This package contains my helper functions for working with models fit with RStanARM. The package is named tristan because I'm working with Stan samples and my name is Tristan.
I plan to incrementally update this package whenever I find myself solving the same old problems from an RStanARM model.
You can install tristan from github with:
# install.packages("devtools")
devtools::install_github("tjmahr/tristan")augment_posterior_predict() and augment_posterior_linpred() generate new data predictions and fitted means for new datasets using RStanARM's posterior_predict() and posterior_linpred(). The RStanARM functions return giant matrices of predicted values, but these functions return a long dataframe of predicted values along with the values of the predictor variables. The name augment follows the convention of the broom package where augment() refers to augmenting a data-set with model predictions.
stan_to_lm() and stan_to_glm() provide a quick way to refit an RStanARM model with its classical counterpart.
tidy_etdi(), tidy_hdpi(), tidy_median() and double_etdi() provide helpers for interval calclulation.
draw_coef(), draw_fixef(), draw_ranef() and draw_var_corr() work like the coef(), fixef(), ranef(), and VarCorr() functions for rstanarm mixed effects models, but they return a tidy dataframe of posterior samples instead of point estimates.
Fit a simple linear model.
library(tidyverse)
library(rstanarm)
library(tristan)
# Scaling makes the model run much faster
scale_v <- function(...) as.vector(scale(...))
iris$z.Sepal.Length <- scale_v(iris$Sepal.Length)
iris$z.Petal.Length <- scale_v(iris$Petal.Length)
# Just to ensure that NA values don't break the prediction function
iris[2:6, "Species"] <- NA
model <- stan_glm(
z.Sepal.Length ~ z.Petal.Length * Species,
data = iris,
family = gaussian(),
prior = normal(0, 2))print(model)
#> stan_glm
#> family: gaussian [identity]
#> formula: z.Sepal.Length ~ z.Petal.Length * Species
#> ------
#>
#> Estimates:
#> Median MAD_SD
#> (Intercept) 0.0 0.6
#> z.Petal.Length 0.8 0.5
#> Speciesversicolor -0.4 0.7
#> Speciesvirginica -1.3 0.7
#> z.Petal.Length:Speciesversicolor 1.0 0.5
#> z.Petal.Length:Speciesvirginica 1.3 0.6
#> sigma 0.4 0.0
#>
#> Sample avg. posterior predictive
#> distribution of y (X = xbar):
#> Median MAD_SD
#> mean_PPD 0.0 0.0
#>
#> ------
#> For info on the priors used see help('prior_summary.stanreg').Let's plot some samples of the model's linear prediction for the mean. If classical model provide a single "line of best fit", Bayesian models provide a distribution "lines of plausible fit". We'd like to visualize 100 of these lines alongside the raw data.
In classical models, getting the fitted values is easily done by adding a column of fitted() values to dataframe or using predict() on some new observations.
Because the posterior of this model contains 4000 such fitted or predicted values, more data wrangling and reshaping is required. augment_posterior_linpred() automates this task by producing a long dataframe with one row per posterior fitted value.
Here, we tell the model that we want just 100 of those lines (i.e., 100 samples from the posterior distribution).
# Get the fitted means of the data for 100 samples of the posterior distribution
linear_preds <- augment_posterior_linpred(
model = model,
newdata = iris,
nsamples = 100)
linear_preds
#> # A tibble: 14,500 x 10
#> .observation .draw .posterior_value Sepal.Length Sepal.Width
#> <int> <int> <dbl> <dbl> <dbl>
#> 1 1 1 -0.9315514 5.1 3.5
#> 2 1 2 -0.9565635 5.1 3.5
#> 3 1 3 -0.8873909 5.1 3.5
#> 4 1 4 -1.0384317 5.1 3.5
#> 5 1 5 -1.0680745 5.1 3.5
#> 6 1 6 -0.9813867 5.1 3.5
#> 7 1 7 -1.0067503 5.1 3.5
#> 8 1 8 -1.1124061 5.1 3.5
#> 9 1 9 -1.0205787 5.1 3.5
#> 10 1 10 -0.9786489 5.1 3.5
#> # ... with 14,490 more rows, and 5 more variables: Petal.Length <dbl>,
#> # Petal.Width <dbl>, Species <fctr>, z.Sepal.Length <dbl>,
#> # z.Petal.Length <dbl>To plot the lines, we have to unscale the model's fitted values.
