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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+ ,39
+ ,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 = '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] "compendium_views_pr"
> x[,par1]
  [1]  81  55  50 125  40  37  63  44  88  66  57  74  49  52  88  36 108  43
 [19]  75  32  44  85  86  56  50 135  63  81  52  44 113  39  73  48  33  59
 [37]  41  69  64   1  59  32 129  37  31  65 107  74  54  76 715  57  66 106
 [55]  54  32  20  71  21  70 112  66 190  66 165  56  61  53 127  63  38  50
 [73]  52  42  76  67  50  53  39  50  77  57  73  34  39  46  63  35 106  43
 [91]  47  31 162  57  36 263  78  63  54  63  77  79 110  56  56  43 111  71
[109]  62  56  74  60  43  68  53  87  46 105  32 133  79  51 207  67  47  34
[127]  66  76  65   9  42  45  25 115  97  53   2  52  44  22  35  74 103 144
[145]  60 134  89  42  52  98  99  52  29 125 106  95  40 140  43 128 142  73
[163]  72 128  61  73 148  64  45  58  97  50  37  50 105  69  46  57  52  98
[181]  61  89   0  48  91   0   7   3  54  70  36  37 123 247  46  72  41  24
[199]  45  33  27  36  87  90 114  31  45  69  51  34  60  45  54  25  38  52
[217]  67  74  38  30  26  67 132  42  35 118  68  43  76  64  48  64  56  71
[235]  75  39  42  39  93  38  60  71  52  27  59  40  79  44  65  10 124  81
[253]  15  92  42  10  24  64  45  22  56  94  19  35  32  35  48  49  48  62
[271]  96  45  63  71  26  48  29  19  45  45  67  30  36  34  36  34  37  46
[289]  44
> 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   7   9  10  15  19  20  21  22  24  25  26  27  29  30  31  32 
  2   1   1   1   1   1   2   1   2   1   1   2   2   2   2   2   2   2   3   5 
 33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52 
  2   5   5   6   5   4   5   3   2   6   6   6   9   5   2   6   2   7   2   9 
 53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72 
  4   5   1   7   5   1   3   4   3   2   7   5   3   5   5   2   3   2   5   2 
 73  74  75  76  77  78  79  81  85  86  87  88  89  90  91  92  93  94  95  96 
  4   5   2   4   2   1   3   3   1   1   2   2   2   1   1   1   1   1   1   1 
 97  98  99 103 105 106 107 108 110 111 112 113 114 115 118 123 124 125 127 128 
  2   2   1   1   2   3   1   1   1   1   1   1   1   1   1   1   1   2   1   2 
129 132 133 134 135 140 142 144 148 162 165 190 207 247 263 715 
  1   1   1   1   1   1   1   1   1   1   1   1   1   1   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] "compendium_views_pr"
> x[,par1]
  [1]  81  55  50 125  40  37  63  44  88  66  57  74  49  52  88  36 108  43
 [19]  75  32  44  85  86  56  50 135  63  81  52  44 113  39  73  48  33  59
 [37]  41  69  64   1  59  32 129  37  31  65 107  74  54  76 715  57  66 106
 [55]  54  32  20  71  21  70 112  66 190  66 165  56  61  53 127  63  38  50
 [73]  52  42  76  67  50  53  39  50  77  57  73  34  39  46  63  35 106  43
 [91]  47  31 162  57  36 263  78  63  54  63  77  79 110  56  56  43 111  71
[109]  62  56  74  60  43  68  53  87  46 105  32 133  79  51 207  67  47  34
[127]  66  76  65   9  42  45  25 115  97  53   2  52  44  22  35  74 103 144
[145]  60 134  89  42  52  98  99  52  29 125 106  95  40 140  43 128 142  73
[163]  72 128  61  73 148  64  45  58  97  50  37  50 105  69  46  57  52  98
[181]  61  89   0  48  91   0   7   3  54  70  36  37 123 247  46  72  41  24
[199]  45  33  27  36  87  90 114  31  45  69  51  34  60  45  54  25  38  52
[217]  67  74  38  30  26  67 132  42  35 118  68  43  76  64  48  64  56  71
[235]  75  39  42  39  93  38  60  71  52  27  59  40  79  44  65  10 124  81
[253]  15  92  42  10  24  64  45  22  56  94  19  35  32  35  48  49  48  62
[271]  96  45  63  71  26  48  29  19  45  45  67  30  36  34  36  34  37  46
[289]  44
> 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/149761354984984.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

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

