Workshop 10; PLC: Recursive partitioning (no categorization)

*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 10:36:10 +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/t129232284753bdgmnf8uvmt4m.htm/, Retrieved Tue, 14 Dec 2010 11:34:08 +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/t129232284753bdgmnf8uvmt4m.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 «

107.11 236.67 8.92 1 122.23 258.1 9.32 2 134.69 241.52 8.9 3 128.79 190.71 8.53 4 126.16 200.32 8.51 5 119.98 223.41 9.03 6 108.45 201.38 9.6 7 108.43 211.83 9.88 8 98.17 224.41 10.81 9 106.09 211.57 11.61 10 108.81 194.77 11.81 11 103.03 201.86 13.93 12 124.36 225 16.19 1 118.52 278.9 18.05 2 112.2 259.74 17.08 3 114.71 230.45 17.46 4 107.96 238.26 16.9 5 101.21 250.14 15.69 6 102.77 263.81 15.86 7 112.13 247.22 12.98 8 109.36 229.81 12.31 9 110.91 224.27 11.51 10 123.57 213.23 11.73 11 129.95 239.57 11.7 12 124.46 249.7 10.9 1 122.34 212.5 10.57 2 116.61 203.27 10.37 3 114.59 192.05 9.59 4 112.52 190.04 9.09 5 118.67 202.05 9.26 6 116.8 211.91 9.9 7 123.63 210.39 9.61 8 128.04 231.25 9.85 9 134.57 224.3 9.99 10 130.33 209.64 9.9 11 136.47 206.05 10.45 12 139.05 229.7 11.66 1 158.21 264.67 13.61 2 148.07 246.29 12.88 3 137.74 260.91 12.52 4 139.74 265.14 10.93 5 144.08 284.52 12.07 6 145.35 287.48 13.21 7 145.77 321.9 13.68 8 140.56 321.59 14.02 9 121.41 282.39 11 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 0.6797 R-squared 0.4619 RMSE 11.1658 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 107.11 119.48 -12.37 2 122.23 119.48 2.75 3 134.69 119.48 15.21 4 128.79 119.48 9.30999999999999 5 126.16 119.48 6.67999999999999 6 119.98 119.48 0.5 7 108.45 119.48 -11.03 8 108.43 119.48 -11.05 9 98.17 119.48 -21.31 10 106.09 119.48 -13.39 11 108.81 119.48 -10.67 12 103.03 119.48 -16.45 13 124.36 119.48 4.88 14 118.52 142.056111111111 -23.5361111111111 15 112.2 119.48 -7.28 16 114.71 119.48 -4.77000000000001 17 107.96 119.48 -11.52 18 101.21 119.48 -18.27 19 102.77 119.48 -16.71 20 112.13 119.48 -7.35000000000001 21 109.36 119.48 -10.12 22 110.91 119.48 -8.57 23 123.57 119.48 4.08999999999999 24 129.95 119.48 10.47 25 124.46 119.48 4.97999999999999 26 122.34 119.48 2.86 27 116.61 119.48 -2.87 28 114.59 119.48 -4.89 29 112.52 119.48 -6.96000000000001 30 118.67 119.48 -0.810000000000002 31 116.8 119.48 -2.68000000000001 32 123.63 119.48 4.14999999999999 33 128.04 119.48 8.55999999999999 34 134.57 119.48 15.09 35 130.33 119.48 10.85 36 136.47 119.48 16.99 37 139.05 119.48 19.57 38 158.21 142.056111111111 16.1538888888889 39 148.07 119.48 28.59 40 137.74 119.48 18.26 41 139.74 142.056111111111 -2.3161111111111 42 144.08 142.056111111111 2.02388888888891 43 145.35 142.056111111111 3.29388888888889 44 145.77 142.056111111111 3.7138888888889 45 140.56 142.056111111111 -1.49611111111111 46 121.41 142.056111111111 -20.6461111111111 47 120.44 119.48 0.959999999999994 48 116.97 119.48 -2.51000000000001 49 128.03 119.48 8.55 50 128.51 142.056111111111 -13.5461111111111 51 127.76 119.48 8.28 52 134.58 142.056111111111 -7.4761111111111 53 147.64 142.056111111111 5.58388888888888 54 144.46 142.056111111111 2.4038888888889 55 137.6 142.056111111111 -4.45611111111111 56 146.87 142.056111111111 4.8138888888889 57 145.67 142.056111111111 3.61388888888888 58 151.95 142.056111111111 9.89388888888888 59 150.23 142.056111111111 8.17388888888888 60 155.86 142.056111111111 13.8038888888889

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/2b5dq1292322964.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/2b5dq1292322964.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/3b5dq1292322964.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/3b5dq1292322964.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/4lecb1292322964.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129232284753bdgmnf8uvmt4m/4lecb1292322964.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') }

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Software written by Ed van Stee & Patrick Wessa

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