R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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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  Natural language support but running in an English locale

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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(15561600
+ ,15.73
+ ,3.56
+ ,142.86
+ ,14917500
+ ,16.17
+ ,1.33
+ ,380.71
+ ,14805920
+ ,12.00
+ ,0.00
+ ,460.00
+ ,16958000
+ ,12.86
+ ,0.69
+ ,361.43
+ ,17605000
+ ,10.30
+ ,10.05
+ ,140.00
+ ,17131200
+ ,12.97
+ ,0.51
+ ,275.00
+ ,18474600
+ ,12.06
+ ,0.91
+ ,274.29
+ ,17286700
+ ,10.49
+ ,2.67
+ ,212.86
+ ,18574400
+ ,5.97
+ ,1.39
+ ,172.86
+ ,18056000
+ ,9.26
+ ,1.24
+ ,186.43
+ ,19701600
+ ,9.74
+ ,2.79
+ ,77.14
+ ,19061700
+ ,5.46
+ ,3.37
+ ,17.86
+ ,19681900
+ ,2.71
+ ,1.60
+ ,37.14
+ ,34521200
+ ,3.90
+ ,4.73
+ ,42.86
+ ,19922700
+ ,1.51
+ ,0.79
+ ,85.00
+ ,20177900
+ ,5.01
+ ,0.67
+ ,45.00
+ ,19759900
+ ,2.96
+ ,0.00
+ ,206.43
+ ,23076700
+ ,-1.97
+ ,0.60
+ ,178.57
+ ,22532000
+ ,-4.61
+ ,0.40
+ ,285.71
+ ,22029400
+ ,4.27
+ ,2.24
+ ,58.57
+ ,22587000
+ ,4.01
+ ,5.74
+ ,88.57
+ ,23256600
+ ,0.04
+ ,0.06
+ ,309.29
+ ,22680300
+ ,3.04
+ ,0.87
+ ,58.57
+ ,21916400
+ ,2.29
+ ,4.91
+ ,132.14
+ ,19640200
+ ,4.37
+ ,1.93
+ ,3.57
+ ,18813100
+ ,6.39
+ ,0.41
+ ,102.86
+ ,18730000
+ ,5.74
+ ,1.21
+ ,185.71
+ ,18154700
+ ,7.64
+ ,2.01
+ ,177.14
+ ,17848800
+ ,7.07
+ ,0.00
+ ,530.00
+ ,18077500
+ ,6.23
+ ,6.49
+ ,162.86
+ ,17133100
+ ,10.20
+ ,0.00
+ ,553.57
+ ,16602600
+ ,14.07
+ ,0.31
+ ,258.57
+ ,15878900
+ ,12.83
+ ,4.87
+ ,326.43
+ ,15789100
+ ,12.04
+ ,1.37
+ ,580.00
+ ,15422000
+ ,11.97
+ ,0.19
+ ,286.43
+ ,14661400
+ ,12.63
+ ,0.34
+ ,310.71
+ ,15879200
+ ,13.56
+ ,3.60
+ ,148.57
+ ,14339300
+ ,15.66
+ ,0.10
+ ,627.14
+ ,13169600
+ ,16.34
+ ,2.10
+ ,477.86
+ ,14528900
+ ,14.09
+ ,0.10
+ ,385.71
+ ,13375800
+ ,15.03
+ ,7.27
+ ,327.86
+ ,12309900
+ ,16.09
+ ,0.76
+ ,402.14
+ ,11933900
+ ,19.27
+ ,1.09
+ ,567.86
+ ,10061900
+ ,22.50
+ ,0.34
+ ,678.57
+ ,12609600
+ ,16.07
+ ,4.13
+ ,253.57
+ ,11156500
+ ,19.11
+ ,1.89
+ ,459.29
+ ,12187200
+ ,18.66
+ ,3.80
+ ,331.43
+ ,11284300
+ ,18.29
+ ,2.47
+ ,421.43
+ ,10177000
+ ,20.26
+ ,0.00
+ ,595.00
+ ,10970720
+ ,19.20
+ ,1.01
+ ,425.71
+ ,10820680
+ ,20.10
+ ,1.21
+ ,603.57
+ ,11492390
+ ,17.93
+ ,0.54
+ ,420.00
+ ,14573750
+ ,16.11
+ ,2.86
+ ,308.57
+ ,13992820
+ ,16.90
+ ,0.04
+ ,325.00
+ ,14727070
+ ,16.14
+ ,1.03
+ ,319.29
+ ,15685360
+ ,15.04
+ ,0.23
+ ,452.86
+ ,16736210
+ ,13.41
+ ,0.20
+ ,83.57
+ ,17950180
+ ,14.14
+ ,13.87
+ ,99.43
+ ,17002730
+ ,9.59
+ ,0.36
+ ,312.71
+ ,17415160
+ ,10.74
+ ,0.56
+ ,128.00
+ ,17929810
+ ,11.67
+ ,1.98
+ ,152.67
+ ,17865790
+ ,8.09
+ ,3.83
+ ,135.00
+ ,19202360
+ ,10.07
+ ,1.46
+ ,57.71
+ ,19085000
+ ,11.80
+ ,2.00
+ ,190.43
+ ,18188880
+ ,12.01
+ ,4.96
+ ,12.86
+ ,18466410
+ ,6.61
+ ,2.76
+ ,32.43
+ ,18520400
+ ,6.47
+ ,2.10
+ ,38.29
+ ,20025500
+ ,-3.11
+ ,2.09
+ ,210.14
+ ,20636100
+ ,1.94
+ ,2.21
+ ,109.14
+ ,20672000
+ ,1.10
+ ,2.90
+ ,71.43
+ ,22589100
+ ,-3.40
+ ,0.57
+ ,102.29
+ ,21864800
+ ,1.64
+ ,1.79
+ ,48.43
+ ,22750100
+ ,3.11
+ ,0.80
+ ,70.43
+ ,22548746
+ ,-0.16
+ ,2.66
+ ,139.86
+ ,21325495
+ ,3.80
+ ,1.70
+ ,83.14
+ ,21556563
+ ,-2.39
+ ,0.79
+ ,27.71
+ ,21415269
+ ,1.51
+ ,0.30
+ ,96.14
+ ,20401054
+ ,7.24
+ ,8.09
+ ,40.57
+ ,19062253
+ ,2.00
+ ,0.97
+ ,364.71
+ ,19085706
+ ,2.11
+ ,0.07
+ ,207.43
+ ,19279967
+ ,10.54
+ ,1.47
+ ,156.29
+ ,18552045
+ ,11.10
+ ,2.74
+ ,229.00
+ ,17800733
+ ,7.34
+ ,3.14
+ ,160.43
+ ,17142490
+ ,9.53
+ ,0.96
+ ,357.43
