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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

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+ ,150
+ ,117
+ ,140
+ ,39
+ ,120
+ ,123.969
+ ,1.369
+ ,58.106
+ ,156
+ ,148
+ ,89
+ ,41
+ ,135
+ ,27.142
+ ,1.577
+ ,57.140
+ ,51
+ ,41
+ ,40
+ ,13
+ ,27
+ ,135.400
+ ,2.201
+ ,122.422
+ ,118
+ ,105
+ ,125
+ ,32
+ ,87
+ ,21.399
+ ,961
+ ,25.899
+ ,71
+ ,55
+ ,21
+ ,18
+ ,25
+ ,130.115
+ ,1.900
+ ,139.296
+ ,144
+ ,132
+ ,167
+ ,39
+ ,131
+ ,24.874
+ ,1.254
+ ,52.678
+ ,47
+ ,44
+ ,32
+ ,14
+ ,45
+ ,34.988
+ ,1.335
+ ,23.853
+ ,28
+ ,21
+ ,36
+ ,7
+ ,29
+ ,45.549
+ ,1.597
+ ,17.306
+ ,68
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+ ,13
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+ ,6.023
+ ,207
+ ,7.953
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+ ,0
+ ,5
+ ,0
+ ,4
+ ,64.466
+ ,1.645
+ ,89.455
+ ,110
+ ,73
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+ ,30
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+ ,54.990
+ ,2.429
+ ,147.866
+ ,147
+ ,86
+ ,151
+ ,37
+ ,109
+ ,1.644
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+ ,4.245
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+ ,7
+ ,6.179
+ ,474
+ ,21.509
+ ,15
+ ,13
+ ,13
+ ,5
+ ,12
+ ,3.926
+ ,141
+ ,7.670
+ ,4
+ ,4
+ ,3
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+ ,32.755
+ ,1.639
+ ,66.675
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+ ,57
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+ ,34.777
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+ ,14.336
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+ ,73.224
+ ,1.318
+ ,53.608
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+ ,61
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+ ,27.114
+ ,1.018
+ ,30.059
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+ ,20.760
+ ,1.383
+ ,29.668
+ ,40
+ ,32
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+ ,11
+ ,42
+ ,37.636
+ ,1.314
+ ,22.097
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+ ,20
+ ,24
+ ,7
+ ,65.461
+ ,1.335
+ ,96.841
+ ,88
+ ,87
+ ,82
+ ,22
+ ,54
+ ,30.080
+ ,1.403
+ ,41.907
+ ,48
+ ,43
+ ,90
+ ,12
+ ,54
+ ,24.094
+ ,910
+ ,27.080
+ ,76
+ ,67
+ ,25
+ ,19
+ ,14
+ ,69.008
+ ,616
+ ,35.885
+ ,51
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+ ,60
+ ,13
+ ,16
+ ,54.968
+ ,1.407
+ ,41.247
+ ,67
+ ,46
+ ,61
+ ,17
+ ,33
+ ,46.090
+ ,771
+ ,28.313
+ ,59
+ ,56
+ ,85
+ ,15
+ ,32
+ ,27.507
+ ,766
+ ,36.845
+ ,61
+ ,48
+ ,43
+ ,16
+ ,21
+ ,10.672
+ ,473
+ ,16.548
+ ,76
+ ,44
+ ,25
+ ,24
+ ,15
+ ,34.029
+ ,1.376
+ ,36.134
+ ,60
+ ,60
+ ,41
+ ,15
+ ,38
+ ,46.300
+ ,1.232
+ ,55.764
+ ,68
+ ,65
+ ,26
+ ,17
+ ,22
+ ,24.760
+ ,1.521
+ ,28.910
+ ,71
+ ,55
+ ,38
+ ,18
+ ,28
+ ,18.779
+ ,572
+ ,13.339
+ ,76
+ ,38
+ ,12
+ ,20
+ ,10
+ ,21.280
+ ,1.059
+ ,25.319
+ ,62
+ ,52
+ ,29
+ ,16
+ ,31
+ ,40.662
+ ,1.544
+ ,66.956
+ ,61
+ ,60
+ ,49
+ ,16
+ ,32
+ ,28.987
+ ,1.230
+ ,47.487
+ ,67
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+ ,46
+ ,18
+ ,32
+ ,22.827
+ ,1.206
+ ,52.785
+ ,88
+ ,86
+ ,41
+ ,22
+ ,43
+ ,18.513
+ ,1.205
+ ,44.683
+ ,30
+ ,24
+ ,31
+ ,8
+ ,27
+ ,30.594
+ ,1.255
+ ,35.619
+ ,64
+ ,52
+ ,41
+ ,17
+ ,37
+ ,24.006
+ ,613
+ ,21.920
+ ,68
+ ,49
+ ,26
+ ,18
+ ,20
+ ,27.913
+ ,721
+ ,45.608
+ ,64
+ ,61
+ ,23
+ ,16
+ ,32
+ ,42.744
+ ,1.109
+ ,7.721
+ ,91
+ ,61
+ ,14
+ ,23
+ ,0
+ ,12.934
+ ,740
+ ,20.634
+ ,88
+ ,81
+ ,16
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+ ,5
+ ,22.574
+ ,1.126
+ ,29.788
+ ,52
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+ ,41.385
+ ,728
+ ,31.931
