Nothing
test_that("predict methods return a tibble with .fitted (cross-class contract)", {
skip_on_cran()
data(apt, package = "beezdemand")
apt_small <- apt[apt$id %in% unique(apt$id)[1:5], ]
# Fixed
fit_fixed <- fit_demand_fixed(apt_small, equation = "hs", k = 2)
pred_fixed <- predict(fit_fixed, newdata = data.frame(x = c(1, 5, 10)), type = "response")
expect_s3_class(pred_fixed, "tbl_df")
expect_true(".fitted" %in% names(pred_fixed))
# NLME
skip_if_not_installed("nlme")
apt_small$y_ll4 <- ll4(apt_small$y)
fit_nlme <- fit_demand_mixed(apt_small, y_var = "y_ll4", x_var = "x", id_var = "id")
pred_nlme <- predict(fit_nlme, newdata = data.frame(x = c(1, 5, 10)), type = "response")
expect_s3_class(pred_nlme, "tbl_df")
expect_true(".fitted" %in% names(pred_nlme))
expect_equal(nrow(pred_nlme), 3)
# Hurdle
skip_if_not_installed("TMB")
fit_hurdle <- fit_demand_hurdle(apt_small, y_var = "y", x_var = "x", id_var = "id", verbose = 0)
pred_hurdle <- predict(fit_hurdle, newdata = data.frame(x = c(1, 5, 10)), type = "response")
expect_s3_class(pred_hurdle, "tbl_df")
expect_true(".fitted" %in% names(pred_hurdle))
expect_equal(nrow(pred_hurdle), 3)
})
test_that("predict.beezdemand_hurdle supports se.fit and confidence intervals", {
skip_on_cran()
skip_if_not_installed("TMB")
data(apt, package = "beezdemand")
apt_small <- apt[apt$id %in% unique(apt$id)[1:5], ]
fit <- fit_demand_hurdle(apt_small, y_var = "y", x_var = "x", id_var = "id", verbose = 0)
newdata <- data.frame(x = c(1, 5, 10))
pred_se <- predict(fit, newdata = newdata, type = "response", se.fit = TRUE)
expect_true(".se.fit" %in% names(pred_se))
expect_true(all(is.finite(pred_se$.se.fit)))
expect_true(all(pred_se$.se.fit >= 0))
pred_ci <- predict(fit, newdata = newdata, type = "response", interval = "confidence", level = 0.95)
expect_true(all(c(".lower", ".upper") %in% names(pred_ci)))
expect_true(all(pred_ci$.lower <= pred_ci$.fitted))
expect_true(all(pred_ci$.fitted <= pred_ci$.upper))
})
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