R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- array(list(25.94
+ ,23688100
+ ,39.18
+ ,3940.35
+ ,0.02740
+ ,144.7
+ ,5.45
+ ,28.66
+ ,13741000
+ ,35.78
+ ,4696.69
+ ,0.03220
+ ,140.8
+ ,5.73
+ ,33.95
+ ,14143500
+ ,42.54
+ ,4572.83
+ ,0.03760
+ ,137.1
+ ,5.85
+ ,31.01
+ ,16763800
+ ,27.92
+ ,3860.66
+ ,0.03070
+ ,137.7
+ ,6.02
+ ,21.00
+ ,16634600
+ ,25.05
+ ,3400.91
+ ,0.03190
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+ ,6.27
+ ,26.19
+ ,13693300
+ ,32.03
+ ,3966.11
+ ,0.03730
+ ,139.2
+ ,6.53
+ ,25.41
+ ,10545800
+ ,27.95
+ ,3766.99
+ ,0.03660
+ ,143.0
+ ,6.54
+ ,30.47
+ ,9409900
+ ,27.95
+ ,4206.35
+ ,0.03410
+ ,140.8
+ ,6.5
+ ,12.88
+ ,39182200
+ ,24.15
+ ,3672.82
+ ,0.03450
+ ,142.5
+ ,6.52
+ ,9.78
+ ,37005800
+ ,27.57
+ ,3369.63
+ ,0.03450
+ ,135.8
+ ,6.51
+ ,8.25
+ ,15818500
+ ,22.97
+ ,2597.93
+ ,0.03450
+ ,132.6
+ ,6.51
+ ,7.44
+ ,16952000
+ ,17.37
+ ,2470.52
+ ,0.03390
+ ,128.6
+ ,6.4
+ ,10.81
+ ,24563400
+ ,24.45
+ ,2772.73
+ ,0.03730
+ ,115.7
+ ,5.98
+ ,9.12
+ ,14163200
+ ,23.62
+ ,2151.83
+ ,0.03530
+ ,109.2
+ ,5.49
+ ,11.03
+ ,18184800
+ ,21.90
+ ,1840.26
+ ,0.02920
+ ,116.9
+ ,5.31
+ ,12.74
+ ,20810300
+ ,27.12
+ ,2116.24
+ ,0.03270
+ ,109.9
+ ,4.8
+ ,9.98
+ ,12843000
+ ,27.70
+ ,2110.49
+ ,0.03620
+ ,116.1
+ ,4.21
+ ,11.62
+ ,13866700
+ ,29.23
+ ,2160.54
+ ,0.03250
+ ,118.9
+ ,3.97
+ ,9.40
+ ,15119200
+ ,26.50
+ ,2027.13
+ ,0.02720
+ ,116.3
+ ,3.77
+ ,9.27
+ ,8301600
+ ,22.84
+ ,1805.43
+ ,0.02720
+ ,114.0
+ ,3.65
+ ,7.76
+ ,14039600
+ ,20.49
+ ,1498.80
+ ,0.02650
+ ,97.0
+ ,3.07
+ ,8.78
+ ,12139700
+ ,23.28
+ ,1690.20
+ ,0.02130
+ ,85.3
+ ,2.49
+ ,10.65
+ ,9649000
+ ,25.71
+ ,1930.58
+ ,0.01900
+ ,84.9
+ ,2.09
+ ,10.95
+ ,8513600
+ ,26.52
+ ,1950.40
+ ,0.01550
+ ,94.6
+ ,1.82
+ ,12.36
+ ,15278600
+ ,25.51
+ ,1934.03
+ ,0.01140
+ ,97.8
+ ,1.73
+ ,10.85
+ ,15590900
+ ,23.36
+ ,1731.49
+ ,0.01140
+ ,95.0
+ ,1.74
+ ,11.84
+ ,9691100
+ ,24.15
+ ,1845.35
+ ,0.01480
+ ,110.7
+ ,1.73
+ ,12.14
+ ,10882700
+ ,20.92
+ ,1688.23
+ ,0.01640
+ ,108.5
+ ,1.75
+ ,11.65
+ ,10294800
+ ,20.38
+ ,1615.73
+ ,0.01180
+ ,110.3
+ ,1.75
+ ,8.86
+ ,16031900
+ ,21.90
+ ,1463.21
+ ,0.01070
+ ,106.3
+ ,1.75
+ ,7.63
+ ,13683600
+ ,19.21
+ ,1328.26
+ ,0.01460
+ ,97.4
+ ,1.73
+ ,7.38
+ ,8677200
+ ,19.65
+ ,1314.85
+ ,0.01800
+ ,94.5
+ ,1.74
+ ,7.25
+ ,9874100
+ ,17.51
+ ,1172.06
+ ,0.01510
+ ,93.7
+ ,1.75
+ ,8.03
+ ,10725500
+ ,21.41
+ ,1329.75
+ ,0.02030
+ ,79.6
+ ,1.75
+ ,7.75
+ ,8348400
+ ,23.09
+ ,1478.78
+ ,0.02200
+ ,84.9
+ ,1.34
+ ,7.16
+ ,8046200
+ ,20.70
+ ,1335.51
+ ,0.02380
+ ,80.7
+ ,1.24
+ ,7.18
+ ,10862300
