Nothing
# mkuhn, 2024-12-09
# testing inference methods
test_that("kappa homogeneity significance test for independent samples", {
# Cohen's kappa on three studies
k2_studies <- lapply(agreem_binary, kappa2)
expect_named(k2_studies, c("study1", "study2", "study3"))
# cf Solutions in Appendix C to problem 18.3
# in Fleiss, Statistical Methods.. 3rd ed (2003)
expect_identical(purrr::map_dbl(k2_studies, "subjects"),
expected = c(20, 20, 30),
ignore_attr = "names")
expect_equal(purrr::map_dbl(k2_studies, "value"),
expected = c(.39, .48, .35),
tolerance = .025, ignore_attr = "names")
expect_equal(purrr::map_dbl(k2_studies, "se"),
expected = c(.21, .25, .17),
tolerance = .025, ignore_attr = "names")
# homogeneity test for kappa
k2hom <- kappa_test(kappas = k2_studies,
val = "value", se = "se")
expect_s3_class(k2hom, class = "htest")
expect_named(k2hom, expected = c("method", "data.name", "estimate",
"statistic", "p.value",
"alternative", "parameter", "conf.int"))
expect_equal(k2hom$estimate, expected = c(`overall kappa`=0.39),
tolerance = .01)
expect_equal(k2hom$statistic, expected = c(`X^2`=0.18),
tolerance = .1)
expect_equal(k2hom$conf.int, expected = c(.16, .62),
tolerance = .1, ignore_attr = "conf.level")
})
test_that("kappa homogeneity test for correlated samples", {
set.seed(2024-12-09)
k2hom <- kappa_test_corr(ratings = depression,
grpIdx = list(c(1, 2), c(1, 3)),
kappaF = kappa2,
kappaF_args = list(ratingScale = c("neg", "pos")),
B = 2500)
# see Vanbelle (2008), section 5.2
expect_equal(as.numeric(k2hom$statistic), 2.19, tolerance = .05)
expect_equal(k2hom$p.value, 0.14, tolerance = .05)
})
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