Recursive Partitioning Organization (no categorization)

*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: Thu, 16 Dec 2010 23:11:08 +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/17/t1292540968mtuzmcb7ars3eyv.htm/, Retrieved Fri, 17 Dec 2010 00:09:31 +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/17/t1292540968mtuzmcb7ars3eyv.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 «

0 13 26 9 15 25 25 0 16 20 9 15 25 24 0 19 21 9 14 19 21 1 15 31 14 10 18 23 0 14 21 8 10 18 17 0 13 18 8 12 22 19 0 19 26 11 18 29 18 0 15 22 10 12 26 27 0 14 22 9 14 25 23 0 15 29 15 18 23 23 1 16 15 14 9 23 29 0 16 16 11 11 23 21 1 16 24 14 11 24 26 0 17 17 6 17 30 25 1 15 19 20 8 19 25 1 15 22 9 16 24 23 0 20 31 10 21 32 26 1 18 28 8 24 30 20 0 16 38 11 21 29 29 1 16 26 14 14 17 24 0 19 25 11 7 25 23 0 16 25 16 18 26 24 1 17 29 14 18 26 30 0 17 28 11 13 25 22 1 16 15 11 11 23 22 0 15 18 12 13 21 13 1 14 21 9 13 19 24 0 15 25 7 18 35 17 1 12 23 13 14 19 24 0 14 23 10 12 20 21 0 16 19 9 9 21 23 1 14 18 9 12 21 24 1 10 26 16 5 23 24 1 14 18 12 10 19 23 0 16 18 6 11 17 26 1 16 28 14 11 24 24 1 16 17 14 12 15 21 0 14 29 10 12 25 23 1 20 12 4 15 27 28 1 14 25 12 12 29 23 0 14 28 12 16 27 22 0 11 20 14 14 18 24 0 15 17 9 17 25 21 0 16 17 9 13 22 23 1 14 20 10 10 26 23 0 16 31 14 17 23 20 1 14 21 10 12 16 23 1 12 19 9 13 27 21 0 16 23 14 13 25 27 1 9 15 8 11 14 12 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.4849 R-squared 0.2351 RMSE 3.3579 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 25 21.9069767441860 3.09302325581395 2 24 21.9069767441860 2.09302325581395 3 21 21.9069767441860 -0.906976744186046 4 23 24.32 -1.32 5 17 21.9069767441860 -4.90697674418605 6 19 21.9069767441860 -2.90697674418605 7 18 21.9069767441860 -3.90697674418605 8 27 21.9069767441860 5.09302325581395 9 23 21.9069767441860 1.09302325581395 10 23 21.9069767441860 1.09302325581395 11 29 24.32 4.68 12 21 21.9069767441860 -0.906976744186046 13 26 24.32 1.68 14 25 21.9069767441860 3.09302325581395 15 25 24.32 0.68 16 23 24.32 -1.32 17 26 21.9069767441860 4.09302325581395 18 20 24.32 -4.32 19 29 21.9069767441860 7.09302325581395 20 24 24.32 -0.32 21 23 21.9069767441860 1.09302325581395 22 24 21.9069767441860 2.09302325581395 23 30 24.32 5.68 24 22 21.9069767441860 0.0930232558139537 25 22 24.32 -2.32 26 13 21.9069767441860 -8.90697674418605 27 24 24.32 -0.32 28 17 21.9069767441860 -4.90697674418605 29 24 24.32 -0.32 30 21 21.9069767441860 -0.906976744186046 31 23 21.9069767441860 1.09302325581395 32 24 24.32 -0.32 33 24 24.32 -0.32 34 23 24.32 -1.32 35 26 21.9069767441860 4.09302325581395 36 24 24.32 -0.32 37 21 17.3333333333333 3.66666666666667 38 23 21.9069767441860 1.09302325581395 39 28 24.32 3.68 40 23 24.32 -1.32 41 22 21.9069767441860 0.0930232558139537 42 24 21.9069767441860 2.09302325581395 43 21 21.9069767441860 -0.906976744186046 44 23 21.9069767441860 1.09302325581395 45 23 24.32 -1.32 46 20 21.9069767441860 -1.90697674418605 47 23 17.3333333333333 5.66666666666667 48 21 24.32 -3.32 49 27 21.9069767441860 5.09302325581395 50 12 17.3333333333333 -5.33333333333333 51 15 21.9069767441860 -6.90697674418605 52 22 21.9069767441860 0.0930232558139537 53 21 17.3333333333333 3.66666666666667 54 21 24.32 -3.32 55 20 21.9069767441860 -1.90697674418605 56 24 24.32 -0.32 57 24 24.32 -0.32 58 29 21.9069767441860 7.09302325581395 59 25 21.9069767441860 3.09302325581395 60 14 21.9069767441860 -7.90697674418605 61 30 24.32 5.68 62 19 21.9069767441860 -2.90697674418605 63 29 24.32 4.68 64 25 21.9069767441860 3.09302325581395 65 25 24.32 0.68 66 25 