Thank you very much, Martin. Warmest regards, b
Em 13/05/2009, às 09:14, "Martin Maechler" <maech...@stat.math.ethz.ch> escreveu: !#x000a >>>>>> "TS" == Tao Shi <shi...@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 <rip...@stats.ox.ac.uk> >>> To: Benilton Carvalho <bcarv...@jhsph.edu> >>> CC: Tao Shi <shi...@hotmail.com>, maech...@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;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@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, rip...@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. ______________________________________________ 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.