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+ ,21
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+ ,10
+ ,18.653
+ ,689
+ ,37.754
+ ,62
+ ,40
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+ ,16
+ ,27
+ ,18.472
+ ,592
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+ ,61
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+ ,11
+ ,30.976
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+ ,40.557
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+ ,29
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+ ,66
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+ ,68
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+ ,55
+ ,33.747
+ ,705
+ ,43.228
+ ,71
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+ ,23
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+ ,301
+ ,4.103
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+ ,23
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+ ,84
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+ ,13.310
+ ,1.186
+ ,14.063
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+ ,30
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+ ,14
+ ,36
+ ,65.892
+ ,1.451
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+ ,30
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+ ,4.694
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+ ,47
+ ,0
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+ ,14
+ ,37
+ ,51.776
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+ ,64.329
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+ ,742
+ ,21.928
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+ ,16
+ ,38.084
+ ,979
+ ,25.836
+ ,77
+ ,42
+ ,50
+ ,21
+ ,26
+ ,27.717
+ ,675
+ ,22.779
+ ,68
+ ,49
+ ,30
+ ,18
+ ,38
+ ,32.928
+ ,1.241
+ ,40.820
+ ,72
+ ,57
+ ,30
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+ ,23
+ ,11.342
+ ,676
+ ,27.530
+ ,67
+ ,12
+ ,34
+ ,17
+ ,22
+ ,19.499
+ ,1.049
+ ,32.378
+ ,64
+ ,40
+ ,33
+ ,17
+ ,30
+ ,16.380
+ ,620
+ ,10.824
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+ ,43
+ ,34
+ ,16
+ ,16
+ ,36.874
+ ,1.081
+ ,39.613
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+ ,18
+ ,48.259
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+ ,41
+ ,17
+ ,29
+ ,45.833
+ ,1.656
+ ,48.231
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+ ,45
+ ,51
+ ,15
+ ,50
+ ,29.156
+ ,705
+ ,39.725
+ ,59
+ ,50
+ ,19
+ ,15
+ ,12
+ ,35.944
+ ,945
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+ ,35
+ ,37
+ ,10
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+ ,36.278
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+ ,45.097
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+ ,4
+ ,8
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+ ,3.738
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+ ,7
+ ,5
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+ ,20
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+ ,3
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+ ,1
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+ ,2
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+ ,20.055
+ ,733
+ ,27.184
+ ,66
+ ,63
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+ ,17
+ ,17
+ ,25.272
+ ,888
+ ,21.610
+ ,76
+ ,67
+ ,32
+ ,19
+ ,28
+ ,82.206
+ ,849
+ ,20.484
+ ,43
+ ,32
+ ,20
+ ,14
+ ,19
+ ,32.073
+ ,1.182
+ ,20.156
+ ,39
+ ,23
+ ,34
+ ,11
+ ,29
+ ,5.444
+ ,528
+ ,6.012
+ ,14
+ ,7
+ ,6
+ ,4
+ ,8
+ ,20.154
+ ,642
+ ,18.475
+ ,61
+ ,54
+ ,12
+ ,16
+ ,10
+ ,36.944
+ ,947
+ ,12.645
+ ,71
+ ,37
+ ,24
+ ,20
+ ,15
+ ,8.019
+ ,819
+ ,11.017
+ ,44
+ ,35
+ ,16
+ ,12
+ ,15
+ ,30.884
+ ,757
+ ,37.623
+ ,60
+ ,51
