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)

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+ ,0
+ ,17
+ ,16
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+ ,39)
+ ,dim=c(10
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120')
+ ,1:289))
>  y <- array(NA,dim=c(10,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120'),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 = 'yes'
> par3 = '2'
> par2 = 'quantiles'
> par1 = '10'
> 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] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  38  93  60 123 148  90 124  70 168 115
 [19]  71  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124
 [37]  39  92 126   0  70  37  38 120  93  95  77  90  80  31 110  66 138 133
 [55] 113 100   7 140  61  41  96 164  78  49 102 124  99 129  62  73 114  99
 [73]  70 104 116  91  74 138  67 151  72 120 115 105 104 108  98  69 111  99
 [91]  71  27  69 107  73 107  93 129  69 118  73 119 104 107  99  90 197  36
[109]  85 139 106  50  64  31  63  92 106  63  69  41  56  25  65  93 114  38
[127]  44  87 110   0  27  83  30  80  98  82   0  60  28   9  33  59  49 115
[145] 140  49 120  66  21 124 152 139  38 144 120 160 114  39  78 119 141 101
[163]  56 133  83 116  90  36  50  61  97  98  78 117 148  41 105  55 132  44
[181]  21  50   0  73  86   0  13   4  57  48  46  48  32  68  87  43  67  46
[199]  46  56  48  44  60  65  55  38  52  60  54  86  24  52  49  61  61  81
[217]  43  40  40  56  68  79  47  57  41  29   3  60  30  79  47  40  48  36
[235]  42  49  57  12  40  43  33  77  43  45  47  43  45  50  35   7  71  67
[253]   0  62  54   4  25  40  38  19  17  67  14  30  54  35  59  24  58  42
[271]  46  61   3  52  25  40  32   4  49  63  67  32  23   7  54  37  35  51
[289]  39
> 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, 67) [67,197] 
     145      144 
> colnames(x)
 [1] "pageviews"              "time_in_rfc"            "logins"                
 [4] "compendium_views_info"  "compendium_views_pr"    "shared_compendiums"    
 [7] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p1"  
[10] "feedback_messages_p120"
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1] [67,197] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [ 0, 67)
  [9] [ 0, 67) [67,197] [ 0, 67) [67,197] [67,197] [67,197] [67,197] [67,197]
 [17] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [67,197] [67,197]
 [25] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197]
 [33] [67,197] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [ 0, 67)
 [41] [67,197] [ 0, 67) [ 0, 67) [67,197] [67,197] [67,197] [67,197] [67,197]
 [49] [67,197] [ 0, 67) [67,197] [ 0, 67) [67,197] [67,197] [67,197] [67,197]
 [57] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [67,197] [67,197] [67,197] [ 0, 67)
 [65] [67,197] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [67,197]
 [73] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197]
 [81] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197]
 [89] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [67,197] [67,197]
 [97] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197] [67,197]
[105] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [67,197] [ 0, 67)
[113] [ 0, 67) [ 0, 67) [ 0, 67) [67,197] [67,197] [ 0, 67) [67,197] [ 0, 67)
[121] [ 0, 67) [ 0, 67) [ 0, 67) [67,197] [67,197] [ 0, 67) [ 0, 67) [67,197]
[129] [67,197] [ 0, 67) [ 0, 67) [67,197] [ 0, 67) [67,197] [67,197] [67,197]
[137] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197]
[145] [67,197] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [67,197] [67,197] [67,197]
[153] [ 0, 67) [67,197] [67,197] [67,197] [67,197] [ 0, 67) [67,197] [67,197]
[161] [67,197] [67,197] [ 0, 67) [67,197] [67,197] [67,197] [67,197] [ 0, 67)
[169] [ 0, 67) [ 0, 67) [67,197] [67,197] [67,197] [67,197] [67,197] [ 0, 67)
[177] [67,197] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197]
[185] [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[193] [ 0, 67) [67,197] [67,197] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [ 0, 67)
[201] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[209] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197]
[217] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197] [67,197] [ 0, 67) [ 0, 67)
[225] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197] [ 0, 67) [ 0, 67)
[233] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[241] [ 0, 67) [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[249] [ 0, 67) [ 0, 67) [67,197] [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[257] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [67,197] [ 0, 67) [ 0, 67)
[265] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[273] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[281] [67,197] [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67) [ 0, 67)
[289] [ 0, 67)
Levels: [ 0, 67) [67,197]
> 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/1jcj41323874925.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu    1    2
          1 1184  111
          2   42 1281
[1] 0.9142857
[1] 0.968254
[1] 0.9415584
           m.ct.x.pred
m.ct.x.actu   1   2
          1 144  11
          2   7 110
[1] 0.9290323
[1] 0.9401709
[1] 0.9338235
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(feedback_messages_p120) 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p1 
Number of observations:  289 

