R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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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(97.06
+ ,21.454
+ ,631.923
+ ,130.678
+ ,97.73
+ ,23.899
+ ,654.294
+ ,120.877
+ ,98
+ ,24.939
+ ,671.833
+ ,137.114
+ ,97.76
+ ,23.580
+ ,586.840
+ ,134.406
+ ,97.48
+ ,24.562
+ ,600.969
+ ,120.262
+ ,97.77
+ ,24.696
+ ,625.568
+ ,130.846
+ ,97.96
+ ,23.785
+ ,558.110
+ ,120.343
+ ,98.22
+ ,23.812
+ ,630.577
+ ,98.881
+ ,98.51
+ ,21.917
+ ,628.654
+ ,115.678
+ ,98.19
+ ,19.713
+ ,603.184
+ ,120.796
+ ,98.37
+ ,19.282
+ ,656.255
+ ,94.261
+ ,98.31
+ ,18.788
+ ,600.730
+ ,89.151
+ ,98.6
+ ,21.453
+ ,670.326
+ ,119.880
+ ,98.96
+ ,24.482
+ ,678.423
+ ,131.468
+ ,99.11
+ ,27.474
+ ,641.502
+ ,155.089
+ ,99.64
+ ,27.264
+ ,625.311
+ ,149.581
+ ,100.02
+ ,27.349
+ ,628.177
+ ,122.788
+ ,99.98
+ ,30.632
+ ,589.767
+ ,143.900
+ ,100.32
+ ,29.429
+ ,582.471
+ ,112.115
+ ,100.44
+ ,30.084
+ ,636.248
+ ,109.600
+ ,100.51
+ ,26.290
+ ,599.885
+ ,117.446
+ ,101
+ ,24.379
+ ,621.694
+ ,118.456
+ ,100.88
+ ,23.335
+ ,637.406
+ ,101.901
+ ,100.55
+ ,21.346
+ ,595.994
+ ,89.940
+ ,100.82
+ ,21.106
+ ,696.308
+ ,129.143
+ ,101.5
+ ,24.514
+ ,674.201
+ ,126.102
+ ,102.15
+ ,28.353
+ ,648.861
+ ,143.048
+ ,102.39
+ ,30.805
+ ,649.605
+ ,142.258
+ ,102.54
+ ,31.348
+ ,672.392
+ ,131.011
+ ,102.85
+ ,34.556
+ ,598.396
+ ,146.471
+ ,103.47
+ ,33.855
+ ,613.177
+ ,114.073
+ ,103.56
+ ,34.787
+ ,638.104
+ ,114.642
+ ,103.69
+ ,32.529
+ ,615.632
+ ,118.226
+ ,103.49
+ ,29.998
+ ,634.465
+ ,111.338
+ ,103.47
+ ,29.257
+ ,638.686
+ ,108.701
+ ,103.45
+ ,28.155
+ ,604.243
+ ,80.512
+ ,103.48
+ ,30.466
+ ,706.669
+ ,146.865
+ ,103.93
+ ,35.704
+ ,677.185
+ ,137.179
+ ,103.89
+ ,39.327
+ ,644.328
+ ,166.536
+ ,104.4
+ ,39.351
+ ,664.825
+ ,137.070
+ ,104.79
+ ,42.234
+ ,605.707
+ ,127.090
+ ,104.77
+ ,43.630
+ ,600.136
+ ,139.966
+ ,105.13
+ ,43.722
+ ,612.166
+ ,122.243
+ ,105.26
+ ,43.121
+ ,599.659
+ ,109.097
+ ,104.96
+ ,37.985
+ ,634.210
+ ,116.591
+ ,104.75
+ ,37.135
+ ,618.234
+ ,111.964
+ ,105.01
+ ,34.646
+ ,613.576
+ ,109.754
+ ,105.15
+ ,33.026
+ ,627.200
+ ,77.609
+ ,105.2
+ ,35.087
+ ,668.973
+ ,138.445
+ ,105.77
+ ,38.846
+ ,651.479
+ ,127.901
+ ,105.78
+ ,42.013
+ ,619.661
+ ,156.615
+ ,106.26
+ ,43.908
+ ,644.260
+ ,133.264
+ ,106.13
+ ,42.868
+ ,579.936
+ ,143.521
+ ,106.12
+ ,44.423
+ ,601.752
+ ,152.139
+ ,106.57
+ ,44.167
+ ,595.376
+ ,131.523
+ ,106.44
+ ,43.636
+ ,588.902
+ ,113.925
+ ,106.54
+ ,44.382
+ ,634.341
+ ,86.495
+ ,107.1
+ ,42.142
+ ,594.305
+ ,127.877
+ ,108.1
+ ,43.452
+ ,606.200
+ ,107.017
+ ,108.4
+ ,36.912
+ ,610.926
+ ,78.716
+ ,108.84
+ ,42.413
+ ,633.685
+ ,138.278
+ ,109.62
+ ,45.344
+ ,639.696
+ ,144.238
+ ,110.42
+ ,44.873
+ ,659.451
+ ,143.679
+ ,110.67
+ ,47.510
+ ,593.248
+ ,159.932
+ ,111.66
