Nothing
.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"
if (.runThisTest && getRversion() >= "4.0.0" && requiet("testthat") && requiet("marginaleffects") && requiet("ggeffects") && requiet("glmmTMB") && requiet("lme4")) {
data(Owls)
data(Salamanders)
m1 <- suppressWarnings(glmmTMB::glmmTMB(
SiblingNegotiation ~ SexParent + ArrivalTime + (1 | Nest),
data = Owls,
family = nbinom1)
)
m2 <- glmmTMB::glmmTMB(
SiblingNegotiation ~ SexParent + ArrivalTime + (1 | Nest),
data = Owls,
family = nbinom2
)
m4 <- glmmTMB::glmmTMB(
SiblingNegotiation ~ FoodTreatment + ArrivalTime + SexParent + (1 | Nest),
data = Owls,
ziformula = ~ 1,
family = truncated_poisson(link = "log")
)
test_that("validate ggpredict against predict, nbinom", {
nd <- data_grid(m1, "SexParent")
pr <- predict(m1, newdata = nd, type = "link", se.fit = TRUE)
linv <- insight::link_inverse(m1)
dof <- insight::get_df(m1, type = "wald", verbose = FALSE)
tcrit <- stats::qt(0.975, df = dof)
out1 <- data.frame(
predicted = linv(pr$fit),
conf.low = linv(pr$fit - tcrit * pr$se.fit),
conf.high = linv(pr$fit + tcrit * pr$se.fit)
)
out2 <- ggpredict(m1, "SexParent")
expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
})
test_that("validate ggpredict lmer against marginaleffects", {
out1 <- marginaleffects::predictions(
m1,
variables = "SexParent",
newdata = marginaleffects::datagrid(m1),
vcov = FALSE
)
out1 <- out1[order(out1$SexParent), ]
out2 <- ggpredict(
m1,
"SexParent",
condition = c(Nest = "Oleyes"),
type = "random"
)
expect_equal(
out1$estimate,
out2$predicted,
tolerance = 1e-4,
ignore_attr = TRUE
)
})
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m1, c("ArrivalTime", "SexParent")), "data.frame")
expect_s3_class(ggpredict(m2, c("ArrivalTime", "SexParent")), "data.frame")
expect_s3_class(ggpredict(m4, c("FoodTreatment", "ArrivalTime [21,24,30]", "SexParent")), "data.frame")
expect_s3_class(ggpredict(m1, c("ArrivalTime", "SexParent"), type = "re"), "data.frame")
expect_s3_class(ggpredict(m2, c("ArrivalTime", "SexParent"), type = "re"), "data.frame")
expect_s3_class(ggpredict(m4, c("FoodTreatment", "ArrivalTime [21,24,30]", "SexParent"), type = "re"), "data.frame")
})
test_that("ggpredict, glmmTMB", {
expect_message(ggpredict(m1, c("ArrivalTime", "SexParent"), type = "fe.zi"))
})
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(m1, c("ArrivalTime", "SexParent"))
p2 <- ggpredict(m2, c("ArrivalTime", "SexParent"))
p3 <- ggemmeans(m1, c("ArrivalTime", "SexParent"))
p4 <- ggemmeans(m2, c("ArrivalTime", "SexParent"))
expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
})
m3 <- glmmTMB::glmmTMB(count ~ spp + mined + (1 | site), ziformula = ~ spp + mined, family = truncated_poisson, data = Salamanders)
m4 <- glmmTMB::glmmTMB(count ~ spp + mined + (1 | site), ziformula = ~ spp + mined + (1 | site), family = truncated_poisson, data = Salamanders)
m5 <- glmmTMB::glmmTMB(count ~ spp + mined + cover + (1 | site), ziformula = ~ spp + mined, family = truncated_poisson, data = Salamanders)
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(m3, "mined", type = "fe")
p2 <- ggpredict(m3, "mined", type = "fe.zi")
p3 <- ggpredict(m3, "mined", type = "re")
p4 <- ggpredict(m3, "mined", type = "re.zi")
expect_gt(p3$conf.high[1], p1$conf.high[1])
expect_gt(p4$conf.high[1], p2$conf.high[1])
expect_s3_class(ggpredict(m3, "mined", type = "fe.zi"), "data.frame")
})
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(m5, c("mined", "spp", "cover"), type = "fe")
p3 <- ggemmeans(m5, c("mined", "spp", "cover"), type = "fe")
expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
