set.seed(1)
kernels <- c("gaussian", "epanechnikov", "rectangular",
"triangular", "biweight", "cosine", "optcosine")
N <- 1000
test_that("single column multivariate kernels = univariate kernels", {
skip_on_cran()
dat <- mtcars[, 1, drop = FALSE]
for (k in kernels) {
x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = FALSE)
y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = FALSE))
expect_true(suppressWarnings(ks.test(x, y)$p.value >= 0.05))
x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = TRUE)
y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = TRUE))
expect_true(suppressWarnings(ks.test(x, y)$p.value >= 0.05))
}
## Not run:
# if ( requireNamespace("cramer", quietly = TRUE) ) {
#
# library(cramer)
#
# dat <- mtcars[, 1, drop = FALSE]
#
# for (k in kernels) {
#
# x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = FALSE)
# y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = FALSE))
# expect_true(cramer.test(x, y)$result == 0)
#
# x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = TRUE)
# y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = TRUE))
# expect_true(cramer.test(x, y)$result == 0)
#
# }
#
# }
## End(Not run)
})
test_that("marginal distributions of multivariate kernels = univariate kernels", {
skip_on_cran()
dat <- mtcars
for (k in kernels) {
x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = FALSE)
y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = FALSE)[, 1])
expect_true(suppressWarnings(ks.test(x, y)$p.value >= 0.05))
x <- ruvk(N, drop(dat[, 1]), kernel = k, bw = 2, shrinked = TRUE)
y <- drop(rmvk(N, dat, kernel = k, bw = 2, shrinked = TRUE)[, 1])
expect_true(suppressWarnings(ks.test(x, y)$p.value >= 0.05))
}
})
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