tests/testthat/test-clm.R

In JointAI: Joint Analysis and Imputation of Incomplete Data

library("JointAI")

skip_on_cran()
# Sys.setenv(IS_CHECK = "true")

run_clm_models <- function() {
  sink(tempfile())
  on.exit(sink())
  invisible(force(suppressWarnings({
    models <- list(
      # no covariates
      m0a = clm_imp(
        O1 ~ 1,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),
      m0b = clm_imp(
        O2 ~ 1,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),

      # only complete
      m1a = clm_imp(
        O1 ~ C1,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),
      m1b = clm_imp(
        O2 ~ C1,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),

      # only incomplete
      m2a = clm_imp(
        O1 ~ C2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),
      m2b = clm_imp(
        O2 ~ C2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),

      # as covariate
      m3a = lm_imp(
        C1 ~ O1,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),
      m3b = lm_imp(
        C1 ~ O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),

      # complex structures
      m4a = clm_imp(
        O1 ~ M2 + O2 * abs(C1 - C2) + log(C1),
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),
      m4b = clm_imp(
        O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) *
          abs(C1 - C2) +
          log(C1),
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        warn = FALSE,
        mess = FALSE
      ),

      # non-proportional effects
      # - basic model
      m5a = clm_imp(
        O1 ~ C1 + C2 + M2 + O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        nonprop = list(O1 = ~ C1 + C2),
        monitor_params = list(other = "p_O1"),
        warn = FALSE,
        mess = FALSE
      ),

      # - interaction in prop. effects
      m5b = clm_imp(
        O1 ~ C1 * C2 + M2 + O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        nonprop = list(O1 = ~ C1 + C2),
        monitor_params = list(other = "p_O1"),
        warn = FALSE,
        mess = FALSE
      ),

      # - interaction in non-prop effects
      m5c = clm_imp(
        O1 ~ C1 * C2 + M2 + O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        nonprop = list(O1 = ~ C1 * C2),
        monitor_params = list(other = "p_O1"),
        warn = FALSE,
        mess = FALSE
      ),

      # - interaction between non-prop and prop effects
      m5d = clm_imp(
        O1 ~ C1 + M2 * C2 + O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        nonprop = list(O1 = ~ C1 + C2),
        monitor_params = list(other = "p_O1"),
        warn = FALSE,
        mess = FALSE
      ),

      # - all effects non-proportional
      m5e = clm_imp(
        O1 ~ C1 + M2 * C2 + O2,
        data = wideDF,
        n.adapt = 5,
        n.iter = 10,
        seed = 2020,
        nonprop = ~ C1 + M2 * C2 + O2,
        monitor_params = list(other = "p_O1"),
        warn = FALSE,
        mess = FALSE
      )
    )

    models$m6a <- update(models$m5a, rev = "O1")
    models$m6b <- update(models$m5b, rev = "O1")
    models$m6c <- update(models$m5c, rev = "O1")
    models$m6d <- update(models$m5d, rev = "O1")
    models$m6e <- update(models$m5e, rev = "O1")
  })))
  models
}

models <- run_clm_models()
models0 <- set0_list(models)

test_that("models run", {
  for (k in seq_along(models)) {
    expect_s3_class(models[[k]], "JointAI")
  }
})


test_that("there are no duplicate betas/alphas in the jagsmodel", {
  expect_null(unlist(lapply(models, find_dupl_parms)))
})


test_that("MCMC is mcmc.list", {
  for (i in seq_along(models)) {
    expect_s3_class(models[[i]]$MCMC, "mcmc.list")
  }
})

test_that("MCMC samples can be plottet", {
  for (k in seq_along(models)) {
    expect_silent(traceplot(models[[k]]))
    expect_silent(densplot(models[[k]]))
    expect_silent(plot(MC_error(models[[k]])))
  }
})


test_that("data_list remains the same", {
  # skip_on_cran()
  expect_snapshot(lapply(models, "[[", "data_list"))
})

test_that("jagsmodel remains the same", {
  expect_snapshot(lapply(models, "[[", "jagsmodel"))
})


test_that("GRcrit and MCerror give same result", {
  # skip_on_cran()
  expect_snapshot(lapply(models0, GR_crit, multivariate = FALSE))
  expect_snapshot(lapply(models0, MC_error))
})


