R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(79 + ,30 + ,112285 + ,0 + ,3 + ,4 + ,58 + ,28 + ,84786 + ,0 + ,NA + ,NA + ,60 + ,38 + ,83123 + ,1 + ,1 + ,2 + ,108 + ,30 + ,101193 + ,NA + ,NA + ,NA + ,49 + ,22 + ,38361 + ,NA + ,NA + ,NA + ,0 + ,26 + ,68504 + ,NA + ,NA + ,NA + ,121 + ,25 + ,119182 + ,0 + ,2 + ,4 + ,1 + ,18 + ,22807 + ,NA + ,NA + ,NA + ,20 + ,11 + ,17140 + ,NA + ,NA + ,NA + ,43 + ,26 + ,116174 + ,0 + ,1 + ,4 + ,69 + ,25 + ,57635 + ,1 + ,2 + ,4 + ,78 + ,38 + ,66198 + ,1 + ,1 + ,5 + ,86 + ,44 + ,71701 + ,NA + ,NA + ,NA + ,44 + ,30 + ,57793 + ,1 + ,2 + ,4 + ,104 + ,40 + ,80444 + ,NA + ,NA + ,NA + ,63 + ,34 + ,53855 + ,NA + ,NA + ,NA + ,158 + ,47 + ,97668 + ,1 + ,1 + ,2 + ,102 + ,30 + ,133824 + ,0 + ,4 + ,2 + ,77 + ,31 + ,101481 + ,1 + ,2 + ,4 + ,82 + ,23 + ,99645 + ,0 + ,NA + ,NA + ,115 + ,36 + ,114789 + ,NA + ,NA + ,NA + ,101 + ,36 + ,99052 + ,0 + ,1 + ,4 + ,80 + ,30 + ,67654 + ,1 + ,1 + ,5 + ,50 + ,25 + ,65553 + ,0 + ,NA + ,NA + ,83 + ,39 + ,97500 + ,NA + ,NA + ,NA + ,123 + ,34 + ,69112 + ,1 + ,1 + ,4 + ,73 + ,31 + ,82753 + ,1 + ,2 + ,4 + ,81 + ,31 + ,85323 + ,0 + ,4 + ,5 + ,105 + ,33 + ,72654 + ,1 + ,2 + ,4 + ,47 + ,25 + ,30727 + ,1 + ,2 + ,4 + ,105 + ,33 + ,77873 + ,NA + ,NA + ,NA + ,94 + ,35 + ,117478 + ,0 + ,3 + ,3 + ,44 + ,42 + ,74007 + ,0 + ,2 + ,3 + ,114 + ,43 + ,90183 + ,NA + ,NA + ,NA + ,38 + ,30 + ,61542 + ,NA + ,NA + ,NA + ,107 + ,33 + ,101494 + ,0 + ,2 + ,5 + ,30 + ,13 + ,27570 + ,NA + ,NA + ,NA + ,71 + ,32 + ,55813 + ,NA + ,NA + ,NA + ,84 + ,36 + ,79215 + ,1 + ,3 + ,4 + ,0 + ,0 + ,1423 + ,1 + ,2 + ,4 + ,59 + ,28 + ,55461 + ,NA + ,NA + ,NA + ,33 + ,14 + ,31081 + ,0 + ,4 + ,2 + ,42 + ,17 + ,22996 + ,0 + ,4 + ,4 + ,96 + ,32 + ,83122 + ,1 + ,0 + ,5 + ,106 + ,30 + ,70106 + ,NA + ,NA + ,NA + ,56 + ,35 + ,60578 + ,0 + ,3 + ,3 + ,57 + ,20 + ,39992 + ,1 + ,1 + ,3 + ,59 + ,28 + ,79892 + ,0 + ,1 + ,4 + ,39 + ,28 + ,49810 + ,1 + ,1 + ,5 + ,34 + ,39 + ,71570 + ,NA + ,NA + ,NA + ,76 + ,34 + ,100708 + ,1 + ,2 + ,4 + ,20 + ,26 + ,33032 + ,NA + ,NA + ,NA + ,91 + ,39 + ,82875 + ,0 + ,3 + ,4 + ,115 + ,39 + ,139077 + ,NA + ,NA + ,NA + ,85 + ,33 + ,71595 + ,NA + ,NA + ,NA + ,76 + ,28 + ,72260 + ,1 + ,NA + ,NA + ,8 + ,4 + ,5950 + ,1 + ,NA + ,NA + ,79 + ,39 + ,115762 + ,1 + ,1 + ,4 + ,21 + ,18 + ,32551 + ,NA + ,NA + ,NA + ,30 + ,14 + ,31701 + ,NA + ,NA + ,NA + ,76 + ,29 + ,80670 + ,1 + ,3 + ,3 + ,101 + ,44 + ,143558 + ,1 + ,1 + ,4 + ,94 + ,21 + ,117105 + ,1 + ,2 + ,3 + ,27 + ,16 + ,23789 + ,0 + ,2 + ,4 + ,92 + ,28 + ,120733 + ,NA + ,NA + ,NA + ,123 + ,35 + ,105195 + ,1 + ,2 + ,4 + ,75 + ,28 + ,73107 + ,NA + ,NA + ,NA + ,128 + ,38 + ,132068 + ,NA + ,NA + ,NA + ,105 + ,23 + ,149193 + ,0 + ,NA + ,NA + ,55 + ,36 + ,46821 + ,NA + ,NA + ,NA + ,56 + ,32 + ,87011 + ,NA + ,NA + ,NA + ,41 + ,29 + ,95260 + ,1 + ,1 + ,4 + ,72 + ,25 + ,55183 + ,1 + ,NA + ,NA + ,67 + ,27 + ,106671 + ,0 + ,1 + ,5 + ,75 + ,36 + ,73511 + ,1 + ,NA + ,NA + ,114 + ,28 + ,92945 + ,0 + ,2 + ,4 + ,118 + ,23 + ,78664 + ,NA + ,NA + ,NA + ,77 + ,40 + ,70054 + ,NA + ,NA + ,NA + ,22 + ,23 + ,22618 + ,1 + ,1 + ,3 + ,66 + ,40 + ,74011 + ,NA + ,NA + ,NA + ,69 + ,28 + ,83737 + ,0 + ,1 + ,2 + ,105 + ,34 + ,69094 + ,0 + ,4 + ,4 + ,116 + ,33 + ,93133 + ,NA + ,NA + ,NA + ,88 + ,28 + ,95536 + ,0 + ,3 + ,4 + ,73 + ,34 + ,225920 + ,1 + ,NA + ,NA + ,99 + ,30 + ,62133 + ,NA + ,NA + ,NA + ,62 + ,33 + ,61370 + ,1 + ,1 + ,4 + ,53 + ,22 + ,43836 + ,NA + ,NA + ,NA + ,118 + ,38 + ,106117 + ,1 + ,3 + ,5 + ,30 + ,26 + ,38692 + ,NA + ,NA + ,NA + ,100 + ,35 + ,84651 + ,1 + ,2 + ,5 + ,49 + ,8 + ,56622 + ,NA + ,NA + ,NA + ,24 + ,24 + ,15986 + ,1 + ,1 + ,2 + ,67 + ,29 + ,95364 + ,0 + ,3 + ,1 + ,46 + ,20 + ,26706 + ,1 + ,3 + ,4 + ,57 + ,29 + ,89691 + ,1 + ,1 + ,4 + ,75 + ,45 + ,67267 + ,NA + ,NA + ,NA + ,135 + ,37 + ,126846 + ,1 + ,3 + ,4 + ,68 + ,33 + ,41140 + ,NA + ,NA + ,NA + ,124 + ,33 + ,102860 + ,0 + ,2 + ,1 + ,33 + ,25 + ,51715 + ,1 + ,1 + ,4 + ,98 + ,32 + ,55801 + ,1 + ,2 + ,4 + ,58 + ,29 + ,111813 + ,1 + ,1 + ,2 + ,68 + ,28 + ,120293 + ,1 + ,2 + ,4 + ,81 + ,28 + ,138599 + ,NA + ,NA + ,NA + ,131 + ,31 + ,161647 + ,1 + ,2 + ,5 + ,110 + ,52 + ,115929 + ,0 + ,1 + ,3 + ,37 + ,21 + ,24266 + ,1 + ,3 + ,4 + ,130 + ,24 + ,162901 + ,0 + ,NA + ,NA + ,93 + ,41 + ,109825 + ,0 + ,3 + ,2 + ,118 + ,33 + ,129838 + ,1 + ,4 + ,5 + ,39 + ,32 + ,37510 + ,0 + ,NA + ,NA + ,13 + ,19 + ,43750 + ,NA + ,NA + ,NA + ,74 + ,20 + ,40652 + ,NA + ,NA + ,NA + ,81 + ,31 + ,87771 + ,1 + ,3 + ,4 + ,109 + ,31 + ,85872 + ,NA + ,NA + ,NA + ,151 + ,32 + ,89275 + ,NA + ,NA + ,NA + ,51 + ,18 + ,44418 + ,1 + ,3 + ,4 + ,28 + ,23 + ,192565 + ,0 + ,2 + ,2 + ,40 + ,17 + ,35232 + ,1 + ,3 + ,3 + ,56 + ,20 + ,40909 + ,1 + ,3 + ,4 + ,27 + ,12 + ,13294 + ,1 + ,1 + ,2 + ,37 + ,17 + ,32387 + ,NA + ,NA + ,NA + ,83 + ,30 + ,140867 + ,1 + ,1 + ,4 + ,54 + ,31 + ,120662 + ,NA + ,NA + ,NA + ,27 + ,10 + ,21233 + ,NA + ,NA + ,NA + ,28 + ,13 + ,44332 + ,0 + ,3 + ,3 + ,59 + ,22 + ,61056 + ,1 + ,3 + ,4 + ,133 + ,42 + ,101338 + ,1 + ,1 + ,4 + ,12 + ,1 + ,1168 + ,1 + ,3 + ,2 + ,0 + ,9 + ,13497 + ,NA + ,NA + ,NA + ,106 + ,32 + ,65567 + ,1 + ,1 + ,4 + ,23 + ,11 + ,25162 + ,NA + ,NA + ,NA + ,44 + ,25 + ,32334 + ,0 + ,4 + ,2 + ,71 + ,36 + ,40735 + ,1 + ,3 + ,5 + ,116 + ,31 + ,91413 + ,0 + ,3 + ,4 + ,4 + ,0 + ,855 + ,1 + ,NA + ,NA + ,62 + ,24 + ,97068 + ,1 + ,NA + ,NA + ,12 + ,13 + ,44339 + ,0 + ,4 + ,3 + ,18 + ,8 + ,14116 + ,0 + ,NA + ,NA + ,14 + ,13 + ,10288 + ,1 + ,3 + ,3 + ,60 + ,19 + ,65622 + ,1 + ,1 + ,4 + ,7 + ,18 + ,16563 + ,NA + ,NA + ,NA + ,98 + ,33 + ,76643 + ,1 + ,3 + ,4 + ,64 + ,40 + ,110681 + ,NA + ,NA + ,NA + ,29 + ,22 + ,29011 + ,NA + ,NA + ,NA + ,32 + ,38 + ,92696 + ,0 + ,1 + ,4 + ,25 + ,24 + ,94785 + ,0 + ,4 + ,3 + ,16 + ,8 + ,8773 + ,NA + ,NA + ,NA + ,48 + ,35 + ,83209 + ,NA + ,NA + ,NA + ,100 + ,43 + ,93815 + ,1 + ,2 + ,4 + ,46 + ,43 + ,86687 + ,NA + ,2 + ,2 + ,45 + ,14 + ,34553 + ,1 + ,1 + ,4 + ,129 + ,41 + ,105547 + ,0 + ,3 + ,4 + ,130 + ,38 + ,103487 + ,NA + ,NA + ,NA + ,136 + ,45 + ,213688 + ,1 + ,4 + ,2 + ,59 + ,31 + ,71220 + ,0 + ,NA + ,NA + ,25 + ,13 + ,23517 + ,NA + ,NA + ,NA + ,32 + ,28 + ,56926 + ,NA + ,NA + ,NA + ,63 + ,31 + ,91721 + ,1 + ,2 + ,2 + ,95 + ,40 + ,115168 + ,NA + ,NA + ,NA + ,14 + ,30 + ,111194 + ,1 + ,NA + ,NA + ,36 + ,16 + ,51009 + ,0 + ,2 + ,4 + ,113 + ,37 + ,135777 + ,0 + ,3 + ,4 + ,47 + ,30 + ,51513 + ,0 + ,2 + ,4 + ,92 + ,35 + ,74163 + ,0 + ,4 + ,3 + ,70 + ,32 + ,51633 + ,NA + ,NA + ,NA + ,19 + ,27 + ,75345 + ,NA + ,NA + ,NA + ,50 + ,20 + ,33416 + ,0 + ,3 + ,2 + ,41 + ,18 + ,83305 + ,1 + ,1 + ,3 + ,91 + ,31 + ,98952 + ,1 + ,NA + ,NA + ,111 + ,31 + ,102372 + ,0 + ,1 + ,1 + ,41 + ,21 + ,37238 + ,1 + ,3 + ,4 + ,120 + ,39 + ,103772 + ,0 + ,1 + ,3 + ,135 + ,41 + ,123969 + ,NA + ,NA + ,NA + ,27 + ,13 + ,27142 + ,NA + ,NA + ,NA + ,87 + ,32 + ,135400 + ,NA + ,NA + ,NA + ,25 + ,18 + ,21399 + ,1 + ,3 + ,4 + ,131 + ,39 + ,130115 + ,0 + ,1 + ,4 + ,45 + ,14 + ,24874 + ,0 + ,1 + ,3 + ,29 + ,7 + ,34988 + ,1 + ,1 + ,5 + ,58 + ,17 + ,45549 + ,0 + ,4 + ,5 + ,4 + ,0 + ,6023 + ,NA + ,NA + ,NA + ,47 + ,30 + ,64466 + ,1 + ,1 + ,4 + ,109 + ,37 + ,54990 + ,1 + ,2 + ,4 + ,7 + ,0 + ,1644 + ,NA + ,NA + ,NA + ,12 + ,5 + ,6179 + ,NA + ,NA + ,NA + ,0 + ,1 + ,3926 + ,NA + ,NA + ,NA + ,37 + ,16 + ,32755 + ,NA + ,NA + ,NA + ,37 + ,32 + ,34777 + ,1 + ,1 + ,2 + ,46 + ,24 + ,73224 + ,NA + ,NA + ,NA + ,15 + ,17 + ,27114 + ,0 + ,2 + ,4 + ,42 + ,11 + ,20760 + ,NA + ,NA + ,NA + ,7 + ,24 + ,37636 + ,0 + ,4 + ,4 + ,54 + ,22 + ,65461 + ,1 + ,2 + ,4 + ,54 + ,12 + ,30080 + ,0 + ,1 + ,4 + ,14 + ,19 + ,24094 + ,1 + ,2 + ,3 + ,16 + ,13 + ,69008 + ,1 + ,2 + ,4 + ,33 + ,17 + ,54968 + ,NA + ,NA + ,NA + ,32 + ,15 + ,46090 + ,1 + ,NA + ,NA + ,21 + ,16 + ,27507 + ,NA + ,NA + ,NA + ,15 + ,24 + ,10672 + ,NA + ,NA + ,NA + ,38 + ,15 + ,34029 + ,0 + ,3 + ,2 + ,22 + ,17 + ,46300 + ,0 + ,2 + ,3 + ,28 + ,18 + ,24760 + ,NA + ,NA + ,NA + ,10 + ,20 + ,18779 + ,NA + ,NA + ,NA + ,31 + ,16 + ,21280 + ,NA + ,NA + ,NA + ,32 + ,16 + ,40662 + ,1 + ,1 + ,4 + ,32 + ,18 + ,28987 + ,1 + ,1 + ,4 + ,43 + ,22 + ,22827 + ,NA + ,NA + ,NA + ,27 + ,8 + ,18513 + ,NA + ,NA + ,NA + ,37 + ,17 + ,30594 + ,0 + ,1 + ,2 + ,20 + ,18 + ,24006 + ,NA + ,NA + ,NA + ,32 + ,16 + ,27913 + ,0 + ,2 + ,4 + ,0 + ,23 + ,42744 + ,0 + ,1 + ,4 + ,5 + ,22 + ,12934 + ,0 + ,4 + ,4 + ,26 + ,13 + ,22574 + ,NA + ,NA + ,NA + ,10 + ,13 + ,41385 + ,1 + ,1 + ,2 + ,27 + ,16 + ,18653 + ,1 + ,NA + ,NA + ,11 + ,16 + ,18472 + ,NA + ,NA + ,NA + ,29 + ,20 + ,30976 + ,1 + ,4 + ,4 + ,25 + ,22 + ,63339 + ,1 + ,1 + ,5 + ,55 + ,17 + ,25568 + ,0 + ,3 + ,3 + ,23 + ,18 + ,33747 + ,NA + ,NA + ,NA + ,5 + ,17 + ,4154 + ,1 + ,NA + ,NA + ,43 + ,12 + ,19474 + ,0 + ,NA + ,NA + ,23 + ,7 + ,35130 + ,NA + ,NA + ,NA + ,34 + ,17 + ,39067 + ,1 + ,2 + ,4 + ,36 + ,14 + ,13310 + ,NA + ,NA + ,NA + ,35 + ,23 + ,65892 + ,1 + ,3 + ,5 + ,0 + ,17 + ,4143 + ,0 + ,2 + ,4 + ,37 + ,14 + ,28579 + ,1 + ,3 + ,4 + ,28 + ,15 + ,51776 + ,NA + ,NA + ,NA + ,16 + ,17 + ,21152 + ,NA + ,NA + ,NA + ,26 + ,21 + ,38084 + ,1 + ,2 + ,4 + ,38 + ,18 + ,27717 + ,1 + ,2 + ,4 + ,23 + ,18 + ,32928 + ,1 + ,1 + ,2 + ,22 + ,17 + ,11342 + ,NA + ,NA + ,NA + ,30 + ,17 + ,19499 + ,1 + ,1 + ,4 + ,16 + ,16 + ,16380 + ,NA + ,NA + ,NA + ,18 + ,15 + ,36874 + ,1 + ,NA + ,NA + ,28 + ,21 + ,48259 + ,1 + ,NA + ,NA + ,32 + ,16 + ,16734 + ,NA + ,NA + ,NA + ,21 + ,14 + ,28207 + ,0 + ,1 + ,2 + ,23 + ,15 + ,30143 + ,NA + ,NA + ,NA + ,29 + ,17 + ,41369 + ,NA + ,NA + ,NA + ,50 + ,15 + ,45833 + ,0 + ,2 + ,4 + ,12 + ,15 + ,29156 + ,1 + ,1 + ,5 + ,21 + ,10 + ,35944 + ,NA + ,NA + ,NA + ,18 + ,6 + ,36278 + ,NA + ,NA + ,NA + ,27 + ,22 + ,45588 + ,1 + ,3 + ,4 + ,41 + ,21 + ,45097 + ,1 + ,3 + ,3 + ,13 + ,1 + ,3895 + ,NA + ,NA + ,NA + ,12 + ,18 + ,28394 + ,0 + ,2 + ,4 + ,21 + ,17 + ,18632 + ,0 + ,3 + ,2 + ,8 + ,4 + ,2325 + ,0 + ,4 + ,4 + ,26 + ,10 + ,25139 + ,1 + ,3 + ,5 + ,27 + ,16 + ,27975 + ,1 + ,NA + ,NA + ,13 + ,16 + ,14483 + ,NA + ,NA + ,NA + ,16 + ,9 + ,13127 + ,NA + ,NA + ,NA + ,2 + ,16 + ,5839 + ,NA + ,NA + ,NA + ,42 + ,17 + ,24069 + ,NA + ,NA + ,NA + ,5 + ,7 + ,3738 + ,NA + ,NA + ,NA + ,37 + ,15 + ,18625 + ,NA + ,3 + ,4 + ,17 + ,14 + ,36341 + ,NA + ,NA + ,NA + ,38 + ,14 + ,24548 + ,NA + ,NA + ,NA + ,37 + ,18 + ,21792 + ,0 + ,2 + ,4 + ,29 + ,12 + ,26263 + ,0 + ,2 + ,2 + ,32 + ,16 + ,23686 + ,0 + ,1 + ,2 + ,35 + ,21 + ,49303 + ,0 + ,NA + ,NA + ,17 + ,19 + ,25659 + ,NA + ,NA + ,NA + ,20 + ,16 + ,28904 + ,NA + ,NA + ,NA + ,7 + ,1 + ,2781 + ,NA + ,NA + ,NA + ,46 + ,16 + ,29236 + ,NA + ,NA + ,NA + ,24 + ,10 + ,19546 + ,NA + ,NA + ,NA + ,40 + ,19 + ,22818 + ,NA + ,NA + ,NA + ,3 + ,12 + ,32689 + ,NA + ,NA + ,NA + ,10 + ,2 + ,5752 + ,1 + ,NA + ,NA + ,37 + ,14 + ,22197 + ,NA + ,NA + ,NA + ,17 + ,17 + ,20055 + ,0 + ,2 + ,4 + ,28 + ,19 + ,25272 + ,NA + ,NA + ,NA + ,19 + ,14 + ,82206 + ,NA + ,NA + ,NA + ,29 + ,11 + ,32073 + ,NA + ,NA + ,NA + ,8 + ,4 + ,5444 + ,NA + ,NA + ,NA + ,10 + ,16 + ,20154 + ,1 + ,2 + ,4 + ,15 + ,20 + ,36944 + ,NA + ,NA + ,NA + ,15 + ,12 + ,8019 + ,NA + ,NA + ,NA + ,28 + ,15 + ,30884 + ,NA + ,NA + ,NA + ,17 + ,16 + ,19540 + ,1 + ,NA + ,NA) + ,dim=c(6 + ,289) + ,dimnames=list(c('blogged_computations' + ,'compendiums_reviewed' + ,'totsize' + ,'Gender' + ,'Restless' + ,'Software') + ,1:289)) > y <- array(NA,dim=c(6,289),dimnames=list(c('blogged_computations','compendiums_reviewed','totsize','Gender','Restless','Software'),1:289)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "blogged_computations" > x[,par1] [1] 79 58 60 108 49 0 121 1 20 43 69 78 86 44 104 63 158 102 [19] 77 82 115 101 80 50 83 123 73 81 105 47 105 94 44 114 38 107 [37] 30 71 84 0 59 33 42 96 106 56 57 59 39 34 76 20 91 115 [55] 85 76 8 79 21 30 76 101 94 27 92 123 75 128 105 55 56 41 [73] 72 67 75 114 118 77 22 66 69 105 116 88 73 99 62 53 118 30 [91] 100 49 24 67 46 57 75 135 68 124 33 98 58 68 81 131 110 37 [109] 130 93 118 39 13 74 81 109 151 51 28 40 56 27 37 83 54 27 [127] 28 59 133 12 0 106 23 44 71 116 4 62 12 18 14 60 7 98 [145] 64 29 32 25 16 48 100 46 45 129 130 136 59 25 32 63 95 14 [163] 36 113 47 92 70 19 50 41 91 111 41 120 135 27 87 25 131 45 [181] 29 58 4 47 109 7 12 0 37 37 46 15 42 7 54 54 14 16 [199] 33 32 21 15 38 22 28 10 31 32 32 43 27 37 20 32 0 5 [217] 26 10 27 11 29 25 55 23 5 43 23 34 36 35 0 37 28 16 [235] 26 38 23 22 30 16 18 28 32 21 23 29 50 12 21 18 27 41 [253] 13 12 21 8 26 27 13 16 2 42 5 37 17 38 37 29 32 35 [271] 17 20 7 46 24 40 3 10 37 17 28 19 29 8 10 15 15 28 [289] 17 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$centers,1:par3),ncol=2) + clm <- clm[sort.list(clm[,1]),] + for (i in 1:par3) { + cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') + } + cl$cluster <- as.factor(cl$cluster) + print(cl$cluster) + x[,par1] <- cl$cluster + } > if (par2 == 'quantiles') { + x[,par1] <- cut2(x[,par1],g=par3) + } > if (par2 == 'hclust') { + hc <- hclust(dist(x[,par1])^2, 'cen') + print(hc) + memb <- cutree(hc, k = par3) + dum <- c(mean(x[memb==1,par1])) + for (i in 2:par3) { + dum <- c(dum, mean(x[memb==i,par1])) + } + hcm <- matrix(cbind(dum,1:par3),ncol=2) + hcm <- hcm[sort.list(hcm[,1]),] + for (i in 1:par3) { + memb[memb==hcm[i,2]] <- paste('C',i,sep='') + } + memb <- as.factor(memb) + print(memb) + x[,par1] <- memb + } > if (par2=='equal') { + ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) + x[,par1] <- as.factor(ed) + } > table(x[,par1]) 0 1 2 3 4 5 7 8 10 11 12 13 14 15 16 17 18 19 20 21 6 1 1 1 2 3 4 3 4 1 5 3 3 4 5 4 3 2 4 5 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 3 5 2 4 3 8 7 6 4 1 8 3 2 2 2 9 4 2 2 4 42 43 44 45 46 47 48 49 50 51 53 54 55 56 57 58 59 60 62 63 3 3 3 2 4 3 1 2 3 1 1 3 2 3 2 3 4 2 2 2 64 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 1 1 2 2 2 1 2 1 2 1 3 3 2 1 2 1 3 1 2 1 85 86 87 88 91 92 93 94 95 96 98 99 100 101 102 104 105 106 107 108 1 1 1 1 2 2 1 2 1 1 2 1 2 2 1 1 4 2 1 1 109 110 111 113 114 115 116 118 120 121 123 124 128 129 130 131 133 135 136 151 2 1 1 1 2 2 2 3 1 1 2 1 1 1 2 2 1 2 1 1 158 1 > colnames(x) [1] "blogged_computations" "compendiums_reviewed" "totsize" [4] "Gender" "Restless" "Software" > colnames(x)[par1] [1] "blogged_computations" > x[,par1] [1] 79 58 60 108 49 0 121 1 20 43 69 78 86 44 104 63 158 102 [19] 77 82 115 101 80 50 83 123 73 81 105 47 105 94 44 114 38 107 [37] 30 71 84 0 59 33 42 96 106 56 57 59 39 34 76 20 91 115 [55] 85 76 8 79 21 30 76 101 94 27 92 123 75 128 105 55 56 41 [73] 72 67 75 114 118 77 22 66 69 105 116 88 73 99 62 53 118 30 [91] 100 49 24 67 46 57 75 135 68 124 33 98 58 68 81 131 110 37 [109] 130 93 118 39 13 74 81 109 151 51 28 40 56 27 37 83 54 27 [127] 28 59 133 12 0 106 23 44 71 116 4 62 12 18 14 60 7 98 [145] 64 29 32 25 16 48 100 46 45 129 130 136 59 25 32 63 95 14 [163] 36 113 47 92 70 19 50 41 91 111 41 120 135 27 87 25 131 45 [181] 29 58 4 47 109 7 12 0 37 37 46 15 42 7 54 54 14 16 [199] 33 32 21 15 38 22 28 10 31 32 32 43 27 37 20 32 0 5 [217] 26 10 27 11 29 25 55 23 5 43 23 34 36 35 0 37 28 16 [235] 26 38 23 22 30 16 18 28 32 21 23 29 50 12 21 18 27 41 [253] 13 12 21 8 26 27 13 16 2 42 5 37 17 38 37 29 32 35 [271] 17 20 7 46 24 40 3 10 37 17 28 19 29 8 10 15 15 28 [289] 17 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/1d4sq1323958557.tab") + } + } > m Conditional inference tree with 8 terminal nodes Response: blogged_computations Inputs: compendiums_reviewed, totsize, Gender, Restless, Software Number of observations: 289 1) compendiums_reviewed <= 27; criterion = 1, statistic = 167.343 2) totsize <= 94785; criterion = 1, statistic = 70.743 3) totsize <= 18472; criterion = 1, statistic = 40.1 4) totsize <= 6023; criterion = 0.997, statistic = 11.521 5)* weights = 16 4) totsize > 6023 6)* weights = 19 3) totsize > 18472 7) totsize <= 44339; criterion = 0.989, statistic = 9.368 8)* weights = 101 7) totsize > 44339 9)* weights = 30 2) totsize > 94785 10)* weights = 9 1) compendiums_reviewed > 27 11) totsize <= 87771; criterion = 1, statistic = 20.138 12)* weights = 58 11) totsize > 87771 13) compendiums_reviewed <= 31; criterion = 0.994, statistic = 10.468 14)* weights = 21 13) compendiums_reviewed > 31 15)* weights = 35 > postscript(file="/var/wessaorg/rcomp/tmp/2qy2n1323958557.