Nothing
test_that("illegal initializations are rejected", {
k <- 9.0
theta <- 0.5
expect_silent(GammaModVar$new("gamma", "GBP", k, theta))
expect_error(GammaModVar$new(42.0, 42.0, k, theta),
class = "description_not_string")
expect_error(GammaModVar$new("gamma", 42L, k, theta),
class = "units_not_string")
expect_error(GammaModVar$new("gamma", "GBP", "9", theta),
class = "shape_not_numeric")
expect_error(GammaModVar$new("gamma", "GBP", k, "0.5"),
class = "scale_not_numeric")
expect_error(GammaModVar$new("gamma", "GBP", -1.0, theta),
class = "shape_not_supported")
expect_error(GammaModVar$new("gamma", "GBP", k, 0.0),
class = "scale_not_supported")
})
test_that("properties are correct", {
k <- 9.0
theta <- 0.5
g <- GammaModVar$new("gamma", "GBP", k, theta)
expect_false(g$is_expression())
expect_true(g$is_probabilistic())
})
test_that("modvar has correct distribution name", {
k <- 9.0
theta <- 0.5
g <- GammaModVar$new("gamma", "GBP", k, theta)
expect_identical(g$distribution(), "Ga(9,0.5)")
})
test_that("get() is initialized correctly", {
k <- 9.0
theta <- 0.5
g <- GammaModVar$new("gamma", "GBP", k, theta)
expect_intol(g$get(), k * theta, 0.01)
})
test_that("mean, mode, sd and quantiles are returned correctly", {
k <- 9.0
theta <- 0.5
g <- GammaModVar$new("gamma", "GBP", k, theta)
expect_intol(g$mean(), k * theta, 0.01)
expect_intol(g$SD(), sqrt(k) * theta, 0.01)
expect_intol(g$mode(), (k - 1.0) * theta, 0.01)
probs <- c(0.025, 0.975)
q <- g$quantile(probs)
expect_intol(q[[1L]], 2.06, 0.01)
expect_intol(q[[2L]], 7.88, 0.01)
})
test_that("stub quantile function checks inputs and has correct output", {
k <- 9.0
theta <- 0.5
g <- GammaModVar$new("gamma", "GBP", k, theta)
probs <- c(0.1, 0.2, 0.5)
expect_silent(g$quantile(probs))
probs <- c(0.1, NA, 0.5)
expect_error(g$quantile(probs), class = "probs_not_defined")
probs <- c(0.1, "boo", 0.5)
expect_error(g$quantile(probs), class = "probs_not_numeric")
probs <- c(0.1, 0.4, 1.5)
expect_error(g$quantile(probs), class = "probs_out_of_range")
probs <- c(0.1, 0.2, 0.5)
expect_length(g$quantile(probs), 3L)
})
test_that("random sampling is from a Gamma distribution", {
k <- 9.0
theta <- 0.5
n <- 1000L
g <- GammaModVar$new("gamma", "GBP", k, theta)
samp <- vapply(seq_len(n), FUN.VALUE = 1.0, FUN = function(i) {
g$set("random")
rv <- g$get()
return(rv)
})
expect_length(samp, n)
# 99.9% confidence limits; expected test failure rate is 0.1%;
# skip for CRAN
skip_on_cran()
ht <- ks.test(samp, stats::rgamma(n, shape = k, scale = theta))
expect_gt(ht$p.value, 0.001)
})
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