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

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Type 'q()' to quit R.

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+ ,17.82972222
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+ ,9.674855276
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+ ,14.12694444
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+ ,16
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+ ,13.46103292
+ ,0
+ ,20.14861111
+ ,21
+ ,16
+ ,39
+ ,19540
+ ,9.81357389
+ ,0)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('Tijd'
+ ,'totblogs'
+ ,'Reviews'
+ ,'LFM'
+ ,'totsize'
+ ,'Score'
+ ,'Dum')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('Tijd','totblogs','Reviews','LFM','totsize','Score','Dum'),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 = 'equal'
> par1 = '6'
> 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] "Score"
> x[,par1]
  [1] 14.3600978 11.3090029 12.4581089 13.5039428  7.2193583  7.3813672
  [7] 15.2609623  3.1654150 11.7814391  9.2778084 14.2278049 13.3753608
 [13]  9.4877505 15.2151167 10.4271201 17.8497865 15.8953488 12.5511544
 [19] 11.4339609 15.5350763 14.5223933 13.8933158  9.7122786 14.1294250
 [25] 12.8953736 12.1602659 13.0118169 13.2987368  7.7754151 13.2842973
 [31] 15.1145104 13.7479724 16.4294262  8.5843773 16.4741068 10.8353995
 [37] 14.4803578  9.8171533  5.3452329 14.3931087 12.8763059 10.7687797
 [43] 10.4969408  8.4436714  8.4077828 13.9406525  6.1632646 14.4368829
 [49] 17.7758385 12.2486023 11.4568557  1.1530933 17.3568684  5.2246521
 [55]  5.2386969 12.6052315 17.9840404 15.3027933 16.2306506 11.1772860
 [61] 17.6425016 14.4328892  9.8414415 13.1018580 12.7822915  9.8413530
 [67] 12.5226713 12.8517713 13.1788982  9.4599263 13.1446023  5.6799940
 [73] 13.6210990 11.4493559 12.4561227 14.5225559 14.0091600 12.8791785
 [79] 10.9145354 10.6771017  7.5005063 15.7151189  8.4514144 13.0428111
 [85]  5.4757356  6.3185620 11.6710285 12.8826250 12.3410312 15.3188731
 [91] 10.0045028 14.8683566  8.7068681 12.3840196 14.2369919 12.9817708
 [97] 14.7511222 15.6821242 18.1400865 15.9186706 14.9617935 16.0407861
[103]  7.9587693  6.1170225  7.4086366 12.7050119 12.2118057 13.7541755
[109] 10.1639840 11.3802787 14.0940622 15.3888242  0.6945124 10.9899281
[115]  3.8477272 10.3653796 15.0728277  0.4068218 11.0744988  2.6517992
[121] 13.4007988 14.2665410 11.1935173  9.4335546  2.2985822 11.5599419
[127] 16.7003244 14.8407483 17.3840383 15.7303959 19.3626588 11.3260538
[133]  7.8518410 13.3022526 17.8976280  9.4632072 17.4191588  9.3322388
[139] 13.4187927 11.3772591  8.7023235 14.1961167 14.3980936  7.3013839
[145] 15.7202726 14.0340547 14.7549827 17.0370713  0.4987245 10.8241040
[151] 13.9900326  0.2952181  1.8336022  0.5994427  6.5123223  8.5944392
[157] 10.1746301 11.7068595  0.3366300 12.8154581 18.4813493 19.1500000
[163] 11.9897651 15.9631169 12.3686764 17.8535942 13.3397208 17.4358625
[169]  7.9717640 16.4942397  9.5896766 13.0201353 18.3099841  5.3114406
