R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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+ ,30
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+ ,29
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+ ,62792
+ ,46
+ ,28
+ ,35
+ ,64
+ ,894
+ ,72535
+ ,44
+ ,17
+ ,14)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('LFM'
+ ,'PV'
+ ,'Time'
+ ,'CV'
+ ,'Blogs'
+ ,'logins')
+ ,1:289))
>  y <- array(NA,dim=c(6,289),dimnames=list(c('LFM','PV','Time','CV','Blogs','logins'),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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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] "LFM"
> 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] "LFM"    "PV"     "Time"   "CV"     "Blogs"  "logins"
> colnames(x)[par1]
[1] "LFM"
> 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/1ig6i1324481123.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  LFM 
Inputs:  PV, Time, CV, Blogs, logins 
Number of observations:  289 

1) Blogs <= 54; criterion = 1, statistic = 169.699
  2) Time <= 43410; criterion = 1, statistic = 64.424
    3) CV <= 34; criterion = 0.996, statistic = 11.044
      4)*  weights = 16 
    3) CV > 34
      5)*  weights = 10 
  2) Time > 43410
    6) Time <= 136084; criterion = 1, statistic = 30.49
      7)*  weights = 127 
    6) Time > 136084
      8)*  weights = 21 
1) Blogs > 54
  9) Blogs <= 74; criterion = 1, statistic = 18.895
    10)*  weights = 35 
  9) Blogs > 74
    11)*  weights = 80 
> postscript(file="/var/wessaorg/rcomp/tmp/2mvmn1324481123.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/3ttae1324481123.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       115 129.75000 -14.7500000
2       109 109.94286  -0.9428571
3       146 109.94286  36.0571429
4       116 129.75000 -13.7500000
5        68  65.21260   2.7874016
6       101  65.21260  35.7874016
7        96 129.75000 -33.7500000
8        67  49.90000  17.1000000
9        44  65.21260 -21.2125984
10      100  99.66667   0.3333333
11       93 109.94286 -16.9428571
12      140 129.75000  10.2500000
13      166 129.75000  36.2500000
14       99  65.21260  33.7874016
15      139 129.75000   9.2500000
16      130 109.94286  20.0571429
17      181 129.75000  51.2500000
18      116 129.75000 -13.7500000
19      116 129.75000 -13.7500000
20       88 129.75000 -41.7500000
21      139 129.75000   9.2500000
22      135 129.75000   5.2500000
23      108 129.75000 -21.7500000
24       89  99.66667 -10.6666667
25      156 129.75000  26.2500000
26      129 129.75000  -0.7500000
27      118 109.94286   8.0571429
28      118 129.75000 -11.7500000
29      125 129.75000  -4.7500000
30       95  65.21260  29.7874016
31      126 129.75000  -3.7500000
32      135 129.75000   5.2500000
33      154  99.66667  54.3333333
34      165 129.75000  35.2500000
35      113  65.21260  47.7874016
36      127 129.75000  -2.7500000
37       52  65.21260 -13.2125984
38      121 109.94286  11.0571429
39      136 129.75000   6.2500000
40        0  11.93750 -11.9375000
41      108 109.94286  -1.9428571
42       46  65.21260 -19.2125984
43       54  65.21260 -11.2125984
44      124 129.75000  -5.7500000
45      115 129.75000 -14.7500000
46      128 109.94286  18.0571429
47       80 109.94286 -29.9428571
48       97 109.94286 -12.9428571
49      104  65.21260  38.7874016
50       59  65.21260  -6.2125984
51      125 129.75000  -4.7500000
52       82  65.21260  16.7874016
53      149 129.75000  19.2500000
54      149 129.75000  19.2500000
55      122 129.75000  -7.7500000
56      118 129.75000 -11.7500000
57       12  11.93750   0.0625000
58      144 129.75000  14.2500000
59       67  65.21260   1.7874016
60       52  65.21260 -13.2125984
61      108 129.75000 -21.7500000
62      166 129.75000  36.2500000
63       80 129.75000 -49.7500000
64       60  65.21260  -5.2125984
65      107 129.75000 -22.7500000
66      127 129.75000  -2.7500000
67      107 129.75000 -22.7500000
68      146 129.75000  16.2500000
69       84 129.75000 -45.7500000
