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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+ ,2650
+ ,20484
+ ,20
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+ ,2370
+ ,20156
+ ,34
+ ,34
+ ,4
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+ ,6012
+ ,6
+ ,6
+ ,16
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+ ,12
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+ ,1352
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+ ,16
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+ ,10205
+ ,37623
+ ,72
+ ,72
+ ,16
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(6,289),dimnames=list(c('compendiums_reviewed','totsize','totrevisions','totseconds','tothyperlinks','totblogs'),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 = ''
> 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
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] "compendiums_reviewed" "totsize"              "totrevisions"        
[4] "totseconds"           "tothyperlinks"        "totblogs"            
> 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/1zqqr1324322064.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  compendiums_reviewed 
Inputs:  totsize, totrevisions, totseconds, tothyperlinks, totblogs 
Number of observations:  289 

1) totsize <= 46300; criterion = 1, statistic = 155.371
  2) totsize <= 8773; criterion = 1, statistic = 46.745
    3)*  weights = 19 
  2) totsize > 8773
    4) totseconds <= 52678; criterion = 0.995, statistic = 10.672
      5)*  weights = 107 
    4) totseconds > 52678
      6)*  weights = 21 
1) totsize > 46300
  7) totseconds <= 64329; criterion = 1, statistic = 18.829
    8)*  weights = 23 
  7) totseconds > 64329
    9)*  weights = 119 
> postscript(file="/var/wessaorg/rcomp/tmp/2mlfd1324322064.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/378qg1324322064.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 32.907563  -2.907563025
2        28 32.907563  -4.907563025
3        38 32.907563   5.092436975
4        30 32.907563  -2.907563025
5        22 21.571429   0.428571429
6        26 24.086957   1.913043478
7        25 32.907563  -7.907563025
8        18 15.990654   2.009345794
9        11 15.990654  -4.990654206
10       26 32.907563  -6.907563025
11       25 32.907563  -7.907563025
12       38 32.907563   5.092436975
13       44 32.907563  11.092436975
14       30 24.086957   5.913043478
15       40 32.907563   7.092436975
16       34 32.907563   1.092436975
17       47 32.907563  14.092436975
18       30 32.907563  -2.907563025
19       31 32.907563  -1.907563025
20       23 32.907563  -9.907563025
21       36 32.907563   3.092436975
22       36 32.907563   3.092436975
23       30 32.907563  -2.907563025
24       25 32.907563  -7.907563025
25       39 32.907563   6.092436975
26       34 32.907563   1.092436975
27       31 32.907563  -1.907563025
28       31 32.907563  -1.907563025
29       33 32.907563   0.092436975
30       25 21.571429   3.428571429
31       33 32.907563   0.092436975
32       35 32.907563   2.092436975
33       42 32.907563   9.092436975
34       43 32.907563  10.092436975
35       30 24.086957   5.913043478
36       33 32.907563   0.092436975
37       13 21.571429  -8.571428571
38       32 32.907563  -0.907563025
39       36 32.907563   3.092436975
40        0  5.263158  -5.263157895
41       28 32.907563  -4.907563025
42       14 21.571429  -7.571428571
43       17 15.990654   1.009345794
44       32 32.907563  -0.907563025
45       30 32.907563  -2.907563025
46       35 32.907563   2.092436975
47       20 15.990654   4.009345794
48       28 32.907563  -4.907563025
49       28 24.086957   3.913043478
50       39 32.907563   6.092436975
51       34 32.907563   1.092436975
52       26 21.571429   4.428571429
53       39 32.907563   6.092436975
54       39 32.907563   6.092436975
55       33 32.907563   0.092436975
56       28 32.907563  -4.907563025
57        4  5.263158  -1.263157895
58       39 32.907563   6.092436975
59       18 15.990654   2.009345794
60       14 15.990654  -1.990654206
61       29 32.907563  -3.907563025
62       44 32.907563  11.092436975
63       21 32.907563 -11.907563025
64       16 15.990654   0.009345794
65       28 32.907563  -4.907563025
66       35 32.907563   2.092436975
67       28 24.086957   3.913043478
