R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

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+ ,1
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+ ,0
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+ ,72535
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time'
+ ,'compinfo'
+ ,'comppr'
+ ,'sharedcomp'
+ ,'bloggedcomp'
+ ,'compreviewed')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','time','compinfo','comppr','sharedcomp','bloggedcomp','compreviewed'),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

Attaching package: 'zoo'

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

    as.Date, 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)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


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] "sharedcomp"
> x[,par1]
  [1]  3  4 12  2  1  3  0  0  0  5  0  0  7  7  3  9  0  4  3  0  7  0  1  5  7
 [26]  0  0  5  0  0  0  3  4  1  4  2  0  0  0  0  2  1  0  2 10  6  0  5  4  1
 [51]  2  2  0  8  3  0  0  8  5  3  1  5  1  1  5  0 12  8  8  8  8  2  0  5  8
 [76]  2  5 12  6  7  2  0  4  3  6  2  0  1  0  5  2  0  0  5  0  1  0  1  1  2
[101]  6  1  4  2  3  0 10  0  9  7  0  0  4  4  0  0  0  1  0  1  0  0  4  0  4
[126]  4  3  0  0  0  5  0  4  0  0  1  0  5  0  0  0  0  0  2  7  1  8  2  0  2
[151]  0  0  1  3  0  3  0  0  0  4  4 11  0  0  4  0  1  0  0  0  9  1  3 10  5
[176]  0  2  0  1  2  4  0  0  2  1  0  0  0  1  0  2  0  3  6  0  2  0  2  1  1
[201]  2  1  0  1  3  0  0  0  0  1  4  0  0  0  7  2  0  7  3  0  0  6  2  0  0
[226]  3  0  1  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  2  0  1  1  0  0  0
[251]  0  0  0  1  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0
[276]  6  3  1  2  0  0  0  3  1  0  0  1  0  0
> 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   3   4   5   6   7   8   9  10  11  12 
137  40  27  19  18  14   7   9   8   3   3   1   3 
> colnames(x)
[1] "pageviews"    "time"         "compinfo"     "comppr"       "sharedcomp"  
[6] "bloggedcomp"  "compreviewed"
> colnames(x)[par1]
[1] "sharedcomp"
> x[,par1]
  [1]  3  4 12  2  1  3  0  0  0  5  0  0  7  7  3  9  0  4  3  0  7  0  1  5  7
 [26]  0  0  5  0  0  0  3  4  1  4  2  0  0  0  0  2  1  0  2 10  6  0  5  4  1
 [51]  2  2  0  8  3  0  0  8  5  3  1  5  1  1  5  0 12  8  8  8  8  2  0  5  8
 [76]  2  5 12  6  7  2  0  4  3  6  2  0  1  0  5  2  0  0  5  0  1  0  1  1  2
[101]  6  1  4  2  3  0 10  0  9  7  0  0  4  4  0  0  0  1  0  1  0  0  4  0  4
[126]  4  3  0  0  0  5  0  4  0  0  1  0  5  0  0  0  0  0  2  7  1  8  2  0  2
[151]  0  0  1  3  0  3  0  0  0  4  4 11  0  0  4  0  1  0  0  0  9  1  3 10  5
[176]  0  2  0  1  2  4  0  0  2  1  0  0  0  1  0  2  0  3  6  0  2  0  2  1  1
[201]  2  1  0  1  3  0  0  0  0  1  4  0  0  0  7  2  0  7  3  0  0  6  2  0  0
[226]  3  0  1  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  2  0  1  1  0  0  0
[251]  0  0  0  1  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0
[276]  6  3  1  2  0  0  0  3  1  0  0  1  0  0
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/115kw1354894783.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

Response:  sharedcomp 
Inputs:  pageviews, time, compinfo, comppr, bloggedcomp, compreviewed 
Number of observations:  289 

