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

R is free software and comes with ABSOLUTELY NO WARRANTY.
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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.

> x <- array(list(1.3866
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+ ,38.2885
+ ,1.2255
+ ,110.79
+ ,37.795
+ ,1.2384
+ ,113.06
+ ,37.92
+ ,1.2307
+ ,112.62
+ ,38.034
+ ,1.2155
+ ,110.65
+ ,38.029
+ ,1.2218
+ ,112.36
+ ,38.063
+ ,1.2268
+ ,113.74
+ ,37.9828
+ ,1.206
+ ,111.73
+ ,37.745
+ ,1.1959
+ ,109.86
+ ,37.969
+ ,1.1942
+ ,109.32
+ ,38.007
+ ,1.201
+ ,109.99
+ ,38.0615
+ ,1.2045
+ ,109.84
+ ,38.0912
+ ,1.2127
+ ,111.13
+ ,38.091
+ ,1.2249
+ ,112.43
+ ,38.431
+ ,1.2258
+ ,111.77
+ ,38.48
+ ,1.2277
+ ,112.15
+ ,38.35
+ ,1.2363
+ ,112.89
+ ,38.214
+ ,1.2372
+ ,112.12
+ ,38.384
+ ,1.2391
+ ,113.1
+ ,38.1375
+ ,1.2258
+ ,111.09
+ ,38.0075
+ ,1.2271
+ ,110.76
+ ,38.0524
+ ,1.2262
+ ,109.59
+ ,38.235
+ ,1.2294
+ ,109.99
+ ,38.31
+ ,1.2339
+ ,110.25
+ ,38.2615
+ ,1.2198
+ ,108.31
+ ,38.13
+ ,1.2271
+ ,108.79
+ ,38.282
+ ,1.2328
+ ,108.14
+ ,38.581
+ ,1.2548
+ ,109.88
+ ,39.0801
+ ,1.2531
+ ,109.93
+ ,39.0387
+ ,1.2579
+ ,110.46
+ ,39.1015
+ ,1.2567
+ ,109.56
+ ,39.1503
+ ,1.266
+ ,111.49
+ ,39.14
+ ,1.2637
+ ,111.85
+ ,39.0275
+ ,1.2572
+ ,111.35
+ ,38.7665
+ ,1.2569
+ ,110.95
+ ,38.691
+ ,1.2703
+ ,112.49
+ ,38.849
+ ,1.2828
+ ,113.11
+ ,39.1644
+ ,1.3
+ ,112.54
+ ,39.4907
+ ,1.2957
+ ,112.84
+ ,39.5095
+ ,1.2844
+ ,111.5
+ ,39.2795
+ ,1.2817
+ ,111.52
+ ,39.0437
+ ,1.285
+ ,111.57
+ ,39.1355
+ ,1.2897
+ ,112.48
+ ,39.143
+ ,1.2931
+ ,112.31
+ ,39.185
+ ,1.3033
+ ,113.79
+ ,39.355
+ ,1.2992
+ ,114.01
+ ,39.297
+ ,1.3069
+ ,113.64
+ ,39.4514
+ ,1.3028
+ ,112.62
+ ,39.4173
+ ,1.3073
+ ,113.27
+ ,39.4305
+ ,1.3221
+ ,113.51
+ ,39.384
+ ,1.3206
+ ,112.92
+ ,39.3261
+ ,1.3184
+ ,113.66
+ ,39.301
+ ,1.3176
+ ,113.14
+ ,39.35
+ ,1.3253
+ ,113.48
+ ,39.64
+ ,1.3133
+ ,113.23
+ ,39.4723
+ ,1.3016
+ ,110.56
+ ,39.3685
+ ,1.279
+ ,109.5
+ ,39.1906
+ ,1.2799
+ ,109.78
+ ,39.1183
+ ,1.282
+ ,109.49
+ ,39.1325
+ ,1.286
+ ,109.66
+ ,39.1144
+ ,1.288
+ ,109.93
+ ,39.1614
+ ,1.2836
+ ,109.82
+ ,39.0908
+ ,1.2711
+ ,108.54
+ ,38.9199
+ ,1.2704
+ ,108.23
+ ,38.913
+ ,1.2611
+ ,106.19
+ ,38.9655
+ ,1.2613
+ ,106.49
+ ,39.029
+ ,1.2693
+ ,107.15
+ ,39.089
+ ,1.2713
+ ,107.74
+ ,39.07
+ ,1.27
+ ,107.54
+ ,39.0046
+ ,1.268
+ ,107.07
+ ,39.1038
+ ,1.28
+ ,107.54
+ ,39.3572
+ ,1.2818
+ ,107.81
+ ,39.388
+ ,1.2834
+ ,108.38
+ ,39.382
+ ,1.2874
+ ,108.42
+ ,39.4398
+ ,1.2744
+ ,106.86
+ ,39.2537
+ ,1.2697
+ ,106.41
+ ,39.2301
+ ,1.2715
+ ,106.46
+ ,39.2763
+ ,1.2725
+ ,106.84
+ ,39.282
+ ,1.2801
+ ,107.69
+ ,39.3325
+ ,1.285
+ ,107.04
+ ,39.557
+ ,1.2989
+ ,111.04
+ ,40.1
+ ,1.3078
+ ,111.93
+ ,40.5875
+ ,1.306
+ ,111.98
+ ,40.485
+ ,1.3074
+ ,112.07
+ ,40.55
+ ,1.312
+ ,112.05
+ ,40.7955
+ ,1.3364
+ ,113.14
+ ,41.456
+ ,1.3323
+ ,112.49
+ ,41.3557
+ ,1.3412
+ ,113.2
+ ,41.374
+ ,1.3477
+ ,113.52
+ ,41.2235
+ ,1.346
+ ,113.22
+ ,41.15
+ ,1.3611
+ ,113.85
+ ,41.3725
+ ,1.3648
+ ,113.68
+ ,41.6923
+ ,1.3726
+ ,114.26
+ ,41.8
+ ,1.3705
+ ,114.1
+ ,41.8045
+ ,1.378
+ ,114.8
+ ,41.64
+ ,1.3856
+ ,114.98
+ ,41.36
+ ,1.397
+ ,115.1
+ ,41.5745
+ ,1.3874
+ ,114.21
+ ,41.593
+ ,1.3936
+ ,114.24
+ ,41.575
+ ,1.3833
+ ,113.35
+ ,41.68
+ ,1.3958
+ ,114.23
+ ,42.0055
+ ,1.4101
+ ,114.43
+ ,42.3188
+ ,1.4089
+ ,114.28
+ ,42.565
+ ,1.3896
+ ,113
