skip_on_os("mac")
skip_if(getRversion() < "3.6.0")
skip_if_not_installed("fixest")
requiet("fixest")
# avoid warnings
fixest::setFixest_nthreads(1)
data(trade)
m1 <- femlm(Euros ~ log(dist_km) | Origin + Destination + Product, data = trade)
m2 <- femlm(log1p(Euros) ~ log(dist_km) | Origin + Destination + Product, data = trade, family = "gaussian")
m3 <- feglm(Euros ~ log(dist_km) | Origin + Destination + Product, data = trade, family = "poisson")
m4 <- feols(
Sepal.Width ~ Petal.Length | Species | Sepal.Length ~ Petal.Width,
data = iris
)
test_that("robust variance-covariance", {
mod <- feols(mpg ~ hp + drat | cyl, data = mtcars)
# default is clustered
expect_equal(
sqrt(diag(vcov(mod))),
sqrt(diag(get_varcov(mod, vcov = ~cyl)))
)
# HC1
expect_equal(
sqrt(diag(vcov(mod, vcov = "HC1"))),
sqrt(diag(get_varcov(mod, vcov = "HC1")))
)
expect_true(all(
sqrt(diag(vcov(mod))) !=
sqrt(diag(get_varcov(mod, vcov = "HC1")))
))
})
test_that("model_info", {
expect_true(model_info(m1)$is_count)
expect_true(model_info(m2)$is_linear)
expect_true(model_info(m3)$is_count)
})
test_that("find_predictors", {
expect_identical(find_predictors(m1), list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product")))
expect_identical(find_predictors(m2), list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product")))
expect_identical(find_predictors(m3), list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product")))
expect_identical(find_predictors(m4), list(
conditional = c("Petal.Length", "Sepal.Length"), cluster = "Species",
instruments = "Petal.Width", endogenous = "Sepal.Length"
))
expect_identical(
find_predictors(m1, component = "all"),
list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product"))
)
expect_identical(
find_predictors(m2, component = "all"),
list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product"))
)
expect_identical(
find_predictors(m3, component = "all"),
list(conditional = "dist_km", cluster = c("Origin", "Destination", "Product"))
)
expect_identical(
find_predictors(m4, component = "all"),
list(
conditional = c("Petal.Length", "Sepal.Length"),
cluster = "Species",
instruments = "Petal.Width",
endogenous = "Sepal.Length"
)
)
})
test_that("find_random", {
expect_null(find_random(m1))
expect_null(find_random(m2))
expect_null(find_random(m3))
})
test_that("get_varcov", {
expect_equal(vcov(m1), get_varcov(m1), tolerance = 1e-3)
expect_equal(vcov(m4), get_varcov(m4), tolerance = 1e-3)
})
test_that("get_random", {
expect_warning(expect_null(get_random(m1)))
})
test_that("find_response", {
expect_identical(find_response(m1), "Euros")
expect_identical(find_response(m2), "Euros")
expect_identical(find_response(m3), "Euros")
})
test_that("get_response", {
expect_equal(get_response(m1), trade$Euros)
expect_equal(get_response(m2), trade$Euros)
expect_equal(get_response(m3), trade$Euros)
})
test_that("get_predictors", {
expect_equal(colnames(get_predictors(m1)), c("dist_km", "Origin", "Destination", "Product"))
expect_equal(colnames(get_predictors(m2)), c("dist_km", "Origin", "Destination", "Product"))
expect_equal(colnames(get_predictors(m3)), c("dist_km", "Origin", "Destination", "Product"))
})
test_that("link_inverse", {
expect_equal(link_inverse(m1)(0.2), exp(0.2), tolerance = 1e-4)
expect_equal(link_inverse(m2)(0.2), 0.2, tolerance = 1e-4)
expect_equal(link_inverse(m3)(0.2), exp(0.2), tolerance = 1e-4)
})
test_that("link_function", {
expect_equal(link_function(m1)(0.2), log(0.2), tolerance = 1e-4)
expect_equal(link_function(m2)(0.2), 0.2, tolerance = 1e-4)
expect_equal(link_function(m3)(0.2), log(0.2), tolerance = 1e-4)
})
test_that("get_data", {
expect_equal(nrow(get_data(m1)), 38325)
expect_equal(colnames(get_data(m1)), c("Euros", "dist_km", "Origin", "Destination", "Product"))
expect_equal(nrow(get_data(m2)), 38325)
expect_equal(colnames(get_data(m2)), c("Euros", "dist_km", "Origin", "Destination", "Product"))
