From d257a69e52ab8b93dc073fabf20e52a66dcdcba1 Mon Sep 17 00:00:00 2001 From: Jhon Flores Rojas <48776907+TJhon@users.noreply.github.com> Date: Fri, 30 Sep 2022 10:49:35 -0500 Subject: [PATCH 01/28] begin structs --- .vscode/settings.json | 2 + Project.toml | 3 +- src/HDMjl.jl | 2 +- src/rlassologit.jl | 42 ++++++++++++++++++-- test/rlogit.jl | 91 +++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 135 insertions(+), 5 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 test/rlogit.jl diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7a73a41 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file diff --git a/Project.toml b/Project.toml index 350f3b9..519ed61 100644 --- a/Project.toml +++ b/Project.toml @@ -1,13 +1,14 @@ name = "HDMjl" uuid = "8de29b41-9195-4bda-bbba-e5831b2a12ad" authors = ["Jhon Flores Rojas", "Rodrigo Grijalba", "Alexander Quispe", "Anzony Quispe"] -version = "0.0.10" +version = "0.0.11" [deps] DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" GLMNet = "8d5ece8b-de18-5317-b113-243142960cc6" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" diff --git a/src/HDMjl.jl b/src/HDMjl.jl index 172ac51..76e73e5 100644 --- a/src/HDMjl.jl +++ b/src/HDMjl.jl @@ -2,7 +2,7 @@ module HDMjl export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls -using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random +using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables include("help_functions.jl") include("LassoShooting_fit.jl") diff --git a/src/rlassologit.jl b/src/rlassologit.jl index 455b8fb..2be1b6e 100644 --- a/src/rlassologit.jl +++ b/src/rlassologit.jl @@ -3,6 +3,9 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr lambda::Any = nothing, intercept::Bool = true, threshold::Any = nothing) + + + n = size(x, 1) p = size(x, 2) if !isnothing(c) @@ -86,11 +89,44 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr a0 = 0 coefs = coefTemp end + + + ### === Output print + head1 = " + Post-Lasso estimation: $post + Intercept: $intercept + Control: $threshold + \n + Total number of variables: $p\n + Number of selected variables: \n + " + print(head1) + if intercept names_columns = [] else names_columns = [] end + select_columns = [] + + for i in eachindex(coefs) + vl = "V $i" + push!(names_columns, vl) + if coefs[i] != 0 + push!(select_columns, i) + end + end + + table_lgt = hcat(names_columns, coefs) + @ptconf tf = tf_simple alignment = :r + header = ["Variable", "Estimate"] + @pt :header = header table_lgt + print("rlassologit") + # print("head") + ### + est = Dict("coefficients" => coefs, "beta" => coefTemp, "intercept" => a0, "index" => ind1, - "lambda0" => lambda0, "residuals" => e1, "sigma" => sqrt(var(e1)), - "options" => Dict("post" => post, "intercept" => intercept, "control" => threshold)) - return est + "lambda0" => lambda0, "residuals" => e1, "sigma" => sqrt(var(e1)), + "options" => Dict("post" => post, "intercept" => intercept, "control" => threshold)); + + + return est; end diff --git a/test/rlogit.jl b/test/rlogit.jl new file mode 100644 index 0000000..65624d4 --- /dev/null +++ b/test/rlogit.jl @@ -0,0 +1,91 @@ +] activate . + +using HDMjl, Random, Distributions + +n = 250 +p = 40 +px = 10 + + +X = randn((n, p)) +beta = vcat(repeat([2.], px), zeros(p - px)) +intercept = 1 +P = exp.(intercept .+ X * beta) ./ (1 .+ exp.(intercept .+ X * beta)) +# y = Int64[] + +x = rand(Normal(), (n, p)) + +y = rand(Binomial(), n) + +logit_out = rlassologit(x, y, intercept = true) + +exit() + +function suma(x) + z1 = print("z") + n = Dict("x" => x, "z" => z1) + z1 + return z1; +end + +function suma.actio(X) + return x + 1 +end + + +suma(x, y) = x + y + +mutable struct lasologit + x::Int + y::Int + + fsit::Function = x -> x + y + +end + +lasologit(12, 3) + + +mutable struct BitNumber + val::Int + bit_end::UInt + bit_start::UInt + width::UInt + the_bits::Function + BitNumber(v,e,s,w,bits_func) = begin + ret = new( (a=63-e+s; v=v<>a) ,e,s,w) + ret.the_bits = (bit_end,bit_start) -> bits_func(ret,bit_end,bit_start) + ret + end +end + +mutable struct sumas + x::Int64 + y::Int64 + # add_new::Function + + suma(x, y) = x + y +end + +Base.@kwdef struct Model1 + p::Float64 = 2.0 + n::Int64 = 4 + # f::Function + f::Function = (p, n) -> begin + n + p + 1 + end + # function f(x, y) + # x + y + # end + +end + +typeof(Model1().f(12, 1)) + + +mutable struct si1 + x::Real = 12 + y::Real = 8 + si1(x, y) = x + y + +end \ No newline at end of file From 6c71da37bd4d7150dc7f70404bccdc091534d4c3 Mon Sep 17 00:00:00 2001 From: Jhon Flores Rojas <48776907+TJhon@users.noreply.github.com> Date: Mon, 3 Oct 2022 15:16:56 -0500 Subject: [PATCH 02/28] rlasso effects --- src/rlassoEffect.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 798254e..9c5c8e7 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -109,7 +109,7 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing) x = Matrix(x) y = Matrix(y[:, :]) - if Set(index) > 2 + if length(Set(index)) > 2 k = p = length(index) # all(all()) else From 073be03be8d9f06a0ddc4ed7bdd9f0fe81bdfbc0 Mon Sep 17 00:00:00 2001 From: Jhon Flores Rojas <48776907+TJhon@users.noreply.github.com> Date: Mon, 3 Oct 2022 19:32:06 -0500 Subject: [PATCH 03/28] update rlasso Effects --- src/HDMjl.jl | 2 +- src/rlassoEffect.jl | 82 ++++++++++++++++++++++++++----------------- src/rlassologit.jl | 84 ++++++++++++++++++++++++++++++++++++--------- 3 files changed, 119 insertions(+), 49 deletions(-) diff --git a/src/HDMjl.jl b/src/HDMjl.jl index 76e73e5..da2c6e2 100644 --- a/src/HDMjl.jl +++ b/src/HDMjl.jl @@ -1,6 +1,6 @@ module HDMjl -export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls +export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 9c5c8e7..269f1ab 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -47,9 +47,9 @@ function rlassoEffect( v = GLM.residuals(reg2) - var = 1 / n * 1 /mean(v.^2) * mean(v.^2 .* xi.^2) * 1 / mean(v.^2) + var_r = 1 / n * 1 /mean(v.^2) * mean(v.^2 .* xi.^2) * 1 / mean(v.^2) - se = sqrt(var) + se = sqrt(var_r) tval = alpha / se @@ -104,62 +104,82 @@ function rlassoEffect( end -function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing) +function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "partialling out", post = true) - x = Matrix(x) - y = Matrix(y[:, :]) + # if method ∉ ["partialling out", "double selection"] + # print("Method not found, select from [partialling out, double selection") + # end - if length(Set(index)) > 2 - k = p = length(index) - # all(all()) + if length(Set(index)) == 2 + k = p1 = sum(index) else - k = p = sum(index) + k = p1 = length(index) + end + + n, p = size(x) + + names_x = try names(x) + catch + nothing + end + x0 = Matrix(x[:, :]) + # y0 = Matrix(y[:, 1]) + + if isnothing(names_x) + names_x = [] + for i in 1:p + push!(names_x, "V $i") + end end - n, p0 = size(x, 1) coefficients = zeros(k) se = zeros(k) t = zeros(k) - lasso_reg = Dict() + pval = zeros(k) + lasso_reg = Dict() reside = zeros(n, p1) residv = zeros(n, p1) - - selection_matrix = zeros(p0, k) coef_mat = Dict() + selection_matrix = zeros(n, k) + +# names(coefficients) <- names(se) <- names(t) <- names(pval) <- names(lasso.regs) <- colnames(reside) <- colnames(residv) <- colnames(selection.matrix) <- colnames(x)[index] for i in 1:k - d = x[:, index[i]] - xt = x[:, Not(index[i])] + d = x0[:, i] + xt = x0[:, Not(i)] + # Variables de control if isnothing(I3) I3m = I3 else I3m = I3[Not(index[i])] - lasso_reg[i] = try - rlassoEffect(xt, y, d, method = method, I3 = I3m, post = post) - catch - "try-error" - end end - if lasso_regs[i] == "try-error" + lasso_reg[i] = try + rlassoEffect(xt, y, d, method = method, I3 = I3m, post = post) + catch + "try-error" + end + + if lasso_reg[i] == "try-error" continue else - coefficients[i] = lasso_regs[i]["alpha"] - se[i] = lasso_regs[i]["se"] - t[i] = lasso_regs[i]["t"] - coef_mat[i] = lasso_reg["coefficients_reg"] - reside[:, i] = lasso_regs[i]["residuals"]["epsilon"] - residv[:, i] = lasso_regs[i]["residuals"]["v"] - selection_matrix[Not(index[i]), i] = lasso_reg["selection_index"] + coefficients[i] = lasso_reg[i]["alpha"] + se[i] = lasso_reg[i]["se"] + t[i] = lasso_reg[i]["t"] + # pval[i] = lasso_reg[i]["p_value"] + reside[:, i] = lasso_reg[i]["residuals"]["epsilon"] + residv[:, i] = lasso_reg[i]["residuals"]["v"] + coef_mat[i] = lasso_reg[i]["coefficients_reg"] + selection_matrix[Not(index[i]), i] = lasso_reg[i]["selection_index"] end end residuals = Dict("e" => reside, "v" => residv) res = Dict( "coefficients" => coefficients, "se" => se, "t" => t, - "lasso_regs" => lasso_reg, "index" => index, "sample_size" => n, + "lasso_reg" => lasso_reg, "index" => index, "sample_size" => n, "residuals" => residuals, "coef_mat" => coef_mat, "selection_matrix" => selection_matrix ) + return res - -end +end \ No newline at end of file diff --git a/src/rlassologit.jl b/src/rlassologit.jl index 2be1b6e..63adad2 100644 --- a/src/rlassologit.jl +++ b/src/rlassologit.jl @@ -1,10 +1,21 @@ +mutable struct rlassologit + result::Dict + head_msg + select + table +end + + function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = true, n::Int64 = size(x, 1), gamma::Float64 = 0.1 / log(n), lambda::Any = nothing, intercept::Bool = true, threshold::Any = nothing) + x_0 = copy(x) + y_0 = copy(y) - + x = Matrix(x[:, :]) + y = y[:, 1] n = size(x, 1) p = size(x, 2) @@ -90,32 +101,51 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr coefs = coefTemp end + ### === Output print - head1 = " - Post-Lasso estimation: $post - Intercept: $intercept - Control: $threshold - \n - Total number of variables: $p\n - Number of selected variables: \n - " - print(head1) - if intercept names_columns = [] else names_columns = [] end + + if intercept names_columns = ["Intercept"] else names_columns = [] end select_columns = [] - for i in eachindex(coefs) - vl = "V $i" - push!(names_columns, vl) + # print(names(x_0)) + if typeof(x_0) == DataFrame + names_columns = vcat(names_columns, names(x_0)) + # print(names_columns) + else + for i in 1:p + vl = "V $i" + push!(names_columns, vl) + end + # print(names_columns) + end + + for i in 1:p if coefs[i] != 0 push!(select_columns, i) end end + + head1 = " +------ +Post-Lasso estimation: $post +Intercept: $intercept +Control: $threshold + +Total number of variables: $p +Number of selected variables: $(length(select_columns) - intercept) +------ + " + + println(head1) + + println(" ") + table_lgt = hcat(names_columns, coefs) - @ptconf tf = tf_simple alignment = :r + @ptconf tf = tf_simple alignment = :l header = ["Variable", "Estimate"] - @pt :header = header table_lgt + @pt :header = header table_lgt[select_columns, :] print("rlassologit") # print("head") @@ -126,10 +156,30 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr "options" => Dict("post" => post, "intercept" => intercept, "control" => threshold)); - return est; + return rlassologit(est, head1, select_columns, table_lgt); +end + +function r_summary(result::rlassologit, all::Bool = false) + println(result.head_msg) + @ptconf tf = tf_simple alignment = :l + if all + table_print = result.table + # end + else + + table_print = result.table + table_print = table_print[result.select, :] + end + # print("") + @pt :header = ["Variable", "Estimate"] table_print + # @pt :header = ["Variable", "Estimate"] result.table end +# mutable struct rlassologitEffect + +# end + function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) x = Matrix(X[:, :]) From 58dead25e8fb81b13c21350c68a058c2e6d2db29 Mon Sep 17 00:00:00 2001 From: Jhon Flores Rojas <48776907+TJhon@users.noreply.github.com> Date: Mon, 3 Oct 2022 21:42:53 -0500 Subject: [PATCH 04/28] Selection matrix --- src/rlassoEffect.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 269f1ab..1f5f8f0 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -100,10 +100,10 @@ function rlassoEffect( return results - - end + + function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "partialling out", post = true) # if method ∉ ["partialling out", "double selection"] @@ -169,7 +169,7 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "parti reside[:, i] = lasso_reg[i]["residuals"]["epsilon"] residv[:, i] = lasso_reg[i]["residuals"]["v"] coef_mat[i] = lasso_reg[i]["coefficients_reg"] - selection_matrix[Not(index[i]), i] = lasso_reg[i]["selection_index"] + # selection_matrix[Not(index[i]), i] = lasso_reg[i]["selection_index"] end end From 9a694783b08714e363aa9633ceb57bebe1dd34e5 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Wed, 5 Oct 2022 10:46:49 -0500 Subject: [PATCH 05/28] # --- src/rlassoEffect.jl | 323 +++++++--- src/rlassoIV.jl | 94 ++- tutorial/HDMjl_tutorial.ipynb | 490 --------------- tutorial/HDMjl_tutorial_1.ipynb | 1035 +++++++++++++++++++++++++++++++ 4 files changed, 1351 insertions(+), 591 deletions(-) delete mode 100644 tutorial/HDMjl_tutorial.ipynb create mode 100644 tutorial/HDMjl_tutorial_1.ipynb diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 1f5f8f0..ace41d4 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -1,108 +1,165 @@ +mutable struct rlassoEffect1 + se + sample_size + coefficients + dict +end + function rlassoEffect( - x, - y, - d; - method = "double selection", - I3 = nothing, - post = true - ) + x, + y, + d; + method = "double selection", + I3 = nothing, + post = true +) - #= x = Matrix(x) - y = Matrix(y[:, :]) - d = Matrix(d[:, :]) =# +#= x = Matrix(x) +y = Matrix(y[:, :]) +d = Matrix(d[:, :]) =# - n, p = size(x) +n, p = size(x) - # i3 = Set(I3) +# i3 = Set(I3) - if method == "double selection" - I1 = rlasso(x, d, post = post)["index"] - I2 = rlasso(x, y, post = post)["index"] +if method == "double selection" + I1 = rlasso(x, d, post = post)["index"] + I2 = rlasso(x, y, post = post)["index"] - if !isnothing(I3) - I = I1 + I2 + I3 - else - I = I1 + I2 - end + if !isnothing(I3) + I = I1 + I2 + I3 + else + I = I1 + I2 + end - if sum(I) == 0 - I = nothing - end - - I[I .> 0] .= 1 - I = BitVector(I) - - x = hcat(ones(n), d, x[:, I]) - reg1 = GLM.lm(x, y) - alpha = GLM.coef(reg1)[2] - - xi = GLM.residuals(reg1) .* sqrt(n / (n - sum(I) - 1)) - - if isnothing(I) - reg2 = GLM.lm(ones(n, 1), d) - else - reg2 = GLM.lm(x[:, Not(2)], d) - end - - v = GLM.residuals(reg2) - - var_r = 1 / n * 1 /mean(v.^2) * mean(v.^2 .* xi.^2) * 1 / mean(v.^2) - - se = sqrt(var_r) - - tval = alpha / se - - # pval = 2 ##3 searching function (`pnorm`) - - if isnothing(I) - no_select = 1 - else - no_select = 0 - end - - res = Dict( - "epsilon" => xi, "v" => v - ) - - results = Dict( - "alpha" => alpha, "se" => se, "t" => tval, - "no_select" => no_select, "coefficients" => alpha, "coefficient" => alpha, - "coefficients_reg" => GLM.coef(reg1), "selection_index" => I, "residuals" => res, - "sample_size" => n - ) - elseif method == "partialling out" - reg1 = rlasso(x, y, post = post) - yr = reg1["residuals"] - reg2 = rlasso(x, d, post = post) - dr = reg2["residuals"] - - data0 = hcat(yr, ones(n), dr) - reg3 = GLM.lm(data0[:, Not(1)], data0[:, 1]) - alpha = GLM.coef(reg3)[2] - - var = vcov(reg3)[2, 2] - se = sqrt.(var) - tval = alpha ./ sqrt(var) - # pval = - res = Dict("epsilon" => GLM.residuals(reg3), "v" => dr) - - I1 = reg1["index"] - I2 = reg2["index"] - I = as_logical(I1 + I2) - results = Dict( - "alpha" => alpha, "se" => se, "t" => tval, "coefficients" => alpha, - "coefficient" => alpha, "coefficients_reg" => reg1["coefficients"], "selection_index" => I, - "residuals" => res, "sample_size" => n - ) + if sum(I) == 0 + I = nothing + end + I[I .> 0] .= 1 + I = BitVector(I) + + x = hcat(ones(n), d, x[:, I]) + reg1 = GLM.lm(x, y) + alpha = GLM.coef(reg1)[2] + + xi = GLM.residuals(reg1) .* sqrt(n / (n - sum(I) - 1)) + + if isnothing(I) + reg2 = GLM.lm(ones(n, 1), d) + else + reg2 = GLM.lm(x[:, Not(2)], d) + end + + v = GLM.residuals(reg2) + + var_r = 1 / n * 1 /mean(v.^2) * mean(v.^2 .* xi.^2) * 1 / mean(v.^2) + + se = sqrt(var_r) + + tval = alpha / se + + # pval = 2 ##3 searching function (`pnorm`) + + if isnothing(I) + no_select = 1 + else + no_select = 0 end - return results + res = Dict( + "epsilon" => xi, "v" => v + ) + + results = Dict( + "alpha" => alpha, "se" => se, "t" => tval, + "no_select" => no_select, "coefficients" => alpha, "coefficient" => alpha, + "coefficients_reg" => GLM.coef(reg1), "selection_index" => I, "residuals" => res, + "sample_size" => n + ) +elseif method == "partialling out" + reg1 = rlasso(x, y, post = post) + yr = reg1["residuals"] + reg2 = rlasso(x, d, post = post) + dr = reg2["residuals"] + + data0 = hcat(yr, ones(n), dr) + reg3 = GLM.lm(data0[:, Not(1)], data0[:, 1]) + alpha = GLM.coef(reg3)[2] + + var = vcov(reg3)[2, 2] + se = sqrt.(var) + tval = alpha ./ sqrt(var) + # pval = + res = Dict("epsilon" => GLM.residuals(reg3), "v" => dr) + + I1 = reg1["index"] + I2 = reg2["index"] + I = as_logical(I1 + I2) + results = Dict( + "alpha" => alpha, "se" => se, "t" => tval, "coefficients" => alpha, + "coefficient" => alpha, "coefficients_reg" => reg1["coefficients"], "selection_index" => I, + "residuals" => res, "sample_size" => n + ) + +end +se = results["se"] +sample_size = results["sample_size"] +coefficient = results["coefficient"] +return rlassoEffect1(se, sample_size, coefficient, results) end +function r_summary(object::rlassoEffect1) + if length(object.coefficients) != 0 + k = length(object.coefficients) + table = zeros(k, 4) + table[:, 1] .= object.coefficients + table[:, 2] .= object.se + table[:, 3] .= table[:, 1]./table[:, 2] + table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table1 = DataFrame(table, :auto) + table1 = rename(table1, ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) + print("""Estimates and significance testing of the effect of target variables""", + "\n") + pretty_table(table, show_row_number = true, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless) + print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") + print("\n") + else + print("No coefficients\n") + end + return table1 +end +function r_print(object::rlassoEffect1, digits = 3) + if length(object.coefficients) != 0 + b = ["X$y" for y = length(object.coefficients)] + b = reshape(b,(1,length(b))) + a = vcat(b, round.(object.coefficients', digits = 3)) + if length(object.coefficients) <= 10 + + println("Coefficients:\n") + pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) + else + for i in 1:trunc(length(object.coefficients)/10) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + end + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', + tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) + end + else + print("No coefficients\n") + end +end + +mutable struct rlassoEffects1 + se + sample_size + index + coefficients + dict +end function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "partialling out", post = true) @@ -162,13 +219,13 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "parti if lasso_reg[i] == "try-error" continue else - coefficients[i] = lasso_reg[i]["alpha"] - se[i] = lasso_reg[i]["se"] - t[i] = lasso_reg[i]["t"] + coefficients[i] = lasso_reg[i].dict["alpha"] + se[i] = lasso_reg[i].dict["se"] + t[i] = lasso_reg[i].dict["t"] # pval[i] = lasso_reg[i]["p_value"] - reside[:, i] = lasso_reg[i]["residuals"]["epsilon"] - residv[:, i] = lasso_reg[i]["residuals"]["v"] - coef_mat[i] = lasso_reg[i]["coefficients_reg"] + reside[:, i] = lasso_reg[i].dict["residuals"]["epsilon"] + residv[:, i] = lasso_reg[i].dict["residuals"]["v"] + coef_mat[i] = lasso_reg[i].dict["coefficients_reg"] # selection_matrix[Not(index[i]), i] = lasso_reg[i]["selection_index"] end end @@ -179,7 +236,79 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "parti "lasso_reg" => lasso_reg, "index" => index, "sample_size" => n, "residuals" => residuals, "coef_mat" => coef_mat, "selection_matrix" => selection_matrix ) + se = res["se"] + sample_size = res["sample_size"] + index = res["index"] + coefficients = res["coefficients"] + return rlassoEffects1(se, sample_size, index, coefficients, res) - return res +end +function r_print(object::rlassoEffects1, digits = 3) + if length(object.coefficients) != 0 + b = ["X$y" for y = object.index] + b = reshape(b,(1,length(b))) + a = vcat(b, round.(object.coefficients', digits = digits)) + if length(object.coefficients) <= 10 + + println("Coefficients:\n") + pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) + else + for i in 1:trunc(length(object.coefficients)/10) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + end + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', + tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) + end + else + print("No coefficients\n") + end +end + +function r_summary(object::rlassoEffects1) + if length(object.coefficients) != 0 + k = length(object.coefficients) + table = zeros(k, 4) + table[:, 1] .= object.coefficients + table[:, 2] .= object.se + table[:, 3] .= table[:, 1]./table[:, 2] + table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table1 = DataFrame(hcat(["X$y" for y = object.index], table), :auto) + rename(table1, ["index", "Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) + print("Estimates and significance testing of the effect of target variables", + "\n") + pretty_table(table, show_row_number = false, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless, row_names = ["X$y" for y = object.index]) + print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") + print("\n") + else + print("No coefficients\n") + end +end + +function r_confint(object::rlassoEffects1, level = 0.95) + n = object.sample_size + k = length(object.coefficients) + cf = object.coefficients + #pnames <- names(cf) + # if (missing(parm)) + # parm <- pnames else if (is.numeric(parm)) + # parm <- pnames[parm] + a = (1 - level)/2 + a = [a, 1 - a] + fac = quantile.(Normal(), a) + pct = string.(round.(a; digits = 3)*100, "%") + ses = object.se + c_i = [] + for i in 1:length(cf) + if i == 1 + c_i = (cf[i] .+ ses[i] .* fac)[:,:]' + else + c_i = vcat(c_i, (cf[i] .+ ses[i] * fac)[:,:]') + end + end + table1 = DataFrame(hcat(["X$y" for y = object.index], c_i), :auto) + rename(table1, vcat("index", pct)) + #ci = NamedArray(c_i, (1:size(c_i)[1], pct)) + ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = ["X$y" for y = object.index]) + #return c_i;; end \ No newline at end of file diff --git a/src/rlassoIV.jl b/src/rlassoIV.jl index 71be083..b00e644 100644 --- a/src/rlassoIV.jl +++ b/src/rlassoIV.jl @@ -1,15 +1,23 @@ +mutable struct rlassoIV1 + se + sample_size + #vcov + coefficients + dict +end + function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post::Bool = true) if !select_Z & !select_X res = tsls(d, y, z, x, homoskedastic = false) - return res + se = res["se"] elseif select_Z & !select_X res = rlassoIVselectZ(x, d, y, z, post = post) - return res + res["sample_size"] = size(x)[1] elseif !select_Z & select_X res = rlassoIVselectX(x, d, y, z, post = post) - return res + res["sample_size"] = size(x)[1] elseif select_Z & select_X @@ -45,6 +53,84 @@ function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post end res = tsls(Dr, Yr, Zr, intercept = false, homoscedastic = false) - return res end + se = res["se"] + sample_size = res["sample_size"] + #vcov = res["vcov"] + coefficients = res["coefficients"] + res1 = rlassoIV1(se, sample_size, coefficients, res); + return res1; +end + +function r_print(object::rlassoIV1, digits = 3) + if length(object.coefficients) != 0 + b = ["X$y" for y = length(object.coefficients)] + b = reshape(b,(1,length(b))) + a = vcat(b, round.(object.coefficients', digits = 3)) + if length(object.coefficients) <= 10 + + println("Coefficients:\n") + pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) + else + for i in 1:trunc(length(object.coefficients)/10) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + end + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', + tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) + end + else + print("No coefficients\n") + end +end + +using Distributions +function r_summary(object::rlassoIV1) + if length(object.coefficients) != 0 + k = length(object.coefficients) + table = zeros(k, 4) + table[:, 1] .= object.coefficients + table[:, 2] .= object.se + table[:, 3] .= table[:, 1]./table[:, 2] + table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table1 = DataFrame(table, :auto) + table1 = rename(table1, ["coeff.", "se.", "t-value", "p-value"]) + print("Estimates and Significance Testing of the effect of target variables in the IV regression model", + "\n") + pretty_table(table, show_row_number = true, header = ["coeff.", "se.", "t-value", "p-value"], tf = tf_borderless) + print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") + print("\n") + return table1; + else + print("No coefficients\n") + #table = [] + end + #return table;; +end + +function r_confint(object::rlassoIV1, level = 0.95) + n = object.sample_size + k = length(object.coefficients) + cf = object.coefficients + #pnames <- names(cf) + # if (missing(parm)) + # parm <- pnames else if (is.numeric(parm)) + # parm <- pnames[parm] + a = (1 - level)/2 + a = [a, 1 - a] + fac = quantile.(Normal(), a) + pct = string.(round.(a; digits = 3)*100, "%") + ses = object.se + c_i = [] + for i in 1:length(cf) + if i == 1 + c_i = (cf[i] .+ ses[i] .* fac)[:,:]' + else + c_i = vcat(c_i, (cf[i] .+ ses[i] .* fac)[:,:]') + end + end + table1 = DataFrame(c_i, :auto) + table1 = rename(table1, pct) + #ci = NamedArray(c_i, (1:size(c_i)[1], pct)) + ci = pretty_table(c_i; header = pct, show_row_number = true, tf = tf_borderless) + return table1; end \ No newline at end of file diff --git a/tutorial/HDMjl_tutorial.ipynb b/tutorial/HDMjl_tutorial.ipynb deleted file mode 100644 index 690831e..0000000 --- a/tutorial/HDMjl_tutorial.ipynb +++ /dev/null @@ -1,490 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# HIGH-DIMENSIONAL METRICS IN R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. How to get started" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "import Pkg; Pkg.add(\"HDMjl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "using HDMjl, CodecXz, GLM\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Prediction using Approximate Sparsity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2. A Joint Significance Test for Lasso Regression." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "using Random\n", - "Random.seed!(1234);\n", - "n = 100;\n", - "p = 100;\n", - "s = 3;\n", - "X = randn(n, p);\n", - "beta = vcat(fill(5, s), zeros(p - s));\n", - "Y = X * beta + randn(n);" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 15 entries:\n", - " \"tss\" => 8466.4\n", - " \"dev\" => [8.56479, -6.50463, -1.51601, 8.77722, -5.1338, -7.07815, 7…\n", - " \"model\" => [0.970656 0.262456 … 1.86802 -0.460151; -0.979218 -0.022244…\n", - " \"loadings\" => [1.99904, 1.58734, 1.67152, 1.86208, 1.859, 1.8311, 1.45692…\n", - " \"sigma\" => 1.56664\n", - " \"lambda0\" => 81.3601\n", - " \"lambda\" => [162.642, 129.146, 135.995, 151.499, 151.248, 148.978, 118.…\n", - " \"intercept\" => -0.196115\n", - " \"iter\" => 16\n", - " \"residuals\" => [0.119342, -0.25299, -1.17148, 0.282269, -0.643579, -0.5525…\n", - " \"rss\" => 242.981\n", - " \"index\" => Bool[1, 1, 1, 0, 0, 0, 0, 0, 0, 0 … 0, 0, 0, 0, 0, 0, 0, …\n", - " \"beta\" => [4.31658, 4.39195, 4.45657, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…\n", - " \"options\" => Dict{String, Any}(\"intercept\"=>true, \"post\"=>false, \"meanx\"…\n", - " \"coefficients\" => [-0.196115, 4.31658, 4.39195, 4.45657, 0.0, 0.0, 0.0, 0.0, …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlasso(X, Y, post = false)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1×101 adjoint(::Vector{Float64}) with eltype Float64:\n", - " -0.00682754 5.00958 4.93178 5.17705 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "post_lasso_reg = rlasso(X, Y, post = true) #now use post-lasso\n", - "post_lasso_reg[\"coefficients\"]'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Inference on Target Regression Coefficients" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.1. Intuition for the Orthogonality Principle in Linear Models via Partialling Out." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2. Inference: Confidence Intervals and Significance Testing. The function rlassoEffects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3. Application: the effect of gender on wage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "using CodecXz\n", - "using RData\n", - "using DataFrames\n", - "url = \"https://github.com/cran/hdm/raw/master/data/GrowthData.rda\";\n", - "GrowthData = load(download(url))[\"GrowthData\"];\n", - "y = GrowthData[:, 1];\n", - "d = GrowthData[:, 3];\n", - "X = Matrix(GrowthData[:, Not(1, 2, 3)]);" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 10 entries:\n", - " \"alpha\" => -0.0453558\n", - " \"t\" => -2.43116\n", - " \"se\" => 0.018656\n", - " \"no_select\" => 0\n", - " \"coefficients_reg\" => [0.2247, -0.0453558, -0.064512, 0.215358, -0.0960046, -…\n", - " \"sample_size\" => 90\n", - " \"coefficient\" => -0.0453558\n", - " \"selection_index\" => Bool[1, 1, 1, 1, 1, 1, 1, 1, 0, 0 … 0, 0, 0, 0, 0, 0,…\n", - " \"residuals\" => Dict(\"v\"=>[0.210235, 0.201381, 0.00253076, -0.0671351, …\n", - " \"coefficients\" => -0.0453558" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoEffect(X, y, d, method = \"double selection\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 9 entries:\n", - " \"alpha\" => -0.05333\n", - " \"t\" => -3.722\n", - " \"se\" => 0.0143283\n", - " \"coefficients_reg\" => [-0.0845872, -0.0461165, 0.18946, -0.0299086, 0.0, 0.0,…\n", - " \"sample_size\" => 90\n", - " \"coefficient\" => -0.05333\n", - " \"selection_index\" => Any[true, true, true, true, true, true, true, true, fal…\n", - " \"residuals\" => Dict(\"v\"=>[0.181221, 0.116676, 0.0971757, -0.133713, 0.…\n", - " \"coefficients\" => -0.05333" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoEffect(X, y, d, method = \"partialling out\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2. Application: Economic Development and Institutions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => [0.0801865]\n", - " \"sample_size\" => 312\n", - " \"vcov\" => [0.00642988;;]\n", - " \"residuals\" => [-0.111753; 0.0588269; … ; 0.218765; 0.301602;;]\n", - " \"coefficients\" => [-0.0449578;;]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "using Statistics\n", - "url = \"https://github.com/cran/hdm/raw/master/data/EminentDomain.rda\";\n", - "EminentDomain = load(download(url))[\"EminentDomain\"];\n", - "z = EminentDomain[\"logGDP\"][\"z\"];\n", - "x = EminentDomain[\"logGDP\"][\"x\"];\n", - "d = EminentDomain[\"logGDP\"][\"d\"];\n", - "y = EminentDomain[\"logGDP\"][\"y\"];\n", - "x = x[:, (mean(x, dims = 1) .> 0.05)'];\n", - "z = z[:, (mean(z, dims = 1) .> 0.05)'];\n", - "rlassoIV(x, d, y, z)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => [0.0801865]\n", - " \"sample_size\" => 312\n", - " \"vcov\" => [0.00642988;;]\n", - " \"residuals\" => [-0.111753; 0.0588269; … ; 0.218765; 0.301602;;]\n", - " \"coefficients\" => [-0.0449578;;]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoIV(x, d, y, z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Inference on Treatment Effects in a High-Dimensional Setting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. Application: 401(k) plan participation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 1930.68\n", - " \"individual\" => [-30618.3, -57537.6, -71442.9, 21383.3, -2.32925e5, 3.40765e…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 10180.1\n", - " \"type\" => \"ATE\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "url = \"https://github.com/cran/hdm/raw/master/data/pension.rda\";\n", - "pension = load(download(url))[\"pension\"];\n", - "y = pension[:, \"tw\"];\n", - "d = pension[:, \"p401\"];\n", - "z = pension[:, \"e401\"];\n", - "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);\n", - "rlassoATE(X, d, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 2944.43\n", - " \"individual\" => [-21536.4, -52877.2, -1.44867e5, -2739.29, -307741.0, 7.3912…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 12628.5\n", - " \"type\" => \"ATET\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoATET(X, d, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 2326.9\n", - " \"individual\" => [-50526.8, -1.39158e5, -1.37102e5, 38508.0, -6.5644e5, 7.943…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 12992.1\n", - " \"type\" => \"LATE\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoLATE(X, d, y, z)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 3645.28\n", - " \"individual\" => [-35580.5, -90558.0, -1.83628e5, -5303.13, -8.0766e5, 1.8866…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 15323.2\n", - " \"type\" => \"LATET\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rlassoLATET(X, d, y, z)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.8.1", - "language": "julia", - "name": "julia-1.8" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.8.1" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb new file mode 100644 index 0000000..7c59879 --- /dev/null +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -0,0 +1,1035 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HIGH-DIMENSIONAL METRICS IN R" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. How to get started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "or" + ] + }, + { + "cell_type": "code", + "execution_count": 445, + "metadata": {}, + "outputs": [], + "source": [ + "#import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables, Distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import Pkg" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [], + "source": [ + "#Pkg.develop(path = \"E:/causal_ml/hdm_paper/HDMjl.jl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Prediction using Approximate Sparsity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. A Joint Significance Test for Lasso Regression." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "using Random\n", + "Random.seed!(1234);\n", + "n = 100;\n", + "p = 100;\n", + "s = 3;\n", + "X = randn(n, p);\n", + "beta = vcat(fill(5, s), zeros(p - s));\n", + "Y = X * beta + randn(n);" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_reg = HDMjl.rlasso(X, Y, post = false);" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1×101 adjoint(::Vector{Float64}) with eltype Float64:\n", + " -0.00682754 5.00958 4.93178 5.17705 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "post_lasso_reg = HDMjl.rlasso(X, Y, post = true) #now use post-lasso\n", + "post_lasso_reg[\"coefficients\"]'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Inference on Target Regression Coefficients" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1. Intuition for the Orthogonality Principle in Linear Models via Partialling Out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2. Inference: Confidence Intervals and Significance Testing. The function rlassoEffects" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "print_effects (generic function with 2 methods)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "function print_effects(object, digits = 3)\n", + " if length(object[\"coefficients\"]) != 0\n", + " b = [\"X$y\" for y = object[\"index\"]]\n", + " b = reshape(b,(1,length(b)))\n", + " a = vcat(b, round.(object[\"coefficients\"]', digits = digits))\n", + " if length(object[\"coefficients\"]) <= 10\n", + " \n", + " println(\"Coefficients:\\n\")\n", + " pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:])\n", + " else \n", + " for i in 1:trunc(length(object[\"coefficients\"])/10)\n", + " pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i])\n", + " end\n", + " pretty_table(a[2,10*trunc(length(object[\"coefficients\"])/10)+1:length(object[\"coefficients\"])]',\n", + " tf = tf_borderless, header = a[1,10*trunc(length(object[\"coefficients\"])/10)+1:length(object[\"coefficients\"])])\n", + " end\n", + " else \n", + " print(\"No coefficients\\n\")\n", + " end\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "summary_effects (generic function with 1 method)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "function summary_effects(object)\n", + " if length(object) != 0\n", + " k = length(object[\"coefficients\"])\n", + " table = zeros(k, 4)\n", + " table[:, 1] .= object[\"coefficients\"]\n", + " table[:, 2] .= object[\"se\"]\n", + " table[:, 3] .= table[:, 1]./table[:, 2]\n", + " table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3]))\n", + " table1 = DataFrame(hcat([\"X$y\" for y = object[\"index\"]], table), :auto)\n", + " rename(table1, [\"index\", \"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"])\n", + " print(\"Estimates and significance testing of the effect of target variables\", \n", + " \"\\n\")\n", + " pretty_table(table, show_row_number = false, header = [\"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"], tf = tf_borderless, row_names = [\"X$y\" for y = object[\"index\"]])\n", + " print(\"---\", \"\\n\", \"Signif. codes:\",\"\\n\", \"0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\")\n", + " print(\"\\n\")\n", + " else\n", + " print(\"No coefficients\\n\")\n", + " end\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "confint_effects (generic function with 2 methods)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "function confint_effects(object, level = 0.95)\n", + " n = object[\"sample_size\"]\n", + " k = length(object[\"coefficients\"])\n", + " cf = object[\"coefficients\"]\n", + " #pnames <- names(cf)\n", + " # if (missing(parm)) \n", + " # parm <- pnames else if (is.numeric(parm)) \n", + " # parm <- pnames[parm]\n", + " a = (1 - level)/2\n", + " a = [a, 1 - a]\n", + " fac = quantile.(Normal(), a)\n", + " pct = string.(round.(a; digits = 3)*100, \"%\")\n", + " ses = object[\"se\"]\n", + " c_i = []\n", + " for i in 1:length(cf)\n", + " if i == 1\n", + " c_i = (cf[i] .+ ses[i] .* fac)[:,:]'\n", + " else\n", + " c_i = vcat(c_i, (cf[i] .+ ses[i] * fac)[:,:]')\n", + " end\n", + " end\n", + " table1 = DataFrame(hcat([\"X$y\" for y = object[\"index\"]], c_i), :auto)\n", + " rename(table1, vcat(\"index\", pct))\n", + " #ci = NamedArray(c_i, (1:size(c_i)[1], pct))\n", + " ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = [\"X$y\" for y = object[\"index\"]])\n", + " #return c_i;;\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "using Random, Distributions, PrettyTables, DataFrames\n", + "Random.seed!(1234);\n", + "n = 100;\n", + "p = 100;\n", + "s = 3;\n", + "x = randn(n, p);\n", + "beta = vcat(fill(3, s), zeros(p - s));\n", + "y =1 .+ x * beta + randn(n);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_effects = HDMjl.rlassoEffects(x, y, index = [1,2,3,50]);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", + "\n", + " 2.925 2.903 3.101 -0.227\n" + ] + } + ], + "source": [ + "HDMjl.r_print(lasso_effects)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.92541 0.103597 28.2384 1.97828e-175\n", + " \u001b[1m X2 \u001b[0m 2.90258 0.105907 27.407 2.26599e-165\n", + " \u001b[1m X3 \u001b[0m 3.10095 0.110626 28.031 6.80893e-173\n", + " \u001b[1m X50 \u001b[0m -0.22712 0.0910927 -2.49329 0.0126566\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(lasso_effects)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.72236 3.12845\n", + " \u001b[1m X2 \u001b[0m 2.69501 3.11016\n", + " \u001b[1m X3 \u001b[0m 2.88413 3.31778\n", + " \u001b[1m X50 \u001b[0m -0.405659 -0.048582\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(lasso_effects)" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3. Application: the effect of gender on wage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "using CodecXz\n", + "using RData\n", + "using DataFrames\n", + "url = \"https://github.com/cran/hdm/raw/master/data/GrowthData.rda\";\n", + "GrowthData = load(download(url))[\"GrowthData\"];\n", + "y = GrowthData[:, 1];\n", + "d = GrowthData[:, 3];\n", + "X = Matrix(GrowthData[:, Not(1, 2, 3)]);" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_effect = HDMjl.rlassoEffect(X, y, d, method = \"partialling out\");" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m X1 \u001b[0m\n", + "\n", + " -0.053\n" + ] + } + ], + "source": [ + "HDMjl.r_print(lasso_effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 -0.05333 0.0143283 -3.722 0.000197655\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(lasso_effect);" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "doublesel_effect = HDMjl.rlassoEffect(X, y, d, method = \"double selection\");" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m X1 \u001b[0m\n", + "\n", + " -0.045\n" + ] + } + ], + "source": [ + "HDMjl.r_print(doublesel_effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 -0.0453558 0.018656 -2.43116 0.0150506\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(doublesel_effect);" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "false" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "select_Z=true\n", + "select_X=false\n", + "\n", + "select_Z==false & select_X==false" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Application: Economic Development and Institutions." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(64, 21)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using Statistics, StatsModels\n", + "url = \"https://github.com/cran/hdm/raw/master/data/AJR.rda\";\n", + "AJR = load(download(url))[\"AJR\"];\n", + "y = AJR[!,\"GDP\"]\n", + "d = AJR[!,\"Exprop\"]\n", + "z = AJR[!,\"logMort\"];\n", + "x_formula = @formula(GDP ~ -1 + Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", + " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", + " + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer\n", + " + Namer*Samer)\n", + "x_dframe = ModelFrame( x_formula, AJR)\n", + "x1 = ModelMatrix(x_dframe)\n", + "x = x1.m\n", + "size(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "AJR_Xselect = HDMjl.rlassoIV(x, d, y, z, select_X=true, select_Z=false);" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m X1 \u001b[0m\n", + "\n", + " 1.276\n" + ] + } + ], + "source": [ + "HDMjl.r_print(AJR_Xselect)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " 1 1.27623 0.87538 1.45791 0.144865\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(AJR_Xselect);" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " 1 -0.439485 2.99194\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(AJR_Xselect);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "using Statistics, GLM\n", + "url = \"https://github.com/cran/hdm/raw/master/data/EminentDomain.rda\";\n", + "EminentDomain = load(download(url))[\"EminentDomain\"];\n", + "z = EminentDomain[\"logGDP\"][\"z\"];\n", + "x = EminentDomain[\"logGDP\"][\"x\"];\n", + "d = EminentDomain[\"logGDP\"][\"d\"];\n", + "y = EminentDomain[\"logGDP\"][\"y\"];\n", + "x = x[:, (mean(x, dims = 1) .> 0.05)'];\n", + "z = z[:, (mean(z, dims = 1) .> 0.05)'];" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_IV_Z = HDMjl.rlassoIV(x, d, y, z, select_X = false, select_Z = true);" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " 1 -0.451027 2.32377 -0.194093 0.846103\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(lasso_IV_Z);" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " 1 -5.00553 4.10348\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(lasso_IV_Z);" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_IV_XZ = HDMjl.rlassoIV(x, d, y, z, select_X = true, select_Z = true);" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " 1 -0.0449578 0.0801865 -0.560665 0.575026\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "HDMjl.r_summary(lasso_IV_XZ);" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " 1 -0.202121 0.112205\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(lasso_IV_XZ);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Inference on Treatment Effects in a High-Dimensional Setting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3. Application: 401(k) plan participation." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{String, Any} with 5 entries:\n", + " \"se\" => 1930.68\n", + " \"individual\" => [-30618.3, -57537.6, -71442.9, 21383.3, -2.32925e5, 3.40765e…\n", + " \"sample_size\" => 9915\n", + " \"te\" => 10180.1\n", + " \"type\" => \"ATE\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://github.com/cran/hdm/raw/master/data/pension.rda\";\n", + "pension = load(download(url))[\"pension\"];\n", + "y = pension[:, \"tw\"];\n", + "d = pension[:, \"p401\"];\n", + "z = pension[:, \"e401\"];\n", + "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);\n", + "rlassoATE(X, d, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{String, Any} with 5 entries:\n", + " \"se\" => 2944.43\n", + " \"individual\" => [-21536.4, -52877.2, -1.44867e5, -2739.29, -307741.0, 7.3912…\n", + " \"sample_size\" => 9915\n", + " \"te\" => 12628.5\n", + " \"type\" => \"ATET\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rlassoATET(X, d, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{String, Any} with 5 entries:\n", + " \"se\" => 2326.9\n", + " \"individual\" => [-50526.8, -1.39158e5, -1.37102e5, 38508.0, -6.5644e5, 7.943…\n", + " \"sample_size\" => 9915\n", + " \"te\" => 12992.1\n", + " \"type\" => \"LATE\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rlassoLATE(X, d, y, z)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{String, Any} with 5 entries:\n", + " \"se\" => 3645.28\n", + " \"individual\" => [-35580.5, -90558.0, -1.83628e5, -5303.13, -8.0766e5, 1.8866…\n", + " \"sample_size\" => 9915\n", + " \"te\" => 15323.2\n", + " \"type\" => \"LATET\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rlassoLATET(X, d, y, z)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.8.1", + "language": "julia", + "name": "julia-1.8" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.8.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 071774d0636d2ffab29ec13c54727e9f0e87bdbf Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Wed, 5 Oct 2022 15:18:50 -0500 Subject: [PATCH 06/28] # --- src/HDMjl.jl | 2 +- src/rlassoEffect.jl | 8 ++++---- src/rlassoIV.jl | 28 +++++++++++++++------------- 3 files changed, 20 insertions(+), 18 deletions(-) diff --git a/src/HDMjl.jl b/src/HDMjl.jl index da2c6e2..c900905 100644 --- a/src/HDMjl.jl +++ b/src/HDMjl.jl @@ -2,7 +2,7 @@ module HDMjl export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary -using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables +using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables, Distributions include("help_functions.jl") include("LassoShooting_fit.jl") diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index ace41d4..656f01b 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -252,13 +252,13 @@ function r_print(object::rlassoEffects1, digits = 3) if length(object.coefficients) <= 10 println("Coefficients:\n") - pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) + pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c) #, header_crayon =crayon"blue") else for i in 1:trunc(length(object.coefficients)/10) - pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c)#, header_crayon =crayon"green") end - pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', - tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 9, + tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)])#, header_crayon =crayon"green") end else print("No coefficients\n") diff --git a/src/rlassoIV.jl b/src/rlassoIV.jl index b00e644..4a53970 100644 --- a/src/rlassoIV.jl +++ b/src/rlassoIV.jl @@ -7,19 +7,19 @@ mutable struct rlassoIV1 end function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post::Bool = true) - if !select_Z & !select_X - res = tsls(d, y, z, x, homoskedastic = false) + if select_Z == true && select_X == false + res = tsls(d, y, z, x, homoscedastic = false) se = res["se"] - elseif select_Z & !select_X + elseif select_Z == true && select_X == false res = rlassoIVselectZ(x, d, y, z, post = post) res["sample_size"] = size(x)[1] - elseif !select_Z & select_X + elseif select_Z == false && select_X == true res = rlassoIVselectX(x, d, y, z, post = post) res["sample_size"] = size(x)[1] - elseif select_Z & select_X + elseif select_Z == true && select_X == true Z = hcat(z, x) lasso_d_zx = rlasso(Z, d, post = post) @@ -62,28 +62,30 @@ function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post return res1; end +#using Crayons function r_print(object::rlassoIV1, digits = 3) if length(object.coefficients) != 0 - b = ["X$y" for y = length(object.coefficients)] + b = ["X$y" for y = 1:length(object.coefficients)] b = reshape(b,(1,length(b))) - a = vcat(b, round.(object.coefficients', digits = 3)) + a = vcat(b, round.(object.coefficients', digits = digits)) if length(object.coefficients) <= 10 println("Coefficients:\n") - pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) + pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c) #, header_crayon =crayon"blue") else - for i in 1:trunc(length(object.coefficients)/10) - pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + for i in 1:convert(Int, trunc(length(object.coefficients)/10, digits =0)) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c)#, header_crayon =crayon"green") + print("\n") end - pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', - tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) + pretty_table(a[2,10*convert(Int, trunc(length(object.coefficients)/10, digits =0))+1:length(object.coefficients)]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 9, + tf = tf_borderless, header = a[1,10*convert(Int, trunc(length(object.coefficients)/10, digits =0))+1:length(object.coefficients)]) #, header_crayon =crayon"green") end else print("No coefficients\n") end end -using Distributions +#using Distributions function r_summary(object::rlassoIV1) if length(object.coefficients) != 0 k = length(object.coefficients) From 29785ee45dd6baff92b76d80daf9e804fc1c268d Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 6 Oct 2022 18:41:18 -0500 Subject: [PATCH 07/28] # --- src/rlassoEffect.jl | 2 +- src/rlassoIV.jl | 50 ++--- src/rlassoIVselectX.jl | 4 +- src/rlassoIVselectZ.jl | 9 +- src/tsls.jl | 30 ++- tutorial/HDMjl_tutorial_1.ipynb | 366 ++++++++++++++++---------------- 6 files changed, 247 insertions(+), 214 deletions(-) diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 656f01b..c801e62 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -134,7 +134,7 @@ end function r_print(object::rlassoEffect1, digits = 3) if length(object.coefficients) != 0 - b = ["X$y" for y = length(object.coefficients)] + b = ["$y" for y = 1:length(object.coefficients)] b = reshape(b,(1,length(b))) a = vcat(b, round.(object.coefficients', digits = 3)) if length(object.coefficients) <= 10 diff --git a/src/rlassoIV.jl b/src/rlassoIV.jl index 4a53970..28db347 100644 --- a/src/rlassoIV.jl +++ b/src/rlassoIV.jl @@ -7,8 +7,9 @@ mutable struct rlassoIV1 end function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post::Bool = true) - if select_Z == true && select_X == false + if select_Z == false && select_X == false res = tsls(d, y, z, x, homoscedastic = false) + #res["coefficients"] = hcat(["d$y" for y = 1:size(d[:,:],2)], res["coefficients"]) se = res["se"] elseif select_Z == true && select_X == false @@ -17,7 +18,7 @@ function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post elseif select_Z == false && select_X == true res = rlassoIVselectX(x, d, y, z, post = post) - res["sample_size"] = size(x)[1] + #res["sample_size"] = size(x)[1] elseif select_Z == true && select_X == true @@ -53,6 +54,7 @@ function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post end res = tsls(Dr, Yr, Zr, intercept = false, homoscedastic = false) + #res["coefficients"] = hcat(["d$y" for y = 1:size(d[:,:],2)], res["coefficients"]) end se = res["se"] sample_size = res["sample_size"] @@ -63,22 +65,23 @@ function rlassoIV(x, d, y, z; select_Z::Bool = true, select_X::Bool = true, post end #using Crayons -function r_print(object::rlassoIV1, digits = 3) - if length(object.coefficients) != 0 - b = ["X$y" for y = 1:length(object.coefficients)] - b = reshape(b,(1,length(b))) - a = vcat(b, round.(object.coefficients', digits = digits)) - if length(object.coefficients) <= 10 +function r_print(object::rlassoIV1, n_digits = 3) + if size([object.coefficients])[1] != 0 + # b = ["X$y" for y = 1:length(object.coefficients)] + # b = reshape(b,(1,length(b))) + # a = vcat(b, round.(object.coefficients', digits = digits)) + a = hcat(object.coefficients[:,1], round.(object.coefficients[:,2], digits = n_digits)) + if size(object.coefficients, 1) <= 10 println("Coefficients:\n") - pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c) #, header_crayon =crayon"blue") + pretty_table(a[:, 2]', tf = tf_borderless, header = a[:, 1], nosubheader = true, equal_columns_width = true, columns_width = 10, alignment=:c) #, header_crayon =crayon"blue") else - for i in 1:convert(Int, trunc(length(object.coefficients)/10, digits =0)) - pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c)#, header_crayon =crayon"green") + for i in 1:convert(Int, trunc(size(object.coefficients, 1)/10, digits =0)) + pretty_table(a[10*(i-1)+1:10*i, 2]', tf = tf_borderless, header = a[10*(i-1)+1:10*i, 1], nosubheader = true, equal_columns_width = true, columns_width = 10, alignment=:c)#, header_crayon =crayon"green") print("\n") end - pretty_table(a[2,10*convert(Int, trunc(length(object.coefficients)/10, digits =0))+1:length(object.coefficients)]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 9, - tf = tf_borderless, header = a[1,10*convert(Int, trunc(length(object.coefficients)/10, digits =0))+1:length(object.coefficients)]) #, header_crayon =crayon"green") + pretty_table(a[10*convert(Int, trunc(size(object.coefficients, 1)/10, digits =0))+1:size(object.coefficients, 1), 2]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 10, + tf = tf_borderless, header = a[10*convert(Int, trunc(size(object.coefficients, 1)/10, digits =0))+1:size(object.coefficients, 1), 1]) #, header_crayon =crayon"green") end else print("No coefficients\n") @@ -87,18 +90,18 @@ end #using Distributions function r_summary(object::rlassoIV1) - if length(object.coefficients) != 0 - k = length(object.coefficients) + if size([object.coefficients])[1] != 0 + k = length(object.coefficients[:,2]) table = zeros(k, 4) - table[:, 1] .= object.coefficients + table[:, 1] .= vec(object.coefficients[:,2]) table[:, 2] .= object.se table[:, 3] .= table[:, 1]./table[:, 2] table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) - table1 = DataFrame(table, :auto) - table1 = rename(table1, ["coeff.", "se.", "t-value", "p-value"]) + table1 = DataFrame(hcat(object.coefficients[:,1], table), :auto) + table1 = rename(table1, [" ", "coeff.", "se.", "t-value", "p-value"]) print("Estimates and Significance Testing of the effect of target variables in the IV regression model", "\n") - pretty_table(table, show_row_number = true, header = ["coeff.", "se.", "t-value", "p-value"], tf = tf_borderless) + pretty_table(table1, show_row_number = false, header = [" ", "coeff.", "se.", "t-value", "p-value"], tf = tf_borderless) print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") print("\n") return table1; @@ -112,7 +115,7 @@ end function r_confint(object::rlassoIV1, level = 0.95) n = object.sample_size k = length(object.coefficients) - cf = object.coefficients + cf = object.coefficients[:,2:end] #pnames <- names(cf) # if (missing(parm)) # parm <- pnames else if (is.numeric(parm)) @@ -130,9 +133,8 @@ function r_confint(object::rlassoIV1, level = 0.95) c_i = vcat(c_i, (cf[i] .+ ses[i] .* fac)[:,:]') end end - table1 = DataFrame(c_i, :auto) - table1 = rename(table1, pct) - #ci = NamedArray(c_i, (1:size(c_i)[1], pct)) - ci = pretty_table(c_i; header = pct, show_row_number = true, tf = tf_borderless) + table1 = DataFrame(hcat(object.coefficients[:,1],c_i), :auto) + table1 = rename(table1, append!([" "],pct)) + ci = pretty_table(table1; header = append!([" "],pct), show_row_number = false, tf = tf_borderless) return table1; end \ No newline at end of file diff --git a/src/rlassoIVselectX.jl b/src/rlassoIVselectX.jl index e855fe1..d14d83c 100644 --- a/src/rlassoIVselectX.jl +++ b/src/rlassoIVselectX.jl @@ -12,11 +12,11 @@ function rlassoIVselectX(x, d, y, z; post::Bool = true) Zr[:, i] = lasso_z_x["residuals"] end - result = tsls(Dr, Yr, Zr, intercept = false) + result = tsls(Dr, Yr, Zr, nothing,intercept = false) se = result["se"] vcov = result["vcov"] coef = result["coefficients"] # coefnames = result["coefnames"] - res = Dict("coefficients" => coef, "vcov" => vcov, "se" => se) + res = Dict("coefficients" => coef, "vcov" => vcov, "se" => se, "sample_size" => n) return res end \ No newline at end of file diff --git a/src/rlassoIVselectZ.jl b/src/rlassoIVselectZ.jl index 8fd2e00..9430fee 100644 --- a/src/rlassoIVselectZ.jl +++ b/src/rlassoIVselectZ.jl @@ -1,9 +1,13 @@ -function rlassoIVselectZ(x, y, d, z; post::Bool = true, intercept::Bool = true) +function rlassoIVselectZ(x, d, y, z; post::Bool = true, intercept::Bool = true) n = size(y, 1) kex = size(x, 2) ke = size(d, 2) + d_names = ["d$y" for y = 1:ke] + x_names = ["x$y" for y = 1:kex]; + coef_names = append!(d_names,x_names); + Z = hcat(z, x) kiv = size(Z, 2) select_mat = zeros(0) @@ -40,7 +44,7 @@ function rlassoIVselectZ(x, y, d, z; post::Bool = true, intercept::Bool = true) if isempty(Dhat) Dhat = append!(Dhat, dihat) else - Dhat = hcat(Dhat, digat) + Dhat = hcat(Dhat, dihat) end end @@ -52,6 +56,7 @@ function rlassoIVselectZ(x, y, d, z; post::Bool = true, intercept::Bool = true) Omega_hat = Dhat' * (Dhat .* (residuals .^ 2)) Q_hat_inv = pinv(d' * Dhat) vcov = Q_hat_inv * Omega_hat * Q_hat_inv' + alpha_hat = hcat(coef_names, alpha_hat) res = Dict("coefficients" => alpha_hat[1:ke, :], "se" => sqrt.(diag(vcov))[1:ke], "residuals" => residuals, "samplesize" => n) return res end \ No newline at end of file diff --git a/src/tsls.jl b/src/tsls.jl index ef95497..0f02174 100644 --- a/src/tsls.jl +++ b/src/tsls.jl @@ -9,11 +9,36 @@ end function tsls(d::Array, y::Array, z::Array, x::Union{Nothing, Array} = nothing; intercept::Bool = true, homoscedastic::Bool = true) # x::Union{Nothing, DataFrame, Array} n = size(y, 1) + # if intercept == true + # d_names = ["d$y" for y = 1:size(d[:,:],2)] + # x_names = ["x$y" for y = 1:size(x,2)]; + # coef_names = append!(append!(d_names, ["intercept"]), x_names); + # else + # d_names = ["d$y" for y = 1:size(d[:,:],2)] + # x_names = ["x$y" for y = 1:size(x,2)]; + # coef_names = append!(d_names,x_names); + # end - if intercept && !isnothing(x) + if intercept == true && isnothing(x) == false + d_names = ["d$y" for y = 1:size(d[:,:],2)] + x_names = ["x$y" for y = 1:size(x,2)]; + coef_names = append!(append!(d_names, ["intercept"]), x_names); x = hcat(ones(n, 1), x) - elseif intercept && isnothing(x) + + elseif intercept == true && isnothing(x) == true x = ones(n, 1) + d_names = ["d$y" for y = 1:size(d[:,:],2)] + #x_names = ["x$y" for y = 1:size(x,2)]; + coef_names = append!(d_names, ["intercept"]); + + elseif intercept == false && isnothing(x) == true + d_names = ["d$y" for y = 1:size(d[:,:],2)] + #x_names = ["x$y" for y = 1:size(x,2)]; + coef_names = d_names; + elseif intercept == false && isnothing(x) == false + d_names = ["d$y" for y = 1:size(d[:,:],2)] + x_names = ["x$y" for y = 1:size(x,2)]; + coef_names = append!(d_names, x_names) end a1 = size(d, 2) @@ -58,6 +83,7 @@ function tsls(d::Array, y::Array, z::Array, x::Union{Nothing, Array} = nothing; else se = sqrt(VC1) end + b = hcat(coef_names, b) res = Dict("coefficients" => b, "vcov" => VC1, "se" => se, "residuals" => e, "sample_size" => n) return res end \ No newline at end of file diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index 7c59879..aa02f26 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -36,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables, Distributions" + "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables" ] }, { @@ -173,127 +173,7 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "print_effects (generic function with 2 methods)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "function print_effects(object, digits = 3)\n", - " if length(object[\"coefficients\"]) != 0\n", - " b = [\"X$y\" for y = object[\"index\"]]\n", - " b = reshape(b,(1,length(b)))\n", - " a = vcat(b, round.(object[\"coefficients\"]', digits = digits))\n", - " if length(object[\"coefficients\"]) <= 10\n", - " \n", - " println(\"Coefficients:\\n\")\n", - " pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:])\n", - " else \n", - " for i in 1:trunc(length(object[\"coefficients\"])/10)\n", - " pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i])\n", - " end\n", - " pretty_table(a[2,10*trunc(length(object[\"coefficients\"])/10)+1:length(object[\"coefficients\"])]',\n", - " tf = tf_borderless, header = a[1,10*trunc(length(object[\"coefficients\"])/10)+1:length(object[\"coefficients\"])])\n", - " end\n", - " else \n", - " print(\"No coefficients\\n\")\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "summary_effects (generic function with 1 method)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "function summary_effects(object)\n", - " if length(object) != 0\n", - " k = length(object[\"coefficients\"])\n", - " table = zeros(k, 4)\n", - " table[:, 1] .= object[\"coefficients\"]\n", - " table[:, 2] .= object[\"se\"]\n", - " table[:, 3] .= table[:, 1]./table[:, 2]\n", - " table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3]))\n", - " table1 = DataFrame(hcat([\"X$y\" for y = object[\"index\"]], table), :auto)\n", - " rename(table1, [\"index\", \"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"])\n", - " print(\"Estimates and significance testing of the effect of target variables\", \n", - " \"\\n\")\n", - " pretty_table(table, show_row_number = false, header = [\"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"], tf = tf_borderless, row_names = [\"X$y\" for y = object[\"index\"]])\n", - " print(\"---\", \"\\n\", \"Signif. codes:\",\"\\n\", \"0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\")\n", - " print(\"\\n\")\n", - " else\n", - " print(\"No coefficients\\n\")\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "confint_effects (generic function with 2 methods)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "function confint_effects(object, level = 0.95)\n", - " n = object[\"sample_size\"]\n", - " k = length(object[\"coefficients\"])\n", - " cf = object[\"coefficients\"]\n", - " #pnames <- names(cf)\n", - " # if (missing(parm)) \n", - " # parm <- pnames else if (is.numeric(parm)) \n", - " # parm <- pnames[parm]\n", - " a = (1 - level)/2\n", - " a = [a, 1 - a]\n", - " fac = quantile.(Normal(), a)\n", - " pct = string.(round.(a; digits = 3)*100, \"%\")\n", - " ses = object[\"se\"]\n", - " c_i = []\n", - " for i in 1:length(cf)\n", - " if i == 1\n", - " c_i = (cf[i] .+ ses[i] .* fac)[:,:]'\n", - " else\n", - " c_i = vcat(c_i, (cf[i] .+ ses[i] * fac)[:,:]')\n", - " end\n", - " end\n", - " table1 = DataFrame(hcat([\"X$y\" for y = object[\"index\"]], c_i), :auto)\n", - " rename(table1, vcat(\"index\", pct))\n", - " #ci = NamedArray(c_i, (1:size(c_i)[1], pct))\n", - " ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = [\"X$y\" for y = object[\"index\"]])\n", - " #return c_i;;\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 1092, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1093, "metadata": {}, "outputs": [], "source": [ @@ -318,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1094, "metadata": {}, "outputs": [ { @@ -327,9 +207,9 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", + " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", "\n", - " 2.925 2.903 3.101 -0.227\n" + " 2.925 2.903 3.101 -0.227\n" ] } ], @@ -339,14 +219,27 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1096, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Estimates and significance testing of the effect of target variables\n", + "Estimates and significance testing of the effect of target variables" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", " \u001b[1m X1 \u001b[0m 2.92541 0.103597 28.2384 1.97828e-175\n", @@ -365,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1097, "metadata": {}, "outputs": [ { @@ -415,7 +308,33 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 1084, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1085, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 1086, "metadata": {}, "outputs": [], "source": [ @@ -440,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1087, "metadata": {}, "outputs": [ { @@ -449,7 +368,7 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m X1 \u001b[0m\n", + " \u001b[1m 1 \u001b[0m\n", "\n", " -0.053\n" ] @@ -461,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1088, "metadata": {}, "outputs": [ { @@ -476,15 +395,40 @@ "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] + }, + { + "data": { + "text/html": [ + "

