PAPER BAEYENS (Recursive Partitioning1)

*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 13:24:14 +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/t1292937883nur7e34p1ghqy41.htm/, Retrieved Tue, 21 Dec 2010 14:24:43 +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/t1292937883nur7e34p1ghqy41.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 «

13 5 14 12 3 18 15 0 11 12 7 12 10 4 16 12 1 18 15 6 14 9 3 14 12 12 15 11 0 15 11 5 17 11 6 19 15 6 10 7 6 16 11 2 18 11 1 14 10 5 14 14 7 17 10 3 14 6 3 16 11 3 18 15 7 11 11 8 14 12 6 12 14 3 17 15 5 9 9 5 16 13 10 14 13 2 15 16 6 11 13 4 16 12 6 13 14 8 17 11 4 15 9 5 14 16 10 16 12 6 9 10 7 15 13 4 17 16 10 13 14 4 15 15 3 16 5 3 16 8 3 12 11 3 12 16 7 11 17 15 15 9 0 15 9 0 17 13 4 13 10 5 16 6 5 14 12 2 11 8 3 12 14 0 12 12 9 15 11 2 16 16 7 15 8 7 12 15 0 12 7 0 8 16 10 13 14 2 11 16 1 14 9 8 15 14 6 10 11 11 11 13 3 12 15 8 15 5 6 15 15 9 14 13 9 16 11 8 15 11 8 15 12 7 13 12 6 12 12 5 17 12 4 13 14 6 15 6 3 13 7 2 15 14 12 16 14 8 15 10 5 16 13 9 15 12 6 14 9 5 15 12 2 14 16 4 13 10 7 7 14 5 17 10 6 13 16 7 15 15 8 14 12 6 13 10 0 16 8 1 12 8 5 14 11 5 17 13 5 15 16 7 17 16 7 12 14 1 16 11 3 11 4 4 15 14 8 9 9 6 16 14 6 15 8 2 10 8 2 10 11 3 15 12 3 11 11 0 13 14 2 14 15 8 18 16 8 16 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.2538 R-squared 0.0644 RMSE 3.0013 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 5 5.88571428571429 -0.885714285714286 2 3 4.30232558139535 -1.30232558139535 3 0 5.88571428571429 -5.88571428571429 4 7 4.30232558139535 2.69767441860465 5 4 4.30232558139535 -0.302325581395348 6 1 4.30232558139535 -3.30232558139535 7 6 5.88571428571429 0.114285714285714 8 3 4.30232558139535 -1.30232558139535 9 12 4.30232558139535 7.69767441860465 10 0 4.30232558139535 -4.30232558139535 11 5 4.30232558139535 0.697674418604652 12 6 4.30232558139535 1.69767441860465 13 6 5.88571428571429 0.114285714285714 14 6 4.30232558139535 1.69767441860465 15 2 4.30232558139535 -2.30232558139535 16 1 4.30232558139535 -3.30232558139535 17 5 4.30232558139535 0.697674418604652 18 7 5.88571428571429 1.11428571428571 19 3 4.30232558139535 -1.30232558139535 20 3 4.30232558139535 -1.30232558139535 21 3 4.30232558139535 -1.30232558139535 22 7 5.88571428571429 1.11428571428571 23 8 4.30232558139535 3.69767441860465 24 6 4.30232558139535 1.69767441860465 25 3 5.88571428571429 -2.88571428571429 26 5 5.88571428571429 -0.885714285714286 27 5 4.30232558139535 0.697674418604652 28 10 5.88571428571429 4.11428571428571 29 2 5.88571428571429 -3.88571428571429 30 6 5.88571428571429 0.114285714285714 31 4 5.88571428571429 -1.88571428571429 32 6 4.30232558139535 1.69767441860465 33 8 5.88571428571429 2.11428571428571 34 4 4.30232558139535 -0.302325581395348 35 5 4.30232558139535 0.697674418604652 36 10 5.88571428571429 4.11428571428571 37 6 4.30232558139535 1.69767441860465 38 7 4.30232558139535 2.69767441860465 39 4 5.88571428571429 -1.88571428571429 40 10 5.88571428571429 4.11428571428571 41 4 5.88571428571429 -1.88571428571429 42 3 5.88571428571429 -2.88571428571429 43 3 4.30232558139535 -1.30232558139535 44 3 4.30232558139535 -1.30232558139535 45 3 4.30232558139535 -1.30232558139535 46 7 5.88571428571429 1.11428571428571 47 15 5.88571428571429 9.11428571428571 48 0 4.30232558139535 -4.30232558139535 49 0 4.30232558139535 -4.30232558139535 50 4 5.88571428571429 -1.88571428571429 51 5 4.30232558139535 0.697674418604652 52 5 4.30232558139535 0.697674418604652 53 2 4.30232558139535 -2.30232558139535 54 3 4.30232558139535 -1.30232558139535 55 0 5.88571428571429 -5.88571428571429 56 9 4.30232558139535 4.69767441860465 57 2 4.30232558139535 -2.30232558139535 58 7 5.88571428571429 1.11428571428571 59 7 4.30232558139535 2.69767441860465 60 0 5.88571428571429 -5.88571428571429 61 0 4.30232558139535 -4.30232558139535 62 10 5.88571428571429 4.11428571428571 63 2 5.88571428571429 -3.88571428571429 64 1 5.88571428571429 -4.88571428571429 65 8 4.30232558139535 3.69767441860465 66 6 5.88571428571429 0.114285714285714 67 11 4.30232558139535 6.69767441860465 68 3 5.88571428571429 -2.88571428571429 69 8 5.88571428571429 