+ ,19.00
+ ,1320.91
+ ,0.02600
+ ,78.8
+ ,1.24
+ ,7.51
+ ,8100300
+ ,19.04
+ ,1337.52
+ ,0.02980
+ ,64.8
+ ,1.26
+ ,7.07
+ ,7287500
+ ,19.45
+ ,1341.17
+ ,0.03020
+ ,61.4
+ ,1.25
+ ,7.11
+ ,14002500
+ ,20.54
+ ,1464.31
+ ,0.02220
+ ,81.0
+ ,1.26
+ ,8.98
+ ,19037900
+ ,19.77
+ ,1595.91
+ ,0.02060
+ ,83.6
+ ,1.26
+ ,9.53
+ ,10774600
+ ,20.60
+ ,1622.80
+ ,0.02110
+ ,83.5
+ ,1.22
+ ,10.54
+ ,8960600
+ ,21.21
+ ,1735.02
+ ,0.02110
+ ,77.0
+ ,1.01
+ ,11.31
+ ,7773300
+ ,21.30
+ ,1810.45
+ ,0.02160
+ ,81.7
+ ,1.03
+ ,10.36
+ ,9579700
+ ,22.33
+ ,1786.94
+ ,0.02320
+ ,77.0
+ ,1.01
+ ,11.44
+ ,11270700
+ ,21.12
+ ,1932.21
+ ,0.02040
+ ,81.7
+ ,1.01
+ ,10.45
+ ,9492800
+ ,20.77
+ ,1960.26
+ ,0.01770
+ ,92.5
+ ,1
+ ,10.69
+ ,9136800
+ ,22.11
+ ,2003.37
+ ,0.01880
+ ,91.7
+ ,0.98
+ ,11.28
+ ,14487600
+ ,22.34
+ ,2066.15
+ ,0.01930
+ ,96.4
+ ,1
+ ,11.96
+ ,10133200
+ ,21.43
+ ,2029.82
+ ,0.01690
+ ,88.5
+ ,1.01
+ ,13.52
+ ,18659700
+ ,20.14
+ ,1994.22
+ ,0.01740
+ ,88.5
+ ,1
+ ,12.89
+ ,15980700
+ ,21.11
+ ,1920.15
+ ,0.02290
+ ,93.0
+ ,1
+ ,14.03
+ ,9732100
+ ,21.19
+ ,1986.74
+ ,0.03050
+ ,93.1
+ ,1
+ ,16.27
+ ,14626300
+ ,23.07
+ ,2047.79
+ ,0.03270
+ ,102.8
+ ,1.03
+ ,16.17
+ ,16904000
+ ,23.01
+ ,1887.36
+ ,0.02990
+ ,105.7
+ ,1.26
+ ,17.25
+ ,13616700
+ ,22.12
+ ,1838.10
+ ,0.02650
+ ,98.7
+ ,1.43
+ ,19.38
+ ,13772900
+ ,22.40
+ ,1896.84
+ ,0.02540
+ ,96.7
+ ,1.61
+ ,26.20
+ ,28749200
+ ,22.66
+ ,1974.99
+ ,0.03190
+ ,92.9
+ ,1.76
+ ,33.53
+ ,31408300
+ ,24.21
+ ,2096.81
+ ,0.03520
+ ,92.6
+ ,1.93
+ ,32.20
+ ,26342800
+ ,24.13
+ ,2175.44
+ ,0.03260
+ ,102.7
+ ,2.16
+ ,38.45
+ ,48909500
+ ,23.73
+ ,2062.41
+ ,0.02970
+ ,105.1
+ ,2.28
+ ,44.86
+ ,41542400
+ ,22.79
+ ,2051.72
+ ,0.03010
+ ,104.4
+ ,2.5
+ ,41.67
+ ,24857200
+ ,21.89
+ ,1999.23
+ ,0.03150
+ ,103.0
+ ,2.63
+ ,36.06
+ ,34093700
+ ,22.92
+ ,1921.65
+ ,0.03510
+ ,97.5
+ ,2.79
+ ,39.76
+ ,22555200
+ ,23.44
+ ,2068.22
+ ,0.02800
+ ,103.1
+ ,3
+ ,36.81
+ ,19067500
+ ,22.57
+ ,2056.96
+ ,0.02530
+ ,106.2
+ ,3.04
+ ,42.65
+ ,19029100
+ ,23.27
+ ,2184.83
+ ,0.03170
+ ,103.6
+ ,3.26
+ ,46.89
+ ,15223200
+ ,24.95
+ ,2152.09
+ ,0.03640
+ ,105.5
+ ,3.5
+ ,53.61
+ ,21903700
+ ,23.45
+ ,2151.69
+ ,0.04690
+ ,87.5
+ ,3.62
+ ,57.59
+ ,33306600
+ ,23.42
+ ,2120.30
+ ,0.04350
+ ,85.2
+ ,3.78
+ ,67.82
+ ,23898100
+ ,25.30
+ ,2232.82
+ ,0.03460
+ ,98.3
+ ,4
+ ,71.89
+ ,23279600
+ ,23.90
+ ,2205.32
+ ,0.03420
+ ,103.8
+ ,4.16
+ ,75.51
+ ,40699800
+ ,25.73
+ ,2305.82
+ ,0.03990
+ ,106.8
+ ,4.29
+ ,68.49
+ ,37646000
+ ,24.64
+ ,2281.39
+ ,0.03600
+ ,102.7
+ ,4.49
+ ,62.72
+ ,37277000
+ ,24.95
