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(30
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+ ,819
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+ ,72
+ ,16
+ ,72535
+ ,14
+ ,17
+ ,19540
+ ,894
+ ,329
+ ,44
+ ,6095
+ ,27)
+ ,dim=c(10
+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'totsize'
+ ,'pageviews'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'totrevisions'
+ ,'tothyperlinks')
+ ,1:289))
>  y <- array(NA,dim=c(10,289),dimnames=list(c('compendiums_reviewed','time_in_rfc','logins','blogged_computations','totsize','pageviews','compendium_views_info','compendium_views_pr','totrevisions','tothyperlinks'),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 = 'none'
> par2 = 'none'
> par1 = '1'
> 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)
Warning message:
NAs introduced by coercion 
> 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] "compendiums_reviewed"  "time_in_rfc"           "logins"               
 [4] "blogged_computations"  "totsize"               "pageviews"            
 [7] "compendium_views_info" "compendium_views_pr"   "totrevisions"         
[10] "tothyperlinks"        
> 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/1nu8q1324133728.tab") 
+ }
+ }
> m

	 Conditional inference tree with 8 terminal nodes

Response:  compendiums_reviewed 
Inputs:  time_in_rfc, logins, blogged_computations, totsize, pageviews, compendium_views_info, compendium_views_pr, totrevisions, tothyperlinks 
Number of observations:  289 