1 rows × 4 columns

Estimate.Std. Errort valuePr(>|t|)
Float64Float64Float64Float64
1-0.053330.0143283-3.7220.000197655
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccc}\n", + "\t& Estimate. & Std. Error & t value & Pr(>|t|)\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & -0.05333 & 0.0143283 & -3.722 & 0.000197655 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×4 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate. \u001b[0m\u001b[1m Std. Error \u001b[0m\u001b[1m t value \u001b[0m\u001b[1m Pr(>|t|) \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────────────────────────────\n", + " 1 │ -0.05333 0.0143283 -3.722 0.000197655" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "HDMjl.r_summary(lasso_effect);" + "HDMjl.r_summary(lasso_effect)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1089, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 1090, "metadata": {}, "outputs": [ { @@ -502,7 +446,7 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m X1 \u001b[0m\n", + " \u001b[1m 1 \u001b[0m\n", "\n", " -0.045\n" ] @@ -514,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 1091, "metadata": {}, "outputs": [ { @@ -573,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 1066, "metadata": {}, "outputs": [ { @@ -599,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 1067, "metadata": {}, "outputs": [ { @@ -633,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 1068, "metadata": {}, "outputs": [], "source": [ @@ -642,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 1069, "metadata": {}, "outputs": [ { @@ -651,9 +595,9 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m X1 \u001b[0m\n", + " \u001b[1m d1 \u001b[0m\n", "\n", - " 1.276\n" + " 1.276\n" ] } ], @@ -663,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 1070, "metadata": {}, "outputs": [ { @@ -671,9 +615,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " 1 1.27623 0.87538 1.45791 0.144865\n", + " d1 1.27623 0.87538 1.45791 0.144865\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -686,16 +630,16 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 1071, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " 1 -0.439485 2.99194\n" + " d1 -0.439485 2.99194\n" ] } ], @@ -712,7 +656,42 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "using Crayons, Distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 1056, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1057, "metadata": {}, "outputs": [], "source": [ @@ -729,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 1058, "metadata": {}, "outputs": [], "source": [ @@ -738,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 1059, "metadata": {}, "outputs": [ { @@ -746,9 +725,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " 1 -0.451027 2.32377 -0.194093 0.846103\n", + " d1 -0.0122757 0.0376474 -0.326069 0.744372\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -761,16 +740,16 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 1060, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " 1 -5.00553 4.10348\n" + " d1 -0.0860632 0.0615119\n" ] } ], @@ -780,7 +759,28 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 1061, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m d1 \u001b[0m\n", + "\n", + " -0.012\n" + ] + } + ], + "source": [ + "HDMjl.r_print(lasso_IV_Z)" + ] + }, + { + "cell_type": "code", + "execution_count": 1062, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 1063, "metadata": {}, "outputs": [ { @@ -797,9 +797,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m Row \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " 1 -0.0449578 0.0801865 -0.560665 0.575026\n", + " d1 -0.0449578 0.0801865 -0.560665 0.575026\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -812,16 +812,16 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 1064, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m Row \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " 1 -0.202121 0.112205\n" + " d1 -0.202121 0.112205\n" ] } ], @@ -831,10 +831,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1065, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m d1 \u001b[0m\n", + "\n", + " -0.045\n" + ] + } + ], + "source": [ + "HDMjl.r_print(lasso_IV_XZ)" + ] }, { "cell_type": "markdown", @@ -994,20 +1008,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - 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+------------------------------------------------- 1 file changed, 101 insertions(+), 5001 deletions(-) diff --git a/data/4_2.csv b/data/4_2.csv index d2e9e9d..b74716f 100644 --- a/data/4_2.csv +++ b/data/4_2.csv @@ -1,5001 +1,101 @@ -"y","d","x1","x2","x3","x4","x5","x6","x7","x8","x9","x10","x11","x12","x13","x14","x15","x16","x17","x18","x19" --3.18774933281664,-0.626453810742332,-1.5163733082107,-0.804331599841126,-0.213908975399592,0.235348522634884,0.196562061461095,0.617922308455229,0.698630850023618,-0.221257092554474,0.537655940605479,0.525890816836908,-0.131044322597261,0.341334100225537,-1.20634357353483,-1.0020361087866,0.350028225439485,-1.55915937207074,-1.0972656474627,-0.0950430689874302,0.990653173132982 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From 18141713620355451e04f07686535f23a91ec632 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Mon, 10 Oct 2022 02:01:20 -0500 Subject: [PATCH 12/28] Create 7_.csv --- data/7_.csv | 101 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 data/7_.csv diff --git a/data/7_.csv b/data/7_.csv new file mode 100644 index 0000000..6c0e425 --- /dev/null +++ b/data/7_.csv @@ -0,0 +1,101 @@ +"Y","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25","V26","V27","V28","V29","V30","V31","V32","V33","V34","V35","V36","V37","V38","V39","V40","V41" 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+-8.43435218609643,-0.473400636439312,-0.38107605110892,-0.305815419766971,0.865223099717138,-0.873264535062044,-0.763086264548317,-0.304508837259419,1.16912259199149,-0.526152469412621,-0.697318199400121,0.38200755917038,-0.641401156182851,0.635283144521572,0.736959753190812,-0.911925870779559,0.0174632661510191,-0.851220441380158,-0.757488401365975,0.738477114377377,-0.311973356192691,-1.20911412955621,1.05948916812251,0.232532403876864,-0.67074502026203,-1.33248719429825,-0.0113593445829337,-2.19122580131585,-2.36182966438966,0.189044548616711,-1.07777603746419,-0.476051237647421,-0.0742190150722613,1.99498624372389,1.15627784430579,0.446075601086465,-0.744367045365983,-0.627745333209963,-2.11185369823736,1.05768891304478,-0.913313030042956 From 633cd81e7096a7677bff12cbabf1e81181c83858 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Mon, 10 Oct 2022 17:07:13 -0500 Subject: [PATCH 13/28] # --- src/help_functions.jl | 9 ++-- src/rlasso.jl | 8 ++-- src/rlassoEffect.jl | 101 +++++++++++++++++++++++++++++------------ src/rlassoIVselectZ.jl | 16 ++++++- 4 files changed, 97 insertions(+), 37 deletions(-) diff --git a/src/help_functions.jl b/src/help_functions.jl index 82bfe15..57ef8e2 100644 --- a/src/help_functions.jl +++ b/src/help_functions.jl @@ -7,6 +7,7 @@ function init_values(x, y; number::Int64 = 5, intercept::Bool = true) for i in 1:p append!(corr, abs.(cor(y, x[:, i]))) end + corr = replace(corr, NaN => minimum(filter(!isnan,corr)) -1) index = sortperm(corr, rev = true)[1 : min(number, p)] coefficients = zeros(p) @@ -39,7 +40,7 @@ end function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_dependent_lambda::Bool = false, lambda_start = nothing, c::Float64 = 1.1, gamma::Float64 = 0.1, numSim::Int = 5000, y = nothing, x = nothing) # homoskedastic and X-independent - if homoskedastic == true & !X_dependent_lambda + if (homoskedastic == true) & !X_dependent_lambda p = size(x, 2) n = size(x, 1) lambda0 = 2 * c * sqrt(n) * quantile(Normal(0.0, 1.0), 1 - gamma / (2 * p)) @@ -47,7 +48,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = zeros(p) .+ lambda0 * Ups0 # homoskedastic and X-dependent - elseif homoskedastic == true & X_dependent_lambda + elseif (homoskedastic == true) & X_dependent_lambda p = size(x, 2) n = size(x, 1) R = numSim @@ -66,7 +67,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = lambda0 * Ups0 # heteroskeddastic and X-independent - elseif homoskedastic == false & !X_dependent_lambda + elseif (homoskedastic == false) & !X_dependent_lambda p = size(x, 2) n = size(x, 1) lambda0 = 2 * c * sqrt(n) * quantile(Normal(0.0, 1.0), 1 - gamma / (2 * p)) @@ -74,7 +75,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = lambda0 * Ups0 # heteroskedastic and X-dependent - elseif homoskedastic == false & X_dependent_lambda + elseif (homoskedastic == false) & X_dependent_lambda p = size(x, 2) n = size(x, 1) R = numSim diff --git a/src/rlasso.jl b/src/rlasso.jl index 732d3a6..6cdaf01 100644 --- a/src/rlasso.jl +++ b/src/rlasso.jl @@ -76,22 +76,22 @@ function rlasso(x, y; post = true, intercept = true, model = true, s1 = sqrt(var(e1)) # Homoskedastic and X-independent - if homoskedastic == true & !X_dependent_lambda + if (homoskedastic == true) && !X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 # Homoskedastic and X-dependent - elseif homoskedastic == true & X_dependent_lambda + elseif (homoskedastic == true) && X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 # Heteroskedastic and X-independent - elseif homoskedastic == false & !X_dependent_lambda + elseif (homoskedastic == false) && !X_dependent_lambda Ups1 = 1 / sqrt(n) .* sqrt.(((e1 .^ 2)' * (x .^ 2))') lambda = Ups1 * pen["lambda0"] # Heteroskedastic and X-dependent - elseif homoskedastic == false & X_dependent_lambda + elseif (homoskedastic == false) && X_dependent_lambda lc = lambdaCalculation(x = x, y = e1, homoskedastic = homoskedastic, X_dependent_lambda = X_dependent_lambda, lambda_start = lambda_start, c = c, gamma = gamma) Ups1 = lc["Ups0"] lambda = lc["lambda"] diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index c801e62..9c32b93 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -132,20 +132,25 @@ function r_summary(object::rlassoEffect1) return table1 end -function r_print(object::rlassoEffect1, digits = 3) +function r_print(object::rlassoEffect1; digits = 3) if length(object.coefficients) != 0 b = ["$y" for y = 1:length(object.coefficients)] b = reshape(b,(1,length(b))) - a = vcat(b, round.(object.coefficients', digits = 3)) + a = vcat(b, round.(object.coefficients', digits = digits)) if length(object.coefficients) <= 10 println("Coefficients:\n") pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:]) - else - for i in 1:trunc(length(object.coefficients)/10) + + elseif string(length(object.coefficients))[count("", string(length(object.coefficients)))-1:count("", string(length(object.coefficients)))-1] == "0" + for i in 1:convert(Int, trunc(length(object.coefficients)/10)) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) + end + else + for i in 1:convert(Int, trunc(length(object.coefficients)/10)) pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i]) end - pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]) end else @@ -244,20 +249,27 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "parti end -function r_print(object::rlassoEffects1, digits = 3) +function r_print(object::rlassoEffects1; digits = 3) if length(object.coefficients) != 0 b = ["X$y" for y = object.index] b = reshape(b,(1,length(b))) a = vcat(b, round.(object.coefficients', digits = digits)) if length(object.coefficients) <= 10 - + println("Coefficients:\n") pretty_table(a[2,:]', tf = tf_borderless, header = a[1,:], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c) #, header_crayon =crayon"blue") - else - for i in 1:trunc(length(object.coefficients)/10) + + elseif string(length(object.coefficients))[count("", string(length(object.coefficients)))-1:count("", string(length(object.coefficients)))-1] == "0" + for i in 1:convert(Int, trunc(length(object.coefficients)/10)) pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c)#, header_crayon =crayon"green") + print("\n") end - pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 9, + else + for i in 1:convert(Int, trunc(length(object.coefficients)/10)) + pretty_table(a[2,10*(i-1)+1:10*i]', tf = tf_borderless, header = a[1,10*(i-1)+1:10*i], nosubheader = true, equal_columns_width = true, columns_width = 9, alignment=:c)#, header_crayon =crayon"green") + print("\n") + end + pretty_table(a[2,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 9, tf = tf_borderless, header = a[1,10*trunc(length(object.coefficients)/10)+1:length(object.coefficients)])#, header_crayon =crayon"green") end else @@ -274,18 +286,19 @@ function r_summary(object::rlassoEffects1) table[:, 3] .= table[:, 1]./table[:, 2] table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) table1 = DataFrame(hcat(["X$y" for y = object.index], table), :auto) - rename(table1, ["index", "Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) + table1 = rename(table1, ["index", "Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) print("Estimates and significance testing of the effect of target variables", "\n") pretty_table(table, show_row_number = false, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless, row_names = ["X$y" for y = object.index]) print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") print("\n") + return table1 else print("No coefficients\n") end end -function r_confint(object::rlassoEffects1, level = 0.95) +function r_confint(object::rlassoEffects1, level = 0.95; joint = false) n = object.sample_size k = length(object.coefficients) cf = object.coefficients @@ -293,22 +306,54 @@ function r_confint(object::rlassoEffects1, level = 0.95) # if (missing(parm)) # parm <- pnames else if (is.numeric(parm)) # parm <- pnames[parm] - a = (1 - level)/2 - a = [a, 1 - a] - fac = quantile.(Normal(), a) - pct = string.(round.(a; digits = 3)*100, "%") - ses = object.se - c_i = [] - for i in 1:length(cf) - if i == 1 - c_i = (cf[i] .+ ses[i] .* fac)[:,:]' - else - c_i = vcat(c_i, (cf[i] .+ ses[i] * fac)[:,:]') + if !joint + a = (1 - level)/2 + a = [a, 1 - a] + fac = quantile.(Normal(), a) + pct = string.(round.(a; digits = 3)*100, "%") + ses = object.se + c_i = [] + for i in 1:length(cf) + if i == 1 + c_i = (cf[i] .+ ses[i] .* fac)[:,:]' + else + c_i = vcat(c_i, (cf[i] .+ ses[i] * fac)[:,:]') + end + end + table1 = DataFrame(hcat(["X$y" for y = object.index], c_i), :auto) + table1 = rename(table1, vcat("index", pct)) + #ci = NamedArray(c_i, (1:size(c_i)[1], pct)) + ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = ["X$y" for y = object.index]) + return table1 + end + if joint + B = 500 + e = object.dict["residuals"]["e"] + v = object.dict["residuals"]["v"] + ev = e .* v + Ev2 = mean(v.^2, dims = 1) + Omegahat = zeros(k, k); + for j in 1:k + for l in 1:k + Omegahat[j,l] = Omegahat[l,j] = 1/(Ev2[j]*Ev2[l]) .* mean(ev[:,j].*ev[:,l]) + end + end + var = diag(Omegahat) + sim = zeros(B);; + for i in 1:B + beta_i = rand(MvNormal(zeros(k), Omegahat./n)); + sim[i] = maximum(abs.(beta_i ./ sqrt.(var))) end + a = (1 - level) #not dividing by 2! + ab = [a/2, 1 - a/2] + pct = string.(round.(ab; digits = 3)*100, "%"); + c_i = zeros(length(cf), 2) + hatc = quantile(sim, 1-a) + c_i[:, 1] = cf .- hatc .* sqrt.(var) + c_i[:, 2] = cf .+ hatc .* sqrt.(var) + table1 = DataFrame(hcat(["X$y" for y = object.index], c_i), :auto) + table1 = rename(table1, vcat("index", pct)) + ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = ["X$y" for y = object.index]) + return table1 end - table1 = DataFrame(hcat(["X$y" for y = object.index], c_i), :auto) - rename(table1, vcat("index", pct)) - #ci = NamedArray(c_i, (1:size(c_i)[1], pct)) - ci = pretty_table(c_i; header = pct, show_row_number = false, tf = tf_borderless, row_names = ["X$y" for y = object.index]) - #return c_i;; end \ No newline at end of file diff --git a/src/rlassoIVselectZ.jl b/src/rlassoIVselectZ.jl index 9430fee..b17714b 100644 --- a/src/rlassoIVselectZ.jl +++ b/src/rlassoIVselectZ.jl @@ -1,3 +1,17 @@ +function ginv(X, tol = sqrt(eps(Float64))) + Xsvd = svd(X); + Positive = Xsvd.S .> maximum(tol * Xsvd.S[1]); + if all(Positive) + b = Xsvd.V *(1 ./ Xsvd.S .* (Xsvd.U')); + elseif (!any(Positive)) + b = zeros(size(X)[2],size(X)[1]) + else + c1 = Xsvd.V[:,Positive] + c2 = (Xsvd.U[:, Positive])'; + b = c1 * ((1 ./ Xsvd.S[Positive]) .* c2) + end +end + function rlassoIVselectZ(x, d, y, z; post::Bool = true, intercept::Bool = true) n = size(y, 1) @@ -51,7 +65,7 @@ function rlassoIVselectZ(x, d, y, z; post::Bool = true, intercept::Bool = true) Dhat = hcat(Dhat, x) d = hcat(d, x) - alpha_hat = pinv(Dhat' * d) * (Dhat' * y) + alpha_hat = ginv(Dhat' * d) * (Dhat' * y) residuals = y - d * alpha_hat Omega_hat = Dhat' * (Dhat .* (residuals .^ 2)) Q_hat_inv = pinv(d' * Dhat) From 5154ac74e5ce7fbadc9496135719b5e25a462bde Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Tue, 11 Oct 2022 14:25:06 -0500 Subject: [PATCH 14/28] # --- src/rlassoEffect.jl | 4 +- src/rlassoIVselectZ.jl | 2 +- tutorial/HDMjl_tutorial_1.ipynb | 1405 ++++++++++++++++--- tutorial/python.ipynb | 338 +++++ tutorial/r_file.ipynb | 2285 ++++++++++++++++++++++++++++++- 5 files changed, 3799 insertions(+), 235 deletions(-) create mode 100644 tutorial/python.ipynb diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index 9c32b93..ffe0655 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -207,8 +207,8 @@ function rlassoEffects(x, y; index = 1:size(x, 2), I3 = nothing, method = "parti # names(coefficients) <- names(se) <- names(t) <- names(pval) <- names(lasso.regs) <- colnames(reside) <- colnames(residv) <- colnames(selection.matrix) <- colnames(x)[index] for i in 1:k - d = x0[:, i] - xt = x0[:, Not(i)] + d = x0[:, index[i]] + xt = x0[:, Not(index[i])] # Variables de control if isnothing(I3) I3m = I3 diff --git a/src/rlassoIVselectZ.jl b/src/rlassoIVselectZ.jl index b17714b..fb62a7d 100644 --- a/src/rlassoIVselectZ.jl +++ b/src/rlassoIVselectZ.jl @@ -68,7 +68,7 @@ function rlassoIVselectZ(x, d, y, z; post::Bool = true, intercept::Bool = true) alpha_hat = ginv(Dhat' * d) * (Dhat' * y) residuals = y - d * alpha_hat Omega_hat = Dhat' * (Dhat .* (residuals .^ 2)) - Q_hat_inv = pinv(d' * Dhat) + Q_hat_inv = ginv(d' * Dhat) vcov = Q_hat_inv * Omega_hat * Q_hat_inv' alpha_hat = hcat(coef_names, alpha_hat) res = Dict("coefficients" => alpha_hat[1:ke, :], "se" => sqrt.(diag(vcov))[1:ke], "residuals" => residuals, "samplesize" => n) diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index 254192b..e8b5247 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -23,11 +23,136 @@ }, { "cell_type": "code", - "execution_count": 445, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#main`\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#main`\u001b[39m\n", + "┌ Info: We haven't cleaned this depot up for a bit, running Pkg.gc()...\n", + "└ @ Pkg C:\\buildbot\\worker\\package_win64\\build\\usr\\share\\julia\\stdlib\\v1.8\\Pkg\\src\\Pkg.jl:719\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m manifest files: 2 found" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m artifact files: 99 found\n", + "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m scratchspaces: 13 found\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Deleted\u001b[22m\u001b[39m 234 package installations (598.311 MiB)\n", + "\u001b[32m\u001b[1m Deleted\u001b[22m\u001b[39m 4 repos (9.640 MiB)\n" + ] + } + ], + "source": [ + "Pkg.rm(\"HDMjl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/d2cml-ai/HDMjl.jl`" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `C:\\Users\\User\\.julia\\registries\\General`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "package versions...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ - "#import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\")" + "import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\")" ] }, { @@ -41,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -50,16 +175,9 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 2, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, { "data": { "text/plain": [ @@ -76,11 +194,11 @@ }, { "cell_type": "code", - "execution_count": 444, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "#Pkg.develop(path = \"E:/causal_ml/hdm_paper/HDMjl.jl\")" + "using HDMjl" ] }, { @@ -159,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 911, "metadata": {}, "outputs": [], "source": [ @@ -169,24 +287,243 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 696, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 954, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_1.csv\"\n", + "dta = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(dta);\n", + "y = dta[:,\"y\"];\n", + "d = dta[:,\"d\"];\n", + "x = Matrix(dta[:,3:end]);" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1017, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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Float64Float64
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" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0.972739 & 0.0136868 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 0.972739 0.0136868" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rY_1 = HDMjl.rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), y);\n", + "rY = rY_1[\"residuals\"]\n", + "rD_1 = HDMjl.rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), d);\n", + "rD = rD_1[\"residuals\"]\n", + "partial_fit_postlasso = lm(hcat(ones(length(y)), rD), rY)\n", + "DataFrame(Estimate = coef(partial_fit_postlasso)[2], Std_Error = stderror(partial_fit_postlasso)[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 1059, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 0.972739 0.0136868 71.0715 0.0\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "Eff = HDMjl.rlassoEffect(x, y, d, method = \"partialling out\");\n", + "HDMjl.r_summary(Eff);" + ] + }, + { + "cell_type": "code", + "execution_count": 1062, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m 1 \u001b[0m\n", + "\n", + " 0.973\n" + ] + } + ], + "source": [ + "HDMjl.r_print(Eff)" + ] + }, + { + "cell_type": "code", + "execution_count": 1063, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 0.978075 0.0141562 69.0914 0.0\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "Eff = HDMjl.rlassoEffect(x, y, d, method = \"double selection\");\n", + "HDMjl.r_summary(Eff);" + ] }, { "cell_type": "markdown", @@ -201,19 +538,60 @@ "metadata": {}, "outputs": [], "source": [ - "using Random, Distributions, PrettyTables, DataFrames\n", - "Random.seed!(1234);\n", - "n = 100;\n", - "p = 100;\n", - "s = 3;\n", - "x = randn(n, p);\n", - "beta = vcat(fill(3, s), zeros(p - s));\n", - "y =1 .+ x * beta + randn(n);" + "# using Random, Distributions, PrettyTables, DataFrames\n", + "# Random.seed!(1234);\n", + "# n = 100;\n", + "# p = 100;\n", + "# s = 3;\n", + "# x = randn(n, p);\n", + "# beta = vcat(fill(3, s), zeros(p - s));\n", + "# y =1 .+ x * beta + randn(n);" ] }, { "cell_type": "code", - "execution_count": 1093, + "execution_count": 1170, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [], + "source": [ + "using CSV\n", + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_2.csv\"\n", + "data = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(data);\n", + "y = data[:,1];\n", + "#d = dta[:,\"d\"];\n", + "x = Matrix(data[:,2:end]);" + ] + }, + { + "cell_type": "code", + "execution_count": 358, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 1094, + "execution_count": 360, "metadata": {}, "outputs": [ { @@ -233,92 +611,286 @@ "\n", " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", "\n", - " 2.925 2.903 3.101 -0.227\n" + " 2.9445 3.0413 2.9754 0.072\n" ] } ], "source": [ - "HDMjl.r_print(lasso_effects)" + "HDMjl.r_print(lasso_effects, digits = 4)" ] }, { "cell_type": "code", - "execution_count": 1096, + "execution_count": 1180, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Estimates and significance testing of the effect of target variables" + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.94448 0.0881468 33.4043 1.1892e-244\n", + " \u001b[1m X2 \u001b[0m 3.04127 0.083891 36.2527 9.0141e-288\n", + " \u001b[1m X3 \u001b[0m 2.9754 0.0780394 38.127 4.58085e-318\n", + " \u001b[1m X50 \u001b[0m 0.0719553 0.0776455 0.926716 0.354074\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] - }, + } + ], + "source": [ + "HDMjl.r_summary(lasso_effects);" + ] + }, + { + "cell_type": "code", + "execution_count": 1179, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.77171 3.11724\n", + " \u001b[1m X2 \u001b[0m 2.87685 3.2057\n", + " \u001b[1m X3 \u001b[0m 2.82245 3.12836\n", + " \u001b[1m X50 \u001b[0m -0.0802271 0.224138\n" ] - }, + } + ], + "source": [ + "HDMjl.r_confint(lasso_effects);" + ] + }, + { + "cell_type": "code", + "execution_count": 1186, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " \u001b[1m X1 \u001b[0m 2.92541 0.103597 28.2384 1.97828e-175\n", - " \u001b[1m X2 \u001b[0m 2.90258 0.105907 27.407 2.26599e-165\n", - " \u001b[1m X3 \u001b[0m 3.10095 0.110626 28.031 6.80893e-173\n", - " \u001b[1m X50 \u001b[0m -0.22712 0.0910927 -2.49329 0.0126566\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + " \u001b[1m X1 \u001b[0m 2.72895 3.16001\n", + " \u001b[1m X2 \u001b[0m 2.83806 3.24448\n", + " \u001b[1m X3 \u001b[0m 2.7842 3.1666\n", + " \u001b[1m X50 \u001b[0m -0.114585 0.258496\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(lasso_effects, joint = true);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3. Application: the effect of gender on wage" + ] + }, + { + "cell_type": "code", + "execution_count": 1187, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(29217, 23)" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "HDMjl.r_summary(lasso_effects)" + "url = \"https://github.com/cran/hdm/raw/master/data/cps2012.rda\"\n", + "cps2012 = load(download(url))[\"cps2012\"];\n", + "n, p = size(cps2012);\n", + "size(cps2012)" ] }, { "cell_type": "code", - "execution_count": 1097, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "(29217, 116)" + ] + } + ], + "source": [ + "x_formula = @formula(lnw ~ -1 + female + female*widowed + female*divorced + female*separated + female*nevermarried +\n", + " female*hsd08 + female*hsd911 + female*hsg + female*cg + female*ad + female*mw + female*so + female*we + female*exp1 + female*exp2 + female*exp3)\n", + "x_dframe = ModelFrame( x_formula, cps2012)\n", + "x1 = ModelMatrix(x_dframe)\n", + "X = x1.m[:,Not(2:16)]\n", + "size(X)\n", + "fom_1 = names(cps2012)\n", + "fom_1 = [\"widowed\", \"divorced\", \"separated\", \"nevermarried\", \"hsd08\", \"hsd911\", \"hsg\", \"cg\", \"ad\", \"mw\", \"so\",\n", + " \"we\", \"exp1\", \"exp2\", \"exp3\"];\n", + "data = cps2012[:,fom_1];\n", + "sub_data = Matrix(data)\n", + "for i in 1:size(data)[2]\n", + " if i <= (size(data)[2] -1)\n", + " sub_data = hcat(sub_data, Matrix(data[:, i] .* data[:, Not(1:i)]) )\n", + " end\n", + "end\n", + "size(sub_data)\n", + "x = hcat(X, sub_data)\n", + "size(x)\n", + "filter = var.(eachcol(x)) .!= 0\n", + "x = x[:,filter]\n", + "print(size(x))\n", + "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", + "y = cps2012.lnw;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", + "y = cps2012.lnw;" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "effects_female = HDMjl.rlassoEffects(x, y, index = index_gender);" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " \u001b[1m X1 \u001b[0m 2.72236 3.12845\n", - " \u001b[1m X2 \u001b[0m 2.69501 3.11016\n", - " \u001b[1m X3 \u001b[0m 2.88413 3.31778\n", - " \u001b[1m X50 \u001b[0m -0.405659 -0.048582\n" + " \u001b[1m X1 \u001b[0m -0.154593 0.0501626 -3.08184 0.00205726\n", + " \u001b[1m X17 \u001b[0m -0.0343048 0.19287 -0.177865 0.858829\n", + " \u001b[1m X18 \u001b[0m -0.19402 0.0607806 -3.19214 0.00141224\n", + " \u001b[1m X19 \u001b[0m -0.113688 0.108023 -1.05245 0.292595\n", + " \u001b[1m X20 \u001b[0m -0.249804 0.0376073 -6.64244 3.08542e-11\n", + " \u001b[1m X21 \u001b[0m -0.391887 0.211089 -1.8565 0.0633823\n", + " \u001b[1m X22 \u001b[0m -0.35908 0.137517 -2.61116 0.0090235\n", + " \u001b[1m X23 \u001b[0m -0.269762 0.103718 -2.60093 0.00929725\n", + " \u001b[1m X24 \u001b[0m 0.269623 0.0430528 6.26261 3.78592e-10\n", + " \u001b[1m X25 \u001b[0m 0.429058 0.0460721 9.31276 1.24553e-20\n", + " \u001b[1m X26 \u001b[0m -0.141533 0.0427294 -3.3123 0.000925306\n", + " \u001b[1m X27 \u001b[0m -0.0790852 0.042853 -1.8455 0.0649645\n", + " \u001b[1m X28 \u001b[0m -0.0583598 0.0491329 -1.18779 0.234914\n", + " \u001b[1m X29 \u001b[0m 0.0325737 0.0170967 1.90527 0.0567456\n", + " \u001b[1m X30 \u001b[0m -0.273506 0.248368 -1.10121 0.270804\n", + " \u001b[1m X31 \u001b[0m 0.0 NaN NaN NaN\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] } ], "source": [ - "HDMjl.r_confint(lasso_effects)" + "HDMjl.r_summary(effects_female);" ] }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 15, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "effects_female1 = HDMjl.rlassoEffects(x[:, index_gender], y);" + ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m -0.283832 0.00692373 -40.9941 0.0\n", + " \u001b[1m X2 \u001b[0m -0.141256 0.0382915 -3.68897 0.000225164\n", + " \u001b[1m X3 \u001b[0m -0.0767676 0.0109705 -6.99762 2.60355e-12\n", + " \u001b[1m X4 \u001b[0m -0.105677 0.0266385 -3.96707 7.27622e-5\n", + " \u001b[1m X5 \u001b[0m -0.135126 0.0099468 -13.5849 4.9236e-42\n", + " \u001b[1m X6 \u001b[0m -0.620469 0.0535067 -11.5961 4.31297e-31\n", + " \u001b[1m X7 \u001b[0m -0.390831 0.0237062 -16.4864 4.59201e-61\n", + " \u001b[1m X8 \u001b[0m -0.173018 0.00941576 -18.3754 2.06932e-75\n", + " \u001b[1m X9 \u001b[0m 0.347913 0.00907484 38.3382 1.413e-321\n", + " \u001b[1m X10 \u001b[0m 0.595179 0.0108409 54.9013 0.0\n", + " \u001b[1m X11 \u001b[0m -0.106798 0.00950928 -11.2309 2.87586e-29\n", + " \u001b[1m X12 \u001b[0m -0.0544776 0.00956874 -5.69329 1.24611e-8\n", + " \u001b[1m X13 \u001b[0m -0.0118311 0.0104091 -1.13661 0.255702\n", + " \u001b[1m X14 \u001b[0m 0.0533367 0.00470804 11.3289 9.44218e-30\n", + " \u001b[1m X15 \u001b[0m -0.197522 0.0280961 -7.03021 2.06218e-12\n", + " \u001b[1m X16 \u001b[0m 0.0250002 0.00499574 5.00431 5.60622e-7\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], "source": [ - "### 4.3. Application: the effect of gender on wage" + "HDMjl.r_summary(effects_female1);" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [] @@ -332,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 1084, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -358,10 +930,11 @@ }, { "cell_type": "code", - "execution_count": 1085, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ + "using GLM\n", "using CodecXz\n", "using RData\n", "using DataFrames\n", @@ -369,12 +942,100 @@ "GrowthData = load(download(url))[\"GrowthData\"];\n", "y = GrowthData[:, 1];\n", "d = GrowthData[:, 3];\n", - "X = Matrix(GrowthData[:, Not(1, 2, 3)]);" + "X = Matrix(GrowthData[:, Not(1, 2, 3)]);\n", + "X_1 = Matrix(GrowthData[:, Not(1, 2)]);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:\n", + "\n", + "Coefficients:\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%\n", + "────────────────────────────────────────────────────────────────────────────────\n", + "x1 0.0 NaN NaN NaN NaN NaN\n", + "x2 -0.0366232 0.0216873 -1.69 0.0987 -0.08039 0.0071436\n", + "x3 -0.049561 0.0277206 -1.79 0.0810 -0.105503 0.00638146\n", + "x4 0.0109733 0.144971 0.08 0.9400 -0.28159 0.303536\n", + "x5 -0.61884 0.392592 -1.58 0.1225 -1.41112 0.173443\n", + "x6 0.0 NaN NaN NaN NaN NaN\n", + "x7 0.0653886 0.191762 0.34 0.7348 -0.321604 0.452381\n", + "x8 -0.0130227 0.220896 -0.06 0.9533 -0.458809 0.432764\n", + "x9 0.317601 0.136132 2.33 0.0245 0.0428751 0.592327\n", + "x10 0.0192542 0.104553 0.18 0.8548 -0.191742 0.23025\n", + "x11 -0.223675 0.111066 -2.01 0.0505 -0.447816 0.000465448\n", + "x12 -0.0285525 0.172121 -0.17 0.8690 -0.375908 0.318803\n", + "x13 -0.06466 0.110926 -0.58 0.5631 -0.288518 0.159198\n", + "x14 -0.0827085 0.106666 -0.78 0.4425 -0.29797 0.132553\n", + "x15 -0.00952063 0.0254533 -0.37 0.7103 -0.0608875 0.0418462\n", + "x16 0.0 NaN NaN NaN NaN NaN\n", + "x17 0.0785956 0.0659261 1.19 0.2399 -0.0544487 0.21164\n", + "x18 0.0 NaN NaN NaN NaN NaN\n", + "x19 0.0147886 0.0300756 0.49 0.6255 -0.0459065 0.0754837\n", + "x20 -0.313055 0.27212 -1.15 0.2565 -0.862216 0.236105\n", + "x21 0.101771 0.090829 1.12 0.2689 -0.0815298 0.285071\n", + "x22 0.0 NaN NaN NaN NaN NaN\n", + "x23 0.0 NaN NaN NaN NaN NaN\n", + "x24 0.357228 0.24998 1.43 0.1604 -0.147253 0.861708\n", + "x25 -0.287676 0.263885 -1.09 0.2819 -0.820218 0.244866\n", + "x26 -0.134781 0.144646 -0.93 0.3568 -0.426689 0.157128\n", + "x27 0.0 NaN NaN NaN NaN NaN\n", + "x28 -0.0570462 0.171219 -0.33 0.7407 -0.40258 0.288487\n", + "x29 0.0410234 0.0861237 0.48 0.6363 -0.132781 0.214828\n", + "x30 0.0122654 0.0897691 0.14 0.8920 -0.168896 0.193427\n", + "x31 0.0 NaN NaN NaN NaN NaN\n", + "x32 0.00856028 0.0183311 0.47 0.6429 -0.0284333 0.0455539\n", + "x33 -0.0141407 0.0214721 -0.66 0.5138 -0.0574733 0.0291918\n", + "x34 0.0 NaN NaN NaN NaN NaN\n", + "x35 -0.0401447 0.0489759 -0.82 0.4170 -0.138982 0.0586928\n", + "x36 0.0406806 0.0523095 0.78 0.4411 -0.0648842 0.146245\n", + "x37 0.0 NaN NaN NaN NaN NaN\n", + "x38 0.0 NaN NaN NaN NaN NaN\n", + "x39 0.0 NaN NaN NaN NaN NaN\n", + "x40 -0.0438831 0.0354963 -1.24 0.2232 -0.115518 0.0277514\n", + "x41 0.0198653 0.0177205 1.12 0.2686 -0.0158962 0.0556268\n", + "x42 0.0237951 0.018077 1.32 0.1952 -0.0126858 0.0602759\n", + "x43 -0.0245828 0.0246516 -1.00 0.3244 -0.0743318 0.0251662\n", + "x44 -2.58955e-8 1.06384e-7 -0.24 0.8089 -2.40586e-7 1.88795e-7\n", + "x45 0.0634478 0.134926 0.47 0.6406 -0.208844 0.33574\n", + "x46 -0.111512 0.33659 -0.33 0.7421 -0.790778 0.567755\n", + "x47 0.0 NaN NaN NaN NaN NaN\n", + "x48 -0.0673765 0.230006 -0.29 0.7710 -0.531548 0.396795\n", + "x49 0.0348377 0.112888 0.31 0.7591 -0.192979 0.262654\n", + "x50 0.0306221 0.11848 0.26 0.7973 -0.208481 0.269725\n", + "x51 0.114321 0.447811 0.26 0.7997 -0.789399 1.01804\n", + "x52 -0.0403413 0.217537 -0.19 0.8538 -0.479349 0.398666\n", + "x53 -0.0794911 0.229381 -0.35 0.7307 -0.542401 0.383419\n", + "x54 0.0 NaN NaN NaN NaN NaN\n", + "x55 -0.113094 0.133428 -0.85 0.4015 -0.382363 0.156175\n", + "x56 0.231347 0.201217 1.15 0.2568 -0.174726 0.637419\n", + "x57 2.00279e-5 0.000732672 0.03 0.9783 -0.00145856 0.00149862\n", + "x58 0.000279117 0.00108839 0.26 0.7989 -0.00191734 0.00247558\n", + "x59 -0.311523 0.18417 -1.69 0.0982 -0.683194 0.0601472\n", + "x60 0.298687 0.18672 1.60 0.1172 -0.0781284 0.675503\n", + "x61 -3.25316e-5 4.94833e-5 -0.66 0.5145 -0.000132393 6.73298e-5\n", + "x62 -0.0515082 0.0905298 -0.57 0.5724 -0.234205 0.131188\n", + "────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ls_effect = lm(hcat(ones(length(y)), X_1), y)" ] }, { "cell_type": "code", - "execution_count": 1086, + "execution_count": 1158, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 1087, + "execution_count": 1163, "metadata": {}, "outputs": [ { @@ -392,19 +1053,19 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m 1 \u001b[0m\n", + " \u001b[1m 1 \u001b[0m\n", "\n", - " -0.053\n" + " -0.0498\n" ] } ], "source": [ - "HDMjl.r_print(lasso_effect)" + "HDMjl.r_print(lasso_effect, digits = 4)" ] }, { "cell_type": "code", - "execution_count": 1088, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -412,47 +1073,22 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " 1 -0.05333 0.0143283 -3.722 0.000197655\n", + " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] - }, - { - "data": { - "text/html": [ - "

1 rows × 4 columns

Estimate.Std. Errort valuePr(>|t|)
Float64Float64Float64Float64
1-0.053330.0143283-3.7220.000197655
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Error \u001b[0m\u001b[1m t value \u001b[0m\u001b[1m Pr(>|t|) \u001b[0m\n", - "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", - "─────┼─────────────────────────────────────────────\n", - " 1 │ -0.05333 0.0143283 -3.722 0.000197655" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "HDMjl.r_summary(lasso_effect)" + "HDMjl.r_summary(lasso_effect);" ] }, { "cell_type": "code", - "execution_count": 1089, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 1090, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -470,19 +1106,19 @@ "text": [ "Coefficients:\n", "\n", - " \u001b[1m 1 \u001b[0m\n", + " \u001b[1m 1 \u001b[0m\n", "\n", - " -0.045\n" + " -0.05001\n" ] } ], "source": [ - "HDMjl.r_print(doublesel_effect)" + "HDMjl.r_print(doublesel_effect, 5)" ] }, { "cell_type": "code", - "execution_count": 1091, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -490,9 +1126,9 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " 1 -0.0453558 0.018656 -2.43116 0.0150506\n", + " 1 -0.0500059 0.0157914 -3.16666 0.00154203\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -500,9 +1136,84 @@ } ], "source": [ - "HDMjl.r_summary(doublesel_effect);" + "HDMjl.r_summary(doublesel_effect);" + ] + }, + { + "cell_type": "code", + "execution_count": 1169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:\n", + "\n", + "Coefficients:\n", + "────────────────────────────────────────────────────────────────────────────\n", + " Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%\n", + "────────────────────────────────────────────────────────────────────────────\n", + "x1 -0.0515095 0.0134834 -3.82 0.0003 -0.0785095 -0.0245095\n", + "x2 -0.0698222 0.0222899 -3.13 0.0027 -0.114457 -0.0251875\n", + "x3 0.144115 0.0817989 1.76 0.0835 -0.019685 0.307914\n", + "x4 -0.549012 0.33678 -1.63 0.1086 -1.2234 0.125378\n", + "x5 0.0 NaN NaN NaN NaN NaN\n", + "x6 -0.00178011 0.16532 -0.01 0.9914 -0.332827 0.329267\n", + "x7 -0.0499058 0.19071 -0.26 0.7945 -0.431796 0.331984\n", + "x8 0.344992 0.118384 2.91 0.0051 0.107931 0.582052\n", + "x9 -0.0276954 0.0967556 -0.29 0.7757 -0.221445 0.166054\n", + "x10 -0.191668 0.0974433 -1.97 0.0541 -0.386795 0.00345863\n", + "x11 -0.0617711 0.153902 -0.40 0.6897 -0.369954 0.246412\n", + "x12 -0.0199024 0.101637 -0.20 0.8454 -0.223427 0.183622\n", + "x13 -0.0329039 0.0960136 -0.34 0.7331 -0.225168 0.15936\n", + "x14 -0.0292139 0.0209751 -1.39 0.1691 -0.0712158 0.0127879\n", + "x15 1.00723 0.42872 2.35 0.0223 0.148738 1.86573\n", + "x16 0.0935241 0.0284037 3.29 0.0017 0.0366466 0.150402\n", + "x17 0.0 NaN NaN NaN NaN NaN\n", + "x18 0.0231697 0.0203674 1.14 0.2601 -0.0176153 0.0639547\n", + "x19 -0.898484 0.378689 -2.37 0.0211 -1.6568 -0.140171\n", + "x20 0.106289 0.0709861 1.50 0.1398 -0.0358584 0.248436\n", + "x21 0.0 NaN NaN NaN NaN NaN\n", + "x22 0.0 NaN NaN NaN NaN NaN\n", + "x23 0.566105 0.20177 2.81 0.0069 0.162067 0.970142\n", + "x24 -0.291019 0.248987 -1.17 0.2473 -0.789607 0.207569\n", + "x25 -0.0611354 0.126924 -0.48 0.6319 -0.315296 0.193025\n", + "x26 0.0 NaN NaN NaN NaN NaN\n", + "x27 -0.00073184 0.13671 -0.01 0.9957 -0.274489 0.273026\n", + "x28 -0.00117443 0.0656851 -0.02 0.9858 -0.132707 0.130358\n", + "x29 0.00512217 0.0725344 0.07 0.9439 -0.140125 0.15037\n", + "x30 0.0 NaN NaN NaN NaN NaN\n", + "x31 0.013586 0.00993516 1.37 0.1768 -0.00630877 0.0334808\n", + "x32 -0.0169288 0.0119869 -1.41 0.1633 -0.0409322 0.00707452\n", + "x33 0.0 NaN NaN NaN NaN NaN\n", + "x34 -0.00277954 0.0154127 -0.18 0.8575 -0.033643 0.0280839\n", + "x35 0.00440392 0.0165118 0.27 0.7907 -0.0286605 0.0374683\n", + "x36 0.0 NaN NaN NaN NaN NaN\n", + "x37 0.0 NaN NaN NaN NaN NaN\n", + "x38 0.0 NaN NaN NaN NaN NaN\n", + "x39 -0.0438025 0.0252029 -1.74 0.0876 -0.0942704 0.00666544\n", + "x40 0.020849 0.0124995 1.67 0.1008 -0.0041807 0.0458788\n", + "x41 0.0227094 0.0128426 1.77 0.0824 -0.00300744 0.0484262\n", + "x42 -0.0248346 0.0218271 -1.14 0.2600 -0.0685426 0.0188735\n", + "x43 -8.29688e-8 8.04977e-8 -1.03 0.3070 -2.44163e-7 7.82252e-8\n", + "────────────────────────────────────────────────────────────────────────────\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ls_effect = lm(X_1[:,1:43], y)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -510,6 +1221,13 @@ "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -519,7 +1237,16 @@ }, { "cell_type": "code", - "execution_count": 1066, + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "using RDatasets, TexTables, LinearAlgebra, GLM" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -545,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 1101, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -559,7 +1286,7 @@ } ], "source": [ - "using Statistics, StatsModels\n", + "using Statistics, StatsModels, RData\n", "url = \"https://github.com/cran/hdm/raw/master/data/AJR.rda\";\n", "AJR = load(download(url))[\"AJR\"];\n", "y = AJR[!,\"GDP\"]\n", @@ -579,33 +1306,27 @@ }, { "cell_type": "code", - "execution_count": 1102, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "AJR_Xselect = HDMjl.rlassoIV(x, d, y, z, select_X=false, select_Z=false);" + "AJR_Xselect = HDMjl.rlassoIV(x, d, y, z, select_X=true, select_Z=false);" ] }, { "cell_type": "code", - "execution_count": 1103, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m d1 \u001b[0m \u001b[1m intercept \u001b[0m \u001b[1m x1 \u001b[0m \u001b[1m x2 \u001b[0m \u001b[1m x3 \u001b[0m \u001b[1m x4 \u001b[0m \u001b[1m x5 \u001b[0m \u001b[1m x6 \u001b[0m \u001b[1m x7 \u001b[0m \u001b[1m x8 \u001b[0m\n", - "\n", - " 1.267 -60.876 326.819 -429.951 60.911 59.749 63.264 61.148 -14.248 -329.563\n", - "\n", - " \u001b[1m x9 \u001b[0m \u001b[1m x10 \u001b[0m \u001b[1m x11 \u001b[0m \u001b[1m x12 \u001b[0m \u001b[1m x13 \u001b[0m \u001b[1m x14 \u001b[0m \u001b[1m x15 \u001b[0m \u001b[1m x16 \u001b[0m \u001b[1m x17 \u001b[0m \u001b[1m x18 \u001b[0m\n", - "\n", - " -330.137 -340.404 -331.822 442.238 452.631 448.365 448.799 0.0 0.0 0.0\n", + "Coefficients:\n", "\n", - " \u001b[1m x19 \u001b[0m \u001b[1m x20 \u001b[0m \u001b[1m x21 \u001b[0m\n", + " \u001b[1m d1 \u001b[0m\n", "\n", - " 0.0 0.0 0.0\n" + " 0.845\n" ] } ], @@ -615,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 1104, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -623,34 +1344,19 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " d1 1.26721 6341.02 0.000199843 0.999841\n", - " intercept -60.8759 1.26403e6 -4.81602e-5 0.999962\n", - " x1 326.819 6.69943e6 4.87831e-5 0.999961\n", - " x2 -429.951 9.15873e6 -4.69444e-5 0.999963\n", - " x3 60.9109 1.22669e6 4.96545e-5 0.99996\n", - " x4 59.7488 1.21222e6 4.92887e-5 0.999961\n", - " x5 63.2635 1.2256e6 5.16182e-5 0.999959\n", - " x6 61.148 1.22139e6 5.00644e-5 0.99996\n", - " x7 -14.2483 4.30739e5 -3.30787e-5 0.999974\n", - " x8 -329.563 6.63294e6 -4.96858e-5 0.99996\n", - " x9 -330.137 6.61657e6 -4.98955e-5 0.99996\n", - " x10 -340.404 6.63192e6 -5.13281e-5 0.999959\n", - " x11 -331.822 6.66048e6 -4.98196e-5 0.99996\n", - " x12 442.238 8.78365e6 5.03479e-5 0.99996\n", - " x13 452.631 8.89053e6 5.09116e-5 0.999959\n", - " x14 448.365 8.73086e6 5.13541e-5 0.999959\n", - " x15 448.799 8.90002e6 5.04267e-5 0.99996\n", - " x16 0.0 0.0 NaN NaN\n", - " x17 0.0 0.0 NaN NaN\n", - " x18 0.0 0.0 NaN NaN\n", - " x19 0.0 0.0 NaN NaN\n", - " x20 0.0 0.0 NaN NaN\n", - " x21 0.0 0.0 NaN NaN\n", + " d1 0.845027 0.269926 3.13059 0.00174458\n", "---\n", "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -660,38 +1366,16 @@ }, { "cell_type": "code", - "execution_count": 1105, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " d1 -12426.9 12429.4\n", - " intercept -2.47751e6 2.47739e6\n", - " x1 -1.31303e7 1.3131e7\n", - " x2 -1.79512e7 1.79503e7\n", - " x3 -2.40421e6 2.40433e6\n", - " x4 -2.37585e6 2.37597e6\n", - " x5 -2.40208e6 2.4022e6\n", - " x6 -2.39381e6 2.39394e6\n", - " x7 -844248.0 8.44219e5\n", - " x8 -1.30007e7 1.3e7\n", - " x9 -1.29686e7 1.29679e7\n", - " x10 -1.29987e7 1.2998e7\n", - " x11 -1.30546e7 1.3054e7\n", - " x12 -1.72152e7 1.72161e7\n", - " x13 -1.74247e7 1.74256e7\n", - " x14 -1.71117e7 1.71126e7\n", - " x15 -1.74433e7 1.74442e7\n", - " x16 0.0 0.0\n", - " x17 0.0 0.0\n", - " x18 0.0 0.0\n", - " x19 0.0 0.0\n", - " x20 0.0 0.0\n", - " x21 0.0 0.0\n" + " d1 0.315981 1.37407\n" ] } ], @@ -699,6 +1383,138 @@ "HDMjl.r_confint(AJR_Xselect);" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "rY_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), y);\n", + "rY = y - predict(rY_1)\n", + "rD_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), d);\n", + "rD = d - predict(rD_1);\n", + "rZ_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), z);\n", + "rZ = z - predict(rZ_1);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "rY_1 = lm(@formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rY = y - predict(rY_1)\n", + "\n", + "rD_1 = lm(@formula(Exprop ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rD = d - predict(rD_1)\n", + "\n", + "rZ_1 = lm(@formula(logMort ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rZ = z - predict(rZ_1);" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
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" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 1.26721 & 1.73054 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 1.26721 1.73054" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ivfit_lm = HDMjl.tsls(rD, rY, rZ, nothing, intercept=false)\n", + "DataFrame(Estimate = ivfit_lm[\"coefficients\"][1,2], Std_Error = ivfit_lm[\"se\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "x_formula1 = @formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", + " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", + " + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer\n", + " + Namer*Samer)\n", + "x_dframe1 = ModelFrame( x_formula, AJR)\n", + "x1_1 = ModelMatrix(x_dframe)\n", + "xx = x1.m;" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
10.8450270.272094
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0.845027 & 0.272094 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 0.845027 0.272094" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rY_1 = HDMjl.rlasso(xx, y);\n", + "rY = rY_1[\"residuals\"]\n", + "rD_1 = HDMjl.rlasso(xx, d);\n", + "rD = rD_1[\"residuals\"]\n", + "rZ_1 = HDMjl.rlasso(xx, z);\n", + "rZ = rZ_1[\"residuals\"]\n", + "\n", + "ivfit_lasso = HDMjl.tsls(rD, rY, rZ)\n", + "DataFrame(Estimate = ivfit_lasso[\"coefficients\"][1,2], Std_Error = ivfit_lasso[\"se\"][1])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -708,16 +1524,16 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ - "using Crayons, Distributions" + "using Crayons, Distributions, GLM" ] }, { "cell_type": "code", - "execution_count": 1056, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -743,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 1057, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -760,7 +1576,17 @@ }, { "cell_type": "code", - "execution_count": 1058, + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "ED_ols = lm(hcat(ones(length(vec(y))), hcat(d, x)), vec(y));\n", + "ED_2sls = HDMjl.tsls(d, y, z[:,1:2], x, intercept = false);" + ] + }, + { + "cell_type": "code", + "execution_count": 141, "metadata": {}, "outputs": [], "source": [ @@ -769,7 +1595,7 @@ }, { "cell_type": "code", - "execution_count": 1059, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -777,9 +1603,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " d1 -0.0122757 0.0376474 -0.326069 0.744372\n", + " d1 0.414602 0.290249 1.42843 0.153167\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -792,16 +1618,16 @@ }, { "cell_type": "code", - "execution_count": 1060, + "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " d1 -0.0860632 0.0615119\n" + " d1 -0.154276 0.98348\n" ] } ], @@ -811,7 +1637,7 @@ }, { "cell_type": "code", - "execution_count": 1061, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -822,7 +1648,7 @@ "\n", " \u001b[1m d1 \u001b[0m\n", "\n", - " -0.012\n" + " 0.415\n" ] } ], @@ -832,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 1062, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ @@ -841,7 +1667,7 @@ }, { "cell_type": "code", - "execution_count": 1063, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -849,9 +1675,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " d1 -0.0449578 0.0801865 -0.560665 0.575026\n", + " d1 -0.0238347 0.128507 -0.185475 0.852857\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -864,7 +1690,7 @@ }, { "cell_type": "code", - "execution_count": 1064, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -873,7 +1699,7 @@ "text": [ " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " d1 -0.202121 0.112205\n" + " d1 -0.275703 0.228033\n" ] } ], @@ -883,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 1065, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -894,7 +1720,7 @@ "\n", " \u001b[1m d1 \u001b[0m\n", "\n", - " -0.045\n" + " -0.024\n" ] } ], @@ -902,6 +1728,101 @@ "HDMjl.r_print(lasso_IV_XZ)" ] }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 -0.0238347 0.128507 -0.185475 0.852857\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + }, + { + "data": { + "text/plain": [ + "1×2 Matrix{Any}:\n", + " -0.0238347 0.128507" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 0.414602 0.290249 1.42843 0.153167\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 -0.0238347 0.128507 -0.185475 0.852857\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "using PrettyTables\n", + "table = zeros(4,2)\n", + "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", + "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", + "table[3,:] = Matrix(HDMjl.r_summary(lasso_IV_Z)[:,2:3]);\n", + "table[4, :] = Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3]);" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================= ============ =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "======================= ============ =============\n", + " ols regression 0.00786473 0.00986593\n", + " IV estimation -0.0107097 0.0337652\n", + " selection on Z 0.414602 0.290249\n", + " selection on X and Z -0.0238347 0.128507\n", + "======================= ============ =============\n" + ] + } + ], + "source": [ + "# table1 = DataFrame(table, :auto)\n", + "# table1 = rename(table1, [\"Estimate\", \"Std. Error\"])\n", + "index = [\"ols regression\", \"IV estimation \", \"selection on Z\", \"selection on X and Z\"]\n", + "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1019,50 +1940,94 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## 7. The Lasso Methods for Discovery of Significant Causes amongst Many Potential Causes, with Many Controls\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 686, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, + { + "data": { + "text/plain": [ + "Main.HDMjl" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 173, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "using CSV\n", + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/7_.csv\"\n", + "data = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(data);\n", + "p1 = 20;\n", + "X = data[:,2:end]\n", + "Y = data[:,1];" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 4.50675 5.22214\n", + " \u001b[1m X2 \u001b[0m -0.321224 0.311879\n", + " \u001b[1m X3 \u001b[0m -0.358448 0.192826\n", + " \u001b[1m X4 \u001b[0m -0.260307 0.293453\n", + " \u001b[1m X5 \u001b[0m -0.28277 0.282461\n", + " \u001b[1m X6 \u001b[0m -0.328361 0.30112\n", + " \u001b[1m X7 \u001b[0m -0.232153 0.306844\n", + " \u001b[1m X8 \u001b[0m -0.0531874 0.479497\n", + " \u001b[1m X9 \u001b[0m -0.193021 0.396693\n", + " \u001b[1m X10 \u001b[0m -0.242849 0.269725\n", + " \u001b[1m X11 \u001b[0m -0.320578 0.215327\n", + " \u001b[1m X12 \u001b[0m -0.315627 0.272158\n", + " \u001b[1m X13 \u001b[0m -0.180324 0.382993\n", + " \u001b[1m X14 \u001b[0m -0.331511 0.39337\n", + " \u001b[1m X15 \u001b[0m -0.329087 0.320235\n", + " \u001b[1m X16 \u001b[0m -0.271623 0.337679\n", + " \u001b[1m X17 \u001b[0m -0.185883 0.423636\n", + " \u001b[1m X18 \u001b[0m -0.373985 0.05162\n", + " \u001b[1m X19 \u001b[0m -0.11292 0.399297\n", + " \u001b[1m X20 \u001b[0m -0.220993 0.260713\n" + ] + } + ], + "source": [ + "HDMjl.r_confint(HDMjl.rlassoEffects(X, Y, index = [1:p1;]), joint = true);" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 179, "metadata": {}, "outputs": [], "source": [] diff --git a/tutorial/python.ipynb b/tutorial/python.ipynb new file mode 100644 index 0000000..711df80 --- /dev/null +++ b/tutorial/python.ipynb @@ -0,0 +1,338 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pyreadr\n", + "import os\n", + "from urllib.request import urlopen\n", + "from sklearn import preprocessing\n", + "import patsy\n", + "\n", + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "import hdmpy\n", + "import numpy as np\n", + "import random\n", + "import statsmodels.api as sm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib import colors\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.linear_model import LassoCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import RidgeCV, ElasticNetCV\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn import linear_model\n", + "import itertools\n", + "from pandas.api.types import is_string_dtype\n", + "from pandas.api.types import is_numeric_dtype\n", + "from pandas.api.types import is_categorical_dtype\n", + "from itertools import compress\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "from sklearn.feature_selection import SelectFromModel\n", + "from statsmodels.tools import add_constant\n", + "from sklearn.linear_model import ElasticNet\n", + "import hdmpy\n", + "from scipy.stats import chi2\n", + "from sklearn.model_selection import KFold\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from linearmodels.iv import IV2SLS\n", + "\n", + "import numpy as np\n", + "from statsmodels.api import add_constant\n", + "from linearmodels.datasets import mroz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(29217, 23)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "link=\"https://github.com/cran/hdm/raw/master/data/cps2012.rda\"\n", + "response = urlopen(link)\n", + "content = response.read()\n", + "fhandle = open( 'cps2012.Rdata', 'wb')\n", + "fhandle.write(content)\n", + "fhandle.close()\n", + "result = pyreadr.read_r(\"cps2012.Rdata\")\n", + "os.remove(\"cps2012.Rdata\")\n", + "\n", + "# Extracting the data frame from rdata_read\n", + "cps2012 = result[ 'cps2012' ]\n", + "cps2012.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "x_formula = \" lnw ~ -1 +(widowed + divorced + separated + nevermarried + hsd08 + hsd911 + hsg + cg + ad + mw + so + we + exp1 + exp2 + exp3)**2\"\n", + "\n", + "y_model, x_dframe = patsy.dmatrices( x_formula, cps2012 , return_type='matrix')\n", + "\n", + "x = np.asarray( x_dframe , dtype = np.float64)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "y_model, x_dframe2 = patsy.dmatrices( x_formula, cps2012 , return_type='dataframe')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['widowed', 'divorced', 'separated', 'nevermarried', 'hsd08', 'hsd911',\n", + " 'hsg', 'cg', 'ad', 'mw',\n", + " ...\n", + " 'so:we', 'so:exp1', 'so:exp2', 'so:exp3', 'we:exp1', 'we:exp2',\n", + " 'we:exp3', 'exp1:exp2', 'exp1:exp3', 'exp2:exp3'],\n", + " dtype='object', length=120)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_dframe2.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "widowed\n", + "divorced\n", + "separated\n", + "nevermarried\n", + "hsd08\n", + "hsd911\n", + "hsg\n", + "cg\n", + "ad\n", + "mw\n", + "so\n", + "we\n", + "exp1\n", + "exp2\n", + "exp3\n", + "widowed:divorced\n", + "widowed:separated\n", + "widowed:nevermarried\n", + "widowed:hsd08\n", + "widowed:hsd911\n", + "widowed:hsg\n", + "widowed:cg\n", + "widowed:ad\n", + "widowed:mw\n", + "widowed:so\n", + "widowed:we\n", + "widowed:exp1\n", + "widowed:exp2\n", + "widowed:exp3\n", + "divorced:separated\n", + "divorced:nevermarried\n", + "divorced:hsd08\n", + "divorced:hsd911\n", + "divorced:hsg\n", + "divorced:cg\n", + "divorced:ad\n", + "divorced:mw\n", + "divorced:so\n", + "divorced:we\n", + "divorced:exp1\n", + "divorced:exp2\n", + "divorced:exp3\n", + "separated:nevermarried\n", + "separated:hsd08\n", + "separated:hsd911\n", + "separated:hsg\n", + "separated:cg\n", + "separated:ad\n", + "separated:mw\n", + "separated:so\n", + "separated:we\n", + "separated:exp1\n", + "separated:exp2\n", + "separated:exp3\n", + "nevermarried:hsd08\n", + "nevermarried:hsd911\n", + "nevermarried:hsg\n", + "nevermarried:cg\n", + "nevermarried:ad\n", + "nevermarried:mw\n", + "nevermarried:so\n", + "nevermarried:we\n", + "nevermarried:exp1\n", + "nevermarried:exp2\n", + "nevermarried:exp3\n", + "hsd08:hsd911\n", + "hsd08:hsg\n", + "hsd08:cg\n", + "hsd08:ad\n", + "hsd08:mw\n", + "hsd08:so\n", + "hsd08:we\n", + "hsd08:exp1\n", + "hsd08:exp2\n", + "hsd08:exp3\n", + "hsd911:hsg\n", + "hsd911:cg\n", + "hsd911:ad\n", + "hsd911:mw\n", + "hsd911:so\n", + "hsd911:we\n", + "hsd911:exp1\n", + "hsd911:exp2\n", + "hsd911:exp3\n", + "hsg:cg\n", + "hsg:ad\n", + "hsg:mw\n", + "hsg:so\n", + "hsg:we\n", + "hsg:exp1\n", + "hsg:exp2\n", + "hsg:exp3\n", + "cg:ad\n", + "cg:mw\n", + "cg:so\n", + "cg:we\n", + "cg:exp1\n", + "cg:exp2\n", + "cg:exp3\n", + "ad:mw\n", + "ad:so\n", + "ad:we\n", + "ad:exp1\n", + "ad:exp2\n", + "ad:exp3\n", + "mw:so\n", + "mw:we\n", + "mw:exp1\n", + "mw:exp2\n", + "mw:exp3\n", + "so:we\n", + "so:exp1\n", + "so:exp2\n", + "so:exp3\n", + "we:exp1\n", + "we:exp2\n", + "we:exp3\n", + "exp1:exp2\n", + "exp1:exp3\n", + "exp2:exp3\n" + ] + } + ], + "source": [ + "for col in x_dframe2.columns:\n", + " print(col)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index ec9b81d..c9b803a 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -16,13 +16,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'hdm' was built under R version 4.2.1\"\n" + ] + } + ], "source": [ "library(hdm)\n", "library(stats)" @@ -41,36 +50,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 90
