R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(2981.85
+ ,2819.19
+ ,11394.84
+ ,10539.51
+ ,10407
+ ,44.23
+ ,3080.58
+ ,2892.56
+ ,11545.71
+ ,10723.78
+ ,10463
+ ,45.85
+ ,3106.22
+ ,2866.08
+ ,11809.38
+ ,10682.06
+ ,10556
+ ,53.38
+ ,3119.31
+ ,2817.41
+ ,11395.64
+ ,10283.19
+ ,10646
+ ,53.26
+ ,3061.26
+ ,2934.75
+ ,11082.38
+ ,10377.18
+ ,10702
+ ,51.8
+ ,3097.31
+ ,3036.54
+ ,11402.75
+ ,10486.64
+ ,11353
+ ,55.3
+ ,3161.69
+ ,3139.5
+ ,11716.87
+ ,10545.38
+ ,11346
+ ,57.81
+ ,3257.16
+ ,3114.31
+ ,12204.98
+ ,10554.27
+ ,11451
+ ,63.96
+ ,3277.01
+ ,3261.3
+ ,12986.62
+ ,10532.54
+ ,11964
+ ,63.77
+ ,3295.32
+ ,3201.79
+ ,13392.79
+ ,10324.31
+ ,12574
+ ,59.15
+ ,3363.99
+ ,3264.53
+ ,14368.05
+ ,10695.25
+ ,13031
+ ,56.12
+ ,3494.17
+ ,3349.1
+ ,15650.83
+ ,10827.81
+ ,13812
+ ,57.42
+ ,3667.03
+ ,3446.17
+ ,16102.64
+ ,10872.48
+ ,14544
+ ,63.52
+ ,3813.06
+ ,3469.48
+ ,16187.64
+ ,10971.19
+ ,14931
+ ,61.71
+ ,3917.96
+ ,3507.13
+ ,16311.54
+ ,11145.65
+ ,14886
+ ,63.01
+ ,3895.51
+ ,3536.2
+ ,17232.97
+ ,11234.68
+ ,16005
+ ,68.18
+ ,3801.06
+ ,3359.05
+ ,16397.83
+ ,11333.88
+ ,17064
+ ,72.03
+ ,3570.12
+ ,3378.85
+ ,14990.31
+ ,10997.97
+ ,15168
+ ,69.75
+ ,3701.61
+ ,3449.15
+ ,15147.55
+ ,11036.89
+ ,16050
+ ,74.41
+ ,3862.27
+ ,3522.89
+ ,15786.78
+ ,11257.35
+ ,15839
+ ,74.33
+ ,3970.1
+ ,3551.04
+ ,15934.09
+ ,11533.59
+ ,15137
+ ,64.24
+ ,4138.52
+ ,3669.15
+ ,16519.44
+ ,11963.12
+ ,14954
+ ,60.03
+ ,4199.75
+ ,3602
+ ,16101.07
+ ,12185.15
+ ,15648
+ ,59.44
+ ,4290.89
+ ,3697.22
+ ,16775.08
+ ,12377.62
+ ,15305
+ ,62.5
+ ,4443.91
+ ,3760.9
+ ,17286.32
+ ,12512.89
+ ,15579
+ ,55.04
+ ,4502.64
+ ,3665.08
+ ,17741.23
+ ,12631.48
+ ,16348
+ ,58.34
+ ,4356.98
+ ,3708.8
+ ,17128.37
+ ,12268.53
+ ,15928
+ ,61.92
+ ,4591.27
+ ,3858.21
+ ,17460.53
+ ,12754.8
+ ,16171
+ ,67.65
+ ,4696.96
+ ,3933.16
+ ,17611.14
+ ,13407.75
+ ,15937
+ ,67.68
+ ,4621.4
+ ,3946.98
+ ,18001.37
+ ,13480.21
+ ,15713
+ ,70.3
+ ,4562.84
+ ,3794.29
+ ,17974.77
+ ,13673.28
+ ,15594
+ ,75.26
+ ,4202.52
+ ,3765.56
+ ,16460.95
+ ,13239.71
+ ,15683
+ ,71.44
+ ,4296.49
+ ,3820.33
+ ,16235.39
+ ,13557.69
+ ,16438
+ ,76.36
+ ,4435.23
+ ,3885.12
+ ,16903.36
+ ,13901.28
+ ,17032
+ ,81.71
+ ,4105.18
+ ,3752.67
+ ,15543.76
+ ,13200.58
+ ,17696
+ ,92.6
+ ,4116.68
+ ,3683.79
+ ,15532.18
+ ,13406.97
+ ,17745
+ ,90.6
+ ,3844.49
+ ,3240.75
+ ,13731.31
+ ,12538.12
+ ,19394
+ ,92.23
+ ,3720.98
+ ,3188.82
+ ,13547.84
+ ,12419.57
+ ,20148
+ ,94.09
+ ,3674.4
+ ,3017.98
+ ,12602.93
+ ,12193.88
+ ,20108
+ ,102.79
+ ,3857.62
+ ,3237.2
+ ,13357.7
+ ,12656.63
+ ,18584
+ ,109.65
+ ,3801.06
+ ,3182.53
+ ,13995.33
+ ,12812.48
+ ,18441
+ ,124.05
+ ,3504.37
+ ,2906.42
+ ,14084.6
+ ,12056.67
+ ,18391
+ ,132.69
+ ,3032.6
+ ,2881.35
+ ,13168.91
+ ,11322.38
+ ,19178
+ ,135.81
+ ,3047.03
+ ,2915.64
+ ,12989.35
+ ,11530.75
+ ,18079
+ ,116.07
+ ,2962.34
+ ,2635.13
+ ,12123.53
+ ,11114.08
+ ,18483
+ ,101.42
+ ,2197.82
+ ,2331.43
+ ,9117.03
+ ,9181.73
+ ,19644
+ ,75.73
+ ,2014.45
+ ,2159.04
+ ,8531.45
+ ,8614.55
+ ,19195
+ ,55.48
+ ,1862.83
+ ,NA
+ ,8460.94
+ ,8595.56
+ ,19650
+ ,43.8
+ ,1905.41
+ ,1983.48
+ ,8331.49
+ ,8396.2
+ ,20830
+ ,45.29
+ ,1810.99
+ ,1770.41
+ ,7694.78
+ ,7690.5
+ ,23595
+ ,44.01
+ ,1670.07
+ ,1815.99
+ ,7764.58
+ ,7235.47
+ ,22937
+ ,47.48
+ ,1864.44
+ ,2026.97
+ ,8767.96
+ ,7992.12
+ ,21814
+ ,51.07
+ ,2052.02
+ ,2124.81
+ ,9304.43
+ ,8398.37
+ ,21928
+ ,57.84
+ ,2029.6
+ ,2098.28
+ ,9810.31
+ ,8593