+ ,2339.79
+ ,0.03360
+ ,107.5
+ ,4.59
+ ,70.39
+ ,39246800
+ ,22.15
+ ,2322.57
+ ,0.03550
+ ,109.8
+ ,4.79
+ ,59.77
+ ,27418400
+ ,20.85
+ ,2178.88
+ ,0.04170
+ ,104.7
+ ,4.94
+ ,57.27
+ ,30318700
+ ,21.45
+ ,2172.09
+ ,0.04320
+ ,105.7
+ ,4.99
+ ,67.96
+ ,32808100
+ ,22.15
+ ,2091.47
+ ,0.04150
+ ,107.0
+ ,5.24
+ ,67.85
+ ,28668200
+ ,23.75
+ ,2183.75
+ ,0.03820
+ ,100.2
+ ,5.25
+ ,76.98
+ ,32370300
+ ,25.27
+ ,2258.43
+ ,0.02060
+ ,105.9
+ ,5.25
+ ,81.08
+ ,24171100
+ ,26.53
+ ,2366.71
+ ,0.01310
+ ,105.1
+ ,5.25
+ ,91.66
+ ,25009100
+ ,27.22
+ ,2431.77
+ ,0.01970
+ ,105.3
+ ,5.25
+ ,84.84
+ ,32084300
+ ,27.69
+ ,2415.29
+ ,0.02540
+ ,110.0
+ ,5.24
+ ,85.73
+ ,50117500
+ ,28.61
+ ,2463.93
+ ,0.02080
+ ,110.2
+ ,5.25
+ ,84.61
+ ,27522200
+ ,26.21
+ ,2416.15
+ ,0.02420
+ ,111.2
+ ,5.26
+ ,92.91
+ ,26816800
+ ,25.93
+ ,2421.64
+ ,0.02780
+ ,108.2
+ ,5.26
+ ,99.80
+ ,25136100
+ ,27.86
+ ,2525.09
+ ,0.02570
+ ,106.3
+ ,5.25
+ ,121.19
+ ,30295600
+ ,28.65
+ ,2604.52
+ ,0.02690
+ ,108.5
+ ,5.25
+ ,122.04
+ ,41526100
+ ,27.51
+ ,2603.23
+ ,0.02690
+ ,105.3
+ ,5.25
+ ,131.76
+ ,43845100
+ ,27.06
+ ,2546.27
+ ,0.02360
+ ,111.9
+ ,5.26
+ ,138.48
+ ,39188900
+ ,26.91
+ ,2596.36
+ ,0.01970
+ ,105.6
+ ,5.02
+ ,153.47
+ ,40496400
+ ,27.60
+ ,2701.50
+ ,0.02760
+ ,99.5
+ ,4.94
+ ,189.95
+ ,37438400
+ ,34.48
+ ,2859.12
+ ,0.03540
+ ,95.2
+ ,4.76
+ ,182.22
+ ,46553700
+ ,31.58
+ ,2660.96
+ ,0.04310
+ ,87.8
+ ,4.49
+ ,198.08
+ ,31771400
+ ,33.46
+ ,2652.28
+ ,0.04080
+ ,90.6
+ ,4.24
+ ,135.36
+ ,62108100
+ ,30.64
+ ,2389.86
+ ,0.04280
+ ,87.9
+ ,3.94
+ ,125.02
+ ,46645400
+ ,25.66
+ ,2271.48
+ ,0.04030
+ ,76.4
+ ,2.98
+ ,143.50
+ ,42313100
+ ,26.78
+ ,2279.10
+ ,0.03980
+ ,65.9
+ ,2.61
+ ,173.95
+ ,38841700
+ ,26.91
+ ,2412.80
+ ,0.03940
+ ,62.3
+ ,2.28
+ ,188.75
+ ,32650300
+ ,26.82
+ ,2522.66
+ ,0.04180
+ ,57.2
+ ,1.98
+ ,167.44
+ ,34281100
+ ,26.05
+ ,2292.98
+ ,0.05020
+ ,50.4
+ ,2
+ ,158.95
+ ,33096200
+ ,24.36
+ ,2325.55
+ ,0.05600
+ ,51.9
+ ,2.01
+ ,169.53
+ ,23273800
+ ,25.94
+ ,2367.52
+ ,0.05370
+ ,58.5
+ ,2
+ ,113.66
+ ,43697600
+ ,25.37
+ ,2091.88
+ ,0.04940
+ ,61.4
+ ,1.81
+ ,107.59
+ ,66902300
+ ,21.23
+ ,1720.95
+ ,0.03660
+ ,38.8
+ ,0.97
+ ,92.67
+ ,44957200
+ ,19.35
+ ,1535.57
+ ,0.01070
+ ,44.9
+ ,0.39
+ ,85.35
+ ,33800900
+ ,18.61
+ ,1577.03
+ ,0.00090
+ ,38.6
+ ,0.16
+ ,90.13
+ ,33487900
+ ,16.37
+ ,1476.42
+ ,0.00030
+ ,4.0
+ ,0.15
+ ,89.31
+ ,27394900
+ ,15.56
+ ,1377.84
+ ,0.00240
+ ,25.3
+ ,0.22
+ ,105.12
+ ,25963400
+ ,17.70
+ ,1528.59
+ ,-0.00380
+ ,26.9
+ ,0.18
+ ,125.83
+ ,20952600
+ ,19.52
+ ,1717.30
+ ,-0.00740
+ ,40.8
