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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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.

> 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 = 'yes'
> par3 = '3'
> par2 = 'quantiles'
> 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])

[1.64,2.50) [2.50,3.06) [3.06,4.70] 
         44          44          43 
> colnames(x)
[1] "Eonia"                 "Werkloosheid"          "Consumentenvertrouwen"
[4] "BEL20"                 "Goudprijs"             "Olieprijs"            
[7] "CPI"                  
> colnames(x)[par1]
[1] "BEL20"
> x[,par1]
  [1] [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
  [7] [2.50,3.06) [3.06,4.70] [2.50,3.06) [2.50,3.06) [3.06,4.70] [2.50,3.06)
 [13] [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
 [19] [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
 [25] [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
 [31] [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50)
 [37] [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50)
 [43] [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50)
 [49] [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50)
 [55] [1.64,2.50) [1.64,2.50) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
 [61] [2.50,3.06) [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [67] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [73] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [79] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [85] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [91] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
 [97] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70] [3.06,4.70]
[103] [2.50,3.06) [2.50,3.06) [2.50,3.06) [1.64,2.50) [1.64,2.50) [1.64,2.50)
[109] [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50) [1.64,2.50)
[115] [1.64,2.50) [1.64,2.50) [1.64,2.50) [2.50,3.06) [1.64,2.50) [1.64,2.50)
[121] [2.50,3.06) [1.64,2.50) [2.50,3.06) [2.50,3.06) [1.64,2.50) [1.64,2.50)
[127] [1.64,2.50) [2.50,3.06) [2.50,3.06) [2.50,3.06) [2.50,3.06)
Levels: [1.64,2.50) [2.50,3.06) [3.06,4.70]
> 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/16tz81293304943.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2   3
          1 365  30   1
          2 102 277  13
          3   3  33 354
[1] 0.9217172
[1] 0.7066327
[1] 0.9076923
[1] 0.8455008
           m.ct.x.pred
m.ct.x.actu  1  2  3
          1 36  8  0
          2 14 32  2
          3  2  1 37
[1] 0.8181818
[1] 0.6666667
[1] 0.925
[1] 0.7954545
> m

	 Conditional inference tree with 6 terminal nodes

Response:  as.factor(BEL20) 
Inputs:  Eonia, Werkloosheid, Consumentenvertrouwen, Goudprijs, Olieprijs, CPI 
Number of observations:  131 

1) Consumentenvertrouwen <= 2; criterion = 1, statistic = 32.384
  2) Olieprijs <= 51.07; criterion = 1, statistic = 23.816
    3) Eonia <= 3.3; criterion = 0.98, statistic = 11.399
      4)*  weights = 39 
    3) Eonia > 3.3
      5)*  weights = 9 
  2) Olieprijs > 51.07
    6) CPI <= 127.19; criterion = 1, statistic = 44.782
      7)*  weights = 38 
    6) CPI > 127.19
      8) Olieprijs <= 75.73; criterion = 0.996, statistic = 14.861
        9)*  weights = 12 
      8) Olieprijs > 75.73
        10)*  weights = 14 
1) Consumentenvertrouwen > 2
  11)*  weights = 19 
> postscript(file="/var/www/html/rcomp/tmp/26tz81293304943.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/www/html/rcomp/tmp/36tz81293304943.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)
+ }
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
       [,1] [,2]
  [1,]    2    2
  [2,]    2    2
  [3,]    2    2
  [4,]    2    2
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    3    2
  [9,]    2    2
 [10,]    2    2
 [11,]    3    2
 [12,]    2    2
 [13,]    2    2
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    2
 [19,]    2    2
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    2    2
 [24,]    2    2
 [25,]    2    1
 [26,]    2    1
 [27,]    2    1
 [28,]    2    2
 [29,]    2    2
 [30,]    2    2
 [31,]    1    1
 [32,]    1    1
 [33,]    1    2
 [34,]    1    1
 [35,]    1    1
 [36,]    1    1
 [37,]    1    1
 [38,]    1    1
 [39,]    1    1
 [40,]    1    1
 [41,]    1    1
 [42,]    1    1
 [43,]    1    1
 [44,]    1    1
 [45,]    1    1
 [46,]    1    1
 [47,]    1    1
 [48,]    1    1
 [49,]    1    1
 [50,]    1    1
 [51,]    1    1
 [52,]    1    1
 [53,]    1    1
 [54,]    1    1
 [55,]    1    1
 [56,]    1    1
 [57,]    2    1
 [58,]    2    1
 [59,]    2    1
 [60,]    2    1
 [61,]    2    1
 [62,]    3    1
 [63,]    3    3
 [64,]    3    3
 [65,]    3    3
 [66,]    3    3
 [67,]    3    3
 [68,]    3    3
 [69,]    3    3
 [70,]    3    3
 [71,]    3    3
 [72,]    3    3
 [73,]    3    3
 [74,]    3    3
 [75,]    3    3
 [76,]    3    3
 [77,]    3    3
 [78,]    3    3
 [79,]    3    3
 [80,]    3    3
 [81,]    3    3
 [82,]    3    3
 [83,]    3    3
 [84,]    3    3
 [85,]    3    3
 [86,]    3    3
 [87,]    3    3
 [88,]    3    3
 [89,]    3    3
 [90,]    3    3
 [91,]    3    3
 [92,]    3    3
 [93,]    3    3
 [94,]    3    3
 [95,]    3    3
 [96,]    3    3
 [97,]    3    3
 [98,]    3    3
 [99,]    3    3
[100,]    3    3
[101,]    3    2
[102,]    3    2
[103,]    2    2
[104,]    2    2
[105,]    2    2
[106,]    1    1
[107,]    1    1
[108,]    1    1
[109,]    1    1
[110,]    1    1
[111,]    1    1
[112,]    1    1
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    1    1
[118,]    2    1
[119,]    1    2
[120,]    1    1
[121,]    2    2
[122,]    1    1
[123,]    2    2
[124,]    2    2
[125,]    1    2
[126,]    1    1
[127,]    1    1
[128,]    2    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
             
              [1.64,2.50) [2.50,3.06) [3.06,4.70]
  [1.64,2.50)          41           3           0
  [2.50,3.06)           9          35           0
  [3.06,4.70]           1           4          38
> postscript(file="/var/www/html/rcomp/tmp/49uge1293304943.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/www/html/rcomp/tmp/5vue21293304943.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/6gdcq1293304943.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/79mct1293304943.tab") 
+ }
> 
> try(system("convert tmp/26tz81293304943.ps tmp/26tz81293304943.png",intern=TRUE))
character(0)
> try(system("convert tmp/36tz81293304943.ps tmp/36tz81293304943.png",intern=TRUE))
character(0)
> try(system("convert tmp/49uge1293304943.ps tmp/49uge1293304943.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.056   0.516   6.584