1) feedback_messages_p1 <= 78; criterion = 1, statistic = 182.718
  2)*  weights = 138 
1) feedback_messages_p1 > 78
  3) feedback_messages_p1 <= 93; criterion = 0.994, statistic = 11.5
    4)*  weights = 30 
  3) feedback_messages_p1 > 93
    5)*  weights = 121 
> postscript(file="/var/wessaorg/rcomp/tmp/27k5s1323874925.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/3e05r1323874925.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,]    1    1
  [6,]    2    2
  [7,]    2    2
  [8,]    1    1
  [9,]    1    1
 [10,]    2    2
 [11,]    1    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,]    1    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,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    2    2
 [36,]    2    2
 [37,]    1    1
 [38,]    2    2
 [39,]    2    2
 [40,]    1    1
 [41,]    2    2
 [42,]    1    1
 [43,]    1    1
 [44,]    2    2
 [45,]    2    2
 [46,]    2    2
 [47,]    2    2
 [48,]    2    2
 [49,]    2    2
 [50,]    1    1
 [51,]    2    2
 [52,]    1    2
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    1    1
 [58,]    2    2
 [59,]    1    1
 [60,]    1    1
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    1    2
 [70,]    2    2
 [71,]    2    2
 [72,]    2    2
 [73,]    2    2
 [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,]    1    1
 [93,]    2    2
 [94,]    2    2
 [95,]    2    1
 [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,]    1    1
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    1    2
[113,]    1    1
[114,]    1    1
[115,]    1    2
[116,]    2    2
[117,]    2    2
[118,]    1    1
[119,]    2    2
[120,]    1    1
[121,]    1    1
[122,]    1    1
[123,]    1    1
[124,]    2    2
[125,]    2    2
[126,]    1    1
[127,]    1    1
[128,]    2    2
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    2    2
[135,]    2    2
[136,]    2    2
[137,]    1    1
[138,]    1    2
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    2    2
[145,]    2    2
[146,]    1    1
[147,]    2    2
[148,]    1    2
[149,]    1    1
[150,]    2    2
[151,]    2    2
[152,]    2    2
[153,]    1    1
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    2    2
[158,]    1    1
[159,]    2    2
[160,]    2    2
[161,]    2    2
[162,]    2    2
[163,]    1    1
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    1    2
[169,]    1    1
[170,]    1    1
[171,]    2    2
[172,]    2    2
[173,]    2    2
[174,]    2    2
[175,]    2    2
[176,]    1    1
[177,]    2    2
[178,]    1    1
[179,]    2    2
[180,]    1    1
[181,]    1    1
[182,]    1    1
[183,]    1    1
[184,]    2    2
[185,]    2    2
[186,]    1    1
[187,]    1    1
[188,]    1    1
[189,]    1    1
[190,]    1    2
[191,]    1    2
[192,]    1    1
[193,]    1    1
[194,]    2    2
[195,]    2    2
[196,]    1    1
[197,]    2    1
[198,]    1    1
[199,]    1    1
[200,]    1    1
[201,]    1    1
[202,]    1    1
[203,]    1    1
[204,]    1    1
[205,]    1    1
[206,]    1    1
[207,]    1    1
[208,]    1    1
[209,]    1    1
[210,]    2    2
[211,]    1    1
[212,]    1    1
[213,]    1    1
[214,]    1    1
[215,]    1    2
[216,]    2    2
[217,]    1    1
[218,]    1    1
[219,]    1    1
[220,]    1    1
[221,]    2    1
[222,]    2    2
[223,]    1    1
[224,]    1    1
[225,]    1    1
[226,]    1    1
[227,]    1    1
[228,]    1    1
[229,]    1    1
[230,]    2    2
[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    1
[242,]    2    2
[243,]    1    1
[244,]    1    1
[245,]    1    1
[246,]    1    1
[247,]    1    1
[248,]    1    1
[249,]    1    1
[250,]    1    1
[251,]    2    2
[252,]    2    2
[253,]    1    1
[254,]    1    1
[255,]    1    1
[256,]    1    1
[257,]    1    1
[258,]    1    1
[259,]    1    1
[260,]    1    1
[261,]    1    1
[262,]    2    1
[263,]    1    1
[264,]    1    1
[265,]    1    1
[266,]    1    1
[267,]    1    1
[268,]    1    1
[269,]    1    1
[270,]    1    1
[271,]    1    1
[272,]    1    1
[273,]    1    1
[274,]    1    1
[275,]    1    1
[276,]    1    1
[277,]    1    1
[278,]    1    1
[279,]    1    1
[280,]    1    1
[281,]    2    1
[282,]    1    1
[283,]    1    1
[284,]    1    1
[285,]    1    1
[286,]    1    1
[287,]    1    1
[288,]    1    1
[289,]    1    1
          
           [ 0, 67) [67,197]
  [ 0, 67)      133       12
  [67,197]        5      139
> postscript(file="/var/wessaorg/rcomp/tmp/4zs7m1323874925.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/5dphg1323874925.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/650qb1323874925.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/75sac1323874925.tab") 
+ }
> 
> try(system("convert tmp/27k5s1323874925.ps tmp/27k5s1323874925.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e05r1323874925.ps tmp/3e05r1323874925.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zs7m1323874925.ps tmp/4zs7m1323874925.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.484   0.232   5.717