tests/testthat/test-GammaModVar.R

In rdecision: Decision Analytic Modelling in Health Economics

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)
})