tests/testthat/tests_auxiliary_functions.R

In psobczyk/varclust: Variables Clustering

context("Testing auxiliary functions")

library(varclust)

test_that("missclassification works ok", {
  part1 <- c(rep(1, 10), rep(2, 10))
  part2 <- c(rep(1, 15), rep(2, 5))
  expect_equal(misclassification(part1, part2, 15, 2), 0.25)
  expect_equal(misclassification(part1, part2, 15, 2), misclassification(part2, part1, 15, 2))
  expect_equal(misclassification(part1, part2, 25, 2), misclassification(part2, part1, 15, 2))
  expect_false(misclassification(part1, part2, 8, 2) == misclassification(part2, part1, 15, 2))
  expect_error(misclassification(part1, c(3, part2), 15, 2))
})
test_that("integration", {
  part1 <- c(rep(1, 10), rep(2, 10))
  part2 <- c(rep(1, 15), rep(2, 5))
  expect_equal(integration(part2, part1)[1], 0.75)
  expect_equal(integration(part2, part1)[2], 0.5)
})

test_that("choose cluster BIC", {
  set.seed(1)
  sim.data <- data.simulation(n = 100, SNR = 1, K = 2, numb.vars = 4, max.dim = 2)
  pca1 <- summary(prcomp(x = sim.data$signals[, 1:4]))$x[, 1:2]
  pca2 <- summary(prcomp(x = sim.data$signals[, 5:8]))$x[, 1:2]
  expect_equal(choose.cluster.BIC(sim.data$signals[, 1], list(pca1, pca2), 2), 1)
  expect_equal(choose.cluster.BIC(sim.data$signals[, 5], list(pca1, pca2), 2), 2)
})

test_that("choose cluster BIC - when n is large we can choose the right model", {
  set.seed(1)
  sim.data <- data.simulation(n = 100, SNR = 10, K = 2, numb.vars = 10, max.dim = 2)
  pca1 <- summary(prcomp(x = sim.data$X[, 1:10]))$x[, 1:2]
  pca2 <- summary(prcomp(x = sim.data$X[, 11:20]))$x[, 1:2]
  expect_equal(choose.cluster.BIC(sim.data$X[, 1], list(pca1, pca2), 2), 1)
  expect_equal(choose.cluster.BIC(sim.data$X[, 11], list(pca1, pca2), 2), 2)
})

test_that("choose cluster BIC - warning for suspiciously low noise", {
  set.seed(1)
  sim.data <- data.simulation(n = 100, SNR = 1, K = 2, numb.vars = 4, max.dim = 2)
  pca1 <- summary(prcomp(x = sim.data$signals[, 1:4]))$x[, 1:2]
  pca2 <- summary(prcomp(x = sim.data$signals[, 1:4]))$x[, 1:3]
  expect_warning(choose.cluster.BIC(sim.data$signals[, 1], list(pca1, pca2), 2, TRUE))
})

test_that("sim.data - factors are orthogonal", {
  set.seed(1)
  sim.data <- data.simulation(n = 50, SNR = 1, K = 2, numb.vars = 20, max.dim = 2)
  expect_gt(cor.test(sim.data$factors[, 1], sim.data$factors[, 2])$p.value, 0.05)
  expect_gt(cor.test(sim.data$factors[, 3], sim.data$factors[, 4])$p.value, 0.05)
})

test_that("warning for -inf", {
  load("test_data/small_matrix.rda")
  segmentation <- c(rep(1, each = 98), 2, 2)
  expect_warning(cluster.pca.BIC(X, segmentation, c(5, 3), 2, 7), "The dimensionality *")
})