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#' Create a dotplot of variable importance values
#'
#' A lattice \code{\link[lattice:xyplot]{dotplot}} is created from an object of
#' class \code{varImp.train}.
#'
#'
#' @param x an object of class \code{varImp.train}
#' @param top the number of predictors to plot
#' @param \dots options passed to \code{\link[lattice:xyplot]{dotplot}}
#' @return an object of class \code{trellis}.
#' @author Max Kuhn
#' @seealso \code{\link{varImp}}, \code{\link[lattice:xyplot]{dotplot}}
#' @keywords hplot
#' @examples
#'
#'
#' data(iris)
#' TrainData <- iris[,1:4]
#' TrainClasses <- iris[,5]
#'
#' knnFit <- train(TrainData, TrainClasses, "knn")
#'
#' knnImp <- varImp(knnFit)
#'
#' dotPlot(knnImp)
#'
#'
#' @export dotPlot
dotPlot <- function (x, top = min(20, dim(x$importance)[1]), ...)
{
varSubset <- sortImp(x, top)
plotObj <- stack(varSubset)
if(dim(varSubset)[2] == 1)
{
plotObj <- varSubset
names(plotObj) <- "values"
plotObj$ind <- "Overall"
} else plotObj <- stack(varSubset)
plotObj$Var <- rep(rownames(varSubset), dim(varSubset)[2])
plotObj$Var <- factor(plotObj$Var, levels = rev(rownames(varSubset)))
if(dim(varSubset)[2] < 3)
{
if(dim(varSubset)[2] > 1) plotObj <- plotObj[plotObj$ind == levels(plotObj$ind)[1],]
out <- dotplot(
Var ~ values,
data = plotObj,
as.table = TRUE,
xlab = "Importance",
...)
} else {
out <- dotplot(
Var ~ values,
data = plotObj,
groups = plotObj$ind,
auto.key = list(columns = min(3, length(levels(plotObj$ind)))),
as.table = TRUE,
xlab = "Importance",
...)
}
out
}
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