Workshop 7 recursive part. 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: Fri, 24 Dec 2010 13:00:30 +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/24/t1293195506sq478rn9vfh5cu9.htm/, Retrieved Fri, 24 Dec 2010 13:58:30 +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/24/t1293195506sq478rn9vfh5cu9.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 «

13 13 14 13 3 2 12 12 8 13 5 1 15 10 12 16 6 0 12 9 7 12 6 3 10 10 10 11 5 3 12 12 7 12 3 1 15 13 16 18 8 3 9 12 11 11 4 1 12 12 14 14 4 4 11 6 6 9 4 0 11 5 16 14 6 3 11 12 11 12 6 2 15 11 16 11 5 4 7 14 12 12 4 3 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 2 14 11 15 16 6 3 10 11 7 9 4 1 6 7 9 11 4 1 11 9 7 13 2 2 15 11 14 15 7 3 11 11 15 10 5 4 12 12 7 11 4 2 14 12 15 13 6 1 15 11 17 16 6 2 9 11 15 15 7 2 13 8 14 14 5 4 13 9 14 14 6 2 16 12 8 14 4 3 13 10 8 8 4 3 12 10 14 13 7 3 14 12 14 15 7 4 11 8 8 13 4 2 9 12 11 11 4 2 16 11 16 15 6 4 12 12 10 15 6 3 10 7 8 9 5 4 13 11 14 13 6 2 16 11 16 16 7 5 14 12 13 13 6 3 15 9 5 11 3 1 5 15 8 12 3 1 8 11 10 12 4 1 11 11 8 12 6 2 16 11 13 14 7 3 17 11 15 14 5 9 9 15 6 8 4 0 9 11 12 13 5 0 13 12 16 16 6 2 10 12 5 13 6 2 6 9 15 11 6 3 12 12 12 14 5 1 8 12 8 13 4 2 14 13 13 13 5 0 12 11 14 13 5 5 11 9 12 12 4 2 16 9 16 16 6 4 8 11 10 15 2 3 15 11 15 15 8 0 7 12 8 12 3 0 16 12 16 14 6 4 14 9 19 12 6 1 16 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.6745 R-squared 0.4549 RMSE 2.1611 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 13 11.2631578947368 1.73684210526316 2 12 11.1481481481481 0.851851851851851 3 15 14.32 0.68 4 12 11.1481481481481 0.851851851851851 5 10 11.1481481481481 -1.14814814814815 6 12 8.93548387096774 3.06451612903226 7 15 14.32 0.68 8 9 8.93548387096774 0.064516129032258 9 12 11.2631578947368 0.736842105263158 10 11 8.93548387096774 2.06451612903226 11 11 14.32 -3.32 12 11 11.1481481481481 -0.148148148148149 13 15 12.7931034482759 2.20689655172414 14 7 8.93548387096774 -1.93548387096774 15 11 11.1481481481481 -0.148148148148149 16 11 8.93548387096774 2.06451612903226 17 10 11.1481481481481 -1.14814814814815 18 14 14.32 -0.32 19 10 8.93548387096774 1.06451612903226 20 6 8.93548387096774 -2.93548387096774 21 11 11.2631578947368 -0.263157894736842 22 15 14.32 0.68 23 11 12.7931034482759 -1.79310344827586 24 12 8.93548387096774 3.06451612903226 25 14 12.7931034482759 1.20689655172414 26 15 14.32 0.68 27 9 14.32 -5.32 28 13 14.32 -1.32 29 13 14.32 -1.32 30 16 11.2631578947368 4.73684210526316 31 13 8.93548387096774 4.06451612903226 32 12 12.7931034482759 -0.793103448275861 33 14 14.32 -0.32 34 11 11.2631578947368 -0.263157894736842 35 9 8.93548387096774 0.064516129032258 36 16 14.32 1.68 37 12 11.1481481481481 0.851851851851851 38 10 11.1481481481481 -1.14814814814815 39 13 12.7931034482759 0.206896551724139 40 16 14.32 1.68 41 14 12.7931034482759 1.20689655172414 42 15 8.93548387096774 6.06451612903226 43 5 8.93548387096774 -3.93548387096774 44 8 8.93548387096774 -0.935483870967742 45 11 11.1481481481481 -0.148148148148149 46 16 14.32 1.68 47 17 14.32 2.68 48 9 8.93548387096774 0.064516129032258 49 9 12.7931034482759 -3.79310344827586 50 13 14.32 -1.32 51 10 11.1481481481481 -1.14814814814815 52 6 12.7931034482759 -6.79310344827586 53 12 14.32 -2.32 54 8 11.2631578947368 -3.26315789473684 55 14 12.7931034482759 1.20689655172414 56 12 12.7931034482759 -0.793103448275861 57 11 8.93548387096774 2.06451612903226 58 16 14.32 1.68 59 8 11.2631578947368 -3.26315789473684 60 15 14.32 0.68 61 7 8.93548387096774 -1.93548387096774 62 16 14.32 1.68 63 14 12.7931034482759 1.20689655172414 64 16 14.32 1.68 65 9 11.1481481481481 -2.14814814814815 66 14 12.7931034482759 1.20689655172414 67 11 12.7931034482759 -1.79310344827586 68 13 11.1481481481481 1.85185185185185 69 15 12.7931034482759 2.20689655172414 70 5 8.93548387096774 -3.93548387096774 71 15 12.7931034482759 2.20689655172414 72 13 12.7931034482759 0.206896551724139 73 11 11.1481481481481 -0.148148148148149 74 11 14.32 -3.32 75 12 12.7931034482759 -0.793103448275861 76 12 12.7931034482759 -0.793103448275861 77 12 12.7931034482759 -0.793103448275861 78 12 12.7931034482759 -0.793103448275861 79 14 11.2631578947368 2.73684210526316 80 6 8.93548387096774 -2.93548387096774 81 7 8.93548387096774 -1.93548387096774 82 14 12.7931034482759 1.20689655172414 83 14 14.32 -0.32 84 10 11.1481481481481 -1.14814814814815 85 13 8.93548387096774 4.06451612903226 86 12 12.7931034482759 -0.793103448275861 87 9 8.93548387096774 0.064516129032258 88 12 11.1481481481481 0.851851851851851 89 16 14.32 1.68 90 10 8.93548387096774 1.06451612903226 91 14 11.1481481481481 2.85185185185185 92 10 14.32 -4.32 93 16 14.32 1.68 94 15 12.7931034482759 2.20689655172414 95 12 11.1481481481481 0.851851851851851 96 10 8.93548387096774 1.06451612903226 97 8 11.1481481481481 -3.14814814814815 98 8 8.93548387096774 -0.935483870967742 99 11 14.32 -3.32 100 13 11.1481481481481 1.85185185185185 101 16 14.32 1.68 102 16 14.32 1.68 103 14 14.32 -0.32 104 11 11.2631578947368 -0.263157894736842 105 4 8.93548387096774 -4.93548387096774 106 14 11.1481481481481 2.85185185185185 107 9 11.2631578947368 -2.26315789473684 108 14 14.32 -0.32 109 8 11.2631578947368 -3.26315789473684 110 8 11.2631578947368 -3.26315789473684 111 11 14.32 -3.32 112 12 12.7931034482759 -0.793103448275861 113 11 11.1481481481481 -0.148148148148149 114 14 14.32 -0.32 115 15 14.32 0.68 116 16 14.32 1.68 117 16 14.32 1.68 118 11 11.1481481481481 -0.148148148148149 119 14 14.32 -0.32 120 14 8.93548387096774 5.06451612903226 121 12 11.2631578947368 0.736842105263158 122 14 12.7931034482759 1.20689655172414 123 8 8.93548387096774 -0.935483870967742 124 13 14.32 -1.32 125 16 14.32 1.68 126 12 11.1481481481481 0.851851851851851 127 16 14.32 1.68 128 12 12.7931034482759 -0.793103448275861 129 11 11.1481481481481 -0.148148148148149 130 4 8.93548387096774 -4.93548387096774 131 16 14.32 1.68 132 15 12.7931034482759 2.20689655172414 133 10 11.2631578947368 -1.26315789473684 134 13 11.1481481481481 1.85185185185185 135 15 14.32 0.68 136 12 11.2631578947368 0.736842105263158 137 14 14.32 -0.32 138 7 8.93548387096774 -1.93548387096774 139 19 14.32 4.68 140 12 14.32 -2.32 141 12 11.2631578947368 0.736842105263158 142 13 14.32 -1.32 143 15 11.2631578947368 3.73684210526316 144 8 8.93548387096774 -0.935483870967742 145 12 11.2631578947368 0.736842105263158 146 10 11.1481481481481 -1.14814814814815 147 8 11.1481481481481 -3.14814814814815 148 10 14.32 -4.32 149 15 14.32 0.68 150 16 14.32 1.68 151 13 12.7931034482759 0.206896551724139 152 16 14.32 1.68 153 9 8.93548387096774 0.064516129032258 154 14 14.32 -0.32 155 14 12.7931034482759 1.20689655172414 156 12 11.2631578947368 0.736842105263158

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/2wupo1293195617.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/2wupo1293195617.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/3wupo1293195617.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/3wupo1293195617.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/473791293195617.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293195506sq478rn9vfh5cu9/473791293195617.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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