Recursive Partioning Parental Criticism

*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: Sat, 11 Dec 2010 08:16:31 +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/11/t1292055307jg0yjnyduy9y0vd.htm/, Retrieved Sat, 11 Dec 2010 09:15:08 +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/11/t1292055307jg0yjnyduy9y0vd.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 69 26 9 15 6 25 25 1 53 20 9 15 6 25 24 1 43 21 9 14 13 19 21 0 60 31 14 10 8 18 23 1 49 21 8 10 7 18 17 1 62 18 8 12 9 22 19 1 45 26 11 18 5 29 18 1 50 22 10 12 8 26 27 1 75 22 9 14 9 25 23 1 82 29 15 18 11 23 23 0 60 15 14 9 8 23 29 1 59 16 11 11 11 23 21 1 21 24 14 11 12 24 26 1 62 17 6 17 8 30 25 0 54 19 20 8 7 19 25 1 47 22 9 16 9 24 23 1 59 31 10 21 12 32 26 0 37 28 8 24 20 30 20 0 43 38 11 21 7 29 29 1 48 26 14 14 8 17 24 0 79 25 11 7 8 25 23 0 62 25 16 18 16 26 24 1 16 29 14 18 10 26 30 0 38 28 11 13 6 25 22 1 58 15 11 11 8 23 22 0 60 18 12 13 9 21 13 0 67 21 9 13 9 19 24 0 55 25 7 18 11 35 17 1 47 23 13 14 12 19 24 0 59 23 10 12 8 20 21 1 49 19 9 9 7 21 23 0 47 18 9 12 8 21 24 1 57 18 13 8 9 24 24 0 39 26 16 5 4 23 24 1 49 18 12 10 8 19 23 1 26 18 6 11 8 17 26 0 53 28 14 11 8 24 24 0 75 17 14 12 6 15 21 1 65 29 10 12 8 25 23 1 49 12 4 15 4 27 28 0 48 25 12 12 7 29 23 0 45 28 12 16 14 27 22 0 31 20 14 14 10 18 24 1 61 17 9 17 9 25 21 1 49 17 9 13 6 22 23 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 0.5598 R-squared 0.3134 RMSE 2.1812 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 6 8.975 -2.975 2 6 8.975 -2.975 3 13 8.975 4.025 4 8 7.68478260869565 0.315217391304348 5 7 7.68478260869565 -0.684782608695652 6 9 7.68478260869565 1.31521739130435 7 5 12.2307692307692 -7.23076923076923 8 8 7.68478260869565 0.315217391304348 9 9 8.975 0.0250000000000004 10 11 12.2307692307692 -1.23076923076923 11 8 7.68478260869565 0.315217391304348 12 11 7.68478260869565 3.31521739130435 13 12 7.68478260869565 4.31521739130435 14 8 8.975 -0.975 15 7 7.68478260869565 -0.684782608695652 16 9 8.975 0.0250000000000004 17 12 12.2307692307692 -0.23076923076923 18 20 12.2307692307692 7.76923076923077 19 7 12.2307692307692 -5.23076923076923 20 8 8.975 -0.975 21 8 5 3 22 16 12.2307692307692 3.76923076923077 23 10 12.2307692307692 -2.23076923076923 24 6 7.68478260869565 -1.68478260869565 25 8 7.68478260869565 0.315217391304348 26 9 7.68478260869565 1.31521739130435 27 9 7.68478260869565 1.31521739130435 28 11 12.2307692307692 -1.23076923076923 29 12 8.975 3.025 30 8 7.68478260869565 0.315217391304348 31 7 7.68478260869565 -0.684782608695652 32 8 7.68478260869565 0.315217391304348 33 9 7.68478260869565 1.31521739130435 34 4 5 -1 35 8 7.68478260869565 0.315217391304348 36 8 7.68478260869565 0.315217391304348 37 8 7.68478260869565 0.315217391304348 38 6 7.68478260869565 -1.68478260869565 39 8 7.68478260869565 0.315217391304348 40 4 8.975 -4.975 41 7 7.68478260869565 -0.684782608695652 42 14 8.975 5.025 43 10 8.975 1.025 44 9 8.975 0.0250000000000004 45 6 7.68478260869565 -1.68478260869565 46 8 7.68478260869565 0.315217391304348 47 11 8.975 2.025 48 8 7.68478260869565 0.315217391304348 49 8 7.68478260869565 0.315217391304348 50 10 7.68478260869565 2.31521739130435 51 8 7.68478260869565 0.315217391304348 52 10 7.68478260869565 2.31521739130435 53 7 7.68478260869565 -0.684782608695652 54 8 7.68478260869565 0.315217391304348 55 7 7.68478260869565 -0.684782608695652 56 9 7.68478260869565 1.31521739130435 57 5 5 0 58 7 8.975 -1.975 59 7 7.68478260869565 -0.684782608695652 60 7 7.68478260869565 -0.684782608695652 61 9 7.68478260869565 1.31521739130435 62 5 7.68478260869565 -2.68478260869565 63 8 7.68478260869565 