R version 2.12.0 (2010-10-15)
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ISBN 3-900051-07-0
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> x <- array(list(8
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+ ,18.6)
+ ,dim=c(5
+ ,240)
+ ,dimnames=list(c('cylinders'
+ ,'engine.displacement'
+ ,'horsepower'
+ ,'weight'
+ ,'acceleration
')
+ ,1:240))
>  y <- array(NA,dim=c(5,240),dimnames=list(c('cylinders','engine.displacement','horsepower','weight','acceleration
'),1:240))
>  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'
> #'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] "acceleration."
> x[,par1]
  [1] 11.5 11.0 10.5 10.0  8.5 10.0 10.0  8.0 10.0 15.0 15.5 20.5 17.5 17.5 12.5
 [16] 14.0 15.0 18.5 14.5 14.0 15.5 15.5 12.0 13.0 12.0 12.0 19.0 15.0 14.0 14.0
 [31] 14.5 19.0 19.0 20.5 17.0 16.5 12.0 13.5 13.0 11.0 13.5 12.5 13.5 14.0 16.0
 [46] 14.5 18.0 16.0 14.5 15.0 13.0 11.5 14.5 12.5 12.0 13.0 11.0 11.0 16.5 18.0
 [61] 16.5 16.0 14.0 12.5 15.0 19.5 16.5 18.5 14.0 13.0  9.5 15.5 14.0 11.0 14.0
 [76] 11.0 16.5 16.0 16.5 21.0 17.0 18.0 14.0 14.5 16.0 15.5 15.5 14.5 19.0 14.5
 [91] 14.0 15.0 16.0 16.0 19.5 11.5 14.0 13.5 21.0 19.0 19.0 13.5 12.0 17.0 16.0
[106] 13.5 16.5 14.5 15.0 17.0 13.5 17.5 16.9 14.9 15.3 13.0 13.9 12.8 14.5 17.6
[121] 22.2 22.1 17.7 16.2 17.8 17.0 16.4 15.7 13.2 16.7 12.1 15.0 14.0 14.8 18.6
[136] 16.8 12.5 13.7 16.9 17.7 11.1 11.4 14.5 14.5 18.2 15.8 15.9 16.4 14.5 12.8
[151] 21.5 14.4 18.6 13.2 12.8 18.2 15.8 17.2 17.2 16.7 18.7 13.2 13.4 13.7 16.5
[166] 14.7 14.5 17.6 15.9 13.6 15.8 14.9 16.6 18.2 17.3 16.6 15.4 13.2 15.2 14.3
[181] 15.0 14.0 15.2 15.0 24.8 22.2 14.9 19.2 16.0 11.3 13.2 14.7 15.5 16.4 18.1
[196] 20.1 15.8 15.5 15.0 15.2 14.4 19.2 19.9 13.8 15.3 15.1 15.7 16.4 12.6 12.9
[211] 16.4 16.1 19.4 17.3 14.9 16.2 14.2 14.8 20.4 13.8 15.8 17.1 16.6 18.6 18.0
[226] 16.0 18.0 15.3 17.6 14.7 14.5 14.5 15.7 16.4 17.0 13.9 17.3 15.6 11.6 18.6
> 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])

   8  8.5  9.5   10 10.5   11 11.1 11.3 11.4 11.5 11.6   12 12.1 12.5 12.6 12.8 
   1    1    1    4    1    6    1    1    1    3    1    6    1    5    1    3 
12.9   13 13.2 13.4 13.5 13.6 13.7 13.8 13.9   14 14.2 14.3 14.4 14.5 14.7 14.8 
   1    6    5    1    7    1    2    2    2   14    1    1    2   16    3    2 
14.9   15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9   16 16.1 16.2 16.4 16.5 
   4   11    1    3    3    1    8    1    3    5    2   10    1    2    6    8 
16.6 16.7 16.8 16.9   17 17.1 17.2 17.3 17.5 17.6 17.7 17.8   18 18.1 18.2 18.5 
   3    2    1    2    6    1    2    3    3    3    2    1    5    1    3    2 
18.6 18.7   19 19.2 19.4 19.5 19.9 20.1 20.4 20.5   21 21.5 22.1 22.2 24.8 
   4    1    6    2    1    2    1    1    1    2    2    1    1    2    1 
> colnames(x)
[1] "cylinders"           "engine.displacement" "horsepower"         
[4] "weight"              "acceleration."      
