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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+ ,14
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+ ,0
+ ,17
+ ,16
+ ,64
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(14
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(14,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','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 = '9'
> 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] "feedback_messages_p1"
> x[,par1]
  [1] 115 109 146 116  68 101  96  67  44 100  93 140 166  99 139 130 181 116
 [19] 116  88 139 135 108  89 156 129 118 118 125  95 126 135 154 165 113 127
 [37]  52 121 136   0 108  46  54 124 115 128  80  97 104  59 125  82 149 149
 [55] 122 118  12 144  67  52 108 166  80  60 107 127 107 146  84 141 123 111
 [73]  98 105 135 107  85 155  88 155 104 132 127 108 129 116 122  85 147  99
 [91]  87  28  90 109  78 111 158 141 122 124  93 124 112 108  99 117 199  78
[109]  91 158 126 122  71  75 115 119 124  72  91  45  78  39  68 119 117  39
[127]  50  88 155   0  36 123  32  99 136 117   0  88  39  25  52  75  71 124
[145] 151  71 145  87  27 131 162 165  54 159 147 170 119  49 104 120 150 112
[163]  59 136 107 130 115 107  75  71 120 116  79 150 156  51 118  71 144  47
[181]  28  68   0 110 147   0  15   4  64 111  85  68  40  80  88  48  76  51
[199]  67  59  61  76  60  68  71  76  62  61  67  88  30  64  68  64  91  88
[217]  52  49  62  61  76  88  66  71  68  48  25  68  41  90  66  54  59  60
[235]  77  68  72  67  64  63  59  84  64  56  54  67  58  59  40  22  83  81
[253]   2  72  61  15  32  62  58  36  59  68  21  55  54  55  72  41  61  67
[271]  76  64   3  63  40  69  48   8  52  66  76  43  39  14  61  71  44  60
[289]  64
> 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   2   3   4   8  12  14  15  21  22  25  27  28  30  32  36  39  40  41  43 
  5   1   1   1   1   1   1   2   1   1   2   1   2   1   2   2   4   3   2   1 
 44  45  46  47  48  49  50  51  52  54  55  56  58  59  60  61  62  63  64  66 
  2   1   1   1   3   2   1   2   5   5   2   1   2   7   4   6   3   2   7   3 
 67  68  69  71  72  75  76  77  78  79  80  81  82  83  84  85  87  88  89  90 
  7  10   1   8   4   3   6   1   3   1   3   1   1   1   2   3   2   8   1   2 
 91  93  95  96  97  98  99 100 101 104 105 107 108 109 110 111 112 113 115 116 
  3   2   1   1   1   1   4   1   1   3   1   5   5   2   1   3   2   1   4   5 
117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 135 136 139 140 
  3   4   3   2   1   4   2   5   2   2   3   1   2   2   1   1   3   3   2   1 
141 144 145 146 147 149 150 151 154 155 156 158 159 162 165 166 170 181 199 
  2   2   1   2   3   2   2   1   1   3   2   2   1   1   2   2   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] "totsize"               "totrevisions"          "totseconds"           
[13] "tothyperlinks"         "totblogs"             
> colnames(x)[par1]
[1] "feedback_messages_p1"
> x[,par1]
  [1] 115 109 146 116  68 101  96  67  44 100  93 140 166  99 139 130 181 116
 [19] 116  88 139 135 108  89 156 129 118 118 125  95 126 135 154 165 113 127
 [37]  52 121 136   0 108  46  54 124 115 128  80  97 104  59 125  82 149 149
 [55] 122 118  12 144  67  52 108 166  80  60 107 127 107 146  84 141 123 111
 [73]  98 105 135 107  85 155  88 155 104 132 127 108 129 116 122  85 147  99
 [91]  87  28  90 109  78 111 158 141 122 124  93 124 112 108  99 117 199  78
[109]  91 158 126 122  71  75 115 119 124  72  91  45  78  39  68 119 117  39
[127]  50  88 155   0  36 123  32  99 136 117   0  88  39  25  52  75  71 124
[145] 151  71 145  87  27 131 162 165  54 159 147 170 119  49 104 120 150 112
[163]  59 136 107 130 115 107  75  71 120 116  79 150 156  51 118  71 144  47
[181]  28  68   0 110 147   0  15   4  64 111  85  68  40  80  88  48  76  51
[199]  67  59  61  76  60  68  71  76  62  61  67  88  30  64  68  64  91  88
[217]  52  49  62  61  76  88  66  71  68  48  25  68  41  90  66  54  59  60
[235]  77  68  72  67  64  63  59  84  64  56  54  67  58  59  40  22  83  81