unscale <- function(scaled, original) {
(scaled * sd(original, na.rm = TRUE)) + mean(original, na.rm = TRUE)
}
linear_preds$.posterior_value <- unscale(
scaled = linear_preds$.posterior_value,
original = iris$Sepal.Length)Now, we can do a spaghetti plot of linear predictions.
ggplot(iris) +
aes(x = Petal.Length, y = Sepal.Length, color = Species) +
geom_point() +
geom_line(aes(y = .posterior_value, group = interaction(Species, .draw)),
data = linear_preds, alpha = .20)
augment_posterior_predict() similarly tidies values from the posterior_predict() function. posterior_predict() incorporates the error terms from the model, so it can be used predict new fake data from the model.
Let's create a range of values within each species, and get posterior predictions for those values.
library(modelr)
# Within each species, generate a sequence of z.Petal.Length values
newdata <- iris %>%
group_by(Species) %>%
# Expand the range x value a little bit so that the points do not bulge out
# left/right sides of the uncertainty ribbon in the plot
data_grid(z.Petal.Length = z.Petal.Length %>%
seq_range(n = 80, expand = .10)) %>%
ungroup()
newdata$Petal.Length <- unscale(newdata$z.Petal.Length, iris$Petal.Length)
# Get posterior predictions
posterior_preds <- augment_posterior_predict(model, newdata)
posterior_preds
#> # A tibble: 960,000 x 6
#> .observation .draw .posterior_value Species z.Petal.Length Petal.Length
#> <int> <int> <dbl> <fctr> <dbl> <dbl>
#> 1 1 1 -1.3809285 setosa -1.587834 0.955
#> 2 1 2 -1.0053184 setosa -1.587834 0.955
#> 3 1 3 -2.0321063 setosa -1.587834 0.955
#> 4 1 4 -1.1646054 setosa -1.587834 0.955
#> 5 1 5 -1.1044528 setosa -1.587834 0.955
#> 6 1 6 -0.9158609 setosa -1.587834 0.955
#> 7 1 7 -0.8546550 setosa -1.587834 0.955
#> 8 1 8 -1.6653748 setosa -1.587834 0.955
#> 9 1 9 -1.5790256 setosa -1.587834 0.955
#> 10 1 10 -1.5077607 setosa -1.587834 0.955
#> # ... with 959,990 more rows
posterior_preds$.posterior_value <- unscale(
scaled = posterior_preds$.posterior_value,
original = iris$Sepal.Length)Take a second to appreciate the size of that table. It has 4000 predictions for each the 320 observations in newdata.
Now, we might inspect whether 95% of the data falls inside the 95% interval of posterior-predicted values (among other questions we could ask the model.)
ggplot(iris) +
aes(x = Petal.Length, y = Sepal.Length, color = Species) +
geom_point() +
stat_summary(aes(y = .posterior_value, group = Species, color = NULL),
data = posterior_preds, alpha = 0.4, fill = "grey60",
geom = "ribbon",
fun.data = median_hilow, fun.args = list(conf.int = .95))
There are some quick functions for refitting RStanARM models using classical versions. These functions basically inject the values of model$formula and model$data into lm() or glm(). (Seriously, see the call sections in the two outputs below.) Therefore, don't use these functions for serious comparisons of classical versus Bayesian models.
Refit with a linear model:
arm::display(stan_to_lm(model))
#> Please manually fit model if original model used any
#> arguments besides `formula` and `data`.