2.11428571428571 70 6 4.30232558139535 1.69767441860465 71 9 5.88571428571429 3.11428571428571 72 9 5.88571428571429 3.11428571428571 73 8 4.30232558139535 3.69767441860465 74 8 4.30232558139535 3.69767441860465 75 7 4.30232558139535 2.69767441860465 76 6 4.30232558139535 1.69767441860465 77 5 4.30232558139535 0.697674418604652 78 4 4.30232558139535 -0.302325581395348 79 6 5.88571428571429 0.114285714285714 80 3 4.30232558139535 -1.30232558139535 81 2 4.30232558139535 -2.30232558139535 82 12 5.88571428571429 6.11428571428571 83 8 5.88571428571429 2.11428571428571 84 5 4.30232558139535 0.697674418604652 85 9 5.88571428571429 3.11428571428571 86 6 4.30232558139535 1.69767441860465 87 5 4.30232558139535 0.697674418604652 88 2 4.30232558139535 -2.30232558139535 89 4 5.88571428571429 -1.88571428571429 90 7 4.30232558139535 2.69767441860465 91 5 5.88571428571429 -0.885714285714286 92 6 4.30232558139535 1.69767441860465 93 7 5.88571428571429 1.11428571428571 94 8 5.88571428571429 2.11428571428571 95 6 4.30232558139535 1.69767441860465 96 0 4.30232558139535 -4.30232558139535 97 1 4.30232558139535 -3.30232558139535 98 5 4.30232558139535 0.697674418604652 99 5 4.30232558139535 0.697674418604652 100 5 5.88571428571429 -0.885714285714286 101 7 5.88571428571429 1.11428571428571 102 7 5.88571428571429 1.11428571428571 103 1 5.88571428571429 -4.88571428571429 104 3 4.30232558139535 -1.30232558139535 105 4 4.30232558139535 -0.302325581395348 106 8 5.88571428571429 2.11428571428571 107 6 4.30232558139535 1.69767441860465 108 6 5.88571428571429 0.114285714285714 109 2 4.30232558139535 -2.30232558139535 110 2 4.30232558139535 -2.30232558139535 111 3 4.30232558139535 -1.30232558139535 112 3 4.30232558139535 -1.30232558139535 113 0 4.30232558139535 -4.30232558139535 114 2 5.88571428571429 -3.88571428571429 115 8 5.88571428571429 2.11428571428571 116 8 5.88571428571429 2.11428571428571 117 0 5.88571428571429 -5.88571428571429 118 5 4.30232558139535 0.697674418604652 119 9 5.88571428571429 3.11428571428571 120 6 5.88571428571429 0.114285714285714 121 6 4.30232558139535 1.69767441860465 122 3 5.88571428571429 -2.88571428571429 123 9 4.30232558139535 4.69767441860465 124 7 5.88571428571429 1.11428571428571 125 8 5.88571428571429 2.11428571428571 126 0 4.30232558139535 -4.30232558139535 127 7 5.88571428571429 1.11428571428571 128 0 4.30232558139535 -4.30232558139535 129 5 4.30232558139535 0.697674418604652 130 0 4.30232558139535 -4.30232558139535 131 14 5.88571428571429 8.11428571428571 132 5 5.88571428571429 -0.885714285714286 133 2 4.30232558139535 -2.30232558139535 134 8 5.88571428571429 2.11428571428571 135 4 5.88571428571429 -1.88571428571429 136 2 4.30232558139535 -2.30232558139535 137 6 5.88571428571429 0.114285714285714 138 3 4.30232558139535 -1.30232558139535 139 5 5.88571428571429 -0.885714285714286 140 9 4.30232558139535 4.69767441860465 141 3 4.30232558139535 -1.30232558139535 142 3 5.88571428571429 -2.88571428571429 143 0 5.88571428571429 -5.88571428571429 144 10 4.30232558139535 5.69767441860465 145 4 4.30232558139535 -0.302325581395348 146 2 4.30232558139535 -2.30232558139535 147 3 4.30232558139535 -1.30232558139535 148 10 4.30232558139535 5.69767441860465 149 7 5.88571428571429 1.11428571428571 150 0 5.88571428571429 -5.88571428571429 151 6 5.88571428571429 0.114285714285714 152 8 5.88571428571429 2.11428571428571 153 0 4.30232558139535 -4.30232558139535 154 4 5.88571428571429 -1.88571428571429 155 10 5.88571428571429 4.11428571428571 156 5 4.30232558139535 0.697674418604652

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/2k8rk1292937844.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/2k8rk1292937844.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/3k8rk1292937844.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/3k8rk1292937844.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/46rpq1292937844.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937883nur7e34p1ghqy41/46rpq1292937844.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; 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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