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("fpc-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('fpc')
Loading required package: MASS
Loading required package: cluster
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "adcoord"
> 
> ### * adcoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: adcoord
> ### Title: Asymmetric discriminant coordinates
> ### Aliases: adcoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0)
>   grface <- as.integer(attr(face,"grouping"))
>   adcf <- adcoord(face,grface==2)
>   adcf2 <- adcoord(face,grface==4)
>   plot(adcf$proj,col=1+(grface==2))
>   plot(adcf2$proj,col=1+(grface==4))
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "ancoord"
> 
> ### * ancoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ancoord
> ### Title: Asymmetric neighborhood based discriminant coordinates
> ### Aliases: ancoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0)
>   grface <- as.integer(attr(face,"grouping"))
>   ancf2 <- ancoord(face,grface==4)
>   plot(ancf2$proj,col=1+(grface==4))
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "awcoord"
> 
> ### * awcoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: awcoord
> ### Title: Asymmetric weighted discriminant coordinates
> ### Aliases: awcoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0)
>   grface <- as.integer(attr(face,"grouping"))
>   awcf <- awcoord(face,grface==1)
>   # awcf2 <- ancoord(face,grface==1, method="mcd")
>   plot(awcf$proj,col=1+(grface==1))
>   # plot(awcf2$proj,col=1+(grface==1))
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "batcoord"
> 
> ### * batcoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: batcoord
> ### Title: Bhattacharyya discriminant projection
> ### Aliases: batcoord batvarcoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
> set.seed(4634)
> face <- rFace(600,dMoNo=2,dNoEy=0)
> grface <- as.integer(attr(face,"grouping"))
> bcf2 <- batcoord(face,grface==2)
> plot(bcf2$proj,col=1+(grface==2))
> bcfv2 <- batcoord(face,grface==2,dom="variance")
> plot(bcfv2$proj,col=1+(grface==2))
> bcfvv2 <- batvarcoord(face,grface==2)
> plot(bcfvv2$proj,col=1+(grface==2))
> 
> 
> 
> cleanEx(); ..nameEx <- "c.weight"
> 
> ### * c.weight
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: c.weight
> ### Title: Weight function for AWC
> ### Aliases: c.weight
> ### Keywords: arith
> 
> ### ** Examples
> 
>   c.weight(4,1)
[1] 0.25
> 
> 
> 
> cleanEx(); ..nameEx <- "can"
> 
> ### * can
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: can
> ### Title: Generation of the tuning constant for regression fixed point
> ###   clusters
> ### Aliases: can
> ### Keywords: arith
> 
> ### ** Examples
> 
>   can(429,3)
[1] 8.806634
> 
> 
> 
> cleanEx(); ..nameEx <- "clusexpect"
> 
> ### * clusexpect
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: clusexpect
> ### Title: Expected value of the number of times a fixed point cluster is
> ###   found
> ### Aliases: clusexpect
> ### Keywords: univar cluster
> 
> ### ** Examples
> 
>   clusexpect(500,4,150,2000)
[1] 1.357889
> 
> 
> 
> cleanEx(); ..nameEx <- "cluster.stats"
> 
> ### * cluster.stats
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cluster.stats
> ### Title: Cluster validation statistics
> ### Aliases: cluster.stats
> ### Keywords: cluster multivariate
> 
> ### ** Examples
>   
>   set.seed(20000)
>   face <- rFace(200,dMoNo=2,dNoEy=0,p=2)
>   dface <- dist(face)
>   complete3 <- cutree(hclust(dface),3)
>   cluster.stats(dface,complete3,
+                 alt.clustering=as.integer(attr(face,"grouping")))
Warning in as.dist.default(separation) : non-square matrix
$n
[1] 200

$cluster.number
[1] 3

$cluster.size
[1] 136  60   4

$diameter
[1] 10.795191  5.756704  9.000000

$average.distance
[1] 3.032856 2.205095 7.047772

$median.distance
[1] 2.842125 1.478951 8.321658

$separation
[1] 5.874239 5.874239 7.222125

$average.toother
[1] 13.77209 13.02913 20.75148

$separation.matrix
          [,1]     [,2]      [,3]
[1,]  0.000000 5.874239 14.975187
[2,]  5.874239 0.000000  7.222125
[3,] 14.975187 7.222125  0.000000