70      141 109.94286  31.0571429
71      123 109.94286  13.0571429
72      111  99.66667  11.3333333
73       98 109.94286 -11.9428571
74      105 109.94286  -4.9428571
75      135 129.75000   5.2500000
76      107 129.75000 -22.7500000
77       85 129.75000 -44.7500000
78      155 129.75000  25.2500000
79       88  65.21260  22.7874016
80      155 109.94286  45.0571429
81      104 109.94286  -5.9428571
82      132 129.75000   2.2500000
83      127 129.75000  -2.7500000
84      108 129.75000 -21.7500000
85      129 109.94286  19.0571429
86      116 129.75000 -13.7500000
87      122 109.94286  12.0571429
88       85  65.21260  19.7874016
89      147 129.75000  17.2500000
90       99  99.66667  -0.6666667
91       87 129.75000 -42.7500000
92       28  65.21260 -37.2125984
93       90  65.21260  24.7874016
94      109 109.94286  -0.9428571
95       78  65.21260  12.7874016
96      111 109.94286   1.0571429
97      158 129.75000  28.2500000
98      141 129.75000  11.2500000
99      122 109.94286  12.0571429
100     124 129.75000  -5.7500000
101      93  99.66667  -6.6666667
102     124 129.75000  -5.7500000
103     112 109.94286   2.0571429
104     108 109.94286  -1.9428571
105      99 129.75000 -30.7500000
106     117 129.75000 -12.7500000
107     199 129.75000  69.2500000
108      78  65.21260  12.7874016
109      91 129.75000 -38.7500000
110     158 129.75000  28.2500000
111     126 129.75000  -3.7500000
112     122  99.66667  22.3333333
113      71  49.90000  21.1000000
114      75 109.94286 -34.9428571
115     115 129.75000 -14.7500000
116     119 129.75000 -10.7500000
117     124 129.75000  -5.7500000
118      72  99.66667 -27.6666667
119      91  65.21260  25.7874016
120      45  65.21260 -20.2125984
121      78 109.94286 -31.9428571
122      39  65.21260 -26.2125984
123      68  65.21260   2.7874016
124     119 129.75000 -10.7500000
125     117  99.66667  17.3333333
126      39  65.21260 -26.2125984
127      50  65.21260 -15.2125984
128      88 109.94286 -21.9428571
129     155 129.75000  25.2500000
130       0  11.93750 -11.9375000
131      36  49.90000 -13.9000000
132     123 129.75000  -6.7500000
133      32  65.21260 -33.2125984
134      99  65.21260  33.7874016
135     136 109.94286  26.0571429
136     117 129.75000 -12.7500000
137       0  11.93750 -11.9375000
138      88 109.94286 -21.9428571
139      39  49.90000 -10.9000000
140      25  11.93750  13.0625000
141      52  65.21260 -13.2125984
142      75 109.94286 -34.9428571
143      71  65.21260   5.7874016
144     124 129.75000  -5.7500000
145     151 109.94286  41.0571429
146      71  65.21260   5.7874016
147     145  65.21260  79.7874016
148      87  65.21260  21.7874016
149      27  49.90000 -22.9000000
150     131  99.66667  31.3333333
151     162 129.75000  32.2500000
152     165  99.66667  65.3333333
153      54  65.21260 -11.2125984
154     159 129.75000  29.2500000
155     147 129.75000  17.2500000
156     170 129.75000  40.2500000
157     119 109.94286   9.0571429
158      49  65.21260 -16.2125984
159     104  65.21260  38.7874016
160     120 109.94286  10.0571429
161     150 129.75000  20.2500000
162     112  65.21260  46.7874016
163      59  65.21260  -6.2125984
164     136 129.75000   6.2500000
165     107  65.21260  41.7874016
166     130 129.75000   0.2500000
167     115 109.94286   5.0571429
168     107  99.66667   7.3333333
169      75  65.21260   9.7874016
170      71  99.66667 -28.6666667
171     120 129.75000  -9.7500000
172     116 129.75000 -13.7500000
173      79  99.66667 -20.6666667
174     150 129.75000  20.2500000
175     156 129.75000  26.2500000
176      51  65.21260 -14.2125984
177     118 129.75000 -11.7500000
178      71  65.21260   5.7874016
179     144 129.75000  14.2500000
180      47  65.21260 -18.2125984
181      28  65.21260 -37.2125984
182      68 109.94286 -41.9428571
183       0  11.93750 -11.9375000
184     110  99.66667  10.3333333
185     147 129.75000  17.2500000
186       0  11.93750 -11.9375000
187      15  65.21260 -50.2125984
188       4  11.93750  -7.9375000