+ ,49.554
+ ,606.677
+ ,136.781
+ ,112.28
+ ,47.369
+ ,599.434
+ ,148.173
+ ,112.87
+ ,45.998
+ ,569.578
+ ,125.673
+ ,112.18
+ ,48.140
+ ,629.873
+ ,105.573
+ ,112.36
+ ,48.441
+ ,613.438
+ ,122.405
+ ,112.16
+ ,44.928
+ ,604.172
+ ,128.045
+ ,111.49
+ ,40.454
+ ,658.328
+ ,94.467
+ ,111.25
+ ,38.661
+ ,612.633
+ ,85.573
+ ,111.36
+ ,37.246
+ ,707.372
+ ,121.501
+ ,111.74
+ ,36.843
+ ,739.770
+ ,125.074
+ ,111.1
+ ,36.424
+ ,777.535
+ ,144.979
+ ,111.33
+ ,37.594
+ ,685.030
+ ,142.120
+ ,111.25
+ ,38.144
+ ,730.234
+ ,124.213
+ ,111.04
+ ,38.737
+ ,714.154
+ ,144.407
+ ,110.97
+ ,34.560
+ ,630.872
+ ,125.170
+ ,111.31
+ ,36.080
+ ,719.492
+ ,109.267
+ ,111.02
+ ,33.508
+ ,677.023
+ ,122.354
+ ,111.07
+ ,35.462
+ ,679.272
+ ,122.589
+ ,111.36
+ ,33.374
+ ,718.317
+ ,104.982
+ ,111.54
+ ,32.110
+ ,645.672
+ ,90.542)
+ ,dim=c(4
+ ,84)
+ ,dimnames=list(c('CPI'
+ ,'vacatures'
+ ,'werklozen'
+ ,'inschrijvingen')
+ ,1:84))
>  y <- array(NA,dim=c(4,84),dimnames=list(c('CPI','vacatures','werklozen','inschrijvingen'),1:84))
>  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 = '3'
> par2 = 'none'
> par1 = '1'
> #'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

Attaching package: 'zoo'


	The following object(s) are masked from package:base :

	 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)

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] "CPI"
> x[,par1]
 [1]  97.06  97.73  98.00  97.76  97.48  97.77  97.96  98.22  98.51  98.19
[11]  98.37  98.31  98.60  98.96  99.11  99.64 100.02  99.98 100.32 100.44
[21] 100.51 101.00 100.88 100.55 100.82 101.50 102.15 102.39 102.54 102.85
[31] 103.47 103.56 103.69 103.49 103.47 103.45 103.48 103.93 103.89 104.40
[41] 104.79 104.77 105.13 105.26 104.96 104.75 105.01 105.15 105.20 105.77
[51] 105.78 106.26 106.13 106.12 106.57 106.44 106.54 107.10 108.10 108.40
[61] 108.84 109.62 110.42 110.67 111.66 112.28 112.87 112.18 112.36 112.16
[71] 111.49 111.25 111.36 111.74 111.10 111.33 111.25 111.04 110.97 111.31
[81] 111.02 111.07 111.36 111.54
> 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])

 97.06  97.48  97.73  97.76  97.77  97.96     98  98.19  98.22  98.31  98.37 
     1      1      1      1      1      1      1      1      1      1      1 
 98.51   98.6  98.96  99.11  99.64  99.98 100.02 100.32 100.44 100.51 100.55 
     1      1      1      1      1      1      1      1      1      1      1 
100.82 100.88    101  101.5 102.15 102.39 102.54 102.85 103.45 103.47 103.48 
     1      1      1      1      1      1      1      1      1      2      1 
103.49 103.56 103.69 103.89 103.93  104.4 104.75 104.77 104.79 104.96 105.01 
     1      1      1      1      1      1      1      1      1      1      1 
105.13 105.15  105.2 105.26 105.77 105.78 106.12 106.13 106.26 106.44 106.54 
