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: Sat, 25 Dec 2010 09:46:58 +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/25/t1293270479m63028km9fe0643.htm/, Retrieved Sat, 25 Dec 2010 10:48:00 +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/25/t1293270479m63028km9fe0643.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:

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13 13 14 13 3 12 12 8 13 5 15 10 12 16 6 12 9 7 12 6 10 10 10 11 5 12 12 7 12 3 15 13 16 18 8 9 12 11 11 4 12 12 14 14 4 11 6 6 9 4 11 5 16 14 6 11 12 11 12 6 15 11 16 11 5 7 14 12 12 4 11 14 7 13 6 11 12 13 11 4 10 12 11 12 6 14 11 15 16 6 10 11 7 9 4 6 7 9 11 4 11 9 7 13 2 15 11 14 15 7 11 11 15 10 5 12 12 7 11 4 14 12 15 13 6 15 11 17 16 6 9 11 15 15 7 13 8 14 14 5 13 9 14 14 6 16 12 8 14 4 13 10 8 8 4 12 10 14 13 7 14 12 14 15 7 11 8 8 13 4 9 12 11 11 4 16 11 16 15 6 12 12 10 15 6 10 7 8 9 5 13 11 14 13 6 16 11 16 16 7 14 12 13 13 6 15 9 5 11 3 5 15 8 12 3 8 11 10 12 4 11 11 8 12 6 16 11 13 14 7 17 11 15 14 5 9 15 6 8 4 9 11 12 13 5 13 12 16 16 6 10 12 5 13 6 6 9 15 11 6 12 12 12 14 5 8 12 8 13 4 14 13 13 13 5 12 11 14 13 5 11 9 12 12 4 16 9 16 16 6 8 11 10 15 2 15 11 15 15 8 7 12 8 12 3 16 12 16 14 6 14 9 19 12 6 16 11 14 15 6 9 9 6 12 5 14 12 13 13 5 11 12 15 12 6 13 12 7 12 5 15 12 13 13 6 5 14 4 5 2 15 11 14 13 5 13 12 13 13 5 11 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 Goodness of Fit Correlation 0.655 R-squared 0.429 RMSE 2.6073 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 14 10 4 2 8 12.2857142857143 -4.28571428571429 3 12 14.3529411764706 -2.35294117647059 4 7 12.2857142857143 -5.28571428571429 5 10 10.5555555555556 -0.555555555555555 6 7 10 -3 7 16 14.3529411764706 1.64705882352941 8 11 8.5 2.5 9 14 10 4 10 6 8.5 -2.5 11 16 10.5555555555556 5.44444444444444 12 11 10.5555555555556 0.444444444444445 13 16 14.3529411764706 1.64705882352941 14 12 8.5 3.5 15 7 10.5555555555556 -3.55555555555556 16 13 8.5 4.5 17 11 10.5555555555556 0.444444444444445 18 15 14.3529411764706 0.647058823529411 19 7 8.5 -1.5 20 9 8.5 0.5 21 7 8.5 -1.5 22 14 14.3529411764706 -0.352941176470589 23 15 10.5555555555556 4.44444444444444 24 7 10 -3 25 15 14.3529411764706 0.647058823529411 26 17 14.3529411764706 2.64705882352941 27 15 10.5555555555556 4.44444444444444 28 14 12.2857142857143 1.71428571428571 29 14 12.2857142857143 1.71428571428571 30 8 10 -2 31 8 10 -2 32 14 12.2857142857143 1.71428571428571 33 14 14.3529411764706 -0.352941176470589 34 8 8.5 -0.5 35 11 8.5 2.5 36 16 14.3529411764706 1.64705882352941 37 10 12.2857142857143 -2.28571428571429 38 8 10.5555555555556 -2.55555555555556 39 14 12.2857142857143 1.71428571428571 40 16 14.3529411764706 1.64705882352941 41 13 14.3529411764706 -1.35294117647059 42 5 10 -5 43 8 8.5 -0.5 44 10 8.5 1.5 45 8 10.5555555555556 -2.55555555555556 46 13 14.3529411764706 -1.35294117647059 47 15 14.3529411764706 0.647058823529411 48 6 8.5 -2.5 49 12 10.5555555555556 1.44444444444444 50 16 12.2857142857143 3.71428571428571 51 5 10.5555555555556 -5.55555555555556 52 15 10.5555555555556 4.44444444444444 53 12 12.2857142857143 -0.285714285714286 54 8 8.5 -0.5 55 13 14.3529411764706 -1.35294117647059 56 14 12.2857142857143 1.71428571428571 57 12 8.5 3.5 58 16 14.3529411764706 1.64705882352941 59 10 8.5 1.5 60 15 14.3529411764706 0.647058823529411 61 8 8.5 -0.5 62 16 14.3529411764706 1.64705882352941 63 19 14.3529411764706 4.64705882352941 64 14 14.3529411764706 -0.352941176470589 65 6 10.5555555555556 -4.55555555555556 66 13 14.3529411764706 -1.35294117647059 67 15 10.5555555555556 