R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(3.04
+ ,493
+ ,9
+ ,3.030
+ ,9.026
+ ,25.64
+ ,104.8
+ ,3.28
+ ,481
+ ,11
+ ,2.803
+ ,9.787
+ ,27.97
+ ,105.2
+ ,3.51
+ ,462
+ ,13
+ ,2.768
+ ,9.536
+ ,27.62
+ ,105.6
+ ,3.69
+ ,457
+ ,12
+ ,2.883
+ ,9.490
+ ,23.31
+ ,105.8
+ ,3.92
+ ,442
+ ,13
+ ,2.863
+ ,9.736
+ ,29.07
+ ,106.1
+ ,4.29
+ ,439
+ ,15
+ ,2.897
+ ,9.694
+ ,29.58
+ ,106.5
+ ,4.31
+ ,488
+ ,13
+ ,3.013
+ ,9.647
+ ,28.63
+ ,106.71
+ ,4.42
+ ,521
+ ,16
+ ,3.143
+ ,9.753
+ ,29.92
+ ,106.68
+ ,4.59
+ ,501
+ ,10
+ ,3.033
+ ,10.070
+ ,32.68
+ ,107.41
+ ,4.76
+ ,485
+ ,14
+ ,3.046
+ ,10.137
+ ,31.54
+ ,107.15
+ ,4.83
+ ,464
+ ,14
+ ,3.111
+ ,9.984
+ ,32.43
+ ,107.5
+ ,4.83
+ ,460
+ ,45
+ ,3.013
+ ,9.732
+ ,26.54
+ ,107.22
+ ,4.76
+ ,467
+ ,13
+ ,2.987
+ ,9.103
+ ,25.85
+ ,107.11
+ ,4.99
+ ,460
+ ,8
+ ,2.996
+ ,9.155
+ ,27.60
+ ,107.57
+ ,4.78
+ ,448
+ ,7
+ ,2.833
+ ,9.308
+ ,25.71
+ ,107.81
+ ,5.06
+ ,443
+ ,3
+ ,2.849
+ ,9.394
+ ,25.38
+ ,108.75
+ ,4.65
+ ,436
+ ,3
+ ,2.795
+ ,9.948
+ ,28.57
+ ,109.43
+ ,4.54
+ ,431
+ ,4
+ ,2.845
+ ,10.177
+ ,27.64
+ ,109.62
+ ,4.51
+ ,484
+ ,4
+ ,2.915
+ ,10.002
+ ,25.36
+ ,109.54
+ ,4.49
+ ,510
+ ,0
+ ,2.893
+ ,9.728
+ ,25.90
+ ,109.53
+ ,3.99
+ ,513
+ ,-4
+ ,2.604
+ ,10.002
+ ,26.29
+ ,109.84
+ ,3.97
+ ,503
+ ,-14
+ ,2.642
+ ,10.063
+ ,21.74
+ ,109.67
+ ,3.51
+ ,471
+ ,-18
+ ,2.660
+ ,10.018
+ ,19.20
+ ,109.79
+ ,3.34
+ ,471
+ ,-8
+ ,2.639
+ ,9.960
+ ,19.32
+ ,109.56
+ ,3.29
+ ,476
+ ,-1
+ ,2.720
+ ,10.236
+ ,19.82
+ ,110.22
+ ,3.28
+ ,475
+ ,1
+ ,2.746
+ ,10.893
+ ,20.36
+ ,110.4
+ ,3.26
+ ,470
+ ,2
+ ,2.736
+ ,10.756
+ ,24.31
+ ,110.69
+ ,3.32
+ ,461
+ ,0
+ ,2.812
+ ,10.940
+ ,25.97
+ ,110.72
+ ,3.31
+ ,455
+ ,1
+ ,2.799
+ ,10.997
+ ,25.61
+ ,110.89
+ ,3.35
+ ,456
+ ,0
+ ,2.555
+ ,10.827
+ ,24.67
+ ,110.58
+ ,3.30
+ ,517
+ ,-1
+ ,2.305
+ ,10.166
+ ,25.59
+ ,110.94
+ ,3.29
+ ,525
+ ,-3
+ ,2.215
+ ,10.186
+ ,26.09
+ ,110.91
+ ,3.32
+ ,523
+ ,-3
+ ,2.066
+ ,10.457
+ ,28.37
+ ,111.22
+ ,3.30
+ ,519
+ ,-3
+ ,1.940
+ ,10.368
+ ,27.34
+ ,111.09
+ ,3.30
+ ,509
+ ,-4
+ ,2.042
+ ,10.244
+ ,24.46
+ ,111
+ ,3.09
+ ,512
+ ,-8
+ ,1.995
+ ,10.511
+ ,27.46
+ ,111.06
+ ,2.79
+ ,519
+ ,-9
+ ,1.947
+ ,10.812
+ ,30.23
+ ,111.55
+ ,2.76
+ ,517
+ ,-13
+ ,1.766
+ ,10.738
+ ,32.33
+ ,112.32
+ ,2.75
+ ,510
+ ,-18
+ ,1.635
+ ,10.171
+ ,29.87
+ ,112.64
+ ,2.56
+ ,509
+ ,-11
+ ,1.833
+ ,9.721
+ ,24.87
+ ,112.36
+ ,2.56
+ ,501
+ ,-9
+ ,1.910
+ ,9.897
+ ,25.48
+ ,112.04
+ ,2.21
+ ,507
+ ,-10
+ ,1.960
+ ,9.828
+ ,27.28
+ ,112.37
+ ,2.08
+ ,569
+ ,-13
+ ,1.970
+ ,9.924
+ ,28.24
+ ,112.59
+ ,2.10
+ ,580
+ ,-11
+ ,2.061
+ ,10.371
+ ,29.58
+ ,112.89
+ ,2.02
+ ,578
+ ,-5
+ ,2.093
+ ,10.846
+ ,26.95
+ ,113.22
+ ,2.01
+ ,565
+ ,-15
+ ,2.121
+ ,10.413
+ ,29.08
+ ,112.85