     1      1      1      1      1      1      1      1      1      1      1 
106.57  107.1  108.1  108.4 108.84 109.62 110.42 110.67 110.97 111.02 111.04 
     1      1      1      1      1      1      1      1      1      1      1 
111.07  111.1 111.25 111.31 111.33 111.36 111.49 111.54 111.66 111.74 112.16 
     1      1      2      1      1      2      1      1      1      1      1 
112.18 112.28 112.36 112.87 
     1      1      1      1 
> colnames(x)
[1] "CPI"            "vacatures"      "werklozen"      "inschrijvingen"
> colnames(x)[par1]
[1] "CPI"
> x[,par1]
 [1]  97.06  97.73  98.00  97.76  97.48  97.77  97.96  98.22  98.51  98.19
[11]  98.37  98.31  98.60  98.96  99.11  99.64 100.02  99.98 100.32 100.44
[21] 100.51 101.00 100.88 100.55 100.82 101.50 102.15 102.39 102.54 102.85
[31] 103.47 103.56 103.69 103.49 103.47 103.45 103.48 103.93 103.89 104.40
[41] 104.79 104.77 105.13 105.26 104.96 104.75 105.01 105.15 105.20 105.77
[51] 105.78 106.26 106.13 106.12 106.57 106.44 106.54 107.10 108.10 108.40
[61] 108.84 109.62 110.42 110.67 111.66 112.28 112.87 112.18 112.36 112.16
[71] 111.49 111.25 111.36 111.74 111.10 111.33 111.25 111.04 110.97 111.31
[81] 111.02 111.07 111.36 111.54
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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/www/html/rcomp/tmp/1gvms1292790759.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  CPI 
Inputs:  vacatures, werklozen, inschrijvingen 
Number of observations:  84 

1) vacatures <= 31.348; criterion = 1, statistic = 53.161
  2) vacatures <= 27.474; criterion = 0.999, statistic = 14.072
    3)*  weights = 23 
  2) vacatures > 27.474
    4)*  weights = 10 
1) vacatures > 31.348
  5) werklozen <= 677.185; criterion = 0.954, statistic = 5.844
    6) vacatures <= 44.423; criterion = 0.991, statistic = 8.742
      7)*  weights = 33 
    6) vacatures > 44.423
      8)*  weights = 9 
  5) werklozen > 677.185
    9)*  weights = 9 
> postscript(file="/var/www/html/rcomp/tmp/2gvms1292790759.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/www/html/rcomp/tmp/3943d1292790759.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)
+ }
   Actuals Forecasts   Residuals
1    97.06  98.99783 -1.93782609
2    97.73  98.99783 -1.26782609
3    98.00  98.99783 -0.99782609
4    97.76  98.99783 -1.23782609
5    97.48  98.99783 -1.51782609
6    97.77  98.99783 -1.22782609
7    97.96  98.99783 -1.03782609
8    98.22  98.99783 -0.77782609
9    98.51  98.99783 -0.48782609
10   98.19  98.99783 -0.80782609
11   98.37  98.99783 -0.62782609
12   98.31  98.99783 -0.68782609
13   98.60  98.99783 -0.39782609
14   98.96  98.99783 -0.03782609
15   99.11  98.99783  0.11217391
16   99.64  98.99783  0.64217391
17  100.02  98.99783  1.02217391
18   99.98 102.17100 -2.19100000
19  100.32 102.17100 -1.85100000
20  100.44 102.17100 -1.73100000
