Recursive Partitioning (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: Sat, 11 Dec 2010 07:44:08 +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/11/t12920533535giw9pr4p1sv9or.htm/, Retrieved Sat, 11 Dec 2010 08:42:33 +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/11/t12920533535giw9pr4p1sv9or.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 69 26 9 15 6 25 25 1 53 20 9 15 6 25 24 1 43 21 9 14 13 19 21 0 60 31 14 10 8 18 23 1 49 21 8 10 7 18 17 1 62 18 8 12 9 22 19 1 45 26 11 18 5 29 18 1 50 22 10 12 8 26 27 1 75 22 9 14 9 25 23 1 82 29 15 18 11 23 23 0 60 15 14 9 8 23 29 1 59 16 11 11 11 23 21 1 21 24 14 11 12 24 26 1 62 17 6 17 8 30 25 0 54 19 20 8 7 19 25 1 47 22 9 16 9 24 23 1 59 31 10 21 12 32 26 0 37 28 8 24 20 30 20 0 43 38 11 21 7 29 29 1 48 26 14 14 8 17 24 0 79 25 11 7 8 25 23 0 62 25 16 18 16 26 24 1 16 29 14 18 10 26 30 0 38 28 11 13 6 25 22 1 58 15 11 11 8 23 22 0 60 18 12 13 9 21 13 0 67 21 9 13 9 19 24 0 55 25 7 18 11 35 17 1 47 23 13 14 12 19 24 0 59 23 10 12 8 20 21 1 49 19 9 9 7 21 23 0 47 18 9 12 8 21 24 1 57 18 13 8 9 24 24 0 39 26 16 5 4 23 24 1 49 18 12 10 8 19 23 1 26 18 6 11 8 17 26 0 53 28 14 11 8 24 24 0 75 17 14 12 6 15 21 1 65 29 10 12 8 25 23 1 49 12 4 15 4 27 28 0 48 25 12 12 7 29 23 0 45 28 12 16 14 27 22 0 31 20 14 14 10 18 24 1 61 17 9 17 9 25 21 1 49 17 9 13 6 22 23 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 NA R-squared NA RMSE 13.3831 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 69 52.0723684210526 16.9276315789474 2 53 52.0723684210526 0.92763157894737 3 43 52.0723684210526 -9.07236842105263 4 60 52.0723684210526 7.92763157894737 5 49 52.0723684210526 -3.07236842105263 6 62 52.0723684210526 9.92763157894737 7 45 52.0723684210526 -7.07236842105263 8 50 52.0723684210526 -2.07236842105263 9 75 52.0723684210526 22.9276315789474 10 82 52.0723684210526 29.9276315789474 11 60 52.0723684210526 7.92763157894737 12 59 52.0723684210526 6.92763157894737 13 21 52.0723684210526 -31.0723684210526 14 62 52.0723684210526 9.92763157894737 15 54 52.0723684210526 1.92763157894737 16 47 52.0723684210526 -5.07236842105263 17 59 52.0723684210526 6.92763157894737 18 37 52.0723684210526 -15.0723684210526 19 43 52.0723684210526 -9.07236842105263 20 48 52.0723684210526 -4.07236842105263 21 79 52.0723684210526 26.9276315789474 22 62 52.0723684210526 9.92763157894737 23 16 52.0723684210526 -36.0723684210526 24 38 52.0723684210526 -14.0723684210526 25 58 52.0723684210526 5.92763157894737 26 60 52.0723684210526 7.92763157894737 27 67 52.0723684210526 14.9276315789474 28 55 52.0723684210526 2.92763157894737 29 47 52.0723684210526 -5.07236842105263 30 59 52.0723684210526 6.92763157894737 31 49 52.0723684210526 -3.07236842105263 32 47 52.0723684210526 -5.07236842105263 33 57 52.0723684210526 4.92763157894737 34 39 52.0723684210526 -13.0723684210526 35 49 52.0723684210526 -3.07236842105263 36 26 52.0723684210526 -26.0723684210526 37 53 52.0723684210526 0.92763157894737 38 75 52.0723684210526 22.9276315789474 39 65 52.0723684210526 12.9276315789474 40 49 52.0723684210526 -3.07236842105263 41 48 52.0723684210526 -4.07236842105263 42 45 52.0723684210526 -7.07236842105263 43 31 52.0723684210526 -21.0723684210526 44 61 52.0723684210526 8.92763157894737 45 49 52.0723684210526 -3.07236842105263 46 69 52.0723684210526 16.9276315789474 47 54 52.0723684210526 1.92763157894737 48 80 52.0723684210526 27.9276315789474 49 57 52.0723684210526 4.92763157894737 50 34 52.0723684210526 -18.0723684210526 51 69 52.0723684210526 16.9276315789474 52 44 52.0723684210526 -8.07236842105263 53 70 52.0723684210526 17.9276315789474 54 51 52.0723684210526 -1.07236842105263 55 66 52.0723684210526 13.9276315789474 56 18 52.0723684210526 -34.0723684210526 57 74 52.0723684210526 21.9276315789474 58 59 52.0723684210526 6.92763157894737 59 48 52.0723684210526 -4.07236842105263 60 55 52.0723684210526 2.92763157894737 61 44 52.0723684210526 -8.07236842105263 62 56 52.0723684210526 3.92763157894737 63 65 52.0723684210526 12.9276315789474 64 77 52.0723684210526 24.9276315789474 65 46 52.0723684210526 -6.07236842105263 66 70 52.0723684210526 17.9276315789474 67 39 