\HeaderA{varImpStruct-class}{Class "varImpStruct" -- collect data on variable importance
from various machine learning methods}{varImpStruct.Rdash.class}
\aliasA{getVarImp}{varImpStruct-class}{getVarImp}
\aliasA{getVarImp,classifierOutput-method}{varImpStruct-class}{getVarImp,classifierOutput.Rdash.method}
\aliasA{getVarImp,classifOutput-method}{varImpStruct-class}{getVarImp,classifOutput.Rdash.method}
\aliasA{plot,varImpStruct,ANY-method}{varImpStruct-class}{plot,varImpStruct,ANY.Rdash.method}
\aliasA{plot,varImpStruct-method}{varImpStruct-class}{plot,varImpStruct.Rdash.method}
\aliasA{show,varImpStruct-method}{varImpStruct-class}{show,varImpStruct.Rdash.method}
\keyword{classes}{varImpStruct-class}
\begin{Description}\relax
collects data on variable importance
\end{Description}
\begin{Section}{Objects from the Class}
Objects can be created by calls of the form \code{new("varImpStruct", ...)}.
These are matrices of importance measures with separate 
slots identifying algorithm generating the measures and
variable names.
\end{Section}
\begin{Section}{Slots}
\describe{
\item[\code{.Data}:] Object of class \code{"matrix"} actual importance
measures 
\item[\code{method}:] Object of class \code{"character"} tag 
\item[\code{varnames}:] Object of class \code{"character"} conformant
vector of names of variables 
}
\end{Section}
\begin{Section}{Extends}
Class \code{"matrix"}, from data part.
Class \code{"structure"}, by class \code{"matrix"}.
Class \code{"array"}, by class \code{"matrix"}.
Class \code{"vector"}, by class "matrix", with explicit coerce.
Class \code{"vector"}, by class "matrix", with explicit coerce.
\end{Section}
\begin{Section}{Methods}
\describe{
\item[plot] \code{signature(x = "varImpStruct")}: make a bar plot,
you can supply an argument resolveenv which will look up variable
names in a translation environment 
\item[show] \code{signature(object = "varImpStruct")}: simple abbreviated
display 
\item[getVarImp] \code{signature(object = "classifOutput")}: extractor
of variable importance structure
}
\end{Section}
\begin{Examples}
\begin{ExampleCode}
library(golubEsets)
data(Golub_Merge)
library(hu6800)
smallG <- Golub_Merge[1:60,]
set.seed(1234)
opar=par(no.readonly=TRUE)
par(las=2, mar=c(10,11,5,5))
rf <- randomForestB( smallG, "ALL.AML", 1:40, importance=TRUE )
plot( getVarImp( rf ), n=10, resolveenv=hu6800SYMBOL )
gb <- gbmB( smallG, "ALL.AML", 1:40 , n.minobsinnode=3 , n.trees=6000)
plot( getVarImp( gb ), n=10, resolveenv=hu6800SYMBOL )
set.seed(1234)
rf2 <- MLearn(ALL.AML~., smallG, randomForestI, 1:40, importance=TRUE,
 sampsize=table(smallG$ALL.AML[1:40]), mtry=sqrt(ncol(exprs(smallG))))
plot( getVarImp( rf2 ), n=10, resolveenv=hu6800SYMBOL ) # translation?
par(opar)
\end{ExampleCode}
\end{Examples}

