workshop 10 recursive partioning

*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: Mon, 13 Dec 2010 11:29:31 +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/13/t12922398082olfowpl1rsa24x.htm/, Retrieved Mon, 13 Dec 2010 12:30:09 +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/13/t12922398082olfowpl1rsa24x.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 «

216.234 627 213.586 696 209.465 825 204.045 677 200.237 656 203.666 785 241.476 412 260.307 352 243.324 839 244.460 729 233.575 696 237.217 641 235.243 695 230.354 638 227.184 762 221.678 635 217.142 721 219.452 854 256.446 418 265.845 367 248.624 824 241.114 687 229.245 601 231.805 676 219.277 740 219.313 691 212.610 683 214.771 594 211.142 729 211.457 731 240.048 386 240.636 331 230.580 707 208.795 715 197.922 657 194.596 653 194.581 642 185.686 643 178.106 718 172.608 654 167.302 632 168.053 731 202.300 392 202.388 344 182.516 792 173.476 852 166.444 649 171.297 629 169.701 685 164.182 617 161.914 715 159.612 715 151.001 629 158.114 916 186.530 531 187.069 357 174.330 917 169.362 828 166.827 708 178.037 858 186.413 775 189.226 785 191.563 1006 188.906 789 186.005 734 195.309 906 223.532 532 226.899 387 214.126 991 206.903 841 204.442 892 220.375 782

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 0.3947 R-squared 0.1558 RMSE 25.5878 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 216.234 200.557935483871 15.676064516129 2 213.586 200.557935483871 13.028064516129 3 209.465 200.557935483871 8.90706451612903 4 204.045 200.557935483871 3.48706451612901 5 200.237 200.557935483871 -0.320935483870983 6 203.666 200.557935483871 3.10806451612902 7 241.476 232.3414 9.13459999999998 8 260.307 232.3414 27.9656 9 243.324 200.557935483871 42.766064516129 10 244.46 200.557935483871 43.902064516129 11 233.575 200.557935483871 33.017064516129 12 237.217 200.557935483871 36.659064516129 13 235.243 200.557935483871 34.685064516129 14 230.354 200.557935483871 29.796064516129 15 227.184 200.557935483871 26.626064516129 16 221.678 200.557935483871 21.120064516129 17 217.142 200.557935483871 16.584064516129 18 219.452 200.557935483871 18.894064516129 19 256.446 232.3414 24.1046 20 265.845 232.3414 33.5036 21 248.624 200.557935483871 48.066064516129 22 241.114 200.557935483871 40.556064516129 23 229.245 200.557935483871 28.687064516129 24 231.805 200.557935483871 31.247064516129 25 219.277 200.557935483871 18.719064516129 26 219.313 200.557935483871 18.755064516129 27 212.61 200.557935483871 12.052064516129 28 214.771 200.557935483871 14.213064516129 29 211.142 200.557935483871 10.584064516129 30 211.457 200.557935483871 10.899064516129 31 240.048 232.3414 7.70659999999998 32 240.636 232.3414 8.29459999999997 33 230.58 200.557935483871 30.022064516129 34 208.795 200.557935483871 8.23706451612901 35 197.922 200.557935483871 -2.63593548387098 36 194.596 200.557935483871 -5.96193548387097 37 194.581 200.557935483871 -5.97693548387099 38 185.686 200.557935483871 -14.871935483871 39 178.106 200.557935483871 -22.451935483871 40 172.608 200.557935483871 -27.949935483871 41 167.302 200.557935483871 -33.255935483871 42 168.053 200.557935483871 -32.504935483871 43 202.3 232.3414 -30.0414 44 202.388 232.3414 -29.9534 45 182.516 200.557935483871 -18.041935483871 46 173.476 200.557935483871 -27.081935483871 47 166.444 200.557935483871 -34.113935483871 48 171.297 200.557935483871 -29.260935483871 49 169.701 200.557935483871 -30.856935483871 50 164.182 200.557935483871 -36.375935483871 51 161.914 200.557935483871 -38.643935483871 52 159.612 200.557935483871 -40.945935483871 53 151.001 200.557935483871 -49.556935483871 54 158.114 200.557935483871 -42.443935483871 55 186.53 200.557935483871 -14.027935483871 56 187.069 232.3414 -45.2724 57 174.33 200.557935483871 -26.227935483871 58 169.362 200.557935483871 -31.195935483871 59 166.827 200.557935483871 -33.730935483871 60 178.037 200.557935483871 -22.520935483871 61 186.413 200.557935483871 -14.144935483871 62 189.226 200.557935483871 -11.331935483871 63 191.563 200.557935483871 -8.99493548387099 64 188.906 200.557935483871 -11.651935483871 65 186.005 200.557935483871 -14.552935483871 66 195.309 200.557935483871 -5.24893548387098 67 223.532 200.557935483871 22.974064516129 68 226.899 232.3414 -5.44240000000002 69 214.126 200.557935483871 13.568064516129 70 206.903 200.557935483871 6.34506451612901 71 204.442 200.557935483871 3.88406451612903 72 220.375 200.557935483871 19.817064516129

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/2btor1292239765.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/2btor1292239765.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/3btor1292239765.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/3btor1292239765.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/4m2od1292239765.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922398082olfowpl1rsa24x/4m2od1292239765.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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Software written by Ed van Stee & Patrick Wessa

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