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 = ''
> 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] "feedback_messages_p120" "totsize"                "totrevisions"          
[13] "totseconds"             "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/135np1354985989.tab") 
+ }
+ }
> m

	 Conditional inference tree with 19 terminal nodes

Response:  feedback_messages_p1 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p120, 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 = 0.989, 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) feedback_messages_p120 <= 62; criterion = 0.957, statistic = 8.74
          19)*  weights = 7 
        18) feedback_messages_p120 > 62
          20)*  weights = 16 
1) compendiums_reviewed > 24
  21) compendiums_reviewed <= 35; criterion = 1, statistic = 102.185
    22) compendiums_reviewed <= 30; criterion = 1, statistic = 59.866
      23) compendiums_reviewed <= 26; criterion = 1, statistic = 26.484
        24)*  weights = 11 
      23) compendiums_reviewed > 26
        25)*  weights = 32 
    22) compendiums_reviewed > 30
      26) feedback_messages_p120 <= 94; criterion = 0.992, statistic = 11.939
        27)*  weights = 14 
      26) feedback_messages_p120 > 94
        28) compendiums_reviewed <= 33; criterion = 1, statistic = 21.471
          29) compendiums_reviewed <= 32; criterion = 0.995, statistic = 12.603
            30)*  weights = 11 
          29) compendiums_reviewed > 32
            31)*  weights = 9 
        28) compendiums_reviewed > 33
          32)*  weights = 9 
  21) compendiums_reviewed > 35
    33) feedback_messages_p120 <= 134; criterion = 1, statistic = 25.583
      34) feedback_messages_p120 <= 110; criterion = 0.96, statistic = 8.872
        35)*  weights = 7 
      34) feedback_messages_p120 > 110
        36)*  weights = 16 
    33) feedback_messages_p120 > 134
      37)*  weights = 18 
> postscript(file="/var/wessaorg/rcomp/tmp/2esfk1354985989.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/31fpj1354985989.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       146 134.571429  11.42857143
4       116 109.031250   6.96875000
5        68  80.428571 -12.42857143
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      140 142.687500  -2.68750000
13      166 161.500000   4.50000000
14       99 109.031250 -10.03125000
15      139 142.687500  -3.68750000
16      130 116.642857  13.35714286
17      181 161.500000  19.50000000
18      116 109.031250   6.96875000
19      116 116.642857  -0.64285714
20       88  87.375000   0.62500000
21      139 142.687500  -3.68750000
22      135 142.687500  -7.68750000
23      108 109.031250  -1.03125000
24       89  95.000000  -6.00000000
25      156 161.500000  -5.50000000
26      129 129.555556  -0.55555556
27      118 120.272727  -2.27272727
28      118 116.642857   1.35714286
29      125 124.777778   0.22222222
30       95  95.000000   0.00000000
31      126 124.777778   1.22222222
32      135 129.555556   5.44444444
33      154 142.687500  11.31250000
34      165 161.500000   3.50000000
35      113 109.031250   3.96875000
36      127 124.777778   2.22222222
37       52  49.950000   2.05000000
38      121 116.642857   4.35714286
39      136 142.687500  -6.68750000
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      124 120.272727   3.72727273
45      115 109.031250   5.96875000
46      128 129.555556  -1.55555556
47       80  76.095238   3.90476190
48       97 109.031250 -12.03125000
49      104 109.031250  -5.03125000
50       59 134.571429 -75.57142857
51      125 129.555556  -4.55555556
52       82  95.000000 -13.00000000
53      149 161.500000 -12.50000000
54      149 142.687500   6.31250000
55      122 124.777778  -2.77777778
56      118 109.031250   8.96875000
57       12   5.615385   6.38461538
58      144 161.500000 -17.50000000
59       67  70.000000  -3.00000000
60       52  49.950000   2.05000000
61      108 109.031250  -1.03125000
62      166 161.500000   4.50000000
63       80  76.095238   3.90476190
64       60  63.052632  -3.05263158
65      107 109.031250  -2.03125000
66      127 129.555556  -2.55555556
67      107 109.031250  -2.03125000
68      146 142.687500   3.31250000
69       84  80.428571   3.57142857
70      141 134.571429   6.42857143
71      123 120.272727   2.72727273
72      111 109.031250   1.96875000
73       98  95.000000   3.00000000
74      105 109.031250  -4.03125000
75      135 142.687500  -7.68750000
76      107 109.031250  -2.03125000
77       85  87.375000  -2.37500000
78      155 161.500000  -6.50000000
79       88  87.375000   0.62500000
80      155 161.500000  -6.50000000
81      104 109.031250  -5.03125000
82      132 129.555556   2.44444444
