% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bk_method.R
\name{Bk_permutations}
\alias{Bk_permutations}
\title{Bk permutation - Calculating Fowlkes-Mallows Index for two dendrogram}
\usage{
Bk_permutations(tree1, tree2, k, R = 1000,
  warn = dendextend_options("warn"), ...)
}
\arguments{
\item{tree1}{a dendrogram/hclust/phylo object.}

\item{tree2}{a dendrogram/hclust/phylo object.}

\item{k}{an integer scalar or vector with the desired number 
of cluster groups.
If missing - the Bk will be calculated for a default k range of
2:(nleaves-1).
No point in checking k=1/k=n, since both will give Bk=1.}

\item{R}{integer (Default is 1000). The number of Bk permutation to perform for each k.}

\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.
If set to TRUE, extra checks are made to varify that the two clusters have
the same size and the same labels.}

\item{...}{Ignored (passed to FM_index_R/FM_index_profdpm).}
}
\value{
A list (of the length of k's), where each element of the list has
R (number of permutations) calculations of Fowlkes-Mallows index 
between two dendrogram after having their labels shuffled.

The names of the lists' items is the k for which it was calculated.
}
\description{
Bk is the calculation of Fowlkes-Mallows index for a series of k cuts 
for two dendrograms.

Bk permutation calculates the Bk under the null hypothesis of no similarirty
between the two trees by randomally shuffling the labels of the two trees
and calculating their Bk.
}
\details{
From Wikipedia:

Fowlkes-Mallows index (see references) is an external evaluation method 
that is used to determine the similarity between two clusterings
(clusters obtained after a clustering algorithm). This measure of similarity
could be either between two hierarchical clusterings or a clustering and
a benchmark classification. A higher the value for the Fowlkes-Mallows index
indicates a greater similarity between the clusters and the benchmark 
classifications.
}
\examples{

\dontrun{

set.seed(23235)
ss <- TRUE # sample(1:150, 10 )
hc1 <- hclust(dist(iris[ss,-5]), "com")
hc2 <- hclust(dist(iris[ss,-5]), "single")
# tree1 <- as.treerogram(hc1)
# tree2 <- as.treerogram(hc2)
#    cutree(tree1)   

some_Bk <- Bk(hc1, hc2, k = 20)
some_Bk_permu <- Bk_permutations(hc1, hc2, k = 20)

# we can see that the Bk is much higher than the permutation Bks: 
plot(x=rep(1,1000), y= some_Bk_permu[[1]], 
        main = "Bk distribution under H0",
        ylim = c(0,1))
points(1, y= some_Bk, pch = 19, col = 2 )

}
}
\references{
Fowlkes, E. B.; Mallows, C. L. (1 September 1983).
"A Method for Comparing Two Hierarchical Clusterings".
Journal of the American Statistical Association 78 (383): 553.

\url{http://en.wikipedia.org/wiki/Fowlkes-Mallows_index}
}
\seealso{
\code{\link{FM_index}}, \link{Bk}
}