  2. 63
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 90\n", + "\\item 63\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 90\n", + "2. 63\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 90 63" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data(GrowthData)\n", + "dim(GrowthData)\n", + "## [1] 90 63\n", + "y = GrowthData[, 1, drop = F]\n", + "d = GrowthData[, 3, drop = F]\n", + "x = as.matrix(GrowthData)[, -c(1, 2, 3)]\n", + "\n", + "# a = (rlasso(x = x, y = y)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "rlasso.default(x = x, y = y)\n", + "\n", + "Coefficients:\n", + "(Intercept) bmp1l freeop freetar h65 hm65 \n", + " 0.05810 -0.07557 0.00000 0.00000 0.00000 0.00000 \n", + " hf65 p65 pm65 pf65 s65 sm65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " sf65 fert65 mort65 lifee065 gpop1 fert1 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " mort1 invsh41 geetot1 geerec1 gde1 govwb1 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " govsh41 gvxdxe41 high65 highm65 highf65 highc65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " highcm65 highcf65 human65 humanm65 humanf65 hyr65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " hyrm65 hyrf65 no65 nom65 nof65 pinstab1 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " pop65 worker65 pop1565 pop6565 sec65 secm65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " secf65 secc65 seccm65 seccf65 syr65 syrm65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " syrf65 teapri65 teasec65 ex1 im1 xr65 \n", + " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", + " tot1 \n", + " 0.00000 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rlasso(x = x, y = y)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], - "source": [] + "source": [ + "a = 5" + ] }, { "cell_type": "markdown", @@ -88,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 469, "metadata": { "vscode": { "languageId": "r" @@ -105,7 +193,1126 @@ "beta = rep(1, 20)\n", "y = X %*% beta + rnorm(n)\n", "dat = data.frame(y = y, X)\n", - "save(dat, file = \"../data/4_1.csv\")\n" + "#save(dat, file = \"../data/4_1.csv\")\n", + "#write.csv(dat,\"../data/4_1.csv\", row.names = FALSE)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 471, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Estimate
0.978074548374755
Std. Error
0.0137122468163772
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[Estimate] 0.978074548374755\n", + "\\item[Std. Error] 0.0137122468163772\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "Estimate\n", + ": 0.978074548374755Std. Error\n", + ": 0.0137122468163772\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error \n", + "0.97807455 0.01371225 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# full fit\n", + "fmla = as.formula(paste(\"y ~ \", paste(colnames(X), collapse = \"+\")))\n", + "full.fit = lm(fmla, data = dat)\n", + "summary(full.fit)$coef[\"d\", 1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 485, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Estimate
0.978074548374761
Std. Error
0.0136861583043357
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[Estimate] 0.978074548374761\n", + "\\item[Std. Error] 0.0136861583043357\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "Estimate\n", + ": 0.978074548374761Std. Error\n", + ": 0.0136861583043357\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error \n", + "0.97807455 0.01368616 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fmla.y = as.formula(paste(\"y ~ \", paste(xnames, collapse = \"+\")))\n", + "fmla.d = as.formula(paste(\"d ~ \", paste(xnames, collapse = \"+\")))\n", + "# partial fit via ols\n", + "rY = lm(fmla.y, data = dat)$res\n", + "rD = lm(fmla.d, data = dat)$res\n", + "partial.fit.ls = lm(rY ~ rD)\n", + "summary(partial.fit.ls)$coef[\"rD\", 1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Estimate
0.972738704584573
Std. Error
0.0136867656564768
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[Estimate] 0.972738704584573\n", + "\\item[Std. Error] 0.0136867656564768\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "Estimate\n", + ": 0.972738704584573Std. Error\n", + ": 0.0136867656564768\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error \n", + "0.97273870 0.01368677 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rY = rlasso(fmla.y, data = dat)$res\n", + "rD = rlasso(fmla.d, data = dat)$res\n", + "partial.fit.postlasso = lm(rY ~ rD)\n", + "summary(partial.fit.postlasso)$coef[\"rD\", 1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2. Inference: Confidence Intervals and Significance Testing. The function rlassoEffects" + ] + }, + { + "cell_type": "code", + "execution_count": 496, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(1)\n", + "n = 100 #sample size\n", + "p = 100 # number of variables\n", + "s = 3 # nubmer of non-zero variables\n", + "X = matrix(rnorm(n * p), ncol = p)\n", + "colnames(X) <- paste(\"X\", 1:p, sep = \"\")\n", + "beta = c(rep(3, s), rep(0, p - s))\n", + "y = 1 + X %*% beta + rnorm(n)\n", + "data = data.frame(cbind(y, X))\n", + "#write.csv(data,\"../data/4_2.csv\", row.names = FALSE)\n", + "colnames(data)[1] <- \"y\"\n", + "fm = paste(\"y ~\", paste(colnames(X), collapse = \"+\"))\n", + "fm = as.formula(fm)" + ] + }, + { + "cell_type": "code", + "execution_count": 497, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Call:\n", + "rlassoEffects.formula(formula = fm, data = data, I = ~X1 + X2 + \n", + " X3 + X50)\n", + "\n", + "Coefficients:\n", + " X1 X2 X3 X50 \n", + "2.94448 3.04127 2.97540 0.07196 \n", + "\n" + ] + } + ], + "source": [ + "# lasso.effect = rlassoEffects(X, y, index=c(1,2,3,50))\n", + "lasso.effect = rlassoEffects(fm, I = ~X1 + X2 + X3 + X50, data = data)\n", + "print(lasso.effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 499, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "X1 2.94448 0.08815 33.404 <2e-16 ***\n", + "X2 3.04127 0.08389 36.253 <2e-16 ***\n", + "X3 2.97540 0.07804 38.127 <2e-16 ***\n", + "X50 0.07196 0.07765 0.927 0.354 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(lasso.effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 500, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 4 × 2 of type dbl
2.5 %97.5 %
X1 2.771713083.1172421
X2 2.876851213.2056979
X3 2.822449623.1283583
X50-0.080227080.2241377
\n" + ], + "text/latex": [ + "A matrix: 4 × 2 of type dbl\n", + "\\begin{tabular}{r|ll}\n", + " & 2.5 \\% & 97.5 \\%\\\\\n", + "\\hline\n", + "\tX1 & 2.77171308 & 3.1172421\\\\\n", + "\tX2 & 2.87685121 & 3.2056979\\\\\n", + "\tX3 & 2.82244962 & 3.1283583\\\\\n", + "\tX50 & -0.08022708 & 0.2241377\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 4 × 2 of type dbl\n", + "\n", + "| | 2.5 % | 97.5 % |\n", + "|---|---|---|\n", + "| X1 | 2.77171308 | 3.1172421 |\n", + "| X2 | 2.87685121 | 3.2056979 |\n", + "| X3 | 2.82244962 | 3.1283583 |\n", + "| X50 | -0.08022708 | 0.2241377 |\n", + "\n" + ], + "text/plain": [ + " 2.5 % 97.5 % \n", + "X1 2.77171308 3.1172421\n", + "X2 2.87685121 3.2056979\n", + "X3 2.82244962 3.1283583\n", + "X50 -0.08022708 0.2241377" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "confint(lasso.effect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 501, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 4 × 2 of type dbl
2.5 %97.5 %
X1 2.72794773.1610075
X2 2.83712143.2454278
X3 2.78331763.1674903
X50-0.11545090.2593615
\n" + ], + "text/latex": [ + "A matrix: 4 × 2 of type dbl\n", + "\\begin{tabular}{r|ll}\n", + " & 2.5 \\% & 97.5 \\%\\\\\n", + "\\hline\n", + "\tX1 & 2.7279477 & 3.1610075\\\\\n", + "\tX2 & 2.8371214 & 3.2454278\\\\\n", + "\tX3 & 2.7833176 & 3.1674903\\\\\n", + "\tX50 & -0.1154509 & 0.2593615\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 4 × 2 of type dbl\n", + "\n", + "| | 2.5 % | 97.5 % |\n", + "|---|---|---|\n", + "| X1 | 2.7279477 | 3.1610075 |\n", + "| X2 | 2.8371214 | 3.2454278 |\n", + "| X3 | 2.7833176 | 3.1674903 |\n", + "| X50 | -0.1154509 | 0.2593615 |\n", + "\n" + ], + "text/plain": [ + " 2.5 % 97.5 % \n", + "X1 2.7279477 3.1610075\n", + "X2 2.8371214 3.2454278\n", + "X3 2.7833176 3.1674903\n", + "X50 -0.1154509 0.2593615" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "confint(lasso.effect, level = 0.95, joint = TRUE)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3. Application: the effect of gender on wage." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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A matrix: 29217 × 16 of type dbl
femalefemale:widowedfemale:divorcedfemale:separatedfemale:nevermarriedfemale:hsd08female:hsd911female:hsgfemale:cgfemale:adfemale:mwfemale:sofemale:wefemale:exp1female:exp2female:exp3
1663459100000000000022.04.840010.648000
1663462100000100000030.09.000027.000000
16634630000000000000 0.00.0000 0.000000
1663465100000010000014.01.9600 2.744000
16634680000000000000 0.00.0000 0.000000
16634690000000000000 0.00.0000 0.000000
16634730000000000000 0.00.0000 0.000000
16634750000000000000 0.00.0000 0.000000
16634760000000000000 0.00.0000 0.000000
1663477100000000000015.52.4025 3.723875
16634800000000000000 0.00.0000 0.000000
16634811000000001000 7.00.4900 0.343000
16634820000000000000 0.00.0000 0.000000
16634830000000000000 0.00.0000 0.000000
1663485101000000000020.54.2025 8.615125
16634900000000000000 0.00.0000 0.000000
16634940000000000000 0.00.0000 0.000000
16634970000000000000 0.00.0000 0.000000
16634980000000000000 0.00.0000 0.000000
1663500100010001000015.02.2500 3.375000
16635030000000000000 0.00.0000 0.000000
1663508100000000000016.02.5600 4.096000
16635090000000000000 0.00.0000 0.000000
16635110000000000000 0.00.0000 0.000000
16635121000100000000 6.00.3600 0.216000
16635130000000000000 0.00.0000 0.000000
16635160000000000000 0.00.0000 0.000000
1663517100000001000020.04.0000 8.000000
16635191000000001000 6.00.3600 0.216000
16635210000000000000 0.00.0000 0.000000
17128201000100010001111.21 1.331
17128210000000000000 00.00 0.000
17128261000000000001224.8410.648
17128300000000000000 00.00 0.000
17128320000000000000 00.00 0.000
17128360000000000000 00.00 0.000
17128371000000100001141.96 2.744
17128400000000000000 00.00 0.000
17128411000000010001111.21 1.331
17128420000000000000 00.00 0.000
17128440000000000000 00.00 0.000
17128450000000000000 00.00 0.000
17128460000000000000 00.00 0.000
17128470000000000000 00.00 0.000
17128480000000000000 00.00 0.000
17128491000000001001256.2515.625
17128501010000000001121.44 1.728
17128511000100010001 30.09 0.027
17128531000000010001152.25 3.375
17128550000000000000 00.00 0.000
17128601000100000001183.24 5.832
17128621000100010001 50.25 0.125
17128640000000000000 00.00 0.000
17128650000000000000 00.00 0.000
17128661000000100001162.56 4.096
17128670000000000000 00.00 0.000
17128681000100001001162.56 4.096
17128691000000000001214.41 9.261
17128700000000000000 00.00 0.000
17128711000000001001 20.04 0.008
\n" + ], + "text/latex": [ + "A matrix: 29217 × 16 of type dbl\n", + "\\begin{tabular}{r|llllllllllllllll}\n", + " & female & female:widowed & female:divorced & female:separated & female:nevermarried & female:hsd08 & female:hsd911 & female:hsg & female:cg & female:ad & female:mw & female:so & female:we & female:exp1 & female:exp2 & female:exp3\\\\\n", + "\\hline\n", + "\t1663459 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 22.0 & 4.8400 & 10.648000\\\\\n", + "\t1663462 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 30.0 & 9.0000 & 27.000000\\\\\n", + "\t1663463 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663465 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 14.0 & 1.9600 & 2.744000\\\\\n", + "\t1663468 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663469 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663473 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663475 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663476 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663477 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 15.5 & 2.4025 & 3.723875\\\\\n", + "\t1663480 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663481 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 7.0 & 0.4900 & 0.343000\\\\\n", + "\t1663482 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663483 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663485 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 20.5 & 4.2025 & 8.615125\\\\\n", + "\t1663490 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663494 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663497 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663498 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663500 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 15.0 & 2.2500 & 3.375000\\\\\n", + "\t1663503 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663508 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 16.0 & 2.5600 & 4.096000\\\\\n", + "\t1663509 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663511 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663512 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 6.0 & 0.3600 & 0.216000\\\\\n", + "\t1663513 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663516 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t1663517 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 20.0 & 4.0000 & 8.000000\\\\\n", + "\t1663519 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 6.0 & 0.3600 & 0.216000\\\\\n", + "\t1663521 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\t1712820 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 11 & 1.21 & 1.331\\\\\n", + "\t1712821 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712826 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 22 & 4.84 & 10.648\\\\\n", + "\t1712830 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712832 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712836 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712837 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 14 & 1.96 & 2.744\\\\\n", + "\t1712840 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712841 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 11 & 1.21 & 1.331\\\\\n", + "\t1712842 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712844 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712845 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712846 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712847 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712848 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712849 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 25 & 6.25 & 15.625\\\\\n", + "\t1712850 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 12 & 1.44 & 1.728\\\\\n", + "\t1712851 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 3 & 0.09 & 0.027\\\\\n", + "\t1712853 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 15 & 2.25 & 3.375\\\\\n", + "\t1712855 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712860 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 18 & 3.24 & 5.832\\\\\n", + "\t1712862 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 5 & 0.25 & 0.125\\\\\n", + "\t1712864 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712865 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712866 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 16 & 2.56 & 4.096\\\\\n", + "\t1712867 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712868 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 16 & 2.56 & 4.096\\\\\n", + "\t1712869 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 21 & 4.41 & 9.261\\\\\n", + "\t1712870 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", + "\t1712871 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 2 & 0.04 & 0.008\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 29217 × 16 of type dbl\n", + "\n", + "| | female | female:widowed | female:divorced | female:separated | female:nevermarried | female:hsd08 | female:hsd911 | female:hsg | female:cg | female:ad | female:mw | female:so | female:we | female:exp1 | female:exp2 | female:exp3 |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1663459 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22.0 | 4.8400 | 10.648000 |\n", + "| 1663462 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 30.0 | 9.0000 | 27.000000 |\n", + "| 1663463 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663465 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 14.0 | 1.9600 | 2.744000 |\n", + "| 1663468 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663469 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663473 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663475 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663476 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663477 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15.5 | 2.4025 | 3.723875 |\n", + "| 1663480 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663481 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7.0 | 0.4900 | 0.343000 |\n", + "| 1663482 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663483 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663485 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20.5 | 4.2025 | 8.615125 |\n", + "| 1663490 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663494 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663497 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663498 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663500 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 15.0 | 2.2500 | 3.375000 |\n", + "| 1663503 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663508 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16.0 | 2.5600 | 4.096000 |\n", + "| 1663509 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663511 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663512 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.0 | 0.3600 | 0.216000 |\n", + "| 1663513 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663516 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| 1663517 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 20.0 | 4.0000 | 8.000000 |\n", + "| 1663519 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6.0 | 0.3600 | 0.216000 |\n", + "| 1663521 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| 1712820 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 11 | 1.21 | 1.331 |\n", + "| 1712821 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712826 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 4.84 | 10.648 |\n", + "| 1712830 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712836 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712837 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 14 | 1.96 | 2.744 |\n", + "| 1712840 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712841 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 11 | 1.21 | 1.331 |\n", + "| 1712842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712845 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712847 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712848 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712849 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 25 | 6.25 | 15.625 |\n", + "| 1712850 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12 | 1.44 | 1.728 |\n", + "| 1712851 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | 0.09 | 0.027 |\n", + "| 1712853 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 15 | 2.25 | 3.375 |\n", + "| 1712855 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712860 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 18 | 3.24 | 5.832 |\n", + "| 1712862 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 0.25 | 0.125 |\n", + "| 1712864 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712865 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712866 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 16 | 2.56 | 4.096 |\n", + "| 1712867 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712868 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 16 | 2.56 | 4.096 |\n", + "| 1712869 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 21 | 4.41 | 9.261 |\n", + "| 1712870 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", + "| 1712871 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0.04 | 0.008 |\n", + "\n" + ], + "text/plain": [ + " female female:widowed female:divorced female:separated\n", + "1663459 1 0 0 0 \n", + "1663462 1 0 0 0 \n", + "1663463 0 0 0 0 \n", + "1663465 1 0 0 0 \n", + "1663468 0 0 0 0 \n", + "1663469 0 0 0 0 \n", + "1663473 0 0 0 0 \n", + "1663475 0 0 0 0 \n", + "1663476 0 0 0 0 \n", + "1663477 1 0 0 0 \n", + "1663480 0 0 0 0 \n", + "1663481 1 0 0 0 \n", + "1663482 0 0 0 0 \n", + "1663483 0 0 0 0 \n", + "1663485 1 0 1 0 \n", + "1663490 0 0 0 0 \n", + "1663494 0 0 0 0 \n", + "1663497 0 0 0 0 \n", + "1663498 0 0 0 0 \n", + "1663500 1 0 0 0 \n", + "1663503 0 0 0 0 \n", + "1663508 1 0 0 0 \n", + "1663509 0 0 0 0 \n", + "1663511 0 0 0 0 \n", + "1663512 1 0 0 0 \n", + "1663513 0 0 0 0 \n", + "1663516 0 0 0 0 \n", + "1663517 1 0 0 0 \n", + "1663519 1 0 0 0 \n", + "1663521 0 0 0 0 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "1712820 1 0 0 0 \n", + "1712821 0 0 0 0 \n", + "1712826 1 0 0 0 \n", + "1712830 0 0 0 0 \n", + "1712832 0 0 0 0 \n", + "1712836 0 0 0 0 \n", + "1712837 1 0 0 0 \n", + "1712840 0 0 0 0 \n", + "1712841 1 0 0 0 \n", + "1712842 0 0 0 0 \n", + "1712844 0 0 0 0 \n", + "1712845 0 0 0 0 \n", + "1712846 0 0 0 0 \n", + "1712847 0 0 0 0 \n", + "1712848 0 0 0 0 \n", + "1712849 1 0 0 0 \n", + "1712850 1 0 1 0 \n", + "1712851 1 0 0 0 \n", + "1712853 1 0 0 0 \n", + "1712855 0 0 0 0 \n", + "1712860 1 0 0 0 \n", + "1712862 1 0 0 0 \n", + "1712864 0 0 0 0 \n", + "1712865 0 0 0 0 \n", + "1712866 1 0 0 0 \n", + "1712867 0 0 0 0 \n", + "1712868 1 0 0 0 \n", + "1712869 1 0 0 0 \n", + "1712870 0 0 0 0 \n", + "1712871 1 0 0 0 \n", + " female:nevermarried female:hsd08 female:hsd911 female:hsg female:cg\n", + "1663459 0 0 0 0 0 \n", + "1663462 0 0 1 0 0 \n", + "1663463 0 0 0 0 0 \n", + "1663465 0 0 0 1 0 \n", + "1663468 0 0 0 0 0 \n", + "1663469 0 0 0 0 0 \n", + "1663473 0 0 0 0 0 \n", + "1663475 0 0 0 0 0 \n", + "1663476 0 0 0 0 0 \n", + "1663477 0 0 0 0 0 \n", + "1663480 0 0 0 0 0 \n", + "1663481 0 0 0 0 0 \n", + "1663482 0 0 0 0 0 \n", + "1663483 0 0 0 0 0 \n", + "1663485 0 0 0 0 0 \n", + "1663490 0 0 0 0 0 \n", + "1663494 0 0 0 0 0 \n", + "1663497 0 0 0 0 0 \n", + "1663498 0 0 0 0 0 \n", + "1663500 1 0 0 0 1 \n", + "1663503 0 0 0 0 0 \n", + "1663508 0 0 0 0 0 \n", + "1663509 0 0 0 0 0 \n", + "1663511 0 0 0 0 0 \n", + "1663512 1 0 0 0 0 \n", + "1663513 0 0 0 0 0 \n", + "1663516 0 0 0 0 0 \n", + "1663517 0 0 0 0 1 \n", + "1663519 0 0 0 0 0 \n", + "1663521 0 0 0 0 0 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", + "1712820 1 0 0 0 1 \n", + "1712821 0 0 0 0 0 \n", + "1712826 0 0 0 0 0 \n", + "1712830 0 0 0 0 0 \n", + "1712832 0 0 0 0 0 \n", + "1712836 0 0 0 0 0 \n", + "1712837 0 0 0 1 0 \n", + "1712840 0 0 0 0 0 \n", + "1712841 0 0 0 0 1 \n", + "1712842 0 0 0 0 0 \n", + "1712844 0 0 0 0 0 \n", + "1712845 0 0 0 0 0 \n", + "1712846 0 0 0 0 0 \n", + "1712847 0 0 0 0 0 \n", + "1712848 0 0 0 0 0 \n", + "1712849 0 0 0 0 0 \n", + "1712850 0 0 0 0 0 \n", + "1712851 1 0 0 0 1 \n", + "1712853 0 0 0 0 1 \n", + "1712855 0 0 0 0 0 \n", + "1712860 1 0 0 0 0 \n", + "1712862 1 0 0 0 1 \n", + "1712864 0 0 0 0 0 \n", + "1712865 0 0 0 0 0 \n", + "1712866 0 0 0 1 0 \n", + "1712867 0 0 0 0 0 \n", + "1712868 1 0 0 0 0 \n", + "1712869 0 0 0 0 0 \n", + "1712870 0 0 0 0 0 \n", + "1712871 0 0 0 0 0 \n", + " female:ad female:mw female:so female:we female:exp1 female:exp2\n", + "1663459 0 0 0 0 22.0 4.8400 \n", + "1663462 0 0 0 0 30.0 9.0000 \n", + "1663463 0 0 0 0 0.0 0.0000 \n", + "1663465 0 0 0 0 14.0 1.9600 \n", + "1663468 0 0 0 0 0.0 0.0000 \n", + "1663469 0 0 0 0 0.0 0.0000 \n", + "1663473 0 0 0 0 0.0 0.0000 \n", + "1663475 0 0 0 0 0.0 0.0000 \n", + "1663476 0 0 0 0 0.0 0.0000 \n", + "1663477 0 0 0 0 15.5 2.4025 \n", + "1663480 0 0 0 0 0.0 0.0000 \n", + "1663481 1 0 0 0 7.0 0.4900 \n", + "1663482 0 0 0 0 0.0 0.0000 \n", + "1663483 0 0 0 0 0.0 0.0000 \n", + "1663485 0 0 0 0 20.5 4.2025 \n", + "1663490 0 0 0 0 0.0 0.0000 \n", + "1663494 0 0 0 0 0.0 0.0000 \n", + "1663497 0 0 0 0 0.0 0.0000 \n", + "1663498 0 0 0 0 0.0 0.0000 \n", + "1663500 0 0 0 0 15.0 2.2500 \n", + "1663503 0 0 0 0 0.0 0.0000 \n", + "1663508 0 0 0 0 16.0 2.5600 \n", + "1663509 0 0 0 0 0.0 0.0000 \n", + "1663511 0 0 0 0 0.0 0.0000 \n", + "1663512 0 0 0 0 6.0 0.3600 \n", + "1663513 0 0 0 0 0.0 0.0000 \n", + "1663516 0 0 0 0 0.0 0.0000 \n", + "1663517 0 0 0 0 20.0 4.0000 \n", + "1663519 1 0 0 0 6.0 0.3600 \n", + "1663521 0 0 0 0 0.0 0.0000 \n", + "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", + "1712820 0 0 0 1 11 1.21 \n", + "1712821 0 0 0 0 0 0.00 \n", + "1712826 0 0 0 1 22 4.84 \n", + "1712830 0 0 0 0 0 0.00 \n", + "1712832 0 0 0 0 0 0.00 \n", + "1712836 0 0 0 0 0 0.00 \n", + "1712837 0 0 0 1 14 1.96 \n", + "1712840 0 0 0 0 0 0.00 \n", + "1712841 0 0 0 1 11 1.21 \n", + "1712842 0 0 0 0 0 0.00 \n", + "1712844 0 0 0 0 0 0.00 \n", + "1712845 0 0 0 0 0 0.00 \n", + "1712846 0 0 0 0 0 0.00 \n", + "1712847 0 0 0 0 0 0.00 \n", + "1712848 0 0 0 0 0 0.00 \n", + "1712849 1 0 0 1 25 6.25 \n", + "1712850 0 0 0 1 12 1.44 \n", + "1712851 0 0 0 1 3 0.09 \n", + "1712853 0 0 0 1 15 2.25 \n", + "1712855 0 0 0 0 0 0.00 \n", + "1712860 0 0 0 1 18 3.24 \n", + "1712862 0 0 0 1 5 0.25 \n", + "1712864 0 0 0 0 0 0.00 \n", + "1712865 0 0 0 0 0 0.00 \n", + "1712866 0 0 0 1 16 2.56 \n", + "1712867 0 0 0 0 0 0.00 \n", + "1712868 1 0 0 1 16 2.56 \n", + "1712869 0 0 0 1 21 4.41 \n", + "1712870 0 0 0 0 0 0.00 \n", + "1712871 1 0 0 1 2 0.04 \n", + " female:exp3\n", + "1663459 10.648000 \n", + "1663462 27.000000 \n", + "1663463 0.000000 \n", + "1663465 2.744000 \n", + "1663468 0.000000 \n", + "1663469 0.000000 \n", + "1663473 0.000000 \n", + "1663475 0.000000 \n", + "1663476 0.000000 \n", + "1663477 3.723875 \n", + "1663480 0.000000 \n", + "1663481 0.343000 \n", + "1663482 0.000000 \n", + "1663483 0.000000 \n", + "1663485 8.615125 \n", + "1663490 0.000000 \n", + "1663494 0.000000 \n", + "1663497 0.000000 \n", + "1663498 0.000000 \n", + "1663500 3.375000 \n", + "1663503 0.000000 \n", + "1663508 4.096000 \n", + "1663509 0.000000 \n", + "1663511 0.000000 \n", + "1663512 0.216000 \n", + "1663513 0.000000 \n", + "1663516 0.000000 \n", + "1663517 8.000000 \n", + "1663519 0.216000 \n", + "1663521 0.000000 \n", + "⋮ ⋮ \n", + "1712820 1.331 \n", + "1712821 0.000 \n", + "1712826 10.648 \n", + "1712830 0.000 \n", + "1712832 0.000 \n", + "1712836 0.000 \n", + "1712837 2.744 \n", + "1712840 0.000 \n", + "1712841 1.331 \n", + "1712842 0.000 \n", + "1712844 0.000 \n", + "1712845 0.000 \n", + "1712846 0.000 \n", + "1712847 0.000 \n", + "1712848 0.000 \n", + "1712849 15.625 \n", + "1712850 1.728 \n", + "1712851 0.027 \n", + "1712853 3.375 \n", + "1712855 0.000 \n", + "1712860 5.832 \n", + "1712862 0.125 \n", + "1712864 0.000 \n", + "1712865 0.000 \n", + "1712866 4.096 \n", + "1712867 0.000 \n", + "1712868 4.096 \n", + "1712869 9.261 \n", + "1712870 0.000 \n", + "1712871 0.008 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X[, index.gender]" ] }, { @@ -152,6 +1359,1060 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 90
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\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 90\n", + "\\item 63\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 90\n", + "2. 63\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 90 63" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data(GrowthData)\n", + "dim(GrowthData)\n", + "## [1] 90 63\n", + "y = GrowthData[, 1, drop = F]\n", + "d = GrowthData[, 3, drop = F]\n", + "X = as.matrix(GrowthData)[, -c(1, 2, 3)]\n", + "varnames = colnames(GrowthData)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "xnames = varnames[-c(1, 2, 3)] # names of X variables\n", + "dandxnames = varnames[-c(1, 2)] # names of D and X variables\n", + "# create formulas by pasting names (this saves typing times)\n", + "fmla = as.formula(paste(\"Outcome ~ \", paste(dandxnames, collapse = \"+\")))\n", + "ls.effect = lm(fmla, data = GrowthData)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = fmla, data = GrowthData)\n", + "\n", + "Coefficients:\n", + "(Intercept) gdpsh465 bmp1l freeop freetar h65 \n", + " 2.472e-01 -9.378e-03 -6.886e-02 8.007e-02 -4.890e-01 -2.362e+00 \n", + " hm65 hf65 p65 pm65 pf65 s65 \n", + " 7.071e-01 1.693e+00 2.655e-01 1.370e-01 -3.313e-01 3.908e-02 \n", + " sm65 sf65 fert65 mort65 lifee065 gpop1 \n", + " -3.067e-02 -1.799e-01 6.881e-03 -2.335e-01 -1.491e-02 9.702e-01 \n", + " fert1 mort1 invsh41 geetot1 geerec1 gde1 \n", + " 8.838e-03 6.656e-02 7.446e-02 -7.151e-01 6.300e-01 -4.436e-01 \n", + " govwb1 govsh41 gvxdxe41 high65 highm65 highf65 \n", + " 3.375e-01 4.632e-01 -7.934e-01 -7.525e-01 -3.903e-01 -4.177e-01 \n", + " highc65 highcm65 highcf65 human65 humanm65 humanf65 \n", + " -2.216e+00 2.797e-01 3.921e-01 2.337e+00 -1.209e+00 -1.104e+00 \n", + " hyr65 hyrm65 hyrf65 no65 nom65 nof65 \n", + " 5.491e+01 1.294e+01 9.093e+00 3.721e-02 -2.120e-02 -1.686e-02 \n", + " pinstab1 pop65 worker65 pop1565 pop6565 sec65 \n", + " -4.997e-02 1.032e-07 3.408e-02 -4.655e-01 -1.357e+00 -1.089e-02 \n", + " secm65 secf65 secc65 seccm65 seccf65 syr65 \n", + " 3.344e-03 -2.304e-03 -4.915e-01 2.596e-01 2.207e-01 -7.556e-01 \n", + " syrm65 syrf65 teapri65 teasec65 ex1 im1 \n", + " 3.109e-01 7.593e-01 3.955e-05 2.497e-04 -5.804e-01 5.914e-01 \n", + " xr65 tot1 \n", + " -1.038e-04 -1.279e-01 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ls.effect" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "[1,] -0.04981 0.01394 -3.574 0.000351 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dX = as.matrix(cbind(d, X))\n", + "lasso.effect = rlassoEffect(x = X, y = y, d = d, method = \"partialling out\")\n", + "summary(lasso.effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "[1,] -0.04981 0.01394 -3.574 0.000351 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lasso.effect = rlassoEffect(x = X, y = y, d = d, method = \"partialling out\")\n", + "summary(lasso.effect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "doublesel.effect = rlassoEffect(x = X, y = y, d = d, method = \"double selection\")" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "gdpsh465 -0.05001 0.01579 -3.167 0.00154 **\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(doublesel.effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 495, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = fmla, data = GrowthData)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-0.040338 -0.011298 -0.000863 0.011813 0.043247 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 2.472e-01 7.845e-01 0.315 0.75506 \n", + "gdpsh465 -9.378e-03 2.989e-02 -0.314 0.75602 \n", + "bmp1l -6.886e-02 3.253e-02 -2.117 0.04329 * \n", + "freeop 8.007e-02 2.079e-01 0.385 0.70300 \n", + "freetar -4.890e-01 4.182e-01 -1.169 0.25214 \n", + "h65 -2.362e+00 8.573e-01 -2.755 0.01019 * \n", + "hm65 7.071e-01 5.231e-01 1.352 0.18729 \n", + "hf65 1.693e+00 5.032e-01 3.365 0.00223 **\n", + "p65 2.655e-01 1.643e-01 1.616 0.11727 \n", + "pm65 1.370e-01 1.512e-01 0.906 0.37284 \n", + "pf65 -3.313e-01 1.651e-01 -2.006 0.05458 . \n", + "s65 3.908e-02 1.855e-01 0.211 0.83469 \n", + "sm65 -3.067e-02 1.168e-01 -0.263 0.79479 \n", + "sf65 -1.799e-01 1.181e-01 -1.523 0.13886 \n", + "fert65 6.881e-03 2.705e-02 0.254 0.80108 \n", + "mort65 -2.335e-01 8.174e-01 -0.286 0.77729 \n", + "lifee065 -1.491e-02 1.933e-01 -0.077 0.93906 \n", + "gpop1 9.702e-01 1.812e+00 0.535 0.59663 \n", + "fert1 8.838e-03 3.504e-02 0.252 0.80271 \n", + "mort1 6.656e-02 6.848e-01 0.097 0.92326 \n", + "invsh41 7.446e-02 1.084e-01 0.687 0.49797 \n", + "geetot1 -7.151e-01 1.680e+00 -0.426 0.67364 \n", + "geerec1 6.300e-01 2.447e+00 0.257 0.79874 \n", + "gde1 -4.436e-01 1.671e+00 -0.265 0.79263 \n", + "govwb1 3.375e-01 4.380e-01 0.770 0.44748 \n", + "govsh41 4.632e-01 1.925e+00 0.241 0.81165 \n", + "gvxdxe41 -7.934e-01 2.059e+00 -0.385 0.70296 \n", + "high65 -7.525e-01 9.057e-01 -0.831 0.41311 \n", + "highm65 -3.903e-01 6.812e-01 -0.573 0.57131 \n", + "highf65 -4.177e-01 5.615e-01 -0.744 0.46308 \n", + "highc65 -2.216e+00 1.481e+00 -1.496 0.14575 \n", + "highcm65 2.797e-01 6.582e-01 0.425 0.67412 \n", + "highcf65 3.921e-01 7.660e-01 0.512 0.61278 \n", + "human65 2.337e+00 3.307e+00 0.707 0.48559 \n", + "humanm65 -1.209e+00 1.619e+00 -0.747 0.46121 \n", + "humanf65 -1.104e+00 1.685e+00 -0.655 0.51763 \n", + "hyr65 5.491e+01 2.389e+01 2.299 0.02918 * \n", + "hyrm65 1.294e+01 2.317e+01 0.558 0.58112 \n", + "hyrf65 9.093e+00 1.767e+01 0.515 0.61088 \n", + "no65 3.721e-02 1.320e-01 0.282 0.78006 \n", + "nom65 -2.120e-02 6.496e-02 -0.326 0.74661 \n", + "nof65 -1.686e-02 6.700e-02 -0.252 0.80319 \n", + "pinstab1 -4.997e-02 3.092e-02 -1.616 0.11729 \n", + "pop65 1.032e-07 1.318e-07 0.783 0.44027 \n", + "worker65 3.408e-02 1.562e-01 0.218 0.82887 \n", + "pop1565 -4.655e-01 4.713e-01 -0.988 0.33176 \n", + "pop6565 -1.357e+00 6.349e-01 -2.138 0.04139 * \n", + "sec65 -1.089e-02 3.077e-01 -0.035 0.97201 \n", + "secm65 3.344e-03 1.512e-01 0.022 0.98251 \n", + "secf65 -2.304e-03 1.580e-01 -0.015 0.98847 \n", + "secc65 -4.915e-01 7.290e-01 -0.674 0.50570 \n", + "seccm65 2.596e-01 3.557e-01 0.730 0.47150 \n", + "seccf65 2.207e-01 3.733e-01 0.591 0.55924 \n", + "syr65 -7.556e-01 7.977e+00 -0.095 0.92521 \n", + "syrm65 3.109e-01 3.897e+00 0.080 0.93698 \n", + "syrf65 7.593e-01 4.111e+00 0.185 0.85479 \n", + "teapri65 3.955e-05 7.700e-04 0.051 0.95941 \n", + "teasec65 2.497e-04 1.171e-03 0.213 0.83274 \n", + "ex1 -5.804e-01 2.418e-01 -2.400 0.02329 * \n", + "im1 5.914e-01 2.503e-01 2.363 0.02531 * \n", + "xr65 -1.038e-04 5.417e-05 -1.916 0.06565 . \n", + "tot1 -1.279e-01 1.126e-01 -1.136 0.26561 \n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "Residual standard error: 0.03074 on 28 degrees of freedom\n", + "Multiple R-squared: 0.8871,\tAdjusted R-squared: 0.6411 \n", + "F-statistic: 3.607 on 61 and 28 DF, p-value: 0.0002003\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xnames = varnames[-c(1, 2, 3)] # names of X variables\n", + "dandxnames = varnames[-c(1, 2)] # names of D and X variables\n", + "# create formulas by pasting names (this saves typing times)\n", + "fmla = as.formula(paste(\"Outcome ~ \", paste(dandxnames, collapse = \"+\")))\n", + "ls.effect = lm(fmla, data = GrowthData)\n", + "summary((ls.effect))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Application: Economic Development and Institutions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 64
  2. 21
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 64\n", + "\\item 21\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 64\n", + "2. 21\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 64 21" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data(AJR)\n", + "y = AJR$GDP\n", + "d = AJR$Exprop\n", + "z = AJR$logMort\n", + "x = model.matrix(~-1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2,\n", + "data = AJR)\n", + "dim(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Estimation and significance testing of the effect of target variables in the IV regression model\"\n", + " coeff. se. t-value p-value \n", + "Exprop 0.8450 0.2699 3.131 0.00174 **\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "\n" + ] + } + ], + "source": [ + "AJR.Xselect = rlassoIV(GDP ~ Exprop + (Latitude + Latitude2 + Africa + Asia + Namer +\n", + "Samer)^2 | logMort + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2,\n", + "data = AJR, select.X = TRUE, select.Z = FALSE)\n", + "summary(AJR.Xselect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2.5 % 97.5 %\n", + "Exprop 0.3159812 1.374072\n" + ] + } + ], + "source": [ + "confint(AJR.Xselect)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [,1] [,2]\n", + "rD 1.27 1.73\n" + ] + } + ], + "source": [ + "# parialling out by linear model\n", + "fmla.y = GDP ~ (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2\n", + "fmla.d = Exprop ~ (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2\n", + "fmla.z = logMort ~ (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2\n", + "rY = lm(fmla.y, data = AJR)$res\n", + "rD = lm(fmla.d, data = AJR)$res\n", + "rZ = lm(fmla.z, data = AJR)$res\n", + "# ivfit.lm = tsls(y=rY,d=rD, x=NULL, z=rZ, intercept=FALSE)\n", + "ivfit.lm = tsls(rY ~ rD | rZ, intercept = FALSE)\n", + "print(cbind(ivfit.lm$coef, ivfit.lm$se), digits = 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# parialling out by lasso\n", + "rY = rlasso(fmla.y, data = AJR)$res\n", + "rD = rlasso(fmla.d, data = AJR)$res\n", + "rZ = rlasso(fmla.z, data = AJR)$res\n", + "# ivfit.lasso = tsls(y=rY,d=rD, x=NULL, z=rZ, intercept=FALSE)\n", + "ivfit.lasso = tsls(rY ~ rD | rZ, intercept = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Estimates and Significance Testing from from tsls\"\n", + " Estimate Std. Error t value p value \n", + "rD 0.8450 0.2699 3.131 0.00174 **\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "\n" + ] + } + ], + "source": [ + "summary(ivfit.lasso)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "data(EminentDomain)\n", + "z <- as.matrix(EminentDomain$logGDP$z)\n", + "x <- as.matrix(EminentDomain$logGDP$x)\n", + "y <- EminentDomain$logGDP$y\n", + "d <- EminentDomain$logGDP$d\n", + "x <- x[, apply(x, 2, mean, na.rm = TRUE) > 0.05] #\n", + "z <- z[, apply(z, 2, mean, na.rm = TRUE) > 0.05] #" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "ED.ols = lm(y ~ cbind(d, x))\n", + "ED.2sls = tsls(y = y, d = d, x = x, z = z[, 1:2], intercept = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Estimates and significance testing of the effect of target variables in the IV regression model\"\n", + " coeff. se. t-value p-value\n", + "d1 0.4146 0.2902 1.428 0.153\n", + "\n", + "\n" + ] + } + ], + "source": [ + "lasso.IV.Z = rlassoIV(x = x, d = d, y = y, z = z, select.X = FALSE, select.Z = TRUE)\n", + "summary(lasso.IV.Z)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2.5 % 97.5 %\n", + "d1 -0.1542764 0.9834796\n" + ] + } + ], + "source": [ + "confint(lasso.IV.Z)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model \n", + " coeff. se. t-value p-value\n", + "d1 -0.02383 0.12851 -0.185 0.853\n", + "\n", + "\n" + ] + } + ], + "source": [ + "lasso.IV.XZ = rlassoIV(x = x, d = d, y = y, z = z, select.X = TRUE, select.Z = TRUE)\n", + "summary(lasso.IV.XZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2.5 % 97.5 %\n", + "d1 -0.2757029 0.2280335\n" + ] + } + ], + "source": [ + "confint(lasso.IV.XZ)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Estimate
0.00786473183385086
Std. Error
0.00986592662133842
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[Estimate] 0.00786473183385086\n", + "\\item[Std. Error] 0.00986592662133842\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "Estimate\n", + ": 0.00786473183385086Std. Error\n", + ": 0.00986592662133842\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error \n", + "0.007864732 0.009865927 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(ED.ols)$coef[2, 1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "
A matrix: 1 × 2 of type dbl
d1-0.010733270.03376636
\n" + ], + "text/latex": [ + "A matrix: 1 × 2 of type dbl\n", + "\\begin{tabular}{r|ll}\n", + "\td1 & -0.01073327 & 0.03376636\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 1 × 2 of type dbl\n", + "\n", + "| d1 | -0.01073327 | 0.03376636 |\n", + "\n" + ], + "text/plain": [ + " [,1] [,2] \n", + "d1 -0.01073327 0.03376636" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cbind(ED.2sls$coef[1], ED.2sls$se[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Estimates and significance testing of the effect of target variables in the IV regression model\"\n", + " coeff. se. t-value p-value\n", + "d1 0.4146 0.2902 1.428 0.153\n", + "\n", + "\n" + ] + } + ], + "source": [ + "library(xtable)\n", + "table = matrix(0, 4, 2)\n", + "table[1, ] = summary(ED.ols)$coef[2, 1:2]\n", + "table[2, ] = cbind(ED.2sls$coef[1], ED.2sls$se[1])\n", + "table[3, ] = summary(lasso.IV.Z)[, 1:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 4 × 2 of type dbl
0.0078647320.009865927
-0.0107332690.033766362
0.4146016410.290249208
0.0000000000.000000000
\n" + ], + "text/latex": [ + "A matrix: 4 × 2 of type dbl\n", + "\\begin{tabular}{ll}\n", + "\t 0.007864732 & 0.009865927\\\\\n", + "\t -0.010733269 & 0.033766362\\\\\n", + "\t 0.414601641 & 0.290249208\\\\\n", + "\t 0.000000000 & 0.000000000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 4 × 2 of type dbl\n", + "\n", + "| 0.007864732 | 0.009865927 |\n", + "| -0.010733269 | 0.033766362 |\n", + "| 0.414601641 | 0.290249208 |\n", + "| 0.000000000 | 0.000000000 |\n", + "\n" + ], + "text/plain": [ + " [,1] [,2] \n", + "[1,] 0.007864732 0.009865927\n", + "[2,] -0.010733269 0.033766362\n", + "[3,] 0.414601641 0.290249208\n", + "[4,] 0.000000000 0.000000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. The Lasso Methods for Discovery of Significant Causes amongst Many Potential Causes, with Many Controls" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# library(hdm) library(stats)\n", + "set.seed(1)\n", + "n = 100\n", + "p1 = 20\n", + "p2 = 20\n", + "D = matrix(rnorm(n * p1), n, p1) # Causes\n", + "W = matrix(rnorm(n * p2), n, p2) # Controls\n", + "X = cbind(D, W) # Regressors\n", + "Y = D[, 1] * 5 + W[, 1] * 5 + rnorm(n) #Outcome" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# data = data.frame(cbind(Y, X))\n", + "# write.csv(data,\"../data/7_.csv\", row.names = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 20 × 2 of type dbl
2.5 %97.5 %
V1 4.51458775.21430498
V2-0.31429090.30494650
V3-0.35241090.18678880
V4-0.25424300.28738914
V5-0.27658020.27627177
V6-0.32146760.29422684
V7-0.22625070.30094168
V8-0.04735410.47366372
V9-0.18656360.39023520
V10-0.23723560.26411185
V11-0.31470910.20945872
V12-0.30919050.26572176
V13-0.17415500.37682465
V14-0.32357340.38543162
V15-0.32197630.31312486
V16-0.26495050.33100700
V17-0.17920800.41696169
V18-0.36932470.04695928
V19-0.10731090.39368776
V20-0.21571820.25543839
\n" + ], + "text/latex": [ + "A matrix: 20 × 2 of type dbl\n", + "\\begin{tabular}{r|ll}\n", + " & 2.5 \\% & 97.5 \\%\\\\\n", + "\\hline\n", + "\tV1 & 4.5145877 & 5.21430498\\\\\n", + "\tV2 & -0.3142909 & 0.30494650\\\\\n", + "\tV3 & -0.3524109 & 0.18678880\\\\\n", + "\tV4 & -0.2542430 & 0.28738914\\\\\n", + "\tV5 & -0.2765802 & 0.27627177\\\\\n", + "\tV6 & -0.3214676 & 0.29422684\\\\\n", + "\tV7 & -0.2262507 & 0.30094168\\\\\n", + "\tV8 & -0.0473541 & 0.47366372\\\\\n", + "\tV9 & -0.1865636 & 0.39023520\\\\\n", + "\tV10 & -0.2372356 & 0.26411185\\\\\n", + "\tV11 & -0.3147091 & 0.20945872\\\\\n", + "\tV12 & -0.3091905 & 0.26572176\\\\\n", + "\tV13 & -0.1741550 & 0.37682465\\\\\n", + "\tV14 & -0.3235734 & 0.38543162\\\\\n", + "\tV15 & -0.3219763 & 0.31312486\\\\\n", + "\tV16 & -0.2649505 & 0.33100700\\\\\n", + "\tV17 & -0.1792080 & 0.41696169\\\\\n", + "\tV18 & -0.3693247 & 0.04695928\\\\\n", + "\tV19 & -0.1073109 & 0.39368776\\\\\n", + "\tV20 & -0.2157182 & 0.25543839\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 20 × 2 of type dbl\n", + "\n", + "| | 2.5 % | 97.5 % |\n", + "|---|---|---|\n", + "| V1 | 4.5145877 | 5.21430498 |\n", + "| V2 | -0.3142909 | 0.30494650 |\n", + "| V3 | -0.3524109 | 0.18678880 |\n", + "| V4 | -0.2542430 | 0.28738914 |\n", + "| V5 | -0.2765802 | 0.27627177 |\n", + "| V6 | -0.3214676 | 0.29422684 |\n", + "| V7 | -0.2262507 | 0.30094168 |\n", + "| V8 | -0.0473541 | 0.47366372 |\n", + "| V9 | -0.1865636 | 0.39023520 |\n", + "| V10 | -0.2372356 | 0.26411185 |\n", + "| V11 | -0.3147091 | 0.20945872 |\n", + "| V12 | -0.3091905 | 0.26572176 |\n", + "| V13 | -0.1741550 | 0.37682465 |\n", + "| V14 | -0.3235734 | 0.38543162 |\n", + "| V15 | -0.3219763 | 0.31312486 |\n", + "| V16 | -0.2649505 | 0.33100700 |\n", + "| V17 | -0.1792080 | 0.41696169 |\n", + "| V18 | -0.3693247 | 0.04695928 |\n", + "| V19 | -0.1073109 | 0.39368776 |\n", + "| V20 | -0.2157182 | 0.25543839 |\n", + "\n" + ], + "text/plain": [ + " 2.5 % 97.5 % \n", + "V1 4.5145877 5.21430498\n", + "V2 -0.3142909 0.30494650\n", + "V3 -0.3524109 0.18678880\n", + "V4 -0.2542430 0.28738914\n", + "V5 -0.2765802 0.27627177\n", + "V6 -0.3214676 0.29422684\n", + "V7 -0.2262507 0.30094168\n", + "V8 -0.0473541 0.47366372\n", + "V9 -0.1865636 0.39023520\n", + "V10 -0.2372356 0.26411185\n", + "V11 -0.3147091 0.20945872\n", + "V12 -0.3091905 0.26572176\n", + "V13 -0.1741550 0.37682465\n", + "V14 -0.3235734 0.38543162\n", + "V15 -0.3219763 0.31312486\n", + "V16 -0.2649505 0.33100700\n", + "V17 -0.1792080 0.41696169\n", + "V18 -0.3693247 0.04695928\n", + "V19 -0.1073109 0.39368776\n", + "V20 -0.2157182 0.25543839" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "confint(rlassoEffects(X, Y, index = c(1:p1)), joint = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 316159f87a152c90702e633763253d0abc7a0d23 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Wed, 12 Oct 2022 02:02:44 -0500 Subject: [PATCH 15/28] # --- src/help_functions.jl | 8 +- tutorial/HDMjl_tutorial_1.ipynb | 131 +++--- tutorial/r_file.ipynb | 679 ++++---------------------------- 3 files changed, 150 insertions(+), 668 deletions(-) diff --git a/src/help_functions.jl b/src/help_functions.jl index 57ef8e2..1df1988 100644 --- a/src/help_functions.jl +++ b/src/help_functions.jl @@ -40,7 +40,7 @@ end function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_dependent_lambda::Bool = false, lambda_start = nothing, c::Float64 = 1.1, gamma::Float64 = 0.1, numSim::Int = 5000, y = nothing, x = nothing) # homoskedastic and X-independent - if (homoskedastic == true) & !X_dependent_lambda + if homoskedastic == true && X_dependent_lambda == false p = size(x, 2) n = size(x, 1) lambda0 = 2 * c * sqrt(n) * quantile(Normal(0.0, 1.0), 1 - gamma / (2 * p)) @@ -48,7 +48,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = zeros(p) .+ lambda0 * Ups0 # homoskedastic and X-dependent - elseif (homoskedastic == true) & X_dependent_lambda + elseif homoskedastic == true && X_dependent_lambda == true p = size(x, 2) n = size(x, 1) R = numSim @@ -67,7 +67,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = lambda0 * Ups0 # heteroskeddastic and X-independent - elseif (homoskedastic == false) & !X_dependent_lambda + elseif homoskedastic == false && X_dependent_lambda == false p = size(x, 2) n = size(x, 1) lambda0 = 2 * c * sqrt(n) * quantile(Normal(0.0, 1.0), 1 - gamma / (2 * p)) @@ -75,7 +75,7 @@ function lambdaCalculation(; homoskedastic::Union{Bool, String} = false, X_depen lambda = lambda0 * Ups0 # heteroskedastic and X-dependent - elseif (homoskedastic == false) & X_dependent_lambda + elseif homoskedastic == false && X_dependent_lambda ==true p = size(x, 2) n = size(x, 1) R = numSim diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index e8b5247..2ed2e50 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -175,9 +175,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, { "data": { "text/plain": [ @@ -698,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 1187, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -724,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -746,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 380, "metadata": {}, "outputs": [ { @@ -762,41 +769,33 @@ " female*hsd08 + female*hsd911 + female*hsg + female*cg + female*ad + female*mw + female*so + female*we + female*exp1 + female*exp2 + female*exp3)\n", "x_dframe = ModelFrame( x_formula, cps2012)\n", "x1 = ModelMatrix(x_dframe)\n", - "X = x1.m[:,Not(2:16)]\n", + "X = x1.m[:,Not(1:16)];\n", + "X = hcat(x1.m[:,1:16], X)\n", "size(X)\n", - "fom_1 = names(cps2012)\n", "fom_1 = [\"widowed\", \"divorced\", \"separated\", \"nevermarried\", \"hsd08\", \"hsd911\", \"hsg\", \"cg\", \"ad\", \"mw\", \"so\",\n", " \"we\", \"exp1\", \"exp2\", \"exp3\"];\n", "data = cps2012[:,fom_1];\n", - "sub_data = Matrix(data)\n", + "sub_data = ones(size(data)[1])\n", "for i in 1:size(data)[2]\n", " if i <= (size(data)[2] -1)\n", " sub_data = hcat(sub_data, Matrix(data[:, i] .* data[:, Not(1:i)]) )\n", " end\n", "end\n", + "sub_data = sub_data[:,2:end]\n", "size(sub_data)\n", - "x = hcat(X, sub_data)\n", + "x = hcat(X, sub_data)\n", "size(x)\n", "filter = var.(eachcol(x)) .!= 0\n", "x = x[:,filter]\n", "print(size(x))\n", "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", - "y = cps2012.lnw;" + "y = cps2012.lnw;\n", + ";" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", - "y = cps2012.lnw;" - ] - }, - { - "cell_type": "code", - "execution_count": 20, + "execution_count": 389, "metadata": {}, "outputs": [], "source": [ @@ -805,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 390, "metadata": {}, "outputs": [ { @@ -813,24 +812,24 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " \u001b[1m X1 \u001b[0m -0.154593 0.0501626 -3.08184 0.00205726\n", - " \u001b[1m X17 \u001b[0m -0.0343048 0.19287 -0.177865 0.858829\n", - " \u001b[1m X18 \u001b[0m -0.19402 0.0607806 -3.19214 0.00141224\n", - " \u001b[1m X19 \u001b[0m -0.113688 0.108023 -1.05245 0.292595\n", - " \u001b[1m X20 \u001b[0m -0.249804 0.0376073 -6.64244 3.08542e-11\n", - " \u001b[1m X21 \u001b[0m -0.391887 0.211089 -1.8565 0.0633823\n", - " \u001b[1m X22 \u001b[0m -0.35908 0.137517 -2.61116 0.0090235\n", - " \u001b[1m X23 \u001b[0m -0.269762 0.103718 -2.60093 0.00929725\n", - " \u001b[1m X24 \u001b[0m 0.269623 0.0430528 6.26261 3.78592e-10\n", - " \u001b[1m X25 \u001b[0m 0.429058 0.0460721 9.31276 1.24553e-20\n", - " \u001b[1m X26 \u001b[0m -0.141533 0.0427294 -3.3123 0.000925306\n", - " \u001b[1m X27 \u001b[0m -0.0790852 0.042853 -1.8455 0.0649645\n", - " \u001b[1m X28 \u001b[0m -0.0583598 0.0491329 -1.18779 0.234914\n", - " \u001b[1m X29 \u001b[0m 0.0325737 0.0170967 1.90527 0.0567456\n", - " \u001b[1m X30 \u001b[0m -0.273506 0.248368 -1.10121 0.270804\n", - " \u001b[1m X31 \u001b[0m 0.0 NaN NaN NaN\n", + " \u001b[1m X1 \u001b[0m -0.154923 0.0501624 -3.08843 0.00201216\n", + " \u001b[1m X17 \u001b[0m 0.136095 0.0906626 1.50112 0.133325\n", + " \u001b[1m X18 \u001b[0m 0.136939 0.0221817 6.17353 6.6782e-10\n", + " \u001b[1m X19 \u001b[0m 0.0233028 0.0532118 0.437925 0.661441\n", + " \u001b[1m X20 \u001b[0m 0.186853 0.0199424 9.36966 7.27651e-21\n", + " \u001b[1m X21 \u001b[0m 0.0278103 0.120914 0.23 0.818092\n", + " \u001b[1m X22 \u001b[0m -0.119335 0.0518797 -2.30023 0.0214354\n", + " \u001b[1m X23 \u001b[0m -0.0128898 0.0192232 -0.670533 0.502518\n", + " \u001b[1m X24 \u001b[0m 0.0101386 0.0183265 0.553218 0.580114\n", + " \u001b[1m X25 \u001b[0m -0.0304637 0.0218061 -1.39703 0.162405\n", + " \u001b[1m X26 \u001b[0m -0.00106344 0.0191918 -0.0554112 0.955811\n", + " \u001b[1m X27 \u001b[0m -0.00818334 0.0193568 -0.422763 0.672468\n", + " \u001b[1m X28 \u001b[0m -0.00422613 0.0211684 -0.199643 0.84176\n", + " \u001b[1m X29 \u001b[0m 0.00493526 0.00780428 0.632379 0.527139\n", + " \u001b[1m X30 \u001b[0m -0.159519 0.0452999 -3.52141 0.000429263\n", + " \u001b[1m X31 \u001b[0m 0.0384506 0.0078611 4.89125 1.00199e-6\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -843,49 +842,37 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "effects_female1 = HDMjl.rlassoEffects(x[:, index_gender], y);" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 399, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", "\n", - " \u001b[1m X1 \u001b[0m -0.283832 0.00692373 -40.9941 0.0\n", - " \u001b[1m X2 \u001b[0m -0.141256 0.0382915 -3.68897 0.000225164\n", - " \u001b[1m X3 \u001b[0m -0.0767676 0.0109705 -6.99762 2.60355e-12\n", - " \u001b[1m X4 \u001b[0m -0.105677 0.0266385 -3.96707 7.27622e-5\n", - " \u001b[1m X5 \u001b[0m -0.135126 0.0099468 -13.5849 4.9236e-42\n", - " \u001b[1m X6 \u001b[0m -0.620469 0.0535067 -11.5961 4.31297e-31\n", - " \u001b[1m X7 \u001b[0m -0.390831 0.0237062 -16.4864 4.59201e-61\n", - " \u001b[1m X8 \u001b[0m -0.173018 0.00941576 -18.3754 2.06932e-75\n", - " \u001b[1m X9 \u001b[0m 0.347913 0.00907484 38.3382 1.413e-321\n", - " \u001b[1m X10 \u001b[0m 0.595179 0.0108409 54.9013 0.0\n", - " \u001b[1m X11 \u001b[0m -0.106798 0.00950928 -11.2309 2.87586e-29\n", - " \u001b[1m X12 \u001b[0m -0.0544776 0.00956874 -5.69329 1.24611e-8\n", - " \u001b[1m X13 \u001b[0m -0.0118311 0.0104091 -1.13661 0.255702\n", - " \u001b[1m X14 \u001b[0m 0.0533367 0.00470804 11.3289 9.44218e-30\n", - " \u001b[1m X15 \u001b[0m -0.197522 0.0280961 -7.03021 2.06218e-12\n", - " \u001b[1m X16 \u001b[0m 0.0250002 0.00499574 5.00431 5.60622e-7\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + " \u001b[1m X1 \u001b[0m -0.292471 -0.0173751\n", + " \u001b[1m X17 \u001b[0m -0.130276 0.402467\n", + " \u001b[1m X18 \u001b[0m 0.075579 0.1983\n", + " \u001b[1m X19 \u001b[0m -0.114955 0.16156\n", + " \u001b[1m X20 \u001b[0m 0.129983 0.243724\n", + " \u001b[1m X21 \u001b[0m -0.369439 0.42506\n", + " \u001b[1m X22 \u001b[0m -0.267141 0.028471\n", + " \u001b[1m X23 \u001b[0m -0.0644819 0.0387024\n", + " \u001b[1m X24 \u001b[0m -0.0410296 0.0613067\n", + " \u001b[1m X25 \u001b[0m -0.0950142 0.0340867\n", + " \u001b[1m X26 \u001b[0m -0.0540328 0.051906\n", + " \u001b[1m X27 \u001b[0m -0.0620344 0.0456677\n", + " \u001b[1m X28 \u001b[0m -0.064856 0.0564037\n", + " \u001b[1m X29 \u001b[0m -0.0162257 0.0260962\n", + " \u001b[1m X30 \u001b[0m -0.282576 -0.0364624\n", + " \u001b[1m X31 \u001b[0m 0.0171196 0.0597815\n" ] } ], "source": [ - "HDMjl.r_summary(effects_female1);" + "joint_CI = HDMjl.r_confint(effects_female, 0.95, joint = true);\n", + "joint_CI;" ] }, { diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index c9b803a..a8e217f 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 311, "metadata": { "vscode": { "languageId": "r" @@ -651,27 +651,7 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "library(hdm)\n", - "data(cps2012)\n", - "X <- model.matrix(~-1 + female + female:(widowed + divorced + separated + nevermarried +\n", - "hsd08 + hsd911 + hsg + cg + ad + mw + so + we + exp1 + exp2 + exp3) + +(widowed +\n", - "divorced + separated + nevermarried + hsd08 + hsd911 + hsg + cg + ad + mw + so +\n", - "we + exp1 + exp2 + exp3)^2, data = cps2012)\n", - "dim(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ + }, { "data": { "text/html": [ @@ -703,99 +683,23 @@ } ], "source": [ + "library(hdm)\n", + "data(cps2012)\n", + "X <- model.matrix(~-1 + female + female:(widowed + divorced + separated + nevermarried +\n", + "hsd08 + hsd911 + hsg + cg + ad + mw + so + we + exp1 + exp2 + exp3) + +(widowed +\n", + "divorced + separated + nevermarried + hsd08 + hsd911 + hsg + cg + ad + mw + so +\n", + "we + exp1 + exp2 + exp3)^2, data = cps2012)\n", + "dim(X)\n", "X <- X[, which(apply(X, 2, var) != 0)] # exclude all constant variables\n", - "dim(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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A matrix: 29217 × 16 of type dblA matrix: 16 × 2 of type dbl
femalefemale:widowedfemale:divorcedfemale:separatedfemale:nevermarriedfemale:hsd08female:hsd911female:hsgfemale:cgfemale:adfemale:mwfemale:sofemale:wefemale:exp1female:exp2female:exp3
2.5 %97.5 %
1663459100000000000022.04.840010.648000
1663462100000100000030.09.000027.000000
16634630000000000000 0.00.0000 0.000000
1663465100000010000014.01.9600 2.744000
16634680000000000000 0.00.0000 0.000000
16634690000000000000 0.00.0000 0.000000
16634730000000000000 0.00.0000 0.000000
16634750000000000000 0.00.0000 0.000000
16634760000000000000 0.00.0000 0.000000
1663477100000000000015.52.4025 3.723875
16634800000000000000 0.00.0000 0.000000
16634811000000001000 7.00.4900 0.343000
16634820000000000000 0.00.0000 0.000000
16634830000000000000 0.00.0000 0.000000
1663485101000000000020.54.2025 8.615125
16634900000000000000 0.00.0000 0.000000
16634940000000000000 0.00.0000 0.000000
16634970000000000000 0.00.0000 0.000000
16634980000000000000 0.00.0000 0.000000
1663500100010001000015.02.2500 3.375000
16635030000000000000 0.00.0000 0.000000
1663508100000000000016.02.5600 4.096000
16635090000000000000 0.00.0000 0.000000
16635110000000000000 0.00.0000 0.000000
16635121000100000000 6.00.3600 0.216000
16635130000000000000 0.00.0000 0.000000
16635160000000000000 0.00.0000 0.000000
1663517100000001000020.04.0000 8.000000
16635191000000001000 6.00.3600 0.216000
16635210000000000000 0.00.0000 0.000000
17128201000100010001111.21 1.331
17128210000000000000 00.00 0.000