1) compreviewed <= 22; criterion = 1, statistic = 43.682
  2)*  weights = 148 
1) compreviewed > 22
  3)*  weights = 141 
> postscript(file="/var/fisher/rcomp/tmp/2w0cd1354894783.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/fisher/rcomp/tmp/39mev1354894783.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         3 3.1205674 -0.1205674
2         4 3.1205674  0.8794326
3        12 3.1205674  8.8794326
4         2 3.1205674 -1.1205674
5         1 0.8513514  0.1486486
6         3 3.1205674 -0.1205674
7         0 3.1205674 -3.1205674
8         0 0.8513514 -0.8513514
9         0 0.8513514 -0.8513514
10        5 3.1205674  1.8794326
11        0 3.1205674 -3.1205674
12        0 3.1205674 -3.1205674
13        7 3.1205674  3.8794326
14        7 3.1205674  3.8794326
15        3 3.1205674 -0.1205674
16        9 3.1205674  5.8794326
17        0 3.1205674 -3.1205674
18        4 3.1205674  0.8794326
19        3 3.1205674 -0.1205674
20        0 3.1205674 -3.1205674
21        7 3.1205674  3.8794326
22        0 3.1205674 -3.1205674
23        1 3.1205674 -2.1205674
24        5 3.1205674  1.8794326
25        7 3.1205674  3.8794326
26        0 3.1205674 -3.1205674
27        0 3.1205674 -3.1205674
28        5 3.1205674  1.8794326
29        0 3.1205674 -3.1205674
30        0 3.1205674 -3.1205674
31        0 3.1205674 -3.1205674
32        3 3.1205674 -0.1205674
33        4 3.1205674  0.8794326
34        1 3.1205674 -2.1205674
35        4 3.1205674  0.8794326
36        2 3.1205674 -1.1205674
37        0 0.8513514 -0.8513514
38        0 3.1205674 -3.1205674
39        0 3.1205674 -3.1205674
40        0 0.8513514 -0.8513514
41        2 3.1205674 -1.1205674
42        1 0.8513514  0.1486486
43        0 0.8513514 -0.8513514
44        2 3.1205674 -1.1205674
45       10 3.1205674  6.8794326
46        6 3.1205674  2.8794326
47        0 0.8513514 -0.8513514
48        5 3.1205674  1.8794326
49        4 3.1205674  0.8794326
50        1 3.1205674 -2.1205674
51        2 3.1205674 -1.1205674
52        2 3.1205674 -1.1205674
53        0 3.1205674 -3.1205674
54        8 3.1205674  4.8794326
55        3 3.1205674 -0.1205674
56        0 3.1205674 -3.1205674
57        0 0.8513514 -0.8513514
58        8 3.1205674  4.8794326
59        5 0.8513514  4.1486486
60        3 0.8513514  2.1486486
61        1 3.1205674 -2.1205674
62        5 3.1205674  1.8794326
63        1 0.8513514  0.1486486
64        1 0.8513514  0.1486486
65        5 3.1205674  1.8794326
66        0 3.1205674 -3.1205674
67       12 3.1205674  8.8794326
68        8 3.1205674  4.8794326
69        8 3.1205674  4.8794326
70        8 3.1205674  4.8794326
71        8 3.1205674  4.8794326
72        2 3.1205674 -1.1205674
73        0 3.1205674 -3.1205674
74        5 3.1205674  1.8794326
75        8 3.1205674  4.8794326
76        2 3.1205674 -1.1205674
77        5 3.1205674  1.8794326
78       12 3.1205674  8.8794326
79        6 3.1205674  2.8794326
80        7 3.1205674  3.8794326
81        2 3.1205674 -1.1205674
82        0 3.1205674 -3.1205674
83        4 3.1205674  0.8794326
84        3 3.1205674 -0.1205674
85        6 3.1205674  2.8794326
86        2 3.1205674 -1.1205674
87        0 3.1205674 -3.1205674
88        1 0.8513514  0.1486486
89        0 3.1205674 -3.1205674
90        5 3.1205674  1.8794326
91        2 3.1205674 -1.1205674
92        0 0.8513514 -0.8513514
93        0 3.1205674 -3.1205674
94        5 3.1205674  1.8794326
95        0 0.8513514 -0.8513514
96        1 3.1205674 -2.1205674
97        0 3.1205674 -3.1205674
