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R Under development (unstable) (2023-03-07 r83950) -- "Unsuffered Consequences"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> ## This came from a bug report on R-help by ge yreyt <tothri2000@yahoo.ca>
> ## Date: Mon, 9 Jun 2003 16:06:53 -0400 (EDT)
> library(cluster)
> if(FALSE) # manual testing
+ library(cluster, lib="~/R/Pkgs/cluster.Rcheck")
> 
> data(iris)
> 
> .proctime00 <- proc.time()
> 
> mdist <- as.dist(1 - cor(t(iris[,1:4])))#dissimlarity
> ## this is always the same:
> hc <- diana(mdist, diss = TRUE, stand = FALSE)
> 
> maxk <- 15                # at most 15 clusters
> silh.wid <- numeric(maxk)  # myind[k] := the silh.value for k clusters
> silh.wid[1] <- NA # 1-cluster: silhouette not defined
> 
> op <- par(mfrow = c(4,4), mar = .1+ c(2,1,2,1), mgp=c(1.5, .6,0))
> for(k in 2:maxk) {
+     cat("\n", k,":\n==\n")
+     k.gr <- cutree(as.hclust(hc), k = k)
+     cat("grouping table: "); print(table(k.gr))
+     si <- silhouette(k.gr, mdist)
+     cat("silhouette:\n"); print(summary(si))
+     plot(si, main = paste("k =",k),
+          col = 2:(k+1), do.n.k=FALSE, do.clus.stat=FALSE)
+     silh.wid[k] <- summary(si)$avg.width
+     ##      ===
+ }

 2 :
==
grouping table: k.gr
  1   2 
 50 100 
silhouette:
Silhouette of 150 units in 2 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       50       100 
0.9829965 0.9362626 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.5884  0.9437  0.9611  0.9518  0.9815  0.9918 

 3 :
==
grouping table: k.gr
 1  2  3 
50 50 50 
silhouette:
Silhouette of 150 units in 3 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       50        50        50 
0.9773277 0.6926798 0.7467236 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.03353 0.76937 0.86121 0.80558 0.97564 0.98919 

 4 :
==
grouping table: k.gr
 1  2  3  4 
35 15 50 50 
silhouette:
Silhouette of 150 units in 4 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        15        50        50 
0.5653722 0.5226372 0.6926798 0.7467236 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.03353 0.56621 0.75102 0.66399 0.84240 0.89390 

 5 :
==
grouping table: k.gr
 1  2  3  4  5 
35 15 29 21 50 
silhouette:
Silhouette of 150 units in 5 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        15        29        21        50 
0.5653722 0.5226372 0.5776362 0.4625437 0.5296735 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.5404  0.3937  0.6252  0.5372  0.7392  0.8136 

 6 :
==
grouping table: k.gr
 1  2  3  4  5  6 
35 15 29 21 29 21 
silhouette:
Silhouette of 150 units in 6 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        15        29        21        29        21 
0.5653722 0.5226372 0.5776362 0.3732981 0.3383135 0.5945444 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.1094  0.3351  0.5257  0.4968  0.6938  0.8136 

 7 :
==
grouping table: k.gr
 1  2  3  4  5  6  7 
35 14  1 29 21 29 21 
silhouette:
Silhouette of 150 units in 7 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        14         1        29        21        29        21 
0.4165289 0.6671435 0.0000000 0.5776362 0.3732981 0.3383135 0.5945444 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.3264  0.3001  0.5234  0.4720  0.6970  0.8301 

 8 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8 
35 14  1 29 10 11 29 21 
silhouette:
Silhouette of 150 units in 8 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        14         1        29        10        11        29        21 
0.4165289 0.6671435 0.0000000 0.4209012 0.6943265 0.7262601 0.2053018 0.5945444 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.6258  0.2576  0.5842  0.4633  0.7132  0.8887 

 9 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 
35 14  1 26 10 11  3 29 21 
silhouette:
Silhouette of 150 units in 9 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        14         1        26        10        11         3        29 
0.4165289 0.6671435 0.0000000 0.5318152 0.6673269 0.6944652 0.7957279 0.2053018 
       21 
0.5945444 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.6258  0.3150  0.5896  0.4859  0.7263  0.8870 

 10 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 
35 14  1 26 10 11  3 16 13 21 
silhouette:
Silhouette of 150 units in 10 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        14         1        26        10        11         3        16 
0.4165289 0.6671435 0.0000000 0.5318152 0.6319149 0.6145837 0.7957279 0.4640123 
       13        21 
0.6615431 0.4228530 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.5870  0.3535  0.6068  0.5208  0.7349  0.8803 