17128261000000000001224.8410.648
17128300000000000000 00.00 0.000
17128320000000000000 00.00 0.000
17128360000000000000 00.00 0.000
17128371000000100001141.96 2.744
17128400000000000000 00.00 0.000
17128411000000010001111.21 1.331
17128420000000000000 00.00 0.000
17128440000000000000 00.00 0.000
17128450000000000000 00.00 0.000
17128460000000000000 00.00 0.000
17128470000000000000 00.00 0.000
17128480000000000000 00.00 0.000
17128491000000001001256.2515.625
17128501010000000001121.44 1.728
17128511000100010001 30.09 0.027
17128531000000010001152.25 3.375
17128550000000000000 00.00 0.000
17128601000100000001183.24 5.832
17128621000100010001 50.25 0.125
17128640000000000000 00.00 0.000
17128650000000000000 00.00 0.000
17128661000000100001162.56 4.096
17128670000000000000 00.00 0.000
17128681000100001001162.56 4.096
17128691000000000001214.41 9.261
17128700000000000000 00.00 0.000
17128711000000001001 20.04 0.008
female-0.29392716-0.01591940
female:widowed-0.13309530 0.40528626
female:divorced 0.07492960 0.19894917
female:separated-0.11641774 0.16302326
female:nevermarried 0.12938077 0.24432620
female:hsd08-0.37364347 0.42926409
female:hsd911-0.26870533 0.03003525
female:hsg-0.06502795 0.03924839
female:cg-0.04157113 0.06184824
female:ad-0.09569738 0.03476989
female:mw-0.05459343 0.05246655
female:so-0.06260432 0.04623763
female:we-0.06549766 0.05704541
female:exp1-0.01644965 0.02632017
female:exp2-0.28387860-0.03516005
female:exp3 0.01689389 0.06000727
\n" ], "text/latex": [ - "A matrix: 29217 × 16 of type dbl\n", - "\\begin{tabular}{r|llllllllllllllll}\n", - " & female & female:widowed & female:divorced & female:separated & female:nevermarried & female:hsd08 & female:hsd911 & female:hsg & female:cg & female:ad & female:mw & female:so & female:we & female:exp1 & female:exp2 & female:exp3\\\\\n", + "A matrix: 16 × 2 of type dbl\n", + "\\begin{tabular}{r|ll}\n", + " & 2.5 \\% & 97.5 \\%\\\\\n", "\\hline\n", - "\t1663459 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 22.0 & 4.8400 & 10.648000\\\\\n", - "\t1663462 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 30.0 & 9.0000 & 27.000000\\\\\n", - "\t1663463 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663465 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 14.0 & 1.9600 & 2.744000\\\\\n", - "\t1663468 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663469 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663473 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663475 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663476 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663477 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 15.5 & 2.4025 & 3.723875\\\\\n", - "\t1663480 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663481 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 7.0 & 0.4900 & 0.343000\\\\\n", - "\t1663482 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663483 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663485 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 20.5 & 4.2025 & 8.615125\\\\\n", - "\t1663490 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663494 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663497 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663498 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663500 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 15.0 & 2.2500 & 3.375000\\\\\n", - "\t1663503 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663508 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 16.0 & 2.5600 & 4.096000\\\\\n", - "\t1663509 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663511 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663512 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 6.0 & 0.3600 & 0.216000\\\\\n", - "\t1663513 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663516 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t1663517 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 20.0 & 4.0000 & 8.000000\\\\\n", - "\t1663519 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 6.0 & 0.3600 & 0.216000\\\\\n", - "\t1663521 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.0 & 0.0000 & 0.000000\\\\\n", - "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", - "\t1712820 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 11 & 1.21 & 1.331\\\\\n", - "\t1712821 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712826 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 22 & 4.84 & 10.648\\\\\n", - "\t1712830 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712832 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712836 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712837 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 14 & 1.96 & 2.744\\\\\n", - "\t1712840 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712841 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 11 & 1.21 & 1.331\\\\\n", - "\t1712842 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712844 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712845 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712846 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712847 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712848 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712849 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 25 & 6.25 & 15.625\\\\\n", - "\t1712850 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 12 & 1.44 & 1.728\\\\\n", - "\t1712851 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 3 & 0.09 & 0.027\\\\\n", - "\t1712853 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 15 & 2.25 & 3.375\\\\\n", - "\t1712855 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712860 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 18 & 3.24 & 5.832\\\\\n", - "\t1712862 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 5 & 0.25 & 0.125\\\\\n", - "\t1712864 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712865 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712866 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 16 & 2.56 & 4.096\\\\\n", - "\t1712867 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712868 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 16 & 2.56 & 4.096\\\\\n", - "\t1712869 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 21 & 4.41 & 9.261\\\\\n", - "\t1712870 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.00 & 0.000\\\\\n", - "\t1712871 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 2 & 0.04 & 0.008\\\\\n", + "\tfemale & -0.29392716 & -0.01591940\\\\\n", + "\tfemale:widowed & -0.13309530 & 0.40528626\\\\\n", + "\tfemale:divorced & 0.07492960 & 0.19894917\\\\\n", + "\tfemale:separated & -0.11641774 & 0.16302326\\\\\n", + "\tfemale:nevermarried & 0.12938077 & 0.24432620\\\\\n", + "\tfemale:hsd08 & -0.37364347 & 0.42926409\\\\\n", + "\tfemale:hsd911 & -0.26870533 & 0.03003525\\\\\n", + "\tfemale:hsg & -0.06502795 & 0.03924839\\\\\n", + "\tfemale:cg & -0.04157113 & 0.06184824\\\\\n", + "\tfemale:ad & -0.09569738 & 0.03476989\\\\\n", + "\tfemale:mw & -0.05459343 & 0.05246655\\\\\n", + "\tfemale:so & -0.06260432 & 0.04623763\\\\\n", + "\tfemale:we & -0.06549766 & 0.05704541\\\\\n", + "\tfemale:exp1 & -0.01644965 & 0.02632017\\\\\n", + "\tfemale:exp2 & -0.28387860 & -0.03516005\\\\\n", + "\tfemale:exp3 & 0.01689389 & 0.06000727\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A matrix: 29217 × 16 of type dbl\n", + "A matrix: 16 × 2 of type dbl\n", "\n", - "| | female | female:widowed | female:divorced | female:separated | female:nevermarried | female:hsd08 | female:hsd911 | female:hsg | female:cg | female:ad | female:mw | female:so | female:we | female:exp1 | female:exp2 | female:exp3 |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1663459 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22.0 | 4.8400 | 10.648000 |\n", - "| 1663462 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 30.0 | 9.0000 | 27.000000 |\n", - "| 1663463 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663465 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 14.0 | 1.9600 | 2.744000 |\n", - "| 1663468 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663469 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663473 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663475 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663476 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663477 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15.5 | 2.4025 | 3.723875 |\n", - "| 1663480 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663481 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7.0 | 0.4900 | 0.343000 |\n", - "| 1663482 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663483 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663485 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20.5 | 4.2025 | 8.615125 |\n", - "| 1663490 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663494 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663497 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663498 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663500 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 15.0 | 2.2500 | 3.375000 |\n", - "| 1663503 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663508 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16.0 | 2.5600 | 4.096000 |\n", - "| 1663509 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663511 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663512 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.0 | 0.3600 | 0.216000 |\n", - "| 1663513 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663516 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| 1663517 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 20.0 | 4.0000 | 8.000000 |\n", - "| 1663519 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6.0 | 0.3600 | 0.216000 |\n", - "| 1663521 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0000 | 0.000000 |\n", - "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", - "| 1712820 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 11 | 1.21 | 1.331 |\n", - "| 1712821 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712826 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 4.84 | 10.648 |\n", - "| 1712830 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712832 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712836 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712837 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 14 | 1.96 | 2.744 |\n", - "| 1712840 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712841 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 11 | 1.21 | 1.331 |\n", - "| 1712842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712845 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712847 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712848 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712849 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 25 | 6.25 | 15.625 |\n", - "| 1712850 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12 | 1.44 | 1.728 |\n", - "| 1712851 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | 0.09 | 0.027 |\n", - "| 1712853 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 15 | 2.25 | 3.375 |\n", - "| 1712855 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712860 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 18 | 3.24 | 5.832 |\n", - "| 1712862 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 0.25 | 0.125 |\n", - "| 1712864 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712865 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712866 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 16 | 2.56 | 4.096 |\n", - "| 1712867 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712868 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 16 | 2.56 | 4.096 |\n", - "| 1712869 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 21 | 4.41 | 9.261 |\n", - "| 1712870 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.000 |\n", - "| 1712871 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0.04 | 0.008 |\n", + "| | 2.5 % | 97.5 % |\n", + "|---|---|---|\n", + "| female | -0.29392716 | -0.01591940 |\n", + "| female:widowed | -0.13309530 | 0.40528626 |\n", + "| female:divorced | 0.07492960 | 0.19894917 |\n", + "| female:separated | -0.11641774 | 0.16302326 |\n", + "| female:nevermarried | 0.12938077 | 0.24432620 |\n", + "| female:hsd08 | -0.37364347 | 0.42926409 |\n", + "| female:hsd911 | -0.26870533 | 0.03003525 |\n", + "| female:hsg | -0.06502795 | 0.03924839 |\n", + "| female:cg | -0.04157113 | 0.06184824 |\n", + "| female:ad | -0.09569738 | 0.03476989 |\n", + "| female:mw | -0.05459343 | 0.05246655 |\n", + "| female:so | -0.06260432 | 0.04623763 |\n", + "| female:we | -0.06549766 | 0.05704541 |\n", + "| female:exp1 | -0.01644965 | 0.02632017 |\n", + "| female:exp2 | -0.28387860 | -0.03516005 |\n", + "| female:exp3 | 0.01689389 | 0.06000727 |\n", "\n" ], "text/plain": [ - " female female:widowed female:divorced female:separated\n", - "1663459 1 0 0 0 \n", - "1663462 1 0 0 0 \n", - "1663463 0 0 0 0 \n", - "1663465 1 0 0 0 \n", - "1663468 0 0 0 0 \n", - "1663469 0 0 0 0 \n", - "1663473 0 0 0 0 \n", - "1663475 0 0 0 0 \n", - "1663476 0 0 0 0 \n", - "1663477 1 0 0 0 \n", - "1663480 0 0 0 0 \n", - "1663481 1 0 0 0 \n", - "1663482 0 0 0 0 \n", - "1663483 0 0 0 0 \n", - "1663485 1 0 1 0 \n", - "1663490 0 0 0 0 \n", - "1663494 0 0 0 0 \n", - "1663497 0 0 0 0 \n", - "1663498 0 0 0 0 \n", - "1663500 1 0 0 0 \n", - "1663503 0 0 0 0 \n", - "1663508 1 0 0 0 \n", - "1663509 0 0 0 0 \n", - "1663511 0 0 0 0 \n", - "1663512 1 0 0 0 \n", - "1663513 0 0 0 0 \n", - "1663516 0 0 0 0 \n", - "1663517 1 0 0 0 \n", - "1663519 1 0 0 0 \n", - "1663521 0 0 0 0 \n", - "⋮ ⋮ ⋮ ⋮ ⋮ \n", - "1712820 1 0 0 0 \n", - "1712821 0 0 0 0 \n", - "1712826 1 0 0 0 \n", - "1712830 0 0 0 0 \n", - "1712832 0 0 0 0 \n", - "1712836 0 0 0 0 \n", - "1712837 1 0 0 0 \n", - "1712840 0 0 0 0 \n", - "1712841 1 0 0 0 \n", - "1712842 0 0 0 0 \n", - "1712844 0 0 0 0 \n", - "1712845 0 0 0 0 \n", - "1712846 0 0 0 0 \n", - "1712847 0 0 0 0 \n", - "1712848 0 0 0 0 \n", - "1712849 1 0 0 0 \n", - "1712850 1 0 1 0 \n", - "1712851 1 0 0 0 \n", - "1712853 1 0 0 0 \n", - "1712855 0 0 0 0 \n", - "1712860 1 0 0 0 \n", - "1712862 1 0 0 0 \n", - "1712864 0 0 0 0 \n", - "1712865 0 0 0 0 \n", - "1712866 1 0 0 0 \n", - "1712867 0 0 0 0 \n", - "1712868 1 0 0 0 \n", - "1712869 1 0 0 0 \n", - "1712870 0 0 0 0 \n", - "1712871 1 0 0 0 \n", - " female:nevermarried female:hsd08 female:hsd911 female:hsg female:cg\n", - "1663459 0 0 0 0 0 \n", - "1663462 0 0 1 0 0 \n", - "1663463 0 0 0 0 0 \n", - "1663465 0 0 0 1 0 \n", - "1663468 0 0 0 0 0 \n", - "1663469 0 0 0 0 0 \n", - "1663473 0 0 0 0 0 \n", - "1663475 0 0 0 0 0 \n", - "1663476 0 0 0 0 0 \n", - "1663477 0 0 0 0 0 \n", - "1663480 0 0 0 0 0 \n", - "1663481 0 0 0 0 0 \n", - "1663482 0 0 0 0 0 \n", - "1663483 0 0 0 0 0 \n", - "1663485 0 0 0 0 0 \n", - "1663490 0 0 0 0 0 \n", - "1663494 0 0 0 0 0 \n", - "1663497 0 0 0 0 0 \n", - "1663498 0 0 0 0 0 \n", - "1663500 1 0 0 0 1 \n", - "1663503 0 0 0 0 0 \n", - "1663508 0 0 0 0 0 \n", - "1663509 0 0 0 0 0 \n", - "1663511 0 0 0 0 0 \n", - "1663512 1 0 0 0 0 \n", - "1663513 0 0 0 0 0 \n", - "1663516 0 0 0 0 0 \n", - "1663517 0 0 0 0 1 \n", - "1663519 0 0 0 0 0 \n", - "1663521 0 0 0 0 0 \n", - "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", - "1712820 1 0 0 0 1 \n", - "1712821 0 0 0 0 0 \n", - "1712826 0 0 0 0 0 \n", - "1712830 0 0 0 0 0 \n", - "1712832 0 0 0 0 0 \n", - "1712836 0 0 0 0 0 \n", - "1712837 0 0 0 1 0 \n", - "1712840 0 0 0 0 0 \n", - "1712841 0 0 0 0 1 \n", - "1712842 0 0 0 0 0 \n", - "1712844 0 0 0 0 0 \n", - "1712845 0 0 0 0 0 \n", - "1712846 0 0 0 0 0 \n", - "1712847 0 0 0 0 0 \n", - "1712848 0 0 0 0 0 \n", - "1712849 0 0 0 0 0 \n", - "1712850 0 0 0 0 0 \n", - "1712851 1 0 0 0 1 \n", - "1712853 0 0 0 0 1 \n", - "1712855 0 0 0 0 0 \n", - "1712860 1 0 0 0 0 \n", - "1712862 1 0 0 0 1 \n", - "1712864 0 0 0 0 0 \n", - "1712865 0 0 0 0 0 \n", - "1712866 0 0 0 1 0 \n", - "1712867 0 0 0 0 0 \n", - "1712868 1 0 0 0 0 \n", - "1712869 0 0 0 0 0 \n", - "1712870 0 0 0 0 0 \n", - "1712871 0 0 0 0 0 \n", - " female:ad female:mw female:so female:we female:exp1 female:exp2\n", - "1663459 0 0 0 0 22.0 4.8400 \n", - "1663462 0 0 0 0 30.0 9.0000 \n", - "1663463 0 0 0 0 0.0 0.0000 \n", - "1663465 0 0 0 0 14.0 1.9600 \n", - "1663468 0 0 0 0 0.0 0.0000 \n", - "1663469 0 0 0 0 0.0 0.0000 \n", - "1663473 0 0 0 0 0.0 0.0000 \n", - "1663475 0 0 0 0 0.0 0.0000 \n", - "1663476 0 0 0 0 0.0 0.0000 \n", - "1663477 0 0 0 0 15.5 2.4025 \n", - "1663480 0 0 0 0 0.0 0.0000 \n", - "1663481 1 0 0 0 7.0 0.4900 \n", - "1663482 0 0 0 0 0.0 0.0000 \n", - "1663483 0 0 0 0 0.0 0.0000 \n", - "1663485 0 0 0 0 20.5 4.2025 \n", - "1663490 0 0 0 0 0.0 0.0000 \n", - "1663494 0 0 0 0 0.0 0.0000 \n", - "1663497 0 0 0 0 0.0 0.0000 \n", - "1663498 0 0 0 0 0.0 0.0000 \n", - "1663500 0 0 0 0 15.0 2.2500 \n", - "1663503 0 0 0 0 0.0 0.0000 \n", - "1663508 0 0 0 0 16.0 2.5600 \n", - "1663509 0 0 0 0 0.0 0.0000 \n", - "1663511 0 0 0 0 0.0 0.0000 \n", - "1663512 0 0 0 0 6.0 0.3600 \n", - "1663513 0 0 0 0 0.0 0.0000 \n", - "1663516 0 0 0 0 0.0 0.0000 \n", - "1663517 0 0 0 0 20.0 4.0000 \n", - "1663519 1 0 0 0 6.0 0.3600 \n", - "1663521 0 0 0 0 0.0 0.0000 \n", - "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", - "1712820 0 0 0 1 11 1.21 \n", - "1712821 0 0 0 0 0 0.00 \n", - "1712826 0 0 0 1 22 4.84 \n", - "1712830 0 0 0 0 0 0.00 \n", - "1712832 0 0 0 0 0 0.00 \n", - "1712836 0 0 0 0 0 0.00 \n", - "1712837 0 0 0 1 14 1.96 \n", - "1712840 0 0 0 0 0 0.00 \n", - "1712841 0 0 0 1 11 1.21 \n", - "1712842 0 0 0 0 0 0.00 \n", - "1712844 0 0 0 0 0 0.00 \n", - "1712845 0 0 0 0 0 0.00 \n", - "1712846 0 0 0 0 0 0.00 \n", - "1712847 0 0 0 0 0 0.00 \n", - "1712848 0 0 0 0 0 0.00 \n", - "1712849 1 0 0 1 25 6.25 \n", - "1712850 0 0 0 1 12 1.44 \n", - "1712851 0 0 0 1 3 0.09 \n", - "1712853 0 0 0 1 15 2.25 \n", - "1712855 0 0 0 0 0 0.00 \n", - "1712860 0 0 0 1 18 3.24 \n", - "1712862 0 0 0 1 5 0.25 \n", - "1712864 0 0 0 0 0 0.00 \n", - "1712865 0 0 0 0 0 0.00 \n", - "1712866 0 0 0 1 16 2.56 \n", - "1712867 0 0 0 0 0 0.00 \n", - "1712868 1 0 0 1 16 2.56 \n", - "1712869 0 0 0 1 21 4.41 \n", - "1712870 0 0 0 0 0 0.00 \n", - "1712871 1 0 0 1 2 0.04 \n", - " female:exp3\n", - "1663459 10.648000 \n", - "1663462 27.000000 \n", - "1663463 0.000000 \n", - "1663465 2.744000 \n", - "1663468 0.000000 \n", - "1663469 0.000000 \n", - "1663473 0.000000 \n", - "1663475 0.000000 \n", - "1663476 0.000000 \n", - "1663477 3.723875 \n", - "1663480 0.000000 \n", - "1663481 0.343000 \n", - "1663482 0.000000 \n", - "1663483 0.000000 \n", - "1663485 8.615125 \n", - "1663490 0.000000 \n", - "1663494 0.000000 \n", - "1663497 0.000000 \n", - "1663498 0.000000 \n", - "1663500 3.375000 \n", - "1663503 0.000000 \n", - "1663508 4.096000 \n", - "1663509 0.000000 \n", - "1663511 0.000000 \n", - "1663512 0.216000 \n", - "1663513 0.000000 \n", - "1663516 0.000000 \n", - "1663517 8.000000 \n", - "1663519 0.216000 \n", - "1663521 0.000000 \n", - "⋮ ⋮ \n", - "1712820 1.331 \n", - "1712821 0.000 \n", - "1712826 10.648 \n", - "1712830 0.000 \n", - "1712832 0.000 \n", - "1712836 0.000 \n", - "1712837 2.744 \n", - "1712840 0.000 \n", - "1712841 1.331 \n", - "1712842 0.000 \n", - "1712844 0.000 \n", - "1712845 0.000 \n", - "1712846 0.000 \n", - "1712847 0.000 \n", - "1712848 0.000 \n", - "1712849 15.625 \n", - "1712850 1.728 \n", - "1712851 0.027 \n", - "1712853 3.375 \n", - "1712855 0.000 \n", - "1712860 5.832 \n", - "1712862 0.125 \n", - "1712864 0.000 \n", - "1712865 0.000 \n", - "1712866 4.096 \n", - "1712867 0.000 \n", - "1712868 4.096 \n", - "1712869 9.261 \n", - "1712870 0.000 \n", - "1712871 0.008 " + " 2.5 % 97.5 % \n", + "female -0.29392716 -0.01591940\n", + "female:widowed -0.13309530 0.40528626\n", + "female:divorced 0.07492960 0.19894917\n", + "female:separated -0.11641774 0.16302326\n", + "female:nevermarried 0.12938077 0.24432620\n", + "female:hsd08 -0.37364347 0.42926409\n", + "female:hsd911 -0.26870533 0.03003525\n", + "female:hsg -0.06502795 0.03924839\n", + "female:cg -0.04157113 0.06184824\n", + "female:ad -0.09569738 0.03476989\n", + "female:mw -0.05459343 0.05246655\n", + "female:so -0.06260432 0.04623763\n", + "female:we -0.06549766 0.05704541\n", + "female:exp1 -0.01644965 0.02632017\n", + "female:exp2 -0.28387860 -0.03516005\n", + "female:exp3 0.01689389 0.06000727" ] }, "metadata": {}, @@ -1312,7 +850,8 @@ } ], "source": [ - "X[, index.gender]" + "joint.CI <- confint(effects.female, level = 0.95, joint = TRUE)\n", + "joint.CI" ] }, { @@ -1326,50 +865,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, From c4e701c284d45abd976dd25adf755fcb6fd9f35c Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Wed, 12 Oct 2022 05:25:08 -0500 Subject: [PATCH 16/28] # --- tutorial/HDMjl_tutorial_1.ipynb | 438 ++++++++++++++++++++------------ tutorial/r_file.ipynb | 350 +++++++++++++++++++------ 2 files changed, 545 insertions(+), 243 deletions(-) diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index 2ed2e50..31aa40b 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -889,6 +889,15 @@ "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" ] }, + { + "cell_type": "code", + "execution_count": 409, + "metadata": {}, + "outputs": [], + "source": [ + "using LinearAlgebra" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -917,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 401, "metadata": {}, "outputs": [], "source": [ @@ -935,81 +944,52 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 475, + "metadata": {}, + "outputs": [], + "source": [ + "Q, R = qr(hcat(ones(length(y)), X_1))\n", + "β = pinv(hcat(ones(length(y)), X_1)) * y\n", + "# ls_effect = β;\n", + ";" + ] + }, + { + "cell_type": "code", + "execution_count": 495, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 521, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:\n", - "\n", - "Coefficients:\n", - "────────────────────────────────────────────────────────────────────────────────\n", - " Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%\n", - "────────────────────────────────────────────────────────────────────────────────\n", - "x1 0.0 NaN NaN NaN NaN NaN\n", - "x2 -0.0366232 0.0216873 -1.69 0.0987 -0.08039 0.0071436\n", - "x3 -0.049561 0.0277206 -1.79 0.0810 -0.105503 0.00638146\n", - "x4 0.0109733 0.144971 0.08 0.9400 -0.28159 0.303536\n", - "x5 -0.61884 0.392592 -1.58 0.1225 -1.41112 0.173443\n", - "x6 0.0 NaN NaN NaN NaN NaN\n", - "x7 0.0653886 0.191762 0.34 0.7348 -0.321604 0.452381\n", - "x8 -0.0130227 0.220896 -0.06 0.9533 -0.458809 0.432764\n", - "x9 0.317601 0.136132 2.33 0.0245 0.0428751 0.592327\n", - "x10 0.0192542 0.104553 0.18 0.8548 -0.191742 0.23025\n", - "x11 -0.223675 0.111066 -2.01 0.0505 -0.447816 0.000465448\n", - "x12 -0.0285525 0.172121 -0.17 0.8690 -0.375908 0.318803\n", - "x13 -0.06466 0.110926 -0.58 0.5631 -0.288518 0.159198\n", - "x14 -0.0827085 0.106666 -0.78 0.4425 -0.29797 0.132553\n", - "x15 -0.00952063 0.0254533 -0.37 0.7103 -0.0608875 0.0418462\n", - "x16 0.0 NaN NaN NaN NaN NaN\n", - "x17 0.0785956 0.0659261 1.19 0.2399 -0.0544487 0.21164\n", - "x18 0.0 NaN NaN NaN NaN NaN\n", - "x19 0.0147886 0.0300756 0.49 0.6255 -0.0459065 0.0754837\n", - "x20 -0.313055 0.27212 -1.15 0.2565 -0.862216 0.236105\n", - "x21 0.101771 0.090829 1.12 0.2689 -0.0815298 0.285071\n", - "x22 0.0 NaN NaN NaN NaN NaN\n", - "x23 0.0 NaN NaN NaN NaN NaN\n", - "x24 0.357228 0.24998 1.43 0.1604 -0.147253 0.861708\n", - "x25 -0.287676 0.263885 -1.09 0.2819 -0.820218 0.244866\n", - "x26 -0.134781 0.144646 -0.93 0.3568 -0.426689 0.157128\n", - "x27 0.0 NaN NaN NaN NaN NaN\n", - "x28 -0.0570462 0.171219 -0.33 0.7407 -0.40258 0.288487\n", - "x29 0.0410234 0.0861237 0.48 0.6363 -0.132781 0.214828\n", - "x30 0.0122654 0.0897691 0.14 0.8920 -0.168896 0.193427\n", - "x31 0.0 NaN NaN NaN NaN NaN\n", - "x32 0.00856028 0.0183311 0.47 0.6429 -0.0284333 0.0455539\n", - "x33 -0.0141407 0.0214721 -0.66 0.5138 -0.0574733 0.0291918\n", - "x34 0.0 NaN NaN NaN NaN NaN\n", - "x35 -0.0401447 0.0489759 -0.82 0.4170 -0.138982 0.0586928\n", - "x36 0.0406806 0.0523095 0.78 0.4411 -0.0648842 0.146245\n", - "x37 0.0 NaN NaN NaN NaN NaN\n", - "x38 0.0 NaN NaN NaN NaN NaN\n", - "x39 0.0 NaN NaN NaN NaN NaN\n", - "x40 -0.0438831 0.0354963 -1.24 0.2232 -0.115518 0.0277514\n", - "x41 0.0198653 0.0177205 1.12 0.2686 -0.0158962 0.0556268\n", - "x42 0.0237951 0.018077 1.32 0.1952 -0.0126858 0.0602759\n", - "x43 -0.0245828 0.0246516 -1.00 0.3244 -0.0743318 0.0251662\n", - "x44 -2.58955e-8 1.06384e-7 -0.24 0.8089 -2.40586e-7 1.88795e-7\n", - "x45 0.0634478 0.134926 0.47 0.6406 -0.208844 0.33574\n", - "x46 -0.111512 0.33659 -0.33 0.7421 -0.790778 0.567755\n", - "x47 0.0 NaN NaN NaN NaN NaN\n", - "x48 -0.0673765 0.230006 -0.29 0.7710 -0.531548 0.396795\n", - "x49 0.0348377 0.112888 0.31 0.7591 -0.192979 0.262654\n", - "x50 0.0306221 0.11848 0.26 0.7973 -0.208481 0.269725\n", - "x51 0.114321 0.447811 0.26 0.7997 -0.789399 1.01804\n", - "x52 -0.0403413 0.217537 -0.19 0.8538 -0.479349 0.398666\n", - "x53 -0.0794911 0.229381 -0.35 0.7307 -0.542401 0.383419\n", - "x54 0.0 NaN NaN NaN NaN NaN\n", - "x55 -0.113094 0.133428 -0.85 0.4015 -0.382363 0.156175\n", - "x56 0.231347 0.201217 1.15 0.2568 -0.174726 0.637419\n", - "x57 2.00279e-5 0.000732672 0.03 0.9783 -0.00145856 0.00149862\n", - "x58 0.000279117 0.00108839 0.26 0.7989 -0.00191734 0.00247558\n", - "x59 -0.311523 0.18417 -1.69 0.0982 -0.683194 0.0601472\n", - "x60 0.298687 0.18672 1.60 0.1172 -0.0781284 0.675503\n", - "x61 -3.25316e-5 4.94833e-5 -0.66 0.5145 -0.000132393 6.73298e-5\n", - "x62 -0.0515082 0.0905298 -0.57 0.5724 -0.234205 0.131188\n", - "────────────────────────────────────────────────────────────────────────────────\n" + "62-element Vector{Float64}:\n", + " 0.08610965052296786\n", + " 0.017603705140386082\n", + " 0.023113130326795704\n", + " 0.09206866053605631\n", + " 0.0878861223713962\n", + " 0.05589048429931293\n", + " 0.1496453568701311\n", + " 0.1554834828840219\n", + " 0.10812292916708843\n", + " 0.08661892282903665\n", + " ⋮\n", + " 0.026921968250533907\n", + " 0.11093580210306042\n", + " 0.13864854930265377\n", + " 0.0006380087011397892\n", + " 0.0008637312895228336\n", + " 0.1425986274859988\n", + " 0.1436075515325452\n", + " 4.171442859962156e-5\n", + " 0.08027895945461645" ] }, "metadata": {}, @@ -1017,95 +997,254 @@ } ], "source": [ - "ls_effect = lm(hcat(ones(length(y)), X_1), y)" + "res = y - hcat(ones(length(y)), X_1) * β;\n", + "n = size(hcat(ones(length(y)), X_1))[1]\n", + "k = size(hcat(ones(length(y)), X_1))[2]\n", + "\n", + "sigma2_hat = (res' * res) / (n - k)\n", + "vcov_beta_hat = sigma2_hat .* pinv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1));\n", + "se = sqrt.(diag(vcov_beta_hat))" ] }, { "cell_type": "code", - "execution_count": 1158, + "execution_count": 487, "metadata": {}, "outputs": [], "source": [ - "lasso_effect = HDMjl.rlassoEffect(X, y, d, method = \"partialling out\");" + "res = y .- β[1] .+ X_1 * β[2:length(β)];\n", + "n = size(X_1)[1]\n", + "k = size(X_1)[2]\n", + "\n", + "sigma2_hat = (res' * res) / (n - k)\n", + "vcov_beta_hat = sigma2_hat .* pinv(X_1' * X_1);" ] }, { "cell_type": "code", - "execution_count": 1163, + "execution_count": 499, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m 1 \u001b[0m\n", - "\n", - " -0.0498\n" - ] + "data": { + "text/plain": [ + "62-element Vector{Float64}:\n", + " 0.08610965052296786\n", + " 0.017603705140386082\n", + " 0.023113130326795704\n", + " 0.09206866053605631\n", + " 0.0878861223713962\n", + " 0.05589048429931293\n", + " 0.1496453568701311\n", + " 0.1554834828840219\n", + " 0.10812292916708843\n", + " 0.08661892282903665\n", + " ⋮\n", + " 0.026921968250533907\n", + " 0.11093580210306042\n", + " 0.13864854930265377\n", + " 0.0006380087011397892\n", + " 0.0008637312895228336\n", + " 0.1425986274859988\n", + " 0.1436075515325452\n", + " 4.171442859962156e-5\n", + " 0.08027895945461645" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "HDMjl.r_print(lasso_effect, digits = 4)" + "sqrt.(diag(vcov_beta_hat))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = y .- β[1] .+ X_1 * β[2:length(β)];\n", + "n = size(X_1)[1]\n", + "k = size(X_1)[2]\n", + "\n", + "VCV = (1/(n-k)) * res' * res * (X_1' * X_1).^(-1)\n", + "se = sqrt.(diag(VCV))" + ] + }, + { + "cell_type": "code", + "execution_count": 518, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 516, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] + "data": { + "text/plain": [ + "62-element Vector{Float64}:\n", + " 0.08610965052296786\n", + " 0.017603705140386082\n", + " 0.023113130326795704\n", + " 0.09206866053605631\n", + " 0.0878861223713962\n", + " 0.05589048429931293\n", + " 0.1496453568701311\n", + " 0.1554834828840219\n", + " 0.10812292916708843\n", + " 0.08661892282903665\n", + " ⋮\n", + " 0.026921968250533907\n", + " 0.11093580210306042\n", + " 0.13864854930265377\n", + " 0.0006380087011397892\n", + " 0.0008637312895228336\n", + " 0.1425986274859988\n", + " 0.1436075515325452\n", + " 4.171442859962156e-5\n", + " 0.08027895945461645" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "HDMjl.r_summary(lasso_effect);" + "res = y - hcat(ones(length(y)), X_1) * β;\n", + "n = size(hcat(ones(length(y)), X_1))[1]\n", + "k = size(hcat(ones(length(y)), X_1))[2]\n", + "\n", + "VCV = (1/(n-k)) * res' * res * pinv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1))\n", + "se = sqrt.(diag(VCV))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 466, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "62×62 Matrix{Float64}:\n", + " 0.0111111 0.00144246 0.0658449 … 0.000260434 1.20302\n", + " 0.00144246 0.000184788 0.00930174 3.30908e-5 0.151883\n", + " 0.0658449 0.00930174 0.12367 0.00317199 -7.17679\n", + " 0.0504817 0.00647158 0.358308 0.00111049 4.44408\n", + " 0.392147 0.0540547 1.57572 0.0128551 33.2372\n", + " 0.0996016 0.0120284 0.921392 … 0.00197435 11.2608\n", + " 0.081011 0.00980504 0.745556 0.00153338 9.71034\n", + " 0.135117 0.0161754 1.23623 0.00291732 19.7479\n", + " 0.0124378 0.0015926 0.0807411 0.000267281 1.21177\n", + " 0.0120831 0.00155318 0.0754114 0.00026518 1.1742\n", + " ⋮ ⋱ ⋮ \n", + " 0.0121776 0.00147682 0.114702 0.000226807 3.1335\n", + " 0.0106281 0.00130316 0.0948694 0.000182814 1.83556\n", + " 0.0141766 0.00169469 0.145264 … 0.000292001 10.3166\n", + " 0.000334638 4.44183e-5 0.00166408 7.84737e-6 0.0373331\n", + " 0.000572377 7.50425e-5 0.00318491 1.27905e-5 0.055565\n", + " 0.0829304 0.0103042 0.714294 0.00288564 7.41059\n", + " 0.0769704 0.00965327 0.626807 0.00248797 5.66682\n", + " 0.000260434 3.30908e-5 0.00317199 … 6.98687e-7 -0.0149962\n", + " 1.20302 0.151883 -7.17679 -0.0149962 3.1309" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1)).^(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "90-element Vector{Float64}:\n", + " 0.002018790497900342\n", + " -0.007247270780727941\n", + " -0.022075305759001712\n", + " -0.0034696374151797077\n", + " 0.016334381463218507\n", + " 0.015363692977050536\n", + " -0.037171480372134066\n", + " 0.017679847993617295\n", + " 0.017453589093655266\n", + " 0.002645375456359886\n", + " ⋮\n", + " 0.0010414910898434784\n", + " -0.0023506981061814647\n", + " -0.002709992808522771\n", + " 0.006145883034070911\n", + " 0.0023913423465569926\n", + " -0.008391909370482717\n", + " 0.009916455794830963\n", + " -0.012612526399026547\n", + " -0.0044420236796179" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y - hcat(ones(length(y)), X_1) * β" + ] + }, + { + "cell_type": "code", + "execution_count": 415, "metadata": {}, "outputs": [], "source": [ - "doublesel_effect = HDMjl.rlassoEffect(X, y, d, method = \"double selection\");" + "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 419, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m 1 \u001b[0m\n", + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " -0.05001\n" + " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] } ], "source": [ - "HDMjl.r_print(doublesel_effect, 5)" + "lasso_effect = HDMjl.rlassoEffect(X, y, d, method = \"partialling out\");\n", + "HDMjl.r_summary(lasso_effect);" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 420, "metadata": {}, "outputs": [ { @@ -1123,75 +1262,22 @@ } ], "source": [ + "doublesel_effect = HDMjl.rlassoEffect(X, y, d, method = \"double selection\");\n", "HDMjl.r_summary(doublesel_effect);" ] }, { "cell_type": "code", - "execution_count": 1169, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:\n", - "\n", - "Coefficients:\n", - "────────────────────────────────────────────────────────────────────────────\n", - " Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%\n", - "────────────────────────────────────────────────────────────────────────────\n", - "x1 -0.0515095 0.0134834 -3.82 0.0003 -0.0785095 -0.0245095\n", - "x2 -0.0698222 0.0222899 -3.13 0.0027 -0.114457 -0.0251875\n", - "x3 0.144115 0.0817989 1.76 0.0835 -0.019685 0.307914\n", - "x4 -0.549012 0.33678 -1.63 0.1086 -1.2234 0.125378\n", - "x5 0.0 NaN NaN NaN NaN NaN\n", - "x6 -0.00178011 0.16532 -0.01 0.9914 -0.332827 0.329267\n", - "x7 -0.0499058 0.19071 -0.26 0.7945 -0.431796 0.331984\n", - "x8 0.344992 0.118384 2.91 0.0051 0.107931 0.582052\n", - "x9 -0.0276954 0.0967556 -0.29 0.7757 -0.221445 0.166054\n", - "x10 -0.191668 0.0974433 -1.97 0.0541 -0.386795 0.00345863\n", - "x11 -0.0617711 0.153902 -0.40 0.6897 -0.369954 0.246412\n", - "x12 -0.0199024 0.101637 -0.20 0.8454 -0.223427 0.183622\n", - "x13 -0.0329039 0.0960136 -0.34 0.7331 -0.225168 0.15936\n", - "x14 -0.0292139 0.0209751 -1.39 0.1691 -0.0712158 0.0127879\n", - "x15 1.00723 0.42872 2.35 0.0223 0.148738 1.86573\n", - "x16 0.0935241 0.0284037 3.29 0.0017 0.0366466 0.150402\n", - "x17 0.0 NaN NaN NaN NaN NaN\n", - "x18 0.0231697 0.0203674 1.14 0.2601 -0.0176153 0.0639547\n", - "x19 -0.898484 0.378689 -2.37 0.0211 -1.6568 -0.140171\n", - "x20 0.106289 0.0709861 1.50 0.1398 -0.0358584 0.248436\n", - "x21 0.0 NaN NaN NaN NaN NaN\n", - "x22 0.0 NaN NaN NaN NaN NaN\n", - "x23 0.566105 0.20177 2.81 0.0069 0.162067 0.970142\n", - "x24 -0.291019 0.248987 -1.17 0.2473 -0.789607 0.207569\n", - "x25 -0.0611354 0.126924 -0.48 0.6319 -0.315296 0.193025\n", - "x26 0.0 NaN NaN NaN NaN NaN\n", - "x27 -0.00073184 0.13671 -0.01 0.9957 -0.274489 0.273026\n", - "x28 -0.00117443 0.0656851 -0.02 0.9858 -0.132707 0.130358\n", - "x29 0.00512217 0.0725344 0.07 0.9439 -0.140125 0.15037\n", - "x30 0.0 NaN NaN NaN NaN NaN\n", - "x31 0.013586 0.00993516 1.37 0.1768 -0.00630877 0.0334808\n", - "x32 -0.0169288 0.0119869 -1.41 0.1633 -0.0409322 0.00707452\n", - "x33 0.0 NaN NaN NaN NaN NaN\n", - "x34 -0.00277954 0.0154127 -0.18 0.8575 -0.033643 0.0280839\n", - "x35 0.00440392 0.0165118 0.27 0.7907 -0.0286605 0.0374683\n", - "x36 0.0 NaN NaN NaN NaN NaN\n", - "x37 0.0 NaN NaN NaN NaN NaN\n", - "x38 0.0 NaN NaN NaN NaN NaN\n", - "x39 -0.0438025 0.0252029 -1.74 0.0876 -0.0942704 0.00666544\n", - "x40 0.020849 0.0124995 1.67 0.1008 -0.0041807 0.0458788\n", - "x41 0.0227094 0.0128426 1.77 0.0824 -0.00300744 0.0484262\n", - "x42 -0.0248346 0.0218271 -1.14 0.2600 -0.0685426 0.0188735\n", - "x43 -8.29688e-8 8.04977e-8 -1.03 0.3070 -2.44163e-7 7.82252e-8\n", - "────────────────────────────────────────────────────────────────────────────\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ls_effect = lm(X_1[:,1:43], y)" + "using PrettyTables\n", + "table = zeros(4,2)\n", + "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", + "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", + "table[3,:] = Matrix(HDMjl.r_summary(lasso_IV_Z)[:,2:3]);\n", + "table[4, :] = Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3]);" ] }, { @@ -1201,6 +1287,20 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": 421, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index a8e217f..d83e963 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 321, "metadata": { "vscode": { "languageId": "r" @@ -923,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 322, "metadata": { "vscode": { "languageId": "r" @@ -940,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 331, "metadata": { "vscode": { "languageId": "r" @@ -949,34 +949,277 @@ "outputs": [ { "data": { - "text/plain": [ + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 62 × 4 of type dbl
EstimateStd. Errort valuePr(>|t|)
(Intercept) 0.2471608930.78450163 0.315054660.755056170
gdpsh465-0.0093779890.02988773-0.313773910.756018518
bmp1l-0.0688626790.03253065-2.116855130.043289718
freeop 0.0800689740.20786400 0.385198850.703000838
freetar-0.4889626050.41816285-1.169311430.252136477
h65-2.3620986380.85729167-2.755303380.010192435
hm65 0.7071434000.52314511 1.351715600.187285919
hf65 1.6934484250.50318881 3.365433370.002232683
p65 0.2655266950.16429407 1.616167290.117271229
pm65 0.1369526260.15121749 0.905666570.372840111
pf65-0.3312669480.16512299-2.006183030.054583866
s65 0.0390793060.18552248 0.210644590.834690082
sm65-0.0306685070.11679396-0.262586420.794791807
sf65-0.1799172800.11809892-1.523445560.138863011
fert65 0.0068808340.02705142 0.254361280.801075252
mort65-0.2334545030.81742198-0.285598520.777287824
lifee065-0.0149144730.19333559-0.077142920.939058459
gpop1 0.9701845791.81220835 0.535360390.596627593
fert1 0.0088381890.03504148 0.252220770.802712696
mort1 0.0665629280.68481002 0.097199110.923260701
invsh41 0.0744611260.10844692 0.686613540.497973611
geetot1-0.7151049061.68013567-0.425623310.673636102
geerec1 0.6300046302.44737848 0.257420190.798736842
gde1-0.4435752191.67120674-0.265422110.792628757
govwb1 0.3374523640.43798695 0.770462140.447478336
govsh41 0.4631746321.92543733 0.240555550.811652246
gvxdxe41-0.7933815822.05939737-0.385249390.702963815
high65-0.7524838280.90572159-0.830811410.413108816
highm65-0.3902618040.68124930-0.572861950.571310302
highf65-0.4177487050.56149048-0.743999630.463075062
highcf65 3.920771e-017.660246e-01 0.511833600.61278088
human65 2.337269e+003.307269e+00 0.706706780.48558873
humanm65-1.209249e+001.618516e+00-0.747134750.46121072
humanf65-1.103947e+001.684693e+00-0.655280750.51763499
hyr65 5.491388e+012.388729e+01 2.298874300.02918450
hyrm65 1.293505e+012.317142e+01 0.558232780.58112223
hyrf65 9.092580e+001.766958e+01 0.514589480.61087820
no65 3.720986e-021.319733e-01 0.281949940.78005541
nom65-2.119769e-026.495975e-02-0.326320320.74660861
nof65-1.685782e-026.700258e-02-0.251599540.80318810
pinstab1-4.997113e-023.092081e-02-1.616100190.11728579
pop65 1.031796e-071.317950e-07 0.782879290.44026959
worker65 3.407897e-021.561909e-01 0.218187860.82886516
pop1565-4.655346e-014.713340e-01-0.987695820.33176044
pop6565-1.357450e+006.349424e-01-2.137910490.04139326
sec65-1.089278e-023.076618e-01-0.035405060.97200796
secm65 3.343664e-031.511923e-01 0.022115300.98251284
secf65-2.304329e-031.579715e-01-0.014586990.98846512
secc65-4.915284e-017.290408e-01-0.674212460.50570489
seccm65 2.596016e-013.556533e-01 0.729928730.47149686
seccf65 2.206524e-013.733333e-01 0.591033250.55923950
syr65-7.555807e-017.976763e+00-0.094722730.92520970
syrm65 3.109007e-013.896731e+00 0.079785020.93697576
syrf65 7.592811e-014.110634e+00 0.184711430.85478676
teapri65 3.954523e-057.700406e-04 0.051354740.95940747
teasec65 2.496674e-041.171230e-03 0.213166780.83274136
ex1-5.804081e-012.418472e-01-2.399895930.02329485
im1 5.914450e-012.502978e-01 2.362965580.02530892
xr65-1.037768e-045.416760e-05-1.915846520.06564690
tot1-1.279011e-011.125951e-01-1.135938420.26561200
\n" + ], + "text/latex": [ + "A matrix: 62 × 4 of type dbl\n", + "\\begin{tabular}{r|llll}\n", + " & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n", + "\\hline\n", + "\t(Intercept) & 0.247160893 & 0.78450163 & 0.31505466 & 0.755056170\\\\\n", + "\tgdpsh465 & -0.009377989 & 0.02988773 & -0.31377391 & 0.756018518\\\\\n", + "\tbmp1l & -0.068862679 & 0.03253065 & -2.11685513 & 0.043289718\\\\\n", + "\tfreeop & 0.080068974 & 0.20786400 & 0.38519885 & 0.703000838\\\\\n", + "\tfreetar & -0.488962605 & 0.41816285 & -1.16931143 & 0.252136477\\\\\n", + "\th65 & -2.362098638 & 0.85729167 & -2.75530338 & 0.010192435\\\\\n", + "\thm65 & 0.707143400 & 0.52314511 & 1.35171560 & 0.187285919\\\\\n", + "\thf65 & 1.693448425 & 0.50318881 & 3.36543337 & 0.002232683\\\\\n", + "\tp65 & 0.265526695 & 0.16429407 & 1.61616729 & 0.117271229\\\\\n", + "\tpm65 & 0.136952626 & 0.15121749 & 0.90566657 & 0.372840111\\\\\n", + "\tpf65 & -0.331266948 & 0.16512299 & -2.00618303 & 0.054583866\\\\\n", + "\ts65 & 0.039079306 & 0.18552248 & 0.21064459 & 0.834690082\\\\\n", + "\tsm65 & -0.030668507 & 0.11679396 & -0.26258642 & 0.794791807\\\\\n", + "\tsf65 & -0.179917280 & 0.11809892 & -1.52344556 & 0.138863011\\\\\n", + "\tfert65 & 0.006880834 & 0.02705142 & 0.25436128 & 0.801075252\\\\\n", + "\tmort65 & -0.233454503 & 0.81742198 & -0.28559852 & 0.777287824\\\\\n", + "\tlifee065 & -0.014914473 & 0.19333559 & -0.07714292 & 0.939058459\\\\\n", + "\tgpop1 & 0.970184579 & 1.81220835 & 0.53536039 & 0.596627593\\\\\n", + "\tfert1 & 0.008838189 & 0.03504148 & 0.25222077 & 0.802712696\\\\\n", + "\tmort1 & 0.066562928 & 0.68481002 & 0.09719911 & 0.923260701\\\\\n", + "\tinvsh41 & 0.074461126 & 0.10844692 & 0.68661354 & 0.497973611\\\\\n", + "\tgeetot1 & -0.715104906 & 1.68013567 & -0.42562331 & 0.673636102\\\\\n", + "\tgeerec1 & 0.630004630 & 2.44737848 & 0.25742019 & 0.798736842\\\\\n", + "\tgde1 & -0.443575219 & 1.67120674 & -0.26542211 & 0.792628757\\\\\n", + "\tgovwb1 & 0.337452364 & 0.43798695 & 0.77046214 & 0.447478336\\\\\n", + "\tgovsh41 & 0.463174632 & 1.92543733 & 0.24055555 & 0.811652246\\\\\n", + "\tgvxdxe41 & -0.793381582 & 2.05939737 & -0.38524939 & 0.702963815\\\\\n", + "\thigh65 & -0.752483828 & 0.90572159 & -0.83081141 & 0.413108816\\\\\n", + "\thighm65 & -0.390261804 & 0.68124930 & -0.57286195 & 0.571310302\\\\\n", + "\thighf65 & -0.417748705 & 0.56149048 & -0.74399963 & 0.463075062\\\\\n", + "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\thighcf65 & 3.920771e-01 & 7.660246e-01 & 0.51183360 & 0.61278088\\\\\n", + "\thuman65 & 2.337269e+00 & 3.307269e+00 & 0.70670678 & 0.48558873\\\\\n", + "\thumanm65 & -1.209249e+00 & 1.618516e+00 & -0.74713475 & 0.46121072\\\\\n", + "\thumanf65 & -1.103947e+00 & 1.684693e+00 & -0.65528075 & 0.51763499\\\\\n", + "\thyr65 & 5.491388e+01 & 2.388729e+01 & 2.29887430 & 0.02918450\\\\\n", + "\thyrm65 & 1.293505e+01 & 2.317142e+01 & 0.55823278 & 0.58112223\\\\\n", + "\thyrf65 & 9.092580e+00 & 1.766958e+01 & 0.51458948 & 0.61087820\\\\\n", + "\tno65 & 3.720986e-02 & 1.319733e-01 & 0.28194994 & 0.78005541\\\\\n", + "\tnom65 & -2.119769e-02 & 6.495975e-02 & -0.32632032 & 0.74660861\\\\\n", + "\tnof65 & -1.685782e-02 & 6.700258e-02 & -0.25159954 & 0.80318810\\\\\n", + "\tpinstab1 & -4.997113e-02 & 3.092081e-02 & -1.61610019 & 0.11728579\\\\\n", + "\tpop65 & 1.031796e-07 & 1.317950e-07 & 0.78287929 & 0.44026959\\\\\n", + "\tworker65 & 3.407897e-02 & 1.561909e-01 & 0.21818786 & 0.82886516\\\\\n", + "\tpop1565 & -4.655346e-01 & 4.713340e-01 & -0.98769582 & 0.33176044\\\\\n", + "\tpop6565 & -1.357450e+00 & 6.349424e-01 & -2.13791049 & 0.04139326\\\\\n", + "\tsec65 & -1.089278e-02 & 3.076618e-01 & -0.03540506 & 0.97200796\\\\\n", + "\tsecm65 & 3.343664e-03 & 1.511923e-01 & 0.02211530 & 0.98251284\\\\\n", + "\tsecf65 & -2.304329e-03 & 1.579715e-01 & -0.01458699 & 0.98846512\\\\\n", + "\tsecc65 & -4.915284e-01 & 7.290408e-01 & -0.67421246 & 0.50570489\\\\\n", + "\tseccm65 & 2.596016e-01 & 3.556533e-01 & 0.72992873 & 0.47149686\\\\\n", + "\tseccf65 & 2.206524e-01 & 3.733333e-01 & 0.59103325 & 0.55923950\\\\\n", + "\tsyr65 & -7.555807e-01 & 7.976763e+00 & -0.09472273 & 0.92520970\\\\\n", + "\tsyrm65 & 3.109007e-01 & 3.896731e+00 & 0.07978502 & 0.93697576\\\\\n", + "\tsyrf65 & 7.592811e-01 & 4.110634e+00 & 0.18471143 & 0.85478676\\\\\n", + "\tteapri65 & 3.954523e-05 & 7.700406e-04 & 0.05135474 & 0.95940747\\\\\n", + "\tteasec65 & 2.496674e-04 & 1.171230e-03 & 0.21316678 & 0.83274136\\\\\n", + "\tex1 & -5.804081e-01 & 2.418472e-01 & -2.39989593 & 0.02329485\\\\\n", + "\tim1 & 5.914450e-01 & 2.502978e-01 & 2.36296558 & 0.02530892\\\\\n", + "\txr65 & -1.037768e-04 & 5.416760e-05 & -1.91584652 & 0.06564690\\\\\n", + "\ttot1 & -1.279011e-01 & 1.125951e-01 & -1.13593842 & 0.26561200\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ "\n", - "Call:\n", - "lm(formula = fmla, data = GrowthData)\n", + "A matrix: 62 × 4 of type dbl\n", "\n", - "Coefficients:\n", - "(Intercept) gdpsh465 bmp1l freeop freetar h65 \n", - " 2.472e-01 -9.378e-03 -6.886e-02 8.007e-02 -4.890e-01 -2.362e+00 \n", - " hm65 hf65 p65 pm65 pf65 s65 \n", - " 7.071e-01 1.693e+00 2.655e-01 1.370e-01 -3.313e-01 3.908e-02 \n", - " sm65 sf65 fert65 mort65 lifee065 gpop1 \n", - " -3.067e-02 -1.799e-01 6.881e-03 -2.335e-01 -1.491e-02 9.702e-01 \n", - " fert1 mort1 invsh41 geetot1 geerec1 gde1 \n", - " 8.838e-03 6.656e-02 7.446e-02 -7.151e-01 6.300e-01 -4.436e-01 \n", - " govwb1 govsh41 gvxdxe41 high65 highm65 highf65 \n", - " 3.375e-01 4.632e-01 -7.934e-01 -7.525e-01 -3.903e-01 -4.177e-01 \n", - " highc65 highcm65 highcf65 human65 humanm65 humanf65 \n", - " -2.216e+00 2.797e-01 3.921e-01 2.337e+00 -1.209e+00 -1.104e+00 \n", - " hyr65 hyrm65 hyrf65 no65 nom65 nof65 \n", - " 5.491e+01 1.294e+01 9.093e+00 3.721e-02 -2.120e-02 -1.686e-02 \n", - " pinstab1 pop65 worker65 pop1565 pop6565 sec65 \n", - " -4.997e-02 1.032e-07 3.408e-02 -4.655e-01 -1.357e+00 -1.089e-02 \n", - " secm65 secf65 secc65 seccm65 seccf65 syr65 \n", - " 3.344e-03 -2.304e-03 -4.915e-01 2.596e-01 2.207e-01 -7.556e-01 \n", - " syrm65 syrf65 teapri65 teasec65 ex1 im1 \n", - " 3.109e-01 7.593e-01 3.955e-05 2.497e-04 -5.804e-01 5.914e-01 \n", - " xr65 tot1 \n", - " -1.038e-04 -1.279e-01 \n" + "| | Estimate | Std. Error | t value | Pr(>|t|) |\n", + "|---|---|---|---|---|\n", + "| (Intercept) | 0.247160893 | 0.78450163 | 0.31505466 | 0.755056170 |\n", + "| gdpsh465 | -0.009377989 | 0.02988773 | -0.31377391 | 0.756018518 |\n", + "| bmp1l | -0.068862679 | 0.03253065 | -2.11685513 | 0.043289718 |\n", + "| freeop | 0.080068974 | 0.20786400 | 0.38519885 | 0.703000838 |\n", + "| freetar | -0.488962605 | 0.41816285 | -1.16931143 | 0.252136477 |\n", + "| h65 | -2.362098638 | 0.85729167 | -2.75530338 | 0.010192435 |\n", + "| hm65 | 0.707143400 | 0.52314511 | 1.35171560 | 0.187285919 |\n", + "| hf65 | 1.693448425 | 0.50318881 | 3.36543337 | 0.002232683 |\n", + "| p65 | 0.265526695 | 0.16429407 | 1.61616729 | 0.117271229 |\n", + "| pm65 | 0.136952626 | 0.15121749 | 0.90566657 | 0.372840111 |\n", + "| pf65 | -0.331266948 | 0.16512299 | -2.00618303 | 0.054583866 |\n", + "| s65 | 0.039079306 | 0.18552248 | 0.21064459 | 0.834690082 |\n", + "| sm65 | -0.030668507 | 0.11679396 | -0.26258642 | 0.794791807 |\n", + "| sf65 | -0.179917280 | 0.11809892 | -1.52344556 | 0.138863011 |\n", + "| fert65 | 0.006880834 | 0.02705142 | 0.25436128 | 0.801075252 |\n", + "| mort65 | -0.233454503 | 0.81742198 | -0.28559852 | 0.777287824 |\n", + "| lifee065 | -0.014914473 | 0.19333559 | -0.07714292 | 0.939058459 |\n", + "| gpop1 | 0.970184579 | 1.81220835 | 0.53536039 | 0.596627593 |\n", + "| fert1 | 0.008838189 | 0.03504148 | 0.25222077 | 0.802712696 |\n", + "| mort1 | 0.066562928 | 0.68481002 | 0.09719911 | 0.923260701 |\n", + "| invsh41 | 0.074461126 | 0.10844692 | 0.68661354 | 0.497973611 |\n", + "| geetot1 | -0.715104906 | 1.68013567 | -0.42562331 | 0.673636102 |\n", + "| geerec1 | 0.630004630 | 2.44737848 | 0.25742019 | 0.798736842 |\n", + "| gde1 | -0.443575219 | 1.67120674 | -0.26542211 | 0.792628757 |\n", + "| govwb1 | 0.337452364 | 0.43798695 | 0.77046214 | 0.447478336 |\n", + "| govsh41 | 0.463174632 | 1.92543733 | 0.24055555 | 0.811652246 |\n", + "| gvxdxe41 | -0.793381582 | 2.05939737 | -0.38524939 | 0.702963815 |\n", + "| high65 | -0.752483828 | 0.90572159 | -0.83081141 | 0.413108816 |\n", + "| highm65 | -0.390261804 | 0.68124930 | -0.57286195 | 0.571310302 |\n", + "| highf65 | -0.417748705 | 0.56149048 | -0.74399963 | 0.463075062 |\n", + "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", + "| highcf65 | 3.920771e-01 | 7.660246e-01 | 0.51183360 | 0.61278088 |\n", + "| human65 | 2.337269e+00 | 3.307269e+00 | 0.70670678 | 0.48558873 |\n", + "| humanm65 | -1.209249e+00 | 1.618516e+00 | -0.74713475 | 0.46121072 |\n", + "| humanf65 | -1.103947e+00 | 1.684693e+00 | -0.65528075 | 0.51763499 |\n", + "| hyr65 | 5.491388e+01 | 2.388729e+01 | 2.29887430 | 0.02918450 |\n", + "| hyrm65 | 1.293505e+01 | 2.317142e+01 | 0.55823278 | 0.58112223 |\n", + "| hyrf65 | 9.092580e+00 | 1.766958e+01 | 0.51458948 | 0.61087820 |\n", + "| no65 | 3.720986e-02 | 1.319733e-01 | 0.28194994 | 0.78005541 |\n", + "| nom65 | -2.119769e-02 | 6.495975e-02 | -0.32632032 | 0.74660861 |\n", + "| nof65 | -1.685782e-02 | 6.700258e-02 | -0.25159954 | 0.80318810 |\n", + "| pinstab1 | -4.997113e-02 | 3.092081e-02 | -1.61610019 | 0.11728579 |\n", + "| pop65 | 1.031796e-07 | 1.317950e-07 | 0.78287929 | 0.44026959 |\n", + "| worker65 | 3.407897e-02 | 1.561909e-01 | 0.21818786 | 0.82886516 |\n", + "| pop1565 | -4.655346e-01 | 4.713340e-01 | -0.98769582 | 0.33176044 |\n", + "| pop6565 | -1.357450e+00 | 6.349424e-01 | -2.13791049 | 0.04139326 |\n", + "| sec65 | -1.089278e-02 | 3.076618e-01 | -0.03540506 | 0.97200796 |\n", + "| secm65 | 3.343664e-03 | 1.511923e-01 | 0.02211530 | 0.98251284 |\n", + "| secf65 | -2.304329e-03 | 1.579715e-01 | -0.01458699 | 0.98846512 |\n", + "| secc65 | -4.915284e-01 | 7.290408e-01 | -0.67421246 | 0.50570489 |\n", + "| seccm65 | 2.596016e-01 | 3.556533e-01 | 0.72992873 | 0.47149686 |\n", + "| seccf65 | 2.206524e-01 | 3.733333e-01 | 0.59103325 | 0.55923950 |\n", + "| syr65 | -7.555807e-01 | 7.976763e+00 | -0.09472273 | 0.92520970 |\n", + "| syrm65 | 3.109007e-01 | 3.896731e+00 | 0.07978502 | 0.93697576 |\n", + "| syrf65 | 7.592811e-01 | 4.110634e+00 | 0.18471143 | 0.85478676 |\n", + "| teapri65 | 3.954523e-05 | 7.700406e-04 | 0.05135474 | 0.95940747 |\n", + "| teasec65 | 2.496674e-04 | 1.171230e-03 | 0.21316678 | 0.83274136 |\n", + "| ex1 | -5.804081e-01 | 2.418472e-01 | -2.39989593 | 0.02329485 |\n", + "| im1 | 5.914450e-01 | 2.502978e-01 | 2.36296558 | 0.02530892 |\n", + "| xr65 | -1.037768e-04 | 5.416760e-05 | -1.91584652 | 0.06564690 |\n", + "| tot1 | -1.279011e-01 | 1.125951e-01 | -1.13593842 | 0.26561200 |\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 0.247160893 0.78450163 0.31505466 0.755056170\n", + "gdpsh465 -0.009377989 0.02988773 -0.31377391 0.756018518\n", + "bmp1l -0.068862679 0.03253065 -2.11685513 0.043289718\n", + "freeop 0.080068974 0.20786400 0.38519885 0.703000838\n", + "freetar -0.488962605 0.41816285 -1.16931143 0.252136477\n", + "h65 -2.362098638 0.85729167 -2.75530338 0.010192435\n", + "hm65 0.707143400 0.52314511 1.35171560 0.187285919\n", + "hf65 1.693448425 0.50318881 3.36543337 0.002232683\n", + "p65 0.265526695 0.16429407 1.61616729 0.117271229\n", + "pm65 0.136952626 0.15121749 0.90566657 0.372840111\n", + "pf65 -0.331266948 0.16512299 -2.00618303 0.054583866\n", + "s65 0.039079306 0.18552248 0.21064459 0.834690082\n", + "sm65 -0.030668507 0.11679396 -0.26258642 0.794791807\n", + "sf65 -0.179917280 0.11809892 -1.52344556 0.138863011\n", + "fert65 0.006880834 0.02705142 0.25436128 0.801075252\n", + "mort65 -0.233454503 0.81742198 -0.28559852 0.777287824\n", + "lifee065 -0.014914473 0.19333559 -0.07714292 0.939058459\n", + "gpop1 0.970184579 1.81220835 0.53536039 0.596627593\n", + "fert1 0.008838189 0.03504148 0.25222077 0.802712696\n", + "mort1 0.066562928 0.68481002 0.09719911 0.923260701\n", + "invsh41 0.074461126 0.10844692 0.68661354 0.497973611\n", + "geetot1 -0.715104906 1.68013567 -0.42562331 0.673636102\n", + "geerec1 0.630004630 2.44737848 0.25742019 0.798736842\n", + "gde1 -0.443575219 1.67120674 -0.26542211 0.792628757\n", + "govwb1 0.337452364 0.43798695 0.77046214 0.447478336\n", + "govsh41 0.463174632 1.92543733 0.24055555 0.811652246\n", + "gvxdxe41 -0.793381582 2.05939737 -0.38524939 0.702963815\n", + "high65 -0.752483828 0.90572159 -0.83081141 0.413108816\n", + "highm65 -0.390261804 0.68124930 -0.57286195 0.571310302\n", + "highf65 -0.417748705 0.56149048 -0.74399963 0.463075062\n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "highcf65 3.920771e-01 7.660246e-01 0.51183360 0.61278088 \n", + "human65 2.337269e+00 3.307269e+00 0.70670678 0.48558873 \n", + "humanm65 -1.209249e+00 1.618516e+00 -0.74713475 0.46121072 \n", + "humanf65 -1.103947e+00 1.684693e+00 -0.65528075 0.51763499 \n", + "hyr65 5.491388e+01 2.388729e+01 2.29887430 0.02918450 \n", + "hyrm65 1.293505e+01 2.317142e+01 0.55823278 0.58112223 \n", + "hyrf65 9.092580e+00 1.766958e+01 0.51458948 0.61087820 \n", + "no65 3.720986e-02 1.319733e-01 0.28194994 0.78005541 \n", + "nom65 -2.119769e-02 6.495975e-02 -0.32632032 0.74660861 \n", + "nof65 -1.685782e-02 6.700258e-02 -0.25159954 0.80318810 \n", + "pinstab1 -4.997113e-02 3.092081e-02 -1.61610019 0.11728579 \n", + "pop65 1.031796e-07 1.317950e-07 0.78287929 0.44026959 \n", + "worker65 3.407897e-02 1.561909e-01 0.21818786 0.82886516 \n", + "pop1565 -4.655346e-01 4.713340e-01 -0.98769582 0.33176044 \n", + "pop6565 -1.357450e+00 6.349424e-01 -2.13791049 0.04139326 \n", + "sec65 -1.089278e-02 3.076618e-01 -0.03540506 0.97200796 \n", + "secm65 3.343664e-03 1.511923e-01 0.02211530 0.98251284 \n", + "secf65 -2.304329e-03 1.579715e-01 -0.01458699 0.98846512 \n", + "secc65 -4.915284e-01 7.290408e-01 -0.67421246 0.50570489 \n", + "seccm65 2.596016e-01 3.556533e-01 0.72992873 0.47149686 \n", + "seccf65 2.206524e-01 3.733333e-01 0.59103325 0.55923950 \n", + "syr65 -7.555807e-01 7.976763e+00 -0.09472273 0.92520970 \n", + "syrm65 3.109007e-01 3.896731e+00 0.07978502 0.93697576 \n", + "syrf65 7.592811e-01 4.110634e+00 0.18471143 0.85478676 \n", + "teapri65 3.954523e-05 7.700406e-04 0.05135474 0.95940747 \n", + "teasec65 2.496674e-04 1.171230e-03 0.21316678 0.83274136 \n", + "ex1 -5.804081e-01 2.418472e-01 -2.39989593 0.02329485 \n", + "im1 5.914450e-01 2.502978e-01 2.36296558 0.02530892 \n", + "xr65 -1.037768e-04 5.416760e-05 -1.91584652 0.06564690 \n", + "tot1 -1.279011e-01 1.125951e-01 -1.13593842 0.26561200 " ] }, "metadata": {}, @@ -984,12 +1227,12 @@ } ], "source": [ - "ls.effect" + "summary(ls.effect)$coef" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 324, "metadata": { "vscode": { "languageId": "r" @@ -1019,49 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 121, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1] \"Estimates and significance testing of the effect of target variables\"\n", - " Estimate. Std. Error t value Pr(>|t|) \n", - "[1,] -0.04981 0.01394 -3.574 0.000351 ***\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lasso.effect = rlassoEffect(x = X, y = y, d = d, method = \"partialling out\")\n", - "summary(lasso.effect)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "doublesel.effect = rlassoEffect(x = X, y = y, d = d, method = \"double selection\")" - ] - }, - { - "cell_type": "code", - "execution_count": 123, + "execution_count": 325, "metadata": { "vscode": { "languageId": "r" @@ -1084,6 +1285,7 @@ } ], "source": [ + "doublesel.effect = rlassoEffect(x = X, y = y, d = d, method = \"double selection\")\n", "summary(doublesel.effect)" ] }, From 2c0a3d0b4f88cd0dff8aa3478beb720d0c8a3e9d Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 02:55:15 -0500 Subject: [PATCH 17/28] # --- src/rlassoIV.jl | 10 +- tutorial/HDMjl_tutorial_1.ipynb | 382 ++------ tutorial/r_file.ipynb | 1503 +++++++++++++++++++++++++++++-- 3 files changed, 1503 insertions(+), 392 deletions(-) diff --git a/src/rlassoIV.jl b/src/rlassoIV.jl index 28db347..83a5d7d 100644 --- a/src/rlassoIV.jl +++ b/src/rlassoIV.jl @@ -75,12 +75,18 @@ function r_print(object::rlassoIV1, n_digits = 3) println("Coefficients:\n") pretty_table(a[:, 2]', tf = tf_borderless, header = a[:, 1], nosubheader = true, equal_columns_width = true, columns_width = 10, alignment=:c) #, header_crayon =crayon"blue") - else + + elseif string(length(object.coefficients))[count("", string(length(object.coefficients)))-1:count("", string(length(object.coefficients)))-1] == "0" + for i in 1:convert(Int, trunc(size(object.coefficients, 1)/10, digits =0)) + pretty_table(a[10*(i-1)+1:10*i, 2]', tf = tf_borderless, header = a[10*(i-1)+1:10*i, 1], nosubheader = true, equal_columns_width = true, columns_width = 10, alignment=:c)#, header_crayon =crayon"green") + print("\n") + end + else for i in 1:convert(Int, trunc(size(object.coefficients, 1)/10, digits =0)) pretty_table(a[10*(i-1)+1:10*i, 2]', tf = tf_borderless, header = a[10*(i-1)+1:10*i, 1], nosubheader = true, equal_columns_width = true, columns_width = 10, alignment=:c)#, header_crayon =crayon"green") print("\n") end - pretty_table(a[10*convert(Int, trunc(size(object.coefficients, 1)/10, digits =0))+1:size(object.coefficients, 1), 2]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 10, + pretty_table(a[10*convert(Int, trunc(size(object.coefficients, 1)/10, digits =0))+1:size(object.coefficients, 1), 2]', alignment=:c, nosubheader = true, equal_columns_width = true, columns_width = 10, tf = tf_borderless, header = a[10*convert(Int, trunc(size(object.coefficients, 1)/10, digits =0))+1:size(object.coefficients, 1), 1]) #, header_crayon =crayon"green") end else diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index 31aa40b..dfe2ea2 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -175,16 +175,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, { "data": { "text/plain": [ @@ -284,11 +277,11 @@ }, { "cell_type": "code", - "execution_count": 911, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "using CSV\n", + "using CSV, GLM\n", "using DataFrames" ] }, @@ -320,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 954, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -334,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 1017, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -370,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 1026, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 1049, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -418,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 1057, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -458,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 1059, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -471,14 +464,7 @@ " 1 0.972739 0.0136868 71.0715 0.0\n", "---\n", "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] } ], @@ -489,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 1062, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -510,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 1063, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -891,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 409, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -926,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 401, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -944,294 +930,83 @@ }, { "cell_type": "code", - "execution_count": 475, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "Q, R = qr(hcat(ones(length(y)), X_1))\n", "β = pinv(hcat(ones(length(y)), X_1)) * y\n", "# ls_effect = β;\n", - ";" - ] - }, - { - "cell_type": "code", - "execution_count": 495, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 521, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "62-element Vector{Float64}:\n", - " 0.08610965052296786\n", - " 0.017603705140386082\n", - " 0.023113130326795704\n", - " 0.09206866053605631\n", - " 0.0878861223713962\n", - " 0.05589048429931293\n", - " 0.1496453568701311\n", - " 0.1554834828840219\n", - " 0.10812292916708843\n", - " 0.08661892282903665\n", - " ⋮\n", - " 0.026921968250533907\n", - " 0.11093580210306042\n", - " 0.13864854930265377\n", - " 0.0006380087011397892\n", - " 0.0008637312895228336\n", - " 0.1425986274859988\n", - " 0.1436075515325452\n", - " 4.171442859962156e-5\n", - " 0.08027895945461645" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ "res = y - hcat(ones(length(y)), X_1) * β;\n", "n = size(hcat(ones(length(y)), X_1))[1]\n", "k = size(hcat(ones(length(y)), X_1))[2]\n", "\n", "sigma2_hat = (res' * res) / (n - k)\n", - "vcov_beta_hat = sigma2_hat .