+ ,72
+ ,15
+ ,28
+ ,19.540
+ ,894
+ ,35.873
+ ,64
+ ,39
+ ,27
+ ,16
+ ,17)
+ ,dim=c(8
+ ,289)
+ ,dimnames=list(c('y'
+ ,'x1'
+ ,'x2'
+ ,'x3'
+ ,'x4'
+ ,'x5'
+ ,'x6'
+ ,'x7')
+ ,1:289))
>  y <- array(NA,dim=c(8,289),dimnames=list(c('y','x1','x2','x3','x4','x5','x6','x7'),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] "y"
> x[,par1]
  [1] 112.285  84.786  83.123 101.193  38.361  68.504 119.182  22.807  17.140
 [10] 116.174  57.635  66.198  71.701  57.793  80.444  53.855  97.668 133.824
 [19] 101.481  99.645 114.789  99.052  67.654  65.553  97.500  69.112  82.753
 [28]  85.323  72.654  30.727  77.873 117.478  74.007  90.183  61.542 101.494
 [37]  27.570  55.813  79.215   1.423  55.461  31.081  22.996  83.122  70.106
 [46]  60.578  39.992  79.892  49.810  71.570 100.708  33.032  82.875 139.077
 [55]  71.595  72.260   5.950 115.762  32.551  31.701  80.670 143.558 117.105
 [64]  23.789 120.733 105.195  73.107 132.068 149.193  46.821  87.011  95.260
 [73]  55.183 106.671  73.511  92.945  78.664  70.054  22.618  74.011  83.737
 [82]  69.094  93.133  95.536 225.920  62.133  61.370  43.836 106.117  38.692
 [91]  84.651  56.622  15.986  95.364  26.706  89.691  67.267 126.846  41.140
[100] 102.860  51.715  55.801 111.813 120.293 138.599 161.647 115.929  24.266
[109] 162.901 109.825 129.838  37.510  43.750  40.652  87.771  85.872  89.275
[118]  44.418 192.565  35.232  40.909  13.294  32.387 140.867 120.662  21.233
[127]  44.332  61.056 101.338   1.168  13.497  65.567  25.162  32.334  40.735
[136]  91.413 855.000  97.068  44.339  14.116  10.288  65.622  16.563  76.643
[145] 110.681  29.011  92.696  94.785   8.773  83.209  93.815  86.687  34.553
[154] 105.547 103.487 213.688  71.220  23.517  56.926  91.721 115.168 111.194
[163]  51.009 135.777  51.513  74.163  51.633  75.345  33.416  83.305  98.952
[172] 102.372  37.238 103.772 123.969  27.142 135.400  21.399 130.115  24.874
[181]  34.988  45.549   6.023  64.466  54.990   1.644   6.179   3.926  32.755
[190]  34.777  73.224  27.114  20.760  37.636  65.461  30.080  24.094  69.008
[199]  54.968  46.090  27.507  10.672  34.029  46.300  24.760  18.779  21.280
[208]  40.662  28.987  22.827  18.513  30.594  24.006  27.913  42.744  12.934
[217]  22.574  41.385  18.653  18.472  30.976  63.339  25.568  33.747   4.154
[226]  19.474  35.130  39.067  13.310  65.892   4.143  28.579  51.776  21.152
[235]  38.084  27.717  32.928  11.342  19.499  16.380  36.874  48.259  16.734
[244]  28.207  30.143  41.369  45.833  29.156  35.944  36.278  45.588  45.097
[253]   3.895  28.394  18.632   2.325  25.139  27.975  14.483  13.127   5.839
[262]  24.069   3.738  18.625  36.341  24.548  21.792  26.263  23.686  49.303
[271]  25.659  28.904   2.781  29.236  19.546  22.818  32.689   5.752  22.197
[280]  20.055  25.272  82.206  32.073   5.444  20.154  36.944   8.019  30.884
[289]  19.540
> 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])

  1.168   1.423   1.644   2.325   2.781   3.738   3.895   3.926   4.143   4.154 
      1       1       1       1       1       1       1       1       1       1 
  5.444   5.752   5.839    5.95   6.023   6.179   8.019   8.773  10.288  10.672 
      1       1       1       1       1       1       1       1       1       1 
 11.342  12.934  13.127  13.294   13.31  13.497  14.116  14.483  15.986   16.38 
      1       1       1       1       1       1       1       1       1       1 
 16.563  16.734   17.14  18.472  18.513  18.625  18.632  18.653  18.779  19.474 
      1       1       1       1       1       1       1       1       1       1 
 19.499   19.54  19.546  20.055  20.154   20.76  21.152  21.233   21.28  21.399 
