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 'q()' to quit R.

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+ ,0
+ ,17
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+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(15
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(15,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120','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 = '8'
> 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] "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] "pageviews"              "time_in_rfc"            "logins"                
 [4] "compendium_views_info"  "compendium_views_pr"    "shared_compendiums"    
 [7] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p1"  
[10] "feedback_messages_p120" "totsize"                "totrevisions"          
[13] "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/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/1bgym1354985630.tab") 
+ }
+ }
> m

	 Conditional inference tree with 17 terminal nodes

Response:  compendiums_reviewed 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, feedback_messages_p1, feedback_messages_p120, totsize, totrevisions, totseconds, tothyperlinks, totblogs 
Number of observations:  289 

1) feedback_messages_p1 <= 91; criterion = 1, statistic = 278.87
  2) feedback_messages_p1 <= 40; criterion = 1, statistic = 141.89
    3) feedback_messages_p1 <= 15; criterion = 1, statistic = 29.742
      4)*  weights = 13 
    3) feedback_messages_p1 > 15
      5)*  weights = 19 
  2) feedback_messages_p1 > 40
    6) feedback_messages_p1 <= 72; criterion = 1, statistic = 82.048
      7) feedback_messages_p1 <= 58; criterion = 1, statistic = 29.024
        8)*  weights = 31 
      7) feedback_messages_p1 > 58
        9) compendium_views_info <= 555; criterion = 0.977, statistic = 9.883
          10) feedback_messages_p1 <= 66; criterion = 1, statistic = 29.337
            11) feedback_messages_p1 <= 60; criterion = 0.986, statistic = 10.778
              12)*  weights = 10 
            11) feedback_messages_p1 > 60
              13)*  weights = 19 
          10) feedback_messages_p1 > 66
            14)*  weights = 26 
        9) compendium_views_info > 555
          15)*  weights = 7 
    6) feedback_messages_p1 > 72
      16) feedback_messages_p1 <= 81; criterion = 0.992, statistic = 11.81
        17)*  weights = 18 
      16) feedback_messages_p1 > 81
        18)*  weights = 23 
1) feedback_messages_p1 > 91
  19) feedback_messages_p1 <= 132; criterion = 1, statistic = 117.918
    20) feedback_messages_p1 <= 109; criterion = 1, statistic = 70.83
      21) feedback_messages_p1 <= 101; criterion = 0.997, statistic = 13.669
        22)*  weights = 12 
      21) feedback_messages_p1 > 101
        23)*  weights = 16 
    20) feedback_messages_p1 > 109
      24) feedback_messages_p1 <= 120; criterion = 1, statistic = 39.831
        25)*  weights = 28 
      24) feedback_messages_p1 > 120
        26) feedback_messages_p1 <= 126; criterion = 0.998, statistic = 14.842
          27)*  weights = 16 
        26) feedback_messages_p1 > 126
          28)*  weights = 10 
  19) feedback_messages_p1 > 132
    29) feedback_messages_p1 <= 156; criterion = 1, statistic = 36.259
      30) feedback_messages_p1 <= 147; criterion = 1, statistic = 20.153
        31)*  weights = 19 
      30) feedback_messages_p1 > 147
        32)*  weights = 11 
    29) feedback_messages_p1 > 156
      33)*  weights = 11 
> postscript(file="/var/fisher/rcomp/tmp/2746a1354985630.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/31ufg1354985630.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 30.464286 -0.46428571
2        28 28.250000 -0.25000000
3        38 37.263158  0.73684211
4        30 30.464286 -0.46428571
5        22 18.230769  3.76923077
6        26 26.166667 -0.16666667
7        25 26.166667 -1.16666667
8        18 18.230769 -0.23076923
9        11 13.677419 -2.67741935
10       26 26.166667 -0.16666667
11       25 26.166667 -1.16666667
12       38 37.263158  0.73684211
13       44 44.363636 -0.36363636
14       30 26.166667  3.83333333
15       40 37.263158  2.73684211
16       34 34.200000 -0.20000000
17       47 44.363636  2.63636364
18       30 30.464286 -0.46428571
19       31 30.464286  0.53571429
20       23 23.565217 -0.56521739
21       36 37.263158 -1.26315789
22       36 37.263158 -1.26315789
23       30 28.250000  1.75000000
24       25 23.565217  1.43478261
25       39 40.090909 -1.09090909
26       34 34.200000 -0.20000000
27       31 30.464286  0.53571429
28       31 30.464286  0.53571429
29       33 32.625000  0.37500000
30       25 26.166667 -1.16666667
31       33 32.625000  0.37500000