[253]   2  72  61  15  32  62  58  36  59  68  21  55  54  55  72  41  61  67
[271]  76  64   3  63  40  69  48   8  52  66  76  43  39  14  61  71  44  60
[289]  64
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> if (par2 != 'none') {
+ m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
+ if (par4=='yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ a<-table.element(a,'Prediction (training)',par3+1,TRUE)
+ a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Actual',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ a<-table.row.end(a)
+ for (i in 1:10) {
+ ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
+ m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
+ if (i==1) {
+ m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
+ m.ct.i.actu <- x[ind==1,par1]
+ m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
+ m.ct.x.actu <- x[ind==2,par1]
+ } else {
+ m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
+ m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
+ m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
+ m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
+ }
+ }
+ print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
+ numer <- numer + m.ct.i.tab[i,i]
+ }
+ print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
+ print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
+ numer <- numer + m.ct.x.tab[i,i]
+ }
+ print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
+ a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
+ for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
+ a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
+ a<-table.row.end(a)
+ }
+ a<-table.row.start(a)
+ a<-table.element(a,'Overall',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.i.cp,4))
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.x.cp,4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1jrqo1354986415.tab") 
+ }
+ }
> m

	 Conditional inference tree with 14 terminal nodes

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

1) compendiums_reviewed <= 24; criterion = 1, statistic = 278.87
  2) compendiums_reviewed <= 12; criterion = 1, statistic = 155.057
    3) compendiums_reviewed <= 5; criterion = 1, statistic = 34.821
      4)*  weights = 13 
    3) compendiums_reviewed > 5
      5) compendiums_reviewed <= 8; criterion = 1, statistic = 18.901
        6)*  weights = 8 
      5) compendiums_reviewed > 8
        7)*  weights = 16 
  2) compendiums_reviewed > 12
    8) compendiums_reviewed <= 17; criterion = 1, statistic = 107.104
      9) compendiums_reviewed <= 14; criterion = 1, statistic = 42.306
        10)*  weights = 20 
      9) compendiums_reviewed > 14
        11) compendiums_reviewed <= 15; criterion = 0.99, statistic = 11.322
          12)*  weights = 9 
        11) compendiums_reviewed > 15
          13)*  weights = 38 
    8) compendiums_reviewed > 17
      14) compendiums_reviewed <= 21; criterion = 1, statistic = 37.025
        15) compendiums_reviewed <= 18; criterion = 1, statistic = 17.655
          16)*  weights = 14 
        15) compendiums_reviewed > 18
          17)*  weights = 21 
      14) compendiums_reviewed > 21
        18)*  weights = 23 
1) compendiums_reviewed > 24
  19) compendiums_reviewed <= 35; criterion = 1, statistic = 102.185
    20) compendiums_reviewed <= 30; criterion = 1, statistic = 59.866
      21) compendiums_reviewed <= 26; criterion = 1, statistic = 26.484
        22)*  weights = 11 
      21) compendiums_reviewed > 26
        23)*  weights = 32 
    20) compendiums_reviewed > 30
      24)*  weights = 43 
  19) compendiums_reviewed > 35
    25) compendiums_reviewed <= 40; criterion = 1, statistic = 17.988
      26)*  weights = 27 
    25) compendiums_reviewed > 40
      27)*  weights = 14 
> postscript(file="/var/wessaorg/rcomp/tmp/2ilre1354986415.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3xco01354986415.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       115 109.031250   5.96875000
2       109 109.031250  -0.03125000
3       146 141.407407   4.59259259
4       116 109.031250   6.96875000
