
Thanks, Martin!

...Tao

> From: maechler@stat.math.ethz.ch
> Date: Wed, 13 May 2009 14:13:48 +0200
> To: shitao@hotmail.com
> CC: ripley@stats.ox.ac.uk; bcarvalh@jhsph.edu; r-help@r-project.org; maechler@stat.math.ethz.ch
> Subject: Re: [R] silhouette: clustering labels have to be consecutive integers starting
> 
> >>>>> "TS" == Tao Shi <shitao@hotmail.com>
> >>>>>     on Wed, 10 Oct 2007 06:15:53 +0000 writes:
> 
>     TS> Thank you very much, Benilton and Prof. Ripley, for the
>     TS> speedy replies!
> 
>     TS> Looking forward to the fix!
>     TS> ....Tao
> 
> I have finally re-stumbled onto this e-mail thread,
> and indeed found fixed the problem.
> 
> Version 1.12.0 of 'cluster' should become visible within a few days,
> and will allow to call
> 
>     silhoutte(g, dis)
> 
> on a grouping vector of k different integer values which need
> *not* necessarily be in 1:k.
> 
> Martin Maechler,
> ETH Zurich
> 
> 
>     >> From: Prof Brian Ripley <ripley@stats.ox.ac.uk>
>     >> To: Benilton Carvalho <bcarvalh@jhsph.edu>
>     >> CC: Tao Shi <shitao@hotmail.com>, maechler@stat.math.ethz.ch,        
>     >> r-help@r-project.org
>     >> Subject: Re: [R] silhouette: clustering labels have to be consecutive 
>     >> intergers starting from 1?
>     >> Date: Wed, 10 Oct 2007 05:33:03 +0100 (BST)
>     >> 
>     >> It is a C-level problem in package cluster: valgrind gives
>     >> 
>     >> ==11377== Invalid write of size 8
>     >> ==11377==    at 0xA4015D3: sildist (sildist.c:35)
>     >> ==11377==    by 0x4706D8: do_dotCode (dotcode.c:1750)
>     >> 
>     >> This is a matter for the package maintainer (Cc:ed here), not R-help.
>     >> 
>     >> On Tue, 9 Oct 2007, Benilton Carvalho wrote:
>     >> 
>     >>> that happened to me with R-2.4.0 (alpha) and was fixed on R-2.4.0
>     >>> (final)...
>     >>> 
>     >>> http://tolstoy.newcastle.edu.au/R/e2/help/06/11/5061.html
>     >>> 
>     >>> then i stopped using... now, the problem seems to be back. The same
>     >>> examples still apply.
>     >>> 
>     >>> This fails:
>     >>> 
>     >>> require(cluster)
>     >>> set.seed(1)
>     >>> x <- rnorm(100)
>     >>> g <- sample(2:4, 100, rep=T)
>     >>> for (i in 1:100){
>     >>> print(i)
>     >>> tmp <- silhouette(g, dist(x))
>     >>> }
>     >>> 
>     >>> and this works:
>     >>> 
>     >>> require(cluster)
>     >>> set.seed(1)
>     >>> x <- rnorm(100)
>     >>> g <- sample(2:4, 100, rep=T)
>     >>> for (i in 1:100){
>     >>> print(i)
>     >>> tmp <- silhouette(as.integer(factor(g)), dist(x))
>     >>> }
>     >>> 
>     >>> and here's the sessionInfo():
>     >>> 
>     >>> > sessionInfo()
>     >>> R version 2.6.0 (2007-10-03)
>     >>> x86_64-unknown-linux-gnu
>     >>> 
>     >>> locale:
>     >>> LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.U
>     >>> TF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-
>     >>> 8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_ID
>     >>> ENTIFICATION=C
>     >>> 
>     >>> attached base packages:
>     >>> [1] stats     graphics  grDevices utils     datasets  methods   base
>     >>> 
>     >>> other attached packages:
>     >>> [1] cluster_1.11.9
>     >>> 
>     >>> 
>     >>> (Red Hat EL 2.6.9-42 smp - AMD opteron 848)
>     >>> 
>     >>> b
>     >>> 
>     >>> On Oct 9, 2007, at 8:35 PM, Tao Shi wrote:
>     >>> 
>     >>>> Hi list,
>     >>>> 
>     >>>> When I was using 'silhouette' from the 'cluster' package to
>     >>>> calculate clustering performances, R crashed.  I traced the problem
>     >>>> to the fact that my clustering labels only have 2's and 3's.  when
>     >>>> I replaced them with 1's and 2's, the problem was solved.  Is the
>     >>>> function purposely written in this way so when I have clustering
>     >>>> labels, "2" and "3", for example, the function somehow takes the
>     >>>> 'missing' cluster "2" into account when it calculates silhouette
>     >>>> widths?
