if (requiet("afex")) {
data(obk.long, package = "afex")
obk.long$treatment <- as.character(obk.long$treatment)
obk.long$phase <- as.character(obk.long$phase)
Mc <- suppressWarnings(suppressMessages(
afex::aov_car(
value ~ treatment * gender + age + Error(id / (phase * hour)),
factorize = FALSE,
data = obk.long, include_aov = FALSE
)
))
Mc2 <- suppressWarnings(suppressMessages(
afex::aov_car(
value ~ treatment * gender + exp(age) + Error(id / (phase * hour)),
factorize = FALSE,
data = obk.long, include_aov = FALSE
)
))
M <- suppressWarnings(suppressMessages(
afex::aov_car(
value ~ treatment * gender + Error(id / (phase * hour)),
data = obk.long, include_aov = FALSE
)
))
B <- suppressWarnings(suppressMessages(
afex::aov_car(
value ~ treatment * gender + Error(id),
data = obk.long, include_aov = FALSE
)
))
W <- suppressWarnings(suppressMessages(
afex::aov_car(
value ~ Error(id / (phase * hour)),
data = obk.long, include_aov = FALSE
)
))
mods <- list(Mc, Mc2, M, B, W)
test_that("afex_aov: afex", {
expect_equal(unique(unlist(sapply(mods, model_name))), "afex_aov")
expect_equal(unique(unlist(sapply(mods, find_algorithm))), "OLS")
expect_equal(unique(unlist(sapply(mods, find_statistic))), "F-statistic")
expect_null(unique(unlist(sapply(mods, find_offset))))
expect_null(unique(unlist(sapply(mods, find_random_slopes))))
expect_null(unique(unlist(sapply(mods, find_smooth))))
expect_null(unique(unlist(sapply(mods, find_weights))))
expect_null(unique(unlist(sapply(mods, get_call))))
expect_null(unique(unlist(sapply(mods, get_weights))))
expect_null(unique(unlist(suppressWarnings(sapply(mods, get_variance)))))
expect_true(unique(sapply(mods, all_models_equal)))
expect_true(unique(sapply(mods, has_intercept)))
expect_true(unique(sapply(mods, is_model)))
expect_true(unique(sapply(mods, is_model_supported)))
expect_false(unique(sapply(mods, is_gam_model)))
# expect_false(unique(sapply(mods, is_multivariate)))
expect_false(unique(sapply(mods, is_nullmodel)))
# expect_equal(get_family(Mc2), gaussian())
expect_equal(link_function(Mc2), gaussian()$linkfun)
expect_equal(link_inverse(Mc2), gaussian()$linkinv)
})
test_that("afex_aov: model values", {
expect_equal(suppressWarnings(sapply(mods, get_auxiliary)),
c(1.75262, 1.77497, 1.77038, 1.29973, 2.08001),
tolerance = 0.01
)
expect_equal(suppressWarnings(sapply(mods, get_df)),
c(134, 134, 149, 9, 224),
tolerance = 0.01
)
expect_equal(sapply(mods, get_loglikelihood),
c(-411.04, -414.088, -431.688, -22.295, -517.397),
tolerance = 0.01
)
expect_equal(suppressWarnings(sapply(mods, get_sigma)),
c(1.75262, 1.77497, 1.77038, 1.29973, 2.08001),
tolerance = 0.01
)
expect_equal(sapply(mods, n_obs),
c(240, 240, 240, 16, 240),
tolerance = 0.01
)
expect_equal(sapply(mods, n_parameters),
c(105, 105, 90, 6, 15),
tolerance = 0.01
)
expect_equal(sapply(mods, is_mixed_model),
c(TRUE, TRUE, TRUE, FALSE, TRUE),
tolerance = 0.01
)
expect_equal(sapply(mods, get_deviance),
c(411.603, 422.17, 467, 15.204, 969.125),
tolerance = 0.01
)
})
test_that("afex_aov: formula and parameters", {
# find_formula
expect_equal(
find_terms(Mc2),
list(
response = "value",
conditional = c("phase", "hour", "treatment", "gender", "exp(age)"),
error = "Error(id/(phase * hour))"
)
)
expect_equal(length(find_interactions(Mc2)$conditional), 14L)
expect_equal(
find_variables(Mc2),
list(
response = "value",
fixed = c("treatment", "gender", "age", "phase", "hour"),
random = "id"
)
)
expect_equal(
find_predictors(Mc2, effects = "all"),
list(
fixed = c("treatment", "gender", "age", "phase", "hour"),
random = "id"
)
)
expect_equal(
find_random(Mc2),
list(random = "id")
)
expect_equal(find_response(Mc2), "value")
})
test_that("afex_aov: formula and parameters", {
expect_equal(dim(get_data(Mc2)), c(240, 7))
expect_equal(dim(get_statistic(Mc2)), c(19, 2))
expect_equal(dim(get_modelmatrix(Mc2)), c(16, 7))
expect_equal(length(find_parameters(Mc2)), 15L)
expect_equal(length(get_intercept(Mc2)), 15L)
expect_equal(dim(get_parameters(Mc2)), c(15 * 7, 3))
expect_equal(dim(get_varcov(Mc2)), c(15 * 7, 15 * 7))
expect_equal(length(get_predicted(Mc2)), n_obs(Mc2))
expect_equal(length(get_residuals(Mc2)), n_obs(Mc2))
})
}
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