test_that("is.residualDiagnostics and as.residualDiagnostics work", {
expect_false(is.residualDiagnostics(mtcars))
expect_error(as.residualDiagnostics(1))
expect_error(as.residualDiagnostics(list(1, 2, 3, 4, 5)))
})
test_that("residualDiagnostics works with linear models", {
m <- stats::lm(mpg ~ hp, data = mtcars)
rd <- suppressWarnings(residualDiagnostics(m))
expect_s3_class(rd, "residualDiagnostics.lm")
rd <- suppressWarnings(residualDiagnostics(m, standardized = FALSE))
expect_s3_class(rd, "residualDiagnostics.lm")
expect_true(is.residualDiagnostics(rd))
})
test_that("internal function, .quantilePercentiles works", {
x <- JWileymisc:::.quantilePercentiles(
data.frame(
Predicted = mtcars$mpg,
Residuals = mtcars$hp))
expect_s3_class(x, "data.table")
expect_equal(nrow(x), 1000)
expect_false(anyNA(x))
x <- JWileymisc:::.quantilePercentiles(
data.frame(
Predicted = 1:10,
Residuals = 1))
expect_s3_class(x, "data.table")
expect_equal(nrow(x), 1000)
expect_true(anyNA(x))
})
test_that("is.modelDiagnostics and as.modelDiagnostics work", {
expect_false(is.modelDiagnostics(mtcars))
expect_error(as.modelDiagnostics(1))
expect_error(as.modelDiagnostics(1:3))
expect_error(as.modelDiagnostics(list(1, 2, 3)))
})
test_that("modelDiagnostics works with linear models", {
m <- stats::lm(mpg ~ hp * factor(cyl), data = mtcars)
md <- suppressWarnings(modelDiagnostics(m))
expect_s3_class(md, "modelDiagnostics.lm")
expect_invisible(td <- plot(md, plot = FALSE, ask = FALSE))
expect_length(td, 2)
expect_s3_class(td$ResPlot, "ggplot")
expect_s3_class(td$ResFittedPlot, "ggplot")
expect_invisible(td <- plot(md, plot = TRUE, ask = FALSE))
expect_invisible(td <- plot(md, plot = TRUE, ask = FALSE, ncol = 1))
md2 <- suppressWarnings(modelDiagnostics(m, ev.perc = .2))
expect_s3_class(md2, "modelDiagnostics.lm")
m <- stats::lm(EffectType ~ 1, data = data.frame(EffectType = 1:10))
expect_error(modelDiagnostics(m))
md <- modelDiagnostics(stats::lm(Y ~ 1, data = data.frame(Y = 1:10)))
expect_invisible(td <- plot(md, plot = FALSE, ask = FALSE, ncol = 1))
md <- modelDiagnostics(stats::lm(Y ~ 1, data = data.frame(Y = 1:505)))
expect_warning(plot(md, plot = TRUE, ask = FALSE, ncol = 1))
})
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