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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+ ,5
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+ ,0
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+ ,0
+ ,55
+ ,0)
+ ,dim=c(11
+ ,269)
+ ,dimnames=list(c('hours'
+ ,'lfm'
+ ,'blogs'
+ ,'uk'
+ ,'spr'
+ ,'hours_blogs'
+ ,'hours_uk'
+ ,'hours_spr'
+ ,'blogs_uk'
+ ,'blogs_spr'
+ ,'uk_spr')
+ ,1:269))
>  y <- array(NA,dim=c(11,269),dimnames=list(c('hours','lfm','blogs','uk','spr','hours_blogs','hours_uk','hours_spr','blogs_uk','blogs_spr','uk_spr'),1:269))
>  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 = '2'
> par4 <- 'no'
> par3 <- '3'
> par2 <- 'none'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/
> #Source of accompanying publication: 
> #
> 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] "lfm"
> x[,par1]
  [1] 122 114 140 143 122 127 113 118 161 134  96 104 135 110 128 142 117  94
 [19] 135 121 103 118 127 116 129 115 135 133 113 111  92 118 134 106 137 100
 [37] 102 134 130 144 120  91 100 134 161 128 124 115 123 117 111 146 101 131
 [55] 122  78 120 115 142  94 114 108 119 117  86 138 119 117 117  76 119 119
 [73] 124 116 118 102 116 103 117 108 122  90 133 116 110  90  74  75 107  90
 [91]  96 115  91  77 108  83  77  99 115  99 106  77 115  67   8  69  88 107
[109] 120   3   1   0 111  69 116 103 139 135 113  99  76 110 121  95  66 111
[127]  77 101 108 135  70 124  92 104 113  95  89  83  96  95 110 106  78 115
[145]  74  93  88 104  86 104  99 101  53  96  58 117  82  57  71 105  60  77
[163]  73  78  81 101 118  59 101  22  77 100  39  42  80  48 131  46  89  51
[181] 108  86 105  85 103  83  77  26  73  42  71 105  73  98 108  57  37  70
[199]  73  47  73  91 110  78  92  52  88 100  33  42  81  67   8  46  83  87
[217]  82  63  27  14  83 168  67  21  55  54 118  69  77  72  53  40 102  25
[235]  31  77  38  23  91  58  42  44  58  35  88  25  39  48  64  65  95  29
[253]   2  83  11  16   9  46  41  14  63   9   0  58  18  42  26  38   1
> 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   8   9  11  14  16  18  21  22  23  25  26  27  29  31  33  35 
  2   2   1   1   2   2   1   2   1   1   1   1   1   2   2   1   1   1   1   1 
 37  38  39  40  41  42  44  46  47  48  51  52  53  54  55  57  58  59  60  63 
  1   2   2   1   1   5   1   3   1   2   1   1   2   1   1   2   4   1   1   2 
 64  65  66  67  69  70  71  72  73  74  75  76  77  78  80  81  82  83  85  86 
  1   1   1   3   3   2   2   1   5   2   1   2   9   4   1   2   2   6   1   3 
 87  88  89  90  91  92  93  94  95  96  98  99 100 101 102 103 104 105 106 107 
  1   4   2   3   4   3   1   2   4   4   1   4   4   5   3   4   4   3   3   2 
108 110 111 113 114 115 116 117 118 119 120 121 122 123 124 127 128 129 130 131 
  6   5   4   4   2   7   5   7   6   4   3   2   4   1   3   2   2   1   1   2 
133 134 135 137 138 139 140 142 143 144 146 161 168 
  2   4   5   1   1   1   1   2   1   1   1   2   1 
> colnames(x)
 [1] "hours"       "lfm"         "blogs"       "uk"          "spr"        
 [6] "hours_blogs" "hours_uk"    "hours_spr"   "blogs_uk"    "blogs_spr"  
[11] "uk_spr"     
> colnames(x)[par1]
[1] "lfm"
> x[,par1]
  [1] 122 114 140 143 122 127 113 118 161 134  96 104 135 110 128 142 117  94
 [19] 135 121 103 118 127 116 129 115 135 133 113 111  92 118 134 106 137 100
 [37] 102 134 130 144 120  91 100 134 161 128 124 115 123 117 111 146 101 131
 [55] 122  78 120 115 142  94 114 108 119 117  86 138 119 117 117  76 119 119
 [73] 124 116 118 102 116 103 117 108 122  90 133 116 110  90  74  75 107  90
 [91]  96 115  91  77 108  83  77  99 115  99 106  77 115  67   8  69  88 107
[109] 120   3   1   0 111  69 116 103 139 135 113  99  76 110 121  95  66 111
