Recursive partitioning nieuwbouw -lening

*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, 24 Dec 2010 15:51:31 +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/24/t1293205769d7it04y3rygxzur.htm/, Retrieved Fri, 24 Dec 2010 16:49:30 +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/24/t1293205769d7it04y3rygxzur.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:

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3.48 4143 3.6 4429 3.66 5219 3.45 4929 3.3 5761 3.14 5592 3.21 4163 3.12 4962 3.14 5208 3.4 4755 3.42 4491 3.29 5732 3.49 5731 3.52 5040 3.81 6102 4.03 4904 3.98 5369 4.1 5578 3.96 4619 3.83 4731 3.72 5011 3.82 5299 3.76 4146 3.98 4625 4.14 4736 4 4219 4.13 5116 4.28 4205 4.46 4121 4.63 5103 4.49 4300 4.41 4578 4.5 3809 4.39 5657 4.33 4248 4.45 3830 4.17 4736 4.13 4839 4.33 4411 4.47 4570 4.63 4104 4.9 4801 4.77 3953 4.51 3828 4.63 4440 4.36 4026 3.95 4109 3.74 4785 4.15 3224 4.14 3552 3.97 3940 3.81 3913 4.07 3681 3.84 4309 3.63 3830 3.55 4143 3.6 4087 3.63 3818 3.55 3380 3.69 3430 3.53 3458 3.43 3970 3.4 5260 3.41 5024 3.09 5634 3.35 6549 3.22 4676

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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.3889 R-squared 0.1512 RMSE 0.4249 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 3.48 4.00282608695652 -0.522826086956522 2 3.6 4.00282608695652 -0.402826086956522 3 3.66 3.61619047619048 0.0438095238095237 4 3.45 3.61619047619048 -0.166190476190476 5 3.3 3.61619047619048 -0.316190476190477 6 3.14 3.61619047619048 -0.476190476190476 7 3.21 4.00282608695652 -0.792826086956522 8 3.12 3.61619047619048 -0.496190476190476 9 3.14 3.61619047619048 -0.476190476190476 10 3.4 4.00282608695652 -0.602826086956522 11 3.42 4.00282608695652 -0.582826086956522 12 3.29 3.61619047619048 -0.326190476190476 13 3.49 3.61619047619048 -0.126190476190476 14 3.52 3.61619047619048 -0.0961904761904764 15 3.81 3.61619047619048 0.193809523809524 16 4.03 4.00282608695652 0.0271739130434785 17 3.98 3.61619047619048 0.363809523809524 18 4.1 3.61619047619048 0.483809523809523 19 3.96 4.00282608695652 -0.0428260869565218 20 3.83 4.00282608695652 -0.172826086956522 21 3.72 3.61619047619048 0.103809523809524 22 3.82 3.61619047619048 0.203809523809523 23 3.76 4.00282608695652 -0.242826086956522 24 3.98 4.00282608695652 -0.0228260869565218 25 4.14 4.00282608695652 0.137173913043478 26 4 4.00282608695652 -0.00282608695652176 27 4.13 3.61619047619048 0.513809523809523 28 4.28 4.00282608695652 0.277173913043478 29 4.46 4.00282608695652 0.457173913043478 30 4.63 3.61619047619048 1.01380952380952 31 4.49 4.00282608695652 0.487173913043478 32 4.41 4.00282608695652 0.407173913043478 33 4.5 4.00282608695652 0.497173913043478 34 4.39 3.61619047619048 0.773809523809523 35 4.33 4.00282608695652 0.327173913043478 36 4.45 4.00282608695652 0.447173913043478 37 4.17 4.00282608695652 0.167173913043478 38 4.13 4.00282608695652 0.127173913043478 39 4.33 4.00282608695652 0.327173913043478 40 4.47 4.00282608695652 0.467173913043478 41 4.63 4.00282608695652 0.627173913043478 42 4.9 4.00282608695652 0.897173913043479 43 4.77 4.00282608695652 0.767173913043478 44 4.51 4.00282608695652 0.507173913043478 45 4.63 4.00282608695652 0.627173913043478 46 4.36 4.00282608695652 0.357173913043479 47 3.95 4.00282608695652 -0.0528260869565216 48 3.74 4.00282608695652 -0.262826086956522 49 4.15 4.00282608695652 0.147173913043479 50 4.14 4.00282608695652 0.137173913043478 51 3.97 4.00282608695652 -0.0328260869565216 52 3.81 4.00282608695652 -0.192826086956522 53 4.07 4.00282608695652 0.0671739130434785 54 3.84 4.00282608695652 -0.162826086956522 55 3.63 4.00282608695652 -0.372826086956522 56 3.55 4.00282608695652 -0.452826086956522 57 3.6 4.00282608695652 -0.402826086956522 58 3.63 4.00282608695652 -0.372826086956522 59 3.55 4.00282608695652 -0.452826086956522 60 3.69 4.00282608695652 -0.312826086956522 61 3.53 4.00282608695652 -0.472826086956522 62 3.43 4.00282608695652 -0.572826086956522 63 3.4 3.61619047619048 -0.216190476190476 64 3.41 3.61619047619048 -0.206190476190476 65 3.09 3.61619047619048 -0.526190476190477 66 3.35 3.61619047619048 -0.266190476190476 67 3.22 4.00282608695652 -0.782826086956522

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/2e4q91293205885.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/2e4q91293205885.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/3e4q91293205885.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/3e4q91293205885.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/47w7c1293205885.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293205769d7it04y3rygxzur/47w7c1293205885.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; par4 = yes ;

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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Software written by Ed van Stee & Patrick Wessa

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