Ws 10 - 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: Sun, 12 Dec 2010 13:50:54 +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/t129216283473z637e0sh8b09x.htm/, Retrieved Sun, 12 Dec 2010 15:07:18 +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/t129216283473z637e0sh8b09x.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 «

104.37 1 167.16 101.56 100.93 104.89 2 179.84 102.13 101.18 105.15 3 174.44 102.39 101.11 105.72 4 180.35 102.42 102.42 106.38 5 193.17 103.87 102.37 106.40 6 195.16 104.44 101.95 106.47 7 202.43 104.97 102.20 106.59 8 189.91 105.17 103.35 106.76 9 195.98 105.35 103.65 107.35 10 212.09 104.65 102.06 107.81 11 205.81 106.62 102.66 108.03 12 204.31 107.05 102.32 109.08 1 196.07 112.30 102.21 109.86 2 199.98 114.70 102.33 110.29 3 199.1 115.40 104.41 110.34 4 198.31 115.64 104.33 110.59 5 195.72 115.66 105.27 110.64 6 223.04 114.50 105.34 110.83 7 238.41 115.14 104.88 111.51 8 259.73 115.41 105.49 113.32 9 326.54 119.32 105.90 115.89 10 335.15 124.77 105.39 116.51 11 321.81 130.96 104.40 117.44 12 368.62 141.02 106.19 118.25 1 369.59 150.60 106.54 118.65 2 425 151.10 108.26 118.52 3 439.72 157.19 106.95 119.07 4 362.23 157.28 108.32 119.12 5 328.76 156.54 108.35 119.28 6 348.55 159.62 109.29 119.30 7 328.18 163.77 109.46 119.44 8 329.34 165.08 109.50 119.57 9 295.55 164.75 109.84 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 0.9898 R-squared 0.9796 RMSE 0.7976 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 104.37 106.326666666667 -1.95666666666665 2 104.89 106.326666666667 -1.43666666666665 3 105.15 106.326666666667 -1.17666666666665 4 105.72 106.326666666667 -0.606666666666655 5 106.38 106.326666666667 0.0533333333333417 6 106.4 106.326666666667 0.0733333333333519 7 106.47 106.326666666667 0.143333333333345 8 106.59 106.326666666667 0.263333333333350 9 106.76 106.326666666667 0.433333333333351 10 107.35 106.326666666667 1.02333333333334 11 107.81 106.326666666667 1.48333333333335 12 108.03 106.326666666667 1.70333333333335 13 109.08 110.717777777778 -1.63777777777779 14 109.86 110.717777777778 -0.857777777777784 15 110.29 110.717777777778 -0.427777777777777 16 110.34 110.717777777778 -0.37777777777778 17 110.59 110.717777777778 -0.12777777777778 18 110.64 110.717777777778 -0.0777777777777828 19 110.83 110.717777777778 0.112222222222215 20 111.51 110.717777777778 0.792222222222222 21 113.32 110.717777777778 2.60222222222221 22 115.89 117.837142857143 -1.94714285714286 23 116.51 117.837142857143 -1.32714285714286 24 117.44 117.837142857143 -0.397142857142867 25 118.25 117.837142857143 0.412857142857135 26 118.65 117.837142857143 0.81285714285714 27 118.52 117.837142857143 0.682857142857131 28 119.07 119.335714285714 -0.265714285714282 29 119.12 119.335714285714 -0.215714285714270 30 119.28 119.335714285714 -0.0557142857142736 31 119.3 119.335714285714 -0.0357142857142776 32 119.44 119.335714285714 0.104285714285723 33 119.57 119.335714285714 0.234285714285718 34 119.93 119.621428571429 0.308571428571454 35 120.03 119.621428571429 0.408571428571449 36 119.66 119.621428571429 0.0385714285714442 37 119.46 119.621428571429 -0.161428571428559 38 119.48 119.621428571429 -0.141428571428548 39 119.56 119.621428571429 -0.0614285714285501 40 119.43 119.621428571429 -0.191428571428546 41 119.57 119.335714285714 0.234285714285718 42 119.59 119.621428571429 -0.0314285714285489 43 119.5 119.621428571429 -0.121428571428552 44 119.54 119.621428571429 -0.0814285714285461 45 119.56 119.621428571429 -0.0614285714285501 46 119.61 119.621428571429 -0.0114285714285529 47 119.64 119.621428571429 0.0185714285714482 48 119.6 117.837142857143 1.76285714285713 49 119.71 119.621428571429 0.0885714285714414 50 119.72 119.8625 -0.142499999999998 51 119.66 119.8625 -0.202500000000001 52 119.76 119.8625 -0.102499999999992 53 119.8 119.8625 -0.0625 54 119.88 119.8625 0.0174999999999983 55 119.78 119.8625 -0.082499999999996 56 120.08 119.8625 0.217500000000001 57 120.22 119.8625 0.357500000000002

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/20xc11292161846.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/20xc11292161846.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/3botm1292161846.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/3botm1292161846.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/4mfbp1292161846.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t129216283473z637e0sh8b09x/4mfbp1292161846.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') }

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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