Nothing
context("predictor pre-processing")
library(testthat)
library(sparsediscrim)
library(modeldata)
# ------------------------------------------------------------------------------
data(scat, package = "modeldata")
# ------------------------------------------------------------------------------
test_that("formula method", {
expect_warning(
mod <- lda_diag(Species ~ ., data = scat[1:90, ]),
"had zero variance"
)
expect_equal(
mod$N,
sum(complete.cases(scat[1:90, ]))
)
missing_rows <- which(!complete.cases(scat[-(1:90), -1]))
pred_cls <- predict(mod, newdata = scat[-(1:90), -1])
pred_prb <- predict(mod, newdata = scat[-(1:90), -1], type = "prob")
expect_true(
all(!is.na(pred_prb[-missing_rows,]))
)
expect_true(
all(is.na(pred_cls[missing_rows]))
)
expect_true(
all(!is.na(pred_cls[-missing_rows]))
)
expect_true(
all(is.na(pred_cls[missing_rows]))
)
})
# ------------------------------------------------------------------------------
test_that("x/y method", {
mod <- lda_diag(x = scat[1:90, 6:12], y = scat$Species[1:90])
expect_equal(
mod$N,
sum(complete.cases(scat[1:90, 6:12]))
)
missing_rows <- which(!complete.cases(scat[-(1:90), 6:12]))
pred_cls <- predict(mod, newdata = scat[-(1:90), 6:12])
pred_prb <- predict(mod, newdata = scat[-(1:90), 6:12], type = "prob")
expect_true(
all(!is.na(pred_prb[-missing_rows,]))
)
expect_true(
all(is.na(pred_cls[missing_rows]))
)
expect_true(
all(!is.na(pred_cls[-missing_rows]))
)
expect_true(
all(is.na(pred_cls[missing_rows]))
)
})
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