tests/testthat/test-ch4.R

In ocbe-uio/contingencytables: Statistical Analysis of Contingency Tables

context("Chapter 4")

test_that("Chapter 4 functions basically work", {
  expect_output(
    object = print(Adjusted_inv_sinh_CI_OR_2x2(lampasona_2013)),
    regexp = "estimate = 5.6250 \\(95% CI 1.1414 to 21.7873\\)"
  )
  expect_output(
    object = print(Adjusted_inv_sinh_CI_ratio_2x2(ritland_2007)),
    regexp = "estimate = 0.0000 \\(95% CI 0.0000 to 1.1524\\)"
  )
  expect_output(
    object = print(Adjusted_inv_sinh_CI_ratio_2x2(matrix(c(1, 0, 3, 4), 2))),
    regexp = "inverse sinh CI: estimate =    Inf \\(95% CI 0.2603 to    Inf\\)"
  )
  expect_output(
    object = print(Adjusted_log_CI_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 0.9241 to 27.0523\\)"
  )
  expect_output(
    object = print(Wald_CI_2x2(perondi_2004)),
    regexp = "estimate = 0.1765 \\(95% CI 0.0292 to 0.3238\\)"
  )
  expect_output(
    object = print(AgrestiCaffo_CI_2x2(perondi_2004)),
    regexp = "estimate = 0.1765 \\(95% CI 0.0116 to 0.3217\\)"
  )
  expect_output(
    object = print(BaptistaPike_exact_conditional_CI_2x2(rbind(c(1, 7), c(5, 7)))),
    regexp = "estimate = 0.2000 \\(95% CI 0.0151 to 1.7523\\)"
  )
  expect_output(
    object = print(BaptistaPike_exact_conditional_CI_2x2(matrix(c(1, 0, 0, 1), 2))),
    regexp = "Baptista-Pike .+ =    Inf \\(95% CI 0.1111 to    Inf\\)"
  )

  expect_output(
    object = print(BaptistaPike_midP_CI_2x2(rbind(c(15, 30), c(2, 3)))),
    regexp = "estimate = 0.7500 \\(95% CI 0.1415 to 4.6034\\)"
  )
  expect_output(
    object = print(BaptistaPike_midP_CI_2x2(matrix(c(1, 0, 1, 1), 2))),
    regexp = "estimate =    Inf \\(95% CI 0.0556 to    Inf\\)"
  )
  expect_output(
    object = print(Cornfield_exact_conditional_CI_2x2(lampasona_2013)),
    regexp = "estimate = 5.6250 \\(95% CI 1.0226 to 31.5025\\)"
  )
  expect_output(
    object = print(Cornfield_exact_conditional_CI_2x2(matrix(c(1, 0, 0, 1), 2))),
    regexp = "conditional CI: estimate =    Inf \\(95% CI 0.0526 to    Inf\\)"
  )
  expect_output(
    object = print(Cornfield_midP_CI_2x2(tea)),
    regexp = "estimate = 9.0000 \\(95% CI 0.3101 to 308.5568\\)"
  )
  expect_output(
    object = print(Cornfield_midP_CI_2x2(matrix(c(3, 1, 0, 3), 2))),
    regexp = "Cornfield .+Inf \\(95% CI 0.5605 to    Inf\\)"
  )
  expect_output(
    object = print(Fisher_exact_test_2x2(ritland_2007)),
    regexp = "P = 0.06287"
  )
  expect_output(
    object = print(Fisher_exact_test_2x2(tea * 0)),
    regexp = "The Fisher exact test \\(Pearson\\): P = 1.00000"
  )
  expect_output(
    object = print(Exact_unconditional_test_2x2(tea)),
    regexp = "The Suissa-Shuster exact unconditional test: P = 0.28916"
  )
  expect_output(
    object = print(Exact_unconditional_test_2x2(tea, gamma= 0)),
    regexp = "The Suissa-Shuster exact unconditional test: P = 0.28906"
  )
  expect_output(
    object = print(Exact_unconditional_test_2x2(matrix(c(0, 1, 0, 0), 2))),
    regexp = "The Suissa-Shuster exact unconditional test: P = 1.0000"
  )
  expect_output(
    object = print(Fisher_midP_test_2x2(ritland_2007)),
    regexp = "The Fisher mid-P test \\(Fisher-Irwin\\): P = 0.04466"
  )
  expect_output(
    object = print(Fisher_midP_test_2x2(ritland_2007, "Pearson")),
    regexp = "The Fisher mid-P test \\(Pearson\\): P = 0.04466"
