tests/testthat/test-cook_weisberg.R

In skedastic: Handling Heteroskedasticity in the Linear Regression Model

context("cook_weisberg works for two lm examples across all argument permutations")



theargs <- formals(cook_weisberg)

carslm <- lm(dist ~ speed, data = cars)
ncars <- nrow(model.matrix(carslm))
carsauxmat <- cbind(1, matrix(data = runif(ncars * 5), nrow = ncars, ncol = 5))

bostonlm <- lm(medv ~ crim + zn + indus + chas + nox + rm +
                 age + dis + rad + tax + ptratio + b + lstat, data = BostonHousing)
nboston <- nrow(model.matrix(bostonlm))
bostonauxmat <- cbind(1, matrix(data = runif(nboston * 5), nrow = nboston, ncol = 5))

theargs <- list("hetfun" = c("mult", "add", "logmult"),
                "auxdesign" = list(NA, carsauxmat,
               bostonauxmat, "fitted.values"), "mainlm" = list(carslm, bostonlm))

allargs <- expand.grid(theargs, stringsAsFactors = FALSE)
dim1 <- vapply(1:nrow(allargs), function(i) ifelse(is.null(dim(allargs$auxdesign[[i]])),
               0L, dim(allargs$auxdesign[[i]])[1]), NA_integer_)
allargs <- allargs[-which(vapply(1:nrow(allargs),
               function(i) dim1[i] == nboston &
               "speed" %in% colnames(model.matrix(allargs$mainlm[[i]])), NA)), ]
dim1 <- vapply(1:nrow(allargs), function(i) ifelse(is.null(dim(allargs$auxdesign[[i]])),
               0L, dim(allargs$auxdesign[[i]])[1]), NA_integer_)
allargs <- allargs[-which(vapply(1:nrow(allargs),
               function(i) dim1[i] == ncars &
               "crim" %in% colnames(model.matrix(allargs$mainlm[[i]])), NA)), ]
dim1 <- vapply(1:nrow(allargs), function(i) ifelse(is.null(dim(allargs$auxdesign[[i]])),
               0L, dim(allargs$auxdesign[[i]])[1]), NA_integer_)
allargs <- allargs[-which(vapply(1:nrow(allargs),
               function(i) allargs$hetfun[i] == "logmult" & dim1[i] == 0, NA)), ]

test_that("linear regression works with all combinations of formals", {
  pvals <- vapply(1:nrow(allargs), function(i) do.call(what = cook_weisberg,
                args = append(list("statonly" = FALSE),
                unlist(allargs[i, ], recursive = FALSE)))$p.value, NA_real_)
  lapply(1:length(pvals), function(i) expect_true(is.btwn01(pvals[i])))
})