skip_on_cran()
skip_if_not_installed("lme4")
skip_if_not_installed("GLMMadaptive")
skip_if_not_installed("glmmTMB")

data("fish")
data("cbpp", package = "lme4")

m1 <- GLMMadaptive::mixed_model(
  count ~ child + camper,
  random = ~ 1 | persons,
  zi_fixed = ~ child + livebait,
  data = fish,
  family = GLMMadaptive::zi.poisson()
)
m2 <- GLMMadaptive::mixed_model(
  cbind(incidence, size - incidence) ~ period,
  random = ~ 1 | herd,
  data = cbpp,
  family = binomial
)

test_that("ci", {
  expect_equal(
    ci(m1)$CI_low,
    c(0.08708, -1.35715, 0.58599, -0.99993, 0.75543, -2.1166),
    tolerance = 1e-3
  )
  expect_equal(
    ci(m1, component = "cond")$CI_low,
    c(0.08708, -1.35715, 0.58599),
    tolerance = 1e-3
  )
  expect_equal(
    ci(m1, component = "zi")$CI_low,
    c(-0.99993, 0.75543, -2.1166),
    tolerance = 1e-3
  )

  expect_equal(
    ci(m2)$CI_low,
    c(-1.8572, -1.59265, -1.76827, -2.41754),
    tolerance = 1e-3
  )
  expect_equal(
    ci(m2, component = "cond")$CI_low,
    c(-1.8572, -1.59265, -1.76827, -2.41754),
    tolerance = 1e-3
  )

  expect_null(suppressMessages(ci(m2, component = "zi")))
})



test_that("se", {
  expect_equal(
    standard_error(m1)$SE,
    c(0.540016, 0.094847, 0.09356, 0.468122, 0.29416, 0.507634),
    tolerance = 1e-3
  )
  expect_equal(
    standard_error(m1, component = "cond")$SE,
    c(0.540016, 0.094847, 0.09356),
    tolerance = 1e-3
  )
  expect_equal(
    standard_error(m1, component = "zi")$SE,
    c(0.468122, 0.29416, 0.507634),
    tolerance = 1e-3
  )

  expect_equal(
    standard_error(m2)$SE,
    c(0.233543, 0.306776, 0.326777, 0.427606),
    tolerance = 1e-3
  )
  expect_equal(
    standard_error(m2, component = "cond")$SE,
    c(0.233543, 0.306776, 0.326777, 0.427606),
    tolerance = 1e-3
  )

  expect_null(suppressMessages(standard_error(m2, component = "zi")))
})


test_that("p_value", {
  expect_equal(
    p_value(m1)$p,
    c(0.0339, 0, 0, 0.86023, 1e-05, 0.02713),
    tolerance = 1e-3
  )
  expect_equal(
    p_value(m1, component = "cond")$p,
    c(0.0339, 0, 0),
    tolerance = 1e-3
  )
  expect_equal(
    p_value(m1, component = "zi")$p,
    c(0.86023, 1e-05, 0.02713),
    tolerance = 1e-3
  )

  expect_equal(
    p_value(m2)$p,
    c(0, 0.00123, 0.00056, 0.00022),
    tolerance = 1e-3
  )
  expect_equal(
    p_value(m2, component = "cond")$p,
    c(0, 0.00123, 0.00056, 0.00022),
    tolerance = 1e-3
  )

  expect_null(suppressMessages(p_value(m2, component = "zi")))
})


test_that("model_parameters", {
  expect_equal(
    model_parameters(m1, effects = "fixed")$Coefficient,
    c(1.14549, -1.17125, 0.76937, -0.08243, 1.33197, -1.12165),
    tolerance = 1e-3
  )
  expect_equal(
    model_parameters(m2, effects = "fixed")$Coefficient,
    c(-1.39946, -0.99138, -1.1278, -1.57945),
    tolerance = 1e-3
  )
})

test_that("model_parameters.mixed-ran_pars", {
  skip_on_os(c("mac", "linux", "solaris"))
  skip_if_not_installed("glmmTMB")

  data("Salamanders", package = "glmmTMB")
  model <- GLMMadaptive::mixed_model(
    count ~ spp + mined,
    random = ~ DOY | site,
    zi_fixed = ~ spp + mined,
    zi_random = ~ DOP | site,
    family = GLMMadaptive::zi.negative.binomial(),
    data = Salamanders,
    control = list(nAGQ = 1)
  )

  params <- model_parameters(model, effects = "random")
  expect_identical(c(nrow(params), ncol(params)), c(7L, 9L))
  expect_identical(
    colnames(params),
    c("Parameter", "Coefficient", "SE", "CI", "CI_low", "CI_high", "Effects", "Group", "Component")
  )
  expect_identical(
    params$Parameter,
    c(
      "SD (Intercept)", "SD (DOY)", "Cor (Intercept~DOY)", "SD (Observations)",
      "SD (Intercept)", "SD (DOP)", "Cor (Intercept~DOP)"
    )
  )
  expect_identical(
    params$Component,
    c(
      "conditional", "conditional", "conditional", "conditional",
      "zero_inflated", "zero_inflated", "zero_inflated"
    )
  )
  expect_equal(
    params$Coefficient,
    c(0.56552, 0.29951, 0.06307, 1.61936, 1.02233, 0.38209, -0.17162),
    tolerance = 1e-2
  )
})