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make a display function and include example
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@eohne
eohne committed Jul 29, 2024
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3 changes: 2 additions & 1 deletion .gitignore
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/.vscode
/docs/build
/Example Data
/keys
/keys
/example.jl
113 changes: 112 additions & 1 deletion README.md
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Expand Up @@ -19,4 +19,115 @@ julia> using Pkg; Pkg.add("http://github.com/eohne/LinearHypothesisTest.jl")
```

[docs-stable-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-stable-url]: https://eohne.github.io/LinearHypothesisTest.jl/dev/
[docs-stable-url]: https://eohne.github.io/LinearHypothesisTest.jl/dev/

## Example
The below regression etc are just toy examples to show how the functions work.
### Load some required packages and data:
```julia
using GLM, FixedEffectModels, CSV, DataFrames
using LinearHypothesisTest
data = CSV.File("test/lalonde.csv") |> DataFrame;

m_lm = lm(@formula(treat ~ age + educ + race + married + nodegree ),data);
m_glm = glm(@formula(treat ~ age + educ + race + married + nodegree ),data,Binomial(),LogitLink());
m_fe = reg(data,@formula(treat ~ age + educ + race + married + nodegree + fe(re74)),Vcov.cluster(:Column1));
```
### Example Linear Model (GLM) `m_lm`
```julia
julia> m_lm
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

treat ~ 1 + age + educ + race + married + nodegree

Coefficients:
─────────────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.371995 0.126153 2.95 0.0033 0.124247 0.619744
age 0.00135367 0.00164096 0.82 0.4097 -0.00186898 0.00457632
educ 0.0183177 0.00818698 2.24 0.0256 0.00223943 0.0343959
race: hispan -0.449351 0.0497604 -9.03 <1e-17 -0.547075 -0.351628
race: white -0.539054 0.0334271 -16.13 <1e-48 -0.604701 -0.473407
married -0.109849 0.0336998 -3.26 0.0012 -0.176031 -0.0436663
nodegree 0.103103 0.0439286 2.35 0.0192 0.0168322 0.189373
─────────────────────────────────────────────────────────────────────────────────
```
Let's test if the coefficent of `age + 2*educ = married`:
```julia
julia> LinearHypothesisTests(m_lm, "age + 2*educ = married")

F-Test
------------------------------
St Error: 0.037
Value: 15.9
5% Crit Value: 3.857
P-Value: 0.01%
------------------------------
```

### Example Logit Model (GLM) `m_glm`
```julia
julia> m_glm
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Binomial{Float64}, LogitLink}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

treat ~ 1 + age + educ + race + married + nodegree

Coefficients:
──────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────
(Intercept) -1.55217 0.975408 -1.59 0.1115 -3.46394 0.35959
age 0.0102973 0.0132934 0.77 0.4386 -0.0157572 0.0363518
educ 0.151612 0.0656824 2.31 0.0210 0.0228773 0.280348
race: hispan -2.12709 0.363592 -5.85 <1e-08 -2.83972 -1.41446
race: white -3.12657 0.285138 -10.97 <1e-27 -3.68543 -2.5677
married -0.929693 0.271283 -3.43 0.0006 -1.4614 -0.397989
nodegree 0.787192 0.335072 2.35 0.0188 0.130463 1.44392
──────────────────────────────────────────────────────────────────────────────
```
Let's test whether `age + educ =0`
```julia
julia> LinearHypothesisTests(m_glm, "age + educ = 0")

