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: Fri, 10 Dec 2010 17:08:51 +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/10/t1292000843ufs3m87efchc18k.htm/, Retrieved Fri, 10 Dec 2010 18:07:28 +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/10/t1292000843ufs3m87efchc18k.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 26 21 21 23 17 23 4 1 20 16 15 24 17 20 4 1 19 19 18 22 18 20 6 2 19 18 11 20 21 21 8 1 20 16 8 24 20 24 8 1 25 23 19 27 28 22 4 2 25 17 4 28 19 23 4 1 22 12 20 27 22 20 8 1 26 19 16 24 16 25 5 1 22 16 14 23 18 23 4 2 17 19 10 24 25 27 4 2 22 20 13 27 17 27 4 1 19 13 14 27 14 22 4 1 24 20 8 28 11 24 4 1 26 27 23 27 27 25 4 2 21 17 11 23 20 22 8 1 13 8 9 24 22 28 4 2 26 25 24 28 22 28 4 2 20 26 5 27 21 27 4 1 22 13 15 25 23 25 8 2 14 19 5 19 17 16 4 1 21 15 19 24 24 28 7 1 7 5 6 20 14 21 4 2 23 16 13 28 17 24 4 1 17 14 11 26 23 27 5 1 25 24 17 23 24 14 4 1 25 24 17 23 24 14 4 1 19 9 5 20 8 27 4 2 20 19 9 11 22 20 4 1 23 19 15 24 23 21 4 2 22 25 17 25 25 22 4 1 22 19 17 23 21 21 4 1 21 18 20 18 24 12 15 2 15 15 12 20 15 20 10 2 20 12 7 20 22 24 4 2 22 21 16 24 21 19 8 1 18 12 7 23 25 28 4 2 20 15 14 25 16 23 4 2 28 28 24 28 28 27 4 1 22 25 15 26 23 22 4 1 18 19 15 26 21 27 7 1 23 20 10 23 21 26 4 1 20 24 14 22 26 22 6 2 25 26 18 24 22 21 5 2 26 25 12 21 21 19 4 1 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 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.5142 R-squared 0.2644 RMSE 2.2454 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 4 4.67241379310345 -0.672413793103448 2 4 5.6 -1.6 3 6 7.88888888888889 -1.88888888888889 4 8 5.6 2.4 5 8 5.6 2.4 6 4 4.67241379310345 -0.672413793103448 7 4 6.06666666666667 -2.06666666666667 8 8 6.06666666666667 1.93333333333333 9 5 4.67241379310345 0.327586206896552 10 4 6.06666666666667 -2.06666666666667 11 4 5.9375 -1.9375 12 4 4.67241379310345 -0.672413793103448 13 4 5.6 -1.6 14 4 4.67241379310345 -0.672413793103448 15 4 4.67241379310345 -0.672413793103448 16 8 5.6 2.4 17 4 5.9375 -1.9375 18 4 4.67241379310345 -0.672413793103448 19 4 5.6 -1.6 20 8 6.06666666666667 1.93333333333333 21 4 5.9375 -1.9375 22 7 7.88888888888889 -0.88888888888889 23 4 5.9375 -1.9375 24 4 6.06666666666667 -2.06666666666667 25 5 9.35714285714286 -4.35714285714286 26 4 4.67241379310345 -0.672413793103448 27 4 4.67241379310345 -0.672413793103448 28 4 5.6 -1.6 29 4 5.6 -1.6 30 4 4.67241379310345 -0.672413793103448 31 4 4.67241379310345 -0.672413793103448 32 4 4.67241379310345 -0.672413793103448 33 15 7.88888888888889 7.11111111111111 34 10 9.35714285714286 0.642857142857142 35 4 5.6 -1.6 36 8 4.67241379310345 3.32758620689655 37 4 5.6 -1.6 38 4 5.6 -1.6 39 4 4.67241379310345 -0.672413793103448 40 4 4.67241379310345 -0.672413793103448 41 7 5.6 1.4 42 4 4.67241379310345 -0.672413793103448 43 6 5.6 0.4 44 5 4.67241379310345 0.327586206896552 45 4 4.67241379310345 -0.672413793103448 46 16 5.9375 10.0625 47 5 5.9375 -0.9375 48 12 6.06666666666667 5.93333333333333 49 6 7.88888888888889 -1.88888888888889 50 9 5.9375 3.0625 51 9 5.6 3.4 52 4 5.6 -1.6 53 5 4.67241379310345 0.327586206896552 54 4 5.9375 -1.9375 55 4 4.67241379310345 -0.672413793103448 56 5 5.6 -0.6 57 4 5.6 -1.6 58 4 6.06666666666667 -2.06666666666667 59 4 5.9375 -1.9375 60 5 4.67241379310345 0.327586206896552 61 4 4.67241379310345 -0.672413793103448 62 6 5.6 0.4 63 4 5.6 -1.6 64 4 4.67241379310345 -0.672413793103448 65 18 9.35714285714286 8.64285714285714 66 4 4.67241379310345 -0.672413793103448 67 6 4.67241379310345 1.32758620689655 68 4 