 11 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 
35 14  1 26 10 11  3 16 13 11 10 
silhouette:
Silhouette of 150 units in 11 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        14         1        26        10        11         3        16 
0.4165289 0.6671435 0.0000000 0.5318152 0.6319149 0.6145837 0.7957279 0.4064279 
       13        11        10 
0.5866228 0.4297258 0.6590274 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.3264  0.3730  0.5984  0.5244  0.7302  0.8505 

 12 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 
35 11  3  1 26 10 11  3 16 13 11 10 
silhouette:
Silhouette of 150 units in 12 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       35        11         3         1        26        10        11         3 
0.2883758 0.7044155 0.4092330 0.0000000 0.5318152 0.6319149 0.6145837 0.7957279 
       16        13        11        10 
0.4064279 0.5866228 0.4297258 0.6590274 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.6007  0.3395  0.5817  0.4921  0.7216  0.8700 

 13 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 
28 11  3  7  1 26 10 11  3 16 13 11 10 
silhouette:
Silhouette of 150 units in 13 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       28        11         3         7         1        26        10        11 
0.3783869 0.6827810 0.4092330 0.4285753 0.0000000 0.5318152 0.6319149 0.6145837 
        3        16        13        11        10 
0.7957279 0.4064279 0.5866228 0.4297258 0.6590274 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.4013  0.3314  0.5704  0.5138  0.7274  0.8531 

 14 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 
19 11  3  9  7  1 26 10 11  3 16 13 11 10 
silhouette:
Silhouette of 150 units in 14 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       19        11         3         9         7         1        26        10 
0.5419530 0.6171802 0.3959926 0.4525348 0.1669077 0.0000000 0.5318152 0.6319149 
       11         3        16        13        11        10 
0.6145837 0.7957279 0.4064279 0.5866228 0.4297258 0.6590274 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.5929  0.3795  0.5875  0.5217  0.7263  0.8505 

 15 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 
19 11  3  9  7  1 18 10 11  8  3 16 13 11 10 
silhouette:
Silhouette of 150 units in 15 clusters from silhouette.default(x = k.gr, dist = mdist) :
 Cluster sizes and average silhouette widths:
       19        11         3         9         7         1        18        10 
0.5419530 0.6171802 0.3959926 0.4525348 0.1669077 0.0000000 0.6616381 0.5871805 
       11         8         3        16        13        11        10 
0.5171407 0.6705138 0.7444822 0.4064279 0.5866228 0.4297258 0.6590274 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.5929  0.3859  0.6211  0.5335  0.7478  0.8551 
> par(op)
> 
> summary(si.p <- silhouette(50 - k.gr, mdist))
Silhouette of 150 units in 15 clusters from silhouette.default(x = 50 - k.gr, dist = mdist) :
 Cluster sizes, ids = (35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49), and average silhouette widths:
       10        11        13        16         3         8        11        10 
0.6590274 0.4297258 0.5866228 0.4064279 0.7444822 0.6705138 0.5171407 0.5871805 
       18         1         7         9         3        11        19 
0.6616381 0.0000000 0.1669077 0.4525348 0.3959926 0.6171802 0.5419530 
Individual silhouette widths:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.5929  0.3859  0.6211  0.5335  0.7478  0.8551 
> stopifnot(identical(si.p[,3],         si[,3]),
+     identical(si.p[, 1:2], 50 - si[, 1:2]))
> 
> # the widths:
> silh.wid
 [1]        NA 0.9518406 0.8055770 0.6639850 0.5371742 0.4967654 0.4720384
 [8] 0.4633064 0.4858965 0.5207776 0.5243911 0.4920638 0.5138220 0.5217026
[15] 0.5335255
> #select the number of k clusters with the largest si value :
> (myk <- which.min(silh.wid)) # -> 8 (here)
[1] 8
> 
> postscript(file="silhouette-ex.ps")
> ## MM:  plot to see how the decision is made
> plot(silh.wid, type = 'b', col= "blue", xlab = "k")
> axis(1, at=myk, col.axis= "red", font.axis= 2)
> 
> ##--- PAM()'s silhouette should give same as silh*.default()!
> Eq <- function(x,y, tol = 1e-12) x == y | abs(x - y) < tol * abs((x+y)/2)
> 
> for(k in 2:40) {
+     cat("\n", k,":\n==\n")
+     p.k <- pam(mdist, k = k)
+     k.gr <- p.k$clustering
+     si.p <- silhouette(p.k)
+     si.g <- silhouette(k.gr, mdist)
+     ## since the obs.order may differ (within cluster):
+     si.g <- si.g[ as.integer(rownames(si.p)), ]
+     cat("grouping table: "); print(table(k.gr))
+     if(!isTRUE(all.equal(c(si.g), c(si.p)))) {
+   cat("silhouettes differ:")
+   if(any(neq <- !Eq(si.g[,3], si.p[,3]))) {
+       cat("\n")
+       print( cbind(si.p[], si.g[,2:3])[ neq, ] )
+   } else cat(" -- but not in col.3 !\n")
+     }
+ }