189      64  65.21260  -1.2125984
190     111  65.21260  45.7874016
191      85  99.66667 -14.6666667
192      68  65.21260   2.7874016
193      40  65.21260 -25.2125984
194      80  65.21260  14.7874016
195      88  99.66667 -11.6666667
196      48  65.21260 -17.2125984
197      76  65.21260  10.7874016
198      51  65.21260 -14.2125984
199      67  65.21260   1.7874016
200      59  65.21260  -6.2125984
201      61  65.21260  -4.2125984
202      76  65.21260  10.7874016
203      60  65.21260  -5.2125984
204      68  65.21260   2.7874016
205      71  65.21260   5.7874016
206      76  65.21260  10.7874016
207      62  65.21260  -3.2125984
208      61  99.66667 -38.6666667
209      67  65.21260   1.7874016
210      88  65.21260  22.7874016
211      30  65.21260 -35.2125984
212      64  65.21260  -1.2125984
213      68  65.21260   2.7874016
214      64  65.21260  -1.2125984
215      91  65.21260  25.7874016
216      88  65.21260  22.7874016
217      52  65.21260 -13.2125984
218      49  65.21260 -16.2125984
219      62  65.21260  -3.2125984
220      61  65.21260  -4.2125984
221      76  65.21260  10.7874016
222      88  65.21260  22.7874016
223      66 109.94286 -43.9428571
224      71  65.21260   5.7874016
225      68  49.90000  18.1000000
226      48  65.21260 -17.2125984
227      25  65.21260 -40.2125984
228      68  65.21260   2.7874016
229      41  65.21260 -24.2125984
230      90  65.21260  24.7874016
231      66  49.90000  16.1000000
232      54  65.21260 -11.2125984
233      59  65.21260  -6.2125984
234      60  65.21260  -5.2125984
235      77  65.21260  11.7874016
236      68  65.21260   2.7874016
237      72  99.66667 -27.6666667
238      67  65.21260   1.7874016
239      64  65.21260  -1.2125984
240      63  49.90000  13.1000000
241      59  65.21260  -6.2125984
242      84  99.66667 -15.6666667
243      64  65.21260  -1.2125984
244      56  65.21260  -9.2125984
245      54  65.21260 -11.2125984
246      67  65.21260   1.7874016
247      58  65.21260  -7.2125984
248      59  65.21260  -6.2125984
249      40  65.21260 -25.2125984
250      22  11.93750  10.0625000
251      83  99.66667 -16.6666667
252      81  65.21260  15.7874016
253       2  11.93750  -9.9375000
254      72  65.21260   6.7874016
255      61  65.21260  -4.2125984
256      15  11.93750   3.0625000
257      32  11.93750  20.0625000
258      62  65.21260  -3.2125984
259      58  65.21260  -7.2125984
260      36  11.93750  24.0625000
261      59  49.90000   9.1000000
262      68  65.21260   2.7874016
263      21  11.93750   9.0625000
264      55  65.21260 -10.2125984
265      54  65.21260 -11.2125984
266      55  65.21260 -10.2125984
267      72  65.21260   6.7874016
268      41  65.21260 -24.2125984
269      61  65.21260  -4.2125984
270      67  65.21260   1.7874016
271      76  65.21260  10.7874016
272      64  65.21260  -1.2125984
273       3  49.90000 -46.9000000
274      63  65.21260  -2.2125984
275      40  65.21260 -25.2125984
276      69  65.21260   3.7874016
277      48  65.21260 -17.2125984
278       8  11.93750  -3.9375000
279      52  65.21260 -13.2125984
280      66  65.21260   0.7874016
281      76  65.21260  10.7874016
282      43  65.21260 -22.2125984
283      39  65.21260 -26.2125984
284      14  11.93750   2.0625000
285      61  65.21260  -4.2125984
286      71  65.21260   5.7874016
287      44  65.21260 -21.2125984
288      60  65.21260  -5.2125984
289      64  65.21260  -1.2125984
> 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/4xfma1324481123.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/5tahw1324481123.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/6ekt31324481123.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/7gdbm1324481123.tab") 
+ }
> 
> try(system("convert tmp/2mvmn1324481123.ps tmp/2mvmn1324481123.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ttae1324481123.ps tmp/3ttae1324481123.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xfma1324481123.ps tmp/4xfma1324481123.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.935   0.310   5.297