RP organisatie

*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: Sun, 12 Dec 2010 19:11:16 +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/12/t1292180948b35hpamczwdcx3w.htm/, Retrieved Sun, 12 Dec 2010 20:09:11 +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/12/t1292180948b35hpamczwdcx3w.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 «

1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 1 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 0.5344 R-squared 0.2856 RMSE 3.224 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 25 23.2368421052632 1.76315789473684 2 24 23.2368421052632 0.763157894736842 3 21 20.6046511627907 0.395348837209301 4 23 24.3269230769231 -1.32692307692308 5 17 20.6046511627907 -3.6046511627907 6 19 20.6046511627907 -1.6046511627907 7 18 23.2368421052632 -5.23684210526316 8 27 23.2368421052632 3.76315789473684 9 23 23.2368421052632 -0.236842105263158 10 23 20.6046511627907 2.3953488372093 11 29 24.3269230769231 4.67307692307692 12 21 20.6046511627907 0.395348837209301 13 26 24.3269230769231 1.67307692307692 14 25 23.2368421052632 1.76315789473684 15 25 24.3269230769231 0.673076923076923 16 23 24.3269230769231 -1.32692307692308 17 26 23.2368421052632 2.76315789473684 18 20 24.3269230769231 -4.32692307692308 19 29 23.2368421052632 5.76315789473684 20 24 24.3269230769231 -0.326923076923077 21 23 23.2368421052632 -0.236842105263158 22 24 23.2368421052632 0.763157894736842 23 30 24.3269230769231 5.67307692307692 24 22 23.2368421052632 -1.23684210526316 25 22 24.3269230769231 -2.32692307692308 26 13 20.6046511627907 -7.6046511627907 27 24 24.3269230769231 -0.326923076923077 28 17 23.2368421052632 -6.23684210526316 29 24 24.3269230769231 -0.326923076923077 30 21 20.6046511627907 0.395348837209301 31 23 20.6046511627907 2.3953488372093 32 24 24.3269230769231 -0.326923076923077 33 24 24.3269230769231 -0.326923076923077 34 24 24.3269230769231 -0.326923076923077 35 23 24.3269230769231 -1.32692307692308 36 26 20.6046511627907 5.3953488372093 37 24 24.3269230769231 -0.326923076923077 38 21 17.7272727272727 3.27272727272727 39 23 23.2368421052632 -0.236842105263158 40 28 24.3269230769231 3.67307692307692 41 22 23.2368421052632 -1.23684210526316 42 24 20.6046511627907 3.3953488372093 43 21 23.2368421052632 -2.23684210526316 44 23 20.6046511627907 2.3953488372093 45 23 24.3269230769231 -1.32692307692308 46 20 20.6046511627907 -0.604651162790699 47 23 17.7272727272727 5.27272727272727 48 21 24.3269230769231 -3.32692307692308 49 27 23.2368421052632 3.76315789473684 50 12 17.7272727272727 -5.72727272727273 51 15 20.6046511627907 -5.6046511627907 52 22 20.6046511627907 1.3953488372093 53 21 17.7272727272727 3.27272727272727 54 21 24.3269230769231 -3.32692307692308 55 20 20.6046511627907 -0.604651162790699 56 24 24.3269230769231 -0.326923076923077 57 24 24.3269230769231 -0.326923076923077 58 29 20.6046511627907 8.3953488372093 59 25 23.2368421052632 1.76315789473684 60 14 20.6046511627907 -6.6046511627907 61 30 24.3269230769231 5.67307692307692 62 19 20.6046511627907 -1.6046511627907 63 29 24.3269230769231 4.67307692307692 64 25 23.2368421052632 1.76315789473684 65 25 24.3269230769231 0.673076923076923 66 25 24.3269230769231 0.673076923076923 67 16 17.7272727272727 -1.72727272727273 68 25 20.6046511627907 4.3953488372093 69 28 24.3269230769231 3.67307692307692 70 24 24.3269230769231 -0.326923076923077 71 25 23.2368421052632 1.76315789473684 72 21 20.6046511627907 0.395348837209301 73 22 24.3269230769231 -2.32692307692308 74 20 