1) pageviews <= 3201; criterion = 1, statistic = 91.384
  2) pageviews <= 1036; criterion = 1, statistic = 83.865
    3) pageviews <= 554; criterion = 1, statistic = 37.502
      4) compendiums_reviewed <= 7; criterion = 0.998, statistic = 14.59
        5)*  weights = 14 
      4) compendiums_reviewed > 7
        6)*  weights = 8 
    3) pageviews > 554
      7)*  weights = 75 
  2) pageviews > 1036
    8) pageviews <= 1954; criterion = 0.999, statistic = 15.669
      9) totseconds <= 32378; criterion = 0.993, statistic = 12.007
        10)*  weights = 26 
      9) totseconds > 32378
        11)*  weights = 110 
    8) pageviews > 1954
      12)*  weights = 49 
1) pageviews > 3201
  13)*  weights = 7 
> postscript(file="/var/fisher/rcomp/tmp/28dul1354984984.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/3acfn1354984984.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        81  61.20000   19.80000000
2        55  45.06667    9.93333333
3        50  61.20000  -11.20000000
4       125  91.28571   33.71428571
5        40  45.06667   -5.06666667
6        37  30.62500    6.37500000
7        63  91.28571  -28.28571429
8        44  30.62500   13.37500000
9        88  93.50000   -5.50000000
10       66  61.20000    4.80000000
11       57  61.20000   -4.20000000
12       74  61.20000   12.80000000
13       49  61.20000  -12.20000000
14       52  61.20000   -9.20000000
15       88  91.28571   -3.28571429
16       36  61.20000  -25.20000000
17      108 212.28571 -104.28571429
18       43  61.20000  -18.20000000
19       75  91.28571  -16.28571429
20       32  45.06667  -13.06666667
21       44  61.20000  -17.20000000
22       85  61.20000   23.80000000
23       86  91.28571   -5.28571429
24       56  61.20000   -5.20000000
25       50  61.20000  -11.20000000
26      135  91.28571   43.71428571
27       63  91.28571  -28.28571429
28       81  91.28571  -10.28571429
29       52  61.20000   -9.20000000
30       44  61.20000  -17.20000000
31      113  61.20000   51.80000000
32       39  61.20000  -22.20000000
33       73  61.20000   11.80000000
34       48  61.20000  -13.20000000
35       33  45.06667  -12.06666667
36       59  91.28571  -32.28571429
37       41  45.06667   -4.06666667
38       69  61.20000    7.80000000
39       64  91.28571  -27.28571429
40        1  10.64286   -9.64285714
41       59  61.20000   -2.20000000
42       32  45.06667  -13.06666667
43      129  93.50000   35.50000000
44       37  91.28571  -54.28571429
45       31  91.28571  -60.28571429
46       65  61.20000    3.80000000
47      107  91.28571   15.71428571
48       74  61.20000   12.80000000
49       54  61.20000   -7.20000000
50       76  91.28571  -15.28571429
51      715 212.28571  502.71428571
52       57  45.06667   11.93333333
53       66  61.20000    4.80000000
54      106 212.28571 -106.28571429
55       54  61.20000   -7.20000000
56       32  61.20000  -29.20000000
57       20  10.64286    9.35714286
58       71  91.28571  -20.28571429
59       21  45.06667  -24.06666667
60       70  61.20000    8.80000000
61      112  91.28571   20.71428571
62       66  91.28571  -25.28571429
63      190 212.28571  -22.28571429
64       66  61.20000    4.80000000
65      165  91.28571   73.71428571
66       56  91.28571  -35.28571429
67       61  61.20000   -0.20000000
68       53  91.28571  -38.28571429
69      127  91.28571   35.71428571
70       63  91.28571  -28.28571429
71       38  61.20000  -23.20000000
72       50  61.20000  -11.20000000
73       52  61.20000   -9.20000000
74       42  61.20000  -19.20000000
75       76  91.28571  -15.28571429
76       67  61.20000    5.80000000
77       50  61.20000  -11.20000000
78       53  61.20000   -8.20000000
79       39  45.06667   -6.06666667
80       50  61.20000  -11.20000000
81       77  61.20000   15.80000000
82       57  61.20000   -4.20000000
83       73  61.20000   11.80000000
84       34  61.20000  -27.20000000
85       39  61.20000  -22.20000000
86       46  61.20000  -15.20000000
