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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bk_method.R
\name{Bk_plot}
\alias{Bk_plot}
\title{Bk plot - ploting the Fowlkes-Mallows Index of two dendrogram for various k's}
\usage{
Bk_plot(tree1, tree2, k, add_E = TRUE, rejection_line_asymptotic = TRUE,
rejection_line_permutation = FALSE, R = 1000, k_permutation,
conf.level = 0.95, p.adjust.methods = c("none", "bonferroni"),
col_line_Bk = 1, col_line_asymptotic = 2, col_line_permutation = 4,
warn = dendextend_options("warn"), main = "Bk plot",
xlab = "k (number of clusters)", ylab = "Bk (Fowlkes-Mallows Index)",
xlim, ylim = c(0, 1), try_cutree_hclust = TRUE, ...)
}
\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{add_E}{logical (TRUE). Should we add a line of the Expected Bk value
for each k, under the null hypothesis of no relation between the clusterings?}
\item{rejection_line_asymptotic}{logical (TRUE). Should we add a line of
the one sided rejection region based on the asymptotic distribution
of Bk values, for each k, under the null hypothesis of no relation
between the clusterings?}
\item{rejection_line_permutation}{logical (FALSE). Should we add a line of
the one sided rejection region based on the asymptotic distribution
of Bk values, for each k, under the null hypothesis of no relation
between the clusterings?}
\item{R}{integer (Default is 1000). The number of Bk permutation to perform
for each k. Applicable only if rejection_line_permutation is TRUE.}
\item{k_permutation}{the k's to be used for permutation (sometimes we might
be only interested in some k's and it is not important to run the simulation
for all possible ks). If missing - k itself will be used.}
\item{conf.level}{the level of one sided confidence interval used for creation
of the rejection lines.}
\item{p.adjust.methods}{a character scalar of either "none" (default), or
"bonferroni". This controls the multiple correction method to use for the
critical rejection values. Currently only the Bonferroni method
is implemented (based on the number of different k values).}
\item{col_line_Bk}{the color of the Bk line.}
\item{col_line_asymptotic}{the color of the rejection asymptotic Bk line.}
\item{col_line_permutation}{the color of the rejection asymptotic Bk line.}
\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{main}{passed to \link{plot}.}
\item{xlab}{passed to \link{plot}.}
\item{ylab}{passed to \link{plot}.}
\item{xlim}{passed to \link{plot}. If missign, xlim is from 2 to nleaves-1}
\item{ylim}{passed to \link{plot}.}
\item{try_cutree_hclust}{logical (TRUE). Since cutree for hclust is MUCH
faster than for dendrogram - Bk_plot 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 functions.
If try_cutree_hclust=FALSE, it will force to use cutree.dendrogram and
not cutree.hclust.}
\item{...}{Ignored.}
}
\value{
After plotting the Bk plot.
Returns (invisible) the output of the elements used for constructing the plot:
The Bk values, Bk permutations (if used), Bk theoratical values, etc.
}
\description{
Bk is the calculation of Fowlkes-Mallows index for a series of k cuts
for two dendrograms.
A Bk plot is simply a scatter plot of Bk versus k.
This plot helps in identifiying the similarity between two dendrograms in
different levels of k (number of clusters).
}
\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.
The default Bk plot comes with a line with dots (type "b") of the Bk values.
Also with a fragmented (lty=2) line (of the same color) of the expected Bk
line under H0,
And a solid red line of the upper critical Bk values for rejection
}
\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)
Bk_plot(hc1, hc2, k = 2:20, xlim = c(2,149))
Bk_plot(hc1, hc2)
Bk_plot(hc1, hc2, k = 3)
Bk_plot(hc1, hc2, k = 3:10)
Bk_plot(hc1, hc2)
Bk_plot(hc1, hc2,p.adjust.methods="bonferroni") # higher rejection lines
# this one can take a bit of time:
Bk_plot(hc1, hc2, rejection_line_permutation=TRUE,
k_permutation = c(2,4,6,8,10,20,30,40,50), R= 100)
# we can see that the permutation line is VERY close to the asymptotic line.
# This is great since it means one can often use the asymptotic results
# Without having to do many simulations.
# works just as well for dendrograms:
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)
Bk_plot(dend1, dend2, k = 2:3, try_cutree_hclust = FALSE) # slower than hclust, but works...
Bk_plot(hc1, dend2, k = 2:3, try_cutree_hclust = FALSE) # slower than hclust, but works...
Bk_plot(dend1, dend1, k = 2:3, try_cutree_hclust = TRUE) # slower than hclust, but works...
Bk_plot(hc1, hc1, k = 2:3) # slower than hclust, but works...
# for some reason it can't turn dend2 back to hclust :(
a= Bk_plot(hc1, hc2, k = 2:3, try_cutree_hclust = TRUE) # slower than hclust, but works...
hc1_mixed <- as.hclust(sample(as.dendrogram(hc1)))
Bk_plot(tree1=hc1, tree2=hc1_mixed,
add_E=FALSE,
rejection_line_permutation=TRUE, k_permutation = c(2,4,6,8,10,20,30,40,50), R= 100)
}
}
\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}, \link{Bk_permutations}
}
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