stepFlexclust {flexclust} | R Documentation |
Runs clustering algorithms repeatedly for different numbers of clusters and returns the minimum within cluster distance solution for each.
stepFlexclust(x, k, nrep=3, verbose=TRUE, FUN = kcca, drop=TRUE, group=NULL, simple=FALSE, save.data=FALSE, seed=NULL, multicore=TRUE, ...) stepcclust(...) ## S4 method for signature 'stepFlexclust, missing': plot(x, y, type=c("barplot", "lines"), totaldist=NULL, xlab=NULL, ylab=NULL, ...) ## S4 method for signature 'stepFlexclust': getModel(object, which=1)
x, ... |
passed to kcca or cclust . |
k |
A vector of integers passed in turn to the k argument
of kcca |
nrep |
For each value of k run kcca
nrep times and keep only the best solution. |
FUN |
Cluster function to use, typically kcca or
cclust . |
verbose |
If TRUE , show progress information during
computations. |
drop |
If TRUE and K is of length 1, then a single
cluster object is returned instead of a "stepFlexclust"
object. |
group |
An optional grouping vector for the data, see
kcca for details. |
simple |
Return an object of class kccasimple ? |
save.data |
Save a copy of x in the return object? |
seed |
If not NULL , a call to set.seed() is made
before any clustering is done. |
multicore |
If TRUE , use package multicore for parallel
processing if available. Availability of multicore is checked
when flexclust is loaded and stored in
getOption("flexclust")$have_multicore . Set to FALSE
for debugging and more sensible error messages in case something
goes wrong. |
y |
Not used. |
type |
Create a barplot or lines plot. |
totaldist |
Include value for 1-cluster solution in plot? Default
is TRUE if K contains 2 , else FALSE . |
xlab, ylab |
Graphical parameters. |
object |
Object of class "stepFlexclust" . |
which |
Number of model to get. If character, interpreted as number of clusters. |
stepcclust
is a simple wrapper for
stepFlexclust(...,FUN=cclust)
.
Friedrich Leisch
data("Nclus") plot(Nclus) cl1 = stepFlexclust(Nclus, k=2:7, FUN=cclust) cl1 plot(cl1) # two ways to do the same: getModel(cl1, 4) cl1[[4]] opar=par("mfrow") par(mfrow=c(2,2)) for(k in 3:6){ image(getModel(cl1, as.character(k)), data=Nclus) title(main=paste(k, "clusters")) } par(opar)