# p2 <- ggpredict(m5, c("mined", "spp", "cover"), type = "fe.zi")
# p4 <- ggemmeans(m5, c("mined", "spp", "cover"), type = "fe.zi")
# expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
})
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(m3, "mined", type = "fe")
p2 <- ggpredict(m3, c("mined", "spp"), type = "fe.zi")
p3 <- ggemmeans(m3, "mined", type = "fe", condition = c(spp = "GP"))
p4 <- ggemmeans(m3, c("mined", "spp"), type = "fe.zi")
p5 <- ggpredict(m3, c("mined", "spp"), type = "fe")
p6 <- ggemmeans(m3, c("mined", "spp"), type = "fe")
expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
# expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
expect_equal(p5$predicted[1], p6$predicted[1], tolerance = 1e-3)
})
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(m4, "mined", type = "fe")
p2 <- ggpredict(m4, "mined", type = "fe.zi")
p3 <- ggpredict(m4, "mined", type = "re")
p4 <- ggpredict(m4, "mined", type = "re.zi")
expect_gt(p3$conf.high[1], p1$conf.high[1])
expect_gt(p4$conf.high[1], p2$conf.high[1])
p1 <- ggpredict(m4, c("spp", "mined"), type = "fe")
p2 <- ggpredict(m4, c("spp", "mined"), type = "fe.zi")
p3 <- ggpredict(m4, c("spp", "mined"), type = "re")
p4 <- ggpredict(m4, c("spp", "mined"), type = "re.zi")
expect_gt(p3$conf.high[1], p1$conf.high[1])
expect_gt(p4$conf.high[1], p2$conf.high[1])
})
test_that("ggpredict, glmmTMB", {
p <- ggpredict(m3, "spp", type = "fe.zi")
expect_true(all(p$conf.low > 0))
set.seed(100)
p <- ggpredict(m3, "spp", type = "fe.zi")
expect_true(all(p$conf.low > 0))
})
test_that("ggpredict, glmmTMB-simulate", {
expect_s3_class(ggpredict(m3, "mined", type = "sim"), "data.frame")
expect_s3_class(ggpredict(m3, c("spp", "mined"), type = "sim"), "data.frame")
expect_s3_class(ggpredict(m4, "mined", type = "sim"), "data.frame")
expect_s3_class(ggpredict(m4, c("spp", "mined"), type = "sim"), "data.frame")
})
md <- glmmTMB::glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined,
dispformula = ~ DOY,
family = truncated_poisson,
data = Salamanders
)
test_that("ggpredict, glmmTMB", {
p1 <- ggpredict(md, c("spp", "mined"), type = "fe")
p2 <- ggpredict(md, c("spp", "mined"), type = "fe.zi")
p3 <- suppressWarnings(ggpredict(md, c("spp", "mined"), type = "re"))
p4 <- suppressWarnings(ggpredict(md, c("spp", "mined"), type = "re.zi"))
expect_gt(p3$conf.high[1], p1$conf.high[1])
expect_gt(p4$conf.high[1], p2$conf.high[1])
})
data(efc_test)
m5 <- glmmTMB::glmmTMB(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test, ziformula = ~ c172code,
family = binomial(link = "logit")
)
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m5, "c161sex", type = "fe"), "data.frame")
expect_s3_class(ggpredict(m5, "c161sex", type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m5, "c161sex", type = "re"), "data.frame")
expect_s3_class(ggpredict(m5, "c161sex", type = "re.zi"), "data.frame")
})
data(efc_test)
m6 <- glmmTMB::glmmTMB(
negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
data = efc_test,
family = binomial(link = "logit")
)
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m6, "c161sex", type = "fe"), "data.frame")
expect_s3_class(ggpredict(m6, "c161sex", type = "re"), "data.frame")
})
test_that("validate ggpredict against predict, binomial", {
nd <- data_grid(m6, "e42dep")
pr <- predict(m6, newdata = nd, type = "link", se.fit = TRUE)
linv <- insight::link_inverse(m6)
dof <- insight::get_df(m6, type = "wald", verbose = FALSE)
tcrit <- stats::qt(0.975, df = dof)
out1 <- data.frame(
predicted = linv(pr$fit),
conf.low = linv(pr$fit - tcrit * pr$se.fit),
conf.high = linv(pr$fit + tcrit * pr$se.fit)
)
out2 <- ggpredict(m6, "e42dep")
expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
})
data(efc_test)
efc_test$tot_sc_e <- as.numeric(efc_test$tot_sc_e)