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/35uqq1323958557.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > if (par2 == 'none') { + forec <- predict(m) + result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) + colnames(result) <- c('Actuals','Forecasts','Residuals') + print(result) + } Actuals Forecasts Residuals 1 79 80.71429 -1.71428571 2 58 73.18966 -15.18965517 3 60 73.18966 -13.18965517 4 108 80.71429 27.28571429 5 49 29.36634 19.63366337 6 0 41.03333 -41.03333333 7 121 81.33333 39.66666667 8 1 29.36634 -28.36633663 9 20 16.78947 3.21052632 10 43 81.33333 -38.33333333 11 69 41.03333 27.96666667 12 78 73.18966 4.81034483 13 86 73.18966 12.81034483 14 44 73.18966 -29.18965517 15 104 73.18966 30.81034483 16 63 73.18966 -10.18965517 17 158 109.62857 48.37142857 18 102 80.71429 21.28571429 19 77 80.71429 -3.71428571 20 82 81.33333 0.66666667 21 115 109.62857 5.37142857 22 101 109.62857 -8.62857143 23 80 73.18966 6.81034483 24 50 41.03333 8.96666667 25 83 109.62857 -26.62857143 26 123 73.18966 49.81034483 27 73 73.18966 -0.18965517 28 81 73.18966 7.81034483 29 105 73.18966 31.81034483 30 47 29.36634 17.63366337 31 105 73.18966 31.81034483 32 94 109.62857 -15.62857143 33 44 73.18966 -29.18965517 34 114 109.62857 4.37142857 35 38 73.18966 -35.18965517 36 107 109.62857 -2.62857143 37 30 29.36634 0.63366337 38 71 73.18966 -2.18965517 39 84 73.18966 10.81034483 40 0 5.81250 -5.81250000 41 59 73.18966 -14.18965517 42 33 29.36634 3.63366337 43 42 29.36634 12.63366337 44 96 73.18966 22.81034483 45 106 73.18966 32.81034483 46 56 73.18966 -17.18965517 47 57 29.36634 27.63366337 48 59 73.18966 -14.18965517 49 39 73.18966 -34.18965517 50 34 73.18966 -39.18965517 51 76 109.62857 -33.62857143 52 20 29.36634 -9.36633663 53 91 73.18966 17.81034483 54 115 109.62857 5.37142857 55 85 73.18966 11.81034483 56 76 73.18966 2.81034483 57 8 5.81250 2.18750000 58 79 109.62857 -30.62857143 59 21 29.36634 -8.36633663 60 30 29.36634 0.63366337 61 76 73.18966 2.81034483 62 101 109.62857 -8.62857143 63 94 81.33333 12.66666667 64 27 29.36634 -2.36633663 65 92 80.71429 11.28571429 66 123 109.62857 13.37142857 67 75 73.18966 1.81034483 68 128 109.62857 18.37142857 69 105 81.33333 23.66666667 70 55 73.18966 -18.18965517 71 56 73.18966 -17.18965517 72 41 80.71429 -39.71428571 73 72 41.03333 30.96666667 74 67 81.33333 -14.33333333 75 75 73.18966 1.81034483 76 114 80.71429 33.28571429 77 118 41.03333 76.96666667 78 77 73.18966 3.81034483 79 22 29.36634 -7.36633663 80 66 73.18966 -7.18965517 81 69 73.18966 -4.18965517 82 105 73.18966 31.81034483 83 116 109.62857 6.37142857 84 88 80.71429 7.28571429 85 73 109.62857 -36.62857143 86 99 73.18966 25.81034483 87 62 73.18966 -11.18965517 88 53 29.36634 23.63366337 89 118 109.62857 