+ ,42.3575
+ ,1.3859
+ ,113.16
+ ,42.29
+ ,1.3861
+ ,112.59
+ ,42.695
+ ,1.4016
+ ,113.65
+ ,43.0028
+ ,1.3934
+ ,113.18
+ ,42.4507
+ ,1.4031
+ ,113.21
+ ,42.4705
+ ,1.3912
+ ,113.11
+ ,42.2875
+ ,1.3803
+ ,112.78
+ ,42.3172
+ ,1.3857
+ ,112.57
+ ,42.55
+ ,1.3857
+ ,111.87
+ ,42.7523
+ ,1.3926
+ ,111.94
+ ,42.8993
+ ,1.4018
+ ,113.18
+ ,43.1555
+ ,1.4014
+ ,113.67
+ ,43.1885
+ ,1.4244
+ ,115.15
+ ,43.43
+ ,1.4084
+ ,114.41
+ ,43.31
+ ,1.3917
+ ,112.88
+ ,42.815
+ ,1.3945
+ ,112.44
+ ,42.7017
+ ,1.377
+ ,113.48
+ ,42.28
+ ,1.37
+ ,112.78
+ ,41.922
+ ,1.3711
+ ,112.59
+ ,42.17
+ ,1.3626
+ ,113.31
+ ,42.1962
+ ,1.3612
+ ,113.21
+ ,42.3215
+ ,1.3481
+ ,112.5
+ ,42.3173
+ ,1.3647
+ ,113.72
+ ,42.391
+ ,1.3674
+ ,114.09
+ ,42.463
+ ,1.3647
+ ,113.97
+ ,42.4125
+ ,1.3496
+ ,112.5
+ ,42.304
+ ,1.3339
+ ,111.28
+ ,41.813
+ ,1.3321
+ ,111.35
+ ,41.651
+ ,1.3225
+ ,110.92
+ ,41.539
+ ,1.3146
+ ,110.73
+ ,41.1575
+ ,1.2998
+ ,109
+ ,40.9545)
+ ,dim=c(3
+ ,491)
+ ,dimnames=list(c('Dollar'
+ ,'Yen'
+ ,'Roebel')
+ ,1:491))
>  y <- array(NA,dim=c(3,491),dimnames=list(c('Dollar','Yen','Roebel'),1:491))
>  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 = '2'
> par2 = 'quantiles'
> par1 = '3'
> #'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] "Roebel"
> x[,par1]
  [1] 40.7819 39.5915 38.8859 39.9068 41.4700 41.5613 41.6005 41.4113 41.8400
 [10] 42.2892 43.1521 43.5998 43.1160 42.4185 42.3687 42.2975 42.8528 43.5350
 [19] 44.7265 45.7293 45.7585 46.1685 46.5075 46.5270 46.6010 46.4607 46.7135
 [28] 46.4113 45.5500 44.6081 44.4395 44.9847 45.7558 45.3942 45.6970 45.5664
 [37] 46.0205 45.9195 45.8005 45.5350 45.4977 45.5782 45.7697 45.2445 45.0615
 [46] 45.2865 44.7910 44.7625 44.7644 44.9973 44.7265 45.1465 44.7465 45.1795
 [55] 45.6515 45.4920 45.2775 45.2115 45.4110 45.4005 44.7692 44.8913 45.0320
 [64] 44.8790 44.8330 44.8257 44.7815 44.4790 44.6317 44.5043 44.3217 44.1005
 [73] 44.0470 43.6835 43.7864 44.1807 43.9595 43.9370 43.9910 43.8650 43.6710
 [82] 43.9300 43.8630 43.7095 43.9435 43.7360 43.6295 43.5980 43.8726 43.8935
 [91] 43.5957 43.7155 43.5280 43.3415 43.3374 43.3320 43.3869 43.5016 43.4875
[100] 43.6023 43.3886 43.3105 43.4455 43.5185 43.5755 43.6217 43.6440 43.5789
[109] 43.5215 43.5033 43.6320 43.2630 43.3717 43.2745 43.2647 43.3240 43.4455
[118] 43.4098 43.4100 43.9300 43.8104 43.5400 43.8580 43.8375 43.8810 43.8870
[127] 43.8009 43.7877 43.8110 44.0625 44.1250 44.5200 45.4005 45.8900 45.1890
[136] 44.9035 44.9351 44.8010 43.9800 44.1100 44.2661 44.3610 44.0990 43.8435
[145] 43.8914 44.2170 44.5060 44.5400 44.4465 44.8420 44.8946 44.9510 45.4450
[154] 45.0035 45.7690 46.0900 45.4120 45.1200 45.4800 45.1050 45.0560 45.2200
[163] 45.3900 45.0410 44.9399 44.9315 45.1935 45.3466 45.4645 45.5685 45.3921
[172] 45.3400 45.1308 45.1005 45.3700 45.2000 44.9614 44.8015 44.9152 45.0950
[181] 44.9271 44.6026 44.5000 44.5400 44.5532 44.4070 44.2590 44.1365 44.1120
[190] 43.8814 43.9800 43.7294 43.9119 43.9550 43.9000 43.7065 43.6939 43.6587
[199] 43.5885 43.8885 43.8216 43.7510 43.6990 43.7425 43.6390 43.5890 43.6060
[208] 43.5325 43.3850 43.3745 43.2360 43.1957 43.0100 43.1401 43.0487 43.1972
[217] 43.2461 43.0866 43.0865 43.0194 43.0800 43.0070 42.9278 42.9545 42.7995
[226] 42.9048 42.9468 43.0800 43.1274 43.1625 43.4500 43.8310 43.7769 43.9800
[235] 43.9200 44.1100 44.0300 44.1582 44.1400 45.0700 44.8737 44.8505 44.3730
