context("mixture.test")
if (!exists(".Random.seed")) set.seed(NULL)
old_seed <- .Random.seed
set.seed(1)
n0 <- 500 # 100 sample points from \eqn{F_0}
nm <- 500 # 100 sample points from \eqn{F_0}
n1 <- 250 # of which 50 are from \eqn{F_1}
p <- 2 # two dimensions
X0 <- matrix(rnorm(n0*p), ncol=p) # \eqn{F_0} is standard normal, \eqn{F_1} has mean c(10,10):
Xm <- matrix(c(rnorm((nm-n1)*p), rnorm(n1*p)+10), ncol=p, byrow = TRUE)
test_that("mixture.test runs smooth and gives reasonable results", {
expect_gt(
mixture.test(X0, Xm, alpha=0.5, calc.CI=FALSE)$p.value, #should not be significant
0.05)
expect_lte(
mixture.test(X0, Xm, alpha=1, calc.CI=FALSE)$p.value, #should be significant
0.05)
expect_lt(
mixture.test(X0, Xm)$conf.int[2],
1) #should exclude 1
})
n0 <- 5000 # 100 sample points from \eqn{F_0}
nm <- 500 # 100 sample points from \eqn{F_0}
n1 <- 250 # of which 50 are from \eqn{F_1}
p <- 2 # two dimensions
X0 <- matrix(rnorm(n0*p), ncol=p) # \eqn{F_0} is standard normal, \eqn{F_1} has mean c(10,10):
Xm <- matrix(c(rnorm((nm-n1)*p), rnorm(n1*p)+10), ncol=p, byrow = TRUE)
test_that("mixture.test for scewed sample numbers", {
expect_gt(
mixture.test(X0, Xm, alpha=0.5, calc.CI=FALSE)$p.value, #should not be significant
0.05)
expect_lte(
mixture.test(X0, Xm, alpha=1, calc.CI=FALSE)$p.value, #should be significant
0.05)
expect_lt(
mixture.test(X0, Xm)$conf.int[2],
1) #should exclude 1
})
n0 <- 5000 # 100 sample points from \eqn{F_0}
nm <- 500 # 100 sample points from \eqn{F_0}
n1 <- 250 # of which 50 are from \eqn{F_1}
X0 <- rnorm(n0) # \eqn{F_0} is standard normal, \eqn{F_1} has mean 10:
Xm <- c(rnorm((nm-n1)), rnorm(n1)+10)
test_that("mixture.test with vectors", {
expect_gt(
mixture.test(X0, Xm, alpha=0.5, calc.CI=FALSE)$p.value, #should not be significant
0.05)
expect_lte(
mixture.test(X0, Xm, alpha=1, calc.CI=FALSE)$p.value, #should be significant
0.05)
expect_lt(
mixture.test(X0, Xm)$conf.int[2],
1) #should exclude 1
})
.Random.seed <- old_seed
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