+ ,0.15
+ ,135.81
+ ,17702900
+ ,20.26
+ ,1774.33
+ ,-0.01280
+ ,54.8
+ ,0.18
+ ,142.43
+ ,21282100
+ ,23.05
+ ,1835.04
+ ,-0.01430
+ ,49.3
+ ,0.21
+ ,163.39
+ ,18449100
+ ,22.81
+ ,1978.50
+ ,-0.02100
+ ,47.4
+ ,0.16
+ ,168.21
+ ,14415700
+ ,24.04
+ ,2009.06
+ ,-0.01480
+ ,54.5
+ ,0.16
+ ,185.35
+ ,17906300
+ ,25.08
+ ,2122.42
+ ,-0.01290
+ ,53.4
+ ,0.15
+ ,188.50
+ ,22197500
+ ,27.04
+ ,2045.11
+ ,-0.00180
+ ,48.7
+ ,0.12
+ ,199.91
+ ,15856500
+ ,28.81
+ ,2144.60
+ ,0.01840
+ ,50.6
+ ,0.12
+ ,210.73
+ ,19068700
+ ,29.86
+ ,2269.15
+ ,0.02720
+ ,53.6
+ ,0.12
+ ,192.06
+ ,30855100
+ ,27.61
+ ,2147.35
+ ,0.02630
+ ,56.5
+ ,0.11
+ ,204.62
+ ,21209000
+ ,28.22
+ ,2238.26
+ ,0.02140
+ ,46.4
+ ,0.13
+ ,235.00
+ ,19541600
+ ,28.83
+ ,2397.96
+ ,0.02310
+ ,52.3
+ ,0.16
+ ,261.09
+ ,21955000
+ ,30.06
+ ,2461.19
+ ,0.02240
+ ,57.7
+ ,0.2
+ ,256.88
+ ,33725900
+ ,25.51
+ ,2257.04
+ ,0.02020
+ ,62.7
+ ,0.2
+ ,251.53
+ ,28192800
+ ,22.75
+ ,2109.24
+ ,0.01050
+ ,54.3
+ ,0.18
+ ,257.25
+ ,27377000
+ ,25.52
+ ,2254.70
+ ,0.01240
+ ,51.0
+ ,0.18
+ ,243.10
+ ,16228100
+ ,23.33
+ ,2114.03
+ ,0.01150
+ ,53.2
+ ,0.19
+ ,283.75
+ ,21278900
+ ,24.34
+ ,2368.62
+ ,0.01140
+ ,48.6
+ ,0.19
+ ,300.98
+ ,21457400
+ ,26.51
+ ,2507.41
+ ,0.01170
+ ,49.9
+ ,0.19)
+ ,dim=c(7
+ ,130)
+ ,dimnames=list(c('APPLE'
+ ,'VOLUME'
+ ,'MICROSOFT'
+ ,'NASDAQ'
+ ,'INFLATION'
+ ,'CONS.CONF'
+ ,'FED.FUNDS.RATE')
+ ,1:130))
>  y <- array(NA,dim=c(7,130),dimnames=list(c('APPLE','VOLUME','MICROSOFT','NASDAQ','INFLATION','CONS.CONF','FED.FUNDS.RATE'),1:130))
>  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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo
Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)

Attaching package: 'Hmisc'

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

    untangle.specials

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

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

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "APPLE"
> x[,par1]
  [1]  25.94  28.66  33.95  31.01  21.00  26.19  25.41  30.47  12.88   9.78
 [11]   8.25   7.44  10.81   9.12  11.03  12.74   9.98  11.62   9.40   9.27
 [21]   7.76   8.78  10.65  10.95  12.36  10.85  11.84  12.14  11.65   8.86
 [31]   7.63   7.38   7.25   8.03   7.75   7.16   7.18   7.51   7.07   7.11
 [41]   8.98   9.53  10.54  11.31  10.36  11.44  10.45  10.69  11.28  11.96
 [51]  13.52  12.89  14.03  16.27  16.17  17.25  19.38  26.20  33.53  32.20
 [61]  38.45  44.86  41.67  36.06  39.76  36.81  42.65  46.89  53.61  57.59
 [71]  67.82  71.89  75.51  68.49  62.72  70.39  59.77  57.27  67.96  67.85
