R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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Type '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(10.81
+ ,24563400
+ ,-0.2643
+ ,24.45
+ ,2772.73
+ ,0.0373
+ ,115.7
+ ,5.98
+ ,9.12
+ ,14163200
+ ,-0.2643
+ ,23.62
+ ,2151.83
+ ,0.0353
+ ,109.2
+ ,5.49
+ ,11.03
+ ,18184800
+ ,-0.2643
+ ,21.90
+ ,1840.26
+ ,0.0292
+ ,116.9
+ ,5.31
+ ,12.74
+ ,20810300
+ ,-0.1918
+ ,27.12
+ ,2116.24
+ ,0.0327
+ ,109.9
+ ,4.8
+ ,9.98
+ ,12843000
+ ,-0.1918
+ ,27.70
+ ,2110.49
+ ,0.0362
+ ,116.1
+ ,4.21
+ ,11.62
+ ,13866700
+ ,-0.1918
+ ,29.23
+ ,2160.54
+ ,0.0325
+ ,118.9
+ ,3.97
+ ,9.40
+ ,15119200
+ ,-0.2246
+ ,26.50
+ ,2027.13
+ ,0.0272
+ ,116.3
+ ,3.77
+ ,9.27
+ ,8301600
+ ,-0.2246
+ ,22.84
+ ,1805.43
+ ,0.0272
+ ,114.0
+ ,3.65
+ ,7.76
+ ,14039600
+ ,-0.2246
+ ,20.49
+ ,1498.80
+ ,0.0265
+ ,97.0
+ ,3.07
+ ,8.78
+ ,12139700
+ ,0.3654
+ ,23.28
+ ,1690.20
+ ,0.0213
+ ,85.3
+ ,2.49
+ ,10.65
+ ,9649000
+ ,0.3654
+ ,25.71
+ ,1930.58
+ ,0.019
+ ,84.9
+ ,2.09
+ ,10.95
+ ,8513600
+ ,0.3654
+ ,26.52
+ ,1950.40
+ ,0.0155
+ ,94.6
+ ,1.82
+ ,12.36
+ ,15278600
+ ,0.0447
+ ,25.51
+ ,1934.03
+ ,0.0114
+ ,97.8
+ ,1.73
+ ,10.85
+ ,15590900
+ ,0.0447
+ ,23.36
+ ,1731.49
+ ,0.0114
+ ,95.0
+ ,1.74
+ ,11.84
+ ,9691100
+ ,0.0447
+ ,24.15
+ ,1845.35
+ ,0.0148
+ ,110.7
+ ,1.73
+ ,12.14
+ ,10882700
+ ,-0.0312
+ ,20.92
+ ,1688.23
+ ,0.0164
+ ,108.5
+ ,1.75
+ ,11.65
+ ,10294800
+ ,-0.0312
+ ,20.38
+ ,1615.73
+ ,0.0118
+ ,110.3
+ ,1.75
+ ,8.86
+ ,16031900
+ ,-0.0312
+ ,21.90
+ ,1463.21
+ ,0.0107
+ ,106.3
+ ,1.75
+ ,7.63
+ ,13683600
+ ,-0.0048
+ ,19.21
+ ,1328.26
+ ,0.0146
+ ,97.4
+ ,1.73
+ ,7.38
+ ,8677200
+ ,-0.0048
+ ,19.65
+ ,1314.85
+ ,0.018
+ ,94.5
+ ,1.74
+ ,7.25
+ ,9874100
+ ,-0.0048
+ ,17.51
+ ,1172.06
+ ,0.0151
+ ,93.7
+ ,1.75
+ ,8.03
+ ,10725500
+ ,0.0705
+ ,21.41
+ ,1329.75
+ ,0.0203
+ ,79.6
+ ,1.75
+ ,7.75
+ ,8348400
+ ,0.0705
+ ,23.09
+ ,1478.78
+ ,0.022
+ ,84.9
+ ,1.34
+ ,7.16
+ ,8046200
+ ,0.0705
+ ,20.70
+ ,1335.51
+ ,0.0238
+ ,80.7
+ ,1.24
+ ,7.18
+ ,10862300
+ ,-0.0134
+ ,19.00
+ ,1320.91
+ ,0.026
+ ,78.8
+ ,1.24
+ ,7.51
+ ,8100300
+ ,-0.0134
+ ,19.04
+ ,1337.52
+ ,0.0298
+ ,64.8
+ ,1.26
+ ,7.07
+ ,7287500
+ ,-0.0134
+ ,19.45
+ ,1341.17
+ ,0.0302
+ ,61.4
+ ,1.25
+ ,7.11
+ ,14002500
+ ,0.0812
+ ,20.54
+ ,1464.31
+ ,0.0222
+ ,81.0
+ ,1.26
+ ,8.98
+ ,19037900
+ ,0.0812
+ ,19.77
+ ,1595.91
+ ,0.0206
+ ,83.6
+ ,1.26
+ ,9.53
+ ,10774600
+ ,0.0812
+ ,20.60
+ ,1622.80
+ ,0.0211
+ ,83.5
+ ,1.22
+ ,10.54
+ ,8960600
+ ,0.1885
+ ,21.21
+ ,1735.02
+ ,0.0211
+ ,77.0
+ ,1.01
+ ,11.31
+ ,7773300
+ ,0.1885
+ ,21.30
+ ,1810.45
+ ,0.0216
+ ,81.7
+ ,1.03
+ ,10.36
+ ,9579700
+ ,0.1885
+ ,22.33
+ ,1786.94
+ ,0.0232
+ ,77.0
+ ,1.01
+ ,11.44
+ ,11270700
+ ,0.3628
+ ,21.12
+ ,1932.21
+ ,0.0204
+ ,81.7
+ ,1.01
+ ,10.45
+ ,9492800
+ ,0.3628
+ ,20.77
+ ,1960.26
+ ,0.0177
+ ,92.5
+ ,1