5        68  85.260870 -17.26086957
6       101  95.000000   6.00000000
7        96  95.000000   1.00000000
8        67  70.000000  -3.00000000
9        44  40.375000   3.62500000
10      100  95.000000   5.00000000
11       93  95.000000  -2.00000000
12      140 141.407407  -1.40740741
13      166 165.285714   0.71428571
14       99 109.031250 -10.03125000
15      139 141.407407  -2.40740741
16      130 121.976744   8.02325581
17      181 165.285714  15.71428571
18      116 109.031250   6.96875000
19      116 121.976744  -5.97674419
20       88  85.260870   2.73913043
21      139 141.407407  -2.40740741
22      135 141.407407  -6.40740741
23      108 109.031250  -1.03125000
24       89  95.000000  -6.00000000
25      156 141.407407  14.59259259
26      129 121.976744   7.02325581
27      118 121.976744  -3.97674419
28      118 121.976744  -3.97674419
29      125 121.976744   3.02325581
30       95  95.000000   0.00000000
31      126 121.976744   4.02325581
32      135 121.976744  13.02325581
33      154 165.285714 -11.28571429
34      165 165.285714  -0.28571429
35      113 109.031250   3.96875000
36      127 121.976744   5.02325581
37       52  49.950000   2.05000000
38      121 121.976744  -0.97674419
39      136 141.407407  -5.40740741
40        0   5.615385  -5.61538462
41      108 109.031250  -1.03125000
42       46  49.950000  -3.95000000
43       54  63.052632  -9.05263158
44      124 121.976744   2.02325581
45      115 109.031250   5.96875000
46      128 121.976744   6.02325581
47       80  76.095238   3.90476190
48       97 109.031250 -12.03125000
49      104 109.031250  -5.03125000
50       59 141.407407 -82.40740741
51      125 121.976744   3.02325581
52       82  95.000000 -13.00000000
53      149 141.407407   7.59259259
54      149 141.407407   7.59259259
55      122 121.976744   0.02325581
56      118 109.031250   8.96875000
57       12   5.615385   6.38461538
58      144 141.407407   2.59259259
59       67  70.000000  -3.00000000
60       52  49.950000   2.05000000
61      108 109.031250  -1.03125000
62      166 165.285714   0.71428571
63       80  76.095238   3.90476190
64       60  63.052632  -3.05263158
65      107 109.031250  -2.03125000
66      127 121.976744   5.02325581
67      107 109.031250  -2.03125000
68      146 141.407407   4.59259259
69       84  85.260870  -1.26086957
70      141 141.407407  -0.40740741
71      123 121.976744   1.02325581
72      111 109.031250   1.96875000
73       98  95.000000   3.00000000
74      105 109.031250  -4.03125000
75      135 141.407407  -6.40740741
76      107 109.031250  -2.03125000
77       85  85.260870  -0.26086957
78      155 141.407407  13.59259259
79       88  85.260870   2.73913043
80      155 141.407407  13.59259259
81      104 109.031250  -5.03125000
82      132 121.976744  10.02325581
83      127 121.976744   5.02325581
84      108 109.031250  -1.03125000
85      129 121.976744   7.02325581
86      116 109.031250   6.96875000
87      122 121.976744   0.02325581
88       85  85.260870  -0.26086957
89      147 141.407407   5.59259259
90       99  95.000000   4.00000000
91       87 121.976744 -34.97674419
92       28  25.750000   2.25000000
93       90  85.260870   4.73913043
94      109 109.031250  -0.03125000
95       78  76.095238   1.90476190
96      111 109.031250   1.96875000
97      158 165.285714  -7.28571429
98      141 141.407407  -0.40740741
99      122 121.976744   0.02325581
100     124 121.976744   2.02325581
101      93  95.000000  -2.00000000
102     124 121.976744   2.02325581
103     112 109.031250   2.96875000
104     108 109.031250  -1.03125000
105      99 109.031250 -10.03125000
106     117 121.976744  -4.97674419
107     199 165.285714  33.71428571
108      78  76.095238   1.90476190
109      91  85.260870   5.73913043
110     158 165.285714  -7.28571429
111     126 121.976744   4.02325581
112     122 121.976744   0.02325581
113      71  76.095238  -5.09523810
114      75  76.095238  -1.09523810
115     115 121.976744  -6.97674419
116     119 121.976744  -2.97674419
117     124 121.976744   2.02325581
118      72  70.000000   2.00000000