F-Test
------------------------------
St Error: 0.069
Value: 5.429
5% Crit Value: 3.857
P-Value: 2.01%
------------------------------
```

### Fixed Effects Example:
```julia
julia> m_fe
FixedEffectModel
==================================================================================
Number of obs: 266 Converged: true
dof (model): 6 dof (residuals): 265
R²: 0.359 R² adjusted: 0.320
F-statistic: 19.0944 P-value: 0.000
R² within: 0.294 Iterations: 1
==================================================================================
Estimate Std. Error t-stat Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────────
age 0.00445118 0.00324399 1.37213 0.1712 -0.00193609 0.0108385
educ 0.0220794 0.0137164 1.60971 0.1087 -0.00492753 0.0490864
race: hispan -0.423725 0.100488 -4.21668 <1e-04 -0.621581 -0.225869
race: white -0.531621 0.0599516 -8.86749 <1e-15 -0.649663 -0.413578
married -0.130539 0.0730434 -1.78715 0.0751 -0.274359 0.0132798
nodegree 0.0991155 0.0807286 1.22776 0.2206 -0.0598355 0.258066
==================================================================================
```
```julia
julia> LinearHypothesisTests(m_fe, "age + educ + race: hispan = married")

F-Test
------------------------------
St Error: 0.14
Value: 3.611
5% Crit Value: 3.877
P-Value: 5.85%
------------------------------
```
113 changes: 112 additions & 1 deletion docs/src/index.md
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Expand Up @@ -22,4 +22,115 @@ julia> using Pkg; Pkg.add("http://github.com/eohne/LinearHypothesisTest.jl")
# Function Definition
````@docs
LinearHypothesisTests
````
````

# Example
The below regression etc are just toy examples to show how the functions work.
## Load some required packages and data:
```julia
using GLM, FixedEffectModels, CSV, DataFrames
using LinearHypothesisTest
data = CSV.File("test/lalonde.csv") |> DataFrame;

m_lm = lm(@formula(treat ~ age + educ + race + married + nodegree ),data);
m_glm = glm(@formula(treat ~ age + educ + race + married + nodegree ),data,Binomial(),LogitLink());
m_fe = reg(data,@formula(treat ~ age + educ + race + married + nodegree + fe(re74)),Vcov.cluster(:Column1));
```
## Example Linear Model (GLM) `m_lm`
```julia
julia> m_lm
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

treat ~ 1 + age + educ + race + married + nodegree

Coefficients:
─────────────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
─────────────────────────────────────────────────────────────────────────────────
(Intercept) 0.371995 0.126153 2.95 0.0033 0.124247 0.619744
age 0.00135367 0.00164096 0.82 0.4097 -0.00186898 0.00457632
educ 0.0183177 0.00818698 2.24 0.0256 0.00223943 0.0343959
race: hispan -0.449351 0.0497604 -9.03 <1e-17 -0.547075 -0.351628
race: white -0.539054 0.0334271 -16.13 <1e-48 -0.604701 -0.473407
married -0.109849 0.0336998 -3.26 0.0012 -0.176031 -0.0436663
nodegree 0.103103 0.0439286 2.35 0.0192 0.0168322 0.189373
─────────────────────────────────────────────────────────────────────────────────
```
Let's test if the coefficent of `age + 2*educ = married`:
```julia
julia> LinearHypothesisTests(m_lm, "age + 2*educ = married")

F-Test
------------------------------
St Error: 0.037
Value: 15.9
5% Crit Value: 3.857
P-Value: 0.01%
------------------------------
```

## Example Logit Model (GLM) `m_glm`
```julia
julia> m_glm
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Binomial{Float64}, LogitLink}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

treat ~ 1 + age + educ + race + married + nodegree

Coefficients:
──────────────────────────────────────────────────────────────────────────────
Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────
(Intercept) -1.55217 0.975408 -1.59 0.1115 -3.46394 0.35959
age 0.0102973 0.0132934 0.77 0.4386 -0.0157572 0.0363518
educ 0.151612 0.0656824 2.31 0.0210 0.0228773 0.280348
race: hispan -2.12709 0.363592 -5.85 <1e-08 -2.83972 -1.41446
race: white -3.12657 0.285138 -10.97 <1e-27 -3.68543 -2.5677
married -0.929693 0.271283 -3.43 0.0006 -1.4614 -0.397989
nodegree 0.787192 0.335072 2.35 0.0188 0.130463 1.44392
──────────────────────────────────────────────────────────────────────────────
```
Let's test whether `age + educ =0`
```julia
julia> LinearHypothesisTests(m_glm, "age + educ = 0")

F-Test
------------------------------
St Error: 0.069
Value: 5.429
5% Crit Value: 3.857
P-Value: 2.01%
------------------------------
```