+ ,17593173
+ ,9.71
+ ,0.00
+ ,542.00
+ ,17633859
+ ,10.14
+ ,0.00
+ ,578.43
+ ,17336613
+ ,13.93
+ ,2.80
+ ,427.43
+ ,17008347
+ ,8.33
+ ,0.23
+ ,130.29
+ ,17951965
+ ,8.31
+ ,2.69
+ ,174.29
+ ,14520929
+ ,13.83
+ ,0.23
+ ,679.14
+ ,16941217
+ ,14.50
+ ,3.60
+ ,389.43
+ ,15436824
+ ,16.71
+ ,0.93
+ ,532.57
+ ,14744261
+ ,16.49
+ ,2.56
+ ,253.71
+ ,14248004
+ ,14.57
+ ,0.74
+ ,414.14
+ ,11540953
+ ,19.04
+ ,0.07
+ ,719.71
+ ,12881661
+ ,22.84
+ ,0.76
+ ,639.86
+ ,15185757
+ ,22.23
+ ,2.73
+ ,619.71
+ ,13554339
+ ,19.56
+ ,4.30
+ ,507.14
+ ,13575106
+ ,19.76
+ ,0.19
+ ,463.86
+ ,12238400
+ ,18.36
+ ,1.19
+ ,254.14
+ ,13303614
+ ,16.99
+ ,1.43
+ ,226.29
+ ,14151478
+ ,16.87
+ ,9.63
+ ,299.57
+ ,14172009
+ ,18.50
+ ,10.44
+ ,274.00
+ ,14022320
+ ,16.51
+ ,4.36
+ ,253.29)
+ ,dim=c(4
+ ,104)
+ ,dimnames=list(c('Kijkcijfers'
+ ,'Temperatuur'
+ ,'Neerslag'
+ ,'Zonneschijnduur')
+ ,1:104))
>  y <- array(NA,dim=c(4,104),dimnames=list(c('Kijkcijfers','Temperatuur','Neerslag','Zonneschijnduur'),1:104))
>  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

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] "Kijkcijfers"
> x[,par1]
  [1] 15561600 14917500 14805920 16958000 17605000 17131200 18474600 17286700
  [9] 18574400 18056000 19701600 19061700 19681900 34521200 19922700 20177900
 [17] 19759900 23076700 22532000 22029400 22587000 23256600 22680300 21916400
 [25] 19640200 18813100 18730000 18154700 17848800 18077500 17133100 16602600
 [33] 15878900 15789100 15422000 14661400 15879200 14339300 13169600 14528900
 [41] 13375800 12309900 11933900 10061900 12609600 11156500 12187200 11284300
 [49] 10177000 10970720 10820680 11492390 14573750 13992820 14727070 15685360
 [57] 16736210 17950180 17002730 17415160 17929810 17865790 19202360 19085000
 [65] 18188880 18466410 18520400 20025500 20636100 20672000 22589100 21864800
 [73] 22750100 22548746 21325495 21556563 21415269 20401054 19062253 19085706
 [81] 19279967 18552045 17800733 17142490 17593173 17633859 17336613 17008347
 [89] 17951965 14520929 16941217 15436824 14744261 14248004 11540953 12881661
 [97] 15185757 13554339 13575106 12238400 13303614 14151478 14172009 14022320
> 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])

10061900 10177000 10820680 10970720 11156500 11284300 11492390 11540953 
       1        1        1        1        1        1        1        1 
11933900 12187200 12238400 12309900 12609600 12881661 13169600 13303614 
       1        1        1        1        1        1        1        1 
13375800 13554339 13575106 13992820 14022320 14151478 14172009 14248004 
       1        1        1        1        1        1        1        1 
14339300 14520929 14528900 14573750 14661400 14727070 14744261 14805920 
       1        1        1        1        1        1        1        1 
14917500 15185757 15422000 15436824 15561600 15685360 15789100 15878900 
       1        1        1        1        1        1        1        1 
15879200 16602600 16736210 16941217 16958000 17002730 17008347 17131200 
       1        1        1        1        1        1        1        1 
17133100 17142490 17286700 17336613 17415160 17593173 17605000 17633859 
       1        1        1        1        1        1        1        1 
17800733 17848800 17865790 17929810 17950180 17951965 18056000 18077500 
       1        1        1        1        1        1        1        1 
18154700 18188880 18466410 18474600 18520400 18552045 18574400 18730000 
       1        1        1        1        1        1        1        1 
