*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: Wed, 22 Dec 2010 15:30:04 +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/22/t1293031714rqaaqm18m8phc05.htm/, Retrieved Wed, 22 Dec 2010 16:28:35 +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/22/t1293031714rqaaqm18m8phc05.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 «

186448 17822 1942 16739 4872 1020 190530 22422 2547 17851 4905 1200 194207 18817 2033 17034 4971 1279 190855 22043 2049 18055 4971 1308 200779 19191 2007 18216 4930 1173 204428 23171 2660 18960 5001 1291 207617 19463 2063 17903 5059 1466 212071 22522 2113 18842 5085 1507 214239 20265 2145 18907 5111 1478 215883 24249 2866 19862 5190 1629 223484 20299 2163 18836 5076 1712 221529 25455 2157 19846 5134 1727 225247 21089 2201 19511 4804 1519 226699 26237 2838 20318 4579 1617 231406 21362 2142 19843 4526 1637 232324 26489 2253 20975 4550 1633 237192 21828 2258 20485 4566 1469 236727 27496 2979 21407 4588 1657 240698 21991 2288 20404 4564 1599 240688 27611 2431 21454 4723 1420 245283 22512 2393 21558 4553 1495 243556 28581 3244 22442 4556 1623 247826 23000 2476 21201 4542 1346 245798 28385 2490 21804 4234 1613 250479 23387 2547 22537 4341 1563 249216 30192 3461 22736 4269 2071 251896 24346 2549 21525 4217 1584 247616 30393 2496 22427 4207 1843 249994 24753 2532 23437 4267 1598 246552 31 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 0.9351 R-squared 0.8743 RMSE 6413.5013 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 186448 206716.692307692 -20268.6923076923 2 190530 206716.692307692 -16186.6923076923 3 194207 206716.692307692 -12509.6923076923 4 190855 206716.692307692 -15861.6923076923 5 200779 206716.692307692 -5937.69230769231 6 204428 206716.692307692 -2288.69230769231 7 207617 206716.692307692 900.307692307688 8 212071 206716.692307692 5354.30769230769 9 214239 206716.692307692 7522.30769230769 10 215883 206716.692307692 9166.30769230769 11 223484 206716.692307692 16767.3076923077 12 221529 206716.692307692 14812.3076923077 13 225247 206716.692307692 18530.3076923077 14 226699 236695 -9996 15 231406 236695 -5289 16 232324 236695 -4371 17 237192 236695 497 18 236727 236695 32 19 240698 236695 4003 20 240688 236695 3993 21 245283 248923.666666667 -3640.66666666666 22 243556 246268.076923077 -2712.07692307694 23 247826 236695 11131 24 245798 246268.076923077 -470.076923076937 25 250479 248923.666666667 1555.33333333334 26 249216 246268.076923077 2947.92307692306 27 251896 248923.666666667 2972.33333333334 28 247616 246268.076923077 1347.92307692306 29 249994 248923.666666667 1070.33333333334 30 246552 246268.076923077 283.923076923063 31 248771 248923.666666667 -152.666666666657 32 247551 246268.076923077 1282.92307692306 33 249745 248923.666666667 821.333333333343 34 245742 246268.076923077 -526.076923076937 35 249019 251678.588235294 -2659.58823529413 36 245841 246268.076923077 -427.076923076937 37 248771 248923.666666667 -152.666666666657 38 244723 246268.076923077 -1545.07692307694 39 246878 248923.666666667 -2045.66666666666 40 246014 246268.076923077 -254.076923076937 41 248496 248923.666666667 -427.666666666657 42 244351 246268.076923077 -1917.07692307694 43 248016 246268.076923077 1747.92307692306 44 246509 246268.076923077 240.923076923063 45 249426 251678.588235294 -2252.58823529413 46 247840 251678.588235294 -3838.58823529413 47 251035 251678.588235294 -643.588235294126 48 250161 251678.588235294 -1517.58823529413 49 254278 251678.588235294 2599.41176470587 50 250801 251678.588235294 -877.588235294126 51 253985 251678.588235294 2306.41176470587 52 249174 247902 1272 53 251287 251678.588235294 -391.588235294126 54 247947 247902 45 55 249992 251678.588235294 -1686.58823529413 56 243805 247902 -4097 57 255812 251678.588235294 4133.41176470587 58 250417 247902 2515 59 253033 251678.588235294 1354.41176470587 60 248705 247902 803 61 253950 251678.588235294 2271.41176470587 62 251484 247902 3582 63 251093 251678.588235294 -585.588235294126 64 245996 247902 -1906 65 252721 251678.588235294 1042.41176470587 66 248019 247902 117 67 250464 251678.588235294 -1214.58823529413 68 245571 247902 -2331 69 252690 261916.714285714 -9226.7142857143 70 250183 261916.714285714 -11733.7142857143 71 253639 251678.588235294 1960.41176470587 72 254436 261916.714285714 -7480.71428571429 73 265280 261916.714285714 3363.28571428571 74 268705 261916.714285714 6788.28571428571 75 270643 261916.714285714 8726.2857142857 76 271480 261916.714285714 9563.2857142857

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/2nnd91293031796.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/2nnd91293031796.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/3nnd91293031796.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/3nnd91293031796.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/4ywuu1293031796.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293031714rqaaqm18m8phc05/4ywuu1293031796.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') }

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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