Nothing
testthat::test_that("m_test works correctly", {
testthat::skip_on_cran()
psi.funs <- c("huber", "hampel", "bisquare")
# Exemplary input vectors ----
set.seed(108)
x <- rnorm(30)
y <- rnorm(30)
for (i in seq_along(psi.funs)) {
psi <- psi.funs[i]
# Create and compare snapshots of test output ----
# Permutation test
testthat::expect_snapshot_output(m_test(x = x[1:5], y = y[1:5],
method = "permutation",
psi = psi))
testthat::expect_snapshot_output(m_test(x = x[1:5], y = y[1:5],
method = "permutation",
psi = psi,
scale.test = TRUE))
# Randomization test
testthat::expect_snapshot_output(m_test(x = x[1:10], y = y[1:10],
psi = psi,
method = "randomization",
n.rep = 10000))
testthat::expect_snapshot_output(m_test(x = x[1:10], y = y[1:10],
psi = psi,
method = "randomization",
n.rep = 10000, scale.test = TRUE))
# Asymptotic test
testthat::expect_snapshot_output(m_test(x = x, y = y,
method = "asymptotic",
psi = psi))
testthat::expect_snapshot_output(m_test(x = x, y = y,
method = "asymptotic",
psi = psi,
scale.test = TRUE))
# Automatic selection of the method to compute the p-value ----
# Asymptotic test for large samples
testthat::expect_equal(m_test(x = x, y = y, psi = psi)$method,
paste("Asymptotic test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator"))
# Randomization test for small samples
testthat::expect_equal(m_test(x = x[1:10], y = y[1:10], psi = psi, n.rep = 100)$method,
paste("Randomization test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator", paste0("(", 100), "random permutations)"))
# Permutation test if sample size is small and 'n.rep' equals the number of
# possible splits
testthat::expect_equal(m_test(x = x[1:5], y = y[1:5], psi = psi, n.rep = 252)$method,
paste("Exact permutation test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator"))
# User-specified selection of the method to compute the p-value ----
# Asymptotic test
testthat::expect_equal(m_test(x = x, y = y, method = "asymptotic", psi = psi)$method,
paste("Asymptotic test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator"))
# Randomization test for small samples
testthat::expect_equal(m_test(x = x[1:5], y = y[1:5], method = "randomization",
psi = psi, n.rep = 100)$method,
paste("Randomization test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator", paste0("(", 100), "random permutations)"))
# Permutation test if sample size is small and 'n.rep' equals the number of
# possible splits
testthat::expect_equal(m_test(x = x[1:5], y = y[1:5], method = "permutation", psi = psi)$method,
paste("Exact permutation test based on", paste0(toupper(substring(psi, 1, 1)), substring(psi, 2, nchar(psi))), "M-estimator"))
# One of the sample contains less than five non-missing observations ----
testthat::expect_error(m_test(x = x[1:4], y = y, psi = psi))
testthat::expect_error(
suppressWarnings(
m_test(x = x, y = c(y[1:4], rep(NA_real_, 10)), psi = psi)
)
)
# Computation of the p-values ----
# The p-values should be related by the following equations:
# (i) p.two.sided = 2 * min(p.less, p.greater)
# (ii) p.less = 1 - p.greater,
# where p.two.sided is the p-value for the two.sided alternative and
# p.greater and p.less are the p-values for the one-sided alternatives.
#
# For the permutation and the randomization test, we need to increase the
# tolerance in the comparison. This is because the null distributions are
# discrete.
# Asymptotic test
p.two.sided <- m_test(x = x, y = y, method = "asymptotic", psi = psi,
alternative = "two.sided")$p.value
p.greater <- m_test(x = x, y = y, method = "asymptotic", psi = psi,
alternative = "greater")$p.value
p.less <- m_test(x = x, y = y, method = "asymptotic", psi = psi,
alternative = "less")$p.value
testthat::expect_equal(p.two.sided, 2 * min(p.less, p.greater))
testthat::expect_equal(p.less, 1 - p.greater)
# Permutation test
p.two.sided <- m_test(x = x[1:5], y = y[1:5], method = "permutation", psi = psi,
alternative = "two.sided")$p.value
p.greater <- m_test(x = x[1:5], y = y[1:5], method = "permutation", psi = psi,
alternative = "greater")$p.value
p.less <- m_test(x = x[1:5], y = y[1:5], method = "permutation", psi = psi,
alternative = "less")$p.value
testthat::expect_equal(p.two.sided, 2 * min(p.less, p.greater))
# In the comparison of the one-sided p-values, we need to add the number of
# values in the permutation distribution that are equal to the value of the
# test statistic. Because of the discrete null distribution, the value of the
# test statistic is included in the computation of the left-sided and the
# computation of the right-sided p-value. Hence, it is counted more than once
# so that p.less + p.greater > 1.
perm.dist <- m_est_perm_distribution(x = x[1:5], y = y[1:5], randomization = FALSE,
psi = psi, k = robustbase::.Mpsi.tuning.default(psi))
m.statistic <- m_test_statistic(x = x[1:5], y = y[1:5], psi = psi)$statistic
testthat::expect_equal(p.less, 1 - p.greater + sum(m.statistic == perm.dist)/252)
# Randomization test
set.seed(168)
p.two.sided <- m_test(x = x[1:10], y = y[1:10], method = "randomization",
psi = psi, alternative = "two.sided", n.rep = 10000)$p.value
set.seed(168)
p.greater <- m_test(x = x[1:10], y = y[1:10], method = "randomization", psi = psi,
alternative = "greater", n.rep = 10000)$p.value
set.seed(168)
p.less <- m_test(x = x[1:10], y = y[1:10], method = "randomization", psi = psi,
alternative = "less", n.rep = 10000)$p.value
# We increase the tolerance for the comparisons. One reason is the discrete
# null distribution. Moreover, as we use the correction by Phipson and Smyth
# (2011), it would be necessary to compute and add the integral in equation (2)
# of their paper, which would make this test case more complicated.
testthat::expect_true(abs(p.two.sided - 2 * min(p.less, p.greater)) < 10^(-2))
testthat::expect_true(abs(1 - (p.less + p.greater)) < 10^(-2))
# Test for scale difference ----
# One of the samples contains zeros
testthat::expect_message(m_test(x = x[1:10], y = c(y[1:9], 0),
method = "asymptotic", psi = psi, scale.test = TRUE))
}
})
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