.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"

if (.runThisTest && getRversion() >= "4.0.0" && requiet("testthat") && requiet("marginaleffects") && requiet("ggeffects") && requiet("glmmTMB") && requiet("lme4")) {
  data(Owls)
  data(Salamanders)

  m1 <- suppressWarnings(glmmTMB::glmmTMB(
    SiblingNegotiation ~ SexParent + ArrivalTime + (1 | Nest),
    data = Owls,
    family = nbinom1)
  )
  m2 <- glmmTMB::glmmTMB(
    SiblingNegotiation ~ SexParent + ArrivalTime + (1 | Nest),
    data = Owls,
    family = nbinom2
  )
  m4 <- glmmTMB::glmmTMB(
    SiblingNegotiation ~ FoodTreatment + ArrivalTime + SexParent + (1 | Nest),
    data = Owls,
    ziformula =  ~ 1,
    family = truncated_poisson(link = "log")
  )

  test_that("validate ggpredict against predict, nbinom", {
    nd <- data_grid(m1, "SexParent")
    pr <- predict(m1, newdata = nd, type = "link", se.fit = TRUE)
    linv <- insight::link_inverse(m1)
    dof <- insight::get_df(m1, type = "wald", verbose = FALSE)
    tcrit <- stats::qt(0.975, df = dof)
    out1 <- data.frame(
      predicted = linv(pr$fit),
      conf.low = linv(pr$fit - tcrit * pr$se.fit),
      conf.high = linv(pr$fit + tcrit * pr$se.fit)
    )
    out2 <- ggpredict(m1, "SexParent")

    expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
  })

  test_that("validate ggpredict lmer against marginaleffects", {
    out1 <- marginaleffects::predictions(
      m1,
      variables = "SexParent",
      newdata = marginaleffects::datagrid(m1),
      vcov = FALSE
    )
    out1 <- out1[order(out1$SexParent), ]
    out2 <- ggpredict(
      m1,
      "SexParent",
      condition = c(Nest = "Oleyes"),
      type = "random"
    )
    expect_equal(
      out1$estimate,
      out2$predicted,
      tolerance = 1e-4,
      ignore_attr = TRUE
    )
  })

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m1, c("ArrivalTime", "SexParent")), "data.frame")
    expect_s3_class(ggpredict(m2, c("ArrivalTime", "SexParent")), "data.frame")
    expect_s3_class(ggpredict(m4, c("FoodTreatment", "ArrivalTime [21,24,30]", "SexParent")), "data.frame")
    expect_s3_class(ggpredict(m1, c("ArrivalTime", "SexParent"), type = "re"), "data.frame")
    expect_s3_class(ggpredict(m2, c("ArrivalTime", "SexParent"), type = "re"), "data.frame")
    expect_s3_class(ggpredict(m4, c("FoodTreatment", "ArrivalTime [21,24,30]", "SexParent"), type = "re"), "data.frame")

  })


  test_that("ggpredict, glmmTMB", {
    expect_message(ggpredict(m1, c("ArrivalTime", "SexParent"), type = "fe.zi"))
  })


  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(m1, c("ArrivalTime", "SexParent"))
    p2 <- ggpredict(m2, c("ArrivalTime", "SexParent"))
    p3 <- ggemmeans(m1, c("ArrivalTime", "SexParent"))
    p4 <- ggemmeans(m2, c("ArrivalTime", "SexParent"))
    expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
    expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
  })


  m3 <- glmmTMB::glmmTMB(count ~ spp + mined + (1 | site), ziformula = ~ spp + mined, family = truncated_poisson, data = Salamanders)
  m4 <- glmmTMB::glmmTMB(count ~ spp + mined + (1 | site), ziformula = ~ spp + mined + (1 | site), family = truncated_poisson, data = Salamanders)
  m5 <- glmmTMB::glmmTMB(count ~ spp + mined + cover + (1 | site), ziformula = ~ spp + mined, family = truncated_poisson, data = Salamanders)

  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(m3, "mined", type = "fe")
    p2 <- ggpredict(m3, "mined", type = "fe.zi")
    p3 <- ggpredict(m3, "mined", type = "re")
    p4 <- ggpredict(m3, "mined", type = "re.zi")
    expect_gt(p3$conf.high[1], p1$conf.high[1])
    expect_gt(p4$conf.high[1], p2$conf.high[1])
    expect_s3_class(ggpredict(m3, "mined", type = "fe.zi"), "data.frame")
  })

  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(m5, c("mined", "spp", "cover"), type = "fe")
    p3 <- ggemmeans(m5, c("mined", "spp", "cover"), type = "fe")
    expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)

    # p2 <- ggpredict(m5, c("mined", "spp", "cover"), type = "fe.zi")
    # p4 <- ggemmeans(m5, c("mined", "spp", "cover"), type = "fe.zi")
    # expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
  })

