R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-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(119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,0.04374 + ,0.426 + ,0.02971 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,0.06134 + ,0.626 + ,0.04368 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,0.05233 + ,0.482 + ,0.0359 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,0.05492 + ,0.517 + ,0.03772 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,0.06425 + ,0.584 + ,0.04465 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,0.04701 + ,0.456 + ,0.03243 + ,1 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00003 + ,0.00155 + ,0.00202 + ,0.01608 + ,0.14 + ,0.01351 + ,1 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00003 + ,0.00144 + ,0.00182 + ,0.01567 + ,0.134 + ,0.01256 + ,1 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00006 + ,0.00293 + 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,0.013 + ,0.117 + ,0.01144 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00146 + ,0.00184 + ,0.01185 + ,0.106 + ,0.01095 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0.02574 + ,0.255 + ,0.01758 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0.04087 + ,0.405 + ,0.02745 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0.02751 + ,0.263 + ,0.01879 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0.02308 + ,0.256 + ,0.01667 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.02296 + ,0.241 + ,0.01588 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.01884 + ,0.19 + ,0.01373 + ,0) + ,dim=c(11 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'MDVP:APQ' + ,'status') + ,1:195)) > y <- array(NA,dim=c(11,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','MDVP:Shimmer','MDVP:Shimmer(dB)','MDVP:APQ','status'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'yes' > par3 = '2' > par2 = 'equal' > par1 = '11' > par4 <- 'yes' > par3 <- '2' > par2 <- 'equal' > par1 <- '11' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object is masked from 'package:survival': untangle.specials The following objects are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "status" > x[,par1] [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 [38] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) C1 C2 48 147 > colnames(x) [1] "MDVP.Fo.Hz." "MDVP.Fhi.Hz." "MDVP.Flo.Hz." "MDVP.Jitter..." [5] "MDVP.Jitter.Abs." "MDVP.RAP" "MDVP.PPQ" "MDVP.Shimmer" [9] "MDVP.Shimmer.dB." "MDVP.APQ" "status" > colnames(x)[par1] [1] "status" > x[,par1] [1] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 [26] C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 [51] C1 C1 C1 C1 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 [76] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 [101] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 [126] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 [151] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [176] C1 C1 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 Levels: C1 C2 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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/fisher/rcomp/tmp/13jig1386664094.