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 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

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+ ,15
+ ,16
+ ,30884
+ ,757
+ ,62792
+ ,35
+ ,28
+ ,72)
+ ,dim=c(6
+ ,241)
+ ,dimnames=list(c('totsize'
+ ,'pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'tothyperlinks')
+ ,1:241))
>  y <- array(NA,dim=c(6,241),dimnames=list(c('totsize','pageviews','time_in_rfc','logins','blogged_computations','tothyperlinks'),1:241))
>  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 = '5'
> 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] "blogged_computations"
> x[,par1]
  [1]  79  58 121  43 102  82 101  50  81  94  44 107  33  42  56  59  91  27
 [19] 105  67 114  69 105  88  67 124 110 130  93  39  28  28  44 116  12  18
 [37]  32  25 129  59  36 113  47  92  50 111 120 131  45  58   0  21  50  12
 [55]  21   8  37  29  32  35  17  60  69  78  44 158  77  80 123  73 105  47
 [73]  84   0  96  57  39  76  76   8  79 101  94 123  41  72  75  22  73  62
 [91] 118 100  24  46  57 135  33  98  58  68 131  37 118  81  51  40  56  27
[109]  83  59 133  12 106  71   4  62  14  60  98 100  45 136  63  14  41  91
[127]  41  25  29  47  37  26  38  23  30  18  28  12  27  41  26  27  10  10
[145]  17 108  49   0   1  20  86 104  63 115  83 105 114  38  30  71  59 106
[163]  34  20 115  85  21  92  75 128  55  56 118  77  66 116  99  53  30  49
[181]  75  68  81  13  74 109 151  37  54  27   0  23   7  64  29  16  48  46
[199] 130  25  32  95  70  19 135  27  87   4  28  16  22  16  32  23  29  21
[217]  18  13  13  16   2  42   5  37  17  38  17  20   7  46  24  40   3  37
[235]  28  19  29   8  15  15  28
> 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   4   5   7   8  10  12  13  14  15  16  17  18  19  20  21  22 
  4   1   1   1   2   1   2   3   2   4   3   2   2   4   4   3   2   3   4   2 
 23  24  25  26  27  28  29  30  32  33  34  35  36  37  38  39  40  41  42  43 
  3   2   3   2   6   6   5   3   4   2   1   1   1   6   3   2   2   4   2   1 
 44  45  46  47  48  49  50  51  53  54  55  56  57  58  59  60  62  63  64  66 
  3   2   3   3   1   2   3   1   1   1   1   3   2   3   4   2   2   2   1   1 
 67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86 
  2   2   2   1   2   1   2   1   3   2   2   1   2   1   3   1   2   1   1   1 
 87  88  91  92  93  94  95  96  98  99 100 101 102 104 105 106 107 108 109 110 
  1   1   2   2   1   2   1   1   2   1   2   2   1   1   4   2   1   1   1   1 
111 113 114 115 116 118 120 121 123 124 128 129 130 131 133 135 136 151 158 
  1   1   2   2   2   3   1   1   2   1   1   1   2   2   1   2   1   1   1 
> colnames(x)
[1] "totsize"              "pageviews"            "time_in_rfc"         
[4] "logins"               "blogged_computations" "tothyperlinks"       
> colnames(x)[par1]
[1] "blogged_computations"
> x[,par1]
  [1]  79  58 121  43 102  82 101  50  81  94  44 107  33  42  56  59  91  27
 [19] 105  67 114  69 105  88  67 124 110 130  93  39  28  28  44 116  12  18
 [37]  32  25 129  59  36 113  47  92  50 111 120 131  45  58   0  21  50  12
 [55]  21   8  37  29  32  35  17  60  69  78  44 158  77  80 123  73 105  47
 [73]  84   0  96  57  39  76  76   8  79 101  94 123  41  72  75  22  73  62
 [91] 118 100  24  46  57 135  33  98  58  68 131  37 118  81  51  40  56  27
[109]  83  59 133  12 106  71   4  62  14  60  98 100  45 136  63  14  41  91
[127]  41  25  29  47  37  26  38  23  30  18  28  12  27  41  26  27  10  10
[145]  17 108  49   0   1  20  86 104  63 115  83 105 114  38  30  71  59 106
[163]  34  20 115  85  21  92  75 128  55  56 118  77  66 116  99  53  30  49
