R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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+ ,0
+ ,17
+ ,16
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+ ,39)
+ ,dim=c(10
+ ,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')
+ ,1:289))
>  y <- array(NA,dim=c(10,289),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'),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 = '10'
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo
Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)

Attaching package: 'Hmisc'

The following object(s) are masked from 'package:survival':

    untangle.specials

The following object(s) are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  38  93  60 123 148  90 124  70 168 115
 [19]  71  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124
 [37]  39  92 126   0  70  37  38 120  93  95  77  90  80  31 110  66 138 133
 [55] 113 100   7 140  61  41  96 164  78  49 102 124  99 129  62  73 114  99
 [73]  70 104 116  91  74 138  67 151  72 120 115 105 104 108  98  69 111  99
 [91]  71  27  69 107  73 107  93 129  69 118  73 119 104 107  99  90 197  36
[109]  85 139 106  50  64  31  63  92 106  63  69  41  56  25  65  93 114  38
[127]  44  87 110   0  27  83  30  80  98  82   0  60  28   9  33  59  49 115
[145] 140  49 120  66  21 124 152 139  38 144 120 160 114  39  78 119 141 101
[163]  56 133  83 116  90  36  50  61  97  98  78 117 148  41 105  55 132  44
[181]  21  50   0  73  86   0  13   4  57  48  46  48  32  68  87  43  67  46
[199]  46  56  48  44  60  65  55  38  52  60  54  86  24  52  49  61  61  81
[217]  43  40  40  56  68  79  47  57  41  29   3  60  30  79  47  40  48  36
[235]  42  49  57  12  40  43  33  77  43  45  47  43  45  50  35   7  71  67
[253]   0  62  54   4  25  40  38  19  17  67  14  30  54  35  59  24  58  42
[271]  46  61   3  52  25  40  32   4  49  63  67  32  23   7  54  37  35  51
[289]  39
> 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   3   4   7   9  12  13  14  17  19  21  22  23  24  25  27  28  29  30  31 
  6   2   3   3   1   1   1   1   1   1   2   1   1   2   3   2   1   1   3   2 
 32  33  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52 
  3   2   3   3   2   6   3   6   4   2   5   3   2   4   3   4   6   4   2   3 
 54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73 
  4   2   4   3   1   2   5   5   2   3   1   2   3   5   2   4   4   3   1   4 
 74  77  78  79  80  81  82  83  84  85  86  87  90  91  92  93  94  95  96  97 
  1   2   3   2   2   2   1   2   1   1   2   2   4   2   2   5   2   1   1   1 
 98  99 100 101 102 103 104 105 106 107 108 109 110 111 113 114 115 116 117 118 
  3   4   1   1   1   2   3   2   2   3   2   1   3   1   1   4   3   2   2   1 
119 120 122 123 124 126 129 132 133 134 138 139 140 141 144 148 151 152 156 158 
  2   6   1   1   4   1   2   1   3   1   2   2   2   1   1   2   1   1   1   1 
160 164 168 197 
  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"
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  38  93  60 123 148  90 124  70 168 115
 [19]  71  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124
 [37]  39  92 126   0  70  37  38 120  93  95  77  90  80  31 110  66 138 133
 [55] 113 100   7 140  61  41  96 164  78  49 102 124  99 129  62  73 114  99
 [73]  70 104 116  91  74 138  67 151  72 120 115 105 104 108  98  69 111  99
 [91]  71  27  69 107  73 107  93 129  69 118  73 119 104 107  99  90 197  36
[109]  85 139 106  50  64  31  63  92 106  63  69  41  56  25  65  93 114  38
[127]  44  87 110   0  27  83  30  80  98  82   0  60  28   9  33  59  49 115
[145] 140  49 120  66  21 124 152 139  38 144 120 160 114  39  78 119 141 101
[163]  56 133  83 116  90  36  50  61  97  98  78 117 148  41 105  55 132  44
[181]  21  50   0  73  86   0  13   4  57  48  46  48  32  68  87  43  67  46
[199]  46  56  48  44  60  65  55  38  52  60  54  86  24  52  49  61  61  81
[217]  43  40  40  56  68  79  47  57  41  29   3  60  30  79  47  40  48  36