# test_that("summary output remained the same on Windows", {
#   # skip_on_cran()
#   skip_on_os(c("mac", "linux", "solaris"))

#   expect_snapshot(lapply(models0, print))
#   expect_snapshot(lapply(models0, coef))
#   expect_snapshot(lapply(models0, confint))
#   expect_snapshot(lapply(models0, summary))
#   expect_snapshot(lapply(models0, function(x) coef(summary(x))))
# })

test_that("summary output remained the same", {
  # skip_on_cran()
  # skip_on_os(c("windows"))
  expect_snapshot(lapply(models0, print))
  expect_snapshot(lapply(models0, coef))
  expect_snapshot(lapply(models0, confint))
  expect_snapshot(lapply(models0, summary))
  expect_snapshot(lapply(models0, function(x) coef(summary(x))))
})


test_that("prediction works", {
  expect_equal(
    class(predict(models$m4a, type = "lp", warn = FALSE)$fitted),
    "array"
  )
  expect_equal(
    class(predict(models$m4a, type = "prob", warn = FALSE)$fitted),
    "array"
  )

  expect_s3_class(
    predict(models$m4a, type = "class", warn = FALSE)$fitted,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m4a, type = "response", warn = FALSE)$fitted,
    "data.frame"
  )

  expect_s3_class(
    predict(models$m4a, type = "lp", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m4a, type = "prob", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m4a, type = "class", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m4a, type = "response", warn = FALSE)$newdata,
    "data.frame"
  )

  expect_equal(
    class(predict(models$m5d, type = "lp", warn = FALSE)$fitted),
    "array"
  )
  expect_equal(
    class(predict(models$m5d, type = "prob", warn = FALSE)$fitted),
    "array"
  )

  expect_s3_class(
    predict(models$m5d, type = "class", warn = FALSE)$fitted,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m5d, type = "response", warn = FALSE)$fitted,
    "data.frame"
  )

  expect_s3_class(
    predict(models$m5d, type = "lp", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m5d, type = "prob", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m5d, type = "class", warn = FALSE)$newdata,
    "data.frame"
  )
  expect_s3_class(
    predict(models$m5d, type = "response", warn = FALSE)$newdata,
    "data.frame"
  )

  expect_s3_class(
    predict(models$m5e, type = "prob", warn = FALSE)$newdata,
    "data.frame"
  )

  expect_equal(check_predprob(models$m5a), 0)
  expect_equal(check_predprob(models$m5b), 0)
  expect_equal(check_predprob(models$m5c), 0)
  expect_equal(check_predprob(models$m5d), 0)
  expect_equal(check_predprob(models$m5e), 0)

  expect_equal(check_predprob(models$m6a), 0)
  expect_equal(check_predprob(models$m6b), 0)
  expect_equal(check_predprob(models$m6c), 0)
  expect_equal(check_predprob(models$m6d), 0)
  expect_equal(check_predprob(models$m6e), 0)
})


test_that("residuals", {
  # residuals are not yet implemented
  expect_error(residuals(models$m4a, type = "working"))
})


test_that("model can be plottet", {
  for (i in seq_along(models)) {
    if (models[[i]]$analysis_type == "clm") {
      expect_error(plot(models[[i]]))
    } else {
      expect_silent(plot(models[[i]]))
    }
  }
})


test_that("wrong models give errors", {
  expect_error(clm_imp(y ~ O1 + C1 + C2, data = wideDF))
  expect_error(clm_imp(
    O2 ~ O1 + C1 + C2 + (1 | id),
    data = longDF,
    warn = FALSE
  ))
  expect_error(clm_imp(
    O2 ~ O1 + C1 + C2 + (1 | id),
    data = wideDF,
    warn = FALSE
  ))
  expect_s3_class(
    suppressWarnings(
      clm_imp(O2 ~ I(O1^2) + C1 + C2, data = wideDF, warn = FALSE)
    ),
    "JointAI_errored"
  )
  expect_error(clm_imp(
    O2 ~ O1 + C1,
    data = wideDF,
    nonprop = list(O2 = ~C2),
    warn = FALSE
  ))
})
# Sys.setenv(IS_CHECK = "")