[R] silhouette: clustering labels have to be consecutive integers starting
Carvalho, Benilton
bcarvalh at jhsph.edu
Wed May 13 14:46:59 CEST 2009
Thank you very much, Martin.
Warmest regards, b
Em 13/05/2009, às 09:14, "Martin Maechler"
<maechler at stat.math.ethz.ch> escreveu:
!#x000a
>>>>>> "TS" == Tao Shi <shitao at 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 at stats.ox.ac.uk>
>>> To: Benilton Carvalho <bcarvalh at jhsph.edu>
>>> CC: Tao Shi <shitao at hotmail.com>, maechler at stat.math.ethz.ch,
>>> r-help at 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;L
>>>> C_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-
>>>> 8;L
>>>> C_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 at 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 at 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 at 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.
More information about the R-help
mailing list