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+ ,9136800
+ ,0.3628
+ ,22.11
+ ,2003.37
+ ,0.0188
+ ,91.7
+ ,0.98
+ ,11.28
+ ,14487600
+ ,0.2942
+ ,22.34
+ ,2066.15
+ ,0.0193
+ ,96.4
+ ,1
+ ,11.96
+ ,10133200
+ ,0.2942
+ ,21.43
+ ,2029.82
+ ,0.0169
+ ,88.5
+ ,1.01
+ ,13.52
+ ,18659700
+ ,0.2942
+ ,20.14
+ ,1994.22
+ ,0.0174
+ ,88.5
+ ,1
+ ,12.89
+ ,15980700
+ ,0.3036
+ ,21.11
+ ,1920.15
+ ,0.0229
+ ,93.0
+ ,1
+ ,14.03
+ ,9732100
+ ,0.3036
+ ,21.19
+ ,1986.74
+ ,0.0305
+ ,93.1
+ ,1
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+ ,14626300
+ ,0.3036
+ ,23.07
+ ,2047.79
+ ,0.0327
+ ,102.8
+ ,1.03
+ ,16.17
+ ,16904000
+ ,0.3703
+ ,23.01
+ ,1887.36
+ ,0.0299
+ ,105.7
+ ,1.26
+ ,17.25
+ ,13616700
+ ,0.3703
+ ,22.12
+ ,1838.10
+ ,0.0265
+ ,98.7
+ ,1.43
+ ,19.38
+ ,13772900
+ ,0.3703
+ ,22.40
+ ,1896.84
+ ,0.0254
+ ,96.7
+ ,1.61
+ ,26.20
+ ,28749200
+ ,0.7398
+ ,22.66
+ ,1974.99
+ ,0.0319
+ ,92.9
+ ,1.76
+ ,33.53
+ ,31408300
+ ,0.7398
+ ,24.21
+ ,2096.81
+ ,0.0352
+ ,92.6
+ ,1.93
+ ,32.20
+ ,26342800
+ ,0.7398
+ ,24.13
+ ,2175.44
+ ,0.0326
+ ,102.7
+ ,2.16
+ ,38.45
+ ,48909500
+ ,0.6988
+ ,23.73
+ ,2062.41
+ ,0.0297
+ ,105.1
+ ,2.28
+ ,44.86
+ ,41542400
+ ,0.6988
+ ,22.79
+ ,2051.72
+ ,0.0301
+ ,104.4
+ ,2.5
+ ,41.67
+ ,24857200
+ ,0.6988
+ ,21.89
+ ,1999.23
+ ,0.0315
+ ,103.0
+ ,2.63
+ ,36.06
+ ,34093700
+ ,0.7478
+ ,22.92
+ ,1921.65
+ ,0.0351
+ ,97.5
+ ,2.79
+ ,39.76
+ ,22555200
+ ,0.7478
+ ,23.44
+ ,2068.22
+ ,0.028
+ ,103.1
+ ,3
+ ,36.81
+ ,19067500
+ ,0.7478
+ ,22.57
+ ,2056.96
+ ,0.0253
+ ,106.2
+ ,3.04
+ ,42.65
+ ,19029100
+ ,0.5651
+ ,23.27
+ ,2184.83
+ ,0.0317
+ ,103.6
+ ,3.26
+ ,46.89
+ ,15223200
+ ,0.5651
+ ,24.95
+ ,2152.09
+ ,0.0364
+ ,105.5
+ ,3.5
+ ,53.61
+ ,21903700
+ ,0.5651
+ ,23.45
+ ,2151.69
+ ,0.0469
+ ,87.5
+ ,3.62
+ ,57.59
+ ,33306600
+ ,0.6473
+ ,23.42
+ ,2120.30
+ ,0.0435
+ ,85.2
+ ,3.78
+ ,67.82
+ ,23898100
+ ,0.6473
+ ,25.30
+ ,2232.82
+ ,0.0346
+ ,98.3
+ ,4
+ ,71.89
+ ,23279600
+ ,0.6473
+ ,23.90
+ ,2205.32
+ ,0.0342
+ ,103.8
+ ,4.16
+ ,75.51
+ ,40699800
+ ,0.3441
+ ,25.73
+ ,2305.82
+ ,0.0399
+ ,106.8
+ ,4.29
+ ,68.49
+ ,37646000
+ ,0.3441
+ ,24.64
+ ,2281.39
+ ,0.036
+ ,102.7
+ ,4.49
+ ,62.72
+ ,37277000
+ ,0.3441
+ ,24.95
+ ,2339.79
+ ,0.0336
+ ,107.5
+ ,4.59
+ ,70.39
+ ,39246800
+ ,0.2415
+ ,22.15
+ ,2322.57
+ ,0.0355
+ ,109.8
+ ,4.79
+ ,59.77
+ ,27418400
+ ,0.2415
+ ,20.85
+ ,2178.88
+ ,0.0417
+ ,104.7
+ ,4.94
+ ,57.27
+ ,30318700
+ ,0.2415
+ ,21.45
+ ,2172.09
+ ,0.0432
+ ,105.7
+ ,4.99
+ ,67.96
+ ,32808100
+ ,0.3151
+ ,22.15
+ ,2091.47
+ ,0.0415
+ ,107.0
+ ,5.24
+ ,67.85
+ ,28668200
+ ,0.3151
+ ,23.75
+ ,2183.75
+ ,0.0382
+ ,100.2
+ ,5.25
+ ,76.98
+ ,32370300
+ ,0.3151
+ ,25.27
+ ,2258.43
+ ,0.0206
+ ,105.9
+ ,5.25
+ ,81.08
+ ,24171100
+ ,0.239
+ ,26.53
+ ,2366.71
+ ,0.0131
+ ,105.1
+ ,5.25
+ ,91.66
+ ,25009100
+ ,0.239
+ ,27.22
+ ,2431.77
+ ,0.0197
+ ,105.3
+ ,5.25
+ ,84.84
+ ,32084300
+ ,0.239
+ ,27.69
+ ,2415.29
+ ,0.0254
