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# Creates a summary various aspects of several randomForests (one for each variable)
# when method randomForest is used.
yaiRFsummary = function(object, nTop=0)
{
if (class(object) != "yai") stop ("arg must be of class yai")
if (object$method != "randomForest") stop ("method must be randomForest")
if (!requireNamespace ("randomForest")) stop("install randomForest and try again")
scaledImportance = yaiVarImp(object, nTop, plot=FALSE)
error = vector(mode="numeric",length=length(names(object$ranForest)))
errtag = vector(mode="character",length=length(names(object$ranForest)))
levels = vector(mode="integer",length=length(names(object$ranForest)))
ntree = vector(mode="integer",length=length(names(object$ranForest)))
type = vector(mode="character",length=length(names(object$ranForest)))
i = 0
for (Rf in object$ranForest)
{
i = i+1
type[i] = Rf$type
if(Rf$type == "regression")
{
error [i] = round(100*Rf$rsq[length(Rf$rsq)], digits=2)
errtag[i] = "%var explained"
levels[i] = NA
}
else if(Rf$type == "classification")
{
error [i] = Rf$err.rate[Rf$ntree,"OOB"]
errtag[i] = "OOB error rate"
levels[i] = nrow(Rf$confusion)
}
else
{
error [i] = NA
errtag[i] = "N/A"
levels[i] = NA
}
ntree [i] = Rf$ntree
}
forestAttributes=data.frame(ntree,error,errtag,levels,type)
rownames(forestAttributes)=names(object$ranForest)
list(forestAttributes=forestAttributes,scaledImportance=scaledImportance)
}
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