skip_on_cran()
skip_on_os(c("mac", "solaris"))
skip_if_not_installed("GLMMadaptive")
skip_if_not_installed("glmmTMB")
skip_if_not_installed("pscl")
skip_if_not_installed("emmeans")
test_that("ggpredict", {
data(fish, package = "ggeffects")
set.seed(123)
m1 <- GLMMadaptive::mixed_model(
count ~ child + camper,
random = ~ 1 | persons,
zi_fixed = ~ child + livebait,
zi_random = ~ 1 | persons,
data = fish,
family = GLMMadaptive::zi.poisson()
)
set.seed(123)
nd <- new_data(m1, "livebait")
p1 <- predict(m1, newdata = nd, type_pred = "response", type = "zero_part")
p2 <- suppressWarnings(ggpredict(m1, "livebait", type = "zi_prob"))
expect_equal(unname(p1), p2$predicted, tolerance = 1e-3)
})
test_that("ggpredict", {
m2 <- glmmTMB::glmmTMB(
count ~ child + camper + (1 | persons),
ziformula = ~ child + livebait + (1 | persons),
data = fish,
family = poisson()
)
set.seed(123)
nd <- new_data(m2, "livebait")
p1 <- predict(m2, newdata = nd, type = "zprob")
p2 <- suppressWarnings(ggpredict(m2, "livebait", type = "zi_prob"))
expect_equal(unname(p1), p2$predicted, tolerance = 1e-3)
})
test_that("ggpredict", {
data(Salamanders, package = "glmmTMB")
m3 <- pscl::zeroinfl(count ~ mined | mined, dist = "poisson", data = Salamanders)
set.seed(123)
nd <- new_data(m3, "mined")
p1 <- predict(m3, newdata = nd, type = "zero")
p2 <- suppressWarnings(ggpredict(m3, "mined", type = "zi_prob"))
expect_equal(unname(p1), p2$predicted, tolerance = 1e-3)
})
test_that("ggpredict", {
data(Salamanders, package = "glmmTMB")
m3 <- pscl::zeroinfl(count ~ mined | mined, dist = "poisson", data = Salamanders)
set.seed(123)
p3 <- suppressWarnings(ggemmeans(m3, "mined", type = "zi_prob"))
expect_equal(p3$predicted, c(0.8409091, 0.3809524), tolerance = 1e-3)
})
test_that("ggpredict pscl, sandwich", {
skip_if_not_installed("sandwich")
data(Salamanders, package = "glmmTMB")
m1 <- pscl::zeroinfl(count ~ mined | mined, dist = "poisson", data = Salamanders)
out <- ggpredict(
m1,
"mined",
type = "count",
vcov_fun = "vcovCL",
vcov_type = "HC0",
vcov_args = list(cluster = Salamanders$site)
)
expect_named(out, c("x", "predicted", "std.error", "conf.low", "conf.high", "group"))
expect_equal(out$conf.low, c(1.08279, 3.06608), tolerance = 1e-3)
expect_message(expect_message({
out <- ggpredict(
m1,
"mined",
type = "count",
vcov_fun = "vcovCR",
vcov_type = "CR0",
vcov_args = list(cluster = Salamanders$site)
)
}, regex = "robust"), regex = "variance-covariance")
expect_named(out, c("x", "predicted", "group"))
expect_null(out$conf.low)
})
test_that("ggemmeans glmmTMB, zprob", {
data(Salamanders, package = "glmmTMB")
m <- glmmTMB::glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined,
family = glmmTMB::truncated_poisson,
data = Salamanders
)
out <- as.data.frame(emmeans::emmeans(m, "spp", component = "zi"))
pred <- ggemmeans(m, "spp", type = "zi_prob")
expect_equal(pred$predicted, plogis(out$emmean), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$conf.low, plogis(out$asymp.LCL), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$conf.high, plogis(out$asymp.UCL), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$x, out$spp, ignore_attr = TRUE)
m <- glmmTMB::glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined + (1 | site),
family = glmmTMB::truncated_poisson,
data = Salamanders
)
out <- as.data.frame(emmeans::emmeans(m, "spp", component = "zi"))
pred <- ggemmeans(m, "spp", type = "zi_prob")
expect_equal(pred$predicted, plogis(out$emmean), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$conf.low, plogis(out$asymp.LCL), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$conf.high, plogis(out$asymp.UCL), tolerance = 1e-3, ignore_attr = TRUE)
expect_equal(pred$x, out$spp, ignore_attr = TRUE)
})
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