$average.between
[1] 13.72910

$average.within
[1] 2.901325

$n.between
[1] 8944

$n.within
[1] 10956

$clus.avg.silwidths
        1         2         3 
0.7522817 0.8179603 0.3548236 

$avg.silwidth
[1] 0.7640361

$g2
NULL

$g3
NULL

$hubertgamma
[1] 0.8834354

$dunn
[1] 0.5441534

$wb.ratio
[1] 0.2113267

$corrected.rand
[1] 0.345064

>   
> 
> 
> 
> cleanEx(); ..nameEx <- "cmahal"
> 
> ### * cmahal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cmahal
> ### Title: Generation of tuning constant for Mahalanobis fixed point
> ###   clusters.
> ### Aliases: cmahal
> ### Keywords: cluster
> 
> ### ** Examples
> 
>   plot(1:100,cmahal(100,3,nmin=5,cmin=qchisq(0.99,3),nc1=90),
+        xlab="FPC size", ylab="cmahal")
> 
> 
> 
> cleanEx(); ..nameEx <- "concomp"
> 
> ### * concomp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: con.comp
> ### Title: Connectivity components of an undirected graph
> ### Aliases: con.comp
> ### Keywords: array cluster
> 
> ### ** Examples
> 
>   set.seed(1000)
>   x <- rnorm(20)
>   m <- matrix(0,nrow=20,ncol=20)
>   for(i in 1:20)
+     for(j in 1:20)
+       m[i,j] <- abs(x[i]-x[j])
>   d <- m<0.2
>   cc <- con.comp(d)
>   max(cc) # number of connectivity components
[1] 6
>   plot(x,cc)
>   # The same should be produced by
>   # cutree(hclust(as.dist(m),method="single"),h=0.2).
> 
> 
> 
> cleanEx(); ..nameEx <- "cov.wml"
> 
> ### * cov.wml
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cov.wml
> ### Title: Weighted Covariance Matrices (Maximum Likelihood)
> ### Aliases: cov.wml
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>   x <- c(1,2,3,4,5,6,7,8,9,10)
>   y <- c(1,2,3,8,7,6,5,8,9,10)
>   cov.wml(cbind(x,y),wt=c(0,0,0,1,1,1,1,1,0,0))
$cov
     x     y
x  2.0 -0.40
y -0.4  1.36

$center
  x   y 
6.0 6.8 

$n.obs
[1] 10

$wt
 [1] 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.0 0.0

>   cov.wt(cbind(x,y),wt=c(0,0,0,1,1,1,1,1,0,0))
$cov
     x    y
x  2.5 -0.5
y -0.5  1.7

