R Under development (unstable) (2024-04-29 r86495 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # This tests a few things that are not run in the examples.
> 
> library(fpc)
> library(MASS)
> library(diptest)
> library(mclust)
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
> options(digits=3)
> 
> set.seed(4634)
> face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
> grface <- as.integer(attr(face,"grouping"))
> # discrproj(face,grface, clnum=1, method="bc")$units
> discrproj(face,grface, clnum=1, method="anc")$units
        [,1]    [,2]    [,3]
[1,] -1.3912 -0.3093  0.1093
[2,]  0.6211 -0.2233  0.0164
[3,] -0.0313  0.0749 -0.8074
> discrproj(face,grface, clnum=1, method="awc")$units
       [,1]    [,2]     [,3]
[1,]  0.215 -0.3389 -0.51886
[2,] -0.370  0.0144 -0.00893
[3,]  0.111  0.7914 -0.23574
> 
> 
> pamk(face,krange=1:5,criterion="ch",usepam=FALSE,critout=TRUE)
1  clusters  0 
2  clusters  1321 
3  clusters  963 
4  clusters  833 
5  clusters  934 
$pamobject
Call:	 clara(x = sdata, k = k) 
Medoids:
      [,1]  [,2] [,3]
[1,] 0.119  3.53 1.49
[2,] 1.742 17.02 1.12
Objective function:	 2.44
Clustering vector: 	 int [1:300] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:	    	 202 98 
Best sample:
 [1]   5  11  18  21  28  40  50  61  62  65  79  82  83  86  93  94 105 119 130
[20] 160 172 180 182 194 195 202 206 208 217 223 230 231 239 248 250 256 259 261
[39] 264 268 271 274 277 299

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      

$nc
[1] 2

$crit
[1]    0 1321  963  833  934

> 
> set.seed(20000)
> face50 <- rFace(50,dMoNo=2,dNoEy=0,p=2)
> pamk(dist(face50),krange=1:5,criterion="asw",critout=TRUE)
1  clusters  0 
2  clusters  0.742 
3  clusters  0.748 
4  clusters  0.581 
5  clusters  0.544 
$pamobject
Medoids:
     ID       
[1,] "22" "22"
[2,] "34" "34"
[3,] "49" "49"
Clustering vector:
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 
 1  1  1  1  1  1  2  2  2  2  2  2  2  2  2  2  2  2  2  2  3  2  3  3 
Objective function:
build  swap 
 2.14  2.09 

Available components:
[1] "medoids"    "id.med"     "clustering" "objective"  "isolation" 
[6] "clusinfo"   "silinfo"    "diss"       "call"      

$nc
[1] 3

$crit
[1] 0.000 0.742 0.748 0.581 0.544

> 
> x <- c(1,2,3,6,6,7,8,120)
> ff8 <- fixmahal(x)
> summary(ff8)
  *  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:  1 

FPCs with less than  4  points were skipped.
FPCs with ratio of times found to number of points less than  0.1  were skipped.
0  iteration runs led to  0  skipped clusters.
  Weight 1 for r^2<=  3.84  weight 0 for r^2>  7.88  
  Constant ca=  3.84  corresponding to alpha=  0.95 

 FPC  1 
  Times found (group members):  9 
  Ratio to size:  1.29 
  Mean:
[1] 4.71
  Covariance matrix:
     [,1]
[1,]  6.2
  Number of points (sum of weights):  7 

Number of points (rounded weights) in intersection of representative FPCs
     [,1]
[1,]    7
>   # ...dataset a bit too small for the defaults...
> ff9 <- fixmahal(x, mnc=3, startn=3)
> summary(ff9)
  *  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:  3 

FPCs with less than  3  points were skipped.
FPCs with ratio of times found to number of points less than  0.1  were skipped.
0  iteration runs led to  0  skipped clusters.
  Weight 1 for r^2<=  3.84  weight 0 for r^2>  7.88  
  Constant ca=  3.84  corresponding to alpha=  0.95 

 FPC  1 
  Times found (group members):  4 
  Ratio to size:  1.33 
  Mean:
[1] 6.33
  Covariance matrix:
      [,1]
[1,] 0.222
  Number of points (sum of weights):  3 