#> stats::lm(formula = stats::formula(model), data = model$data,
#> weights = if (length(model$weights) == 0) NULL else model$weights,
#> offset = model$offset)
#> coef.est coef.se
#> (Intercept) 0.33 0.80
#> z.Petal.Length 1.03 0.61
#> Speciesversicolor -0.72 0.81
#> Speciesvirginica -1.59 0.83
#> z.Petal.Length:Speciesversicolor 0.74 0.65
#> z.Petal.Length:Speciesvirginica 1.10 0.64
#> ---
#> n = 145, k = 6
#> residual sd = 0.41, R-Squared = 0.84Refit with a generalized linear model:
arm::display(stan_to_glm(model))
#> Please manually fit model if original model used any
#> arguments besides `formula`, `family`, and `data`.
#> stats::glm(formula = stats::formula(model), family = model$family,
#> data = model$data, weights = if (length(model$weights) ==
#> 0) NULL else model$weights, offset = model$offset)
#> coef.est coef.se
#> (Intercept) 0.33 0.80
#> z.Petal.Length 1.03 0.61
#> Speciesversicolor -0.72 0.81
#> Speciesvirginica -1.59 0.83
#> z.Petal.Length:Speciesversicolor 0.74 0.65
#> z.Petal.Length:Speciesvirginica 1.10 0.64
#> ---
#> n = 145, k = 6
#> residual deviance = 23.3, null deviance = 142.0 (difference = 118.7)
#> overdispersion parameter = 0.2
#> residual sd is sqrt(overdispersion) = 0.41calculate_classical_r2() returns the unadjusted R2 for each draw of the posterior distribution.
df_r2 <- data_frame(
R2 = calculate_model_r2(model)
)
df_r2
#> # A tibble: 4,000 x 1
#> R2
#> <dbl>
#> 1 0.8269287
#> 2 0.8286182
#> 3 0.8309418
#> 4 0.8337803
#> 5 0.8316979
#> 6 0.8295612
#> 7 0.8325530
#> 8 0.8318201
#> 9 0.8276650
#> 10 0.8280340
#> # ... with 3,990 more rowsTwo more helper functions compute tidy data-frames of posterior density intervals. tidy_hpdi() provides the highest-density posterior interval for model parameters, while tidy_etdi() computes the equal-tailed density intervals (the typical sort of intervals used for uncertain intervals.)
tidy_hpdi(model)
#> # A tibble: 7 x 5
#> term interval density lower upper
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 (Intercept) HPDI 0.9 -1.03894068 1.0757546
#> 2 z.Petal.Length HPDI 0.9 0.03453807 1.6560589
#> 3 Speciesversicolor HPDI 0.9 -1.42748700 0.6884022
#> 4 Speciesvirginica HPDI 0.9 -2.38524882 -0.2048360
#> 5 z.Petal.Length:Speciesversicolor HPDI 0.9 0.06651501 1.8544207
#> 6 z.Petal.Length:Speciesvirginica HPDI 0.9 0.46379409 2.2085139
#> 7 sigma HPDI 0.9 0.37288522 0.4534112
tidy_etdi(model)
#> # A tibble: 7 x 5
#> term interval density lower upper
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 (Intercept) ETDI 0.9 -1.01524062 1.1076903
#> 2 z.Petal.Length ETDI 0.9 -0.01548979 1.6149531
#> 3 Speciesversicolor ETDI 0.9 -1.49737884 0.6347173
#> 4 Speciesvirginica ETDI 0.9 -2.37986191 -0.1925737
#> 5 z.Petal.Length:Speciesversicolor ETDI 0.9 0.07317261 1.8652106
#> 6 z.Petal.Length:Speciesvirginica ETDI 0.9 0.44271242 2.1940697
#> 7 sigma ETDI 0.9 0.37262531 0.4531580The functions also work on single vectors of numbers, for quick one-off calculations.
