ranger_wrapper: Wrapper for fitting a random forest using ranger.

In benkeser/nlpred: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

Wrapper for fitting a random forest using ranger.

Description

Compatible learner wrappers for this package should have a specific format. Namely they should take as input a list called train that contains named objects $Y and $X, that contain, respectively, the outcomes and predictors in a particular training fold. Other options may be passed in to the function as well. The function must output a list with the following named objects: test_pred = predictions of test$Y based on the learner fit using train$X; train_pred = prediction of train$Y based on the learner fit using train$X; model = the fitted model (only necessary if you desire to look at this model later, not used for internal computations); train_y = a copy of train$Y; test_y = a copy of test$Y.

Usage

ranger_wrapper(
  train,
  test,
  num.trees = 500,
  mtry = floor(sqrt(ncol(train$X))),
  write.forest = TRUE,
  probability = TRUE,
  min.node.size = 5,
  replace = TRUE,
  sample.fraction = ifelse(replace, 1, 0.632),
  num.threads = 1,
  verbose = TRUE,
  ...
)

Arguments

train	A list with named objects Y and X (see description).
test	A list with named objects Y and X (see description).
num.trees	See ranger.
mtry	See ranger.
write.forest	See ranger.
probability	See ranger.
min.node.size	See ranger.
replace	See ranger.
sample.fraction	See ranger.
num.threads	See ranger.
verbose	See ranger.
...	Other options (passed to ranger)

Details

This particular wrapper implements the ranger ensemble methodology. We refer readers to the original package's documentation for more details.

Value

A list with named objects (see description).

Examples

# simulate data
# make list of training data
train_X <- data.frame(x1 = runif(50))
train_Y <- rbinom(50, 1, plogis(train_X$x1))
train <- list(Y = train_Y, X = train_X)
# make list of test data
test_X <- data.frame(x1 = runif(50))
test_Y <- rbinom(50, 1, plogis(train_X$x1))
test <- list(Y = test_Y, X = test_X)
# fit ranger
rf_wrap <- ranger_wrapper(train = train, test = test)

benkeser/nlpred documentation built on June 11, 2022, 3:05 a.m.