SL.ranger.imp {flevr}

Super Learner wrapper for a ranger object with variable importance

Description

Super Learner wrapper for a ranger object with variable importance

Usage

SL.ranger.imp(
  Y,
  X,
  newX,
  family,
  obsWeights = rep(1, length(Y)),
  num.trees = 500,
  mtry = floor(sqrt(ncol(X))),
  write.forest = TRUE,
  probability = family$family == "binomial",
  min.node.size = ifelse(family$family == "gaussian", 5, 1),
  replace = TRUE,
  sample.fraction = ifelse(replace, 1, 0.632),
  num.threads = 1,
  verbose = FALSE,
  importance = "impurity",
  ...
)

Arguments

Y

Outcome variable

X

Training dataframe

newX

Test dataframe

family

Gaussian or binomial

obsWeights

Observation-level weights

num.trees

Number of trees.

mtry

Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.

write.forest

Save ranger.forest object, required for prediction. Set to FALSE to reduce memory usage if no prediction intended.

probability

Grow a probability forest as in Malley et al. (2012).

min.node.size

Minimal node size. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.

replace

Sample with replacement.

sample.fraction

Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement.

num.threads

Number of threads to use.

verbose

If TRUE, display additional output during execution.

importance

Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index for classification, the variance of the responses for regression and the sum of test statistics (see splitrule) for survival.

...

Any additional arguments, not currently used.

Value

a named list with elements pred (predictions on newX) and fit (the fitted ranger object).

References

Breiman, L. (2001). Random forests. Machine learning 45:5-32.

Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, in press. http://arxiv.org/abs/1508.04409.

See Also

SL.ranger ranger predict.ranger

Examples

data("biomarkers")
# subset to complete cases for illustration
cc <- complete.cases(biomarkers)
dat_cc <- biomarkers[cc, ]
# use only the mucinous outcome, not the high-malignancy outcome
y <- dat_cc$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# get the fit
set.seed(20231129)
fit <- SL.ranger.imp(Y = y, X = x, newX = x, family = binomial())
fit