 2 :
==
grouping table: k.gr
  1   2 
 50 100 

 3 :
==
grouping table: k.gr
 1  2  3 
50 50 50 

 4 :
==
grouping table: k.gr
 1  2  3  4 
50 43 37 20 

 5 :
==
grouping table: k.gr
 1  2  3  4  5 
50 25 35 20 20 

 6 :
==
grouping table: k.gr
 1  2  3  4  5  6 
33 17 25 35 20 20 

 7 :
==
grouping table: k.gr
 1  2  3  4  5  6  7 
33 17 17 14 18 31 20 

 8 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8 
21 13 16 17 14 18 31 20 

 9 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 
21 13 16 12 20 11 19 17 21 

 10 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 
21 13 16 18 10 15 14  7 16 20 

 11 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 
21 13 16 19 10 14  7  6 15 13 16 

 12 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 
21 13 16 17 10 12  9  3  5 15 13 16 

 13 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 
21 12 16  1 18 11 12  9  3 15 13  4 15 

 14 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 
20 10  7 13 18 10 12  9  3  7 10 13  4 14 

 15 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 
20 11  5 13  1 18 10 12  9  3  7 10 13  4 14 

 16 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 
20 11  5 13  1 12  8  9 11  9  3  7 10 13  4 14 

 17 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 
20 11  5 13  1 12  8  7  9 10  3  3  9 13  4 13  9 

 18 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 
20 11  5  9  4  1 12  8  7  9 10  3  3  9 13  4 13  9 

 19 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 
20 11  5  9  4  1 10  8  8  9  8  3  3  9 13  3  4 13  9 

 20 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 
20 11  5  9  4  1 10  8  8  9  8  3  3  9 12  3  4  6  9  8 

 21 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 
20 11  5  9  4  1 10  8  8  7  8  3  3  7 11  3  4  6  9  8  5 

 22 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 
 9 11  5  9 11  4  1 10  8  8  7  8  3  3  7 11  3  4  6  9  8  5 

 23 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 
 9 11  5  9 11  4  1 10  8  8  7  8  3  3  7 11  3  4  6  8  8  5  1 

 24 :
==
grouping table: k.gr
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 
15 10  5 10  3  3  3  1 10  8  8  7  8  3  3  7 11  3  4  6  8  8  5  1 

 25 :
==
grouping table: k.gr
 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 
 8  4  5  9 11  7  2  3  1 10  8  8  7  8  3  3  7 11  3  4  6  8  8  5  1 

 26 :
==
grouping table: k.gr
 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 
 8  4  5  9 11  7  2  3  1 10  8  8  7  8  3  3  7  7  3  4  6  8  8  4  5  1 

 27 :
==
grouping table: k.gr
 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 
 8  4  5  9 11  7  2  3  1 10  8  7  7  8  3  2  7  7  3  2  4  6  8  8  4  5 
27 
 1 

 28 :
==
grouping table: k.gr
 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 
 8  4  4  9 11  7  2  3  1  1 10  8  7  7  8  3  2  7  7  3  2  4  6  8  8  4 
27 28 
 5  1 

 29 :
==
grouping table: k.gr
 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 
 8  4  4  9 11  7  2  3  1  1 10  8  7  7  8  2  2  7  7  3  2  1  4  6  8  8 
27 28 29 
 4  5  1 

 30 :
==
grouping table: k.gr
 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 
 8  4 11 10  6  3  2  3  1  1  1 10  8  7  7  8  2  2  7  7  3  2  1  4  6  8 
27 28 29 30 
 8  4  5  1 

 31 :
==
grouping table: k.gr
 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 
 8  4 11 10  6  3  2  3  1  1  1 10  8  7  7  8  2  2  7  7  3  2  1  4  6  7 
27 28 29 30 31 
 7  4  5  1  2 

 32 :
==
grouping table: k.gr
 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 
 7  4  3 10 10  6  2  2  3  1  1  1 10  8  7  7  8  2  2  7  7  3  2  1  4  6 
27 28 29 30 31 32 
 7  7  4  5  1  2 

 33 :
==
grouping table: k.gr
 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 
 7  4  3 10 10  6  2  2  3  1  1  1 10  8  7  7  8  2  2  7  7  3  2  1  1  6 
27 28 29 30 31 32 33 
 7  3  7  4  5  1  2 