+ ,110.0
+ ,5.24
+ ,85.73
+ ,50117500
+ ,0.2127
+ ,28.61
+ ,2463.93
+ ,0.0208
+ ,110.2
+ ,5.25
+ ,84.61
+ ,27522200
+ ,0.2127
+ ,26.21
+ ,2416.15
+ ,0.0242
+ ,111.2
+ ,5.26
+ ,92.91
+ ,26816800
+ ,0.2127
+ ,25.93
+ ,2421.64
+ ,0.0278
+ ,108.2
+ ,5.26
+ ,99.80
+ ,25136100
+ ,0.273
+ ,27.86
+ ,2525.09
+ ,0.0257
+ ,106.3
+ ,5.25
+ ,121.19
+ ,30295600
+ ,0.273
+ ,28.65
+ ,2604.52
+ ,0.0269
+ ,108.5
+ ,5.25
+ ,122.04
+ ,41526100
+ ,0.273
+ ,27.51
+ ,2603.23
+ ,0.0269
+ ,105.3
+ ,5.25
+ ,131.76
+ ,43845100
+ ,0.3657
+ ,27.06
+ ,2546.27
+ ,0.0236
+ ,111.9
+ ,5.26
+ ,138.48
+ ,39188900
+ ,0.3657
+ ,26.91
+ ,2596.36
+ ,0.0197
+ ,105.6
+ ,5.02
+ ,153.47
+ ,40496400
+ ,0.3657
+ ,27.60
+ ,2701.50
+ ,0.0276
+ ,99.5
+ ,4.94
+ ,189.95
+ ,37438400
+ ,0.4643
+ ,34.48
+ ,2859.12
+ ,0.0354
+ ,95.2
+ ,4.76
+ ,182.22
+ ,46553700
+ ,0.4643
+ ,31.58
+ ,2660.96
+ ,0.0431
+ ,87.8
+ ,4.49
+ ,198.08
+ ,31771400
+ ,0.4643
+ ,33.46
+ ,2652.28
+ ,0.0408
+ ,90.6
+ ,4.24
+ ,135.36
+ ,62108100
+ ,0.5096
+ ,30.64
+ ,2389.86
+ ,0.0428
+ ,87.9
+ ,3.94
+ ,125.02
+ ,46645400
+ ,0.5096
+ ,25.66
+ ,2271.48
+ ,0.0403
+ ,76.4
+ ,2.98
+ ,143.50
+ ,42313100
+ ,0.5096
+ ,26.78
+ ,2279.10
+ ,0.0398
+ ,65.9
+ ,2.61
+ ,173.95
+ ,38841700
+ ,0.3592
+ ,26.91
+ ,2412.80
+ ,0.0394
+ ,62.3
+ ,2.28
+ ,188.75
+ ,32650300
+ ,0.3592
+ ,26.82
+ ,2522.66
+ ,0.0418
+ ,57.2
+ ,1.98
+ ,167.44
+ ,34281100
+ ,0.3592
+ ,26.05
+ ,2292.98
+ ,0.0502
+ ,50.4
+ ,2
+ ,158.95
+ ,33096200
+ ,0.7439
+ ,24.36
+ ,2325.55
+ ,0.056
+ ,51.9
+ ,2.01
+ ,169.53
+ ,23273800
+ ,0.7439
+ ,25.94
+ ,2367.52
+ ,0.0537
+ ,58.5
+ ,2
+ ,113.66
+ ,43697600
+ ,0.7439
+ ,25.37
+ ,2091.88
+ ,0.0494
+ ,61.4
+ ,1.81
+ ,107.59
+ ,66902300
+ ,0.139
+ ,21.23
+ ,1720.95
+ ,0.0366
+ ,38.8
+ ,0.97
+ ,92.67
+ ,44957200
+ ,0.139
+ ,19.35
+ ,1535.57
+ ,0.0107
+ ,44.9
+ ,0.39
+ ,85.35
+ ,33800900
+ ,0.139
+ ,18.61
+ ,1577.03
+ ,0.0009
+ ,38.6
+ ,0.16
+ ,90.13
+ ,33487900
+ ,0.1383
+ ,16.37
+ ,1476.42
+ ,0.0003
+ ,4.0
+ ,0.15
+ ,89.31
+ ,27394900
+ ,0.1383
+ ,15.56
+ ,1377.84
+ ,0.0024
+ ,25.3
+ ,0.22
+ ,105.12
+ ,25963400
+ ,0.1383
+ ,17.70
+ ,1528.59
+ ,-0.0038
+ ,26.9
+ ,0.18
+ ,125.83
+ ,20952600
+ ,0.2874
+ ,19.52
+ ,1717.30
+ ,-0.0074
+ ,40.8
+ ,0.15
+ ,135.81
+ ,17702900
+ ,0.2874
+ ,20.26
+ ,1774.33
+ ,-0.0128
+ ,54.8
+ ,0.18
+ ,142.43
+ ,21282100
+ ,0.2874
+ ,23.05
+ ,1835.04
+ ,-0.0143
+ ,49.3
+ ,0.21
+ ,163.39
+ ,18449100
+ ,0.0596
+ ,22.81
+ ,1978.50
+ ,-0.021
+ ,47.4
+ ,0.16
+ ,168.21
+ ,14415700
+ ,0.0596
+ ,24.04
+ ,2009.06
+ ,-0.0148
+ ,54.5
+ ,0.16
+ ,185.35
+ ,17906300
+ ,0.0596
+ ,25.08
+ ,2122.42
+ ,-0.0129
+ ,53.4
+ ,0.15
+ ,188.50
+ ,22197500
+ ,0.3201
+ ,27.04
+ ,2045.11
+ ,-0.0018
+ ,48.7
+ ,0.12
+ ,199.91
+ ,15856500
+ ,0.3201
+ ,28.81
+ ,2144.60
+ ,0.0184
+ ,50.6
+ ,0.12
+ ,210.73
+ ,19068700
+ ,0.3201
+ ,29.86
+ ,2269.15
+ ,0.0272
+ ,53.6
+ ,0.12
+ ,192.06
+ ,30855100
+ ,0.486
+ ,27.61
+ ,2147.35
+ ,0.0263