  )
  expect_output(
    object = print(Fisher_midP_test_2x2(ritland_2007, "LR")),
    regexp = "The Fisher mid-P test \\(LR\\): P = 0.04466"
  )
  expect_output(
    object = print(Gart_adjusted_logit_CI_2x2(ritland_2007)),
    regexp = "estimate = 0.0000 \\(95% CI 0.0064 to 1.9804\\)"
  )
  expect_output(
    object = print(Independence_smoothed_logit_CI_2x2(lampasona_2013)),
    regexp = "estimate = 5.6250 \\(95% CI 1.0206 to 23.7777\\)"
  )
  expect_output(
    object = print(Inv_sinh_CI_OR_2x2(lampasona_2013)),
    regexp = "estimate = 5.6250 \\(95% CI 1.2472 to 25.3686\\)"
  )
  expect_output(
    object = print(Inv_sinh_CI_OR_2x2(matrix(c(0, 0, 0, 0), 2))),
    regexp = "estimate =    NaN \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(Inv_sinh_CI_ratio_2x2(matrix(c(10, 0, 10, 20), 2))),
    regexp = "The inverse sinh CI.+Inf \\(95% CI 2.6032 to    Inf\\)"
  )
  expect_output(
    object = print(Inv_sinh_CI_ratio_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 1.1671 to 41.9827\\)"
  )
  expect_output(
    object = print(Katz_log_CI_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 0.9096 to 53.8695\\)"
  )
  expect_output(
    object = print(Koopman_asymptotic_score_CI_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 1.2209 to 42.5757\\)"
  )
  expect_output(
    object = print(Koopman_asymptotic_score_CI_2x2(matrix(c(0, 0, 1, 2), 2))),
    regexp = "estimate =    NaN \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(Koopman_asymptotic_score_CI_2x2(matrix(c(3, 0, 1, 2), 2))),
    regexp = "estimate =    Inf \\(95% CI 0.8257 to    Inf\\)"
  )
  expect_output(
    object = print(LR_test_2x2(tea)),
    regexp = "P = 0.14798, T = 2.093 \\(df = 1\\)"
  )
  expect_output(
    object = print(Mee_asymptotic_score_CI_2x2(perondi_2004)),
    regexp = "estimate = 0.1765 \\(95% CI 0.0284 to 0.3439\\)"
  )
  expect_output(
    object = print(Mee_asymptotic_score_CI_2x2(matrix(c(2e6, 2, 2, 2e6), 2))),
    regexp = "Mee asymptotic .+ 1.0000 \\(95% CI 1.0000 to 1.0000\\)"
  )
  expect_output(
    object = print(Mee_asymptotic_score_CI_2x2(matrix(c(2, 2e6, 2e6, 2), 2))),
    regexp = "Mee asymptotic .+ -1.0000 \\(95% CI -1.0000 to -1.0000\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_difference_2x2(ritland_2007)),
    regexp = "estimate = -0.2083 \\(95% CI -0.3164 to -0.0056\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_difference_2x2(matrix(c(2e6, 2, 2, 2e6), 2))),
    regexp = "Miettinen-Nurminen .+ 1.0000 \\(95% CI 1.0000 to 1.0000\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_difference_2x2(matrix(c(2, 2e6, 2e6, 2), 2))),
    regexp = "Miettinen-Nurminen .+ -1.0000 \\(95% CI -1.0000 to -1.0000\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_OR_2x2(lampasona_2013)),
    regexp = "5.6250 \\(95% CI 1.0934 to 28.9419\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_OR_2x2(matrix(c(1, 0, 10, 2), 2))),
    regexp = "score CI: estimate =    Inf \\(95% CI 0.0313 to    Inf\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_ratio_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 1.2086 to 43.0330\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_ratio_2x2(matrix(c(0, 0, 1, 2), 2))),
    regexp = "estimate =    NaN \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(MiettinenNurminen_asymptotic_score_CI_ratio_2x2(matrix(c(3, 0, 1, 2), 2))),
