Nothing
test_that("run.test produces correct structure and types", {
# Simulation setup
taus <- c(0.25, 0.5, 0.75)
hh <- 0.2
alpha <- c(0.05, 0.1)
m <- length(taus) # number of quantiles
dg <- 2 # number of groups
n.sim <- 100
n <- 500
Qy.p <- matrix(rnorm(m * dg), m, dg)
Qy.m <- matrix(rnorm(m * dg), m, dg)
bias.p <- matrix(runif(m * dg, 0, 0.05), m, dg)
bias.m <- matrix(runif(m * dg, 0, 0.05), m, dg)
Dc.p <- array(rnorm(dg * m * n.sim), dim = c(dg, m, n.sim))
Dc.m <- array(rnorm(dg * m * n.sim), dim = c(dg, m, n.sim))
Dr.p <- array(rnorm(dg * m * n.sim), dim = c(dg, m, n.sim))
Dr.m <- array(rnorm(dg * m * n.sim), dim = c(dg, m, n.sim))
for (test.type in 1:4) { # set 'std.opt = 1'
out <- run.test(n.sam = n, dz = 1, taus = taus, hh = hh,
Dc.p = Dc.p, Dc.m = Dc.m, Dr.p = Dr.p, Dr.m = Dr.m,
Qy.p = Qy.p, Qy.m = Qy.m, bias.p = bias.p, bias.m = bias.m,
cov = 1, bias = 1, alpha = alpha, n.sim = n.sim,
test.type = test.type, std.opt = 1)
expect_type(out, "list")
expect_named(out, c("test.stat", "cr.value", "p.val"))
expect_length(out$test.stat, dg)
expect_equal(dim(out$cr.value), c(dg, length(alpha)))
expect_length(out$p.val, dg)
expect_true(all(out$p.val >= n.sim^{-1})) # p-values are bounded below
}
for (test.type in 1:4) { # set 'std.opt = 0'
out2 <- run.test(n.sam = n, dz = 1, taus = taus, hh = hh,
Dc.p = Dc.p, Dc.m = Dc.m, Dr.p = Dr.p, Dr.m = Dr.m,
Qy.p = Qy.p, Qy.m = Qy.m, bias.p = bias.p, bias.m = bias.m,
cov = 1, bias = 1, alpha = alpha, n.sim = n.sim,
test.type = test.type, std.opt = 0)
expect_type(out2, "list")
expect_named(out2, c("test.stat", "cr.value", "p.val"))
expect_length(out2$test.stat, dg)
expect_equal(dim(out2$cr.value), c(dg, length(alpha)))
expect_length(out2$p.val, dg)
expect_true(all(out2$p.val >= n.sim^{-1})) # p-values are bounded below
}
})
test_that("run.test works with dz = 0 (no covariates)", {
set.seed(456)
m <- 2
n.sim <- 500
n <- 300
Qy.p <- matrix(rnorm(m), m, 1)
Qy.m <- matrix(rnorm(m), m, 1)
bias.p <- matrix(runif(m, 0, 0.05), m, 1)
bias.m <- matrix(runif(m, 0, 0.05), m, 1)
Dc.p <- array(rnorm(m * n.sim), dim = c(m, n.sim))
Dc.m <- array(rnorm(m * n.sim), dim = c(m, n.sim))
Dr.p <- array(rnorm(m * n.sim), dim = c(m, n.sim))
Dr.m <- array(rnorm(m * n.sim), dim = c(m, n.sim))
taus <- c(0.4, 0.6)
hh <- 0.15
alpha <- c(0.05)
out <- run.test(n.sam = n, dz = 0, taus = taus, hh = hh,
Dc.p = Dc.p, Dc.m = Dc.m, Dr.p = Dr.p, Dr.m = Dr.m,
Qy.p = Qy.p, Qy.m = Qy.m, bias.p = bias.p, bias.m = bias.m,
cov = 0, bias = 0, alpha = alpha, n.sim = n.sim,
test.type = 2, std.opt = 0)
#expect_s3_class(out$cr.value, "matrix")
expect_equal(length(out$p.val), 1)
})
test_that("run.test handles scalar vs vector cases in hh and std.opt", {
# Same data as earlier
set.seed(789)
m <- 3; dg <- 1; n.sim <- 250; n <- 300
Qy.p <- matrix(rnorm(m), m, 1)
Qy.m <- matrix(rnorm(m), m, 1)
bias.p <- matrix(runif(m, 0, 0.05), m, 1)
bias.m <- matrix(runif(m, 0, 0.05), m, 1)
Dc.p <- array(rnorm(m * n.sim), dim = c(1, m, n.sim))
Dc.m <- array(rnorm(m * n.sim), dim = c(1, m, n.sim))
Dr.p <- array(rnorm(m * n.sim), dim = c(1, m, n.sim))
Dr.m <- array(rnorm(m * n.sim), dim = c(1, m, n.sim))
taus <- c(0.25, 0.5, 0.75)
hh <- rep(0.2, m)
alpha <- 0.1
# Should work even if hh is vector and std.opt = 1
expect_silent({
out <- run.test(n.sam = n, dz = 1, taus = taus, hh = hh,
Dc.p = Dc.p, Dc.m = Dc.m, Dr.p = Dr.p, Dr.m = Dr.m,
Qy.p = Qy.p, Qy.m = Qy.m, bias.p = bias.p, bias.m = bias.m,
cov = 1, bias = 0, alpha = alpha, n.sim = n.sim,
test.type = 1, std.opt = 1)
})
})
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