dc <- mtcars
# scale continuous variables
dc <- sapply(mtcars[, 1:7], scale)
# code factor variables
dc <- cbind(as.data.frame(dc),
vs = as.factor(mtcars$vs),
am = as.factor(mtcars$am),
gear = as.factor(mtcars$gear),
carb = as.factor(mtcars$carb))
# as here I basically only use functions from other packages, I only test if
# the d argument is set correctly
# set up a function to run the function that calls ConsensusClusterPlus
# to manually turn off any device or changed plotting codes
# set pdf(NULL) because otherwise the test leaves a Rplots.pdf file
run_ccc <- function(...) {
pdf(NULL)
op <- par(no.readonly = TRUE)
on.exit(par(op))
on.exit(dev.off(), add = TRUE)
crosssectional_consensus_cluster(...)
}
test_that("the d argument is set correctly", {
# this base case should work
expect_type(run_ccc(
data = dc,
reps = 3,
seed = 1
),
"list")
# no extra d argument is allowed
expect_error(run_ccc(
data = dc,
d = dc,
reps = 3,
seed = 1
))
})
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