[235]  42  49  57  12  40  43  33  77  43  45  47  43  45  50  35   7  71  67
[253]   0  62  54   4  25  40  38  19  17  67  14  30  54  35  59  24  58  42
[271]  46  61   3  52  25  40  32   4  49  63  67  32  23   7  54  37  35  51
[289]  39
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
> 
> if (par2 != 'none') {
+ m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
+ if (par4=='yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ a<-table.element(a,'Prediction (training)',par3+1,TRUE)
+ a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Actual',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ a<-table.row.end(a)
+ for (i in 1:10) {
+ ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
+ m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
+ if (i==1) {
+ m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
+ m.ct.i.actu <- x[ind==1,par1]
+ m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
+ m.ct.x.actu <- x[ind==2,par1]
+ } else {
+ m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
+ m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
+ m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
+ m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
+ }
+ }
+ print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
+ numer <- numer + m.ct.i.tab[i,i]
+ }
+ print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
+ print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
+ numer <- numer + m.ct.x.tab[i,i]
+ }
+ print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
+ a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
+ for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
+ a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
+ a<-table.row.end(a)
+ }
+ a<-table.row.start(a)
+ a<-table.element(a,'Overall',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.i.cp,4))
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.x.cp,4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/1rhpe1323874710.tab") 
+ }
+ }
> m

	 Conditional inference tree with 9 terminal nodes

Response:  feedback_messages_p120 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p1 
Number of observations:  289 

1) feedback_messages_p1 <= 93; criterion = 1, statistic = 254.277
  2) feedback_messages_p1 <= 43; criterion = 1, statistic = 130.974
    3) feedback_messages_p1 <= 25; criterion = 1, statistic = 28.685
      4)*  weights = 17 
    3) feedback_messages_p1 > 25
      5)*  weights = 18 
  2) feedback_messages_p1 > 43
    6) feedback_messages_p1 <= 78; criterion = 1, statistic = 66.371
      7) feedback_messages_p1 <= 59; criterion = 1, statistic = 18.857
        8)*  weights = 35 
      7) feedback_messages_p1 > 59
        9) time_in_rfc <= 52164; criterion = 0.98, statistic = 9.341
          10)*  weights = 11 
        9) time_in_rfc > 52164
          11)*  weights = 57 
    6) feedback_messages_p1 > 78
      12)*  weights = 30 
1) feedback_messages_p1 > 93
  13) feedback_messages_p1 <= 123; criterion = 1, statistic = 69.748
    14)*  weights = 61 
  13) feedback_messages_p1 > 123
    15) feedback_messages_p1 <= 158; criterion = 1, statistic = 27.701
      16)*  weights = 51 
    15) feedback_messages_p1 > 158
      17)*  weights = 9 
> postscript(file="/var/www/rcomp/tmp/2gztp1323874710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/3gi1r1323874710.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        94  92.016393   1.98360656
2       103  92.016393  10.98360656
3        93 119.588235 -26.58823529
4       103  92.016393  10.98360656
5        51  53.438596  -2.43859649
6        70  92.016393 -22.01639344
7        91  92.016393  -1.01639344
8        22  38.818182 -16.81818182
9        38  40.428571  -2.42857143
10       93  92.016393   0.98360656
11       60  72.100000 -12.10000000
12      123 119.588235   3.41176471
13      148 158.888889 -10.88888889
14       90  92.016393  -2.01639344
15      124 119.588235   4.41176471
