R/featurePlot.R

In caret: Classification and Regression Training

"featurePlot" <-
function(x, y, 
   plot = if(is.factor(y)) "strip" else "scatter", 
   labels = c("Feature", ""), ...)
{
   if(!is.data.frame(x))  x <- as.data.frame(x)
   numFeat <- dim(x)[2]
   
   if(plot != "pairs")
   {
      stackX <- stack(x)
      stackX$.y <- rep(y, numFeat)
   } else {
      if(!is.factor(y))
      {
         x <- data.frame(cbind(x, y))
      } 
   }
   
   if(is.factor(y))
   {
      featPlot <- switch(tolower(plot),
         strip = stripplot(values ~ .y|ind, stackX, 
            xlab = labels[1], ylab = labels[2], ...),
         box =, boxplot = bwplot(values ~ .y|ind, stackX, 
            xlab = labels[1], ylab = labels[2], ...),
         density = densityplot(~values |ind, stackX, 
            groups = stackX$.y, 
            xlab = labels[1], ylab = labels[2], ...),
         pairs = splom(~x, groups = y, ...),
         ellipse =  splom(~x, groups = y,
            panel = function(x, y, groups, subscripts, ...)
            {
               require(ellipse)
               lineInfo <-  trellis.par.get("superpose.line")   
               pointInfo <-  trellis.par.get("superpose.symbol")               
               uniqueGroups <- sort(unique(groups))
               for (i in seq(along=uniqueGroups)) 
               {
                  id <- which(groups[subscripts] == uniqueGroups[i])
                  panel.xyplot(x[id], y[id], pch = pointInfo$pch[i],
                      col = pointInfo$col[i], cex = pointInfo$cex[i], ...)
                  groupVar<-var(cbind(x[id],y[id]))
                  groupMean<-cbind(mean(x[id]),mean(y[id]))
                  groupEllipse<-ellipse(groupVar, centre = groupMean, level = 0.95)
                  panel.xyplot(groupEllipse[,1], groupEllipse[,2], type="l", col = lineInfo$col[i], lty = lineInfo$lty[i], ...)
               }
            },
         ...)   
      )
   } else {
      featPlot <- switch(tolower(plot),
         scatter =, xyplot = xyplot(.y ~ values|ind, stackX, 
            scales = list( x = list(relation = "free")),
            xlab = labels[1], ylab = labels[2], ...),
         pairs = splom(~x,  ...))   
   }
         
   featPlot
}