87       63  61.20000    1.80000000
88       35  61.20000  -26.20000000
89      106  91.28571   14.71428571
90       43  61.20000  -18.20000000
91       47  61.20000  -14.20000000
92       31  45.06667  -14.06666667
93      162  93.50000   68.50000000
94       57  61.20000   -4.20000000
95       36  45.06667   -9.06666667
96      263  91.28571  171.71428571
97       78  91.28571  -13.28571429
98       63  91.28571  -28.28571429
99       54  61.20000   -7.20000000
100      63  61.20000    1.80000000
101      77  61.20000   15.80000000
102      79  91.28571  -12.28571429
103     110  91.28571   18.71428571
104      56  61.20000   -5.20000000
105      56  61.20000   -5.20000000
106      43  61.20000  -18.20000000
107     111  91.28571   19.71428571
108      71  93.50000  -22.50000000
109      62  91.28571  -29.28571429
110      56  91.28571  -35.28571429
111      74  61.20000   12.80000000
112      60  61.20000   -1.20000000
113      43  45.06667   -2.06666667
114      68  61.20000    6.80000000
115      53  61.20000   -8.20000000
116      87  91.28571   -4.28571429
117      46  91.28571  -45.28571429
118     105  91.28571   13.71428571
119      32  45.06667  -13.06666667
120     133  93.50000   39.50000000
121      79  61.20000   17.80000000
122      51  93.50000  -42.50000000
123     207  91.28571  115.71428571
124      67  61.20000    5.80000000
125      47  61.20000  -14.20000000
126      34  45.06667  -11.06666667
127      66  61.20000    4.80000000
128      76  61.20000   14.80000000
129      65  61.20000    3.80000000
130       9  10.64286   -1.64285714
131      42  45.06667   -3.06666667
132      45  61.20000  -16.20000000
133      25  30.62500   -5.62500000
134     115  91.28571   23.71428571
135      97  61.20000   35.80000000
136      53  91.28571  -38.28571429
137       2  10.64286   -8.64285714
138      52  61.20000   -9.20000000
139      44  45.06667   -1.06666667
140      22  30.62500   -8.62500000
141      35  45.06667  -10.06666667
142      74  61.20000   12.80000000
143     103  93.50000    9.50000000
144     144  91.28571   52.71428571
145      60  61.20000   -1.20000000
146     134  93.50000   40.50000000
147      89  93.50000   -4.50000000
148      42  61.20000  -19.20000000
149      52  45.06667    6.93333333
150      98  61.20000   36.80000000
151      99  91.28571    7.71428571
152      52  61.20000   -9.20000000
153      29  61.20000  -32.20000000
154     125  91.28571   33.71428571
155     106  91.28571   14.71428571
156      95  91.28571    3.71428571
157      40  45.06667   -5.06666667
158     140  93.50000   46.50000000
159      43  45.06667   -2.06666667
160     128  91.28571   36.71428571
161     142 212.28571  -70.28571429
162      73  45.06667   27.93333333
163      72  61.20000   10.80000000
164     128 212.28571  -84.28571429
165      61  61.20000   -0.20000000
166      73  61.20000   11.80000000
167     148  61.20000   86.80000000
168      64  61.20000    2.80000000
169      45  45.06667   -0.06666667
170      58  61.20000   -3.20000000
171      97 212.28571 -115.28571429
172      50  61.20000  -11.20000000
173      37  91.28571  -54.28571429
174      50  91.28571  -41.28571429
175     105  61.20000   43.80000000
176      69  61.20000    7.80000000
177      46  91.28571  -45.28571429
178      57  45.06667   11.93333333
179      52  61.20000   -9.20000000
180      98  61.20000   36.80000000
181      61  93.50000  -32.50000000
182      89  93.50000   -4.50000000
183       0  10.64286  -10.64285714
184      48  61.20000  -13.20000000
185      91  91.28571   -0.28571429
186       0  10.64286  -10.64285714
187       7  10.64286   -3.64285714
188       3  10.64286   -7.64285714
189      54  61.20000   -7.20000000
190      70  45.06667   24.93333333
191      36  61.20000  -25.20000000
192      37  45.06667   -8.06666667
193     123  93.50000   29.50000000
194     247  93.50000  153.50000000
195      46  61.20000  -15.20000000
196      72  61.20000   10.80000000
