Recursive Partitioning (geen categorieën)

*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 17:06:19 +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/t1293210497mog60jagei6zmcp.htm/, Retrieved Fri, 24 Dec 2010 18:08:17 +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/t1293210497mog60jagei6zmcp.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 1 167.16 101.56 100.93 104.89 2 2 179.84 102.13 101.18 105.15 3 3 174.44 102.39 101.11 105.72 4 4 180.35 102.42 102.42 106.38 5 5 193.17 103.87 102.37 106.40 6 6 195.16 104.44 101.95 106.47 7 7 202.43 104.97 102.20 106.59 8 8 189.91 105.17 103.35 106.76 9 9 195.98 105.35 103.65 107.35 10 10 212.09 104.65 102.06 107.81 11 11 205.81 106.62 102.66 108.03 12 12 204.31 107.05 102.32 109.08 1 13 196.07 112.30 102.21 109.86 2 14 199.98 114.70 102.33 110.29 3 15 199.1 115.40 104.41 110.34 4 16 198.31 115.64 104.33 110.59 5 17 195.72 115.66 105.27 110.64 6 18 223.04 114.50 105.34 110.83 7 19 238.41 115.14 104.88 111.51 8 20 259.73 115.41 105.49 113.32 9 21 326.54 119.32 105.90 115.89 10 22 335.15 124.77 105.39 116.51 11 23 321.81 130.96 104.40 117.44 12 24 368.62 141.02 106.19 118.25 1 25 369.59 150.60 106.54 118.65 2 26 425 151.10 108.26 118.52 3 27 439.72 157.19 106.95 119.07 4 28 362.23 157.28 108.32 119.12 5 29 328.76 156.54 108.35 119.28 6 30 348.55 159.62 109.29 119.30 7 31 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.9897 R-squared 0.9796 RMSE 0.7977 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 104.37 106.326666666667 -1.95666666666666 2 104.89 106.326666666667 -1.43666666666667 3 105.15 106.326666666667 -1.17666666666666 4 105.72 106.326666666667 -0.606666666666669 5 106.38 106.326666666667 0.0533333333333275 6 106.4 106.326666666667 0.0733333333333377 7 106.47 106.326666666667 0.143333333333331 8 106.59 106.326666666667 0.263333333333335 9 106.76 106.326666666667 0.433333333333337 10 107.35 106.326666666667 1.02333333333333 11 107.81 106.326666666667 1.48333333333333 12 108.03 106.326666666667 1.70333333333333 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 -1.94714285714285 23 116.51 117.837142857143 -1.32714285714285 24 117.44 117.837142857143 -0.397142857142853 25 118.25 117.837142857143 0.412857142857149 26 118.65 117.837142857143 0.812857142857155 27 118.52 117.837142857143 0.682857142857145 28 119.07 119.335714285714 -0.265714285714296 29 119.12 119.335714285714 -0.215714285714284 30 119.28 119.335714285714 -0.0557142857142878 31 119.3 119.335714285714 -0.0357142857142918 32 119.44 119.335714285714 0.104285714285709 33 119.57 119.335714285714 0.234285714285704 34 119.93 119.614615384615 0.315384615384616 35 120.03 119.614615384615 0.41538461538461 36 119.66 119.614615384615 0.0453846153846058 37 119.46 119.614615384615 -0.154615384615397 38 119.48 119.614615384615 -0.134615384615387 39 119.56 119.614615384615 -0.0546153846153885 40 119.43 119.614615384615 -0.184615384615384 41 119.57 119.335714285714 0.234285714285704 42 119.59 119.614615384615 -0.0246153846153874 43 119.5 119.614615384615 -0.114615384615391 44 119.54 119.614615384615 -0.0746153846153845 45 119.56 119.614615384615 -0.0546153846153885 46 119.61 119.614615384615 -0.00461538461539135 47 119.64 119.614615384615 0.0253846153846098 48 119.6 117.837142857143 1.76285714285714 49 119.71 119.845555555556 -0.135555555555555 50 119.72 119.845555555556 -0.125555555555550 51 119.66 119.845555555556 -0.185555555555553 52 119.76 119.845555555556 -0.085555555555544 53 119.8 119.845555555556 -0.045555555555552 54 119.88 119.845555555556 0.0344444444444463 55 119.78 119.845555555556 -0.065555555555548 56 120.08 119.845555555556 0.234444444444449 57 120.22 119.845555555556 0.37444444444445

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/2krdv1293210371.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/2krdv1293210371.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/3krdv1293210371.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/3krdv1293210371.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/4didy1293210371.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293210497mog60jagei6zmcp/4didy1293210371.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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