Apple Inc - 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: Tue, 14 Dec 2010 15:16:31 +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/14/t1292340508wje2o5qexifj85q.htm/, Retrieved Tue, 14 Dec 2010 16:28:29 +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/14/t1292340508wje2o5qexifj85q.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:

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10.81 -0.2643 0 0 24563400 24.45 115.7 9.12 -0.2643 0 0 14163200 23.62 109.2 11.03 -0.2643 0 0 18184800 21.90 116.9 12.74 -0.1918 0 0 20810300 27.12 109.9 9.98 -0.1918 0 0 12843000 27.70 116.1 11.62 -0.1918 0 0 13866700 29.23 118.9 9.40 -0.2246 0 0 15119200 26.50 116.3 9.27 -0.2246 0 0 8301600 22.84 114.0 7.76 -0.2246 0 0 14039600 20.49 97.0 8.78 0.3654 0 0 12139700 23.28 85.3 10.65 0.3654 0 0 9649000 25.71 84.9 10.95 0.3654 0 0 8513600 26.52 94.6 12.36 0.0447 0 0 15278600 25.51 97.8 10.85 0.0447 0 0 15590900 23.36 95.0 11.84 0.0447 0 0 9691100 24.15 110.7 12.14 -0.0312 0 0 10882700 20.92 108.5 11.65 -0.0312 0 0 10294800 20.38 110.3 8.86 -0.0312 0 0 16031900 21.90 106.3 7.63 -0.0048 0 0 13683600 19.21 97.4 7.38 -0.0048 0 0 8677200 19.65 94.5 7.25 -0.0048 0 0 9874100 17.51 93.7 8.03 0.0705 0 0 10725500 21.41 79.6 7.75 0.0705 0 0 8348400 23.09 84.9 7.16 0.0705 0 0 8046200 20.70 80.7 7.18 -0.0134 0 0 10862300 19.00 78.8 7.51 -0.0134 0 0 8100300 19.04 64.8 7.07 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 0.9578 R-squared 0.9174 RMSE 21.741 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 10.81 56.7588 -45.9488 2 9.12 10.1563157894737 -1.03631578947368 3 11.03 10.1563157894737 0.873684210526315 4 12.74 10.1563157894737 2.58368421052632 5 9.98 10.1563157894737 -0.176315789473684 6 11.62 10.1563157894737 1.46368421052632 7 9.4 10.1563157894737 -0.756315789473684 8 9.27 10.1563157894737 -0.886315789473684 9 7.76 10.1563157894737 -2.39631578947368 10 8.78 23.2811111111111 -14.5011111111111 11 10.65 23.2811111111111 -12.6311111111111 12 10.95 23.2811111111111 -12.3311111111111 13 12.36 10.1563157894737 2.20368421052632 14 10.85 10.1563157894737 0.693684210526316 15 11.84 10.1563157894737 1.68368421052632 16 12.14 10.1563157894737 1.98368421052632 17 11.65 10.1563157894737 1.49368421052632 18 8.86 10.1563157894737 -1.29631578947368 19 7.63 10.1563157894737 -2.52631578947368 20 7.38 10.1563157894737 -2.77631578947368 21 7.25 10.1563157894737 -2.90631578947368 22 8.03 10.1563157894737 -2.12631578947368 23 7.75 10.1563157894737 -2.40631578947368 24 7.16 10.1563157894737 -2.99631578947368 25 7.18 10.1563157894737 -2.97631578947368 26 7.51 10.1563157894737 -2.64631578947368 27 7.07 10.1563157894737 -3.08631578947368 28 7.11 10.1563157894737 -3.04631578947368 29 8.98 10.1563157894737 -1.17631578947368 30 9.53 10.1563157894737 -0.626315789473685 31 10.54 10.1563157894737 0.383684210526315 32 11.31 10.1563157894737 1.15368421052632 33 10.36 10.1563157894737 0.203684210526315 34 11.44 10.1563157894737 1.28368421052632 35 10.45 10.1563157894737 0.293684210526315 36 10.69 10.1563157894737 0.533684210526316 37 11.28 10.1563157894737 1.12368421052632 38 11.96 10.1563157894737 1.80368421052632 39 13.52 10.1563157894737 3.36368421052632 40 12.89 10.1563157894737 2.73368421052632 41 14.03 10.1563157894737 3.87368421052632 42 