*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, 28 Dec 2010 23:02:39 +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/29/t12935772502x9e0h4b39kr25o.htm/, Retrieved Wed, 29 Dec 2010 00:00:51 +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/29/t12935772502x9e0h4b39kr25o.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 «

99 94,6 106,3 95,9 128,9 104,7 111,1 102,8 102,9 98,1 130 113,9 87 80,9 87,5 95,7 117,6 113,2 103,4 105,9 110,8 108,8 112,6 102,3 102,5 99 112,4 100,7 135,6 115,5 105,1 100,7 127,7 109,9 137 114,6 91 85,4 90,5 100,5 122,4 114,8 123,3 116,5 124,3 112,9 120 102 118,1 106 119 105,3 142,7 118,8 123,6 106,1 129,6 109,3 151,6 117,2 110,4 92,5 99,2 104,2 130,5 112,5 136,2 122,4 129,7 113,3 128 100 121,6 110,7 135,8 112,8 143,8 109,8 147,5 117,3 136,2 109,1 156,6 115,9 123,3 96 104,5 99,8 139,8 116,8 136,5 115,7 112,1 99,4 118,5 94,3 94,4 91 102,3 93,2 111,4 103,1 99,2 94,1 87,8 91,8 115,8 102,7 79,7 82,6 72,7 89,1 104,5 104,5 103 105,1 95,1 95,1 104,2 88,7 78,3 86,3

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.8499 R-squared 0.7224 RMSE 9.9318 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 99 109.064516129032 -10.0645161290323 2 106.3 109.064516129032 -2.76451612903226 3 128.9 109.064516129032 19.8354838709677 4 111.1 109.064516129032 2.03548387096774 5 102.9 109.064516129032 -6.16451612903225 6 130 134.363636363636 -4.36363636363637 7 87 86.8875 0.112499999999997 8 87.5 109.064516129032 -21.5645161290323 9 117.6 134.363636363636 -16.7636363636364 10 103.4 109.064516129032 -5.66451612903225 11 110.8 109.064516129032 1.73548387096774 12 112.6 109.064516129032 3.53548387096774 13 102.5 109.064516129032 -6.56451612903226 14 112.4 109.064516129032 3.33548387096775 15 135.6 134.363636363636 1.23636363636362 16 105.1 109.064516129032 -3.96451612903226 17 127.7 134.363636363636 -6.66363636363637 18 137 134.363636363636 2.63636363636363 19 91 86.8875 4.1125 20 90.5 109.064516129032 -18.5645161290323 21 122.4 134.363636363636 -11.9636363636364 22 123.3 134.363636363636 -11.0636363636364 23 124.3 134.363636363636 -10.0636363636364 24 120 109.064516129032 10.9354838709677 25 118.1 109.064516129032 9.03548387096774 26 119 109.064516129032 9.93548387096774 27 142.7 134.363636363636 8.33636363636361 28 123.6 109.064516129032 14.5354838709677 29 129.6 134.363636363636 -4.76363636363638 30 151.6 134.363636363636 17.2363636363636 31 110.4 109.064516129032 1.33548387096775 32 99.2 109.064516129032 -9.86451612903225 33 130.5 134.363636363636 -3.86363636363637 34 136.2 134.363636363636 1.83636363636361 35 129.7 134.363636363636 -4.66363636363639 36 128 109.064516129032 18.9354838709677 37 121.6 134.363636363636 -12.7636363636364 38 135.8 134.363636363636 1.43636363636364 39 143.8 134.363636363636 9.43636363636364 40 147.5 134.363636363636 13.1363636363636 41 136.2 134.363636363636 1.83636363636361 42 156.6 134.363636363636 22.2363636363636 43 123.3 109.064516129032 14.2354838709677 44 104.5 109.064516129032 -4.56451612903226 45 139.8 134.363636363636 5.43636363636364 46 136.5 134.363636363636 2.13636363636363 47 112.1 109.064516129032 3.03548387096774 48 118.5 109.064516129032 9.43548387096774 49 94.4 86.8875 7.5125 50 102.3 109.064516129032 -6.76451612903226 51 111.4 109.064516129032 2.33548387096775 52 99.2 109.064516129032 -9.86451612903225 53 87.8 86.8875 0.912499999999994 54 115.8 109.064516129032 6.73548387096774 55 79.7 86.8875 -7.1875 56 72.7 86.8875 -14.1875 57 104.5 109.064516129032 -4.56451612903226 58 103 109.064516129032 -6.06451612903226 59 95.1 109.064516129032 -13.9645161290323 60 104.2 86.8875 17.3125 61 78.3 86.8875 -8.5875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/2yh631293577352.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/2yh631293577352.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/3yh631293577352.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/3yh631293577352.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/4885o1293577352.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12935772502x9e0h4b39kr25o/4885o1293577352.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = none ; 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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