tests/testthat/test-covariate.R

In campsis: Generic PK/PD Simulation Platform Campsis

library(testthat)

context("Test all methods from the covariate class")

test_that("Constant covariate", {
  covariate <- Covariate("WT", ConstantDistribution(70))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@value, 70)
  expect_equal(is(covariate, "covariate"), TRUE)
  expect_equal(is(covariate, "fixed_covariate"), TRUE)
  expect_equal(is(covariate, "time_varying_covariate"), FALSE)

  # No distribution argument is provided
  expect_error(Covariate("WT"))

  # Two values provided
  expect_error(Covariate("WT", ConstantDistribution(c(60, 70))))
})

test_that("Fixed covariate", {
  covariate <- Covariate("WT", FixedDistribution(c(50, 60, 70)))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@values, c(50, 60, 70))

  # No distribution argument is provided
  expect_error(Covariate("WT"))

  # Empty values list
  expect_error(Covariate("WT", FixedDistribution(numeric(0))))
})

test_that("Function covariate", {
  # Example 1
  covariate <- Covariate(name = "VAR", FunctionDistribution(fun = "sample.int", args = list(n = 10, size = "n")))
  expect_equal(covariate@name, "VAR")
  expect_equal(covariate@distribution@args, list(n = 10, size = "n"))

  set.seed(1)
  covariate <- covariate %>% sample(n = as.integer(5))
  expect_equal(covariate@distribution@sampled_values, c(9, 4, 7, 1, 2))

  # Example 2
  covariate <- Covariate(name = "WT", FunctionDistribution(fun = "rnorm", args = list(mean = 70, sd = 5)))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@args, list(mean = 70, sd = 5))

  set.seed(1)
  covariate <- covariate %>% sample(n = as.integer(5))
  expect_equal(round(covariate@distribution@sampled_values), c(67, 71, 66, 78, 72))
})

test_that("Bootstrap covariate", {
  set.seed(1)
  data <- rnorm(n = 10, mean = 70, sd = 10)

  # Example 1: replacement=FALSE (default), random=FALSE (default)
  covariate <- Covariate(name = "WT", BootstrapDistribution(data = data))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@data, data)
  expect_equal(covariate@distribution@replacement, FALSE)
  expect_equal(covariate@distribution@random, FALSE)
  covariate <- covariate %>% sample(n = as.integer(5))
  expect_equal(covariate@distribution@sampled_values, data[1:5])

  # Example 2: replacement=FALSE (default), random=TRUE (default)
  set.seed(1)
  covariate <- Covariate(name = "WT", BootstrapDistribution(data = data, replacement = FALSE, random = TRUE))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@data, data)
  expect_equal(covariate@distribution@replacement, FALSE)
  expect_equal(covariate@distribution@random, TRUE)
  covariate <- covariate %>% sample(n = as.integer(5))
  expect_equal(covariate@distribution@sampled_values, data[c(9, 4, 7, 1, 2)])

  # Example 3: replacement=TRUE (default), random=TRUE (default)
  covariate <- Covariate(name = "WT", BootstrapDistribution(data = data, replacement = TRUE, random = FALSE))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@data, data)
  expect_equal(covariate@distribution@replacement, TRUE)
  expect_equal(covariate@distribution@random, FALSE)
  covariate <- covariate %>% sample(n = as.integer(12))
  expect_equal(covariate@distribution@sampled_values, c(data, data[1:2]))

  # Example 4: replacement=TRUE (default), random=TRUE (default)
  set.seed(1)
  covariate <- Covariate(name = "WT", BootstrapDistribution(data = data, replacement = TRUE, random = TRUE))
  expect_equal(covariate@name, "WT")
  expect_equal(covariate@distribution@data, data)
  expect_equal(covariate@distribution@replacement, TRUE)
  expect_equal(covariate@distribution@random, TRUE)
  covariate <- covariate %>% sample(n = as.integer(12))
  expect_equal(covariate@distribution@sampled_values, data[c(9, 4, 7, 1, 2, 7, 2, 3, 1, 5, 5, 10)]) # match(covariate@values, data)
})

test_that("Event-related covariate", {
  covariate <- EventCovariate("DOSE", 100)
  expect_equal(covariate@name, "DOSE")
  expect_equal(covariate@distribution@value, 100)
  expect_equal(is(covariate, "covariate"), TRUE)
  expect_equal(is(covariate, "event_covariate"), TRUE)

  # No initial distribution argument is provided
  expect_error(EventCovariate("DOSE"))
})

test_that("Time-varying covariate", {
  expect_error(TimeVaryingCovariate("BW", table = data.frame()), regexp = "TIME and VALUE are mandatory columns")

  expect_error(
    TimeVaryingCovariate("BW", data.frame(TIME = 1, VALUE = 50)),
    regexp = "Please provide a value for time 0"
  )

  expect_error(
    TimeVaryingCovariate("BW", table = data.frame(ID = c(1, 2, 3), TIME = c(0, 1, 1), VALUE = c(50, 50, 50))),
    regexp = "Some ID's don't have a value for time 0: 2,3"
  )

  expect_error(
    TimeVaryingCovariate("BW", table = data.frame(ID = c(1, 1, 2), TIME = c(0, 0, 0), VALUE = c(50, 50, 100))),
    regexp = "Some ID's have several values for time 0"
  )

  bw1 <- data.frame(ID = 1, TIME = c(0), VALUE = c(70)) # Constant
  bw2 <- data.frame(ID = 2, TIME = c(0, 24), VALUE = c(100, 90))
  bw3 <- data.frame(ID = 3, TIME = c(0, 12, 25, 36), VALUE = c(90, 80, 70, 60))
  bw4 <- data.frame(ID = 4, TIME = c(0, 12, 25, 36), VALUE = c(50, 40, 30, 20))
  cov <- TimeVaryingCovariate("BW", table = dplyr::bind_rows(bw1, bw2, bw3, bw4))

  expect_equal(cov@distribution, FixedDistribution(c(70, 100, 90, 50)))
  expect_equal(
    cov@table,
    data.frame(ID = c(2, 3, 3, 3, 4, 4, 4), TIME = c(24, 12, 25, 36, 12, 25, 36), VALUE = c(90, 80, 70, 60, 40, 30, 20))
  )
})