> colnames(x)[par1]
[1] "acceleration."
> x[,par1]
  [1] 11.5 11.0 10.5 10.0  8.5 10.0 10.0  8.0 10.0 15.0 15.5 20.5 17.5 17.5 12.5
 [16] 14.0 15.0 18.5 14.5 14.0 15.5 15.5 12.0 13.0 12.0 12.0 19.0 15.0 14.0 14.0
 [31] 14.5 19.0 19.0 20.5 17.0 16.5 12.0 13.5 13.0 11.0 13.5 12.5 13.5 14.0 16.0
 [46] 14.5 18.0 16.0 14.5 15.0 13.0 11.5 14.5 12.5 12.0 13.0 11.0 11.0 16.5 18.0
 [61] 16.5 16.0 14.0 12.5 15.0 19.5 16.5 18.5 14.0 13.0  9.5 15.5 14.0 11.0 14.0
 [76] 11.0 16.5 16.0 16.5 21.0 17.0 18.0 14.0 14.5 16.0 15.5 15.5 14.5 19.0 14.5
 [91] 14.0 15.0 16.0 16.0 19.5 11.5 14.0 13.5 21.0 19.0 19.0 13.5 12.0 17.0 16.0
[106] 13.5 16.5 14.5 15.0 17.0 13.5 17.5 16.9 14.9 15.3 13.0 13.9 12.8 14.5 17.6
[121] 22.2 22.1 17.7 16.2 17.8 17.0 16.4 15.7 13.2 16.7 12.1 15.0 14.0 14.8 18.6
[136] 16.8 12.5 13.7 16.9 17.7 11.1 11.4 14.5 14.5 18.2 15.8 15.9 16.4 14.5 12.8
[151] 21.5 14.4 18.6 13.2 12.8 18.2 15.8 17.2 17.2 16.7 18.7 13.2 13.4 13.7 16.5
[166] 14.7 14.5 17.6 15.9 13.6 15.8 14.9 16.6 18.2 17.3 16.6 15.4 13.2 15.2 14.3
[181] 15.0 14.0 15.2 15.0 24.8 22.2 14.9 19.2 16.0 11.3 13.2 14.7 15.5 16.4 18.1
[196] 20.1 15.8 15.5 15.0 15.2 14.4 19.2 19.9 13.8 15.3 15.1 15.7 16.4 12.6 12.9
[211] 16.4 16.1 19.4 17.3 14.9 16.2 14.2 14.8 20.4 13.8 15.8 17.1 16.6 18.6 18.0
[226] 16.0 18.0 15.3 17.6 14.7 14.5 14.5 15.7 16.4 17.0 13.9 17.3 15.6 11.6 18.6
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/1gf1g1292338110.tab") 
+ }
+ }
> m

	 Conditional inference tree with 9 terminal nodes

Response:  acceleration. 
