context("cvFit - repeated CV")
## load packages
library("cvTools", quietly=TRUE)
## set seed for reproducibility
set.seed(1234)
## generate data for tests
n <- 20
x <- rnorm(n)
y <- x + rnorm(n)
x <- as.matrix(x)
xy <- data.frame(x, y)
## fit models via lmrob() and lts()
lmrobFit <- lmrob(y~x, data=xy)
ltsFit <- ltsReg(x, y, alpha=0.75)
ltsFit$call[[1]] <- as.name("ltsReg")
## set up cross-validation folds
K <- 5
R <- 2
folds <- cvFolds(n, K, R)
## run tests
test_that("returned object has class \"cv\" and correct dimensions", {
## MM-regression
lmrobCV <- cvFit(lmrobFit, data=xy, y=xy$y, cost=rtmspe, folds=folds)
expect_is(lmrobCV, "cv")
lmrobRTMSPE <- lmrobCV$cv
expect_is(lmrobRTMSPE, "numeric")
expect_equal(length(lmrobRTMSPE), 1)
lmrobReps <- lmrobCV$reps
expect_is(lmrobReps, "matrix")
expect_equal(dim(lmrobReps), c(R, 1))
lmrobSE <- lmrobCV$se
expect_is(lmrobSE, "numeric")
expect_equal(length(lmrobSE), 1)
expect_false(any(is.na(lmrobSE)))
## reweighted and raw LTS
ltsCV <- cvFit(ltsFit, x=x, y=y, cost=rtmspe, folds=folds,
predictArgs=list(fit="both"))
expect_is(ltsCV, "cv")
ltsRTMSPE <- ltsCV$cv
expect_is(ltsRTMSPE, "numeric")
expect_equal(length(ltsRTMSPE), 2)
expect_equal(ltsFit$reps, NULL)
ltsReps <- ltsCV$reps
expect_is(ltsReps, "matrix")
expect_equal(dim(ltsReps), c(R, 2))
ltsSE <- ltsCV$se
expect_is(ltsSE, "numeric")
expect_equal(length(ltsSE), 2)
expect_false(any(is.na(ltsSE)))
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.