Nothing
context("The change-points in the mean structure for a multivariate
time series of multiplicative structure via minimising an information
criterion related to the Linf distance")
# In this file we test the ic_Linf function
test_that("The correct change-points are given when the function is used correctly", {
set.seed(1)
num.nodes <- 40 # number of nodes
etaA.1 <- 0.95
etaA.2 <- 0.05
pcor1 <- GeneNet::ggm.simulate.pcor(num.nodes, etaA = etaA.1)
pcor2 <- GeneNet::ggm.simulate.pcor(num.nodes, etaA = etaA.2)
n <- 100
data1 <- GeneNet::ggm.simulate.data(n, pcor1)
data2 <- GeneNet::ggm.simulate.data(n, pcor2)
X <- rbind(data1, data2) ## change-point at 100
sgn <- sign(stats::cor(X))
expect_equal(ic_Linf(t(hdbinseg::gen.input(t(X), -1, TRUE, diag = FALSE, sgn)))$changepoints, c(100))
})
test_that("Error and warning messages are given correctly", {
set.seed(100)
test_data <- matrix(data = rchisq(1000, df = 5), nrow = 500, ncol = 2)
# A string vector is given as the input for X.
expect_error(ic_Linf(X = "I am not a numeric matrix"))
# The threshold constant is set equal to zero.
expect_error(ic_Linf(X = test_data, thr_const = 0))
# The value for lambda is negative.
expect_error(ic_Linf(X = test_data, points = -2))
# The value for lambda is a positive real number instead of a positive integer.
expect_warning(ic_Linf(X = test_data, points = 3.4))
})
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