*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 18:18:28 +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/t1292955415aogqnmnty5l5kgk.htm/, Retrieved Tue, 21 Dec 2010 19:16:56 +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/t1292955415aogqnmnty5l5kgk.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 «

235.1 9700 280.7 9081 264.6 9084 240.7 9743 201.4 8587 240.8 9731 241.1 9563 223.8 9998 206.1 9437 174.7 10038 203.3 9918 220.5 9252 299.5 9737 347.4 9035 338.3 9133 327.7 9487 351.6 8700 396.6 9627 438.8 8947 395.6 9283 363.5 8829 378.8 9947 357 9628 369 9318 464.8 9605 479.1 8640 431.3 9214 366.5 9567 326.3 8547 355.1 9185 331.6 9470 261.3 9123 249 9278 205.5 10170 235.6 9434 240.9 9655 264.9 9429 253.8 8739 232.3 9552 193.8 9687 177 9019 213.2 9672 207.2 9206 180.6 9069 188.6 9788 175.4 10312 199 10105 179.6 9863 225.8 9656 234 9295 200.2 9946 183.6 9701 178.2 9049 203.2 10190 208.5 9706 191.8 9765 172.8 9893 148 9994 159.4 10433 154.5 10073 213.2 10112 196.4 9266 182.8 9820 176.4 10097 153.6 9115 173.2 10411 171 9678 151.2 10408 161.9 10153 157.2 10368 201.7 10581 236.4 10597 356.1 10680 398.3 9738 403.7 9556

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 0.5105 R-squared 0.2606 RMSE 74.51 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 235.1 211.05 24.05 2 280.7 299.518918918919 -18.8189189189189 3 264.6 299.518918918919 -34.9189189189189 4 240.7 211.05 29.65 5 201.4 299.518918918919 -98.118918918919 6 240.8 211.05 29.75 7 241.1 299.518918918919 -58.4189189189189 8 223.8 211.05 12.75 9 206.1 299.518918918919 -93.418918918919 10 174.7 211.05 -36.35 11 203.3 211.05 -7.75 12 220.5 299.518918918919 -79.018918918919 13 299.5 211.05 88.45 14 347.4 299.518918918919 47.881081081081 15 338.3 299.518918918919 38.7810810810811 16 327.7 299.518918918919 28.1810810810811 17 351.6 299.518918918919 52.0810810810811 18 396.6 299.518918918919 97.081081081081 19 438.8 299.518918918919 139.281081081081 20 395.6 299.518918918919 96.081081081081 21 363.5 299.518918918919 63.9810810810811 22 378.8 211.05 167.75 23 357 299.518918918919 57.4810810810811 24 369 299.518918918919 69.4810810810811 25 464.8 299.518918918919 165.281081081081 26 479.1 299.518918918919 179.581081081081 27 431.3 299.518918918919 131.781081081081 28 366.5 299.518918918919 66.9810810810811 29 326.3 299.518918918919 26.7810810810811 30 355.1 299.518918918919 55.5810810810811 31 331.6 299.518918918919 32.0810810810811 32 261.3 299.518918918919 -38.2189189189189 33 249 299.518918918919 -50.5189189189189 34 205.5 211.05 -5.55000000000001 35 235.6 299.518918918919 -63.918918918919 36 240.9 211.05 29.85 37 264.9 299.518918918919 -34.618918918919 38 253.8 299.518918918919 -45.7189189189189 39 232.3 299.518918918919 -67.2189189189189 40 193.8 211.05 -17.25 41 177 299.518918918919 -122.518918918919 42 213.2 211.05 2.14999999999998 43 207.2 299.518918918919 -92.318918918919 44 180.6 299.518918918919 -118.918918918919 45 188.6 211.05 -22.45 46 175.4 211.05 -35.65 47 199 211.05 -12.05 48 179.6 211.05 -31.45 49 225.8 211.05 14.75 50 234 299.518918918919 -65.5189189189189 51 200.2 211.05 -10.85 52 183.6 211.05 -27.45 53 178.2 299.518918918919 -121.318918918919 54 203.2 211.05 -7.85000000000002 55 208.5 211.05 -2.55000000000001 56 191.8 211.05 -19.25 57 172.8 211.05 -38.25 58 148 211.05 -63.05 59 159.4 211.05 -51.65 60 154.5 211.05 -56.55 61 213.2 211.05 2.14999999999998 62 196.4 299.518918918919 -103.118918918919 63 182.8 211.05 -28.25 64 176.4 211.05 -34.65 65 153.6 299.518918918919 -145.918918918919 66 173.2 211.05 -37.85 67 171 211.05 -40.05 68 151.2 211.05 -59.85 69 161.9 211.05 -49.15 70 157.2 211.05 -53.85 71 201.7 211.05 -9.35000000000002 72 236.4 211.05 25.35 73 356.1 211.05 145.05 74 398.3 211.05 187.25 75 403.7 299.518918918919 104.181081081081

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/25a5f1292955502.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/25a5f1292955502.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/35a5f1292955502.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/35a5f1292955502.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/4yj401292955502.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292955415aogqnmnty5l5kgk/4yj401292955502.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 2 ; 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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