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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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> x <- array(list(108.35
+ ,98.68
+ ,100.70
+ ,104.38
+ ,97.72
+ ,15.38
+ ,31.27
+ ,109.87
+ ,99.21
+ ,99.62
+ ,103.97
+ ,98.01
+ ,15.03
+ ,35.83
+ ,111.30
+ ,99.36
+ ,99.83
+ ,103.32
+ ,97.78
+ ,15.21
+ ,37.12
+ ,115.50
+ ,100.72
+ ,100.74
+ ,105.01
+ ,98.04
+ ,15.20
+ ,36.77
+ ,116.22
+ ,102.27
+ ,100.84
+ ,104.88
+ ,98.54
+ ,14.60
+ ,35.17
+ ,116.63
+ ,102.62
+ ,100.85
+ ,104.46
+ ,98.39
+ ,13.79
+ ,37.25
+ ,116.84
+ ,102.97
+ ,99.71
+ ,104.71
+ ,98.58
+ ,14.54
+ ,33.77
+ ,116.63
+ ,102.88
+ ,100.80
+ ,106.09
+ ,98.91
+ ,14.31
+ ,30.59
+ ,117.03
+ ,102.90
+ ,100.06
+ ,106.54
+ ,98.68
+ ,13.93
+ ,33.59
+ ,117.00
+ ,103.01
+ ,100.57
+ ,104.36
+ ,98.59
+ ,14.82
+ ,37.24
+ ,117.14
+ ,103.02
+ ,99.79
+ ,105.31
+ ,99.13
+ ,14.46
+ ,34.81
+ ,116.64
+ ,103.73
+ ,99.90
+ ,105.07
+ ,98.70
+ ,14.85
+ ,34.94
+ ,117.24
+ ,104.18
+ ,100.12
+ ,105.39
+ ,99.00
+ ,14.95
+ ,34.47
+ ,117.52
+ ,103.73
+ ,100.40
+ ,105.65
+ ,98.80
+ ,14.43
+ ,30.48
+ ,117.83
+ ,103.78
+ ,100.51
+ ,108.25
+ ,98.80
+ ,14.84
+ ,30.94
+ ,119.79
+ ,103.61
+ ,100.70
+ ,107.71
+ ,99.29
+ ,14.39
+ ,30.60
+ ,120.86
+ ,103.84
+ ,100.62
+ ,108.58
+ ,99.69
+ ,15.70
+ ,28.42
+ ,120.75
+ ,103.86
+ ,99.70
+ ,108.27
+ ,100.01
+ ,15.34
+ ,25.89
+ ,120.63
+ ,104.14
+ ,99.48
+ ,107.62
+ ,99.85
+ ,13.98
+ ,26.32
+ ,120.89
+ ,104.05
+ ,99.36
+ ,108.80
+ ,99.66
+ ,14.75
+ ,27.18
+ ,120.23
+ ,104.01
+ ,99.39
+ ,109.26
+ ,101.18
+ ,14.81
+ ,25.85
+ ,121.19
+ ,104.49
+ ,99.45
+ ,108.58
+ ,101.47
+ ,14.67
+ ,26.32
+ ,120.79
+ ,104.83
+ ,99.28
+ ,107.05
+ ,101.28
+ ,15.03
+ ,23.07
+ ,120.09
+ ,104.78
+ ,99.40
+ ,109.20
+ ,101.80
+ ,14.34
+ ,20.19
+ ,120.86
+ ,104.95
+ ,99.10
+ ,109.52
+ ,102.48
+ ,12.54
+ ,18.65
+ ,121.10
+ ,105.28
+ ,99.48
+ ,111.12
+ ,102.32
+ ,11.37
+ ,17.74
+ ,121.47
+ ,105.28
+ ,99.74
+ ,108.74
+ ,102.30
+ ,12.58
+ ,17.26
+ ,122.01
+ ,105.91
+ ,100.42
+ ,110.53
+ ,102.84
+ ,13.06
+ ,16.01
+ ,123.94
+ ,106.81
+ ,100.80
+ ,110.44
+ ,102.36
+ ,12.50
+ ,17.94
+ ,125.78
+ ,106.39
+ ,100.66
+ ,111.02
+ ,102.16
+ ,11.11
+ ,15.53
+ ,125.31
+ ,107.02
+ ,101.03
+ ,111.13
+ ,102.57
+ ,12.39
+ ,14.49
+ ,125.79
+ ,106.92
+ ,101.22
+ ,110.90
+ ,102.49
+ ,12.34
+ ,15.35
+ ,126.12
+ ,107.01
+ ,101.23
+ ,111.32
+ ,104.11
+ ,11.54
+ ,14.67
+ ,125.57
+ ,106.79
+ ,100.10
+ ,109.37
+ ,104.78
+ ,10.22
+ ,12.95
+ ,125.44
+ ,107.41
+ ,99.98
+ ,110.18
+ ,104.13
+ ,8.50
+ ,8.81
+ ,126.12
+ ,107.13
+ ,99.91
+ ,110.74
+ ,104.22
+ ,9.06
+ ,9.33
+ ,126.01
+ ,107.54
+ ,99.84
+ ,111.70
+ ,104.73
+ ,9.28
+ ,9.31
+ ,126.50
+ ,108.48
+ ,99.68
+ ,111.33
+ ,104.99
+ ,7.24
+ ,9.03
+ ,126.13
+ ,108.50
+ ,99.74
+ ,110.86
+ ,104.70
+ ,7.58
+ ,10.96
+ ,126.66
