Nothing
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
rfout <-
mobforest.analysis(
as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls =
mobforest.control(ntree = 3, mtry = 2, replace = T, alpha = 0.05,
bonferroni = T, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
# Run Tests
test_that("rfout was successful in all calculations.", {
# OOB Predictions
expect_equal(unname(round(rfout@oob_predictions@pred_mat[90, 1], 4)), 25.6958)
# Model Used
expect_equal(rfout@model_used, "medv ~ lstat | rad + tax + crim")
# Training Respones - The correct subset was used
expect_equal(round(mean(rfout@train_response$medv), 4), 21.6922)
# Variable Importance Matrix
expect_equal(
unname(round(rfout@varimp_object@varimp_matrix[2, 1], 4)), 3.5593)
# General R2 is calculated correctly
expect_equal(round(rfout@general_predictions@overall_r2, 4), 0.6611)
})
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