119      91  85.260870   5.73913043
120      45  63.052632 -18.05263158
121      78  76.095238   1.90476190
122      39  40.375000  -1.37500000
123      68  63.052632   4.94736842
124     119 109.031250   9.96875000
125     117 121.976744  -4.97674419
126      39  40.375000  -1.37500000
127      50  49.950000   0.05000000
128      88  85.260870   2.73913043
129     155 165.285714 -10.28571429
130       0   5.615385  -5.61538462
131      36  40.375000  -4.37500000
132     123 121.976744   1.02325581
133      32  40.375000  -8.37500000
134      99  95.000000   4.00000000
135     136 141.407407  -5.40740741
136     117 121.976744  -4.97674419
137       0   5.615385  -5.61538462
138      88  85.260870   2.73913043
139      39  49.950000 -10.95000000
140      25  25.750000  -0.75000000
141      52  49.950000   2.05000000
142      75  76.095238  -1.09523810
143      71  70.000000   1.00000000
144     124 121.976744   2.02325581
145     151 141.407407   9.59259259
146      71  85.260870 -14.26086957
147     145 141.407407   3.59259259
148      87  85.260870   1.73913043
149      27  25.750000   1.25000000
150     131 121.976744   9.02325581
151     162 165.285714  -3.28571429
152     165 165.285714  -0.28571429
153      54  49.950000   4.05000000
154     159 165.285714  -6.28571429
155     147 141.407407   5.59259259
156     170 165.285714   4.71428571
157     119 121.976744  -2.97674419
158      49  49.950000  -0.95000000
159     104 109.031250  -5.03125000
160     120 121.976744  -1.97674419
161     150 141.407407   8.59259259
162     112 109.031250   2.96875000
163      59  63.052632  -4.05263158
164     136 141.407407  -5.40740741
165     107 109.031250  -2.03125000
166     130 121.976744   8.02325581
167     115 121.976744  -6.97674419
168     107 109.031250  -2.03125000
169      75  76.095238  -1.09523810
170      71  70.000000   1.00000000
171     120 121.976744  -1.97674419
172     116 121.976744  -5.97674419
173      79  76.095238   2.90476190
174     150 141.407407   8.59259259
175     156 165.285714  -9.28571429
176      51  49.950000   1.05000000
177     118 121.976744  -3.97674419
178      71  70.000000   1.00000000
179     144 141.407407   2.59259259
180      47  49.950000  -2.95000000
181      28  25.750000   2.25000000
182      68  63.052632   4.94736842
183       0   5.615385  -5.61538462
184     110 109.031250   0.96875000
185     147 141.407407   5.59259259
186       0   5.615385  -5.61538462
187      15   5.615385   9.38461538
188       4   5.615385  -1.61538462
189      64  63.052632   0.94736842
190     111 121.976744 -10.97674419
191      85  85.260870  -0.26086957
192      68  63.052632   4.94736842
193      40  40.375000  -0.37500000
194      80  85.260870  -5.26086957
195      88  85.260870   2.73913043
196      48  40.375000   7.62500000
197      76  76.095238  -0.09523810
198      51  49.950000   1.05000000
199      67  63.052632   3.94736842
200      59  58.111111   0.88888889
201      61  63.052632  -2.05263158
202      76  85.260870  -9.26086957
203      60  58.111111   1.88888889
204      68  63.052632   4.94736842
205      71  70.000000   1.00000000
206      76  76.095238  -0.09523810
207      62  63.052632  -1.05263158
208      61  63.052632  -2.05263158
209      67  70.000000  -3.00000000
210      88  85.260870   2.73913043
211      30  25.750000   4.25000000
212      64  63.052632   0.94736842
213      68  70.000000  -2.00000000
214      64  63.052632   0.94736842
215      91  85.260870   5.73913043
216      88  85.260870   2.73913043
217      52  49.950000   2.05000000
218      49  49.950000  -0.95000000
219      62  63.052632  -1.05263158
220      61  63.052632  -2.05263158
221      76  76.095238  -0.09523810
222      88  85.260870   2.73913043
223      66  63.052632   2.94736842
224      71  70.000000   1.00000000
225      68  63.052632   4.94736842
226      48  40.375000   7.62500000
227      25  25.750000  -0.75000000
228      68  63.052632   4.94736842
229      41  49.950000  -8.95000000
230      90  85.260870   4.73913043
231      66  63.052632   2.94736842
232      54  49.950000   4.05000000