$center
  x   y 
6.0 6.8 

$n.obs
[1] 10

$wt
 [1] 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.0 0.0

> 
> 
> 
> cleanEx(); ..nameEx <- "discrcoord"
> 
> ### * discrcoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: discrcoord
> ### Title: Discriminant coordinates/canonical variates
> ### Aliases: discrcoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0)
>   grface <- as.integer(attr(face,"grouping"))
>   dcf <- discrcoord(face,grface)
>   plot(dcf$proj,col=grface)
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "discrproj"
> 
> ### * discrproj
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: discrproj
> ### Title: Linear dimension reduction for classification
> ### Aliases: discrproj
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
> set.seed(4634)
> face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
> grface <- as.integer(attr(face,"grouping"))
> discrproj(face,grface, method="nc")$units
            [,1]       [,2]        [,3]
[1,]  0.84359426 1.14608710 -0.02209846
[2,] -0.33649001 0.27903245 -0.01187219
[3,] -0.06808816 0.03965075  1.00303099
> discrproj(face,grface, method="wnc")$units
            [,1]        [,2]         [,3]
[1,] -0.07433352 1.420772600  0.039179197
[2,] -0.43725296 0.002745807 -0.005281343
[3,] -0.03831248 0.001676168 -1.005389819
> discrproj(face,grface==1, method="arc")$units
           [,1]        [,2]        [,3]
[1,] -1.2952782  0.50007753 -0.33960121
[2,]  0.6533164  0.05164296 -0.08010673
[3,] -0.0805180 -0.66005066 -0.46509088
> 
> 
> 
> cleanEx(); ..nameEx <- "fixmahal"
> 
> ### * fixmahal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fixmahal
> ### Title: Mahalanobis Fixed Point Clusters
> ### Aliases: fixmahal summary.mfpc plot.mfpc fpclusters.mfpc
> ###   print.summary.mfpc print.mfpc fpmi
> ### Keywords: cluster multivariate robust
> 
> ### ** Examples
> 
>   set.seed(20000)
>   face <- rFace(400,dMoNo=2,dNoEy=0, p=3)
>   # The first example uses grouping information via init.group.
>   initg <- list()
>   grface <- as.integer(attr(face,"grouping"))
>   for (i in 1:5) initg[[i]] <- (grface==i)
>   ff0 <- fixmahal(face, pointit=FALSE, init.group=initg)
>   summary(ff0)
  *  Mahalanobis Fixed Point Clusters  *

Often a clear cluster in the data leads to several similar FPCs.
The summary shows the representative FPCs of groups of similar FPCs.

Method  fuzzy  was used.
Number of representative FPCs:  5 

FPCs with less than  16  points were skipped.
0  iteration runs led to  0  skipped clusters.
  Weight 1 for r^2<=  6.251389  weight 0 for r^2>  11.34487  
  Constant ca=  6.251389  corresponding to alpha=  0.9 

 FPC  1 
  Times found (group members):  1 
  Mean:
[1] -2.059856 17.120851  1.144176
  Covariance matrix:
              [,1]          [,2]        [,3]
[1,]  0.1380273772 -0.0005850577 -0.02221587
[2,] -0.0005850577  0.1493604214  0.05120692
[3,] -0.0222158688  0.0512069203  0.99970636
  Number of points (sum of weights):  38.26940 

 FPC  2 
  Times found (group members):  1 
  Mean:
[1]  1.990335 16.990115  1.168523
  Covariance matrix:
            [,1]        [,2]       [,3]
[1,] 0.155013896 0.002647451 0.04614691
[2,] 0.002647451 0.102943657 0.04792916
[3,] 0.046146913 0.047929158 1.19384338
  Number of points (sum of weights):  73.87413 

 FPC  3 
  Times found (group members):  1 
  Mean:
[1] -0.01706032  3.08725211  0.47432734
  Covariance matrix:
            [,1]        [,2]        [,3]
[1,] 0.188194114 0.001181821 0.009039074
[2,] 0.001181821 0.042873492 0.004792551
[3,] 0.009039074 0.004792551 0.124434523
  Number of points (sum of weights):  88.37003 

 FPC  4 
  Times found (group members):  2 
  Mean:
[1] 0.01031798 3.84049017 0.57963428
  Covariance matrix:
           [,1]       [,2]       [,3]
[1,] 0.17635831 0.01533119 0.02026135
[2,] 0.01533119 4.36327556 0.11767498
[3,] 0.02026135 0.11767498 0.22623388
  Number of points (sum of weights):  183.4207 

 FPC  5 
  Times found (group members):  1 
  Mean:
[1] 0.4248803 6.8480685 0.5883182
  Covariance matrix:
          [,1]        [,2]       [,3]
[1,] 0.8496890  4.53978778 0.01344090
[2,] 4.5397878 33.51962628 0.07652022
[3,] 0.0134409  0.07652022 0.23095281
  Number of points (sum of weights):  245.1377 