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 1 233 206 2 45 1283 [1] 0.5307517 [1] 0.9661145 [1] 0.8579513 m.ct.x.pred m.ct.x.actu 1 2 1 18 23 2 6 136 [1] 0.4390244 [1] 0.9577465 [1] 0.8415301 > m Conditional inference tree with 8 terminal nodes Response: as.factor(status) Inputs: MDVP.Fo.Hz., MDVP.Fhi.Hz., MDVP.Flo.Hz., MDVP.Jitter..., MDVP.Jitter.Abs., MDVP.RAP, MDVP.PPQ, MDVP.Shimmer, MDVP.Shimmer.dB., MDVP.APQ Number of observations: 195 1) MDVP.Fo.Hz. <= 193.03; criterion = 1, statistic = 28.537 2) MDVP.Shimmer <= 0.02498; criterion = 0.989, statistic = 10.625 3) MDVP.Fo.Hz. <= 129.336; criterion = 0.972, statistic = 8.886 4) MDVP.RAP <= 0.00186; criterion = 0.993, statistic = 11.524 5)* weights = 22 4) MDVP.RAP > 0.00186 6)* weights = 17 3) MDVP.Fo.Hz. > 129.336 7) MDVP.Jitter... <= 0.00502; criterion = 1, statistic = 16.745 8)* weights = 28 7) MDVP.Jitter... > 0.00502 9)* weights = 7 2) MDVP.Shimmer > 0.02498 10)* weights = 80 1) MDVP.Fo.Hz. > 193.03 11) MDVP.Shimmer <= 0.0203; criterion = 0.988, statistic = 10.533 12) MDVP.Jitter.Abs. <= 1e-05; criterion = 0.996, statistic = 12.616 13)* weights = 20 12) MDVP.Jitter.Abs. > 1e-05 14)* weights = 7 11) MDVP.Shimmer > 0.0203 15)* weights = 14 > postscript(file="/var/fisher/rcomp/tmp/29ejr1386664094.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/fisher/rcomp/tmp/3oepn1386664094.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) + } > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } [,1] [,2] [1,] 2 2 [2,] 2 2 [3,] 2 2 [4,] 2 2 [5,] 2 2 [6,] 2 2 [7,] 2 1 [8,] 2 1 [9,] 2 2 [10,] 2 2 [11,] 2 2 [12,] 2 2 [13,] 2 2 [14,] 2 2 [15,] 2 2 [16,] 2 2 [17,] 2 2 [18,] 2 2 [19,] 2 2 [20,] 2 2 [21,] 2 2 [22,] 2 2 [23,] 2 2 [24,] 2 2 [25,] 2 2 [26,] 2 2 [27,] 2 2 [28,] 2 2 [29,] 2 2 [30,] 2 2 [31,] 1 1 [32,] 1 1 [33,] 1 1 [34,] 1 1 [35,] 1 1 [36,] 1 1 [37,] 2 2 [38,] 2 2 [39,] 2 2 [40,] 2 2 [41,] 2 2 [42,] 2 2 [43,] 1 1 [44,] 1 1 [45,] 1 1 [46,] 1 1 [47,] 1 1 [48,] 1 1 [49,] 1 1 [50,] 1 1 [51,] 1 1 [52,] 1 1 [53,] 1 1 [54,] 1 1 [55,] 2 2 [56,] 2 2 [57,] 2 2 [58,] 2 2 [59,] 2 2 [60,] 2 2 [61,] 1 1 [62,] 1 1 [63,] 1 1 [64,] 1 1 [65,] 1 1 [66,] 1 1 [67,] 2 2 [68,] 2 2 [69,] 2 2 [70,] 2 2 [71,] 2 2 [72,] 2 2 [73,] 2 1 [74,] 2 2 [75,] 2 2 [76,] 2 2 [77,] 2 2 [78,] 2 2 [79,] 2 2 [80,] 2 2 [81,] 2 2 [82,] 2 2 [83,] 2 2 [84,] 2 2 [85,] 2 2 [86,] 2 2 [87,] 2 2 [88,] 2 2 [89,] 2 2 [90,] 2 2 [91,] 2 2 [92,] 2 2 [93,] 2 2 [94,] 2 2 [95,] 2 2 [96,] 2 2 [97,] 2 2 [98,] 2 2 [99,] 2 2 [100,] 2 2 [101,] 2 2 [102,] 2 2 [103,] 2 2 [104,] 2 2 [105,] 2 2 [106,] 2 2 [107,] 2 2 [108,] 2 2 [109,] 2 2 [110,] 2 2 [111,] 2 2 [112,] 2 2 [113,] 2 2 [114,] 2 2 [115,] 2 2 [116,] 2 2 [117,] 2 2 [118,] 2 2 [119,] 2 2 [120,] 2 2 [121,] 2 2 [122,] 2 2 [123,] 2 2 [124,] 2 2 [125,] 2 2 [126,] 2 2 [127,] 2 2 [128,] 2 2 [129,] 2 2 [130,] 2 1 [131,] 2 2 [132,] 2 2 [133,] 2 1 [134,] 2 1 [135,] 2 2 [136,] 2 2 [137,] 2 2 [138,] 2 2 [139,] 2 2 [140,] 2 2 [141,] 2 2 [142,] 2 2 [143,] 2 2 [144,] 2 2 [145,] 2 2 [146,] 2 2 [147,] 2 2 [148,] 2 2 [149,] 2 2 [150,] 2 2 [151,] 2 2 [152,] 2 2 [153,] 2 2 [154,] 2 2 [155,] 2 2 [156,] 2 2 [157,] 2 2 [158,] 2 2 [159,] 2 2 [160,] 2 2 [161,] 2 2 [162,] 2 2 [163,] 2 2 [164,] 2 2 [165,] 2 2 [166,] 1 2 [167,] 1 1 [168,] 1 1 [169,] 1 2 [170,] 1 2 [171,] 1 2 [172,] 1 1 [173,] 1 1 [174,] 1 1 [175,] 1 1 [176,] 1 1 [177,] 1 1 [178,] 2 2 [179,] 2 2 [180,] 2 2 [181,] 2 2 [182,] 2 2 [183,] 2 2 [184,] 1 1 [185,] 1 2 [186,] 1 1 [187,] 1 2 [188,] 1 1 [189,] 1 1 [190,] 1 2 [191,] 1 2 [192,] 1 2 [193,] 1 2 [194,] 1 2 [195,] 1 2 C1 C2 C1 36 12 C2 6 141 > postscript(file="/var/fisher/rcomp/tmp/4ewgr1386664094.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/fisher/rcomp/tmp/5lqhe1386664094.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/fisher/rcomp/tmp/66crp1386664094.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/fisher/rcomp/tmp/7qbgw1386664094.tab") + } > > try(system("convert tmp/29ejr1386664094.ps tmp/29ejr1386664094.png",intern=TRUE)) character(0) > try(system("convert tmp/3oepn1386664094.ps tmp/3oepn1386664094.png",intern=TRUE)) character(0) > try(system("convert tmp/4ewgr1386664094.ps tmp/4ewgr1386664094.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 13.188 2.637 15.789