+ ,1.97
+ ,547
+ ,-6
+ ,2.175
+ ,10.709
+ ,28.76
+ ,113.06
+ ,2.06
+ ,555
+ ,-6
+ ,2.197
+ ,10.662
+ ,29.59
+ ,112.99
+ ,2.02
+ ,562
+ ,-3
+ ,2.350
+ ,10.570
+ ,30.70
+ ,113.32
+ ,2.03
+ ,561
+ ,-1
+ ,2.440
+ ,10.297
+ ,30.52
+ ,113.74
+ ,2.01
+ ,555
+ ,-3
+ ,2.409
+ ,10.635
+ ,32.67
+ ,113.91
+ ,2.08
+ ,544
+ ,-4
+ ,2.473
+ ,10.872
+ ,33.19
+ ,114.52
+ ,2.02
+ ,537
+ ,-6
+ ,2.408
+ ,10.296
+ ,37.13
+ ,114.96
+ ,2.03
+ ,543
+ ,0
+ ,2.455
+ ,10.383
+ ,35.54
+ ,114.91
+ ,2.07
+ ,594
+ ,-4
+ ,2.448
+ ,10.431
+ ,37.75
+ ,115.3
+ ,2.04
+ ,611
+ ,-2
+ ,2.498
+ ,10.574
+ ,41.84
+ ,115.44
+ ,2.05
+ ,613
+ ,-2
+ ,2.646
+ ,10.653
+ ,42.94
+ ,115.52
+ ,2.11
+ ,611
+ ,-6
+ ,2.757
+ ,10.805
+ ,49.14
+ ,116.08
+ ,2.09
+ ,594
+ ,-7
+ ,2.849
+ ,10.872
+ ,44.61
+ ,115.94
+ ,2.05
+ ,595
+ ,-6
+ ,2.921
+ ,10.625
+ ,40.22
+ ,115.56
+ ,2.08
+ ,591
+ ,-6
+ ,2.982
+ ,10.407
+ ,44.23
+ ,115.88
+ ,2.06
+ ,589
+ ,-3
+ ,3.081
+ ,10.463
+ ,45.85
+ ,116.66
+ ,2.06
+ ,584
+ ,-2
+ ,3.106
+ ,10.556
+ ,53.38
+ ,117.41
+ ,2.08
+ ,573
+ ,-5
+ ,3.119
+ ,10.646
+ ,53.26
+ ,117.68
+ ,2.07
+ ,567
+ ,-11
+ ,3.061
+ ,10.702
+ ,51.80
+ ,117.85
+ ,2.06
+ ,569
+ ,-11
+ ,3.097
+ ,11.353
+ ,55.30
+ ,118.21
+ ,2.07
+ ,621
+ ,-11
+ ,3.162
+ ,11.346
+ ,57.81
+ ,118.92
+ ,2.06
+ ,629
+ ,-10
+ ,3.257
+ ,11.451
+ ,63.96
+ ,119.03
+ ,2.09
+ ,628
+ ,-14
+ ,3.277
+ ,11.964
+ ,63.77
+ ,119.17
+ ,2.07
+ ,612
+ ,-8
+ ,3.295
+ ,12.574
+ ,59.15
+ ,118.95
+ ,2.09
+ ,595
+ ,-9
+ ,3.364
+ ,13.031
+ ,56.12
+ ,118.92
+ ,2.28
+ ,597
+ ,-5
+ ,3.494
+ ,13.812
+ ,57.42
+ ,118.9
+ ,2.33
+ ,593
+ ,-1
+ ,3.667
+ ,14.544
+ ,63.52
+ ,118.92
+ ,2.35
+ ,590
+ ,-2
+ ,3.813
+ ,14.931
+ ,61.71
+ ,119.44
+ ,2.52
+ ,580
+ ,-5
+ ,3.918
+ ,14.886
+ ,63.01
+ ,119.40
+ ,2.63
+ ,574
+ ,-4
+ ,3.896
+ ,16.005
+ ,68.18
+ ,119.98
+ ,2.58
+ ,573
+ ,-6
+ ,3.801
+ ,17.064
+ ,72.03
+ ,120.43
+ ,2.70
+ ,573
+ ,-2
+ ,3.570
+ ,15.168
+ ,69.75
+ ,120.41
+ ,2.81
+ ,620
+ ,-2
+ ,3.702
+ ,16.050
+ ,74.41
+ ,120.82
+ ,2.97
+ ,626
+ ,-2
+ ,3.862
+ ,15.839
+ ,74.33
+ ,120.97
+ ,3.04
+ ,620
+ ,-2
+ ,3.970
+ ,15.137
+ ,64.24
+ ,120.63
+ ,3.28
+ ,588
+ ,2
+ ,4.139
+ ,14.954
+ ,60.03
+ ,120.38
+ ,3.33
+ ,566
+ ,1
+ ,4.200
+ ,15.648
+ ,59.44
+ ,120.68
+ ,3.50
+ ,557
+ ,-8
+ ,4.291
+ ,15.305
+ ,62.50
+ ,120.84
+ ,3.56
+ ,561
+ ,-1
+ ,4.444
+ ,15.579
+ ,55.04
+ ,120.90
+ ,3.57
+ ,549
+ ,1
+ ,4.503
+ ,16.348
+ ,58.34
+ ,121.56
+ ,3.69
+ ,532
+ ,-1
+ ,4.357
+ ,15.928
+ ,61.92
+ ,121.57
+ ,3.82
+ ,526
+ ,2
+ ,4.591
+ ,16.171
+ ,67.65
+ ,122.12
+ ,3.79
+ ,511
+ ,2
+ ,4.697
+ ,15.937
+ ,67.68
+ ,121.97
+ ,3.96
+ ,499
+ ,1
+ ,4.621
+ ,15.713
+ ,70.30
+ ,121.96
+ ,4.06
+ ,555
+ ,-1
+ ,4.563
+ ,15.594
+ ,75.26
+ ,122.48
+ ,4.05
+ ,565
+ ,-2
+ ,4.203
+ ,15.683
+ ,71.44
+ ,122.33
+ ,4.03
+ ,542
+ ,-2