83      127 124.777778   2.22222222
84      108 109.031250  -1.03125000
85      129 129.555556  -0.55555556
86      116 109.031250   6.96875000
87      122 124.777778  -2.77777778
88       85  87.375000  -2.37500000
89      147 142.687500   4.31250000
90       99  95.000000   4.00000000
91       87 116.642857 -29.64285714
92       28  25.750000   2.25000000
93       90  87.375000   2.62500000
94      109 109.031250  -0.03125000
95       78  76.095238   1.90476190
96      111 109.031250   1.96875000
97      158 134.571429  23.42857143
98      141 142.687500  -1.68750000
99      122 116.642857   5.35714286
100     124 124.777778  -0.77777778
101      93  95.000000  -2.00000000
102     124 120.272727   3.72727273
103     112 109.031250   2.96875000
104     108 109.031250  -1.03125000
105      99 109.031250 -10.03125000
106     117 116.642857   0.35714286
107     199 161.500000  37.50000000
108      78  76.095238   1.90476190
109      91  87.375000   3.62500000
110     158 161.500000  -3.50000000
111     126 124.777778   1.22222222
112     122 116.642857   5.35714286
113      71  76.095238  -5.09523810
114      75  76.095238  -1.09523810
115     115 116.642857  -1.64285714
116     119 116.642857   2.35714286
117     124 120.272727   3.72727273
118      72  70.000000   2.00000000
119      91  87.375000   3.62500000
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     117 120.272727  -3.27272727
126      39  40.375000  -1.37500000
127      50  49.950000   0.05000000
128      88  87.375000   0.62500000
129     155 134.571429  20.42857143
130       0   5.615385  -5.61538462
131      36  40.375000  -4.37500000
132     123 116.642857   6.35714286
133      32  40.375000  -8.37500000
134      99  95.000000   4.00000000
135     136 134.571429   1.42857143
136     117 116.642857   0.35714286
137       0   5.615385  -5.61538462
138      88  80.428571   7.57142857
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     124 124.777778  -0.77777778
145     151 161.500000 -10.50000000
146      71  80.428571  -9.42857143
147     145 142.687500   2.31250000
148      87  87.375000  -0.37500000
149      27  25.750000   1.25000000
150     131 129.555556   1.44444444
151     162 161.500000   0.50000000
152     165 161.500000   3.50000000
153      54  49.950000   4.05000000
154     159 161.500000  -2.50000000
155     147 142.687500   4.31250000
156     170 161.500000   8.50000000
157     119 120.272727  -1.27272727
158      49  49.950000  -0.95000000
159     104 109.031250  -5.03125000
160     120 120.272727  -0.27272727
161     150 161.500000 -11.50000000
162     112 109.031250   2.96875000
163      59  63.052632  -4.05263158
164     136 142.687500  -6.68750000
165     107 109.031250  -2.03125000
166     130 129.555556   0.44444444
167     115 116.642857  -1.64285714
168     107 109.031250  -2.03125000
169      75  76.095238  -1.09523810
170      71  70.000000   1.00000000
171     120 120.272727  -0.27272727
172     116 120.272727  -4.27272727
173      79  76.095238   2.90476190
174     150 142.687500   7.31250000
175     156 161.500000  -5.50000000
176      51  49.950000   1.05000000
177     118 120.272727  -2.27272727
178      71  70.000000   1.00000000
179     144 142.687500   1.31250000
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     147 134.571429  12.42857143
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     111 116.642857  -5.64285714
191      85  80.428571   4.57142857
192      68  63.052632   4.94736842
193      40  40.375000  -0.37500000
194      80  87.375000  -7.37500000
195      88  87.375000   0.62500000
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  80.428571  -4.42857143
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  87.375000   0.62500000
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  80.428571  10.57142857
216      88  87.375000   0.62500000
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  87.375000   0.62500000
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  87.375000   2.62500000
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  87.375000  -4.37500000
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/4jv2e1354985989.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/583ad1354985989.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/62wye1354985989.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/7w39u1354985989.tab") 
+ }
> 
> try(system("convert tmp/2esfk1354985989.ps tmp/2esfk1354985989.png",intern=TRUE))
character(0)
> try(system("convert tmp/31fpj1354985989.ps tmp/31fpj1354985989.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jv2e1354985989.ps tmp/4jv2e1354985989.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 12.698   0.580  13.591