tests/testthat/test-FDM.R

In vital: Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data

# Check Functional data models

test_that("Functional data model", {
  if(requireNamespace("feasts", quietly = TRUE)) {
    library(feasts)
    hu <- aus_mortality |>
      dplyr::filter(State == "Victoria") |>
      model(hu = FDM(log(Mortality)))
    fc <- forecast(hu)
    expect_no_error(autoplot(fc))
    expect_equal(dim(hu), c(3L, 4L))
    expect_equal(NROW(tidy(hu)), 0L)
    expect_equal(colnames(glance(hu)), c("Sex", "State", "Code", ".model", "nobs", "varprop"))
    expect_no_error(residuals(hu, type = "innov"))
    expect_no_error(residuals(hu, type = "response"))
    expect_no_error(fitted(hu))
    expect_equal(NROW(generate(hu, times = 2)), 1212L)
    expect_equal(NROW(fc), 606L)
    expect_equal(fc |>
      dplyr::filter(Sex == "female", Code == "VIC", Age == 0, Year == 2021) |>
      dplyr::pull(.mean), 0.00246, tolerance = 1e-5)
    expect_equal(forecast(hu, bootstrap = TRUE, times = 7) |>
                   head(1) |>
                   dplyr::pull(Mortality) |>
                   unlist() |>
                   length(), 7)
    expect_equal(colnames(time_components(hu)), c("Sex", "State", "Code", "Year", "mean", paste0("beta",1:6)))
    expect_equal(colnames(age_components(hu)), c("Sex", "State", "Code", "Age", "mean", paste0("phi",1:6)))
  }
})