Nothing
test_that("mixed models with Type = 2 work", {
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("emmeans")
testthat::skip_if_not_installed("MEMSS")
testthat::skip_on_cran()
data("Machines", package = "MEMSS")
# simple model with random-slopes for repeated-measures factor
m1 <- mixed(score ~ Machine + (1|Worker),
data=Machines, type = 2, method = "LRT",
progress = FALSE)
expect_doppelganger("afex_plot lmm, type = 2", afex_plot(m1, "Machine"))
})
test_that("response variable y works", {
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("emmeans")
testthat::skip_if_not_installed("MEMSS")
testthat::skip_on_cran()
data("Machines", package = "MEMSS")
Machines$y <- rnorm(nrow(Machines), mean = -100)
## was a problem if it came before the DV column
Machines <- Machines[, c("y", "Worker", "Machine", "score")]
# simple model with random-slopes for repeated-measures factor
m1 <- mixed(score ~ Machine + (1|Worker), data=Machines, progress = FALSE)
m1
pp <- afex_plot(m1, "Machine")
expect_doppelganger("afex_plot dv = y works", pp)
})
test_that("merMod objects with missing data can be plotted", {
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("emmeans")
testthat::skip_if_not_installed("MEMSS")
testthat::skip_on_cran()
data("Machines", package = "MEMSS")
Machines[1, "score"] <- NA
m1 <- lme4::lmer(score ~ Machine + (1|Worker), data=Machines)
pp <- afex_plot(m1, "Machine")
expect_is(pp, "ggplot")
m2 <- mixed(score ~ Machine + (1|Worker), data=Machines,
progress = FALSE)
pp2 <- afex_plot(m2, "Machine")
expect_doppelganger("afex_plot merMod objects with missing data", pp2)
})
test_that("binomial models plot data correctly with factor DVs", {
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("emmeans")
set.seed(898765)
## long format binomial GLM (https://stats.stackexchange.com/q/322038/442):
drc4 <- function(x, b =1.0, c = 0, d = 1, e = 0){
(d - c)/ (1 + exp(-b * (log(x) - log(e))))
}
# simulate long form of dataset
nReps = 30
dfLong <- data.frame(dose = factor(rep(letters[1:3], each = nReps)))
dfLong$mortality <-rbinom(n = dim(dfLong)[1], size = 1,
prob = drc4(as.numeric(dfLong$dose), b = 2, e = 5))
fitLong <- glm( mortality ~ dose, data = dfLong,
family = "binomial")
p1 <- afex_plot(fitLong, "dose")
expect_doppelganger("afex_plot binomial glm with factor", p1)
dfLong$mortality <- factor(dfLong$mortality)
fitLong2 <- glm( mortality ~ dose, data = dfLong,
family = "binomial")
p2 <- afex_plot(fitLong2, "dose")
expect_doppelganger("afex_plot binomial glm with factor 2", p2)
expect_equivalent(p1, p2, check.environment=FALSE)
})
test_that("non-factor IVs work with factor_levels argument", {
testthat::skip_if_not_installed("ggplot2")
testthat::skip_if_not_installed("emmeans")
testthat::skip_if_not_installed("MEMSS")
data("Machines", package = "MEMSS")
## transform Machine to character and disable check_contrasts
Machines$Machine <- as.character(Machines$Machine)
m2 <- mixed(score ~ Machine + (Machine||Worker), data=Machines, expand_re = TRUE,
check_contrasts = FALSE, progress = FALSE)
df_out <- afex_plot(m2, "Machine",
factor_levels = list(Machine = list("B" = "beta", "A" = "a", "C" = "c")),
return = "data")
expect_s3_class(df_out$data$Machine, "factor")
expect_s3_class(df_out$means$Machine, "factor")
})
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