+ ,4.296
+ ,16.438
+ ,76.36
+ ,122.44
+ ,3.94
+ ,527
+ ,-1
+ ,4.435
+ ,17.032
+ ,81.71
+ ,123.08
+ ,4.02
+ ,510
+ ,-8
+ ,4.105
+ ,17.696
+ ,92.60
+ ,124.23
+ ,3.88
+ ,514
+ ,-4
+ ,4.117
+ ,17.745
+ ,90.60
+ ,124.58
+ ,4.02
+ ,517
+ ,-6
+ ,3.844
+ ,19.394
+ ,92.23
+ ,125.08
+ ,4.03
+ ,508
+ ,-3
+ ,3.721
+ ,20.148
+ ,94.09
+ ,125.98
+ ,4.09
+ ,493
+ ,-3
+ ,3.674
+ ,20.108
+ ,102.79
+ ,126.90
+ ,3.99
+ ,490
+ ,-7
+ ,3.858
+ ,18.584
+ ,109.65
+ ,127.19
+ ,4.01
+ ,469
+ ,-9
+ ,3.801
+ ,18.441
+ ,124.05
+ ,128.33
+ ,4.01
+ ,478
+ ,-11
+ ,3.504
+ ,18.391
+ ,132.69
+ ,129.04
+ ,4.19
+ ,528
+ ,-13
+ ,3.033
+ ,19.178
+ ,135.81
+ ,129.72
+ ,4.30
+ ,534
+ ,-11
+ ,3.047
+ ,18.079
+ ,116.07
+ ,128.92
+ ,4.27
+ ,518
+ ,-9
+ ,2.962
+ ,18.483
+ ,101.42
+ ,129.13
+ ,3.82
+ ,506
+ ,-17
+ ,2.198
+ ,19.644
+ ,75.73
+ ,128.90
+ ,3.15
+ ,502
+ ,-22
+ ,2.014
+ ,19.195
+ ,55.48
+ ,128.13
+ ,2.49
+ ,516
+ ,-25
+ ,1.863
+ ,19.650
+ ,43.80
+ ,127.85
+ ,1.81
+ ,528
+ ,-20
+ ,1.905
+ ,20.830
+ ,45.29
+ ,127.98
+ ,1.26
+ ,533
+ ,-24
+ ,1.811
+ ,23.595
+ ,44.01
+ ,128.42
+ ,1.06
+ ,536
+ ,-24
+ ,1.670
+ ,22.937
+ ,47.48
+ ,127.68
+ ,0.84
+ ,537
+ ,-22
+ ,1.864
+ ,21.814
+ ,51.07
+ ,127.95
+ ,0.78
+ ,524
+ ,-19
+ ,2.052
+ ,21.928
+ ,57.84
+ ,127.85
+ ,0.70
+ ,536
+ ,-18
+ ,2.030
+ ,21.777
+ ,69.04
+ ,127.61
+ ,0.36
+ ,587
+ ,-17
+ ,2.071
+ ,21.383
+ ,65.61
+ ,127.53
+ ,0.35
+ ,597
+ ,-11
+ ,2.293
+ ,21.467
+ ,72.87
+ ,127.92
+ ,0.36
+ ,581
+ ,-11
+ ,2.443
+ ,22.052
+ ,68.41
+ ,127.59
+ ,0.36
+ ,564
+ ,-12
+ ,2.513
+ ,22.680
+ ,73.25
+ ,127.65
+ ,0.36
+ ,558
+ ,-10
+ ,2.467
+ ,24.320
+ ,77.43
+ ,127.98
+ ,0.35
+ ,575
+ ,-15
+ ,2.503
+ ,24.977
+ ,75.28
+ ,128.19
+ ,0.34
+ ,580
+ ,-15
+ ,2.540
+ ,25.204
+ ,77.33
+ ,128.77
+ ,0.34
+ ,575
+ ,-15
+ ,2.483
+ ,25.739
+ ,74.31
+ ,129.31
+ ,0.35
+ ,563
+ ,-13
+ ,2.626
+ ,26.434
+ ,79.70
+ ,129.80
+ ,0.35
+ ,552
+ ,-8
+ ,2.656
+ ,27.525
+ ,85.47
+ ,130.24
+ ,0.34
+ ,537
+ ,-13
+ ,2.447
+ ,30.695
+ ,77.98
+ ,130.76
+ ,0.35
+ ,545
+ ,-9
+ ,2.467
+ ,32.436
+ ,75.69
+ ,130.75
+ ,0.48
+ ,601
+ ,-7
+ ,2.462
+ ,30.160
+ ,75.20
+ ,130.81
+ ,0.43
+ ,604
+ ,-4
+ ,2.505
+ ,30.236
+ ,77.21
+ ,130.89
+ ,0.45
+ ,586
+ ,-4
+ ,2.579
+ ,31.293
+ ,77.85
+ ,131.30
+ ,0.70
+ ,564
+ ,-2
+ ,2.649
+ ,31.077
+ ,83.53
+ ,131.49
+ ,0.59
+ ,549
+ ,0
+ ,2.637
+ ,32.226
+ ,85.99
+ ,131.65)
+ ,dim=c(7
+ ,131)
+ ,dimnames=list(c('Eonia'
+ ,'Werkloosheid'
+ ,'Consumentenvertrouwen'
+ ,'BEL20'
+ ,'Goudprijs'
+ ,'Olieprijs'
+ ,'CPI')
+ ,1:131))
>  y <- array(NA,dim=c(7,131),dimnames=list(c('Eonia','Werkloosheid','Consumentenvertrouwen','BEL20','Goudprijs','Olieprijs','CPI'),1:131))
>  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 = '4'
> #'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

