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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+ ,16
+ ,19540
+ ,27
+ ,21)
+ ,dim=c(8
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'pageviews'
+ ,'compendium_views_info'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'totale_size'
+ ,'totale_hyperlinks'
+ ,'totale_blogs')
+ ,1:289))
>  y <- array(NA,dim=c(8,289),dimnames=list(c('time_in_rfc','pageviews','compendium_views_info','blogged_computations','compendiums_reviewed','totale_size','totale_hyperlinks','totale_blogs'),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 = '3'
> par2 = 'none'
> 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
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] "compendiums_reviewed"
> x[,par1]
  [1] 30 28 38 30 22 26 25 18 11 26 25 38 44 30 40 34 47 30 31 23 36 36 30 25 39
 [26] 34 31 31 33 25 33 35 42 43 30 33 13 32 36  0 28 14 17 32 30 35 20 28 28 39
 [51] 34 26 39 39 33 28  4 39 18 14 29 44 21 16 28 35 28 38 23 36 32 29 25 27 36
 [76] 28 23 40 23 40 28 34 33 28 34 30 33 22 38 26 35  8 24 29 20 29 45 37 33 33
[101] 25 32 29 28 28 31 52 21 24 41 33 32 19 20 31 31 32 18 23 17 20 12 17 30 31
[126] 10 13 22 42  1  9 32 11 25 36 31  0 24 13  8 13 19 18 33 40 22 38 24  8 35
[151] 43 43 14 41 38 45 31 13 28 31 40 30 16 37 30 35 32 27 20 18 31 31 21 39 41
[176] 13 32 18 39 14  7 17  0 30 37  0  5  1 16 32 24 17 11 24 22 12 19 13 17 15
[201] 16 24 15 17 18 20 16 16 18 22  8 17 18 16 23 22 13 13 16 16 20 22 17 18 17
[226] 12  7 17 14 23 17 14 15 17 21 18 18 17 17 16 15 21 16 14 15 17 15 15 10  6
[251] 22 21  1 18 17  4 10 16 16  9 16 17  7 15 14 14 18 12 16 21 19 16  1 16 10
[276] 19 12  2 14 17 19 14 11  4 16 20 12 15 16
> 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  1  2  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
 4  4  1  3  1  1  3  4  2  4  4  6  9 11  9 19 19 14  6  8  7  9  7  7  7  4 
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 47 52 
 2 13  5 12 12 10 10  5  6  6  3  6  7  5  3  2  3  2  2  1  1 
> colnames(x)
[1] "time_in_rfc"           "pageviews"             "compendium_views_info"
[4] "blogged_computations"  "compendiums_reviewed"  "totale_size"          
[7] "totale_hyperlinks"     "totale_blogs"         
> colnames(x)[par1]
[1] "compendiums_reviewed"
> x[,par1]
  [1] 30 28 38 30 22 26 25 18 11 26 25 38 44 30 40 34 47 30 31 23 36 36 30 25 39
 [26] 34 31 31 33 25 33 35 42 43 30 33 13 32 36  0 28 14 17 32 30 35 20 28 28 39
 [51] 34 26 39 39 33 28  4 39 18 14 29 44 21 16 28 35 28 38 23 36 32 29 25 27 36
 [76] 28 23 40 23 40 28 34 33 28 34 30 33 22 38 26 35  8 24 29 20 29 45 37 33 33
[101] 25 32 29 28 28 31 52 21 24 41 33 32 19 20 31 31 32 18 23 17 20 12 17 30 31
[126] 10 13 22 42  1  9 32 11 25 36 31  0 24 13  8 13 19 18 33 40 22 38 24  8 35
[151] 43 43 14 41 38 45 31 13 28 31 40 30 16 37 30 35 32 27 20 18 31 31 21 39 41
[176] 13 32 18 39 14  7 17  0 30 37  0  5  1 16 32 24 17 11 24 22 12 19 13 17 15
[201] 16 24 15 17 18 20 16 16 18 22  8 17 18 16 23 22 13 13 16 16 20 22 17 18 17
[226] 12  7 17 14 23 17 14 15 17 21 18 18 17 17 16 15 21 16 14 15 17 15 15 10  6
[251] 22 21  1 18 17  4 10 16 16  9 16 17  7 15 14 14 18 12 16 21 19 16  1 16 10
[276] 19 12  2 14 17 19 14 11  4 16 20 12 15 16
> 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/1hzyo1323895647.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  compendiums_reviewed 
Inputs:  time_in_rfc, pageviews, compendium_views_info, blogged_computations, totale_size, totale_hyperlinks, totale_blogs 
Number of observations:  289 

1) blogged_computations <= 54; criterion = 1, statistic = 167.343
  2) totale_size <= 56622; criterion = 1, statistic = 66.676
    3) totale_size <= 8773; criterion = 1, statistic = 42.98
      4)*  weights = 19 
