Nothing
test_that("lmmelsm returns correct structures.", {
set.seed(13)
n <- 50
K <- 10
F <- 2
J <- 8
lambda <- matrix(c(.8,.8,.8,.8,0,0,0,0,
0,0,0,0,.8,.8,.8,.8),byrow=TRUE,nrow = F)
resid <- rep(1, J)
nu <- rep(0, J)
mu_beta <- matrix(c(.4,.5,.6,.7), ncol = F)
logsd_beta <- matrix(c(.4,.5,.6,.7), ncol = F)
P_random_ind <- 1
Q_random_ind <- 2
mu_logsd_betas_cor <- diag(1, 2 * F + F + F)
mu_logsd_betas_sigma <- rep(.3, 2 * F + F + F)
epsilon_cor <- diag(1,2,2)
d <- LMMELSM:::simulate_lmmelsm(
n = n,
K = K,
lambda = lambda,
resid = resid,
nu = nu,
mu_beta = mu_beta,
logsd_beta = logsd_beta,
P_random_ind = P_random_ind,
Q_random_ind = Q_random_ind,
mu_logsd_betas_cor = mu_logsd_betas_cor,
mu_logsd_betas_sigma = mu_logsd_betas_sigma,
epsilon_cor = epsilon_cor
)
fit <- lmmelsm(list(fac1 ~ obs_1 + obs_2 + obs_3 + obs_4,
fac2 ~ obs_5 + obs_6 + obs_7 + obs_8,
location ~ loc_1 + loc_2 | loc_1,
scale ~ sca_1 + sca_2 | sca_2),
group = subject, data = d$df, iter = 10, chains = 1, cores = 1)
expect_s3_class(fit, "lmmelsm")
expect_named(fit, c("fit", "meta", "data","stan_data", "stan_args"))
expect_equal(fit$meta$group_spec$K, K)
expect_equal(fit$meta$indicator_spec$J, J)
expect_equal(fit$meta$indicator_spec$N, n*K)
fit_sum <- summary(fit)
expect_s3_class(fit_sum, "summary.lmmelsm")
expect_named(fit_sum, c("meta", "summary"))
expect_named(fit_sum$summary,
c("lambda",
"nu",
"sigma",
"mu_coef",
"logsd_coef",
"zeta",
"random_mu_intercept",
"random_logsd_intercept",
"random_mu_coef",
"random_logsd_coef",
"random_sigma",
"random_correlation",
"factor_correlation"),
ignore.order = TRUE)
fit_ranef <- ranef(fit)
expect_type(fit_ranef, "list")
expect_named(fit_ranef, c("random_mu_intercept",
"random_logsd_intercept",
"random_mu_coef",
"random_logsd_coef"), ignore.order = TRUE)
expect_equal(nrow(fit_ranef$random_mu_intercept), K*F)
expect_equal(nrow(fit_ranef$random_logsd_intercept), K*F)
fit_coef <- coef(fit)
expect_named(fit_coef, c("mu_intercept",
"logsd_intercept",
"mu_coef",
"logsd_coef"))
expect_equal(nrow(fit_coef$mu_intercept), K*F)
expect_equal(nrow(fit_coef$logsd_intercept), K*F)
})
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