Inputs:  cylinders, engine.displacement, horsepower, weight 
Number of observations:  240 

1) horsepower <= 110; criterion = 1, statistic = 111.143
  2) horsepower <= 61; criterion = 1, statistic = 14.76
    3)*  weights = 9 
  2) horsepower > 61
    4) weight <= 2830; criterion = 0.999, statistic = 13.066
      5) horsepower <= 72; criterion = 1, statistic = 18.584
        6)*  weights = 33 
      5) horsepower > 72
        7)*  weights = 72 
    4) weight > 2830
      8) horsepower <= 90; criterion = 0.992, statistic = 9.449
        9)*  weights = 20 
      8) horsepower > 90
        10) weight <= 3425; criterion = 0.996, statistic = 10.778
          11)*  weights = 20 
        10) weight > 3425
          12)*  weights = 10 
1) horsepower > 110
  13) horsepower <= 158; criterion = 1, statistic = 18.155
    14)*  weights = 49 
  13) horsepower > 158
    15) engine.displacement <= 360; criterion = 0.988, statistic = 8.749
      16)*  weights = 13 
    15) engine.displacement > 360
      17)*  weights = 14 
> postscript(file="/var/www/rcomp/tmp/2gf1g1292338110.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/rcomp/tmp/3gf1g1292338110.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      11.5  12.64615 -1.14615385
2      11.0  13.58776 -2.58775510
3      10.5  13.58776 -3.08775510
4      10.0  10.72143 -0.72142857
5       8.5  10.72143 -2.22142857
6      10.0  10.72143 -0.72142857
7      10.0  10.72143 -0.72142857
8       8.0  12.64615 -4.64615385
9      10.0  10.72143 -0.72142857
10     15.0  15.28750 -0.28750000
11     15.5  15.28750  0.21250000
12     20.5  19.61111  0.88888889
13     17.5  15.28750  2.21250000
14     17.5  15.28750  2.21250000
15     12.5  13.58776 -1.08775510
16     14.0  12.64615  1.35384615
17     15.0  12.64615  2.35384615
18     18.5  12.64615  5.85384615
19     14.5  15.28750 -0.78750000
20     14.0  15.28750 -1.28750000
21     15.5  16.04500 -0.54500000
22     15.5  16.04500 -0.54500000
23     12.0  12.64615 -0.64615385
24     13.0  13.58776 -0.58775510
25     12.0  10.72143  1.27857143
26     12.0  10.72143  1.27857143
27     19.0  16.78485  2.21515152
28     15.0  16.04500 -1.04500000
29     14.0  15.28750 -1.28750000
30     14.0  15.28750 -1.28750000
31     14.5  15.28750 -0.78750000
32     19.0  16.78485  2.21515152
33     19.0  19.61111 -0.61111111
34     20.5  16.78485  3.71515152
35     17.0  15.28750  1.71250000
36     16.5  15.28750  1.21250000
37     12.0  12.64615 -0.64615385
38     13.5  13.58776 -0.08775510
39     13.0  13.58776 -0.58775510
40     11.0  10.72143  0.27857143
41     13.5  13.58776 -0.08775510
42     12.5  10.72143  1.77857143
43     13.5  15.28750 -1.78750000
44     14.0  13.58776  0.41224490
45     16.0  13.58776  2.41224490
46     14.5  13.58776  0.91224490
47     18.0  15.28750  2.71250000
48     16.0  15.28750  0.71250000
49     14.5  15.28750 -0.78750000
50     15.0  15.28750 -0.28750000
51     13.0  12.64615  0.35384615
52     11.5  13.58776 -2.08775510
53     14.5  13.58776  0.91224490
54     12.5  13.58776 -1.08775510
55     12.0  13.58776 -1.58775510
56     13.0  13.58776 -0.58775510
57     11.0  10.72143  0.27857143
58     11.0  10.72143  0.27857143
59     16.5  16.04500  0.45500000
60     18.0  16.04500  1.95500000
61     16.5  18.34500 -1.84500000
62     16.0  16.04500 -0.04500000
63     14.0  13.58776  0.41224490
