# Tests for Random Effects Functions
# Note: Random effects functionality is currently a placeholder/stub
# These tests verify function signatures and expected behavior

test_that("gmm_survey_re exists and has correct signature", {
  # Function should exist
  expect_true(exists("gmm_survey_re"))
  expect_true(is.function(gmm_survey_re))

  # Should be exported
  expect_true("gmm_survey_re" %in% getNamespaceExports("surveymixr"))
})

test_that("gmm_survey_re gives informative error for unimplemented functionality", {
  # Create simple test data
  sim_data <- simulate_gmm_survey(
    n_individuals = 50,
    n_times = 3,
    n_classes = 2,
    seed = 777
  )

  # Should throw informative error about pending implementation
  expect_error(
    gmm_survey_re(
      data = sim_data,
      id = "id",
      time = "time",
      outcome = "outcome",
      n_classes = 2,
      random = ~ 1,  # Random intercepts
      starts = 5,
      verbose = FALSE
    ),
    "Full implementation pending"
  )
})

test_that("gmm_survey_re validates random formula", {
  sim_data <- simulate_gmm_survey(
    n_individuals = 50,
    n_times = 3,
    n_classes = 2,
    seed = 778
  )

  # Should error if random is not a formula
  expect_error(
    gmm_survey_re(
      data = sim_data,
      id = "id",
      time = "time",
      outcome = "outcome",
      n_classes = 2,
      random = "not_a_formula",  # Invalid
      starts = 5,
      verbose = FALSE
    ),
    "random must be a formula"
  )
})

test_that("get_random_effects exists and has correct signature", {
  # Function should exist
  expect_true(exists("get_random_effects"))
  expect_true(is.function(get_random_effects))

  # Should be exported
  expect_true("get_random_effects" %in% getNamespaceExports("surveymixr"))
})

test_that("calculate_icc exists and has correct signature", {
  # Function should exist
  expect_true(exists("calculate_icc"))
  expect_true(is.function(calculate_icc))

  # Should be exported
  expect_true("calculate_icc" %in% getNamespaceExports("surveymixr"))
})

test_that("variance_components exists and has correct signature", {
  # Function should exist
  expect_true(exists("variance_components"))
  expect_true(is.function(variance_components))

  # Should be exported
  expect_true("variance_components" %in% getNamespaceExports("surveymixr"))
})

test_that("SurveyMixrRE class is defined", {
  skip("SurveyMixrRE class not yet fully implemented - pending v0.3.0")

  # S4 class should exist
  expect_true("SurveyMixrRE" %in% getClasses())

  # Should be exported
  expect_true("SurveyMixrRE" %in% getNamespaceExports("surveymixr"))

  # Should inherit from SurveyMixr
  expect_true("SurveyMixr" %in% extends("SurveyMixrRE"))
})

test_that("plot_random_effects exists", {
  # Function should exist
  expect_true(exists("plot_random_effects"))
  expect_true(is.function(plot_random_effects))

  # Should be exported
  expect_true("plot_random_effects" %in% getNamespaceExports("surveymixr"))
})

# Future tests (activate once implementation is complete):
#
# test_that("gmm_survey_re fits model with random intercepts", {
#   sim_data <- simulate_gmm_survey(
#     n_individuals = 150,
#     n_times = 4,
#     n_classes = 2,
#     seed = 801
#   )
#
#   fit <- gmm_survey_re(
#     data = sim_data,
#     id = "id",
#     time = "time",
#     outcome = "outcome",
#     n_classes = 2,
#     random = ~ 1,
#     starts = 10,
#     verbose = FALSE
#   )
#
#   expect_s4_class(fit, "SurveyMixrRE")
#   expect_true(fit@convergence_info$converged)
#   expect_true(!is.null(fit@random_effects))
#   expect_true(!is.null(fit@variance_components))
# })
#
# test_that("gmm_survey_re fits model with random slopes", {
#   sim_data <- simulate_gmm_survey(
#     n_individuals = 150,
#     n_times = 4,
#     n_classes = 2,
#     seed = 802
#   )
#
#   fit <- gmm_survey_re(
#     data = sim_data,
#     id = "id",
#     time = "time",
#     outcome = "outcome",
#     n_classes = 2,
#     random = ~ 1 + time,  # Random intercepts and slopes
#     starts = 10,
#     verbose = FALSE
#   )
#
#   expect_s4_class(fit, "SurveyMixrRE")
#   expect_true(ncol(fit@variance_components[[1]]) == 2)  # 2x2 covariance matrix
# })
#
# test_that("get_random_effects extracts BLUPs correctly", {
#   # (Implementation pending)
# })
#
# test_that("calculate_icc computes ICC for each class", {
#   # (Implementation pending)
# })
#
# test_that("variance_components returns proper covariance matrices", {
#   # (Implementation pending)
# })