R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("flexclust-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('flexclust')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "cclust"
> 
> ### * cclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cclust
> ### Title: Convex Clustering
> ### Aliases: cclust
> ### Keywords: cluster
> 
> ### ** Examples
> 
> ## a 2-dimensional example
> x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2),
+          matrix(rnorm(100,mean=1,sd=0.3),ncol=2))
> cl<-cclust(x,2)
> plot(x, col=predict(cl))
> points(cl@centers, pch="x", cex=2, col=3) 
> 
> ## a 3-dimensional example 
> x<-rbind(matrix(rnorm(150,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=2,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=4,sd=0.3),ncol=3))
> cl<-cclust(x, 6, method="neuralgas")
> pairs(x, col=predict(cl))
> plot(cl, data=x)
Loading required package: ellipse
> 
> 
> 
> cleanEx(); ..nameEx <- "flexclustControl-class"
> 
> ### * flexclustControl-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: flexclustControl-class
> ### Title: Classes "flexclustControl" and "cclustControl"
> ### Aliases: flexclustControl-class coerce,list,flexclustControl-method
> ###   coerce,NULL,flexclustControl-method cclustControl-class
> ###   coerce,list,cclustControl-method coerce,NULL,cclustControl-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> ## have a look at the defaults
> new("flexclustControl")
An object of class "flexclustControl"
Slot "iter.max":
[1] 200

Slot "tolerance":
[1] 1e-06

Slot "verbose":
[1] 0

Slot "classify":
[1] "auto"

Slot "gamma":
[1] 1

Slot "simann":
[1]  0.30  0.95 10.00

Slot "ntry":
[1] 5

Slot "min.size":
[1] 2

> 
> ## corce a list
> mycont = list(iter=500, tol=0.001, class="w")
> as(mycont, "flexclustControl")
An object of class "flexclustControl"
Slot "iter.max":
[1] 500

Slot "tolerance":
[1] 0.001

Slot "verbose":
[1] 0

Slot "classify":
[1] "weighted"

Slot "gamma":
[1] 1

Slot "simann":
[1]  0.30  0.95 10.00

Slot "ntry":
[1] 5

Slot "min.size":
[1] 2

> 
> ## some additional slots
> as(mycont, "cclustControl")
An object of class "cclustControl"
Slot "pol.rate":
[1] 1 0

Slot "exp.rate":
[1] 1e-01 1e-04

Slot "ng.rate":
[1] 5e-01 5e-03 1e+01 1e-02

Slot "method":
[1] "polynomial"

Slot "iter.max":
[1] 500

Slot "tolerance":
[1] 0.001

Slot "verbose":
[1] 0

Slot "classify":
[1] "w"

Slot "gamma":
[1] 1

Slot "simann":
[1]  0.30  0.95 10.00

Slot "ntry":
[1] 5

Slot "min.size":
[1] 2

> 
> 
> 
> cleanEx(); ..nameEx <- "kcca"
> 
> ### * kcca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kcca
> ### Title: K-Centroids Cluster Analysis
> ### Aliases: kcca kccaFamily flexclust-class kcca-class kccaFamily-class
> ###   show,kcca-method
> ### Keywords: cluster
> 
> ### ** Examples
> 
> data("Nclus", package="flexmix")
> plot(Nclus)
> 
> ## try kmeans 
> cl1 = kcca(Nclus, k=4)
> cl1
kcca object of family 'kmeans' 

call:
kcca(x = Nclus, k = 4)

cluster sizes:

  1   2   3   4 
 99  64  88 299 

convergence after 10 iterations
sum of within cluster distances : 1117.346 
> 
> image(cl1)
> points(Nclus)
> 
> ## A barplot of the centroids 
> barplot(cl1)
> 
> ## now use k-medians, cluster centroids should be similar ...
> cl2 = kcca(Nclus, k=4, family=kccaFamily("kmedians"))
> cl2
kcca object of family 'kmedians' 

call:
kcca(x = Nclus, k = 4, family = kccaFamily("kmedians"))

cluster sizes:

  1   2   3   4 
200 105 147  98 

convergence after 8 iterations
sum of within cluster distances : 963.1808 
> 
> ## ... but the boundaries of the partitions have a different shape
> image(cl2)
> points(Nclus)
> 
> 
> 
> cleanEx(); ..nameEx <- "qtclust"
> 
> ### * qtclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qtclust
> ### Title: QT Clustering
> ### Aliases: qtclust
> ### Keywords: cluster
> 
> ### ** Examples
> 
> x <- matrix(10*runif(1000), ncol=2)
> 
> ## maximum distrance of point to cluster center is 3
> cl1 <- qtclust(x, radius=3)
> 
> ## maximum distrance of point to cluster center is 1
> ## -> more clusters, longer runtime
> cl2 <- qtclust(x, radius=1)
> 
> opar <- par(c("mfrow","mar"))
> par(mfrow=c(2,1), mar=c(2.1,2.1,1,1))
> plot(x, col=predict(cl1), xlab="", ylab="")
> plot(x, col=predict(cl2), xlab="", ylab="")
> par(opar)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "stepFlexclust"
> 
> ### * stepFlexclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stepFlexclust
> ### Title: Run FlexClust Algorithms Repeatedly
> ### Aliases: stepFlexclust stepFlexclust-class show,stepFlexclust-method
> ###   plot,stepFlexclust,missing-method getModel
> ###   getModel,stepFlexclust-method
> ### Keywords: cluster
> 
> ### ** Examples
> 
> data("Nclus", package="flexmix")
> plot(Nclus)
> 
> cl1 = stepFlexclust(Nclus, K=2:7, FUN=cclust)
2 : * * *
3 : * * *
4 : * * *
5 : * * *
6 : * * *
7 : * * *
> cl1
stepFlexclust object of family 'kmeans' 

call:
stepFlexclust(Nclus, K = 2:7, FUN = cclust)

  iter converged   distsum
2    5      TRUE 1681.2730
3    6      TRUE 1220.3927
4    6      TRUE  742.5601
5    8      TRUE  685.9157
6   11      TRUE  649.6834
7   21      TRUE  564.5816
> 
> plot(cl1)
> 
> getModel(cl1, 4)
kcca object of family 'kmeans' 

call:
stepFlexclust(Nclus, K = 5, FUN = cclust)

cluster sizes:

  1   2   3   4   5 
 63  98  86 198 105 

convergence after 8 iterations
sum of within cluster distances : 685.9157 
> 
> 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"))
+ }
Loading required package: ellipse
> par(opar)
NULL
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  14.76 0.31 15.14 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')