[175] 16.1454403  9.6148321  6.9700193 17.8328823 10.4363182 12.4952189
[181] 13.1301414 12.5995365 16.8927283 15.9582222 17.4462451 13.3609634
[187] 11.7020832 12.6700532  9.9429226 12.4778703 16.1794073 11.3426961
[193] 10.6145874 13.6336296 19.3000000 14.1786204 12.1311030 14.2912223
[199] 15.9042453 15.3053788 12.7171195 10.1041787 15.1063345 14.9747254
[205] 13.1494039  8.8033703 11.3767137 16.4882902 15.2869052 15.8744937
[211]  8.9918917 14.5206900 10.7332929 12.1125599 13.1203908 11.1761845
[217]  9.8524521 10.8382635 10.0160248  9.9842933 13.9065466 17.4382945
[223] 14.1365141 12.9546588  6.4058756 13.0082774  8.8438968 14.9527512
[229]  9.9853994 15.8528907  6.3048718 11.7074413 14.6847188  9.0637407
[235] 14.0966964 11.4580741 14.5766967  8.0134599 10.9171930  9.6804496
[241] 12.3063118 19.1500000  9.9740386 10.6139696 12.3996917 13.8923511
[247] 15.3368091 11.2757048 11.4689064  7.7054735 17.8091989 17.4519668
[253]  2.0809109 13.0982075 11.2913554  2.5071723  7.8236645 10.9666254
[259]  8.2841739  5.6926153  4.6872765 13.2598721  3.5800935  9.7331168
[265] 11.8865130 11.3271032 12.9961406 10.5171551 13.7161684 15.3244789
[271] 13.8227477 11.7115875  1.9083708 13.8208843  8.2413274 10.5053078
[277]  8.1809177  2.3226555 12.2859831 12.5490645 13.1110906 11.7934321
[283] 10.8128618  3.0687721  9.6748553 11.3997302  7.1662655 13.4610329
[289]  9.8135739
> 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 
 74 215 
> colnames(x)
[1] "Tijd"     "totblogs" "Reviews"  "LFM"      "totsize"  "Score"    "Dum"     
> colnames(x)[par1]
[1] "Score"
> x[,par1]
  [1] C2 C2 C2 C2 C1 C1 C2 C1 C2 C1 C2 C2 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C2 C2
 [26] C2 C2 C2 C1 C2 C2 C2 C2 C1 C2 C2 C2 C1 C1 C2 C2 C2 C2 C1 C1 C2 C1 C2 C2 C2
 [51] C2 C1 C2 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C2 C1 C2 C2 C2
 [76] C2 C2 C2 C2 C2 C1 C2 C1 C2 C1 C1 C2 C2 C2 C2 C2 C2 C1 C2 C2 C2 C2 C2 C2 C2
[101] C2 C2 C1 C1 C1 C2 C2 C2 C2 C2 C2 C2 C1 C2 C1 C2 C2 C1 C2 C1 C2 C2 C2 C1 C1
[126] C2 C2 C2 C2 C2 C2 C2 C1 C2 C2 C1 C2 C1 C2 C2 C1 C2 C2 C1 C2 C2 C2 C2 C1 C2
[151] C2 C1 C1 C1 C1 C1 C2 C2 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C2 C1 C2 C2 C1 C2
[176] C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2
[201] C2 C2 C2 C2 C2 C1 C2 C2 C2 C2 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1
[226] C2 C1 C2 C2 C2 C1 C2 C2 C1 C2 C2 C2 C1 C2 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1
[251] C2 C2 C1 C2 C2 C1 C1 C2 C1 C1 C1 C2 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C1 C2 C1
[276] C2 C1 C1 C2 C2 C2 C2 C2 C1 C1 C2 C1 C2 C1
Levels: C1 C2
> 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/1951d1323795536.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu    1    2
          1  582   86
          2   94 1830
[1] 0.8712575
[1] 0.9511435
[1] 0.9305556
           m.ct.x.pred
m.ct.x.actu   1   2
          1  61  11
          2  21 205
[1] 0.8472222
[1] 0.9070796
[1] 0.8926174
> m