    3) totale_size > 8773
      5) pageviews <= 108446; criterion = 0.998, statistic = 13.063
        6)*  weights = 101 
      5) pageviews > 108446
        7)*  weights = 29 
  2) totale_size > 56622
    8)*  weights = 25 
1) blogged_computations > 54
  9) blogged_computations <= 74; criterion = 1, statistic = 19.205
    10)*  weights = 35 
  9) blogged_computations > 74
    11)*  weights = 80 
> postscript(file="/var/wessaorg/rcomp/tmp/2hnms1323895647.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/36syj1323895647.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        30 34.362500  -4.36250000
2        28 28.885714  -0.88571429
3        38 28.885714   9.11428571
4        30 34.362500  -4.36250000
5        22 19.931034   2.06896552
6        26 27.680000  -1.68000000
7        25 34.362500  -9.36250000
8        18 15.881188   2.11881188
9        11 15.881188  -4.88118812
10       26 27.680000  -1.68000000
11       25 28.885714  -3.88571429
12       38 34.362500   3.63750000
13       44 34.362500   9.63750000
14       30 27.680000   2.32000000
15       40 34.362500   5.63750000
16       34 28.885714   5.11428571
17       47 34.362500  12.63750000
18       30 34.362500  -4.36250000
19       31 34.362500  -3.36250000
20       23 34.362500 -11.36250000
21       36 34.362500   1.63750000
22       36 34.362500   1.63750000
23       30 34.362500  -4.36250000
24       25 27.680000  -2.68000000
25       39 34.362500   4.63750000
26       34 34.362500  -0.36250000
27       31 28.885714   2.11428571
28       31 34.362500  -3.36250000
29       33 34.362500  -1.36250000
30       25 19.931034   5.06896552
31       33 34.362500  -1.36250000
32       35 34.362500   0.63750000
33       42 27.680000  14.32000000
34       43 34.362500   8.63750000
35       30 27.680000   2.32000000
36       33 34.362500  -1.36250000
37       13 15.881188  -2.88118812
38       32 28.885714   3.11428571
39       36 34.362500   1.63750000
40        0  5.263158  -5.26315789
41       28 28.885714  -0.88571429
42       14 15.881188  -1.88118812
43       17 15.881188   1.11881188
44       32 34.362500  -2.36250000
45       30 34.362500  -4.36250000
46       35 28.885714   6.11428571
47       20 28.885714  -8.88571429
48       28 28.885714  -0.88571429
49       28 19.931034   8.06896552
50       39 27.680000  11.32000000
51       34 34.362500  -0.36250000
52       26 15.881188  10.11881188
53       39 34.362500   4.63750000
54       39 34.362500   4.63750000
55       33 34.362500  -1.36250000
56       28 34.362500  -6.36250000
57        4  5.263158  -1.26315789
58       39 34.362500   4.63750000
59       18 15.881188   2.11881188
60       14 15.881188  -1.88118812
61       29 34.362500  -5.36250000
62       44 34.362500   9.63750000
63       21 34.362500 -13.36250000
64       16 15.881188   0.11881188
65       28 34.362500  -6.36250000
66       35 34.362500   0.63750000
67       28 34.362500  -6.36250000
68       38 34.362500   3.63750000
69       23 34.362500 -11.36250000
70       36 28.885714   7.11428571
71       32 28.885714   3.11428571
72       29 27.680000   1.32000000
73       25 28.885714  -3.88571429
74       27 28.885714  -1.88571429
75       36 34.362500   1.63750000
76       28 34.362500  -6.36250000
77       23 34.362500 -11.36250000
78       40 34.362500   5.63750000
79       23 15.881188   7.11881188
80       40 28.885714  11.11428571
81       28 28.885714  -0.88571429
82       34 34.362500  -0.36250000
83       33 34.362500  -1.36250000
84       28 34.362500  -6.36250000
85       34 28.885714   5.11428571
86       30 34.362500  -4.36250000
87       33 28.885714   4.11428571
88       22 19.931034   2.06896552
89       38 34.362500   3.63750000
90       26 19.931034   6.06896552
91       35 34.362500   0.63750000
