skip_if_not_installed("lmerTest")
skip_if_not_installed("pbkrtest")
skip_if_not_installed("lme4")
mtcars$cyl <- as.factor(mtcars$cyl)
model <- suppressMessages(lme4::lmer(mpg ~ as.factor(gear) * hp + as.factor(am) + wt + (1 | cyl), data = mtcars))
model2 <- suppressMessages(lmerTest::lmer(mpg ~ as.factor(gear) * hp + as.factor(am) + wt + (1 | cyl), data = mtcars))
mp0 <- model_parameters(model, digits = 5, effects = "fixed")
mp1 <- model_parameters(model, digits = 5, ci_method = "normal", effects = "fixed")
mp2 <- model_parameters(model, digits = 5, ci_method = "s", effects = "fixed")
mp3 <- model_parameters(model, digits = 5, ci_method = "kr", effects = "fixed")
mp4 <- model_parameters(model, digits = 5, ci_method = "wald", effects = "fixed")
test_that("model_parameters, ci_method default (residual)", {
expect_equal(
mp0$SE,
c(
2.77457,
3.69574,
3.521,
0.01574,
1.58514,
0.86316,
0.02973,
0.01668
),
tolerance = 1e-3
)
expect_equal(mp0$df, c(22, 22, 22, 22, 22, 22, 22, 22), tolerance = 1e-3)
expect_equal(
mp0$p,
c(
0,
0.00258,
0.14297,
0.17095,
0.84778,
0.00578,
0.00151,
0.32653
),
tolerance = 1e-3
)
expect_equal(
mp0$CI_low,
c(
24.54722,
4.89698,
-1.95317,
-0.05493,
-2.97949,
-4.42848,
-0.16933,
-0.05133
),
tolerance = 1e-3
)
})
test_that("model_parameters, ci_method normal", {
expect_equal(
mp1$SE,
c(
2.77457,
3.69574,
3.521,
0.01574,
1.58514,
0.86316,
0.02973,
0.01668
),
tolerance = 1e-3
)
expect_equal(
mp1$df,
c(22, 22, 22, 22, 22, 22, 22, 22),
tolerance = 1e-3
)
expect_equal(
mp1$p,
c(0, 0.00068, 0.12872, 0.15695, 0.846, 0.00224, 0.00029, 0.31562),
tolerance = 1e-3
)
expect_equal(
mp1$CI_low,
c(
24.86326,
5.31796,
-1.5521,
-0.05313,
-2.79893,
-4.33015,
-0.16595,
-0.04943
),
tolerance = 1e-3
)
})
test_that("model_parameters, ci_method satterthwaite", {
expect_equal(
mp2$SE,
c(
2.77457,
3.69574,
3.521,
0.01574,
1.58514,
0.86316,
0.02973,
0.01668
),
tolerance = 1e-3
)
expect_equal(mp2$df, c(24, 24, 24, 24, 24, 24, 24, 24), tolerance = 1e-3)
expect_equal(
mp2$p,
c(
0,
0.00236,
0.14179,
0.16979,
0.84763,
0.00542,
0.00136,
0.32563
),
tolerance = 1e-3
)
expect_equal(
mp2$CI_low,
c(
24.57489,
4.93385,
-1.91805,
-0.05477,
-2.96368,
-4.41987,
-0.16904,
-0.05117
),
tolerance = 1e-3
)
})
test_that("model_parameters, ci_method kenward", {
expect_equal(
mp3$SE,
c(
2.97608,
6.10454,
3.98754,
0.02032,
1.60327,
0.91599,
0.05509,
0.01962
),
tolerance = 1e-3
)
expect_equal(
mp3$df,
c(
19.39553,
5.27602,
23.57086,
8.97297,
22.7421,
23.76299,
2.72622,
22.82714
),
tolerance = 1e-3
)
expect_equal(
mp3$p,
c(
0,
0.09176,
0.19257,
0.30147,
0.84942,
0.00828,
0.15478,
0.40248
),
tolerance = 1e-3
)
expect_equal(
mp3$CI_low,
c(
24.08091,
-2.887,
-2.88887,
-0.06828,
-3.01082,
-4.5299,
-0.29339,
-0.05735
),
tolerance = 1e-3
)
})
test_that("model_parameters, ci_method wald (t)", {
expect_equal(
mp4$SE,
c(
2.77457,
3.69574,
3.521,
0.01574,
1.58514,
0.86316,
0.02973,
0.01668
),
tolerance = 1e-3
)
expect_equal(mp4$df, c(22, 22, 22, 22, 22, 22, 22, 22), tolerance = 1e-3)
expect_equal(
mp4$p,
c(
0,
0.00258,
0.14297,
0.17095,
0.84778,
0.00578,
0.00151,
0.32653
),
tolerance = 1e-3
)
expect_equal(
mp4$CI_low,
c(
24.54722,
4.89698,
-1.95317,
-0.05493,
-2.97949,
-4.42848,
-0.16933,
-0.05133
),
tolerance = 1e-3
)
})
test_that("model_parameters, satterthwaite compare", {
s <- summary(model2)
expect_equal(mp2$df, as.vector(s$coefficients[, "df"]), tolerance = 1e-4)
expect_equal(mp2$t, as.vector(s$coefficients[, "t value"]), tolerance = 1e-4)
expect_equal(mp2$p, as.vector(s$coefficients[, "Pr(>|t|)"]), tolerance = 1e-4)
expect_equal(mp2$SE, as.vector(s$coefficients[, "Std. Error"]), tolerance = 1e-4)
})
test_that("model_parameters, satterthwaite Conf Int-1", {
ci1 <- ci_satterthwaite(model)
expect_equal(mp2$CI_low, ci1$CI_low, tolerance = 1e-4)
ci2 <- ci_satterthwaite(model2)
expect_equal(mp2$CI_low, ci2$CI_low, tolerance = 1e-4)
})
test_that("model_parameters, satterthwaite Conf Int-2", {
coef.table <- as.data.frame(summary(model2)$coefficients)
coef.table$CI_low <- coef.table$Estimate - (coef.table$"Std. Error" * qt(0.975, df = coef.table$df))
coef.table$CI_high <- coef.table$Estimate + (coef.table$"Std. Error" * qt(0.975, df = coef.table$df))
expect_equal(mp2$CI_low, coef.table$CI_low, tolerance = 1e-4)
expect_equal(mp2$CI_high, coef.table$CI_high, tolerance = 1e-4)
})
test_that("model_parameters, Kenward-Roger compare", {
s <- summary(model2, ddf = "Kenward-Roger")
expect_equal(mp3$df, as.vector(s$coefficients[, "df"]), tolerance = 1e-4)
expect_equal(mp3$t, as.vector(s$coefficients[, "t value"]), tolerance = 1e-4)
expect_equal(mp3$p, as.vector(s$coefficients[, "Pr(>|t|)"]), tolerance = 1e-4)
expect_equal(mp3$SE, as.vector(s$coefficients[, "Std. Error"]), tolerance = 1e-4)
})
model <- lm(mpg ~ as.factor(gear) * hp + as.factor(am) + wt, data = mtcars)
test_that("model_parameters, ci_method-lm", {
expect_s3_class(model_parameters(model), "parameters_model")
expect_message(model_parameters(model, ci_method = "kenward"))
})
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