64     12.5  12.64615 -0.14615385
65     15.0  15.28750 -0.28750000
66     19.5  16.78485  2.71515152
67     16.5  15.28750  1.21250000
68     18.5  15.28750  3.21250000
69     14.0  15.28750 -1.28750000
70     13.0  13.58776 -0.58775510
71      9.5  10.72143 -1.22142857
72     15.5  15.28750  0.21250000
73     14.0  15.28750 -1.28750000
74     11.0  13.58776 -2.58775510
75     14.0  15.28750 -1.28750000
76     11.0  12.64615 -1.64615385
77     16.5  16.04500  0.45500000
78     16.0  16.04500 -0.04500000
79     16.5  15.28750  1.21250000
80     21.0  16.78485  4.21515152
81     17.0  18.15000 -1.15000000
82     18.0  18.15000 -0.15000000
83     14.0  13.58776  0.41224490
84     14.5  13.58776  0.91224490
85     16.0  13.58776  2.41224490
86     15.5  13.58776  1.91224490
87     15.5  16.78485 -1.28484848
88     14.5  15.28750 -0.78750000
89     19.0  19.61111 -0.61111111
90     14.5  15.28750 -0.78750000
91     14.0  15.28750 -1.28750000
92     15.0  15.28750 -0.28750000
93     16.0  16.78485 -0.78484848
94     16.0  16.04500 -0.04500000
95     19.5  18.34500  1.15500000
96     11.5  10.72143  0.77857143
97     14.0  13.58776  0.41224490
98     13.5  13.58776 -0.08775510
99     21.0  18.15000  2.85000000
100    19.0  18.15000  0.85000000
101    19.0  18.15000  0.85000000
102    13.5  16.04500 -2.54500000
103    12.0  13.58776 -1.58775510
104    17.0  15.28750  1.71250000
105    16.0  16.04500 -0.04500000
106    13.5  15.28750 -1.78750000
107    16.5  16.78485 -0.28484848
108    14.5  16.04500 -1.54500000
109    15.0  15.28750 -0.28750000
110    17.0  18.34500 -1.34500000
111    13.5  13.58776 -0.08775510
112    17.5  19.61111 -2.11111111
113    16.9  15.28750  1.61250000
114    14.9  15.28750 -0.38750000
115    15.3  15.28750  0.01250000
116    13.0  13.58776 -0.58775510
117    13.9  13.58776  0.31224490
118    12.8  13.58776 -0.78775510
119    14.5  16.04500 -1.54500000
120    17.6  18.34500 -0.74500000
121    22.2  19.61111  2.58888889
122    22.1  19.61111  2.48888889
123    17.7  18.15000 -0.45000000
124    16.2  18.15000 -1.95000000
125    17.8  16.04500  1.75500000
126    17.0  16.78485  0.21515152
127    16.4  15.28750  1.11250000
128    15.7  16.04500 -0.34500000
129    13.2  13.58776 -0.38775510
130    16.7  13.58776  3.11224490
131    12.1  12.64615 -0.54615385
132    15.0  13.58776  1.41224490
133    14.0  13.58776  0.41224490
134    14.8  15.28750 -0.48750000
135    18.6  19.61111 -1.01111111
136    16.8  16.78485  0.01515152
137    12.5  13.58776 -1.08775510
138    13.7  13.58776  0.11224490
139    16.9  16.04500  0.85500000
140    17.7  18.15000 -0.45000000
141    11.1  10.72143  0.37857143
142    11.4  12.64615 -1.24615385
143    14.5  13.58776  0.91224490
144    14.5  15.28750 -0.78750000
145    18.2  15.28750  2.91250000
146    15.8  15.28750  0.51250000
147    15.9  15.28750  0.61250000
148    16.4  16.78485 -0.38484848
149    14.5  15.28750 -0.78750000
150    12.8  15.28750 -2.48750000
151    21.5  19.61111  1.88888889
152    14.4  16.78485 -2.38484848
153    18.6  16.78485  1.81515152
154    13.2  13.58776 -0.38775510
155    12.8  13.58776 -0.78775510
156    18.2  16.04500  2.15500000
157    15.8  18.34500 -2.54500000
158    17.2  18.15000 -0.95000000
159    17.2  18.34500 -1.14500000
160    16.7  18.34500 -1.64500000
161    18.7  18.15000  0.55000000
