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: Sun, 19 Dec 2010 20:32:45 +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/19/t1292792743cbz1sa5fkwnd237.htm/, Retrieved Sun, 19 Dec 2010 22:05:46 +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/19/t1292792743cbz1sa5fkwnd237.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 «

97,06 21.454 631.923 130.678 97,73 23.899 654.294 120.877 98 24.939 671.833 137.114 97,76 23.580 586.840 134.406 97,48 24.562 600.969 120.262 97,77 24.696 625.568 130.846 97,96 23.785 558.110 120.343 98,22 23.812 630.577 98.881 98,51 21.917 628.654 115.678 98,19 19.713 603.184 120.796 98,37 19.282 656.255 94.261 98,31 18.788 600.730 89.151 98,6 21.453 670.326 119.880 98,96 24.482 678.423 131.468 99,11 27.474 641.502 155.089 99,64 27.264 625.311 149.581 100,02 27.349 628.177 122.788 99,98 30.632 589.767 143.900 100,32 29.429 582.471 112.115 100,44 30.084 636.248 109.600 100,51 26.290 599.885 117.446 101 24.379 621.694 118.456 100,88 23.335 637.406 101.901 100,55 21.346 595.994 89.940 100,82 21.106 696.308 129.143 101,5 24.514 674.201 126.102 102,15 28.353 648.861 143.048 102,39 30.805 649.605 142.258 102,54 31.348 672.392 131.011 102,85 34.556 598.396 146.471 103,47 33.855 613.177 114.073 103,56 34.787 638.104 114.642 103,69 32.529 615.632 118.226 103,49 29.998 634.465 111.338 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 0.9295 R-squared 0.8639 RMSE 1.7922 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 97.06 98.9978260869565 -1.93782608695653 2 97.73 98.9978260869565 -1.26782608695653 3 98 98.9978260869565 -0.997826086956536 4 97.76 98.9978260869565 -1.23782608695653 5 97.48 98.9978260869565 -1.51782608695653 6 97.77 98.9978260869565 -1.22782608695654 7 97.96 98.9978260869565 -1.03782608695654 8 98.22 98.9978260869565 -0.777826086956537 9 98.51 98.9978260869565 -0.487826086956531 10 98.19 98.9978260869565 -0.807826086956538 11 98.37 98.9978260869565 -0.627826086956532 12 98.31 98.9978260869565 -0.687826086956534 13 98.6 98.9978260869565 -0.397826086956542 14 98.96 98.9978260869565 -0.0378260869565423 15 99.11 98.9978260869565 0.112173913043463 16 99.64 98.9978260869565 0.642173913043464 17 100.02 98.9978260869565 1.02217391304346 18 99.98 102.171 -2.19100000000000 19 100.32 102.171 -1.85100000000001 20 100.44 102.171 -1.73100000000001 21 100.51 98.9978260869565 1.51217391304347 22 101 98.9978260869565 2.00217391304346 23 100.88 98.9978260869565 1.88217391304346 24 100.55 98.9978260869565 1.55217391304346 25 100.82 98.9978260869565 1.82217391304346 26 101.5 98.9978260869565 2.50217391304346 27 102.15 102.171 -0.0210000000000008 28 102.39 102.171 0.218999999999994 29 102.54 102.171 0.369 30 102.85 106.337272727273 -3.48727272727272 31 103.47 106.337272727273 -2.86727272727272 32 103.56 106.337272727273 -2.77727272727272 33 103.69 106.337272727273 -2.64727272727272 34 103.49 102.171 1.31899999999999 35 103.47 102.171 1.29899999999999 36 103.45 102.171 1.27900000000000 37 103.48 102.171 1.30900000000000 38 103.93 106.337272727273 -2.40727272727271 39 103.89 106.337272727273 -2.44727272727272 40 104.4 106.337272727273 -1.93727272727271 41 104.79 106.337272727273 -1.54727272727271 42 104.77 106.337272727273 -1.56727272727272 43 105.13 106.337272727273 -1.20727272727272 44 105.26 106.337272727273 -1.07727272727271 45 104.96 106.337272727273 -1.37727272727273 46 104.75 106.337272727273 -1.58727272727272 47 105.01 106.337272727273 -1.32727272727271 48 105.15 106.337272727273 -1.18727272727271 49 105.2 106.337272727273 -1.13727272727272 50 105.77 106.337272727273 -0.567272727272723 51 105.78 106.337272727273 -0.557272727272718 52 106.26 106.337272727273 -0.0772727272727138 53 106.13 106.337272727273 -0.207272727272724 54 106.12 106.337272727273 -0.217272727272714 55 106.57 106.337272727273 0.232727272727274 56 106.44 106.337272727273 0.102727272727279 57 106.54 106.337272727273 0.202727272727287 58 107.1 106.337272727273 0.762727272727275 59 108.1 106.337272727273 1.76272727272728 60 108.4 106.337272727273 2.06272727272729 61 108.84 106.337272727273 2.50272727272728 62 109.62 111.58 -1.95999999999999 63 110.42 111.58 -1.16000000000000 64 110.67 111.58 -0.909999999999997 65 111.66 111.58 0.0799999999999983 66 112.28 111.58 0.700000000000003 67 112.87 111.58 1.29000000000001 68 112.18 111.58 0.600000000000009 69 112.36 111.58 0.780000000000001 70 112.16 111.58 0.579999999999998 71 111.49 106.337272727273 5.15272727272728 72 111.25 106.337272727273 4.91272727272728 73 111.36 111.284444444444 0.0755555555555674 74 111.74 111.284444444444 0.455555555555563 75 111.1 111.284444444444 -0.184444444444438 76 111.33 111.284444444444 0.0455555555555662 77 111.25 111.284444444444 -0.0344444444444321 78 111.04 111.284444444444 -0.244444444444426 79 110.97 106.337272727273 4.63272727272728 80 111.31 111.284444444444 0.0255555555555702 81 111.02 106.337272727273 4.68272727272728 82 111.07 111.284444444444 -0.214444444444439 83 111.36 111.284444444444 0.0755555555555674 84 111.54 106.337272727273 5.20272727272729

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/2gvms1292790759.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/2gvms1292790759.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/3943d1292790759.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/3943d1292790759.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/42wky1292790759.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292792743cbz1sa5fkwnd237/42wky1292790759.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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