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*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Sun, 12 Dec 2010 19:14:56 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm.htm/, Retrieved Sun, 12 Dec 2010 20:17:01 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

15 10 77 46 15 12 13 6 11 6 4 15 16 0 9 20 63 37 12 7 11 4 26 5 4 23 24 1 12 16 73 45 15 13 14 6 26 20 10 26 22 1 15 10 76 46 12 11 12 5 15 12 6 19 21 1 17 8 90 55 14 16 12 5 10 11 5 19 23 1 14 14 67 40 8 10 6 4 21 12 8 16 23 1 9 19 69 43 11 15 10 5 27 11 9 23 21 0 11 23 54 33 4 4 10 2 21 13 8 19 22 1 13 9 54 33 13 7 12 5 21 9 11 24 20 1 16 12 76 47 19 15 15 6 22 14 6 19 12 0 16 14 75 44 10 5 13 6 29 12 8 25 23 0 15 13 76 47 15 16 18 8 29 18 11 23 23 0 10 11 80 49 6 15 11 6 29 9 5 31 30 1 16 11 89 55 7 13 12 3 30 15 10 29 22 0 12 10 73 43 14 13 13 6 19 12 7 18 21 1 15 12 74 46 16 15 14 6 19 12 7 17 21 1 13 18 78 51 16 15 16 7 22 12 13 22 15 0 18 12 76 47 14 10 16 8 18 15 10 21 22 0 13 10 69 42 15 17 16 6 28 11 8 24 24 1 17 15 74 42 14 14 15 7 17 13 6 22 23 0 14 15 82 48 12 9 13 4 18 10 8 16 15 0 13 12 77 45 9 6 8 4 20 17 7 22 24 1 13 9 84 51 12 11 14 2 16 13 5 21 24 0 15 11 75 46 14 13 15 6 17 17 9 25 21 0 15 16 79 47 14 10 16 6 25 15 11 22 21 0 13 17 79 47 10 4 13 6 22 13 11 24 18 0 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.668 R-squared 0.4462 RMSE 2.1615 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 15 14.1132075471698 0.88679245283019 2 12 9.56818181818182 2.43181818181818 3 15 14.1132075471698 0.88679245283019 4 12 11.2083333333333 0.791666666666666 5 14 12.5 1.5 6 8 9.56818181818182 -1.56818181818182 7 11 12.5 -1.5 8 4 9.56818181818182 -5.56818181818182 9 13 11.2083333333333 1.79166666666667 10 19 14.1132075471698 4.88679245283019 11 10 11.2083333333333 -1.20833333333333 12 15 14.1132075471698 0.88679245283019 13 6 14.1132075471698 -8.11320754716981 14 7 9.56818181818182 -2.56818181818182 15 14 14.1132075471698 -0.113207547169811 16 16 14.1132075471698 1.88679245283019 17 16 14.1132075471698 1.88679245283019 18 14 11.2083333333333 2.79166666666667 19 15 14.1132075471698 0.88679245283019 20 14 14.1132075471698 -0.113207547169811 21 12 9.56818181818182 2.43181818181818 22 9 9.56818181818182 -0.568181818181818 23 12 9.56818181818182 2.43181818181818 24 14 14.1132075471698 -0.113207547169811 25 14 11.2083333333333 2.79166666666667 26 10 11.2083333333333 -1.20833333333333 27 16 14.1132075471698 1.88679245283019 28 10 9.56818181818182 0.431818181818182 29 8 9.56818181818182 -1.56818181818182 30 12 11.2083333333333 0.791666666666666 31 8 9.56818181818182 -1.56818181818182 32 13 14.1132075471698 -1.11320754716981 33 11 9.56818181818182 1.43181818181818 34 12 9.56818181818182 2.43181818181818 35 16 14.1132075471698 1.88679245283019 36 16 14.1132075471698 1.88679245283019 37 13 14.1132075471698 -1.11320754716981 38 14 14.1132075471698 -0.113207547169811 39 5 9.56818181818182 -4.56818181818182 40 14 14.1132075471698 -0.113207547169811 41 13 9.56818181818182 3.43181818181818 42 15 14.1132075471698 0.88679245283019 43 11 12.5 -1.5 44 15 14.1132075471698 0.88679245283019 45 16 14.1132075471698 1.88679245283019 46 13 12.5 0.5 47 11 14.1132075471698 -3.11320754716981 48 12 14.1132075471698 -2.11320754716981 49 12 11.2083333333333 0.791666666666666 50 10 14.1132075471698 -4.11320754716981 51 8 11.2083333333333 -3.20833333333333 52 9 9.56818181818182 -0.568181818181818 53 12 12.5 -0.5 54 14 14.1132075471698 -0.113207547169811 55 12 14.1132075471698 -2.11320754716981 56 11 9.56818181818182 1.43181818181818 57 14 14.1132075471698 -0.113207547169811 58 7 9.56818181818182 -2.56818181818182 59 16 14.1132075471698 1.88679245283019 60 11 11.2083333333333 -0.208333333333334 61 16 14.1132075471698 1.88679245283019 62 13 11.2083333333333 1.79166666666667 63 11 11.2083333333333 -0.208333333333334 64 13 14.1132075471698 -1.11320754716981 65 14 14.1132075471698 -0.113207547169811 66 10 11.2083333333333 -1.20833333333333 