if (require("testthat") &&
require("insight") &&
require("stats")) {
context("insight, lm")
data(iris)
data(mtcars)
m1 <- lm(Sepal.Length ~ Petal.Width + Species, data = iris)
m2 <-
lm(log(mpg) ~ log(hp) + cyl + I(cyl^2) + poly(wt, degree = 2, raw = TRUE),
data = mtcars
)
test_that("model_info", {
expect_true(model_info(m1)$is_linear)
expect_false(model_info(m1)$is_bayesian)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = c("Petal.Width", "Species")))
expect_identical(
find_predictors(m1, flatten = TRUE),
c("Petal.Width", "Species")
)
expect_null(find_predictors(m1, effects = "random"))
expect_identical(find_predictors(m2), list(conditional = c("hp", "cyl", "wt")))
expect_identical(find_predictors(m2, flatten = TRUE), c("hp", "cyl", "wt"))
expect_null(find_predictors(m2, effects = "random"))
})
test_that("find_response", {
expect_identical(find_response(m1), "Sepal.Length")
expect_identical(find_response(m2), "mpg")
})
test_that("link_inverse", {
expect_identical(link_inverse(m1)(.2), .2)
expect_identical(link_inverse(m2)(.2), .2)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 150)
expect_equal(
colnames(get_data(m1)),
c("Sepal.Length", "Petal.Width", "Species")
)
expect_equal(nrow(get_data(m2)), 32)
expect_equal(colnames(get_data(m2)), c("mpg", "hp", "cyl", "wt"))
})
test_that("find_formula", {
expect_length(find_formula(m1), 1)
expect_equal(
find_formula(m1),
list(conditional = as.formula("Sepal.Length ~ Petal.Width + Species"))
)
expect_length(find_formula(m2), 1)
expect_equal(
find_formula(m2),
list(
conditional = as.formula(
"log(mpg) ~ log(hp) + cyl + I(cyl^2) + poly(wt, degree = 2, raw = TRUE)"
)
)
)
})
test_that("find_terms", {
expect_equal(
find_terms(m1),
list(
response = "Sepal.Length",
conditional = c("Petal.Width", "Species")
)
)
expect_equal(
find_terms(m2),
list(
response = "log(mpg)",
conditional = c(
"log(hp)",
"cyl",
"I(cyl^2)",
"poly(wt, degree = 2, raw = TRUE)"
)
)
)
expect_equal(
find_terms(m1, flatten = TRUE),
c("Sepal.Length", "Petal.Width", "Species")
)
expect_equal(
find_terms(m2, flatten = TRUE),
c(
"log(mpg)",
"log(hp)",
"cyl",
"I(cyl^2)",
"poly(wt, degree = 2, raw = TRUE)"
)
)
})
test_that("find_variables", {
expect_equal(
find_variables(m1),
list(
response = "Sepal.Length",
conditional = c("Petal.Width", "Species")
)
)
expect_equal(find_variables(m2), list(
response = "mpg",
conditional = c("hp", "cyl", "wt")
))
expect_equal(
find_variables(m1, flatten = TRUE),
c("Sepal.Length", "Petal.Width", "Species")
)
expect_equal(
find_variables(m2, flatten = TRUE),
c("mpg", "hp", "cyl", "wt")
)
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(
conditional = c(
"(Intercept)",
"Petal.Width",
"Speciesversicolor",
"Speciesvirginica"
)
)
)
expect_equal(nrow(get_parameters(m1)), 4)
expect_equal(
get_parameters(m1)$Parameter,
c(
"(Intercept)",
"Petal.Width",
"Speciesversicolor",
"Speciesvirginica"
)
)
})
test_that("linkfun", {
expect_false(is.null(link_function(m1)))
expect_false(is.null(link_function(m2)))
})
test_that("find_algorithm", {
expect_equal(find_algorithm(m1), list(algorithm = "OLS"))
})
test_that("get_variance", {
expect_warning(expect_null(get_variance(m1)))
expect_warning(expect_null(get_variance_dispersion(m1)))
expect_warning(expect_null(get_variance_distribution(m1)))
expect_warning(expect_null(get_variance_fixed(m1)))
expect_warning(expect_null(get_variance_intercept(m1)))
expect_warning(expect_null(get_variance_random(m1)))
expect_warning(expect_null(get_variance_residual(m1)))
})
test_that("is_model", {
expect_true(is_model(m1))
})
test_that("all_models_equal", {
expect_true(all_models_equal(m1, m2))
})
test_that("get_varcov", {
expect_equal(diag(get_varcov(m1)), diag(vcov(m1)))
})
test_that("get_statistic", {
expect_equal(get_statistic(m1)$Statistic, c(57.5427, 4.7298, -0.2615, -0.1398), tolerance = 1e-3)
})
test_that("find_statistic", {
expect_equal(find_statistic(m1), "t-statistic")
})
data("DNase")
DNase1 <- subset(DNase, Run == 1)
m3 <-
stats::nls(
density ~ stats::SSlogis(log(conc), Asym, xmid, scal),
DNase1,
start = list(
Asym = 1,
xmid = 1,
scal = 1
)
)
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
m4 <- glm(counts ~ outcome + treatment, family = poisson())
test_that("is_model", {
expect_true(is_model(m3))
})
test_that("is_model", {
expect_false(is_model_supported(m3))
})
test_that("all_models_equal", {
expect_false(all_models_equal(m1, m2, m3))
expect_false(all_models_equal(m1, m2, m4))
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "t-statistic")
expect_identical(find_statistic(m2), "t-statistic")
expect_identical(find_statistic(m3), "t-statistic")
expect_identical(find_statistic(m4), "z-statistic")
})
}
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