Attaching package: 'zoo'


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

	 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)

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] "BEL20"
> x[,par1]
  [1] 3.030 2.803 2.768 2.883 2.863 2.897 3.013 3.143 3.033 3.046 3.111 3.013
 [13] 2.987 2.996 2.833 2.849 2.795 2.845 2.915 2.893 2.604 2.642 2.660 2.639
 [25] 2.720 2.746 2.736 2.812 2.799 2.555 2.305 2.215 2.066 1.940 2.042 1.995
 [37] 1.947 1.766 1.635 1.833 1.910 1.960 1.970 2.061 2.093 2.121 2.175 2.197
 [49] 2.350 2.440 2.409 2.473 2.408 2.455 2.448 2.498 2.646 2.757 2.849 2.921
 [61] 2.982 3.081 3.106 3.119 3.061 3.097 3.162 3.257 3.277 3.295 3.364 3.494
 [73] 3.667 3.813 3.918 3.896 3.801 3.570 3.702 3.862 3.970 4.139 4.200 4.291
 [85] 4.444 4.503 4.357 4.591 4.697 4.621 4.563 4.203 4.296 4.435 4.105 4.117
 [97] 3.844 3.721 3.674 3.858 3.801 3.504 3.033 3.047 2.962 2.198 2.014 1.863
[109] 1.905 1.811 1.670 1.864 2.052 2.030 2.071 2.293 2.443 2.513 2.467 2.503
[121] 2.540 2.483 2.626 2.656 2.447 2.467 2.462 2.505 2.579 2.649 2.637
> 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])

1.635  1.67 1.766 1.811 1.833 1.863 1.864 1.905  1.91  1.94 1.947  1.96  1.97 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
1.995 2.014  2.03 2.042 2.052 2.061 2.066 2.071 2.093 2.121 2.175 2.197 2.198 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
2.215 2.293 2.305  2.35 2.408 2.409  2.44 2.443 2.447 2.448 2.455 2.462 2.467 
    1     1     1     1     1     1     1     1     1     1     1     1     2 
2.473 2.483 2.498 2.503 2.505 2.513  2.54 2.555 2.579 2.604 2.626 2.637 2.639 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
2.642 2.646 2.649 2.656  2.66  2.72 2.736 2.746 2.757 2.768 2.795 2.799 2.803 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
2.812 2.833 2.845 2.849 2.863 2.883 2.893 2.897 2.915 2.921 2.962 2.982 2.987 
    1     1     1     2     1     1     1     1     1     1     1     1     1 
2.996 3.013  3.03 3.033 3.046 3.047 3.061 3.081 3.097 3.106 3.111 3.119 3.143 
    1     2     1     2     1     1     1     1     1     1     1     1     1 
3.162 3.257 3.277 3.295 3.364 3.494 3.504  3.57 3.667 3.674 3.702 3.721 3.801 
    1     1     1     1     1     1     1     1     1     1     1     1     2 
3.813 3.844 3.858 3.862 3.896 3.918  3.97 4.105 4.117 4.139   4.2 4.203 4.291 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
4.296 4.357 4.435 4.444 4.503 4.563 4.591 4.621 4.697 
    1     1     1     1     1     1     1     1     1 
> colnames(x)
[1] "Eonia"                 "Werkloosheid"          "Consumentenvertrouwen"
[4] "BEL20"                 "Goudprijs"             "Olieprijs"            
[7] "CPI"                  
> colnames(x)[par1]
