WS 10 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: Tue, 14 Dec 2010 11:22:20 +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/t1292325703fkicdyd3f5trozi.htm/, Retrieved Tue, 14 Dec 2010 12:21:44 +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/t1292325703fkicdyd3f5trozi.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 «

1 24 24 13 13 13 1 25 25 12 12 13 1 17 30 15 10 16 0 18 19 12 9 12 1 18 22 10 10 11 1 16 22 12 12 12 1 20 25 15 13 18 1 16 23 9 12 11 1 18 17 12 12 14 1 17 21 11 6 9 0 23 19 11 5 14 1 30 19 11 12 12 0 23 15 15 11 11 1 18 16 7 14 12 1 15 23 11 14 13 0 12 27 11 12 11 0 21 22 10 12 12 1 15 14 14 11 16 0 20 22 10 11 9 1 31 23 6 7 11 0 27 23 11 9 13 1 34 21 15 11 15 1 21 19 11 11 10 1 31 18 12 12 11 0 19 20 14 12 13 1 16 23 15 11 16 0 20 25 9 11 15 1 21 19 13 8 14 1 22 24 13 9 14 0 17 22 16 12 14 1 24 25 13 10 8 0 25 26 12 10 13 1 26 29 14 12 15 1 25 32 11 8 13 0 17 25 9 12 11 0 32 29 16 11 15 0 33 28 12 12 15 0 13 17 10 7 9 1 32 28 13 11 13 0 25 29 16 11 16 0 29 26 14 12 13 1 22 25 15 9 11 0 18 14 5 15 12 0 17 25 8 11 12 1 20 26 11 11 12 1 15 20 16 11 14 1 20 18 17 11 14 1 33 32 9 15 8 1 29 25 9 11 13 0 23 25 13 12 16 1 26 23 10 12 13 0 18 21 6 9 11 0 20 20 12 12 14 1 11 15 8 12 13 0 28 30 14 13 13 1 26 24 12 11 13 1 22 26 11 9 12 1 17 24 16 9 16 0 12 22 8 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 4 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.4334 R-squared 0.1878 RMSE 5.1239 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 24 20.9719626168224 3.02803738317757 2 25 20.9719626168224 4.02803738317757 3 17 26.0689655172414 -9.06896551724138 4 18 20.9719626168224 -2.97196261682243 5 18 20.9719626168224 -2.97196261682243 6 16 20.9719626168224 -4.97196261682243 7 20 20.9719626168224 -0.97196261682243 8 16 20.9719626168224 -4.97196261682243 9 18 17.55 0.449999999999999 10 17 20.9719626168224 -3.97196261682243 11 23 20.9719626168224 2.02803738317757 12 30 20.9719626168224 9.02803738317757 13 23 17.55 5.45 14 18 17.55 0.449999999999999 15 15 20.9719626168224 -5.97196261682243 16 12 26.0689655172414 -14.0689655172414 17 21 20.9719626168224 0.0280373831775691 18 15 17.55 -2.55 19 20 20.9719626168224 -0.97196261682243 20 31 20.9719626168224 10.0280373831776 21 27 20.9719626168224 6.02803738317757 22 34 20.9719626168224 13.0280373831776 23 21 20.9719626168224 0.0280373831775691 24 31 20.9719626168224 10.0280373831776 25 19 20.9719626168224 -1.97196261682243 26 16 20.9719626168224 -4.97196261682243 27 20 20.9719626168224 -0.97196261682243 28 21 20.9719626168224 0.0280373831775691 29 22 20.9719626168224 1.02803738317757 30 17 20.9719626168224 -3.97196261682243 31 24 20.9719626168224 3.02803738317757 32 25 26.0689655172414 -1.06896551724138 33 26 26.0689655172414 -0.0689655172413808 34 25 26.0689655172414 -1.06896551724138 35 17 20.9719626168224 -3.97196261682243 36 32 26.0689655172414 5.93103448275862 37 33 26.0689655172414 6.93103448275862 38 13 17.55 -4.55 39 32 26.0689655172414 5.93103448275862 40 25 26.0689655172414 -1.06896551724138 41 29 26.0689655172414 2.93103448275862 42 22 20.9719626168224 1.02803738317757 43 18 17.55 0.449999999999999 44 17 20.9719626168224 -3.97196261682243 45 20 26.0689655172414 -6.06896551724138 46 15 20.9719626168224 -5.97196261682243 47 20 20.9719626168224 -0.97196261682243 48 33 26.0689655172414 6.93103448275862 49 29 20.9719626168224 8.02803738317757 50 23 20.9719626168224 2.02803738317757 51 26 20.9719626168224 5.02803738317757 52 18 20.9719626168224 -2.97196261682243 53 20 20.9719626168224 -0.97196261682243 54 11 17.55 -6.55 55 28 26.0689655172414 1.93103448275862 56 26 20.9719626168224 5.02803738317757 57 22 26.0689655172414 -4.06896551724138 58 17 20.9719626168224 -3.97196261682243 59 12 20.9719626168224 -8.97196261682243 60 14 17.55 -3.55 61 17 20.9719626168224 -3.97196261682243 62 21 20.9719626168224 0.0280373831775691 63 19 20.9719626168224 -1.97196261682243 64 18 20.9719626168224 -2.97196261682243 65 10 20.9719626168224 -10.9719626168224 66 29 26.0689655172414 