	 Conditional inference tree with 9 terminal nodes

Response:  as.factor(Score) 
Inputs:  Tijd, totblogs, Reviews, LFM, totsize, Dum 
Number of observations:  289 

1) LFM <= 38; criterion = 1, statistic = 67.212
  2) Tijd <= 20.41778; criterion = 0.997, statistic = 12.268
    3) totblogs <= 23; criterion = 0.988, statistic = 9.538
      4)*  weights = 26 
    3) totblogs > 23
      5)*  weights = 12 
  2) Tijd > 20.41778
    6)*  weights = 19 
1) LFM > 38
  7) totblogs <= 83; criterion = 1, statistic = 20.434
    8) Dum <= 0; criterion = 1, statistic = 47.424
      9) totsize <= 20154; criterion = 0.991, statistic = 10.118
        10)*  weights = 14 
      9) totsize > 20154
        11)*  weights = 76 
    8) Dum > 0
      12) Reviews <= 32; criterion = 1, statistic = 20.698
        13) totsize <= 68504; criterion = 0.995, statistic = 11.25
          14)*  weights = 24 
        13) totsize > 68504
          15)*  weights = 10 
      12) Reviews > 32
        16)*  weights = 12 
  7) totblogs > 83
    17)*  weights = 96 
> postscript(file="/var/wessaorg/rcomp/tmp/2rfdg1323795536.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/386ms1323795536.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,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    2    2
 [10,]    1    1
 [11,]    2    2
 [12,]    2    2
 [13,]    1    1
 [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,]    1    1
 [24,]    2    2
 [25,]    2    2
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    1    1
 [30,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    1    1
 [35,]    2    2
 [36,]    2    1
 [37,]    2    2
 [38,]    1    1
 [39,]    1    2
 [40,]    2    2
 [41,]    2    2
 [42,]    2    2
 [43,]    2    2
 [44,]    1    1
 [45,]    1    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,]    1    1
 [55,]    1    1
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    2    2
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    1    2
 [71,]    2    2
 [72,]    1    1
 [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,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    2    2
 [85,]    1    2
 [86,]    1    1
 [87,]    2    2
 [88,]    2    2
 [89,]    2    2
 [90,]    2    2
 [91,]    2    2
 [92,]    2    2
 [93,]    1    1
 [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,]    1    1
[104,]    1    1
[105,]    1    2
[106,]    2    2
[107,]    2    2
[108,]    2    2
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    2    2
[113,]    1    1
[114,]    2    2
[115,]    1    1
[116,]    2    2
[117,]    2    2
[118,]    1    1
[119,]    2    2
[120,]    1    1
[121,]    2    2
[122,]    2    2
[123,]    2    2
[124,]    1    2
[125,]    1    1
[126,]    2    2
[127,]    2    2
[128,]    2    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    2    2
[133,]    1    1
[134,]    2    2
[135,]    2    2
[136,]    1    2
[137,]    2    2
[138,]    1    1
[139,]    2    2
[140,]    2    2
[141,]    1    2
[142,]    2    2
[143,]    2    2
[144,]    1    1
[145,]    2    2
[146,]    2    2
[147,]    2    2
[148,]    2    2
[149,]    1    1
[150,]    2    2
[151,]    2    2
[152,]    1    1
[153,]    1    1
[154,]    1    1
[155,]    1    1
[156,]    1    2
[157,]    2    2
[158,]    2    2
[159,]    1    1
[160,]    2    2
[161,]    2    2
[162,]    2    2
[163,]    2    2
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    2    2
[169,]    1    1
[170,]    2    2
[171,]    1    2
[172,]    2    2
[173,]    2    2
[174,]    1    1
[175,]    2    2
[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,]    2    2
[185,]    2    2
[186,]    2    2
[187,]    2    2
[188,]    2    2
[189,]    2    2
[190,]    2    2
[191,]    2    2
[192,]    2    2
[193,]    2    2
[194,]    2    2
[195,]    2    2
[196,]    2    2
[197,]    2    2
[198,]    2    2
[199,]    2    2
[200,]    2    2
[201,]    2    2
[202,]    2    2
[203,]    2    2
[204,]    2    2
[205,]    2    2
[206,]    1    1
[207,]    2    2
[208,]    2    2
[209,]    2    2
[210,]    2    2
[211,]    1    2
[212,]    2    2
[213,]    2    2
[214,]    2    2
[215,]    2    2
[216,]    2    2
[217,]    2    2
[218,]    2    2
[219,]    2    2
[220,]    2    2
[221,]    2    2
[222,]    2    2
[223,]    2    2
[224,]    2    2
[225,]    1    2
[226,]    2    2
[227,]    1    1
[228,]    2    2
[229,]    2    1
[230,]    2    2
[231,]    1    2
[232,]    2    2
[233,]    2    2
[234,]    1    1
[235,]    2    2
[236,]    2    2
[237,]    2    2
[238,]    1    1
[239,]    2    2
[240,]    1    2
[241,]    2    2
[242,]    2    2
[243,]    2    2
[244,]    2    2
[245,]    2    2
[246,]    2    2
[247,]    2    2
[248,]    2    2
[249,]    2    2
[250,]    1    1
[251,]    2    2
[252,]    2    2
[253,]    1    1
[254,]    2    2
[255,]    2    2
[256,]    1    1
[257,]    1    1
[258,]    2    2
[259,]    1    1
[260,]    1    1
[261,]    1    1
[262,]    2    2
[263,]    1    1
[264,]    1    1
[265,]    2    2
[266,]    2    2
[267,]    2    2
[268,]    2    1
[269,]    2    2
[270,]    2    2
[271,]    2    2
[272,]    2    2
[273,]    1    1
[274,]    2    2
[275,]    1    1
[276,]    2    2
[277,]    1    1
[278,]    1    1
[279,]    2    2
[280,]    2    2
[281,]    2    2
[282,]    2    2
[283,]    2    2
[284,]    1    1
[285,]    1    2
[286,]    2    1
[287,]    1    1
[288,]    2    2
[289,]    1    2
    
      C1  C2
  C1  58  16
  C2   4 211
> postscript(file="/var/wessaorg/rcomp/tmp/4kfn41323795536.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/55v0i1323795536.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/6u4fu1323795537.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/7a0f51323795537.tab") 
+ }
> 
> try(system("convert tmp/2rfdg1323795536.ps tmp/2rfdg1323795536.png",intern=TRUE))
character(0)
> try(system("convert tmp/386ms1323795536.ps tmp/386ms1323795536.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kfn41323795536.ps tmp/4kfn41323795536.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.976   0.287   5.260