92        8 15.881188  -7.88118812
93       24 19.931034   4.06896552
94       29 28.885714   0.11428571
95       20 19.931034   0.06896552
96       29 28.885714   0.11428571
97       45 34.362500  10.63750000
98       37 34.362500   2.63750000
99       33 28.885714   4.11428571
100      33 34.362500  -1.36250000
101      25 19.931034   5.06896552
102      32 34.362500  -2.36250000
103      29 28.885714   0.11428571
104      28 28.885714  -0.88571429
105      28 34.362500  -6.36250000
106      31 34.362500  -3.36250000
107      52 34.362500  17.63750000
108      21 15.881188   5.11881188
109      24 34.362500 -10.36250000
110      41 34.362500   6.63750000
111      33 34.362500  -1.36250000
112      32 19.931034  12.06896552
113      19 15.881188   3.11881188
114      20 28.885714  -8.88571429
115      31 34.362500  -3.36250000
116      31 34.362500  -3.36250000
117      32 34.362500  -2.36250000
118      18 19.931034  -1.93103448
119      23 27.680000  -4.68000000
120      17 15.881188   1.11881188
121      20 28.885714  -8.88571429
122      12 15.881188  -3.88118812
123      17 19.931034  -2.93103448
124      30 34.362500  -4.36250000
125      31 27.680000   3.32000000
126      10 15.881188  -5.88118812
127      13 15.881188  -2.88118812
128      22 28.885714  -6.88571429
129      42 34.362500   7.63750000
130       1  5.263158  -4.26315789
131       9 15.881188  -6.88118812
132      32 34.362500  -2.36250000
133      11 15.881188  -4.88118812
134      25 15.881188   9.11881188
135      36 28.885714   7.11428571
136      31 34.362500  -3.36250000
137       0  5.263158  -5.26315789
138      24 28.885714  -4.88571429
139      13 15.881188  -2.88118812
140       8 15.881188  -7.88118812
141      13 15.881188  -2.88118812
142      19 28.885714  -9.88571429
143      18 15.881188   2.11881188
144      33 34.362500  -1.36250000
145      40 28.885714  11.11428571
146      22 15.881188   6.11881188
147      38 27.680000  10.32000000
148      24 27.680000  -3.68000000
149       8  5.263158   2.73684211
150      35 27.680000   7.32000000
151      43 34.362500   8.63750000
152      43 27.680000  15.32000000
153      14 15.881188  -1.88118812
154      41 34.362500   6.63750000
155      38 34.362500   3.63750000
156      45 34.362500  10.63750000
157      31 28.885714   2.11428571
158      13 15.881188  -2.88118812
159      28 27.680000   0.32000000
160      31 28.885714   2.11428571
161      40 34.362500   5.63750000
162      30 27.680000   2.32000000
163      16 19.931034  -3.93103448
164      37 34.362500   2.63750000
165      30 19.931034  10.06896552
166      35 34.362500   0.63750000
167      32 28.885714   3.11428571
168      27 27.680000  -0.68000000
169      20 19.931034   0.06896552
170      18 27.680000  -9.68000000
171      31 34.362500  -3.36250000
172      31 34.362500  -3.36250000
173      21 19.931034   1.06896552
174      39 34.362500   4.63750000
175      41 34.362500   6.63750000
176      13 19.931034  -6.93103448
177      32 34.362500  -2.36250000
178      18 15.881188   2.11881188
179      39 34.362500   4.63750000
180      14 19.931034  -5.93103448
181       7 15.881188  -8.88118812
182      17 28.885714 -11.88571429
183       0  5.263158  -5.26315789
184      30 27.680000   2.32000000
185      37 34.362500   2.63750000
186       0  5.263158  -5.26315789
187       5  5.263158  -0.26315789
188       1  5.263158  -4.26315789
189      16 19.931034  -3.93103448
190      32 15.881188  16.11881188
191      24 27.680000  -3.68000000
192      17 15.881188   1.11881188
193      11 15.881188  -4.88118812
194      24 15.881188   8.11881188
195      22 27.680000  -5.68000000
196      12 19.931034  -7.93103448
197      19 15.881188   3.11881188
198      13 27.680000 -14.68000000
199      17 15.881188   1.11881188
