if (requiet("MASS")) {
data(housing, package = "MASS")
m1 <- polr(Sat ~ Infl + Type + Cont, data = housing, weights = Freq)
test_that("model_info", {
expect_true(model_info(m1)$is_ordinal)
expect_false(model_info(m1)$is_multinomial)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("Infl", "Type", "Cont")))
expect_identical(
find_predictors(m1, flatten = TRUE),
c("Infl", "Type", "Cont")
)
expect_null(find_predictors(m1, effects = "random"))
})
test_that("find_response", {
expect_identical(find_response(m1), "Sat")
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), plogis(0.2), tolerance = 1e-5)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 72)
expect_equal(
colnames(get_data(m1)),
c("Sat", "Infl", "Type", "Cont", "Freq")
)
})
test_that("get_df", {
expect_equal(
get_df(m1, type = "residual"),
df.residual(m1),
ignore_attr = TRUE
)
expect_equal(
get_df(m1, type = "normal"),
Inf,
ignore_attr = TRUE
)
expect_equal(
get_df(m1, type = "wald"),
df.residual(m1), # model has t-statistic
ignore_attr = TRUE
)
})
test_that("find_formula", {
expect_length(find_formula(m1), 1)
expect_equal(
find_formula(m1),
list(conditional = as.formula("Sat ~ Infl + Type + Cont")),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_equal(find_terms(m1), list(
response = "Sat",
conditional = c("Infl", "Type", "Cont")
))
expect_equal(
find_terms(m1, flatten = TRUE),
c("Sat", "Infl", "Type", "Cont")
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 1681)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional =
c(
"Intercept: Low|Medium",
"Intercept: Medium|High",
"InflMedium",
"InflHigh",
"TypeApartment",
"TypeAtrium",
"TypeTerrace",
"ContHigh"
)
)
)
})
test_that("get_parameters", {
expect_equal(
get_parameters(m1),
data.frame(
Parameter = c(
"Intercept: Low|Medium",
"Intercept: Medium|High",
"InflMedium",
"InflHigh",
"TypeApartment",
"TypeAtrium",
"TypeTerrace",
"ContHigh"
),
Estimate = c(
-0.4961353438375,
0.690708290379271,
0.566393738890106,
1.28881906381232,
-0.572350146429611,
-0.366186566153346,
-1.09101490767244,
0.360284149947385
),
stringsAsFactors = FALSE,
row.names = NULL
)
)
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "t-statistic")
})
test_that("get_predicted", {
p1 <- get_predicted(m1, predict = "expectation")
p2 <- get_predicted(m1, predict = "classification")
p3 <- get_predicted(m1, predict = NULL, type = "probs")
p4 <- get_predicted(m1, predict = NULL, type = "class")
expect_s3_class(p1, "get_predicted")
expect_s3_class(p2, "get_predicted")
expect_s3_class(p3, "get_predicted")
expect_s3_class(p4, "get_predicted")
expect_equal(p1, p3)
expect_equal(p2, p4)
expect_true(inherits(p1, "data.frame"))
expect_true(inherits(p2, "factor"))
expect_true(inherits(p3, "data.frame"))
expect_true(inherits(p4, "factor"))
expect_true(all(c("Row", "Response", "Predicted") %in% colnames(p1)))
expect_true(all(c("Row", "Response", "Predicted") %in% colnames(p3)))
d <- get_datagrid(m1, at = "Type", verbose = FALSE)
p1 <- get_predicted(m1, predict = "expectation", data = d, verbose = FALSE)
expect_equal(colnames(p1), c("Row", "Type", "Response", "Predicted"))
expect_equal(dim(p1), c(12, 4))
})
}
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