      1       1       1       1       1       1       1       1       1       1 
 21.792  22.197  22.574  22.618  22.807  22.818  22.827  22.996  23.517  23.686 
      1       1       1       1       1       1       1       1       1       1 
 23.789  24.006  24.069  24.094  24.266  24.548   24.76  24.874  25.139  25.162 
      1       1       1       1       1       1       1       1       1       1 
 25.272  25.568  25.659  26.263  26.706  27.114  27.142  27.507   27.57  27.717 
      1       1       1       1       1       1       1       1       1       1 
 27.913  27.975  28.207  28.394  28.579  28.904  28.987  29.011  29.156  29.236 
      1       1       1       1       1       1       1       1       1       1 
  30.08  30.143  30.594  30.727  30.884  30.976  31.081  31.701  32.073  32.334 
      1       1       1       1       1       1       1       1       1       1 
 32.387  32.551  32.689  32.755  32.928  33.032  33.416  33.747  34.029  34.553 
      1       1       1       1       1       1       1       1       1       1 
 34.777  34.988   35.13  35.232  35.944  36.278  36.341  36.874  36.944  37.238 
      1       1       1       1       1       1       1       1       1       1 
  37.51  37.636  38.084  38.361  38.692  39.067  39.992  40.652  40.662  40.735 
      1       1       1       1       1       1       1       1       1       1 
 40.909   41.14  41.369  41.385  42.744   43.75  43.836  44.332  44.339  44.418 
      1       1       1       1       1       1       1       1       1       1 
 45.097  45.549  45.588  45.833   46.09    46.3  46.821  48.259  49.303   49.81 
      1       1       1       1       1       1       1       1       1       1 
 51.009  51.513  51.633  51.715  51.776  53.855  54.968   54.99  55.183  55.461 
      1       1       1       1       1       1       1       1       1       1 
 55.801  55.813  56.622  56.926  57.635  57.793  60.578  61.056   61.37  61.542 
      1       1       1       1       1       1       1       1       1       1 
 62.133  63.339  64.466  65.461  65.553  65.567  65.622  65.892  66.198  67.267 
      1       1       1       1       1       1       1       1       1       1 
 67.654  68.504  69.008  69.094  69.112  70.054  70.106   71.22   71.57  71.595 
      1       1       1       1       1       1       1       1       1       1 
 71.701   72.26  72.654  73.107  73.224  73.511  74.007  74.011  74.163  75.345 
      1       1       1       1       1       1       1       1       1       1 
 76.643  77.873  78.664  79.215  79.892  80.444   80.67  82.206  82.753  82.875 
      1       1       1       1       1       1       1       1       1       1 
 83.122  83.123  83.209  83.305  83.737  84.651  84.786  85.323  85.872  86.687 
      1       1       1       1       1       1       1       1       1       1 
 87.011  87.771  89.275  89.691  90.183  91.413  91.721  92.696  92.945  93.133 
      1       1       1       1       1       1       1       1       1       1 
 93.815  94.785   95.26  95.364  95.536  97.068    97.5  97.668  98.952  99.052 
      1       1       1       1       1       1       1       1       1       1 
 99.645 100.708 101.193 101.338 101.481 101.494 102.372  102.86 103.487 103.772 
      1       1       1       1       1       1       1       1       1       1 
105.195 105.547 106.117 106.671 109.825 110.681 111.194 111.813 112.285 114.789 
      1       1       1       1       1       1       1       1       1       1 
115.168 115.762 115.929 116.174 117.105 117.478 119.182 120.293 120.662 120.733 
