ws 10

*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, 14 Dec 2010 10:55:35 +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/14/t1292324038b0ijsrfjsgjkrm0.htm/, Retrieved Tue, 14 Dec 2010 11:54:04 +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/14/t1292324038b0ijsrfjsgjkrm0.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 «

0 1 24 14 11 12 24 26 1 1 25 11 7 8 25 23 1 0 17 6 17 8 30 25 0 1 18 12 10 8 19 23 1 0 16 10 12 7 22 29 1 1 20 10 11 4 25 25 1 1 16 11 11 11 23 21 1 1 18 16 12 7 17 22 1 1 17 11 13 7 21 25 0 1 23 13 14 12 19 24 1 1 30 12 16 10 19 18 1 1 18 12 10 8 16 15 0 1 15 11 11 8 23 22 0 1 12 4 15 4 27 28 1 1 21 9 9 9 22 20 0 1 20 8 17 7 22 24 1 1 27 15 11 9 23 21 0 1 34 16 18 11 21 20 1 1 21 9 14 13 19 21 0 1 31 14 10 8 18 23 0 1 19 11 11 8 20 28 1 1 16 8 15 9 23 24 1 1 20 9 15 6 25 24 0 1 21 9 13 9 19 24 0 1 22 9 16 9 24 23 1 1 17 9 13 6 22 23 0 1 24 10 9 6 25 29 1 1 25 16 18 16 26 24 1 1 26 11 18 5 29 18 1 1 25 8 12 7 32 25 1 1 17 9 17 9 25 21 0 1 32 16 9 6 29 26 0 1 33 11 9 6 28 22 0 0 32 12 18 12 28 22 0 1 25 12 12 7 29 23 0 1 29 14 18 10 26 30 1 1 22 9 14 9 25 23 0 1 18 10 15 8 14 17 1 1 17 9 16 5 25 23 0 1 20 10 10 8 26 23 0 1 15 12 11 8 20 25 1 1 20 14 14 10 18 24 0 1 33 14 9 6 32 24 1 1 23 14 17 7 25 21 0 1 26 16 5 4 23 24 0 1 18 9 12 8 21 24 1 1 20 10 12 8 20 28 1 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 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.6076 R-squared 0.3691 RMSE 2.7913 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 11 17.8333333333333 -6.83333333333333 2 7 11.8021978021978 -4.8021978021978 3 17 11.8021978021978 5.1978021978022 4 10 11.8021978021978 -1.80219780219780 5 12 11.8021978021978 0.197802197802197 6 11 11.8021978021978 -0.802197802197803 7 11 13 -2 8 12 11.8021978021978 0.197802197802197 9 13 11.8021978021978 1.19780219780220 10 14 13 1 11 16 17.8333333333333 -1.83333333333333 12 10 11.8021978021978 -1.80219780219780 13 11 11.8021978021978 -0.802197802197803 14 15 11.8021978021978 3.1978021978022 15 9 11.8021978021978 -2.8021978021978 16 17 11.8021978021978 5.1978021978022 17 11 11.8021978021978 -0.802197802197803 18 18 17.8333333333333 0.166666666666668 19 14 13 1 20 10 11.8021978021978 -1.80219780219780 21 11 11.8021978021978 -0.802197802197803 22 15 11.8021978021978 3.1978021978022 23 15 11.8021978021978 3.1978021978022 24 13 11.8021978021978 1.19780219780220 25 16 11.8021978021978 4.1978021978022 26 13 11.8021978021978 1.19780219780220 27 9 11.8021978021978 -2.8021978021978 28 18 17.8333333333333 0.166666666666668 29 18 11.8021978021978 6.1978021978022 30 12 11.8021978021978 0.197802197802197 31 17 11.8021978021978 5.1978021978022 32 9 11.8021978021978 -2.8021978021978 33 9 11.8021978021978 -2.8021978021978 34 18 17.8333333333333 0.166666666666668 35 12 11.8021978021978 0.197802197802197 36 18 17.8333333333333 0.166666666666668 37 14 11.8021978021978 2.19780219780220 38 15 11.8021978021978 3.1978021978022 39 16 11.8021978021978 4.1978021978022 40 10 11.8021978021978 -1.80219780219780 41 11 11.8021978021978 -0.802197802197803 42 14 13 1 43 9 11.8021978021978 -2.8021978021978 44 17 11.8021978021978 5.1978021978022 45 5 11.8021978021978 -6.8021978021978 46 12 11.8021978021978 0.197802197802197 47 12 11.8021978021978 0.197802197802197 48 6 11.8021978021978 -5.8021978021978 49 24 17.8333333333333 6.16666666666667 50 12 11.8021978021978 