162    13.2  13.58776 -0.38775510
163    13.4  12.64615  0.75384615
164    13.7  13.58776  0.11224490
165    16.5  16.78485 -0.28484848
166    14.7  15.28750 -0.58750000
167    14.5  15.28750 -0.78750000
168    17.6  18.34500 -0.74500000
169    15.9  15.28750  0.61250000
170    13.6  13.58776  0.01224490
171    15.8  13.58776  2.21224490
172    14.9  16.78485 -1.88484848
173    16.6  16.78485 -0.18484848
174    18.2  18.34500 -0.14500000
175    17.3  18.34500 -1.04500000
176    16.6  16.04500  0.55500000
177    15.4  13.58776  1.81224490
178    13.2  13.58776 -0.38775510
179    15.2  13.58776  1.61224490
180    14.3  13.58776  0.71224490
181    15.0  13.58776  1.41224490
182    14.0  16.78485 -2.78484848
183    15.2  16.78485 -1.58484848
184    15.0  15.28750 -0.28750000
185    24.8  18.34500  6.45500000
186    22.2  18.34500  3.85500000
187    14.9  16.78485 -1.88484848
188    19.2  16.78485  2.41515152
189    16.0  15.28750  0.71250000
190    11.3  13.58776 -2.28775510
191    13.2  15.28750 -2.08750000
192    14.7  15.28750 -0.58750000
193    15.5  16.78485 -1.28484848
194    16.4  16.78485 -0.38484848
195    18.1  18.34500 -0.24500000
196    20.1  18.34500  1.75500000
197    15.8  15.28750  0.51250000
198    15.5  15.28750  0.21250000
199    15.0  15.28750 -0.28750000
200    15.2  15.28750 -0.08750000
201    14.4  15.28750 -0.88750000
202    19.2  16.78485  2.41515152
203    19.9  18.34500  1.55500000
204    13.8  16.78485 -2.98484848
205    15.3  16.78485 -1.48484848
206    15.1  15.28750 -0.18750000
207    15.7  15.28750  0.41250000
208    16.4  15.28750  1.11250000
209    12.6  15.28750 -2.68750000
210    12.9  15.28750 -2.38750000
211    16.4  16.78485 -0.38484848
212    16.1  19.61111 -3.51111111
213    19.4  16.78485  2.61515152
214    17.3  16.78485  0.51515152
215    14.9  16.78485 -1.88484848
216    16.2  16.78485 -0.58484848
217    14.2  15.28750 -1.08750000
218    14.8  15.28750 -0.48750000
219    20.4  18.34500  2.05500000
220    13.8  13.58776  0.21224490
221    15.8  16.04500 -0.24500000
222    17.1  18.34500 -1.24500000
223    16.6  18.34500 -1.74500000
224    18.6  15.28750  3.31250000
225    18.0  15.28750  2.71250000
226    16.0  15.28750  0.71250000
227    18.0  15.28750  2.71250000
228    15.3  15.28750  0.01250000
229    17.6  16.78485  0.81515152
230    14.7  16.78485 -2.08484848
231    14.5  15.28750 -0.78750000
232    14.5  15.28750 -0.78750000
233    15.7  16.78485 -1.08484848
234    16.4  16.04500  0.35500000
235    17.0  18.34500 -1.34500000
236    13.9  15.28750 -1.38750000
237    17.3  18.34500 -1.04500000
238    15.6  15.28750  0.31250000
239    11.6  15.28750 -3.68750000
240    18.6  15.28750  3.31250000
> 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/rcomp/tmp/49oji1292338110.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/rcomp/tmp/55ggr1292338110.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/rcomp/tmp/6x7yu1292338110.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/rcomp/tmp/718e01292338110.tab") 
+ }
> 
> try(system("convert tmp/2gf1g1292338110.ps tmp/2gf1g1292338110.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gf1g1292338110.ps tmp/3gf1g1292338110.png",intern=TRUE))
character(0)
> try(system("convert tmp/49oji1292338110.ps tmp/49oji1292338110.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.840   0.780   4.629