Paper 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: Sun, 19 Dec 2010 11:16:52 +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/19/t12927573138z5gcvugy05y2gt.htm/, Retrieved Sun, 19 Dec 2010 12:15:13 +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/19/t12927573138z5gcvugy05y2gt.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 41 25 15 9 3 1 38 25 15 9 4 1 37 19 14 9 4 1 42 18 10 8 4 1 40 23 18 15 3 1 43 25 14 9 4 1 40 23 11 11 4 1 45 30 17 6 5 1 45 32 21 10 4 1 44 25 7 11 4 1 42 26 18 16 4 1 32 25 13 11 5 1 32 25 13 11 5 1 41 35 18 7 4 1 38 20 12 10 4 1 38 21 9 9 4 1 24 23 11 15 3 1 46 17 11 6 5 1 42 27 16 12 4 1 46 25 12 10 4 1 43 18 14 14 5 1 38 22 13 9 4 1 39 23 17 14 4 1 40 25 13 14 3 1 37 19 13 9 2 1 41 20 12 8 4 1 46 26 12 10 4 1 26 16 12 9 3 1 37 22 9 9 3 1 39 25 17 9 4 1 44 29 18 11 5 1 38 22 12 10 2 1 38 32 12 8 0 1 38 23 9 14 4 1 33 18 13 10 3 1 43 26 11 14 4 1 41 14 13 15 2 1 49 20 6 8 4 1 45 25 11 10 5 1 31 21 18 13 3 1 30 21 18 13 3 1 38 23 15 10 4 1 39 24 11 11 4 1 40 21 14 10 4 1 36 17 12 16 2 1 49 29 8 6 5 1 41 25 11 11 4 1 42 25 17 14 3 1 41 25 16 9 5 1 43 21 13 11 4 1 46 23 15 8 3 1 41 25 16 8 5 1 39 25 7 11 4 1 42 24 16 16 4 1 35 21 13 12 5 1 36 22 15 14 3 1 48 14 12 8 4 1 41 20 12 10 4 1 47 21 24 14 3 1 41 22 15 10 3 1 31 19 8 5 5 1 36 28 18 12 4 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.2598 R-squared 0.0675 RMSE 4.7958 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 41 38.1111111111111 2.88888888888889 2 38 40.7802197802198 -2.78021978021978 3 37 40.7802197802198 -3.78021978021978 4 42 40.7802197802198 1.21978021978022 5 40 38.1111111111111 1.88888888888889 6 43 40.7802197802198 2.21978021978022 7 40 40.7802197802198 -0.780219780219781 8 45 40.7802197802198 4.21978021978022 9 45 40.7802197802198 4.21978021978022 10 44 40.7802197802198 3.21978021978022 11 42 40.7802197802198 1.21978021978022 12 32 40.7802197802198 -8.78021978021978 13 32 40.7802197802198 -8.78021978021978 14 41 40.7802197802198 0.219780219780219 15 38 40.7802197802198 -2.78021978021978 16 38 40.7802197802198 -2.78021978021978 17 24 38.1111111111111 -14.1111111111111 18 46 40.7802197802198 5.21978021978022 19 42 40.7802197802198 1.21978021978022 20 46 40.7802197802198 5.21978021978022 21 43 40.7802197802198 2.21978021978022 22 38 40.7802197802198 -2.78021978021978 23 39 40.7802197802198 -1.78021978021978 24 40 38.1111111111111 1.88888888888889 25 37 38.1111111111111 -1.11111111111111 26 41 40.7802197802198 0.219780219780219 27 46 40.7802197802198 5.21978021978022 28 26 38.1111111111111 -12.1111111111111 29 37 38.1111111111111 -1.11111111111111 30 39 40.7802197802198 -1.78021978021978 31 44 40.7802197802198 3.21978021978022 32 38 38.1111111111111 -0.111111111111114 33 38 38.1111111111111 -0.111111111111114 34 38 40.7802197802198 -2.78021978021978 35 33 38.1111111111111 -5.11111111111111 36 43 40.7802197802198 2.21978021978022 37 41 38.1111111111111 2.88888888888889 38 49 40.7802197802198 8.21978021978022 39 45 40.7802197802198 4.21978021978022 40 31 38.1111111111111 -7.11111111111111 41 30 38.1111111111111 -8.11111111111111 42 38 40.7802197802198 -2.78021978021978 43 39 40.7802197802198 -1.78021978021978 44 40 40.7802197802198 -0.780219780219781 45 36 38.1111111111111 -2.11111111111111 46 49 40.7802197802198 8.21978021978022 47 41 40.7802197802198 0.219780219780219 48 42 38.1111111111111 3.88888888888889 49 41 40.7802197802198 0.219780219780219 50 43 40.7802197802198 2.21978021978022 51 46 38.1111111111111 7.88888888888889 52 41 40.7802197802198 0.219780219780219 53 39 40.7802197802198 -1.78021978021978 54 42 40.7802197802198 1.21978021978022 55 35 40.7802197802198 -5.78021978021978 56 36 38.1111111111111 -2.11111111111111 57 48 40.7802197802198 7.21978021978022 58 41 40.7802197802198 0.219780219780219 59 47 38.1111111111111 8.88888888888889 60 41 38.1111111111111 2.88888888888889 61 31 40.7802197802198 -9.78021978021978 62 36 40.7802197802198 -4.78021978021978 63 46 40.7802197802198 5.21978021978022 64 44 40.7802197802198 3.21978021978022 65 43 38.1111111111111 4.88888888888889 66 40 40.7802197802198 -0.780219780219781 67 40 38.1111111111111 1.88888888888889 68 46 38.1111111111111 7.88888888888889 69 39 40.7802197802198 -1.78021978021978 70 44 40.7802197802198 3.21978021978022 71 38 38.1111111111111 -0.111111111111114 72 39 40.7802197802198 -1.78021978021978 73 41 40.7802197802198 0.219780219780219 74 39 38.1111111111111 0.888888888888886 75 40 40.7802197802198 -0.780219780219781 76 44 40.7802197802198 3.21978021978022 77 42 40.7802197802198 1.21978021978022 78 46 40.7802197802198 5.21978021978022 79 44 40.7802197802198 3.21978021978022 80 37 40.7802197802198 -3.78021978021978 81 39 38.1111111111111 0.888888888888886 82 40 38.1111111111111 1.88888888888889 83 42 38.1111111111111 3.88888888888889 84 37 38.1111111111111 -1.11111111111111 85 33 38.1111111111111 -5.11111111111111 86 35 40.7802197802198 -5.78021978021978 87 42 38.1111111111111 3.88888888888889 88 36 38.1111111111111 -2.11111111111111 89 44 40.7802197802198 3.21978021978022 90 45 40.7802197802198 4.21978021978022 91 47 40.7802197802198 6.21978021978022 92 40 40.7802197802198 -0.780219780219781 93 49 40.7802197802198 8.21978021978022 94 48 40.7802197802198 7.21978021978022 95 29 40.7802197802198 -11.7802197802198 96 45 40.7802197802198 4.21978021978022 97 29 38.1111111111111 -9.11111111111111 98 41 40.7802197802198 0.219780219780219 99 34 38.1111111111111 -4.11111111111111 100 38 38.1111111111111 -0.111111111111114 101 37 38.1111111111111 -1.11111111111111 102 48 40.7802197802198 7.21978021978022 103 39 40.7802197802198 -1.78021978021978 104 34 40.7802197802198 -6.78021978021978 105 35 38.1111111111111 -3.11111111111111 106 41 38.1111111111111 2.88888888888889 107 43 40.7802197802198 2.21978021978022 108 41 38.1111111111111 2.88888888888889 109 39 38.1111111111111 0.888888888888886 110 36 40.7802197802198 -4.78021978021978 111 32 40.7802197802198 -8.78021978021978 112 46 40.7802197802198 5.21978021978022 113 42 40.7802197802198 1.21978021978022 114 42 38.1111111111111 3.88888888888889 115 45 38.1111111111111 6.88888888888889 116 39 40.7802197802198 -1.78021978021978 117 45 40.7802197802198 4.21978021978022 118 48 40.7802197802198 7.21978021978022 119 28 40.7802197802198 -12.7802197802198 120 35 38.1111111111111 -3.11111111111111 121 38 40.7802197802198 -2.78021978021978 122 42 40.7802197802198 1.21978021978022 123 36 38.1111111111111 -2.11111111111111 124 37 40.7802197802198 -3.78021978021978 125 38 38.1111111111111 -0.111111111111114 126 43 40.7802197802198 2.21978021978022 127 35 38.1111111111111 -3.11111111111111 128 36 40.7802197802198 -4.78021978021978 129 33 38.1111111111111 -5.11111111111111 130 39 40.7802197802198 -1.78021978021978 131 32 40.7802197802198 -8.78021978021978 132 45 38.1111111111111 6.88888888888889 133 35 40.7802197802198 -5.78021978021978 134 38 38.1111111111111 -0.111111111111114 135 36 38.1111111111111 -2.11111111111111 136 42 38.1111111111111 3.88888888888889 137 41 40.7802197802198 0.219780219780219 138 47 38.1111111111111 8.88888888888889 139 35 38.1111111111111 -3.11111111111111 140 43 38.1111111111111 4.88888888888889 141 40 40.7802197802198 -0.780219780219781 142 46 40.7802197802198 5.21978021978022 143 44 40.7802197802198 3.21978021978022 144 35 38.1111111111111 -3.11111111111111 145 29 40.7802197802198 -11.7802197802198

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/28ifc1292757404.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/28ifc1292757404.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/38ifc1292757404.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/38ifc1292757404.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/4j9ef1292757404.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927573138z5gcvugy05y2gt/4j9ef1292757404.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by