tests/testthat/test-diagnostics.R

In jkbest2/spatq: Use spatially varying catchability to standardize fishery-dependent data

context("Test diagnostic functions")

test_that("Null eigenvalue detection works", {
  ## Construct a nearly-singular "covariance" matrix
  n <- 10
  x <- matrix(rnorm(n * n), nrow = n)
  x[, n] <- x[, seq_len(n - 1)] %*% rep(1/9, n - 1) + rnorm(n, 0, 0.001)
  X <- t(x) %*% x

  pars <- rep(0, n)
  names(pars) <- letters[seq_len(n)]
  row.names(X) <- names(pars)
  sdr <- list(par.fixed = pars, cov.fixed = X)

  nulleigs <- fixpar_nulleigs(sdr)
  ## Make sure that the last column is detected as contributing most to singularity.
  expect_equal(nulleigs$sortedpars[length(nulleigs$values), 1], letters[n])
})

test_that("Map correlations work", {
  d <- c(100, 100, 5)
  true <- array(rnorm(prod(d)), dim = d)
  est <- 10 * (true + rnorm(prod(d)))

  mc <- map_correlation(true, est)
  expect_true(0.6 < mc && mc < 0.8)

  mcyr <- map_correlation(true, est, byyear = TRUE)
  expect_equal(length(mcyr), d[3])
  expect_true(all(0.6 < mcyr & mcyr < 0.8))
})