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> > > ______________________________________________ > [email protected] 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, [EMAIL PROTECTED] 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 ______________________________________________ [email protected] 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.