[1] "BEL20"
> x[,par1]
  [1] 3.030 2.803 2.768 2.883 2.863 2.897 3.013 3.143 3.033 3.046 3.111 3.013
 [13] 2.987 2.996 2.833 2.849 2.795 2.845 2.915 2.893 2.604 2.642 2.660 2.639
 [25] 2.720 2.746 2.736 2.812 2.799 2.555 2.305 2.215 2.066 1.940 2.042 1.995
 [37] 1.947 1.766 1.635 1.833 1.910 1.960 1.970 2.061 2.093 2.121 2.175 2.197
 [49] 2.350 2.440 2.409 2.473 2.408 2.455 2.448 2.498 2.646 2.757 2.849 2.921
 [61] 2.982 3.081 3.106 3.119 3.061 3.097 3.162 3.257 3.277 3.295 3.364 3.494
 [73] 3.667 3.813 3.918 3.896 3.801 3.570 3.702 3.862 3.970 4.139 4.200 4.291
 [85] 4.444 4.503 4.357 4.591 4.697 4.621 4.563 4.203 4.296 4.435 4.105 4.117
 [97] 3.844 3.721 3.674 3.858 3.801 3.504 3.033 3.047 2.962 2.198 2.014 1.863
[109] 1.905 1.811 1.670 1.864 2.052 2.030 2.071 2.293 2.443 2.513 2.467 2.503
[121] 2.540 2.483 2.626 2.656 2.447 2.467 2.462 2.505 2.579 2.649 2.637
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> if (par2 != 'none') {
+ m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
+ if (par4=='yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ a<-table.element(a,'Prediction (training)',par3+1,TRUE)
+ a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Actual',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ a<-table.row.end(a)
+ for (i in 1:10) {
+ ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
+ m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
+ if (i==1) {
+ m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
+ m.ct.i.actu <- x[ind==1,par1]
+ m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
+ m.ct.x.actu <- x[ind==2,par1]
+ } else {
+ m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
+ m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
+ m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
+ m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
+ }
+ }
+ print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
+ numer <- numer + m.ct.i.tab[i,i]
+ }
+ print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
+ print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
+ numer <- numer + m.ct.x.tab[i,i]
+ }
+ print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
+ a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
+ for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
+ a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
+ a<-table.row.end(a)
+ }
+ a<-table.row.start(a)
+ a<-table.element(a,'Overall',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.i.cp,4))
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.x.cp,4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1f6jt1293040702.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  BEL20 
Inputs:  Eonia, Werkloosheid, Consumentenvertrouwen, Goudprijs, Olieprijs, CPI 