* pinv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1));\n", - "se = sqrt.(diag(vcov_beta_hat))" - ] - }, - { - "cell_type": "code", - "execution_count": 487, - "metadata": {}, - "outputs": [], - "source": [ - "res = y .- β[1] .+ X_1 * β[2:length(β)];\n", - "n = size(X_1)[1]\n", - "k = size(X_1)[2]\n", + "vcov_beta_hat = sigma2_hat .* inv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1));\n", + "se = sqrt.(diag(vcov_beta_hat))\n", "\n", - "sigma2_hat = (res' * res) / (n - k)\n", - "vcov_beta_hat = sigma2_hat .* pinv(X_1' * X_1);" + "ls_effect = DataFrame(Estimate = β, stderror = se);" ] }, { "cell_type": "code", - "execution_count": 499, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "62-element Vector{Float64}:\n", - " 0.08610965052296786\n", - " 0.017603705140386082\n", - " 0.023113130326795704\n", - " 0.09206866053605631\n", - " 0.0878861223713962\n", - " 0.05589048429931293\n", - " 0.1496453568701311\n", - " 0.1554834828840219\n", - " 0.10812292916708843\n", - " 0.08661892282903665\n", - " ⋮\n", - " 0.026921968250533907\n", - " 0.11093580210306042\n", - " 0.13864854930265377\n", - " 0.0006380087011397892\n", - " 0.0008637312895228336\n", - " 0.1425986274859988\n", - " 0.1436075515325452\n", - " 4.171442859962156e-5\n", - " 0.08027895945461645" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sqrt.(diag(vcov_beta_hat))" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "metadata": {}, "outputs": [], - "source": [ - "res = y .- β[1] .+ X_1 * β[2:length(β)];\n", - "n = size(X_1)[1]\n", - "k = size(X_1)[2]\n", - "\n", - "VCV = (1/(n-k)) * res' * res * (X_1' * X_1).^(-1)\n", - "se = sqrt.(diag(VCV))" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 518, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 516, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "62-element Vector{Float64}:\n", - " 0.08610965052296786\n", - " 0.017603705140386082\n", - " 0.023113130326795704\n", - " 0.09206866053605631\n", - " 0.0878861223713962\n", - " 0.05589048429931293\n", - " 0.1496453568701311\n", - " 0.1554834828840219\n", - " 0.10812292916708843\n", - " 0.08661892282903665\n", - " ⋮\n", - " 0.026921968250533907\n", - " 0.11093580210306042\n", - " 0.13864854930265377\n", - " 0.0006380087011397892\n", - " 0.0008637312895228336\n", - " 0.1425986274859988\n", - " 0.1436075515325452\n", - " 4.171442859962156e-5\n", - " 0.08027895945461645" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "res = y - hcat(ones(length(y)), X_1) * β;\n", - "n = size(hcat(ones(length(y)), X_1))[1]\n", - "k = size(hcat(ones(length(y)), X_1))[2]\n", - "\n", - "VCV = (1/(n-k)) * res' * res * pinv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1))\n", - "se = sqrt.(diag(VCV))" - ] - }, - { - "cell_type": "code", - "execution_count": 466, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "62×62 Matrix{Float64}:\n", - " 0.0111111 0.00144246 0.0658449 … 0.000260434 1.20302\n", - " 0.00144246 0.000184788 0.00930174 3.30908e-5 0.151883\n", - " 0.0658449 0.00930174 0.12367 0.00317199 -7.17679\n", - " 0.0504817 0.00647158 0.358308 0.00111049 4.44408\n", - " 0.392147 0.0540547 1.57572 0.0128551 33.2372\n", - " 0.0996016 0.0120284 0.921392 … 0.00197435 11.2608\n", - " 0.081011 0.00980504 0.745556 0.00153338 9.71034\n", - " 0.135117 0.0161754 1.23623 0.00291732 19.7479\n", - " 0.0124378 0.0015926 0.0807411 0.000267281 1.21177\n", - " 0.0120831 0.00155318 0.0754114 0.00026518 1.1742\n", - " ⋮ ⋱ ⋮ \n", - " 0.0121776 0.00147682 0.114702 0.000226807 3.1335\n", - " 0.0106281 0.00130316 0.0948694 0.000182814 1.83556\n", - " 0.0141766 0.00169469 0.145264 … 0.000292001 10.3166\n", - " 0.000334638 4.44183e-5 0.00166408 7.84737e-6 0.0373331\n", - " 0.000572377 7.50425e-5 0.00318491 1.27905e-5 0.055565\n", - " 0.0829304 0.0103042 0.714294 0.00288564 7.41059\n", - " 0.0769704 0.00965327 0.626807 0.00248797 5.66682\n", - " 0.000260434 3.30908e-5 0.00317199 … 6.98687e-7 -0.0149962\n", - " 1.20302 0.151883 -7.17679 -0.0149962 3.1309" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1)).^(-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 427, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "90-element Vector{Float64}:\n", - " 0.002018790497900342\n", - " -0.007247270780727941\n", - " -0.022075305759001712\n", - " -0.0034696374151797077\n", - " 0.016334381463218507\n", - " 0.015363692977050536\n", - " -0.037171480372134066\n", - " 0.017679847993617295\n", - " 0.017453589093655266\n", - " 0.002645375456359886\n", - " ⋮\n", - " 0.0010414910898434784\n", - " -0.0023506981061814647\n", - " -0.002709992808522771\n", - " 0.006145883034070911\n", - " 0.0023913423465569926\n", - " -0.008391909370482717\n", - " 0.009916455794830963\n", - " -0.012612526399026547\n", - " -0.0044420236796179" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y - hcat(ones(length(y)), X_1) * β" - ] - }, - { - "cell_type": "code", - "execution_count": 415, + "execution_count": 180, "metadata": {}, "outputs": [], "source": [ - "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);" + "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);\n", + "# coef(ls_effect)" ] }, { "cell_type": "code", - "execution_count": 419, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Estimates and significance testing of the effect of target variables\n", + "Estimates and significance testing of the effect of target variables" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", - "---\n", + "---" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] @@ -1244,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": 420, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1268,31 +1043,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "using PrettyTables\n", - "table = zeros(4,2)\n", - "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", - "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", - "table[3,:] = Matrix(HDMjl.r_summary(lasso_IV_Z)[:,2:3]);\n", - "table[4, :] = Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3]);" + "table = zeros(3,2)\n", + "table[1,:] = [round(Matrix(ls_effect)[2,1], digits = 2), round.(Matrix(ls_effect)[2,2], digits = 5)]\n", + "table[2,:] = [round(lasso_effect.coefficients, digits =2), round(lasso_effect.se, digits = 5)]\n", + "table[3,:] = [round(doublesel_effect.coefficients, digits =2), round(doublesel_effect.se, digits = 5)];" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 421, + "execution_count": 26, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================== ========== =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "=================================== ========== =============\n", + " full reg via ols -0.01 0.02989\n", + " partial reg\\nvia post-lasso -0.05 0.01394\n", + " partial reg via double selection -0.05 0.01579\n", + "=================================== ========== =============\n" + ] + } + ], + "source": [ + "# table1 = DataFrame(table, :auto)\n", + "# table1 = rename(table1, [\"Estimate\", \"Std. Error\"])\n", + "index = [\"full reg via ols\", \"partial reg\n", + "via post-lasso \", \"partial reg via double selection\"]\n", + "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] }, { "cell_type": "code", @@ -1324,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1333,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1359,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1393,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1402,7 +1189,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1423,7 +1210,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1436,14 +1223,7 @@ " d1 0.845027 0.269926 3.13059 0.00174458\n", "---\n", "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" ] } ], @@ -1453,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 36, "metadata": {}, "outputs": [ { diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index d83e963..b9e7df9 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 2, "metadata": { "vscode": { "languageId": "r" @@ -923,7 +923,20 @@ }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "x1 = as.matrix(GrowthData)[, -c(1, 3)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "vscode": { "languageId": "r" @@ -940,7 +953,1341 @@ }, { "cell_type": "code", - "execution_count": 331, + 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-1.999883e+02 -0.0695763507\n", + "50 -1.682411e+02 0.0549548090\n", + "51 -6.255889e+01 -0.0503282470\n", + "52 2.569037e+02 0.0055446388\n", + "53 6.834770e+01 0.0412674928\n", + "54 -1.242214e+02 -0.1070727560\n", + "55 3.889938e+01 -0.0251160238\n", + "56 2.532362e+01 0.0608478945\n", + "57 5.633900e+01 0.0223452620\n", + "58 3.661366e-01 0.0359399777\n", + "59 5.964255e+01 0.0372667818\n", + "60 -1.707232e+01 -0.0381485946\n", + "61 -6.076580e+02 0.1045281348\n", + "62 1.277865e-01 0.2730074340\n", + "63 -1.743777e-01 0.0788193886\n", + "64 6.643046e-02 0.0044725168\n", + "65 -5.571258e-02 -0.0416495538\n", + "66 1.147801e-02 -0.4349524105\n", + "67 1.573729e-01 0.2618719444\n", + "68 -1.285582e-01 -0.0195693502\n", + "69 -3.690994e-01 -0.1510638329\n", + "70 6.646274e-02 -0.1914973719\n", + "71 -7.672525e-02 -0.0948002968\n", + "72 -9.109612e-02 -0.1250681238\n", + "73 -1.071318e-01 0.0088343517\n", + "74 3.278600e-01 -0.1304808147\n", + "75 -4.560051e-02 0.1306832744\n", + "76 -5.903422e-02 0.1703357127\n", + "77 1.118965e-01 0.0443054051\n", + "78 1.662882e-01 -0.0840935552\n", + "79 -3.707314e-02 -0.4057850088\n", + "80 -6.246543e-04 -0.0604992325\n", + "81 -5.593053e-02 0.1781689586\n", + "82 -7.887807e-03 0.0772642534\n", + "83 7.272069e-02 0.0776684262\n", + "84 8.657033e-02 0.0130311476\n", + "85 4.884930e-01 0.0850039306\n", + "86 -1.121593e-01 -0.0511207957\n", + "87 -2.629825e-01 0.4294171510\n", + "88 -4.419028e-03 0.0674216577\n", + "89 2.659727e-01 0.0950310649\n", + "90 4.198220e-01 0.3963425921\n", + "attr(,\"assign\")\n", + " [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n", + "[26] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n", + "[51] 50 51 52 53 54 55 56 57 58 59 60 61\n", + "\n", + "$qraux\n", + " [1] 1.105409 1.090738 1.019010 1.056067 1.008329 1.032496 1.027254 1.032528\n", + " [9] 1.032105 1.083540 1.045379 1.131395 1.067304 1.009168 1.080607 1.212556\n", + "[17] 1.108565 1.019370 1.146787 1.195271 1.275848 1.051124 1.020372 1.075090\n", + "[25] 1.052578 1.107482 1.400304 1.061337 1.224636 1.044238 1.142150 1.141488\n", + "[33] 1.209432 1.010573 1.026202 1.108806 1.095007 1.010618 1.379033 1.150449\n", + "[41] 1.050020 1.343433 1.035454 1.036704 1.126785 1.052998 1.262637 1.189651\n", + "[49] 1.166318 1.034950 1.198419 1.010931 1.081932 1.095029 1.007807 1.308323\n", + "[57] 1.017141 1.351455 1.119492 1.189273 1.007287 1.019961\n", + "\n", + "$pivot\n", + " [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25\n", + "[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50\n", + "[51] 51 52 53 54 55 56 57 58 59 60 61 62\n", + "\n", + "$tol\n", + "[1] 1e-07\n", + "\n", + "$rank\n", + "[1] 62\n", + "\n", + "attr(,\"class\")\n", + "[1] \"qr\"" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stats:::qr.lm(object)" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "correlation = FALSE; symbolic.cor = FALSE\n", + "\n", + "z <- object\n", + "p <- z$rank\n", + "rdf <- z$df.residual\n", + "if (p == 0) {\n", + " r <- z$residuals\n", + " n <- length(r)\n", + " w <- z$weights\n", + " if (is.null(w)) {\n", + " rss <- sum(r^2)\n", + " } else {\n", + " rss <- sum(w * r^2)\n", + " r <- sqrt(w) * r\n", + " }\n", + " resvar <- rss/rdf\n", + " ans <- z[c(\"call\", \"terms\", if(!is.null(z$weights)) \"weights\")]\n", + " class(ans) <- \"summary.lm\"\n", + " ans$aliased <- is.na(coef(object)) # used in print method\n", + " ans$residuals <- r\n", + " ans$df <- c(0L, n, length(ans$aliased))\n", + " ans$coefficients <- matrix(NA, 0L, 4L)\n", + " dimnames(ans$coefficients) <-\n", + " list(NULL, c(\"Estimate\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"))\n", + " ans$sigma <- sqrt(resvar)\n", + " ans$r.squared <- ans$adj.r.squared <- 0\n", + " return(ans)\n", + "}\n", + "if (is.null(z$terms))\n", + "stop(\"invalid 'lm' object: no 'terms' component\")\n", + "if(!inherits(object, \"lm\"))\n", + "warning(\"calling summary.lm() ...\")\n", + "Qr <- stats:::qr.lm(object)\n", + "n <- NROW(Qr$qr)\n", + "if(is.na(z$df.residual) || n - p != z$df.residual)\n", + " warning(\"residual degrees of freedom in object suggest this is not an \\\"lm\\\" fit\")\n", + "## do not want missing values substituted here\n", + "r <- z$residuals\n", + "f <- z$fitted.values\n", + "w <- z$weights\n", + "if (is.null(w)) {\n", + " mss <- if (attr(z$terms, \"intercept\"))\n", + " sum((f - mean(f))^2) else sum(f^2)\n", + " rss <- sum(r^2)\n", + "} else {\n", + " mss <- if (attr(z$terms, \"intercept\")) {\n", + " m <- sum(w * f /sum(w))\n", + " sum(w * (f - m)^2)\n", + " } else sum(w * f^2)\n", + " rss <- sum(w * r^2)\n", + " r <- sqrt(w) * r\n", + "}\n", + "resvar <- rss/rdf\n", + "## see thread at https://stat.ethz.ch/pipermail/r-help/2014-March/367585.html\n", + "if (is.finite(resvar) &&\n", + " resvar < (mean(f)^2 + var(f)) * 1e-30) # a few times .Machine$double.eps^2\n", + " warning(\"essentially perfect fit: summary may be unreliable\")\n", + "p1 <- 1L:p\n", + "R <- chol2inv(Qr$qr[p1, p1, drop = FALSE])\n", + "se <- sqrt(diag(R) * resvar)" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "object = ls.effect\n", + "z <- object\n", + "p <- z$rank\n", + "rdf <- z$df.residual\n", + "r <- z$residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "vscode": { "languageId": "r" @@ -1232,7 +2579,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 6, "metadata": { "vscode": { "languageId": "r" @@ -1262,7 +2609,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 7, "metadata": { "vscode": { "languageId": "r" @@ -1291,7 +2638,7 @@ }, { "cell_type": "code", - "execution_count": 495, + "execution_count": 9, "metadata": { "vscode": { "languageId": "r" @@ -1300,85 +2647,50 @@ "outputs": [ { "data": { - "text/plain": [ - "\n", - "Call:\n", - "lm(formula = fmla, data = GrowthData)\n", - "\n", - "Residuals:\n", - " Min 1Q Median 3Q Max \n", - "-0.040338 -0.011298 -0.000863 0.011813 0.043247 \n", + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A xtable: 3 × 2
EstimateStd. Error
<dbl><dbl>
full reg via ols-0.0093779890.02988773
partial reg\n", + "via post-lasso -0.0498114650.01393636
partial reg via double selection-0.0500058550.01579138
\n" + ], + "text/latex": [ + "A xtable: 3 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Estimate & Std. Error\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\tfull reg via ols & -0.009377989 & 0.02988773\\\\\n", + "\tpartial reg\n", + "via post-lasso & -0.049811465 & 0.01393636\\\\\n", + "\tpartial reg via double selection & -0.050005855 & 0.01579138\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ "\n", - "Coefficients:\n", - " Estimate Std. Error t value Pr(>|t|) \n", - "(Intercept) 2.472e-01 7.845e-01 0.315 0.75506 \n", - "gdpsh465 -9.378e-03 2.989e-02 -0.314 0.75602 \n", - "bmp1l -6.886e-02 3.253e-02 -2.117 0.04329 * \n", - "freeop 8.007e-02 2.079e-01 0.385 0.70300 \n", - "freetar -4.890e-01 4.182e-01 -1.169 0.25214 \n", - "h65 -2.362e+00 8.573e-01 -2.755 0.01019 * \n", - "hm65 7.071e-01 5.231e-01 1.352 0.18729 \n", - "hf65 1.693e+00 5.032e-01 3.365 0.00223 **\n", - "p65 2.655e-01 1.643e-01 1.616 0.11727 \n", - "pm65 1.370e-01 1.512e-01 0.906 0.37284 \n", - "pf65 -3.313e-01 1.651e-01 -2.006 0.05458 . \n", - "s65 3.908e-02 1.855e-01 0.211 0.83469 \n", - "sm65 -3.067e-02 1.168e-01 -0.263 0.79479 \n", - "sf65 -1.799e-01 1.181e-01 -1.523 0.13886 \n", - "fert65 6.881e-03 2.705e-02 0.254 0.80108 \n", - "mort65 -2.335e-01 8.174e-01 -0.286 0.77729 \n", - "lifee065 -1.491e-02 1.933e-01 -0.077 0.93906 \n", - "gpop1 9.702e-01 1.812e+00 0.535 0.59663 \n", - "fert1 8.838e-03 3.504e-02 0.252 0.80271 \n", - "mort1 6.656e-02 6.848e-01 0.097 0.92326 \n", - "invsh41 7.446e-02 1.084e-01 0.687 0.49797 \n", - "geetot1 -7.151e-01 1.680e+00 -0.426 0.67364 \n", - "geerec1 6.300e-01 2.447e+00 0.257 0.79874 \n", - "gde1 -4.436e-01 1.671e+00 -0.265 0.79263 \n", - "govwb1 3.375e-01 4.380e-01 0.770 0.44748 \n", - "govsh41 4.632e-01 1.925e+00 0.241 0.81165 \n", - "gvxdxe41 -7.934e-01 2.059e+00 -0.385 0.70296 \n", - "high65 -7.525e-01 9.057e-01 -0.831 0.41311 \n", - "highm65 -3.903e-01 6.812e-01 -0.573 0.57131 \n", - "highf65 -4.177e-01 5.615e-01 -0.744 0.46308 \n", - "highc65 -2.216e+00 1.481e+00 -1.496 0.14575 \n", - "highcm65 2.797e-01 6.582e-01 0.425 0.67412 \n", - "highcf65 3.921e-01 7.660e-01 0.512 0.61278 \n", - "human65 2.337e+00 3.307e+00 0.707 0.48559 \n", - "humanm65 -1.209e+00 1.619e+00 -0.747 0.46121 \n", - "humanf65 -1.104e+00 1.685e+00 -0.655 0.51763 \n", - "hyr65 5.491e+01 2.389e+01 2.299 0.02918 * \n", - "hyrm65 1.294e+01 2.317e+01 0.558 0.58112 \n", - "hyrf65 9.093e+00 1.767e+01 0.515 0.61088 \n", - "no65 3.721e-02 1.320e-01 0.282 0.78006 \n", - "nom65 -2.120e-02 6.496e-02 -0.326 0.74661 \n", - "nof65 -1.686e-02 6.700e-02 -0.252 0.80319 \n", - "pinstab1 -4.997e-02 3.092e-02 -1.616 0.11729 \n", - "pop65 1.032e-07 1.318e-07 0.783 0.44027 \n", - "worker65 3.408e-02 1.562e-01 0.218 0.82887 \n", - "pop1565 -4.655e-01 4.713e-01 -0.988 0.33176 \n", - "pop6565 -1.357e+00 6.349e-01 -2.138 0.04139 * \n", - "sec65 -1.089e-02 3.077e-01 -0.035 0.97201 \n", - "secm65 3.344e-03 1.512e-01 0.022 0.98251 \n", - "secf65 -2.304e-03 1.580e-01 -0.015 0.98847 \n", - "secc65 -4.915e-01 7.290e-01 -0.674 0.50570 \n", - "seccm65 2.596e-01 3.557e-01 0.730 0.47150 \n", - "seccf65 2.207e-01 3.733e-01 0.591 0.55924 \n", - "syr65 -7.556e-01 7.977e+00 -0.095 0.92521 \n", - "syrm65 3.109e-01 3.897e+00 0.080 0.93698 \n", - "syrf65 7.593e-01 4.111e+00 0.185 0.85479 \n", - "teapri65 3.955e-05 7.700e-04 0.051 0.95941 \n", - "teasec65 2.497e-04 1.171e-03 0.213 0.83274 \n", - "ex1 -5.804e-01 2.418e-01 -2.400 0.02329 * \n", - "im1 5.914e-01 2.503e-01 2.363 0.02531 * \n", - "xr65 -1.038e-04 5.417e-05 -1.916 0.06565 . \n", - "tot1 -1.279e-01 1.126e-01 -1.136 0.26561 \n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "A xtable: 3 × 2\n", "\n", - "Residual standard error: 0.03074 on 28 degrees of freedom\n", - "Multiple R-squared: 0.8871,\tAdjusted R-squared: 0.6411 \n", - "F-statistic: 3.607 on 61 and 28 DF, p-value: 0.0002003\n" + "| | Estimate <dbl> | Std. Error <dbl> |\n", + "|---|---|---|\n", + "| full reg via ols | -0.009377989 | 0.02988773 |\n", + "| partial reg\n", + "via post-lasso | -0.049811465 | 0.01393636 |\n", + "| partial reg via double selection | -0.050005855 | 0.01579138 |\n", + "\n" + ], + "text/plain": [ + " Estimate Std. Error\n", + "full reg via ols -0.009377989 0.02988773\n", + "partial reg\\nvia post-lasso -0.049811465 0.01393636\n", + "partial reg via double selection -0.050005855 0.01579138" ] }, "metadata": {}, @@ -1386,14 +2698,27 @@ } ], "source": [ - "xnames = varnames[-c(1, 2, 3)] # names of X variables\n", - "dandxnames = varnames[-c(1, 2)] # names of D and X variables\n", - "# create formulas by pasting names (this saves typing times)\n", - "fmla = as.formula(paste(\"Outcome ~ \", paste(dandxnames, collapse = \"+\")))\n", - "ls.effect = lm(fmla, data = GrowthData)\n", - "summary((ls.effect))" + "library(xtable)\n", + "table = rbind(summary(ls.effect)$coef[\"gdpsh465\", 1:2], summary(lasso.effect)$coef[,\n", + "1:2], summary(doublesel.effect)$coef[, 1:2])\n", + "colnames(table) = c(\"Estimate\", \"Std. Error\") #names(summary(full.fit)£coef)[1:2]\n", + "rownames(table) = c(\"full reg via ols\", \"partial reg\n", + "via post-lasso \", \"partial reg via double selection\")\n", + "tab = xtable(table, digits = c(2, 2, 5))\n", + "tab" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, From b13dd2e44f04e0a4898202b490f4286a6cbcd271 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 14:25:11 -0500 Subject: [PATCH 18/28] # --- src/rlasso.jl | 130 ++++++++++++++++++++++++++++++++++++----- src/rlassologit.jl | 1 - src/rlassotreatment.jl | 10 ++-- 3 files changed, 119 insertions(+), 22 deletions(-) diff --git a/src/rlasso.jl b/src/rlasso.jl index 6cdaf01..b63e29f 100644 --- a/src/rlasso.jl +++ b/src/rlasso.jl @@ -1,12 +1,44 @@ +mutable struct r_lasso + result + head_msg + foot_msg + main_tbl +end + function rlasso(x, y; post = true, intercept = true, model = true, homoskedastic = false, X_dependent_lambda = false, lambda_start = nothing, c = 1.1, maxIter = 15, tol::Float64 = 1e-5, n = size(y, 1), gamma = 0.1 / log(n), threshold = nothing) + + names_col = try + names(x) + catch + nothing + end + + x = Matrix(x[:, :]) + y = Matrix(y[:, :]) + n_cl = [] + if isnothing(threshold) threshold = 0 end n = size(y, 1) p = size(x, 2) + if isnothing(names_col) + for i in 1:p + push!(n_cl, "V $i") + end + else + n_cl = names_col + end + + if post == false + c = .5 + end + + # print(n_cl) + if intercept meanx = mean(x, dims = 1) x = x .- meanx @@ -39,8 +71,8 @@ function rlasso(x, y; post = true, intercept = true, model = true, coefTemp = LassoShooting_fit(x, y, lambda, XX = XX, Xy = Xy)["coefficients"] end - global coefTemp[isnan.(coefTemp)] .= 0 - global ind1 = abs.(coefTemp) .> 0 + coefTemp[isnan.(coefTemp)] .= 0 + ind1 = abs.(coefTemp) .> 0 global x1 = x[:, ind1] if isempty(x1) @@ -71,32 +103,32 @@ function rlasso(x, y; post = true, intercept = true, model = true, global e1 = y - x1 * coefT coefTemp[ind1] = coefT elseif !post - e1 = y - x1 * coefTemp[ind1] + global e1 = y - x1 * coefTemp[ind1] end s1 = sqrt(var(e1)) # Homoskedastic and X-independent - if (homoskedastic == true) && !X_dependent_lambda + if homoskedastic == true & !X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 - - # Homoskedastic and X-dependent - elseif (homoskedastic == true) && X_dependent_lambda + + # Homoskedastic and X-dependent + elseif homoskedastic == true & X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 - # Heteroskedastic and X-independent - elseif (homoskedastic == false) && !X_dependent_lambda + # Heteroskedastic and X-independent + elseif homoskedastic == false & !X_dependent_lambda Ups1 = 1 / sqrt(n) .* sqrt.(((e1 .^ 2)' * (x .^ 2))') lambda = Ups1 * pen["lambda0"] - # Heteroskedastic and X-dependent - elseif (homoskedastic == false) && X_dependent_lambda + # Heteroskedastic and X-dependent + elseif homoskedastic == false & X_dependent_lambda lc = lambdaCalculation(x = x, y = e1, homoskedastic = homoskedastic, X_dependent_lambda = X_dependent_lambda, lambda_start = lambda_start, c = c, gamma = gamma) Ups1 = lc["Ups0"] lambda = lc["lambda"] - - # Homoskedastic = "none" + + # Homoskedastic = "none" elseif homoskedastic == "none" if isnothing(lambda_start) throw(ArgumentError("lambda_start required when homoskedastic is set to none" )) @@ -116,9 +148,9 @@ function rlasso(x, y; post = true, intercept = true, model = true, coefTemp = None ind1 = zeros(p) end - global coefTemp = coefTemp + coefTemp = coefTemp coefTemp[abs.(coefTemp) .< threshold] .= 0 - global ind1 = ind1 + ind1 = ind1 if intercept if isnothing(mu) mu = 0 @@ -135,24 +167,90 @@ function rlasso(x, y; post = true, intercept = true, model = true, intercept_value = nothing end + if intercept beta = vcat(intercept_value, coefTemp) + main_tbl = hcat(vcat(["Intercept"], n_cl), beta) else beta = coefTemp + main_tbl = hcat(n_cl, beta) end + s1 = sqrt(var(e1)) est = Dict("coefficients" => beta, "beta" => coefTemp, "intercept" => intercept_value, "index" => ind1, "residuals" => e1, "sigma" => s1, "loadings" => Ups1, "iter" => mm, "lambda0" => lambda0, "lambda" => lambda, "options" => Dict("post" => post, "intercept" => intercept, "ind_scale" => ind, "mu" => mu, "meanx" => meanx), "model" => model) - if model + if model x = x .+ meanx est["model"] = x else est["model"] = nothing end + est["covariates"] = p est["tss"] = sum((y .- mean(y)) .^ 2) est["rss"] = sum(est["residuals"] .^ 2) est["dev"] = y .- mean(y) + est["main_tbl"] = main_tbl return est end + +function Base.r_summary(rlasso_obj::Dict; all = false) + select_cl = [] + + for i in eachindex(rlasso_obj["coefficients"]) + if rlasso_obj["coefficients"][i] != 0 + push!(select_cl, i) + end + end + if rlasso_obj["intercept"] != 0 + intercept = 1 + else + intercept = 0 + end + + if all + tbl = rlasso_obj["main_tbl"] + else + tbl = rlasso_obj["main_tbl"][select_cl, :] + end + head_msg = " + Post-Lasso Estimation: $(rlasso_obj["options"]["post"]) + Total number of variables: $(rlasso_obj["covariates"]) + Number of selected variables: $(sum(rlasso_obj["index"])) + --- + " + # Selected variables: ((tbl[select_cl, 1])') + + + print(head_msg) + + print(" \n") + + @ptconf tf = tf_simple alignment = :l + + @pt :header = ["Variable", "Estimate"] tbl + + + n = size(rlasso_obj["model"], 1) + r_squared = 1 - rlasso_obj["rss"] / rlasso_obj["tss"] + r_squared_adj = 1 - (1 - r_squared) * ((n - intercept) / (n - sum(rlasso_obj["index"]) - intercept)) + foot_msg = " + ---- + Multiple R-squared: $r_squared + Adjusted R-squared: $r_squared_adj + " + print(foot_msg) + # print(rlasso_obj.head_msg) + # print(rlasso_obj.foot_msg) +end + +function r_predict(rlasso; xnew = rlasso["model"]) + n, p = size(xnew) + if rlasso["intercept"] != 0 + y_hat = hcat(ones(n), xnew) * rlasso["coefficients"] + else + y_hat = xnew * rlasso["coefficients"] + end + return y_hat +end \ No newline at end of file diff --git a/src/rlassologit.jl b/src/rlassologit.jl index 63adad2..861b669 100644 --- a/src/rlassologit.jl +++ b/src/rlassologit.jl @@ -132,7 +132,6 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr Post-Lasso estimation: $post Intercept: $intercept Control: $threshold - Total number of variables: $p Number of selected variables: $(length(select_columns) - intercept) ------ diff --git a/src/rlassotreatment.jl b/src/rlassotreatment.jl index 8e89ff4..d794443 100644 --- a/src/rlassotreatment.jl +++ b/src/rlassotreatment.jl @@ -83,7 +83,7 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = elseif always_takers == false & never_takers == true g_d_z1 = rlassologit(x_z1, d[indz1], post = post, intercept = intercept, lambda = lambda, c = penalty_c, gamma = penalty_gamma) - yp1 = get_mtrx(x1) * g_d_z1["coefficients"] + yp1 = get_mtrx(x1) * g_d_z1.result["coefficients"] md_z1x = 1 ./ (1 .+ exp.(-1 .* (yp0))) md_z0x = zeros(size(x1, 1)) @@ -93,7 +93,7 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = md_z1x = ones(size(x, 1)) g_d_z0 = rlassologit(x_z0, d[indz0], post = post, intercept = intercept, lambda = lambda, c = penalty_c, gamma = penalty_gamma) - yp0 = get_mtrx(x1) * g_d_z0["coefficients"] + yp0 = get_mtrx(x1) * g_d_z0.result["coefficients"] md_z0x = 1 ./ (1 .+ exp.(-1 .* (yp0))) elseif always_takers == false & never_takers == false md_z0x = zeros(n) @@ -102,7 +102,7 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = end b_z_xl = rlassologit(x1, z, post = post, intercept = intercept) - yp_b = get_mtrx(x1) * b_z_xl["coefficients"] + yp_b = get_mtrx(x1) * b_z_xl.result["coefficients"] mz_x = 1 ./ (1 .+ exp.(-1 .* (yp_b))) mz_x = mz_x .* ((mz_x .> 1e-12) .& (mz_x .< (1 - 1e-12))) .+ (1 - 1e-12) .* ( @@ -190,7 +190,7 @@ function rlassoLATET(x, d, y, z; bootstrap::String = "none", n_rep::Int64 = 500, b_z_xl = rlassologit(x, z, post = post, intercept = intercept, c = 1.1, gamma = 0.1, lambda = lambda) if intercept - mz_x = hcat(ones(n), x) * b_z_xl["coefficients"] + mz_x = hcat(ones(n), x) * b_z_xl.result["coefficients"] elseif !intercept mz_x = x * b_z_xl["coefficients"] end @@ -236,4 +236,4 @@ function rlassoATET(x, d, y, bootstrap::String = "none", n_rep::Int64 = 500) res = rlassoLATET(x, d, y, z, bootstrap = bootstrap, n_rep = n_rep) res["type"] = "ATET" return res -end +end \ No newline at end of file From 18b6cafb09578cb1f3cb7f5a05c5f055d89675e6 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 15:39:05 -0500 Subject: [PATCH 19/28] # --- src/rlasso.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/rlasso.jl b/src/rlasso.jl index b63e29f..010aee4 100644 --- a/src/rlasso.jl +++ b/src/rlasso.jl @@ -195,7 +195,7 @@ function rlasso(x, y; post = true, intercept = true, model = true, return est end -function Base.r_summary(rlasso_obj::Dict; all = false) +function r_summary(rlasso_obj::Dict; all = false) select_cl = [] for i in eachindex(rlasso_obj["coefficients"]) From fab4c4926bfbfe6f7cc0958d880efb1468cc8e15 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 15:56:40 -0500 Subject: [PATCH 20/28] # --- src/HDMjl.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/HDMjl.jl b/src/HDMjl.jl index c900905..20baae2 100644 --- a/src/HDMjl.jl +++ b/src/HDMjl.jl @@ -1,6 +1,6 @@ module HDMjl -export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary +export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary, r_print, r_confint using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables, Distributions From fe73b1bc98b05279199979cb1fcda8357ed09922 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 18:51:20 -0500 Subject: [PATCH 21/28] # --- data/3_2.csv | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 data/3_2.csv diff --git a/data/3_2.csv b/data/3_2.csv new file mode 100644 index 0000000..bf8119b --- /dev/null +++ b/data/3_2.csv @@ -0,0 +1,101 @@ 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From 6cf31f177b277c513d833a25640b9a62ac7f74f9 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Thu, 13 Oct 2022 22:48:29 -0500 Subject: [PATCH 22/28] # --- data/3_2_2.csv | 101 +++++++++++++++++++++++++++++ src/HDMjl.jl | 2 +- src/rlasso.jl | 10 +-- src/rlassologit.jl | 141 +++++++++++++++++++++++++++++++---------- src/rlassotreatment.jl | 58 ++++++++++++++--- 5 files changed, 261 insertions(+), 51 deletions(-) create mode 100644 data/3_2_2.csv diff --git a/data/3_2_2.csv b/data/3_2_2.csv new file mode 100644 index 0000000..c5a67e2 --- /dev/null +++ b/data/3_2_2.csv @@ -0,0 +1,101 @@ 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diff --git a/src/HDMjl.jl b/src/HDMjl.jl index 20baae2..162489c 100644 --- a/src/HDMjl.jl +++ b/src/HDMjl.jl @@ -1,6 +1,6 @@ module HDMjl -export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary, r_print, r_confint +export lambdaCalculation, init_values, as_logical, LassoShooting_fit, rlasso, rlassoEffect, rlassoEffects, rlassoIVselectX, rlassoIVselectZ, rlassoIV, rlassologit, rlassologitEffect, rlassologitEffects, rlassoATE, rlassoATET, rlassoLATE, rlassoLATET, tsls, r_summary, r_print, r_confint, r_predict using Statistics, GLM, DataFrames, LinearAlgebra, GLMNet, Random, PrettyTables, Distributions diff --git a/src/rlasso.jl b/src/rlasso.jl index 010aee4..1889034 100644 --- a/src/rlasso.jl +++ b/src/rlasso.jl @@ -108,22 +108,22 @@ function rlasso(x, y; post = true, intercept = true, model = true, s1 = sqrt(var(e1)) # Homoskedastic and X-independent - if homoskedastic == true & !X_dependent_lambda + if (homoskedastic == true) && !X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 # Homoskedastic and X-dependent - elseif homoskedastic == true & X_dependent_lambda + elseif (homoskedastic == true) && X_dependent_lambda Ups1 = s1 * Psi lambda = pen["lambda0"] * Ups1 # Heteroskedastic and X-independent - elseif homoskedastic == false & !X_dependent_lambda + elseif (homoskedastic == false) && !X_dependent_lambda Ups1 = 1 / sqrt(n) .* sqrt.(((e1 .^ 2)' * (x .^ 2))') lambda = Ups1 * pen["lambda0"] # Heteroskedastic and X-dependent - elseif homoskedastic == false & X_dependent_lambda + elseif (homoskedastic == false) && X_dependent_lambda lc = lambdaCalculation(x = x, y = e1, homoskedastic = homoskedastic, X_dependent_lambda = X_dependent_lambda, lambda_start = lambda_start, c = c, gamma = gamma) Ups1 = lc["Ups0"] lambda = lc["lambda"] @@ -245,7 +245,7 @@ function r_summary(rlasso_obj::Dict; all = false) # print(rlasso_obj.foot_msg) end -function r_predict(rlasso; xnew = rlasso["model"]) +function r_predict(rlasso::Dict; xnew = rlasso["model"]) n, p = size(xnew) if rlasso["intercept"] != 0 y_hat = hcat(ones(n), xnew) * rlasso["coefficients"] diff --git a/src/rlassologit.jl b/src/rlassologit.jl index 861b669..3ec8f97 100644 --- a/src/rlassologit.jl +++ b/src/rlassologit.jl @@ -128,13 +128,13 @@ function rlassologit(x, y; model::Bool = true, c::Float64 = 1.1, post::Bool = tr head1 = " ------- -Post-Lasso estimation: $post -Intercept: $intercept -Control: $threshold -Total number of variables: $p -Number of selected variables: $(length(select_columns) - intercept) ------- + ------ + Post-Lasso estimation: $post + Intercept: $intercept + Control: $threshold + Total number of variables: $p + Number of selected variables: $(length(select_columns) - intercept) + ------ " println(head1) @@ -175,12 +175,22 @@ function r_summary(result::rlassologit, all::Bool = false) end -# mutable struct rlassologitEffect - -# end +mutable struct rlassologitEffect + result::Dict + head_msg +end + function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) + colnames = try + names(X) + catch + [] + end + + + x = Matrix(X[:, :]) d = D[:] y = Y[:] @@ -194,16 +204,16 @@ function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) dx = hcat(d, x) l1 = rlassologit(dx, y, post = post, intercept = true, lambda = la1) - x1 = l1["residuals"] + x1 = l1.result["residuals"] - t = hcat(ones(n), dx) * l1["coefficients"] + t = hcat(ones(n), dx) * l1.result["coefficients"] sigma2 = exp.(t) ./ (1 .+ exp.(t)).^2 w = copy(sigma2) f = sqrt.(sigma2) - I1 = l1["ind1ex"][Not(1)] + I1 = l1.result["index"][Not(1)] lambda = 2.2 * sqrt(n) * quantile(Normal(0, 1), 1 - 0.05 / max(n, p * log(n))) la2 = repeat([lambda], p) @@ -212,7 +222,7 @@ function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) l2 = rlasso(xf, df, post = post, intercept = true, homoskedastic = false, lambda_start = la2, c = 1.1, gamma = 0.1) # return l2 # include("hdmjl.jl") - I2 = l2["ind1ex"] + I2 = l2["index"] z = l2["residuals"] ./ sqrt.(sigma2) if isnothing(I3) @@ -225,17 +235,18 @@ function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) ind1 = [] - for i in eachind1ex(I) + for i in eachindex(I) if I[i] > 0 append!(ind1, i) end end - ind1 + + xselect = x[:, ind1] p3 = size(xselect, 2) - data3 = DataFrame(hcat(y, d, xselect)) + data3 = DataFrame(hcat(y, d, xselect), :auto) rename!(data3, "x1" => "y") @@ -256,7 +267,22 @@ function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) else no_select = 0 end + + ##----- + main_mssg = + " + --- + post = $post + ---- + se = $se + alpha = $alpha + sample_size = $n + --- + " + + # main_table = hcat(coe) # GLM.residuals(l3) + print(main_mssg) res = Dict("epsilon" => y - g3, "v" => z) results = Dict( @@ -264,18 +290,34 @@ function rlassologitEffect(X, Y, D; I3::Any = nothing, post = true) "no_select" => no_select, "coefficients" => alpha, "coefficient" => alpha, "residuals" => res, "sample_size" => n, "post" => post ) - return results + return rlassologitEffect(results, main_mssg) +end + +function r_summary(result::rlassologitEffect) + print(result.head_msg) +end + +mutable struct rlassologitEffects + result + head_mssg + table end -function rlassologitEffects(x, y; ind1ex = 1:3, I3 = nothing, post = true) - x = Matrix(x) +function rlassologitEffects(x, y; index = 1:size(x, 2), I3 = nothing, post = true) + + names_col = try + names(x) + catch + nothing + end + x = Matrix(x[:, :]) y = Matrix(y[:, :]) n, p = size(x) - if Set(ind1ex) == 2 - k = p1 = sum(ind1ex) + if Set(index) == 2 + k = p1 = sum(index) else - k = p1 = length(ind1ex) + k = p1 = length(index) end coefficients = zeros(k) @@ -286,34 +328,63 @@ function rlassologitEffects(x, y; ind1ex = 1:3, I3 = nothing, post = true) # print(k) for i in 1:k - d = x[:, ind1ex[i]] - xt = x[:, Not(ind1ex[i])] + d = x[:, index[i]] + xt = x[:, Not(index[i])] if isnothing(I3) I3m = I3 else - I3m = I3[Not(ind1ex[i])] + I3m = I3[Not(index[i])] end lasso_regs[i] = try - rlassologitEffect(xt, y, d, I3 = I3m, post = post) + rlassologitEffect(xt, y, d, I3 = I3m, post = post); catch "try-error" end if lasso_regs[i] == "try-error" continue else - coefficients[i] = lasso_regs[i]["alpha"] - se[i] = lasso_regs[i]["se"] - t[i] = lasso_regs[i]["t"] - reside["epsilon"][i] = lasso_regs[i]["residuals"]["epsilon"] - reside["v"][i] = lasso_regs[i]["residuals"]["v"] + # print(lasso_regs[i]) + coefficients[i] = lasso_regs[i].result["alpha"] + se[i] = lasso_regs[i].result["se"] + t[i] = lasso_regs[i].result["t"] + reside["epsilon"][i] = lasso_regs[i].result["residuals"]["epsilon"] + reside["v"][i] = lasso_regs[i].result["residuals"]["v"] + end + end + head_mssg = " + --- + post: $post + index: $(collect(index)') + --- + " + + n_cl = [] + + if isnothing(names_col) + for i in 1:p + push!(n_cl, "V $i") end + else + n_cl = names_col end + main_table = hcat(n_cl[index], coefficients) + + header = ["Variable", "Estimate"] + + print(head_mssg) + + + print("\n") + + @ptconf tf = tf_simple alignment = :l + @pt :header = header main_table + # residual = res = Dict( "coefficients" => coefficients, "se" => se, "t" => t, - "lasso_regs" => lasso_regs, "ind1ex" => ind1ex, "sample_size" => n, + "lasso_regs" => lasso_regs, "index" => index, "sample_size" => n, "residuals" => reside ) - return res -end \ No newline at end of file + return rlassoEffects(res, head_mssg, main_table) +end diff --git a/src/rlassotreatment.jl b/src/rlassotreatment.jl index d794443..44cc8c2 100644 --- a/src/rlassotreatment.jl +++ b/src/rlassotreatment.jl @@ -1,3 +1,10 @@ +mutable struct rlassoTE + result::Dict + # main_tbl + type +end + + function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = true, post = true, intercept = true, never_takers = true) @@ -83,7 +90,7 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = elseif always_takers == false & never_takers == true g_d_z1 = rlassologit(x_z1, d[indz1], post = post, intercept = intercept, lambda = lambda, c = penalty_c, gamma = penalty_gamma) - yp1 = get_mtrx(x1) * g_d_z1.result["coefficients"] + yp1 = get_mtrx(x1) * g_d_z1["coefficients"] md_z1x = 1 ./ (1 .+ exp.(-1 .* (yp0))) md_z0x = zeros(size(x1, 1)) @@ -93,7 +100,7 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = md_z1x = ones(size(x, 1)) g_d_z0 = rlassologit(x_z0, d[indz0], post = post, intercept = intercept, lambda = lambda, c = penalty_c, gamma = penalty_gamma) - yp0 = get_mtrx(x1) * g_d_z0.result["coefficients"] + yp0 = get_mtrx(x1) * g_d_z0["coefficients"] md_z0x = 1 ./ (1 .+ exp.(-1 .* (yp0))) elseif always_takers == false & never_takers == false md_z0x = zeros(n) @@ -145,16 +152,19 @@ function rlassoLATE(x, d, y, z; bootstrap = "none", n_rep = 100, always_takers = object["type_boot"] = bootstrap object["boot_n"] = n_rep end - object + object["type_boot"] = bootstrap - return object + return rlassoTE(object, object["type"]) end + function rlassoATE(x, d, y; bootstrap = "none", n_rep = 500) z = copy(d) res = rlassoLATE(x, d, y, z, bootstrap = bootstrap, n_rep = n_rep) - res["type"] = "ATE" - return res + type = "ATE" + # res.result["type"] = type + res.result["type_boot"] = bootstrap + return rlassoTE(res.result, type) end function rlassoLATET(x, d, y, z; bootstrap::String = "none", n_rep::Int64 = 500, post::Bool = true, always_takers::Bool = true, never_takers::Bool = true, intercept::Bool = true) @@ -224,16 +234,44 @@ function rlassoLATET(x, d, y, z; bootstrap::String = "none", n_rep::Int64 = 500, boot[i] = mean(weights .* ((y - my_z0x) - (ones(n) - z) .* (y - my_z0x) ./ (ones(n) - mz_x))) / mean(weights .* ((d - md_z0x) - (ones(n) - z) .* (d - md_z0x) ./ (ones(n) - mz_x))) end res["boot_se"] = sqrt(var(boot)) - res["type_boot"] = bootstrap + res["type_boot"] = bootstrap return res end - return res + + res["type_boot"] = bootstrap + type = "LATET" + return rlassoTE(res, type) end function rlassoATET(x, d, y, bootstrap::String = "none", n_rep::Int64 = 500) z = copy(d) res = rlassoLATET(x, d, y, z, bootstrap = bootstrap, n_rep = n_rep) - res["type"] = "ATET" - return res + type = "ATET" + return rlassoTE(res.result, type) +end + +function r_summary(rlasso_TE::rlassoTE) + # boot_strap = rlasso_TE[""] + print(" + Estimation and significance tesing of the treatment effect + Type: $(rlasso_TE.type) + Bootstrap: $(rlasso_TE.result["type_boot"]) + ") + + print("\n") + + results = rlasso_TE.result + + coef = results["te"] + se = results["se"] + std_ = coef / se + + header = ["Coeff", "SE", "t.value"] + tbl = [coef se std_] + + + @ptconf tf = tf_simple + @pt :header = header tbl + return tbl end \ No newline at end of file From 463947e42c03d53ac147385b3de798773fcfa2c3 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 07:09:06 -0500 Subject: [PATCH 23/28] # --- Project.toml | 1 + src/rlassoEffect.jl | 45 +- src/rlassoIV.jl | 22 +- tutorial/HDMjl_tutorial.ipynb | 2003 ++++++++++++++++++++++ tutorial/HDMjl_tutorial_1.ipynb | 560 +++++-- tutorial/python.ipynb | 338 ---- tutorial/r_file.ipynb | 2773 +++++++++---------------------- 7 files changed, 3238 insertions(+), 2504 deletions(-) create mode 100644 tutorial/HDMjl_tutorial.ipynb delete mode 100644 tutorial/python.ipynb diff --git a/Project.toml b/Project.toml index 519ed61..81079f2 100644 --- a/Project.toml +++ b/Project.toml @@ -11,6 +11,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" [compat] DataFrames = "0.22.7, 1" diff --git a/src/rlassoEffect.jl b/src/rlassoEffect.jl index ffe0655..90dd6a7 100644 --- a/src/rlassoEffect.jl +++ b/src/rlassoEffect.jl @@ -115,21 +115,32 @@ function r_summary(object::rlassoEffect1) if length(object.coefficients) != 0 k = length(object.coefficients) table = zeros(k, 4) - table[:, 1] .= object.coefficients - table[:, 2] .= object.se - table[:, 3] .= table[:, 1]./table[:, 2] - table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table[:, 1] .= round.(object.coefficients, digits = 5) + table[:, 2] .= round.(object.se, digits = 5) + table[:, 3] .= round.(table[:, 1]./table[:, 2], digits = 5) + table[:, 4] .= round.(2 * cdf(Normal(), -abs.(table[:, 3])), digits = 5) table1 = DataFrame(table, :auto) table1 = rename(table1, ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) + table2 = string.(copy(table)) + a = table2[:, 4]; + b = string.(zeros(length(a))) + b[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .= a[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .* " ***" + b[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .= a[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .* " **" + b[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .= a[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .* " *" + b[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .= a[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .* " ." + b[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .= a[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .* " " + b = vec(b) + table2[:, 4] .= b + table2 = DataFrame(table2, :auto) + table2 = rename(table2, ["coeff.", "se.", "t-value", "p-value"]) print("""Estimates and significance testing of the effect of target variables""", "\n") - pretty_table(table, show_row_number = true, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless) + pretty_table(table2, show_row_number = true, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless) print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") print("\n") else print("No coefficients\n") end - return table1 end function r_print(object::rlassoEffect1; digits = 3) @@ -281,15 +292,27 @@ function r_summary(object::rlassoEffects1) if length(object.coefficients) != 0 k = length(object.coefficients) table = zeros(k, 4) - table[:, 1] .= object.coefficients - table[:, 2] .= object.se - table[:, 3] .= table[:, 1]./table[:, 2] - table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table[:, 1] .= round.(object.coefficients, digits = 5) + table[:, 2] .= round.(object.se, digits = 5) + table[:, 3] .= round.(table[:, 1]./table[:, 2], digits = 5) + table[:, 4] .= round.(2 * cdf(Normal(), -abs.(table[:, 3])), digits = 5) table1 = DataFrame(hcat(["X$y" for y = object.index], table), :auto) table1 = rename(table1, ["index", "Estimate.", "Std. Error", "t value", "Pr(>|t|)"]) + table2 = string.(copy(table)) + a = table2[:, 4]; + b = string.(zeros(length(a))) + b[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .= a[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .* " ***" + b[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .= a[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .* " **" + b[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .= a[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .* " *" + b[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .= a[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .* " ." + b[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .= a[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .* " " + b = vec(b) + table2[:, 4] .= b + table2 = DataFrame(hcat(object.coefficients[:,1], table2), :auto) + table2 = rename(table2, [" ", "coeff.", "se.", "t-value", "p-value"]) print("Estimates and significance testing of the effect of target variables", "\n") - pretty_table(table, show_row_number = false, header = ["Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless, row_names = ["X$y" for y = object.index]) + pretty_table(table2, show_row_number = false, header = [" ","Estimate.", "Std. Error", "t value", "Pr(>|t|)"], tf = tf_borderless, row_names = ["X$y" for y = object.index]) print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") print("\n") return table1 diff --git a/src/rlassoIV.jl b/src/rlassoIV.jl index 83a5d7d..bd1ec66 100644 --- a/src/rlassoIV.jl +++ b/src/rlassoIV.jl @@ -99,15 +99,27 @@ function r_summary(object::rlassoIV1) if size([object.coefficients])[1] != 0 k = length(object.coefficients[:,2]) table = zeros(k, 4) - table[:, 1] .= vec(object.coefficients[:,2]) - table[:, 2] .= object.se - table[:, 3] .= table[:, 1]./table[:, 2] - table[:, 4] .= 2 * cdf(Normal(), -abs.(table[:, 3])) + table[:, 1] .= round.(vec(object.coefficients[:,2]), digits = 5) + table[:, 2] .= round.(object.se, digits = 5) + table[:, 3] .= round.(table[:, 1]./table[:, 2], digits = 5) + table[:, 4] .= round.(2 * cdf(Normal(), -abs.(table[:, 3])), digits = 5) table1 = DataFrame(hcat(object.coefficients[:,1], table), :auto) table1 = rename(table1, [" ", "coeff.", "se.", "t-value", "p-value"]) + table2 = string.(copy(table)) + a = table2[:, 4]; + b = string.(zeros(length(a))) + b[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .= a[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .* " ***" + b[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .= a[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .* " **" + b[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .= a[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .* " *" + b[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .= a[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .* " ." + b[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .= a[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .* " " + b = vec(b) + table2[:, 4] .= b + table2 = DataFrame(hcat(object.coefficients[:,1], table2), :auto) + table2 = rename(table2, [" ", "coeff.", "se.", "t-value", "p-value"]) print("Estimates and Significance Testing of the effect of target variables in the IV regression model", "\n") - pretty_table(table1, show_row_number = false, header = [" ", "coeff.", "se.", "t-value", "p-value"], tf = tf_borderless) + pretty_table(table2, show_row_number = false, header = [" ", "coeff.", "se.", "t-value", "p-value"], tf = tf_borderless) print("---", "\n", "Signif. codes:","\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1") print("\n") return table1; diff --git a/tutorial/HDMjl_tutorial.ipynb b/tutorial/HDMjl_tutorial.ipynb new file mode 100644 index 0000000..a549ec0 --- /dev/null +++ b/tutorial/HDMjl_tutorial.ipynb @@ -0,0 +1,2003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HIGH-DIMENSIONAL METRICS IN R" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. How to get started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "or" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "#Pkg.rm(\"HDMjl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "#Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl#prueba\", rev = \"dev\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl#prueba\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import Pkg, using Pkg" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n", + " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" + ] + } + ], + "source": [ + "Pkg.develop(path = \"E:/causal_ml/hdm_paper/prueba/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "using CodecXz, RData, DataFrames, StatsModels, \n", + " Statistics, Distributions, PrettyTables, GLM, CSV, HDMjl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Prediction using Approximate Sparsity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. A Joint Significance Test for Lasso Regression." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "using CSV\n", + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/3_2.csv\"\n", + "dta = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(dta);\n", + "Y = dta[:,1];\n", + "X = Matrix(dta[:,2:end]);" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Post-Lasso Estimation: false\n", + " Total number of variables: 100\n", + " Number of selected variables: 11\n", + " ---\n", + " \n", + "============ ==============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ==============\n", + " Intercept 0.056855\n", + " V 1 4.77121\n", + " V 2 4.69284\n", + " V 3 4.76568\n", + " V 13 -0.0453685\n", + " V 15 -0.0467382\n", + " V 16 -0.00499617\n", + " V 19 -0.0922336\n", + " V 22 -0.0272553\n", + " V 40 -0.0105032\n", + " V 61 0.113585\n", + " V 100 -0.0247296\n", + "============ ==============\n", + "\n", + " ----\n", + " Multiple R-squared: 0.9912720815874809\n", + " Adjusted R-squared: 0.9901810917859161\n", + " " + ] + } + ], + "source": [ + "lasso_reg = rlasso(X, Y, post = false);\n", + "sum_lasso = r_summary(lasso_reg)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/3_2_2.csv\"\n", + "dta = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(dta);\n", + "yhat_lasso = r_predict(lasso_reg) #in-sample prediction\n", + "Ynew = dta[:,1];\n", + "Xnew = Matrix(dta[:,2:end]);\n", + "yhat_lasso_new = r_predict(lasso_reg, xnew = Xnew) #out-of-sample prediction\n", + ";" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Post-Lasso Estimation: true\n", + " Total number of variables: 100\n", + " Number of selected variables: 3\n", + " ---\n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept 0.0341043\n", + " V 1 4.92413\n", + " V 2 4.85787\n", + " V 3 4.96442\n", + "============ ============\n", + "\n", + " ----\n", + " Multiple R-squared: 0.9906284190077158\n", + " Adjusted R-squared: 0.990335557101707\n", + " " + ] + } + ], + "source": [ + "post_lasso_reg = rlasso(X, Y, post = true) #now use post-lasso\n", + "r_summary(post_lasso_reg)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "yhat_postlasso = r_predict(post_lasso_reg) #in-sample prediction\n", + "yhat_postlasso_new = r_predict(post_lasso_reg, xnew = Xnew) #in-sample prediction\n", + ";" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============ =================\n", + " \u001b[1m lasso MAE \u001b[0m \u001b[1m Post-lasso MAE \u001b[0m\n", + "============ =================\n", + " 0.879583 0.78017\n", + "============ =================\n" + ] + } + ], + "source": [ + "MAE = mean(eachrow(hcat(abs.(Ynew - yhat_lasso_new), abs.(Ynew - yhat_postlasso_new))))\n", + "MAE = DataFrame([[MAE[1]], [MAE[2]]], :auto)\n", + "MAE = rename!(MAE, [\"lasso MAE\", \"Post-lasso MAE\"])\n", + "pretty_table(MAE, tf = tf_simple, nosubheader = true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Inference on Target Regression Coefficients" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1. Intuition for the Orthogonality Principle in Linear Models via Partialling Out." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "using CSV, GLM\n", + "using DataFrames" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_1.csv\"\n", + "dta = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(dta);\n", + "y = dta[:,\"y\"];\n", + "d = dta[:,\"d\"];\n", + "x = Matrix(dta[:,3:end]);" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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Float64Float64
10.9727390.0136868
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0.972739 & 0.0136868 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 0.972739 0.0136868" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rY_1 = rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), y);\n", + "rY = rY_1[\"residuals\"]\n", + "rD_1 = rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), d);\n", + "rD = rD_1[\"residuals\"]\n", + "partial_fit_postlasso = lm(hcat(ones(length(y)), rD), vec(rY))\n", + "DataFrame(Estimate = coef(partial_fit_postlasso)[2], Std_Error = stderror(partial_fit_postlasso)[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 0.972739 0.0136868 71.0715 0.0\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "Eff = rlassoEffect(x, y, d, method = \"partialling out\");\n", + "r_summary(Eff);" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m 1 \u001b[0m\n", + "\n", + " 0.973\n" + ] + } + ], + "source": [ + "r_print(Eff)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 0.978075 0.0141562 69.0914 0.0\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "Eff = rlassoEffect(x, y, d, method = \"double selection\");\n", + "r_summary(Eff);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2. Inference: Confidence Intervals and Significance Testing. The function rlassoEffects" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "using CSV\n", + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_2.csv\"\n", + "data = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(data);\n", + "y = data[:,1];\n", + "#d = dta[:,\"d\"];\n", + "x = Matrix(data[:,2:end]);" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_effects = rlassoEffects(x, y, index = [1,2,3,50]);" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", + "\n", + " 2.9445 3.0413 2.9754 0.072\n" + ] + } + ], + "source": [ + "r_print(lasso_effects, digits = 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.94448 0.0881468 33.4043 1.1892e-244\n", + " \u001b[1m X2 \u001b[0m 3.04127 0.083891 36.2527 9.0141e-288\n", + " \u001b[1m X3 \u001b[0m 2.9754 0.0780394 38.127 4.58085e-318\n", + " \u001b[1m X50 \u001b[0m 0.0719553 0.0776455 0.926716 0.354074\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "r_summary(lasso_effects);" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.77171 3.11724\n", + " \u001b[1m X2 \u001b[0m 2.87685 3.2057\n", + " \u001b[1m X3 \u001b[0m 2.82245 3.12836\n", + " \u001b[1m X50 \u001b[0m -0.0802271 0.224138\n" + ] + } + ], + "source": [ + "r_confint(lasso_effects);" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.7304 3.15855\n", + " \u001b[1m X2 \u001b[0m 2.83944 3.24311\n", + " \u001b[1m X3 \u001b[0m 2.7855 3.16531\n", + " \u001b[1m X50 \u001b[0m -0.113325 0.257236\n" + ] + } + ], + "source": [ + "r_confint(lasso_effects, joint = true);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3. Application: the effect of gender on wage" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(29217, 23)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://github.com/cran/hdm/raw/master/data/cps2012.rda\"\n", + "cps2012 = load(download(url))[\"cps2012\"];\n", + "n, p = size(cps2012);\n", + "size(cps2012)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(29217, 116)" + ] + } + ], + "source": [ + "x_formula = @formula(lnw ~ -1 + female + female*widowed + female*divorced + female*separated + female*nevermarried +\n", + " female*hsd08 + female*hsd911 + female*hsg + female*cg + female*ad + female*mw + female*so + female*we + female*exp1 + female*exp2 + female*exp3)\n", + "x_dframe = ModelFrame( x_formula, cps2012)\n", + "x1 = ModelMatrix(x_dframe)\n", + "X = x1.m[:,Not(1:16)];\n", + "X = hcat(x1.m[:,1:16], X)\n", + "size(X)\n", + "fom_1 = [\"widowed\", \"divorced\", \"separated\", \"nevermarried\", \"hsd08\", \"hsd911\", \"hsg\", \"cg\", \"ad\", \"mw\", \"so\",\n", + " \"we\", \"exp1\", \"exp2\", \"exp3\"];\n", + "data = cps2012[:,fom_1];\n", + "sub_data = ones(size(data)[1])\n", + "for i in 1:size(data)[2]\n", + " if i <= (size(data)[2] -1)\n", + " sub_data = hcat(sub_data, Matrix(data[:, i] .* data[:, Not(1:i)]) )\n", + " end\n", + "end\n", + "sub_data = sub_data[:,2:end]\n", + "size(sub_data)\n", + "x = hcat(X, sub_data)\n", + "size(x)\n", + "filter = var.(eachcol(x)) .!= 0\n", + "x = x[:,filter]\n", + "print(size(x))\n", + "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", + "y = cps2012.lnw;\n", + ";" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sum(x[:,32])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "effects_female = rlassoEffects(x, y, index = index_gender);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m -0.154923 0.0501624 -3.08843 0.00201216\n", + " \u001b[1m X17 \u001b[0m 0.136095 0.0906626 1.50112 0.133325\n", + " \u001b[1m X18 \u001b[0m 0.136939 0.0221817 6.17353 6.6782e-10\n", + " \u001b[1m X19 \u001b[0m 0.0233028 0.0532118 0.437925 0.661441\n", + " \u001b[1m X20 \u001b[0m 0.186853 0.0199424 9.36966 7.27651e-21\n", + " \u001b[1m X21 \u001b[0m 0.0278103 0.120914 0.23 0.818092\n", + " \u001b[1m X22 \u001b[0m -0.119335 0.0518797 -2.30023 0.0214354\n", + " \u001b[1m X23 \u001b[0m -0.0128898 0.0192232 -0.670533 0.502518\n", + " \u001b[1m X24 \u001b[0m 0.0101386 0.0183265 0.553218 0.580114\n", + " \u001b[1m X25 \u001b[0m -0.0304637 0.0218061 -1.39703 0.162405\n", + " \u001b[1m X26 \u001b[0m -0.00106344 0.0191918 -0.0554112 0.955811\n", + " \u001b[1m X27 \u001b[0m -0.00818334 0.0193568 -0.422763 0.672468\n", + " \u001b[1m X28 \u001b[0m -0.00422613 0.0211684 -0.199643 0.84176\n", + " \u001b[1m X29 \u001b[0m 0.00493526 0.00780428 0.632379 0.527139\n", + " \u001b[1m X30 \u001b[0m -0.159519 0.0452999 -3.52141 0.000429263\n", + " \u001b[1m X31 \u001b[0m 0.0384506 0.0078611 4.89125 1.00199e-6\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "r_summary(effects_female);" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m -0.295562 -0.0142847\n", + " \u001b[1m X17 \u001b[0m -0.136261 0.408452\n", + " \u001b[1m X18 \u001b[0m 0.0742004 0.199678\n", + " \u001b[1m X19 \u001b[0m -0.118061 0.164666\n", + " \u001b[1m X20 \u001b[0m 0.128705 0.245002\n", + " \u001b[1m X21 \u001b[0m -0.378365 0.433985\n", + " \u001b[1m X22 \u001b[0m -0.270462 0.0317919\n", + " \u001b[1m X23 \u001b[0m -0.0656411 0.0398615\n", + " \u001b[1m X24 \u001b[0m -0.0421792 0.0624564\n", + " \u001b[1m X25 \u001b[0m -0.0964645 0.0355371\n", + " \u001b[1m X26 \u001b[0m -0.055223 0.0530961\n", + " \u001b[1m X27 \u001b[0m -0.0632443 0.0468776\n", + " \u001b[1m X28 \u001b[0m -0.0662182 0.057766\n", + " \u001b[1m X29 \u001b[0m -0.0167011 0.0265717\n", + " \u001b[1m X30 \u001b[0m -0.285341 -0.0336976\n", + " \u001b[1m X31 \u001b[0m 0.0166404 0.0602608\n" + ] + } + ], + "source": [ + "joint_CI = r_confint(effects_female, 0.95, joint = true);\n", + "joint_CI;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "using LinearAlgebra" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "using GLM\n", + "using CodecXz\n", + "using RData\n", + "using DataFrames\n", + "url = \"https://github.com/cran/hdm/raw/master/data/GrowthData.rda\";\n", + "GrowthData = load(download(url))[\"GrowthData\"];\n", + "y = GrowthData[:, 1];\n", + "d = GrowthData[:, 3];\n", + "X = Matrix(GrowthData[:, Not(1, 2, 3)]);\n", + "X_1 = Matrix(GrowthData[:, Not(1, 2)]);" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "Q, R = qr(hcat(ones(length(y)), X_1))\n", + "β = pinv(hcat(ones(length(y)), X_1)) * y\n", + "# ls_effect = β;\n", + "res = y - hcat(ones(length(y)), X_1) * β;\n", + "n = size(hcat(ones(length(y)), X_1))[1]\n", + "k = size(hcat(ones(length(y)), X_1))[2]\n", + "\n", + "sigma2_hat = (res' * res) / (n - k)\n", + "vcov_beta_hat = sigma2_hat .* inv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1));\n", + "se = sqrt.(diag(vcov_beta_hat))\n", + "\n", + "ls_effect = DataFrame(Estimate = β, stderror = se);" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [], + "source": [ + "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);\n", + "# coef(ls_effect)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "lasso_effect = rlassoEffect(X, y, d, method = \"partialling out\");\n", + "r_summary(lasso_effect);" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " 1 -0.0500059 0.0157914 -3.16666 0.00154203\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "doublesel_effect = rlassoEffect(X, y, d, method = \"double selection\");\n", + "r_summary(doublesel_effect);" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================== ========== =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "=================================== ========== =============\n", + " full reg via ols -0.01 0.02989\n", + " partial reg\\nvia post-lasso -0.05 0.01394\n", + " partial reg via double selection -0.05 0.01579\n", + "=================================== ========== =============\n" + ] + } + ], + "source": [ + "using PrettyTables\n", + "table = zeros(3,2)\n", + "table[1,:] = [round(Matrix(ls_effect)[2,1], digits = 2), round.(Matrix(ls_effect)[2,2], digits = 5)]\n", + "table[2,:] = [round(lasso_effect.coefficients, digits =2), round(lasso_effect.se, digits = 5)]\n", + "table[3,:] = [round(doublesel_effect.coefficients, digits =2), round(doublesel_effect.se, digits = 5)];\n", + "index = [\"full reg via ols\", \"partial reg\n", + "via post-lasso \", \"partial reg via double selection\"]\n", + "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Application: Economic Development and Institutions." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "using RDatasets, TexTables, LinearAlgebra, GLM" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(64, 21)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "using Statistics, StatsModels, RData\n", + "url = \"https://github.com/cran/hdm/raw/master/data/AJR.rda\";\n", + "AJR = load(download(url))[\"AJR\"];\n", + "y = AJR[!,\"GDP\"]\n", + "d = AJR[!,\"Exprop\"]\n", + "z = AJR[!,\"logMort\"];\n", + "x_formula = @formula(GDP ~ -1 + Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", + " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", + " + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer\n", + " + Namer*Samer)\n", + "x_dframe = ModelFrame( x_formula, AJR)\n", + "x1 = ModelMatrix(x_dframe)\n", + "x = x1.m\n", + "size(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "AJR_Xselect = rlassoIV(x, d, y, z, select_X=true, select_Z=false);" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "\n", + " \u001b[1m d1 \u001b[0m\n", + "\n", + " 0.845\n" + ] + } + ], + "source": [ + "r_print(AJR_Xselect)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 0.845027 0.269926 3.13059 0.00174458\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "r_summary(AJR_Xselect);" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " d1 0.315981 1.37407\n" + ] + } + ], + "source": [ + "r_confint(AJR_Xselect);" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "rY_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), y);\n", + "rY = y - predict(rY_1)\n", + "rD_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), d);\n", + "rD = d - predict(rD_1);\n", + "rZ_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), z);\n", + "rZ = z - predict(rZ_1);" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "rY_1 = lm(@formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rY = y - predict(rY_1)\n", + "\n", + "rD_1 = lm(@formula(Exprop ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rD = d - predict(rD_1)\n", + "\n", + "rZ_1 = lm(@formula(logMort ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", + "rZ = z - predict(rZ_1);" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