>     >>>> 
>     >>>> Thanks,
>     >>>> 
>     >>>> ....Tao
>     >>>> 
>     >>>> ##============================================
>     >>>> ## sorry about the long attachment
>     >>>> 
>     >>>>> R.Version()
>     >>>> $platform
>     >>>> [1] "i386-pc-mingw32"
>     >>>> 
>     >>>> $arch
>     >>>> [1] "i386"
>     >>>> 
>     >>>> $os
>     >>>> [1] "mingw32"
>     >>>> 
>     >>>> $system
>     >>>> [1] "i386, mingw32"
>     >>>> 
>     >>>> $status
>     >>>> [1] ""
>     >>>> 
>     >>>> $major
>     >>>> [1] "2"
>     >>>> 
>     >>>> $minor
>     >>>> [1] "5.1"
>     >>>> 
>     >>>> $year
>     >>>> [1] "2007"
>     >>>> 
>     >>>> $month
>     >>>> [1] "06"
>     >>>> 
>     >>>> $day
>     >>>> [1] "27"
>     >>>> 
>     >>>> $`svn rev`
>     >>>> [1] "42083"
>     >>>> 
>     >>>> $language
>     >>>> [1] "R"
>     >>>> 
>     >>>> $version.string
>     >>>> [1] "R version 2.5.1 (2007-06-27)"
>     >>>> 
>     >>>>> library(cluster)
>     >>>>> cl1   ## clustering labels
>     >>>> [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2
>     >>>> [30] 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
>     >>>> [59] 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
>     >>>> [88] 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
>     >>>> [117] 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
>     >>>> [146] 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
>     >>>> [175] 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
>     >>>> [204] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
>     >>>>> x1  ## 1-d input vector
>     >>>> [1] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
>     >>>> [6] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
>     >>>> [11] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
>     >>>> [16] 1.5707963 1.5707963 1.5707963 1.5707963 1.5707963
>     >>>> [21] 1.0163758 0.7657763 0.7370084 0.6999689 0.7366476
>     >>>> [26] 0.7883921 0.6925395 0.7729240 0.7202391 0.7910149
>     >>>> [31] 0.7397698 0.7958092 0.6978596 0.7350255 0.7294362
>     >>>> [36] 0.6125713 0.7174000 0.7413046 0.7044205 0.7568104
>     >>>> [41] 0.7048469 0.7334515 0.7143170 0.7002311 0.7540981
>     >>>> [46] 0.7627527 0.7712762 0.8193611 0.7801148 0.9061762
>     >>>> [51] 0.8248195 0.7932630 0.7248037 0.7423547 0.6419314
>     >>>> [56] 0.6001092 0.7572272 0.7631742 0.7085384 0.8710853
>     >>>> [61] 0.6589563 0.7464943 0.7487340 0.7751280 0.7946542
>     >>>> [66] 0.7666081 0.8508109 0.8314308 0.7442471 0.8006093
>     >>>> [71] 0.7949156 0.7852447 0.7630048 0.7104764 0.6768218
>     >>>> [76] 0.6806351 0.7255355 0.7431389 0.7523627 0.7670515
>     >>>> [81] 0.8118214 0.7215615 0.8186164 0.6941610 0.8285453
>     >>>> [86] 0.8395170 0.8088044 0.8182706 0.7550723 0.7948639
>     >>>> [91] 0.7204830 0.7109068 0.7756949 0.6837856 0.7055604
>     >>>> [96] 0.6126666 0.7201964 0.6849890 0.7779753 0.7845284
>     >>>> [101] 0.9370788 0.8242935 0.6908860 0.6446151 0.7660386
>     >>>> [106] 0.8141526 0.8111984 0.8624186 0.7865335 0.8213035