1) blogged_computations <= 54; criterion = 1, statistic = 167.343
  2) totsize <= 56622; criterion = 1, statistic = 66.676
    3) totsize <= 8773; criterion = 1, statistic = 42.98
      4)*  weights = 19 
    3) totsize > 8773
      5) time_in_rfc <= 108446; criterion = 0.997, statistic = 13.063
        6) compendium_views_pr <= 35; criterion = 0.956, statistic = 7.872
          7)*  weights = 24 
        6) compendium_views_pr > 35
          8)*  weights = 77 
      5) time_in_rfc > 108446
        9)*  weights = 29 
  2) totsize > 56622
    10) compendium_views_pr <= 46; criterion = 0.959, statistic = 8.029
      11)*  weights = 9 
    10) compendium_views_pr > 46
      12)*  weights = 16 
1) blogged_computations > 54
  13) blogged_computations <= 74; criterion = 1, statistic = 19.205
    14)*  weights = 35 
  13) blogged_computations > 74
    15)*  weights = 80 
> postscript(file="/var/wessaorg/rcomp/tmp/2noso1324133728.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/3vzel1324133728.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 22.666667   3.33333333
7        25 34.362500  -9.36250000
8        18 16.636364   1.36363636
9        11 16.636364  -5.63636364
10       26 30.500000  -4.50000000
11       25 28.885714  -3.88571429
12       38 34.362500   3.63750000
13       44 34.362500   9.63750000
14       30 30.500000  -0.50000000
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 30.500000  -5.50000000
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 30.500000  11.50000000
34       43 34.362500   8.63750000
35       30 22.666667   7.33333333
36       33 34.362500  -1.36250000
37       13 16.636364  -3.63636364
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 13.458333   0.54166667
43       17 16.636364   0.36363636
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 30.500000   8.50000000
51       34 34.362500  -0.36250000
52       26 16.636364   9.36363636
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 13.458333   4.54166667
60       14 16.636364  -2.63636364
61       29 34.362500  -5.36250000
62       44 34.362500   9.63750000
63       21 34.362500 -13.36250000
64       16 16.636364  -0.63636364
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 30.500000  -1.50000000
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 16.636364   6.36363636
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 13.458333  -5.45833333
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 16.636364   4.36363636
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 16.636364   2.36363636
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 22.666667   0.33333333
120      17 16.636364   0.36363636
121      20 28.885714  -8.88571429
122      12 16.636364  -4.63636364
123      17 19.931034  -2.93103448
124      30 34.362500  -4.36250000
125      31 30.500000   0.50000000
126      10 13.458333  -3.45833333
127      13 16.636364  -3.63636364
128      22 28.885714  -6.88571429
129      42 34.362500   7.63750000
130       1  5.263158  -4.26315789
131       9 16.636364  -7.63636364
132      32 34.362500  -2.36250000
133      11 13.458333  -2.45833333
134      25 16.636364   8.36363636
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 16.636364  -3.63636364
140       8 13.458333  -5.45833333
141      13 13.458333  -0.45833333
142      19 28.885714  -9.88571429
143      18 16.636364   1.36363636
144      33 34.362500  -1.36250000
145      40 28.885714  11.11428571
146      22 16.636364   5.36363636
147      38 30.500000   7.50000000
148      24 22.666667   1.33333333
149       8  5.263158   2.73684211
150      35 30.500000   4.50000000
151      43 34.362500   8.63750000
152      43 30.500000  12.50000000
153      14 13.458333   0.54166667
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 16.636364  -3.63636364
159      28 22.666667   5.33333333
160      31 28.885714   2.11428571
161      40 34.362500   5.63750000
162      30 30.500000  -0.50000000
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 30.500000  -3.50000000
169      20 19.931034   0.06896552
170      18 30.500000 -12.50000000
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 16.636364   1.36363636
179      39 34.362500   4.63750000
180      14 19.931034  -5.93103448
181       7 16.636364  -9.63636364
182      17 28.885714 -11.88571429
183       0  5.263158  -5.26315789
184      30 30.500000  -0.50000000
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 16.636364  15.36363636
191      24 22.666667   1.33333333
192      17 16.636364   0.36363636
193      11 16.636364  -5.63636364
194      24 16.636364   7.36363636
195      22 22.666667  -0.66666667
196      12 19.931034  -7.93103448
197      19 16.636364   2.36363636
198      13 22.666667  -9.66666667
199      17 16.636364   0.36363636
200      15 13.458333   1.54166667
201      16 13.458333   2.54166667
202      24 16.636364   7.36363636
203      15 19.931034  -4.93103448
204      17 16.636364   0.36363636
205      18 16.636364   1.36363636
206      20 13.458333   6.54166667
207      16 16.636364  -0.63636364
208      16 19.931034  -3.93103448
209      18 19.931034  -1.93103448
210      22 19.931034   2.06896552
211       8 16.636364  -8.63636364
212      17 19.931034  -2.93103448
213      18 16.636364   1.36363636
214      16 13.458333   2.54166667
215      23 16.636364   6.36363636
216      22 16.636364   5.36363636
217      13 16.636364  -3.63636364
218      13 16.636364  -3.63636364
219      16 16.636364  -0.63636364
220      16 13.458333   2.54166667
221      20 13.458333   6.54166667
222      22 30.500000  -8.50000000
223      17 28.885714 -11.88571429
224      18 16.636364   1.36363636
225      17  5.263158  11.73684211
226      12 19.931034  -7.93103448
227       7 16.636364  -9.63636364
228      17 16.636364   0.36363636
229      14 16.636364  -2.63636364
230      23 30.500000  -7.50000000
231      17  5.263158  11.73684211
232      14 16.636364  -2.63636364
233      15 16.636364  -1.63636364
234      17 16.636364   0.36363636
235      21 16.636364   4.36363636
236      18 16.636364   1.36363636
237      18 19.931034  -1.93103448
238      17 16.636364   0.36363636
239      17 16.636364   0.36363636
240      16 16.636364  -0.63636364
241      15 16.636364  -1.63636364
242      21 19.931034   1.06896552
243      16 16.636364  -0.63636364
244      14 13.458333   0.54166667
245      15 16.636364  -1.63636364
246      17 16.636364   0.36363636
247      15 16.636364  -1.63636364
248      15 16.636364  -1.63636364
249      10 16.636364  -6.63636364
250       6 13.458333  -7.45833333
251      22 19.931034   2.06896552
252      21 16.636364   4.36363636
253       1  5.263158  -4.26315789
254      18 16.636364   1.36363636
255      17 16.636364   0.36363636
256       4  5.263158  -1.26315789
257      10 13.458333  -3.45833333
258      16 16.636364  -0.63636364
259      16 16.636364  -0.63636364
260       9 13.458333  -4.45833333
261      16  5.263158  10.73684211
262      17 16.636364   0.36363636
263       7  5.263158   1.73684211
264      15 13.458333   1.54166667
265      14 13.458333   0.54166667
266      14 13.458333   0.54166667
267      18 16.636364   1.36363636
268      12 16.636364  -4.63636364
269      16 19.931034  -3.93103448
270      21 16.636364   4.36363636
271      19 16.636364   2.36363636
272      16 16.636364  -0.63636364
273       1  5.263158  -4.26315789
274      16 16.636364  -0.63636364
275      10 13.458333  -3.45833333
276      19 16.636364   2.36363636
277      12 13.458333  -1.45833333
278       2  5.263158  -3.26315789
279      14 16.636364  -2.63636364
280      17 16.636364   0.36363636
281      19 16.636364   2.36363636
282      14 22.666667  -8.66666667
283      11 16.636364  -5.63636364
284       4  5.263158  -1.26315789
285      16 16.636364  -0.63636364
286      20 13.458333   6.54166667
287      12  5.263158   6.73684211
288      15 16.636364  -1.63636364
289      16 16.636364  -0.63636364
> 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/491gq1324133728.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/5wab61324133728.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/6bsdq1324133728.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/7r2lx1324133728.tab") 
+ }
> 
> try(system("convert tmp/2noso1324133728.ps tmp/2noso1324133728.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vzel1324133728.ps tmp/3vzel1324133728.png",intern=TRUE))
character(0)
> try(system("convert tmp/491gq1324133728.ps tmp/491gq1324133728.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  6.325   0.267   6.595