67 15 14.1132075471698 0.88679245283019 68 11 11.2083333333333 -0.208333333333334 69 6 9.56818181818182 -3.56818181818182 70 11 9.56818181818182 1.43181818181818 71 12 9.56818181818182 2.43181818181818 72 12 11.2083333333333 0.791666666666666 73 8 11.2083333333333 -3.20833333333333 74 9 9.56818181818182 -0.568181818181818 75 10 9.56818181818182 0.431818181818182 76 16 14.1132075471698 1.88679245283019 77 15 12.5 2.5 78 14 14.1132075471698 -0.113207547169811 79 12 14.1132075471698 -2.11320754716981 80 12 11.2083333333333 0.791666666666666 81 12 9.56818181818182 2.43181818181818 82 8 9.56818181818182 -1.56818181818182 83 16 14.1132075471698 1.88679245283019 84 11 9.56818181818182 1.43181818181818 85 12 9.56818181818182 2.43181818181818 86 9 12.5 -3.5 87 14 12.5 1.5 88 15 14.1132075471698 0.88679245283019 89 8 9.56818181818182 -1.56818181818182 90 12 12.5 -0.5 91 10 11.2083333333333 -1.20833333333333 92 16 14.1132075471698 1.88679245283019 93 8 9.56818181818182 -1.56818181818182 94 9 11.2083333333333 -2.20833333333333 95 8 9.56818181818182 -1.56818181818182 96 11 11.2083333333333 -0.208333333333334 97 16 14.1132075471698 1.88679245283019 98 5 9.56818181818182 -4.56818181818182 99 15 12.5 2.5 100 15 14.1132075471698 0.88679245283019 101 12 12.5 -0.5 102 12 14.1132075471698 -2.11320754716981 103 16 14.1132075471698 1.88679245283019 104 12 14.1132075471698 -2.11320754716981 105 10 11.2083333333333 -1.20833333333333 106 12 11.2083333333333 0.791666666666666 107 11 14.1132075471698 -3.11320754716981 108 16 14.1132075471698 1.88679245283019 109 7 9.56818181818182 -2.56818181818182 110 9 9.56818181818182 -0.568181818181818 111 11 9.56818181818182 1.43181818181818 112 6 9.56818181818182 -3.56818181818182 113 14 14.1132075471698 -0.113207547169811 114 11 11.2083333333333 -0.208333333333334 115 11 9.56818181818182 1.43181818181818 116 16 9.56818181818182 6.43181818181818 117 7 9.56818181818182 -2.56818181818182 118 8 9.56818181818182 -1.56818181818182 119 10 9.56818181818182 0.431818181818182 120 14 12.5 1.5 121 9 9.56818181818182 -0.568181818181818 122 13 14.1132075471698 -1.11320754716981 123 13 9.56818181818182 3.43181818181818 124 12 12.5 -0.5 125 11 12.5 -1.5 126 10 14.1132075471698 -4.11320754716981 127 12 12.5 -0.5 128 14 14.1132075471698 -0.113207547169811 129 11 14.1132075471698 -3.11320754716981 130 13 9.56818181818182 3.43181818181818 131 14 14.1132075471698 -0.113207547169811 132 13 12.5 0.5 133 16 14.1132075471698 1.88679245283019 134 13 11.2083333333333 1.79166666666667 135 9 9.56818181818182 -0.568181818181818 136 14 9.56818181818182 4.43181818181818 137 15 14.1132075471698 0.88679245283019

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/20btx1292181287.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/20btx1292181287.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/30btx1292181287.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/30btx1292181287.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/4sksi1292181287.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181418wge6c7m5u3xlmfm/4sksi1292181287.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = quantiles ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 4 ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] 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]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='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='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() 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) } bitmap(file='test2.png') 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() 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$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$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='mytable.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='mytable2.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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