WS10 Feedback Recursive Partitioning

*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: Wed, 22 Dec 2010 16:40:24 +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/22/t1293035980ak1xqkyvvpo0phz.htm/, Retrieved Wed, 22 Dec 2010 17:39:41 +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/22/t1293035980ak1xqkyvvpo0phz.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 «

211868 43880 229527 43110 229139 44496 198563 44164 195722 40399 202196 36763 205816 37903 212588 35532 214320 35533 220375 32110 204442 33374 206903 35462 214126 33508 226899 36080 223532 34560 195309 38737 186005 38144 188906 37594 191563 36424 189226 36843 186413 37246 178037 38661 166827 40454 169362 44928 174330 48441 187069 48140 186530 45998 158114 47369 151001 49554 159612 47510 161914 44873 164182 45344 169701 42413 171297 36912 166444 43452 173476 42142 182516 44382 202388 43636 202300 44167 168053 44423 167302 42868 172608 43908 178106 42013 185686 38846 194581 35087 194596 33026 197922 34646 208795 37135 230580 37985 240636 43121 240048 43722 211457 43630 211142 42234 214771 39351 212610 39327 219313 35704 219277 30466 231805 28155 229245 29257 241114 29998 248624 32529 265845 34787 256446 33855 219452 34556 217142 31348 221678 30805 227184 28353 230354 24514 235243 21106 237217 21346 233575 23335 244460 24379 243324 26290 26 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 4 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.2955 R-squared 0.0873 RMSE 26037.693 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 211868 199626.275 12241.725 2 229527 199626.275 29900.725 3 229139 199626.275 29512.725 4 198563 199626.275 -1063.27499999999 5 195722 199626.275 -3904.27499999999 6 202196 199626.275 2569.72500000001 7 205816 199626.275 6189.725 8 212588 199626.275 12961.725 9 214320 199626.275 14693.725 10 220375 199626.275 20748.725 11 204442 199626.275 4815.72500000001 12 206903 199626.275 7276.725 13 214126 199626.275 14499.725 14 226899 199626.275 27272.725 15 223532 199626.275 23905.725 16 195309 199626.275 -4317.27499999999 17 186005 199626.275 -13621.275 18 188906 199626.275 -10720.275 19 191563 199626.275 -8063.275 20 189226 199626.275 -10400.275 21 186413 199626.275 -13213.275 22 178037 199626.275 -21589.275 23 166827 199626.275 -32799.275 24 169362 168012.666666667 1349.33333333334 25 174330 168012.666666667 6317.33333333334 26 187069 168012.666666667 19056.3333333333 27 186530 168012.666666667 18517.3333333333 28 158114 168012.666666667 -9898.66666666666 29 151001 168012.666666667 -17011.6666666667 30 159612 168012.666666667 -8400.66666666666 31 161914 168012.666666667 -6098.66666666666 32 164182 168012.666666667 -3830.66666666666 33 169701 199626.275 -29925.275 34 171297 199626.275 -28329.275 35 166444 199626.275 -33182.275 36 173476 199626.275 -26150.275 37 182516 199626.275 -17110.275 38 202388 199626.275 2761.72500000001 39 202300 199626.275 2673.72500000001 40 168053 199626.275 -31573.275 41 167302 199626.275 -32324.275 42 172608 199626.275 -27018.275 43 178106 199626.275 -21520.275 44 185686 199626.275 -13940.275 45 194581 199626.275 -5045.27499999999 46 194596 199626.275 -5030.27499999999 47 197922 199626.275 -1704.27499999999 48 208795 199626.275 9168.725 49 230580 199626.275 30953.725 50 240636 199626.275 41009.725 51 240048 199626.275 40421.725 52 211457 199626.275 11830.725 53 211142 199626.275 11515.725 54 214771 199626.275 15144.725 55 212610 199626.275 12983.725 56 219313 199626.275 19686.725 57 219277 199626.275 19650.725 58 231805 199626.275 32178.725 59 229245 199626.275 29618.725 60 241114 199626.275 41487.725 61 248624 199626.275 48997.725 62 265845 199626.275 66218.725 63 256446 199626.275 56819.725 64 219452 199626.275 19825.725 65 217142 199626.275 17515.725 66 221678 199626.275 22051.725 67 227184 199626.275 27557.725 68 230354 199626.275 30727.725 69 235243 199626.275 35616.725 70 237217 199626.275 37590.725 71 233575 199626.275 33948.725 72 244460 199626.275 44833.725 73 243324 199626.275 43697.725 74 260307 199626.275 60680.725 75 241476 199626.275 41849.725 76 203666 199626.275 4039.72500000001 77 200237 199626.275 610.725000000006 78 204045 199626.275 4418.72500000001 79 209465 199626.275 9838.725 80 213586 199626.275 13959.725 81 216234 199626.275 16607.725 82 213188 199626.275 13561.725 83 208679 199626.275 9052.725 84 217859 199626.275 18232.725 85 227247 199626.275 27620.725 86 243477 199626.275 43850.725 87 232571 199626.275 32944.725 88 191531 199626.275 -8095.275 89 186029 199626.275 -13597.275 90 189733 199626.275 -9893.275 91 190420 199626.275 -9206.275 92 194163 199626.275 -5463.27499999999 93 198770 199626.275 -856.274999999994 94 195198 199626.275 -4428.27499999999 95 193111 199626.275 -6515.275 96 195411 199626.275 -4215.27499999999 97 202108 199626.275 2481.72500000001 98 215706 199626.275 16079.725 99 206348 199626.275 6721.725 100 166972 199626.275 -32654.275 101 166070 199626.275 -33556.275 102 169292 199626.275 -30334.275 103 175041 199626.275 -24585.275 104 177876 199626.275 -21750.275 105 181140 199626.275 -18486.275 106 179566 199626.275 -20060.275 107 175335 199626.275 -24291.275 108 184128 199626.275 -15498.275 109 189917 199626.275 -9709.275 110 194690 199626.275 -4936.27499999999 111 179612 199626.275 -20014.275 112 150605 199626.275 -49021.275 113 150569 199626.275 -49057.275 114 153745 199626.275 -45881.275 115 155511 199626.275 -44115.275 116 159044 199626.275 -40582.275 117 163095 199626.275 -36531.275 118 159585 199626.275 -40041.275 119 158644 199626.275 -40982.275 120 166618 199626.275 -33008.275 121 176512 199626.275 -23114.275 122 200765 199626.275 1138.72500000001 123 182698 199626.275 -16928.275 124 153730 199626.275 -45896.275 125 156145 199626.275 -43481.275 126 161570 199626.275 -38056.275 127 165688 199626.275 -33938.275 128 173666 199626.275 -25960.275 129 180144 199626.275 -19482.275

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/2zhgy1293036018.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/2zhgy1293036018.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/3zhgy1293036018.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/3zhgy1293036018.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/4aqxj1293036018.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293035980ak1xqkyvvpo0phz/4aqxj1293036018.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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