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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+ ,1
+ ,17
+ ,22
+ ,88
+ ,56
+ ,23494
+ ,8181
+ ,43485
+ ,28
+ ,22)
+ ,dim=c(15
+ ,144)
+ ,dimnames=list(c('Variabele1'
+ ,'Variabele2'
+ ,'Variabele3'
+ ,'Variabele4'
+ ,'Variabele5'
+ ,'Variabele6'
+ ,'Variabele7'
+ ,'Variabele8'
+ ,'Variabele9'
+ ,'Variabele10'
+ ,'Variabele11'
+ ,'Variabele12'
+ ,'Variabele13'
+ ,'Variabele14'
+ ,'Variabele15
')
+ ,1:144))
>  y <- array(NA,dim=c(15,144),dimnames=list(c('Variabele1','Variabele2','Variabele3','Variabele4','Variabele5','Variabele6','Variabele7','Variabele8','Variabele9','Variabele10','Variabele11','Variabele12','Variabele13','Variabele14','Variabele15
'),1:144))
>  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 = '8'
> par2 = 'equal'
> par1 = '2'
> #'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] "Variabele2"
> x[,par1]
  [1] 162687 201906   7215 146367 257045 524450 188294 195674 177020 325899
 [11] 121844 203938 107220 220751 172905 156326 145178  89171 172624  32443
 [21]  87927 241285 195820 146946 159763 207078 212394 201536 394662 217808
 [31] 182286 181740 137978 255929 236489      0 230761 130843 157118 253254
 [41] 269329 161273 107181 195891 139667 171101  81407 247563 239807 172743
 [51]  48188 169355 315622 241518 195583 159913 220241 101694 151985 202536
 [61] 173505 150518 141273 125612 166049 124197 195043 138708 116552  31970
 [71] 255587 151184 135926 119629 171518 108949 183471 159966  93786  84971
 [81]  88797 304603  75101 145043  95827 173924 241957 115367 118408 164078
 [91] 158931 184139 152856 144014  62535 245196 199841  19349 247280 159408
[101]  72128 104253 151090 137382  87448  27676 165507 132148      0  95778
[111] 109001 158833 147690  89887   3616      0 199005 160930 177948 136061
[121]  43410 184277 108858 141744  60493  19764 177559 140281 164249  11796
[131]  10674 151322   6836 174712   5118  40248      0 127628  88837   7131
[141]   9056  87957 144470 111408
> 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])

C1 C2 C3 C4 C5 C6 C7 C8 
22 31 61 24  4  0  1  1 
> colnames(x)
 [1] "Variabele1"   "Variabele2"   "Variabele3"   "Variabele4"   "Variabele5"  
 [6] "Variabele6"   "Variabele7"   "Variabele8"   "Variabele9"   "Variabele10" 
[11] "Variabele11"  "Variabele12"  "Variabele13"  "Variabele14"  "Variabele15."
> colnames(x)[par1]
[1] "Variabele2"
> x[,par1]
  [1] C3 C4 C1 C3 C4 C8 C3 C3 C3 C5 C2 C4 C2 C4 C3 C3 C3 C2 C3 C1 C2 C4 C3 C3 C3
 [26] C4 C4 C4 C7 C4 C3 C3 C3 C4 C4 C1 C4 C2 C3 C4 C5 C3 C2 C3 C3 C3 C2 C4 C4 C3
 [51] C1 C3 C5 C4 C3 C3 C4 C2 C3 C4 C3 C3 C3 C2 C3 C2 C3 C3 C2 C1 C4 C3 C3 C2 C3
 [76] C2 C3 C3 C2 C2 C2 C5 C2 C3 C2 C3 C4 C2 C2 C3 C3 C3 C3 C3 C1 C4 C4 C1 C4 C3
[101] C2 C2 C3 C3 C2 C1 C3 C3 C1 C2 C2 C3 C3 C2 C1 C1 C4 C3 C3 C3 C1 C3 C2 C3 C1
[126] C1 C3 C3 C3 C1 C1 C3 C1 C3 C1 C1 C1 C2 C2 C1 C1 C2 C3 C2
Levels: C1 C2 C3 C4 C5 C6 C7 C8
> 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/1bi1c1324660146.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

Response:  as.factor(Variabele2) 
Inputs:  Variabele1, Variabele3, Variabele4, Variabele5, Variabele6, Variabele7, Variabele8, Variabele9, Variabele10, Variabele11, Variabele12, Variabele13, Variabele14, Variabele15. 
Number of observations:  144 

