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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> x <- array(list(1418
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+ ,27
+ ,0
+ ,10
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+ ,947
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+ ,21
+ ,0
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+ ,819
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+ ,19
+ ,1
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+ ,12
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+ ,35
+ ,0
+ ,28
+ ,15
+ ,30884
+ ,894
+ ,72535
+ ,14
+ ,0
+ ,17
+ ,16
+ ,19540)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time'
+ ,'logins'
+ ,'shared'
+ ,'blogged'
+ ,'reviewed'
+ ,'totsize')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','time','logins','shared','blogged','reviewed','totsize'),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 = '2'
> par2 = 'quantiles'
> par1 = '7'
> #'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] "totsize"
> x[,par1]
  [1] 112285  84786  83123 101193  38361  68504 119182  22807  17140 116174
 [11]  57635  66198  71701  57793  80444  53855  97668 133824 101481  99645
 [21] 114789  99052  67654  65553  97500  69112  82753  85323  72654  30727
 [31]  77873 117478  74007  90183  61542 101494  27570  55813  79215   1423
 [41]  55461  31081  22996  83122  70106  60578  39992  79892  49810  71570
 [51] 100708  33032  82875 139077  71595  72260   5950 115762  32551  31701
 [61]  80670 143558 117105  23789 120733 105195  73107 132068 149193  46821
 [71]  87011  95260  55183 106671  73511  92945  78664  70054  22618  74011
 [81]  83737  69094  93133  95536 225920  62133  61370  43836 106117  38692
 [91]  84651  56622  15986  95364  26706  89691  67267 126846  41140 102860
[101]  51715  55801 111813 120293 138599 161647 115929  24266 162901 109825
[111] 129838  37510  43750  40652  87771  85872  89275  44418 192565  35232
[121]  40909  13294  32387 140867 120662  21233  44332  61056 101338   1168
[131]  13497  65567  25162  32334  40735  91413    855  97068  44339  14116
[141]  10288  65622  16563  76643 110681  29011  92696  94785   8773  83209
[151]  93815  86687  34553 105547 103487 213688  71220  23517  56926  91721
[161] 115168 111194  51009 135777  51513  74163  51633  75345  33416  83305
[171]  98952 102372  37238 103772 123969  27142 135400  21399 130115  24874
[181]  34988  45549   6023  64466  54990   1644   6179   3926  32755  34777
[191]  73224  27114  20760  37636  65461  30080  24094  69008  54968  46090
[201]  27507  10672  34029  46300  24760  18779  21280  40662  28987  22827
[211]  18513  30594  24006  27913  42744  12934  22574  41385  18653  18472
[221]  30976  63339  25568  33747   4154  19474  35130  39067  13310  65892
[231]   4143  28579  51776  21152  38084  27717  32928  11342  19499  16380
[241]  36874  48259  16734  28207  30143  41369  45833  29156  35944  36278
[251]  45588  45097   3895  28394  18632   2325  25139  27975  14483  13127
[261]   5839  24069   3738  18625  36341  24548  21792  26263  23686  49303
[271]  25659  28904   2781  29236  19546  22818  32689   5752  22197  20055
[281]  25272  82206  32073   5444  20154  36944   8019  30884  19540
> 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])