18813100 19061700 19062253 19085000 19085706 19202360 19279967 19640200 
       1        1        1        1        1        1        1        1 
19681900 19701600 19759900 19922700 20025500 20177900 20401054 20636100 
       1        1        1        1        1        1        1        1 
20672000 21325495 21415269 21556563 21864800 21916400 22029400 22532000 
       1        1        1        1        1        1        1        1 
22548746 22587000 22589100 22680300 22750100 23076700 23256600 34521200 
       1        1        1        1        1        1        1        1 
> colnames(x)
[1] "Kijkcijfers"     "Temperatuur"     "Neerslag"        "Zonneschijnduur"
> colnames(x)[par1]
[1] "Kijkcijfers"
> x[,par1]
  [1] 15561600 14917500 14805920 16958000 17605000 17131200 18474600 17286700
  [9] 18574400 18056000 19701600 19061700 19681900 34521200 19922700 20177900
 [17] 19759900 23076700 22532000 22029400 22587000 23256600 22680300 21916400
 [25] 19640200 18813100 18730000 18154700 17848800 18077500 17133100 16602600
 [33] 15878900 15789100 15422000 14661400 15879200 14339300 13169600 14528900
 [41] 13375800 12309900 11933900 10061900 12609600 11156500 12187200 11284300
 [49] 10177000 10970720 10820680 11492390 14573750 13992820 14727070 15685360
 [57] 16736210 17950180 17002730 17415160 17929810 17865790 19202360 19085000
 [65] 18188880 18466410 18520400 20025500 20636100 20672000 22589100 21864800
 [73] 22750100 22548746 21325495 21556563 21415269 20401054 19062253 19085706
 [81] 19279967 18552045 17800733 17142490 17593173 17633859 17336613 17008347
 [89] 17951965 14520929 16941217 15436824 14744261 14248004 11540953 12881661
 [97] 15185757 13554339 13575106 12238400 13303614 14151478 14172009 14022320
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/118va1292953966.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  Kijkcijfers 
Inputs:  Temperatuur, Neerslag, Zonneschijnduur 
Number of observations:  104 

1) Temperatuur <= 11.8; criterion = 1, statistic = 75.073
  2) Temperatuur <= 4.27; criterion = 1, statistic = 16.782
    3)*  weights = 23 
  2) Temperatuur > 4.27
    4) Zonneschijnduur <= 102.86; criterion = 0.996, statistic = 10.215
      5)*  weights = 9 
    4) Zonneschijnduur > 102.86
      6)*  weights = 22 
1) Temperatuur > 11.8
  7) Temperatuur <= 15.73; criterion = 1, statistic = 27.732
    8) Zonneschijnduur <= 275; criterion = 0.953, statistic = 5.803
      9)*  weights = 8 
    8) Zonneschijnduur > 275
      10)*  weights = 14 
  7) Temperatuur > 15.73
    11)*  weights = 28 
> postscript(file="/var/www/html/freestat/rcomp/tmp/218va1292953966.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/freestat/rcomp/tmp/3tiud1292953966.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   15561600  17065559 -1503958.75
2   14917500  12899698  2017801.71
3   14805920  15320817  -514897.36
4   16958000  15320817  1637182.64
5   17605000  17896694  -291694.05
6   17131200  17065559    65641.25
7   18474600  17065559  1409041.25
8   17286700  17896694  -609994.05
9   18574400  17896694   677705.95
10  18056000  17896694   159305.95
11  19701600  19331636   369964.00
12  19061700  19331636  -269936.00
13  19681900  21978075 -2296175.30
14  34521200  21978075 12543124.70
15  19922700  21978075 -2055375.30
16  20177900  19331636   846264.00
17  19759900  21978075 -2218175.30
18  23076700  21978075  1098624.70
19  22532000  21978075   553924.70
20  22029400  21978075    51324.70
21  22587000  21978075   608924.70
22  23256600  21978075  1278524.70
23  22680300  21978075   702224.70