+ ,108.27
+ ,99.71
+ ,109.48
+ ,104.69
+ ,7.81
+ ,14.26
+ ,126.33
+ ,109.42
+ ,99.35
+ ,108.77
+ ,104.85
+ ,8.54
+ ,14.20
+ ,126.61
+ ,110.09
+ ,99.21
+ ,109.81
+ ,104.24
+ ,9.27
+ ,13.70
+ ,126.36
+ ,109.98
+ ,99.21
+ ,109.15
+ ,104.74
+ ,10.11
+ ,17.46
+ ,126.83
+ ,109.99
+ ,99.16
+ ,109.63
+ ,104.20
+ ,9.21
+ ,18.73
+ ,125.90
+ ,109.54
+ ,99.20
+ ,111.32
+ ,105.62
+ ,10.71
+ ,20.37
+ ,126.29
+ ,108.85
+ ,99.08
+ ,109.75
+ ,106.08
+ ,10.85
+ ,18.72
+ ,126.37
+ ,106.76
+ ,98.16
+ ,110.37
+ ,105.46
+ ,11.77
+ ,21.60
+ ,125.11
+ ,107.56
+ ,98.00
+ ,108.30
+ ,105.42
+ ,11.81
+ ,22.75)
+ ,dim=c(7
+ ,48)
+ ,dimnames=list(c('Coffee'
+ ,'Tea'
+ ,'Sugar'
+ ,'Water'
+ ,'Soda'
+ ,'SaraLee'
+ ,'Starbucks')
+ ,1:48))
>  y <- array(NA,dim=c(7,48),dimnames=list(c('Coffee','Tea','Sugar','Water','Soda','SaraLee','Starbucks'),1:48))
>  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 = '0'
> 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] "Coffee"
> x[,par1]
 [1] 108.35 109.87 111.30 115.50 116.22 116.63 116.84 116.63 117.03 117.00
[11] 117.14 116.64 117.24 117.52 117.83 119.79 120.86 120.75 120.63 120.89
[21] 120.23 121.19 120.79 120.09 120.86 121.10 121.47 122.01 123.94 125.78
[31] 125.31 125.79 126.12 125.57 125.44 126.12 126.01 126.50 126.13 126.66
[41] 126.33 126.61 126.36 126.83 125.90 126.29 126.37 125.11
> 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])

108.35 109.87  111.3  115.5 116.22 116.63 116.64 116.84    117 117.03 117.14 
     1      1      1      1      1      2      1      1      1      1      1 
117.24 117.52 117.83 119.79 120.09 120.23 120.63 120.75 120.79 120.86 120.89 
     1      1      1      1      1      1      1      1      1      2      1 
 121.1 121.19 121.47 122.01 123.94 125.11 125.31 125.44 125.57 125.78 125.79 
     1      1      1      1      1      1      1      1      1      1      1 
 125.9 126.01 126.12 126.13 126.29 126.33 126.36 126.37  126.5 126.61 126.66 
     1      1      2      1      1      1      1      1      1      1      1 
126.83 
     1 
> colnames(x)
[1] "Coffee"    "Tea"       "Sugar"     "Water"     "Soda"      "SaraLee"  
[7] "Starbucks"
> colnames(x)[par1]
[1] "Coffee"
> x[,par1]
 [1] 108.35 109.87 111.30 115.50 116.22 116.63 116.84 116.63 117.03 117.00
[11] 117.14 116.64 117.24 117.52 117.83 119.79 120.86 120.75 120.63 120.89
[21] 120.23 121.19 120.79 120.09 120.86 121.10 121.47 122.01 123.94 125.78
[31] 125.31 125.79 126.12 125.57 125.44 126.12 126.01 126.50 126.13 126.66
[41] 126.33 126.61 126.36 126.83 125.90 126.29 126.37 125.11
> 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/1gvbb1293019758.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  Coffee 
Inputs:  Tea, Sugar, Water, Soda, SaraLee, Starbucks 
Number of observations:  48 

