WS 10 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 14:32:29 +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/t1292077943xzkm3de77vmb2a4.htm/, Retrieved Sat, 11 Dec 2010 15:32:24 +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/t1292077943xzkm3de77vmb2a4.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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12008 4.0 9169 5.9 8788 7.1 8417 10.5 8247 15.1 8197 16.8 8236 15.3 8253 18.4 7733 16.1 8366 11.3 8626 7.9 8863 5.6 10102 3.4 8463 4.8 9114 6.5 8563 8.5 8872 15.1 8301 15.7 8301 18.7 8278 19.2 7736 12.9 7973 14.4 8268 6.2 9476 3.3 11100 4.6 8962 7.1 9173 7.8 8738 9.9 8459 13.6 8078 17.1 8411 17.8 8291 18.6 7810 14.7 8616 10.5 8312 8.6 9692 4.4 9911 2.3 8915 2.8 9452 8.8 9112 10.7 8472 13.9 8230 19.3 8384 19.5 8625 20.4 8221 15.3 8649 7.9 8625 8.3 10443 4.5 10357 3.2 8586 5.0 8892 6.6 8329 11.1 8101 12.8 7922 16.3 8120 17.4 7838 18.9 7735 15.8 8406 11.7 8209 6.4 9451 2.9 10041 4.7 9411 2.4 10405 7.2 8467 10.7 8464 13.4 8102 18.3 7627 18.4 7513 16.8 7510 16.6 8291 14.1 8064 6.1 9383 3.5 9706 1.7 8579 2.3 9474 4.5 8318 9.3 8213 14.2 8059 17.3 9111 23.0 7708 16.3 7680 18.4 8014 14.2 8007 9.1 8718 5.9 9486 7.2 9113 6.8 9025 8.0 8476 14.3 7952 14.6 7759 17.5 7835 17.2 7600 17.2 7651 14.1 8319 10.4 8812 6.8 8630 4.1

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 0.7724 R-squared 0.5966 RMSE 506.1489 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 12008 9804.64705882353 2203.35294117647 2 9169 8760.34375 408.65625 3 8788 8760.34375 27.65625 4 8417 8760.34375 -343.34375 5 8247 8116.8085106383 130.191489361702 6 8197 8116.8085106383 80.1914893617022 7 8236 8116.8085106383 119.191489361702 8 8253 8116.8085106383 136.191489361702 9 7733 8116.8085106383 -383.808510638298 10 8366 8116.8085106383 249.191489361702 11 8626 8760.34375 -134.34375 12 8863 8760.34375 102.65625 13 10102 9804.64705882353 297.35294117647 14 8463 8760.34375 -297.34375 15 9114 8760.34375 353.65625 16 8563 8760.34375 -197.34375 17 8872 8116.8085106383 755.191489361702 18 8301 8116.8085106383 184.191489361702 19 8301 8116.8085106383 184.191489361702 20 8278 8116.8085106383 161.191489361702 21 7736 8116.8085106383 -380.808510638298 22 7973 8116.8085106383 -143.808510638298 23 8268 8760.34375 -492.34375 24 9476 9804.64705882353 -328.64705882353 25 11100 9804.64705882353 1295.35294117647 26 8962 8760.34375 201.65625 27 9173 8760.34375 412.65625 28 8738 8760.34375 -22.34375 29 8459 8116.8085106383 342.191489361702 30 8078 8116.8085106383 -38.8085106382978 31 8411 8116.8085106383 294.191489361702 32 8291 8116.8085106383 174.191489361702 33 7810 8116.8085106383 -306.808510638298 34 8616 8760.34375 -144.34375 35 8312 8760.34375 -448.34375 36 9692 9804.64705882353 -112.64705882353 37 9911 9804.64705882353 106.35294117647 38 8915 9804.64705882353 -889.64705882353 39 9452 8760.34375 691.65625 40 9112 8760.34375 351.65625 41 8472 8116.8085106383 355.191489361702 42 8230 8116.8085106383 113.191489361702 43 8384 8116.8085106383 267.191489361702 44 8625 8116.8085106383 508.191489361702 45 8221 8116.8085106383 104.191489361702 46 8649 8760.34375 -111.34375 47 8625 8760.34375 -135.34375 48 10443 9804.64705882353 638.35294117647 49 10357 9804.64705882353 552.35294117647 50 8586 8760.34375 -174.34375 51 8892 8760.34375 131.65625 52 8329 8116.8085106383 212.191489361702 53 8101 8116.8085106383 -15.8085106382978 54 7922 8116.8085106383 -194.808510638298 55 8120 8116.8085106383 3.19148936170222 56 7838 8116.8085106383 -278.808510638298 57 7735 8116.8085106383 -381.808510638298 58 8406 8116.8085106383 289.191489361702 59 8209 8760.34375 -551.34375 60 9451 9804.64705882353 -353.64705882353 61 10041 9804.64705882353 236.35294117647 62 9411 9804.64705882353 -393.64705882353 63 10405 8760.34375 1644.65625 64 8467 8760.34375 -293.34375 65 8464 8116.8085106383 347.191489361702 66 8102 8116.8085106383 -14.8085106382978 67 7627 8116.8085106383 -489.808510638298 68 7513 8116.8085106383 -603.808510638298 69 7510 8116.8085106383 -606.808510638298 70 8291 8116.8085106383 174.191489361702 71 8064 8760.34375 -696.34375 72 9383 9804.64705882353 -421.64705882353 73 9706 9804.64705882353 -98.6470588235297 74 8579 9804.64705882353 -1225.64705882353 75 9474 9804.64705882353 -330.64705882353 76 8318 8760.34375 -442.34375 77 8213 8116.8085106383 96.1914893617022 78 8059 8116.8085106383 -57.8085106382978 79 9111 8116.8085106383 994.191489361702 80 7708 8116.8085106383 -408.808510638298 81 7680 8116.8085106383 -436.808510638298 82 8014 8116.8085106383 -102.808510638298 83 8007 8760.34375 -753.34375 84 8718 8760.34375 -42.34375 85 9486 8760.34375 725.65625 86 9113 8760.34375 352.65625 87 9025 8760.34375 264.65625 88 8476 8116.8085106383 359.191489361702 89 7952 8116.8085106383 -164.808510638298 90 7759 8116.8085106383 -357.808510638298 91 7835 8116.8085106383 -281.808510638298 92 7600 8116.8085106383 -516.808510638298 93 7651 8116.8085106383 -465.808510638298 94 8319 8760.34375 -441.34375 95 8812 8760.34375 51.65625 96 8630 9804.64705882353 -1174.64705882353

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/2xytb1292077943.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/2xytb1292077943.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/3xytb1292077943.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/3xytb1292077943.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/4p7se1292077943.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292077943xzkm3de77vmb2a4/4p7se1292077943.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') }

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Software written by Ed van Stee & Patrick Wessa

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