paper - recursive partitioning

*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: Tue, 21 Dec 2010 21:38:27 +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/21/t12929673801gw07krom1iz9iy.htm/, Retrieved Tue, 21 Dec 2010 22:36:24 +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/21/t12929673801gw07krom1iz9iy.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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631923 -12 -10.8 654294 -13 -12.2 671833 -16 -14.1 586840 -10 -15.2 600969 -4 -15.8 625568 -9 -15.8 558110 -8 -14.9 630577 -9 -12.6 628654 -3 -9.9 603184 -13 -7.8 656255 -3 -6 600730 -1 -5 670326 -2 -4.5 678423 0 -3.9 641502 0 -2.9 625311 -3 -1.5 628177 0 -0.5 589767 5 0 582471 3 0.5 636248 4 0.9 599885 3 0.8 621694 1 0.1 637406 -1 -1 596994 0 -2 696308 -2 -3 674201 -1 -3.7 648861 2 -4.7 649605 0 -6.4 672392 -6 -7.5 598396 -7 -7.8 613177 -6 -7.7 638104 -4 -6.6 615632 -9 -4.2 634465 -2 -2 638686 -3 -0.7 604243 2 0.1 706669 3 0.9 677185 1 2.1 644328 0 3.5 644825 1 4.9 605707 1 5.7 600136 3 6.2 612166 5 6.5 599659 5 6.5 634210 4 6.3 618234 11 6.2 613576 8 6.4 627200 -1 6.3 668973 4 5.8 651479 4 5.1 619661 4 5.1 644260 6 5.8 579936 6 6.7 601752 6 7.1 595376 6 6.7 588902 4 5.5 634341 1 4.2 594305 6 3 606200 0 2.2 610926 2 2 633685 -2 1.8 639696 0 1.8 659451 1 1.5 593248 -3 0.4 606677 -3 -0.9 599434 -5 -1.7 569578 -7 -2.6 629873 -7 -4.4 613438 -5 - 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.557 R-squared 0.3102 RMSE 34250.6269 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 631923 627585.342465753 4337.65753424657 2 654294 627585.342465753 26708.6575342466 3 671833 627585.342465753 44247.6575342466 4 586840 627585.342465753 -40745.3424657534 5 600969 627585.342465753 -26616.3424657534 6 625568 627585.342465753 -2017.34246575343 7 558110 627585.342465753 -69475.3424657534 8 630577 627585.342465753 2991.65753424657 9 628654 627585.342465753 1068.65753424657 10 603184 627585.342465753 -24401.3424657534 11 656255 627585.342465753 28669.6575342466 12 600730 627585.342465753 -26855.3424657534 13 670326 627585.342465753 42740.6575342466 14 678423 627585.342465753 50837.6575342466 15 641502 627585.342465753 13916.6575342466 16 625311 627585.342465753 -2274.34246575343 17 628177 627585.342465753 591.657534246566 18 589767 627585.342465753 -37818.3424657534 19 582471 627585.342465753 -45114.3424657534 20 636248 627585.342465753 8662.65753424657 21 599885 627585.342465753 -27700.3424657534 22 621694 627585.342465753 -5891.34246575343 23 637406 627585.342465753 9820.65753424657 24 596994 627585.342465753 -30591.3424657534 25 696308 627585.342465753 68722.6575342466 26 674201 627585.342465753 46615.6575342466 27 648861 627585.342465753 21275.6575342466 28 649605 627585.342465753 22019.6575342466 29 672392 627585.342465753 44806.6575342466 30 598396 627585.342465753 -29189.3424657534 31 613177 627585.342465753 -14408.3424657534 32 638104 627585.342465753 10518.6575342466 33 615632 627585.342465753 -11953.3424657534 34 634465 627585.342465753 6879.65753424657 35 638686 627585.342465753 11100.6575342466 36 604243 627585.342465753 -23342.3424657534 37 706669 627585.342465753 79083.6575342466 38 677185 627585.342465753 49599.6575342466 39 644328 627585.342465753 16742.6575342466 40 644825 627585.342465753 17239.6575342466 41 605707 627585.342465753 -21878.3424657534 42 600136 627585.342465753 -27449.3424657534 43 612166 627585.342465753 -15419.3424657534 44 599659 627585.342465753 -27926.3424657534 45 634210 627585.342465753 6624.65753424657 46 618234 627585.342465753 -9351.34246575343 47 613576 627585.342465753 -14009.3424657534 48 627200 627585.342465753 -385.342465753434 49 668973 627585.342465753 41387.6575342466 50 651479 627585.342465753 23893.6575342466 51 619661 627585.342465753 -7924.34246575343 52 644260 627585.342465753 16674.6575342466 53 579936 627585.342465753 -47649.3424657534 54 601752 627585.342465753 -25833.3424657534 55 595376 627585.342465753 -32209.3424657534 56 588902 627585.342465753 -38683.3424657534 57 634341 627585.342465753 6755.65753424657 58 594305 627585.342465753 -33280.3424657534 59 606200 627585.342465753 -21385.3424657534 60 610926 627585.342465753 -16659.3424657534 61 633685 627585.342465753 6099.65753424657 62 639696 627585.342465753 12110.6575342466 63 659451 627585.342465753 31865.6575342466 64 593248 627585.342465753 -34337.3424657534 65 606677 627585.342465753 -20908.3424657534 66 599434 627585.342465753 -28151.3424657534 67 569578 627585.342465753 -58007.3424657534 68 629873 627585.342465753 2287.65753424657 69 613438 627585.342465753 -14147.3424657534 70 604172 627585.342465753 -23413.3424657534 71 658328 695676.636363636 -37348.6363636364 72 612633 695676.636363636 -83043.6363636364 73 707372 695676.636363636 11695.3636363636 74 739770 695676.636363636 44093.3636363636 75 777535 695676.636363636 81858.3636363636 76 685030 695676.636363636 -10646.6363636364 77 730234 695676.636363636 34557.3636363636 78 714154 695676.636363636 18477.3636363636 79 630872 695676.636363636 -64804.6363636364 80 719492 695676.636363636 23815.3636363636 81 677023 695676.636363636 -18653.6363636364 82 679272 627585.342465753 51686.6575342466 83 718317 627585.342465753 90731.6575342466 84 645672 627585.342465753 18086.6575342466

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/27iad1292967501.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/27iad1292967501.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/37iad1292967501.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/37iad1292967501.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/4irrg1292967501.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929673801gw07krom1iz9iy/4irrg1292967501.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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') }

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Software written by Ed van Stee & Patrick Wessa

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