Nothing
# Setup -------------------------------------------------------------------
library(afex)
expected_statistics <- read_stats("../data/afex.json")
# aov_ez() ----------------------------------------------------------------
test_that("aov_ez works", {
data(md_12.1)
model <- aov_ez(
"id",
"rt",
md_12.1,
within = c("angle", "noise"),
anova_table = list(correction = "none", es = "none")
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez
)
})
test_that("aov_ez default works", {
data(md_12.1)
model <- aov_ez(
"id",
"rt",
md_12.1,
within = c("angle", "noise")
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez_default
)
})
test_that("aov_ez covariate works", {
data(obk.long, package = "afex")
model <- aov_ez(
"id",
"value",
obk.long,
between = c("treatment", "gender"),
within = c("phase", "hour"), covariate = "age",
observed = c("gender", "age"),
factorize = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez_covariate
)
})
test_that("aov_ez aggregate works", {
data(obk.long, package = "afex")
model <- aov_ez(
"id",
"value",
obk.long,
c("treatment", "gender"),
"hour",
observed = "gender",
fun_aggregate = mean
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez_aggregate
)
})
test_that("aov_ez aggregate over both within-subjected factors works", {
data(obk.long, package = "afex")
model <- aov_ez(
"id",
"value",
obk.long,
between = c("treatment", "gender"),
observed = "gender",
fun_aggregate = mean
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez_aggregate_both
)
})
test_that("aov_ez p-value adjustment works", {
data(obk.long, package = "afex")
model <- aov_ez(
"id", "value",
obk.long,
between = "treatment",
within = c("phase", "hour"),
anova_table = list(p_adjust_method = "holm")
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_ez_p
)
})
test_that("aov_car works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + Error(id / (phase * hour)),
data = obk.long, observed = "gender"
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car
)
})
test_that("aov_car covariate works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + age + Error(id / (phase * hour)),
data = obk.long, observed = c("gender", "age"),
factorize = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_covariate
)
})
test_that("aov_car aggregating over one within-subjects works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + Error(id / hour),
data = obk.long, observed = "gender",
fun_aggregate = mean
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_aggregate
)
})
test_that("aov_car aggregating over both within-subjects works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + Error(id),
data = obk.long, observed = "gender",
fun_aggregate = mean
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_aggregate_both
)
})
test_that("aov_car only within-subject works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ Error(id / (phase * hour)),
data = obk.long
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_within
)
})
test_that("aov_car no df-correctiona and partial eta-squared works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + Error(id / (phase * hour)),
data = obk.long,
anova_table = list(correction = "none", es = "pes")
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_no_df_pes
)
})
test_that("aov_car no df-correction and no MSE works", {
data(obk.long, package = "afex")
model <- aov_car(
value ~ treatment * gender + Error(id / (phase * hour)),
data = obk.long, observed = "gender",
anova_table = list(correction = "none", MSE = FALSE)
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$aov_car_no_df_no_MSE
)
})
test_that("mixed simple model with random-slopes works", {
data("Machines", package = "MEMSS")
model <- mixed(
score ~ Machine + (Machine | Worker),
data = Machines, progress = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$mixed
)
})
test_that("mixed with expanded random effects terms works", {
data("Machines", package = "MEMSS")
model <- mixed(
score ~ Machine + (Machine || Worker),
data = Machines, expand_re = TRUE, progress = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$mixed_expand_RE
)
})
test_that("mixed with random intercept plus random slope works", {
data(md_15.1)
model <- mixed(
iq ~ timecat + (1 + time | id),
data = md_15.1, progress = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$mixed_random_interecept
)
})
test_that("mixed with treatment contrasts checked works", {
data(md_16.1)
model <- mixed(
severity ~ sex + (1 | id),
data = md_16.1, check_contrasts = FALSE, progress = FALSE
)
expect_equal_models(
model = model,
expected_tidy_model = expected_statistics$mixed_contrast
)
})
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