| 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, ...)
## 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)
... |
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? |
x |
Object of class "stepFlexclust". |
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. |
Friedrich Leisch
data("Nclus")
plot(Nclus)
cl1 = stepFlexclust(Nclus, k=2:7, FUN=cclust)
cl1
plot(cl1)
getModel(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)