clusGap {cluster}  R Documentation 
clusGap()
calculates a goodness of clustering measure, the
“gap” statistic. For each number of clusters k, it
compares log(W(k)) with
E*[log(W(k))] where the latter is defined via
bootstrapping, i.e., simulating from a reference (H_0)
distribution, a uniform distribution on the hypercube determined by
the ranges of x
, after first centering, and then
svd
(aka ‘PCA’)rotating them when (as by
default) spaceH0 = "scaledPCA"
.
maxSE(f, SE.f)
determines the location of the maximum
of f
, taking a “1SE rule” into account for the
*SE*
methods. The default method "firstSEmax"
looks for
the smallest k such that its value f(k) is not more than 1
standard error away from the first local maximum.
This is similar but not the same as "Tibs2001SEmax"
, Tibshirani
et al's recommendation of determining the number of clusters from the
gap statistics and their standard deviations.
clusGap(x, FUNcluster, K.max, B = 100, d.power = 1, spaceH0 = c("scaledPCA", "original"), verbose = interactive(), ...) maxSE(f, SE.f, method = c("firstSEmax", "Tibs2001SEmax", "globalSEmax", "firstmax", "globalmax"), SE.factor = 1) ## S3 method for class 'clusGap' print(x, method = "firstSEmax", SE.factor = 1, ...) ## S3 method for class 'clusGap' plot(x, type = "b", xlab = "k", ylab = expression(Gap[k]), main = NULL, do.arrows = TRUE, arrowArgs = list(col="red3", length=1/16, angle=90, code=3), ...)
x 
numeric matrix or 
FUNcluster 
a 
K.max 
the maximum number of clusters to consider, must be at least two. 
B 
integer, number of Monte Carlo (“bootstrap”) samples. 
d.power 
a positive integer specifying the power p which
is applied to the euclidean distances ( 
spaceH0 
a 
verbose 
integer or logical, determining if “progress” output should be printed. The default prints one bit per bootstrap sample. 
... 
(for 
f 
numeric vector of ‘function values’, of length K, whose (“1 SE respected”) maximum we want. 
SE.f 
numeric vector of length K of standard errors of 
method 
character string indicating how the “optimal” number of clusters, k^, is computed from the gap statistics (and their standard deviations), or more generally how the location k^ of the maximum of f[k] should be determined.
See the examples for a comparison in a simple case. 
SE.factor 
[When 
type, xlab, ylab, main 
arguments with the same meaning as in

do.arrows 
logical indicating if (1 SE )“error bars”
should be drawn, via 
arrowArgs 
a list of arguments passed to 
The main result <res>$Tab[,"gap"]
of course is from
bootstrapping aka Monte Carlo simulation and hence random, or
equivalently, depending on the initial random seed (see
set.seed()
).
On the other hand, in our experience, using B = 500
gives
quite precise results such that the gap plot is basically unchanged
after an another run.
clusGap(..)
returns an object of S3 class "clusGap"
,
basically a list with components
Tab 
a matrix with 
call 
the 
spaceH0 
the 
n 
number of observations, i.e., 
B 
input 
FUNcluster 
input function 
This function is originally based on the functions gap
of
(Bioconductor) package SAGx by Per Broberg,
gapStat()
from former package SLmisc by Matthias Kohl
and ideas from gap()
and its methods of package lga by
Justin Harrington.
The current implementation is by Martin Maechler.
The implementation of spaceH0 = "original"
is based on code
proposed by Juan Gonzalez.
Tibshirani, R., Walther, G. and Hastie, T. (2001). Estimating the number of data clusters via the Gap statistic. Journal of the Royal Statistical Society B, 63, 411–423.
Tibshirani, R., Walther, G. and Hastie, T. (2000). Estimating the number of clusters in a dataset via the Gap statistic. Technical Report. Stanford.
Dudoit, S. and Fridlyand, J. (2002) A predictionbased resampling method for estimating the number of clusters in a dataset. Genome Biology 3(7). doi: 10.1186/gb200237research0036
Per Broberg (2006). SAGx: Statistical Analysis of the GeneChip. R package version 1.9.7. http://www.bioconductor.org/packages/release/bioc/html/SAGx.html
silhouette
for a much simpler less sophisticated
goodness of clustering measure.
cluster.stats()
in package fpc for
alternative measures.
###  maxSE() methods  (mets < eval(formals(maxSE)$method)) fk < c(2,3,5,4,7,8,5,4) sk < c(1,1,2,1,1,3,1,1)/2 ## use plot.clusGap(): plot(structure(class="clusGap", list(Tab = cbind(gap=fk, SE.sim=sk)))) ## Note that 'firstmax' and 'globalmax' are always at 3 and 6 : sapply(c(1/4, 1,2,4), function(SEf) sapply(mets, function(M) maxSE(fk, sk, method = M, SE.factor = SEf))) ###  clusGap()  ## ridiculously nicely separated clusters in 3 D : x < rbind(matrix(rnorm(150, sd = 0.1), ncol = 3), matrix(rnorm(150, mean = 1, sd = 0.1), ncol = 3), matrix(rnorm(150, mean = 2, sd = 0.1), ncol = 3), matrix(rnorm(150, mean = 3, sd = 0.1), ncol = 3)) ## Slightly faster way to use pam (see below) pam1 < function(x,k) list(cluster = pam(x,k, cluster.only=TRUE)) ## We do not recommend using hier.clustering here, but if you want, ## there is factoextra::hcut () or a cheap version of it hclusCut < function(x, k, d.meth = "euclidean", ...) list(cluster = cutree(hclust(dist(x, method=d.meth), ...), k=k)) ## You can manually set it before running this : doExtras < TRUE # or FALSE if(!(exists("doExtras") && is.logical(doExtras))) doExtras < cluster:::doExtras() if(doExtras) { ## Note we use B = 60 in the following examples to keep them "speedy". ##  rather keep the default B = 500 for your analysis! ## note we can pass 'nstart = 20' to kmeans() : gskmn < clusGap(x, FUN = kmeans, nstart = 20, K.max = 8, B = 60) gskmn #> its print() method plot(gskmn, main = "clusGap(., FUN = kmeans, n.start=20, B= 60)") set.seed(12); system.time( gsPam0 < clusGap(x, FUN = pam, K.max = 8, B = 60) ) set.seed(12); system.time( gsPam1 < clusGap(x, FUN = pam1, K.max = 8, B = 60) ) ## and show that it gives the "same": not.eq < c("call", "FUNcluster"); n < names(gsPam0) eq < n[!(n %in% not.eq)] stopifnot(identical(gsPam1[eq], gsPam0[eq])) print(gsPam1, method="globalSEmax") print(gsPam1, method="globalmax") print(gsHc < clusGap(x, FUN = hclusCut, K.max = 8, B = 60)) }# end {doExtras} gs.pam.RU < clusGap(ruspini, FUN = pam1, K.max = 8, B = 60) gs.pam.RU plot(gs.pam.RU, main = "Gap statistic for the 'ruspini' data") mtext("k = 4 is best .. and k = 5 pretty close") ## This takes a minute.. ## No clustering ==> k = 1 ("one cluster") should be optimal: Z < matrix(rnorm(256*3), 256,3) gsP.Z < clusGap(Z, FUN = pam1, K.max = 8, B = 200) plot(gsP.Z, main = "clusGap(<iid_rnorm_p=3>) ==> k = 1 cluster is optimal") gsP.Z