*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: Tue, 21 Dec 2010 12:56:27 +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/21/t129293609950oiry7uzggwbbu.htm/, Retrieved Tue, 21 Dec 2010 13:54:59 +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/21/t129293609950oiry7uzggwbbu.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 «

14 11 23 8 1 6 7 22 24 4 2 5 22 23 24 7 2 20 12 21 21 4 2 12 15 19 21 4 2 11 9 12 19 5 2 12 20 24 12 15 1 11 10 21 21 5 1 9 12 21 25 7 2 13 23 26 27 4 2 9 10 18 21 4 1 14 11 21 27 7 1 12 20 22 20 8 1 18 11 26 16 4 2 9 22 20 26 8 1 15 19 20 24 4 2 12 20 26 25 5 2 12 16 27 25 16 1 12 12 27 27 7 1 15 14 16 23 4 2 11 14 26 22 6 1 13 9 20 10 4 1 10 19 25 25 5 2 17 17 16 18 4 1 13 14 20 21 4 1 17 19 20 20 6 1 15 20 24 18 4 1 13 20 24 25 4 1 17 9 22 28 4 1 21 10 18 27 8 1 12 6 21 20 5 2 12 15 17 20 4 1 15 9 15 20 10 2 8 24 28 27 4 2 15 11 23 23 4 1 16 4 19 23 4 2 9 12 15 22 5 2 13 22 26 26 5 1 11 16 20 21 4 1 9 14 11 17 6 1 15 13 17 27 4 2 9 13 16 16 4 2 15 10 21 26 4 1 14 12 18 17 4 1 8 13 17 24 4 2 11 16 21 23 4 2 14 18 18 20 6 1 14 10 16 10 4 1 12 12 13 21 5 1 15 9 28 25 4 1 11 7 25 28 4 1 11 16 24 25 5 2 9 12 15 20 10 2 8 15 21 20 10 1 13 15 11 27 4 1 12 8 27 26 4 1 24 14 23 19 4 2 11 13 21 26 8 1 11 18 16 20 4 2 16 11 20 22 14 1 12 12 21 19 4 2 18 12 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 23 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.2927 R-squared 0.0857 RMSE 4.1015 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 11 19.8934426229508 -8.89344262295082 2 22 19.8934426229508 2.10655737704918 3 23 23.1923076923077 -0.192307692307693 4 21 19.8934426229508 1.10655737704918 5 19 19.8934426229508 -0.893442622950818 6 12 19.8934426229508 -7.89344262295082 7 24 23.1923076923077 0.807692307692307 8 21 19.8934426229508 1.10655737704918 9 21 19.8934426229508 1.10655737704918 10 26 23.1923076923077 2.80769230769231 11 18 19.8934426229508 -1.89344262295082 12 21 19.8934426229508 1.10655737704918 13 22 23.1923076923077 -1.19230769230769 14 26 19.8934426229508 6.10655737704918 15 20 23.1923076923077 -3.19230769230769 16 20 23.1923076923077 -3.19230769230769 17 26 23.1923076923077 2.80769230769231 18 27 19.8934426229508 7.10655737704918 19 27 19.8934426229508 7.10655737704918 20 16 19.8934426229508 -3.89344262295082 21 26 19.8934426229508 6.10655737704918 22 20 19.8934426229508 0.106557377049182 23 25 23.1923076923077 1.80769230769231 24 16 19.8934426229508 -3.89344262295082 25 20 19.8934426229508 0.106557377049182 26 20 23.1923076923077 -3.19230769230769 27 24 23.1923076923077 0.807692307692307 28 24 23.1923076923077 0.807692307692307 29 22 19.8934426229508 2.10655737704918 30 18 19.8934426229508 -1.89344262295082 31 21 19.8934426229508 1.10655737704918 32 17 19.8934426229508 -2.89344262295082 33 15 19.8934426229508 -4.89344262295082 34 28 23.1923076923077 4.80769230769231 35 23 19.8934426229508 3.10655737704918 36 19 19.8934426229508 -0.893442622950818 37 15 19.8934426229508 -4.89344262295082 38 26 23.1923076923077 2.80769230769231 39 20 19.8934426229508 0.106557377049182 40 11 19.8934426229508 -8.89344262295082 41 17 19.8934426229508 -2.89344262295082 42 16 19.8934426229508 -3.89344262295082 43 21 19.8934426229508 1.10655737704918 44 18 19.8934426229508 -1.89344262295082 45 17 19.8934426229508 -2.89344262295082 46 21 19.8934426229508 1.10655737704918 47 18 19.8934426229508 -1.89344262295082 48 16 19.8934426229508 -3.89344262295082 49 13 19.8934426229508 -6.89344262295082 50 28 19.8934426229508 8.10655737704918 51 25 19.8934426229508 5.10655737704918 52 24 19.8934426229508 4.10655737704918 53 15 19.8934426229508 -4.89344262295082 54 21 19.8934426229508 1.10655737704918 55 11 19.8934426229508 -8.89344262295082 56 27 19.8934426229508 7.10655737704918 57 23 19.8934426229508 3.10655737704918 58 21 19.8934426229508 1.10655737704918 59 16 19.8934426229508 -3.89344262295082 60 20 19.8934426229508 0.106557377049182 61 21 19.8934426229508 1.10655737704918 62 10 19.8934426229508 -9.89344262295082 63 18 23.1923076923077 -5.19230769230769 64 20 19.8934426229508 