r2_intervals <- bind_rows(
tidy_hpdi(calculate_model_r2(model)),
tidy_etdi(calculate_model_r2(model))
)
r2_intervals
#> # A tibble: 2 x 5
#> term interval density lower upper
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 calculate_model_r2(model) HPDI 0.9 0.8234095 0.8349006
#> 2 calculate_model_r2(model) ETDI 0.9 0.8211217 0.8338705We can compare the difference between highest-posterior density intervals and equal-tailed intervals.
ggplot(df_r2) +
aes(x = R2) +
geom_histogram() +
geom_vline(aes(xintercept = lower, color = interval),
data = r2_intervals, size = 1, linetype = "dashed") +
geom_vline(aes(xintercept = upper, color = interval),
data = r2_intervals, size = 1, linetype = "dashed") +
labs(color = "90% interval",
x = expression(R^2),
y = "Num. posterior samples")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
double_etdi() that provides all the values needed to make a double interval (caterpillar) plot.
df1 <- double_etdi(calculate_model_r2(model), .95, .90)
df2 <- double_etdi(model, .95, .90)
df <- bind_rows(df1, df2)
df
#> # A tibble: 8 x 9
#> term outer_lower inner_lower estimate
#> <chr> <dbl> <dbl> <dbl>
#> 1 calculate_model_r2(model) 0.8189694 0.82112172 0.829535546
#> 2 (Intercept) -1.2148612 -1.01524062 0.006074363
#> 3 z.Petal.Length -0.1528441 -0.01548979 0.780169581
#> 4 Speciesversicolor -1.6635555 -1.49737884 -0.395108335
#> 5 Speciesvirginica -2.5544321 -2.37986191 -1.256114326
#> 6 z.Petal.Length:Speciesversicolor -0.0711872 0.07317261 0.974786719
#> 7 z.Petal.Length:Speciesvirginica 0.2931280 0.44271242 1.323619152
#> 8 sigma 0.3660297 0.37262531 0.410974065
#> # ... with 5 more variables: inner_upper <dbl>, outer_upper <dbl>,
#> # est_type <chr>, inner_density <dbl>, outer_density <dbl>
ggplot(df) +
aes(x = estimate, y = term, yend = term) +
geom_vline(color = "white", size = 2, xintercept = 0) +
geom_segment(aes(x = outer_lower, xend = outer_upper)) +
geom_segment(aes(x = inner_lower, xend = inner_upper), size = 2) +
geom_point(size = 4)
This is such a finicky area that it will never 100% of the time but it works well enough, especially if the new observations are subsets of the original dataset.
cbpp <- lme4::cbpp
m2 <- stan_glmer(
cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial,
prior_covariance = decov(4, 1, 1),
prior = normal(0, 1))
#> trying deprecated constructor; please alert package maintainerAdd a new herd to the data.
# Add a new herd
newdata <- cbpp %>%
tibble::add_row(herd = "16", incidence = 0, size = 20, period = 1) %>%
tibble::add_row(herd = "16", incidence = 0, size = 20, period = 2) %>%
tibble::add_row(herd = "16", incidence = 0, size = 20, period = 3) %>%
tibble::add_row(herd = "16", incidence = 0, size = 20, period = NA)
d <- augment_posterior_predict(m2, newdata) %>%
dplyr::filter(herd == "16")
d
#> # A tibble: 12,000 x 7
#> .observation .draw .posterior_value herd incidence size period
#> <int> <int> <int> <fctr> <dbl> <dbl> <fctr>
#> 1 57 1 4 16 0 20 1
#> 2 57 2 3 16 0 20 1
#> 3 57 3 3 16 0 20 1
#> 4 57 4 2 16 0 20 1
#> 5 57 5 6 16 0 20 1
#> 6 57 6 6 16 0 20 1
#> 7 57 7 2 16 0 20 1
#> 8 57 8 5 16 0 20 1
#> 9 57 9 9 16 0 20 1
#> 10 57 10 3 16 0 20 1
#> # ... with 11,990 more rowsPlot the predictions.