      1       1       1       1       1       1       1       1       1       1 
123.969 126.846 129.838 130.115 132.068 133.824   135.4 135.777 138.599 139.077 
      1       1       1       1       1       1       1       1       1       1 
140.867 143.558 149.193 161.647 162.901 192.565 213.688  225.92     855 
      1       1       1       1       1       1       1       1       1 
> colnames(x)
[1] "y"  "x1" "x2" "x3" "x4" "x5" "x6" "x7"
> colnames(x)[par1]
[1] "y"
> x[,par1]
  [1] 112.285  84.786  83.123 101.193  38.361  68.504 119.182  22.807  17.140
 [10] 116.174  57.635  66.198  71.701  57.793  80.444  53.855  97.668 133.824
 [19] 101.481  99.645 114.789  99.052  67.654  65.553  97.500  69.112  82.753
 [28]  85.323  72.654  30.727  77.873 117.478  74.007  90.183  61.542 101.494
 [37]  27.570  55.813  79.215   1.423  55.461  31.081  22.996  83.122  70.106
 [46]  60.578  39.992  79.892  49.810  71.570 100.708  33.032  82.875 139.077
 [55]  71.595  72.260   5.950 115.762  32.551  31.701  80.670 143.558 117.105
 [64]  23.789 120.733 105.195  73.107 132.068 149.193  46.821  87.011  95.260
 [73]  55.183 106.671  73.511  92.945  78.664  70.054  22.618  74.011  83.737
 [82]  69.094  93.133  95.536 225.920  62.133  61.370  43.836 106.117  38.692
 [91]  84.651  56.622  15.986  95.364  26.706  89.691  67.267 126.846  41.140
[100] 102.860  51.715  55.801 111.813 120.293 138.599 161.647 115.929  24.266
[109] 162.901 109.825 129.838  37.510  43.750  40.652  87.771  85.872  89.275
[118]  44.418 192.565  35.232  40.909  13.294  32.387 140.867 120.662  21.233
[127]  44.332  61.056 101.338   1.168  13.497  65.567  25.162  32.334  40.735
[136]  91.413 855.000  97.068  44.339  14.116  10.288  65.622  16.563  76.643
[145] 110.681  29.011  92.696  94.785   8.773  83.209  93.815  86.687  34.553
[154] 105.547 103.487 213.688  71.220  23.517  56.926  91.721 115.168 111.194
[163]  51.009 135.777  51.513  74.163  51.633  75.345  33.416  83.305  98.952
[172] 102.372  37.238 103.772 123.969  27.142 135.400  21.399 130.115  24.874
[181]  34.988  45.549   6.023  64.466  54.990   1.644   6.179   3.926  32.755
[190]  34.777  73.224  27.114  20.760  37.636  65.461  30.080  24.094  69.008
[199]  54.968  46.090  27.507  10.672  34.029  46.300  24.760  18.779  21.280
[208]  40.662  28.987  22.827  18.513  30.594  24.006  27.913  42.744  12.934
[217]  22.574  41.385  18.653  18.472  30.976  63.339  25.568  33.747   4.154
[226]  19.474  35.130  39.067  13.310  65.892   4.143  28.579  51.776  21.152
[235]  38.084  27.717  32.928  11.342  19.499  16.380  36.874  48.259  16.734
[244]  28.207  30.143  41.369  45.833  29.156  35.944  36.278  45.588  45.097
[253]   3.895  28.394  18.632   2.325  25.139  27.975  14.483  13.127   5.839
[262]  24.069   3.738  18.625  36.341  24.548  21.792  26.263  23.686  49.303
[271]  25.659  28.904   2.781  29.236  19.546  22.818  32.689   5.752  22.197
[280]  20.055  25.272  82.206  32.073   5.444  20.154  36.944   8.019  30.884
[289]  19.540
> 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/1viq91323985798.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  y 
Inputs:  x1, x2, x3, x4, x5, x6, x7 
Number of observations:  289 

1) x2 <= 67.808; criterion = 1, statistic = 66.005
  2)*  weights = 167 
1) x2 > 67.808
  3) x2 <= 120.642; criterion = 1, statistic = 36.68
    4) x5 <= 82; criterion = 0.999, statistic = 14.382
      5)*  weights = 24 
    4) x5 > 82
      6)*  weights = 49 
  3) x2 > 120.642
    7)*  weights = 49 
> postscript(file="/var/wessaorg/rcomp/tmp/2k1161323985798.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/3yssu1323985798.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   112.285 113.15051  -0.86551020