 [81]  76.98  81.08  91.66  84.84  85.73  84.61  92.91  99.80 121.19 122.04
 [91] 131.76 138.48 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95
[101] 188.75 167.44 158.95 169.53 113.66 107.59  92.67  85.35  90.13  89.31
[111] 105.12 125.83 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73
[121] 192.06 204.62 235.00 261.09 256.88 251.53 257.25 243.10 283.75 300.98
> 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   7.44   7.51   7.63   7.75   7.76 
     1      1      1      1      1      1      1      1      1      1      1 
  8.03   8.25   8.78   8.86   8.98   9.12   9.27    9.4   9.53   9.78   9.98 
     1      1      1      1      1      1      1      1      1      1      1 
 10.36  10.45  10.54  10.65  10.69  10.81  10.85  10.95  11.03  11.28  11.31 
     1      1      1      1      1      1      1      1      1      1      1 
 11.44  11.62  11.65  11.84  11.96  12.14  12.36  12.74  12.88  12.89  13.52 
     1      1      1      1      1      1      1      1      1      1      1 
 14.03  16.17  16.27  17.25  19.38     21  25.41  25.94  26.19   26.2  28.66 
     1      1      1      1      1      1      1      1      1      1      1 
 30.47  31.01   32.2  33.53  33.95  36.06  36.81  38.45  39.76  41.67  42.65 
     1      1      1      1      1      1      1      1      1      1      1 
 44.86  46.89  53.61  57.27  57.59  59.77  62.72  67.82  67.85  67.96  68.49 
     1      1      1      1      1      1      1      1      1      1      1 
 70.39  71.89  75.51  76.98  81.08  84.61  84.84  85.35  85.73  89.31  90.13 
     1      1      1      1      1      1      1      1      1      1      1 
 91.66  92.67  92.91   99.8 105.12 107.59 113.66 121.19 122.04 125.02 125.83 
     1      1      1      1      1      1      1      1      1      1      1 
131.76 135.36 135.81 138.48 142.43  143.5 153.47 158.95 163.39 167.44 168.21 
     1      1      1      1      1      1      1      1      1      1      1 
169.53 173.95 182.22 185.35  188.5 188.75 189.95 192.06 198.08 199.91 204.62 
     1      1      1      1      1      1      1      1      1      1      1 
210.73    235  243.1 251.53 256.88 257.25 261.09 283.75 300.98 
     1      1      1      1      1      1      1      1      1 
> colnames(x)
[1] "APPLE"          "VOLUME"         "MICROSOFT"      "NASDAQ"        
[5] "INFLATION"      "CONS.CONF"      "FED.FUNDS.RATE"
> colnames(x)[par1]
[1] "APPLE"
> x[,par1]
  [1]  25.94  28.66  33.95  31.01  21.00  26.19  25.41  30.47  12.88   9.78
 [11]   8.25   7.44  10.81   9.12  11.03  12.74   9.98  11.62   9.40   9.27