98        1 3.1205674 -2.1205674
99        1 3.1205674 -2.1205674
100       2 3.1205674 -1.1205674
101       6 3.1205674  2.8794326
102       1 3.1205674 -2.1205674
103       4 3.1205674  0.8794326
104       2 3.1205674 -1.1205674
105       3 3.1205674 -0.1205674
106       0 3.1205674 -3.1205674
107      10 3.1205674  6.8794326
108       0 0.8513514 -0.8513514
109       9 3.1205674  5.8794326
110       7 3.1205674  3.8794326
111       0 3.1205674 -3.1205674
112       0 3.1205674 -3.1205674
113       4 0.8513514  3.1486486
114       4 0.8513514  3.1486486
115       0 3.1205674 -3.1205674
116       0 3.1205674 -3.1205674
117       0 3.1205674 -3.1205674
118       1 0.8513514  0.1486486
119       0 3.1205674 -3.1205674
120       1 0.8513514  0.1486486
121       0 0.8513514 -0.8513514
122       0 0.8513514 -0.8513514
123       4 0.8513514  3.1486486
124       0 3.1205674 -3.1205674
125       4 3.1205674  0.8794326
126       4 0.8513514  3.1486486
127       3 0.8513514  2.1486486
128       0 0.8513514 -0.8513514
129       0 3.1205674 -3.1205674
130       0 0.8513514 -0.8513514
131       5 0.8513514  4.1486486
132       0 3.1205674 -3.1205674
133       4 0.8513514  3.1486486
134       0 3.1205674 -3.1205674
135       0 3.1205674 -3.1205674
136       1 3.1205674 -2.1205674
137       0 0.8513514 -0.8513514
138       5 3.1205674  1.8794326
139       0 0.8513514 -0.8513514
140       0 0.8513514 -0.8513514
141       0 0.8513514 -0.8513514
142       0 0.8513514 -0.8513514
143       0 0.8513514 -0.8513514
144       2 3.1205674 -1.1205674
145       7 3.1205674  3.8794326
146       1 0.8513514  0.1486486
147       8 3.1205674  4.8794326
148       2 3.1205674 -1.1205674
149       0 0.8513514 -0.8513514
150       2 3.1205674 -1.1205674
151       0 3.1205674 -3.1205674
152       0 3.1205674 -3.1205674
153       1 0.8513514  0.1486486
154       3 3.1205674 -0.1205674
155       0 3.1205674 -3.1205674
156       3 3.1205674 -0.1205674
157       0 3.1205674 -3.1205674
158       0 0.8513514 -0.8513514
159       0 3.1205674 -3.1205674
160       4 3.1205674  0.8794326
161       4 3.1205674  0.8794326
162      11 3.1205674  7.8794326
163       0 0.8513514 -0.8513514
164       0 3.1205674 -3.1205674
165       4 3.1205674  0.8794326
166       0 3.1205674 -3.1205674
167       1 3.1205674 -2.1205674
168       0 3.1205674 -3.1205674
169       0 0.8513514 -0.8513514
170       0 0.8513514 -0.8513514
171       9 3.1205674  5.8794326
172       1 3.1205674 -2.1205674
173       3 0.8513514  2.1486486
174      10 3.1205674  6.8794326
175       5 3.1205674  1.8794326
176       0 0.8513514 -0.8513514
177       2 3.1205674 -1.1205674
178       0 0.8513514 -0.8513514
179       1 3.1205674 -2.1205674
180       2 0.8513514  1.1486486
181       4 0.8513514  3.1486486
182       0 0.8513514 -0.8513514
183       0 0.8513514 -0.8513514
184       2 3.1205674 -1.1205674
185       1 3.1205674 -2.1205674
186       0 0.8513514 -0.8513514
187       0 0.8513514 -0.8513514
188       0 0.8513514 -0.8513514
189       1 0.8513514  0.1486486
190       0 3.1205674 -3.1205674
191       2 3.1205674 -1.1205674
192       0 0.8513514 -0.8513514
193       3 0.8513514  2.1486486
194       6 3.1205674  2.8794326
195       0 0.8513514 -0.8513514
196       2 0.8513514  1.1486486
197       0 0.8513514 -0.8513514
198       2 0.8513514  1.1486486
199       1 0.8513514  0.1486486
200       1 0.8513514  0.1486486
201       2 0.8513514  1.1486486
202       1 3.1205674 -2.1205674
203       0 0.8513514 -0.8513514
204       1 0.8513514  0.1486486