Number of points (rounded weights) in intersection of representative FPCs
     [,1] [,2] [,3] [,4] [,5]
[1,]   38    0    0    0    0
[2,]    0   74    0    0   54
[3,]    0    0   88   88   88
[4,]    0    0   88  183  182
[5,]    0   54   88  182  245
>   cff0 <- fpclusters(ff0)
>   plot(face, col=1+cff0[[1]])
>   plot(face, col=1+cff0[[4]]) # Why does this come out as a cluster? 
>   plot(ff0, face, 4) # A bit clearer...
>   # Without grouping information, examples need more time:
>   # ff1 <- fixmahal(face)
>   # summary(ff1)
>   # cff1 <- fpclusters(ff1)
>   # plot(face, col=1+cff1[[1]])
>   # plot(face, col=1+cff1[[6]]) # Why does this come out as a cluster? 
>   # plot(ff1, face, 6) # A bit clearer...
>   # ff2 <- fixmahal(face,method="ml")
>   # summary(ff2)
>   # ff3 <- fixmahal(face,method="ml",calpha=0.95,subset=50)
>   # summary(ff3)
>   ## ...fast, but lots of clusters. mer=0.3 may be useful here.
>   # set.seed(3000)
>   # face2 <- rFace(400,dMoNo=2,dNoEy=0)
>   # ff5 <- fixmahal(face2)
>   # summary(ff5)
>   ## misses right eye of face data; with p=6,
>   ## initial configurations are too large for 40 point clusters 
>   # ff6 <- fixmahal(face2, startn=30)
>   # summary(ff6)
>   # cff6 <- fpclusters(ff6)
>   # plot(face2, col=1+cff6[[3]])
>   # plot(ff6, face2, 3)
>   # x <- c(1,2,3,6,6,7,8,120)
>   # ff8 <- fixmahal(x)
>   # summary(ff8)
>   # ...dataset a bit too small for the defaults...
>   # ff9 <- fixmahal(x, mnc=3, startn=3)
>   # summary(ff9)
> 
> 
> 
> cleanEx(); ..nameEx <- "fixreg"
> 
> ### * fixreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fixreg
> ### Title: Linear Regression Fixed Point Clusters
> ### Aliases: fixreg summary.rfpc plot.rfpc fpclusters.rfpc
> ###   print.summary.rfpc print.rfpc rfpi
> ### Keywords: cluster robust regression
> 
> ### ** Examples
> 
> set.seed(190000)
> data(tonedata)
> # Note: If you do not use the installed package, replace this by
> # tonedata <- read.table("(path/)tonedata.txt", header=TRUE)
> attach(tonedata)
> tonefix <- fixreg(stretchratio,tuned,mtf=1,ir=20)
> summary(tonefix)
  *  Fixed Point Clusters  *

Often a clear cluster in the data leads to several similar FPCs.
The summary shows the representative FPCs of groups of similar FPCs,
which were found at least  1  times.

Constant ca=  10.07010 
Number of representative FPCs:  3 

FPCs with less than  50  points were skipped.
0  iterations led to skipped FPCs.

 FPC  1 
  Times found (group members):  17 
  Ratio to estimated expectation:  1.587200 
  Regression parameters:
 Intercept          X 
1.90507095 0.04772128 
  Error variance:  0.002815126 
  Number of points:  122 

 FPC  2 
  Times found (group members):  3 
  Ratio to estimated expectation:  2.082420 
  Regression parameters:
  Intercept           X 
0.003507292 0.998653704 
  Error variance:  3.688176e-05 
  Number of points:  63 

 FPC  3 
  Times found (group members):  1 
  Ratio to estimated expectation:  0.602382 
  Regression parameters:
Intercept         X 
0.7941830 0.6018805 
  Error variance:  0.001118718 
  Number of points:  66 