0.315217391304348 64 8 7.68478260869565 0.315217391304348 65 8 7.68478260869565 0.315217391304348 66 9 7.68478260869565 1.31521739130435 67 6 7.68478260869565 -1.68478260869565 68 8 7.68478260869565 0.315217391304348 69 6 5 1 70 4 5 -1 71 6 7.68478260869565 -1.68478260869565 72 4 7.68478260869565 -3.68478260869565 73 12 12.2307692307692 -0.23076923076923 74 6 7.68478260869565 -1.68478260869565 75 11 7.68478260869565 3.31521739130435 76 8 7.68478260869565 0.315217391304348 77 10 7.68478260869565 2.31521739130435 78 10 8.975 1.025 79 4 7.68478260869565 -3.68478260869565 80 8 7.68478260869565 0.315217391304348 81 9 8.975 0.0250000000000004 82 9 8.975 0.0250000000000004 83 7 7.68478260869565 -0.684782608695652 84 7 7.68478260869565 -0.684782608695652 85 11 7.68478260869565 3.31521739130435 86 8 7.68478260869565 0.315217391304348 87 8 7.68478260869565 0.315217391304348 88 7 8.975 -1.975 89 5 8.975 -3.975 90 7 7.68478260869565 -0.684782608695652 91 9 8.975 0.0250000000000004 92 8 7.68478260869565 0.315217391304348 93 6 7.68478260869565 -1.68478260869565 94 8 8.975 -0.975 95 10 12.2307692307692 -2.23076923076923 96 10 8.975 1.025 97 8 7.68478260869565 0.315217391304348 98 11 8.975 2.025 99 8 8.975 -0.975 100 8 7.68478260869565 0.315217391304348 101 6 7.68478260869565 -1.68478260869565 102 20 12.2307692307692 7.76923076923077 103 6 8.975 -2.975 104 12 12.2307692307692 -0.23076923076923 105 9 8.975 0.0250000000000004 106 5 7.68478260869565 -2.68478260869565 107 10 8.975 1.025 108 5 7.68478260869565 -2.68478260869565 109 6 7.68478260869565 -1.68478260869565 110 10 8.975 1.025 111 6 7.68478260869565 -1.68478260869565 112 10 8.975 1.025 113 5 7.68478260869565 -2.68478260869565 114 13 12.2307692307692 0.76923076923077 115 7 7.68478260869565 -0.684782608695652 116 9 7.68478260869565 1.31521739130435 117 11 8.975 2.025 118 8 7.68478260869565 0.315217391304348 119 5 7.68478260869565 -2.68478260869565 120 4 7.68478260869565 -3.68478260869565 121 9 7.68478260869565 1.31521739130435 122 7 7.68478260869565 -0.684782608695652 123 5 7.68478260869565 -2.68478260869565 124 5 7.68478260869565 -2.68478260869565 125 4 5 -1 126 7 7.68478260869565 -0.684782608695652 127 9 8.975 0.0250000000000004 128 8 7.68478260869565 0.315217391304348 129 8 8.975 -0.975 130 11 8.975 2.025 131 10 8.975 1.025 132 9 8.975 0.0250000000000004 133 12 8.975 3.025 134 10 7.68478260869565 2.31521739130435 135 10 7.68478260869565 2.31521739130435 136 7 8.975 -1.975 137 10 8.975 1.025 138 6 8.975 -2.975 139 6 7.68478260869565 -1.68478260869565 140 11 7.68478260869565 3.31521739130435 141 8 7.68478260869565 0.315217391304348 142 9 8.975 0.0250000000000004 143 9 7.68478260869565 1.31521739130435 144 13 7.68478260869565 5.31521739130435 145 11 7.68478260869565 3.31521739130435 146 4 5 -1 147 9 7.68478260869565 1.31521739130435 148 5 7.68478260869565 -2.68478260869565 149 4 7.68478260869565 -3.68478260869565 150 9 7.68478260869565 1.31521739130435 151 8 7.68478260869565 0.315217391304348 152 9 7.68478260869565 1.31521739130435

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/2g4ah1292055384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/2g4ah1292055384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/3g4ah1292055384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/3g4ah1292055384.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/4rva21292055384.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292055307jg0yjnyduy9y0vd/4rva21292055384.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 6 ; 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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