68       38 32.907563   5.092436975
69       23 32.907563  -9.907563025
70       36 32.907563   3.092436975
71       32 32.907563  -0.907563025
72       29 32.907563  -3.907563025
73       25 32.907563  -7.907563025
74       27 32.907563  -5.907563025
75       36 32.907563   3.092436975
76       28 32.907563  -4.907563025
77       23 32.907563  -9.907563025
78       40 32.907563   7.092436975
79       23 15.990654   7.009345794
80       40 32.907563   7.092436975
81       28 32.907563  -4.907563025
82       34 32.907563   1.092436975
83       33 32.907563   0.092436975
84       28 32.907563  -4.907563025
85       34 32.907563   1.092436975
86       30 32.907563  -2.907563025
87       33 32.907563   0.092436975
88       22 21.571429   0.428571429
89       38 32.907563   5.092436975
90       26 21.571429   4.428571429
91       35 32.907563   2.092436975
92        8 24.086957 -16.086956522
93       24 15.990654   8.009345794
94       29 32.907563  -3.907563025
95       20 21.571429  -1.571428571
96       29 32.907563  -3.907563025
97       45 32.907563  12.092436975
98       37 32.907563   4.092436975
99       33 21.571429  11.428571429
100      33 32.907563   0.092436975
101      25 32.907563  -7.907563025
102      32 32.907563  -0.907563025
103      29 32.907563  -3.907563025
104      28 32.907563  -4.907563025
105      28 32.907563  -4.907563025
106      31 32.907563  -1.907563025
107      52 32.907563  19.092436975
108      21 15.990654   5.009345794
109      24 32.907563  -8.907563025
110      41 32.907563   8.092436975
111      33 32.907563   0.092436975
112      32 21.571429  10.428571429
113      19 15.990654   3.009345794
114      20 21.571429  -1.571428571
115      31 32.907563  -1.907563025
116      31 32.907563  -1.907563025
117      32 32.907563  -0.907563025
118      18 21.571429  -3.571428571
119      23 32.907563  -9.907563025
120      17 15.990654   1.009345794
121      20 21.571429  -1.571428571
122      12 15.990654  -3.990654206
123      17 15.990654   1.009345794
124      30 32.907563  -2.907563025
125      31 32.907563  -1.907563025
126      10 15.990654  -5.990654206
127      13 15.990654  -2.990654206
128      22 24.086957  -2.086956522
129      42 32.907563   9.092436975
130       1  5.263158  -4.263157895
131       9 15.990654  -6.990654206
132      32 32.907563  -0.907563025
133      11 15.990654  -4.990654206
134      25 15.990654   9.009345794
135      36 21.571429  14.428571429
136      31 32.907563  -1.907563025
137       0  5.263158  -5.263157895
138      24 32.907563  -8.907563025
139      13 15.990654  -2.990654206
140       8 15.990654  -7.990654206
141      13 15.990654  -2.990654206
142      19 32.907563 -13.907563025
143      18 15.990654   2.009345794
144      33 32.907563   0.092436975
145      40 32.907563   7.092436975
146      22 15.990654   6.009345794
147      38 24.086957  13.913043478
148      24 24.086957  -0.086956522
149       8  5.263158   2.736842105
150      35 32.907563   2.092436975
151      43 32.907563  10.092436975
152      43 32.907563  10.092436975
153      14 15.990654  -1.990654206
154      41 32.907563   8.092436975
155      38 32.907563   5.092436975
156      45 32.907563  12.092436975
157      31 32.907563  -1.907563025
158      13 15.990654  -2.990654206
159      28 24.086957   3.913043478
160      31 32.907563  -1.907563025
161      40 32.907563   7.092436975
162      30 24.086957   5.913043478
163      16 24.086957  -8.086956522
164      37 32.907563   4.092436975
165      30 24.086957   5.913043478
166      35 32.907563   2.092436975
167      32 32.907563  -0.907563025
168      27 24.086957   2.913043478
169      20 21.571429  -1.571428571
170      18 32.907563 -14.907563025
171      31 32.907563  -1.907563025
172      31 32.907563  -1.907563025
173      21 15.990654   5.009345794
174      39 32.907563   6.092436975
175      41 24.086957  16.913043478
176      13 21.571429  -8.571428571
177      32 32.907563  -0.907563025
178      18 15.990654   2.009345794
179      39 32.907563   6.092436975
180      14 15.990654  -1.990654206
181       7 15.990654  -8.990654206
182      17 15.990654   1.009345794
183       0  5.263158  -5.263157895
184      30 32.907563  -2.907563025
185      37 32.907563   4.092436975
186       0  5.263158  -5.263157895
187       5  5.263158  -0.263157895