>     >>>> [111] 0.8059171 0.6735751 0.7815353 0.6972508 0.6699396
>     >>>> [116] 0.6293971 0.7475913 0.7700821 0.8258339 0.8096144
>     >>>> [121] 0.7058171 0.7516635 0.7323909 0.7229136 0.8344846
>     >>>> [126] 0.7205433 0.8287774 0.8322097 0.7767547 0.7402277
>     >>>> [131] 0.7939879 0.7797308 0.7112453 0.7091554 0.6417382
>     >>>> [136] 0.6369171 0.7059020 0.7496380 0.7298359 0.8202566
>     >>>> [141] 0.7331830 0.7344492 0.8316894 0.7323979 0.7977615
>     >>>> [146] 0.7841205 0.7587060 0.8056685 0.7895643 0.8140731
>     >>>> [151] 0.7890221 0.8016008 0.7381577 0.6936453 0.7133525
>     >>>> [156] 0.7121459 0.6851448 0.7946275 0.8077618 0.7899059
>     >>>> [161] 0.7128826 0.7546289 0.7042451 0.6606403 0.7525233
>     >>>> [166] 0.7527548 0.8098887 0.8254190 0.7873064 0.8139340
>     >>>> [171] 0.7903462 0.8377651 0.6709983 0.7423632 0.6632082
>     >>>> [176] 0.5676717 0.6925125 0.7077083 0.7488877 0.7630604
>     >>>> [181] 0.7843001 0.7524471 0.6871823 0.7144443 0.7692206
>     >>>> [186] 0.8690710 0.9282786 0.7844991 0.7094671 0.7578409
>     >>>> [191] 0.8026643 0.7759241 0.6997376 0.6167209 0.6682289
>     >>>> [196] 0.6572018 0.7615807 0.7415752 0.7659161 0.7040360
>     >>>> [201] 0.6874460 0.7052109 0.8290970 0.6915149 0.7173107
>     >>>> [206] 0.7848961 0.7943846 0.8437946 0.7817344 0.8867006
>     >>>> [211] 0.7575857 0.8390473 0.7382348 0.6789859 0.7129010
>     >>>> [216] 0.6938173 0.7384170 0.6747648 0.7203337 0.7278963
>     >>>>> silhouette(cl1, dist(x1)^2)  #####  CRASHED! ######
>     >>>>> silhouette(ifelse(cl1==3,2,1), dist(x1)^2)
>     >>>> cluster neighbor sil_width
>     >>>> [1,]       2        1 1.0000000
>     >>>> [2,]       2        1 1.0000000
>     >>>> [3,]       2        1 1.0000000
>     >>>> [4,]       2        1 1.0000000
>     >>>> [5,]       2        1 1.0000000
>     >>>> [6,]       2        1 1.0000000
>     >>>> [7,]       2        1 1.0000000
>     >>>> [8,]       2        1 1.0000000
>     >>>> [9,]       2        1 1.0000000
>     >>>> [10,]       2        1 1.0000000
>     >>>> [11,]       2        1 1.0000000
>     >>>> [12,]       2        1 1.0000000
>     >>>> [13,]       2        1 1.0000000
>     >>>> [14,]       2        1 1.0000000
>     >>>> [15,]       2        1 1.0000000
>     >>>> [16,]       2        1 1.0000000
>     >>>> [17,]       2        1 1.0000000
>     >>>> [18,]       2        1 1.0000000
>     >>>> [19,]       2        1 1.0000000
>     >>>> [20,]       2        1 1.0000000
>     >>>> [21,]       1        2 0.7592857
>     >>>> [22,]       1        2 0.9934455
>     >>>> [23,]       1        2 0.9937880
>     >>>> [24,]       1        2 0.9909544
>     >>>> [25,]       1        2 0.9937769
>     >>>> [26,]       1        2 0.9912442
>     >>>> [27,]       1        2 0.9900156
>     >>>> [28,]       1        2 0.9929499
>     >>>> [29,]       1        2 0.9929125
>     >>>> [30,]       1        2 0.9908637
>     >>>> [31,]       1        2 0.9938610
>     >>>> [32,]       1        2 0.9900958