## Fixed Effects Example:
```julia
julia> m_fe
FixedEffectModel
==================================================================================
Number of obs: 266 Converged: true
dof (model): 6 dof (residuals): 265
R²: 0.359 R² adjusted: 0.320
F-statistic: 19.0944 P-value: 0.000
R² within: 0.294 Iterations: 1
==================================================================================
Estimate Std. Error t-stat Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────────
age 0.00445118 0.00324399 1.37213 0.1712 -0.00193609 0.0108385
educ 0.0220794 0.0137164 1.60971 0.1087 -0.00492753 0.0490864
race: hispan -0.423725 0.100488 -4.21668 <1e-04 -0.621581 -0.225869
race: white -0.531621 0.0599516 -8.86749 <1e-15 -0.649663 -0.413578
married -0.130539 0.0730434 -1.78715 0.0751 -0.274359 0.0132798
nodegree 0.0991155 0.0807286 1.22776 0.2206 -0.0598355 0.258066
==================================================================================
```
```julia
julia> LinearHypothesisTests(m_fe, "age + educ + race: hispan = married")

F-Test
------------------------------
St Error: 0.14
Value: 3.611
5% Crit Value: 3.877
P-Value: 5.85%
------------------------------
```
31 changes: 29 additions & 2 deletions src/LinearHypothesisTest.jl
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Expand Up @@ -3,10 +3,38 @@ module LinearHypothesisTest
using Distributions

export LinearHypothesisTests
export display


include("f_chi_tests.jl");

"""
LinearHypothesisTests
#Fields
- `test::String` The type of test.
- `se::Number` Standard error
- `val::Number` Value of the test statistic (F or Chis val)
- `pvalue::Number` The pvalue
- `crit::Number` The critical value for the 5% sign. level
"""
mutable struct LinearHypothesisTests
test::String
se::Number
value::Number
pvalue::Number
crit::Number
end

function Base.display(x::LinearHypothesisTests)
print("\n$(x.test)")
print("\n------------------------------\n")
print("St Error:\t\t$(round(x.se,digits=3))\n")
print("Value:\t\t\t$(round(x.value,digits=3))\n")
print("5% Crit Value:\t\t$(round(x.crit,digits=3))\n")
print("P-Value:\t\t$(round(x.pvalue*100,digits=2))%\n")
print("------------------------------\n")
end

"""
LinearHypothesisTests(lhs::Vector{<:Number},rhs::Number,coef::Vector{<:Number},vcov::Matrix{Float64},dof=nothing;test="")
Expand All @@ -33,7 +61,6 @@ function LinearHypothesisTests(lhs::Vector{<:Number},rhs::Number,coef::Vector{<:
se,SSH,p,crit=_lh_test_stats_chi(lhs,rhs,coef,vcov)
test_type = "Chisq-Test"
end
return (;se = se,SSH=SSH,pval=p,test_type = test_type,crit=crit)
return LinearHypothesisTests(test_type,se, SSH,p, crit)
end

end
8 changes: 4 additions & 4 deletions test/runtests.jl
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Expand Up @@ -11,15 +11,15 @@ h = "educ + age + 2*married=race: hispan"

@testset "LinearHypothesisTests" begin
ta = LinearHypothesisTests(glm_r,h,test="F")
@test ta.SSH ≈ 7.31491822963153
@test ta.value ≈ 7.31491822963153

ta = LinearHypothesisTests(fe_r,h,test="F")
@test ta.SSH ≈ 0.26826901009093007
@test ta.value ≈ 0.26826901009093007

ta = LinearHypothesisTests(glm_r,h,test="C")
@test ta.SSH ≈ 7.31491822963153
@test ta.value ≈ 7.31491822963153

ta = LinearHypothesisTests(fe_r,h,test="C")
@test ta.SSH ≈ 0.26826901009093007
@test ta.value ≈ 0.26826901009093007

end

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