[244] 44.0750 43.9725 44.0940 44.1910 43.9685 43.7900 43.6041 43.1707 42.7100
[253] 42.7550 43.3316 43.5000 43.1540 43.1600 43.1000 42.8500 42.6175 42.5000
[262] 42.6285 42.6974 43.0400 42.6730 42.5015 42.5380 42.3735 42.0140 41.8618
[271] 42.1824 42.6050 42.7345 42.6150 42.4650 42.3400 42.2510 42.0475 41.8600
[280] 41.6850 41.7350 41.7060 41.7640 41.5800 41.3730 41.0880 41.1370 41.1587
[289] 41.1850 40.8190 40.6330 40.8580 40.7940 40.6900 40.5950 40.7305 40.5471
[298] 40.5145 40.7000 40.7000 40.5220 40.6165 40.3985 40.2815 40.2450 40.3055
[307] 40.2696 40.2510 40.1270 39.9500 39.6750 39.9540 39.8828 39.6200 39.5415
[316] 39.5250 39.8145 39.6675 39.6950 39.5985 39.2735 39.1435 39.1742 39.2025
[325] 39.3946 39.5025 39.4845 39.3300 39.2950 39.2675 39.2535 38.9845 38.9285
[334] 38.8592 38.7700 38.7900 38.8205 38.7577 38.8390 38.7800 38.5400 38.5110
[343] 38.6150 38.8980 38.8691 38.3840 38.0277 37.7200 37.7325 37.6260 37.6030
[352] 37.7800 38.5590 39.0459 38.4500 38.5050 38.2885 37.7950 37.9200 38.0340
[361] 38.0290 38.0630 37.9828 37.7450 37.9690 38.0070 38.0615 38.0912 38.0910
[370] 38.4310 38.4800 38.3500 38.2140 38.3840 38.1375 38.0075 38.0524 38.2350
[379] 38.3100 38.2615 38.1300 38.2820 38.5810 39.0801 39.0387 39.1015 39.1503
[388] 39.1400 39.0275 38.7665 38.6910 38.8490 39.1644 39.4907 39.5095 39.2795
[397] 39.0437 39.1355 39.1430 39.1850 39.3550 39.2970 39.4514 39.4173 39.4305
[406] 39.3840 39.3261 39.3010 39.3500 39.6400 39.4723 39.3685 39.1906 39.1183
[415] 39.1325 39.1144 39.1614 39.0908 38.9199 38.9130 38.9655 39.0290 39.0890
[424] 39.0700 39.0046 39.1038 39.3572 39.3880 39.3820 39.4398 39.2537 39.2301
[433] 39.2763 39.2820 39.3325 39.5570 40.1000 40.5875 40.4850 40.5500 40.7955
[442] 41.4560 41.3557 41.3740 41.2235 41.1500 41.3725 41.6923 41.8000 41.8045
[451] 41.6400 41.3600 41.5745 41.5930 41.5750 41.6800 42.0055 42.3188 42.5650
[460] 42.3575 42.2900 42.6950 43.0028 42.4507 42.4705 42.2875 42.3172 42.5500
[469] 42.7523 42.8993 43.1555 43.1885 43.4300 43.3100 42.8150 42.7017 42.2800
[478] 41.9220 42.1700 42.1962 42.3215 42.3173 42.3910 42.4630 42.4125 42.3040
[487] 41.8130 41.6510 41.5390 41.1575 40.9545
> 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])

[37.6,42.9) [42.9,46.7] 
        246         245 
> colnames(x)
[1] "Dollar" "Yen"    "Roebel"
> colnames(x)[par1]
[1] "Roebel"
> x[,par1]
  [1] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
  [7] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [42.9,46.7] [42.9,46.7]
 [13] [42.9,46.7] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [42.9,46.7]
 [19] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [25] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [31] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [37] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [43] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [49] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [55] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [61] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [67] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [73] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [79] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [85] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [91] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
