tests/testthat/test-growth.R

In INLAvaan: Approximate Bayesian Latent Variable Analysis

mod <- "
  # intercept and slope with fixed coefficients
  i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
  s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4

  # regressions
  i ~ x1 + x2
  s ~ x1 + x2

  # time-varying covariates
  t1 ~ c1
  t2 ~ c2
  t3 ~ c3
  t4 ~ c4
"
dat <- lavaan::Demo.growth
fit_lav <- lavaan::cfa(mod, dat)
NSAMP <- 3

test_that("Method: marggaus (fast)", {
  expect_no_error({
    fit <- agrowth(
      mod,
      dat,
      marginal_method = "marggaus",
      vb_correction = FALSE,
      test = "none",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
})

test_that("Method: skewnorm", {
  expect_no_error({
    fit <- agrowth(
      mod,
      dat,
      marginal_method = "skewnorm",
      vb_correction = FALSE,
      test = "none",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
  # Convergence (dx ~ 0) depends on the optimiser path, which varies with the
  # platform's BLAS/compiler -- too fragile to assert on CRAN's check farm.
  skip_on_cran()
  expect_equal(fit@optim$dx, rep(0, length(coef(fit))), tolerance = 1e-3)
})

test_that("Method: asymgaus", {
  expect_no_error({
    fit <- agrowth(
      mod,
      dat,
      marginal_method = "asymgaus",
      vb_correction = FALSE,
      test = "none",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
})

test_that("Method: sampling", {
  expect_no_error({
    fit <- agrowth(
      mod,
      dat,
      marginal_method = "sampling",
      vb_correction = FALSE,
      test = "none",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
})

test_that("Gradients are correct (Finite Difference Check)", {
  # Analytic-vs-finite-difference agreement is sensitive to BLAS/compiler
  # differences across CRAN check flavours -- too fragile to assert there.
  skip_on_cran()
  suppressMessages(
    tmp <- capture.output(fit <- agrowth(mod, dat, test = "none", debug = TRUE))
  )
  test_df <- read.table(text = tmp, skip = 1)[, -1]
  colnames(test_df) <- c("fd", "analytic", "diff")

  expect_equal(
    as.numeric(test_df$fd),
    as.numeric(test_df$diff),
    tolerance = 1e-3
  )
  expect_equal(
    as.numeric(test_df$diff),
    rep(0, nrow(test_df)),
    tolerance = 1e-3
  )
})