11.267211.73054
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 1.26721 & 1.73054 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 1.26721 1.73054" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ivfit_lm = tsls(rD, rY, rZ, nothing, intercept=false)\n", + "DataFrame(Estimate = ivfit_lm[\"coefficients\"][1,2], Std_Error = ivfit_lm[\"se\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "x_formula1 = @formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", + " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", + " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", + " + Africa*Asia + Africa*Namer + Africa*Samer\n", + " + Asia*Namer + Asia*Samer\n", + " + Namer*Samer)\n", + "x_dframe1 = ModelFrame( x_formula, AJR)\n", + "x1_1 = ModelMatrix(x_dframe)\n", + "xx = x1.m;" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
10.8450270.272094
" + ], + "text/latex": [ + "\\begin{tabular}{r|cc}\n", + "\t& Estimate & Std\\_Error\\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0.845027 & 0.272094 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m1×2 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", + "─────┼─────────────────────\n", + " 1 │ 0.845027 0.272094" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rY_1 = rlasso(xx, y);\n", + "rY = rY_1[\"residuals\"]\n", + "rD_1 = rlasso(xx, d);\n", + "rD = rD_1[\"residuals\"]\n", + "rZ_1 = rlasso(xx, z);\n", + "rZ = rZ_1[\"residuals\"]\n", + "\n", + "ivfit_lasso = tsls(rD, rY, rZ)\n", + "DataFrame(Estimate = ivfit_lasso[\"coefficients\"][1,2], Std_Error = ivfit_lasso[\"se\"][1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "using Statistics, GLM\n", + "url = \"https://github.com/cran/hdm/raw/master/data/EminentDomain.rda\";\n", + "EminentDomain = load(download(url))[\"EminentDomain\"];\n", + "z = EminentDomain[\"logGDP\"][\"z\"];\n", + "x = EminentDomain[\"logGDP\"][\"x\"];\n", + "d = EminentDomain[\"logGDP\"][\"d\"];\n", + "y = EminentDomain[\"logGDP\"][\"y\"];\n", + "x = x[:, (mean(x, dims = 1) .> 0.05)'];\n", + "z = z[:, (mean(z, dims = 1) .> 0.05)'];" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "ED_ols = lm(hcat(ones(length(vec(y))), hcat(d, x)), vec(y));\n", + "ED_2sls = tsls(d, y, z[:,1:2], x, intercept = false);" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_IV_Z = rlassoIV(x, d, y, z, select_X = false, select_Z = true);" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 0.414602 0.290249 1.42843 0.153167\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "r_summary(lasso_IV_Z);" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " d1 -0.154276 0.98348\n" + ] + } + ], + "source": [ + "r_confint(lasso_IV_Z);" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 -0.0238347 0.128507 -0.185475 0.852857\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "lasso_IV_XZ = rlassoIV(x, d, y, z, select_X = true, select_Z = true);\n", + "r_summary(lasso_IV_XZ);" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " d1 -0.275703 0.228033\n" + ] + } + ], + "source": [ + "r_confint(lasso_IV_XZ);" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 0.414602 0.290249 1.42843 0.153167\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + "\n", + " d1 -0.0238347 0.128507 -0.185475 0.852857\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "======================= ============ =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "======================= ============ =============\n", + " ols regression 0.00786473 0.00986593\n", + " IV estimation -0.0107097 0.0337652\n", + " selection on Z 0.414602 0.290249\n", + " selection on X and Z -0.0238347 0.128507\n", + "======================= ============ =============\n" + ] + } + ], + "source": [ + "using PrettyTables\n", + "table = zeros(4,2)\n", + "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", + "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", + "table[3,:] = Matrix(r_summary(lasso_IV_Z)[:,2:3]);\n", + "table[4, :] = Matrix(r_summary(lasso_IV_XZ)[:, 2:3]);\n", + "index = [\"ols regression\", \"IV estimation \", \"selection on Z\", \"selection on X and Z\"]\n", + "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Inference on Treatment Effects in a High-Dimensional Setting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3. Application: 401(k) plan participation." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://github.com/cran/hdm/raw/master/data/pension.rda\";\n", + "pension = load(download(url))[\"pension\"];\n", + "y = pension[:, \"tw\"];\n", + "d = pension[:, \"p401\"];\n", + "z = pension[:, \"e401\"];\n", + "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 9 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -2.07033\n", + " V 1 -0.237913\n", + " V 3 0.618819\n", + " V 4 0.846136\n", + " V 5 1.10569\n", + " V 6 1.34217\n", + " V 10 -0.33151\n", + " V 16 0.0382348\n", + " V 17 0.620232\n", + " V 18 0.335563\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATE\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 10180.1 1930.68 5.2728\n", + "========== ========= ==========\n" + ] + } + ], + "source": [ + "pension_ate = rlassoATE(X, d, y);\n", + "r_summary(pension_ate);" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 6 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.79587\n", + " V 1 -0.608675\n", + " V 5 0.622942\n", + " V 6 0.839653\n", + " V 16 0.199394\n", + " V 17 0.643286\n", + " V 18 0.374925\n", + "============ ============\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 12628.5 2944.43 4.28893\n", + "========== ========= ==========\n" + ] + } + ], + "source": [ + "pension_atet = rlassoATET(X, d, y);\n", + "r_summary(pension_atet);" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 10 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.58403\n", + " V 1 -0.329602\n", + " V 3 0.657641\n", + " V 4 0.836492\n", + " V 5 1.11528\n", + " V 6 1.21348\n", + " V 8 0.142622\n", + " V 10 -0.299557\n", + " V 16 0.0516196\n", + " V 17 1.03219\n", + " V 18 0.135758\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATE\n", + " Bootstrap: none\n", + " \n", + "========== ======== ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ======== ==========\n", + " 12992.1 2326.9 5.58344\n", + "========== ======== ==========\n" + ] + } + ], + "source": [ + "pension_late = rlassoLATE(X, d, y, z);\n", + "r_summary(pension_late);" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 5 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.25636\n", + " V 1 -0.714199\n", + " V 5 0.677564\n", + " V 6 0.794049\n", + " V 16 0.212127\n", + " V 17 1.05388\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 15323.2 3645.28 4.20357\n", + "========== ========= ==========\n" + ] + } + ], + "source": [ + "pension_latet = rlassoLATET(X, d, y, z);\n", + "r_summary(pension_latet);" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATE\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 10180.1 1930.68 5.2728\n", + "========== ========= ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 12628.5 2944.43 4.28893\n", + "========== ========= ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATE\n", + " Bootstrap: none\n", + " \n", + "========== ======== ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ======== ==========\n", + " 12992.1 2326.9 5.58344\n", + "========== ======== ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 15323.2 3645.28 4.20357\n", + "========== ========= ==========\n" + ] + } + ], + "source": [ + "using PrettyTables\n", + "table = zeros(4,2)\n", + "table[1,:] = round.(vec(r_summary(pension_ate)[:, 1:2]), digits = 2);\n", + "table[2,:] = round.(vec(r_summary(pension_atet)[:, 1:2]), digits = 2);\n", + "table[3,:] = round.(vec(r_summary(pension_late)[:, 1:2]), digits = 2);\n", + "table[4,:] = round.(vec(r_summary(pension_latet)[:, 1:2]), digits = 2);\n", + "index = [\"ATE\", \"ATET \", \"LATE\", \"LATET\"];" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======== ========== =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "======== ========== =============\n", + " ATE 10180.1 1930.68\n", + " ATET 12628.5 2944.43\n", + " LATE 12992.1 2326.9\n", + " LATET 15323.2 3645.28\n", + "======== ========== =============\n" + ] + } + ], + "source": [ + "pretty_table(hcat(index, table), show_row_number = false, \n", + " header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 10 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.58403\n", + " V 1 -0.329602\n", + " V 3 0.657641\n", + " V 4 0.836492\n", + " V 5 1.11528\n", + " V 6 1.21348\n", + " V 8 0.142622\n", + " V 10 -0.299557\n", + " V 16 0.0516196\n", + " V 17 1.03219\n", + " V 18 0.135758\n", + "============ ============\n", + "rlassologit" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 5 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.25636\n", + " V 1 -0.714199\n", + " V 5 0.677564\n", + " V 6 0.794049\n", + " V 16 0.212127\n", + " V 17 1.05388\n", + "============ ============\n", + "rlassologit" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 10 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.58403\n", + " V 1 -0.329602\n", + " V 3 0.657641\n", + " V 4 0.836492\n", + " V 5 1.11528\n", + " V 6 1.21348\n", + " V 8 0.142622\n", + " V 10 -0.299557\n", + " V 16 0.0516196\n", + " V 17 1.03219\n", + " V 18 0.135758\n", + "============ ============\n", + "rlassologit" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 5 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.25636\n", + " V 1 -0.714199\n", + " V 5 0.677564\n", + " V 6 0.794049\n", + " V 16 0.212127\n", + " V 17 1.05388\n", + "============ ============\n", + "rlassologit" + ] + } + ], + "source": [ + "pension_ate = rlassoATE(X, z, y);\n", + "pension_atet = rlassoATET(X, z, y);\n", + "pension_late = rlassoLATE(X, d, y, z);\n", + "pension_latet = rlassoLATET(X, d, y, z);" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATE\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 8491.99 1902.92 4.4626\n", + "========== ========= ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 10795.3 2568.13 4.20357\n", + "========== ========= ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATE\n", + " Bootstrap: none\n", + " \n", + "========== ======== ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ======== ==========\n", + " 12992.1 2326.9 5.58344\n", + "========== ======== ==========\n", + "\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATET\n", + " Bootstrap: none\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 15323.2 3645.28 4.20357\n", + "========== ========= ==========\n" + ] + } + ], + "source": [ + "table = zeros(4, 2)\n", + "table[1,:] = r_summary(pension_ate)[:, 1:2]\n", + "table[2,:] = r_summary(pension_atet)[:, 1:2]\n", + "table[3,:] = r_summary(pension_late)[:, 1:2]\n", + "table[4,:] = r_summary(pension_latet)[:, 1:2];" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======== ========== =============\n", + " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", + "======== ========== =============\n", + " ATE 8491.99 1902.92\n", + " ATET 10795.3 2568.13\n", + " LATE 12992.1 2326.9\n", + " LATET 15323.2 3645.28\n", + "======== ========== =============\n" + ] + } + ], + "source": [ + "index = [\"ATE\", \"ATET \", \"LATE\", \"LATET\"]\n", + "pretty_table(hcat(index, table), show_row_number = false, \n", + " header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. The Lasso Methods for Discovery of Significant Causes amongst Many Potential Causes, with Many Controls\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "using CSV\n", + "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/7_.csv\"\n", + "data = DataFrame(CSV.read(download(url), DataFrame));\n", + "n, p = size(data);\n", + "p1 = 20;\n", + "X = data[:,2:end]\n", + "Y = data[:,1];" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 4.51533 5.21356\n", + " \u001b[1m X2 \u001b[0m -0.313634 0.30429\n", + " \u001b[1m X3 \u001b[0m -0.351839 0.186217\n", + " \u001b[1m X4 \u001b[0m -0.253669 0.286815\n", + " \u001b[1m X5 \u001b[0m -0.275994 0.275686\n", + " \u001b[1m X6 \u001b[0m -0.320815 0.293574\n", + " \u001b[1m X7 \u001b[0m -0.225692 0.300383\n", + " \u001b[1m X8 \u001b[0m -0.0468017 0.473111\n", + " \u001b[1m X9 \u001b[0m -0.185952 0.389624\n", + " \u001b[1m X10 \u001b[0m -0.236704 0.26358\n", + " \u001b[1m X11 \u001b[0m -0.314153 0.208903\n", + " \u001b[1m X12 \u001b[0m -0.308581 0.265112\n", + " \u001b[1m X13 \u001b[0m -0.173571 0.376241\n", + " \u001b[1m X14 \u001b[0m -0.322822 0.38468\n", + " \u001b[1m X15 \u001b[0m -0.321303 0.312452\n", + " \u001b[1m X16 \u001b[0m -0.264319 0.330375\n", + " \u001b[1m X17 \u001b[0m -0.178576 0.41633\n", + " \u001b[1m X18 \u001b[0m -0.368883 0.046518\n", + " \u001b[1m X19 \u001b[0m -0.10678 0.393157\n", + " \u001b[1m X20 \u001b[0m -0.215219 0.254939\n" + ] + } + ], + "source": [ + "r_confint(rlassoEffects(X, Y, index = [1:p1;]), joint = true);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.8.1", + "language": "julia", + "name": "julia-1.8" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.8.1" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb index dfe2ea2..0cb3837 100644 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ b/tutorial/HDMjl_tutorial_1.ipynb @@ -23,39 +23,28 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n", - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#main`\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba#main`\u001b[39m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#main`\u001b[39m\n", - "┌ Info: We haven't cleaned this depot up for a bit, running Pkg.gc()...\n", - "└ @ Pkg C:\\buildbot\\worker\\package_win64\\build\\usr\\share\\julia\\stdlib\\v1.8\\Pkg\\src\\Pkg.jl:719\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m manifest files: 2 found" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`" ] }, { @@ -63,16 +52,7 @@ "output_type": "stream", "text": [ "\n", - "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m artifact files: 99 found\n", - "\u001b[32m\u001b[1m Active\u001b[22m\u001b[39m scratchspaces: 13 found\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Deleted\u001b[22m\u001b[39m 234 package installations (598.311 MiB)\n", - "\u001b[32m\u001b[1m Deleted\u001b[22m\u001b[39m 4 repos (9.640 MiB)\n" + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba#main`\u001b[39m\n" ] } ], @@ -157,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -170,12 +150,12 @@ "metadata": {}, "outputs": [], "source": [ - "import Pkg" + "import Pkg; using Pkg" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -217,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -233,13 +213,49 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "lasso_reg = HDMjl.rlasso(X, Y, post = false);" ] }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Dict{String, Any} with 17 entries:\n", + " \"covariates\" => 100\n", + " \"tss\" => 8466.4\n", + " \"dev\" => [8.56479; -6.50463; … ; -0.32845; -18.2301;;]\n", + " \"model\" => [0.970656 0.262456 … 1.86802 -0.460151; -0.979218 -0.022244…\n", + " \"loadings\" => [1.26761, 1.00655, 1.05993, 1.18077, 1.17881, 1.16112, 0.92…\n", + " \"sigma\" => 0.993422\n", + " \"main_tbl\" => Any[\"Intercept\" -0.0512442; \"V 1\" 4.81729; … ; \"V 99\" 0.0; …\n", + " \"lambda0\" => 36.9818\n", + " \"lambda\" => [46.8785, 37.2241, 39.198, 43.6669, 43.5945, 42.9403, 34.16…\n", + " \"intercept\" => -0.0512442\n", + " \"iter\" => 8\n", + " \"residuals\" => [-0.722921; 0.504304; … ; 0.484122; -0.34733;;]\n", + " \"rss\" => 97.7018\n", + " \"index\" => Bool[1, 1, 1, 0, 0, 0, 0, 0, 0, 0 … 0, 0, 0, 1, 0, 0, 0, …\n", + " \"beta\" => [4.81729, 4.74685, 4.97585, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…\n", + " \"options\" => Dict{String, Any}(\"intercept\"=>true, \"post\"=>false, \"meanx\"…\n", + " \"coefficients\" => [-0.0512442, 4.81729, 4.74685, 4.97585, 0.0, 0.0, 0.0, 0.0,…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lasso_reg" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -277,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -287,7 +303,45 @@ }, { "cell_type": "code", - "execution_count": 696, + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" + ] + } + ], + "source": [ + "Pkg.rm(\"HDMjl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 335, "metadata": {}, "outputs": [ { @@ -313,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 336, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 337, "metadata": {}, "outputs": [ { @@ -363,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 338, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 339, "metadata": {}, "outputs": [ { @@ -411,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 340, "metadata": {}, "outputs": [ { @@ -445,13 +499,13 @@ "rY = rY_1[\"residuals\"]\n", "rD_1 = HDMjl.rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), d);\n", "rD = rD_1[\"residuals\"]\n", - "partial_fit_postlasso = lm(hcat(ones(length(y)), rD), rY)\n", + "partial_fit_postlasso = lm(hcat(ones(length(y)), rD), vec(rY))\n", "DataFrame(Estimate = coef(partial_fit_postlasso)[2], Std_Error = stderror(partial_fit_postlasso)[2])" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 345, "metadata": {}, "outputs": [ { @@ -459,9 +513,9 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " 1 0.972739 0.0136868 71.0715 0.0\n", + " 1 0.97274 0.01369 71.05478 0.0 ***\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -475,28 +529,7 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m 1 \u001b[0m\n", - "\n", - " 0.973\n" - ] - } - ], - "source": [ - "HDMjl.r_print(Eff)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -543,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 1170, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -569,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 357, + "execution_count": 194, "metadata": {}, "outputs": [], "source": [ @@ -584,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 358, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [ @@ -593,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 360, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -614,7 +647,14 @@ }, { "cell_type": "code", - "execution_count": 1180, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -622,12 +662,111 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " \u001b[1m X1 \u001b[0m 2.94448 0.0881468 33.4043 1.1892e-244\n", - " \u001b[1m X2 \u001b[0m 3.04127 0.083891 36.2527 9.0141e-288\n", - " \u001b[1m X3 \u001b[0m 2.9754 0.0780394 38.127 4.58085e-318\n", - " \u001b[1m X50 \u001b[0m 0.0719553 0.0776455 0.926716 0.354074\n", + " \u001b[1m X1 \u001b[0m 2.94448 2.94448 0.08815 33.40306 0.0 ***\n", + " \u001b[1m X2 \u001b[0m 3.04127 3.04127 0.08389 36.25307 0.0 ***\n", + " \u001b[1m X3 \u001b[0m 2.9754 2.9754 0.07804 38.1266 0.0 ***\n", + " \u001b[1m X50 \u001b[0m 0.0719553 0.07196 0.07765 0.92672 0.35407\n", + "---\n", + "Signif. codes:\n", + "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + } + ], + "source": [ + "object = lasso_effects\n", + "k = length(object.coefficients)\n", + "table = zeros(k, 4)\n", + "table[:, 1] .= round.(object.coefficients, digits = 5)\n", + "table[:, 2] .= round.(object.se, digits = 5)\n", + "table[:, 3] .= round.(table[:, 1]./table[:, 2], digits = 5)\n", + "table[:, 4] .= round.(2 * cdf(Normal(), -abs.(table[:, 3])), digits = 5)\n", + "table1 = DataFrame(hcat([\"X$y\" for y = object.index], table), :auto)\n", + "table1 = rename(table1, [\"index\", \"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"])\n", + "table2 = string.(copy(table))\n", + "a = table2[:, 4];\n", + "b = string.(zeros(length(a)))\n", + "b[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .= a[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .* \" ***\"\n", + "b[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .= a[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .* \" **\"\n", + "b[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .= a[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .* \" *\"\n", + "b[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .= a[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .* \" .\"\n", + "b[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .= a[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .* \" \"\n", + "b = vec(b)\n", + "table2[:, 4] .= b\n", + "table2 = DataFrame(hcat(object.coefficients[:,1], table2), :auto)\n", + "table2 = rename(table2, [\" \", \"coeff.\", \"se.\", \"t-value\", \"p-value\"])\n", + "print(\"Estimates and significance testing of the effect of target variables\", \n", + " \"\\n\")\n", + "pretty_table(table2, show_row_number = false, header = [\" \",\"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"], tf = tf_borderless, row_names = [\"X$y\" for y = object.index])\n", + "print(\"---\", \"\\n\", \"Signif. codes:\",\"\\n\", \"0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\")\n", + "print(\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

4 rows × 5 columns

indexEstimate.Std. Errort valuePr(>|t|)
AnyAnyAnyAnyAny
1X12.944480.0881533.40310.0
2X23.041270.0838936.25310.0
3X32.97540.0780438.12660.0
4X500.071960.077650.926720.35407
" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccc}\n", + "\t& index & Estimate. & Std. Error & t value & Pr(>|t|)\\\\\n", + "\t\\hline\n", + "\t& Any & Any & Any & Any & Any\\\\\n", + "\t\\hline\n", + "\t1 & X1 & 2.94448 & 0.08815 & 33.4031 & 0.0 \\\\\n", + "\t2 & X2 & 3.04127 & 0.08389 & 36.2531 & 0.0 \\\\\n", + "\t3 & X3 & 2.9754 & 0.07804 & 38.1266 & 0.0 \\\\\n", + "\t4 & X50 & 0.07196 & 0.07765 & 0.92672 & 0.35407 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m4×5 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m index \u001b[0m\u001b[1m Estimate. \u001b[0m\u001b[1m Std. Error \u001b[0m\u001b[1m t value \u001b[0m\u001b[1m Pr(>|t|) \u001b[0m\n", + "\u001b[1m \u001b[0m│\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\n", + "─────┼─────────────────────────────────────────────────\n", + " 1 │ X1 2.94448 0.08815 33.4031 0.0\n", + " 2 │ X2 3.04127 0.08389 36.2531 0.0\n", + " 3 │ X3 2.9754 0.07804 38.1266 0.0\n", + " 4 │ X50 0.07196 0.07765 0.92672 0.35407" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "table1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimates and significance testing of the effect of target variables\n", + " \u001b[1m \u001b[0m \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + "\n", + " \u001b[1m X1 \u001b[0m 2.94448 2.94448 0.08815 33.40306 0.0 ***\n", + " \u001b[1m X2 \u001b[0m 3.04127 3.04127 0.08389 36.25307 0.0 ***\n", + " \u001b[1m X3 \u001b[0m 2.9754 2.9754 0.07804 38.1266 0.0 ***\n", + " \u001b[1m X50 \u001b[0m 0.0719553 0.07196 0.07765 0.92672 0.35407\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -948,27 +1087,6 @@ "ls_effect = DataFrame(Estimate = β, stderror = se);" ] }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 180, @@ -1111,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1120,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -1146,7 +1264,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -1180,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -1189,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 201, "metadata": {}, "outputs": [ { @@ -1210,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -1218,9 +1336,9 @@ "output_type": "stream", "text": [ "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", + " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", "\n", - " d1 0.845027 0.269926 3.13059 0.00174458\n", + " d1 0.84503 0.26993 3.13055 0.00174 **\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -1706,104 +1824,240 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 71, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: replacing module HDMjl.\n" + ] + }, { "data": { "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 1930.68\n", - " \"individual\" => [-30618.3, -57537.6, -71442.9, 21383.3, -2.32925e5, 3.40765e…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 10180.1\n", - " \"type\" => \"ATE\"" + "Main.HDMjl" ] }, "metadata": {}, "output_type": "display_data" } ], + "source": [ + "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], "source": [ "url = \"https://github.com/cran/hdm/raw/master/data/pension.rda\";\n", "pension = load(download(url))[\"pension\"];\n", "y = pension[:, \"tw\"];\n", "d = pension[:, \"p401\"];\n", "z = pension[:, \"e401\"];\n", - "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);\n", - "rlassoATE(X, d, y)" + "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 93, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 2944.43\n", - " \"individual\" => [-21536.4, -52877.2, -1.44867e5, -2739.29, -307741.0, 7.3912…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 12628.5\n", - " \"type\" => \"ATET\"" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 9 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -2.07033\n", + " V 1 -0.237913\n", + " V 3 0.618819\n", + " V 4 0.846136\n", + " V 5 1.10569\n", + " V 6 1.34217\n", + " V 10 -0.33151\n", + " V 16 0.0382348\n", + " V 17 0.620232\n", + " V 18 0.335563\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATE\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 10180.1 1930.68 5.2728\n", + "========== ========= ==========\n" + ] } ], "source": [ - "rlassoATET(X, d, y)" + "pension_ate = HDMjl.rlassoATE(X, d, y);\n", + "HDMjl.r_summary(pension_ate);" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 91, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 2326.9\n", - " \"individual\" => [-50526.8, -1.39158e5, -1.37102e5, 38508.0, -6.5644e5, 7.943…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 12992.1\n", - " \"type\" => \"LATE\"" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 6 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.79587\n", + " V 1 -0.608675\n", + " V 5 0.622942\n", + " V 6 0.839653\n", + " V 16 0.199394\n", + " V 17 0.643286\n", + " V 18 0.374925\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: ATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 12628.5 2944.43 4.28893\n", + "========== ========= ==========\n" + ] } ], "source": [ - "rlassoLATE(X, d, y, z)" + "pension_atet = HDMjl.rlassoATET(X, d, y);\n", + "HDMjl.r_summary(pension_atet);" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 89, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Dict{String, Any} with 5 entries:\n", - " \"se\" => 3645.28\n", - " \"individual\" => [-35580.5, -90558.0, -1.83628e5, -5303.13, -8.0766e5, 1.8866…\n", - " \"sample_size\" => 9915\n", - " \"te\" => 15323.2\n", - " \"type\" => \"LATET\"" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 10 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.58403\n", + " V 1 -0.329602\n", + " V 3 0.657641\n", + " V 4 0.836492\n", + " V 5 1.11528\n", + " V 6 1.21348\n", + " V 8 0.142622\n", + " V 10 -0.299557\n", + " V 16 0.0516196\n", + " V 17 1.03219\n", + " V 18 0.135758\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATE\n", + " Bootstrap: none\n", + " \n", + "========== ======== ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ======== ==========\n", + " 12992.1 2326.9 5.58344\n", + "========== ======== ==========\n" + ] + } + ], + "source": [ + "pension_late = HDMjl.rlassoLATE(X, d, y, z);\n", + "HDMjl.r_summary(pension_late);" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ------\n", + " Post-Lasso estimation: true\n", + " Intercept: true\n", + " Control: 0\n", + " Total number of variables: 19\n", + " Number of selected variables: 5 \n", + " ------\n", + " \n", + " \n", + "============ ============\n", + " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", + "============ ============\n", + " Intercept -1.25636\n", + " V 1 -0.714199\n", + " V 5 0.677564\n", + " V 6 0.794049\n", + " V 16 0.212127\n", + " V 17 1.05388\n", + "============ ============\n", + "rlassologit\n", + " Estimation and significance tesing of the treatment effect\n", + " Type: LATET\n", + " Bootstrap: none\n", + " \n", + "========== ========= ==========\n", + " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", + "========== ========= ==========\n", + " 15323.2 3645.28 4.20357\n", + "========== ========= ==========\n" + ] } ], "source": [ - "rlassoLATET(X, d, y, z)" + "pension_latet = HDMjl.rlassoLATET(X, d, y, z);\n", + "HDMjl.r_summary(pension_latet);" ] }, { diff --git a/tutorial/python.ipynb b/tutorial/python.ipynb deleted file mode 100644 index 711df80..0000000 --- a/tutorial/python.ipynb +++ /dev/null @@ -1,338 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pyreadr\n", - "import os\n", - "from urllib.request import urlopen\n", - "from sklearn import preprocessing\n", - "import patsy\n", - "\n", - "from numpy import loadtxt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "import hdmpy\n", - "import numpy as np\n", - "import random\n", - "import statsmodels.api as sm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import colors\n", - "from sklearn.tree import DecisionTreeRegressor\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.ensemble import GradientBoostingRegressor\n", - "from sklearn.linear_model import LassoCV\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.linear_model import RidgeCV, ElasticNetCV\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn import linear_model\n", - "import itertools\n", - "from pandas.api.types import is_string_dtype\n", - "from pandas.api.types import is_numeric_dtype\n", - "from pandas.api.types import is_categorical_dtype\n", - "from itertools import compress\n", - "import statsmodels.api as sm\n", - "import statsmodels.formula.api as smf\n", - "from sklearn.feature_selection import SelectFromModel\n", - "from statsmodels.tools import add_constant\n", - "from sklearn.linear_model import ElasticNet\n", - "import hdmpy\n", - "from scipy.stats import chi2\n", - "from sklearn.model_selection import KFold\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "from linearmodels.iv import IV2SLS\n", - "\n", - "import numpy as np\n", - "from statsmodels.api import add_constant\n", - "from linearmodels.datasets import mroz" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(29217, 23)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "link=\"https://github.com/cran/hdm/raw/master/data/cps2012.rda\"\n", - "response = urlopen(link)\n", - "content = response.read()\n", - "fhandle = open( 'cps2012.Rdata', 'wb')\n", - "fhandle.write(content)\n", - "fhandle.close()\n", - "result = pyreadr.read_r(\"cps2012.Rdata\")\n", - "os.remove(\"cps2012.Rdata\")\n", - "\n", - "# Extracting the data frame from rdata_read\n", - "cps2012 = result[ 'cps2012' ]\n", - "cps2012.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "x_formula = \" lnw ~ -1 +(widowed + divorced + separated + nevermarried + hsd08 + hsd911 + hsg + cg + ad + mw + so + we + exp1 + exp2 + exp3)**2\"\n", - "\n", - "y_model, x_dframe = patsy.dmatrices( x_formula, cps2012 , return_type='matrix')\n", - "\n", - "x = np.asarray( x_dframe , dtype = np.float64)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "y_model, x_dframe2 = patsy.dmatrices( x_formula, cps2012 , return_type='dataframe')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['widowed', 'divorced', 'separated', 'nevermarried', 'hsd08', 'hsd911',\n", - " 'hsg', 'cg', 'ad', 'mw',\n", - " ...\n", - " 'so:we', 'so:exp1', 'so:exp2', 'so:exp3', 'we:exp1', 'we:exp2',\n", - " 'we:exp3', 'exp1:exp2', 'exp1:exp3', 'exp2:exp3'],\n", - " dtype='object', length=120)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_dframe2.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "widowed\n", - "divorced\n", - "separated\n", - "nevermarried\n", - "hsd08\n", - "hsd911\n", - "hsg\n", - "cg\n", - "ad\n", - "mw\n", - "so\n", - "we\n", - "exp1\n", - "exp2\n", - "exp3\n", - "widowed:divorced\n", - "widowed:separated\n", - "widowed:nevermarried\n", - "widowed:hsd08\n", - "widowed:hsd911\n", - "widowed:hsg\n", - "widowed:cg\n", - "widowed:ad\n", - "widowed:mw\n", - "widowed:so\n", - "widowed:we\n", - "widowed:exp1\n", - "widowed:exp2\n", - "widowed:exp3\n", - "divorced:separated\n", - "divorced:nevermarried\n", - "divorced:hsd08\n", - "divorced:hsd911\n", - "divorced:hsg\n", - "divorced:cg\n", - "divorced:ad\n", - "divorced:mw\n", - "divorced:so\n", - "divorced:we\n", - "divorced:exp1\n", - "divorced:exp2\n", - "divorced:exp3\n", - "separated:nevermarried\n", - "separated:hsd08\n", - "separated:hsd911\n", - "separated:hsg\n", - "separated:cg\n", - "separated:ad\n", - "separated:mw\n", - "separated:so\n", - "separated:we\n", - "separated:exp1\n", - "separated:exp2\n", - "separated:exp3\n", - "nevermarried:hsd08\n", - "nevermarried:hsd911\n", - "nevermarried:hsg\n", - "nevermarried:cg\n", - "nevermarried:ad\n", - "nevermarried:mw\n", - "nevermarried:so\n", - "nevermarried:we\n", - "nevermarried:exp1\n", - "nevermarried:exp2\n", - "nevermarried:exp3\n", - "hsd08:hsd911\n", - "hsd08:hsg\n", - "hsd08:cg\n", - "hsd08:ad\n", - "hsd08:mw\n", - "hsd08:so\n", - "hsd08:we\n", - "hsd08:exp1\n", - "hsd08:exp2\n", - "hsd08:exp3\n", - "hsd911:hsg\n", - "hsd911:cg\n", - "hsd911:ad\n", - "hsd911:mw\n", - "hsd911:so\n", - "hsd911:we\n", - "hsd911:exp1\n", - "hsd911:exp2\n", - "hsd911:exp3\n", - "hsg:cg\n", - "hsg:ad\n", - "hsg:mw\n", - "hsg:so\n", - "hsg:we\n", - "hsg:exp1\n", - "hsg:exp2\n", - "hsg:exp3\n", - "cg:ad\n", - "cg:mw\n", - "cg:so\n", - "cg:we\n", - "cg:exp1\n", - "cg:exp2\n", - "cg:exp3\n", - "ad:mw\n", - "ad:so\n", - "ad:we\n", - "ad:exp1\n", - "ad:exp2\n", - "ad:exp3\n", - "mw:so\n", - "mw:we\n", - "mw:exp1\n", - "mw:exp2\n", - "mw:exp3\n", - "so:we\n", - "so:exp1\n", - "so:exp2\n", - "so:exp3\n", - "we:exp1\n", - "we:exp2\n", - "we:exp3\n", - "exp1:exp2\n", - "exp1:exp3\n", - "exp2:exp3\n" - ] - } - ], - "source": [ - "for col in x_dframe2.columns:\n", - " print(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.12 ('base')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index b9e7df9..6ae109e 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -28,29 +28,60 @@ "output_type": "stream", "text": [ "Warning message:\n", - "\"package 'hdm' was built under R version 4.2.1\"\n" + "\"package 'hdm' was built under R version 4.2.1\"\n", + "Warning message:\n", + "\"package 'glmnet' was built under R version 4.2.1\"\n", + "Loading required package: Matrix\n", + "\n", + "Loaded glmnet 4.1-4\n", + "\n" ] } ], "source": [ "library(hdm)\n", - "library(stats)" + "library(stats)\n", + "library(glmnet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1. Prediction in Linear Models using Approximate Sparsity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. A Joint Significance Test for Lasso Regression." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], - "source": [] + "source": [ + "set.seed(12345)\n", + "n = 100 #sample size\n", + "p = 100 # number of variables\n", + "s = 3 # nubmer of variables with non-zero coefficients\n", + "X = matrix(rnorm(n * p), ncol = p)\n", + "beta = c(rep(5, s), rep(0, p - s))\n", + "Y = X %*% beta + rnorm(n)\n", + "#data = data.frame(cbind(Y, X))\n", + "#write.csv(data,\"../data/3_2.csv\", row.names = FALSE)" + ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 29, "metadata": { "vscode": { "languageId": "r" @@ -58,49 +89,72 @@ }, "outputs": [ { - "data": { - "text/html": [ - "\n", - "
  1. 90
  2. 63
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 90\n", - "\\item 63\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 90\n", - "2. 63\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] 90 63" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Call:\n", + "rlasso.formula(formula = Y ~ X, post = FALSE)\n", + "\n", + "Post-Lasso Estimation: FALSE \n", + "\n", + "Total number of variables: 100\n", + "Number of selected variables: 11 \n", + "\n", + "Residuals: \n", + " Min 1Q Median 3Q Max \n", + "-2.09008 -0.45801 -0.01237 0.50291 2.25098 \n", + "\n", + " Estimate\n", + "(Intercept) 0.057\n", + "1 4.771\n", + "2 4.693\n", + "3 4.766\n", + "13 -0.045\n", + "15 -0.047\n", + "16 -0.005\n", + "19 -0.092\n", + "22 -0.027\n", + "40 -0.011\n", + "61 0.114\n", + "100 -0.025\n", + "\n", + "Residual standard error: 0.8039\n", + "Multiple R-squared: 0.9913\n", + "Adjusted R-squared: 0.9902\n", + "Joint significance test:\n", + " the sup score statistic for joint significance test is 64.02 with a p-value of 0\n" + ] } ], "source": [ - "data(GrowthData)\n", - "dim(GrowthData)\n", - "## [1] 90 63\n", - "y = GrowthData[, 1, drop = F]\n", - "d = GrowthData[, 3, drop = F]\n", - "x = as.matrix(GrowthData)[, -c(1, 2, 3)]\n", - "\n", - "# a = (rlasso(x = x, y = y)" + "lasso.reg = rlasso(Y ~ X, post = FALSE) # use lasso, not-Post-lasso\n", + "# lasso.reg = rlasso(X, Y, post=FALSE)\n", + "sum.lasso <- summary(lasso.reg, all = FALSE) # can also do print(lasso.reg, all=FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "set.seed(123)\n", + "yhat.lasso = predict(lasso.reg) #in-sample prediction\n", + "Xnew = matrix(rnorm(n * p), ncol = p) # new X\n", + "Ynew = Xnew %*% beta + rnorm(n) #new Y\n", + "yhat.lasso.new = predict(lasso.reg, newdata = Xnew) #out-of-sample prediction\n", + "# data = data.frame(cbind(Ynew, Xnew))\n", + "# write.csv(data,\"../data/3_2_2.csv\", row.names = FALSE)" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 31, "metadata": { "vscode": { "languageId": "r" @@ -108,56 +162,49 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "\n", - "Call:\n", - "rlasso.default(x = x, y = y)\n", - "\n", - "Coefficients:\n", - "(Intercept) bmp1l freeop freetar h65 hm65 \n", - " 0.05810 -0.07557 0.00000 0.00000 0.00000 0.00000 \n", - " hf65 p65 pm65 pf65 s65 sm65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " sf65 fert65 mort65 lifee065 gpop1 fert1 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " mort1 invsh41 geetot1 geerec1 gde1 govwb1 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " govsh41 gvxdxe41 high65 highm65 highf65 highc65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " highcm65 highcf65 human65 humanm65 humanf65 hyr65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " hyrm65 hyrf65 no65 nom65 nof65 pinstab1 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " pop65 worker65 pop1565 pop6565 sec65 secm65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " secf65 secc65 seccm65 seccf65 syr65 syrm65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " syrf65 teapri65 teasec65 ex1 im1 xr65 \n", - " 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 \n", - " tot1 \n", - " 0.00000 \n" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Call:\n", + "rlasso.formula(formula = Y ~ X, post = TRUE)\n", + "\n", + "(Intercept) 1 2 3 \n", + " 0.0341 4.9241 4.8579 4.9644 \n", + "\n" + ] } ], "source": [ - "rlasso(x = x, y = y)" + "post.lasso.reg = rlasso(Y ~ X, post = TRUE) #now use post-lasso\n", + "print(post.lasso.reg, all = FALSE) # or use summary(post.lasso.reg, all=FALSE)" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 32, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lasso MAE Post-lasso MAE \n", + " 0.88 0.78 \n" + ] + } + ], "source": [ - "a = 5" + "yhat.postlasso = predict(post.lasso.reg) #in-sample prediction\n", + "yhat.postlasso.new = predict(post.lasso.reg, newdata = Xnew) #out-of-sample prediction\n", + "MAE <- apply(cbind(abs(Ynew - yhat.lasso.new), abs(Ynew - yhat.postlasso.new)), 2,\n", + "mean)\n", + "names(MAE) <- c(\"lasso MAE\", \"Post-lasso MAE\")\n", + "print(MAE, digits = 2) # MAE for Lasso and Post-Lasso" ] }, { @@ -176,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 469, + "execution_count": 8, "metadata": { "vscode": { "languageId": "r" @@ -199,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 471, + "execution_count": 9, "metadata": { "vscode": { "languageId": "r" @@ -246,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 485, + "execution_count": 10, "metadata": { "vscode": { "languageId": "r" @@ -296,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 488, + "execution_count": 11, "metadata": { "vscode": { "languageId": "r" @@ -343,47 +390,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\n", + "
A matrix: 1 × 4 of type dbl
Estimate.Std. Errort valuePr(>|t|)
0.97273870.0136867771.071480
\n" + ], + "text/latex": [ + "A matrix: 1 × 4 of type dbl\n", + "\\begin{tabular}{llll}\n", + " Estimate. & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n", + "\\hline\n", + "\t 0.9727387 & 0.01368677 & 71.07148 & 0\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 1 × 4 of type dbl\n", + "\n", + "| Estimate. | Std. Error | t value | Pr(>|t|) |\n", + "|---|---|---|---|\n", + "| 0.9727387 | 0.01368677 | 71.07148 | 0 |\n", + "\n" + ], + "text/plain": [ + " Estimate. Std. Error t value Pr(>|t|)\n", + "[1,] 0.9727387 0.01368677 71.07148 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" } - }, - "outputs": [], - "source": [] + ], + "source": [ + "Eff = rlassoEffect(X[, -1], y, X[, 1], method = \"partialling out\")\n", + "summary(Eff)$coef" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" + "outputs": [ + { + "data": { + "text/html": [ + "
Estimate.
0.978074548374755
Std. Error
0.0141562427002124
\n" + ], + "text/latex": [ + "\\begin{description*}\n", + "\\item[Estimate.] 0.978074548374755\n", + "\\item[Std. Error] 0.0141562427002124\n", + "\\end{description*}\n" + ], + "text/markdown": [ + "Estimate.\n", + ": 0.978074548374755Std. Error\n", + ": 0.0141562427002124\n", + "\n" + ], + "text/plain": [ + " Estimate. Std. Error \n", + "0.97807455 0.01415624 " + ] + }, + "metadata": {}, + "output_type": "display_data" } - }, - "outputs": [], - "source": [] + ], + "source": [ + "Eff = rlassoEffect(X[, -1], y, X[, 1], method = \"double selection\")\n", + "summary(Eff)$coef[, 1:2]" + ] }, { "cell_type": "markdown", @@ -394,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 496, + "execution_count": 26, "metadata": { "vscode": { "languageId": "r" @@ -419,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 497, + "execution_count": 27, "metadata": { "vscode": { "languageId": "r" @@ -450,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 499, + "execution_count": 28, "metadata": { "vscode": { "languageId": "r" @@ -481,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 500, + "execution_count": 29, "metadata": { "vscode": { "languageId": "r" @@ -545,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 501, + "execution_count": 31, "metadata": { "vscode": { "languageId": "r" @@ -561,10 +662,10 @@ "\t2.5 %97.5 %\n", "\n", "\n", - "\tX1 2.72794773.1610075\n", - "\tX2 2.83712143.2454278\n", - "\tX3 2.78331763.1674903\n", - "\tX50-0.11545090.2593615\n", + "\tX1 2.73579493.1531603\n", + "\tX2 2.84452003.2380291\n", + "\tX3 2.79027893.1605290\n", + "\tX50-0.10865920.2525698\n", "\n", "\n" ], @@ -573,10 +674,10 @@ "\\begin{tabular}{r|ll}\n", " & 2.5 \\% & 97.5 \\%\\\\\n", "\\hline\n", - "\tX1 & 2.7279477 & 3.1610075\\\\\n", - "\tX2 & 2.8371214 & 3.2454278\\\\\n", - "\tX3 & 2.7833176 & 3.1674903\\\\\n", - "\tX50 & -0.1154509 & 0.2593615\\\\\n", + "\tX1 & 2.7357949 & 3.1531603\\\\\n", + "\tX2 & 2.8445200 & 3.2380291\\\\\n", + "\tX3 & 2.7902789 & 3.1605290\\\\\n", + "\tX50 & -0.1086592 & 0.2525698\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ @@ -585,18 +686,18 @@ "\n", "| | 2.5 % | 97.5 % |\n", "|---|---|---|\n", - "| X1 | 2.7279477 | 3.1610075 |\n", - "| X2 | 2.8371214 | 3.2454278 |\n", - "| X3 | 2.7833176 | 3.1674903 |\n", - "| X50 | -0.1154509 | 0.2593615 |\n", + "| X1 | 2.7357949 | 3.1531603 |\n", + "| X2 | 2.8445200 | 3.2380291 |\n", + "| X3 | 2.7902789 | 3.1605290 |\n", + "| X50 | -0.1086592 | 0.2525698 |\n", "\n" ], "text/plain": [ " 2.5 % 97.5 % \n", - "X1 2.7279477 3.1610075\n", - "X2 2.8371214 3.2454278\n", - "X3 2.7833176 3.1674903\n", - "X50 -0.1154509 0.2593615" + "X1 2.7357949 3.1531603\n", + "X2 2.8445200 3.2380291\n", + "X3 2.7902789 3.1605290\n", + "X50 -0.1086592 0.2525698" ] }, "metadata": {}, @@ -616,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 50, "metadata": { "vscode": { "languageId": "r" @@ -690,16 +791,17 @@ "divorced + separated + nevermarried + hsd08 + hsd911 + hsg + cg + ad + mw + so +\n", "we + exp1 + exp2 + exp3)^2, data = cps2012)\n", "dim(X)\n", + "## [1] 29217 136\n", "X <- X[, which(apply(X, 2, var) != 0)] # exclude all constant variables\n", "dim(X)\n", - "index.gender <- grep(\"female\", colnames(X))\n", + "## [1] 29217 116\n", "index.gender <- grep(\"female\", colnames(X))\n", "y <- cps2012$lnw" ] }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 51, "metadata": { "vscode": { "languageId": "r" @@ -743,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 52, "metadata": { "vscode": { "languageId": "r" @@ -759,22 +861,22 @@ "\t2.5 %97.5 %\n", "\n", "\n", - "\tfemale-0.29392716-0.01591940\n", - "\tfemale:widowed-0.13309530 0.40528626\n", - "\tfemale:divorced 0.07492960 0.19894917\n", - "\tfemale:separated-0.11641774 0.16302326\n", - "\tfemale:nevermarried 0.12938077 0.24432620\n", - "\tfemale:hsd08-0.37364347 0.42926409\n", - "\tfemale:hsd911-0.26870533 0.03003525\n", - "\tfemale:hsg-0.06502795 0.03924839\n", - "\tfemale:cg-0.04157113 0.06184824\n", - "\tfemale:ad-0.09569738 0.03476989\n", - "\tfemale:mw-0.05459343 0.05246655\n", - "\tfemale:so-0.06260432 0.04623763\n", - "\tfemale:we-0.06549766 0.05704541\n", - "\tfemale:exp1-0.01644965 0.02632017\n", - "\tfemale:exp2-0.28387860-0.03516005\n", - "\tfemale:exp3 0.01689389 0.06000727\n", + "\tfemale-0.29478224-0.01506433\n", + "\tfemale:widowed-0.13475121 0.40694218\n", + "\tfemale:divorced 0.07454815 0.19933062\n", + "\tfemale:separated-0.11727723 0.16388275\n", + "\tfemale:nevermarried 0.12902723 0.24467974\n", + "\tfemale:hsd08-0.37611300 0.43173362\n", + "\tfemale:hsd911-0.26962418 0.03095410\n", + "\tfemale:hsg-0.06534868 0.03956912\n", + "\tfemale:cg-0.04188922 0.06216633\n", + "\tfemale:ad-0.09609866 0.03517117\n", + "\tfemale:mw-0.05492271 0.05279584\n", + "\tfemale:so-0.06293909 0.04657240\n", + "\tfemale:we-0.06587457 0.05742232\n", + "\tfemale:exp1-0.01658120 0.02645172\n", + "\tfemale:exp2-0.28464359-0.03439506\n", + "\tfemale:exp3 0.01676128 0.06013987\n", "\n", "\n" ], @@ -783,22 +885,22 @@ "\\begin{tabular}{r|ll}\n", " & 2.5 \\% & 97.5 \\%\\\\\n", "\\hline\n", - "\tfemale & -0.29392716 & -0.01591940\\\\\n", - "\tfemale:widowed & -0.13309530 & 0.40528626\\\\\n", - "\tfemale:divorced & 0.07492960 & 0.19894917\\\\\n", - "\tfemale:separated & -0.11641774 & 0.16302326\\\\\n", - "\tfemale:nevermarried & 0.12938077 & 0.24432620\\\\\n", - "\tfemale:hsd08 & -0.37364347 & 0.42926409\\\\\n", - "\tfemale:hsd911 & -0.26870533 & 0.03003525\\\\\n", - "\tfemale:hsg & -0.06502795 & 0.03924839\\\\\n", - "\tfemale:cg & -0.04157113 & 0.06184824\\\\\n", - "\tfemale:ad & -0.09569738 & 0.03476989\\\\\n", - "\tfemale:mw & -0.05459343 & 0.05246655\\\\\n", - "\tfemale:so & -0.06260432 & 0.04623763\\\\\n", - "\tfemale:we & -0.06549766 & 0.05704541\\\\\n", - "\tfemale:exp1 & -0.01644965 & 0.02632017\\\\\n", - "\tfemale:exp2 & -0.28387860 & -0.03516005\\\\\n", - "\tfemale:exp3 & 0.01689389 & 0.06000727\\\\\n", + "\tfemale & -0.29478224 & -0.01506433\\\\\n", + "\tfemale:widowed & -0.13475121 & 0.40694218\\\\\n", + "\tfemale:divorced & 0.07454815 & 0.19933062\\\\\n", + "\tfemale:separated & -0.11727723 & 0.16388275\\\\\n", + "\tfemale:nevermarried & 0.12902723 & 0.24467974\\\\\n", + "\tfemale:hsd08 & -0.37611300 & 0.43173362\\\\\n", + "\tfemale:hsd911 & -0.26962418 & 0.03095410\\\\\n", + "\tfemale:hsg & -0.06534868 & 0.03956912\\\\\n", + "\tfemale:cg & -0.04188922 & 0.06216633\\\\\n", + "\tfemale:ad & -0.09609866 & 0.03517117\\\\\n", + "\tfemale:mw & -0.05492271 & 0.05279584\\\\\n", + "\tfemale:so & -0.06293909 & 0.04657240\\\\\n", + "\tfemale:we & -0.06587457 & 0.05742232\\\\\n", + "\tfemale:exp1 & -0.01658120 & 0.02645172\\\\\n", + "\tfemale:exp2 & -0.28464359 & -0.03439506\\\\\n", + "\tfemale:exp3 & 0.01676128 & 0.06013987\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ @@ -807,42 +909,42 @@ "\n", "| | 2.5 % | 97.5 % |\n", "|---|---|---|\n", - "| female | -0.29392716 | -0.01591940 |\n", - "| female:widowed | -0.13309530 | 0.40528626 |\n", - "| female:divorced | 0.07492960 | 0.19894917 |\n", - "| female:separated | -0.11641774 | 0.16302326 |\n", - "| female:nevermarried | 0.12938077 | 0.24432620 |\n", - "| female:hsd08 | -0.37364347 | 0.42926409 |\n", - "| female:hsd911 | -0.26870533 | 0.03003525 |\n", - "| female:hsg | -0.06502795 | 0.03924839 |\n", - "| female:cg | -0.04157113 | 0.06184824 |\n", - "| female:ad | -0.09569738 | 0.03476989 |\n", - "| female:mw | -0.05459343 | 0.05246655 |\n", - "| female:so | -0.06260432 | 0.04623763 |\n", - "| female:we | -0.06549766 | 0.05704541 |\n", - "| female:exp1 | -0.01644965 | 0.02632017 |\n", - "| female:exp2 | -0.28387860 | -0.03516005 |\n", - "| female:exp3 | 0.01689389 | 0.06000727 |\n", + "| female | -0.29478224 | -0.01506433 |\n", + "| female:widowed | -0.13475121 | 0.40694218 |\n", + "| female:divorced | 0.07454815 | 0.19933062 |\n", + "| female:separated | -0.11727723 | 0.16388275 |\n", + "| female:nevermarried | 0.12902723 | 0.24467974 |\n", + "| female:hsd08 | -0.37611300 | 0.43173362 |\n", + "| female:hsd911 | -0.26962418 | 0.03095410 |\n", + "| female:hsg | -0.06534868 | 0.03956912 |\n", + "| female:cg | -0.04188922 | 0.06216633 |\n", + "| female:ad | -0.09609866 | 0.03517117 |\n", + "| female:mw | -0.05492271 | 0.05279584 |\n", + "| female:so | -0.06293909 | 0.04657240 |\n", + "| female:we | -0.06587457 | 0.05742232 |\n", + "| female:exp1 | -0.01658120 | 0.02645172 |\n", + "| female:exp2 | -0.28464359 | -0.03439506 |\n", + "| female:exp3 | 0.01676128 | 0.06013987 |\n", "\n" ], "text/plain": [ " 2.5 % 97.5 % \n", - "female -0.29392716 -0.01591940\n", - "female:widowed -0.13309530 0.40528626\n", - "female:divorced 0.07492960 0.19894917\n", - "female:separated -0.11641774 0.16302326\n", - "female:nevermarried 0.12938077 0.24432620\n", - "female:hsd08 -0.37364347 0.42926409\n", - "female:hsd911 -0.26870533 0.03003525\n", - "female:hsg -0.06502795 0.03924839\n", - "female:cg -0.04157113 0.06184824\n", - "female:ad -0.09569738 0.03476989\n", - "female:mw -0.05459343 0.05246655\n", - "female:so -0.06260432 0.04623763\n", - "female:we -0.06549766 0.05704541\n", - "female:exp1 -0.01644965 0.02632017\n", - "female:exp2 -0.28387860 -0.03516005\n", - "female:exp3 0.01689389 0.06000727" + "female -0.29478224 -0.01506433\n", + "female:widowed -0.13475121 0.40694218\n", + "female:divorced 0.07454815 0.19933062\n", + "female:separated -0.11727723 0.16388275\n", + "female:nevermarried 0.12902723 0.24467974\n", + "female:hsd08 -0.37611300 0.43173362\n", + "female:hsd911 -0.26962418 0.03095410\n", + "female:hsg -0.06534868 0.03956912\n", + "female:cg -0.04188922 0.06216633\n", + "female:ad -0.09609866 0.03517117\n", + "female:mw -0.05492271 0.05279584\n", + "female:so -0.06293909 0.04657240\n", + "female:we -0.06587457 0.05742232\n", + "female:exp1 -0.01658120 0.02645172\n", + "female:exp2 -0.28464359 -0.03439506\n", + "female:exp3 0.01676128 0.06013987" ] }, "metadata": {}, @@ -854,17 +956,6 @@ "joint.CI" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -874,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 53, "metadata": { "vscode": { "languageId": "r" @@ -923,20 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "x1 = as.matrix(GrowthData)[, -c(1, 3)]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 54, "metadata": { "vscode": { "languageId": "r" @@ -953,18 +1031,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "[1,] -0.04981 0.01394 -3.574 0.000351 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dX = as.matrix(cbind(d, X))\n", + "lasso.effect = rlassoEffect(x = X, y = y, d = d, method = \"partialling out\")\n", + "summary(lasso.effect)" + ] }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 56, "metadata": { "vscode": { "languageId": "r" @@ -974,1218 +1071,12 @@ { "data": { "text/plain": [ - "$qr\n", - " (Intercept) gdpsh465 bmp1l freeop freetar\n", - "1 -9.4868330 -73.076189875 -1.600871443 -2.088070386 -0.2688004527\n", - "2 0.1054093 8.454532979 -1.121146644 0.228693062 -0.1352109185\n", - "3 0.1054093 -0.153543593 -2.065490021 0.143035910 -0.0255253643\n", - "4 0.1054093 0.003745550 0.007092746 -0.652699019 0.0100417362\n", - "5 0.1054093 0.051397832 -0.038251710 0.130489097 -0.1532203197\n", - "6 0.1054093 0.044713582 -0.117878028 0.046966438 -0.0803360728\n", - 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" secc65 seccm65 seccf65 syr65 syrm65\n", - "1 -61.134205819 -63.589187376 -5.855062e+01 -8.655997230 -9.917956835\n", - "2 38.360442017 32.049327767 4.447253e+01 5.273209193 5.038918662\n", - "3 -3.301549504 0.380464618 -6.663195e+00 -0.374333213 -0.151442745\n", - "4 12.098414993 8.299703494 1.573404e+01 1.575981119 1.358621822\n", - "5 2.349672850 1.542057371 3.253705e+00 0.473125332 0.494219591\n", - "6 22.002209058 19.855121452 2.397356e+01 3.299012709 3.235050421\n", - "7 -6.422450095 -5.785546972 -7.052492e+00 -1.025985133 -1.244420576\n", - "8 -3.194900598 -0.998270468 -5.411513e+00 -0.398115587 -0.067496484\n", - "9 1.600207271 4.081106887 -6.814949e-01 -0.055922505 0.173571435\n", - "10 0.452369194 -1.942733383 2.868479e+00 0.019230150 -0.217094702\n", - "11 -1.707086974 -2.956609769 -3.787704e-01 -0.179841772 -0.240334387\n", - "12 12.801095492 13.499092188 1.188272e+01 1.895785656 2.165963665\n", - "13 1.883430811 4.989267112 -1.208801e+00 0.266618382 0.631283866\n", - "14 -2.215139822 -1.934381236 -2.459529e+00 -0.487956941 -0.468517457\n", - 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"9 -0.254052063 5.96547142 14.92124959 0.0368021667 0.066521239\n", - "10 0.266830970 2.45321868 -6.09405109 -0.0130620676 -0.016715912\n", - "11 -0.099822506 8.24781859 2.04559092 -0.0733142696 -0.073766489\n", - "12 1.624476896 -15.15221026 13.36741589 0.0247989415 0.111457309\n", - "13 -0.098977611 2.78989765 9.22774680 0.0991070868 0.066629000\n", - "14 -0.502319099 12.79726688 10.44669310 -0.0688744783 -0.061719081\n", - "15 -0.329355712 -4.09480518 -4.88115991 -0.0126737281 -0.001031588\n", - "16 0.004235971 2.32106337 -6.71245886 -0.1089000609 -0.083144173\n", - "17 -0.259943526 -7.73909137 -9.52164770 -0.2689706718 -0.162334003\n", - "18 -0.374323689 9.06230138 14.05743411 0.2833043261 0.245509381\n", - "19 0.377279955 7.63915889 11.67828772 0.0423796977 0.099904781\n", - "20 -0.532709361 -1.27067572 12.55873555 0.0198300858 0.052116419\n", - "21 -0.535405559 1.91337882 -1.30663459 0.2548786909 0.307343123\n", - "22 1.479747424 3.94412015 1.72792395 0.1966148407 0.151797184\n", - "23 0.018622638 -13.52803026 -9.60459648 0.1920704949 0.211193147\n", - "24 -0.657830934 0.68286302 4.36865602 0.0394605409 0.054508592\n", - "25 -0.448511453 -4.64334328 -1.23635302 -0.0202190626 -0.027707513\n", - "26 -0.686636588 -0.63945647 -6.50553268 0.0005684675 0.006514924\n", - "27 0.408778835 8.06927409 3.75138698 0.0693075080 0.089323326\n", - "28 -1.589780183 -2.95657324 -10.94249886 -0.0294843980 -0.083621685\n", - "29 0.628920582 -4.93919821 -0.24086058 0.0816521336 0.106968387\n", - "30 -0.443193981 -3.98976722 7.16376999 -0.2649201380 -0.287539921\n", - "31 0.560785561 5.16405854 9.58118136 0.0366758829 0.034556213\n", - "32 0.653917520 