16.27 10.1563157894737 6.11368421052632 43 16.17 23.2811111111111 -7.11111111111111 44 17.25 23.2811111111111 -6.03111111111111 45 19.38 23.2811111111111 -3.90111111111111 46 26.2 56.7588 -30.5588 47 33.53 56.7588 -23.2288 48 32.2 56.7588 -24.5588 49 38.45 56.7588 -18.3088 50 44.86 56.7588 -11.8988 51 41.67 56.7588 -15.0888 52 36.06 56.7588 -20.6988 53 39.76 56.7588 -16.9988 54 36.81 23.2811111111111 13.5288888888889 55 42.65 23.2811111111111 19.3688888888889 56 46.89 23.2811111111111 23.6088888888889 57 53.61 56.7588 -3.1488 58 57.59 56.7588 0.831200000000003 59 67.82 56.7588 11.0612 60 71.89 56.7588 15.1312 61 75.51 56.7588 18.7512 62 68.49 56.7588 11.7312 63 62.72 56.7588 5.9612 64 70.39 56.7588 13.6312 65 59.77 56.7588 3.0112 66 57.27 56.7588 0.511200000000002 67 67.96 56.7588 11.2012 68 67.85 56.7588 11.0912 69 76.98 56.7588 20.2212 70 81.08 56.7588 24.3212 71 91.66 56.7588 34.9012 72 84.84 56.7588 28.0812 73 85.73 116.526111111111 -30.7961111111111 74 84.61 116.526111111111 -31.9161111111111 75 92.91 116.526111111111 -23.6161111111111 76 99.8 116.526111111111 -16.7261111111111 77 121.19 116.526111111111 4.66388888888889 78 122.04 116.526111111111 5.5138888888889 79 131.76 150.437272727273 -18.6772727272727 80 138.48 150.437272727273 -11.9572727272727 81 153.47 150.437272727273 3.03272727272727 82 189.95 150.437272727273 39.5127272727273 83 182.22 150.437272727273 31.7827272727273 84 198.08 218.733125 -20.653125 85 135.36 150.437272727273 -15.0772727272727 86 125.02 150.437272727273 -25.4172727272727 87 143.5 150.437272727273 -6.93727272727273 88 173.95 150.437272727273 23.5127272727273 89 188.75 218.733125 -29.983125 90 167.44 150.437272727273 17.0027272727273 91 158.95 218.733125 -59.783125 92 169.53 218.733125 -49.203125 93 113.66 150.437272727273 -36.7772727272727 94 107.59 116.526111111111 -8.9361111111111 95 92.67 116.526111111111 -23.8561111111111 96 85.35 116.526111111111 -31.1761111111111 97 90.13 116.526111111111 -26.3961111111111 98 89.31 116.526111111111 -27.2161111111111 99 105.12 116.526111111111 -11.4061111111111 100 125.83 116.526111111111 9.30388888888889 101 135.81 116.526111111111 19.2838888888889 102 142.43 116.526111111111 25.9038888888889 103 163.39 116.526111111111 46.8638888888889 104 168.21 116.526111111111 51.6838888888889 105 185.35 116.526111111111 68.8238888888889 106 188.5 218.733125 -30.233125 107 199.91 218.733125 -18.823125 108 210.73 218.733125 -8.00312500000001 109 192.06 218.733125 -26.673125 110 204.62 218.733125 -14.113125 111 235 218.733125 16.266875 112 261.09 218.733125 42.356875 113 256.88 218.733125 38.146875 114 251.53 218.733125 32.796875 115 257.25 218.733125 38.516875 116 243.1 218.733125 24.366875 117 283.75 218.733125 65.016875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/2um431292339784.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/2um431292339784.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/3um431292339784.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/3um431292339784.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/45v3o1292339784.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292340508wje2o5qexifj85q/45v3o1292339784.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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