Nothing
context("optimal_k")
coeff <- c("(Intercept)" = -3.68, "Parsonnet" = 0.077)
df <- data.frame(Parsonnet=c(0L, 0L, 50L, 50L),
status = c(0, 1, 0, 1))
QA <- 2
# test_that("Output of optimal_k calculation", {
# expected_results <- 0.1255
# works <- round(optimal_k(pmix = df, RA = 2, FALSE), 4)
# expect_equal(works, expected_results)
# })
# test_that("Output of optimal_k calculation", {
# expected_results <- 0.079
# QA <- 1/2
# works <- round(optimal_k(QA, df, coeff, FALSE), 4)
# expect_equal(works, expected_results)
# })
#
# test_that("Output of optimal_k calculation", {
# expected_results <- 0.2213
# works <- round(optimal_k(QA, df, coeff, TRUE), 4)
# expect_equal(works, expected_results)
# })
#
# test_that("Output of optimal_k calculation", {
# expected_results <- 0.1393
# QA <- 1/2
# works <- round(optimal_k(QA, df, coeff, TRUE), 4)
# expect_equal(works, expected_results)
# })
#
# test_that("Different input values for coeff", {
# coefftest <- list(coeff[1], rep(1, 3), NULL, NA)
# lapply(coefftest, function(x) {
# expect_error(do.call(x, optimal_k(QA, df, coeff = x, FALSE)),
# "Model coefficients 'coeff' must be a numeric vector with two elements")})
# })
#
# test_that("Different input values for QA", {
# QAtest <- list(-1, NA, 0)
# lapply(QAtest, function(x) {
# expect_error(do.call(x, optimal_k(QA = QAtest, df, coeff, FALSE)),
# "QA must a positive numeric value")})
# })
#
# test_that("Different input values for yemp", {
# expect_warning(optimal_k(QA, df, coeff, yemp = as.character(TRUE)),
# "Argument 'yemp' must be logical using TRUE as default value")
# expect_warning(optimal_k(QA, df, coeff, yemp = as.numeric(TRUE)),
# "Argument 'yemp' must be logical using TRUE as default value")
# expect_warning(optimal_k(QA, df, coeff, yemp = NA),
# "Argument 'yemp' must be logical using TRUE as default value")
# })
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