[127]  77 101 108 135  70 124  92 104 113  95  89  83  96  95 110 106  78 115
[145]  74  93  88 104  86 104  99 101  53  96  58 117  82  57  71 105  60  77
[163]  73  78  81 101 118  59 101  22  77 100  39  42  80  48 131  46  89  51
[181] 108  86 105  85 103  83  77  26  73  42  71 105  73  98 108  57  37  70
[199]  73  47  73  91 110  78  92  52  88 100  33  42  81  67   8  46  83  87
[217]  82  63  27  14  83 168  67  21  55  54 118  69  77  72  53  40 102  25
[235]  31  77  38  23  91  58  42  44  58  35  88  25  39  48  64  65  95  29
[253]   2  83  11  16   9  46  41  14  63   9   0  58  18  42  26  38   1
> 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/14dgi1355754641.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  lfm 
Inputs:  hours, blogs, uk, spr, hours_blogs, hours_uk, hours_spr, blogs_uk, blogs_spr, uk_spr 
Number of observations:  269 

1) hours_uk <= 19; criterion = 1, statistic = 87.81
  2) blogs <= 60; criterion = 1, statistic = 48.598
    3) hours <= 13; criterion = 1, statistic = 22.325
      4)*  weights = 18 
    3) hours > 13
      5)*  weights = 81 
  2) blogs > 60
    6) hours <= 39; criterion = 0.955, statistic = 8.025
      7)*  weights = 34 
    6) hours > 39
      8)*  weights = 33 
1) hours_uk > 19
  9) hours <= 27; criterion = 1, statistic = 16.848
    10)*  weights = 19 
  9) hours > 27
    11)*  weights = 84 
> postscript(file="/var/wessaorg/rcomp/tmp/2rpcy1355754641.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/3au911355754641.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       122 118.57143   3.4285714
2       114 118.57143  -4.5714286
3       140 118.57143  21.4285714
4       143 118.57143  24.4285714
5       122 118.57143   3.4285714
6       127 118.57143   8.4285714
7       113 118.57143  -5.5714286
8       118 118.57143  -0.5714286
9       161 118.57143  42.4285714
10      134 118.57143  15.4285714
11       96 118.57143 -22.5714286
12      104 118.57143 -14.5714286
13      135 118.57143  16.4285714
14      110 118.57143  -8.5714286
15      128 118.57143   9.4285714
16      142 118.57143  23.4285714
17      117 118.57143  -1.5714286
18       94 118.57143 -24.5714286
19      135 118.57143  16.4285714
20      121 118.57143   2.4285714
21      103 118.57143 -15.5714286
22      118 118.57143  -0.5714286
23      127 118.57143   8.4285714
24      116 118.57143  -2.5714286
25      129 118.57143  10.4285714
26      115 118.57143  -3.5714286
27      135 118.57143  16.4285714
28      133 118.57143  14.4285714
29      113 118.57143  -5.5714286
30      111 118.57143  -7.5714286
31       92 118.57143 -26.5714286
32      118 118.57143  -0.5714286
33      134 118.57143  15.4285714
34      106 118.57143 -12.5714286
35      137 118.57143  18.4285714
36      100 118.57143 -18.5714286
37      102 118.57143 -16.5714286
38      134 118.57143  15.4285714
39      130 118.57143  11.4285714
40      144 118.57143  25.4285714
41      120 118.57143   1.4285714
42       91 118.57143 -27.5714286
43      100 118.57143 -18.5714286
44      134 118.57143  15.4285714
45      161 118.57143  42.4285714
46      128 118.57143   9.4285714
47      124 118.57143   5.4285714
48      115 118.57143  -3.5714286
49      123 118.57143   4.4285714
50      117 118.57143  -1.5714286
51      111 118.57143  -7.5714286
52      146 118.57143  27.4285714
53      101 118.57143 -17.5714286
54      131 118.57143  12.4285714
55      122 118.57143   3.4285714
56       78 118.57143 -40.5714286
57      120 118.57143   1.4285714
58      115 118.57143  -3.5714286
59      142 118.57143  23.4285714
60       94 118.57143 -24.5714286
61      114 118.57143  -4.5714286
62      108 118.57143 -10.5714286
63      119 118.57143   0.4285714
64      117 118.57143  -1.5714286
65       86 118.57143 -32.5714286
66      138 118.57143  19.4285714