 FPC  2 
  Times found (group members):  3 
  Ratio to size:  1 
  Mean:
[1] 2
  Covariance matrix:
      [,1]
[1,] 0.667
  Number of points (sum of weights):  3 

 FPC  3 
  Times found (group members):  2 
  Ratio to size:  0.286 
  Mean:
[1] 4.71
  Covariance matrix:
     [,1]
[1,]  6.2
  Number of points (sum of weights):  7 

Number of points (rounded weights) in intersection of representative FPCs
     [,1] [,2] [,3]
[1,]    3    0    3
[2,]    0    3    3
[3,]    3    3    7
> 
> set.seed(776655)
> v1 <- rnorm(100)
> v2 <- rnorm(100)
> d1 <- sample(1:5,100,replace=TRUE)
> d2 <- sample(1:4,100,replace=TRUE)
> ldata <- cbind(v1,v2,d1,d2)
> fr <- flexmixedruns(ldata,
+     continuous=2,discrete=2,simruns=1,initial.cluster=c(rep(1,5),rep(2,45),
+                                         rep(3,50)),
+                     control=list(minprior=0.1),
+                     n.cluster=3,allout=FALSE)
k=  3  new best fit found in run  1 
k=  3  BIC=  1299 
> print(fr$optsummary)

Call:
flexmix(formula = x ~ 1, k = k, cluster = initial.cluster, model = lcmixed(continuous = continuous, 
    discrete = discrete, ppdim = ppdim, diagonal = diagonal), 
    control = control)

       prior size post>0 ratio
Comp.1 0.204   23     61 0.377
Comp.2 0.284   30     71 0.423
Comp.3 0.512   47     77 0.610

'log Lik.' -569 (df=35)
AIC: 1208   BIC: 1299 

> 
> dface <- dist(face50)
> 
> 
> hclusttreeCBI(face50,minlevel=2,method="complete",scaling=TRUE)
$result

Call:
hclust(d = dist(sdata), method = method)

Cluster method   : complete 
Distance         : euclidean 
Number of objects: 50 


$nc
[1] 48

$clusterlist
$clusterlist[[1]]
 [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]  TRUE  TRUE

$clusterlist[[2]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[3]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[4]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[5]]
 [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]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[6]]
 [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  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[7]]
 [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  TRUE FALSE FALSE
[37] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[8]]
 [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]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[9]]
 [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  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[10]]
 [1] FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[11]]
 [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  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[12]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13]  TRUE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[13]]
 [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  TRUE FALSE FALSE FALSE
[37]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[14]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[15]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[16]]
 [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  TRUE FALSE FALSE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[17]]
 [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  TRUE FALSE FALSE FALSE
[37]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[18]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[19]]
 [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  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[20]]
 [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[21]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[13]  TRUE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[22]]
 [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  TRUE
[25] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[23]]
 [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  TRUE FALSE FALSE  TRUE
[37]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[24]]
 [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]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[25]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE 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

$clusterlist[[26]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE  TRUE 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

$clusterlist[[27]]
 [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  TRUE FALSE  TRUE  TRUE
[37]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[28]]
 [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]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[29]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[30]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[31]]
 [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  TRUE  TRUE FALSE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[32]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[33]]
 [1]  TRUE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[34]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE 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

$clusterlist[[35]]
 [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  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[36]]
 [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[37]]
 [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  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[38]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[39]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE  TRUE 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

$clusterlist[[40]]
 [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 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

$clusterlist[[41]]
 [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[42]]
 [1]  TRUE  TRUE  TRUE FALSE  TRUE 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

$clusterlist[[43]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE  TRUE 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  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[44]]
 [1]  TRUE  TRUE  TRUE FALSE  TRUE 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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[45]]
 [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[46]]
 [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  TRUE FALSE
[49]  TRUE  TRUE

$clusterlist[[47]]
 [1]  TRUE  TRUE  TRUE FALSE  TRUE 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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[49] FALSE FALSE

$clusterlist[[48]]
 [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
[49]  TRUE  TRUE


$partition
 [1] 1 1 1 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 2 1 1 1 1 1 1
[39] 1 1 1 2 2 2 2 2 2 1 2 2

$clustermethod
[1] "hclust, full tree"