24.3269230769231 -4.32692307692308 75 25 24.3269230769231 0.673076923076923 76 27 24.3269230769231 2.67307692307692 77 21 20.6046511627907 0.395348837209301 78 13 17.7272727272727 -4.72727272727273 79 26 23.2368421052632 2.76315789473684 80 26 20.6046511627907 5.3953488372093 81 25 24.3269230769231 0.673076923076923 82 22 20.6046511627907 1.3953488372093 83 19 17.7272727272727 1.27272727272727 84 23 23.2368421052632 -0.236842105263158 85 25 23.2368421052632 1.76315789473684 86 15 17.7272727272727 -2.72727272727273 87 21 23.2368421052632 -2.23684210526316 88 23 23.2368421052632 -0.236842105263158 89 25 20.6046511627907 4.3953488372093 90 24 20.6046511627907 3.3953488372093 91 24 24.3269230769231 -0.326923076923077 92 21 24.3269230769231 -3.32692307692308 93 24 23.2368421052632 0.763157894736842 94 22 24.3269230769231 -2.32692307692308 95 24 23.2368421052632 0.763157894736842 96 28 24.3269230769231 3.67307692307692 97 21 20.6046511627907 0.395348837209301 98 17 17.7272727272727 -0.727272727272727 99 28 20.6046511627907 7.3953488372093 100 24 24.3269230769231 -0.326923076923077 101 10 20.6046511627907 -10.6046511627907 102 20 20.6046511627907 -0.604651162790699 103 22 23.2368421052632 -1.23684210526316 104 19 23.2368421052632 -4.23684210526316 105 22 24.3269230769231 -2.32692307692308 106 22 20.6046511627907 1.3953488372093 107 26 24.3269230769231 1.67307692307692 108 24 23.2368421052632 0.763157894736842 109 22 20.6046511627907 1.3953488372093 110 20 20.6046511627907 -0.604651162790699 111 20 20.6046511627907 -0.604651162790699 112 15 20.6046511627907 -5.6046511627907 113 20 23.2368421052632 -3.23684210526316 114 20 20.6046511627907 -0.604651162790699 115 24 24.3269230769231 -0.326923076923077 116 29 23.2368421052632 5.76315789473684 117 23 24.3269230769231 -1.32692307692308 118 24 20.6046511627907 3.3953488372093 119 22 23.2368421052632 -1.23684210526316 120 16 20.6046511627907 -4.6046511627907 121 23 24.3269230769231 -1.32692307692308 122 27 24.3269230769231 2.67307692307692 123 16 17.7272727272727 -1.72727272727273 124 21 24.3269230769231 -3.32692307692308 125 26 23.2368421052632 2.76315789473684 126 22 24.3269230769231 -2.32692307692308 127 23 24.3269230769231 -1.32692307692308 128 19 23.2368421052632 -4.23684210526316 129 18 20.6046511627907 -2.6046511627907 130 24 24.3269230769231 -0.326923076923077 131 29 24.3269230769231 4.67307692307692 132 22 17.7272727272727 4.27272727272727 133 24 24.3269230769231 -0.326923076923077 134 22 23.2368421052632 -1.23684210526316 135 12 20.6046511627907 -8.6046511627907 136 26 24.3269230769231 1.67307692307692 137 18 23.2368421052632 -5.23684210526316 138 22 24.3269230769231 -2.32692307692308 139 24 20.6046511627907 3.3953488372093 140 21 20.6046511627907 0.395348837209301 141 15 20.6046511627907 -5.6046511627907 142 23 23.2368421052632 -0.236842105263158 143 22 23.2368421052632 -1.23684210526316 144 24 20.6046511627907 3.3953488372093

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/2qzj11292181068.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/2qzj11292181068.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/3qzj11292181068.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/3qzj11292181068.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/4j81m1292181068.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292180948b35hpamczwdcx3w/4j81m1292181068.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 7 ; 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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