  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(m3, "mined", type = "fe")
    p2 <- ggpredict(m3, c("mined", "spp"), type = "fe.zi")
    p3 <- ggemmeans(m3, "mined", type = "fe", condition = c(spp = "GP"))
    p4 <- ggemmeans(m3, c("mined", "spp"), type = "fe.zi")
    p5 <- ggpredict(m3, c("mined", "spp"), type = "fe")
    p6 <- ggemmeans(m3, c("mined", "spp"), type = "fe")
    expect_equal(p1$predicted[1], p3$predicted[1], tolerance = 1e-3)
    # expect_equal(p2$predicted[1], p4$predicted[1], tolerance = 1e-3)
    expect_equal(p5$predicted[1], p6$predicted[1], tolerance = 1e-3)
  })

  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(m4, "mined", type = "fe")
    p2 <- ggpredict(m4, "mined", type = "fe.zi")
    p3 <- ggpredict(m4, "mined", type = "re")
    p4 <- ggpredict(m4, "mined", type = "re.zi")
    expect_gt(p3$conf.high[1], p1$conf.high[1])
    expect_gt(p4$conf.high[1], p2$conf.high[1])

    p1 <- ggpredict(m4, c("spp", "mined"), type = "fe")
    p2 <- ggpredict(m4, c("spp", "mined"), type = "fe.zi")
    p3 <- ggpredict(m4, c("spp", "mined"), type = "re")
    p4 <- ggpredict(m4, c("spp", "mined"), type = "re.zi")
    expect_gt(p3$conf.high[1], p1$conf.high[1])
    expect_gt(p4$conf.high[1], p2$conf.high[1])
  })

  test_that("ggpredict, glmmTMB", {
    p <- ggpredict(m3, "spp", type = "fe.zi")
    expect_true(all(p$conf.low > 0))
    set.seed(100)
    p <- ggpredict(m3, "spp", type = "fe.zi")
    expect_true(all(p$conf.low > 0))
  })


  test_that("ggpredict, glmmTMB-simulate", {
    expect_s3_class(ggpredict(m3, "mined", type = "sim"), "data.frame")
    expect_s3_class(ggpredict(m3, c("spp", "mined"), type = "sim"), "data.frame")
    expect_s3_class(ggpredict(m4, "mined", type = "sim"), "data.frame")
    expect_s3_class(ggpredict(m4, c("spp", "mined"), type = "sim"), "data.frame")
  })

  md <- glmmTMB::glmmTMB(
    count ~ spp + mined + (1 | site),
    ziformula = ~ spp + mined,
    dispformula = ~ DOY,
    family = truncated_poisson,
    data = Salamanders
  )

  test_that("ggpredict, glmmTMB", {
    p1 <- ggpredict(md, c("spp", "mined"), type = "fe")
    p2 <- ggpredict(md, c("spp", "mined"), type = "fe.zi")
    p3 <- suppressWarnings(ggpredict(md, c("spp", "mined"), type = "re"))
    p4 <- suppressWarnings(ggpredict(md, c("spp", "mined"), type = "re.zi"))
    expect_gt(p3$conf.high[1], p1$conf.high[1])
    expect_gt(p4$conf.high[1], p2$conf.high[1])
  })

  data(efc_test)

  m5 <- glmmTMB::glmmTMB(
    negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
    data = efc_test, ziformula = ~ c172code,
    family = binomial(link = "logit")
  )

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m5, "c161sex", type = "fe"), "data.frame")
    expect_s3_class(ggpredict(m5, "c161sex", type = "fe.zi"), "data.frame")
    expect_s3_class(ggpredict(m5, "c161sex", type = "re"), "data.frame")
    expect_s3_class(ggpredict(m5, "c161sex", type = "re.zi"), "data.frame")
  })


  data(efc_test)

  m6 <- glmmTMB::glmmTMB(
    negc7d ~ c12hour + e42dep + c161sex + c172code + (1 | grp),
    data = efc_test,
    family = binomial(link = "logit")
  )

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m6, "c161sex", type = "fe"), "data.frame")
    expect_s3_class(ggpredict(m6, "c161sex", type = "re"), "data.frame")
  })

  test_that("validate ggpredict against predict, binomial", {
    nd <- data_grid(m6, "e42dep")
    pr <- predict(m6, newdata = nd, type = "link", se.fit = TRUE)
    linv <- insight::link_inverse(m6)
    dof <- insight::get_df(m6, type = "wald", verbose = FALSE)
    tcrit <- stats::qt(0.975, df = dof)
    out1 <- data.frame(
      predicted = linv(pr$fit),
      conf.low = linv(pr$fit - tcrit * pr$se.fit),
      conf.high = linv(pr$fit + tcrit * pr$se.fit)
    )
    out2 <- ggpredict(m6, "e42dep")

    expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
  })


  data(efc_test)

  efc_test$tot_sc_e <- as.numeric(efc_test$tot_sc_e)
  efc_test$c172code <- as.factor(efc_test$c172code)

  m7 <- glmmTMB::glmmTMB(
    tot_sc_e ~ neg_c_7 * c172code + c161sex + (1 | grp),
    data = efc_test, ziformula = ~ c172code,
    family = nbinom1
  )