1) Tea <= 105.28; criterion = 1, statistic = 42.801
  2) Tea <= 102.88; criterion = 1, statistic = 23.093
    3)*  weights = 7 
  2) Tea > 102.88
    4) Starbucks <= 28.42; criterion = 0.999, statistic = 13.773
      5)*  weights = 11 
    4) Starbucks > 28.42
      6)*  weights = 9 
1) Tea > 105.28
  7) SaraLee <= 11.11; criterion = 0.964, statistic = 7.518
    8)*  weights = 14 
  7) SaraLee > 11.11
    9)*  weights = 7 
> postscript(file="/var/www/rcomp/tmp/2gvbb1293019758.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/3gvbb1293019758.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   108.35  113.5000 -5.15000000
2   109.87  113.5000 -3.63000000
3   111.30  113.5000 -2.20000000
4   115.50  113.5000  2.00000000
5   116.22  113.5000  2.72000000
6   116.63  113.5000  3.13000000
7   116.84  117.4478 -0.60777778
8   116.63  113.5000  3.13000000
9   117.03  117.4478 -0.41777778
10  117.00  117.4478 -0.44777778
11  117.14  117.4478 -0.30777778
12  116.64  117.4478 -0.80777778
13  117.24  117.4478 -0.20777778
14  117.52  117.4478  0.07222222
15  117.83  117.4478  0.38222222
16  119.79  117.4478  2.34222222
17  120.86  120.8055  0.05454545
18  120.75  120.8055 -0.05545455
19  120.63  120.8055 -0.17545455
20  120.89  120.8055  0.08454545
21  120.23  120.8055 -0.57545455
22  121.19  120.8055  0.38454545
23  120.79  120.8055 -0.01545455
24  120.09  120.8055 -0.71545455
25  120.86  120.8055  0.05454545
26  121.10  120.8055  0.29454545
27  121.47  120.8055  0.66454545
28  122.01  124.9500 -2.94000000
29  123.94  124.9500 -1.01000000
30  125.78  126.1807 -0.40071429
31  125.31  124.9500  0.36000000
32  125.79  124.9500  0.84000000
33  126.12  124.9500  1.17000000
34  125.57  126.1807 -0.61071429
35  125.44  126.1807 -0.74071429
36  126.12  126.1807 -0.06071429
37  126.01  126.1807 -0.17071429
38  126.50  126.1807  0.31928571
39  126.13  126.1807 -0.05071429
40  126.66  126.1807  0.47928571
41  126.33  126.1807  0.14928571
42  126.61  126.1807  0.42928571
43  126.36  126.1807  0.17928571
44  126.83  126.1807  0.64928571
45  125.90  126.1807 -0.28071429
46  126.29  126.1807  0.10928571
47  126.37  124.9500  1.42000000
48  125.11  124.9500  0.16000000
> 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/42vay1293019758.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/55wqm1293019758.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/6qxpa1293019758.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/7j6ov1293019758.tab") 
+ }
> 
> try(system("convert tmp/2gvbb1293019758.ps tmp/2gvbb1293019758.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gvbb1293019758.ps tmp/3gvbb1293019758.png",intern=TRUE))
character(0)
> try(system("convert tmp/42vay1293019758.ps tmp/42vay1293019758.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.140   0.740   2.849