test_that("blavaan, all", {
skip_on_cran()
skip_if_not_or_load_if_installed("blavaan")
skip_if_not_or_load_if_installed("lavaan")
skip_if_not_or_load_if_installed("rstan")
skip_if_not_or_load_if_installed("cmdstanr")
skip_if_not(dir.exists(cmdstanr::cmdstan_default_install_path()))
data("PoliticalDemocracy", package = "lavaan")
model <- "
# latent variable definitions
dem60 =~ y1 + a*y2
dem65 =~ y5 + a*y6
# regressions
dem65 ~ dem60
# residual correlations
y1 ~~ y5
"
model2 <- "
# latent variable definitions
dem60 =~ y1 + a*y2
dem65 =~ y5 + a*y6
# regressions
dem65 ~ 0*dem60
# residual correlations
y1 ~~ 0*y5
"
suppressWarnings(capture.output({
bfit <- blavaan::bsem(model,
data = PoliticalDemocracy,
n.chains = 1, burnin = 50, sample = 100
)
bfit2 <- blavaan::bsem(model2,
data = PoliticalDemocracy,
n.chains = 1, burnin = 50, sample = 100
)
}))
x <- point_estimate(bfit, centrality = "all", dispersion = TRUE)
expect_true(all(c("Median", "MAD", "Mean", "SD", "MAP", "Component") %in% colnames(x)))
expect_identical(nrow(x), 10L)
x <- eti(bfit)
expect_identical(nrow(x), 10L)
x <- hdi(bfit)
expect_identical(nrow(x), 10L)
x <- p_direction(bfit)
expect_identical(nrow(x), 10L)
x <- rope(bfit, range = c(-0.1, 0.1))
expect_identical(nrow(x), 10L)
x <- p_rope(bfit, range = c(-0.1, 0.1))
expect_identical(nrow(x), 10L)
x <- p_map(bfit)
expect_identical(nrow(x), 10L)
x <- p_significance(bfit, threshold = c(-0.1, 0.1))
expect_identical(nrow(x), 10L)
x <- equivalence_test(bfit, range = c(-0.1, 0.1))
expect_identical(nrow(x), 10L)
x <- estimate_density(bfit)
expect_length(unique(x$Parameter), 10)
## Bayes factors ----
# For these models, no BF available, see #627
expect_error(bayesfactor_models(bfit, bfit2), regex = "Could not calculate Bayes")
bfit_prior <- unupdate(bfit)
capture.output(x <- expect_warning(bayesfactor_parameters(bfit, prior = bfit_prior)))
expect_identical(nrow(x), 10L)
x <- expect_warning(si(bfit, prior = bfit_prior))
expect_identical(nrow(x), 10L)
## Prior/posterior checks ----
suppressWarnings(x <- check_prior(bfit))
expect_equal(nrow(x), 13)
x <- check_prior(bfit, simulate_priors = FALSE)
expect_identical(nrow(x), 10L)
x <- diagnostic_posterior(bfit)
expect_identical(nrow(x), 10L)
x <- simulate_prior(bfit)
expect_identical(ncol(x), 13L)
# YES this is 13! We have two parameters with the same prior.
x <- describe_prior(bfit)
expect_identical(nrow(x), 13L)
# YES this is 13! We have two parameters with the same prior.
x <- describe_posterior(bfit, test = "all", rope_range = c(-0.1, 0.1))
expect_identical(nrow(x), 10L)
})
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