VT.predict {aVirtualTwins}

VT.predict generic function

Description

VT.predict generic function

Usage

VT.predict(rfor, newdata, type)

## S4 method for signature 'RandomForest,missing,character'
VT.predict(rfor, type = "binary")

## S4 method for signature 'RandomForest,data.frame,character'
VT.predict(rfor, newdata,
  type = "binary")

## S4 method for signature 'randomForest,missing,character'
VT.predict(rfor, type = "binary")

## S4 method for signature 'randomForest,data.frame,character'
VT.predict(rfor, newdata,
  type = "binary")

## S4 method for signature 'train,ANY,character'
VT.predict(rfor, newdata, type = "binary")

## S4 method for signature 'train,missing,character'
VT.predict(rfor, type = "binary")

Arguments

rfor

random forest model. Can be train, randomForest or RandomForest class.

newdata

Newdata to predict by the random forest model. If missing, OOB predictions are returned.

type

Must be binary or continous, depending on the outcome. Only binary is really available.

Value

vector E(Y=1)

Methods (by class)

• rfor = RandomForest,newdata = missing,type = character: rfor(RandomForest) newdata (missing) type (character)

• rfor = RandomForest,newdata = data.frame,type = character: rfor(RandomForest) newdata (data.frame) type (character)

• rfor = randomForest,newdata = missing,type = character: rfor(randomForest) newdata (missing) type (character)

• rfor = randomForest,newdata = data.frame,type = character: rfor(randomForest) newdata (data.frame) type (character)

• rfor = train,newdata = ANY,type = character: rfor(train) newdata (ANY) type (character)

• rfor = train,newdata = missing,type = character: rfor(train) newdata (missing) type (character)