16       70 119.588235 -49.58823529
17      168 158.888889   9.11111111
18      115  92.016393  22.98360656
19       71  92.016393 -21.01639344
20       66  72.100000  -6.10000000
21      134 119.588235  14.41176471
22      117 119.588235  -2.58823529
23      108  92.016393  15.98360656
24       84  72.100000  11.90000000
25      156 119.588235  36.41176471
26      120 119.588235   0.41176471
27      114  92.016393  21.98360656
28       94  92.016393   1.98360656
29      120 119.588235   0.41176471
30       81  92.016393 -11.01639344
31      110 119.588235  -9.58823529
32      133 119.588235  13.41176471
33      122 119.588235   2.41176471
34      158 158.888889  -0.88888889
35      109  92.016393  16.98360656
36      124 119.588235   4.41176471
37       39  40.428571  -1.42857143
38       92  92.016393  -0.01639344
39      126 119.588235   6.41176471
40        0   4.411765  -4.41176471
41       70  92.016393 -22.01639344
42       37  40.428571  -3.42857143
43       38  40.428571  -2.42857143
44      120 119.588235   0.41176471
45       93  92.016393   0.98360656
46       95 119.588235 -24.58823529
47       77  72.100000   4.90000000
48       90  92.016393  -2.01639344
49       80  92.016393 -12.01639344
50       31  40.428571  -9.42857143
51      110 119.588235  -9.58823529
52       66  72.100000  -6.10000000
53      138 119.588235  18.41176471
54      133 119.588235  13.41176471
55      113  92.016393  20.98360656
56      100  92.016393   7.98360656
57        7   4.411765   2.58823529
58      140 119.588235  20.41176471
59       61  53.438596   7.56140351
60       41  40.428571   0.57142857
61       96  92.016393   3.98360656
62      164 158.888889   5.11111111
63       78  72.100000   5.90000000
64       49  53.438596  -4.43859649
65      102  92.016393   9.98360656
66      124 119.588235   4.41176471
67       99  92.016393   6.98360656
68      129 119.588235   9.41176471
69       62  72.100000 -10.10000000
70       73 119.588235 -46.58823529
71      114  92.016393  21.98360656
72       99  92.016393   6.98360656
73       70  92.016393 -22.01639344
74      104  92.016393  11.98360656
75      116 119.588235  -3.58823529
76       91  92.016393  -1.01639344
77       74  72.100000   1.90000000
78      138 119.588235  18.41176471
79       67  72.100000  -5.10000000
80      151 119.588235  31.41176471
81       72  92.016393 -20.01639344
82      120 119.588235   0.41176471
83      115 119.588235  -4.58823529
84      105  92.016393  12.98360656
85      104 119.588235 -15.58823529
86      108  92.016393  15.98360656
87       98  92.016393   5.98360656
88       69  72.100000  -3.10000000
89      111 119.588235  -8.58823529
90       99  92.016393   6.98360656
91       71  72.100000  -1.10000000
92       27  27.000000   0.00000000
93       69  72.100000  -3.10000000
94      107  92.016393  14.98360656
95       73  53.438596  19.56140351
96      107  92.016393  14.98360656
97       93 119.588235 -26.58823529
98      129 119.588235   9.41176471
99       69  92.016393 -23.01639344
100     118 119.588235  -1.58823529
101      73  72.100000   0.90000000
102     119 119.588235  -0.58823529
103     104  92.016393  11.98360656
104     107  92.016393  14.98360656
105      99  92.016393   6.98360656
106      90  92.016393  -2.01639344
107     197 158.888889  38.11111111
108      36  53.438596 -17.43859649
109      85  72.100000  12.90000000
110     139 119.588235  19.41176471
111     106 119.588235 -13.58823529
112      50  92.016393 -42.01639344
113      64  38.818182  25.18181818
114      31  53.438596 -22.43859649
115      63  92.016393 -29.01639344
116      92  92.016393  -0.01639344
117     106 119.588235 -13.58823529
118      63  53.438596   9.56140351
119      69  72.100000  -3.10000000
120      41  40.428571   0.57142857
121      56  53.438596   2.56140351
122      25  27.000000  -2.00000000
123      65  53.438596  11.56140351
124      93  92.016393   0.98360656
125     114  92.016393  21.98360656