4.44444444444444 68 7 12.2857142857143 -5.28571428571429 69 13 14.3529411764706 -1.35294117647059 70 4 8.5 -4.5 71 14 14.3529411764706 -0.352941176470589 72 13 12.2857142857143 0.714285714285714 73 11 10.5555555555556 0.444444444444445 74 14 10.5555555555556 3.44444444444444 75 12 12.2857142857143 -0.285714285714286 76 15 12.2857142857143 2.71428571428571 77 14 12.2857142857143 1.71428571428571 78 13 12.2857142857143 0.714285714285714 79 8 10 -2 80 6 8.5 -2.5 81 7 8.5 -1.5 82 13 14.3529411764706 -1.35294117647059 83 13 14.3529411764706 -1.35294117647059 84 11 10.5555555555556 0.444444444444445 85 5 10 -5 86 12 12.2857142857143 -0.285714285714286 87 8 8.5 -0.5 88 11 12.2857142857143 -1.28571428571429 89 14 14.3529411764706 -0.352941176470589 90 9 8.5 0.5 91 10 14.3529411764706 -4.35294117647059 92 13 10.5555555555556 2.44444444444444 93 16 14.3529411764706 1.64705882352941 94 16 14.3529411764706 1.64705882352941 95 11 12.2857142857143 -1.28571428571429 96 8 8.5 -0.5 97 4 10.5555555555556 -6.55555555555556 98 7 8.5 -1.5 99 14 10.5555555555556 3.44444444444444 100 11 12.2857142857143 -1.28571428571429 101 17 14.3529411764706 2.64705882352941 102 15 14.3529411764706 0.647058823529411 103 17 14.3529411764706 2.64705882352941 104 5 8.5 -3.5 105 4 8.5 -4.5 106 10 14.3529411764706 -4.35294117647059 107 11 8.5 2.5 108 15 14.3529411764706 0.647058823529411 109 10 8.5 1.5 110 9 8.5 0.5 111 12 10.5555555555556 1.44444444444444 112 15 12.2857142857143 2.71428571428571 113 7 10.5555555555556 -3.55555555555556 114 13 14.3529411764706 -1.35294117647059 115 12 14.3529411764706 -2.35294117647059 116 14 14.3529411764706 -0.352941176470589 117 14 14.3529411764706 -0.352941176470589 118 8 10.5555555555556 -2.55555555555556 119 15 14.3529411764706 0.647058823529411 120 12 10 2 121 12 10 2 122 16 14.3529411764706 1.64705882352941 123 9 8.5 0.5 124 15 12.2857142857143 2.71428571428571 125 15 14.3529411764706 0.647058823529411 126 6 12.2857142857143 -6.28571428571429 127 14 14.3529411764706 -0.352941176470589 128 15 12.2857142857143 2.71428571428571 129 10 10.5555555555556 -0.555555555555555 130 6 8.5 -2.5 131 14 14.3529411764706 -0.352941176470589 132 12 14.3529411764706 -2.35294117647059 133 8 8.5 -0.5 134 11 12.2857142857143 -1.28571428571429 135 13 14.3529411764706 -1.35294117647059 136 9 10 -1 137 15 14.3529411764706 0.647058823529411 138 13 8.5 4.5 139 15 14.3529411764706 0.647058823529411 140 14 12.2857142857143 1.71428571428571 141 16 10 6 142 14 12.2857142857143 1.71428571428571 143 14 10 4 144 10 8.5 1.5 145 10 10 0 146 4 10.5555555555556 -6.55555555555556 147 8 10.5555555555556 -2.55555555555556 148 15 10.5555555555556 4.44444444444444 149 16 14.3529411764706 1.64705882352941 150 12 14.3529411764706 -2.35294117647059 151 12 12.2857142857143 -0.285714285714286 152 15 14.3529411764706 0.647058823529411 153 9 8.5 0.5 154 12 14.3529411764706 -2.35294117647059 155 14 14.3529411764706 -0.352941176470589 156 11 10 1

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/2e0iw1293270411.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/2e0iw1293270411.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/37ahz1293270411.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/37ahz1293270411.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/4zjh21293270411.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293270479m63028km9fe0643/4zjh21293270411.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 3 ; par2 = none ; par3 = 4 ; 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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