> 
> disthclusttreeCBI(dface,minlevel=2,method="complete")
$result

Call:
hclust(d = as.dist(dmatrix), method = method)

Cluster method   : complete 
Distance         : euclidean 
Number of objects: 50 


$nc
[1] 48

$clusterlist
$clusterlist[[1]]
 [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]  TRUE  TRUE

$clusterlist[[2]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[3]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[4]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[5]]
 [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  TRUE FALSE FALSE
[37] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[6]]
 [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  TRUE
[37] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[7]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE 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

$clusterlist[[8]]
 [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  TRUE FALSE FALSE FALSE
[37]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[9]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[13]  TRUE 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

$clusterlist[[10]]
 [1] FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[11]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE 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

$clusterlist[[12]]
 [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  TRUE FALSE FALSE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[13]]
 [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  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[14]]
 [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]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[15]]
 [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]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[16]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[17]]
 [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[18]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[19]]
 [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  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[20]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE 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

$clusterlist[[21]]
 [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  TRUE
[25] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[22]]
 [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  TRUE
[37] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[23]]
 [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  TRUE  TRUE FALSE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[24]]
 [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]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[25]]
 [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  TRUE FALSE  TRUE FALSE
[37]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[26]]
 [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  TRUE
[25] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[27]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
[13] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE 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

$clusterlist[[28]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[13]  TRUE  TRUE 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

$clusterlist[[29]]
 [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  TRUE FALSE  TRUE FALSE
[37]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[30]]
 [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  TRUE FALSE  TRUE
[37] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[31]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[13]  TRUE  TRUE 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

$clusterlist[[32]]
 [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  TRUE
[25] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[33]]
 [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  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[34]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
[13] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 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

$clusterlist[[35]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[36]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[37]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 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

$clusterlist[[38]]
 [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  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[39]]
 [1]  TRUE FALSE  TRUE FALSE  TRUE 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

$clusterlist[[40]]
 [1] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE 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

$clusterlist[[41]]
 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[42]]
 [1] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 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

$clusterlist[[43]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[44]]
 [1]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[45]]
 [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  TRUE FALSE
[49]  TRUE  TRUE

$clusterlist[[46]]
 [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[47]]
 [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  TRUE  TRUE
[49]  TRUE  TRUE

$clusterlist[[48]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[49]  TRUE  TRUE


$partition
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[39] 2 2 2 2 2 2 2 2 2 2 2 2

$clustermethod
[1] "hclust, full tree"

> 
> noisemclustCBI(face50,G=1:5,emModelNames="VVV",nnk=2)
$result
Bayesian Information Criterion (BIC): 
   EII  VII  EEI  VEI  EVI  VVI  EEE  VEE  EVE  VVE  EEV  VEV  EVV  VVV
1 -521 -521 -506 -506 -506 -506 -525 -525 -525 -525 -525 -525 -525 -525
2 -498 -501 -501 -477 -464 -466 -505 -481 -467 -470 -467 -470 -470 -473
3 -468 -467 -480 -466 -476 -461 -482 -470 -479 -465 -480 -468 -486 -469
4 -449 -448 -485 -449 -445 -449 -488 -452 -439 -452 -486 -482 -444 -455
5 -456 -452 -456 -454 -444 -458 -460 -458 -448 -462 -451 -485 -456 -469

Top 3 models based on the BIC criterion: 
EVE,4 EVV,4 EVI,5 
 -439  -444  -444 

$nc
[1] 5

$nccl
[1] 4

$clusterlist
$clusterlist[[1]]
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
[13]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 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

$clusterlist[[2]]
 [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  TRUE
[25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[3]]
 [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  TRUE  TRUE  TRUE  TRUE
[37]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[4]]
 [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  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
[49] FALSE FALSE

$clusterlist[[5]]
 [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE  TRUE 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  TRUE  TRUE
[49]  TRUE  TRUE


$partition
 [1] 5 5 5 1 5 5 5 5 5 1 1 1 1 5 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3
[39] 3 3 3 4 4 4 4 4 5 5 5 5

$nnk
[1] 2

$initnoise
 [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE 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  TRUE  TRUE
[49]  TRUE  TRUE

$clustermethod
[1] "mclustBIC"