efc_test$c172code <- as.factor(efc_test$c172code)
m7 <- glmmTMB::glmmTMB(
tot_sc_e ~ neg_c_7 * c172code + c161sex + (1 | grp),
data = efc_test, ziformula = ~ c172code,
family = nbinom1
)
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m7, "neg_c_7"), "data.frame")
expect_s3_class(ggpredict(m7, "neg_c_7 [all]"), "data.frame")
expect_s3_class(ggpredict(m7, "neg_c_7", type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m7, "neg_c_7 [all]", type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m7, c("neg_c_7", "c172code")), "data.frame")
expect_s3_class(ggpredict(m7, c("neg_c_7 [all]", "c172code")), "data.frame")
expect_s3_class(ggpredict(m7, c("neg_c_7", "c172code"), type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m7, c("neg_c_7 [all]", "c172code"), type = "fe.zi"), "data.frame")
})
m8 <- glmmTMB::glmmTMB(
tot_sc_e ~ neg_c_7 * c172code + (1 | grp),
data = efc_test, ziformula = ~ c172code,
family = nbinom1
)
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m8, "neg_c_7"), "data.frame")
expect_s3_class(ggpredict(m8, "neg_c_7 [all]"), "data.frame")
expect_s3_class(ggpredict(m8, "neg_c_7", type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m8, "neg_c_7 [all]", type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m8, c("neg_c_7", "c172code")), "data.frame")
expect_s3_class(ggpredict(m8, c("neg_c_7 [all]", "c172code")), "data.frame")
expect_s3_class(ggpredict(m8, c("neg_c_7", "c172code"), type = "fe.zi"), "data.frame")
expect_s3_class(ggpredict(m8, c("neg_c_7 [all]", "c172code"), type = "fe.zi"), "data.frame")
})
data(Salamanders)
m9 <- glmmTMB::glmmTMB(
count ~ spp + cover + mined + (1 | site),
ziformula = ~ DOY,
dispformula = ~ spp,
data = Salamanders,
family = nbinom2
)
test_that("ggpredict, glmmTMB", {
expect_s3_class(ggpredict(m9, c("cover", "mined", "spp"), type = "fe"), "data.frame")
expect_s3_class(ggpredict(m9, c("cover", "mined", "spp"), type = "fe.zi"), "data.frame")
expect_s3_class(suppressWarnings(ggpredict(m9, c("cover", "mined", "spp"), type = "re")), "data.frame")
expect_s3_class(suppressWarnings(ggpredict(m9, c("cover", "mined", "spp"), type = "re.zi")), "data.frame")
})
test_that("validate ggpredict against predict, linear, REML-fit", {
data(sleepstudy, package = "lme4")
# REML-fit
m10 <- glmmTMB::glmmTMB(
Reaction ~ Days + (1 + Days | Subject),
data = sleepstudy,
REML = TRUE
)
nd <- data_grid(m10, "Days")
pr <- predict(m10, newdata = nd, type = "link", se.fit = TRUE)
dof <- insight::get_df(m10, type = "wald", verbose = FALSE)
tcrit <- stats::qt(0.975, df = dof)
out1 <- data.frame(
predicted = pr$fit,
conf.low = pr$fit - tcrit * pr$se.fit,
conf.high = pr$fit + tcrit * pr$se.fit
)
out2 <- ggpredict(m10, "Days", type = "random", interval = "confidence")
expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
# ML-fit
m11 <- glmmTMB::glmmTMB(
Reaction ~ Days + (1 + Days | Subject),
data = sleepstudy,
REML = FALSE
)
nd <- data_grid(m11, "Days")
pr <- predict(m11, newdata = nd, type = "link", se.fit = TRUE)
dof <- insight::get_df(m11, type = "wald", verbose = FALSE)
tcrit <- stats::qt(0.975, df = dof)
out1 <- data.frame(
predicted = pr$fit,
conf.low = pr$fit - tcrit * pr$se.fit,
conf.high = pr$fit + tcrit * pr$se.fit
)
out2 <- ggpredict(m11, "Days")
out3 <- ggpredict(m11, "Days", type = "random", interval = "confidence")
expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$predicted, out3$predicted, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.low, out3$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
expect_equal(out1$conf.high, out3$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
})
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.