Splits factor levels into non-overlapping clusters
based on a factorMerger object.
If a stat is "loglikelihood" or "p-value"
then performs bottom-up search through models
on the merging path until spots a model scored worse
than the given threshold (value).
If stat = "GIC", value is interpreted as
GIC penalty and optimal GIC model is returned..
cutTree(factorMerger, stat = "GIC", value = 2)object of a class factorMerger
statistic used in the bottom-up search. Available statistics are:
"loglikelihood", "pvalue", "GIC".
cut threshold or penalty (for GIC)
Returns a factor vector - each observation is given a new cluster label.
By default, cutree returns factor partition
corresponding to the optimal GIC model (with the lowest GIC).