>     >>>> [33,]       1        2 0.9906993
>     >>>> [34,]       1        2 0.9937227
>     >>>> [35,]       1        2 0.9934823
>     >>>> [36,]       1        2 0.9740954
>     >>>> [37,]       1        2 0.9926948
>     >>>> [38,]       1        2 0.9938924
>     >>>> [39,]       1        2 0.9914623
>     >>>> [40,]       1        2 0.9938250
>     >>>> [41,]       1        2 0.9915088
>     >>>> [42,]       1        2 0.9936633
>     >>>> [43,]       1        2 0.9924367
>     >>>> [44,]       1        2 0.9909855
>     >>>> [45,]       1        2 0.9938891
>     >>>> [46,]       1        2 0.9936028
>     >>>> [47,]       1        2 0.9930799
>     >>>> [48,]       1        2 0.9848568
>     >>>> [49,]       1        2 0.9922685
>     >>>> [50,]       1        2 0.9371272
>     >>>> [51,]       1        2 0.9832647
>     >>>> [52,]       1        2 0.9905154
>     >>>> [53,]       1        2 0.9932217
>     >>>> [54,]       1        2 0.9939101
>     >>>> [55,]       1        2 0.9810071
>     >>>> [56,]       1        2 0.9708675
>     >>>> [57,]       1        2 0.9938131
>     >>>> [58,]       1        2 0.9935827
>     >>>> [59,]       1        2 0.9918943
>     >>>> [60,]       1        2 0.9628701
>     >>>> [61,]       1        2 0.9844965
>     >>>> [62,]       1        2 0.9939491
>     >>>> [63,]       1        2 0.9939495
>     >>>> [64,]       1        2 0.9927610
>     >>>> [65,]       1        2 0.9902895
>     >>>> [66,]       1        2 0.9933968
>     >>>> [67,]       1        2 0.9734481
>     >>>> [68,]       1        2 0.9811285
>     >>>> [69,]       1        2 0.9939341
>     >>>> [70,]       1        2 0.9892304
>     >>>> [71,]       1        2 0.9902461
>     >>>> [72,]       1        2 0.9916649
>     >>>> [73,]       1        2 0.9935909
>     >>>> [74,]       1        2 0.9920846
>     >>>> [75,]       1        2 0.9876779
>     >>>> [76,]       1        2 0.9882868
>     >>>> [77,]       1        2 0.9932665
>     >>>> [78,]       1        2 0.9939213
>     >>>> [79,]       1        2 0.9939182
>     >>>> [80,]       1        2 0.9933699
>     >>>> [81,]       1        2 0.9868129
>     >>>> [82,]       1        2 0.9930074
>     >>>> [83,]       1        2 0.9850624
>     >>>> [84,]       1        2 0.9902300
>     >>>> [85,]       1        2 0.9820895
>     >>>> [86,]       1        2 0.9781906
>     >>>> [87,]       1        2 0.9875197
>     >>>> [88,]       1        2 0.9851569
>     >>>> [89,]       1        2 0.9938688
>     >>>> [90,]       1        2 0.9902547
>     >>>> [91,]       1        2 0.9929304
>     >>>> [92,]       1        2 0.9921257
>     >>>> [93,]       1        2 0.9927096
>     >>>> [94,]       1        2 0.9887702
>     >>>> [95,]       1        2 0.9915856
>     >>>> [96,]       1        2 0.9741195
>     >>>> [97,]       1        2 0.9929094
>     >>>> [98,]       1        2 0.9889500
>     >>>> [99,]       1        2 0.9924910
>     >>>> [100,]       1        2 0.9917552
>     >>>> [101,]       1        2 0.9047049
>     >>>> [102,]       1        2 0.9834247