 [97] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[103] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[109] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[115] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[121] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[127] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[133] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[139] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[145] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[151] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[157] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[163] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[169] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[175] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[181] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[187] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[193] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[199] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[205] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[211] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[217] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[223] [37.6,42.9) [42.9,46.7] [37.6,42.9) [37.6,42.9) [42.9,46.7] [42.9,46.7]
[229] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[235] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[241] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[247] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [37.6,42.9)
[253] [37.6,42.9) [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[259] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [42.9,46.7]
[265] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[271] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[277] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[283] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[289] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[295] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[301] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[307] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[313] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[319] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[325] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[331] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[337] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[343] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[349] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[355] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[361] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[367] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[373] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[379] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[385] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[391] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[397] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[403] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[409] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[415] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[421] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[427] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[433] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[439] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[445] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[451] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[457] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[463] [42.9,46.7] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[469] [37.6,42.9) [37.6,42.9) [42.9,46.7] [42.9,46.7] [42.9,46.7] [42.9,46.7]
[475] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[481] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
[487] [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9) [37.6,42.9)
Levels: [37.6,42.9) [42.9,46.7]
> 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/1vwjh1292973293.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  as.factor(Roebel) 
Inputs:  Dollar, Yen 
Number of observations:  491 

1) Yen <= 127.74; criterion = 1, statistic = 247.321
  2) Yen <= 113.64; criterion = 0.999, statistic = 13.131
    3)*  weights = 117 
  2) Yen > 113.64
    4) Dollar <= 1.3182; criterion = 1, statistic = 42.319
      5) Yen <= 118.06; criterion = 0.997, statistic = 10.087
        6)*  weights = 30 
      5) Yen > 118.06
        7)*  weights = 29 
    4) Dollar > 1.3182
      8) Yen <= 120.11; criterion = 0.999, statistic = 12.625
        9)*  weights = 23 
      8) Yen > 120.11
        10)*  weights = 77 
1) Yen > 127.74
  11)*  weights = 215 
> postscript(file="/var/www/html/rcomp/tmp/2vwjh1292973293.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/3vwjh1292973293.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,]    1    1
  [2,]    1    1
  [3,]    1    1
  [4,]    1    1
  [5,]    1    1
  [6,]    1    1
  [7,]    1    1
  [8,]    1    1
  [9,]    1    1
 [10,]    1    1
 [11,]    2    1
 [12,]    2    2
 [13,]    2    1
 [14,]    1    1
 [15,]    1    1
 [16,]    1    1
 [17,]    1    1
 [18,]    2    1
 [19,]    2    1
 [20,]    2    2
 [21,]    2    1
 [22,]    2    1
 [23,]    2    1
 [24,]    2    1
 [25,]    2    1
 [26,]    2    1
 [27,]    2    2
 [28,]    2    2
 [29,]    2    1
 [30,]    2    1
 [31,]    2    1
 [32,]    2    1
 [33,]    2    1
 [34,]    2    1
 [35,]    2    2
 [36,]    2    2
 [37,]    2    2
 [38,]    2    2
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    2    2
 [43,]    2    2
 [44,]    2    2
 [45,]    2    2
 [46,]    2    2
 [47,]    2    2
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    2    2
 [52,]    2    2
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    2    2
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    2    2
 [73,]    2    2
 [74,]    2    2
 [75,]    2    2
 [76,]    2    2
 [77,]    2    2
 [78,]    2    2
 [79,]    2    2
 [80,]    2    2
 [81,]    2    2
 [82,]    2    2
 [83,]    2    2
 [84,]    2    2
 [85,]    2    2
 [86,]    2    2
 [87,]    2    2
 [88,]    2    2
 [89,]    2    2
 [90,]    2    2
 [91,]    2    2
 [92,]    2    2
 [93,]    2    2
 [94,]    2    2
 [95,]    2    2
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    2    2
[103,]    2    2
[104,]    2    2
[105,]    2    2
[106,]    2    2
[107,]    2    2
[108,]    2    2
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    2    2
[113,]    2    2
[114,]    2    2
[115,]    2    2
[116,]    2    2
[117,]    2    2
[118,]    2    2
[119,]    2    2
[120,]    2    2
[121,]    2    2
[122,]    2    2
[123,]    2    2
[124,]    2    2
[125,]    2    2
[126,]    2    2
[127,]    2    2
[128,]    2    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    2    2
[133,]    2    2