2.93103448275862 67 31 20.9719626168224 10.0280373831776 68 19 26.0689655172414 -7.06896551724138 69 9 20.9719626168224 -11.9719626168224 70 20 20.9719626168224 -0.97196261682243 71 28 20.9719626168224 7.02803738317757 72 19 20.9719626168224 -1.97196261682243 73 30 26.0689655172414 3.93103448275862 74 29 20.9719626168224 8.02803738317757 75 26 20.9719626168224 5.02803738317757 76 23 20.9719626168224 2.02803738317757 77 13 20.9719626168224 -7.97196261682243 78 21 20.9719626168224 0.0280373831775691 79 19 20.9719626168224 -1.97196261682243 80 28 20.9719626168224 7.02803738317757 81 23 20.9719626168224 2.02803738317757 82 18 17.55 0.449999999999999 83 21 20.9719626168224 0.0280373831775691 84 20 20.9719626168224 -0.97196261682243 85 23 20.9719626168224 2.02803738317757 86 21 20.9719626168224 0.0280373831775691 87 21 26.0689655172414 -5.06896551724138 88 15 20.9719626168224 -5.97196261682243 89 28 26.0689655172414 1.93103448275862 90 19 17.55 1.45 91 26 17.55 8.45 92 10 20.9719626168224 -10.9719626168224 93 16 17.55 -1.55 94 22 20.9719626168224 1.02803738317757 95 19 20.9719626168224 -1.97196261682243 96 31 26.0689655172414 4.93103448275862 97 31 20.9719626168224 10.0280373831776 98 29 20.9719626168224 8.02803738317757 99 19 20.9719626168224 -1.97196261682243 100 22 20.9719626168224 1.02803738317757 101 23 20.9719626168224 2.02803738317757 102 15 20.9719626168224 -5.97196261682243 103 20 20.9719626168224 -0.97196261682243 104 18 20.9719626168224 -2.97196261682243 105 23 20.9719626168224 2.02803738317757 106 25 17.55 7.45 107 21 20.9719626168224 0.0280373831775691 108 24 20.9719626168224 3.02803738317757 109 25 20.9719626168224 4.02803738317757 110 17 17.55 -0.550000000000001 111 13 17.55 -4.55 112 28 20.9719626168224 7.02803738317757 113 21 20.9719626168224 0.0280373831775691 114 25 26.0689655172414 -1.06896551724138 115 9 26.0689655172414 -17.0689655172414 116 16 20.9719626168224 -4.97196261682243 117 19 20.9719626168224 -1.97196261682243 118 17 20.9719626168224 -3.97196261682243 119 25 20.9719626168224 4.02803738317757 120 20 17.55 2.45 121 29 26.0689655172414 2.93103448275862 122 14 17.55 -3.55 123 22 20.9719626168224 1.02803738317757 124 15 20.9719626168224 -5.97196261682243 125 19 20.9719626168224 -1.97196261682243 126 20 20.9719626168224 -0.97196261682243 127 15 20.9719626168224 -5.97196261682243 128 20 20.9719626168224 -0.97196261682243 129 18 20.9719626168224 -2.97196261682243 130 33 26.0689655172414 6.93103448275862 131 22 20.9719626168224 1.02803738317757 132 16 17.55 -1.55 133 17 20.9719626168224 -3.97196261682243 134 16 20.9719626168224 -4.97196261682243 135 21 17.55 3.45 136 26 26.0689655172414 -0.0689655172413808 137 18 20.9719626168224 -2.97196261682243 138 18 20.9719626168224 -2.97196261682243 139 17 20.9719626168224 -3.97196261682243 140 22 20.9719626168224 1.02803738317757 141 30 20.9719626168224 9.02803738317757 142 30 26.0689655172414 3.93103448275862 143 24 20.9719626168224 3.02803738317757 144 21 20.9719626168224 0.0280373831775691 145 21 20.9719626168224 0.0280373831775691 146 29 26.0689655172414 2.93103448275862 147 31 20.9719626168224 10.0280373831776 148 20 20.9719626168224 -0.97196261682243 149 16 17.55 -1.55 150 22 20.9719626168224 1.02803738317757 151 20 26.0689655172414 -6.06896551724138 152 28 20.9719626168224 7.02803738317757 153 38 26.0689655172414 11.9310344827586 154 22 20.9719626168224 1.02803738317757 155 20 20.9719626168224 -0.97196261682243 156 17 20.9719626168224 -3.97196261682243

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/2dman1292325733.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/2dman1292325733.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/3dman1292325733.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/3dman1292325733.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/45ws81292325733.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292325703fkicdyd3f5trozi/45ws81292325733.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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