126      38  27.000000  11.00000000
127      44  40.428571   3.57142857
128      87  72.100000  14.90000000
129     110 119.588235  -9.58823529
130       0   4.411765  -4.41176471
131      27  27.000000   0.00000000
132      83  92.016393  -9.01639344
133      30  27.000000   3.00000000
134      80  92.016393 -12.01639344
135      98 119.588235 -21.58823529
136      82  92.016393 -10.01639344
137       0   4.411765  -4.41176471
138      60  72.100000 -12.10000000
139      28  27.000000   1.00000000
140       9   4.411765   4.58823529
141      33  40.428571  -7.42857143
142      59  53.438596   5.56140351
143      49  53.438596  -4.43859649
144     115 119.588235  -4.58823529
145     140 119.588235  20.41176471
146      49  53.438596  -4.43859649
147     120 119.588235   0.41176471
148      66  72.100000  -6.10000000
149      21  27.000000  -6.00000000
150     124 119.588235   4.41176471
151     152 158.888889  -6.88888889
152     139 158.888889 -19.88888889
153      38  40.428571  -2.42857143
154     144 158.888889 -14.88888889
155     120 119.588235   0.41176471
156     160 158.888889   1.11111111
157     114  92.016393  21.98360656
158      39  40.428571  -1.42857143
159      78  92.016393 -14.01639344
160     119  92.016393  26.98360656
161     141 119.588235  21.41176471
162     101  92.016393   8.98360656
163      56  40.428571  15.57142857
164     133 119.588235  13.41176471
165      83  92.016393  -9.01639344
166     116 119.588235  -3.58823529
167      90  92.016393  -2.01639344
168      36  92.016393 -56.01639344
169      50  53.438596  -3.43859649
170      61  53.438596   7.56140351
171      97  92.016393   4.98360656
172      98  92.016393   5.98360656
173      78  72.100000   5.90000000
174     117 119.588235  -2.58823529
175     148 119.588235  28.41176471
176      41  40.428571   0.57142857
177     105  92.016393  12.98360656
178      55  53.438596   1.56140351
179     132 119.588235  12.41176471
180      44  40.428571   3.57142857
181      21  27.000000  -6.00000000
182      50  53.438596  -3.43859649
183       0   4.411765  -4.41176471
184      73  92.016393 -19.01639344
185      86 119.588235 -33.58823529
186       0   4.411765  -4.41176471
187      13   4.411765   8.58823529
188       4   4.411765  -0.41176471
189      57  53.438596   3.56140351
190      48  92.016393 -44.01639344
191      46  72.100000 -26.10000000
192      48  53.438596  -5.43859649
193      32  27.000000   5.00000000
194      68  72.100000  -4.10000000
195      87  72.100000  14.90000000
196      43  40.428571   2.57142857
197      67  53.438596  13.56140351
198      46  40.428571   5.57142857
199      46  53.438596  -7.43859649
200      56  40.428571  15.57142857
201      48  53.438596  -5.43859649
202      44  38.818182   5.18181818
203      60  53.438596   6.56140351
204      65  53.438596  11.56140351
205      55  53.438596   1.56140351
206      38  38.818182  -0.81818182
207      52  53.438596  -1.43859649
208      60  53.438596   6.56140351
209      54  53.438596   0.56140351
210      86  72.100000  13.90000000
211      24  27.000000  -3.00000000
212      52  53.438596  -1.43859649
213      49  53.438596  -4.43859649
214      61  53.438596   7.56140351
215      61  72.100000 -11.10000000
216      81  72.100000   8.90000000
217      43  40.428571   2.57142857
218      40  40.428571  -0.42857143
219      40  53.438596 -13.43859649
220      56  53.438596   2.56140351
221      68  53.438596  14.56140351
222      79  72.100000   6.90000000
223      47  53.438596  -6.43859649
224      57  53.438596   3.56140351
225      41  38.818182   2.18181818
226      29  40.428571 -11.42857143
227       3   4.411765  -1.41176471
228      60  53.438596   6.56140351
229      30  27.000000   3.00000000
230      79  72.100000   6.90000000
231      47  38.818182   8.18181818
232      40  40.428571  -0.42857143
233      48  40.428571   7.57142857
234      36  38.818182  -2.81818182
235      42  53.438596 -11.43859649