Number of points in intersection of  representative FPCs
     [,1] [,2] [,3]
[1,]  122   46   61
[2,]   46   63   47
[3,]   61   47   66
> # This is designed to have a fast example; default setting would be better.
> # If you want to see more (and you have a bit more time),
> # try out the following:
> # set.seed(1000)
> # tonefix <- fixreg(stretchratio,tuned)
> ## Default - good for these data
> # summary(tonefix)
> # plot(tonefix,stretchratio,tuned,1)
> # plot(tonefix,stretchratio,tuned,2)
> # plot(tonefix,stretchratio,tuned,3,bw=FALSE,pch=5) 
> # toneclus <- fpclusters(tonefix,stretchratio,tuned)
> # plot(stretchratio,tuned,col=1+toneclus[[2]])
> # tonefix2 <- fixreg(stretchratio,tuned,distcut=1,mtf=1,countmode=50)
> ## Every found fixed point cluster is reported,
> ## no matter how instable it may be.
> # summary(tonefix2)
> # tonefix3 <- fixreg(stretchratio,tuned,ca=7)
> ## ca defaults to 10.07 for these data.
> # summary(tonefix3)
> # subset <- c(rep(FALSE,5),rep(TRUE,24),rep(FALSE,121))
> # tonefix4 <- fixreg(stretchratio,tuned,
> #                    mtf=1,ir=0,init.group=list(subset))
> # summary(tonefix4)
> 
> 
> 
> cleanEx(); ..nameEx <- "itnumber"
> 
> ### * itnumber
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: itnumber
> ### Title: Number of regression fixed point cluster iterations
> ### Aliases: itnumber
> ### Keywords: univar cluster
> 
> ### ** Examples
> 
>   itnumber(500,4,150,2)
[1] 6985
> 
> 
> 
> cleanEx(); ..nameEx <- "mahalanodisc"
> 
> ### * mahalanodisc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mahalanodisc
> ### Title: Mahalanobis for AWC
> ### Aliases: mahalanodisc
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>   x <- cbind(rnorm(50),rnorm(50))
>   mahalanodisc(x,c(0,0),cov(x))
 [1] 0.71349606 0.43761908 1.10821206 4.87324972 2.39439438 5.00225901
 [7] 0.47073953 1.87819557 0.85857646 0.15860222 9.81873721 0.22036263
[13] 1.02517555 7.10170674 2.34179377 0.04013201 3.47942431 3.71699879
[19] 1.01446502 5.67306063 1.50739543 1.37019793 0.41046506 6.84584566
[25] 2.15804831 0.09360798 0.25155881 3.13406307 0.33370927 0.59996983
[31] 2.94471138 0.03615695 1.74305241 2.48897888 3.04452624 0.35439149
[37] 1.39043569 0.10557988 1.93895136 0.93985318 0.36194844 1.61998071
[43] 2.21921981 1.01003313 3.26790679 1.01966858 1.88625521 1.16363811
[49] 1.63183882 1.32346790
>   mahalanodisc(x,c(0,0),matrix(0,ncol=2,nrow=2))
 [1]  5509326690  4083011766  8146378218 38203818405 21621323013 45951521273
 [7]  3724386939 16353404881  6561046188  1115018130 80532501689  1535175268
[13]  8616802470 49056797112 18179246336   376615616 32581377766 30386778449
[19]  6978908370 50729602495 10706287216 11157611277  3785466156 48300585553
[25] 19557806599   880913436  2207799248 21631137965  2341541077  5222100937
[31] 21693942514   288385749 15381782499 23241507778 22490650969  2830764373
[37] 12856458316   960459384 13469697387  6537789871  3213959978 15231367777
[43] 18322921772  8001730717 29924248192  8124564847 17626076738  9192760912
[49] 15122977280 10004589882
> 
> 
> 
> cleanEx(); ..nameEx <- "mahalanofix"
> 
> ### * mahalanofix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mahalanofix
> ### Title: Mahalanobis distances from center of indexed points
> ### Aliases: mahalanofix mahalanofuz
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>   x <- c(1,2,3,4,5,6,7,8,9,10)
>   y <- c(1,2,3,8,7,6,5,8,9,10)
>   mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,1,0,0))
>   mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,0,0,0))
>   mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,1,0,0),method="mcd")
>   mahalanofuz(cbind(x,y),gv=c(0,0,0.5,0.5,1,1,1,0.5,0.5,0))
> 
> 
> 
> cleanEx(); ..nameEx <- "mahalconf"
> 
> ### * mahalconf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mahalconf