205       3 0.8513514  2.1486486
206       0 0.8513514 -0.8513514
207       0 0.8513514 -0.8513514
208       0 0.8513514 -0.8513514
209       0 0.8513514 -0.8513514
210       1 0.8513514  0.1486486
211       4 0.8513514  3.1486486
212       0 0.8513514 -0.8513514
213       0 0.8513514 -0.8513514
214       0 0.8513514 -0.8513514
215       7 3.1205674  3.8794326
216       2 0.8513514  1.1486486
217       0 0.8513514 -0.8513514
218       7 0.8513514  6.1486486
219       3 0.8513514  2.1486486
220       0 0.8513514 -0.8513514
221       0 0.8513514 -0.8513514
222       6 0.8513514  5.1486486
223       2 0.8513514  1.1486486
224       0 0.8513514 -0.8513514
225       0 0.8513514 -0.8513514
226       3 0.8513514  2.1486486
227       0 0.8513514 -0.8513514
228       1 0.8513514  0.1486486
229       1 0.8513514  0.1486486
230       0 3.1205674 -3.1205674
231       1 0.8513514  0.1486486
232       0 0.8513514 -0.8513514
233       0 0.8513514 -0.8513514
234       0 0.8513514 -0.8513514
235       0 0.8513514 -0.8513514
236       0 0.8513514 -0.8513514
237       0 0.8513514 -0.8513514
238       0 0.8513514 -0.8513514
239       0 0.8513514 -0.8513514
240       0 0.8513514 -0.8513514
241       0 0.8513514 -0.8513514
242       0 0.8513514 -0.8513514
243       0 0.8513514 -0.8513514
244       2 0.8513514  1.1486486
245       0 0.8513514 -0.8513514
246       1 0.8513514  0.1486486
247       1 0.8513514  0.1486486
248       0 0.8513514 -0.8513514
249       0 0.8513514 -0.8513514
250       0 0.8513514 -0.8513514
251       0 0.8513514 -0.8513514
252       0 0.8513514 -0.8513514
253       0 0.8513514 -0.8513514
254       1 0.8513514  0.1486486
255       0 0.8513514 -0.8513514
256       0 0.8513514 -0.8513514
257       0 0.8513514 -0.8513514
258       0 0.8513514 -0.8513514
259       1 0.8513514  0.1486486
260       0 0.8513514 -0.8513514
261       0 0.8513514 -0.8513514
262       0 0.8513514 -0.8513514
263       0 0.8513514 -0.8513514
264       0 0.8513514 -0.8513514
265       0 0.8513514 -0.8513514
266       0 0.8513514 -0.8513514
267       0 0.8513514 -0.8513514
268       0 0.8513514 -0.8513514
269       0 0.8513514 -0.8513514
270       0 0.8513514 -0.8513514
271       1 0.8513514  0.1486486
272       0 0.8513514 -0.8513514
273       0 0.8513514 -0.8513514
274       1 0.8513514  0.1486486
275       0 0.8513514 -0.8513514
276       6 0.8513514  5.1486486
277       3 0.8513514  2.1486486
278       1 0.8513514  0.1486486
279       2 0.8513514  1.1486486
280       0 0.8513514 -0.8513514
281       0 0.8513514 -0.8513514
282       0 0.8513514 -0.8513514
283       3 0.8513514  2.1486486
284       1 0.8513514  0.1486486
285       0 0.8513514 -0.8513514
286       0 0.8513514 -0.8513514
287       1 0.8513514  0.1486486
288       0 0.8513514 -0.8513514
289       0 0.8513514 -0.8513514
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/fisher/rcomp/tmp/48d6a1354894783.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/fisher/rcomp/tmp/5iw2c1354894783.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/fisher/rcomp/tmp/69k1c1354894783.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/fisher/rcomp/tmp/7fedh1354894783.tab") 
+ }
> 
> try(system("convert tmp/2w0cd1354894783.ps tmp/2w0cd1354894783.png",intern=TRUE))
character(0)
> try(system("convert tmp/39mev1354894783.ps tmp/39mev1354894783.png",intern=TRUE))
character(0)
> try(system("convert tmp/48d6a1354894783.ps tmp/48d6a1354894783.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  7.158   0.601   7.750