test_that("fit_gamm validates formula", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    y = rnorm(20),
    x = rnorm(20),
    id = factor(rep(1:5, each = 4))
  )
  
  expect_error(
    fit_gamm(
      "y ~ s(x)",
      data = dat,
      random = list(id = ~1)
    )
  )
})

test_that("fit_gamm validates data", {
  
  skip_if_not_installed("mgcv")
  
  expect_error(
    fit_gamm(
      y ~ s(x),
      data = list(
        y = rnorm(20),
        x = rnorm(20)
      ),
      random = list(id = ~1)
    )
  )
})

test_that("GAMM fits without random effects", {
  
  skip_if_not_installed("mgcv")
  
  set.seed(123)
  
  dat <- data.frame(
    x = seq(0, 10, length.out = 50)
  )
  
  dat$y <- sin(dat$x) + rnorm(50, 0, 0.1)
  
  m <- fit_gamm(
    formula = y ~ s(x, k = 5),
    data = dat
  )
  
  expect_s3_class(m, "biomix_gamm")
  expect_s3_class(m, "biomix")
  expect_s3_class(m$fit, "gam")
  expect_null(m$random)
  expect_equal(
    m$model_type,
    "Generalized additive mixed model"
  )
})

test_that("GAMM handles supplied random argument", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    id = factor(rep(1:5, each = 10)),
    x = rep(seq(0, 1, length.out = 10), 5)
  )
  
  dat$y <- sin(dat$x) + rnorm(50, 0, 0.1)
  
  expect_error(
    fit_gamm(
      formula = y ~ s(x, k = 5),
      data = dat,
      random = ~ 1 | id
    )
  )
})
test_that("GAMM gives helpful error for excessive basis dimension", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    x = c(1, 2, 3, 1, 2, 3, 1, 2, 3),
    y = rnorm(9)
  )
  
  expect_error(
    fit_gamm(
      formula = y ~ s(x, k = 10),
      data = dat
    ),
    paste0(
      "The GAM smooth has too many basis dimensions for the ",
      "number of unique covariate combinations"
    )
  )
})
test_that("GAMM validates formula", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    x = rnorm(10),
    y = rnorm(10)
  )
  
  expect_error(
    fit_gamm(
      formula = "y ~ x",
      data = dat
    ),
    "formula must be a formula"
  )
})

test_that("GAMM validates data", {
  
  skip_if_not_installed("mgcv")
  
  expect_error(
    fit_gamm(
      formula = y ~ x,
      data = list(
        x = rnorm(10),
        y = rnorm(10)
      )
    ),
    "data must be a data frame"
  )
})

test_that("GAMM stores random-effects specification", {
  
  skip_if_not_installed("mgcv")
  
  dat <- data.frame(
    id = factor(rep(1:5, each = 10)),
    x = rep(seq(0, 1, length.out = 10), 5)
  )
  
  dat$y <- sin(dat$x) + rnorm(50, 0, 0.1)
  
  random_spec <- list(
    id = ~ 1
  )
  
  # This test should be used after changing the implementation
  # to use mgcv::gamm() for random effects.
  
  m <- fit_gamm(
    formula = y ~ s(x, k = 5),
    data = dat,
    random = random_spec
  )
  
  expect_equal(m$random, random_spec)
})