 34 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1 10  8  7  7  8  2  2  7  7  3  2  1  1 
27 28 29 30 31 32 33 34 
 6  7  3  7  4  5  1  2 

 35 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1 10  8  7  7  8  2  2  5  7  3  2  1  1 
27 28 29 30 31 32 33 34 35 
 6  5  3  7  4  4  5  1  2 

 36 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1 10  8  7  6  8  2  2  5  7  1  3  2  1 
27 28 29 30 31 32 33 34 35 36 
 1  6  5  3  7  4  4  5  1  2 

 37 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1 10  8  3  5  6  8  2  2  5  7  1  3  2 
27 28 29 30 31 32 33 34 35 36 37 
 1  1  6  5  3  7  4  5  1  3  2 

 38 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1 10  8  3  5  6  5  2  2  5  3  7  1  3 
27 28 29 30 31 32 33 34 35 36 37 38 
 2  1  1  6  5  3  7  4  5  1  3  2 

 39 :
==
grouping table: k.gr
 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 
 7  4  3  8  9  6  2  3  3  1  1  2  1  7  3  8  3  5  6  5  2  2  5  3  7  1 
27 28 29 30 31 32 33 34 35 36 37 38 39 
 3  2  1  1  6  5  3  7  4  5  1  3  2 

 40 :
==
grouping table: k.gr
 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 
 5  4  3  7 10  6  2  2  3  3  1  1  2  1  7  3  8  3  5  6  5  2  2  5  3  7 
27 28 29 30 31 32 33 34 35 36 37 38 39 40 
 1  3  2  1  1  6  5  3  7  4  5  1  3  2 
> 
> 
> ## "pathological" case where a_i == b_i == 0 :
> D6 <- structure(c(0, 0, 0, 0.4, 1, 0.05, 1, 1, 0, 1, 1, 0, 0.25, 1, 1),
+                 Labels = LETTERS[1:6], Size = 6, call = as.name("manually"),
+                 class = "dist", Diag = FALSE, Upper = FALSE)
> D6
     A    B    C    D    E
B 0.00                    
C 0.00 0.05               
D 0.00 1.00 1.00          
E 0.40 1.00 1.00 0.25     
F 1.00 0.00 0.00 1.00 1.00
> kl6 <- c(1,1, 2,2, 3,3)
> (skD6 <- silhouette(kl6, D6))# had one NaN
     cluster neighbor sil_width
[1,]       1        2     0.000
[2,]       1        3     1.000
[3,]       2        1    -0.975
[4,]       2        1    -0.500
[5,]       3        2    -0.375
[6,]       3        1    -0.500
attr(,"Ordered")
[1] FALSE
attr(,"call")
silhouette.default(x = kl6, dist = D6)
attr(,"class")
[1] "silhouette"
> summary(skD6)
Silhouette of 6 units in 3 clusters from silhouette.default(x = kl6, dist = D6) :
 Cluster sizes and average silhouette widths:
      2       2       2 
 0.5000 -0.7375 -0.4375 
Individual silhouette widths:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.97500 -0.50000 -0.43750 -0.22500 -0.09375  1.00000 
> plot(silhouette(kl6, D6))# gives error in earlier cluster versions
> dev.off()
pdf 
  2 
> 
> ## checking compatibility with  R-only version
> silhouetteR <- asNamespace("cluster")$silhouetteR
> noCall <- function(si) `attr<-`(si, "call", NULL) # only 'call' is different:
> stopifnot(all.equal(noCall(skD6), noCall(silhouetteR(kl6, D6))))
> 
> ## k=1 : pam(*, k=1) works, but silhouette() is not defined;
> ## ---  FIXME: silhouette.partition() fails:  "invalid partition .."; (which is not strictly true
> ##     ------  -> give something like NA ((or a *different* error message)
> ##  the other methods just give NA (no object!)
> ## drop "call" *and* "iOrd"
> noCliO <- function(si) noCall(`attr<-`(si, "iOrd", NULL))
> for(k in 2:7) {
+     p.k <- pam(ruspini, k=k)
+     ## order the silhouette to be *as* the default:
+     ## spk <- silhouette(p.k);  opk <- spk[order(as.numeric(rownames(spk))), ]
+     ## rather sort*() the other:
+     stopifnot(all.equal(noCall(silhouette(p.k)),
+                         noCliO(sortSilhouette(silhouetteR(p.k$clustering, p.k$diss)))))
+ }
> 
> ## Last Line:
> cat('Time elapsed: ', proc.time() - .proctime00,'\n')
Time elapsed:  1.607 0.036 1.651 0 0 
> 
> 
> proc.time()
   user  system elapsed 
  1.722   0.084   1.859