236      49  53.438596  -4.43859649
237      57  53.438596   3.56140351
238      12  38.818182 -26.81818182
239      40  53.438596 -13.43859649
240      43  38.818182   4.18181818
241      33  40.428571  -7.42857143
242      77  72.100000   4.90000000
243      43  38.818182   4.18181818
244      45  40.428571   4.57142857
245      47  40.428571   6.57142857
246      43  53.438596 -10.43859649
247      45  40.428571   4.57142857
248      50  40.428571   9.57142857
249      35  27.000000   8.00000000
250       7   4.411765   2.58823529
251      71  72.100000  -1.10000000
252      67  72.100000  -5.10000000
253       0   4.411765  -4.41176471
254      62  53.438596   8.56140351
255      54  53.438596   0.56140351
256       4   4.411765  -0.41176471
257      25  27.000000  -2.00000000
258      40  53.438596 -13.43859649
259      38  40.428571  -2.42857143
260      19  27.000000  -8.00000000
261      17  40.428571 -23.42857143
262      67  53.438596  13.56140351
263      14   4.411765   9.58823529
264      30  40.428571 -10.42857143
265      54  40.428571  13.57142857
266      35  40.428571  -5.42857143
267      59  53.438596   5.56140351
268      24  27.000000  -3.00000000
269      58  53.438596   4.56140351
270      42  53.438596 -11.43859649
271      46  53.438596  -7.43859649
272      61  53.438596   7.56140351
273       3   4.411765  -1.41176471
274      52  53.438596  -1.43859649
275      25  27.000000  -2.00000000
276      40  53.438596 -13.43859649
277      32  40.428571  -8.42857143
278       4   4.411765  -0.41176471
279      49  40.428571   8.57142857
280      63  53.438596   9.56140351
281      67  53.438596  13.56140351
282      32  27.000000   5.00000000
283      23  27.000000  -4.00000000
284       7   4.411765   2.58823529
285      54  53.438596   0.56140351
286      37  38.818182  -1.81818182
287      35  40.428571  -5.42857143
288      51  53.438596  -2.43859649
289      39  53.438596 -14.43859649
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/rcomp/tmp/4cv3h1323874710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if(par2=='none') {
+ op <- par(mfrow=c(2,2))
+ plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
+ plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
+ plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
+ plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$Forecasts,result$Actuals)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goodness of Fit',2,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Correlation',1,TRUE)
+ a<-table.element(a,round(detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'R-squared',1,TRUE)
+ a<-table.element(a,round(detcoef*detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'RMSE',1,TRUE)
+ a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/5t07y1323874710.tab") 
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'#',header=TRUE)
+ a<-table.element(a,'Actuals',header=TRUE)
+ a<-table.element(a,'Forecasts',header=TRUE)
+ a<-table.element(a,'Residuals',header=TRUE)
+ a<-table.row.end(a)
+ for (i in 1:length(result$Actuals)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,result$Actuals[i])
+ a<-table.element(a,result$Forecasts[i])
+ a<-table.element(a,result$Residuals[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/6l4yc1323874710.tab") 
+ }
> if (par2 != 'none') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ for (i in 1:par3) {
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ }
+ a<-table.row.end(a)
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (j in 1:par3) {
+ a<-table.element(a,myt[i,j])
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/7yhg91323874710.tab") 
+ }
> 
> try(system("convert tmp/2gztp1323874710.ps tmp/2gztp1323874710.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gi1r1323874710.ps tmp/3gi1r1323874710.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cv3h1323874710.ps tmp/4cv3h1323874710.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  8.764   0.320   9.631