tests/testthat/test-timeSplitter.R

In gforge/Greg: Regression Helper Functions

library(testthat)
library(dplyr)

test_that("Check the number of events and rows are correct", {
  test_data <- data.frame(
    id = 1:4,
    time = c(4, 3.5, 1, 5),
    event = c("censored", "dead", "alive", "dead"),
    age = c(62.2, 55.3, 73.7, 46.3),
    date = as.Date(
      c(
        "2003-01-01",
        "2010-04-01",
        "2013-09-20",
        "2002-02-23"
      )
    )
  )

  split_data <-
    test_data |>
    select(id, event, time, age, date) |>
    timeSplitter(by = 2, # The time that we want to split by
                 event_var = "event",
                 time_var = "time",
                 event_start_status = "alive",
                 time_related_vars = c("age", "date"))

  expect_equal(nrow(split_data), sum(ceil(test_data$time / 2)))
  expect_equal(
    sum(split_data$event == "dead"),
    sum(test_data$event == "dead")
  )
  expect_equal(
    sum(split_data$event == "censored"),
    sum(test_data$event == "censored")
  )
  expect_gt(
    sum(split_data$event == "alive"),
    sum(test_data$event == "alive")
  )
})


test_that("Check that labels are preserved", {
  test_data <- data.frame(
    id = 1:4,
    time = c(4, 3.5, 1, 5),
    event = c("censored", "dead", "alive", "dead"),
    age = c(62.2, 55.3, 73.7, 46.3),
    date = as.Date(
      c(
        "2003-01-01",
        "2010-04-01",
        "2013-09-20",
        "2002-02-23"
      )
    )
  )

  library(Hmisc)
  label(test_data$age) <- "Age (years)"
  split_data <-
    test_data |>
    select(id, event, time, age, date) |>
    timeSplitter(by = 2, # The time that we want to split by
                 event_var = "event",
                 time_var = "time",
                 event_start_status = "alive",
                 time_related_vars = c("age", "date"))

  expect_equivalent(label(split_data$age),
                    label(test_data$age))
})


test_that("Check that age and calendar time is updated", {
  test_data <- data.frame(
    id = 1:4,
    time = c(4, 3.5, 1, 5),
    event = c("censored", "dead", "alive", "dead"),
    age = c(62.2, 55.3, 73.7, 46.3),
    date = as.Date(
      c(
        "2003-01-01",
        "2010-04-01",
        "2013-09-20",
        "2002-02-23"
      )
    )
  )

  library(Hmisc)
  label(test_data$age) <- "Age (years)"
  split_data <-
    test_data |>
    select(id, event, time, age, date) |>
    timeSplitter(by = 2, # The time that we want to split by
                 event_var = "event",
                 time_var = "time",
                 event_start_status = "alive",
                 time_related_vars = c("age", "date"))

  test_data <- cal.yr(test_data)
  library(dplyr)
  for (i in 1:nrow(test_data)) {
    row <- test_data[i, ]
    last_age <- row$age + (ceil(row$time / 2) - 1) * 2
    age <- split_data[split_data$id == row$id, "age"]
    expect_true(max(age) - last_age < .Machine$double.eps,
      info = paste0(
        "Could not identify the split max age (", max(age), ")",
        " to be equal to the calculated (", last_age, ")",
        " for row no ", i
      )
    )
    last_cal <- row$date + (ceil(row$time / 2) - 1) * 2
    cal <- split_data[split_data$id == row$id, "date"]
    expect_true(max(age) - last_age < .Machine$double.eps,
      info = paste0(
        "Could not identify the split max date (", max(cal), ")",
        " to be equal to the calculated (", last_cal, ")",
        " for row no ", i
      )
    )
  }
})