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61 lines
1.8 KiB
R
61 lines
1.8 KiB
R
% Generated by roxygen2 (4.1.1): do not edit by hand
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% Please edit documentation in R/predict.R
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\docType{methods}
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\name{VT.predict}
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\alias{VT.predict}
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\alias{VT.predict,RandomForest,data.frame,character-method}
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\alias{VT.predict,RandomForest,missing,character-method}
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\alias{VT.predict,randomForest,data.frame,character-method}
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\alias{VT.predict,randomForest,missing,character-method}
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\alias{VT.predict,train,ANY,character-method}
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\alias{VT.predict,train,missing,character-method}
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\title{VT.predict generic function}
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\usage{
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VT.predict(rfor, newdata, type)
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\S4method{VT.predict}{RandomForest,missing,character}(rfor, type = "binary")
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\S4method{VT.predict}{RandomForest,data.frame,character}(rfor, newdata,
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type = "binary")
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\S4method{VT.predict}{randomForest,missing,character}(rfor, type = "binary")
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\S4method{VT.predict}{randomForest,data.frame,character}(rfor, newdata,
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type = "binary")
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\S4method{VT.predict}{train,ANY,character}(rfor, newdata, type = "binary")
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\S4method{VT.predict}{train,missing,character}(rfor, type = "binary")
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}
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\arguments{
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\item{rfor}{random forest model. Can be train, randomForest or RandomForest
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class.}
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\item{newdata}{Newdata to predict by the random forest model. If missing, OOB
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predictions are returned.}
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\item{type}{Must be binary or continous, depending on the outcome. Only
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binary is really available.}
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}
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\value{
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vector \eqn{E(Y=1)}
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}
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\description{
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VT.predict generic function
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}
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\section{Methods (by class)}{
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\itemize{
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\item \code{rfor = RandomForest,newdata = missing,type = character}:
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\item \code{rfor = RandomForest,newdata = data.frame,type = character}:
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\item \code{rfor = randomForest,newdata = missing,type = character}:
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\item \code{rfor = randomForest,newdata = data.frame,type = character}:
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\item \code{rfor = train,newdata = ANY,type = character}:
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\item \code{rfor = train,newdata = missing,type = character}:
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}}
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