[134,]    2    2
[135,]    2    2
[136,]    2    2
[137,]    2    2
[138,]    2    2
[139,]    2    2
[140,]    2    2
[141,]    2    2
[142,]    2    2
[143,]    2    2
[144,]    2    2
[145,]    2    2
[146,]    2    2
[147,]    2    2
[148,]    2    2
[149,]    2    2
[150,]    2    2
[151,]    2    2
[152,]    2    2
[153,]    2    2
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    2    2
[158,]    2    2
[159,]    2    2
[160,]    2    2
[161,]    2    2
[162,]    2    2
[163,]    2    2
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    2    2
[169,]    2    2
[170,]    2    2
[171,]    2    2
[172,]    2    2
[173,]    2    2
[174,]    2    2
[175,]    2    2
[176,]    2    2
[177,]    2    2
[178,]    2    2
[179,]    2    2
[180,]    2    2
[181,]    2    2
[182,]    2    2
[183,]    2    2
[184,]    2    2
[185,]    2    2
[186,]    2    2
[187,]    2    2
[188,]    2    2
[189,]    2    2
[190,]    2    2
[191,]    2    2
[192,]    2    2
[193,]    2    2
[194,]    2    2
[195,]    2    2
[196,]    2    2
[197,]    2    2
[198,]    2    2
[199,]    2    2
[200,]    2    2
[201,]    2    2
[202,]    2    2
[203,]    2    2
[204,]    2    2
[205,]    2    2
[206,]    2    2
[207,]    2    2
[208,]    2    2
[209,]    2    2
[210,]    2    2
[211,]    2    2
[212,]    2    2
[213,]    2    2
[214,]    2    2
[215,]    2    2
[216,]    2    2
[217,]    2    2
[218,]    2    2
[219,]    2    2
[220,]    2    2
[221,]    2    2
[222,]    2    2
[223,]    1    2
[224,]    2    2
[225,]    1    2
[226,]    1    2
[227,]    2    2
[228,]    2    2
[229,]    2    2
[230,]    2    2
[231,]    2    2
[232,]    2    2
[233,]    2    2
[234,]    2    2
[235,]    2    2
[236,]    2    2
[237,]    2    2
[238,]    2    2
[239,]    2    2
[240,]    2    2
[241,]    2    2
[242,]    2    2
[243,]    2    2
[244,]    2    2
[245,]    2    2
[246,]    2    2
[247,]    2    2
[248,]    2    2
[249,]    2    2
[250,]    2    2
[251,]    2    2
[252,]    1    2
[253,]    1    2
[254,]    2    2
[255,]    2    2
[256,]    2    2
[257,]    2    2
[258,]    2    2
[259,]    1    2
[260,]    1    2
[261,]    1    2
[262,]    1    2
[263,]    1    2
[264,]    2    2
[265,]    1    2
[266,]    1    2
[267,]    1    2
[268,]    1    2
[269,]    1    2
[270,]    1    2
[271,]    1    1
[272,]    1    1
[273,]    1    1
[274,]    1    1
[275,]    1    1
[276,]    1    1
[277,]    1    1
[278,]    1    1
[279,]    1    1
[280,]    1    1
[281,]    1    1
[282,]    1    1
[283,]    1    1
[284,]    1    1
[285,]    1    1
[286,]    1    1
[287,]    1    1
[288,]    1    1
[289,]    1    1
[290,]    1    1
[291,]    1    1
[292,]    1    1
[293,]    1    1
[294,]    1    1
[295,]    1    1
[296,]    1    1
[297,]    1    1
[298,]    1    1
[299,]    1    1
[300,]    1    1
[301,]    1    1
[302,]    1    1
[303,]    1    1
[304,]    1    1
[305,]    1    1
[306,]    1    1
[307,]    1    1
[308,]    1    1
[309,]    1    1
[310,]    1    1
[311,]    1    1
[312,]    1    1
[313,]    1    1
[314,]    1    1
[315,]    1    1
[316,]    1    1
[317,]    1    1
[318,]    1    1
[319,]    1    1
[320,]    1    1
[321,]    1    1
[322,]    1    1
[323,]    1    1
[324,]    1    1
[325,]    1    1
[326,]    1    1
[327,]    1    1
[328,]    1    1
[329,]    1    1
[330,]    1    1
[331,]    1    1
[332,]    1    1
[333,]    1    1
[334,]    1    1