197      41  45.06667   -4.06666667
198      24  45.06667  -21.06666667
199      45  61.20000  -16.20000000
200      33  45.06667  -12.06666667
201      27  45.06667  -18.06666667
202      36  30.62500    5.37500000
203      87  61.20000   25.80000000
204      90  61.20000   28.80000000
205     114  93.50000   20.50000000
206      31  45.06667  -14.06666667
207      45  93.50000  -48.50000000
208      69  61.20000    7.80000000
209      51  61.20000  -10.20000000
210      34  61.20000  -27.20000000
211      60  61.20000   -1.20000000
212      45  61.20000  -16.20000000
213      54  45.06667    8.93333333
214      25  45.06667  -20.06666667
215      38  93.50000  -55.50000000
216      52  45.06667    6.93333333
217      67  93.50000  -26.50000000
218      74  45.06667   28.93333333
219      38  45.06667   -7.06666667
220      30  45.06667  -15.06666667
221      26  45.06667  -19.06666667
222      67  61.20000    5.80000000
223     132  91.28571   40.71428571
224      42  45.06667   -3.06666667
225      35  30.62500    4.37500000
226     118  61.20000   56.80000000
227      68  45.06667   22.93333333
228      43  45.06667   -2.06666667
229      76  93.50000  -17.50000000
230      64  93.50000  -29.50000000
231      48  45.06667    2.93333333
232      64  61.20000    2.80000000
233      56  61.20000   -5.20000000
234      71  45.06667   25.93333333
235      75  45.06667   29.93333333
236      39  45.06667   -6.06666667
237      42  61.20000  -19.20000000
238      39  45.06667   -6.06666667
239      93  93.50000   -0.50000000
240      38  45.06667   -7.06666667
241      60  61.20000   -1.20000000
242      71  61.20000    9.80000000
243      52  45.06667    6.93333333
244      27  45.06667  -18.06666667
245      59  45.06667   13.93333333
246      40  61.20000  -21.20000000
247      79  61.20000   17.80000000
248      44  45.06667   -1.06666667
249      65  45.06667   19.93333333
250      10  10.64286   -0.64285714
251     124  61.20000   62.80000000
252      81  45.06667   35.93333333
253      15  10.64286    4.35714286
254      92  93.50000   -1.50000000
255      42  93.50000  -51.50000000
256      10  10.64286   -0.64285714
257      24  30.62500   -6.62500000
258      64  45.06667   18.93333333
259      45  45.06667   -0.06666667
260      22  30.62500   -8.62500000
261      56  45.06667   10.93333333
262      94  45.06667   48.93333333
263      19  10.64286    8.35714286
264      35  45.06667  -10.06666667
265      32  45.06667  -13.06666667
266      35  45.06667  -10.06666667
267      48  45.06667    2.93333333
268      49  45.06667    3.93333333
269      48  45.06667    2.93333333
270      62  45.06667   16.93333333
271      96  93.50000    2.50000000
272      45  45.06667   -0.06666667
273      63  45.06667   17.93333333
274      71  61.20000    9.80000000
275      26  45.06667  -19.06666667
276      48  93.50000  -45.50000000
277      29  45.06667  -16.06666667
278      19  10.64286    8.35714286
279      45  61.20000  -16.20000000
280      45  45.06667   -0.06666667
281      67  45.06667   21.93333333
282      30  45.06667  -15.06666667
283      36  93.50000  -57.50000000
284      34  10.64286   23.35714286
285      36  45.06667   -9.06666667
286      34  45.06667  -11.06666667
287      37  45.06667   -8.06666667
288      46  45.06667    0.93333333
289      44  45.06667   -1.06666667
> 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/4j08t1354984984.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/56w4k1354984984.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/6algh1354984984.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/78suc1354984984.tab") 
+ }
> 
> try(system("convert tmp/28dul1354984984.ps tmp/28dul1354984984.png",intern=TRUE))
character(0)
> try(system("convert tmp/3acfn1354984984.ps tmp/3acfn1354984984.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j08t1354984984.ps tmp/4j08t1354984984.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 11.668   0.629  12.288