WS10 Recursive Partitioning

*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: Mon, 13 Dec 2010 09:33:29 +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/13/t12922327726nfcqunamm82vp3.htm/, Retrieved Mon, 13 Dec 2010 10:33:03 +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/13/t12922327726nfcqunamm82vp3.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 «

5 2 1 3 11 16 14 6 12 1 1 1 11 13 11 4 11 1 1 3 15 16 11 5 6 1 1 3 11 6 9 4 12 1 2 3 9 11 11 4 11 1 1 3 14 13 16 6 12 1 1 1 12 15 13 6 7 2 4 3 6 9 11 4 8 1 1 3 4 6 4 4 13 1 1 1 13 11 15 6 12 1 1 1 12 9 13 4 13 1 1 3 10 4 13 6 12 1 1 1 12 8 13 5 12 1 3 3 9 11 11 4 11 2 1 3 16 16 15 6 12 2 1 1 13 5 12 3 12 1 1 1 12 6 14 5 12 1 6 1 11 7 13 6 11 2 1 3 12 16 13 4 13 2 1 1 12 12 12 6 9 1 1 3 11 7 13 2 11 2 1 3 16 13 14 7 11 1 1 1 9 12 13 5 11 2 1 3 8 10 15 2 9 1 1 1 11 12 12 4 11 2 1 4 9 8 10 4 12 2 1 3 16 15 14 6 12 1 1 3 14 15 13 6 10 2 1 3 10 10 11 5 12 1 4 3 14 13 15 6 12 2 1 1 16 16 14 6 12 1 1 3 12 10 13 4 9 2 1 3 13 14 14 6 9 1 1 3 16 16 16 6 12 1 1 3 15 13 13 6 14 2 1 1 5 4 5 2 12 2 1 3 12 7 11 4 11 1 1 1 11 15 10 5 9 1 1 2 15 5 11 3 11 2 1 3 15 14 15 7 7 1 1 1 12 11 15 5 15 1 1 1 5 8 12 3 11 1 1 3 16 14 15 8 12 1 1 3 16 12 15 8 12 2 2 1 12 12 14 5 9 2 1 3 6 15 11 6 12 2 1 3 7 8 12 3 11 2 1 3 14 16 12 5 11 2 2 3 8 9 12 4 8 1 4 3 12 13 13 5 7 2 1 1 10 8 9 5 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 0.6977 R-squared 0.4867 RMSE 0.9957 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 6 5.43859649122807 0.56140350877193 2 4 5.43859649122807 -1.43859649122807 3 5 5.43859649122807 -0.43859649122807 4 4 4.44444444444444 -0.444444444444445 5 4 3.75862068965517 0.241379310344827 6 6 6.75 -0.75 7 6 5.43859649122807 0.56140350877193 8 4 3.75862068965517 0.241379310344827 9 4 3.75862068965517 0.241379310344827 10 6 5.55555555555556 0.444444444444445 11 4 4.44444444444444 -0.444444444444445 12 6 4.44444444444444 1.55555555555556 13 5 4.44444444444444 0.555555555555555 14 4 3.75862068965517 0.241379310344827 15 6 6.75 -0.75 16 3 4.44444444444444 -1.44444444444444 17 5 4.44444444444444 0.555555555555555 18 6 4.44444444444444 1.55555555555556 19 4 5.43859649122807 -1.43859649122807 20 6 5.43859649122807 0.56140350877193 21 2 4.44444444444444 -2.44444444444444 22 7 5.43859649122807 1.56140350877193 23 5 3.75862068965517 1.24137931034483 24 2 3.75862068965517 -1.75862068965517 25 4 5.43859649122807 -1.43859649122807 26 4 3.75862068965517 0.241379310344827 27 6 5.43859649122807 0.56140350877193 28 6 5.43859649122807 0.56140350877193 29 5 4.44444444444444 0.555555555555555 30 6 6.75 -0.75 31 6 5.43859649122807 0.56140350877193 32 4 4.44444444444444 -0.444444444444445 33 6 5.43859649122807 0.56140350877193 34 6 6.75 -0.75 35 6 5.43859649122807 0.56140350877193 36 2 3.75862068965517 -1.75862068965517 37 4 4.44444444444444 -0.444444444444445 38 5 5.43859649122807 -0.43859649122807 39 3 4.44444444444444 -1.44444444444444 40 7 6.75 0.25 41 5 5.55555555555556 -0.555555555555555 42 3 3.75862068965517 -0.758620689655173 43 8 6.75 1.25 44 8 6.75 1.25 45 5 5.43859649122807 -0.43859649122807 46 6 3.75862068965517 2.24137931034483 47 3 3.75862068965517 -0.758620689655173 48 5 5.43859649122807 -0.43859649122807 49 4 3.75862068965517 0.241379310344827 50 5 5.43859649122807 -0.43859649122807 51 5 4.44444444444444 0.555555555555555 52 6 5.43859649122807 0.56140350877193 53 5 5.43859649122807 -0.43859649122807 54 6 6.75 -0.75 55 6 5.43859649122807 0.56140350877193 56 4 3.75862068965517 0.241379310344827 57 8 6.75 1.25 58 6 5.55555555555556 0.444444444444445 59 4 3.75862068965517 0.241379310344827 60 6 5.43859649122807 0.56140350877193 61 5 5.55555555555556 -0.555555555555555 62 5 5.43859649122807 -0.43859649122807 63 6 6.75 -0.75 64 6 