67      119 118.57143   0.4285714
68      117 118.57143  -1.5714286
69      117 118.57143  -1.5714286
70       76 118.57143 -42.5714286
71      119 118.57143   0.4285714
72      119 118.57143   0.4285714
73      124 118.57143   5.4285714
74      116 118.57143  -2.5714286
75      118 118.57143  -0.5714286
76      102 118.57143 -16.5714286
77      116 118.57143  -2.5714286
78      103 118.57143 -15.5714286
79      117 118.57143  -1.5714286
80      108 118.57143 -10.5714286
81      122 118.57143   3.4285714
82       90 118.57143 -28.5714286
83      133 118.57143  14.4285714
84      116 118.57143  -2.5714286
85      110  94.94737  15.0526316
86       90  94.94737  -4.9473684
87       74  94.94737 -20.9473684
88       75  94.94737 -19.9473684
89      107  94.94737  12.0526316
90       90  94.94737  -4.9473684
91       96  94.94737   1.0526316
92      115  94.94737  20.0526316
93       91  94.94737  -3.9473684
94       77  94.94737 -17.9473684
95      108  94.94737  13.0526316
96       83  94.94737 -11.9473684
97       77  94.94737 -17.9473684
98       99  94.94737   4.0526316
99      115  94.94737  20.0526316
100      99  94.94737   4.0526316
101     106  94.94737  11.0526316
102      77  94.94737 -17.9473684
103     115  94.94737  20.0526316
104      67  65.74074   1.2592593
105       8  65.74074 -57.7407407
106      69  65.74074   3.2592593
107      88  65.74074  22.2592593
108     107  65.74074  41.2592593
109     120  65.74074  54.2592593
110       3  22.00000 -19.0000000
111       1  22.00000 -21.0000000
112       0  22.00000 -22.0000000
113     111 102.51515   8.4848485
114      69 102.51515 -33.5151515
115     116 102.51515  13.4848485
116     103 102.51515   0.4848485
117     139 102.51515  36.4848485
118     135 102.51515  32.4848485
119     113 102.51515  10.4848485
120      99  65.74074  33.2592593
121      76 102.51515 -26.5151515
122     110 102.51515   7.4848485
123     121 102.51515  18.4848485
124      95 102.51515  -7.5151515
125      66 102.51515 -36.5151515
126     111 102.51515   8.4848485
127      77  65.74074  11.2592593
128     101 102.51515  -1.5151515
129     108 102.51515   5.4848485
130     135 102.51515  32.4848485
131      70 102.51515 -32.5151515
132     124 102.51515  21.4848485
133      92 102.51515 -10.5151515
134     104 102.51515   1.4848485
135     113 102.51515  10.4848485
136      95 102.51515  -7.5151515
137      89 102.51515 -13.5151515
138      83 102.51515 -19.5151515
139      96 102.51515  -6.5151515
140      95 102.51515  -7.5151515
141     110 102.51515   7.4848485
142     106 102.51515   3.4848485
143      78  65.74074  12.2592593
144     115 102.51515  12.4848485
145      74  65.74074   8.2592593
146      93 102.51515  -9.5151515
147      88  65.74074  22.2592593
148     104 102.51515   1.4848485
149      86 102.51515 -16.5151515
150     104  65.74074  38.2592593
151      99 102.51515  -3.5151515
152     101  80.88235  20.1176471
153      53  80.88235 -27.8823529
154      96  80.88235  15.1176471
155      58  65.74074  -7.7407407
156     117  80.88235  36.1176471
157      82  80.88235   1.1176471
158      57  80.88235 -23.8823529
159      71  80.88235  -9.8823529
160     105  80.88235  24.1176471
161      60  80.88235 -20.8823529
162      77  80.88235  -3.8823529
163      73  65.74074   7.2592593
164      78  65.74074  12.2592593
165      81  80.88235   0.1176471
166     101  65.74074  35.2592593
167     118  80.88235  37.1176471
168      59  65.74074  -6.7407407
169     101  65.74074  35.2592593
170      22  80.88235 -58.8823529
171      77  65.74074  11.2592593
172     100  65.74074  34.2592593
173      39  65.74074 -26.7407407
174      42  65.74074 -23.7407407
175      80  80.88235  -0.8823529
176      48  65.74074 -17.7407407