[181]  75  68  81  13  74 109 151  37  54  27   0  23   7  64  29  16  48  46
[199] 130  25  32  95  70  19 135  27  87   4  28  16  22  16  32  23  29  21
[217]  18  13  13  16   2  42   5  37  17  38  17  20   7  46  24  40   3  37
[235]  28  19  29   8  15  15  28
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/1qfbd1355078171.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  blogged_computations 
Inputs:  totsize, pageviews, time_in_rfc, logins, tothyperlinks 
Number of observations:  241 

1) tothyperlinks <= 79; criterion = 1, statistic = 183.942
  2) tothyperlinks <= 50; criterion = 1, statistic = 83.126
    3) tothyperlinks <= 12; criterion = 1, statistic = 40.766
      4)*  weights = 18 
    3) tothyperlinks > 12
      5) pageviews <= 637; criterion = 1, statistic = 20.443
        6)*  weights = 13 
      5) pageviews > 637
        7) pageviews <= 1241; criterion = 0.984, statistic = 8.688
          8) tothyperlinks <= 28; criterion = 1, statistic = 18.369
            9)*  weights = 24 
          8) tothyperlinks > 28
            10)*  weights = 28 
        7) pageviews > 1241
          11)*  weights = 17 
  2) tothyperlinks > 50
    12) time_in_rfc <= 151101; criterion = 0.992, statistic = 9.989
      13)*  weights = 24 
    12) time_in_rfc > 151101
      14)*  weights = 16 
1) tothyperlinks > 79
  15) tothyperlinks <= 119; criterion = 1, statistic = 27.544
    16)*  weights = 48 
  15) tothyperlinks > 119
    17) pageviews <= 1579; criterion = 0.984, statistic = 8.715
      18)*  weights = 8 
    17) pageviews > 1579
      19)*  weights = 45 
> postscript(file="/var/fisher/rcomp/tmp/2m1vu1355078171.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/fisher/rcomp/tmp/3iln21355078171.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        79  73.625000   5.37500000
2        58  78.000000 -20.00000000
3       121 111.844444   9.15555556
4        43  78.000000 -35.00000000
5       102 111.844444  -9.84444444
6        82  73.625000   8.37500000
7       101 111.844444 -10.84444444
8        50  62.687500 -12.68750000
9        81  78.000000   3.00000000
10       94  78.000000  16.00000000
11       44  73.625000 -29.62500000
12      107 111.844444  -4.84444444
13       33  31.035714   1.96428571
14       42  35.117647   6.88235294
15       56  78.000000 -22.00000000
16       59  62.687500  -3.68750000
17       91 111.844444 -20.84444444
18       27  21.416667   5.58333333
19      105 111.844444  -6.84444444
20       67  78.000000 -11.00000000
21      114 111.844444   2.15555556
22       69  78.000000  -9.00000000
23      105  73.625000  31.37500000
24       88 111.844444 -23.84444444
25       67  78.000000 -11.00000000
26      124 111.844444  12.15555556
27      110 111.844444  -1.84444444
28      130 111.844444  18.15555556
29       93  78.000000  15.00000000
30       39  43.958333  -4.95833333
31       28  43.958333 -15.95833333
32       28  21.416667   6.58333333
33       44  35.117647   8.88235294
34      116 111.844444   4.15555556
35       12  16.076923  -4.07692308
36       18  16.076923   1.92307692
37       32  31.035714   0.96428571
38       25  43.958333 -18.95833333
39      129 111.844444  17.15555556
40       59  62.687500  -3.68750000
41       36  43.958333  -7.95833333
42      113 111.844444   1.15555556
43       47  43.958333   3.04166667
44       92  78.000000  14.00000000
45       50  43.958333   6.04166667
46      111 111.844444  -0.84444444
47      120 111.844444   8.15555556
48      131 111.844444  19.15555556
49       45  35.117647   9.88235294
50       58  35.117647  22.88235294
51        0   6.055556  -6.05555556
52       21  16.076923   4.92307692
53       50  43.958333   6.04166667
54       12  21.416667  -9.41666667
