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skip_on_cran()
skip_if_not_installed("mmrm")
skip_if_not(packageVersion("insight") > "0.18.8")
test_that("model_parameters", {
data(fev_data, package = "mmrm")
m1 <- mmrm::mmrm(
formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
data = fev_data
)
out1 <- coef(summary(m1))
out2 <- model_parameters(m1)
expect_equal(
as.vector(out1[, "Estimate"]),
out2$Coefficient,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_identical(
rownames(out1),
out2$Parameter
)
expect_equal(
as.vector(out1[, "df"]),
out2$df_error,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "Pr(>|t|)"]),
out2$p,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "t value"]),
out2$t,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "Std. Error"]),
out2$SE,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_identical(attributes(out2)$ci_method, "Satterthwaite")
})
test_that("model_parameters", {
data(fev_data, package = "mmrm")
m1 <- mmrm::mmrm(
formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
data = fev_data,
method = "Kenward-Roger"
)
out1 <- coef(summary(m1))
out2 <- model_parameters(m1)
expect_equal(
as.vector(out1[, "Estimate"]),
out2$Coefficient,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_identical(
rownames(out1),
out2$Parameter
)
expect_equal(
as.vector(out1[, "df"]),
out2$df_error,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "Pr(>|t|)"]),
out2$p,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "t value"]),
out2$t,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_equal(
as.vector(out1[, "Std. Error"]),
out2$SE,
tolerance = 1e-4,
ignore_attr = TRUE
)
expect_identical(attributes(out2)$ci_method, "Kenward")
})
|
