Recursive Partitioning (Regression Trees)

*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 15:32:45 +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/t1292081478agl7nenfhg3bp6c.htm/, Retrieved Sat, 11 Dec 2010 16:31:19 +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/t1292081478agl7nenfhg3bp6c.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 «

16198,9 16896,2 0 1 0 0 0 0 0 0 0 0 0 0 0 1 16554,2 16698 0 0 1 0 0 0 0 0 0 0 0 0 0 2 19554,2 19691,6 0 0 0 1 0 0 0 0 0 0 0 0 0 3 15903,8 15930,7 0 0 0 0 1 0 0 0 0 0 0 0 0 4 18003,8 17444,6 0 0 0 0 0 1 0 0 0 0 0 0 0 5 18329,6 17699,4 0 0 0 0 0 0 1 0 0 0 0 0 0 6 16260,7 15189,8 0 0 0 0 0 0 0 1 0 0 0 0 0 7 14851,9 15672,7 0 0 0 0 0 0 0 0 1 0 0 0 0 8 18174,1 17180,8 0 0 0 0 0 0 0 0 0 1 0 0 0 9 18406,6 17664,9 0 0 0 0 0 0 0 0 0 0 1 0 0 10 18466,5 17862,9 0 0 0 0 0 0 0 0 0 0 0 1 0 11 16016,5 16162,3 0 0 0 0 0 0 0 0 0 0 0 0 1 12 17428,5 17463,6 0 1 0 0 0 0 0 0 0 0 0 0 0 13 17167,2 16772,1 0 0 1 0 0 0 0 0 0 0 0 0 0 14 19630 19106,9 0 0 0 1 0 0 0 0 0 0 0 0 0 15 17183,6 16721,3 0 0 0 0 1 0 0 0 0 0 0 0 0 16 18344,7 18161,3 0 0 0 0 0 1 0 0 0 0 0 0 0 17 19301,4 18509,9 0 0 0 0 0 0 1 0 0 0 0 0 0 18 18147,5 17802,7 0 0 0 0 0 0 0 1 0 0 0 0 0 19 16192,9 16409,9 0 0 0 0 0 0 0 0 1 0 0 0 0 20 18374,4 17967,7 0 0 0 0 0 0 0 0 0 1 0 0 0 21 20515,2 20286,6 0 0 0 0 0 0 0 0 0 0 1 0 0 22 18957,2 19537,3 0 0 0 0 0 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 0.9345 R-squared 0.8733 RMSE 712.1775 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 16198.9 16126.4428571429 72.4571428571417 2 16554.2 16126.4428571429 427.757142857143 3 19554.2 19643.7875 -89.5874999999978 4 15903.8 16126.4428571429 -222.642857142859 5 18003.8 17709.8 294 6 18329.6 17709.8 619.799999999999 7 16260.7 14554.3363636364 1706.36363636364 8 14851.9 14554.3363636364 297.563636363637 9 18174.1 17709.8 464.299999999999 10 18406.6 17709.8 696.799999999999 11 18466.5 17709.8 756.7 12 16016.5 16126.4428571429 -109.942857142858 13 17428.5 17709.8 -281.299999999999 14 17167.2 16126.4428571429 1040.75714285714 15 19630 19643.7875 -13.7874999999985 16 17183.6 16126.4428571429 1057.15714285714 17 18344.7 17709.8 634.900000000001 18 19301.4 19643.7875 -342.387499999997 19 18147.5 17709.8 437.700000000001 20 16192.9 16126.4428571429 66.4571428571417 21 18374.4 17709.8 664.600000000002 22 20515.2 19643.7875 871.412500000002 23 18957.2 19643.7875 -686.587499999998 24 16471.5 17709.8 -1238.3 25 18746.8 19643.7875 -896.9875 26 19009.5 19643.7875 -634.287499999999 27 19211.2 19643.7875 -432.587499999998 28 20547.7 19643.7875 903.912500000002 29 19325.8 19643.7875 -317.987499999999 30 20605.5 19643.7875 961.712500000001 31 20056.9 19643.7875 413.112500000003 32 16141.4 17709.8 -1568.4 33 20359.8 19643.7875 716.0125 34 19711.6 19643.7875 67.8125 35 15638.6 16126.4428571429 -487.842857142858 36 14384.5 14554.3363636364 -169.836363636363 37 13855.6 14554.3363636364 -698.736363636363 38 14308.3 14554.3363636364 -246.036363636364 39 15290.6 14554.3363636364 736.263636363637 40 14423.8 14554.3363636364 -130.536363636364 41 13779.7 14554.3363636364 -774.636363636362 42 15686.3 14554.3363636364 1131.96363636364 43 14733.8 14554.3363636364 179.463636363636 44 12522.5 14554.3363636364 -2031.83636363636 45 16189.4 16126.4428571429 62.9571428571417 46 16059.1 16126.4428571429 -67.3428571428576 47 16007.1 16126.4428571429 -119.342857142858 48 15806.8 16126.4428571429 -319.642857142859 49 15160 16126.4428571429 -966.442857142858 50 15692.1 16126.4428571429 -434.342857142858 51 18908.9 19643.7875 -734.887499999997 52 16969.9 17709.8 -739.899999999998 53 16997.5 17709.8 -712.3 54 19858.9 19643.7875 215.112500000003 55 17681.2 17709.8 -28.5999999999985

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/2mptm1292081558.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/2mptm1292081558.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/3mptm1292081558.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/3mptm1292081558.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/4wzap1292081558.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081478agl7nenfhg3bp6c/4wzap1292081558.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; 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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