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test_that("classif_plsdaCaret_binary", {
skip("until klaR pkg was updated")
requirePackagesOrSkip("caret", default.method = "load")
parset.list = list(
list(),
list(ncomp = 4),
list(probMethod = "Bayes"),
list(method = "oscorespls")
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
x = binaryclass.train
y = x[, binaryclass.class.col]
x[, binaryclass.class.col] = NULL
pars = list(x = x, y = y)
pars = c(pars, parset)
m = do.call(caret::plsda, pars)
newx = binaryclass.test
newx[, binaryclass.class.col] = NULL
p = predict(m, newdata = newx, type = "class")
p2 = predict(m, newdata = newx, type = "prob")[, 1, 1]
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.plsdaCaret", binaryclass.df, binaryclass.target,
binaryclass.train.inds,
old.predicts.list, parset.list)
testProbParsets("classif.plsdaCaret", binaryclass.df, binaryclass.target,
binaryclass.train.inds,
old.probs.list, parset.list)
})
test_that("classif_plsdaCaret_multiclass", {
requirePackagesOrSkip("caret", default.method = "load")
parset.list = list(
list(),
list(ncomp = 4),
list(probMethod = "Bayes"),
list(method = "oscorespls")
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(x = multiclass.train[, names(multiclass.train) %nin%
multiclass.target],
y = multiclass.train[, multiclass.target])
pars = c(pars, parset)
m = do.call(caret::plsda, pars)
newx = multiclass.test
newx[, multiclass.class.col] = NULL
p = predict(m, newdata = newx, type = "class")
p2 = predict(m, newdata = newx, type = "prob")[, , 1]
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.plsdaCaret", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.plsdaCaret", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
})
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