ggplot(d, aes(x = interaction(herd, period), y = .posterior_value / size)) +
stat_summary(fun.data = median_hilow) +
theme_grey(base_size = 8)
coef, fixef() and ranef() only give the point estimates for model effects. draw_coef(), draw_fixef() and draw_ranef() will sample them from the posterior distribution.
ranef(m2)
#> $herd
#> (Intercept)
#> 1 0.585052547
#> 2 -0.307232667
#> 3 0.398094621
#> 4 0.007847365
#> 5 -0.218798043
#> 6 -0.459476477
#> 7 0.907308051
#> 8 0.670980547
#> 9 -0.289507045
#> 10 -0.586808540
#> 11 -0.124260855
#> 12 -0.075497166
#> 13 -0.764195670
#> 14 1.039507689
#> 15 -0.589869444
draw_ranef(m2, 100)
#> # A tibble: 1,500 x 6
#> .draw .group_var .group .term .parameter
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 herd 1 (Intercept) b[(Intercept) herd:1]
#> 2 2 herd 1 (Intercept) b[(Intercept) herd:1]
#> 3 3 herd 1 (Intercept) b[(Intercept) herd:1]
#> 4 4 herd 1 (Intercept) b[(Intercept) herd:1]
#> 5 5 herd 1 (Intercept) b[(Intercept) herd:1]
#> 6 6 herd 1 (Intercept) b[(Intercept) herd:1]
#> 7 7 herd 1 (Intercept) b[(Intercept) herd:1]
#> 8 8 herd 1 (Intercept) b[(Intercept) herd:1]
#> 9 9 herd 1 (Intercept) b[(Intercept) herd:1]
#> 10 10 herd 1 (Intercept) b[(Intercept) herd:1]
#> # ... with 1,490 more rows, and 1 more variables: .posterior_value <dbl>
fixef(m2)
#> (Intercept) period2 period3 period4
#> -1.4923155 -0.8623434 -0.9683822 -1.3216858
draw_fixef(m2, 100)
#> # A tibble: 400 x 3
#> .draw .parameter .posterior_value
#> <int> <chr> <dbl>
#> 1 1 (Intercept) -1.369444
#> 2 2 (Intercept) -1.535243
#> 3 3 (Intercept) -1.209551
#> 4 4 (Intercept) -1.488341
#> 5 5 (Intercept) -1.269371
#> 6 6 (Intercept) -1.145897
#> 7 7 (Intercept) -1.871921
#> 8 8 (Intercept) -1.051208
#> 9 9 (Intercept) -1.250272
#> 10 10 (Intercept) -1.368299
#> # ... with 390 more rows
coef(m2)
#> $herd
#> (Intercept) period2 period3 period4
#> 1 -0.9072630 -0.8623434 -0.9683822 -1.321686
#> 2 -1.7995482 -0.8623434 -0.9683822 -1.321686
#> 3 -1.0942209 -0.8623434 -0.9683822 -1.321686
#> 4 -1.4844681 -0.8623434 -0.9683822 -1.321686
#> 5 -1.7111136 -0.8623434 -0.9683822 -1.321686
#> 6 -1.9517920 -0.8623434 -0.9683822 -1.321686
#> 7 -0.5850075 -0.8623434 -0.9683822 -1.321686
#> 8 -0.8213350 -0.8623434 -0.9683822 -1.321686
#> 9 -1.7818226 -0.8623434 -0.9683822 -1.321686
#> 10 -2.0791240 -0.8623434 -0.9683822 -1.321686
#> 11 -1.6165764 -0.8623434 -0.9683822 -1.321686
#> 12 -1.5678127 -0.8623434 -0.9683822 -1.321686
#> 13 -2.2565112 -0.8623434 -0.9683822 -1.321686
#> 14 -0.4528078 -0.8623434 -0.9683822 -1.321686
#> 15 -2.0821850 -0.8623434 -0.9683822 -1.321686
#>
#> attr(,"class")
#> [1] "coef.mer"
draw_coef(m2, 100)
#> # A tibble: 6,000 x 9
#> .draw .group_var .group .term .fixef_parameter
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 herd 1 (Intercept) (Intercept)
#> 2 1 herd 1 period2 period2
#> 3 1 herd 1 period3 period3
#> 4 1 herd 1 period4 period4
#> 5 1 herd 10 (Intercept) (Intercept)
#> 6 1 herd 10 period2 period2
