Nothing
context('yeojohnson functionality')
data(iris)
train <- iris$Petal.Width
yeojohnson_obj <- yeojohnson(train)
test_that('yeojohnson Transforms original data consistently', {
expect_equal(yeojohnson_obj$x.t, predict(yeojohnson_obj))
expect_equal(yeojohnson_obj$x, predict(yeojohnson_obj, inverse = TRUE))
})
test_that('yeojohnson Transforms new data consistently', {
nd <- seq(0, 4, length = 100)
pred <- predict(yeojohnson_obj, newdata = nd)
expect_true(!any(is.na(pred)))
nd2 <- predict(yeojohnson_obj, newdata = pred, inverse = TRUE)
expect_equal(nd, nd2)
})
test_that('yeojohnson correctly handles missing original data', {
b <- yeojohnson(c(NA, train))
expect_equal(as.numeric(NA), b$x.t[1])
expect_equal(as.numeric(NA), predict(b)[1])
expect_equal(as.numeric(NA), predict(b, inverse = TRUE)[1])
})
test_that('yeojohnson correctly handles missing new data', {
b <- yeojohnson(train)
expect_equal(as.numeric(NA), predict(b, newdata = c(1, NA))[2])
expect_equal(as.numeric(NA), predict(b, newdata = c(1, NA), inverse = TRUE)[2])
})
# without standardization
yeojohnson_obj <- yeojohnson(train, standardize = FALSE)
test_that('yeojohnson without standardization Transforms original data consistently', {
expect_equal(yeojohnson_obj$x.t, predict(yeojohnson_obj))
expect_equal(yeojohnson_obj$x, predict(yeojohnson_obj, inverse = TRUE))
})
test_that('yeojohnson without standardization Transforms new data consistently', {
nd <- seq(0, 4, length = 100)
pred <- predict(yeojohnson_obj, newdata = nd)
expect_true(!any(is.na(pred)))
nd2 <- predict(yeojohnson_obj, newdata = pred, inverse = TRUE)
expect_equal(nd, nd2)
})
test_that('yeojohnson without standardization correctly handles missing original data', {
b <- yeojohnson(c(NA, train), standardize = FALSE)
expect_equal(as.numeric(NA), b$x.t[1])
expect_equal(as.numeric(NA), predict(b)[1])
expect_equal(as.numeric(NA), predict(b, inverse = TRUE)[1])
})
test_that('yeojohnson without standardization correctly handles missing new data', {
b <- yeojohnson(train, standardize = FALSE)
expect_equal(as.numeric(NA), predict(b, newdata = c(1, NA))[2])
expect_equal(as.numeric(NA), predict(b, newdata = c(1, NA), inverse = TRUE)[2])
})
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