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.
