tests/testthat/test_truncated.R

In goldingn/greta: Simple and Scalable Statistical Modelling in R

context("truncated distributions")

test_that("truncated normal has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated normal
  compare_truncated_distribution(normal,
    "norm",
    parameters = list(
      mean = -1,
      sd = 2.4
    ),
    truncation = c(-Inf, Inf)
  )

  # positive truncated
  compare_truncated_distribution(normal,
    "norm",
    parameters = list(
      mean = -1,
      sd = 2.4
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(normal,
    "norm",
    parameters = list(
      mean = -1,
      sd = 2.4
    ),
    truncation = c(-Inf, 0)
  )

  # fully truncated
  compare_truncated_distribution(normal,
    "norm",
    parameters = list(
      mean = -1,
      sd = 2.4
    ),
    truncation = c(-2, -1)
  )
})

test_that("truncated lognormal has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(lognormal,
    "lnorm",
    parameters = list(
      meanlog = -1,
      sdlog = 2.4
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(lognormal,
    "lnorm",
    parameters = list(
      meanlog = -1,
      sdlog = 2.4
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(lognormal,
    "lnorm",
    parameters = list(
      meanlog = -1,
      sdlog = 2.4
    ),
    truncation = c(0, 2)
  )

  # fully truncated
  compare_truncated_distribution(lognormal,
    "lnorm",
    parameters = list(
      meanlog = -1,
      sdlog = 2.4
    ),
    truncation = c(2, 4)
  )
})

test_that("truncated gamma has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(gamma,
    "gamma",
    parameters = list(
      shape = 2,
      rate = 2
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(gamma,
    "gamma",
    parameters = list(
      shape = 2,
      rate = 2
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(gamma,
    "gamma",
    parameters = list(
      shape = 2,
      rate = 2
    ),
    truncation = c(0, 2)
  )

  # fully truncated
  compare_truncated_distribution(gamma,
    "gamma",
    parameters = list(
      shape = 2,
      rate = 2
    ),
    truncation = c(1, 2)
  )
})

test_that("truncated inverse gamma has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(inverse_gamma,
    "invgamma",
    parameters = list(
      alpha = 2,
      beta = 1.2
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(inverse_gamma,
    "invgamma",
    parameters = list(
      alpha = 2,
      beta = 1.2
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(inverse_gamma,
    "invgamma",
    parameters = list(
      alpha = 2,
      beta = 1.2
    ),
    truncation = c(0, 2)
  )

  # fully truncated
  compare_truncated_distribution(inverse_gamma,
    "invgamma",
    parameters = list(
      alpha = 2,
      beta = 1.2
    ),
    truncation = c(1, 2)
  )
})

test_that("truncated weibull has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(weibull,
    "weibull",
    parameters = list(
      shape = 2,
      scale = 1.2
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(weibull,
    "weibull",
    parameters = list(
      shape = 2,
      scale = 1.2
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(weibull,
    "weibull",
    parameters = list(
      shape = 2,
      scale = 1.2
    ),
    truncation = c(0, 2)
  )

  # fully truncated
  compare_truncated_distribution(weibull,
    "weibull",
    parameters = list(
      shape = 2,
      scale = 1.2
    ),
    truncation = c(1, 2)
  )
})

test_that("truncated exponential has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(exponential,
    "exp",
    parameters = list(rate = 2),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(exponential,
    "exp",
    parameters = list(rate = 2),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(exponential,
    "exp",
    parameters = list(rate = 2),
    truncation = c(0, 2)
  )

  # fully truncated
  compare_truncated_distribution(exponential,
    "exp",
    parameters = list(rate = 2),
    truncation = c(1, 2)
  )
})

test_that("truncated pareto has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(preto,
    "preto",
    parameters = list(
      a_ = 1.9,
      b_ = 4.3
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(preto,
    "preto",
    parameters = list(
      a_ = 1.9,
      b_ = 4.3
    ),
    truncation = c(7.2, Inf)
  )

  # negative truncated
  compare_truncated_distribution(preto,
    "preto",
    parameters = list(
      a_ = 1.9,
      b_ = 4.3
    ),
    truncation = c(0, 21.3)
  )

  # fully truncated
  compare_truncated_distribution(preto,
    "preto",
    parameters = list(
      a_ = 1.9,
      b_ = 4.3
    ),
    truncation = c(7.2, 21.3)
  )
})

test_that("truncated student has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(student,
    "student",
    parameters = list(
      df = 5,
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(-Inf, Inf)
  )

