varSelectionRF: Variable selection function that can be provided to...

In nonlinearICP: Invariant Causal Prediction for Nonlinear Models

Description

Variable selection function that can be provided to nonlinearICP - it is then applied to pre-select a set of variables before running the ICP procedure on this subset. Here, the variable selection is based on random forest variable importance measures.

Usage

varSelectionRF(X, Y, env, verbose, nSelect = sqrt(ncol(X)),
  useMtry = sqrt(ncol(X)), ntree = 100)

Arguments

X	A (nxp)-dimensional matrix (or data frame) with n observations of p variables.
Y	Response vector (n x 1)
env	Indicator of the experiment or the intervention type an observation belongs to. A numeric vector of length n. Has to contain at least two different unique values.
verbose	If FALSE, most messages are supressed.
nSelect	Number of variables to select. Defaults to sqrt(ncol(X)).
useMtry	Random forest parameter mtry. Defaults to sqrt(ncol(X)).
ntree	Random forest parameter ntree. Defaults to 100.

Value

A vector containing the indices of the selected variables.

Examples

# Example 1
require(CondIndTests)
data("simData")
targetVar <- 2
# choose environments where we did not intervene on var
useEnvs <- which(simData$interventionVar[,targetVar] == 0)
ind <- is.element(simData$environment, useEnvs)
X <- simData$X[ind,-targetVar]
Y <- simData$X[ind,targetVar]
E <- as.factor(simData$environment[ind])
chosenIdx <- varSelectionRF(X = X, Y = Y, env = E, verbose = TRUE)
cat(paste("Variable(s)", paste(chosenIdx, collapse=", "), "was/were chosen."))

nonlinearICP documentation built on May 1, 2019, 7:50 p.m.