+ ,56.5
+ ,0.11
+ ,204.62
+ ,21209000
+ ,0.486
+ ,28.22
+ ,2238.26
+ ,0.0214
+ ,46.4
+ ,0.13
+ ,235.00
+ ,19541600
+ ,0.486
+ ,28.83
+ ,2397.96
+ ,0.0231
+ ,52.3
+ ,0.16
+ ,261.09
+ ,21955000
+ ,0.6129
+ ,30.06
+ ,2461.19
+ ,0.0224
+ ,57.7
+ ,0.2
+ ,256.88
+ ,33725900
+ ,0.6129
+ ,25.51
+ ,2257.04
+ ,0.0202
+ ,62.7
+ ,0.2
+ ,251.53
+ ,28192800
+ ,0.6129
+ ,22.75
+ ,2109.24
+ ,0.0105
+ ,54.3
+ ,0.18
+ ,257.25
+ ,27377000
+ ,0.6665
+ ,25.52
+ ,2254.70
+ ,0.0124
+ ,51.0
+ ,0.18
+ ,243.10
+ ,16228100
+ ,0.6665
+ ,23.33
+ ,2114.03
+ ,0.0115
+ ,53.2
+ ,0.19
+ ,283.75
+ ,21278900
+ ,0.6665
+ ,24.34
+ ,2368.62
+ ,0.0114
+ ,48.6
+ ,0.19)
+ ,dim=c(8
+ ,117)
+ ,dimnames=list(c('APPLE'
+ ,'VOLUME'
+ ,'REV.GROWTH'
+ ,'MICROSOFT'
+ ,'NASDAQ'
+ ,'INFLATION'
+ ,'CONS.CONF'
+ ,'FED.FUNDS.RATE')
+ ,1:117))
>  y <- array(NA,dim=c(8,117),dimnames=list(c('APPLE','VOLUME','REV.GROWTH','MICROSOFT','NASDAQ','INFLATION','CONS.CONF','FED.FUNDS.RATE'),1:117))
>  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 = ''
> par2 = 'none'
> par1 = '1'
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo

Attaching package: 'zoo'

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

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


Attaching package: 'Hmisc'

The following object(s) are masked from 'package:survival':

    untangle.specials

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

    format.pval, round.POSIXt, trunc.POSIXt, units

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "APPLE"
> x[,par1]
  [1]  10.81   9.12  11.03  12.74   9.98  11.62   9.40   9.27   7.76   8.78
 [11]  10.65  10.95  12.36  10.85  11.84  12.14  11.65   8.86   7.63   7.38
 [21]   7.25   8.03   7.75   7.16   7.18   7.51   7.07   7.11   8.98   9.53
 [31]  10.54  11.31  10.36  11.44  10.45  10.69  11.28  11.96  13.52  12.89
 [41]  14.03  16.27  16.17  17.25  19.38  26.20  33.53  32.20  38.45  44.86
 [51]  41.67  36.06  39.76  36.81  42.65  46.89  53.61  57.59  67.82  71.89
 [61]  75.51  68.49  62.72  70.39  59.77  57.27  67.96  67.85  76.98  81.08
 [71]  91.66  84.84  85.73  84.61  92.91  99.80 121.19 122.04 131.76 138.48
 [81] 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95 188.75 167.44
 [91] 158.95 169.53 113.66 107.59  92.67  85.35  90.13  89.31 105.12 125.83
[101] 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73 192.06 204.62
[111] 235.00 261.09 256.88 251.53 257.25 243.10 283.75
> 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])

  7.07   7.11   7.16   7.18   7.25   7.38   7.51   7.63   7.75   7.76   8.03 
     1      1      1      1      1      1      1      1      1      1      1 
  8.78   8.86   8.98   9.12   9.27    9.4   9.53   9.98  10.36  10.45  10.54 
     1      1      1      1      1      1      1      1      1      1      1 
 10.65  10.69  10.81  10.85  10.95  11.03  11.28  11.31  11.44  11.62  11.65 