24  21916400  21978075   -61675.30
25  19640200  19331636   308564.00
26  18813100  19331636  -518536.00
27  18730000  17896694   833305.95
28  18154700  17896694   258005.95
29  17848800  17896694   -47894.05
30  18077500  17896694   180805.95
31  17133100  17896694  -763594.05
32  16602600  17065559  -462958.75
33  15878900  15320817   558082.64
34  15789100  15320817   468282.64
35  15422000  15320817   101182.64
36  14661400  15320817  -659417.36
37  15879200  17065559 -1186358.75
38  14339300  15320817  -981517.36
39  13169600  12899698   269901.71
40  14528900  15320817  -791917.36
41  13375800  15320817 -1945017.36
42  12309900  12899698  -589798.29
43  11933900  12899698  -965798.29
44  10061900  12899698 -2837798.29
45  12609600  12899698  -290098.29
46  11156500  12899698 -1743198.29
47  12187200  12899698  -712498.29
48  11284300  12899698 -1615398.29
49  10177000  12899698 -2722698.29
50  10970720  12899698 -1928978.29
51  10820680  12899698 -2079018.29
52  11492390  12899698 -1407308.29
53  14573750  12899698  1674051.71
54  13992820  12899698  1093121.71
55  14727070  12899698  1827371.71
56  15685360  15320817   364542.64
57  16736210  17065559  -329348.75
58  17950180  17065559   884621.25
59  17002730  17896694  -893964.05
60  17415160  17896694  -481534.05
61  17929810  17896694    33115.95
62  17865790  17896694   -30904.05
63  19202360  19331636  -129276.00
64  19085000  17896694  1188305.95
65  18188880  17065559  1123321.25
66  18466410  19331636  -865226.00
67  18520400  19331636  -811236.00
68  20025500  21978075 -1952575.30
69  20636100  21978075 -1341975.30
70  20672000  21978075 -1306075.30
71  22589100  21978075   611024.70
72  21864800  21978075  -113275.30
73  22750100  21978075   772024.70
74  22548746  21978075   570670.70
75  21325495  21978075  -652580.30
76  21556563  21978075  -421512.30
77  21415269  21978075  -562806.30
78  20401054  19331636  1069418.00
79  19062253  21978075 -2915822.30
80  19085706  21978075 -2892369.30
81  19279967  17896694  1383272.95
82  18552045  17896694   655350.95
83  17800733  17896694   -95961.05
84  17142490  17896694  -754204.05
85  17593173  17896694  -303521.05
86  17633859  17896694  -262835.05
87  17336613  15320817  2015795.64
88  17008347  17896694  -888347.05
89  17951965  17896694    55270.95
90  14520929  15320817  -799888.36
91  16941217  15320817  1620399.64
92  15436824  12899698  2537125.71
93  14744261  12899698  1844562.71
94  14248004  15320817 -1072813.36
95  11540953  12899698 -1358745.29
96  12881661  12899698   -18037.29
97  15185757  12899698  2286058.71
98  13554339  12899698   654640.71
99  13575106  12899698   675407.71
100 12238400  12899698  -661298.29
101 13303614  12899698   403915.71
102 14151478  12899698  1251779.71
103 14172009  12899698  1272310.71
104 14022320  12899698  1122621.71
> 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/html/freestat/rcomp/tmp/4mrcg1292953966.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/freestat/rcomp/tmp/57ram1292953966.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/freestat/rcomp/tmp/6ta9a1292953966.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/freestat/rcomp/tmp/7428d1292953966.tab") 
+ }
> 
> try(system("convert tmp/218va1292953966.ps tmp/218va1292953966.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tiud1292953966.ps tmp/3tiud1292953966.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mrcg1292953966.ps tmp/4mrcg1292953966.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.168   0.807   4.565