tests/testthat/test_powerscale.R

In n-kall/priorsense: Prior Diagnostics and Sensitivity Analysis

univariate_normal_draws <- example_powerscale_model()$draws

test_that("priorsense_data is created", {
  expect_s3_class(
    create_priorsense_data(
      univariate_normal_draws
    ),
    "priorsense_data"
  )
}
)

test_that("powerscale returns powerscaled_draws", {
  expect_s3_class(
    powerscale(
      x = univariate_normal_draws,
      component = "prior",
      alpha = 0.8
    ),
    "powerscaled_draws"
  )
  expect_s3_class(
    powerscale(
      x = univariate_normal_draws,
      component = "likelihood",
      alpha = 0.8
    ),
    "powerscaled_draws"
  )
}
)

test_that("powerscale_seqence returns powerscaled_sequence", {
  expect_s3_class(
    suppressWarnings(powerscale_sequence(
      x = univariate_normal_draws
    )),
    "powerscaled_sequence"
  )
}
)

test_that("powerscale_sensitivity returns powerscaled_sensitivity_summary", {
  expect_s3_class(
    powerscale_sensitivity(
      x = univariate_normal_draws
    ),
    "powerscaled_sensitivity_summary"
  )
}
)

test_that("powerscale_sequence uses input alphas correctly", {

  lower_alpha <- 0.5
  upper_alpha <- 2.5
  pss <- suppressWarnings(powerscale_sequence(
    x = univariate_normal_draws,
    lower_alpha = lower_alpha,
    upper_alpha = upper_alpha,
    symmetric = FALSE,
    length = 10
  ))

  expect_equal(
    pss$alphas[1],
    0.5
  )

  expect_equal(
    get_powerscaling_details(pss$prior_scaled$draws_sequence[[1]])$alpha,
    0.5
  )

  expect_equal(
    pss$alphas[length(pss$alphas)],
    2.5
  )

  expect_equal(
    get_powerscaling_details(pss$prior_scaled$draws_sequence[[length(pss$alphas)]])$alpha,
    2.5
  )

  expect_equal(
    length(pss$alphas),
    10
  )

}
)

test_that("powerscale_sequence adapts alphas and keeps pareto-k low", {
  k_threshold <- 0.7
  pss <- suppressWarnings(powerscale_sequence(
    x = univariate_normal_draws,
    auto_alpha_range = TRUE
  ))

  expect_lt(
    get_powerscaling_details(pss$likelihood_scaled$draws_sequence[[1]])$diagnostics$khat,
    k_threshold
  )

  expect_lt(
    get_powerscaling_details(pss$likelihood_scaled$draws_sequence[[length(pss$likelihood_scaled$draws_sequence)]])$diagnostics$khat,
    k_threshold
  )

  expect_lt(
    attr(pss$prior_scaled$draws_sequence[[1]], "powerscaling")$diagnostics$khat,
    k_threshold
  )

  expect_lt(
    get_powerscaling_details(pss$prior_scaled$draws_sequence[[length(pss$prior_scaled$draws_sequence)]])$diagnostics$khat,
    k_threshold
  )

}
)

test_that("powerscale_sequence gives symmetric range", {

  lower_alpha <- 0.3
  length <- 9
  pss <- suppressWarnings(powerscale_sequence(
    x = univariate_normal_draws,
    symmetric = TRUE,
    lower_alpha = lower_alpha,
    length = length
  ))
  
  expect_equal(
    pss$alphas[1],
    lower_alpha
  )
  
  expect_equal(
    get_powerscaling_details(pss$prior_scaled$draws_sequence[[1]])$alpha,
    lower_alpha
  )
  
  expect_equal(
    pss$alphas[length(pss$alphas)],
    1 / lower_alpha
  )
  
  expect_equal(
    get_powerscaling_details(pss$prior_scaled$draws_sequence[[length(pss$alphas)]])$alpha,
    1 / lower_alpha
  )
  
  expect_equal(
    length(pss$alphas),
    8
  )
  
  expect_equal(
    abs(log(pss$alphas[1])),
    abs(log(pss$alphas[length(pss$alphas)]))
  )
  
}
)