24.32 0.68 67 16 17.3333333333333 -1.33333333333333 68 25 21.9069767441860 3.09302325581395 69 28 24.32 3.68 70 24 24.32 -0.32 71 25 21.9069767441860 3.09302325581395 72 21 21.9069767441860 -0.906976744186046 73 22 24.32 -2.32 74 20 24.32 -4.32 75 25 24.32 0.68 76 27 24.32 2.68 77 21 21.9069767441860 -0.906976744186046 78 13 17.3333333333333 -4.33333333333333 79 26 21.9069767441860 4.09302325581395 80 26 21.9069767441860 4.09302325581395 81 25 24.32 0.68 82 22 21.9069767441860 0.0930232558139537 83 19 17.3333333333333 1.66666666666667 84 23 21.9069767441860 1.09302325581395 85 25 21.9069767441860 3.09302325581395 86 15 17.3333333333333 -2.33333333333333 87 21 21.9069767441860 -0.906976744186046 88 23 21.9069767441860 1.09302325581395 89 25 21.9069767441860 3.09302325581395 90 24 21.9069767441860 2.09302325581395 91 24 24.32 -0.32 92 21 24.32 -3.32 93 24 21.9069767441860 2.09302325581395 94 22 24.32 -2.32 95 24 21.9069767441860 2.09302325581395 96 28 24.32 3.68 97 21 21.9069767441860 -0.906976744186046 98 17 17.3333333333333 -0.333333333333332 99 28 21.9069767441860 6.09302325581395 100 24 24.32 -0.32 101 10 21.9069767441860 -11.9069767441860 102 20 21.9069767441860 -1.90697674418605 103 22 21.9069767441860 0.0930232558139537 104 19 21.9069767441860 -2.90697674418605 105 22 24.32 -2.32 106 22 21.9069767441860 0.0930232558139537 107 26 24.32 1.68 108 24 21.9069767441860 2.09302325581395 109 22 21.9069767441860 0.0930232558139537 110 20 21.9069767441860 -1.90697674418605 111 20 21.9069767441860 -1.90697674418605 112 15 21.9069767441860 -6.90697674418605 113 20 21.9069767441860 -1.90697674418605 114 20 21.9069767441860 -1.90697674418605 115 24 21.9069767441860 2.09302325581395 116 22 21.9069767441860 0.0930232558139537 117 29 21.9069767441860 7.09302325581395 118 23 24.32 -1.32 119 24 21.9069767441860 2.09302325581395 120 22 21.9069767441860 0.0930232558139537 121 16 21.9069767441860 -5.90697674418605 122 23 24.32 -1.32 123 27 24.32 2.68 124 16 17.3333333333333 -1.33333333333333 125 21 24.32 -3.32 126 26 21.9069767441860 4.09302325581395 127 22 24.32 -2.32 128 23 24.32 -1.32 129 19 21.9069767441860 -2.90697674418605 130 18 21.9069767441860 -3.90697674418605 131 24 21.9069767441860 2.09302325581395 132 29 24.32 4.68 133 22 17.3333333333333 4.66666666666667 134 24 24.32 -0.32 135 22 21.9069767441860 0.0930232558139537 136 12 21.9069767441860 -9.90697674418605 137 26 24.32 1.68 138 18 21.9069767441860 -3.90697674418605 139 22 24.32 -2.32 140 24 21.9069767441860 2.09302325581395 141 21 21.9069767441860 -0.906976744186046 142 15 21.9069767441860 -6.90697674418605 143 23 21.9069767441860 1.09302325581395 144 22 21.9069767441860 0.0930232558139537 145 22 21.9069767441860 0.0930232558139537 146 24 21.9069767441860 2.09302325581395 147 23 21.9069767441860 1.09302325581395 148 13 17.3333333333333 -4.33333333333333

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/2yzlj1292541059.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/2yzlj1292541059.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/3yzlj1292541059.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/3yzlj1292541059.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/4rr241292541059.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292540968mtuzmcb7ars3eyv/4rr241292541059.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 7 ; par2 = none ; par3 = 2 ; 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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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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