osx <- tryCatch(
{
si <- Sys.info()
if (!is.null(si["sysname"])) {
si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
} else {
FALSE
}
},
error = function(e) {
FALSE
}
)
.runThisTest <- Sys.getenv("RunAllinsightTests") == "yes"
if (.runThisTest && !osx && requiet("nonnest2")) {
data(iris)
data(mtcars)
test_that("get_loglikelihood - lm", {
x <- lm(Sepal.Length ~ Petal.Width + Species, data = iris)
ll <- loglikelihood(x, estimator = "ML")
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
expect_equal(sum(attributes(ll)$per_obs - nonnest2::llcont(x)), 0)
# REML
ll <- loglikelihood(x, estimator = "REML")
ll2 <- stats::logLik(x, REML = TRUE)
expect_equal(as.numeric(ll), as.numeric(ll2))
# With weights
x <- lm(Sepal.Length ~ Petal.Width + Species, data = iris, weights = Petal.Length)
ll <- loglikelihood(x, estimator = "ML")
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
# log-response
x <- lm(mpg ~ wt, data = mtcars)
expect_equal(as.numeric(get_loglikelihood(x)), -80.01471, tolerance = 1e-3)
x <- lm(log(mpg) ~ wt, data = mtcars)
expect_equal(as.numeric(get_loglikelihood(x)), 19.42433, tolerance = 1e-3)
expect_equal(as.numeric(get_loglikelihood(x, check_response = TRUE)), -75.21614, tolerance = 1e-3)
set.seed(123)
mtcars$wg <- abs(rnorm(nrow(mtcars), mean = 1))
x <- lm(mpg ~ wt, weights = wg, data = mtcars)
expect_equal(as.numeric(get_loglikelihood(x)), -82.03376, tolerance = 1e-3)
x <- lm(log(mpg) ~ wt, weights = wg, data = mtcars)
expect_equal(as.numeric(get_loglikelihood(x)), 18.4205, tolerance = 1e-3)
expect_equal(as.numeric(get_loglikelihood(x, check_response = TRUE)), -75.58669, tolerance = 1e-3)
# sqrt-response
x <- lm(sqrt(mpg) ~ wt, data = mtcars)
expect_equal(as.numeric(get_loglikelihood(x)), -7.395031, tolerance = 1e-3)
expect_equal(as.numeric(get_loglikelihood(x, check_response = TRUE)), -76.89597, tolerance = 1e-3)
})
test_that("get_loglikelihood - glm", {
x <- glm(vs ~ mpg * disp, data = mtcars, family = "binomial")
ll <- loglikelihood(x)
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
expect_equal(sum(attributes(ll)$per_obs - nonnest2::llcont(x)), 0)
x <- glm(cbind(cyl, gear) ~ mpg, data = mtcars, weights = disp, family = binomial)
ll <- loglikelihood(x)
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
# Nonnest2 seems to be giving diffenrent results,
# which sums doesn't add up to base R's result... so commenting off
# expect_equal(sum(attributes(ll)$per_obs - nonnest2::llcont(x)), 0)
})
test_that("get_loglikelihood - (g)lmer", {
if (requiet("lme4")) {
x <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
# REML
ll <- loglikelihood(x, estimator = "REML")
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
# ML
ll <- loglikelihood(x, estimator = "ML")
ll2 <- stats::logLik(x, REML = FALSE)
expect_equal(as.numeric(ll), as.numeric(ll2))
# default
ll <- loglikelihood(x)
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
x <- lme4::glmer(vs ~ mpg + (1 | cyl), data = mtcars, family = "binomial")
ll <- loglikelihood(x, estimator = "REML") # no REML for glmer
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), as.numeric(ll2))
expect_equal(attributes(ll)$df, attributes(ll2)$df)
ll <- loglikelihood(x, estimator = "ML")
ll2 <- stats::logLik(x, REML = FALSE)
expect_equal(as.numeric(ll), as.numeric(ll2))
model <- download_model("lmerMod_1")
expect_equal(get_loglikelihood(model, estimator = "REML"), logLik(model, REML = TRUE), tolerance = 0.01, ignore_attr = TRUE)
expect_equal(get_loglikelihood(model, estimator = "ML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
model <- download_model("merMod_1")
expect_equal(get_loglikelihood(model, estimator = "REML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
expect_equal(get_loglikelihood(model, estimator = "ML"), logLik(model, REML = FALSE), tolerance = 0.01, ignore_attr = TRUE)
}
})
test_that("get_loglikelihood - stanreg", {
.runStanTest <- Sys.getenv("RunAllinsightStanTests") == "yes"
if (requiet("rstanarm") && .runStanTest) {
x <- rstanarm::stan_glm(Sepal.Length ~ Petal.Width, data = iris, refresh = 0)
ref <- lm(Sepal.Length ~ Petal.Width, data = iris)
ll <- loglikelihood(x)
ll2 <- loglikelihood(ref)
expect_equal(as.numeric(ll), as.numeric(ll2), tolerance = 2)
expect_equal(mean(abs(attributes(ll)$per_obs - attributes(ll2)$per_obs)), 0, tolerance = 0.1)
}
})
test_that("get_loglikelihood - ivreg", {
requiet("ivreg")
data("CigaretteDemand", package = "ivreg")
x <- ivreg::ivreg(log(packs) ~ log(rprice) + log(rincome) | salestax + log(rincome), data = CigaretteDemand)
ll <- loglikelihood(x)
expect_equal(as.numeric(ll), 13.26255, tolerance = 1e-3)
})
test_that("get_loglikelihood - plm", {
if (requiet("plm")) {
data("Produc", package = "plm")
x <- suppressWarnings(
plm::plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state", "year")
)
)
ll <- loglikelihood(x)
expect_equal(as.numeric(ll), 1534.532, tolerance = 1e-3)
}
})
if (requiet("estimatr")) {
test_that("get_loglikelihood - iv_robust", {
data(mtcars)
x <- estimatr::iv_robust(mpg ~ gear + cyl | carb + wt, data = mtcars)
ll <- loglikelihood(x)
expect_equal(as.numeric(ll), -84.60057, tolerance = 1e-3)
})
}
if (requiet("mgcv")) {
test_that("get_loglikelihood - mgcv", {
x <- mgcv::gam(Sepal.Length ~ s(Petal.Width), data = iris)
ll <- insight::get_loglikelihood(x)
ll2 <- stats::logLik(x)
expect_equal(as.numeric(ll), -96.26613, tolerance = 1e-3)
# TODO: I'm not sure why this differes :/
# expect_equal(as.numeric(ll), as.numeric(ll2))
x <- mgcv::gamm(Sepal.Length ~ s(Petal.Width), random = list("Species" = ~1), data = iris)
# Which one to get?
})
}
if (requiet("gamm4")) {
test_that("get_loglikelihood - gamm4", {
x <- gamm4::gamm4(Sepal.Length ~ s(Petal.Width), data = iris)
ll <- insight::get_loglikelihood(x)
# It works, but it's quite diferent from the mgcv result
expect_equal(as.numeric(ll), -101.1107, tolerance = 1e-3)
})
}
}
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