>     >>>> [103,]       1        2 0.9897916
>     >>>> [104,]       1        2 0.9815845
>     >>>> [105,]       1        2 0.9934304
>     >>>> [106,]       1        2 0.9862375
>     >>>> [107,]       1        2 0.9869624
>     >>>> [108,]       1        2 0.9677353
>     >>>> [109,]       1        2 0.9914973
>     >>>> [110,]       1        2 0.9843076
>     >>>> [111,]       1        2 0.9881568
>     >>>> [112,]       1        2 0.9871393
>     >>>> [113,]       1        2 0.9921114
>     >>>> [114,]       1        2 0.9906240
>     >>>> [115,]       1        2 0.9865148
>     >>>> [116,]       1        2 0.9781846
>     >>>> [117,]       1        2 0.9939511
>     >>>> [118,]       1        2 0.9931681
>     >>>> [119,]       1        2 0.9829519
>     >>>> [120,]       1        2 0.9873341
>     >>>> [121,]       1        2 0.9916130
>     >>>> [122,]       1        2 0.9939273
>     >>>> [123,]       1        2 0.9936196
>     >>>> [124,]       1        2 0.9930999
>     >>>> [125,]       1        2 0.9800620
>     >>>> [126,]       1        2 0.9929347
>     >>>> [127,]       1        2 0.9820138
>     >>>> [128,]       1        2 0.9808614
>     >>>> [129,]       1        2 0.9926103
>     >>>> [130,]       1        2 0.9938711
>     >>>> [131,]       1        2 0.9903987
>     >>>> [132,]       1        2 0.9923097
>     >>>> [133,]       1        2 0.9921578
>     >>>> [134,]       1        2 0.9919558
>     >>>> [135,]       1        2 0.9809652
>     >>>> [136,]       1        2 0.9799023
>     >>>> [137,]       1        2 0.9916220
>     >>>> [138,]       1        2 0.9939454
>     >>>> [139,]       1        2 0.9935022
>     >>>> [140,]       1        2 0.9846059
>     >>>> [141,]       1        2 0.9936526
>     >>>> [142,]       1        2 0.9937017
>     >>>> [143,]       1        2 0.9810402
>     >>>> [144,]       1        2 0.9936199
>     >>>> [145,]       1        2 0.9897557
>     >>>> [146,]       1        2 0.9918058
>     >>>> [147,]       1        2 0.9937665
>     >>>> [148,]       1        2 0.9882099
>     >>>> [149,]       1        2 0.9910776
>     >>>> [150,]       1        2 0.9862575
>     >>>> [151,]       1        2 0.9911553
>     >>>> [152,]       1        2 0.9890393
>     >>>> [153,]       1        2 0.9938209
>     >>>> [154,]       1        2 0.9901624
>     >>>> [155,]       1        2 0.9923515
>     >>>> [156,]       1        2 0.9922418
>     >>>> [157,]       1        2 0.9889731
>     >>>> [158,]       1        2 0.9902939
>     >>>> [159,]       1        2 0.9877542
>     >>>> [160,]       1        2 0.9910280
>     >>>> [161,]       1        2 0.9923092
>     >>>> [162,]       1        2 0.9938784
>     >>>> [163,]       1        2 0.9914431
>     >>>> [164,]       1        2 0.9848184
>     >>>> [165,]       1        2 0.9939159
>     >>>> [166,]       1        2 0.9939125
>     >>>> [167,]       1        2 0.9872706
>     >>>> [168,]       1        2 0.9830805
>     >>>> [169,]       1        2 0.9913937
>     >>>> [170,]       1        2 0.9862925