> ### Title: Mahalanobis fixed point clusters initial configuration
> ### Aliases: mahalconf
> ### Keywords: multivariate cluster
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0,p=2)
>   mahalconf(face,no=200,startn=20,covall=cov(face),plot="start")
  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
[193]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
[265] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[277]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "minsize"
> 
> ### * minsize
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: minsize
> ### Title: Minimum size of regression fixed point cluster
> ### Aliases: minsize
> ### Keywords: univar cluster
> 
> ### ** Examples
> 
>   minsize(500,4,7000,2)
[1] 127
> 
> 
> 
> cleanEx(); ..nameEx <- "mvdcoord"
> 
> ### * mvdcoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mvdcoord
> ### Title: Mean/variance differences discriminant coordinates
> ### Aliases: mvdcoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
>   grface <- as.integer(attr(face,"grouping"))
>   mcf <- mvdcoord(face,grface)
>   plot(mcf$proj,col=grface)
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "ncoord"
> 
> ### * ncoord
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ncoord
> ### Title: Neighborhood based discriminant coordinates
> ### Aliases: ncoord
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(200,dMoNo=2,dNoEy=0,p=3)
>   grface <- as.integer(attr(face,"grouping"))
>   ncf <- ncoord(face,grface)
>   plot(ncf$proj,col=grface)
>   ncf2 <- ncoord(face,grface,weighted=TRUE)
>   plot(ncf2$proj,col=grface)
>   # ...done in one step by function plotcluster.
> 
> 
> 
> cleanEx(); ..nameEx <- "plotcluster"
> 
> ### * plotcluster
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotcluster
> ### Title: Discriminant projection plot.
> ### Aliases: plotcluster
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
> set.seed(4634)
> face <- rFace(600,dMoNo=2,dNoEy=0)
> grface <- as.integer(attr(face,"grouping"))
> plotcluster(face,grface)
> plotcluster(face,grface==1)
> 
> 
> 
> cleanEx(); ..nameEx <- "rFace"
> 
> ### * rFace
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rFace
> ### Title: "Face-shaped" clustered benchmark datasets
> ### Aliases: rFace
> ### Keywords: data
> 
> ### ** Examples
> 
>   set.seed(4634)
>   face <- rFace(600,dMoNo=2,dNoEy=0)
>   grface <- as.integer(attr(face,"grouping"))
>   plot(face, col = grface)
>   pairs(face, col = grface, main ="rFace(600,dMoNo=2,dNoEy=0)")
> 
> 
> 
> cleanEx(); ..nameEx <- "randcmatrix"
> 
> ### * randcmatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randcmatrix
> ### Title: Random partition matrix
> ### Aliases: randcmatrix
> ### Keywords: cluster
> 
> ### ** Examples
> 
> set.seed(111)
> randcmatrix(10,2,1)
      [,1] [,2]
 [1,]    0    1
 [2,]    0    1
 [3,]    1    0
 [4,]    0    1
 [5,]    1    0
 [6,]    1    0
 [7,]    1    0
 [8,]    0    1
 [9,]    1    0
[10,]    1    0
> 
> 
> 
> cleanEx(); ..nameEx <- "randconf"
> 
> ### * randconf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: randconf
> ### Title: Generate a sample indicator vector
> ### Aliases: randconf
> ### Keywords: distribution
> 
> ### ** Examples
> 
>   randconf(10,3)
 [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "regmix"
> 
> ### * regmix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: regmix
> ### Title: Mixture Model ML for Clusterwise Linear Regression
> ### Aliases: regmix regem
> ### Keywords: cluster regression
> 
> ### ** Examples
> 
> set.seed(12234)
> data(tonedata)
> # Note: If you do not use the installed package, replace this by
> # tonedata <- read.table("(path/)tonedata.txt", header=TRUE)
> attach(tonedata)
> rmt1 <- regmix(stretchratio,tuned,nclust=1:2)
Iteration  1  for  1  clusters.
Iteration  1  for  2  clusters.
> # nclust=1:2 makes the example fast;