 [21]   7.76   8.78  10.65  10.95  12.36  10.85  11.84  12.14  11.65   8.86
 [31]   7.63   7.38   7.25   8.03   7.75   7.16   7.18   7.51   7.07   7.11
 [41]   8.98   9.53  10.54  11.31  10.36  11.44  10.45  10.69  11.28  11.96
 [51]  13.52  12.89  14.03  16.27  16.17  17.25  19.38  26.20  33.53  32.20
 [61]  38.45  44.86  41.67  36.06  39.76  36.81  42.65  46.89  53.61  57.59
 [71]  67.82  71.89  75.51  68.49  62.72  70.39  59.77  57.27  67.96  67.85
 [81]  76.98  81.08  91.66  84.84  85.73  84.61  92.91  99.80 121.19 122.04
 [91] 131.76 138.48 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95
[101] 188.75 167.44 158.95 169.53 113.66 107.59  92.67  85.35  90.13  89.31
[111] 105.12 125.83 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73
[121] 192.06 204.62 235.00 261.09 256.88 251.53 257.25 243.10 283.75 300.98
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/1t8ep1292237033.tab") 
+ }
+ }
> m

	 Conditional inference tree with 9 terminal nodes

Response:  APPLE 
Inputs:  VOLUME, MICROSOFT, NASDAQ, INFLATION, CONS.CONF, FED.FUNDS.RATE 
Number of observations:  130 

1) CONS.CONF <= 62.7; criterion = 1, statistic = 44.873
  2) NASDAQ <= 2091.88; criterion = 1, statistic = 21.304
    3)*  weights = 14 
  2) NASDAQ > 2091.88
    4)*  weights = 18 
1) CONS.CONF > 62.7
  5) VOLUME <= 24857200; criterion = 1, statistic = 54.324
    6) VOLUME <= 20810300; criterion = 1, statistic = 24.456
      7) NASDAQ <= 2151.83; criterion = 1, statistic = 22.125
        8) NASDAQ <= 1805.43; criterion = 0.993, statistic = 10.553
          9) NASDAQ <= 1595.91; criterion = 0.996, statistic = 11.772
            10)*  weights = 12 
          9) NASDAQ > 1595.91
            11)*  weights = 8 
        8) NASDAQ > 1805.43
          12)*  weights = 23 
      7) NASDAQ > 2151.83
        13)*  weights = 12 
    6) VOLUME > 20810300
      14)*  weights = 8 
  5) VOLUME > 24857200
    15) MICROSOFT <= 25.27; criterion = 1, statistic = 21.273
      16)*  weights = 16 
    15) MICROSOFT > 25.27
      17)*  weights = 19 
> postscript(file="/var/www/rcomp/tmp/2t8ep1292237033.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/rcomp/tmp/3t8ep1292237033.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     25.94  49.072500  -23.13250000
2     28.66  26.128333    2.53166667
3     33.95  26.128333    7.82166667
4     31.01  26.128333    4.88166667
5     21.00  26.128333   -5.12833333
6     26.19  26.128333    0.06166667
7     25.41  26.128333   -0.71833333
8     30.47  26.128333    4.34166667
9     12.88  50.825000  -37.94500000
