test_that("empirical_pval() returns correct values", {
tol <- 0.1
expect_equal(empirical_pval(seq(1, 100), 95, alternative = "greater"),
(5 + 1) / (100 + 1))
expect_lt(empirical_pval(rnorm(1e3), 2, alternative = "two.sided"), .05 + tol)
expect_lt(empirical_pval(rnorm(1e3), -2, alternative = "less"), .025 + tol)
})
test_that("empirical_pval() identifies incorrect inputs", {
matrix_input <- matrix(rnorm(1e3), ncol = 10)
multiple_observations <- c(2, 3)
expect_error(empirical_pval(matrix_input, 10))
expect_error(empirical_pval(seq(1, 100), multiple_observations))
expect_error(empirical_pval(95, seq(1, 100)))
})
yrep <- matrix(rnorm(1e3), ncol = 10)
y <- rep(2, 10)
test_that("post_pred_pval() returns a list containing a numeric value and a ggplot", {
x <- post_pred_pval(yrep, y, plot = TRUE)
expect_is(x, "list")
expect_is(x[[1]], "numeric")
expect_is(x[[2]], "ggplot")
})
test_that("post_pred_pval() identifies potentially incorrect inputs", {
expect_warning(post_pred_pval(yrep, c(y, 2)))
})
test_that("post_pred_pval() identifies incorrect inputs", {
expect_error(post_pred_pval(yrep, y, test_statistic = "mean"))
expect_error(post_pred_pval(yrep, y, plot = "no"))
})
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