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test_that("classif_pamr", {
requirePackagesOrSkip("pamr", default.method = "load")
parset.list = list(
list(),
list(threshold.predict = 2, n.threshold = 40L)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
d = list(x = t(binaryclass.train[, -binaryclass.class.col]),
y = binaryclass.train[, binaryclass.class.col])
parset = c(parset, list(data = d))
if ("threshold.predict" %in% names(parset)) {
threshold.predict = parset$threshold.predict
parset$threshold.predict = NULL
} else {
threshold.predict = 1
}
capture.output({
m = do.call(pamr::pamr.train, parset)
})
newdata = t(binaryclass.test[, -binaryclass.class.col])
old.predicts.list[[i]] = pamr::pamr.predict(m, newdata,
threshold = threshold.predict)
old.probs.list[[i]] = pamr::pamr.predict(m, newdata,
type = "posterior", threshold = threshold.predict)[, 1L]
}
testSimpleParsets("classif.pamr", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.pamr", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
})
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