32       35 37.263158 -2.26315789
33       42 40.090909  1.90909091
34       43 44.363636 -1.36363636
35       30 30.464286 -0.46428571
36       33 34.200000 -1.20000000
37       13 13.677419 -0.67741935
38       32 32.625000 -0.62500000
39       36 37.263158 -1.26315789
40        0  1.769231 -1.76923077
41       28 28.250000 -0.25000000
42       14 13.677419  0.32258065
43       17 13.677419  3.32258065
44       32 32.625000 -0.62500000
45       30 30.464286 -0.46428571
46       35 34.200000  0.80000000
47       20 20.500000 -0.50000000
48       28 26.166667  1.83333333
49       28 28.250000 -0.25000000
50       39 20.428571 18.57142857
51       34 32.625000  1.37500000
52       26 23.565217  2.43478261
53       39 40.090909 -1.09090909
54       39 40.090909 -1.09090909
55       33 32.625000  0.37500000
56       28 30.464286 -2.46428571
57        4  1.769231  2.23076923
58       39 37.263158  1.73684211
59       18 18.230769 -0.23076923
60       14 13.677419  0.32258065
61       29 28.250000  0.75000000
62       44 44.363636 -0.36363636
63       21 20.500000  0.50000000
64       16 15.500000  0.50000000
65       28 28.250000 -0.25000000
66       35 34.200000  0.80000000
67       28 28.250000 -0.25000000
68       38 37.263158  0.73684211
69       23 23.565217 -0.56521739
70       36 37.263158 -1.26315789
71       32 32.625000 -0.62500000
72       29 30.464286 -1.46428571
73       25 26.166667 -1.16666667
74       27 28.250000 -1.25000000
75       36 37.263158 -1.26315789
76       28 28.250000 -0.25000000
77       23 23.565217 -0.56521739
78       40 40.090909 -0.09090909
79       23 23.565217 -0.56521739
80       40 40.090909 -0.09090909
81       28 28.250000 -0.25000000
82       34 34.200000 -0.20000000
83       33 34.200000 -1.20000000
84       28 28.250000 -0.25000000
85       34 34.200000 -0.20000000
86       30 30.464286 -0.46428571
87       33 32.625000  0.37500000
88       22 23.565217 -1.56521739
89       38 37.263158  0.73684211
90       26 26.166667 -0.16666667
91       35 23.565217 11.43478261
92        8  9.210526 -1.21052632
93       24 23.565217  0.43478261
94       29 28.250000  0.75000000
95       20 20.500000 -0.50000000
96       29 30.464286 -1.46428571
97       45 44.363636  0.63636364
98       37 37.263158 -0.26315789
99       33 32.625000  0.37500000
100      33 32.625000  0.37500000
101      25 26.166667 -1.16666667
102      32 32.625000 -0.62500000
103      29 30.464286 -1.46428571
104      28 28.250000 -0.25000000
105      28 26.166667  1.83333333
106      31 30.464286  0.53571429
107      52 44.363636  7.63636364
108      21 20.500000  0.50000000
109      24 23.565217  0.43478261
110      41 44.363636 -3.36363636
111      33 32.625000  0.37500000
112      32 32.625000 -0.62500000
113      19 18.230769  0.76923077
114      20 20.500000 -0.50000000
115      31 30.464286  0.53571429
116      31 30.464286  0.53571429
117      32 32.625000 -0.62500000
118      18 20.428571 -2.42857143
119      23 23.565217 -0.56521739
120      17 13.677419  3.32258065
121      20 20.500000 -0.50000000
122      12  9.210526  2.78947368
123      17 20.428571 -3.42857143
124      30 30.464286 -0.46428571
125      31 30.464286  0.53571429
126      10  9.210526  0.78947368
127      13 13.677419 -0.67741935
128      22 23.565217 -1.56521739
129      42 40.090909  1.90909091
130       1  1.769231 -0.76923077
131       9  9.210526 -0.21052632
132      32 32.625000 -0.62500000
133      11  9.210526  1.78947368
134      25 26.166667 -1.16666667
135      36 37.263158 -1.26315789
136      31 30.464286  0.53571429
137       0  1.769231 -1.76923077
138      24 23.565217  0.43478261
139      13  9.210526  3.78947368
140       8  9.210526 -1.21052632
141      13 13.677419 -0.67741935
142      19 20.500000 -1.50000000
143      18 18.230769 -0.23076923
144      33 32.625000  0.37500000
145      40 40.090909 -0.09090909
146      22 18.230769  3.76923077
147      38 37.263158  0.73684211
148      24 23.565217  0.43478261
149       8  9.210526 -1.21052632
150      35 34.200000  0.80000000
151      43 44.363636 -1.36363636
152      43 44.363636 -1.36363636
153      14 13.677419  0.32258065
154      41 44.363636 -3.36363636
155      38 37.263158  0.73684211
156      45 44.363636  0.63636364
157      31 30.464286  0.53571429
158      13 13.677419 -0.67741935
159      28 28.250000 -0.25000000
160      31 30.464286  0.53571429
161      40 40.090909 -0.09090909
162      30 30.464286 -0.46428571
163      16 15.500000  0.50000000
164      37 37.263158 -0.26315789
165      30 28.250000  1.75000000
166      35 34.200000  0.80000000
167      32 30.464286  1.53571429
168      27 28.250000 -1.25000000
169      20 20.500000 -0.50000000