Number of observations:  131 

1) Olieprijs <= 51.07; criterion = 1, statistic = 26.184
  2) Consumentenvertrouwen <= -1; criterion = 1, statistic = 36.508
    3) Consumentenvertrouwen <= -9; criterion = 0.996, statistic = 11.817
      4)*  weights = 16 
    3) Consumentenvertrouwen > -9
      5) Olieprijs <= 37.75; criterion = 0.985, statistic = 9.123
        6)*  weights = 18 
      5) Olieprijs > 37.75
        7)*  weights = 7 
  2) Consumentenvertrouwen > -1
    8) Eonia <= 3.51; criterion = 0.996, statistic = 11.412
      9)*  weights = 9 
    8) Eonia > 3.51
      10)*  weights = 17 
1) Olieprijs > 51.07
  11) Consumentenvertrouwen <= -9; criterion = 1, statistic = 37.163
    12) Consumentenvertrouwen <= -15; criterion = 0.997, statistic = 11.908
      13)*  weights = 8 
    12) Consumentenvertrouwen > -15
      14)*  weights = 18 
  11) Consumentenvertrouwen > -9
    15) Eonia <= 2.08; criterion = 1, statistic = 30.307
      16)*  weights = 9 
    15) Eonia > 2.08
      17) Eonia <= 3.04; criterion = 0.986, statistic = 9.257
        18)*  weights = 10 
      17) Eonia > 3.04
        19)*  weights = 19 
> postscript(file="/var/www/html/rcomp/tmp/28f1w1293040702.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=12.5,height=8.3333333333333) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/38f1w1293040702.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=12.5,height=8.3333333333333) 
> 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     3.030  2.744889  0.285111111
2     2.803  2.744889  0.058111111
3     2.768  2.744889  0.023111111
4     2.883  2.947941 -0.064941176
5     2.863  2.947941 -0.084941176
6     2.897  2.947941 -0.050941176
7     3.013  2.947941  0.065058824
8     3.143  2.947941  0.195058824
9     3.033  2.947941  0.085058824
10    3.046  2.947941  0.098058824
11    3.111  2.947941  0.163058824
12    3.013  2.947941  0.065058824
13    2.987  2.947941  0.039058824
14    2.996  2.947941  0.048058824
15    2.833  2.947941 -0.114941176
16    2.849  2.947941 -0.098941176
17    2.795  2.947941 -0.152941176
18    2.845  2.947941 -0.102941176
19    2.915  2.947941 -0.032941176
20    2.893  2.947941 -0.054941176
21    2.604  2.306611  0.297388889
22    2.642  1.976125  0.665875000
23    2.660  1.976125  0.683875000
24    2.639  2.306611  0.332388889
25    2.720  2.306611  0.413388889
26    2.746  2.744889  0.001111111
27    2.736  2.744889 -0.008888889
28    2.812  2.744889  0.067111111
29    2.799  2.744889  0.054111111
30    2.555  2.744889 -0.189888889
31    2.305  2.306611 -0.001611111
32    2.215  2.306611 -0.091611111
33    2.066  2.306611 -0.240611111
34    1.940  2.306611 -0.366611111
35    2.042  2.306611 -0.264611111