[  855, 46090) [46090,225920] 
           145            144 
> colnames(x)
[1] "pageviews" "time"      "logins"    "shared"    "blogged"   "reviewed" 
[7] "totsize"  
> colnames(x)[par1]
[1] "totsize"
> x[,par1]
  [1] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [  855, 46090)
  [6] [46090,225920] [46090,225920] [  855, 46090) [  855, 46090) [46090,225920]
 [11] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [16] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [21] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [26] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [  855, 46090)
 [31] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [36] [46090,225920] [  855, 46090) [46090,225920] [46090,225920] [  855, 46090)
 [41] [46090,225920] [  855, 46090) [  855, 46090) [46090,225920] [46090,225920]
 [46] [46090,225920] [  855, 46090) [46090,225920] [46090,225920] [46090,225920]
 [51] [46090,225920] [  855, 46090) [46090,225920] [46090,225920] [46090,225920]
 [56] [46090,225920] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090)
 [61] [46090,225920] [46090,225920] [46090,225920] [  855, 46090) [46090,225920]
 [66] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [71] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [76] [46090,225920] [46090,225920] [46090,225920] [  855, 46090) [46090,225920]
 [81] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
 [86] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [  855, 46090)
 [91] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [  855, 46090)
 [96] [46090,225920] [46090,225920] [46090,225920] [  855, 46090) [46090,225920]
[101] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
[106] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [46090,225920]
[111] [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920]
[116] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [  855, 46090)
[121] [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920] [46090,225920]
[126] [  855, 46090) [  855, 46090) [46090,225920] [46090,225920] [  855, 46090)
[131] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090)
[136] [46090,225920] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090)
[141] [  855, 46090) [46090,225920] [  855, 46090) [46090,225920] [46090,225920]
[146] [  855, 46090) [46090,225920] [46090,225920] [  855, 46090) [46090,225920]
[151] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [46090,225920]
[156] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [46090,225920]
[161] [46090,225920] [46090,225920] [46090,225920] [46090,225920] [46090,225920]
[166] [46090,225920] [46090,225920] [46090,225920] [  855, 46090) [46090,225920]
[171] [46090,225920] [46090,225920] [  855, 46090) [46090,225920] [46090,225920]
[176] [  855, 46090) [46090,225920] [  855, 46090) [46090,225920] [  855, 46090)
[181] [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920] [46090,225920]
[186] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[191] [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920]
[196] [  855, 46090) [  855, 46090) [46090,225920] [46090,225920] [46090,225920]
[201] [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920] [  855, 46090)
[206] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[211] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[216] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[221] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090)
[226] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920]
[231] [  855, 46090) [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090)
[236] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[241] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090)
[246] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[251] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[256] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[261] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[266] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [46090,225920]
[271] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[276] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
[281] [  855, 46090) [46090,225920] [  855, 46090) [  855, 46090) [  855, 46090)
[286] [  855, 46090) [  855, 46090) [  855, 46090) [  855, 46090)
Levels: [  855, 46090) [46090,225920]
> 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/1ahbt1323616822.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  as.factor(totsize) 
Inputs:  pageviews, time, logins, shared, blogged, reviewed 
Number of observations:  289 

1) reviewed <= 22; criterion = 1, statistic = 162.108
  2) logins <= 76; criterion = 1, statistic = 21.299
    3)*  weights = 140 
  2) logins > 76
    4)*  weights = 8 
1) reviewed > 22
  5) blogged <= 24; criterion = 1, statistic = 26
    6)*  weights = 9 
  5) blogged > 24
    7) blogged <= 47; criterion = 0.99, statistic = 9.788
      8)*  weights = 22 
    7) blogged > 47
      9)*  weights = 110 
> postscript(file="/var/wessaorg/rcomp/tmp/2ehbb1323616822.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/3wjto1323616822.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    1
  [7,]    2    2
  [8,]    1    1
  [9,]    1    1
 [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,]    1    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,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    2    2
 [52,]    1    1
 [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    1
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    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,]    1    1
 [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,]    1    1
 [89,]    2    2
 [90,]    1    2
 [91,]    2    2
 [92,]    2    1
 [93,]    1    1
 [94,]    2    2
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    1    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,]    2    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    1
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    1    2
[135,]    1    2
[136,]    2    2
[137,]    1    1
[138,]    2    2
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    2    1
[143,]    1    1
[144,]    2    2
[145,]    2    2
[146,]    1    1
[147,]    2    2
[148,]    2    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    1
[163,]    2    1
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    2    1
[169,]    1    1
[170,]    2    1
[171,]    2    2
[172,]    2    2
[173,]    1    1
[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,]    2    2
[192,]    1    1
[193,]    1    1
[194,]    1    1
[195,]    2    1
[196,]    1    1
[197,]    1    1
[198,]    2    1
[199,]    2    1
[200,]    2    1
[201,]    1    1
[202,]    1    1
[203,]    1    1
[204,]    2    1
[205,]    1    1
[206,]    1    1
[207,]    1    1
[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,]    2    1
[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,]    2    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    1
[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,]    1    1
[252,]    1    1
[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,]    1    1
[263,]    1    1
[264,]    1    1
[265,]    1    1
[266,]    1    1
[267,]    1    1
[268,]    1    1
[269,]    1    1
[270,]    2    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,]    1    1
[282,]    2    1
[283,]    1    1
[284,]    1    1
[285,]    1    1
[286,]    1    1
[287,]    1    1
[288,]    1    1
[289,]    1    1
                
                 [  855, 46090) [46090,225920]
  [  855, 46090)            138              7
  [46090,225920]             19            125
> postscript(file="/var/wessaorg/rcomp/tmp/4z8v91323616822.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/537po1323616822.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/6ou5m1323616822.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/73a961323616822.tab") 
+ }
> 
> try(system("convert tmp/2ehbb1323616822.ps tmp/2ehbb1323616822.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wjto1323616822.ps tmp/3wjto1323616822.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z8v91323616822.ps tmp/4z8v91323616822.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.754   0.241   4.105