# testthat for gg_error function
context("gg_error tests")
test_that("gg_error.rfsrc classifications",{
## Load the cached forest
data(rfsrc_iris, package="ggRandomForests")
# Test the cached forest type
expect_is(rfsrc_iris, "rfsrc")
# Test the forest family
expect_match(rfsrc_iris$family, "class")
## Create the correct gg_error object
gg_dta <- gg_error(rfsrc_iris)
# Test object type
expect_is(gg_dta, "gg_error")
# Test classification dimensions
expect_equal(dim(gg_dta)[1], dim(rfsrc_iris$err.rate)[1])
expect_equal(dim(gg_dta)[2], dim(rfsrc_iris$err.rate)[2] + 1)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "ntree")]), rfsrc_iris$err.rate)
## Test plotting the gg_error object
gg_plt <- plot(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
# "Incorrect object type: Expects a gg_error object"
expect_error(gg_error(gg_plt))
expect_error(gg_error.rfsrc(gg_plt))
rfsrc_iris$err.rate <- NULL
expect_error(gg_error(rfsrc_iris))
})
test_that("gg_error.randomForest classifications",{
## Load the cached forest
rf_iris <- randomForest::randomForest(Species ~.,
data = iris)
# Test the cached forest type
expect_is(rf_iris, "randomForest")
# Test the forest family
expect_match(rf_iris$type, "classification")
## Create the correct gg_error object
gg_dta <- gg_error(rf_iris)
# Test object type
expect_is(gg_dta, "gg_error")
# Test classification dimensions
expect_equal(dim(gg_dta)[1], dim(rf_iris$err.rate)[1])
expect_equal(dim(gg_dta)[2], dim(rf_iris$err.rate)[2] + 1)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "ntree")]), rf_iris$err.rate)
## Test plotting the gg_error object
gg_plt <- plot(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
# "Incorrect object type: Expects a gg_error object"
expect_error(gg_error(gg_plt))
expect_error(gg_error.randomForest(gg_plt))
rf_iris$err.rate <- NULL
expect_error(gg_error(rf_iris))
})
test_that("gg_error survival", {
## Load the cached forest
data(rfsrc_pbc, package="ggRandomForests")
# Test the cached forest type
expect_is(rfsrc_pbc, "rfsrc")
# Test the forest family
expect_match(rfsrc_pbc$family, "surv")
## Create the correct gg_error object
gg_dta <- gg_error(rfsrc_pbc)
# Test object type
expect_is(gg_dta, "gg_error")
# Test classification dimensions
expect_equal(dim(gg_dta)[1], length(rfsrc_pbc$err.rate))
expect_equal(dim(gg_dta)[2], 2)
# Test data is correctly pulled from randomForest obect.
tmp <- c(gg_dta[,1])
expect_equivalent(tmp, rfsrc_pbc$err.rate)
## Test plotting the gg_error object
gg_plt <- plot(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
expect_error(gg_error(gg_plt))
# "Incorrect object type: Expects a gg_error object"
})
test_that("gg_error regression",{
## Load the cached forest
data(rfsrc_Boston, package="ggRandomForests")
# Test the cached forest type
expect_is(rfsrc_Boston, "rfsrc")
# Test the forest family
expect_match(rfsrc_Boston$family, "regr")
## Create the correct gg_error object
gg_dta <- gg_error(rfsrc_Boston)
# Test object type
expect_is(gg_dta, "gg_error")
# Test classification dimensions
expect_equal(nrow(gg_dta), length(rfsrc_Boston$err.rate))
expect_equal(ncol(gg_dta), 2)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(c(gg_dta[,1]), rfsrc_Boston$err.rate)
## Test plotting the gg_error object
gg_plt <- plot(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
## Test plotting the gg_error object
gg_plt <- plot.gg_error(rfsrc_Boston)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
# Test the exception for input
expect_error(gg_error(gg_plt))
## Create the correct gg_error object
# gg_dta <- gg_error(rfsrc_Boston, training=TRUE)
# Test object type
# expect_is(gg_dta, "gg_error")
})
test_that("gg_error regression",{
## Load the cached forest
data(Boston, package="MASS")
Boston$chas <- as.logical(Boston$chas)
rf_Boston <- randomForest::randomForest(medv~., data=Boston)
# Test the cached forest type
expect_is(rf_Boston, "randomForest")
# Test the forest family
expect_match(rf_Boston$type, "regression")
## Create the correct gg_error object
gg_dta <- gg_error(rf_Boston)
# Test object type
expect_is(gg_dta, "gg_error")
# Test classification dimensions
expect_equal(nrow(gg_dta), length(rf_Boston$mse))
expect_equal(ncol(gg_dta), 2)
# Test data is correctly pulled from randomForest obect.
expect_equivalent(c(gg_dta[,1]), rf_Boston$mse)
## Test plotting the gg_error object
gg_plt <- plot(gg_dta)
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
# Test return is s ggplot object
expect_is(gg_plt, "ggplot")
# Test the exception for input
expect_error(gg_error(gg_plt))
## Create the correct gg_error object
# gg_dta <- gg_error(rf_Boston, training=TRUE)
# Test object type
# expect_is(gg_dta, "gg_error")
})
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