36    1.995  2.306611 -0.311611111
37    1.947  1.976125 -0.029125000
38    1.766  1.976125 -0.210125000
39    1.635  1.976125 -0.341125000
40    1.833  1.976125 -0.143125000
41    1.910  1.976125 -0.066125000
42    1.960  1.976125 -0.016125000
43    1.970  1.976125 -0.006125000
44    2.061  1.976125  0.084875000
45    2.093  2.306611 -0.213611111
46    2.121  1.976125  0.144875000
47    2.175  2.306611 -0.131611111
48    2.197  2.306611 -0.109611111
49    2.350  2.306611  0.043388889
50    2.440  2.306611  0.133388889
51    2.409  2.306611  0.102388889
52    2.473  2.306611  0.166388889
53    2.408  2.306611  0.101388889
54    2.455  2.744889 -0.289888889
55    2.448  2.306611  0.141388889
56    2.498  2.819143 -0.321142857
57    2.646  2.819143 -0.173142857
58    2.757  2.819143 -0.062142857
59    2.849  2.819143  0.029857143
60    2.921  2.819143  0.101857143
61    2.982  2.819143  0.162857143
62    3.081  2.819143  0.261857143
63    3.106  2.778667  0.327333333
64    3.119  2.778667  0.340333333
65    3.061  2.934500  0.126500000
66    3.097  2.934500  0.162500000
67    3.162  2.934500  0.227500000
68    3.257  2.934500  0.322500000
69    3.277  2.934500  0.342500000
70    3.295  2.778667  0.516333333
71    3.364  2.934500  0.429500000
72    3.494  3.769300 -0.275300000
73    3.667  3.769300 -0.102300000
74    3.813  3.769300  0.043700000
75    3.918  3.769300  0.148700000
76    3.896  3.769300  0.126700000
77    3.801  3.769300  0.031700000
78    3.570  3.769300 -0.199300000
79    3.702  3.769300 -0.067300000
80    3.862  3.769300  0.092700000
81    3.970  3.769300  0.200700000
82    4.139  4.245211 -0.106210526
83    4.200  4.245211 -0.045210526
84    4.291  4.245211  0.045789474
85    4.444  4.245211  0.198789474
86    4.503  4.245211  0.257789474
87    4.357  4.245211  0.111789474
88    4.591  4.245211  0.345789474
89    4.697  4.245211  0.451789474
90    4.621  4.245211  0.375789474
91    4.563  4.245211  0.317789474
92    4.203  4.245211 -0.042210526
93    4.296  4.245211  0.050789474
94    4.435  4.245211  0.189789474
95    4.105  4.245211 -0.140210526
96    4.117  4.245211 -0.128210526
97    3.844  4.245211 -0.401210526
98    3.721  4.245211 -0.524210526
99    3.674  4.245211 -0.571210526
100   3.858  4.245211 -0.387210526
101   3.801  2.934500  0.866500000
102   3.504  2.934500  0.569500000
103   3.033  2.934500  0.098500000
104   3.047  2.934500  0.112500000
105   2.962  2.934500  0.027500000
106   2.198  2.236375 -0.038375000
107   2.014  2.236375 -0.222375000
108   1.863  1.976125 -0.113125000
109   1.905  1.976125 -0.071125000
110   1.811  1.976125 -0.165125000
111   1.670  1.976125 -0.306125000
112   1.864  1.976125 -0.112125000
113   2.052  2.236375 -0.184375000