     1      1      1      1      1      1      1      1      1      1      1 
 11.84  11.96  12.14  12.36  12.74  12.89  13.52  14.03  16.17  16.27  17.25 
     1      1      1      1      1      1      1      1      1      1      1 
 19.38   26.2   32.2  33.53  36.06  36.81  38.45  39.76  41.67  42.65  44.86 
     1      1      1      1      1      1      1      1      1      1      1 
 46.89  53.61  57.27  57.59  59.77  62.72  67.82  67.85  67.96  68.49  70.39 
     1      1      1      1      1      1      1      1      1      1      1 
 71.89  75.51  76.98  81.08  84.61  84.84  85.35  85.73  89.31  90.13  91.66 
     1      1      1      1      1      1      1      1      1      1      1 
 92.67  92.91   99.8 105.12 107.59 113.66 121.19 122.04 125.02 125.83 131.76 
     1      1      1      1      1      1      1      1      1      1      1 
135.36 135.81 138.48 142.43  143.5 153.47 158.95 163.39 167.44 168.21 169.53 
     1      1      1      1      1      1      1      1      1      1      1 
173.95 182.22 185.35  188.5 188.75 189.95 192.06 198.08 199.91 204.62 210.73 
     1      1      1      1      1      1      1      1      1      1      1 
   235  243.1 251.53 256.88 257.25 261.09 283.75 
     1      1      1      1      1      1      1 
> colnames(x)
[1] "APPLE"          "VOLUME"         "REV.GROWTH"     "MICROSOFT"     
[5] "NASDAQ"         "INFLATION"      "CONS.CONF"      "FED.FUNDS.RATE"
> colnames(x)[par1]
[1] "APPLE"
> x[,par1]
  [1]  10.81   9.12  11.03  12.74   9.98  11.62   9.40   9.27   7.76   8.78
 [11]  10.65  10.95  12.36  10.85  11.84  12.14  11.65   8.86   7.63   7.38
 [21]   7.25   8.03   7.75   7.16   7.18   7.51   7.07   7.11   8.98   9.53
 [31]  10.54  11.31  10.36  11.44  10.45  10.69  11.28  11.96  13.52  12.89
 [41]  14.03  16.27  16.17  17.25  19.38  26.20  33.53  32.20  38.45  44.86
 [51]  41.67  36.06  39.76  36.81  42.65  46.89  53.61  57.59  67.82  71.89
 [61]  75.51  68.49  62.72  70.39  59.77  57.27  67.96  67.85  76.98  81.08
 [71]  91.66  84.84  85.73  84.61  92.91  99.80 121.19 122.04 131.76 138.48
 [81] 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95 188.75 167.44
 [91] 158.95 169.53 113.66 107.59  92.67  85.35  90.13  89.31 105.12 125.83
[101] 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73 192.06 204.62
[111] 235.00 261.09 256.88 251.53 257.25 243.10 283.75
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
> 
> if (par2 != 'none') {
+ m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
+ if (par4=='yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ a<-table.element(a,'Prediction (training)',par3+1,TRUE)
+ a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Actual',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ a<-table.row.end(a)
+ for (i in 1:10) {
+ ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
+ m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