  # positive truncated
  compare_truncated_distribution(student,
    "student",
    parameters = list(
      df = 5,
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(student,
    "student",
    parameters = list(
      df = 5,
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(-Inf, 2)
  )

  # fully truncated
  compare_truncated_distribution(student,
    "student",
    parameters = list(
      df = 5,
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(1, 2)
  )
})


test_that("truncated laplace has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(laplace,
    "laplace",
    parameters = list(
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(-Inf, Inf)
  )

  # positive truncated
  compare_truncated_distribution(laplace,
    "laplace",
    parameters = list(
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(laplace,
    "laplace",
    parameters = list(
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(-Inf, 2)
  )

  # fully truncated
  compare_truncated_distribution(laplace,
    "laplace",
    parameters = list(
      mu = 2,
      sigma = 3.4
    ),
    truncation = c(1, 2)
  )
})

test_that("truncated beta has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(beta,
    "beta",
    parameters = list(
      shape1 = 2.1,
      shape2 = 2.3
    ),
    truncation = c(0, 1)
  )

  # positive truncated
  compare_truncated_distribution(beta,
    "beta",
    parameters = list(
      shape1 = 2.1,
      shape2 = 2.3
    ),
    truncation = c(0.1, 1)
  )

  # negative truncated
  compare_truncated_distribution(beta,
    "beta",
    parameters = list(
      shape1 = 2.1,
      shape2 = 2.3
    ),
    truncation = c(0, 0.2)
  )

  # fully truncated
  compare_truncated_distribution(beta,
    "beta",
    parameters = list(
      shape1 = 2.1,
      shape2 = 2.3
    ),
    truncation = c(0.1, 0.2)
  )
})

test_that("truncated cauchy has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(cauchy,
    "cauchy",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(-Inf, Inf)
  )

  # positive truncated
  compare_truncated_distribution(cauchy,
    "cauchy",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(0.1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(cauchy,
    "cauchy",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(-Inf, 0.2)
  )

  # fully truncated
  compare_truncated_distribution(cauchy,
    "cauchy",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(0.1, 0.2)
  )
})

test_that("truncated logistic has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(logistic,
    "logis",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(-Inf, Inf)
  )

  # positive truncated
  compare_truncated_distribution(logistic,
    "logis",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(0.1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(logistic,
    "logis",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(-Inf, 0.2)
  )

  # fully truncated
  compare_truncated_distribution(logistic,
    "logis",
    parameters = list(
      location = -1.3,
      scale = 2.3
    ),
    truncation = c(0.1, 0.2)
  )
})

test_that("truncated f has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(f,
    "f",
    parameters = list(
      df1 = 1.3,
      df2 = 4.7
    ),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(f,
    "f",
    parameters = list(
      df1 = 1.3,
      df2 = 4.7
    ),
    truncation = c(0.1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(f,
    "f",
    parameters = list(
      df1 = 1.3,
      df2 = 4.7
    ),
    truncation = c(0, 0.2)
  )

  # fully truncated
  compare_truncated_distribution(f,
    "f",
    parameters = list(
      df1 = 1.3,
      df2 = 4.7
    ),
    truncation = c(0.1, 0.2)
  )
})

test_that("truncated chi squared has correct densities", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  # non truncated
  compare_truncated_distribution(chi_squared,
    "chisq",
    parameters = list(df = 9.3),
    truncation = c(0, Inf)
  )

  # positive truncated
  compare_truncated_distribution(chi_squared,
    "chisq",
    parameters = list(df = 9.3),
    truncation = c(0.1, Inf)
  )

  # negative truncated
  compare_truncated_distribution(chi_squared,
    "chisq",
    parameters = list(df = 9.3),
    truncation = c(0, 0.2)
  )

  # fully truncated
  compare_truncated_distribution(chi_squared,
    "chisq",
    parameters = list(df = 9.3),
    truncation = c(0.1, 0.2)
  )
})


test_that("bad truncations error", {
  skip_if_not(check_tf_version())
  source("helpers.R")

  expect_error(
    lognormal(0, 1, truncation = c(-1, Inf)),
    "lower bound must be 0 or higher"
  )

  expect_error(
    beta(1, 1, truncation = c(-1, 2)),
    "lower and upper bounds must be between 0 and 1"
  )
})