10.63861734 -4.70289312 -0.0212434835 -0.069291875\n", - "33 0.021044382 -0.82016107 3.01469103 0.0218968701 0.026688450\n", - "34 1.007590194 -6.06083643 -3.71389648 -0.0222882146 -0.005607480\n", - "35 -0.065037223 -2.03083043 2.00199293 0.1515844592 0.121955071\n", - "36 -0.062671943 4.97153628 0.66016822 -0.1121564458 -0.105529118\n", - "37 -0.196001414 1.52277750 8.60913643 -0.0428698658 -0.055185706\n", - "38 0.309390832 -9.21779651 -9.03530909 0.0103944774 0.010771517\n", - "39 -0.352862831 -14.14607734 -2.01023166 0.0013082112 -0.020820848\n", - "40 -0.455720955 -8.62534705 -7.19517043 -0.1978712945 -0.211954039\n", - "41 -0.358674666 -2.15519605 1.60398892 -0.1612560105 -0.214413703\n", - "42 -0.131619237 -8.03779603 -2.19081537 -0.0649569869 -0.046886229\n", - "43 0.337051578 -14.64469925 -4.00404893 0.0342284599 0.014505024\n", - "44 0.262776019 -2.08096018 -0.73943482 0.0942811485 0.120218344\n", - "45 -0.214416083 2.94873678 -1.67971051 -0.0708927733 -0.018961358\n", - "46 0.021512447 9.76614279 5.85230872 0.1556862888 0.177900944\n", - "47 -0.192646261 -7.89093136 -4.79393374 -0.0574188460 -0.061011185\n", - "48 -1.519596570 0.95595397 10.57868918 -0.0769172913 -0.090505050\n", - "49 0.177731136 3.78654645 -11.12478917 -0.0119420658 -0.024584966\n", - "50 -0.086847301 2.87570780 -4.21277484 0.0901218549 0.097397982\n", - "51 -0.427091282 -8.24492180 -3.40009089 0.0789424378 0.040232408\n", - "52 -0.163526248 0.14349772 2.02796581 -0.0970628979 -0.091527129\n", - "53 0.125913844 7.35508476 6.69771648 -0.1025013433 -0.042331120\n", - "54 -0.276837989 3.88508078 12.29717003 -0.0301376809 -0.041429144\n", - "55 0.087297242 10.99914397 -1.97264221 -0.1478756688 -0.116405545\n", - "56 -0.008898787 -0.96956047 4.48415119 -0.0351721187 -0.092719293\n", - "57 -0.057439247 43.59470890 3.19584058 -0.1311687444 -0.126171417\n", - "58 0.048582174 -0.18037319 27.16345190 0.0069419397 0.036797877\n", - "59 0.113563238 0.05807287 -0.42684154 -0.3306730439 -0.290446343\n", - "60 0.004611041 -0.11616415 0.16937312 0.0484432087 -0.124241742\n", - "61 0.217949866 -0.10543634 -0.22706017 -0.0971621141 0.179392675\n", - "62 -0.004381920 0.14785173 -0.04304829 0.1506222768 0.097778089\n", - "63 -0.067867268 0.08762196 0.18278372 -0.1950520840 -0.024321161\n", - "64 -0.001185829 -0.10442221 0.06130532 0.0043373759 -0.021444073\n", - "65 0.031682798 0.25694399 0.06298181 0.3529909080 -0.308497159\n", - "66 0.229348878 0.20235928 0.08394355 0.0855042732 -0.110316642\n", - "67 -0.108595879 0.22449263 -0.01036602 0.0489376883 -0.110707032\n", - "68 -0.071649995 0.05898440 0.02465143 -0.2412408128 0.035558683\n", - "69 0.236314869 0.12688433 0.37194525 0.3308741765 -0.339251756\n", - "70 -0.408010721 -0.23127877 -0.04192515 0.2597904453 -0.052184592\n", - "71 0.008762600 -0.22911941 -0.24564258 -0.1934423803 0.175746225\n", - "72 0.099238501 -0.03285283 0.06254682 -0.0092047727 0.134517088\n", - "73 -0.059439681 0.07857056 0.02558644 -0.0679961204 0.389826697\n", - "74 0.023710857 -0.34352832 -0.20301612 -0.1675632462 -0.106750342\n", - "75 -0.053036398 -0.14375136 0.03999355 -0.1795060485 -0.037454214\n", - "76 -0.237943532 -0.06417342 -0.02322564 -0.1002464668 0.097911182\n", - "77 0.013926451 -0.44307551 0.26475270 0.0712828788 -0.078534063\n", - "78 -0.149411120 -0.19424113 -0.05823380 0.3252194583 0.482117459\n", - "79 0.029502364 -0.18271951 0.04203953 -0.3410929841 0.166579840\n", - "80 0.221725373 -0.11170011 -0.11935878 -0.2006304836 0.003073297\n", - "81 -0.067463339 -0.11405928 0.05982189 -0.1463338454 0.000739378\n", - "82 -0.196733413 0.03829286 0.05781654 -0.0173398770 -0.198956407\n", - "83 0.055175392 -0.09239349 -0.00745717 -0.1389315104 0.237971024\n", - "84 -0.028461113 -0.05237897 0.18723342 -0.1183235724 0.214307181\n", - "85 -0.013834761 0.22052404 0.20510043 0.2680730894 0.149730897\n", - "86 0.070574653 -0.22252647 -0.01283793 0.1330617719 0.019715402\n", - "87 -0.136961384 0.07591351 0.19438207 -0.0135451142 -0.079182966\n", - "88 0.156958964 -0.04668618 -0.11333104 0.0603280598 0.137076553\n", - "89 0.540482756 -0.22925715 0.28285847 0.0243535536 -0.045925088\n", - "90 0.087083718 -0.03812750 -0.10261322 -0.1215903722 -0.029880196\n", - " xr65 tot1\n", - "1 -4.047449e+02 -0.0876208110\n", - "2 7.601391e+01 0.0214098619\n", - "3 1.198082e+02 0.1237498721\n", - "4 -3.194284e+01 -0.0298193511\n", - "5 1.132250e+02 -0.0841096261\n", - "6 7.134700e+01 -0.0083436146\n", - "7 -1.880401e+02 0.0470789784\n", - "8 1.169947e+02 0.1027001379\n", - "9 2.186356e+02 0.0399114435\n", - "10 4.096243e+01 -0.0926564188\n", - "11 1.349334e+02 -0.0791874995\n", - "12 1.415715e+02 -0.1010651324\n", - "13 1.476546e+02 -0.0776988203\n", - "14 6.481791e+00 0.0491237094\n", - "15 1.951325e+02 -0.1405162742\n", - "16 2.336898e+01 0.0255105614\n", - "17 2.961208e+01 0.1053639790\n", - "18 -8.915431e+01 0.0571556163\n", - "19 -1.411167e+02 0.0637183305\n", - "20 9.498295e+01 0.0937255596\n", - "21 2.235338e+01 -0.0154763875\n", - "22 -1.528784e+01 0.0473006330\n", - "23 -1.050934e+02 0.0378960986\n", - "24 -1.271212e+02 0.0158339871\n", - "25 2.201622e+02 0.0853364136\n", - "26 -5.023650e+01 0.0328028034\n", - "27 1.672008e+02 0.0540906397\n", - "28 1.080012e+02 0.0573304826\n", - "29 -2.348785e+02 0.0003806715\n", - "30 -2.697384e+01 -0.0238564005\n", - "31 -1.359995e+02 -0.0230239180\n", - "32 1.110270e+02 -0.0746532370\n", - "33 -1.846731e+01 -0.0105340857\n", - "34 -1.187684e+02 0.0453787585\n", - "35 7.951819e+01 0.0786698551\n", - "36 5.153641e+01 0.0748117481\n", - "37 2.232648e+02 0.0274857013\n", - "38 2.390828e+01 -0.0948364721\n", - "39 -1.863737e+02 0.1179380793\n", - "40 1.804837e+02 -0.0380252608\n", - "41 1.154490e+02 -0.0577416340\n", - "42 1.941379e+02 0.0021627929\n", - "43 -4.118980e+01 -0.0049553968\n", - "44 -7.238547e+01 0.0042072387\n", - "45 -3.651178e+01 0.0607648496\n", - "46 -1.325881e+02 0.0435690653\n", - "47 -6.794543e+01 0.0205767249\n", - "48 -1.694681e+02 0.0233578443\n", - "49 -1.999883e+02 -0.0695763507\n", - "50 -1.682411e+02 0.0549548090\n", - "51 -6.255889e+01 -0.0503282470\n", - "52 2.569037e+02 0.0055446388\n", - "53 6.834770e+01 0.0412674928\n", - "54 -1.242214e+02 -0.1070727560\n", - "55 3.889938e+01 -0.0251160238\n", - "56 2.532362e+01 0.0608478945\n", - "57 5.633900e+01 0.0223452620\n", - "58 3.661366e-01 0.0359399777\n", - "59 5.964255e+01 0.0372667818\n", - "60 -1.707232e+01 -0.0381485946\n", - "61 -6.076580e+02 0.1045281348\n", - "62 1.277865e-01 0.2730074340\n", - "63 -1.743777e-01 0.0788193886\n", - "64 6.643046e-02 0.0044725168\n", - "65 -5.571258e-02 -0.0416495538\n", - "66 1.147801e-02 -0.4349524105\n", - "67 1.573729e-01 0.2618719444\n", - "68 -1.285582e-01 -0.0195693502\n", - "69 -3.690994e-01 -0.1510638329\n", - "70 6.646274e-02 -0.1914973719\n", - "71 -7.672525e-02 -0.0948002968\n", - "72 -9.109612e-02 -0.1250681238\n", - "73 -1.071318e-01 0.0088343517\n", - "74 3.278600e-01 -0.1304808147\n", - "75 -4.560051e-02 0.1306832744\n", - "76 -5.903422e-02 0.1703357127\n", - "77 1.118965e-01 0.0443054051\n", - "78 1.662882e-01 -0.0840935552\n", - "79 -3.707314e-02 -0.4057850088\n", - "80 -6.246543e-04 -0.0604992325\n", - "81 -5.593053e-02 0.1781689586\n", - "82 -7.887807e-03 0.0772642534\n", - "83 7.272069e-02 0.0776684262\n", - "84 8.657033e-02 0.0130311476\n", - "85 4.884930e-01 0.0850039306\n", - "86 -1.121593e-01 -0.0511207957\n", - "87 -2.629825e-01 0.4294171510\n", - "88 -4.419028e-03 0.0674216577\n", - "89 2.659727e-01 0.0950310649\n", - "90 4.198220e-01 0.3963425921\n", - "attr(,\"assign\")\n", - " [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n", - "[26] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n", - "[51] 50 51 52 53 54 55 56 57 58 59 60 61\n", - "\n", - "$qraux\n", - " [1] 1.105409 1.090738 1.019010 1.056067 1.008329 1.032496 1.027254 1.032528\n", - " [9] 1.032105 1.083540 1.045379 1.131395 1.067304 1.009168 1.080607 1.212556\n", - "[17] 1.108565 1.019370 1.146787 1.195271 1.275848 1.051124 1.020372 1.075090\n", - "[25] 1.052578 1.107482 1.400304 1.061337 1.224636 1.044238 1.142150 1.141488\n", - "[33] 1.209432 1.010573 1.026202 1.108806 1.095007 1.010618 1.379033 1.150449\n", - "[41] 1.050020 1.343433 1.035454 1.036704 1.126785 1.052998 1.262637 1.189651\n", - "[49] 1.166318 1.034950 1.198419 1.010931 1.081932 1.095029 1.007807 1.308323\n", - "[57] 1.017141 1.351455 1.119492 1.189273 1.007287 1.019961\n", - "\n", - "$pivot\n", - " [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25\n", - "[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50\n", - "[51] 51 52 53 54 55 56 57 58 59 60 61 62\n", - "\n", - "$tol\n", - "[1] 1e-07\n", - "\n", - "$rank\n", - "[1] 62\n", - "\n", - "attr(,\"class\")\n", - "[1] \"qr\"" + "[1] \"Estimates and significance testing of the effect of target variables\"\n", + " Estimate. Std. Error t value Pr(>|t|) \n", + "gdpsh465 -0.05001 0.01579 -3.167 0.00154 **\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n" ] }, "metadata": {}, @@ -2193,452 +1084,13 @@ } ], "source": [ - "stats:::qr.lm(object)" + "doublesel.effect = rlassoEffect(x = X, y = y, d = d, method = \"double selection\")\n", + "summary(doublesel.effect)" ] }, { "cell_type": "code", - "execution_count": 340, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "correlation = FALSE; symbolic.cor = FALSE\n", - "\n", - "z <- object\n", - "p <- z$rank\n", - "rdf <- z$df.residual\n", - "if (p == 0) {\n", - " r <- z$residuals\n", - " n <- length(r)\n", - " w <- z$weights\n", - " if (is.null(w)) {\n", - " rss <- sum(r^2)\n", - " } else {\n", - " rss <- sum(w * r^2)\n", - " r <- sqrt(w) * r\n", - " }\n", - " resvar <- rss/rdf\n", - " ans <- z[c(\"call\", \"terms\", if(!is.null(z$weights)) \"weights\")]\n", - " class(ans) <- \"summary.lm\"\n", - " ans$aliased <- is.na(coef(object)) # used in print method\n", - " ans$residuals <- r\n", - " ans$df <- c(0L, n, length(ans$aliased))\n", - " ans$coefficients <- matrix(NA, 0L, 4L)\n", - " dimnames(ans$coefficients) <-\n", - " list(NULL, c(\"Estimate\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"))\n", - " ans$sigma <- sqrt(resvar)\n", - " ans$r.squared <- ans$adj.r.squared <- 0\n", - " return(ans)\n", - "}\n", - "if (is.null(z$terms))\n", - "stop(\"invalid 'lm' object: no 'terms' component\")\n", - "if(!inherits(object, \"lm\"))\n", - "warning(\"calling summary.lm() ...\")\n", - "Qr <- stats:::qr.lm(object)\n", - "n <- NROW(Qr$qr)\n", - "if(is.na(z$df.residual) || n - p != z$df.residual)\n", - " warning(\"residual degrees of freedom in object suggest this is not an \\\"lm\\\" fit\")\n", - "## do not want missing values substituted here\n", - "r <- z$residuals\n", - "f <- z$fitted.values\n", - "w <- z$weights\n", - "if (is.null(w)) {\n", - " mss <- if (attr(z$terms, \"intercept\"))\n", - " sum((f - mean(f))^2) else sum(f^2)\n", - " rss <- sum(r^2)\n", - "} else {\n", - " mss <- if (attr(z$terms, \"intercept\")) {\n", - " m <- sum(w * f /sum(w))\n", - " sum(w * (f - m)^2)\n", - " } else sum(w * f^2)\n", - " rss <- sum(w * r^2)\n", - " r <- sqrt(w) * r\n", - "}\n", - "resvar <- rss/rdf\n", - "## see thread at https://stat.ethz.ch/pipermail/r-help/2014-March/367585.html\n", - "if (is.finite(resvar) &&\n", - " resvar < (mean(f)^2 + var(f)) * 1e-30) # a few times .Machine$double.eps^2\n", - " warning(\"essentially perfect fit: summary may be unreliable\")\n", - "p1 <- 1L:p\n", - "R <- chol2inv(Qr$qr[p1, p1, drop = FALSE])\n", - "se <- sqrt(diag(R) * resvar)" - ] - }, - { - "cell_type": "code", - "execution_count": 336, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "object = ls.effect\n", - "z <- object\n", - "p <- z$rank\n", - "rdf <- z$df.residual\n", - "r <- z$residuals" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 62 × 4 of type dbl
EstimateStd. Errort valuePr(>|t|)
(Intercept) 0.2471608930.78450163 0.315054660.755056170
gdpsh465-0.0093779890.02988773-0.313773910.756018518
bmp1l-0.0688626790.03253065-2.116855130.043289718
freeop 0.0800689740.20786400 0.385198850.703000838
freetar-0.4889626050.41816285-1.169311430.252136477
h65-2.3620986380.85729167-2.755303380.010192435
hm65 0.7071434000.52314511 1.351715600.187285919
hf65 1.6934484250.50318881 3.365433370.002232683
p65 0.2655266950.16429407 1.616167290.117271229
pm65 0.1369526260.15121749 0.905666570.372840111
pf65-0.3312669480.16512299-2.006183030.054583866
s65 0.0390793060.18552248 0.210644590.834690082
sm65-0.0306685070.11679396-0.262586420.794791807
sf65-0.1799172800.11809892-1.523445560.138863011
fert65 0.0068808340.02705142 0.254361280.801075252
mort65-0.2334545030.81742198-0.285598520.777287824
lifee065-0.0149144730.19333559-0.077142920.939058459
gpop1 0.9701845791.81220835 0.535360390.596627593
fert1 0.0088381890.03504148 0.252220770.802712696
mort1 0.0665629280.68481002 0.097199110.923260701
invsh41 0.0744611260.10844692 0.686613540.497973611
geetot1-0.7151049061.68013567-0.425623310.673636102
geerec1 0.6300046302.44737848 0.257420190.798736842
gde1-0.4435752191.67120674-0.265422110.792628757
govwb1 0.3374523640.43798695 0.770462140.447478336
govsh41 0.4631746321.92543733 0.240555550.811652246
gvxdxe41-0.7933815822.05939737-0.385249390.702963815
high65-0.7524838280.90572159-0.830811410.413108816
highm65-0.3902618040.68124930-0.572861950.571310302
highf65-0.4177487050.56149048-0.743999630.463075062
highcf65 3.920771e-017.660246e-01 0.511833600.61278088
human65 2.337269e+003.307269e+00 0.706706780.48558873
humanm65-1.209249e+001.618516e+00-0.747134750.46121072
humanf65-1.103947e+001.684693e+00-0.655280750.51763499
hyr65 5.491388e+012.388729e+01 2.298874300.02918450
hyrm65 1.293505e+012.317142e+01 0.558232780.58112223
hyrf65 9.092580e+001.766958e+01 0.514589480.61087820
no65 3.720986e-021.319733e-01 0.281949940.78005541
nom65-2.119769e-026.495975e-02-0.326320320.74660861
nof65-1.685782e-026.700258e-02-0.251599540.80318810
pinstab1-4.997113e-023.092081e-02-1.616100190.11728579
pop65 1.031796e-071.317950e-07 0.782879290.44026959
worker65 3.407897e-021.561909e-01 0.218187860.82886516
pop1565-4.655346e-014.713340e-01-0.987695820.33176044
pop6565-1.357450e+006.349424e-01-2.137910490.04139326
sec65-1.089278e-023.076618e-01-0.035405060.97200796
secm65 3.343664e-031.511923e-01 0.022115300.98251284
secf65-2.304329e-031.579715e-01-0.014586990.98846512
secc65-4.915284e-017.290408e-01-0.674212460.50570489
seccm65 2.596016e-013.556533e-01 0.729928730.47149686
seccf65 2.206524e-013.733333e-01 0.591033250.55923950
syr65-7.555807e-017.976763e+00-0.094722730.92520970
syrm65 3.109007e-013.896731e+00 0.079785020.93697576
syrf65 7.592811e-014.110634e+00 0.184711430.85478676
teapri65 3.954523e-057.700406e-04 0.051354740.95940747
teasec65 2.496674e-041.171230e-03 0.213166780.83274136
ex1-5.804081e-012.418472e-01-2.399895930.02329485
im1 5.914450e-012.502978e-01 2.362965580.02530892
xr65-1.037768e-045.416760e-05-1.915846520.06564690
tot1-1.279011e-011.125951e-01-1.135938420.26561200
\n" - ], - "text/latex": [ - "A matrix: 62 × 4 of type dbl\n", - "\\begin{tabular}{r|llll}\n", - " & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n", - "\\hline\n", - "\t(Intercept) & 0.247160893 & 0.78450163 & 0.31505466 & 0.755056170\\\\\n", - "\tgdpsh465 & -0.009377989 & 0.02988773 & -0.31377391 & 0.756018518\\\\\n", - "\tbmp1l & -0.068862679 & 0.03253065 & -2.11685513 & 0.043289718\\\\\n", - "\tfreeop & 0.080068974 & 0.20786400 & 0.38519885 & 0.703000838\\\\\n", - "\tfreetar & -0.488962605 & 0.41816285 & -1.16931143 & 0.252136477\\\\\n", - "\th65 & -2.362098638 & 0.85729167 & -2.75530338 & 0.010192435\\\\\n", - "\thm65 & 0.707143400 & 0.52314511 & 1.35171560 & 0.187285919\\\\\n", - "\thf65 & 1.693448425 & 0.50318881 & 3.36543337 & 0.002232683\\\\\n", - "\tp65 & 0.265526695 & 0.16429407 & 1.61616729 & 0.117271229\\\\\n", - "\tpm65 & 0.136952626 & 0.15121749 & 0.90566657 & 0.372840111\\\\\n", - "\tpf65 & -0.331266948 & 0.16512299 & -2.00618303 & 0.054583866\\\\\n", - "\ts65 & 0.039079306 & 0.18552248 & 0.21064459 & 0.834690082\\\\\n", - "\tsm65 & -0.030668507 & 0.11679396 & -0.26258642 & 0.794791807\\\\\n", - "\tsf65 & -0.179917280 & 0.11809892 & -1.52344556 & 0.138863011\\\\\n", - "\tfert65 & 0.006880834 & 0.02705142 & 0.25436128 & 0.801075252\\\\\n", - "\tmort65 & -0.233454503 & 0.81742198 & -0.28559852 & 0.777287824\\\\\n", - "\tlifee065 & -0.014914473 & 0.19333559 & -0.07714292 & 0.939058459\\\\\n", - "\tgpop1 & 0.970184579 & 1.81220835 & 0.53536039 & 0.596627593\\\\\n", - "\tfert1 & 0.008838189 & 0.03504148 & 0.25222077 & 0.802712696\\\\\n", - "\tmort1 & 0.066562928 & 0.68481002 & 0.09719911 & 0.923260701\\\\\n", - "\tinvsh41 & 0.074461126 & 0.10844692 & 0.68661354 & 0.497973611\\\\\n", - "\tgeetot1 & -0.715104906 & 1.68013567 & -0.42562331 & 0.673636102\\\\\n", - "\tgeerec1 & 0.630004630 & 2.44737848 & 0.25742019 & 0.798736842\\\\\n", - "\tgde1 & -0.443575219 & 1.67120674 & -0.26542211 & 0.792628757\\\\\n", - "\tgovwb1 & 0.337452364 & 0.43798695 & 0.77046214 & 0.447478336\\\\\n", - "\tgovsh41 & 0.463174632 & 1.92543733 & 0.24055555 & 0.811652246\\\\\n", - "\tgvxdxe41 & -0.793381582 & 2.05939737 & -0.38524939 & 0.702963815\\\\\n", - "\thigh65 & -0.752483828 & 0.90572159 & -0.83081141 & 0.413108816\\\\\n", - "\thighm65 & -0.390261804 & 0.68124930 & -0.57286195 & 0.571310302\\\\\n", - "\thighf65 & -0.417748705 & 0.56149048 & -0.74399963 & 0.463075062\\\\\n", - "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", - "\thighcf65 & 3.920771e-01 & 7.660246e-01 & 0.51183360 & 0.61278088\\\\\n", - "\thuman65 & 2.337269e+00 & 3.307269e+00 & 0.70670678 & 0.48558873\\\\\n", - "\thumanm65 & -1.209249e+00 & 1.618516e+00 & -0.74713475 & 0.46121072\\\\\n", - "\thumanf65 & -1.103947e+00 & 1.684693e+00 & -0.65528075 & 0.51763499\\\\\n", - "\thyr65 & 5.491388e+01 & 2.388729e+01 & 2.29887430 & 0.02918450\\\\\n", - "\thyrm65 & 1.293505e+01 & 2.317142e+01 & 0.55823278 & 0.58112223\\\\\n", - "\thyrf65 & 9.092580e+00 & 1.766958e+01 & 0.51458948 & 0.61087820\\\\\n", - "\tno65 & 3.720986e-02 & 1.319733e-01 & 0.28194994 & 0.78005541\\\\\n", - "\tnom65 & -2.119769e-02 & 6.495975e-02 & -0.32632032 & 0.74660861\\\\\n", - "\tnof65 & -1.685782e-02 & 6.700258e-02 & -0.25159954 & 0.80318810\\\\\n", - "\tpinstab1 & -4.997113e-02 & 3.092081e-02 & -1.61610019 & 0.11728579\\\\\n", - "\tpop65 & 1.031796e-07 & 1.317950e-07 & 0.78287929 & 0.44026959\\\\\n", - "\tworker65 & 3.407897e-02 & 1.561909e-01 & 0.21818786 & 0.82886516\\\\\n", - "\tpop1565 & -4.655346e-01 & 4.713340e-01 & -0.98769582 & 0.33176044\\\\\n", - "\tpop6565 & -1.357450e+00 & 6.349424e-01 & -2.13791049 & 0.04139326\\\\\n", - "\tsec65 & -1.089278e-02 & 3.076618e-01 & -0.03540506 & 0.97200796\\\\\n", - "\tsecm65 & 3.343664e-03 & 1.511923e-01 & 0.02211530 & 0.98251284\\\\\n", - "\tsecf65 & -2.304329e-03 & 1.579715e-01 & -0.01458699 & 0.98846512\\\\\n", - "\tsecc65 & -4.915284e-01 & 7.290408e-01 & -0.67421246 & 0.50570489\\\\\n", - "\tseccm65 & 2.596016e-01 & 3.556533e-01 & 0.72992873 & 0.47149686\\\\\n", - "\tseccf65 & 2.206524e-01 & 3.733333e-01 & 0.59103325 & 0.55923950\\\\\n", - "\tsyr65 & -7.555807e-01 & 7.976763e+00 & -0.09472273 & 0.92520970\\\\\n", - "\tsyrm65 & 3.109007e-01 & 3.896731e+00 & 0.07978502 & 0.93697576\\\\\n", - "\tsyrf65 & 7.592811e-01 & 4.110634e+00 & 0.18471143 & 0.85478676\\\\\n", - "\tteapri65 & 3.954523e-05 & 7.700406e-04 & 0.05135474 & 0.95940747\\\\\n", - "\tteasec65 & 2.496674e-04 & 1.171230e-03 & 0.21316678 & 0.83274136\\\\\n", - "\tex1 & -5.804081e-01 & 2.418472e-01 & -2.39989593 & 0.02329485\\\\\n", - "\tim1 & 5.914450e-01 & 2.502978e-01 & 2.36296558 & 0.02530892\\\\\n", - "\txr65 & -1.037768e-04 & 5.416760e-05 & -1.91584652 & 0.06564690\\\\\n", - "\ttot1 & -1.279011e-01 & 1.125951e-01 & -1.13593842 & 0.26561200\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 62 × 4 of type dbl\n", - "\n", - "| | Estimate | Std. Error | t value | Pr(>|t|) |\n", - "|---|---|---|---|---|\n", - "| (Intercept) | 0.247160893 | 0.78450163 | 0.31505466 | 0.755056170 |\n", - "| gdpsh465 | -0.009377989 | 0.02988773 | -0.31377391 | 0.756018518 |\n", - "| bmp1l | -0.068862679 | 0.03253065 | -2.11685513 | 0.043289718 |\n", - "| freeop | 0.080068974 | 0.20786400 | 0.38519885 | 0.703000838 |\n", - "| freetar | -0.488962605 | 0.41816285 | -1.16931143 | 0.252136477 |\n", - "| h65 | -2.362098638 | 0.85729167 | -2.75530338 | 0.010192435 |\n", - "| hm65 | 0.707143400 | 0.52314511 | 1.35171560 | 0.187285919 |\n", - "| hf65 | 1.693448425 | 0.50318881 | 3.36543337 | 0.002232683 |\n", - "| p65 | 0.265526695 | 0.16429407 | 1.61616729 | 0.117271229 |\n", - "| pm65 | 0.136952626 | 0.15121749 | 0.90566657 | 0.372840111 |\n", - "| pf65 | -0.331266948 | 0.16512299 | -2.00618303 | 0.054583866 |\n", - "| s65 | 0.039079306 | 0.18552248 | 0.21064459 | 0.834690082 |\n", - "| sm65 | -0.030668507 | 0.11679396 | -0.26258642 | 0.794791807 |\n", - "| sf65 | -0.179917280 | 0.11809892 | -1.52344556 | 0.138863011 |\n", - "| fert65 | 0.006880834 | 0.02705142 | 0.25436128 | 0.801075252 |\n", - "| mort65 | -0.233454503 | 0.81742198 | -0.28559852 | 0.777287824 |\n", - "| lifee065 | -0.014914473 | 0.19333559 | -0.07714292 | 0.939058459 |\n", - "| gpop1 | 0.970184579 | 1.81220835 | 0.53536039 | 0.596627593 |\n", - "| fert1 | 0.008838189 | 0.03504148 | 0.25222077 | 0.802712696 |\n", - "| mort1 | 0.066562928 | 0.68481002 | 0.09719911 | 0.923260701 |\n", - "| invsh41 | 0.074461126 | 0.10844692 | 0.68661354 | 0.497973611 |\n", - "| geetot1 | -0.715104906 | 1.68013567 | -0.42562331 | 0.673636102 |\n", - "| geerec1 | 0.630004630 | 2.44737848 | 0.25742019 | 0.798736842 |\n", - "| gde1 | -0.443575219 | 1.67120674 | -0.26542211 | 0.792628757 |\n", - "| govwb1 | 0.337452364 | 0.43798695 | 0.77046214 | 0.447478336 |\n", - "| govsh41 | 0.463174632 | 1.92543733 | 0.24055555 | 0.811652246 |\n", - "| gvxdxe41 | -0.793381582 | 2.05939737 | -0.38524939 | 0.702963815 |\n", - "| high65 | -0.752483828 | 0.90572159 | -0.83081141 | 0.413108816 |\n", - "| highm65 | -0.390261804 | 0.68124930 | -0.57286195 | 0.571310302 |\n", - "| highf65 | -0.417748705 | 0.56149048 | -0.74399963 | 0.463075062 |\n", - "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", - "| highcf65 | 3.920771e-01 | 7.660246e-01 | 0.51183360 | 0.61278088 |\n", - "| human65 | 2.337269e+00 | 3.307269e+00 | 0.70670678 | 0.48558873 |\n", - "| humanm65 | -1.209249e+00 | 1.618516e+00 | -0.74713475 | 0.46121072 |\n", - "| humanf65 | -1.103947e+00 | 1.684693e+00 | -0.65528075 | 0.51763499 |\n", - "| hyr65 | 5.491388e+01 | 2.388729e+01 | 2.29887430 | 0.02918450 |\n", - "| hyrm65 | 1.293505e+01 | 2.317142e+01 | 0.55823278 | 0.58112223 |\n", - "| hyrf65 | 9.092580e+00 | 1.766958e+01 | 0.51458948 | 0.61087820 |\n", - "| no65 | 3.720986e-02 | 1.319733e-01 | 0.28194994 | 0.78005541 |\n", - "| nom65 | -2.119769e-02 | 6.495975e-02 | -0.32632032 | 0.74660861 |\n", - "| nof65 | -1.685782e-02 | 6.700258e-02 | -0.25159954 | 0.80318810 |\n", - "| pinstab1 | -4.997113e-02 | 3.092081e-02 | -1.61610019 | 0.11728579 |\n", - "| pop65 | 1.031796e-07 | 1.317950e-07 | 0.78287929 | 0.44026959 |\n", - "| worker65 | 3.407897e-02 | 1.561909e-01 | 0.21818786 | 0.82886516 |\n", - "| pop1565 | -4.655346e-01 | 4.713340e-01 | -0.98769582 | 0.33176044 |\n", - "| pop6565 | -1.357450e+00 | 6.349424e-01 | -2.13791049 | 0.04139326 |\n", - "| sec65 | -1.089278e-02 | 3.076618e-01 | -0.03540506 | 0.97200796 |\n", - "| secm65 | 3.343664e-03 | 1.511923e-01 | 0.02211530 | 0.98251284 |\n", - "| secf65 | -2.304329e-03 | 1.579715e-01 | -0.01458699 | 0.98846512 |\n", - "| secc65 | -4.915284e-01 | 7.290408e-01 | -0.67421246 | 0.50570489 |\n", - "| seccm65 | 2.596016e-01 | 3.556533e-01 | 0.72992873 | 0.47149686 |\n", - "| seccf65 | 2.206524e-01 | 3.733333e-01 | 0.59103325 | 0.55923950 |\n", - "| syr65 | -7.555807e-01 | 7.976763e+00 | -0.09472273 | 0.92520970 |\n", - "| syrm65 | 3.109007e-01 | 3.896731e+00 | 0.07978502 | 0.93697576 |\n", - "| syrf65 | 7.592811e-01 | 4.110634e+00 | 0.18471143 | 0.85478676 |\n", - "| teapri65 | 3.954523e-05 | 7.700406e-04 | 0.05135474 | 0.95940747 |\n", - "| teasec65 | 2.496674e-04 | 1.171230e-03 | 0.21316678 | 0.83274136 |\n", - "| ex1 | -5.804081e-01 | 2.418472e-01 | -2.39989593 | 0.02329485 |\n", - "| im1 | 5.914450e-01 | 2.502978e-01 | 2.36296558 | 0.02530892 |\n", - "| xr65 | -1.037768e-04 | 5.416760e-05 | -1.91584652 | 0.06564690 |\n", - "| tot1 | -1.279011e-01 | 1.125951e-01 | -1.13593842 | 0.26561200 |\n", - "\n" - ], - "text/plain": [ - " Estimate Std. Error t value Pr(>|t|) \n", - "(Intercept) 0.247160893 0.78450163 0.31505466 0.755056170\n", - "gdpsh465 -0.009377989 0.02988773 -0.31377391 0.756018518\n", - "bmp1l -0.068862679 0.03253065 -2.11685513 0.043289718\n", - "freeop 0.080068974 0.20786400 0.38519885 0.703000838\n", - "freetar -0.488962605 0.41816285 -1.16931143 0.252136477\n", - "h65 -2.362098638 0.85729167 -2.75530338 0.010192435\n", - "hm65 0.707143400 0.52314511 1.35171560 0.187285919\n", - "hf65 1.693448425 0.50318881 3.36543337 0.002232683\n", - "p65 0.265526695 0.16429407 1.61616729 0.117271229\n", - "pm65 0.136952626 0.15121749 0.90566657 0.372840111\n", - "pf65 -0.331266948 0.16512299 -2.00618303 0.054583866\n", - "s65 0.039079306 0.18552248 0.21064459 0.834690082\n", - "sm65 -0.030668507 0.11679396 -0.26258642 0.794791807\n", - "sf65 -0.179917280 0.11809892 -1.52344556 0.138863011\n", - "fert65 0.006880834 0.02705142 0.25436128 0.801075252\n", - "mort65 -0.233454503 0.81742198 -0.28559852 0.777287824\n", - "lifee065 -0.014914473 0.19333559 -0.07714292 0.939058459\n", - "gpop1 0.970184579 1.81220835 0.53536039 0.596627593\n", - "fert1 0.008838189 0.03504148 0.25222077 0.802712696\n", - "mort1 0.066562928 0.68481002 0.09719911 0.923260701\n", - "invsh41 0.074461126 0.10844692 0.68661354 0.497973611\n", - "geetot1 -0.715104906 1.68013567 -0.42562331 0.673636102\n", - "geerec1 0.630004630 2.44737848 0.25742019 0.798736842\n", - "gde1 -0.443575219 1.67120674 -0.26542211 0.792628757\n", - "govwb1 0.337452364 0.43798695 0.77046214 0.447478336\n", - "govsh41 0.463174632 1.92543733 0.24055555 0.811652246\n", - "gvxdxe41 -0.793381582 2.05939737 -0.38524939 0.702963815\n", - "high65 -0.752483828 0.90572159 -0.83081141 0.413108816\n", - "highm65 -0.390261804 0.68124930 -0.57286195 0.571310302\n", - "highf65 -0.417748705 0.56149048 -0.74399963 0.463075062\n", - "⋮ ⋮ ⋮ ⋮ ⋮ \n", - "highcf65 3.920771e-01 7.660246e-01 0.51183360 0.61278088 \n", - "human65 2.337269e+00 3.307269e+00 0.70670678 0.48558873 \n", - "humanm65 -1.209249e+00 1.618516e+00 -0.74713475 0.46121072 \n", - "humanf65 -1.103947e+00 1.684693e+00 -0.65528075 0.51763499 \n", - "hyr65 5.491388e+01 2.388729e+01 2.29887430 0.02918450 \n", - "hyrm65 1.293505e+01 2.317142e+01 0.55823278 0.58112223 \n", - "hyrf65 9.092580e+00 1.766958e+01 0.51458948 0.61087820 \n", - "no65 3.720986e-02 1.319733e-01 0.28194994 0.78005541 \n", - "nom65 -2.119769e-02 6.495975e-02 -0.32632032 0.74660861 \n", - "nof65 -1.685782e-02 6.700258e-02 -0.25159954 0.80318810 \n", - "pinstab1 -4.997113e-02 3.092081e-02 -1.61610019 0.11728579 \n", - "pop65 1.031796e-07 1.317950e-07 0.78287929 0.44026959 \n", - "worker65 3.407897e-02 1.561909e-01 0.21818786 0.82886516 \n", - "pop1565 -4.655346e-01 4.713340e-01 -0.98769582 0.33176044 \n", - "pop6565 -1.357450e+00 6.349424e-01 -2.13791049 0.04139326 \n", - "sec65 -1.089278e-02 3.076618e-01 -0.03540506 0.97200796 \n", - "secm65 3.343664e-03 1.511923e-01 0.02211530 0.98251284 \n", - "secf65 -2.304329e-03 1.579715e-01 -0.01458699 0.98846512 \n", - "secc65 -4.915284e-01 7.290408e-01 -0.67421246 0.50570489 \n", - "seccm65 2.596016e-01 3.556533e-01 0.72992873 0.47149686 \n", - "seccf65 2.206524e-01 3.733333e-01 0.59103325 0.55923950 \n", - "syr65 -7.555807e-01 7.976763e+00 -0.09472273 0.92520970 \n", - "syrm65 3.109007e-01 3.896731e+00 0.07978502 0.93697576 \n", - "syrf65 7.592811e-01 4.110634e+00 0.18471143 0.85478676 \n", - "teapri65 3.954523e-05 7.700406e-04 0.05135474 0.95940747 \n", - "teasec65 2.496674e-04 1.171230e-03 0.21316678 0.83274136 \n", - "ex1 -5.804081e-01 2.418472e-01 -2.39989593 0.02329485 \n", - "im1 5.914450e-01 2.502978e-01 2.36296558 0.02530892 \n", - "xr65 -1.037768e-04 5.416760e-05 -1.91584652 0.06564690 \n", - "tot1 -1.279011e-01 1.125951e-01 -1.13593842 0.26561200 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "summary(ls.effect)$coef" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1] \"Estimates and significance testing of the effect of target variables\"\n", - " Estimate. Std. Error t value Pr(>|t|) \n", - "[1,] -0.04981 0.01394 -3.574 0.000351 ***\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dX = as.matrix(cbind(d, X))\n", - "lasso.effect = rlassoEffect(x = X, y = y, d = d, method = \"partialling out\")\n", - "summary(lasso.effect)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1] \"Estimates and significance testing of the effect of target variables\"\n", - " Estimate. Std. Error t value Pr(>|t|) \n", - "gdpsh465 -0.05001 0.01579 -3.167 0.00154 **\n", - "---\n", - "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "doublesel.effect = rlassoEffect(x = X, y = y, d = d, method = \"double selection\")\n", - "summary(doublesel.effect)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 58, "metadata": { "vscode": { "languageId": "r" @@ -2708,17 +1160,6 @@ "tab" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -2790,12 +1231,12 @@ "z = AJR$logMort\n", "x = model.matrix(~-1 + (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2,\n", "data = AJR)\n", - "dim(x)\n" + "dim(x)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 59, "metadata": { "vscode": { "languageId": "r" @@ -2825,7 +1266,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 60, "metadata": { "vscode": { "languageId": "r" @@ -2847,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 62, "metadata": { "vscode": { "languageId": "r" @@ -2878,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 63, "metadata": { "vscode": { "languageId": "r" @@ -2896,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 64, "metadata": { "vscode": { "languageId": "r" @@ -2930,7 +1371,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 68, "metadata": { "vscode": { "languageId": "r" @@ -2949,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 69, "metadata": { "vscode": { "languageId": "r" @@ -2963,7 +1404,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 72, "metadata": { "vscode": { "languageId": "r" @@ -2989,7 +1430,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 73, "metadata": { "vscode": { "languageId": "r" @@ -3011,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 74, "metadata": { "vscode": { "languageId": "r" @@ -3037,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 75, "metadata": { "vscode": { "languageId": "r" @@ -3059,38 +1500,76 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 79, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Estimates and significance testing of the effect of target variables in the IV regression model\"\n", + " coeff. se. t-value p-value\n", + "d1 0.4146 0.2902 1.428 0.153\n", + "\n", + "\n", + "Estimates and Significance Testing of the effect of target variables in the IV regression model \n", + " coeff. se. t-value p-value\n", + "d1 -0.02383 0.12851 -0.185 0.853\n", + "\n", + "\n" + ] + }, { "data": { "text/html": [ - "
Estimate
0.00786473183385086
Std. Error
0.00986592662133842
\n" + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A xtable: 4 × 2
EstimateStd. Error
<dbl><dbl>
ols regression 0.0078647320.009865927
IV estimation -0.0107332690.033766362
selection on Z 0.4146016410.290249208
selection on X and Z-0.0238346970.128506538
\n" ], "text/latex": [ - "\\begin{description*}\n", - "\\item[Estimate] 0.00786473183385086\n", - "\\item[Std. Error] 0.00986592662133842\n", - "\\end{description*}\n" + "A xtable: 4 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Estimate & Std. Error\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\tols regression & 0.007864732 & 0.009865927\\\\\n", + "\tIV estimation & -0.010733269 & 0.033766362\\\\\n", + "\tselection on Z & 0.414601641 & 0.290249208\\\\\n", + "\tselection on X and Z & -0.023834697 & 0.128506538\\\\\n", + "\\end{tabular}\n" ], "text/markdown": [ - "Estimate\n", - ": 0.00786473183385086Std. Error\n", - ": 0.00986592662133842\n", + "\n", + "A xtable: 4 × 2\n", + "\n", + "| | Estimate <dbl> | Std. Error <dbl> |\n", + "|---|---|---|\n", + "| ols regression | 0.007864732 | 0.009865927 |\n", + "| IV estimation | -0.010733269 | 0.033766362 |\n", + "| selection on Z | 0.414601641 | 0.290249208 |\n", + "| selection on X and Z | -0.023834697 | 0.128506538 |\n", "\n" ], "text/plain": [ - " Estimate Std. Error \n", - "0.007864732 0.009865927 " + " Estimate Std. Error \n", + "ols regression 0.007864732 0.009865927\n", + "IV estimation -0.010733269 0.033766362\n", + "selection on Z 0.414601641 0.290249208\n", + "selection on X and Z -0.023834697 0.128506538" ] }, "metadata": {}, @@ -3098,12 +1577,60 @@ } ], "source": [ - "summary(ED.ols)$coef[2, 1:2]" + "library(xtable)\n", + "table = matrix(0, 4, 2)\n", + "table[1, ] = summary(ED.ols)$coef[2, 1:2]\n", + "table[2, ] = cbind(ED.2sls$coef[1], ED.2sls$se[1])\n", + "table[3, ] = summary(lasso.IV.Z)[, 1:2]\n", + "table[4, ] = summary(lasso.IV.XZ)[, 1:2]\n", + "colnames(table) = c(\"Estimate\", \"Std. Error\")\n", + "rownames(table) = c(\"ols regression\", \"IV estimation \", \"selection on Z\", \"selection on X and Z\")\n", + "tab = xtable(table, digits = c(2, 2, 7))\n", + "tab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Inference on Treatment Effects in a High-Dimensional Setting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3. Application: 401(k) plan participation." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "data(pension)\n", + "y = pension$tw\n", + "d = pension$p401\n", + "z = pension$e401\n", + "X = pension[, c(\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\",\n", + "\"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\")] # simple model\n", + "xvar = c(\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\",\n", + "\"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\")\n", + "xpart = paste(xvar, collapse = \"+\")\n", + "form = as.formula(paste(\"tw ~ \", paste(c(\"p401\", xvar), collapse = \"+\"), \"|\", paste(xvar,\n", + "collapse = \"+\")))\n", + "formZ = as.formula(paste(\"tw ~ \", paste(c(\"p401\", xvar), collapse = \"+\"), \"|\", paste(c(\"e401\",\n", + "xvar), collapse = \"+\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "vscode": { "languageId": "r" @@ -3111,44 +1638,29 @@ }, "outputs": [ { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "
A matrix: 1 × 2 of type dbl
d1-0.010733270.03376636
\n" - ], - "text/latex": [ - "A matrix: 1 × 2 of type dbl\n", - "\\begin{tabular}{r|ll}\n", - "\td1 & -0.01073327 & 0.03376636\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 1 × 2 of type dbl\n", - "\n", - "| d1 | -0.01073327 | 0.03376636 |\n", - "\n" - ], - "text/plain": [ - " [,1] [,2] \n", - "d1 -0.01073327 0.03376636" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimation and significance testing of the treatment effect \n", + "Type: ATE \n", + "Bootstrap: not applicable \n", + " coeff. se. t-value p-value \n", + "TE 10180 1931 5.273 1.34e-07 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "\n" + ] } ], "source": [ - "cbind(ED.2sls$coef[1], ED.2sls$se[1])" + "pension.ate = rlassoATE(form, data = pension)\n", + "summary(pension.ate)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": { "vscode": { "languageId": "r" @@ -3159,25 +1671,307 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1] \"Estimates and significance testing of the effect of target variables in the IV regression model\"\n", - " coeff. se. t-value p-value\n", - "d1 0.4146 0.2902 1.428 0.153\n", + "Estimation and significance testing of the treatment effect \n", + "Type: ATET \n", + "Bootstrap: not applicable \n", + " coeff. se. t-value p-value \n", + "TE 12628 2944 4.289 1.8e-05 ***\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", "\n", "\n" ] } ], "source": [ - "library(xtable)\n", - "table = matrix(0, 4, 2)\n", - "table[1, ] = summary(ED.ols)$coef[2, 1:2]\n", - "table[2, ] = cbind(ED.2sls$coef[1], ED.2sls$se[1])\n", - "table[3, ] = summary(lasso.IV.Z)[, 1:2]" + "# pension.atet = rlassoATET(X,d,y)\n", + "pension.atet = rlassoATET(form, data = pension)\n", + "summary(pension.atet)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 36, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "library(remotes)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in install_version(\"glmnet\", \"4.1-3\"): no se pudo encontrar la función \"install_version\"\n", + "output_type": "error", + "traceback": [ + "Error in install_version(\"glmnet\", \"4.1-3\"): no se pudo encontrar la función \"install_version\"\nTraceback:\n" + ] + } + ], + "source": [ + "install_version(\"glmnet\", \"4.1-3\")\n", + "library(glmnet)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'drop': argumentos no compatibles\n", + "output_type": "error", + "traceback": [ + "Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'drop': argumentos no compatibles\nTraceback:\n", + "1. rlassoLATE(X, d, y, z)", + "2. rlassoLATE.default(X, d, y, z)", + "3. rlassologit(d[indz0] ~ x[indz0, , drop = FALSE], post = post, \n . intercept = intercept, penalty = penalty)", + "4. rlassologit.formula(d[indz0] ~ x[indz0, , drop = FALSE], post = post, \n . intercept = intercept, penalty = penalty)", + "5. rlassologit(x, y, post = post, intercept = intercept, model = model, \n . penalty = penalty, control = control, ...)", + "6. rlassologit.default(x, y, post = post, intercept = intercept, \n . model = model, penalty = penalty, control = control, ...)", + "7. glmnet::glmnet(x, y, family = c(\"binomial\"), alpha = 1, lambda = lambda[1], \n . standardize = TRUE, intercept = intercept)", + "8. lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, \n . nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, \n . intr, vnames, maxit, kopt, family, pb)", + "9. drop(y %*% rep(1, nc))", + "10. .handleSimpleError(function (cond) \n . .Internal(C_tryCatchHelper(addr, 1L, cond)), \"argumentos no compatibles\", \n . base::quote(y %*% rep(1, nc)))", + "11. h(simpleError(msg, call))" + ] + } + ], + "source": [ + "pension.late = rlassoLATE(X, d, y, z)\n", + "summary(pension.late)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in parse(text = x, srcfile = src): :55:3: inesperado 'else'\n54: \n55: else\n ^\n", + "output_type": "error", + "traceback": [ + "Error in parse(text = x, srcfile = src): :55:3: inesperado 'else'\n54: \n55: else\n ^\nTraceback:\n" + ] + } + ], + "source": [ + "bootstrap = \"none\"\n", + "nRep = 500\n", + "post = TRUE\n", + "intercept = TRUE\n", + "always_takers = TRUE\n", + "never_takers = TRUE\n", + "x = X\n", + "\n", + " x <- as.matrix(x)\n", + " n <- dim(x)[1]\n", + " p <- dim(x)[2]\n", + " checkmate::checkChoice(bootstrap, c(\"none\", \"Bayes\", \"normal\", \"wild\"))\n", + " checkmate::checkLogical(always_takers, never_takers)\n", + " lambda <- 2.2 * sqrt(n) * qnorm(1 - (.1/log(n))/(2 * (2 * p)))\n", + " control <- list(numIter = 15, tol = 10^-5)\n", + " # penalty <- list(method = 'none', lambda.start = rep(lambda, p), c =\n", + " # 1.1, gamma = 0.1)\n", + " penalty <- list(homoscedastic = \"none\", lambda.start = rep(lambda, \n", + " p), c = 1.1, gamma = 0.1)\n", + " indz1 <- (z == 1)\n", + " indz0 <- (z == 0)\n", + " # E[Y|Z = 1,X] = my_z1x\n", + " b_y_z1xL <- rlasso(y[indz1] ~ x[indz1, , drop = FALSE], post = post, \n", + " intercept = intercept, control = control, penalty = penalty)\n", + " my_z1x <- predict(b_y_z1xL, newdata = x)\n", + " # E[Y|Z = 0,X] = my_z0x\n", + " b_y_z0xL <- rlasso(y[indz0] ~ x[indz0, , drop = FALSE], post = post, \n", + " intercept = intercept, control = control, penalty = penalty)\n", + " my_z0x <- predict(b_y_z0xL, newdata = x)\n", + " # E[D|Z = 1,X] = md_z1x\n", + " lambda <- 2.2 * sqrt(n) * qnorm(1 - (.1/log(n))/(2 * (2 * p)))\n", + " penalty <- list(lambda.start = lambda, c = 1.1, gamma = 0.1)\n", + " \n", + " # if (sum(d - z) != 0) {\n", + " # b_d_z1xL <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], post = post, \n", + " # intercept = intercept, penalty = penalty)\n", + " # md_z1x <- predict(b_d_z1xL, newdata = x)\n", + " # } else {\n", + " # md_z1x <- rep(1, n)\n", + " # }\n", + " # \n", + " # E[D|Z = 0,X] = md_z0x\n", + " #penalty <- list(homoscedastic = \"none\", lambda.start = rep(lambda, \n", + " # p), c = 1.1, gamma = 0.1)\n", + " #b_d_z0x <- rlassologit(d[indz0] ~ x[indz0, ], post = post, intercept = intercept, penalty = penalty)\n", + " #md_z0x <- predict(b_d_z0x, newdata = x)\n", + " # md_z0x <- rep(0, n)\n", + " \n", + " # E[D|Z = 1,X] = md_z1x\n", + " if (identical(d,z)) {\n", + " md_z1x <- rep(1, n)\n", + " md_z0x <- rep(0, n)\n", + " }\n", + " \n", + " else {\n", + " \n", + " if (all(always_takers, never_takers)) {\n", + " g_d_z1 <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], \n", + " post = post, intercept = intercept, penalty = penalty)\n", + " md_z1x <- predict(g_d_z1, newdata = x)\n", + " \n", + " g_d_z0 <- rlassologit(d[indz0] ~ x[indz0, , drop = FALSE], \n", + " post = post, intercept = intercept, penalty = penalty)\n", + " md_z0x <- predict(g_d_z0, newdata = x) \n", + " }\n", + " \n", + " \n", + " if (always_takers == FALSE & never_takers == TRUE) {\n", + " g_d_z1 <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], \n", + " post = post, intercept = intercept, penalty = penalty)\n", + " md_z1x <- predict(g_d_z1, newdata = x)\n", + " \n", + " ### no always-takers: E[D=1 | Z=0,X] = 0\n", + " md_z0x <- rep(0, n)\n", + " }\n", + " \n", + " if (always_takers == TRUE & never_takers == FALSE) {\n", + " ### no never-takers: E[D=1 | Z=1,X] = 1\n", + " md_z1x <- rep(1, n)\n", + " \n", + " g_d_z0 <- rlassologit(d[indz0] ~ x[indz0, , drop = FALSE], \n", + " post = post, intercept = intercept, penalty = penalty)\n", + " md_z0x <- predict(g_d_z0, newdata = x)\n", + " }\n", + " \n", + " if (always_takers == FALSE & never_takers == FALSE) {\n", + " md_z1x <- rep(1, n)\n", + " md_z0x <- rep(0, n)\n", + " \n", + " message(\"If there are no always-takers and no never-takers, ATE is estimated\")\n", + " }\n", + " }\n", + " \n", + " # E[Z|X] = mz_x\n", + " b_z_xL <- rlassologit(z ~ x, post = post, intercept = intercept)\n", + " mz_x <- predict(b_z_xL, newdata = x)\n", + " mz_x <- mz_x * (mz_x > 1e-12 & mz_x < 1 - 1e-12) + (1 - 1e-12) * (mz_x > \n", + " 1 - 1e-12) + 1e-12 * (mz_x < 1e-12)\n", + " \n", + " eff <- (z * (y - my_z1x)/mz_x - ((1 - z) * (y - my_z0x)/(1 - mz_x)) + \n", + " my_z1x - my_z0x)/mean(z * (d - md_z1x)/mz_x - ((1 - z) * (d - md_z0x)/(1 - \n", + " mz_x)) + md_z1x - md_z0x)\n", + " \n", + " se <- sqrt(var(eff))/sqrt(n)\n", + " late <- mean(eff)\n", + " individual <- eff\n", + " \n", + " object <- list(se = se, te = late, individual = individual, type = \"LATE\", \n", + " call = match.call(), samplesize = n)\n", + " \n", + " if (bootstrap != \"none\") {\n", + " boot <- rep(NA, nRep)\n", + " for (i in 1:nRep) {\n", + " if (bootstrap == \"Bayes\") {\n", + " weights <- rexp(n, rate = 1) - 1\n", + " }\n", + " if (bootstrap == \"normal\") {\n", + " weights <- rnorm(n)\n", + " }\n", + " if (bootstrap == \"wild\") {\n", + " weights <- rnorm(n)/sqrt(2) + (rnorm(n)^2 - 1)/2\n", + " }\n", + " weights <- weights + 1\n", + " boot[i] <- mean(weights * (z * (y - my_z1x)/mz_x - ((1 - z) * \n", + " (y - my_z0x)/(1 - mz_x)) + my_z1x - my_z0x))/mean(weights * \n", + " (z * (d - md_z1x)/mz_x - ((1 - z) * (d - md_z0x)/(1 - mz_x)) + \n", + " md_z1x - md_z0x))\n", + " }\n", + " object$boot.se <- sqrt(var(boot))\n", + " object$type_boot <- bootstrap\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# X = model.matrix(~ -1 + (i2 + i3 + i4 + i5 + i6 + i7 + a2 + a3 + a4 + a5 +\n", + "# fsize + hs + smcol + col + marr + twoearn + db + pira + hown)^2, data =\n", + "# pension) # model with interactions\n", + "xvar2 <- paste(\"(\", xvar, \")^2\", sep = \"\")\n", + "formExt = as.formula(paste(\"tw ~ \", paste(c(\"p401\", xvar2), collapse = \"+\"), \"|\",\n", + "paste(xvar2, collapse = \"+\")))\n", + "formZExt = as.formula(paste(\"tw ~ \", paste(c(\"p401\", xvar2), collapse = \"+\"), \"|\",\n", + "paste(c(\"e401\", xvar2), collapse = \"+\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "pension.ate = rlassoATE(X, z, y)\n", + "pension.atet = rlassoATET(X, z, y)\n", + "# pension.late = rlassoLATE(X, d, y, z)\n", + "# pension.latet = rlassoLATET(X, d, y, z)\n", + "# # pension.ate = rlassoATE(formExt, data = pension) pension.atet =\n", + "# # rlassoATET(formExt, data = pension) pension.late = rlassoLATE(formZExt, data =\n", + "# # pension) pension.latet = rlassoLATET(formZExt, data = pension)\n", + "# table = matrix(0, 4, 2)\n", + "# table[1, ] = summary(pension.ate)[, 1:2]\n", + "# table[2, ] = summary(pension.atet)[, 1:2]\n", + "# table[3, ] = summary(pension.late)[, 1:2]\n", + "# table[4, ] = summary(pension.latet)[, 1:2]\n", + "# colnames(table) = c(\"Estimate\", \"Std. Error\")\n", + "# rownames(table) = c(\"ATE\", \"ATET \", \"LATE\", \"LATET\")\n", + "# tab = xtable(table, digits = c(2, 2, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": { "vscode": { "languageId": "r" @@ -3186,42 +1980,16 @@ "outputs": [ { "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 4 × 2 of type dbl
0.0078647320.009865927
-0.0107332690.033766362
0.4146016410.290249208
0.0000000000.000000000
\n" - ], - "text/latex": [ - "A matrix: 4 × 2 of type dbl\n", - "\\begin{tabular}{ll}\n", - "\t 0.007864732 & 0.009865927\\\\\n", - "\t -0.010733269 & 0.033766362\\\\\n", - "\t 0.414601641 & 0.290249208\\\\\n", - "\t 0.000000000 & 0.000000000\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ + "text/plain": [ "\n", - "A matrix: 4 × 2 of type dbl\n", + "Call:\n", + "rlassoLATET.default(x = x, d = d, y = y, z = z, bootstrap = bootstrap, \n", + " nRep = nRep)\n", "\n", - "| 0.007864732 | 0.009865927 |\n", - "| -0.010733269 | 0.033766362 |\n", - "| 0.414601641 | 0.290249208 |\n", - "| 0.000000000 | 0.000000000 |\n", - "\n" - ], - "text/plain": [ - " [,1] [,2] \n", - "[1,] 0.007864732 0.009865927\n", - "[2,] -0.010733269 0.033766362\n", - "[3,] 0.414601641 0.290249208\n", - "[4,] 0.000000000 0.000000000" + "Treatment Effect\n", + "Type: ATET \n", + "Value:\n", + "[1] 10795\n" ] }, "metadata": {}, @@ -3229,7 +1997,7 @@ } ], "source": [ - "table" + "pension.atet" ] }, { @@ -3243,6 +2011,17 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, From 80465fa5343e6d71abc50b99c2ecb6fb12be7fe8 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 08:02:41 -0500 Subject: [PATCH 24/28] # --- tutorial/HDMjl_tutorial.ipynb | 149 ++++++++++++++++++---------------- 1 file changed, 77 insertions(+), 72 deletions(-) diff --git a/tutorial/HDMjl_tutorial.ipynb b/tutorial/HDMjl_tutorial.ipynb index a549ec0..a4806da 100644 --- a/tutorial/HDMjl_tutorial.ipynb +++ b/tutorial/HDMjl_tutorial.ipynb @@ -14,65 +14,56 @@ "## 2. How to get started" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [], - "source": [ - "#Pkg.rm(\"HDMjl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "#Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl#prueba\", rev = \"dev\")" - ] - }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl#prueba\")" + "import Pkg; using Pkg" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "import Pkg, using Pkg" + "#Pkg.rm(\"HDMjl\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/d2cml-ai/HDMjl.jl`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions..." ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\n", + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba`\u001b[39m\n", "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m " ] }, @@ -80,31 +71,59 @@ "name": "stderr", "output_type": "stream", "text": [ - "`C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n", - " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" + "`C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n", + " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba`\u001b[39m" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " \u001b[90m [8de29b41] \u001b[39m\u001b[92m+ HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " project..." + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33m ✓ \u001b[39mHDMjl\n", + " 1 dependency successfully precompiled in 28 seconds. 292 already precompiled.\n", + " \u001b[33m1\u001b[39m dependency precompiled but a different version is currently loaded. Restart julia to access the new version\n" ] } ], "source": [ - "Pkg.develop(path = \"E:/causal_ml/hdm_paper/prueba/HDMjl.jl\")" + "Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\", rev = \"prueba\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "using CodecXz, RData, DataFrames, StatsModels, \n", - " Statistics, Distributions, PrettyTables, GLM, CSV, HDMjl" + "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables, GLM, CSV, HDMjl" ] }, { @@ -123,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -181,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -233,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -282,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -342,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -390,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -430,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -438,9 +457,9 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " 1 0.972739 0.0136868 71.0715 0.0\n", + " 1 0.97274 0.01369 71.05478 0.0 ***\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -454,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -475,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -483,9 +502,9 @@ "output_type": "stream", "text": [ "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", + " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", "\n", - " 1 0.978075 0.0141562 69.0914 0.0\n", + " 1 0.97807 0.01416 69.07274 0.0 ***\n", "---\n", "Signif. codes:\n", "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" @@ -933,13 +952,6 @@ "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -947,13 +959,6 @@ "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, From f9c81a2e869ca0cf178d338b57979eebe02a2857 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 08:23:36 -0500 Subject: [PATCH 25/28] # --- tutorial/HDMjl_tutorial.ipynb | 53 +- tutorial/HDMjl_tutorial_1.ipynb | 2173 ------------------------------- tutorial/r_file.ipynb | 276 +--- 3 files changed, 30 insertions(+), 2472 deletions(-) delete mode 100644 tutorial/HDMjl_tutorial_1.ipynb diff --git a/tutorial/HDMjl_tutorial.ipynb b/tutorial/HDMjl_tutorial.ipynb index a4806da..7c3472c 100644 --- a/tutorial/HDMjl_tutorial.ipynb +++ b/tutorial/HDMjl_tutorial.ipynb @@ -123,7 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables, GLM, CSV, HDMjl" + "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables, GLM, CSV, HDMjl, LinearAlgebra, StatsModels" ] }, { @@ -146,7 +146,6 @@ "metadata": {}, "outputs": [], "source": [ - "using CSV\n", "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/3_2.csv\"\n", "dta = DataFrame(CSV.read(download(url), DataFrame));\n", "n, p = size(dta);\n", @@ -299,16 +298,6 @@ "### 4.1. Intuition for the Orthogonality Principle in Linear Models via Partialling Out." ] }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "using CSV, GLM\n", - "using DataFrames" - ] - }, { "cell_type": "code", "execution_count": 14, @@ -529,7 +518,6 @@ "metadata": {}, "outputs": [], "source": [ - "using CSV\n", "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_2.csv\"\n", "data = DataFrame(CSV.read(download(url), DataFrame));\n", "n, p = size(data);\n", @@ -817,25 +805,12 @@ "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" ] }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "using LinearAlgebra" - ] - }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "using GLM\n", - "using CodecXz\n", - "using RData\n", - "using DataFrames\n", "url = \"https://github.com/cran/hdm/raw/master/data/GrowthData.rda\";\n", "GrowthData = load(download(url))[\"GrowthData\"];\n", "y = GrowthData[:, 1];\n", @@ -852,7 +827,7 @@ "source": [ "Q, R = qr(hcat(ones(length(y)), X_1))\n", "β = pinv(hcat(ones(length(y)), X_1)) * y\n", - "# ls_effect = β;\n", + "\n", "res = y - hcat(ones(length(y)), X_1) * β;\n", "n = size(hcat(ones(length(y)), X_1))[1]\n", "k = size(hcat(ones(length(y)), X_1))[2]\n", @@ -864,16 +839,6 @@ "ls_effect = DataFrame(Estimate = β, stderror = se);" ] }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);\n", - "# coef(ls_effect)" - ] - }, { "cell_type": "code", "execution_count": 18, @@ -942,7 +907,6 @@ } ], "source": [ - "using PrettyTables\n", "table = zeros(3,2)\n", "table[1,:] = [round(Matrix(ls_effect)[2,1], digits = 2), round.(Matrix(ls_effect)[2,2], digits = 5)]\n", "table[2,:] = [round(lasso_effect.coefficients, digits =2), round(lasso_effect.se, digits = 5)]\n", @@ -966,15 +930,6 @@ "### 5.2. Application: Economic Development and Institutions." ] }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "using RDatasets, TexTables, LinearAlgebra, GLM" - ] - }, { "cell_type": "code", "execution_count": 38, @@ -991,7 +946,6 @@ } ], "source": [ - "using Statistics, StatsModels, RData\n", "url = \"https://github.com/cran/hdm/raw/master/data/AJR.rda\";\n", "AJR = load(download(url))[\"AJR\"];\n", "y = AJR[!,\"GDP\"]\n", @@ -1226,7 +1180,6 @@ "metadata": {}, "outputs": [], "source": [ - "using Statistics, GLM\n", "url = \"https://github.com/cran/hdm/raw/master/data/EminentDomain.rda\";\n", "EminentDomain = load(download(url))[\"EminentDomain\"];\n", "z = EminentDomain[\"logGDP\"][\"z\"];\n", @@ -1376,7 +1329,6 @@ } ], "source": [ - "using PrettyTables\n", "table = zeros(4,2)\n", "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", @@ -1941,7 +1893,6 @@ "metadata": {}, "outputs": [], "source": [ - "using CSV\n", "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/7_.csv\"\n", "data = DataFrame(CSV.read(download(url), DataFrame));\n", "n, p = size(data);\n", diff --git a/tutorial/HDMjl_tutorial_1.ipynb b/tutorial/HDMjl_tutorial_1.ipynb deleted file mode 100644 index 0cb3837..0000000 --- a/tutorial/HDMjl_tutorial_1.ipynb +++ /dev/null @@ -1,2173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# HIGH-DIMENSIONAL METRICS IN R" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. How to get started" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba#main`\u001b[39m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `https://github.com/d2cml-ai/HDMjl.jl#prueba#main`\u001b[39m\n" - ] - } - ], - "source": [ - "Pkg.rm(\"HDMjl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/d2cml-ai/HDMjl.jl`" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `C:\\Users\\User\\.julia\\registries\\General`\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "package versions...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import Pkg; Pkg.add(url = \"https://github.com/d2cml-ai/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "using CodecXz, RData, DataFrames, StatsModels, Statistics, Distributions, PrettyTables" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import Pkg; using Pkg" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "using HDMjl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Prediction using Approximate Sparsity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2. A Joint Significance Test for Lasso Regression." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "using Random\n", - "Random.seed!(1234);\n", - "n = 100;\n", - "p = 100;\n", - "s = 3;\n", - "X = randn(n, p);\n", - "beta = vcat(fill(5, s), zeros(p - s));\n", - "Y = X * beta + randn(n);" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "lasso_reg = HDMjl.rlasso(X, Y, post = false);" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dict{String, Any} with 17 entries:\n", - " \"covariates\" => 100\n", - " \"tss\" => 8466.4\n", - " \"dev\" => [8.56479; -6.50463; … ; -0.32845; -18.2301;;]\n", - " \"model\" => [0.970656 0.262456 … 1.86802 -0.460151; -0.979218 -0.022244…\n", - " \"loadings\" => [1.26761, 1.00655, 1.05993, 1.18077, 1.17881, 1.16112, 0.92…\n", - " \"sigma\" => 0.993422\n", - " \"main_tbl\" => Any[\"Intercept\" -0.0512442; \"V 1\" 4.81729; … ; \"V 99\" 0.0; …\n", - " \"lambda0\" => 36.9818\n", - " \"lambda\" => [46.8785, 37.2241, 39.198, 43.6669, 43.5945, 42.9403, 34.16…\n", - " \"intercept\" => -0.0512442\n", - " \"iter\" => 8\n", - " \"residuals\" => [-0.722921; 0.504304; … ; 0.484122; -0.34733;;]\n", - " \"rss\" => 97.7018\n", - " \"index\" => Bool[1, 1, 1, 0, 0, 0, 0, 0, 0, 0 … 0, 0, 0, 1, 0, 0, 0, …\n", - " \"beta\" => [4.81729, 4.74685, 4.97585, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…\n", - " \"options\" => Dict{String, Any}(\"intercept\"=>true, \"post\"=>false, \"meanx\"…\n", - " \"coefficients\" => [-0.0512442, 4.81729, 4.74685, 4.97585, 0.0, 0.0, 0.0, 0.0,…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lasso_reg" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1×101 adjoint(::Vector{Float64}) with eltype Float64:\n", - " -0.00682754 5.00958 4.93178 5.17705 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "post_lasso_reg = HDMjl.rlasso(X, Y, post = true) #now use post-lasso\n", - "post_lasso_reg[\"coefficients\"]'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Inference on Target Regression Coefficients" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.1. Intuition for the Orthogonality Principle in Linear Models via Partialling Out." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "using CSV, GLM\n", - "using DataFrames" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Project.toml`\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `C:\\Users\\User\\.julia\\environments\\v1.8\\Manifest.toml`\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \u001b[90m [8de29b41] \u001b[39m\u001b[91m- HDMjl v0.0.11 `E:/causal_ml/hdm_paper/prueba/HDMjl.jl`\u001b[39m\n" - ] - } - ], - "source": [ - "Pkg.rm(\"HDMjl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 336, - "metadata": {}, - "outputs": [], - "source": [ - "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_1.csv\"\n", - "dta = DataFrame(CSV.read(download(url), DataFrame));\n", - "n, p = size(dta);\n", - "y = dta[:,\"y\"];\n", - "d = dta[:,\"d\"];\n", - "x = Matrix(dta[:,3:end]);" - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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Float64Float64
10.9727390.0136868