1) Variabele1 <= 778; criterion = 1, statistic = 112.56
  2) Variabele7 <= 7; criterion = 0.998, statistic = 14.305
    3)*  weights = 16 
  2) Variabele7 > 7
    4)*  weights = 7 
1) Variabele1 > 778
  5) Variabele1 <= 1325; criterion = 1, statistic = 80.859
    6)*  weights = 32 
  5) Variabele1 > 1325
    7) Variabele1 <= 1880; criterion = 1, statistic = 58.332
      8) Variabele13 <= 31972; criterion = 0.994, statistic = 15.562
        9)*  weights = 8 
      8) Variabele13 > 31972
        10)*  weights = 32 
    7) Variabele1 > 1880
      11) Variabele1 <= 2897; criterion = 1, statistic = 36.65
        12)*  weights = 42 
      11) Variabele1 > 2897
        13)*  weights = 7 
> postscript(file="/var/wessaorg/rcomp/tmp/2kfj91324660146.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/334cg1324660146.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,]    3    2
  [2,]    4    3
  [3,]    1    1
  [4,]    3    3
  [5,]    4    4
  [6,]    8    4
  [7,]    3    3
  [8,]    3    3
  [9,]    3    3
 [10,]    5    4
 [11,]    2    3
 [12,]    4    3
 [13,]    2    2
 [14,]    4    4
 [15,]    3    3
 [16,]    3    3
 [17,]    3    3
 [18,]    2    2
 [19,]    3    3
 [20,]    1    1
 [21,]    2    2
 [22,]    4    3
 [23,]    3    2
 [24,]    3    3
 [25,]    3    3
 [26,]    4    3
 [27,]    4    3
 [28,]    4    3
 [29,]    7    4
 [30,]    4    3
 [31,]    3    3
 [32,]    3    3
 [33,]    3    3
 [34,]    4    4
 [35,]    4    3
 [36,]    1    1
 [37,]    4    3
 [38,]    2    2
 [39,]    3    3
 [40,]    4    3
 [41,]    5    3
 [42,]    3    3
 [43,]    2    2
 [44,]    3    3
 [45,]    3    3
 [46,]    3    2
 [47,]    2    1
 [48,]    4    3
 [49,]    4    3
 [50,]    3    3
 [51,]    1    1
 [52,]    3    3
 [53,]    5    3
 [54,]    4    3
 [55,]    3    3
 [56,]    3    3
 [57,]    4    3
 [58,]    2    2
 [59,]    3    2
 [60,]    4    3
 [61,]    3    3
 [62,]    3    3
 [63,]    3    3
 [64,]    2    2
 [65,]    3    2
 [66,]    2    3
 [67,]    3    3
 [68,]    3    3
 [69,]    2    2
 [70,]    1    1
 [71,]    4    4
 [72,]    3    3
 [73,]    3    3
 [74,]    2    2
 [75,]    3    3
 [76,]    2    2
 [77,]    3    3
 [78,]    3    3
 [79,]    2    2
 [80,]    2    2
 [81,]    2    2
 [82,]    5    3
 [83,]    2    2
 [84,]    3    3
 [85,]    2    2
 [86,]    3    3
 [87,]    4    3
 [88,]    2    2
 [89,]    2    2
 [90,]    3    3
 [91,]    3    3
 [92,]    3    3
 [93,]    3    2
 [94,]    3    3
 [95,]    1    2
 [96,]    4    3
 [97,]    4    3
 [98,]    1    1
 [99,]    4    3
[100,]    3    3
[101,]    2    1
[102,]    2    2
[103,]    3    2
[104,]    3    3
[105,]    2    2
[106,]    1    1
[107,]    3    3
[108,]    3    2
[109,]    1    1
[110,]    2    2
[111,]    2    2
[112,]    3    3
[113,]    3    3
[114,]    2    2
[115,]    1    1
[116,]    1    1
[117,]    4    2
[118,]    3    3
[119,]    3    3
[120,]    3    2
[121,]    1    1
[122,]    3    3
[123,]    2    2
[124,]    3    3
[125,]    1    1
[126,]    1    1
[127,]    3    3
[128,]    3    2
[129,]    3    3
[130,]    1    1
[131,]    1    1
[132,]    3    3
[133,]    1    1
[134,]    3    3
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    2    2
[139,]    2    2
[140,]    1    1
[141,]    1    1
[142,]    2    2
[143,]    3    2
[144,]    2    2
    
     C1 C2 C3 C4 C5 C6 C7 C8
  C1 21  1  0  0  0  0  0  0
  C2  2 27  2  0  0  0  0  0
  C3  0 11 50  0  0  0  0  0
  C4  0  1 19  4  0  0  0  0
  C5  0  0  3  1  0  0  0  0
  C6  0  0  0  0  0  0  0  0
  C7  0  0  0  1  0  0  0  0
  C8  0  0  0  1  0  0  0  0
> postscript(file="/var/wessaorg/rcomp/tmp/4i51b1324660146.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/50xpc1324660146.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/6mwlu1324660146.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/71smu1324660146.tab") 
+ }
> 
> try(system("convert tmp/2kfj91324660146.ps tmp/2kfj91324660146.png",intern=TRUE))
character(0)
> try(system("convert tmp/334cg1324660146.ps tmp/334cg1324660146.png",intern=TRUE))
character(0)
> try(system("convert tmp/4i51b1324660146.ps tmp/4i51b1324660146.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.067   0.260   4.329