Nothing
skip_if_not(getRversion() >= "4.2.0")
skip_if_not_installed("PROreg", minimum_version = "1.3.0")
test_that("PROreg.BBrm", {
set.seed(1234)
# defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5, -1.1)
sigma <- 0.5
# simulating the covariate and random effects
x <- runif(k, 0, 10)
X <- model.matrix(~x)
z <- as.factor(PROreg::rBI(k, 4, 0.5, 2))
Z <- model.matrix(~ z - 1)
u <- rnorm(5, 0, sigma)
# the linear predictor and simulated response variable
eta <- beta[1] + beta[2] * x + crossprod(t(Z), u)
p <- 1 / (1 + exp(-eta))
y <- PROreg::rBB(k, m, p, phi)
dat <- data.frame(cbind(y, x, z))
dat$z <- as.factor(dat$z)
# apply the model
invisible(capture.output({
model <- PROreg::BBmm(
fixed.formula = y ~ x,
random.formula = ~z,
m = m,
data = dat
)
}))
out <- get_parameters(model)
expect_identical(out$Parameter, c("Intercept", "x"))
expect_equal(out$Estimate, c(1.05328, -1.06242), tolerance = 1e-4)
out <- find_parameters(model)
expect_identical(out$conditional, c("Intercept", "x"))
expect_identical(out$random, "z")
out <- get_statistic(model)
expect_equal(out$Statistic, c(2.64613, -5.22177), tolerance = 1e-4)
out <- find_formula(model)
expect_equal(out, list(conditional = y ~ x, random = ~z), ignore_attr = TRUE)
out <- n_obs(model)
expect_identical(out, 100L)
out <- model_info(model)
expect_true(out$is_betabinomial)
})
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