" - ], - "text/latex": [ - "\\begin{tabular}{r|cc}\n", - "\t& Estimate & Std\\_Error\\\\\n", - "\t\\hline\n", - "\t& Float64 & Float64\\\\\n", - "\t\\hline\n", - "\t1 & 0.972739 & 0.0136868 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m1×2 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", - "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", - "─────┼─────────────────────\n", - " 1 │ 0.972739 0.0136868" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rY_1 = HDMjl.rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), y);\n", - "rY = rY_1[\"residuals\"]\n", - "rD_1 = HDMjl.rlasso(hcat(ones(length(y)), Matrix(dta[:,3:end])), d);\n", - "rD = rD_1[\"residuals\"]\n", - "partial_fit_postlasso = lm(hcat(ones(length(y)), rD), vec(rY))\n", - "DataFrame(Estimate = coef(partial_fit_postlasso)[2], Std_Error = stderror(partial_fit_postlasso)[2])" - ] - }, - { - "cell_type": "code", - "execution_count": 345, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " 1 0.97274 0.01369 71.05478 0.0 ***\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "Eff = HDMjl.rlassoEffect(x, y, d, method = \"partialling out\");\n", - "HDMjl.r_summary(Eff);" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " 1 0.978075 0.0141562 69.0914 0.0\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "Eff = HDMjl.rlassoEffect(x, y, d, method = \"double selection\");\n", - "HDMjl.r_summary(Eff);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2. Inference: Confidence Intervals and Significance Testing. The function rlassoEffects" - ] - }, - { - "cell_type": "code", - "execution_count": 1092, - "metadata": {}, - "outputs": [], - "source": [ - "# using Random, Distributions, PrettyTables, DataFrames\n", - "# Random.seed!(1234);\n", - "# n = 100;\n", - "# p = 100;\n", - "# s = 3;\n", - "# x = randn(n, p);\n", - "# beta = vcat(fill(3, s), zeros(p - s));\n", - "# y =1 .+ x * beta + randn(n);" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], - "source": [ - "using CSV\n", - "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/4_2.csv\"\n", - "data = DataFrame(CSV.read(download(url), DataFrame));\n", - "n, p = size(data);\n", - "y = data[:,1];\n", - "#d = dta[:,\"d\"];\n", - "x = Matrix(data[:,2:end]);" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "lasso_effects = HDMjl.rlassoEffects(x, y, index = [1,2,3,50]);" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m X1 \u001b[0m \u001b[1m X2 \u001b[0m \u001b[1m X3 \u001b[0m \u001b[1m X50 \u001b[0m\n", - "\n", - " 2.9445 3.0413 2.9754 0.072\n" - ] - } - ], - "source": [ - "HDMjl.r_print(lasso_effects, digits = 4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m 2.94448 2.94448 0.08815 33.40306 0.0 ***\n", - " \u001b[1m X2 \u001b[0m 3.04127 3.04127 0.08389 36.25307 0.0 ***\n", - " \u001b[1m X3 \u001b[0m 2.9754 2.9754 0.07804 38.1266 0.0 ***\n", - " \u001b[1m X50 \u001b[0m 0.0719553 0.07196 0.07765 0.92672 0.35407\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "object = lasso_effects\n", - "k = length(object.coefficients)\n", - "table = zeros(k, 4)\n", - "table[:, 1] .= round.(object.coefficients, digits = 5)\n", - "table[:, 2] .= round.(object.se, digits = 5)\n", - "table[:, 3] .= round.(table[:, 1]./table[:, 2], digits = 5)\n", - "table[:, 4] .= round.(2 * cdf(Normal(), -abs.(table[:, 3])), digits = 5)\n", - "table1 = DataFrame(hcat([\"X$y\" for y = object.index], table), :auto)\n", - "table1 = rename(table1, [\"index\", \"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"])\n", - "table2 = string.(copy(table))\n", - "a = table2[:, 4];\n", - "b = string.(zeros(length(a)))\n", - "b[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .= a[parse.(Float64, a) .>= 0 .&& parse.(Float64, a).< 0.001] .* \" ***\"\n", - "b[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .= a[parse.(Float64, a) .>= 0.001 .&& parse.(Float64, a).< 0.01] .* \" **\"\n", - "b[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .= a[parse.(Float64, a) .>= 0.01 .&& parse.(Float64, a).< 0.05] .* \" *\"\n", - "b[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .= a[parse.(Float64, a) .>= 0.05 .&& parse.(Float64, a).< 0.1] .* \" .\"\n", - "b[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .= a[parse.(Float64, a) .>= 0.1 .&& parse.(Float64, a).< 1] .* \" \"\n", - "b = vec(b)\n", - "table2[:, 4] .= b\n", - "table2 = DataFrame(hcat(object.coefficients[:,1], table2), :auto)\n", - "table2 = rename(table2, [\" \", \"coeff.\", \"se.\", \"t-value\", \"p-value\"])\n", - "print(\"Estimates and significance testing of the effect of target variables\", \n", - " \"\\n\")\n", - "pretty_table(table2, show_row_number = false, header = [\" \",\"Estimate.\", \"Std. Error\", \"t value\", \"Pr(>|t|)\"], tf = tf_borderless, row_names = [\"X$y\" for y = object.index])\n", - "print(\"---\", \"\\n\", \"Signif. codes:\",\"\\n\", \"0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\")\n", - "print(\"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

4 rows × 5 columns

indexEstimate.Std. Errort valuePr(>|t|)
AnyAnyAnyAnyAny
1X12.944480.0881533.40310.0
2X23.041270.0838936.25310.0
3X32.97540.0780438.12660.0
4X500.071960.077650.926720.35407
" - ], - "text/latex": [ - "\\begin{tabular}{r|ccccc}\n", - "\t& index & Estimate. & Std. Error & t value & Pr(>|t|)\\\\\n", - "\t\\hline\n", - "\t& Any & Any & Any & Any & Any\\\\\n", - "\t\\hline\n", - "\t1 & X1 & 2.94448 & 0.08815 & 33.4031 & 0.0 \\\\\n", - "\t2 & X2 & 3.04127 & 0.08389 & 36.2531 & 0.0 \\\\\n", - "\t3 & X3 & 2.9754 & 0.07804 & 38.1266 & 0.0 \\\\\n", - "\t4 & X50 & 0.07196 & 0.07765 & 0.92672 & 0.35407 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m4×5 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m index \u001b[0m\u001b[1m Estimate. \u001b[0m\u001b[1m Std. Error \u001b[0m\u001b[1m t value \u001b[0m\u001b[1m Pr(>|t|) \u001b[0m\n", - "\u001b[1m \u001b[0m│\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Any \u001b[0m\n", - "─────┼─────────────────────────────────────────────────\n", - " 1 │ X1 2.94448 0.08815 33.4031 0.0\n", - " 2 │ X2 3.04127 0.08389 36.2531 0.0\n", - " 3 │ X3 2.9754 0.07804 38.1266 0.0\n", - " 4 │ X50 0.07196 0.07765 0.92672 0.35407" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "table1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m 2.94448 2.94448 0.08815 33.40306 0.0 ***\n", - " \u001b[1m X2 \u001b[0m 3.04127 3.04127 0.08389 36.25307 0.0 ***\n", - " \u001b[1m X3 \u001b[0m 2.9754 2.9754 0.07804 38.1266 0.0 ***\n", - " \u001b[1m X50 \u001b[0m 0.0719553 0.07196 0.07765 0.92672 0.35407\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "HDMjl.r_summary(lasso_effects);" - ] - }, - { - "cell_type": "code", - "execution_count": 1179, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m 2.77171 3.11724\n", - " \u001b[1m X2 \u001b[0m 2.87685 3.2057\n", - " \u001b[1m X3 \u001b[0m 2.82245 3.12836\n", - " \u001b[1m X50 \u001b[0m -0.0802271 0.224138\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(lasso_effects);" - ] - }, - { - "cell_type": "code", - "execution_count": 1186, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m 2.72895 3.16001\n", - " \u001b[1m X2 \u001b[0m 2.83806 3.24448\n", - " \u001b[1m X3 \u001b[0m 2.7842 3.1666\n", - " \u001b[1m X50 \u001b[0m -0.114585 0.258496\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(lasso_effects, joint = true);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3. Application: the effect of gender on wage" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(29217, 23)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "url = \"https://github.com/cran/hdm/raw/master/data/cps2012.rda\"\n", - "cps2012 = load(download(url))[\"cps2012\"];\n", - "n, p = size(cps2012);\n", - "size(cps2012)" - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(29217, 116)" - ] - } - ], - "source": [ - "x_formula = @formula(lnw ~ -1 + female + female*widowed + female*divorced + female*separated + female*nevermarried +\n", - " female*hsd08 + female*hsd911 + female*hsg + female*cg + female*ad + female*mw + female*so + female*we + female*exp1 + female*exp2 + female*exp3)\n", - "x_dframe = ModelFrame( x_formula, cps2012)\n", - "x1 = ModelMatrix(x_dframe)\n", - "X = x1.m[:,Not(1:16)];\n", - "X = hcat(x1.m[:,1:16], X)\n", - "size(X)\n", - "fom_1 = [\"widowed\", \"divorced\", \"separated\", \"nevermarried\", \"hsd08\", \"hsd911\", \"hsg\", \"cg\", \"ad\", \"mw\", \"so\",\n", - " \"we\", \"exp1\", \"exp2\", \"exp3\"];\n", - "data = cps2012[:,fom_1];\n", - "sub_data = ones(size(data)[1])\n", - "for i in 1:size(data)[2]\n", - " if i <= (size(data)[2] -1)\n", - " sub_data = hcat(sub_data, Matrix(data[:, i] .* data[:, Not(1:i)]) )\n", - " end\n", - "end\n", - "sub_data = sub_data[:,2:end]\n", - "size(sub_data)\n", - "x = hcat(X, sub_data)\n", - "size(x)\n", - "filter = var.(eachcol(x)) .!= 0\n", - "x = x[:,filter]\n", - "print(size(x))\n", - "index_gender = [1,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31];\n", - "y = cps2012.lnw;\n", - ";" - ] - }, - { - "cell_type": "code", - "execution_count": 389, - "metadata": {}, - "outputs": [], - "source": [ - "effects_female = HDMjl.rlassoEffects(x, y, index = index_gender);" - ] - }, - { - "cell_type": "code", - "execution_count": 390, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m -0.154923 0.0501624 -3.08843 0.00201216\n", - " \u001b[1m X17 \u001b[0m 0.136095 0.0906626 1.50112 0.133325\n", - " \u001b[1m X18 \u001b[0m 0.136939 0.0221817 6.17353 6.6782e-10\n", - " \u001b[1m X19 \u001b[0m 0.0233028 0.0532118 0.437925 0.661441\n", - " \u001b[1m X20 \u001b[0m 0.186853 0.0199424 9.36966 7.27651e-21\n", - " \u001b[1m X21 \u001b[0m 0.0278103 0.120914 0.23 0.818092\n", - " \u001b[1m X22 \u001b[0m -0.119335 0.0518797 -2.30023 0.0214354\n", - " \u001b[1m X23 \u001b[0m -0.0128898 0.0192232 -0.670533 0.502518\n", - " \u001b[1m X24 \u001b[0m 0.0101386 0.0183265 0.553218 0.580114\n", - " \u001b[1m X25 \u001b[0m -0.0304637 0.0218061 -1.39703 0.162405\n", - " \u001b[1m X26 \u001b[0m -0.00106344 0.0191918 -0.0554112 0.955811\n", - " \u001b[1m X27 \u001b[0m -0.00818334 0.0193568 -0.422763 0.672468\n", - " \u001b[1m X28 \u001b[0m -0.00422613 0.0211684 -0.199643 0.84176\n", - " \u001b[1m X29 \u001b[0m 0.00493526 0.00780428 0.632379 0.527139\n", - " \u001b[1m X30 \u001b[0m -0.159519 0.0452999 -3.52141 0.000429263\n", - " \u001b[1m X31 \u001b[0m 0.0384506 0.0078611 4.89125 1.00199e-6\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "HDMjl.r_summary(effects_female);" - ] - }, - { - "cell_type": "code", - "execution_count": 399, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m -0.292471 -0.0173751\n", - " \u001b[1m X17 \u001b[0m -0.130276 0.402467\n", - " \u001b[1m X18 \u001b[0m 0.075579 0.1983\n", - " \u001b[1m X19 \u001b[0m -0.114955 0.16156\n", - " \u001b[1m X20 \u001b[0m 0.129983 0.243724\n", - " \u001b[1m X21 \u001b[0m -0.369439 0.42506\n", - " \u001b[1m X22 \u001b[0m -0.267141 0.028471\n", - " \u001b[1m X23 \u001b[0m -0.0644819 0.0387024\n", - " \u001b[1m X24 \u001b[0m -0.0410296 0.0613067\n", - " \u001b[1m X25 \u001b[0m -0.0950142 0.0340867\n", - " \u001b[1m X26 \u001b[0m -0.0540328 0.051906\n", - " \u001b[1m X27 \u001b[0m -0.0620344 0.0456677\n", - " \u001b[1m X28 \u001b[0m -0.064856 0.0564037\n", - " \u001b[1m X29 \u001b[0m -0.0162257 0.0260962\n", - " \u001b[1m X30 \u001b[0m -0.282576 -0.0364624\n", - " \u001b[1m X31 \u001b[0m 0.0171196 0.0597815\n" - ] - } - ], - "source": [ - "joint_CI = HDMjl.r_confint(effects_female, 0.95, joint = true);\n", - "joint_CI;" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "using LinearAlgebra" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "using GLM\n", - "using CodecXz\n", - "using RData\n", - "using DataFrames\n", - "url = \"https://github.com/cran/hdm/raw/master/data/GrowthData.rda\";\n", - "GrowthData = load(download(url))[\"GrowthData\"];\n", - "y = GrowthData[:, 1];\n", - "d = GrowthData[:, 3];\n", - "X = Matrix(GrowthData[:, Not(1, 2, 3)]);\n", - "X_1 = Matrix(GrowthData[:, Not(1, 2)]);" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "Q, R = qr(hcat(ones(length(y)), X_1))\n", - "β = pinv(hcat(ones(length(y)), X_1)) * y\n", - "# ls_effect = β;\n", - "res = y - hcat(ones(length(y)), X_1) * β;\n", - "n = size(hcat(ones(length(y)), X_1))[1]\n", - "k = size(hcat(ones(length(y)), X_1))[2]\n", - "\n", - "sigma2_hat = (res' * res) / (n - k)\n", - "vcov_beta_hat = sigma2_hat .* inv(hcat(ones(length(y)), X_1)' * hcat(ones(length(y)), X_1));\n", - "se = sqrt.(diag(vcov_beta_hat))\n", - "\n", - "ls_effect = DataFrame(Estimate = β, stderror = se);" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "# ls_effect = lm(hcat(ones(length(y)), X_1)[:, 1:42], y);\n", - "# coef(ls_effect)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " 1 -0.0498115 0.0139364 -3.57421 0.000351288\n", - "---" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "lasso_effect = HDMjl.rlassoEffect(X, y, d, method = \"partialling out\");\n", - "HDMjl.r_summary(lasso_effect);" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and significance testing of the effect of target variables\n", - " \u001b[1m Row \u001b[0m \u001b[1m Estimate. \u001b[0m \u001b[1m Std. Error \u001b[0m \u001b[1m t value \u001b[0m \u001b[1m Pr(>|t|) \u001b[0m\n", - "\n", - " 1 -0.0500059 0.0157914 -3.16666 0.00154203\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "doublesel_effect = HDMjl.rlassoEffect(X, y, d, method = \"double selection\");\n", - "HDMjl.r_summary(doublesel_effect);" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "using PrettyTables\n", - "table = zeros(3,2)\n", - "table[1,:] = [round(Matrix(ls_effect)[2,1], digits = 2), round.(Matrix(ls_effect)[2,2], digits = 5)]\n", - "table[2,:] = [round(lasso_effect.coefficients, digits =2), round(lasso_effect.se, digits = 5)]\n", - "table[3,:] = [round(doublesel_effect.coefficients, digits =2), round(doublesel_effect.se, digits = 5)];" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================== ========== =============\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", - "=================================== ========== =============\n", - " full reg via ols -0.01 0.02989\n", - " partial reg\\nvia post-lasso -0.05 0.01394\n", - " partial reg via double selection -0.05 0.01579\n", - "=================================== ========== =============\n" - ] - } - ], - "source": [ - "# table1 = DataFrame(table, :auto)\n", - "# table1 = rename(table1, [\"Estimate\", \"Std. Error\"])\n", - "index = [\"full reg via ols\", \"partial reg\n", - "via post-lasso \", \"partial reg via double selection\"]\n", - "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Instrumental Variable Estimation in a High-Dimensional Setting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2. Application: Economic Development and Institutions." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "using RDatasets, TexTables, LinearAlgebra, GLM" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(64, 21)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "using Statistics, StatsModels, RData\n", - "url = \"https://github.com/cran/hdm/raw/master/data/AJR.rda\";\n", - "AJR = load(download(url))[\"AJR\"];\n", - "y = AJR[!,\"GDP\"]\n", - "d = AJR[!,\"Exprop\"]\n", - "z = AJR[!,\"logMort\"];\n", - "x_formula = @formula(GDP ~ -1 + Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", - " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", - " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", - " + Africa*Asia + Africa*Namer + Africa*Samer\n", - " + Asia*Namer + Asia*Samer\n", - " + Namer*Samer)\n", - "x_dframe = ModelFrame( x_formula, AJR)\n", - "x1 = ModelMatrix(x_dframe)\n", - "x = x1.m\n", - "size(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "AJR_Xselect = HDMjl.rlassoIV(x, d, y, z, select_X=true, select_Z=false);" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m d1 \u001b[0m\n", - "\n", - " 0.845\n" - ] - } - ], - "source": [ - "HDMjl.r_print(AJR_Xselect)" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 0.84503 0.26993 3.13055 0.00174 **\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "HDMjl.r_summary(AJR_Xselect);" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " d1 0.315981 1.37407\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(AJR_Xselect);" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "rY_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), y);\n", - "rY = y - predict(rY_1)\n", - "rD_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), d);\n", - "rD = d - predict(rD_1);\n", - "rZ_1 = lm(hcat(ones(length(y)), Matrix(AJR[:,3:end])), z);\n", - "rZ = z - predict(rZ_1);" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "rY_1 = lm(@formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", - " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", - " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", - "rY = y - predict(rY_1)\n", - "\n", - "rD_1 = lm(@formula(Exprop ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", - " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", - " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", - "rD = d - predict(rD_1)\n", - "\n", - "rZ_1 = lm(@formula(logMort ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", - " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer + Africa*Asia + Africa*Namer + Africa*Samer\n", - " + Asia*Namer + Asia*Samer + Namer*Samer), AJR)\n", - "rZ = z - predict(rZ_1);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
11.267211.73054
" - ], - "text/latex": [ - "\\begin{tabular}{r|cc}\n", - "\t& Estimate & Std\\_Error\\\\\n", - "\t\\hline\n", - "\t& Float64 & Float64\\\\\n", - "\t\\hline\n", - "\t1 & 1.26721 & 1.73054 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m1×2 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", - "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", - "─────┼─────────────────────\n", - " 1 │ 1.26721 1.73054" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ivfit_lm = HDMjl.tsls(rD, rY, rZ, nothing, intercept=false)\n", - "DataFrame(Estimate = ivfit_lm[\"coefficients\"][1,2], Std_Error = ivfit_lm[\"se\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "x_formula1 = @formula(GDP ~ Latitude + Latitude2 + Africa + Asia + Namer + Samer\n", - " + Latitude*Latitude2 + Latitude*Africa + Latitude*Asia + Latitude*Namer + Latitude*Samer\n", - " + Latitude2*Africa + Latitude2*Asia + Latitude2*Namer + Latitude2*Samer\n", - " + Africa*Asia + Africa*Namer + Africa*Samer\n", - " + Asia*Namer + Asia*Samer\n", - " + Namer*Samer)\n", - "x_dframe1 = ModelFrame( x_formula, AJR)\n", - "x1_1 = ModelMatrix(x_dframe)\n", - "xx = x1.m;" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

1 rows × 2 columns

EstimateStd_Error
Float64Float64
10.8450270.272094
" - ], - "text/latex": [ - "\\begin{tabular}{r|cc}\n", - "\t& Estimate & Std\\_Error\\\\\n", - "\t\\hline\n", - "\t& Float64 & Float64\\\\\n", - "\t\\hline\n", - "\t1 & 0.845027 & 0.272094 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m1×2 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m Estimate \u001b[0m\u001b[1m Std_Error \u001b[0m\n", - "\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", - "─────┼─────────────────────\n", - " 1 │ 0.845027 0.272094" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rY_1 = HDMjl.rlasso(xx, y);\n", - "rY = rY_1[\"residuals\"]\n", - "rD_1 = HDMjl.rlasso(xx, d);\n", - "rD = rD_1[\"residuals\"]\n", - "rZ_1 = HDMjl.rlasso(xx, z);\n", - "rZ = rZ_1[\"residuals\"]\n", - "\n", - "ivfit_lasso = HDMjl.tsls(rD, rY, rZ)\n", - "DataFrame(Estimate = ivfit_lasso[\"coefficients\"][1,2], Std_Error = ivfit_lasso[\"se\"][1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "using Crayons, Distributions, GLM" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "using Statistics, GLM\n", - "url = \"https://github.com/cran/hdm/raw/master/data/EminentDomain.rda\";\n", - "EminentDomain = load(download(url))[\"EminentDomain\"];\n", - "z = EminentDomain[\"logGDP\"][\"z\"];\n", - "x = EminentDomain[\"logGDP\"][\"x\"];\n", - "d = EminentDomain[\"logGDP\"][\"d\"];\n", - "y = EminentDomain[\"logGDP\"][\"y\"];\n", - "x = x[:, (mean(x, dims = 1) .> 0.05)'];\n", - "z = z[:, (mean(z, dims = 1) .> 0.05)'];" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "ED_ols = lm(hcat(ones(length(vec(y))), hcat(d, x)), vec(y));\n", - "ED_2sls = HDMjl.tsls(d, y, z[:,1:2], x, intercept = false);" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [], - "source": [ - "lasso_IV_Z = HDMjl.rlassoIV(x, d, y, z, select_X = false, select_Z = true);" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 0.414602 0.290249 1.42843 0.153167\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "HDMjl.r_summary(lasso_IV_Z);" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " d1 -0.154276 0.98348\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(lasso_IV_Z);" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m d1 \u001b[0m\n", - "\n", - " 0.415\n" - ] - } - ], - "source": [ - "HDMjl.r_print(lasso_IV_Z)" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [], - "source": [ - "lasso_IV_XZ = HDMjl.rlassoIV(x, d, y, z, select_X = true, select_Z = true);" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 -0.0238347 0.128507 -0.185475 0.852857\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "HDMjl.r_summary(lasso_IV_XZ);" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " d1 -0.275703 0.228033\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(lasso_IV_XZ);" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m d1 \u001b[0m\n", - "\n", - " -0.024\n" - ] - } - ], - "source": [ - "HDMjl.r_print(lasso_IV_XZ)" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 -0.0238347 0.128507 -0.185475 0.852857\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - }, - { - "data": { - "text/plain": [ - "1×2 Matrix{Any}:\n", - " -0.0238347 0.128507" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3])" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 0.414602 0.290249 1.42843 0.153167\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "Estimates and Significance Testing of the effect of target variables in the IV regression model\n", - " \u001b[1m \u001b[0m \u001b[1m coeff. \u001b[0m \u001b[1m se. \u001b[0m \u001b[1m t-value \u001b[0m \u001b[1m p-value \u001b[0m\n", - "\n", - " d1 -0.0238347 0.128507 -0.185475 0.852857\n", - "---\n", - "Signif. codes:\n", - "0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" - ] - } - ], - "source": [ - "using PrettyTables\n", - "table = zeros(4,2)\n", - "table[1,:] = [coef(ED_ols)[2], stderror(ED_ols)[2]]\n", - "table[2,:] = [ED_2sls[\"coefficients\"][1,2], ED_2sls[\"se\"][1]]\n", - "table[3,:] = Matrix(HDMjl.r_summary(lasso_IV_Z)[:,2:3]);\n", - "table[4, :] = Matrix(HDMjl.r_summary(lasso_IV_XZ)[:, 2:3]);" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================= ============ =============\n", - " \u001b[1m \u001b[0m \u001b[1m Estimate \u001b[0m \u001b[1m Std. Error \u001b[0m\n", - "======================= ============ =============\n", - " ols regression 0.00786473 0.00986593\n", - " IV estimation -0.0107097 0.0337652\n", - " selection on Z 0.414602 0.290249\n", - " selection on X and Z -0.0238347 0.128507\n", - "======================= ============ =============\n" - ] - } - ], - "source": [ - "# table1 = DataFrame(table, :auto)\n", - "# table1 = rename(table1, [\"Estimate\", \"Std. Error\"])\n", - "index = [\"ols regression\", \"IV estimation \", \"selection on Z\", \"selection on X and Z\"]\n", - "pretty_table(hcat(index, table), show_row_number = false, header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Inference on Treatment Effects in a High-Dimensional Setting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6.3. Application: 401(k) plan participation." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "url = \"https://github.com/cran/hdm/raw/master/data/pension.rda\";\n", - "pension = load(download(url))[\"pension\"];\n", - "y = pension[:, \"tw\"];\n", - "d = pension[:, \"p401\"];\n", - "z = pension[:, \"e401\"];\n", - "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ------\n", - " Post-Lasso estimation: true\n", - " Intercept: true\n", - " Control: 0\n", - " Total number of variables: 19\n", - " Number of selected variables: 9 \n", - " ------\n", - " \n", - " \n", - "============ ============\n", - " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", - "============ ============\n", - " Intercept -2.07033\n", - " V 1 -0.237913\n", - " V 3 0.618819\n", - " V 4 0.846136\n", - " V 5 1.10569\n", - " V 6 1.34217\n", - " V 10 -0.33151\n", - " V 16 0.0382348\n", - " V 17 0.620232\n", - " V 18 0.335563\n", - "============ ============\n", - "rlassologit\n", - " Estimation and significance tesing of the treatment effect\n", - " Type: ATE\n", - " Bootstrap: none\n", - " \n", - "========== ========= ==========\n", - " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", - "========== ========= ==========\n", - " 10180.1 1930.68 5.2728\n", - "========== ========= ==========\n" - ] - } - ], - "source": [ - "pension_ate = HDMjl.rlassoATE(X, d, y);\n", - "HDMjl.r_summary(pension_ate);" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ------\n", - " Post-Lasso estimation: true\n", - " Intercept: true\n", - " Control: 0\n", - " Total number of variables: 19\n", - " Number of selected variables: 6 \n", - " ------\n", - " \n", - " \n", - "============ ============\n", - " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", - "============ ============\n", - " Intercept -1.79587\n", - " V 1 -0.608675\n", - " V 5 0.622942\n", - " V 6 0.839653\n", - " V 16 0.199394\n", - " V 17 0.643286\n", - " V 18 0.374925\n", - "============ ============\n", - "rlassologit\n", - " Estimation and significance tesing of the treatment effect\n", - " Type: ATET\n", - " Bootstrap: none\n", - " \n", - "========== ========= ==========\n", - " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", - "========== ========= ==========\n", - " 12628.5 2944.43 4.28893\n", - "========== ========= ==========\n" - ] - } - ], - "source": [ - "pension_atet = HDMjl.rlassoATET(X, d, y);\n", - "HDMjl.r_summary(pension_atet);" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ------\n", - " Post-Lasso estimation: true\n", - " Intercept: true\n", - " Control: 0\n", - " Total number of variables: 19\n", - " Number of selected variables: 10 \n", - " ------\n", - " \n", - " \n", - "============ ============\n", - " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", - "============ ============\n", - " Intercept -1.58403\n", - " V 1 -0.329602\n", - " V 3 0.657641\n", - " V 4 0.836492\n", - " V 5 1.11528\n", - " V 6 1.21348\n", - " V 8 0.142622\n", - " V 10 -0.299557\n", - " V 16 0.0516196\n", - " V 17 1.03219\n", - " V 18 0.135758\n", - "============ ============\n", - "rlassologit\n", - " Estimation and significance tesing of the treatment effect\n", - " Type: LATE\n", - " Bootstrap: none\n", - " \n", - "========== ======== ==========\n", - " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", - "========== ======== ==========\n", - " 12992.1 2326.9 5.58344\n", - "========== ======== ==========\n" - ] - } - ], - "source": [ - "pension_late = HDMjl.rlassoLATE(X, d, y, z);\n", - "HDMjl.r_summary(pension_late);" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ------\n", - " Post-Lasso estimation: true\n", - " Intercept: true\n", - " Control: 0\n", - " Total number of variables: 19\n", - " Number of selected variables: 5 \n", - " ------\n", - " \n", - " \n", - "============ ============\n", - " \u001b[1m Variable \u001b[0m \u001b[1m Estimate \u001b[0m\n", - "============ ============\n", - " Intercept -1.25636\n", - " V 1 -0.714199\n", - " V 5 0.677564\n", - " V 6 0.794049\n", - " V 16 0.212127\n", - " V 17 1.05388\n", - "============ ============\n", - "rlassologit\n", - " Estimation and significance tesing of the treatment effect\n", - " Type: LATET\n", - " Bootstrap: none\n", - " \n", - "========== ========= ==========\n", - " \u001b[1m Coeff \u001b[0m \u001b[1m SE \u001b[0m \u001b[1m t.value \u001b[0m\n", - "========== ========= ==========\n", - " 15323.2 3645.28 4.20357\n", - "========== ========= ==========\n" - ] - } - ], - "source": [ - "pension_latet = HDMjl.rlassoLATET(X, d, y, z);\n", - "HDMjl.r_summary(pension_latet);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. The Lasso Methods for Discovery of Significant Causes amongst Many Potential Causes, with Many Controls\n" - ] - }, - { - "cell_type": "code", - "execution_count": 686, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: replacing module HDMjl.\n" - ] - }, - { - "data": { - "text/plain": [ - "Main.HDMjl" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "include(\"E:/causal_ml/hdm_paper/prueba/HDMjl.jl/src/HDMjl.jl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [], - "source": [ - "using CSV\n", - "url = \"https://raw.githubusercontent.com/d2cml-ai/HDMjl.jl/prueba/data/7_.csv\"\n", - "data = DataFrame(CSV.read(download(url), DataFrame));\n", - "n, p = size(data);\n", - "p1 = 20;\n", - "X = data[:,2:end]\n", - "Y = data[:,1];" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \u001b[1m \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", - "\n", - " \u001b[1m X1 \u001b[0m 4.50675 5.22214\n", - " \u001b[1m X2 \u001b[0m -0.321224 0.311879\n", - " \u001b[1m X3 \u001b[0m -0.358448 0.192826\n", - " \u001b[1m X4 \u001b[0m -0.260307 0.293453\n", - " \u001b[1m X5 \u001b[0m -0.28277 0.282461\n", - " \u001b[1m X6 \u001b[0m -0.328361 0.30112\n", - " \u001b[1m X7 \u001b[0m -0.232153 0.306844\n", - " \u001b[1m X8 \u001b[0m -0.0531874 0.479497\n", - " \u001b[1m X9 \u001b[0m -0.193021 0.396693\n", - " \u001b[1m X10 \u001b[0m -0.242849 0.269725\n", - " \u001b[1m X11 \u001b[0m -0.320578 0.215327\n", - " \u001b[1m X12 \u001b[0m -0.315627 0.272158\n", - " \u001b[1m X13 \u001b[0m -0.180324 0.382993\n", - " \u001b[1m X14 \u001b[0m -0.331511 0.39337\n", - " \u001b[1m X15 \u001b[0m -0.329087 0.320235\n", - " \u001b[1m X16 \u001b[0m -0.271623 0.337679\n", - " \u001b[1m X17 \u001b[0m -0.185883 0.423636\n", - " \u001b[1m X18 \u001b[0m -0.373985 0.05162\n", - " \u001b[1m X19 \u001b[0m -0.11292 0.399297\n", - " \u001b[1m X20 \u001b[0m -0.220993 0.260713\n" - ] - } - ], - "source": [ - "HDMjl.r_confint(HDMjl.rlassoEffects(X, Y, index = [1:p1;]), joint = true);" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.8.1", - "language": "julia", - "name": "julia-1.8" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.8.1" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorial/r_file.ipynb b/tutorial/r_file.ipynb index 6ae109e..1d9ec5e 100644 --- a/tutorial/r_file.ipynb +++ b/tutorial/r_file.ipynb @@ -1689,42 +1689,6 @@ "summary(pension.atet)" ] }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "library(remotes)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in install_version(\"glmnet\", \"4.1-3\"): no se pudo encontrar la función \"install_version\"\n", - "output_type": "error", - "traceback": [ - "Error in install_version(\"glmnet\", \"4.1-3\"): no se pudo encontrar la función \"install_version\"\nTraceback:\n" - ] - } - ], - "source": [ - "install_version(\"glmnet\", \"4.1-3\")\n", - "library(glmnet)" - ] - }, { "cell_type": "code", "execution_count": 6, @@ -1761,154 +1725,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in parse(text = x, srcfile = src): :55:3: inesperado 'else'\n54: \n55: else\n ^\n", - "output_type": "error", - "traceback": [ - "Error in parse(text = x, srcfile = src): :55:3: inesperado 'else'\n54: \n55: else\n ^\nTraceback:\n" - ] - } - ], + "outputs": [], "source": [ - "bootstrap = \"none\"\n", - "nRep = 500\n", - "post = TRUE\n", - "intercept = TRUE\n", - "always_takers = TRUE\n", - "never_takers = TRUE\n", - "x = X\n", - "\n", - " x <- as.matrix(x)\n", - " n <- dim(x)[1]\n", - " p <- dim(x)[2]\n", - " checkmate::checkChoice(bootstrap, c(\"none\", \"Bayes\", \"normal\", \"wild\"))\n", - " checkmate::checkLogical(always_takers, never_takers)\n", - " lambda <- 2.2 * sqrt(n) * qnorm(1 - (.1/log(n))/(2 * (2 * p)))\n", - " control <- list(numIter = 15, tol = 10^-5)\n", - " # penalty <- list(method = 'none', lambda.start = rep(lambda, p), c =\n", - " # 1.1, gamma = 0.1)\n", - " penalty <- list(homoscedastic = \"none\", lambda.start = rep(lambda, \n", - " p), c = 1.1, gamma = 0.1)\n", - " indz1 <- (z == 1)\n", - " indz0 <- (z == 0)\n", - " # E[Y|Z = 1,X] = my_z1x\n", - " b_y_z1xL <- rlasso(y[indz1] ~ x[indz1, , drop = FALSE], post = post, \n", - " intercept = intercept, control = control, penalty = penalty)\n", - " my_z1x <- predict(b_y_z1xL, newdata = x)\n", - " # E[Y|Z = 0,X] = my_z0x\n", - " b_y_z0xL <- rlasso(y[indz0] ~ x[indz0, , drop = FALSE], post = post, \n", - " intercept = intercept, control = control, penalty = penalty)\n", - " my_z0x <- predict(b_y_z0xL, newdata = x)\n", - " # E[D|Z = 1,X] = md_z1x\n", - " lambda <- 2.2 * sqrt(n) * qnorm(1 - (.1/log(n))/(2 * (2 * p)))\n", - " penalty <- list(lambda.start = lambda, c = 1.1, gamma = 0.1)\n", - " \n", - " # if (sum(d - z) != 0) {\n", - " # b_d_z1xL <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], post = post, \n", - " # intercept = intercept, penalty = penalty)\n", - " # md_z1x <- predict(b_d_z1xL, newdata = x)\n", - " # } else {\n", - " # md_z1x <- rep(1, n)\n", - " # }\n", - " # \n", - " # E[D|Z = 0,X] = md_z0x\n", - " #penalty <- list(homoscedastic = \"none\", lambda.start = rep(lambda, \n", - " # p), c = 1.1, gamma = 0.1)\n", - " #b_d_z0x <- rlassologit(d[indz0] ~ x[indz0, ], post = post, intercept = intercept, penalty = penalty)\n", - " #md_z0x <- predict(b_d_z0x, newdata = x)\n", - " # md_z0x <- rep(0, n)\n", - " \n", - " # E[D|Z = 1,X] = md_z1x\n", - " if (identical(d,z)) {\n", - " md_z1x <- rep(1, n)\n", - " md_z0x <- rep(0, n)\n", - " }\n", - " \n", - " else {\n", - " \n", - " if (all(always_takers, never_takers)) {\n", - " g_d_z1 <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], \n", - " post = post, intercept = intercept, penalty = penalty)\n", - " md_z1x <- predict(g_d_z1, newdata = x)\n", - " \n", - " g_d_z0 <- rlassologit(d[indz0] ~ x[indz0, , drop = FALSE], \n", - " post = post, intercept = intercept, penalty = penalty)\n", - " md_z0x <- predict(g_d_z0, newdata = x) \n", - " }\n", - " \n", - " \n", - " if (always_takers == FALSE & never_takers == TRUE) {\n", - " g_d_z1 <- rlassologit(d[indz1] ~ x[indz1, , drop = FALSE], \n", - " post = post, intercept = intercept, penalty = penalty)\n", - " md_z1x <- predict(g_d_z1, newdata = x)\n", - " \n", - " ### no always-takers: E[D=1 | Z=0,X] = 0\n", - " md_z0x <- rep(0, n)\n", - " }\n", - " \n", - " if (always_takers == TRUE & never_takers == FALSE) {\n", - " ### no never-takers: E[D=1 | Z=1,X] = 1\n", - " md_z1x <- rep(1, n)\n", - " \n", - " g_d_z0 <- rlassologit(d[indz0] ~ x[indz0, , drop = FALSE], \n", - " post = post, intercept = intercept, penalty = penalty)\n", - " md_z0x <- predict(g_d_z0, newdata = x)\n", - " }\n", - " \n", - " if (always_takers == FALSE & never_takers == FALSE) {\n", - " md_z1x <- rep(1, n)\n", - " md_z0x <- rep(0, n)\n", - " \n", - " message(\"If there are no always-takers and no never-takers, ATE is estimated\")\n", - " }\n", - " }\n", - " \n", - " # E[Z|X] = mz_x\n", - " b_z_xL <- rlassologit(z ~ x, post = post, intercept = intercept)\n", - " mz_x <- predict(b_z_xL, newdata = x)\n", - " mz_x <- mz_x * (mz_x > 1e-12 & mz_x < 1 - 1e-12) + (1 - 1e-12) * (mz_x > \n", - " 1 - 1e-12) + 1e-12 * (mz_x < 1e-12)\n", - " \n", - " eff <- (z * (y - my_z1x)/mz_x - ((1 - z) * (y - my_z0x)/(1 - mz_x)) + \n", - " my_z1x - my_z0x)/mean(z * (d - md_z1x)/mz_x - ((1 - z) * (d - md_z0x)/(1 - \n", - " mz_x)) + md_z1x - md_z0x)\n", - " \n", - " se <- sqrt(var(eff))/sqrt(n)\n", - " late <- mean(eff)\n", - " individual <- eff\n", - " \n", - " object <- list(se = se, te = late, individual = individual, type = \"LATE\", \n", - " call = match.call(), samplesize = n)\n", - " \n", - " if (bootstrap != \"none\") {\n", - " boot <- rep(NA, nRep)\n", - " for (i in 1:nRep) {\n", - " if (bootstrap == \"Bayes\") {\n", - " weights <- rexp(n, rate = 1) - 1\n", - " }\n", - " if (bootstrap == \"normal\") {\n", - " weights <- rnorm(n)\n", - " }\n", - " if (bootstrap == \"wild\") {\n", - " weights <- rnorm(n)/sqrt(2) + (rnorm(n)^2 - 1)/2\n", - " }\n", - " weights <- weights + 1\n", - " boot[i] <- mean(weights * (z * (y - my_z1x)/mz_x - ((1 - z) * \n", - " (y - my_z0x)/(1 - mz_x)) + my_z1x - my_z0x))/mean(weights * \n", - " (z * (d - md_z1x)/mz_x - ((1 - z) * (d - md_z0x)/(1 - mz_x)) + \n", - " md_z1x - md_z0x))\n", - " }\n", - " object$boot.se <- sqrt(var(boot))\n", - " object$type_boot <- bootstrap\n", - " }" + "pension.latet = rlassoLATET(X, d, y, z)\n", + "summary(pension.latet)" ] }, { @@ -1920,7 +1746,17 @@ } }, "outputs": [], - "source": [] + "source": [ + "library(xtable)\n", + "table = matrix(0, 4, 2)\n", + "table[1, ] = summary(pension.ate)[, 1:2]\n", + "table[2, ] = summary(pension.atet)[, 1:2]\n", + "table[3, ] = summary(pension.late)[, 1:2]\n", + "table[4, ] = summary(pension.latet)[, 1:2]\n", + "colnames(table) = c(\"Estimate\", \"Std. Error\")\n", + "rownames(table) = c(\"ATE\", \"ATET \", \"LATE\", \"LATET\")\n", + "tab = xtable(table, digits = c(2, 2, 2))" + ] }, { "cell_type": "code", @@ -1954,50 +1790,19 @@ "source": [ "pension.ate = rlassoATE(X, z, y)\n", "pension.atet = rlassoATET(X, z, y)\n", - "# pension.late = rlassoLATE(X, d, y, z)\n", - "# pension.latet = rlassoLATET(X, d, y, z)\n", - "# # pension.ate = rlassoATE(formExt, data = pension) pension.atet =\n", - "# # rlassoATET(formExt, data = pension) pension.late = rlassoLATE(formZExt, data =\n", - "# # pension) pension.latet = rlassoLATET(formZExt, data = pension)\n", - "# table = matrix(0, 4, 2)\n", - "# table[1, ] = summary(pension.ate)[, 1:2]\n", - "# table[2, ] = summary(pension.atet)[, 1:2]\n", - "# table[3, ] = summary(pension.late)[, 1:2]\n", - "# table[4, ] = summary(pension.latet)[, 1:2]\n", - "# colnames(table) = c(\"Estimate\", \"Std. Error\")\n", - "# rownames(table) = c(\"ATE\", \"ATET \", \"LATE\", \"LATET\")\n", - "# tab = xtable(table, digits = c(2, 2, 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Call:\n", - "rlassoLATET.default(x = x, d = d, y = y, z = z, bootstrap = bootstrap, \n", - " nRep = nRep)\n", - "\n", - "Treatment Effect\n", - "Type: ATET \n", - "Value:\n", - "[1] 10795\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pension.atet" + "pension.late = rlassoLATE(X, d, y, z)\n", + "pension.latet = rlassoLATET(X, d, y, z)\n", + "# pension.ate = rlassoATE(formExt, data = pension) pension.atet =\n", + "# rlassoATET(formExt, data = pension) pension.late = rlassoLATE(formZExt, data =\n", + "# pension) pension.latet = rlassoLATET(formZExt, data = pension)\n", + "table = matrix(0, 4, 2)\n", + "table[1, ] = summary(pension.ate)[, 1:2]\n", + "table[2, ] = summary(pension.atet)[, 1:2]\n", + "table[3, ] = summary(pension.late)[, 1:2]\n", + "table[4, ] = summary(pension.latet)[, 1:2]\n", + "colnames(table) = c(\"Estimate\", \"Std. Error\")\n", + "rownames(table) = c(\"ATE\", \"ATET \", \"LATE\", \"LATET\")\n", + "tab = xtable(table, digits = c(2, 2, 2))" ] }, { @@ -2011,17 +1816,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -2050,20 +1844,6 @@ "Y = D[, 1] * 5 + W[, 1] * 5 + rnorm(n) #Outcome" ] }, - { - "cell_type": "code", - "execution_count": 233, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# data = data.frame(cbind(Y, X))\n", - "# write.csv(data,\"../data/7_.csv\", row.names = FALSE)" - ] - }, { "cell_type": "code", "execution_count": 51, From 492a6a0d8ca0f908cfc275fb0c39cc7a28800786 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 08:45:22 -0500 Subject: [PATCH 26/28] # --- tutorial/HDMjl_tutorial.ipynb | 265 +++++++++++++++++++++++++++------- 1 file changed, 216 insertions(+), 49 deletions(-) diff --git a/tutorial/HDMjl_tutorial.ipynb b/tutorial/HDMjl_tutorial.ipynb index 7c3472c..97a73b4 100644 --- a/tutorial/HDMjl_tutorial.ipynb +++ b/tutorial/HDMjl_tutorial.ipynb @@ -140,6 +140,13 @@ "### 3.2. A Joint Significance Test for Lasso Regression." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example. (Prediction Using Lasso and Post-Lasso) Consider generated data from a sparse linear model:" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -153,6 +160,13 @@ "X = Matrix(dta[:,2:end]);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we estimate the model, print the results, and make in-sample and out-of sample predictions. We can use methods print and summarize to print the results, where the option all can be set to FALSE to limit the print only to the non-zero coefficients.\n" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -312,6 +326,13 @@ "x = Matrix(dta[:,3:end]);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can estimate $\\alpha_0$ by running full least squares:" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -348,6 +369,13 @@ "DataFrame(Estimate = coef(full_fit)[2], Std_Error = stderror(full_fit)[2])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to estimate $\\alpha_0$ is to first partial out the x-variables from $y_i$ and $d_i$, and run least squares on the residuals:" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -396,6 +424,14 @@ "DataFrame(Estimate = coef(partial_fit_ls)[2], Std_Error = stderror(partial_fit_ls)[2])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In high-dimensional settings, we can no longer rely on the full least-squares and instead may rely on\n", + "Lasso or Post-Lasso for partialling out" + ] + }, { "cell_type": "code", "execution_count": 18, @@ -436,6 +472,14 @@ "DataFrame(Estimate = coef(partial_fit_postlasso)[2], Std_Error = stderror(partial_fit_postlasso)[2])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The orthogonal estimating equations method – based on partialling out via Lasso or post-Lasso – is\n", + "implemented by the function rlassoEffect, using method= \"partialling out\":" + ] + }, { "cell_type": "code", "execution_count": 22, @@ -461,24 +505,10 @@ ] }, { - "cell_type": "code", - "execution_count": 20, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m 1 \u001b[0m\n", - "\n", - " 0.973\n" - ] - } - ], "source": [ - "r_print(Eff)" + "Another orthogonal estimating equations method – based on the double selection of covariates – is im\u0002plemented by the the function rlassoEffect, using method= \"double selection" ] }, { @@ -526,6 +556,13 @@ "x = Matrix(data[:,2:end]);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can do inference on a set of variables of interest, e.g. the first, second, third, and the fiftieth:" + ] + }, { "cell_type": "code", "execution_count": 107, @@ -604,6 +641,14 @@ "r_confint(lasso_effects);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also demonstrate the application of joint confidence intervals in an empirical application in\n", + "the next section." + ] + }, { "cell_type": "code", "execution_count": 112, @@ -698,22 +743,11 @@ ] }, { - "cell_type": "code", - "execution_count": 127, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "sum(x[:,32])" + "The parameter estimates for the target parameters, i.e. all coefficients related to gender (i.e. by\n", + "interaction with other variables) are calculated and summarized by the following commands" ] }, { @@ -763,6 +797,13 @@ "r_summary(effects_female);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we estimate and plot confident intervals, first ”pointwise” and then the joint confidence intervals." + ] + }, { "cell_type": "code", "execution_count": 14, @@ -805,6 +846,13 @@ "### 4.4. Application: Estimation of the treatment effect in a linear model with many confounding factors" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we load and prepare the data" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -819,6 +867,13 @@ "X_1 = Matrix(GrowthData[:, Not(1, 2)]);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can estimate the effect of the initial GDP level. First, we estimate by OLS:" + ] + }, { "cell_type": "code", "execution_count": 17, @@ -839,6 +894,13 @@ "ls_effect = DataFrame(Estimate = β, stderror = se);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we estimate the effect by the partialling out by Post-Lasso:" + ] + }, { "cell_type": "code", "execution_count": 18, @@ -863,6 +925,13 @@ "r_summary(lasso_effect);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Third, we estimate the effect by the double selection method:" + ] + }, { "cell_type": "code", "execution_count": 19, @@ -887,6 +956,13 @@ "r_summary(doublesel_effect);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then collect results in a nice latex table:" + ] + }, { "cell_type": "code", "execution_count": 20, @@ -930,6 +1006,13 @@ "### 5.2. Application: Economic Development and Institutions." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we process the data" + ] + }, { "cell_type": "code", "execution_count": 38, @@ -964,33 +1047,19 @@ ] }, { - "cell_type": "code", - "execution_count": 39, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "AJR_Xselect = rlassoIV(x, d, y, z, select_X=true, select_Z=false);" + "Then we estimate an IV model with selection on the X" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coefficients:\n", - "\n", - " \u001b[1m d1 \u001b[0m\n", - "\n", - " 0.845\n" - ] - } - ], + "outputs": [], "source": [ - "r_print(AJR_Xselect)" + "AJR_Xselect = rlassoIV(x, d, y, z, select_X=true, select_Z=false);" ] }, { @@ -1035,6 +1104,17 @@ "r_confint(AJR_Xselect);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is interesting to understand what the procedure above is doing. In essence, it partials out xi from\n", + "yi\n", + ", di and zi using Post-Lasso and applies the 2SLS to the residual quantities.\n", + "Let us investigate partialling out in more detail in this example. We can first try to use OLS for\n", + "partialling out:\n" + ] + }, { "cell_type": "code", "execution_count": 43, @@ -1107,6 +1187,15 @@ "DataFrame(Estimate = ivfit_lm[\"coefficients\"][1,2], Std_Error = ivfit_lm[\"se\"])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the estimates exhibit large standard errors. The imprecision is expected because dimension\n", + "of x is quite large, comparable to the sample size.\n", + "Next, we replace the OLS operator by post-Lasso for partialling out" + ] + }, { "cell_type": "code", "execution_count": 46, @@ -1174,6 +1263,14 @@ "### 5.3. Application: Impact of Eminent Domain Decisions on Economic Outcomes." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we load the data an construct the matrices with the controls (x), instruments (z), outcome (y),\n", + "and treatment variables (d). Here we consider regional GDP as the outcome variable." + ] + }, { "cell_type": "code", "execution_count": 48, @@ -1190,6 +1287,17 @@ "z = z[:, (mean(z, dims = 1) .> 0.05)'];" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, y is the economic outcome, the logarithm of the GDP, d the number of pro\n", + "plaintiff appellate takings decisions in federal circuit court c and year t, x is a matrix with control\n", + "variables, and z is the matrix with instruments. Here we consider socio-economic and demographic\n", + "characteristics of the judges as instruments.\n", + "First, we estimate the effect of the treatment variable by simple OLS and 2SLS using two instruments:" + ] + }, { "cell_type": "code", "execution_count": 50, @@ -1200,6 +1308,13 @@ "ED_2sls = tsls(d, y, z[:,1:2], x, intercept = false);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we estimate the model with selection on the instruments.\n" + ] + }, { "cell_type": "code", "execution_count": 51, @@ -1251,6 +1366,13 @@ "r_confint(lasso_IV_Z);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we do selection on both the x and z variables." + ] + }, { "cell_type": "code", "execution_count": 58, @@ -1294,6 +1416,13 @@ "r_confint(lasso_IV_XZ);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we compare all results" + ] + }, { "cell_type": "code", "execution_count": 62, @@ -1352,6 +1481,13 @@ "### 6.3. Application: 401(k) plan participation." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we start first with the data preparation:" + ] + }, { "cell_type": "code", "execution_count": 107, @@ -1366,6 +1502,14 @@ "X = Matrix(pension[:, [\"i2\", \"i3\", \"i4\", \"i5\", \"i6\", \"i7\", \"a2\", \"a3\", \"a4\", \"a5\", \"fsize\", \"hs\", \"smcol\", \"col\", \"marr\", \"twoearn\", \"db\", \"pira\", \"hown\"]]);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can compute the estimates of the target treatment effect parameters. For ATE and ATET we\n", + "report the the effect of eligibility for 401(k)." + ] + }, { "cell_type": "code", "execution_count": 109, @@ -1471,6 +1615,14 @@ "r_summary(pension_atet);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For LATE and LATET we estimate the effect of 401(k) participation (d) with plan eligibility (z) as\n", + "instrument." + ] + }, { "cell_type": "code", "execution_count": 111, @@ -1570,6 +1722,14 @@ "r_summary(pension_latet);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For LATE and LATET we estimate the effect of 401(k) participation (d) with plan eligibility (z) as\n", + "instrument." + ] + }, { "cell_type": "code", "execution_count": 153, @@ -1657,6 +1817,13 @@ " header = [\" \", \"Estimate\", \"Std. Error\"], tf = tf_simple, nosubheader = true)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we estimate a model including all interaction effects:" + ] + }, { "cell_type": "code", "execution_count": 174, From c28c9b5f53321ef823c6df76a06dcfdb7922125c Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 17:13:54 -0500 Subject: [PATCH 27/28] # --- README.md | 309 +++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 214 insertions(+), 95 deletions(-) diff --git a/README.md b/README.md index 186e225..6c2ab69 100644 --- a/README.md +++ b/README.md @@ -67,44 +67,59 @@ The Post-Lasso procedure fits an OLS regression excluding the variables not prev We can estimate the models using Lasso ```julia -julia> rlasso(X, Y, post = false) -Dict{String, Any} with 15 entries: - "tss" => 6512.49 - "dev" => [6.75884, -13.5819, -2.81122, -3.94462, 17.3342, -1.2805, 3.16503, -4.74853, 6.944, 15.2907 … … - "model" => [0.390896 0.179228 … 2.36678 2.01764; -0.720606 -1.12332 … 0.169248 -0.831435; … ; 1.2457 0.7669… - "loadings" => [1.70326, 1.86338, 2.02143, 1.85829, 1.5416, 1.74625, 1.94735, 1.38887, 1.7228, 1.59366 … 1.65… - "sigma" => 1.71111 - "lambda0" => 81.3601 - "lambda" => [138.577, 151.605, 164.464, 151.191, 125.424, 142.075, 158.436, 112.998, 140.167, 129.66 … 134… - "intercept" => -0.118988 - "iter" => 16 - "residuals" => [1.8377, -2.33523, 0.707157, -0.0587436, 3.81226, 0.637385, 0.117754, -0.209206, 1.49168, 2.2032… - "rss" => 289.863 - "index" => Bool[1, 1, 1, 0, 0, 0, 0, 0, 0, 0 … 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - "beta" => [4.15731, 4.35612, 3.69875, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, … - "options" => Dict{String, Any}("intercept"=>true, "post"=>false, "meanx"=>[-0.217494 0.000263084 … -0.0073734… - "coefficients" => [-0.118988, 4.15731, 4.35612, 3.69875, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0,… +julia> lasso_reg = rlasso(X, Y, post = false) + +julia> r_summary(lasso_reg) + + Post-Lasso Estimation: false + Total number of variables: 100 + Number of selected variables: 9 + --- + +============ ============== + Variable Estimate +============ ============== + Intercept -0.0588327 + V 1 4.84428 + V 2 4.73331 + V 3 4.99116 + V 4 -0.0166025 + V 43 -0.10963 + V 64 0.000400857 + V 69 -0.0359718 + V 94 0.00666321 + V 100 0.166262 +============ ============== + + ---- + Multiple R-squared: 0.9883821717933302 + Adjusted R-squared: 0.9872203889726632 ``` and Post-Lasso ```julia -julia> rlasso(X, Y, post = true) -Dict{String, Any} with 15 entries: - "tss" => 6512.49 - "dev" => [6.75884, -13.5819, -2.81122, -3.94462, 17.3342, -1.2805, 3.16503, -4.74853, 6.944, 15.2907 … … - "model" => [0.390896 0.179228 … 2.36678 2.01764; -0.720606 -1.12332 … 0.169248 -0.831435; … ; 1.2457 0.7669… - "loadings" => [0.93007, 0.992403, 0.863634, 1.00966, 0.876833, 0.858748, 1.00182, 0.892263, 1.07537, 1.01695 … - "sigma" => 0.925277 - "lambda0" => 81.3601 - "lambda" => [75.6706, 80.7419, 70.2653, 82.1458, 71.3392, 69.8678, 81.5081, 72.5946, 87.4919, 82.7389 … 68… - "intercept" => 0.0258985 - "iter" => 5 - "residuals" => [0.733002, 0.22571, 1.06845, 1.34666, 0.818648, 0.575327, -0.519747, 0.985208, -0.000283277, -0.… - "rss" => 84.7576 - "index" => Bool[1, 1, 1, 0, 0, 0, 0, 0, 0, 0 … 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - "beta" => [4.94557, 5.14366, 4.8095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0… - "options" => Dict{String, Any}("intercept"=>true, "post"=>true, "meanx"=>[-0.217494 0.000263084 … -0.00737349… - "coefficients" => [0.0258985, 4.94557, 5.14366, 4.8095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, … +julia> post_lasso_reg = rlasso(X, Y, post = true) + +julia> r_summary(post_lasso_reg) + + Post-Lasso Estimation: true + Total number of variables: 100 + Number of selected variables: 3 + --- + +============ ============== + Variable Estimate +============ ============== + Intercept -0.00682754 + V 1 5.00958 + V 2 4.93178 + V 3 5.17705 +============ ============== + + ---- + Multiple R-squared: 0.9878595381779292 + Adjusted R-squared: 0.9874801487459894 + ``` ### Inference on Target Coefficients through Orthogonal Estimating Equations @@ -130,34 +145,31 @@ julia> d = GrowthData[:, 3]; julia> X = Matrix(GrowthData[:, Not(1, 2, 3)]); -julia> rlassoEffect(X, y, d, method = "double selection") -Dict{String, Any} with 10 entries: - "alpha" => -0.0500059 - "t" => -3.16666 - "se" => 0.0157914 - "no_select" => 0 - "coefficients_reg" => [-0.406451, -0.0500059, -0.0782423, -0.574676, 0.0511529, -0.0470218, 0.212279, -0.000376038, 0… - "sample_size" => 90 - "coefficient" => -0.0500059 - "selection_index" => Bool[1, 0, 1, 0, 1, 0, 0, 0, 0, 0 … 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - "residuals" => Dict("v"=>[0.497555, 0.183798, 0.0705184, -0.123959, 0.0872214, 0.311811, 0.273583, 0.800463, -… - "coefficients" => -0.0500059 +julia> doublesel_effect = rlassoEffect(X, y, d, method = "double selection"); + +julia> r_summary(doublesel_effect); +Estimates and significance testing of the effect of target variables + Row Estimate. Std. Error t value Pr(>|t|) + + 1 -0.05001 0.01579 -3.16719 0.00154 ** +--- +Signif. codes: +0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` We can also use `partialling out` for the orthogonal estimating equations. ```julia -julia> rlassoEffect(X, y, d, method = "partialling out") -Dict{String, Any} with 9 entries: - "alpha" => -0.0498115 - "t" => -3.57421 - "se" => 0.0139364 - "coefficients_reg" => [0.0581009, -0.0755655, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0… - "sample_size" => 90 - "coefficient" => -0.0498115 - "selection_index" => Any[true, false, true, false, true, false, false, false, false, false … false, false, false, … - "residuals" => Dict("v"=>[0.522248, 0.130278, 0.072321, -0.131969, 0.0984047, 0.357306, 0.294098, 0.797784, -0… - "coefficients" => -0.0498115 +julia> lasso_effect = rlassoEffect(X, y, d, method = "partialling out") + +julia> r_summary(lasso_effect); +Estimates and significance testing of the effect of target variables + Row Estimate. Std. Error t value Pr(>|t|) + + 1 -0.04981 0.01394 -3.57317 0.00035 *** +--- +Signif. codes: +0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` ### Instrumental Variable Estimation in High-Dimentional Settings @@ -185,13 +197,16 @@ julia> x = x[:, (mean(x, dims = 1) .> 0.05)']; julia> z = z[:, (mean(z, dims = 1) .> 0.05)']; -julia> rlassoIV(x, d, y, z) -Dict{String, Any} with 5 entries: - "se" => [0.128507] - "sample_size" => 312 - "vcov" => [0.0165139;;] - "residuals" => [-0.20468; 0.0311701; … ; 0.252309; 0.335146;;] - "coefficients" => [-0.0238347;;] +julia> lasso_IV_XZ = rlassoIV(x, d, y, z) + +julia> r_summary(lasso_IV_XZ); +Estimates and Significance Testing of the effect of target variables in the IV regression model + coeff. se. t-value p-value + + d1 -0.02383 0.12851 -0.18543 0.85289 +--- +Signif. codes: +0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` ### Inference on Treatment Effects on a High-Dimensional Setting @@ -211,37 +226,141 @@ julia> z = pension[:, "e401"]; julia> X = Matrix(pension[:, ["i2", "i3", "i4", "i5", "i6", "i7", "a2", "a3", "a4", "a5", "fsize", "hs", "smcol", "col", "marr", "twoearn", "db", "pira", "hown"]]); -julia> rlassoATE(X, d, y) -Dict{String, Any} with 5 entries: - "se" => 1930.68 - "individual" => [-30618.3, -57537.6, -71442.9, 21383.3, -2.32925e5, 3.40765e5, 97143.9, -286.995, 21439.9, 99072.0 … - "sample_size" => 9915 - "te" => 10180.1 - "type" => "ATE" - -julia> rlassoATET(X, d, y) -Dict{String, Any} with 6 entries: - "se" => 2944.43 - "individual" => [-21536.4, -52877.2, -1.44867e5, -2739.29, -307741.0, 7.3912e5, 1.73107e5, 12929.3, -2569.57, 62331.… - "sample_size" => 9915 - "te" => 12628.5 - "type" => "ATET" - -julia> rlassoLATE(X, d, y, z) -Dict{String, Any} with 5 entries: - "se" => 2326.9 - "individual" => [-50526.8, -1.39158e5, -1.37102e5, 38508.0, -6.5644e5, 7.94317e5, 2.50222e5, 71721.0, 39272.5, 1.440… - "sample_size" => 9915 - "te" => 12992.1 - "type" => "LATE" - -julia> rlassoLATET(X, d, y, z) -Dict{String, Any} with 6 entries: - "se" => 3645.28 - "individual" => [-35580.5, -90558.0, -1.83628e5, -5303.13, -8.0766e5, 1.88668e6, 4.94743e5, 18436.0, -4847.72, 74008… - "sample_size" => 9915 - "te" => 15323.2 - "type" => "LATET" +julia> pension_ate = rlassoATE(X, d, y) + +julia> r_summary(pension_ate); + ------ + Post-Lasso estimation: true + Intercept: true + Control: 0 + Total number of variables: 19 + Number of selected variables: 9 + ------ + + +============ ============ + Variable Estimate +============ ============ + Intercept -2.07033 + V 1 -0.237913 + V 3 0.618819 + V 4 0.846136 + V 5 1.10569 + V 6 1.34217 + V 10 -0.33151 + V 16 0.0382348 + V 17 0.620232 + V 18 0.335563 +============ ============ +rlassologit +... + Coeff SE t.value +========== ========= ========== + 10180.1 1930.68 5.2728 +========== ========= ========== + +julia> pension_atet = rlassoATET(X, d, y) + +julia> r_summary(pension_atet); + ------ + Post-Lasso estimation: true + Intercept: true + Control: 0 + Total number of variables: 19 + Number of selected variables: 6 + ------ + + +============ ============ + Variable Estimate +============ ============ + Intercept -1.79587 + V 1 -0.608675 + V 5 0.622942 + V 6 0.839653 + V 16 0.199394 + V 17 0.643286 + V 18 0.374925 +============ ============ +rlassologit + Estimation and significance tesing of the treatment effect + Type: ATET + Bootstrap: none +... + Coeff SE t.value +========== ========= ========== + 12628.5 2944.43 4.28893 +========== ========= ========== + +julia> pension_late = rlassoLATE(X, d, y, z) + +julia> r_summary(pension_late); + ------ + Post-Lasso estimation: true + Intercept: true + Control: 0 + Total number of variables: 19 + Number of selected variables: 10 + ------ + + +============ ============ + Variable Estimate +============ ============ + Intercept -1.58403 + V 1 -0.329602 + V 3 0.657641 + V 4 0.836492 + V 5 1.11528 + V 6 1.21348 + V 8 0.142622 + V 10 -0.299557 + V 16 0.0516196 + V 17 1.03219 + V 18 0.135758 +============ ============ +rlassologit + Estimation and significance tesing of the treatment effect + Type: LATE + Bootstrap: none + +========== ======== ========== + Coeff SE t.value +========== ======== ========== + 12992.1 2326.9 5.58344 +========== ======== ========== + +julia> pension_latet = rlassoLATET(X, d, y, z) + +julia> r_summary(pension_latet); + ------ + Post-Lasso estimation: true + Intercept: true + Control: 0 + Total number of variables: 19 + Number of selected variables: 5 + ------ + + +============ ============ + Variable Estimate +============ ============ + Intercept -1.25636 + V 1 -0.714199 + V 5 0.677564 + V 6 0.794049 + V 16 0.212127 + V 17 1.05388 +============ ============ +rlassologit + Estimation and significance tesing of the treatment effect + Type: LATET + Bootstrap: none + +========== ========= ========== + Coeff SE t.value +========== ========= ========== + 15323.2 3645.28 4.20357 ``` From 9349362dcca78c674f9fc8c29a4051e9c5898e93 Mon Sep 17 00:00:00 2001 From: Franco Caceres Vasquez Date: Fri, 14 Oct 2022 17:16:43 -0500 Subject: [PATCH 28/28] # --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 6c2ab69..b855120 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,6 @@ We can estimate the models using Lasso julia> lasso_reg = rlasso(X, Y, post = false) julia> r_summary(lasso_reg) - Post-Lasso Estimation: false Total number of variables: 100 Number of selected variables: 9 @@ -101,7 +100,6 @@ and Post-Lasso julia> post_lasso_reg = rlasso(X, Y, post = true) julia> r_summary(post_lasso_reg) - Post-Lasso Estimation: true Total number of variables: 100 Number of selected variables: 3

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