>     >>>> [171,]       1        2 0.9909633
>     >>>> [172,]       1        2 0.9788584
>     >>>> [173,]       1        2 0.9866989
>     >>>> [174,]       1        2 0.9939102
>     >>>> [175,]       1        2 0.9853007
>     >>>> [176,]       1        2 0.9617883
>     >>>> [177,]       1        2 0.9900120
>     >>>> [178,]       1        2 0.9918102
>     >>>> [179,]       1        2 0.9939489
>     >>>> [180,]       1        2 0.9935882
>     >>>> [181,]       1        2 0.9917836
>     >>>> [182,]       1        2 0.9939170
>     >>>> [183,]       1        2 0.9892708
>     >>>> [184,]       1        2 0.9924478
>     >>>> [185,]       1        2 0.9932287
>     >>>> [186,]       1        2 0.9640487
>     >>>> [187,]       1        2 0.9150126
>     >>>> [188,]       1        2 0.9917589
>     >>>> [189,]       1        2 0.9919865
>     >>>> [190,]       1        2 0.9937946
>     >>>> [191,]       1        2 0.9888295
>     >>>> [192,]       1        2 0.9926884
>     >>>> [193,]       1        2 0.9909269
>     >>>> [194,]       1        2 0.9751339
>     >>>> [195,]       1        2 0.9862132
>     >>>> [196,]       1        2 0.9841566
>     >>>> [197,]       1        2 0.9936557
>     >>>> [198,]       1        2 0.9938973
>     >>>> [199,]       1        2 0.9934375
>     >>>> [200,]       1        2 0.9914201
>     >>>> [201,]       1        2 0.9893087
>     >>>> [202,]       1        2 0.9915481
>     >>>> [203,]       1        2 0.9819092
>     >>>> [204,]       1        2 0.9898774
>     >>>> [205,]       1        2 0.9926876
>     >>>> [206,]       1        2 0.9917091
>     >>>> [207,]       1        2 0.9903339
>     >>>> [208,]       1        2 0.9764847
>     >>>> [209,]       1        2 0.9920887
>     >>>> [210,]       1        2 0.9526866
>     >>>> [211,]       1        2 0.9938025
>     >>>> [212,]       1        2 0.9783714
>     >>>> [213,]       1        2 0.9938230
>     >>>> [214,]       1        2 0.9880267
>     >>>> [215,]       1        2 0.9923108
>     >>>> [216,]       1        2 0.9901850
>     >>>> [217,]       1        2 0.9938279
>     >>>> [218,]       1        2 0.9873388
>     >>>> [219,]       1        2 0.9929195
>     >>>> [220,]       1        2 0.9934017
>     >>>> attr(,"Ordered")
>     >>>> [1] FALSE
>     >>>> attr(,"call")
>     >>>> silhouette.default(x = ifelse(cl1 == 3, 2, 1), dist = dist(x1)^2)
>     >>>> attr(,"class")
>     >>>> [1] "silhouette"
>     >>>> 
>     >>>> ## other examples
>     >>>>> set.seed(1234)
>     >>>>> cl.tmp <- rep(2:3, each=5)
>     >>>>> x.tmp <- c(rep(-1,5), abs(rnorm(5)+3))
>     >>>>> silhouette(cl.tmp, dist(x.tmp))
>     >>>> cluster neighbor  sil_width
>     >>>> [1,]       2        1        NaN
>     >>>> [2,]       2        1        NaN
>     >>>> [3,]       2        1        NaN
>     >>>> [4,]       2        1        NaN
>     >>>> [5,]       2        1        NaN
>     >>>> [6,]       3        2 -0.5736515
>     >>>> [7,]       3        2 -0.1557143
>     >>>> [8,]       3        2 -0.2922523