170      18 20.428571 -2.42857143
171      31 30.464286  0.53571429
172      31 30.464286  0.53571429
173      21 20.500000  0.50000000
174      39 40.090909 -1.09090909
175      41 40.090909  0.90909091
176      13 13.677419 -0.67741935
177      32 30.464286  1.53571429
178      18 18.230769 -0.23076923
179      39 37.263158  1.73684211
180      14 13.677419  0.32258065
181       7  9.210526 -2.21052632
182      17 18.230769 -1.23076923
183       0  1.769231 -1.76923077
184      30 30.464286 -0.46428571
185      37 37.263158 -0.26315789
186       0  1.769231 -1.76923077
187       5  1.769231  3.23076923
188       1  1.769231 -0.76923077
189      16 16.263158 -0.26315789
190      32 30.464286  1.53571429
191      24 23.565217  0.43478261
192      17 18.230769 -1.23076923
193      11  9.210526  1.78947368
194      24 20.500000  3.50000000
195      22 23.565217 -1.56521739
196      12 13.677419 -1.67741935
197      19 20.500000 -1.50000000
198      13 13.677419 -0.67741935
199      17 18.230769 -1.23076923
200      15 15.500000 -0.50000000
201      16 16.263158 -0.26315789
202      24 20.500000  3.50000000
203      15 15.500000 -0.50000000
204      17 18.230769 -1.23076923
205      18 20.428571 -2.42857143
206      20 20.500000 -0.50000000
207      16 16.263158 -0.26315789
208      16 20.428571 -4.42857143
209      18 18.230769 -0.23076923
210      22 23.565217 -1.56521739
211       8  9.210526 -1.21052632
212      17 16.263158  0.73684211
213      18 18.230769 -0.23076923
214      16 16.263158 -0.26315789
215      23 23.565217 -0.56521739
216      22 23.565217 -1.56521739
217      13 13.677419 -0.67741935
218      13 13.677419 -0.67741935
219      16 16.263158 -0.26315789
220      16 16.263158 -0.26315789
221      20 20.500000 -0.50000000
222      22 23.565217 -1.56521739
223      17 20.428571 -3.42857143
224      18 18.230769 -0.23076923
225      17 18.230769 -1.23076923
226      12 13.677419 -1.67741935
227       7  9.210526 -2.21052632
228      17 18.230769 -1.23076923
229      14 13.677419  0.32258065
230      23 23.565217 -0.56521739
231      17 16.263158  0.73684211
232      14 13.677419  0.32258065
233      15 15.500000 -0.50000000
234      17 15.500000  1.50000000
235      21 20.500000  0.50000000
236      18 18.230769 -0.23076923
237      18 18.230769 -0.23076923
238      17 18.230769 -1.23076923
239      17 16.263158  0.73684211
240      16 16.263158 -0.26315789
241      15 15.500000 -0.50000000
242      21 23.565217 -2.56521739
243      16 16.263158 -0.26315789
244      14 13.677419  0.32258065
245      15 13.677419  1.32258065
246      17 18.230769 -1.23076923
247      15 13.677419  1.32258065
248      15 15.500000 -0.50000000
249      10  9.210526  0.78947368
250       6  9.210526 -3.21052632
251      22 23.565217 -1.56521739
252      21 20.500000  0.50000000
253       1  1.769231 -0.76923077
254      18 18.230769 -0.23076923
255      17 16.263158  0.73684211
256       4  1.769231  2.23076923
257      10  9.210526  0.78947368
258      16 16.263158 -0.26315789
259      16 13.677419  2.32258065
260       9  9.210526 -0.21052632
261      16 15.500000  0.50000000
262      17 18.230769 -1.23076923
263       7  9.210526 -2.21052632
264      15 13.677419  1.32258065
265      14 13.677419  0.32258065
266      14 13.677419  0.32258065
267      18 18.230769 -0.23076923
268      12 13.677419 -1.67741935
269      16 16.263158 -0.26315789
270      21 18.230769  2.76923077
271      19 20.500000 -1.50000000
272      16 16.263158 -0.26315789
273       1  1.769231 -0.76923077
274      16 16.263158 -0.26315789
275      10  9.210526  0.78947368
276      19 18.230769  0.76923077
277      12 13.677419 -1.67741935
278       2  1.769231  0.23076923
279      14 13.677419  0.32258065
280      17 16.263158  0.73684211
281      19 20.500000 -1.50000000
282      14 13.677419  0.32258065
283      11  9.210526  1.78947368
284       4  1.769231  2.23076923
285      16 16.263158 -0.26315789
286      20 18.230769  1.76923077
287      12 13.677419 -1.67741935
288      15 15.500000 -0.50000000
289      16 16.263158 -0.26315789
> 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/4x6a71354985630.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/5sw6d1354985630.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/6l8fz1354985630.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/75r2x1354985630.tab") 
+ }
> 
> try(system("convert tmp/2746a1354985630.ps tmp/2746a1354985630.png",intern=TRUE))
character(0)
> try(system("convert tmp/31ufg1354985630.ps tmp/31ufg1354985630.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x6a71354985630.ps tmp/4x6a71354985630.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 12.761   0.650  13.393