+ ,21777
+ ,69.04
+ ,2070.83
+ ,2291.39
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,65.61
+ ,2293.41
+ ,2401.57
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,72.87
+ ,2443.27
+ ,2453.89
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,68.41
+ ,2513.17
+ ,2409.53
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,73.25
+ ,2466.92
+ ,2432.45
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,77.43
+ ,2502.66
+ ,2585.34
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,75.28
+ ,2539.91
+ ,2478.51
+ ,10661.62
+ ,10471.24
+ ,25204
+ ,77.33
+ ,2482.6
+ ,2470.18
+ ,10175.13
+ ,10214.51
+ ,25739
+ ,74.31
+ ,2626.15
+ ,2629.16
+ ,10671.49
+ ,10677.52
+ ,26434
+ ,79.7
+ ,2656.32
+ ,2541.22
+ ,11139.77
+ ,11052.15
+ ,27525
+ ,85.47
+ ,2446.66
+ ,2397.18
+ ,10103.98
+ ,10500.19
+ ,30695
+ ,77.98
+ ,2467.38
+ ,2359.66
+ ,9786.05
+ ,10159.27
+ ,32436
+ ,75.69
+ ,2462.32
+ ,2476.2
+ ,9456.84
+ ,10222.24
+ ,30160
+ ,75.2
+ ,2504.58
+ ,2449.57
+ ,9268.24
+ ,10350.4
+ ,30236
+ ,77.21
+ ,2579.39
+ ,2482.18
+ ,9346.72
+ ,10598.07
+ ,31293
+ ,77.85
+ ,2649.24
+ ,2542.76
+ ,9455.09
+ ,11044.49
+ ,31077
+ ,83.53
+ ,2636.87
+ ,2477.63
+ ,9797.18
+ ,11198.31
+ ,32226
+ ,85.99
+ ,2613.94
+ ,2586.46
+ ,10254.46
+ ,11465.26
+ ,33865
+ ,91.77
+ ,2634.01
+ ,2654.47
+ ,10449.53
+ ,11802.37
+ ,32810
+ ,96.59
+ ,2711.94
+ ,2713.48
+ ,10622.27
+ ,12190
+ ,32242
+ ,103.57
+ ,2646.43
+ ,2582.9
+ ,9852.45
+ ,12081.48
+ ,32700
+ ,114.46
+ ,2717.79
+ ,2661.37
+ ,9644.62
+ ,12434.93
+ ,32819
+ ,122.54
+ ,2701.54
+ ,2631.87
+ ,9650.78
+ ,12579.99
+ ,33947
+ ,115.08
+ ,2572.98
+ ,2561.37
+ ,9541.53
+ ,12097.31
+ ,34148
+ ,113.93
+ ,2488.92
+ ,2510.85
+ ,9996.68
+ ,12512.33
+ ,35261
+ ,116.29
+ ,2204.91
+ ,2238.24
+ ,9072.94
+ ,11326.62
+ ,39506
+ ,110.12
+ ,2123.99
+ ,2159.7
+ ,8695.42
+ ,11175.45
+ ,41591
+ ,110.86
+ ,2149.1
+ ,2318
+ ,8733.56
+ ,11515.93
+ ,39148
+ ,108.53)
+ ,dim=c(6
+ ,82)
+ ,dimnames=list(c('BEL20'
+ ,'DJEuropeStoxx'
+ ,'Nikkei'
+ ,'DowJones'
+ ,'Goudprijs'
+ ,'Brent')
+ ,1:82))
>  y <- array(NA,dim=c(6,82),dimnames=list(c('BEL20','DJEuropeStoxx','Nikkei','DowJones','Goudprijs','Brent'),1:82))
>  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 = '2'
> par2 = 'quantiles'
> 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
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] 2981.85 3080.58 3106.22 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01
[10] 3295.32 3363.99 3494.17 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12
[19] 3701.61 3862.27 3970.10 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98
[28] 4591.27 4696.96 4621.40 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68
[37] 3844.49 3720.98 3674.40 3857.62 3801.06 3504.37 3032.60 3047.03 2962.34
[46] 2197.82 2014.45 1862.83 1905.41 1810.99 1670.07 1864.44 2052.02 2029.60
[55] 2070.83 2293.41 2443.27 2513.17 2466.92 2502.66 2539.91 2482.60 2626.15
[64] 2656.32 2446.66 2467.38 2462.32 2504.58 2579.39 2649.24 2636.87 2613.94
[73] 2634.01 2711.94 2646.43 2717.79 2701.54 2572.98 2488.92 2204.91 2123.99
[82] 2149.10
> 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])

[1670,3061) [3061,4697] 
         41          41 
> colnames(x)
[1] "BEL20"         "DJEuropeStoxx" "Nikkei"        "DowJones"     
[5] "Goudprijs"     "Brent"        
> colnames(x)[par1]
[1] "BEL20"
> x[,par1]
 [1] [1670,3061) [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
