Nothing
test_that("illegal initializations are rejected", {
expect_silent(NormalDistribution$new(0.0, 1.0))
expect_error(NormalDistribution$new("0", 1.0), class = "mu_not_numeric")
expect_error(NormalDistribution$new(0.0, "1"), class = "sigma_not_numeric")
})
test_that("distribution name is correct", {
sn <- NormalDistribution$new(0.0, 1.0)
expect_identical(sn$distribution(), "N(0,1)")
n <- NormalDistribution$new(42.0, 1.0)
expect_identical(n$distribution(), "N(42,1)")
})
test_that("quantile function checks inputs", {
x <- NormalDistribution$new(0.0, 1.0)
probs <- c(0.1, 0.2, 0.5)
expect_silent(x$quantile(probs))
probs <- c(0.1, NA, 0.5)
expect_error(x$quantile(probs), class = "probs_not_defined")
probs <- c(0.1, "boo", 0.5)
expect_error(x$quantile(probs), class = "probs_not_numeric")
probs <- c(0.1, 0.4, 1.5)
expect_error(x$quantile(probs), class = "probs_out_of_range")
probs <- c(0.1, 0.2, 0.5)
expect_length(x$quantile(probs), 3L)
})
test_that("pe, mean, sd and quantiles are returned correctly", {
sn <- NormalDistribution$new(0.0, 1.0)
expect_identical(sn$mean(), 0.0)
expect_identical(sn$SD(), 1.0)
probs <- c(0.025, 0.975)
q <- sn$quantile(probs)
expect_identical(round(q[[1L]], 2L), -1.96)
expect_identical(round(q[[2L]], 2L), 1.96)
})
test_that("random sampling is from a Normal distribution", {
mu <- 0.0
sigma <- 1.0
sn <- NormalDistribution$new(mu, sigma)
# mean
sn$sample(TRUE)
expect_identical(sn$r(), 0.0)
# sample
n <- 1000L
samp <- vapply(seq_len(n), FUN.VALUE = 1.0, FUN = function(i) {
sn$sample()
rv <- sn$r()
return(rv)
})
expect_length(samp, n)
# check sample mean and sd are within 99.9% CI based on CLT; this is exact
# for a normal, and is expected to fail for 0.1% of tests; skip for CRAN
skip_on_cran()
ht <- ks.test(samp, rnorm(n, mean = mu, sd = sigma))
expect_gt(ht$p.value, 0.001)
})
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