2    84.786  87.05663  -2.27063265
3    83.123  87.05663  -3.93363265
4   101.193  87.05663  14.13636735
5    38.361  64.86808 -26.50708333
6    68.504  36.85187  31.65212575
7   119.182 113.15051   6.03148980
8    22.807  36.85187 -14.04487425
9    17.140  36.85187 -19.71187425
10  116.174  87.05663  29.11736735
11   57.635  64.86808  -7.23308333
12   66.198 113.15051 -46.95251020
13   71.701  64.86808   6.83291667
14   57.793  36.85187  20.94112575
15   80.444  87.05663  -6.61263265
16   53.855  87.05663 -33.20163265
17   97.668 113.15051 -15.48251020
18  133.824 113.15051  20.67348980
19  101.481  87.05663  14.42436735
20   99.645  87.05663  12.58836735
21  114.789  87.05663  27.73236735
22   99.052 113.15051 -14.09851020
23   67.654 113.15051 -45.49651020
24   65.553  64.86808   0.68491667
25   97.500  87.05663  10.44336735
26   69.112  87.05663 -17.94463265
27   82.753  87.05663  -4.30363265
28   85.323 113.15051 -27.82751020
29   72.654 113.15051 -40.49651020
30   30.727  36.85187  -6.12487425
31   77.873 113.15051 -35.27751020
32  117.478 113.15051   4.32748980
33   74.007  87.05663 -13.04963265
34   90.183 113.15051 -22.96751020
35   61.542  36.85187  24.69012575
36  101.494 113.15051 -11.65651020
37   27.570  36.85187  -9.28187425
38   55.813  64.86808  -9.05508333
39   79.215 113.15051 -33.93551020
40    1.423  36.85187 -35.42887425
41   55.461  87.05663 -31.59563265
42   31.081  36.85187  -5.77087425
43   22.996  36.85187 -13.85587425
44   83.122 113.15051 -30.02851020
45   70.106  87.05663 -16.95063265
46   60.578  64.86808  -4.29008333
47   39.992  36.85187   3.14012575
48   79.892  64.86808  15.02391667
49   49.810  36.85187  12.95812575
50   71.570  64.86808   6.70191667
51  100.708  64.86808  35.83991667
52   33.032  36.85187  -3.81987425
53   82.875  87.05663  -4.18163265
54  139.077 113.15051  25.92648980
55   71.595  87.05663 -15.46163265
56   72.260 113.15051 -40.89051020
57    5.950  36.85187 -30.90187425
58  115.762 113.15051   2.61148980
59   32.551  36.85187  -4.30087425
60   31.701  36.85187  -5.15087425
61   80.670  87.05663  -6.38663265
62  143.558 113.15051  30.40748980
63  117.105 113.15051   3.95448980
64   23.789  36.85187 -13.06287425
65  120.733 113.15051   7.58248980
66  105.195 113.15051  -7.95551020
67   73.107  36.85187  36.25512575
68  132.068 113.15051  18.91748980
69  149.193 113.15051  36.04248980
70   46.821  64.86808 -18.04708333
71   87.011  87.05663  -0.04563265
72   95.260 113.15051 -17.89051020
73   55.183  87.05663 -31.87363265
74  106.671  87.05663  19.61436735
75   73.511  87.05663 -13.54563265
76   92.945  87.05663   5.88836735
77   78.664  64.86808  13.79591667
78   70.054  87.05663 -17.00263265
79   22.618  36.85187 -14.23387425
80   74.011  87.05663 -13.04563265
81   83.737  87.05663  -3.31963265
82   69.094  87.05663 -17.96263265
83   93.133  87.05663   6.07636735
84   95.536 113.15051 -17.61451020
85  225.920 113.15051 112.76948980
86   62.133  36.85187  25.28112575
87   61.370  36.85187  24.51812575
88   43.836  36.85187   6.98412575
89  106.117 113.15051  -7.03351020
90   38.692  64.86808 -26.17608333
91   84.651  87.05663  -2.40563265
92   56.622  36.85187  19.77012575
93   15.986  36.85187 -20.86587425
94   95.364  87.05663   8.30736735
95   26.706  36.85187 -10.14587425
96   89.691  64.86808  24.82291667
97   67.267 113.15051 -45.88351020
98  126.846  87.05663  39.78936735
99   41.140  64.86808 -23.72808333
100 102.860  87.05663  15.80336735
101  51.715  64.86808 -13.15308333