>     >>>> [9,]       3        2 -0.8340174
>     >>>> [10,]       3        2 -0.1511875
>     >>>> attr(,"Ordered")
>     >>>> [1] FALSE
>     >>>> attr(,"call")
>     >>>> silhouette.default(x = cl.tmp, dist = dist(x.tmp))
>     >>>> attr(,"class")
>     >>>> [1] "silhouette"
>     >>>>> silhouette(ifelse(cl.tmp==2,1,2), dist(x.tmp))
>     >>>> cluster neighbor  sil_width
>     >>>> [1,]       1        2  1.0000000
>     >>>> [2,]       1        2  1.0000000
>     >>>> [3,]       1        2  1.0000000
>     >>>> [4,]       1        2  1.0000000
>     >>>> [5,]       1        2  1.0000000
>     >>>> [6,]       2        1  0.4136253
>     >>>> [7,]       2        1  0.7038917
>     >>>> [8,]       2        1  0.6467668
>     >>>> [9,]       2        1 -0.3360695
>     >>>> [10,]       2        1  0.7054709
>     >>>> attr(,"Ordered")
>     >>>> [1] FALSE
>     >>>> attr(,"call")
>     >>>> silhouette.default(x = ifelse(cl.tmp == 2, 1, 2), dist = dist(x.tmp))
>     >>>> attr(,"class")
>     >>>> [1] "silhouette"
>     >>>>> silhouette(ifelse(cl.tmp==2,1,3), dist(x.tmp))
>     >>>> cluster neighbor  sil_width
>     >>>> [1,]       1        2        NaN
>     >>>> [2,]       1        2        NaN
>     >>>> [3,]       1        2        NaN
>     >>>> [4,]       1        2        NaN
>     >>>> [5,]       1        2        NaN
>     >>>> [6,]       3        1 -0.7694686
>     >>>> [7,]       3        1 -0.8167313
>     >>>> [8,]       3        1 -0.6054665
>     >>>> [9,]       3        1 -0.9037412
>     >>>> [10,]       3        1  0.1875360
>     >>>> attr(,"Ordered")
>     >>>> [1] FALSE
>     >>>> attr(,"call")
>     >>>> silhouette.default(x = ifelse(cl.tmp == 2, 1, 3), dist = dist(x.tmp))
>     >>>> attr(,"class")
>     >>>> [1] "silhouette"
>     >>>> 
>     >>>> _________________________________________________________________
>     >>>> 
>     >>>> It?s free. http://im.live.com/messenger/im/home/?source=TAGHM
>     >>>> 
>     >>>> <mime-attachment.txt>
>     >>> 
>     >>> ______________________________________________
>     >>> R-help@r-project.org mailing list
>     >>> https://stat.ethz.ch/mailman/listinfo/r-help
>     >>> PLEASE do read the posting guide 
>     >>> http://www.R-project.org/posting-guide.html
>     >>> and provide commented, minimal, self-contained, reproducible code.
>     >>> 
>     >> 
>     >> --
>     >> Brian D. Ripley,                  ripley@stats.ox.ac.uk
>     >> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
>     >> University of Oxford,             Tel:  +44 1865 272861 (self)
>     >> 1 South Parks Road,                     +44 1865 272866 (PA)
>     >> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> 
>     TS> ______________________________________________
>     TS> R-help@r-project.org mailing list
>     TS> https://stat.ethz.ch/mailman/listinfo/r-help
>     TS> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>     TS> and provide commented, minimal, self-contained, reproducible code.

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