[335,]    1    1
[336,]    1    1
[337,]    1    1
[338,]    1    1
[339,]    1    1
[340,]    1    1
[341,]    1    2
[342,]    1    2
[343,]    1    2
[344,]    1    1
[345,]    1    2
[346,]    1    1
[347,]    1    1
[348,]    1    1
[349,]    1    1
[350,]    1    1
[351,]    1    1
[352,]    1    1
[353,]    1    1
[354,]    1    1
[355,]    1    1
[356,]    1    1
[357,]    1    1
[358,]    1    1
[359,]    1    1
[360,]    1    1
[361,]    1    1
[362,]    1    1
[363,]    1    1
[364,]    1    1
[365,]    1    1
[366,]    1    1
[367,]    1    1
[368,]    1    1
[369,]    1    1
[370,]    1    1
[371,]    1    1
[372,]    1    1
[373,]    1    1
[374,]    1    1
[375,]    1    1
[376,]    1    1
[377,]    1    1
[378,]    1    1
[379,]    1    1
[380,]    1    1
[381,]    1    1
[382,]    1    1
[383,]    1    1
[384,]    1    1
[385,]    1    1
[386,]    1    1
[387,]    1    1
[388,]    1    1
[389,]    1    1
[390,]    1    1
[391,]    1    1
[392,]    1    1
[393,]    1    1
[394,]    1    1
[395,]    1    1
[396,]    1    1
[397,]    1    1
[398,]    1    1
[399,]    1    1
[400,]    1    1
[401,]    1    1
[402,]    1    1
[403,]    1    1
[404,]    1    1
[405,]    1    1
[406,]    1    1
[407,]    1    1
[408,]    1    1
[409,]    1    1
[410,]    1    1
[411,]    1    1
[412,]    1    1
[413,]    1    1
[414,]    1    1
[415,]    1    1
[416,]    1    1
[417,]    1    1
[418,]    1    1
[419,]    1    1
[420,]    1    1
[421,]    1    1
[422,]    1    1
[423,]    1    1
[424,]    1    1
[425,]    1    1
[426,]    1    1
[427,]    1    1
[428,]    1    1
[429,]    1    1
[430,]    1    1
[431,]    1    1
[432,]    1    1
[433,]    1    1
[434,]    1    1
[435,]    1    1
[436,]    1    1
[437,]    1    1
[438,]    1    1
[439,]    1    1
[440,]    1    1
[441,]    1    1
[442,]    1    1
[443,]    1    1
[444,]    1    1
[445,]    1    1
[446,]    1    1
[447,]    1    1
[448,]    1    1
[449,]    1    1
[450,]    1    1
[451,]    1    1
[452,]    1    1
[453,]    1    1
[454,]    1    1
[455,]    1    1
[456,]    1    1
[457,]    1    1
[458,]    1    1
[459,]    1    1
[460,]    1    1
[461,]    1    1
[462,]    1    1
[463,]    2    1
[464,]    1    1
[465,]    1    1
[466,]    1    1
[467,]    1    1
[468,]    1    1
[469,]    1    1
[470,]    1    1
[471,]    2    1
[472,]    2    1
[473,]    2    1
[474,]    2    1
[475,]    1    1
[476,]    1    1
[477,]    1    1
[478,]    1    1
[479,]    1    1
[480,]    1    1
[481,]    1    1
[482,]    1    1
[483,]    1    1
[484,]    1    1
[485,]    1    1
[486,]    1    1
[487,]    1    1
[488,]    1    1
[489,]    1    1
[490,]    1    1
[491,]    1    1
             
              [37.6,42.9) [42.9,46.7]
  [37.6,42.9)         226          20
  [42.9,46.7]          21         224
> postscript(file="/var/www/html/rcomp/tmp/4o5ik1292973293.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/52xyb1292973293.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/6dofw1292973293.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/7g6ek1292973293.tab") 
+ }
> 
> try(system("convert tmp/2vwjh1292973293.ps tmp/2vwjh1292973293.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vwjh1292973293.ps tmp/3vwjh1292973293.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o5ik1292973293.ps tmp/4o5ik1292973293.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.714   0.517   6.033