WS10: RP (no cat)

*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, 14 Dec 2010 16:28:00 +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/14/t1292344042lbr74d1jrc7ulqx.htm/, Retrieved Tue, 14 Dec 2010 17:27:23 +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/14/t1292344042lbr74d1jrc7ulqx.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 «

0.6000 1.0800 1.0100 1.6100 1.7700 1.3900 1.7700 0.6000 1.0900 1.0000 1.5800 1.7700 1.3500 1.9800 0.6000 1.1000 1.0000 1.6900 1.7700 1.3900 1.9400 0.6000 1.1000 1.0000 1.7800 1.7700 1.3700 1.8500 0.6000 1.1100 1.0600 1.7600 1.7400 1.3800 1.8400 0.6000 1.1000 1.2200 1.8300 1.7800 1.5100 1.8200 0.6000 1.1000 1.2400 1.8000 1.7800 1.5100 1.8300 0.6000 1.1100 1.3400 1.5700 1.7800 1.4500 1.9100 0.6100 1.1100 1.3000 1.4500 1.7800 1.3000 1.8500 0.6100 1.1100 1.0500 1.4000 1.8100 1.2900 1.8100 0.6100 1.1100 1.0000 1.5500 1.8400 1.4400 1.8300 0.6100 1.1100 1.0000 1.5800 1.8000 1.4600 1.7900 0.6100 1.1200 1.0100 1.5800 1.7800 1.5000 1.8000 0.6100 1.1100 1.0200 1.5900 1.7600 1.3900 1.8200 0.6200 1.1100 1.0600 1.8000 1.7400 1.4800 1.8800 0.6200 1.1200 1.0900 1.9900 1.7200 1.5200 2.0100 0.6200 1.1200 1.0900 2.0600 1.7300 1.6800 1.9700 0.6300 1.1100 1.1500 2.0600 1.7700 1.7400 1.9200 0.6300 1.1200 1.2500 2.0800 1.8100 1.7200 1.9800 0.6300 1.1100 1.3700 2.0000 1.8300 1.7400 2.0200 0.6300 1.1100 1.5100 1.8 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 0.708 R-squared 0.5012 RMSE 0.0131 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 1.08 1.1069696969697 -0.0269696969696971 2 1.09 1.1069696969697 -0.0169696969696971 3 1.1 1.1069696969697 -0.00696969696969707 4 1.1 1.1069696969697 -0.00696969696969707 5 1.11 1.1069696969697 0.00303030303030294 6 1.1 1.1069696969697 -0.00696969696969707 7 1.1 1.1069696969697 -0.00696969696969707 8 1.11 1.1069696969697 0.00303030303030294 9 1.11 1.1069696969697 0.00303030303030294 10 1.11 1.1069696969697 0.00303030303030294 11 1.11 1.1069696969697 0.00303030303030294 12 1.11 1.1069696969697 0.00303030303030294 13 1.12 1.1069696969697 0.0130303030303029 14 1.11 1.1069696969697 0.00303030303030294 15 1.11 1.1069696969697 0.00303030303030294 16 1.12 1.1069696969697 0.0130303030303029 17 1.12 1.1069696969697 0.0130303030303029 18 1.11 1.1069696969697 0.00303030303030294 19 1.12 1.1069696969697 0.0130303030303029 20 1.11 1.1069696969697 0.00303030303030294 21 1.11 1.1069696969697 0.00303030303030294 22 1.1 1.1069696969697 -0.00696969696969707 23 1.1 1.1069696969697 -0.00696969696969707 24 1.1 1.1069696969697 -0.00696969696969707 25 1.11 1.1069696969697 0.00303030303030294 26 1.1 1.1069696969697 -0.00696969696969707 27 1.1 1.1069696969697 -0.00696969696969707 28 1.09 1.1069696969697 -0.0169696969696971 29 1.1 1.1069696969697 -0.00696969696969707 30 1.1 1.1069696969697 -0.00696969696969707 31 1.11 1.1069696969697 0.00303030303030294 32 1.13 1.1069696969697 0.0230303030303027 33 1.13 1.1069696969697 0.0230303030303027 34 1.13 1.13333333333333 -0.00333333333333341 35 1.13 1.13333333333333 -0.00333333333333341 36 1.14 1.13333333333333 0.0066666666666666 37 1.14 1.13333333333333 0.0066666666666666 38 1.14 1.13333333333333 0.0066666666666666 39 1.15 1.13333333333333 0.0166666666666666 40 1.15 1.13333333333333 0.0166666666666666 41 1.15 1.13333333333333 0.0166666666666666 42 1.15 1.13333333333333 0.0166666666666666 43 1.15 1.13333333333333 0.0166666666666666 44 1.15 1.13333333333333 0.0166666666666666 45 1.14 1.13333333333333 0.0066666666666666 46 1.14 1.13333333333333 0.0066666666666666 47 1.14 1.13333333333333 0.0066666666666666 48 1.13 1.13333333333333 -0.00333333333333341 49 1.12 1.13333333333333 -0.0133333333333332 50 1.13 1.13333333333333 -0.00333333333333341 51 1.13 1.13333333333333 -0.00333333333333341 52 1.13 1.13333333333333 -0.00333333333333341 53 1.12 1.13333333333333 -0.0133333333333332 54 1.13 1.13333333333333 -0.00333333333333341 55 1.12 1.13333333333333 -0.0133333333333332 56 1.12 1.13333333333333 -0.0133333333333332 57 1.11 1.13333333333333 -0.0233333333333332 58 1.11 1.13333333333333 -0.0233333333333332 59 1.11 1.13333333333333 -0.0233333333333332 60 1.11 1.13333333333333 -0.0233333333333332 61 1.14 1.13333333333333 0.0066666666666666 62 1.15 1.13333333333333 0.0166666666666666 63 1.15 1.13333333333333 0.0166666666666666 64 1.16 1.13333333333333 0.0266666666666666 65 1.15 1.13333333333333 0.0166666666666666 66 1.16 1.13333333333333 0.0266666666666666 67 1.13 1.13333333333333 -0.00333333333333341 68 1.13 1.13333333333333 -0.00333333333333341 69 1.12 1.13333333333333 -0.0133333333333332 70 1.12 1.13333333333333 -0.0133333333333332 71 1.11 1.13333333333333 -0.0233333333333332 72 1.11 1.13333333333333 -0.0233333333333332

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/2x4o41292344074.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/2x4o41292344074.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/3x4o41292344074.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/3x4o41292344074.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/4i45a1292344074.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292344042lbr74d1jrc7ulqx/4i45a1292344074.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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