% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cutree.dendrogram.R
\name{cutree}
\alias{cutree}
\alias{cutree.default}
\alias{cutree.hclust}
\alias{cutree.phylo}
\alias{cutree.dendrogram}
\title{Cut a Tree (Dendrogram/hclust/phylo) into Groups of Data}
\usage{
cutree(tree, k = NULL, h = NULL, ...)

\method{cutree}{default}(tree, k = NULL, h = NULL, ...)

\method{cutree}{hclust}(tree, k = NULL, h = NULL,
  use_labels_not_values = TRUE, order_clusters_as_data = TRUE,
  warn = dendextend_options("warn"), NA_to_0L = TRUE, ...)

\method{cutree}{phylo}(tree, k = NULL, h = NULL, ...)

\method{cutree}{dendrogram}(tree, k = NULL, h = NULL,
  dend_heights_per_k = NULL, use_labels_not_values = TRUE,
  order_clusters_as_data = TRUE, warn = dendextend_options("warn"),
  try_cutree_hclust = TRUE, NA_to_0L = TRUE, ...)
}
\arguments{
\item{tree}{a dendrogram object}

\item{k}{numeric scalar (OR a vector) with the number of clusters
the tree should be cut into.}

\item{h}{numeric scalar (OR a vector) with a height where the tree 
should be cut.}

\item{...}{(not currently in use)}

\item{use_labels_not_values}{logical, defaults to TRUE. If the actual labels of the 
clusters do not matter - and we want to gain speed (say, 10 times faster) - 
then use FALSE (gives the "leaves order" instead of their labels.).
This is passed to \code{cutree_1h.dendrogram}.}

\item{order_clusters_as_data}{logical, defaults to TRUE. There are two ways by which 
to order the clusters: 1) By the order of the original data. 2) by the order of the 
labels in the dendrogram. In order to be consistent with \link[stats]{cutree}, this is set
to TRUE.
This is passed to \code{cutree_1h.dendrogram}.}

\item{warn}{logical (default from dendextend_options("warn") is FALSE).
Set if warning are to be issued, it is safer to keep this at TRUE,
but for keeping the noise down, the default is FALSE.
Should the function send a warning in case the desried k is not available?}

\item{NA_to_0L}{logical. default is TRUE. When no clusters are possible,
Should the function return 0 (TRUE, default), or NA (when set to FALSE).}

\item{dend_heights_per_k}{a named vector that resulted from running.
\code{heights_per_k.dendrogram}. When running the function many times,
supplying this object will help improve the running time if using k!=NULL .}

\item{try_cutree_hclust}{logical. default is TRUE. Since cutree for hclust is 
MUCH faster than for dendrogram - cutree.dendrogram will first try to change the 
dendrogram into an hclust object. If it will fail (for example, with unbranched trees),
it will continue using the cutree.dendrogram function.
If try_cutree_hclust=FALSE, it will force to use cutree.dendrogram and not
cutree.hclust.}
}
\value{
If k or h are scalar - \code{cutree.dendrogram} returns an integer vector with group 
memberships.
Otherwise a matrix with group memberships is returned where each column 
corresponds to the elements of k or h, respectively 
(which are also used as column names).

In case there exists no such k for which exists a relevant split of the 
dendrogram, a warning is issued to the user, and NA is returned.
}
\description{
Cuts a dendrogram tree into several groups 
by specifying the desired number of clusters k(s), or cut height(s).

For \code{hclust.dendrogram} - 
In case there exists no such k for which exists a relevant split of the 
dendrogram, a warning is issued to the user, and NA is returned.
}
\details{
At least one of k or h must be specified, k overrides h if both are given.

as opposed to \link[stats]{cutree} for hclust, \code{cutree.dendrogram} allows the
cutting of trees at a given height also for non-ultrametric trees 
(ultrametric tree == a tree with monotone clustering heights).
}
\examples{

\dontrun{
hc <- hclust(dist(USArrests[c(1,6,13,20, 23),]), "ave")
dend <- as.dendrogram(hc)
unbranch_dend <- unbranch(dend,2)

cutree(hc, k=2:4) # on hclust
cutree(dend, k=2:4) # on dendrogram

cutree(hc, k=2) # on hclust
cutree(dend, k=2) # on dendrogram

cutree(dend, h = c(20, 25.5, 50,170))
cutree(hc, h = c(20, 25.5, 50,170))

# the default (ordered by original data's order)
cutree(dend, k=2:3, order_clusters_as_data = FALSE) 
labels(dend)

# as.hclust(unbranch_dend) # ERROR - can not do this...
cutree(unbranch_dend, k = 2) # all NA's
cutree(unbranch_dend, k = 1:4)
cutree(unbranch_dend, h = c(20, 25.5, 50,170))
cutree(dend, h = c(20, 25.5, 50,170))


library(microbenchmark)
## this shows how as.hclust is expensive - but still worth it if possible
microbenchmark(
      cutree(hc, k=2:4),
      cutree(as.hclust(dend), k=2:4),
      cutree(dend, k=2:4),
      cutree(dend, k=2:4, try_cutree_hclust=FALSE)
   )          
         # the dendrogram is MUCH slower...
         
# Unit: microseconds
##                       expr      min       lq    median        uq       max neval
##        cutree(hc, k = 2:4)   91.270   96.589   99.3885  107.5075   338.758   100
##    tree(as.hclust(dend), 
##			  k = 2:4)           1701.629 1767.700 1854.4895 2029.1875  8736.591   100
##      cutree(dend, k = 2:4) 1807.456 1869.887 1963.3960 2125.2155  5579.705   100
##  cutree(dend, k = 2:4, 
##	try_cutree_hclust = FALSE) 8393.914 8570.852 8755.3490 9686.7930 14194.790   100
         
# and trying to "hclust" is not expensive (which is nice...)         
microbenchmark(
  cutree_unbranch_dend = cutree(unbranch_dend, k=2:4),
  cutree_unbranch_dend_not_trying_to_hclust = 
  cutree(unbranch_dend, k=2:4, try_cutree_hclust=FALSE)
)
         
 
## Unit: milliseconds
##                   expr      min       lq   median       uq      max neval
##cutree_unbranch_dend       7.309329 7.428314 7.494107 7.752234 17.59581   100
##cutree_unbranch_dend_not
##_trying_to_hclust        6.945375 7.079198 7.148629 7.577536 16.99780   100
##There were 50 or more warnings (use warnings() to see the first 50)        
                 
# notice that if cutree can't find clusters for the desired k/h, it will produce 0's instead!
# (It will produce a warning though...)
# This is a different behaviout than stats::cutree                        
# For example:
cutree(as.dendrogram(hclust(dist(c(1,1,1,2,2)))),
      k=5)
                                         
}


}
\seealso{
\code{\link{hclust}}, \code{\link[stats]{cutree}}, 
\code{\link{cutree_1h.dendrogram}}, \code{\link{cutree_1k.dendrogram}},
}
\author{
\code{cutree.dendrogram} was written by Tal Galili.
\code{cutree.hclust} is redirecting the function
to \link[stats]{cutree} from base R.
}