52.0723684210526 -13.0723684210526 68 55 52.0723684210526 2.92763157894737 69 44 52.0723684210526 -8.07236842105263 70 45 52.0723684210526 -7.07236842105263 71 45 52.0723684210526 -7.07236842105263 72 49 52.0723684210526 -3.07236842105263 73 65 52.0723684210526 12.9276315789474 74 45 52.0723684210526 -7.07236842105263 75 71 52.0723684210526 18.9276315789474 76 48 52.0723684210526 -4.07236842105263 77 41 52.0723684210526 -11.0723684210526 78 40 52.0723684210526 -12.0723684210526 79 64 52.0723684210526 11.9276315789474 80 56 52.0723684210526 3.92763157894737 81 52 52.0723684210526 -0.0723684210526301 82 41 52.0723684210526 -11.0723684210526 83 42 52.0723684210526 -10.0723684210526 84 54 52.0723684210526 1.92763157894737 85 40 52.0723684210526 -12.0723684210526 86 40 52.0723684210526 -12.0723684210526 87 51 52.0723684210526 -1.07236842105263 88 48 52.0723684210526 -4.07236842105263 89 80 52.0723684210526 27.9276315789474 90 38 52.0723684210526 -14.0723684210526 91 57 52.0723684210526 4.92763157894737 92 28 52.0723684210526 -24.0723684210526 93 51 52.0723684210526 -1.07236842105263 94 46 52.0723684210526 -6.07236842105263 95 58 52.0723684210526 5.92763157894737 96 67 52.0723684210526 14.9276315789474 97 72 52.0723684210526 19.9276315789474 98 26 52.0723684210526 -26.0723684210526 99 54 52.0723684210526 1.92763157894737 100 53 52.0723684210526 0.92763157894737 101 64 52.0723684210526 11.9276315789474 102 47 52.0723684210526 -5.07236842105263 103 43 52.0723684210526 -9.07236842105263 104 66 52.0723684210526 13.9276315789474 105 54 52.0723684210526 1.92763157894737 106 62 52.0723684210526 9.92763157894737 107 52 52.0723684210526 -0.0723684210526301 108 64 52.0723684210526 11.9276315789474 109 55 52.0723684210526 2.92763157894737 110 57 52.0723684210526 4.92763157894737 111 74 52.0723684210526 21.9276315789474 112 32 52.0723684210526 -20.0723684210526 113 38 52.0723684210526 -14.0723684210526 114 66 52.0723684210526 13.9276315789474 115 37 52.0723684210526 -15.0723684210526 116 26 52.0723684210526 -26.0723684210526 117 64 52.0723684210526 11.9276315789474 118 28 52.0723684210526 -24.0723684210526 119 66 52.0723684210526 13.9276315789474 120 65 52.0723684210526 12.9276315789474 121 48 52.0723684210526 -4.07236842105263 122 44 52.0723684210526 -8.07236842105263 123 64 52.0723684210526 11.9276315789474 124 39 52.0723684210526 -13.0723684210526 125 50 52.0723684210526 -2.07236842105263 126 66 52.0723684210526 13.9276315789474 127 48 52.0723684210526 -4.07236842105263 128 70 52.0723684210526 17.9276315789474 129 66 52.0723684210526 13.9276315789474 130 61 52.0723684210526 8.92763157894737 131 31 52.0723684210526 -21.0723684210526 132 61 52.0723684210526 8.92763157894737 133 54 52.0723684210526 1.92763157894737 134 34 52.0723684210526 -18.0723684210526 135 62 52.0723684210526 9.92763157894737 136 47 52.0723684210526 -5.07236842105263 137 52 52.0723684210526 -0.0723684210526301 138 37 52.0723684210526 -15.0723684210526 139 46 52.0723684210526 -6.07236842105263 140 38 52.0723684210526 -14.0723684210526 141 63 52.0723684210526 10.9276315789474 142 34 52.0723684210526 -18.0723684210526 143 46 52.0723684210526 -6.07236842105263 144 40 52.0723684210526 -12.0723684210526 145 30 52.0723684210526 -22.0723684210526 146 35 52.0723684210526 -17.0723684210526 147 51 52.0723684210526 -1.07236842105263 148 56 52.0723684210526 3.92763157894737 149 68 52.0723684210526 15.9276315789474 150 39 52.0723684210526 -13.0723684210526 151 44 52.0723684210526 -8.07236842105263 152 58 52.0723684210526 5.92763157894737

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/21iwa1292053438.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/21iwa1292053438.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/31iwa1292053438.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/31iwa1292053438.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/4taed1292053438.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920533535giw9pr4p1sv9or/4taed1292053438.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

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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