> # a more serious application would rather use the default.
> rmt1$g
  [1] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2
 [75] 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2
[149] 2 1
> rmt1$bic
[1]   3.732369 225.809992
> # start with initial parameter values
> cln <- 3
> n <- 150
> initcoef <- cbind(c(2,0),c(0,1),c(0,2.5))
> initvar <- c(0.001,0.0001,0.5)
> initeps <- c(0.4,0.3,0.3)
> # computation of m from initial parameters
> m <- matrix(nrow=n, ncol=cln)
> stm <- numeric(0)
> for (i in 1:cln)
+   for (j in 1:n){
+     m[j,i] <- initeps[i]*dnorm(tuned[j],mean=initcoef[1,i]+
+               initcoef[2,i]*stretchratio[j], sd=sqrt(initvar[i]))
+   }
>   for (j in 1:n){
+     stm[j] <- sum(m[j,])
+     for (i in 1:cln)
+       m[j,i] <- m[j,i]/stm[j]
+   } 
> rmt2 <- regem(stretchratio, tuned, m, cln)
> rmt2bic <- 2*rmt2$loglik - log(150)*(4*cln-1)
> rmt2bic
[1] 422.4743
> 
> 
> 
> cleanEx(); ..nameEx <- "simmatrix"
> 
> ### * simmatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simmatrix
> ### Title: Extracting intersections between clusters from fpc-object
> ### Aliases: simmatrix
> ### Keywords: utilities
> 
> ### ** Examples
> 
> set.seed(190000)
> data(tonedata)
> # Note: If you do not use the installed package, replace this by
> # tonedata <- read.table("(path/)tonedata.txt", header=TRUE)
> attach(tonedata)
> tonefix <- fixreg(stretchratio,tuned,mtf=1,ir=20)
> simmatrix(tonefix)[sseg(2,3)]
[1] 47
> 
> 
> 
> cleanEx(); ..nameEx <- "solvecov"
> 
> ### * solvecov
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: solvecov
> ### Title: Inversion of (possibly singular) symmetric matrices
> ### Aliases: solvecov
> ### Keywords: array
> 
> ### ** Examples
> 
>   x <- c(1,0,0,1,0,1,0,0,1)
>   dim(x) <- c(3,3)
>   solvecov(x)
> 
> 
> 
> cleanEx(); ..nameEx <- "sseg"
> 
> ### * sseg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sseg
> ### Title: Position in a similarity vector
> ### Aliases: sseg
> ### Keywords: utilities
> 
> ### ** Examples
> 
> sseg(3,4)
[1] 9
> 
> 
> 
> cleanEx(); ..nameEx <- "tdecomp"
> 
> ### * tdecomp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tdecomp
> ### Title: Root of singularity-corrected eigenvalue decomposition
> ### Aliases: tdecomp
> ### Keywords: array
> 
> ### ** Examples
> 
> x <- rnorm(10)
> y <- rnorm(10)
> z <- cov(cbind(x,y))
> tdecomp(z)
           [,1]      [,2]
[1,] -0.4766383 1.0305950
[2,]  0.6181669 0.2858951
> 
> 
> 
> cleanEx(); ..nameEx <- "wfu"
> 
> ### * wfu
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: wfu
> ### Title: Weight function (for Mahalabobis distances)
> ### Aliases: wfu
> ### Keywords: arith
> 
> ### ** Examples
> 
>   md <- seq(0,10,by=0.1)
>   wfu(md,ca=5,ca2=8)
  [1] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
  [7] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [13] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [19] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [25] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [31] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [37] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [43] 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
 [49] 1.00000000 1.00000000 1.00000000 0.96666667 0.93333333 0.90000000
 [55] 0.86666667 0.83333333 0.80000000 0.76666667 0.73333333 0.70000000
 [61] 0.66666667 0.63333333 0.60000000 0.56666667 0.53333333 0.50000000
 [67] 0.46666667 0.43333333 0.40000000 0.36666667 0.33333333 0.30000000
 [73] 0.26666667 0.23333333 0.20000000 0.16666667 0.13333333 0.10000000
 [79] 0.06666667 0.03333333 0.00000000 0.00000000 0.00000000 0.00000000
 [85] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
 [91] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
 [97] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  6.37 0.2 6.6 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')