tests/testthat/test_model_select.R

In univariateML: Maximum Likelihood Estimation for Univariate Densities

context("model_select")

## Data generation.
set.seed(313)
small_data <- rnorm(100, 0, 3)
tiny_data <- rnorm(10, 0, 3)

## Finds errors with na.
expect_error(model_select(c(tiny_data, NA)))

## Finds incorrect/insufficent models
expect_error(model_select(tiny_data, models = c("norm", "stupid")))
expect_error(model_select(tiny_data, models = c("exp", "lnorm")))

## Finds wrong criterion
expect_error(model_select(tiny_data, criterion = "cv"))

## Checks that na.rm works as intended.
expect_equal(
  coef(model_select(small_data, models = c("norm", "cauchy"))),
  coef(model_select(c(small_data, NA), models = c("norm", "cauchy"), na.rm = TRUE))
)

## Does it find the correct model?
est <- mlnorm(small_data)
est_aic <- model_select(small_data, models = c("norm", "cauchy"), criterion = "aic")
est_select <- model_select(small_data, models = c("norm", "cauchy"), criterion = "loglik")
est_bic <- model_select(small_data, models = c("norm", "cauchy"), criterion = "bic")

attr(est, "call") <- str2lang("f(x = x, na.rm = na.rm)")
attr(est_aic, "call") <- str2lang("f(x = x, na.rm = na.rm)")
attr(est_select, "call") <- str2lang("f(x = x, na.rm = na.rm)")
attr(est_bic, "call") <- str2lang("f(x = x, na.rm = na.rm)")

expect_equal(est, est_aic)
expect_equal(est, est_select)
expect_equal(est, est_bic)

## Check class.
est <- model_select(small_data, models = c("norm", "cauchy"))
expect_equal(attr(est, "model"), "Normal")
expect_equal(class(est), "univariateML")


## Test data frame version
est <- model_select(small_data, models = c("norm", "cauchy", "zipf"), type = "continuous", return = "all")
expect(est |> inherits("data.frame"), "est is not a data frame")
expect_equal(est$model, c("Normal", "Cauchy"))
expect_equal(
  c(est$d_logLik[[1]], est$d_AIC[[1]], est$d_BIC[[1]]),
  c(0, 0, 0)
)