Nothing
context("Hodges_Lehmann")
test_that("output has the correct format",
{
skip_on_os(os = "solaris")
x <- 1:5
y <- HodgesLehmann(x, b_u = 1, control = list(b_n = 1, l = 1))
# X <- matrix(1:9, ncol = 3)
# Y <- HodgesLehmann(X)
expect_true(is.numeric(y))
# expect_true(is.numeric(Y))
expect_equal(length(y), 1)
# expect_equal(length(Y), 1)
# expect_error(HodgesLehmann(x, b_u = 0))
testStructure(hl_test, "kernel")
testStructure(hl_test, "subsampling")
testStructure(hl_test, "bootstrap")
})
# test_that("u_hat computes the correct value",
# {
# skip_on_os(os = "solaris")
#
# x <- rnorm(5)
# expect_error(u_hat(x, -2, kFun = "FT"))
#
# b <- 2
# y <- abs(apply(combn(x, 2), 2, diff) / b)
# y <- ifelse(y > 1, 1, y)
# y <- sum(1 - y)
# expect_equal(2 / (5 * 4 * b) * y * 2 / 3, u_hat(x, b, kFun = "bartlett"))
# })
test_that("HodgesLehmann computes the correct value",
{
skip_on_os("solaris")
x <- c(14, 49, 50, 47, 28)
l <- 2
y <- sqrt(5) * 5.44 * u_hat(x - c(0, rep(34, 4))) /
sqrt(lrv(x, "kernel", control = list(l = l, overlapping = TRUE, distr = TRUE)))
z <- HodgesLehmann(x, control = list(l = l, overlapping = TRUE, distr = TRUE))
attributes(z) <- NULL
expect_equal(z, y)
x <- c(58, 2, 59, 26, 20, 88)
l <- 3
y <- sqrt(6) * 310 / 36 * u_hat(x - c(rep(0, 5), 62)) /
sqrt(lrv(x, "subs", control = list(l = l, overlapping = TRUE, distr = TRUE)))
z <- HodgesLehmann(x, method = "subsampling",
control = list(l = l, overlapping = TRUE, distr = TRUE))
attributes(z) <- NULL
expect_equal(z, y)
})
test_that("The output of hl_test has the correct format",
{
skip_on_os(os = "solaris")
x <- rnorm(10)
res <- suppressWarnings(hl_test(x))
expect_equal(class(res), "htest")
expect_equal(res$alternative, "two-sided")
expect_equal(res$method, "Hodges-Lehmann change point test")
})
test_that("Hodges-Lehmann change point test is performed correctly",
{
skip_on_os(os = "solaris")
skip_on_cran()
## simulation might run too long
suppressWarnings({p <- replicate(200,
{
x <- rnorm(200)
x[101:200] <- x[101:200] + 1
hl_test(x, b_u = 0.05, control = list(b_n = 10))$p.value
})})
expect_equal(mean(p < 0.05), 1, tolerance = 0.1)
# correct change point location
x <- rnorm(100)
x[50:100] <- x[50:100] + 10
expect_equal(attr(hl_test(x, b_u = 0.1)$statistic, "cp-location"), 50, tolerance = 1)
## maybe some more tests
## best to be checked graphically:
## hist(p)
})
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