 [7] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[13] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[19] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[25] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[31] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[37] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697] [3061,4697]
[43] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[49] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[55] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[61] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[67] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[73] [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061) [1670,3061)
[79] [1670,3061) [1670,3061) [1670,3061) [1670,3061)
Levels: [1670,3061) [3061,4697]
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/1j0xi1323824079.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 345  27
          2   5 356
[1] 0.9274194
[1] 0.9861496
[1] 0.9563438
           m.ct.x.pred
m.ct.x.actu  1  2
          1 32  6
          2  3 46
[1] 0.8421053
[1] 0.9387755
[1] 0.8965517
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(BEL20) 
Inputs:  DJEuropeStoxx, Nikkei, DowJones, Goudprijs, Brent 
Number of observations:  82 

1) DJEuropeStoxx <= 2713.48; criterion = 1, statistic = 59.651
  2)*  weights = 37 
1) DJEuropeStoxx > 2713.48
  3) Nikkei <= 13168.91; criterion = 0.981, statistic = 8.369
    4)*  weights = 13 
  3) Nikkei > 13168.91
    5)*  weights = 32 
> postscript(file="/var/wessaorg/rcomp/tmp/2b5py1323824079.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/wessaorg/rcomp/tmp/3pohb1323824079.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    2
 [2,]    2    2
 [3,]    2    2
 [4,]    2    2
 [5,]    2    2
 [6,]    2    2
 [7,]    2    2
 [8,]    2    2
 [9,]    2    2
[10,]    2    2
[11,]    2    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    2
[26,]    2    2
[27,]    2    2
[28,]    2    2
[29,]    2    2
[30,]    2    2
[31,]    2    2
[32,]    2    2
[33,]    2    2
[34,]    2    2
[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,]    1    2
[44,]    1    2
[45,]    1    1
[46,]    1    1
[47,]    1    1
[48,]    1    2
[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,]    1    1
[58,]    1    1
[59,]    1    1
[60,]    1    1
[61,]    1    1
[62,]    1    1
[63,]    1    1
[64,]    1    1
[65,]    1    1
[66,]    1    1
[67,]    1    1
[68,]    1    1
[69,]    1    1
[70,]    1    1
[71,]    1    1
[72,]    1    1
[73,]    1    1
[74,]    1    1
[75,]    1    1
[76,]    1    1
[77,]    1    1
[78,]    1    1
[79,]    1    1
[80,]    1    1
[81,]    1    1
[82,]    1    1
             
              [1670,3061) [3061,4697]
  [1670,3061)          37           4
  [3061,4697]           0          41
> postscript(file="/var/wessaorg/rcomp/tmp/4tghc1323824079.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/wessaorg/rcomp/tmp/52tdn1323824079.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/wessaorg/rcomp/tmp/6vpis1323824079.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/wessaorg/rcomp/tmp/7jkh01323824079.tab") 
+ }
> 
> try(system("convert tmp/2b5py1323824079.ps tmp/2b5py1323824079.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pohb1323824079.ps tmp/3pohb1323824079.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tghc1323824079.ps tmp/4tghc1323824079.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.976   0.252   3.265