177     131  80.88235  50.1176471
178      46  80.88235 -34.8823529
179      89  65.74074  23.2592593
180      51  65.74074 -14.7407407
181     108  80.88235  27.1176471
182      86  65.74074  20.2592593
183     105  80.88235  24.1176471
184      85  80.88235   4.1176471
185     103  65.74074  37.2592593
186      83  65.74074  17.2592593
187      77  65.74074  11.2592593
188      26  65.74074 -39.7407407
189      73  65.74074   7.2592593
190      42  65.74074 -23.7407407
191      71  65.74074   5.2592593
192     105  65.74074  39.2592593
193      73  65.74074   7.2592593
194      98  80.88235  17.1176471
195     108  80.88235  27.1176471
196      57  80.88235 -23.8823529
197      37  65.74074 -28.7407407
198      70  65.74074   4.2592593
199      73  65.74074   7.2592593
200      47  65.74074 -18.7407407
201      73  65.74074   7.2592593
202      91  80.88235  10.1176471
203     110  80.88235  29.1176471
204      78  80.88235  -2.8823529
205      92  80.88235  11.1176471
206      52  80.88235 -28.8823529
207      88  65.74074  22.2592593
208     100  65.74074  34.2592593
209      33  65.74074 -32.7407407
210      42  80.88235 -38.8823529
211      81  80.88235   0.1176471
212      67  65.74074   1.2592593
213       8  65.74074 -57.7407407
214      46  65.74074 -19.7407407
215      83  80.88235   2.1176471
216      87  65.74074  21.2592593
217      82  80.88235   1.1176471
218      63  65.74074  -2.7407407
219      27  65.74074 -38.7407407
220      14  65.74074 -51.7407407
221      83  65.74074  17.2592593
222     168  65.74074 102.2592593
223      67  65.74074   1.2592593
224      21  65.74074 -44.7407407
225      55  80.88235 -25.8823529
226      54  80.88235 -26.8823529
227     118  65.74074  52.2592593
228      69  65.74074   3.2592593
229      77  65.74074  11.2592593
230      72  80.88235  -8.8823529
231      53  65.74074 -12.7407407
232      40  65.74074 -25.7407407
233     102  65.74074  36.2592593
234      25  65.74074 -40.7407407
235      31  65.74074 -34.7407407
236      77  65.74074  11.2592593
237      38  65.74074 -27.7407407
238      23  65.74074 -42.7407407
239      91  65.74074  25.2592593
240      58  65.74074  -7.7407407
241      42  65.74074 -23.7407407
242      44  65.74074 -21.7407407
243      58  65.74074  -7.7407407
244      35  65.74074 -30.7407407
245      88  65.74074  22.2592593
246      25  65.74074 -40.7407407
247      39  65.74074 -26.7407407
248      48  65.74074 -17.7407407
249      64  65.74074  -1.7407407
250      65  65.74074  -0.7407407
251      95  65.74074  29.2592593
252      29  65.74074 -36.7407407
253       2  65.74074 -63.7407407
254      83  65.74074  17.2592593
255      11  22.00000 -11.0000000
256      16  22.00000  -6.0000000
257       9  22.00000 -13.0000000
258      46  22.00000  24.0000000
259      41  22.00000  19.0000000
260      14  22.00000  -8.0000000
261      63  22.00000  41.0000000
262       9  22.00000 -13.0000000
263       0  22.00000 -22.0000000
264      58  22.00000  36.0000000
265      18  22.00000  -4.0000000
266      42  22.00000  20.0000000
267      26  22.00000   4.0000000
268      38  22.00000  16.0000000
269       1  22.00000 -21.0000000
> 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/4ldeb1355754641.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/5gxsk1355754641.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/60mpm1355754641.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/7y7ca1355754641.tab") 
+ }
> 
> try(system("convert tmp/2rpcy1355754641.ps tmp/2rpcy1355754641.png",intern=TRUE))
character(0)
> try(system("convert tmp/3au911355754641.ps tmp/3au911355754641.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ldeb1355754641.ps tmp/4ldeb1355754641.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  9.430   0.669  11.271