R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

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+ ,15
+ ,16
+ ,30884
+ ,757
+ ,62792
+ ,35
+ ,28
+ ,72)
+ ,dim=c(6
+ ,241)
+ ,dimnames=list(c('totsize'
+ ,'pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'tothyperlinks')
+ ,1:241))
>  y <- array(NA,dim=c(6,241),dimnames=list(c('totsize','pageviews','time_in_rfc','logins','blogged_computations','tothyperlinks'),1:241))
>  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 = 'quantiles'
> par1 = '5'
> 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 object(s) 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(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  58 121  43 102  82 101  50  81  94  44 107  33  42  56  59  91  27
 [19] 105  67 114  69 105  88  67 124 110 130  93  39  28  28  44 116  12  18
 [37]  32  25 129  59  36 113  47  92  50 111 120 131  45  58   0  21  50  12
 [55]  21   8  37  29  32  35  17  60  69  78  44 158  77  80 123  73 105  47
 [73]  84   0  96  57  39  76  76   8  79 101  94 123  41  72  75  22  73  62
 [91] 118 100  24  46  57 135  33  98  58  68 131  37 118  81  51  40  56  27
[109]  83  59 133  12 106  71   4  62  14  60  98 100  45 136  63  14  41  91
[127]  41  25  29  47  37  26  38  23  30  18  28  12  27  41  26  27  10  10
[145]  17 108  49   0   1  20  86 104  63 115  83 105 114  38  30  71  59 106
[163]  34  20 115  85  21  92  75 128  55  56 118  77  66 116  99  53  30  49
[181]  75  68  81  13  74 109 151  37  54  27   0  23   7  64  29  16  48  46
[199] 130  25  32  95  70  19 135  27  87   4  28  16  22  16  32  23  29  21
[217]  18  13  13  16   2  42   5  37  17  38  17  20   7  46  24  40   3  37
[235]  28  19  29   8  15  15  28
> 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, 49) [49,158] 
     121      120 
> colnames(x)
[1] "totsize"              "pageviews"            "time_in_rfc"         
[4] "logins"               "blogged_computations" "tothyperlinks"       
> colnames(x)[par1]
[1] "blogged_computations"
> x[,par1]
  [1] [49,158] [49,158] [49,158] [ 0, 49) [49,158] [49,158] [49,158] [49,158]
  [9] [49,158] [49,158] [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [49,158] [49,158]
 [17] [49,158] [ 0, 49) [49,158] [49,158] [49,158] [49,158] [49,158] [49,158]
 [25] [49,158] [49,158] [49,158] [49,158] [49,158] [ 0, 49) [ 0, 49) [ 0, 49)
 [33] [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [49,158] [49,158]
 [41] [ 0, 49) [49,158] [ 0, 49) [49,158] [49,158] [49,158] [49,158] [49,158]
 [49] [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [ 0, 49)
 [57] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [49,158] [49,158] [49,158]
 [65] [ 0, 49) [49,158] [49,158] [49,158] [49,158] [49,158] [49,158] [ 0, 49)
 [73] [49,158] [ 0, 49) [49,158] [49,158] [ 0, 49) [49,158] [49,158] [ 0, 49)
 [81] [49,158] [49,158] [49,158] [49,158] [ 0, 49) [49,158] [49,158] [ 0, 49)
 [89] [49,158] [49,158] [49,158] [49,158] [ 0, 49) [ 0, 49) [49,158] [49,158]
 [97] [ 0, 49) [49,158] [49,158] [49,158] [49,158] [ 0, 49) [49,158] [49,158]
[105] [49,158] [ 0, 49) [49,158] [ 0, 49) [49,158] [49,158] [49,158] [ 0, 49)
[113] [49,158] [49,158] [ 0, 49) [49,158] [ 0, 49) [49,158] [49,158] [49,158]
[121] [ 0, 49) [49,158] [49,158] [ 0, 49) [ 0, 49) [49,158] [ 0, 49) [ 0, 49)
[129] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[137] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[145] [ 0, 49) [49,158] [49,158] [ 0, 49) [ 0, 49) [ 0, 49) [49,158] [49,158]
[153] [49,158] [49,158] [49,158] [49,158] [49,158] [ 0, 49) [ 0, 49) [49,158]
[161] [49,158] [49,158] [ 0, 49) [ 0, 49) [49,158] [49,158] [ 0, 49) [49,158]
[169] [49,158] [49,158] [49,158] [49,158] [49,158] [49,158] [49,158] [49,158]
[177] [49,158] [49,158] [ 0, 49) [49,158] [49,158] [49,158] [49,158] [ 0, 49)
[185] [49,158] [49,158] [49,158] [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [ 0, 49)
[193] [ 0, 49) [49,158] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [49,158] [ 0, 49)
[201] [ 0, 49) [49,158] [49,158] [ 0, 49) [49,158] [ 0, 49) [49,158] [ 0, 49)
[209] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[217] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[225] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[233] [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49) [ 0, 49)
[241] [ 0, 49)
Levels: [ 0, 49) [49,158]
> 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/18u1c1355078255.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu    1    2
          1  962  123
          2   36 1046
[1] 0.8866359
[1] 0.9667283
[1] 0.9266267
           m.ct.x.pred
m.ct.x.actu   1   2
          1 107  18
          2   4 114
[1] 0.856
[1] 0.9661017
[1] 0.909465
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(blogged_computations) 
Inputs:  totsize, pageviews, time_in_rfc, logins, tothyperlinks 
Number of observations:  241 

