Bayesian

I was talking to a colleague about modeling nominal outcomes in STAN, and wrote up this example. Just put it here in case it’s helpful for anyone (probably myself in the future).

Measurement invariance---that a test measures the same construct in the same way across subgroups---needs to hold for subgroup comparisons to be meaningful. There has been tremendous growth in measurement invariance research in the past decade. …

library(SimDesign) library(cmdstanr) [Update: Use parallel computing with two cores.] Adapted from https://cran.r-project.org/web/packages/SimDesign/vignettes/SimDesign-intro.html See https://mc-stan.org/cmdstanr/articles/cmdstanr.html for using cmdstanr Design <- createDesign(sample_size = c(30, 60, 120, 240), distribution = c('norm', 'chi')) Design ## # A tibble: 8 × 2 ## sample_size distribution ## <dbl> <chr> ## 1 30 norm ## 2 60 norm ## 3 120 norm ## 4 240 norm ## 5 30 chi ## 6 60 chi ## 7 120 chi ## 8 240 chi Generate <- function(condition, fixed_objects = NULL) { N <- condition$sample_size dist <- condition$distribution if(dist == 'norm'){ dat <- rnorm(N, mean = 3) } else if(dist == 'chi'){ dat <- rchisq(N, df = 3) } dat } Define Bayes estimator of the mean with STAN