10     9.78 119.258421 -109.47842105
11     8.25  26.128333  -17.87833333
12     7.44  26.128333  -18.68833333
13    10.81  49.072500  -38.26250000
14     9.12  13.544348   -4.42434783
15    11.03  13.544348   -2.51434783
16    12.74  13.544348   -0.80434783
17     9.98  13.544348   -3.56434783
18    11.62  26.128333  -14.50833333
19     9.40  13.544348   -4.14434783
20     9.27  10.390000   -1.12000000
21     7.76   7.716667    0.04333333
22     8.78  10.390000   -1.61000000
23    10.65  13.544348   -2.89434783
24    10.95  13.544348   -2.59434783
25    12.36  13.544348   -1.18434783
26    10.85  10.390000    0.46000000
27    11.84  13.544348   -1.70434783
28    12.14  10.390000    1.75000000
29    11.65  10.390000    1.26000000
30     8.86   7.716667    1.14333333
31     7.63   7.716667   -0.08666667
32     7.38   7.716667   -0.33666667
33     7.25   7.716667   -0.46666667
34     8.03   7.716667    0.31333333
35     7.75   7.716667    0.03333333
36     7.16   7.716667   -0.55666667
37     7.18   7.716667   -0.53666667
38     7.51   7.716667   -0.20666667
39     7.07 115.362143 -108.29214286
40     7.11   7.716667   -0.60666667
41     8.98   7.716667    1.26333333
42     9.53  10.390000   -0.86000000
43    10.54  10.390000    0.15000000
44    11.31  13.544348   -2.23434783
45    10.36  10.390000   -0.03000000
46    11.44  13.544348   -2.10434783
47    10.45  13.544348   -3.09434783
48    10.69  13.544348   -2.85434783
49    11.28  13.544348   -2.26434783
50    11.96  13.544348   -1.58434783
51    13.52  13.544348   -0.02434783
52    12.89  13.544348   -0.65434783
53    14.03  13.544348    0.48565217
54    16.27  13.544348    2.72565217
55    16.17  13.544348    2.62565217
56    17.25  13.544348    3.70565217
57    19.38  13.544348    5.83565217
58    26.20  50.825000  -24.62500000
59    33.53  50.825000  -17.29500000
60    32.20  50.825000  -18.62500000
61    38.45  50.825000  -12.37500000
62    44.86  50.825000   -5.96500000
63    41.67  49.072500   -7.40250000
64    36.06  50.825000  -14.76500000
65    39.76  49.072500   -9.31250000
66    36.81  13.544348   23.26565217
67    42.65  26.128333   16.52166667
68    46.89  26.128333   20.76166667
69    53.61  49.072500    4.53750000
70    57.59  50.825000    6.76500000
71    67.82  49.072500   18.74750000
72    71.89  49.072500   22.81750000
73    75.51 119.258421  -43.74842105
74    68.49  50.825000   17.66500000
75    62.72  50.825000   11.89500000
76    70.39  50.825000   19.56500000
77    59.77  50.825000    8.94500000
78    57.27  50.825000    6.44500000