114   2.030  2.236375 -0.206375000
115   2.071  2.236375 -0.165375000
116   2.293  2.934500 -0.641500000
117   2.443  2.934500 -0.491500000
118   2.513  2.934500 -0.421500000
119   2.467  2.934500 -0.467500000
120   2.503  2.236375  0.266625000
121   2.540  2.236375  0.303625000
122   2.483  2.236375  0.246625000
123   2.626  2.934500 -0.308500000
124   2.656  2.778667 -0.122666667
125   2.447  2.934500 -0.487500000
126   2.467  2.934500 -0.467500000
127   2.462  2.778667 -0.316666667
128   2.505  2.778667 -0.273666667
129   2.579  2.778667 -0.199666667
130   2.649  2.778667 -0.129666667
131   2.637  2.778667 -0.141666667
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/html/rcomp/tmp/4joiy1293040702.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=12.5,height=8.3333333333333) 
> if(par2=='none') {
+ op <- par(mfrow=c(2,2))
+ plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
+ plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
+ plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
+ plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$Forecasts,result$Actuals)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goodness of Fit',2,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Correlation',1,TRUE)
+ a<-table.element(a,round(detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'R-squared',1,TRUE)
+ a<-table.element(a,round(detcoef*detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'RMSE',1,TRUE)
+ a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/5m7y41293040702.tab") 
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'#',header=TRUE)
+ a<-table.element(a,'Actuals',header=TRUE)
+ a<-table.element(a,'Forecasts',header=TRUE)
+ a<-table.element(a,'Residuals',header=TRUE)
+ a<-table.row.end(a)
+ for (i in 1:length(result$Actuals)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,result$Actuals[i])
+ a<-table.element(a,result$Forecasts[i])
+ a<-table.element(a,result$Residuals[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/687xa1293040702.tab") 
+ }
> if (par2 != 'none') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ for (i in 1:par3) {
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ }
+ a<-table.row.end(a)
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (j in 1:par3) {
+ a<-table.element(a,myt[i,j])
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/70hed1293040702.tab") 
+ }
> 
> try(system("convert tmp/28f1w1293040702.ps tmp/28f1w1293040702.png",intern=TRUE))
character(0)
> try(system("convert tmp/38f1w1293040702.ps tmp/38f1w1293040702.png",intern=TRUE))
character(0)
> try(system("convert tmp/4joiy1293040702.ps tmp/4joiy1293040702.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.223   0.643   7.604