8.37142857 90 30 29.36634 0.63366337 91 100 73.18966 26.81034483 92 49 41.03333 7.96666667 93 24 16.78947 7.21052632 94 67 80.71429 -13.71428571 95 46 29.36634 16.63366337 96 57 80.71429 -23.71428571 97 75 73.18966 1.81034483 98 135 109.62857 25.37142857 99 68 73.18966 -5.18965517 100 124 109.62857 14.37142857 101 33 41.03333 -8.03333333 102 98 73.18966 24.81034483 103 58 80.71429 -22.71428571 104 68 80.71429 -12.71428571 105 81 80.71429 0.28571429 106 131 80.71429 50.28571429 107 110 109.62857 0.37142857 108 37 29.36634 7.63366337 109 130 81.33333 48.66666667 110 93 109.62857 -16.62857143 111 118 109.62857 8.37142857 112 39 73.18966 -34.18965517 113 13 29.36634 -16.36633663 114 74 29.36634 44.63366337 115 81 73.18966 7.81034483 116 109 73.18966 35.81034483 117 151 109.62857 41.37142857 118 51 41.03333 9.96666667 119 28 81.33333 -53.33333333 120 40 29.36634 10.63366337 121 56 29.36634 26.63366337 122 27 16.78947 10.21052632 123 37 29.36634 7.63366337 124 83 80.71429 2.28571429 125 54 80.71429 -26.71428571 126 27 29.36634 -2.36633663 127 28 29.36634 -1.36633663 128 59 41.03333 17.96666667 129 133 109.62857 23.37142857 130 12 5.81250 6.18750000 131 0 16.78947 -16.78947368 132 106 73.18966 32.81034483 133 23 29.36634 -6.36633663 134 44 29.36634 14.63366337 135 71 73.18966 -2.18965517 136 116 80.71429 35.28571429 137 4 5.81250 -1.81250000 138 62 81.33333 -19.33333333 139 12 29.36634 -17.36633663 140 18 16.78947 1.21052632 141 14 16.78947 -2.78947368 142 60 41.03333 18.96666667 143 7 16.78947 -9.78947368 144 98 73.18966 24.81034483 145 64 109.62857 -45.62857143 146 29 29.36634 -0.36633663 147 32 109.62857 -77.62857143 148 25 41.03333 -16.03333333 149 16 16.78947 -0.78947368 150 48 73.18966 -25.18965517 151 100 109.62857 -9.62857143 152 46 73.18966 -27.18965517 153 45 29.36634 15.63366337 154 129 109.62857 19.37142857 155 130 109.62857 20.37142857 156 136 109.62857 26.37142857 157 59 73.18966 -14.18965517 158 25 29.36634 -4.36633663 159 32 73.18966 -41.18965517 160 63 80.71429 -17.71428571 161 95 109.62857 -14.62857143 162 14 80.71429 -66.71428571 163 36 41.03333 -5.03333333 164 113 109.62857 3.37142857 165 47 73.18966 -26.18965517 166 92 73.18966 18.81034483 167 70 73.18966 -3.18965517 168 19 41.03333 -22.03333333 169 50 29.36634 20.63366337 170 41 41.03333 -0.03333333 171 91 80.71429 10.28571429 172 111 80.71429 30.28571429 173 41 29.36634 11.63366337 174 120 109.62857 10.37142857 175 135 109.62857 25.37142857 176 27 29.36634 -2.36633663 177 87 109.62857 -22.62857143 178 25 29.36634 -4.36633663 179 131 109.62857 21.37142857 180 45 29.36634 15.63366337 181 29 29.36634 -0.36633663 182 58 41.03333 16.96666667 183 4 5.81250 -1.81250000 184 47 73.18966 -26.18965517 185 109 73.18966 35.81034483 186 7 5.81250 1.18750000 187 12 16.78947 -4.78947368 188 0 5.81250 -5.81250000 189 37 29.36634 7.63366337 190 37 73.18966 -36.18965517 191 46 41.03333 4.96666667 192 15 29.36634 -14.36633663 193 42 29.36634 12.63366337 194 7 29.36634 -22.36633663 195 54 41.03333 12.96666667 196 54 29.36634 24.63366337 197 14 29.36634 -15.36633663 198 16 41.03333 -25.03333333 199 33 41.03333 -8.03333333 