+ if (i==1) {
+ m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
+ m.ct.i.actu <- x[ind==1,par1]
+ m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
+ m.ct.x.actu <- x[ind==2,par1]
+ } else {
+ m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
+ m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
+ m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
+ m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
+ }
+ }
+ print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
+ numer <- numer + m.ct.i.tab[i,i]
+ }
+ print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
+ print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
+ numer <- numer + m.ct.x.tab[i,i]
+ }
+ print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
+ a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
+ for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
+ a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
+ a<-table.row.end(a)
+ }
+ a<-table.row.start(a)
+ a<-table.element(a,'Overall',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.i.cp,4))
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.x.cp,4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15q4p1355275042.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

Response:  APPLE 
Inputs:  VOLUME, REV.GROWTH, MICROSOFT, NASDAQ, INFLATION, CONS.CONF, FED.FUNDS.RATE 
Number of observations:  117 

1) CONS.CONF <= 62.7; criterion = 1, statistic = 37.189
  2) NASDAQ <= 1835.04; criterion = 1, statistic = 20.085
    3)*  weights = 10 
  2) NASDAQ > 1835.04
    4)*  weights = 21 
1) CONS.CONF > 62.7
  5) VOLUME <= 22555200; criterion = 1, statistic = 52.547
    6) REV.GROWTH <= 0.3654; criterion = 1, statistic = 21.821
      7) NASDAQ <= 1595.91; criterion = 1, statistic = 22.028
        8)*  weights = 12 
      7) NASDAQ > 1595.91
        9)*  weights = 28 
    6) REV.GROWTH > 0.3654
      10)*  weights = 8 
  5) VOLUME > 22555200
    11) MICROSOFT <= 26.53; criterion = 1, statistic = 26.618
      12)*  weights = 24 
    11) MICROSOFT > 26.53
      13)*  weights = 14 
> postscript(file="/var/fisher/rcomp/tmp/2lcpe1355275042.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/3i1991355275042.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     10.81  60.485000 -49.675000000
2      9.12  11.308929  -2.188928571
3     11.03  11.308929  -0.278928571
4     12.74  11.308929   1.431071429
5      9.98  11.308929  -1.328928571
6     11.62  11.308929   0.311071429
7      9.40  11.308929  -1.908928571
8      9.27  11.308929  -2.038928571
9      7.76   7.716667   0.043333333
10     8.78  11.308929  -2.528928571
11    10.65  11.308929  -0.658928571
12    10.95  11.308929  -0.358928571
13    12.36  11.308929   1.051071429
14    10.85  11.308929  -0.458928571
15    11.84  11.308929   0.531071429
16    12.14  11.308929   0.831071429
17    11.65  11.308929   0.341071429