1) tothyperlinks <= 57; criterion = 1, statistic = 138.864
  2) totsize <= 43750; criterion = 0.999, statistic = 14.31
    3)*  weights = 90 
  2) totsize > 43750
    4)*  weights = 21 
1) tothyperlinks > 57
  5) time_in_rfc <= 149061; criterion = 0.999, statistic = 14.668
    6)*  weights = 28 
  5) time_in_rfc > 149061
    7)*  weights = 102 
> postscript(file="/var/wessaorg/rcomp/tmp/2gjul1355078255.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/3kcbx1355078255.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,]    1    2
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    2
 [10,]    2    2
 [11,]    1    2
 [12,]    2    2
 [13,]    1    1
 [14,]    1    1
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    1    1
 [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,]    1    1
 [31,]    1    1
 [32,]    1    1
 [33,]    1    1
 [34,]    2    2
 [35,]    1    1
 [36,]    1    1
 [37,]    1    1
 [38,]    1    2
 [39,]    2    2
 [40,]    2    2
 [41,]    1    2
 [42,]    2    2
 [43,]    1    2
 [44,]    2    2
 [45,]    2    2
 [46,]    2    2
 [47,]    2    2
 [48,]    2    2
 [49,]    1    1
 [50,]    2    1
 [51,]    1    1
 [52,]    1    1
 [53,]    2    1
 [54,]    1    1
 [55,]    1    1
 [56,]    1    1
 [57,]    1    1
 [58,]    1    1
 [59,]    1    1
 [60,]    1    1
 [61,]    1    1
 [62,]    2    2
 [63,]    2    2
 [64,]    2    2
 [65,]    1    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    1    1
 [73,]    2    2
 [74,]    1    1
 [75,]    2    2
 [76,]    2    2
 [77,]    1    1
 [78,]    2    2
 [79,]    2    2
 [80,]    1    1
 [81,]    2    2
 [82,]    2    2
 [83,]    2    2
 [84,]    2    2
 [85,]    1    2
 [86,]    2    2
 [87,]    2    2
 [88,]    1    1
 [89,]    2    1
 [90,]    2    2
 [91,]    2    2
 [92,]    2    2
 [93,]    1    1
 [94,]    1    1
 [95,]    2    2
 [96,]    2    2
 [97,]    1    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    1    1
[103,]    2    2
[104,]    2    2
[105,]    2    2
[106,]    1    1
[107,]    2    2
[108,]    1    1
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    1    1
[113,]    2    2
[114,]    2    2
[115,]    1    1
[116,]    2    2
[117,]    1    1
[118,]    2    2
[119,]    2    2
[120,]    2    2
[121,]    1    1
[122,]    2    2
[123,]    2    2
[124,]    1    1
[125,]    1    1
[126,]    2    2
[127,]    1    1
[128,]    1    1
[129,]    1    1
[130,]    1    2
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    1    1
[135,]    1    1
[136,]    1    1
[137,]    1    2
[138,]    1    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    1    1
[145,]    1    1
[146,]    2    2
[147,]    2    2
[148,]    1    1
[149,]    1    1
[150,]    1    1
[151,]    2    2
[152,]    2    2
[153,]    2    2
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    2    2
[158,]    1    1
[159,]    1    1
[160,]    2    2
[161,]    2    2
[162,]    2    2
[163,]    1    1
[164,]    1    1
[165,]    2    2
[166,]    2    2
[167,]    1    1
[168,]    2    2
[169,]    2    2
[170,]    2    2
[171,]    2    2
[172,]    2    2
[173,]    2    2
[174,]    2    2
[175,]    2    2
[176,]    2    2
[177,]    2    2
[178,]    2    1
[179,]    1    1
[180,]    2    2
[181,]    2    2
[182,]    2    2
[183,]    2    2
[184,]    1    1
[185,]    2    2
[186,]    2    2
[187,]    2    2
[188,]    1    2
[189,]    2    2
[190,]    1    1
[191,]    1    1
[192,]    1    1
[193,]    1    1
[194,]    2    2
[195,]    1    1
[196,]    1    1
[197,]    1    1
[198,]    1    2
[199,]    2    2
[200,]    1    1
[201,]    1    1
[202,]    2    2
[203,]    2    2
[204,]    1    2
[205,]    2    2
[206,]    1    1
[207,]    2    2
[208,]    1    1
[209,]    1    1
[210,]    1    1
[211,]    1    1
[212,]    1    1
[213,]    1    1
[214,]    1    1
[215,]    1    1
[216,]    1    1
[217,]    1    1
[218,]    1    1
[219,]    1    1
[220,]    1    1
[221,]    1    1
[222,]    1    1
[223,]    1    1
[224,]    1    1
[225,]    1    1
[226,]    1    1
[227,]    1    1
[228,]    1    1
[229,]    1    1
[230,]    1    1
[231,]    1    1
[232,]    1    1
[233,]    1    1
[234,]    1    1
[235,]    1    1
[236,]    1    1
[237,]    1    1
[238,]    1    1
[239,]    1    1
[240,]    1    1
[241,]    1    2
          
           [ 0, 49) [49,158]
  [ 0, 49)      107       14
  [49,158]        4      116
> postscript(file="/var/wessaorg/rcomp/tmp/4y3nx1355078255.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/5wu2c1355078255.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/6pxdx1355078255.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/7404b1355078255.tab") 
+ }
> 
> try(system("convert tmp/2gjul1355078255.ps tmp/2gjul1355078255.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kcbx1355078255.ps tmp/3kcbx1355078255.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y3nx1355078255.ps tmp/4y3nx1355078255.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.859   0.369   5.211