tests/testthat/test-cor_to_pcor.R

In correlation: Methods for Correlation Analysis

test_that("pcor_to_cor", {
  skip_if_not_or_load_if_installed("ppcor")
  skip_if_not_or_load_if_installed("Hmisc")

  set.seed(333)

  # easycormatrix
  out <- correlation(iris, partial = TRUE, p_adjust = "none")
  pcormat <- summary(out, redundant = TRUE)

  ppcor <- ppcor::pcor(iris[1:4])
  expect_equal(max(as.matrix(pcormat[2:5]) - as.matrix(ppcor$estimate)), 0, tolerance = 0.01)

  # TODO: fix
  # cormat <- pcor_to_cor(pcormat)
  # expect_equal(max(as.matrix(cormat[2:5]) - as.matrix(cor(iris[1:4]))), 0, tolerance = 0.01)

  # hmisc <- Hmisc::rcorr(as.matrix(iris[1:4]), type = c("pearson"))
  # expect_equal(mean(as.matrix(cormat[2:5]) - hmisc$r), 0, tolerance = 0.0001)

  # p <- as.matrix(attributes(cormat)$p[2:5])
  # expect_equal(mean(p - hmisc$P, na.rm = TRUE), 0, tolerance = 0.001)

  # easycorrelation
  cormat <- summary(pcor_to_cor(correlation(iris, partial = TRUE)), redundant = TRUE)

  expect_equal(max(as.matrix(cormat[2:5]) - as.matrix(cor(iris[1:4]))), 0, tolerance = 0.01)

  hmisc <- Hmisc::rcorr(as.matrix(iris[1:4]), type = c("pearson"))
  expect_equal(mean(as.matrix(cormat[2:5]) - hmisc$r), 0, tolerance = 0.0001)

  p <- as.matrix(attributes(cormat)$p[2:5])
  expect_equal(mean(p - hmisc$P, na.rm = TRUE), 0, tolerance = 0.001)
})


test_that("spcor_to_cor", {
  skip_if_not_or_load_if_installed("ppcor")

  set.seed(333)

  # easycormatrix
  out <- correlation(iris)
  cormat <- summary(out, redundant = TRUE)
  spcormat <- cor_to_spcor(cormat, cov = cov(iris[1:4]))

  spcor <- ppcor::spcor(iris[1:4])
  expect_equal(max(spcormat - as.matrix(spcor$estimate)), 0, tolerance = 0.01)
})