#> 7 1 herd 10 period3 period3
#> 8 1 herd 10 period4 period4
#> 9 1 herd 11 (Intercept) (Intercept)
#> 10 1 herd 11 period2 period2
#> # ... with 5,990 more rows, and 4 more variables: .ranef_parameter <chr>,
#> # .fixef_part <dbl>, .ranef_part <dbl>, .total <dbl>
draw_coef(m2, 100) %>%
select(.draw, .group_var, .group, .fixef_parameter, .total) %>%
tidyr::spread(.fixef_parameter, .total)
#> # A tibble: 1,500 x 7
#> .draw .group_var .group `(Intercept)` period2 period3 period4
#> * <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 herd 1 -1.6614772 -0.8450444 -0.8612593 -0.9988943
#> 2 1 herd 10 -2.1102399 -0.8450444 -0.8612593 -0.9988943
#> 3 1 herd 11 -1.2134057 -0.8450444 -0.8612593 -0.9988943
#> 4 1 herd 12 -0.8685274 -0.8450444 -0.8612593 -0.9988943
#> 5 1 herd 13 -2.2248655 -0.8450444 -0.8612593 -0.9988943
#> 6 1 herd 14 -0.4510703 -0.8450444 -0.8612593 -0.9988943
#> 7 1 herd 15 -2.4719634 -0.8450444 -0.8612593 -0.9988943
#> 8 1 herd 2 -1.5590489 -0.8450444 -0.8612593 -0.9988943
#> 9 1 herd 3 -0.5611966 -0.8450444 -0.8612593 -0.9988943
#> 10 1 herd 4 -1.6242694 -0.8450444 -0.8612593 -0.9988943
#> # ... with 1,490 more rowsWe can visually confirm that coef() uses the posterior median values.
all_coefs <- draw_coef(m2)
medians <- coef(m2) %>%
lapply(tibble::rownames_to_column, ".group") %>%
bind_rows(.id = ".group_var") %>%
tidyr::gather(.term, .posterior_median, -.group_var, -.group)
ggplot(all_coefs) +
aes(x = interaction(.group_var, .group), y = .total) +
stat_summary(fun.data = median_hilow) +
facet_wrap(".term") +
geom_point(aes(y = .posterior_median), data = medians, color = "red")
draw_var_corr() provides an analogue to VarCorr(). Let's compare the prior distribution of correlation terms using different settings for decov() prior.
sleep <- lme4::sleepstudy
m0 <- stan_glmer(
Reaction ~ Days + (Days | Subject),
data = sleep,
prior_covariance = decov(.25, 1, 1),
prior = normal(0, 1),
prior_PD = TRUE,
chains = 1)
#> trying deprecated constructor; please alert package maintainer
m1 <- stan_glmer(
Reaction ~ Days + (Days | Subject),
data = sleep,
prior_covariance = decov(1, 1, 1),
prior = normal(0, 1),
prior_PD = TRUE,
chains = 1)
#> trying deprecated constructor; please alert package maintainer
m2 <- stan_glmer(
Reaction ~ Days + (Days | Subject),
data = sleep,
prior_covariance = decov(2, 1, 1),
prior = normal(0, 1),
prior_PD = TRUE,
chains = 1)
#> trying deprecated constructor; please alert package maintainer
m3 <- stan_glmer(
Reaction ~ Days + (Days | Subject),
data = sleep,
prior_covariance = decov(4, 1, 1),
prior = normal(0, 1),
prior_PD = TRUE,
chains = 1)
#> trying deprecated constructor; please alert package maintainer
m4 <- stan_glmer(
Reaction ~ Days + (Days | Subject),
data = sleep,
prior_covariance = decov(8, 1, 1),
prior = normal(0, 1),
prior_PD = TRUE,
chains = 1)
#> trying deprecated constructor; please alert package maintainerVarCorr() returns the mean variance-covariance/standard-deviation-correlation values.
