test_that("Poisson GLMM fits", {
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    y = rpois(100, 5),
    x = rnorm(100),
    id = factor(rep(1:20, each = 5))
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "poisson"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_equal(m$family, "poisson")
})


test_that("negative-binomial GLMM fits", {
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    y = rnbinom(100, size = 2, mu = 5),
    x = rnorm(100),
    id = factor(rep(1:20, each = 5))
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "nbinom2"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_equal(m$family, "nbinom2")
})

test_that("zero-inflated GLMM fits", {
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    y = rpois(100, 2),
    x = rnorm(100),
    id = factor(rep(1:20, each = 5))
  )
  
  dat$y[sample(1:100, 40)] <- 0
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "poisson",
    ziformula = ~1
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_true(m$zero_inflated)
  expect_equal(m$model_type, "Zero-inflated GLMM")
})

test_that("GAMM validates formula", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    y = rnorm(20),
    x = rnorm(20)
  )
  
  expect_error(
    fit_gamm(
      formula = "y ~ s(x)",
      data = dat
    ),
    "formula"
  )
})


test_that("GAMM validates data", {
  
  skip_if_not_installed("mgcv")
  
  expect_error(
    fit_gamm(
      formula = y ~ s(x),
      data = list(
        y = rnorm(20),
        x = rnorm(20)
      )
    ),
    "data"
  )
})

test_that("GAMM fits with smooth term", {
  
  skip_if_not_installed("mgcv")
  
  set.seed(123)
  
  dat <- data.frame(
    id = factor(rep(1:10, each = 10)),
    x = rep(seq(0, 1, length.out = 10), 10)
  )
  
  dat$y <- sin(2 * pi * dat$x) +
    rnorm(100, sd = 0.2)
  
  m <- fit_gamm(
    y ~ s(x, k = 5),
    data = dat,
    random = list(id = ~1)
  )
  
  expect_s3_class(m, "biomix_gamm")
  expect_true(is.list(m))
  expect_true(is.finite(AIC(get_fit(m)$lme)))
})

test_that("fit_glmm rejects non-formula ziformula", {
  
  dat <- data.frame(
    y = rpois(20, 5),
    x = rnorm(20),
    id = factor(rep(1:5, each = 4))
  )
  
  expect_error(
    fit_glmm(
      y ~ x + (1 | id),
      data = dat,
      family = "poisson",
      ziformula = "not a formula"
    ),
    "ziformula must be a formula."
  )
})

test_that("fit_glmm rejects non-formula dispformula", {
  
  dat <- data.frame(
    y = rpois(20, 5),
    x = rnorm(20),
    id = factor(rep(1:5, each = 4))
  )
  
  expect_error(
    fit_glmm(
      y ~ x + (1 | id),
      data = dat,
      family = "poisson",
      dispformula = "not a formula"
    ),
    "dispformula must be a formula."
  )
})

test_that("fit_glmm gives informative error when glmmTMB is unavailable", {
  
  testthat::local_mocked_bindings(
    .has_glmmTMB = function() FALSE,
    .package = "BioMixModel"
  )
  
  dat <- data.frame(
    y = rpois(10, 5),
    x = rnorm(10),
    id = factor(rep(1:5, each = 2))
  )
  
  expect_error(
    fit_glmm(
      y ~ x + (1 | id),
      data = dat,
      family = "poisson"
    ),
    "Package 'glmmTMB' is required for GLMMs."
  )
})

test_that("fit_glmm supports nbinom1", {
  
  dat <- data.frame(
    y = rpois(60, 5),
    x = rnorm(60),
    id = factor(rep(1:20, each = 3))
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "nbinom1"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_identical(m$family, "nbinom1")
  expect_identical(m$model_type, "GLMM")
  expect_false(m$zero_inflated)
})

test_that("fit_glmm supports nbinom2", {
  set.seed(123)
  
  dat <- data.frame(
    x = rnorm(100),
    id = factor(rep(1:20, each = 5))
  )
  
  dat$y <- rnbinom(
    100,
    size = 2,
    mu = exp(1 + 0.5 * dat$x)
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "nbinom2"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_identical(m$family, "nbinom2")
  expect_false(m$zero_inflated)
  expect_equal(m$model_type, "GLMM")
})

test_that("fit_glmm rejects non-formula input", {
  dat <- data.frame(
    y = rpois(10, 5),
    x = rnorm(10),
    id = factor(rep(1:5, each = 2))
  )
  
  expect_error(
    fit_glmm(
      formula = "y ~ x + (1 | id)",
      data = dat,
      family = "poisson"
    ),
    "formula must be a formula."
  )
})


test_that("fit_glmm rejects non-data-frame input", {
  expect_error(
    fit_glmm(
      formula = y ~ x,
      data = list(
        y = rpois(10, 5),
        x = rnorm(10)
      ),
      family = "poisson"
    ),
    "data must be a data frame."
  )
})