55       21  21.416667  -0.41666667
56        8   6.055556   1.94444444
57       37  31.035714   5.96428571
58       29  31.035714  -2.03571429
59       32  31.035714   0.96428571
60       35  43.958333  -8.95833333
61       17  31.035714 -14.03571429
62       60  78.000000 -18.00000000
63       69  78.000000  -9.00000000
64       78 111.844444 -33.84444444
65       44  43.958333   0.04166667
66      158 111.844444  46.15555556
67       77  78.000000  -1.00000000
68       80  78.000000   2.00000000
69      123 111.844444  11.15555556
70       73  78.000000  -5.00000000
71      105  78.000000  27.00000000
72       47  43.958333   3.04166667
73       84  78.000000   6.00000000
74        0   6.055556  -6.05555556
75       96 111.844444 -15.84444444
76       57  62.687500  -5.68750000
77       39  43.958333  -4.95833333
78       76  62.687500  13.31250000
79       76  78.000000  -2.00000000
80        8   6.055556   1.94444444
81       79 111.844444 -32.84444444
82      101 111.844444 -10.84444444
83       94 111.844444 -17.84444444
84      123 111.844444  11.15555556
85       41  78.000000 -37.00000000
86       72  78.000000  -6.00000000
87       75  78.000000  -3.00000000
88       22  21.416667   0.58333333
89       73  62.687500  10.31250000
90       62  62.687500  -0.68750000
91      118 111.844444   6.15555556
92      100 111.844444 -11.84444444
93       24  35.117647 -11.11764706
94       46  31.035714  14.96428571
95       57  62.687500  -5.68750000
96      135 111.844444  23.15555556
97       33  43.958333 -10.95833333
98       98  78.000000  20.00000000
99       58 111.844444 -53.84444444
100      68  78.000000 -10.00000000
101     131 111.844444  19.15555556
102      37  35.117647   1.88235294
103     118 111.844444   6.15555556
104      81  73.625000   7.37500000
105      51  62.687500 -11.68750000
106      40  35.117647   4.88235294
107      56  43.958333  12.04166667
108      27  21.416667   5.58333333
109      83  43.958333  39.04166667
110      59  43.958333  15.04166667
111     133 111.844444  21.15555556
112      12   6.055556   5.94444444
113     106  78.000000  28.00000000
114      71  73.625000  -2.62500000
115       4   6.055556  -2.05555556
116      62  78.000000 -16.00000000
117      14  21.416667  -7.41666667
118      60  43.958333  16.04166667
119      98  78.000000  20.00000000
120     100 111.844444 -11.84444444
121      45  35.117647   9.88235294
122     136 111.844444  24.15555556
123      63  78.000000 -15.00000000
124      14   6.055556   7.94444444
125      41  62.687500 -21.68750000
126      91  78.000000  13.00000000
127      41  35.117647   5.88235294
128      25  21.416667   3.58333333
129      29  35.117647  -6.11764706
130      47  78.000000 -31.00000000
131      37  31.035714   5.96428571
132      26  31.035714  -5.03571429
133      38  31.035714   6.96428571
134      23  31.035714  -8.03571429
135      30  31.035714  -1.03571429
136      18  31.035714 -13.03571429
137      28  78.000000 -50.00000000
138      12  21.416667  -9.41666667
139      27  35.117647  -8.11764706
140      41  43.958333  -2.95833333
141      26  16.076923   9.92307692
142      27  21.416667   5.58333333
143      10   6.055556   3.94444444
144      10   6.055556   3.94444444
145      17  21.416667  -4.41666667
146     108 111.844444  -3.84444444
147      49  43.958333   5.04166667
148       0  16.076923 -16.07692308
149       1   6.055556  -5.05555556
150      20  21.416667  -1.41666667
151      86  78.000000   8.00000000
152     104 111.844444  -7.84444444
153      63  78.000000 -15.00000000
154     115 111.844444   3.15555556
155      83  78.000000   5.00000000
156     105  78.000000  27.00000000
157     114 111.844444   2.15555556