18     8.86   7.716667   1.143333333
19     7.63   7.716667  -0.086666667
20     7.38   7.716667  -0.336666667
21     7.25   7.716667  -0.466666667
22     8.03   7.716667   0.313333333
23     7.75   7.716667   0.033333333
24     7.16   7.716667  -0.556666667
25     7.18   7.716667  -0.536666667
26     7.51   7.716667  -0.206666667
27     7.07  98.131000 -91.061000000
28     7.11   7.716667  -0.606666667
29     8.98   7.716667   1.263333333
30     9.53  11.308929  -1.778928571
31    10.54  11.308929  -0.768928571
32    11.31  11.308929   0.001071429
33    10.36  11.308929  -0.948928571
34    11.44  11.308929   0.131071429
35    10.45  11.308929  -0.858928571
36    10.69  11.308929  -0.618928571
37    11.28  11.308929  -0.028928571
38    11.96  11.308929   0.651071429
39    13.52  11.308929   2.211071429
40    12.89  11.308929   1.581071429
41    14.03  11.308929   2.721071429
42    16.27  11.308929   4.961071429
43    16.17  34.065000 -17.895000000
44    17.25  34.065000 -16.815000000
45    19.38  34.065000 -14.685000000
46    26.20  60.485000 -34.285000000
47    33.53  60.485000 -26.955000000
48    32.20  60.485000 -28.285000000
49    38.45  60.485000 -22.035000000
50    44.86  60.485000 -15.625000000
51    41.67  60.485000 -18.815000000
52    36.06  60.485000 -24.425000000
53    39.76  34.065000   5.695000000
54    36.81  34.065000   2.745000000
55    42.65  34.065000   8.585000000
56    46.89  34.065000  12.825000000
57    53.61  34.065000  19.545000000
58    57.59  60.485000  -2.895000000
59    67.82  60.485000   7.335000000
60    71.89  60.485000  11.405000000
61    75.51  60.485000  15.025000000
62    68.49  60.485000   8.005000000
63    62.72  60.485000   2.235000000
64    70.39  60.485000   9.905000000
65    59.77  60.485000  -0.715000000
66    57.27  60.485000  -3.215000000
67    67.96  60.485000   7.475000000
68    67.85  60.485000   7.365000000
69    76.98  60.485000  16.495000000
70    81.08  60.485000  20.595000000
71    91.66 134.148571 -42.488571429
72    84.84 134.148571 -49.308571429
73    85.73 134.148571 -48.418571429
74    84.61  60.485000  24.125000000
75    92.91  60.485000  32.425000000
76    99.80 134.148571 -34.348571429
77   121.19 134.148571 -12.958571429
78   122.04 134.148571 -12.108571429
79   131.76 134.148571  -2.388571429
80   138.48 134.148571   4.331428571
81   153.47 134.148571  19.321428571
82   189.95 134.148571  55.801428571
83   182.22 134.148571  48.071428571
84   198.08 134.148571  63.931428571
85   135.36 134.148571   1.211428571
86   125.02  60.485000  64.535000000
87   143.50 134.148571   9.351428571
88   173.95 203.507143 -29.557142857
89   188.75 203.507143 -14.757142857
90   167.44 203.507143 -36.067142857
91   158.95 203.507143 -44.557142857
92   169.53 203.507143 -33.977142857
93   113.66 203.507143 -89.847142857
94   107.59  98.131000   9.459000000
95    92.67  98.131000  -5.461000000