233      59  58.111111   0.88888889
234      60  63.052632  -3.05263158
235      77  76.095238   0.90476190
236      68  70.000000  -2.00000000
237      72  70.000000   2.00000000
238      67  63.052632   3.94736842
239      64  63.052632   0.94736842
240      63  63.052632  -0.05263158
241      59  58.111111   0.88888889
242      84  76.095238   7.90476190
243      64  63.052632   0.94736842
244      56  49.950000   6.05000000
245      54  58.111111  -4.11111111
246      67  63.052632   3.94736842
247      58  58.111111  -0.11111111
248      59  58.111111   0.88888889
249      40  40.375000  -0.37500000
250      22  25.750000  -3.75000000
251      83  85.260870  -2.26086957
252      81  76.095238   4.90476190
253       2   5.615385  -3.61538462
254      72  70.000000   2.00000000
255      61  63.052632  -2.05263158
256      15   5.615385   9.38461538
257      32  40.375000  -8.37500000
258      62  63.052632  -1.05263158
259      58  63.052632  -5.05263158
260      36  40.375000  -4.37500000
261      59  63.052632  -4.05263158
262      68  63.052632   4.94736842
263      21  25.750000  -4.75000000
264      55  58.111111  -3.11111111
265      54  49.950000   4.05000000
266      55  49.950000   5.05000000
267      72  70.000000   2.00000000
268      41  40.375000   0.62500000
269      61  63.052632  -2.05263158
270      67  76.095238  -9.09523810
271      76  76.095238  -0.09523810
272      64  63.052632   0.94736842
273       3   5.615385  -2.61538462
274      63  63.052632  -0.05263158
275      40  40.375000  -0.37500000
276      69  76.095238  -7.09523810
277      48  40.375000   7.62500000
278       8   5.615385   2.38461538
279      52  49.950000   2.05000000
280      66  63.052632   2.94736842
281      76  76.095238  -0.09523810
282      43  49.950000  -6.95000000
283      39  40.375000  -1.37500000
284      14   5.615385   8.38461538
285      61  63.052632  -2.05263158
286      71  76.095238  -5.09523810
287      44  40.375000   3.62500000
288      60  58.111111   1.88888889
289      64  63.052632   0.94736842
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/4pwsf1354986415.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if(par2=='none') {
+ op <- par(mfrow=c(2,2))
+ plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
+ plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
+ plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
+ plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$Forecasts,result$Actuals)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goodness of Fit',2,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Correlation',1,TRUE)
+ a<-table.element(a,round(detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'R-squared',1,TRUE)
+ a<-table.element(a,round(detcoef*detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'RMSE',1,TRUE)
+ a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/5n7g61354986415.tab") 
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'#',header=TRUE)
+ a<-table.element(a,'Actuals',header=TRUE)
+ a<-table.element(a,'Forecasts',header=TRUE)
+ a<-table.element(a,'Residuals',header=TRUE)
+ a<-table.row.end(a)
+ for (i in 1:length(result$Actuals)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,result$Actuals[i])
+ a<-table.element(a,result$Forecasts[i])
+ a<-table.element(a,result$Residuals[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/6348e1354986415.tab") 
+ }
> if (par2 != 'none') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ for (i in 1:par3) {
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ }
+ a<-table.row.end(a)
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (j in 1:par3) {
+ a<-table.element(a,myt[i,j])
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/7aimj1354986415.tab") 
+ }
> 
> try(system("convert tmp/2ilre1354986415.ps tmp/2ilre1354986415.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xco01354986415.ps tmp/3xco01354986415.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pwsf1354986415.ps tmp/4pwsf1354986415.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 12.967   0.560  13.508