Last data update: 2014.03.03
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R: Run Flexclust Algorithms Repeatedly
stepFlexclust | R Documentation |
Run Flexclust Algorithms Repeatedly
Description
Runs clustering algorithms repeatedly for different numbers of
clusters and returns the minimum within cluster distance solution for
each.
Usage
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)
Arguments
x, ... |
passed to kcca or cclust .
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k |
A vector of integers passed in turn to the k argument
of kcca
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nrep |
For each value of k run kcca
nrep times and keep only the best solution.
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FUN |
Cluster function to use, typically kcca or
cclust .
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verbose |
If TRUE , show progress information during
computations.
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drop |
If TRUE and K is of length 1, then a single
cluster object is returned instead of a "stepFlexclust"
object.
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group |
An optional grouping vector for the data, see
kcca for details.
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simple |
Return an object of class kccasimple ?
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save.data |
Save a copy of x in the return object?
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seed |
If not NULL , a call to set.seed() is made
before any clustering is done.
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multicore |
If TRUE , use mclapply() from package
parallel for parallel processing.
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y |
Not used.
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type |
Create a barplot or lines plot.
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totaldist |
Include value for 1-cluster solution in plot? Default
is TRUE if K contains 2 , else FALSE .
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xlab, ylab |
Graphical parameters.
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object |
Object of class "stepFlexclust" .
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which |
Number of model to get. If character, interpreted as
number of clusters.
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Details
stepcclust is a simple wrapper for
stepFlexclust(...,FUN=cclust) .
Author(s)
Friedrich Leisch
Examples
data("Nclus")
plot(Nclus)
## multicore off for CRAN checks
cl1 = stepFlexclust(Nclus, k=2:7, FUN=cclust, multicore=FALSE)
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)
Results
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