📄 varimpplot.r
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varImpPlot <- function(x, sort=TRUE, n.var=min(30, nrow(x$importance)), type=NULL, class=NULL, scale=TRUE, main=deparse(substitute(x)), ...) { if (!inherits(x, "randomForest")) stop("This function only works for objects of class `randomForest'") imp <- importance(x, class=class, scale=scale, type=type, ...) ## If there are more than two columns, just use the last two columns. if (ncol(imp) > 2) imp <- imp[, -(1:(ncol(imp) - 2))] nmeas <- ncol(imp) if (nmeas > 1) { op <- par(mfrow=c(1, 2), mar=c(4, 5, 4, 1), mgp=c(2, .8, 0), oma=c(0, 0, 2, 0), no.readonly=TRUE) on.exit(par(op)) } for (i in 1:nmeas) { ord <- if (sort) rev(order(imp[,i], decreasing=TRUE)[1:n.var]) else 1:n.var xmin <- if (colnames(imp)[i] %in% c("IncNodePurity", "MeanDecreaseGini")) 0 else min(imp[ord, i]) dotchart(imp[ord,i], xlab=colnames(imp)[i], ylab="", main=if (nmeas == 1) main else NULL, xlim=c(xmin, max(imp[,i])), ...) } if (nmeas > 1) mtext(outer=TRUE, side=3, text=main, cex=1.2) invisible(imp)}
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