skip_if_not_installed("gamlss")
skip_if_not_installed("gamlss.data")
pb <- gamlss::pb
data(abdom, package = "gamlss.data")
data(usair, package = "gamlss.data")
void <- capture.output({
m_gamlss1 <- gamlss::gamlss(
y ~ pb(x),
sigma.formula = ~ pb(x),
family = "BCT",
data = abdom,
method = mixed(1, 20)
)
})
void <- capture.output({
m_gamlss2 <- gamlss::gamlss(y ~ x1 + x2 + x3,
sigma.formula = ~ x4 + x5 + x6 + x4:x5,
nu.formula = ~ x2 + x5,
tau.formula = ~ x1 + x4 + x5 + x6 + x1:x4,
family = "ZIBNB", data = usair
)
})
test_that("model_info", {
expect_true(model_info(m_gamlss1)$is_linear)
expect_true(model_info(m_gamlss2)$is_zero_inflated)
})
test_that("find_predictors", {
expect_identical(find_predictors(m_gamlss1), list(conditional = "x", sigma = "x"))
expect_identical(find_predictors(m_gamlss1, flatten = TRUE), "x")
expect_null(find_predictors(m_gamlss1, effects = "random"))
})
test_that("find_random", {
expect_null(find_random(m_gamlss1))
})
test_that("get_random", {
expect_warning(get_random(m_gamlss1))
})
test_that("find_response", {
expect_identical(find_response(m_gamlss1), "y")
})
test_that("get_response", {
expect_identical(get_response(m_gamlss1), abdom$y)
})
test_that("get_predictors", {
expect_identical(colnames(get_predictors(m_gamlss1)), "x")
})
test_that("get_data", {
expect_identical(nrow(get_data(m_gamlss1)), 610L)
expect_identical(colnames(get_data(m_gamlss1)), c("y", "x"))
})
test_that("find_formula", {
expect_length(find_formula(m_gamlss1), 4)
expect_equal(
find_formula(m_gamlss1),
list(
conditional = as.formula("y ~ pb(x)"),
sigma = as.formula("~pb(x)"),
nu = as.formula("~1"),
tau = as.formula("~1")
),
ignore_attr = TRUE
)
})
test_that("find_variables", {
expect_identical(
find_variables(m_gamlss1),
list(
response = "y",
conditional = "x",
sigma = "x"
)
)
expect_identical(find_variables(m_gamlss1, flatten = TRUE), c("y", "x"))
})
test_that("find_terms", {
expect_identical(
find_terms(m_gamlss1),
list(
response = "y",
conditional = "pb(x)",
sigma = "pb(x)",
nu = "1",
tau = "1"
)
)
})
test_that("n_obs", {
expect_identical(n_obs(m_gamlss1), 610L)
})
test_that("link_function", {
expect_equal(link_function(m_gamlss1)(0.2), 0.2, tolerance = 1e-5)
})
test_that("link_inverse", {
expect_equal(link_inverse(m_gamlss1)(0.2), 0.2, tolerance = 1e-5)
})
test_that("find_parameters", {
expect_identical(
find_parameters(m_gamlss1),
list(
conditional = c("(Intercept)", "pb(x)"),
sigma = c("(Intercept)", "pb(x)"),
nu = "(Intercept)",
tau = "(Intercept)"
)
)
expect_identical(nrow(get_parameters(m_gamlss1)), 6L)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m_gamlss1))
})
test_that("find_algorithm", {
expect_identical(find_algorithm(m_gamlss1), list(algorithm = "mixed"))
})
test_that("find_statistic", {
expect_identical(find_statistic(m_gamlss1), "t-statistic")
})
test_that("find_formula works with namespace colons", {
data(iris)
m <- gamlss::gamlss(
Sepal.Length ~ Sepal.Width + gamlss::random(Species),
sigma.formula = ~Sepal.Width,
data = iris
)
expect_equal(
find_formula(m),
list(
conditional = Sepal.Length ~ Sepal.Width,
random = ~ 1 | Species,
sigma = ~Sepal.Width
),
ignore_attr = TRUE
)
})
test_that("link_inv for LOGNO", {
data(abdom, package = "gamlss.data")
m1 <- gamlss::gamlss(y ~ x, family = "LOGNO", data = abdom)
expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-4)
expect_equal(link_function(m1)(0.2), log(0.2), tolerance = 1e-4)
})
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