# Test draw.parametric_effects() method

test_that("draw.parametric_effects works for m_2_fac", {
  expect_message(
    peff <- parametric_effects(m_2_fac,
      data = df_2_fac,
      envir = teardown_env()
    ),
    "Interaction terms are not currently supported."
  )
  expect_silent(plt <- draw(peff))

  skip_on_ci()
  expect_doppelganger("draw parametric effects m_2_fac", plt)
})

test_that("draw.parametric_effects works for m_para_sm", {
  expect_message(
    peff <- parametric_effects(m_para_sm,
      data = df_2_fac,
      envir = teardown_env()
    ),
    "Interaction terms are not currently supported."
  )
  expect_silent(plt <- draw(peff, rug = FALSE))

  skip_on_ci()
  expect_doppelganger("draw parametric effects m_para_sm", plt)
})

test_that("draw.parametric_effects works for m_2_fac select term", {
  expect_silent(peff <- parametric_effects(m_2_fac,
    term = "fac", data = df_2_fac,
    envir = teardown_env()
  ))
  expect_silent(plt <- draw(peff))

  skip_on_ci()
  expect_doppelganger("draw parametric effects m_2_fac with term", plt)
})

test_that("draw.parametric_effects works for m_para_sm select term", {
  expect_silent(peff <- parametric_effects(m_para_sm,
    term = "fac", data = df_2_fac,
    envir = teardown_env()
  ))
  expect_silent(plt <- draw(peff, rug = FALSE))

  skip_on_ci()
  expect_doppelganger("draw parametric effects m_para_sm with term", plt)
})

test_that("draw.parametric_effects works with only parametric terms", {
  expect_message(
    peff <- parametric_effects(m_only_para,
      data = df_2_fac,
      envir = teardown_env()
    ),
    "Interaction terms are not currently supported."
  )
  expect_silent(plt <- draw(peff, rug = FALSE))

  skip_on_ci()
  expect_doppelganger("draw parametric effects m_only_para", plt)
})

test_that("issue 45 parametric effects for lss models remains fixed", {
  skip_on_cran()
  # gratia error reprex
  issue_45_data <- function(n = 500, seed = NULL) {
    if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) {
      runif(1)
    }
    if (is.null(seed)) {
      RNGstate <- get(".Random.seed", envir = .GlobalEnv)
    } else {
      R.seed <- get(".Random.seed", envir = .GlobalEnv)
      set.seed(seed)
      RNGstate <- structure(seed, kind = as.list(RNGkind()))
      on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
    }

    ## simulate some data...
    f0 <- function(x) 2 * sin(pi * x)
    f1 <- function(x) exp(2 * x)
    f2 <- function(x) {
      0.2 * x^11 * (10 * (1 - x))^6 + 10 *
        (10 * x)^3 * (1 - x)^10
    }
    x0 <- runif(n)
    x1 <- runif(n)
    x2 <- runif(n)
    x3 <- runif(n)
    x4 <- sample(factor(c("a", "b", "c")), size = n, replace = TRUE)

    ## Simulate probability of potential presence...
    eta1 <- f0(x0) + f1(x1) - 3
    p <- binomial()$linkinv(eta1)
    y <- as.numeric(runif(n) < p) ## 1 for presence, 0 for absence

    ## Simulate y given potentially present (not exactly model fitted!)...
    ind <- y > 0
    eta2 <- f2(x2[ind]) / 3
    y[ind] <- rpois(exp(eta2), exp(eta2))
    df <- data.frame(y = y, x0 = x0, x1 = x1, x2 = x2, x3 = x3, x4 = x4)
    df
  }

  data_45 <- issue_45_data(n = 500, seed = 5)

  # Fit ZIP model...
  b <- gam(list(y ~ s(x2) + x3, ~ s(x0) + x1),
    family = ziplss(),
    data = data_45
  )

  expect_silent(plt1 <- draw(b, rug = FALSE))

  expect_silent(plt2 <- draw(b,
    parametric = TRUE, rug = FALSE,
    data = data_45, envir = teardown_env()
  ))

  # ZIP model with a categorical predictor
  b0 <- gam(list(y ~ s(x2) + x4, ~ s(x0) + x1),
    family = ziplss(),
    data = data_45
  )

  expect_silent(plt3 <- draw(b0, rug = FALSE))

  expect_silent(plt4 <- draw(b0,
    parametric = TRUE, rug = FALSE,
    data = data_45, envir = teardown_env()
  ))

  # ZIP model with linear and categorical predictor
  b1 <- gam(list(y ~ s(x2) + x3 + x4, ~ s(x0) + x1),
    family = ziplss(),
    data = data_45
  )

  expect_silent(plt5 <- draw(b1, rug = FALSE))

  expect_silent(plt6 <- draw(b1,
    parametric = TRUE, rug = FALSE,
    data = data_45, envir = teardown_env()
  ))

  skip_on_ci()
  expect_doppelganger("ziplss with numeric para not plotted", plt1)
  expect_doppelganger("ziplss with numeric para plotted", plt2)
  expect_doppelganger("ziplss with factor para not plotted", plt3)
  expect_doppelganger("ziplss with factor para plotted", plt4)
  expect_doppelganger("ziplss with both parametric not plotted", plt5)
  expect_doppelganger("ziplss with both parametric plotted", plt6)
})

# test #219
test_that("parametric effects works with messing data in model fit", {
  skip_on_cran()
  skip_on_ci()
  skip_if_offline()
  skip_if_not_installed("forcats")
  skip_if_not_installed("readr")

  rats_url <- "https://bit.ly/rat-hormone"
  expect_warning(rats <- readr::read_table(rats_url,
    col_types = "dddddddddddd-"
  ))
  # ignore the warning - it"s due to trailing white space at the ends of each
  #   row in the file

  rats <- rats |>
    mutate(
      treatment = forcats::fct_recode(
        factor(group,
          levels = c(1, 2, 3)
        ),
        Low = "1",
        High = "2",
        Control = "3"
      ),
      treatment = forcats::fct_relevel(
        treatment,
        c("Control", "Low", "High")
      ),
      subject = factor(subject)
    )

  m_rat <- gam(
    response ~ treatment +
      s(time, by = treatment, k = 5) +
      s(subject, bs = "re"),
    data = rats, method = "REML"
  )

  expect_silent(plt <- draw(m_rat,
    residuals = TRUE, rug = FALSE,
    grouped_by = TRUE, parametric = TRUE,
    data = rats, envir = teardown_env()
  ))

  skip_on_ci()
  expect_doppelganger("issue 219 parametric effects", plt)
})