> 
> distnoisemclustCBI(dface,G=5,emModelNames="EEE",nnk=2,
+                         mdsmethod="classical",
+                         mdsdim=2)
$result
Bayesian Information Criterion (BIC): 
   EII VII  EEI VEI EVI VVI  EEE VEE EVE VVE  EEV VEV EVV VVV
5 -461  NA -496  NA  NA  NA -500  NA  NA  NA -496  NA  NA  NA

Top 3 models based on the BIC criterion: 
EII,5 EEI,5 EEV,5 
 -461  -496  -496 

$nc
[1] 6

$nccl
[1] 5

$clusterlist
$clusterlist[[1]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   40    41    42    43    44    45    46    47    48    49    50 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 

$clusterlist[[2]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
 TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   40    41    42    43    44    45    46    47    48    49    50 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 

$clusterlist[[3]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE 
   40    41    42    43    44    45    46    47    48    49    50 
 TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 

$clusterlist[[4]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   40    41    42    43    44    45    46    47    48    49    50 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 

$clusterlist[[5]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   40    41    42    43    44    45    46    47    48    49    50 
FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE 

$clusterlist[[6]]
    1     2     3     4     5     6     7     8     9    10    11    12    13 
 TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 
   40    41    42    43    44    45    46    47    48    49    50 
FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE 


$partition
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
 6  6  6  1  6  6  6  6  6  1  1  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2 
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 
 2  2  2  2  2  2  3  3  3  3  3  3  3  3  3  5  5  5  5  5  6  6  6  6 

$nnk
[1] 2

$initnoise
 [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE 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  TRUE  TRUE
[49]  TRUE  TRUE

$clustermethod
[1] "mclustBIC"

> 
> mahalCBI(face50,clustercut=0.5)
$result
Mahalanobis Fixed Point Cluster object
3  representative stable fixed point clusters
 of totally  7  found fixed point clusters.

$nc
[1] 3

$clusterlist
$clusterlist[[1]]
 [1] 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0
[39] 0 0 0 0 0 0 0 0 0 0 0 0

$clusterlist[[2]]
 [1] 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
[39] 0 0 0 0 0 0 0 0 0 0 0 0

$clusterlist[[3]]
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 0 0 0 0


$partition
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 1

$clustermethod
[1] "fixmahal"

> 
> set.seed(20000)
> face100 <- rFace(100,dMoNo=2,dNoEy=0,p=2)
> cbf <- clusterboot(face100,B=2,clustermethod=speccCBI,showplots=TRUE,k=6,seed=50000)
boot 1 
boot 2 
> cbf$nc
[1] 6
> cbf$noisemethod
[1] FALSE
> cbf$bootmethod
[1] "boot"
> # suppressWarnings(if(require(tclust))
> # print(clusterboot(face100,B=2,clustermethod=tclustCBI,showplots=TRUE,k=5,seed=50000,noisemethod=TRUE)))
> 
> 
> complete3 <- cutree(hclust(dface),3)
> 
> cluster.stats(dface,complete3,G2=TRUE)
$n
[1] 50

$cluster.number
[1] 3

$cluster.size
[1] 32 14  4

$min.cluster.size
[1] 4

$noisen
[1] 0

$diameter
[1] 8.53 4.94 9.00

$average.distance
[1] 2.95 2.15 7.05

$median.distance
[1] 2.94 1.44 8.32

$separation
[1] 8.67 7.46 7.46

$average.toother
[1] 15.5 12.9 20.6

$separation.matrix
      [,1] [,2]  [,3]
[1,]  0.00 8.67 17.24
[2,]  8.67 0.00  7.46
[3,] 17.24 7.46  0.00

$ave.between.matrix
     [,1] [,2] [,3]
[1,]  0.0   13 24.4
[2,] 13.0    0 12.0
[3,] 24.4   12  0.0

