RP - Cannotdo - No Cat

*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, 19 Dec 2010 16:23:40 +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/19/t1292775737a8cz0cr2cj6ophd.htm/, Retrieved Sun, 19 Dec 2010 17:22:20 +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/19/t1292775737a8cz0cr2cj6ophd.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 «

0 1 4 4 2 0 1 2 2 2 0 1 5 5 4 1 1 4 5 3 0 2 1 1 2 0 1 2 4 1 0 4 5 6 4 0 1 1 5 3 0 1 3 4 1 0 2 5 5 4 1 1 2 7 4 0 1 2 2 4 1 2 2 7 3 0 1 2 5 4 0 1 1 5 1 1 1 4 7 4 1 1 3 3 1 0 1 6 6 4 1 1 1 2 4 0 2 3 6 3 1 1 2 1 2 0 2 5 5 6 0 1 5 4 5 0 2 3 4 4 1 1 3 7 6 0 1 5 7 1 1 1 5 5 2 0 2 4 6 4 1 1 2 5 4 0 1 1 1 1 1 2 4 6 2 0 1 6 4 1 0 1 2 2 2 1 1 3 2 2 1 1 2 6 2 1 2 4 6 6 1 1 2 6 2 0 1 1 1 1 1 1 5 6 4 1 1 5 6 3 0 1 1 1 3 1 1 1 1 1 1 1 2 7 4 0 1 4 2 3 0 1 5 3 4 0 1 3 5 3 0 1 3 3 2 1 1 1 4 1 0 1 2 2 5 1 1 3 3 4 1 2 2 7 1 0 2 5 7 2 1 1 4 5 4 0 1 4 1 3 0 1 2 2 2 0 2 3 5 3 1 1 6 2 3 0 1 2 4 2 1 2 3 7 2 1 1 2 2 4 0 1 5 5 4 0 1 5 6 2 0 1 5 3 2 1 1 6 7 5 0 2 4 4 4 1 1 2 3 5 0 1 5 5 5 1 2 2 3 2 1 1 1 2 3 0 1 6 6 4 0 1 6 6 2 1 1 3 5 2 1 1 4 2 2 0 3 5 3 5 0 2 2 4 2 0 2 4 6 3 1 1 3 5 2 1 1 2 2 2 1 1 2 5 2 1 1 3 2 2 0 1 3 1 2 1 1 7 2 1 0 1 2 4 3 0 1 2 5 3 1 1 2 5 3 0 1 5 3 3 0 1 1 2 1 0 3 5 7 4 0 1 2 1 1 0 1 1 5 1 0 1 2 5 1 0 1 2 2 3 0 1 0 6 2 0 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.3238 R-squared 0.1049 RMSE 1.5444 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 4 3.125 0.875 2 2 3.125 -1.125 3 5 3.79245283018868 1.20754716981132 4 4 3.125 0.875 5 1 1.72727272727273 -0.727272727272727 6 2 3.125 -1.125 7 5 3.79245283018868 1.20754716981132 8 1 3.125 -2.125 9 3 3.125 -0.125 10 5 3.79245283018868 1.20754716981132 11 2 3.79245283018868 -1.79245283018868 12 2 3.79245283018868 -1.79245283018868 13 2 3.125 -1.125 14 2 3.79245283018868 -1.79245283018868 15 1 3.125 -2.125 16 4 3.79245283018868 0.207547169811321 17 3 3.125 -0.125 18 6 3.79245283018868 2.20754716981132 19 1 3.79245283018868 -2.79245283018868 20 3 3.125 -0.125 21 2 1.72727272727273 0.272727272727273 22 5 3.79245283018868 1.20754716981132 23 5 3.79245283018868 1.20754716981132 24 3 3.79245283018868 -0.79245283018868 25 3 3.79245283018868 -0.79245283018868 26 5 3.125 1.875 27 5 3.125 1.875 28 4 3.79245283018868 0.207547169811321 29 2 3.79245283018868 -1.79245283018868 30 1 1.72727272727273 -0.727272727272727 31 4 3.125 0.875 32 6 3.125 2.875 33 2 3.125 -1.125 34 3 3.125 -0.125 35 2 3.125 -1.125 36 4 3.79245283018868 0.207547169811321 37 2 3.125 -1.125 38 1 1.72727272727273 -0.727272727272727 39 5 3.79245283018868 1.20754716981132 40 5 3.125 1.875 41 1 1.72727272727273 -0.727272727272727 42 1 1.72727272727273 -0.727272727272727 43 2 3.79245283018868 -1.79245283018868 44 4 3.125 0.875 45 5 3.79245283018868 1.20754716981132 46 3 3.125 -0.125 47 3 3.125 -0.125 48 1 3.125 -2.125 49 2 3.79245283018868 -1.79245283018868 50 3 3.79245283018868 -0.79245283018868 51 2 3.125 -1.125 52 5 3.125 1.875 53 4 3.79245283018868 0.207547169811321 54 4 1.72727272727273 2.27272727272727 55 2 3.125 -1.125 56 3 3.125 -0.125 57 6 3.125 2.875 