tests/testthat/test_brm.R

In n-kall/priorsense: Prior Diagnostics and Sensitivity Analysis

## library(brms)

## set.seed(1234)
## dat <- data.frame(
##   count = rpois(236, lambda = 20),
##   visit = factor(rep(1:4, each = 59)),
##   patient = factor(rep(1:59, 4)),
##   Age = rnorm(236),
##   Trt = factor(sample(0:1, 236, TRUE)),
##   AgeSD = abs(rnorm(236, 1)),
##   Exp = factor(sample(1:5, 236, TRUE), ordered = TRUE),
##   volume = rnorm(236),
##   gender = factor(c(rep("m", 30), rep("f", 29)))
## )

## warmup <- 150
## iter <- 200
## chains <- 1
## stan_model_args <- list(save_dso = FALSE)

## brmsfit_example1 <- brm(
##   bf(count ~ Trt*Age + mo(Exp) + s(Age) + volume +
##       offset(Age) + (1+Trt|visit) + arma(visit, patient),
##      sigma ~ Trt),
##   data = dat, family = student(),
##   prior = set_prior("normal(0,2)", class = "b") +
##     set_prior("cauchy(0,2)", class = "sd") +
##     set_prior("normal(0,3)", dpar = "sigma"),
##   sample_prior = TRUE,
##   warmup = warmup, iter = iter, chains = chains,
##   stan_model_args = stan_model_args, rename = TRUE,
##   save_pars = save_pars(all = TRUE),
##   backend = "rstan"
## )

## test_that("priorsense_data is created", {
##   expect_s3_class(
##     create_priorsense_data(
##       brmsfit_example1
##     ),
##     "priorsense_data"
##   )
## }
## )

## test_that("powerscale returns powerscaled_draws", {
##   expect_s3_class(
##     powerscale(
##       x = brmsfit_example1,
##       component = "prior",
##       alpha = 0.8
##     ),
##     "powerscaled_draws"
##   )
##   expect_s3_class(
##     powerscale(
##       x = brmsfit_example1,
##       component = "likelihood",
##       alpha = 1.3,
##       prediction = \(x) priorsense::predictions_as_draws(x, brms::posterior_epred),
##       moment_match = TRUE
##     ),
##     "powerscaled_draws"
##   )  
## }
## )

## test_that("powerscale_sequence returns powerscaled_sequence", {
##   expect_s3_class(
##     suppressWarnings(powerscale_sequence(
##       x = brmsfit_example1
##     )),
##     "powerscaled_sequence"
##   )
## }
## )

## test_that("powerscale_sensitivity returns powerscaled_sensitivity_summary", {
##   expect_s3_class(
##     powerscale_sensitivity(
##       x = brmsfit_example1
##     ),
##     "powerscaled_sensitivity_summary"
##   )
## }
## )