0.106557377049182 65 21 19.8934426229508 1.10655737704918 66 24 19.8934426229508 4.10655737704918 67 26 19.8934426229508 6.10655737704918 68 23 23.1923076923077 -0.192307692307693 69 22 19.8934426229508 2.10655737704918 70 13 19.8934426229508 -6.89344262295082 71 27 23.1923076923077 3.80769230769231 72 24 19.8934426229508 4.10655737704918 73 19 23.1923076923077 -4.19230769230769 74 17 19.8934426229508 -2.89344262295082 75 16 19.8934426229508 -3.89344262295082 76 20 23.1923076923077 -3.19230769230769 77 8 19.8934426229508 -11.8934426229508 78 16 19.8934426229508 -3.89344262295082 79 17 19.8934426229508 -2.89344262295082 80 23 23.1923076923077 -0.192307692307693 81 18 19.8934426229508 -1.89344262295082 82 24 23.1923076923077 0.807692307692307 83 17 19.8934426229508 -2.89344262295082 84 20 19.8934426229508 0.106557377049182 85 22 19.8934426229508 2.10655737704918 86 22 19.8934426229508 2.10655737704918 87 20 19.8934426229508 0.106557377049182 88 18 19.8934426229508 -1.89344262295082 89 21 19.8934426229508 1.10655737704918 90 23 19.8934426229508 3.10655737704918 91 28 23.1923076923077 4.80769230769231 92 19 19.8934426229508 -0.893442622950818 93 22 19.8934426229508 2.10655737704918 94 17 19.8934426229508 -2.89344262295082 95 25 19.8934426229508 5.10655737704918 96 22 19.8934426229508 2.10655737704918 97 21 19.8934426229508 1.10655737704918 98 15 19.8934426229508 -4.89344262295082 99 20 19.8934426229508 0.106557377049182 100 25 19.8934426229508 5.10655737704918 101 21 19.8934426229508 1.10655737704918 102 24 19.8934426229508 4.10655737704918 103 23 19.8934426229508 3.10655737704918 104 22 19.8934426229508 2.10655737704918 105 14 19.8934426229508 -5.89344262295082 106 11 19.8934426229508 -8.89344262295082 107 22 19.8934426229508 2.10655737704918 108 22 19.8934426229508 2.10655737704918 109 6 19.8934426229508 -13.8934426229508 110 15 19.8934426229508 -4.89344262295082 111 26 19.8934426229508 6.10655737704918 112 26 23.1923076923077 2.80769230769231 113 20 19.8934426229508 0.106557377049182 114 26 19.8934426229508 6.10655737704918 115 15 19.8934426229508 -4.89344262295082 116 25 19.8934426229508 5.10655737704918 117 22 19.8934426229508 2.10655737704918 118 20 19.8934426229508 0.106557377049182 119 18 19.8934426229508 -1.89344262295082 120 23 19.8934426229508 3.10655737704918 121 22 19.8934426229508 2.10655737704918 122 23 19.8934426229508 3.10655737704918 123 17 23.1923076923077 -6.19230769230769 124 20 19.8934426229508 0.106557377049182 125 21 19.8934426229508 1.10655737704918 126 23 19.8934426229508 3.10655737704918 127 25 19.8934426229508 5.10655737704918 128 25 19.8934426229508 5.10655737704918 129 21 19.8934426229508 1.10655737704918 130 22 19.8934426229508 2.10655737704918 131 18 19.8934426229508 -1.89344262295082 132 18 19.8934426229508 -1.89344262295082 133 18 23.1923076923077 -5.19230769230769 134 21 19.8934426229508 1.10655737704918 135 21 19.8934426229508 1.10655737704918 136 25 19.8934426229508 5.10655737704918 137 24 19.8934426229508 4.10655737704918 138 24 19.8934426229508 4.10655737704918 139 28 23.1923076923077 4.80769230769231 140 24 23.1923076923077 0.807692307692307 141 22 19.8934426229508 2.10655737704918 142 22 19.8934426229508 2.10655737704918 143 20 19.8934426229508 0.106557377049182 144 25 19.8934426229508 5.10655737704918 145 13 19.8934426229508 -6.89344262295082 146 21 19.8934426229508 1.10655737704918 147 23 19.8934426229508 3.10655737704918 148 18 19.8934426229508 -1.89344262295082

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/2evgg1292936162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/2evgg1292936162.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/3evgg1292936162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/3evgg1292936162.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/4zwx41292936162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129293609950oiry7uzggwbbu/4zwx41292936162.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; 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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