96    85.35  98.131000 -12.781000000
97    90.13  98.131000  -8.001000000
98    89.31  98.131000  -8.821000000
99   105.12  98.131000   6.989000000
100  125.83  98.131000  27.699000000
101  135.81  98.131000  37.679000000
102  142.43  98.131000  44.299000000
103  163.39 203.507143 -40.117142857
104  168.21 203.507143 -35.297142857
105  185.35 203.507143 -18.157142857
106  188.50 203.507143 -15.007142857
107  199.91 203.507143  -3.597142857
108  210.73 203.507143   7.222857143
109  192.06 203.507143 -11.447142857
110  204.62 203.507143   1.112857143
111  235.00 203.507143  31.492857143
112  261.09 203.507143  57.582857143
113  256.88 203.507143  53.372857143
114  251.53 203.507143  48.022857143
115  257.25 203.507143  53.742857143
116  243.10 203.507143  39.592857143
117  283.75 203.507143  80.242857143
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/fisher/rcomp/tmp/4sdxt1355275042.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if(par2=='none') {
+ op <- par(mfrow=c(2,2))
+ plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
+ plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
+ plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
+ plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$Forecasts,result$Actuals)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goodness of Fit',2,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Correlation',1,TRUE)
+ a<-table.element(a,round(detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'R-squared',1,TRUE)
+ a<-table.element(a,round(detcoef*detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'RMSE',1,TRUE)
+ a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/5tfvt1355275042.tab") 
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'#',header=TRUE)
+ a<-table.element(a,'Actuals',header=TRUE)
+ a<-table.element(a,'Forecasts',header=TRUE)
+ a<-table.element(a,'Residuals',header=TRUE)
+ a<-table.row.end(a)
+ for (i in 1:length(result$Actuals)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,result$Actuals[i])
+ a<-table.element(a,result$Forecasts[i])
+ a<-table.element(a,result$Residuals[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/6k2af1355275042.tab") 
+ }
> if (par2 != 'none') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ for (i in 1:par3) {
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ }
+ a<-table.row.end(a)
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (j in 1:par3) {
+ a<-table.element(a,myt[i,j])
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/7asv91355275042.tab") 
+ }
> 
> try(system("convert tmp/2lcpe1355275042.ps tmp/2lcpe1355275042.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i1991355275042.ps tmp/3i1991355275042.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sdxt1355275042.ps tmp/4sdxt1355275042.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.761   0.627   5.438