5.43859649122807 0.56140350877193 65 6 5.43859649122807 0.56140350877193 66 6 4.44444444444444 1.55555555555556 67 6 5.55555555555556 0.444444444444445 68 6 5.43859649122807 0.56140350877193 69 7 5.43859649122807 1.56140350877193 70 4 3.75862068965517 0.241379310344827 71 4 4.44444444444444 -0.444444444444445 72 3 5.43859649122807 -2.43859649122807 73 6 6.75 -0.75 74 5 5.43859649122807 -0.43859649122807 75 5 5.43859649122807 -0.43859649122807 76 3 3.75862068965517 -0.758620689655173 77 5 5.43859649122807 -0.43859649122807 78 4 4.44444444444444 -0.444444444444445 79 3 4.44444444444444 -1.44444444444444 80 7 6.75 0.25 81 4 3.75862068965517 0.241379310344827 82 4 5.43859649122807 -1.43859649122807 83 5 5.43859649122807 -0.43859649122807 84 6 5.43859649122807 0.56140350877193 85 2 3.75862068965517 -1.75862068965517 86 2 3.75862068965517 -1.75862068965517 87 6 5.43859649122807 0.56140350877193 88 4 3.75862068965517 0.241379310344827 89 5 5.43859649122807 -0.43859649122807 90 6 5.43859649122807 0.56140350877193 91 7 6.75 0.25 92 8 6.75 1.25 93 6 5.55555555555556 0.444444444444445 94 6 5.55555555555556 0.444444444444445 95 3 3.75862068965517 -0.758620689655173 96 7 6.75 0.25 97 3 4.44444444444444 -1.44444444444444 98 6 5.43859649122807 0.56140350877193 99 4 4.44444444444444 -0.444444444444445 100 4 5.55555555555556 -1.55555555555556 101 6 4.44444444444444 1.55555555555556 102 6 5.43859649122807 0.56140350877193 103 6 3.75862068965517 2.24137931034483 104 4 4.44444444444444 -0.444444444444445 105 7 5.43859649122807 1.56140350877193 106 5 5.43859649122807 -0.43859649122807 107 7 6.75 0.25 108 4 3.75862068965517 0.241379310344827 109 6 5.43859649122807 0.56140350877193 110 6 5.43859649122807 0.56140350877193 111 6 5.55555555555556 0.444444444444445 112 5 5.43859649122807 -0.43859649122807 113 5 4.44444444444444 0.555555555555555 114 6 5.43859649122807 0.56140350877193 115 7 5.43859649122807 1.56140350877193 116 4 5.43859649122807 -1.43859649122807 117 4 5.43859649122807 -1.43859649122807 118 8 6.75 1.25 119 6 5.43859649122807 0.56140350877193 120 3 3.75862068965517 -0.758620689655173 121 4 4.44444444444444 -0.444444444444445 122 5 5.43859649122807 -0.43859649122807 123 5 3.75862068965517 1.24137931034483 124 6 5.43859649122807 0.56140350877193 125 8 6.75 1.25 126 2 5.43859649122807 -3.43859649122807 127 4 3.75862068965517 0.241379310344827 128 7 6.75 0.25 129 5 3.75862068965517 1.24137931034483 130 6 5.43859649122807 0.56140350877193 131 6 5.43859649122807 0.56140350877193 132 4 4.44444444444444 -0.444444444444445 133 5 5.43859649122807 -0.43859649122807 134 6 5.43859649122807 0.56140350877193 135 6 6.75 -0.75 136 6 6.75 -0.75 137 6 5.43859649122807 0.56140350877193 138 5 5.43859649122807 -0.43859649122807 139 5 4.44444444444444 0.555555555555555 140 6 6.75 -0.75 141 4 3.75862068965517 0.241379310344827 142 6 4.44444444444444 1.55555555555556 143 3 3.75862068965517 -0.758620689655173 144 6 4.44444444444444 1.55555555555556 145 8 6.75 1.25 146 4 6.75 -2.75

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/2eurq1292232800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/2eurq1292232800.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/3eurq1292232800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/3eurq1292232800.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/4ol8t1292232800.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922327726nfcqunamm82vp3/4ol8t1292232800.ps (open in new window)

Parameters (Session):

par1 = 8 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 8 ; 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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Software written by Ed van Stee & Patrick Wessa

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