tests/testthat/test_estimate_meta_mdiff_two.R

In rcalinjageman/esci: Estimation Statistics with Confidence Intervals

test_that("Compare meta_mdiff_two to ESCI_Original_two_groups", {

  # Comparisons are not perfect to ESCI due to difference in SE calculations

  esci_test <- data.frame(
    study_name = c("McCabe 1", "McCabe 2", paste("Michael", seq(1:10), sep = " ")),
    nbM = c(2.89, 2.69, 2.90, 2.62, 2.96, 2.93, 2.86, 2.50, 2.41, 2.54, 2.73, 2.66),
    nbS = c(0.79, 0.55, 0.58, 0.54, 0.36, 0.60, 0.59, 0.84, 0.78, 0.66, 0.67, 0.65),
    nbN = c(28, 26, 98, 42, 24, 184, 274, 58, 34, 99, 98, 94),
    bM = c(3.12, 3.00, 2.86, 2.85, 3.07, 2.89, 2.91, 2.60, 2.74, 2.72, 2.68, 2.64),
    bS = c(0.65, 0.54, 0.61, 0.57, 0.55, 0.60, 0.52, 0.83, 0.51, 0.68, 0.69, 0.71),
    bN = c(26, 28, 99, 33, 21, 184, 255, 55, 34, 95, 93, 97),
    mod = as.factor(
      c("Simple", "Critique", "Simple","Simple","Simple","Simple","Simple","Critique","Critique","Critique", "Critique","Critique")
    ),
    ds = c(0.31217944,  0.56073138, -0.06693802,  0.41136192,  0.23581389, -0.06652995,  0.08958082,  0.11892778,  0.49506069,  0.26765910, -0.07326, -0.02925)
  )

  esci_test <- esci_test[1:10, ]


  # Random effects
  estimate <- esci::meta_mdiff_two(
    data = esci_test,
    comparison_means = bM,
    comparison_sds = bS,
    comparison_ns = bN,
    reference_means = nbM,
    reference_sds = nbS,
    reference_ns = nbN,
    labels = study_name,
    effect_label = "Brain Photo Rating - No Brain Photo Rating",
    assume_equal_variance = TRUE,
    random_effects = TRUE
  )

  testthat::expect_s3_class(estimate, "esci_estimate")
  testthat::expect_equal(estimate$es_meta$effect_size[[1]], 0.0943163245)
  testthat::expect_equal(estimate$es_meta$LL[[1]], 0.01498243444)
  testthat::expect_equal(estimate$es_meta$UL[[1]], 0.17365021465)
  testthat::expect_equal(estimate$es_meta$diamond_ratio[[1]], 1.397516897)
  suppressWarnings(myplot <- esci::plot_meta(estimate))
  testthat::expect_s3_class(myplot, "ggplot")

  # Fixed effects
  estimate <- esci::meta_mdiff_two(
    data = esci_test,
    comparison_means = bM,
    comparison_sds = bS,
    comparison_ns = bN,
    reference_means = nbM,
    reference_sds = nbS,
    reference_ns = nbN,
    labels = study_name,
    effect_label = "Brain Photo Rating - No Brain Photo Rating",
    assume_equal_variance = TRUE,
    random_effects = FALSE
  )

  testthat::expect_s3_class(estimate, "esci_estimate")
  testthat::expect_equal(estimate$es_meta$effect_size[[1]], 0.0684524822)
  testthat::expect_equal(estimate$es_meta$LL[[1]], 0.01168473203)
  testthat::expect_equal(estimate$es_meta$UL[[1]], 0.12522023244)
  testthat::expect_equal(estimate$es_meta$diamond_ratio[[1]], 1.397516897)
  suppressWarnings(myplot <- esci::plot_meta(estimate))
  testthat::expect_s3_class(myplot, "ggplot")


  # Random effects with moderator
  estimate <- esci::meta_mdiff_two(
    data = esci_test,
    comparison_means = bM,
    comparison_sds = bS,
    comparison_ns = bN,
    reference_means = nbM,
    reference_sds = nbS,
    reference_ns = nbN,
    labels = study_name,
    effect_label = "Brain Photo Rating - No Brain Photo Rating",
    contrast = c(1, -1),
    assume_equal_variance = TRUE,
    moderator = mod,
    random_effects = TRUE
  )

  testthat::expect_s3_class(estimate, "esci_estimate")
  testthat::expect_equal(estimate$es_meta$effect_size[[1]], 0.0943163245)
  testthat::expect_equal(estimate$es_meta$LL[[1]], 0.01498243444)
  testthat::expect_equal(estimate$es_meta$UL[[1]], 0.17365021465)
  testthat::expect_equal(estimate$es_meta$diamond_ratio[[1]], 1.397516897)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[2]], 0.0324369811895)
  testthat::expect_equal(estimate$es_meta_difference$LL[[2]], -0.0308546401245)
  testthat::expect_equal(estimate$es_meta_difference$UL[[2]], 0.0957286025036)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[1]], 0.2166330957690)
  testthat::expect_equal(estimate$es_meta_difference$LL[[1]], 0.0882531509332)
  testthat::expect_equal(estimate$es_meta_difference$UL[[1]], 0.3450130406048)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[3]], 0.1841961145795)
  testthat::expect_equal(estimate$es_meta_difference$LL[[3]], 0.0410624722566)
  testthat::expect_equal(estimate$es_meta_difference$UL[[3]], 0.3273297569023)
  suppressWarnings(myplot <- esci::plot_meta(estimate))
  testthat::expect_s3_class(myplot, "ggplot")


  # Fixed effects with moderator
  estimate <- esci::meta_mdiff_two(
    data = esci_test,
    comparison_means = bM,
    comparison_sds = bS,
    comparison_ns = bN,
    reference_means = nbM,
    reference_sds = nbS,
    reference_ns = nbN,
    labels = study_name,
    effect_label = "Brain Photo Rating - No Brain Photo Rating",
    contrast = c(1, -1),
    assume_equal_variance = TRUE,
    moderator = mod,
    random_effects = FALSE
  )

  testthat::expect_s3_class(estimate, "esci_estimate")
  testthat::expect_equal(estimate$es_meta$effect_size[[1]], 0.0684524822)
  testthat::expect_equal(estimate$es_meta$LL[[1]], 0.01168473203)
  testthat::expect_equal(estimate$es_meta$UL[[1]], 0.12522023244)
  testthat::expect_equal(estimate$es_meta$diamond_ratio[[1]], 1.397516897)
  testthat::expect_equal(estimate$es_meta$diamond_ratio[[1]], 1.397516897)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[2]], 0.0324369811895)
  testthat::expect_equal(estimate$es_meta_difference$LL[[2]], -0.03085464)
  testthat::expect_equal(estimate$es_meta_difference$UL[[2]], 0.095728603)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[1]], 0.216633096)
  testthat::expect_equal(estimate$es_meta_difference$LL[[1]], 0.088253151)
  testthat::expect_equal(estimate$es_meta_difference$UL[[1]], 0.345013041)
  testthat::expect_equal(estimate$es_meta_difference$effect_size[[3]], 0.184196115)
  testthat::expect_equal(estimate$es_meta_difference$LL[[3]], 0.041062472)
  testthat::expect_equal(estimate$es_meta_difference$UL[[3]], 0.327329757)
  suppressWarnings(myplot <- esci::plot_meta(estimate))
  testthat::expect_s3_class(myplot, "ggplot")

})