200 32 41.03333 -9.03333333 201 21 29.36634 -8.36633663 202 15 16.78947 -1.78947368 203 38 29.36634 8.63366337 204 22 41.03333 -19.03333333 205 28 29.36634 -1.36633663 206 10 29.36634 -19.36633663 207 31 29.36634 1.63366337 208 32 29.36634 2.63366337 209 32 29.36634 2.63366337 210 43 29.36634 13.63366337 211 27 29.36634 -2.36633663 212 37 29.36634 7.63366337 213 20 29.36634 -9.36633663 214 32 29.36634 2.63366337 215 0 29.36634 -29.36633663 216 5 16.78947 -11.78947368 217 26 29.36634 -3.36633663 218 10 29.36634 -19.36633663 219 27 29.36634 -2.36633663 220 11 16.78947 -5.78947368 221 29 29.36634 -0.36633663 222 25 41.03333 -16.03333333 223 55 29.36634 25.63366337 224 23 29.36634 -6.36633663 225 5 5.81250 -0.81250000 226 43 29.36634 13.63366337 227 23 29.36634 -6.36633663 228 34 29.36634 4.63366337 229 36 16.78947 19.21052632 230 35 41.03333 -6.03333333 231 0 5.81250 -5.81250000 232 37 29.36634 7.63366337 233 28 41.03333 -13.03333333 234 16 29.36634 -13.36633663 235 26 29.36634 -3.36633663 236 38 29.36634 8.63366337 237 23 29.36634 -6.36633663 238 22 16.78947 5.21052632 239 30 29.36634 0.63366337 240 16 16.78947 -0.78947368 241 18 29.36634 -11.36633663 242 28 41.03333 -13.03333333 243 32 16.78947 15.21052632 244 21 29.36634 -8.36633663 245 23 29.36634 -6.36633663 246 29 29.36634 -0.36633663 247 50 41.03333 8.96666667 248 12 29.36634 -17.36633663 249 21 29.36634 -8.36633663 250 18 29.36634 -11.36633663 251 27 41.03333 -14.03333333 252 41 41.03333 -0.03333333 253 13 5.81250 7.18750000 254 12 29.36634 -17.36633663 255 21 29.36634 -8.36633663 256 8 5.81250 2.18750000 257 26 29.36634 -3.36633663 258 27 29.36634 -2.36633663 259 13 16.78947 -3.78947368 260 16 16.78947 -0.78947368 261 2 5.81250 -3.81250000 262 42 29.36634 12.63366337 263 5 5.81250 -0.81250000 264 37 29.36634 7.63366337 265 17 29.36634 -12.36633663 266 38 29.36634 8.63366337 267 37 29.36634 7.63366337 268 29 29.36634 -0.36633663 269 32 29.36634 2.63366337 270 35 41.03333 -6.03333333 271 17 29.36634 -12.36633663 272 20 29.36634 -9.36633663 273 7 5.81250 1.18750000 274 46 29.36634 16.63366337 275 24 29.36634 -5.36633663 276 40 29.36634 10.63366337 277 3 29.36634 -26.36633663 278 10 5.81250 4.18750000 279 37 29.36634 7.63366337 280 17 29.36634 -12.36633663 281 28 29.36634 -1.36633663 282 19 41.03333 -22.03333333 283 29 29.36634 -0.36633663 284 8 5.81250 2.18750000 285 10 29.36634 -19.36633663 286 15 29.36634 -14.36633663 287 15 16.78947 -1.78947368 288 28 29.36634 -1.36633663 289 17 29.36634 -12.36633663 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/48qkk1323958557.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5zij41323958557.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/6v9s31323958557.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/75ub21323958557.tab") + } > > try(system("convert tmp/2qy2n1323958557.ps tmp/2qy2n1323958557.png",intern=TRUE)) character(0) > try(system("convert tmp/35uqq1323958557.ps tmp/35uqq1323958557.png",intern=TRUE)) character(0) > try(system("convert tmp/48qkk1323958557.ps tmp/48qkk1323958557.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.262 0.298 5.554