Thank you very much, Benilton and Prof. Ripley, for the speedy replies! Looking forward to the fix!
....Tao >From: Prof Brian Ripley <[EMAIL PROTECTED]> >To: Benilton Carvalho <[EMAIL PROTECTED]> >CC: Tao Shi <[EMAIL PROTECTED]>, [EMAIL PROTECTED], >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, [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 ______________________________________________ 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.
