Nothing
test_that("model_index is sampled correctly", {
data <- dreamer_data_linear(
n_cohorts = c(10, 20, 30),
dose = c(0, 2.5, 5),
b1 = 1,
b2 = 2,
sigma = 3
)
output <- dreamer_mcmc(
data = data,
n_adapt = 1e3,
n_burn = 1e3,
n_iter = 1e3,
n_chains = 1,
silent = TRUE,
convergence_warn = FALSE,
mod_linear = model_linear(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
shape = 1,
rate = .001,
w_prior = 0
),
mod_logquad = model_logquad(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
shape = 1,
rate = .001,
w_prior = 1
)
)
expect_true(all(attr(output, "model_index") == 2))
})
test_that("model names attributes", {
data <- dreamer_data_linear(
n_cohorts = c(10, 20, 30),
dose = c(0, 2.5, 5),
b1 = 1,
b2 = 2,
sigma = 3
)
output <- dreamer_mcmc(
data = data,
n_adapt = 1e3,
n_burn = 1e3,
n_iter = 1e3,
n_chains = 1,
silent = TRUE,
convergence_warn = FALSE,
mod_logquad = model_logquad(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
shape = 1,
rate = .001,
w_prior = .5
),
mod_linear = model_linear(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
shape = 1,
rate = .001,
w_prior = .5
)
)
expect_equal(attr(output, "model_names"), c("mod_logquad", "mod_linear"))
expect_equal(attr(output$mod_linear, "model_name"), "mod_linear")
expect_equal(attr(output$mod_logquad, "model_name"), "mod_logquad")
})
test_that("jags modules are restored after MCMC", {
data <- dreamer_data_linear(
n_cohorts = c(10, 20, 30),
dose = c(0, 2.5, 5),
b1 = 1,
b2 = 2,
sigma = 3
)
original_modules <- rjags::list.modules()
output <- dreamer_mcmc(
data = data,
n_adapt = 1e3,
n_burn = 1e3,
n_iter = 1e3,
n_chains = 1,
silent = TRUE,
convergence_warn = FALSE,
mod_linear = model_linear(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
shape = 1,
rate = .001,
w_prior = 0
),
mod_logquad = model_logquad(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
shape = 1,
rate = .001,
w_prior = 1
)
)
expect_equal(rjags::list.modules(), original_modules)
})
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