79    67.96  50.825000   17.13500000
80    67.85  50.825000   17.02500000
81    76.98  50.825000   26.15500000
82    81.08  49.072500   32.00750000
83    91.66 119.258421  -27.59842105
84    84.84 119.258421  -34.41842105
85    85.73 119.258421  -33.52842105
86    84.61 119.258421  -34.64842105
87    92.91 119.258421  -26.34842105
88    99.80 119.258421  -19.45842105
89   121.19 119.258421    1.93157895
90   122.04 119.258421    2.78157895
91   131.76 119.258421   12.50157895
92   138.48 119.258421   19.22157895
93   153.47 119.258421   34.21157895
94   189.95 119.258421   70.69157895
95   182.22 119.258421   62.96157895
96   198.08 119.258421   78.82157895
97   135.36 119.258421   16.10157895
98   125.02 119.258421    5.76157895
99   143.50 119.258421   24.24157895
100  173.95 218.937222  -44.98722222
101  188.75 218.937222  -30.18722222
102  167.44 218.937222  -51.49722222
103  158.95 218.937222  -59.98722222
104  169.53 218.937222  -49.40722222
105  113.66 115.362143   -1.70214286
106  107.59 115.362143   -7.77214286
107   92.67 115.362143  -22.69214286
108   85.35 115.362143  -30.01214286
109   90.13 115.362143  -25.23214286
110   89.31 115.362143  -26.05214286
111  105.12 115.362143  -10.24214286
112  125.83 115.362143   10.46785714
113  135.81 115.362143   20.44785714
114  142.43 115.362143   27.06785714
115  163.39 115.362143   48.02785714
116  168.21 115.362143   52.84785714
117  185.35 218.937222  -33.58722222
118  188.50 115.362143   73.13785714
119  199.91 218.937222  -19.02722222
120  210.73 218.937222   -8.20722222
121  192.06 218.937222  -26.87722222
122  204.62 218.937222  -14.31722222
123  235.00 218.937222   16.06277778
124  261.09 218.937222   42.15277778
125  256.88 218.937222   37.94277778
126  251.53 218.937222   32.59277778
127  257.25 218.937222   38.31277778
128  243.10 218.937222   24.16277778
129  283.75 218.937222   64.81277778
130  300.98 218.937222   82.04277778
> 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/rcomp/tmp/44ivs1292237033.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/rcomp/tmp/5iab11292237033.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/rcomp/tmp/6tjam1292237033.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/rcomp/tmp/7w19a1292237033.tab") 
+ }
> try(system("convert tmp/2t8ep1292237033.ps tmp/2t8ep1292237033.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t8ep1292237033.ps tmp/3t8ep1292237033.png",intern=TRUE))
character(0)
> try(system("convert tmp/44ivs1292237033.ps tmp/44ivs1292237033.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.010   0.680   3.708