102  55.801  87.05663 -31.25563265
103 111.813  87.05663  24.75636735
104 120.293  87.05663  33.23636735
105 138.599 113.15051  25.44848980
106 161.647 113.15051  48.49648980
107 115.929 113.15051   2.77848980
108  24.266  36.85187 -12.58587425
109 162.901 113.15051  49.75048980
110 109.825  87.05663  22.76836735
111 129.838 113.15051  16.68748980
112  37.510  36.85187   0.65812575
113  43.750  36.85187   6.89812575
114  40.652  64.86808 -24.21608333
115  87.771  87.05663   0.71436735
116  85.872  87.05663  -1.18463265
117  89.275  87.05663   2.21836735
118  44.418  64.86808 -20.45008333
119 192.565 113.15051  79.41448980
120  35.232  36.85187  -1.61987425
121  40.909  36.85187   4.05712575
122  13.294  36.85187 -23.55787425
123  32.387  36.85187  -4.46487425
124 140.867  64.86808  75.99891667
125 120.662 113.15051   7.51148980
126  21.233  36.85187 -15.61887425
127  44.332  36.85187   7.48012575
128  61.056  36.85187  24.20412575
129 101.338  87.05663  14.28136735
130   1.168  36.85187 -35.68387425
131  13.497  36.85187 -23.35487425
132  65.567  87.05663 -21.48963265
133  25.162  36.85187 -11.68987425
134  32.334  36.85187  -4.51787425
135  40.735  36.85187   3.88312575
136  91.413 113.15051 -21.73751020
137 855.000  36.85187 818.14812575
138  97.068  87.05663  10.01136735
139  44.339  36.85187   7.48712575
140  14.116  36.85187 -22.73587425
141  10.288  36.85187 -26.56387425
142  65.622  64.86808   0.75391667
143  16.563  36.85187 -20.28887425
144  76.643 113.15051 -36.50751020
145 110.681  87.05663  23.62436735
146  29.011  36.85187  -7.84087425
147  92.696  36.85187  55.84412575
148  94.785  36.85187  57.93312575
149   8.773  36.85187 -28.07887425
150  83.209  36.85187  46.35712575
151  93.815 113.15051 -19.33551020
152  86.687  87.05663  -0.36963265
153  34.553  36.85187  -2.29887425
154 105.547 113.15051  -7.60351020
155 103.487 113.15051  -9.66351020
156 213.688 113.15051 100.53748980
157  71.220  64.86808   6.35191667
158  23.517  36.85187 -13.33487425
159  56.926  36.85187  20.07412575
160  91.721  87.05663   4.66436735
161 115.168 113.15051   2.01748980
162 111.194  36.85187  74.34212575
163  51.009  36.85187  14.15712575
164 135.777 113.15051  22.62648980
165  51.513  36.85187  14.66112575
166  74.163 113.15051 -38.98751020
167  51.633  87.05663 -35.42363265
168  75.345  36.85187  38.49312575
169  33.416  64.86808 -31.45208333
170  83.305  64.86808  18.43691667
171  98.952  87.05663  11.89536735
172 102.372  87.05663  15.31536735
173  37.238  36.85187   0.38612575
174 103.772 113.15051  -9.37851020
175 123.969  36.85187  87.11712575
176  27.142  36.85187  -9.70987425
177 135.400 113.15051  22.24948980
178  21.399  36.85187 -15.45287425
179 130.115 113.15051  16.96448980
180  24.874  36.85187 -11.97787425
181  34.988  36.85187  -1.86387425
182  45.549  36.85187   8.69712575
183   6.023  36.85187 -30.82887425
184  64.466  87.05663 -22.59063265
185  54.990 113.15051 -58.16051020
186   1.644  36.85187 -35.20787425
187   6.179  36.85187 -30.67287425
188   3.926  36.85187 -32.92587425
189  32.755  36.85187  -4.09687425
190  34.777  36.85187  -2.07487425
191  73.224  36.85187  36.37212575
192  27.114  36.85187  -9.73787425
193  20.760  36.85187 -16.09187425
194  37.636  36.85187   0.78412575
195  65.461  64.86808   0.59291667
196  30.080  36.85187  -6.77187425
197  24.094  36.85187 -12.75787425
198  69.008  36.85187  32.15612575
199  54.968  36.85187  18.11612575
200  46.090  36.85187   9.23812575
201  27.507  36.85187  -9.34487425
202  10.672  36.85187 -26.17987425
203  34.029  36.85187  -2.82287425
204  46.300  36.85187   9.44812575