$average.between
[1] 15.2

$average.within
[1] 3.05

$n.between
[1] 632

$n.within
[1] 593

$max.diameter
[1] 9

$min.separation
[1] 7.46

$within.cluster.ss
[1] 319

$clus.avg.silwidths
    1     2     3 
0.764 0.821 0.346 

$avg.silwidth
[1] 0.746

$g2
[1] 1

$g3
NULL

$pearsongamma
[1] 0.838

$dunn
[1] 0.829

$dunn2
[1] 1.71

$entropy
[1] 0.844

$wb.ratio
[1] 0.2

$ch
[1] 229

$cwidegap
[1] 2.90 2.49 8.32

$widestgap
[1] 8.32

$sindex
[1] 7.78

$corrected.rand
NULL

$vi
NULL

> 
> set.seed(55667788)
> 
> data(crabs)
> dc <- crabs[,4:8]
> cmo <- mclustBIC(crabs[,4:8],G=9,modelNames="EEE")
> # set.seed(12345)
> cm <- mclustBIC(crabs[,4:8],G=9,modelNames="EEE",
+                 initialization=list(noise=(1:200)[sample(200,50)]))
> 
> 
> scm <- summary(cm,crabs[,4:8])
> scmo <- summary(cmo,crabs[,4:8])
> 
> set.seed(334455)
> summary(mergenormals(crabs[,4:8],scm,method="ridge.ratio",by=0.05))
* Merging Gaussian mixture components *

 Method:  ridge.ratio , cutoff value:  0.2 
 Original number of components:  9 
   (not including noise which is denoted by clustering=0)
 Number of clusters after merging:  1 
 Values at which clusters were merged: 
     [,1]  [,2]
[1,]    8 0.828
[2,]    7 1.000
[3,]    6 1.000
[4,]    5 0.888
[5,]    4 1.000
[6,]    3 1.000
[7,]    2 0.784
[8,]    1 0.845
 Components assigned to clusters: 
      [,1]
 [1,]    0
 [2,]    1
 [3,]    1
 [4,]    1
 [5,]    1
 [6,]    1
 [7,]    1
 [8,]    1
 [9,]    1
[10,]    1
> summary(mergenormals(crabs[,4:8],scmo,method="ridge.uni",by=0.05))
* Merging Gaussian mixture components *