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m7, "neg_c_7"), "data.frame")
    expect_s3_class(ggpredict(m7, "neg_c_7 [all]"), "data.frame")
    expect_s3_class(ggpredict(m7, "neg_c_7", type = "fe.zi"), "data.frame")
    expect_s3_class(ggpredict(m7, "neg_c_7 [all]", type = "fe.zi"), "data.frame")

    expect_s3_class(ggpredict(m7, c("neg_c_7", "c172code")), "data.frame")
    expect_s3_class(ggpredict(m7, c("neg_c_7 [all]", "c172code")), "data.frame")
    expect_s3_class(ggpredict(m7, c("neg_c_7", "c172code"), type = "fe.zi"), "data.frame")
    expect_s3_class(ggpredict(m7, c("neg_c_7 [all]", "c172code"), type = "fe.zi"), "data.frame")
  })


  m8 <- glmmTMB::glmmTMB(
    tot_sc_e ~ neg_c_7 * c172code + (1 | grp),
    data = efc_test, ziformula = ~ c172code,
    family = nbinom1
  )

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m8, "neg_c_7"), "data.frame")
    expect_s3_class(ggpredict(m8, "neg_c_7 [all]"), "data.frame")
    expect_s3_class(ggpredict(m8, "neg_c_7", type = "fe.zi"), "data.frame")
    expect_s3_class(ggpredict(m8, "neg_c_7 [all]", type = "fe.zi"), "data.frame")

    expect_s3_class(ggpredict(m8, c("neg_c_7", "c172code")), "data.frame")
    expect_s3_class(ggpredict(m8, c("neg_c_7 [all]", "c172code")), "data.frame")
    expect_s3_class(ggpredict(m8, c("neg_c_7", "c172code"), type = "fe.zi"), "data.frame")
    expect_s3_class(ggpredict(m8, c("neg_c_7 [all]", "c172code"), type = "fe.zi"), "data.frame")
  })


  data(Salamanders)
  m9 <- glmmTMB::glmmTMB(
    count ~ spp + cover + mined + (1 | site),
    ziformula =  ~ DOY,
    dispformula = ~ spp,
    data = Salamanders,
    family = nbinom2
  )

  test_that("ggpredict, glmmTMB", {
    expect_s3_class(ggpredict(m9, c("cover", "mined", "spp"), type = "fe"), "data.frame")
    expect_s3_class(ggpredict(m9, c("cover", "mined", "spp"), type = "fe.zi"), "data.frame")
    expect_s3_class(suppressWarnings(ggpredict(m9, c("cover", "mined", "spp"), type = "re")), "data.frame")
    expect_s3_class(suppressWarnings(ggpredict(m9, c("cover", "mined", "spp"), type = "re.zi")), "data.frame")
  })

  test_that("validate ggpredict against predict, linear, REML-fit", {
    data(sleepstudy, package = "lme4")
    # REML-fit
    m10 <- glmmTMB::glmmTMB(
      Reaction ~ Days + (1 + Days | Subject),
      data = sleepstudy,
      REML = TRUE
    )
    nd <- data_grid(m10, "Days")
    pr <- predict(m10, newdata = nd, type = "link", se.fit = TRUE)
    dof <- insight::get_df(m10, type = "wald", verbose = FALSE)
    tcrit <- stats::qt(0.975, df = dof)
    out1 <- data.frame(
      predicted = pr$fit,
      conf.low = pr$fit - tcrit * pr$se.fit,
      conf.high = pr$fit + tcrit * pr$se.fit
    )
    out2 <- ggpredict(m10, "Days", type = "random", interval = "confidence")

    expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)

    # ML-fit
    m11 <- glmmTMB::glmmTMB(
      Reaction ~ Days + (1 + Days | Subject),
      data = sleepstudy,
      REML = FALSE
    )
    nd <- data_grid(m11, "Days")
    pr <- predict(m11, newdata = nd, type = "link", se.fit = TRUE)
    dof <- insight::get_df(m11, type = "wald", verbose = FALSE)
    tcrit <- stats::qt(0.975, df = dof)
    out1 <- data.frame(
      predicted = pr$fit,
      conf.low = pr$fit - tcrit * pr$se.fit,
      conf.high = pr$fit + tcrit * pr$se.fit
    )
    out2 <- ggpredict(m11, "Days")
    out3 <- ggpredict(m11, "Days", type = "random", interval = "confidence")

    expect_equal(out1$predicted, out2$predicted, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$predicted, out3$predicted, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.low, out2$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.low, out3$conf.low, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.high, out2$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
    expect_equal(out1$conf.high, out3$conf.high, tolerance = 1e-4, ignore_attr = TRUE)
  })
}