21  100.51  98.99783  1.51217391
22  101.00  98.99783  2.00217391
23  100.88  98.99783  1.88217391
24  100.55  98.99783  1.55217391
25  100.82  98.99783  1.82217391
26  101.50  98.99783  2.50217391
27  102.15 102.17100 -0.02100000
28  102.39 102.17100  0.21900000
29  102.54 102.17100  0.36900000
30  102.85 106.33727 -3.48727273
31  103.47 106.33727 -2.86727273
32  103.56 106.33727 -2.77727273
33  103.69 106.33727 -2.64727273
34  103.49 102.17100  1.31900000
35  103.47 102.17100  1.29900000
36  103.45 102.17100  1.27900000
37  103.48 102.17100  1.30900000
38  103.93 106.33727 -2.40727273
39  103.89 106.33727 -2.44727273
40  104.40 106.33727 -1.93727273
41  104.79 106.33727 -1.54727273
42  104.77 106.33727 -1.56727273
43  105.13 106.33727 -1.20727273
44  105.26 106.33727 -1.07727273
45  104.96 106.33727 -1.37727273
46  104.75 106.33727 -1.58727273
47  105.01 106.33727 -1.32727273
48  105.15 106.33727 -1.18727273
49  105.20 106.33727 -1.13727273
50  105.77 106.33727 -0.56727273
51  105.78 106.33727 -0.55727273
52  106.26 106.33727 -0.07727273
53  106.13 106.33727 -0.20727273
54  106.12 106.33727 -0.21727273
55  106.57 106.33727  0.23272727
56  106.44 106.33727  0.10272727
57  106.54 106.33727  0.20272727
58  107.10 106.33727  0.76272727
59  108.10 106.33727  1.76272727
60  108.40 106.33727  2.06272727
61  108.84 106.33727  2.50272727
62  109.62 111.58000 -1.96000000
63  110.42 111.58000 -1.16000000
64  110.67 111.58000 -0.91000000
65  111.66 111.58000  0.08000000
66  112.28 111.58000  0.70000000
67  112.87 111.58000  1.29000000
68  112.18 111.58000  0.60000000
69  112.36 111.58000  0.78000000
70  112.16 111.58000  0.58000000
71  111.49 106.33727  5.15272727
72  111.25 106.33727  4.91272727
73  111.36 111.28444  0.07555556
74  111.74 111.28444  0.45555556
75  111.10 111.28444 -0.18444444
76  111.33 111.28444  0.04555556
77  111.25 111.28444 -0.03444444
78  111.04 111.28444 -0.24444444
79  110.97 106.33727  4.63272727
80  111.31 111.28444  0.02555556
81  111.02 106.33727  4.68272727
82  111.07 111.28444 -0.21444444
83  111.36 111.28444  0.07555556
84  111.54 106.33727  5.20272727
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/html/rcomp/tmp/42wky1292790759.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/www/html/rcomp/tmp/5nw1m1292790759.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/www/html/rcomp/tmp/6g5071292790759.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/www/html/rcomp/tmp/7johd1292790759.tab") 
+ }
> 
> try(system("convert tmp/2gvms1292790759.ps tmp/2gvms1292790759.png",intern=TRUE))
character(0)
> try(system("convert tmp/3943d1292790759.ps tmp/3943d1292790759.png",intern=TRUE))
character(0)
> try(system("convert tmp/42wky1292790759.ps tmp/42wky1292790759.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.394   0.617   5.650