# old bug: m4 uses a complex formula and we need to extract all relevant
# variables in order to compute predictions.
nd <- get_data(m4)
tmp <- predict(m4, newdata = nd)
expect_true(is.numeric(tmp) && length(tmp) == nrow(iris))
})
if (requiet("parameters")) {
test_that("get_df", {
expect_equal(get_df(m1, type = "residual"), 38290, ignore_attr = TRUE)
expect_equal(get_df(m1, type = "normal"), Inf, ignore_attr = TRUE)
## TODO: check if statistic is z or t for this model
expect_equal(get_df(m1, type = "wald"), 14, ignore_attr = TRUE)
})
}
test_that("find_formula", {
expect_length(find_formula(m1), 2)
expect_equal(
find_formula(m1),
list(
conditional = as.formula("Euros ~ log(dist_km)"),
cluster = as.formula("~Origin + Destination + Product")
),
ignore_attr = TRUE
)
expect_length(find_formula(m2), 2)
expect_equal(
find_formula(m2),
list(
conditional = as.formula("log1p(Euros) ~ log(dist_km)"),
cluster = as.formula("~Origin + Destination + Product")
),
ignore_attr = TRUE
)
})
test_that("find_terms", {
expect_equal(
find_terms(m1),
list(response = "Euros", conditional = "log(dist_km)", cluster = c("Origin", "Destination", "Product"))
)
expect_equal(
find_terms(m1, flatten = TRUE),
c("Euros", "log(dist_km)", "Origin", "Destination", "Product")
)
expect_equal(
find_terms(m2),
list(response = "log1p(Euros)", conditional = "log(dist_km)", cluster = c("Origin", "Destination", "Product"))
)
expect_equal(
find_terms(m2, flatten = TRUE),
c("log1p(Euros)", "log(dist_km)", "Origin", "Destination", "Product")
)
})
test_that("find_variables", {
expect_equal(
find_variables(m1),
list(response = "Euros", conditional = "dist_km", cluster = c("Origin", "Destination", "Product"))
)
expect_equal(
find_variables(m1, flatten = TRUE),
c("Euros", "dist_km", "Origin", "Destination", "Product")
)
expect_equal(
find_variables(m2),
list(response = "Euros", conditional = "dist_km", cluster = c("Origin", "Destination", "Product"))
)
expect_equal(
find_variables(m1, flatten = TRUE),
c("Euros", "dist_km", "Origin", "Destination", "Product")
)
})
test_that("n_obs", {
expect_equal(n_obs(m1), 38325)
expect_equal(n_obs(m2), 38325)
})
test_that("find_parameters", {
expect_equal(
find_parameters(m1),
list(conditional = "log(dist_km)")
)
expect_equal(
get_parameters(m1),
data.frame(
Parameter = "log(dist_km)",
Estimate = -1.52774702640008,
row.names = NULL,
stringsAsFactors = FALSE
),
tolerance = 1e-4
)
expect_equal(
find_parameters(m2),
list(conditional = "log(dist_km)")
)
expect_equal(
get_parameters(m2),
data.frame(
Parameter = "log(dist_km)",
Estimate = -2.16843021944503,
row.names = NULL,
stringsAsFactors = FALSE
),
tolerance = 1e-4
)
})
test_that("is_multivariate", {
expect_false(is_multivariate(m1))
})
test_that("find_statistic", {
expect_identical(find_statistic(m1), "z-statistic")
expect_identical(find_statistic(m2), "t-statistic")
})
test_that("get_statistic", {
stat <- get_statistic(m1)
expect_equal(stat$Statistic, -13.212695, tolerance = 1e-3)
stat <- get_statistic(m2)
expect_equal(stat$Statistic, -14.065336, tolerance = 1e-3)
})
test_that("get_predicted", {
pred <- get_predicted(m1)
expect_s3_class(pred, "get_predicted")
expect_equal(length(pred), nrow(trade))
a <- get_predicted(m1)
b <- get_predicted(m1, type = "response", predict = NULL)
expect_equal(a, b)
a <- get_predicted(m1, predict = "link")
b <- get_predicted(m1, type = "link", predict = NULL)
expect_equal(a, b)
# these used to raise warnings
expect_warning(get_predicted(m1, ci = 0.4), NA)
expect_warning(get_predicted(m1, predict = NULL, type = "link"), NA)
})
test_that("get_data works when model data has name of reserved words", {
## NOTE check back every now and then and see if tests still work
skip("works interactively")
rep <- data.frame(Y = runif(100) > 0.5, X = rnorm(100))
m <- feglm(Y ~ X, data = rep, family = binomial)
out <- get_data(m)
expect_s3_class(out, "data.frame")
expect_equal(
head(out),
structure(
list(
Y = c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE),
X = c(
-1.37601434046896, -0.0340090992175856, 0.418083058388383,
-0.51688491498936, -1.30634551903768, -0.858343109785566
)
),
is_subset = FALSE, row.names = c(NA, 6L), class = "data.frame"
),
ignore_attr = TRUE,
tolerance = 1e-3
)
})
test_that("find_variables with interaction", {
mod <- suppressMessages(feols(mpg ~ 0 | carb | vs:cyl ~ am:cyl, data = mtcars))
expect_equal(
find_variables(mod),
list(
response = "mpg", conditional = "vs", cluster = "carb",
instruments = c("am", "cyl"), endogenous = c("vs", "cyl")
),
ignore_attr = TRUE
)
# used to produce a warning
mod <- feols(mpg ~ 0 | carb | vs:cyl ~ am:cyl, data = mtcars)
expect_warning(find_variables(mod), NA)
})
test_that("find_predictors with i(f1, i.f2) interaction", {
aq <- airquality
aq$week <- aq$Day %/% 7 + 1
mod <- feols(Ozone ~ i(Month, i.week), aq, notes = FALSE)
expect_equal(
find_predictors(mod),
list(
conditional = c("Month", "week")
),
ignore_attr = TRUE
)
})
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