0.197802197802197 51 12 11.8021978021978 0.197802197802197 52 14 11.8021978021978 2.19780219780220 53 7 11.8021978021978 -4.8021978021978 54 12 11.8021978021978 0.197802197802197 55 14 11.8021978021978 2.19780219780220 56 8 11.8021978021978 -3.8021978021978 57 11 11.8021978021978 -0.802197802197803 58 9 11.8021978021978 -2.8021978021978 59 11 11.8021978021978 -0.802197802197803 60 10 11.8021978021978 -1.80219780219780 61 11 11.8021978021978 -0.802197802197803 62 12 11.8021978021978 0.197802197802197 63 9 11.8021978021978 -2.8021978021978 64 18 17.8333333333333 0.166666666666668 65 15 11.8021978021978 3.1978021978022 66 12 11.8021978021978 0.197802197802197 67 13 11.8021978021978 1.19780219780220 68 14 11.8021978021978 2.19780219780220 69 10 11.8021978021978 -1.80219780219780 70 13 11.8021978021978 1.19780219780220 71 13 13 0 72 11 11.8021978021978 -0.802197802197803 73 13 11.8021978021978 1.19780219780220 74 16 13 3 75 11 11.8021978021978 -0.802197802197803 76 16 17.8333333333333 -1.83333333333333 77 14 11.8021978021978 2.19780219780220 78 8 11.8021978021978 -3.8021978021978 79 9 11.8021978021978 -2.8021978021978 80 15 11.8021978021978 3.1978021978022 81 11 13 -2 82 21 17.8333333333333 3.16666666666667 83 14 11.8021978021978 2.19780219780220 84 18 17.8333333333333 0.166666666666668 85 12 11.8021978021978 0.197802197802197 86 13 11.8021978021978 1.19780219780220 87 12 11.8021978021978 0.197802197802197 88 19 13 6 89 11 13 -2 90 13 17.8333333333333 -4.83333333333333 91 15 17.8333333333333 -2.83333333333333 92 12 11.8021978021978 0.197802197802197 93 16 17.8333333333333 -1.83333333333333 94 18 17.8333333333333 0.166666666666668 95 8 13 -5 96 9 11.8021978021978 -2.8021978021978 97 15 11.8021978021978 3.1978021978022 98 6 11.8021978021978 -5.8021978021978 99 8 11.8021978021978 -3.8021978021978 100 10 11.8021978021978 -1.80219780219780 101 11 11.8021978021978 -0.802197802197803 102 14 11.8021978021978 2.19780219780220 103 11 11.8021978021978 -0.802197802197803 104 12 11.8021978021978 0.197802197802197 105 11 11.8021978021978 -0.802197802197803 106 9 11.8021978021978 -2.8021978021978 107 12 11.8021978021978 0.197802197802197 108 20 17.8333333333333 2.16666666666667 109 13 11.8021978021978 1.19780219780220 110 12 13 -1 111 9 11.8021978021978 -2.8021978021978 112 24 17.8333333333333 6.16666666666667 113 11 11.8021978021978 -0.802197802197803 114 17 11.8021978021978 5.1978021978022 115 11 11.8021978021978 -0.802197802197803 116 11 11.8021978021978 -0.802197802197803 117 16 11.8021978021978 4.1978021978022 118 13 11.8021978021978 1.19780219780220 119 11 11.8021978021978 -0.802197802197803 120 19 17.8333333333333 1.16666666666667

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/27nfk1292324126.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/27nfk1292324126.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/37nfk1292324126.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/37nfk1292324126.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/4iwen1292324126.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292324038b0ijsrfjsgjkrm0/4iwen1292324126.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 5 ; 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') }

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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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