58 2 3.125 -1.125 59 3 3.125 -0.125 60 2 3.79245283018868 -1.79245283018868 61 5 3.79245283018868 1.20754716981132 62 5 3.125 1.875 63 5 3.125 1.875 64 6 3.79245283018868 2.20754716981132 65 4 3.79245283018868 0.207547169811321 66 2 3.79245283018868 -1.79245283018868 67 5 3.79245283018868 1.20754716981132 68 2 3.125 -1.125 69 1 3.125 -2.125 70 6 3.79245283018868 2.20754716981132 71 6 3.125 2.875 72 3 3.125 -0.125 73 4 3.125 0.875 74 5 3.79245283018868 1.20754716981132 75 2 3.125 -1.125 76 4 3.125 0.875 77 3 3.125 -0.125 78 2 3.125 -1.125 79 2 3.125 -1.125 80 3 3.125 -0.125 81 3 1.72727272727273 1.27272727272727 82 7 3.125 3.875 83 2 3.125 -1.125 84 2 3.125 -1.125 85 2 3.125 -1.125 86 5 3.125 1.875 87 1 3.125 -2.125 88 5 3.79245283018868 1.20754716981132 89 2 1.72727272727273 0.272727272727273 90 1 3.125 -2.125 91 2 3.125 -1.125 92 2 3.125 -1.125 93 0 3.125 -3.125 94 5 3.125 1.875 95 3 3.79245283018868 -0.79245283018868 96 2 3.125 -1.125 97 4 3.125 0.875 98 2 3.125 -1.125 99 2 3.125 -1.125 100 4 3.79245283018868 0.207547169811321 101 1 3.79245283018868 -2.79245283018868 102 5 3.125 1.875 103 4 3.125 0.875 104 6 3.125 2.875 105 2 3.125 -1.125 106 5 3.79245283018868 1.20754716981132 107 1 3.125 -2.125 108 7 3.79245283018868 3.20754716981132 109 5 3.125 1.875 110 3 3.125 -0.125 111 4 3.79245283018868 0.207547169811321 112 4 3.79245283018868 0.207547169811321 113 2 3.125 -1.125 114 1 3.125 -2.125 115 6 3.79245283018868 2.20754716981132 116 4 3.125 0.875 117 2 3.125 -1.125 118 7 3.125 3.875 119 4 3.79245283018868 0.207547169811321 120 4 3.125 0.875 121 4 3.79245283018868 0.207547169811321 122 2 3.125 -1.125 123 5 3.79245283018868 1.20754716981132 124 3 3.125 -0.125 125 2 3.125 -1.125 126 3 3.125 -0.125 127 4 3.79245283018868 0.207547169811321 128 5 3.125 1.875 129 6 3.125 2.875 130 2 1.72727272727273 0.272727272727273 131 2 3.79245283018868 -1.79245283018868 132 2 3.79245283018868 -1.79245283018868 133 2 3.79245283018868 -1.79245283018868 134 2 3.79245283018868 -1.79245283018868 135 5 3.79245283018868 1.20754716981132 136 2 3.79245283018868 -1.79245283018868 137 3 3.125 -0.125 138 6 3.79245283018868 2.20754716981132 139 4 3.125 0.875 140 5 3.125 1.875 141 1 1.72727272727273 -0.727272727272727 142 2 3.125 -1.125 143 2 3.125 -1.125 144 2 3.125 -1.125 145 6 3.125 2.875 146 2 3.125 -1.125 147 2 3.125 -1.125 148 1 3.79245283018868 -2.79245283018868 149 5 3.125 1.875 150 3 3.79245283018868 -0.79245283018868 151 6 3.79245283018868 2.20754716981132 152 1 3.125 -2.125

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/2mesh1292775813.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/2mesh1292775813.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/3mesh1292775813.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/3mesh1292775813.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/4w59k1292775813.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd/4w59k1292775813.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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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