wrapper.rf: Wrapper function to call random forests function.

In silkeszy/Pomona: Identification of relevant variables in omics data sets using Random Forests

Wrapper function to call random forests function.

Description

Provides an interface to different parallel implementations of the random forest algorithm. Currently, only the ranger package is supported.

Usage

wrapper.rf(
  x,
  y,
  ntree = 500,
  mtry.prop = 0.2,
  nodesize.prop = 0.1,
  no.threads = 1,
  method = "ranger",
  type = "regression",
  importance = "impurity_corrected",
  case.weights = NULL,
  ...
)

Arguments

x	matrix or data.frame of predictor variables with variables in columns and samples in rows (Note: missing values are not allowed).
y	vector with values of phenotype variable (Note: will be converted to factor if classification mode is used).
ntree	number of trees.
mtry.prop	proportion of variables that should be used at each split.
nodesize.prop	proportion of minimal number of samples in terminal nodes.
no.threads	number of threads used for parallel execution.
method	implementation to be used ("ranger").
type	mode of prediction ("regression", "classification" or "probability").
importance	Variable importance mode ('none', 'impurity', 'impurity_corrected' or 'permutation'). Default is 'impurity_corrected'.
case.weights	Weights for sampling of training observations. Observations with larger weights will be selected with higher probability in the bootstrap (or subsampled) samples for the trees.
...	further arguments needed for holdout.rf function only.

Value

An object of class ranger.

Examples

# simulate toy data set
data = simulation.data.cor(no.samples = 100, group.size = rep(10, 6), no.var.total = 200)

# regression
wrapper.rf(x = data[, -1], y = data[, 1],
           type = "regression", method = "ranger")

silkeszy/Pomona documentation built on March 31, 2022, 11:13 p.m.