VarCorr(m3)
#> Groups Name Std.Dev. Corr
#> Subject (Intercept) 8116.1
#> Days 5426.2 0.128
#> Residual 1351.4
as.data.frame(VarCorr(m3))
#> grp var1 var2 vcov sdcor
#> 1 Subject (Intercept) <NA> 65871265 8116.1114205
#> 2 Subject Days <NA> 29443287 5426.1669244
#> 3 Subject (Intercept) Days 5645130 0.1281837
#> 4 Residual <NA> <NA> 1826242 1351.3853075In this function we compute the sd-cor matrix for each posterior sample.
vcs0 <- draw_var_corr(m0)
vcs1 <- draw_var_corr(m1)
vcs2 <- draw_var_corr(m2)
vcs3 <- draw_var_corr(m3)
vcs4 <- draw_var_corr(m4)
vcs3
#> # A tibble: 3,000 x 7
#> .draw .parameter grp var1 var2
#> <int> <chr> <chr> <chr> <chr>
#> 1 73 Sigma[Subject:(Intercept),(Intercept)] Subject (Intercept) <NA>
#> 2 73 Sigma[Subject:Days,Days] Subject Days <NA>
#> 3 73 Sigma[Subject:Days,(Intercept)] Subject (Intercept) Days
#> 4 243 Sigma[Subject:(Intercept),(Intercept)] Subject (Intercept) <NA>
#> 5 243 Sigma[Subject:Days,Days] Subject Days <NA>
#> 6 243 Sigma[Subject:Days,(Intercept)] Subject (Intercept) Days
#> 7 600 Sigma[Subject:(Intercept),(Intercept)] Subject (Intercept) <NA>
#> 8 600 Sigma[Subject:Days,Days] Subject Days <NA>
#> 9 600 Sigma[Subject:Days,(Intercept)] Subject (Intercept) Days
#> 10 310 Sigma[Subject:(Intercept),(Intercept)] Subject (Intercept) <NA>
#> # ... with 2,990 more rows, and 2 more variables: vcov <dbl>, sdcor <dbl>Filter to just the correlations.
cors <- bind_rows(
vcs0 %>% mutate(model = "decov(.25, 1, 1)"),
vcs1 %>% mutate(model = "decov(1, 1, 1)"),
vcs2 %>% mutate(model = "decov(2, 1, 1)"),
vcs3 %>% mutate(model = "decov(4, 1, 1)"),
vcs4 %>% mutate(model = "decov(8, 1, 1)")) %>%
filter(!is.na(var2))
ggplot(cors) +
aes(y = sdcor, x = .parameter, color = model) +
stat_summary(fun.data = median_hilow, geom = "linerange",
fun.args = list(conf.int = .9),
position = position_dodge(-.8)) +
stat_summary(fun.data = median_hilow, size = 1.5, geom = "linerange",
fun.args = list(conf.int = .7), show.legend = FALSE,
position = position_dodge(-.8)) +
stat_summary(fun.data = median_hilow, size = 2.5, geom = "linerange",
fun.args = list(conf.int = .5), show.legend = FALSE,
position = position_dodge(-.8)) +
coord_flip() +
facet_wrap(".parameter") +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major.y = element_blank()) +
labs(x = NULL, y = "correlation", color = "prior") +
ggtitle("Degrees of regularization with LKJ prior")
#> Warning: position_dodge requires non-overlapping x intervals
#> Warning: position_dodge requires non-overlapping x intervals
#> Warning: position_dodge requires non-overlapping x intervals
ggplot(cors) +
aes(x = sdcor, color = model) +
stat_density(geom = "line", adjust = .2) +
facet_wrap(".parameter")