188       1  5.263158  -4.263157895
189      16 21.571429  -5.571428571
190      32 15.990654  16.009345794
191      24 24.086957  -0.086956522
192      17 15.990654   1.009345794
193      11 15.990654  -4.990654206
194      24 15.990654   8.009345794
195      22 32.907563 -10.907563025
196      12 15.990654  -3.990654206
197      19 15.990654   3.009345794
198      13 24.086957 -11.086956522
199      17 24.086957  -7.086956522
200      15 15.990654  -0.990654206
201      16 15.990654   0.009345794
202      24 15.990654   8.009345794
203      15 15.990654  -0.990654206
204      17 21.571429  -4.571428571
205      18 15.990654   2.009345794
206      20 15.990654   4.009345794
207      16 15.990654   0.009345794
208      16 21.571429  -5.571428571
209      18 15.990654   2.009345794
210      22 21.571429   0.428571429
211       8 15.990654  -7.990654206
212      17 15.990654   1.009345794
213      18 15.990654   2.009345794
214      16 15.990654   0.009345794
215      23 15.990654   7.009345794
216      22 15.990654   6.009345794
217      13 15.990654  -2.990654206
218      13 15.990654  -2.990654206
219      16 15.990654   0.009345794
220      16 15.990654   0.009345794
221      20 15.990654   4.009345794
222      22 32.907563 -10.907563025
223      17 15.990654   1.009345794
224      18 15.990654   2.009345794
225      17  5.263158  11.736842105
226      12 15.990654  -3.990654206
227       7 15.990654  -8.990654206
228      17 15.990654   1.009345794
229      14 15.990654  -1.990654206
230      23 24.086957  -1.086956522
231      17  5.263158  11.736842105
232      14 15.990654  -1.990654206
233      15 24.086957  -9.086956522
234      17 15.990654   1.009345794
235      21 15.990654   5.009345794
236      18 15.990654   2.009345794
237      18 15.990654   2.009345794
238      17 15.990654   1.009345794
239      17 15.990654   1.009345794
240      16 15.990654   0.009345794
241      15 15.990654  -0.990654206
242      21 24.086957  -3.086956522
243      16 15.990654   0.009345794
244      14 15.990654  -1.990654206
245      15 15.990654  -0.990654206
246      17 15.990654   1.009345794
247      15 15.990654  -0.990654206
248      15 15.990654  -0.990654206
249      10 15.990654  -5.990654206
250       6 15.990654  -9.990654206
251      22 21.571429   0.428571429
252      21 15.990654   5.009345794
253       1  5.263158  -4.263157895
254      18 15.990654   2.009345794
255      17 15.990654   1.009345794
256       4  5.263158  -1.263157895
257      10 15.990654  -5.990654206
258      16 15.990654   0.009345794
259      16 15.990654   0.009345794
260       9 15.990654  -6.990654206
261      16  5.263158  10.736842105
262      17 15.990654   1.009345794
263       7  5.263158   1.736842105
264      15 15.990654  -0.990654206
265      14 15.990654  -1.990654206
266      14 15.990654  -1.990654206
267      18 15.990654   2.009345794
268      12 15.990654  -3.990654206
269      16 15.990654   0.009345794
270      21 24.086957  -3.086956522
271      19 15.990654   3.009345794
272      16 15.990654   0.009345794
273       1  5.263158  -4.263157895
274      16 15.990654   0.009345794
275      10 15.990654  -5.990654206
276      19 15.990654   3.009345794
277      12 15.990654  -3.990654206
278       2  5.263158  -3.263157895
279      14 15.990654  -1.990654206
280      17 15.990654   1.009345794
281      19 15.990654   3.009345794
282      14 24.086957 -10.086956522
283      11 15.990654  -4.990654206
284       4  5.263158  -1.263157895
285      16 15.990654   0.009345794
286      20 15.990654   4.009345794
287      12  5.263158   6.736842105
288      15 15.990654  -0.990654206
289      16 15.990654   0.009345794
> 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/4kjq11324322064.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/54ge01324322064.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/6f0ks1324322064.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/7z93o1324322064.tab") 
+ }
> 
> try(system("convert tmp/2mlfd1324322064.ps tmp/2mlfd1324322064.png",intern=TRUE))
character(0)
> try(system("convert tmp/378qg1324322064.ps tmp/378qg1324322064.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kjq11324322064.ps tmp/4kjq11324322064.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.339   0.321   5.693