5.6 -1.6 69 4 4.67241379310345 -0.672413793103448 70 5 5.9375 -0.9375 71 4 7.88888888888889 -3.88888888888889 72 4 4.67241379310345 -0.672413793103448 73 5 6.06666666666667 -1.06666666666667 74 10 4.67241379310345 5.32758620689655 75 5 4.67241379310345 0.327586206896552 76 8 9.35714285714286 -1.35714285714286 77 8 9.35714285714286 -1.35714285714286 78 5 5.6 -0.6 79 4 4.67241379310345 -0.672413793103448 80 4 5.9375 -1.9375 81 4 4.67241379310345 -0.672413793103448 82 5 5.6 -0.6 83 4 4.67241379310345 -0.672413793103448 84 4 5.6 -1.6 85 8 9.35714285714286 -1.35714285714286 86 4 4.67241379310345 -0.672413793103448 87 5 4.67241379310345 0.327586206896552 88 14 9.35714285714286 4.64285714285714 89 8 5.6 2.4 90 8 5.6 2.4 91 4 5.9375 -1.9375 92 4 4.67241379310345 -0.672413793103448 93 6 6.06666666666667 -0.0666666666666664 94 4 4.67241379310345 -0.672413793103448 95 7 9.35714285714286 -2.35714285714286 96 7 5.6 1.4 97 4 4.67241379310345 -0.672413793103448 98 6 5.6 0.4 99 4 5.6 -1.6 100 7 4.67241379310345 2.32758620689655 101 4 5.6 -1.6 102 4 5.6 -1.6 103 8 6.06666666666667 1.93333333333333 104 4 4.67241379310345 -0.672413793103448 105 4 5.6 -1.6 106 10 5.6 4.4 107 8 5.6 2.4 108 6 6.06666666666667 -0.0666666666666664 109 4 6.06666666666667 -2.06666666666667 110 4 4.67241379310345 -0.672413793103448 111 4 4.67241379310345 -0.672413793103448 112 5 4.67241379310345 0.327586206896552 113 4 5.6 -1.6 114 6 5.6 0.4 115 4 4.67241379310345 -0.672413793103448 116 5 5.6 -0.6 117 7 9.35714285714286 -2.35714285714286 118 8 5.6 2.4 119 5 5.6 -0.6 120 8 5.9375 2.0625 121 10 7.88888888888889 2.11111111111111 122 8 7.88888888888889 0.111111111111111 123 5 5.9375 -0.9375 124 12 9.35714285714286 2.64285714285714 125 4 4.67241379310345 -0.672413793103448 126 5 4.67241379310345 0.327586206896552 127 4 4.67241379310345 -0.672413793103448 128 6 5.6 0.4 129 4 9.35714285714286 -5.35714285714286 130 4 4.67241379310345 -0.672413793103448 131 7 9.35714285714286 -2.35714285714286 132 7 4.67241379310345 2.32758620689655 133 10 5.9375 4.0625 134 4 5.6 -1.6 135 5 4.67241379310345 0.327586206896552 136 8 6.06666666666667 1.93333333333333 137 11 5.6 5.4 138 7 9.35714285714286 -2.35714285714286 139 4 4.67241379310345 -0.672413793103448 140 8 4.67241379310345 3.32758620689655 141 6 5.6 0.4 142 7 5.6 1.4 143 5 5.9375 -0.9375 144 4 6.06666666666667 -2.06666666666667 145 8 4.67241379310345 3.32758620689655 146 4 5.6 -1.6 147 8 5.6 2.4 148 6 4.67241379310345 1.32758620689655 149 4 4.67241379310345 -0.672413793103448 150 9 7.88888888888889 1.11111111111111 151 5 5.6 -0.6 152 6 4.67241379310345 1.32758620689655 153 4 4.67241379310345 -0.672413793103448 154 4 4.67241379310345 -0.672413793103448 155 4 4.67241379310345 -0.672413793103448 156 5 5.6 -0.6 157 6 7.88888888888889 -1.88888888888889 158 16 9.35714285714286 6.64285714285714 159 6 6.06666666666667 -0.0666666666666664 160 6 5.6 0.4 161 4 5.6 -1.6 162 4 5.6 -1.6

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/2j82o1292000922.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/2j82o1292000922.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/3j82o1292000922.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/3j82o1292000922.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/4uz191292000922.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1292000843ufs3m87efchc18k/4uz191292000922.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 8 ; 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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