200      15 15.881188  -0.88118812
201      16 15.881188   0.11881188
202      24 15.881188   8.11881188
203      15 19.931034  -4.93103448
204      17 15.881188   1.11881188
205      18 15.881188   2.11881188
206      20 15.881188   4.11881188
207      16 15.881188   0.11881188
208      16 19.931034  -3.93103448
209      18 19.931034  -1.93103448
210      22 19.931034   2.06896552
211       8 15.881188  -7.88118812
212      17 19.931034  -2.93103448
213      18 15.881188   2.11881188
214      16 15.881188   0.11881188
215      23 15.881188   7.11881188
216      22 15.881188   6.11881188
217      13 15.881188  -2.88118812
218      13 15.881188  -2.88118812
219      16 15.881188   0.11881188
220      16 15.881188   0.11881188
221      20 15.881188   4.11881188
222      22 27.680000  -5.68000000
223      17 28.885714 -11.88571429
224      18 15.881188   2.11881188
225      17  5.263158  11.73684211
226      12 19.931034  -7.93103448
227       7 15.881188  -8.88118812
228      17 15.881188   1.11881188
229      14 15.881188  -1.88118812
230      23 27.680000  -4.68000000
231      17  5.263158  11.73684211
232      14 15.881188  -1.88118812
233      15 15.881188  -0.88118812
234      17 15.881188   1.11881188
235      21 15.881188   5.11881188
236      18 15.881188   2.11881188
237      18 19.931034  -1.93103448
238      17 15.881188   1.11881188
239      17 15.881188   1.11881188
240      16 15.881188   0.11881188
241      15 15.881188  -0.88118812
242      21 19.931034   1.06896552
243      16 15.881188   0.11881188
244      14 15.881188  -1.88118812
245      15 15.881188  -0.88118812
246      17 15.881188   1.11881188
247      15 15.881188  -0.88118812
248      15 15.881188  -0.88118812
249      10 15.881188  -5.88118812
250       6 15.881188  -9.88118812
251      22 19.931034   2.06896552
252      21 15.881188   5.11881188
253       1  5.263158  -4.26315789
254      18 15.881188   2.11881188
255      17 15.881188   1.11881188
256       4  5.263158  -1.26315789
257      10 15.881188  -5.88118812
258      16 15.881188   0.11881188
259      16 15.881188   0.11881188
260       9 15.881188  -6.88118812
261      16  5.263158  10.73684211
262      17 15.881188   1.11881188
263       7  5.263158   1.73684211
264      15 15.881188  -0.88118812
265      14 15.881188  -1.88118812
266      14 15.881188  -1.88118812
267      18 15.881188   2.11881188
268      12 15.881188  -3.88118812
269      16 19.931034  -3.93103448
270      21 15.881188   5.11881188
271      19 15.881188   3.11881188
272      16 15.881188   0.11881188
273       1  5.263158  -4.26315789
274      16 15.881188   0.11881188
275      10 15.881188  -5.88118812
276      19 15.881188   3.11881188
277      12 15.881188  -3.88118812
278       2  5.263158  -3.26315789
279      14 15.881188  -1.88118812
280      17 15.881188   1.11881188
281      19 15.881188   3.11881188
282      14 27.680000 -13.68000000
283      11 15.881188  -4.88118812
284       4  5.263158  -1.26315789
285      16 15.881188   0.11881188
286      20 15.881188   4.11881188
287      12  5.263158   6.73684211
288      15 15.881188  -0.88118812
289      16 15.881188   0.11881188
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/40pr51323895647.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/5jief1323895647.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/6i3s61323895647.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/78m441323895647.tab") 
+ }
> 
> try(system("convert tmp/2hnms1323895647.ps tmp/2hnms1323895647.png",intern=TRUE))
character(0)
> try(system("convert tmp/36syj1323895647.ps tmp/36syj1323895647.png",intern=TRUE))
character(0)
> try(system("convert tmp/40pr51323895647.ps tmp/40pr51323895647.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.600   0.301   5.901