Blog 4

*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 17:21:05 +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/t1292347148i9owisgjvgvhhw4.htm/, Retrieved Tue, 14 Dec 2010 18:19:11 +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/t1292347148i9owisgjvgvhhw4.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 «

3.18 0.22 6.62 3.64 3.14 0.22 6.56 3.62 3.02 0.23 6.59 3.61 3.02 0.24 6.56 3.6 3.03 0.25 6.57 3.6 3.04 0.25 6.62 3.63 3.09 0.24 6.69 3.59 3.06 0.24 6.69 3.55 3.06 0.22 6.64 3.54 3.09 0.21 6.6 3.53 3.11 0.21 6.66 3.53 3.1 0.21 6.62 3.53 3.09 0.2 6.64 3.52 3.19 0.2 6.64 3.52 3.22 0.2 6.73 3.48 3.22 0.2 6.73 3.49 3.25 0.2 6.69 3.47 3.25 0.2 6.78 3.46 3.27 0.2 6.77 3.4 3.28 0.2 6.8 3.36 3.24 0.2 6.8 3.3 3.23 0.2 6.74 3.28 3.2 0.2 6.84 3.28 3.19 0.2 6.83 3.24 3.23 0.2 6.89 3.23 3.19 0.2 6.9 3.2 3.16 0.2 6.86 3.15 3.11 0.2 6.78 3.1 3.11 0.2 6.82 3.07 3.07 0.2 6.81 3.03 3.05 0.21 6.81 2.96 3 0.2 6.78 2.88 2.95 0.2 6.79 2.83 2.9 0.19 6.83 2.8 2.88 0.18 6.9 2.8 2.9 0.18 6.79 2.79 2.89 0.17 6.88 2.79 2.89 0.17 6.89 2.78 2.91 0.17 6.91 2.79 2.9 0.17 6.93 2.78 2.9 0.17 6.89 2.78 2.88 0.16 7 2.74 2.83 0.16 7.01 2.71 2.8 0.16 7.15 2.69 2.77 0.16 7.25 2.68 2.78 0.16 7.33 2.68 2.75 0.16 7.39 2.68 2.74 0.15 7.38 2.69 2.73 0.15 7.38 2.68 2.69 0.15 7.35 2.69 2.67 0.15 7.38 2.68 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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.9573 R-squared 0.9164 RMSE 0.0566 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 3.18 3.07615384615385 0.103846153846154 2 3.14 3.07615384615385 0.0638461538461543 3 3.02 3.07615384615385 -0.0561538461538458 4 3.02 3.07615384615385 -0.0561538461538458 5 3.03 3.07615384615385 -0.046153846153846 6 3.04 3.07615384615385 -0.0361538461538458 7 3.09 3.07615384615385 0.0138461538461541 8 3.06 3.07615384615385 -0.0161538461538457 9 3.06 3.07615384615385 -0.0161538461538457 10 3.09 3.07615384615385 0.0138461538461541 11 3.11 3.07615384615385 0.0338461538461541 12 3.1 3.07615384615385 0.0238461538461543 13 3.09 3.18421052631579 -0.094210526315789 14 3.19 3.18421052631579 0.00578947368421101 15 3.22 3.18421052631579 0.0357894736842113 16 3.22 3.18421052631579 0.0357894736842113 17 3.25 3.18421052631579 0.065789473684211 18 3.25 3.18421052631579 0.065789473684211 19 3.27 3.18421052631579 0.085789473684211 20 3.28 3.18421052631579 0.0957894736842109 21 3.24 3.18421052631579 0.0557894736842113 22 3.23 3.18421052631579 0.0457894736842110 23 3.2 3.18421052631579 0.0157894736842112 24 3.19 3.18421052631579 0.00578947368421101 25 3.23 3.18421052631579 0.0457894736842110 26 3.19 3.18421052631579 0.00578947368421101 27 3.16 3.18421052631579 -0.0242105263157888 28 3.11 3.18421052631579 -0.074210526315789 29 3.11 3.18421052631579 -0.074210526315789 30 3.07 3.18421052631579 -0.114210526315789 31 3.05 3.07615384615385 -0.0261538461538460 32 3 3.18421052631579 -0.184210526315789 33 2.95 2.89363636363636 0.0563636363636366 34 2.9 2.89363636363636 0.00636363636363635 35 2.88 2.89363636363636 -0.0136363636363637 36 2.9 2.89363636363636 0.00636363636363635 37 2.89 2.89363636363636 -0.00363636363636344 38 2.89 2.89363636363636 -0.00363636363636344 39 2.91 2.89363636363636 0.0163636363636366 40 2.9 2.89363636363636 0.00636363636363635 41 2.9 2.89363636363636 0.00636363636363635 42 2.88 2.89363636363636 -0.0136363636363637 43 2.83 2.89363636363636 -0.0636363636363635 44 2.8 2.70615384615385 0.0938461538461537 45 2.77 2.70615384615385 0.0638461538461539 46 2.78 2.70615384615385 0.0738461538461537 47 2.75 2.70615384615385 0.0438461538461539 48 2.74 2.70615384615385 0.0338461538461541 49 2.73 2.70615384615385 0.0238461538461539 50 2.69 2.70615384615385 -0.0161538461538462 51 2.67 2.70615384615385 -0.0361538461538462 52 2.66 2.70615384615385 -0.046153846153846 53 2.67 2.70615384615385 -0.0361538461538462 54 2.65 2.70615384615385 -0.0561538461538462 55 2.64 2.70615384615385 -0.066153846153846 56 2.63 2.70615384615385 -0.0761538461538462

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/2oadz1292347258.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/2oadz1292347258.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/3oadz1292347258.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/3oadz1292347258.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/4h1ck1292347258.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292347148i9owisgjvgvhhw4/4h1ck1292347258.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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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