205  24.760  36.85187 -12.09187425
206  18.779  36.85187 -18.07287425
207  21.280  36.85187 -15.57187425
208  40.662  36.85187   3.81012575
209  28.987  36.85187  -7.86487425
210  22.827  36.85187 -14.02487425
211  18.513  36.85187 -18.33887425
212  30.594  36.85187  -6.25787425
213  24.006  36.85187 -12.84587425
214  27.913  36.85187  -8.93887425
215  42.744  36.85187   5.89212575
216  12.934  36.85187 -23.91787425
217  22.574  36.85187 -14.27787425
218  41.385  36.85187   4.53312575
219  18.653  36.85187 -18.19887425
220  18.472  36.85187 -18.37987425
221  30.976  36.85187  -5.87587425
222  63.339  64.86808  -1.52908333
223  25.568  36.85187 -11.28387425
224  33.747  36.85187  -3.10487425
225   4.154  36.85187 -32.69787425
226  19.474  36.85187 -17.37787425
227  35.130  36.85187  -1.72187425
228  39.067  36.85187   2.21512575
229  13.310  36.85187 -23.54187425
230  65.892  36.85187  29.04012575
231   4.143  36.85187 -32.70887425
232  28.579  36.85187  -8.27287425
233  51.776  36.85187  14.92412575
234  21.152  36.85187 -15.69987425
235  38.084  36.85187   1.23212575
236  27.717  36.85187  -9.13487425
237  32.928  36.85187  -3.92387425
238  11.342  36.85187 -25.50987425
239  19.499  36.85187 -17.35287425
240  16.380  36.85187 -20.47187425
241  36.874  36.85187   0.02212575
242  48.259  36.85187  11.40712575
243  16.734  36.85187 -20.11787425
244  28.207  36.85187  -8.64487425
245  30.143  36.85187  -6.70887425
246  41.369  36.85187   4.51712575
247  45.833  36.85187   8.98112575
248  29.156  36.85187  -7.69587425
249  35.944  36.85187  -0.90787425
250  36.278  36.85187  -0.57387425
251  45.588  36.85187   8.73612575
252  45.097  36.85187   8.24512575
253   3.895  36.85187 -32.95687425
254  28.394  36.85187  -8.45787425
255  18.632  36.85187 -18.21987425
256   2.325  36.85187 -34.52687425
257  25.139  36.85187 -11.71287425
258  27.975  36.85187  -8.87687425
259  14.483  36.85187 -22.36887425
260  13.127  36.85187 -23.72487425
261   5.839  36.85187 -31.01287425
262  24.069  36.85187 -12.78287425
263   3.738  36.85187 -33.11387425
264  18.625  36.85187 -18.22687425
265  36.341  36.85187  -0.51087425
266  24.548  36.85187 -12.30387425
267  21.792  36.85187 -15.05987425
268  26.263  36.85187 -10.58887425
269  23.686  36.85187 -13.16587425
270  49.303  36.85187  12.45112575
271  25.659  36.85187 -11.19287425
272  28.904  36.85187  -7.94787425
273   2.781  36.85187 -34.07087425
274  29.236  36.85187  -7.61587425
275  19.546  36.85187 -17.30587425
276  22.818  36.85187 -14.03387425
277  32.689  36.85187  -4.16287425
278   5.752  36.85187 -31.09987425
279  22.197  36.85187 -14.65487425
280  20.055  36.85187 -16.79687425
281  25.272  36.85187 -11.57987425
282  82.206  36.85187  45.35412575
283  32.073  36.85187  -4.77887425
284   5.444  36.85187 -31.40787425
285  20.154  36.85187 -16.69787425
286  36.944  36.85187   0.09212575
287   8.019  36.85187 -28.83287425
288  30.884  36.85187  -5.96787425
289  19.540  36.85187 -17.31187425
> 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/432461323985798.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/58aer1323985798.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/65o0p1323985798.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/75x3h1323985798.tab") 
+ }
> 
> try(system("convert tmp/2k1161323985798.ps tmp/2k1161323985798.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yssu1323985798.ps tmp/3yssu1323985798.png",intern=TRUE))
character(0)
> try(system("convert tmp/432461323985798.ps tmp/432461323985798.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  6.064   0.361   6.465