158      38  21.416667  16.58333333
159      30  31.035714  -1.03571429
160      71  62.687500   8.31250000
161      59  78.000000 -19.00000000
162     106  78.000000  28.00000000
163      34  35.117647  -1.11764706
164      20  21.416667  -1.41666667
165     115 111.844444   3.15555556
166      85  78.000000   7.00000000
167      21  21.416667  -0.41666667
168      92 111.844444 -19.84444444
169      75  62.687500  12.31250000
170     128 111.844444  16.15555556
171      55  62.687500  -7.68750000
172      56  78.000000 -22.00000000
173     118  78.000000  40.00000000
174      77  78.000000  -1.00000000
175      66  78.000000 -12.00000000
176     116  78.000000  38.00000000
177      99  78.000000  21.00000000
178      53  43.958333   9.04166667
179      30  35.117647  -5.11764706
180      49  43.958333   5.04166667
181      75  62.687500  12.31250000
182      68  62.687500   5.31250000
183      81  73.625000   7.37500000
184      13  16.076923  -3.07692308
185      74  62.687500  11.31250000
186     109  78.000000  31.00000000
187     151 111.844444  39.15555556
188      37  43.958333  -6.95833333
189      54  78.000000 -24.00000000
190      27  21.416667   5.58333333
191       0   6.055556  -6.05555556
192      23  16.076923   6.92307692
193       7  21.416667 -14.41666667
194      64  78.000000 -14.00000000
195      29  35.117647  -6.11764706
196      16  16.076923  -0.07692308
197      48  43.958333   4.04166667
198      46  73.625000 -27.62500000
199     130 111.844444  18.15555556
200      25  31.035714  -6.03571429
201      32  31.035714   0.96428571
202      95 111.844444 -16.84444444
203      70  78.000000  -8.00000000
204      19  43.958333 -24.95833333
205     135  78.000000  57.00000000
206      27  35.117647  -8.11764706
207      87 111.844444 -24.84444444
208       4   6.055556  -2.05555556
209      28  35.117647  -7.11764706
210      16  21.416667  -5.41666667
211      22  31.035714  -9.03571429
212      16  16.076923  -0.07692308
213      32  31.035714   0.96428571
214      23  31.035714  -8.03571429
215      29  31.035714  -2.03571429
216      21  31.035714 -10.03571429
217      18  16.076923   1.92307692
218      13  16.076923  -3.07692308
219      13   6.055556   6.94444444
220      16  16.076923  -0.07692308
221       2   6.055556  -4.05555556
222      42  31.035714  10.96428571
223       5   6.055556  -1.05555556
224      37  31.035714   5.96428571
225      17  16.076923   0.92307692
226      38  31.035714   6.96428571
227      17  35.117647 -18.11764706
228      20  21.416667  -1.41666667
229       7   6.055556   0.94444444
230      46  31.035714  14.96428571
231      24  21.416667   2.58333333
232      40  21.416667  18.58333333
233       3   6.055556  -3.05555556
234      37  31.035714   5.96428571
235      28  31.035714  -3.03571429
236      19  21.416667  -2.41666667
237      29  31.035714  -2.03571429
238       8   6.055556   1.94444444
239      15  21.416667  -6.41666667
240      15  21.416667  -6.41666667
241      28  43.958333 -15.95833333
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/fisher/rcomp/tmp/4gcrv1355078171.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/fisher/rcomp/tmp/5rw1u1355078171.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/fisher/rcomp/tmp/6mudc1355078171.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/fisher/rcomp/tmp/7zlzj1355078171.tab") 
+ }
> 
> try(system("convert tmp/2m1vu1355078171.ps tmp/2m1vu1355078171.png",intern=TRUE))
character(0)
> try(system("convert tmp/3iln21355078171.ps tmp/3iln21355078171.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gcrv1355078171.ps tmp/4gcrv1355078171.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  6.453   0.603   7.039