 Method:  ridge.uni , cutoff value:  1 
 Original number of components:  9 
 Number of clusters after merging:  8 
 Values at which clusters were merged: 
     [,1]
[1,]    8
[2,]    9
 Components assigned to clusters: 
      [,1]
 [1,]    1
 [2,]    1
 [3,]    2
 [4,]    3
 [5,]    4
 [6,]    5
 [7,]    6
 [8,]    7
 [9,]    8
> # summary(mergenormals(crabs[,4:8],scm,method="diptantrum",by=0.05))
> # summary(mergenormals(crabs[,4:8],scmo,method="dipuni",by=0.05))
> # summary(mergenormals(crabs[,4:8],scm,method="predictive",M=2))
> 
> set.seed(20000)
> x1 <- rnorm(50)
> y <- rnorm(100)
> x2 <- rnorm(40,mean=20)
> x3 <- rnorm(10,mean=25,sd=100)
> x0 <- cbind(c(x1,x2,x3),y)
> 
> prediction.strength(x0,M=10,Gmax=4,
+                            clustermethod=noisemclustCBI,
+                            classification="qda")
Prediction strength 
Clustering method:  mclustBIC 
Maximum number of clusters:  4 
Resampled data sets:  10 
Mean pred.str. for numbers of clusters:  1 0.815 0.874 0.591 
Cutoff value:  0.8 
Largest number of clusters better than cutoff:  3 
> 
> prediction.strength(dist(x0),M=10,Gmax=4,
+                            clustermethod=claraCBI,
+                            classification="centroids")
Prediction strength 
Clustering method:  clara/pam 
Maximum number of clusters:  4 
Resampled data sets:  10 
Mean pred.str. for numbers of clusters:  1 0 0 0 
Cutoff value:  0.8 
Largest number of clusters better than cutoff:  1 
> 
> 
> set.seed(20000)
> xdata <- c(rnorm(10,0,1),rnorm(10,8,1))
> clustermethod=c("claraCBI","dbscanCBI")
> 
> clustermethodpars <- list()
> clustermethodpars[[1]] <- clustermethodpars[[2]] <- list()
> clustermethodpars[[2]]$eps <- 2
> clustermethodpars[[2]]$MinPts <- 2
> cbs <-  clusterbenchstats(xdata,G=3,clustermethod=clustermethod,
+     distmethod=rep(TRUE,2),ncinput=c(TRUE,FALSE),scaling=FALSE,
+     clustermethodpars=clustermethodpars,nnruns=2,kmruns=2,fnruns=1,avenruns=1,useallg=TRUE)
[1] "claraCBI"
[1] "dbscanCBI"
[1] "Computation of validity statistics"
comsum  1 
comsum  2 
[1] "Simulation"
3  clusters; nn run  1 
3  clusters; nn run  2 
3  clusters; fn run  1 
3  clusters; aven run  1 
3  clusters; km run  1 
3  clusters; km run  2 
[1] "Simulation quantile re-standardisation"
[1] "Simulation sd re-standardisation"
> 
> print(cbs$sstat,aggregate=TRUE,weights=c(1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1),include.othernc=cbs$cm$othernc)
avewithin 
     method    2    3
1  claraCBI   NA 0.67
2 dbscanCBI 0.14   NA
mnnd 
     method    2    3
1  claraCBI   NA 0.37
2 dbscanCBI 0.39   NA
cvnnd 
     method   2    3
1  claraCBI  NA 0.32
2 dbscanCBI 0.3   NA
maxdiameter 
     method    2    3
1  claraCBI   NA 0.66
2 dbscanCBI 0.56   NA
widestgap 
     method    2    3
1  claraCBI   NA 0.59
2 dbscanCBI 0.59   NA
sindex 
     method   2   3
1  claraCBI  NA 0.1
2 dbscanCBI 5.4  NA
minsep 
     method  2    3
1  claraCBI NA -1.6
2 dbscanCBI 17   NA
asw 
     method   2    3
1  claraCBI  NA 0.55
2 dbscanCBI 1.7   NA
dindex 
     method    2   3
1  claraCBI   NA 1.4
2 dbscanCBI -1.3  NA
denscut 
     method    2    3
1  claraCBI   NA -2.3
2 dbscanCBI 0.38   NA
highdgap 
     method    2    3
1  claraCBI   NA 0.73
2 dbscanCBI 0.55   NA
pearsongamma 
     method   2    3
1  claraCBI  NA 0.54
2 dbscanCBI 1.9   NA
withinss 
     method    2    3
1  claraCBI   NA 0.63
2 dbscanCBI 0.42   NA
entropy 
     method   2    3
1  claraCBI  NA 0.85
2 dbscanCBI 1.6   NA
pamc 
     method     2    3
1  claraCBI    NA 0.83
2 dbscanCBI 0.052   NA
dmode 
     method     2   3
1  claraCBI    NA 1.2
2 dbscanCBI -0.87  NA
aggregate 
     method   2    3
1  claraCBI  NA 0.15
2 dbscanCBI 1.2   NA
> print(cbs$qstat,aggregate=TRUE,weights=c(1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1),include.othernc=cbs$cm$othernc)
avewithin 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.38   NA
mnnd 
     method    2    3
1  claraCBI   NA 0.62
2 dbscanCBI 0.75   NA
cvnnd 
     method    2    3
1  claraCBI   NA 0.75
2 dbscanCBI 0.75   NA
maxdiameter 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.38   NA
widestgap 
     method    2    3
1  claraCBI   NA 0.38
2 dbscanCBI 0.38   NA
sindex 
     method  2    3
1  claraCBI NA 0.38
2 dbscanCBI  1   NA
minsep 
     method  2    3
1  claraCBI NA 0.12
2 dbscanCBI  1   NA
asw 
     method  2    3
1  claraCBI NA 0.62
2 dbscanCBI  1   NA
dindex 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.12   NA
denscut 
     method    2    3
1  claraCBI   NA 0.12
2 dbscanCBI 0.25   NA
highdgap 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.38   NA
pearsongamma 
     method  2   3
1  claraCBI NA 0.5
2 dbscanCBI  1  NA
withinss 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.38   NA
entropy 
     method  2    3
1  claraCBI NA 0.75
2 dbscanCBI  1   NA
pamc 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.38   NA
dmode 
     method    2    3
1  claraCBI   NA 0.88
2 dbscanCBI 0.19   NA
aggregate 
     method    2   3
1  claraCBI   NA 0.6
2 dbscanCBI 0.53  NA
> 
> 
> 
> proc.time()
   user  system elapsed 
   6.73    0.40    7.11