    regexp = "estimate =    Inf \\(95% CI 0.7113 to    Inf\\)"
  )
  expect_output(
    object = print(MOVER_R_Wilson_CI_OR_2x2(lampasona_2013)),
    regexp = "estimate = 5.6250 \\(95% CI 1.0433 to 20.5670\\)"
  )
  expect_output(
    object = print(MOVER_R_Wilson_CI_OR_2x2(matrix(c(10, 20, 0, 30), 2))),
    regexp = "MOVER-R Wilson CI: estimate =    Inf \\(95% CI 3.9010 to    Inf\\)"
  )
  expect_output(
    object = print(MOVER_R_Wilson_CI_OR_2x2(matrix(c(1, NA, 3, 4), 2))),
    regexp = "MOVER-R Wilson CI: estimate =\\s+NA \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(MOVER_R_Wilson_CI_ratio_2x2(matrix(c(NA, 1, 2, 3), 2))),
    regexp = "MOVER-R Wilson CI: estimate =\\s+NA \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(MOVER_R_Wilson_CI_ratio_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 1.1537 to 41.9763\\)"
  )
  expect_output(
    object = print(Newcombe_hybrid_score_CI_2x2(perondi_2004)),
    regexp = "estimate = 0.1765 \\(95% CI 0.0189 to 0.3404\\)"
  )
  expect_output(
    object = print(Pearson_chi_squared_test_2x2(tea)),
    regexp = "Pearson chi-squared test: P = 0.15730, T = 2.000 \\(df = 1\\)"
  )
  expect_output(
    object = print(Pearson_chi_squared_test_2x2(tea * 0)),
    regexp = "The Pearson chi-squared test: P = 1.00000, T =   NaN \\(df = 1\\)"
  )
  expect_output(
    object = print(Pearson_chi_squared_test_CC_2x2(tea)),
    regexp = "chi-squared test: P = 0.47950, T = 0.500 \\(df = 1\\)"
  )
  expect_output(
    object = print(Pearson_chi_squared_test_CC_2x2(tea * 0)),
    regexp = "The Pearson chi-squared test: P = 1.00000, T =   NaN \\(df = 1\\)"
  )
  expect_output(
    object = print(PriceBonett_approximate_Bayes_CI_2x2(perondi_2004)),
    regexp = "estimate = 7.0000 \\(95% CI 0.9205 to 36.5449\\)"
  )
  expect_output(
    object = print(Wald_CI_CC_2x2(perondi_2004)),
    regexp = "estimate = 0.1765 \\(95% CI -0.0002 to 0.3532\\)"
  )
  expect_output(
    object = print(the_2x2_table_CIs_difference(ritland_2007)),
    regexp = "Miettinen-Nurminen asymptotic score -0.3164 to -0.0056"
  )
  expect_output(
    object = print(Woolf_logit_CI_2x2(ritland_2007)),
    regexp = "estimate = 0.0000 \\(95% CI 0.0000 to    Inf\\)"
  )
  expect_output(
    object = print(Uncorrected_asymptotic_score_CI_2x2(ritland_2007)),
    regexp = "0.0000 \\(95% CI 0.0000 to 0.9532\\)"
  )
  expect_output(
    object = print(Uncorrected_asymptotic_score_CI_2x2(matrix(c(10, 0, 0, 30), 2))),
    regexp = "score CI: estimate =    Inf \\(95% CI 81.6504 to    Inf\\)"
  )
  expect_output(
    object = print(the_2x2_table_CIs_OR(ritland_2007)),
    regexp = "inverse sinh \\(0.45, 0.25\\)     0.006 to  1.817    5.766"
  )
  expect_output(
    object = print(the_2x2_table_CIs_ratio(ritland_2007)),
    regexp = "Price-Bonett approximate Bayes\\s+0.001 to  3.425    7.836"
  )
  expect_output(
    object = print(Z_unpooled_test_2x2(ritland_2007)),
    regexp = "The Z-unpooled test: P = 0.00001, Z = -4.353"
  )
  expect_output(
    object = print(Z_unpooled_test_2x2(ritland_2007 * 0)),
    regexp = "The Z-unpooled test: P = 1.00000, Z =    NaN"
  )
  expect_output(
    object = print(the_2x2_table_tests(ritland_2007)),
    regexp = "chi-squared w / CC\\s+0.1016\\s+\\(T = 2.680, df = 1\\)"
  )
})