context("implementation of model auxiliary functions")
## Tolerance for comparisons
tolerance <- 1e-05
data("lizards", package = "brglm")
model1 <- glm(formula = grahami/(grahami + opalinus) ~ height + diameter + light + time, family = binomial(logit), weights = (grahami + opalinus), data = lizards)
model2 <- glm(formula = cbind(grahami, opalinus) ~ height + diameter + light + time, family = binomial(logit), data = lizards)
lizards_grahami <- lizards[, c("grahami", "height", "diameter", "light", "time")]
lizards_grahami <- lizards_grahami[rep(seq.int(nrow(lizards_grahami)), lizards_grahami$grahami), ]
lizards_grahami$species <- "grahami"
lizards_grahami$grahami <- NULL
lizards_opalinus <- lizards[, c("opalinus", "height", "diameter", "light", "time")]
lizards_opalinus <- lizards_opalinus[rep(seq.int(nrow(lizards_opalinus)), lizards_opalinus$opalinus), ]
lizards_opalinus$species <- "opalinus"
lizards_opalinus$opalinus <- NULL
lizards1 <- rbind(lizards_grahami, lizards_opalinus)
lizards1$species <- factor(lizards1$species, levels = c("opalinus", "grahami"))
model3 <- glm(formula = species ~ height + diameter + light + time, family = binomial(logit), data = lizards1)
test_that("simulate and get_simulate_function return the same variates for various equivalent representations of the data for logistic regression", {
expect_identical(simulate(model1, seed = 123)[, 1],
get_simulate_function(model1)(seed = 123)[, 1])
expect_identical(simulate(model2, seed = 123)[, 1],
get_simulate_function(model2)(seed = 123)[, 1])
expect_identical(simulate(model3, seed = 123)[, 1],
get_simulate_function(model3)(seed = 123)[, 1])
})
## Create a test data set
test_data <- model1$data[1:8, ]
test_data$grahami <- c(1, 1, 1, 1, 0, 0, 0, 0)
test_data$opalinus <- c(0, 0, 0, 0, 1, 1, 1, 1)
test_data$species <- factor(rep(c("grahami", "opalinus"), each = 4), levels = c("opalinus", "grahami"))
test_that("dmodel returns the same results for various equivalent representations of the data for logistic regression", {
expect_equal(enrich(model1)$auxiliary_functions$dmodel(test_data),
enrich(model3)$auxiliary_functions$dmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model1)$auxiliary_functions$dmodel(test_data),
enrich(model2)$auxiliary_functions$dmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model2)$auxiliary_functions$dmodel(test_data),
enrich(model3)$auxiliary_functions$dmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
})
test_that("pmodel returns the same results for various equivalent representations of the data for logistic regression", {
expect_equal(enrich(model1)$auxiliary_functions$pmodel(test_data),
enrich(model3)$auxiliary_functions$pmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model1)$auxiliary_functions$pmodel(test_data),
enrich(model2)$auxiliary_functions$pmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model2)$auxiliary_functions$pmodel(test_data),
enrich(model3)$auxiliary_functions$pmodel(test_data),
check.attributes = FALSE,
tol = tolerance)
})
ps <- seq(0, 1, length = nrow(test_data))
test_that("qmodel returns the same results for various equivalent representations of the data for logistic regression", {
expect_equal(enrich(model1)$auxiliary_functions$qmodel(ps, test_data),
enrich(model3)$auxiliary_functions$qmodel(ps, test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model1)$auxiliary_functions$qmodel(ps, test_data),
enrich(model2)$auxiliary_functions$qmodel(ps, test_data),
check.attributes = FALSE,
tol = tolerance)
expect_equal(enrich(model2)$auxiliary_functions$qmodel(ps, test_data),
enrich(model3)$auxiliary_functions$qmodel(ps, test_data),
check.attributes = FALSE,
tol = tolerance)
})
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