tests/testthat/test-predictionInterval.R

In ReplicationSuccess: Design and Analysis of Replication Studies

test_that("Output of function 'pReplicate' stays the same.", {
    thetao <- seq(-2, 2, 2)
    grid <- expand.grid(
        priors = c("conditional", "predictive", "EB"),
        tau = c(0, 0.5),
        seo = 1,
        ser = c(0.5, 2),
        stringsAsFactors = FALSE
    )
    out <- lapply(
        seq_len(nrow(grid)),
        function(i) {
            predictionInterval(
                thetao = thetao,
                seo = grid[i, "seo"],
                ser = grid[i, "ser"],
                tau = grid[i, "tau"],
                designPrior = grid[i, "priors"]
            )
        }
    )
    expect_equal(
        out,
                     list(structure(list(lower = c(-2.97998199227003, -0.979981992270027,
1.02001800772997), mean = c(-2, 0, 2), upper = c(-1.02001800772997,
0.979981992270027, 2.97998199227003)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-4.19130635144145, -2.19130635144145,
-0.191306351441454), mean = c(-2, 0, 2), upper = c(0.191306351441454,
2.19130635144145, 4.19130635144145)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-3.45996398454005, -0.979981992270027,
-0.459963984540054), mean = c(-1.5, 0, 1.5), upper = c(0.459963984540054,
0.979981992270027, 3.45996398454005)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-2.97998199227003, -0.979981992270027,
1.02001800772997), mean = c(-2, 0, 2), upper = c(-1.02001800772997,
0.979981992270027, 2.97998199227003)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-4.59278864086811, -2.59278864086811,
-0.592788640868113), mean = c(-2, 0, 2), upper = c(0.592788640868113,
2.59278864086811, 4.59278864086811)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-3.66016587677592, -1.38590382434968,
-0.910165876775924), mean = c(-1.375, 0, 1.375), upper = c(0.910165876775924,
1.38590382434968, 3.66016587677592)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-5.91992796908011, -3.91992796908011,
-1.91992796908011), mean = c(-2, 0, 2), upper = c(1.91992796908011,
3.91992796908011, 5.91992796908011)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-6.38261270288291, -4.38261270288291,
-2.38261270288291), mean = c(-2, 0, 2), upper = c(2.38261270288291,
4.38261270288291, 6.38261270288291)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-5.7716424707947, -3.91992796908011,
-2.7716424707947), mean = c(-1.5, 0, 1.5), upper = c(2.7716424707947,
3.91992796908011, 5.7716424707947)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-5.91992796908011, -3.91992796908011,
-1.91992796908011), mean = c(-2, 0, 2), upper = c(1.91992796908011,
3.91992796908011, 5.91992796908011)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-6.5965229808865, -4.5965229808865,
-2.5965229808865), mean = c(-2, 0, 2), upper = c(2.5965229808865,
4.5965229808865, 6.5965229808865)), class = "data.frame", row.names = c(NA,
-3L)), structure(list(lower = c(-5.80528821432467, -4.04056926533255,
-3.05528821432467), mean = c(-1.375, 0, 1.375), upper = c(3.05528821432467,
4.04056926533255, 5.80528821432467)), class = "data.frame", row.names = c(NA,
-3L))))
})


test_that("numeric test for predictionInterval(): 1", {
    za <- qnorm(p = 0.025, lower.tail = FALSE)
    expect_equal(object = predictionInterval(thetao = za, seo = 1, ser = 1,
                                             designPrior = "conditional"),
                 expected = data.frame(lower = 0, mean = za, upper = 2 * za),
                 tol = 0.0001)
})