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# plotting a randomForest tree!
# https://stats.stackexchange.com/questions/2344/best-way-to-present-a-random-forest-in-a-publication
# I will still need to clean this into this:
# as.dendrogram.randomForest
# to.dendrogram <- function(dfrep,rownum=1,height.increment=0.1){
# if(dfrep[rownum,'status'] == -1){
# rval <- list()
# attr(rval,"members") <- 1
# attr(rval,"height") <- 0.0
# attr(rval,"label") <- dfrep[rownum,'prediction']
# attr(rval,"leaf") <- TRUE
# }else{##note the change "to.dendrogram" and not "to.dendogram"
# left <- to.dendrogram(dfrep,dfrep[rownum,'left daughter'],height.increment)
# right <- to.dendrogram(dfrep,dfrep[rownum,'right daughter'],height.increment)
# rval <- list(left,right)
# attr(rval,"members") <- attr(left,"members") + attr(right,"members")
# attr(rval,"height") <- max(attr(left,"height"),attr(right,"height")) + height.increment
# attr(rval,"leaf") <- FALSE
# attr(rval,"edgetext") <- dfrep[rownum,'split var']
# }
# class(rval) <- "dendrogram"
# return(rval)
# }
# library(randomForest)
# set.seed(1)
# data(iris, envir = environment())
# iris.rf <- randomForest(Species ~ ., iris, proximity=TRUE,
# keep.forest=FALSE)
# MDSplot(iris.rf, iris$Species)
# set.seed(1)
# mod <- randomForest(Species ~ .,data=iris)
# tree <- getTree(mod,1,labelVar=TRUE)
# d <- to.dendrogram(tree)
# str(d)
# plot(d,center=TRUE,leaflab='none',edgePar=list(t.cex=1,p.col=NA,p.lty=0))
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