Nothing
# testthat for gg_rfsrc function
context("gg_rfsrc tests")
test_that("gg_rfsrc classifications", {
## Load the cached forest
rfsrc_iris <- randomForestSRC::rfsrc(
Species ~ .,
data = iris,
forest = TRUE,
importance = TRUE,
save.memory = TRUE)
# Test the cached forest type
expect_is(rfsrc_iris, "rfsrc")
# Test the forest family
expect_is(rfsrc_iris, "class")
## Create the correct gg_error object
gg_dta <- gg_rfsrc(rfsrc_iris)
# Test object type
expect_is(gg_dta, "gg_rfsrc")
# Test classification dimensions
expect_equal(nrow(gg_dta), nrow(rfsrc_iris$predicted.oob))
expect_equal(ncol(gg_dta), ncol(rfsrc_iris$predicted.oob) + 1)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "y")]),
rfsrc_iris$predicted.oob)
## Test plotting the gg_error object
gg_plt <- plot.gg_rfsrc(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
## Create the correct gg_error object
gg_dta <- gg_rfsrc(rfsrc_iris, oob = FALSE)
# Test object type
expect_is(gg_dta, "gg_rfsrc")
# Test classification dimensions
expect_equal(nrow(gg_dta), nrow(rfsrc_iris$predicted))
expect_equal(ncol(gg_dta), ncol(rfsrc_iris$predicted) + 1)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "y")]),
rfsrc_iris$predicted)
rf_iris <- randomForest::randomForest(Species ~ ., data = iris)
gg_dta <- gg_rfsrc(rf_iris)
})
test_that("gg_rfsrc regression", {
data(Boston, package = "MASS")
boston <- Boston
boston$chas <- as.logical(boston$chas)
## Load the cached forest
rfsrc_boston <-
randomForestSRC::rfsrc(
medv ~ .,
data = boston,
forest = TRUE,
importance = TRUE,
tree.err = TRUE,
save.memory = TRUE)
# Test the cached forest type
expect_is(rfsrc_boston, "rfsrc")
# Test the forest family
expect_match(rfsrc_boston$family, "regr")
## Create the correct gg_error object
gg_dta <- gg_rfsrc(rfsrc_boston)
# Test object type
expect_is(gg_dta, "gg_rfsrc")
expect_is(gg_dta, "regr")
## Test plotting the gg_error object
gg_plt <- plot.gg_rfsrc(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
## Create the correct gg_error object
gg_dta <- gg_rfsrc(rfsrc_boston, oob = FALSE)
# Test object type
expect_is(gg_dta, "gg_rfsrc")
# Test classification dimensions
## Create the correct gg_error object
gg_dta <- gg_rfsrc(rfsrc_boston, by = "chas")
# Test object type
expect_is(gg_dta, "gg_rfsrc")
expect_is(gg_dta, "regr")
## Test plotting the gg_error object
gg_plt <- plot.gg_rfsrc(gg_dta)
# Test data is correctly pulled from randomForest obect.
# Predicted values
rfsrc_boston$family <- "test"
expect_error(gg_rfsrc(rfsrc_boston))
# Test exceptions
# Is it an rfsrc object?
expect_error(gg_rfsrc(gg_plt))
# Does it contain the forest?
rfsrc_boston$forest <- NULL
expect_error(gg_rfsrc(rfsrc_boston))
data(Boston, package = "MASS")
rf_boston <- randomForest(medv ~ ., data = Boston)
plot(gg_rfsrc(rf_boston))
})
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