tests/testthat/test-cpt_ts_Linf.R

In ccid: Cross-Covariance Isolate Detect: a New Change-Point Method for Estimating Dynamic Functional Connectivity

context("The change-points in the mean structure for a multivariate time series
        of multiplicative structure through thresholding and extraction of the
        component time series where the changes occurred via taking the maximum
        of contrast function values")

# In this file we test the cpt_ts_Linf function

test_that("The correct result is 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(cpt_ts_Linf(t(hdbinseg::gen.input(t(X), -1, TRUE, diag = FALSE, sgn)))$changepoints, c(100))
  expect_true(is.list(cpt_ts_Linf(t(hdbinseg::gen.input(t(X), -1, TRUE, diag = FALSE, sgn)))$time_series))
})

test_that("Error and warning messages are given correctly", {
  set.seed(100)
  test_data <- matrix(data = rchisq(63750, df = 5), nrow = 50, ncol = 1275)

  # A string vector is given as the input for X.
  expect_error(cpt_ts_Linf(X = "I am not a numeric matrix"))

  # The threshold constant is set equal to zero.
  expect_error(cpt_ts_Linf(X = test_data, thr_const_m = 0))

  # The value for lambda is negative.
  expect_error(cpt_ts_Linf(X = test_data, points_m = -2))

  # The value for lambda is a positive real number instead of a positive integer.
  expect_warning(cpt_ts_Linf(X = test_data, points_m = 3.4))
})