Paper - 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: Fri, 24 Dec 2010 12:31:43 +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/t12931941457snoryv3x9deqxl.htm/, Retrieved Fri, 24 Dec 2010 13:35:49 +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/t12931941457snoryv3x9deqxl.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 2772.73 0.0373 115.7 5.98 9.12 -0.2643 0 0 14163200 23.62 2151.83 0.0353 109.2 5.49 11.03 -0.2643 0 0 18184800 21.90 1840.26 0.0292 116.9 5.31 12.74 -0.1918 0 0 20810300 27.12 2116.24 0.0327 109.9 4.8 9.98 -0.1918 0 0 12843000 27.70 2110.49 0.0362 116.1 4.21 11.62 -0.1918 0 0 13866700 29.23 2160.54 0.0325 118.9 3.97 9.40 -0.2246 0 0 15119200 26.50 2027.13 0.0272 116.3 3.77 9.27 -0.2246 0 0 8301600 22.84 1805.43 0.0272 114.0 3.65 7.76 -0.2246 0 0 14039600 20.49 1498.80 0.0265 97.0 3.07 8.78 0.3654 0 0 12139700 23.28 1690.20 0.0213 85.3 2.49 10.65 0.3654 0 0 9649000 25.71 1930.58 0.019 84.9 2.09 10.95 0.3654 0 0 8513600 26.52 1950.40 0.0155 94.6 1.82 12.36 0.0447 0 0 15278600 25.51 1934.03 0.0114 97.8 1.73 10.85 0.0447 0 0 15590900 23.36 1731.49 0.0114 95.0 1.74 11.84 0.0447 0 0 9691100 24.15 1845.35 0.0148 110.7 1.73 12.14 -0.0312 0 0 10882700 20.92 1688.23 0.0164 108.5 1.75 11.65 -0.0312 0 0 10294800 20.38 1615.73 0.0118 110.3 1.75 8.86 -0.031 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 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.9626 R-squared 0.9266 RMSE 20.5002 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 10.81 49.5305263157895 -38.7205263157895 2 9.12 11.4508 -2.3308 3 11.03 11.4508 -0.4208 4 12.74 11.4508 1.2892 5 9.98 11.4508 -1.4708 6 11.62 11.4508 0.1692 7 9.4 11.4508 -2.0508 8 9.27 11.4508 -2.1808 9 7.76 7.66692307692308 0.0930769230769215 10 8.78 23.2811111111111 -14.5011111111111 11 10.65 23.2811111111111 -12.6311111111111 12 10.95 23.2811111111111 -12.3311111111111 13 12.36 11.4508 0.9092 14 10.85 11.4508 -0.6008 15 11.84 11.4508 0.389200000000001 16 12.14 11.4508 0.689200000000001 17 11.65 11.4508 0.199200000000001 18 8.86 7.66692307692308 1.19307692307692 19 7.63 7.66692307692308 -0.0369230769230784 20 7.38 7.66692307692308 -0.286923076923078 21 7.25 7.66692307692308 -0.416923076923078 22 8.03 7.66692307692308 0.363076923076921 23 7.75 7.66692307692308 0.0830769230769217 24 7.16 7.66692307692308 -0.506923076923078 25 7.18 7.66692307692308 -0.486923076923079 26 7.51 7.66692307692308 -0.156923076923078 27 7.07 7.66692307692308 -0.596923076923078 28 7.11 7.66692307692308 -0.556923076923078 29 8.98 7.66692307692308 1.31307692307692 30 9.53 11.4508 -1.9208 31 10.54 11.4508 -0.9108 32 11.31 11.4508 -0.140799999999999 33 10.36 11.4508 -1.0908 34 11.44 11.4508 -0.0107999999999997 35 10.45 11.4508 -1.0008 36 10.69 11.4508 -0.7608 37 11.28 11.4508 -0.1708 38 11.96 11.4508 0.509200000000002 39 13.52 11.4508 2.0692 40 12.89 11.4508 1.4392 41 14.03 11.4508 2.5792 42 16.27 11.4508 4.8192 43 16.17 23.2811111111111 -7.11111111111111 44 17.25 23.2811111111111 -6.03111111111111 45 19.38 23.2811111111111 -3.90111111111111 46 26.2 49.5305263157895 -23.3305263157895 47 33.53 49.5305263157895 -16.0005263157895 48 32.2 49.5305263157895 -17.3305263157895 49 38.45 49.5305263157895 -11.0805263157895 50 44.86 49.5305263157895 -4.67052631578947 51 41.67 49.5305263157895 -7.86052631578947 52 36.06 49.5305263157895 -13.4705263157895 53 39.76 49.5305263157895 -9.77052631578947 54 36.81 23.2811111111111 13.5288888888889 55 42.65 23.2811111111111 19.3688888888889 56 46.89 23.2811111111111 23.6088888888889 57 53.61 49.5305263157895 4.07947368421053 58 57.59 49.5305263157895 8.05947368421053 59 67.82 87.4663636363636 -19.6463636363636 60 71.89 49.5305263157895 22.3594736842105 61 75.51 87.4663636363636 -11.9563636363636 62 68.49 49.5305263157895 18.9594736842105 63 62.72 49.5305263157895 13.1894736842105 64 70.39 49.5305263157895 20.8594736842105 65 59.77 49.5305263157895 10.2394736842105 66 57.27 49.5305263157895 7.73947368421053 67 67.96 49.5305263157895 18.4294736842105 68 67.85 49.5305263157895 18.3194736842105 69 76.98 87.4663636363636 -10.4863636363636 70 81.08 87.4663636363636 -6.38636363636363 71 91.66 87.4663636363636 4.19363636363637 72 84.84 87.4663636363636 -2.62636363636362 73 85.73 87.4663636363636 -1.73636363636362 74 84.61 87.4663636363636 -2.85636363636362 75 92.91 87.4663636363636 5.44363636363637 76 99.8 87.4663636363636 12.3336363636364 77 121.19 87.4663636363636 33.7236363636364 78 122.04 124.094615384615 -2.05461538461537 79 131.76 150.437272727273 -18.6772727272728 80 138.48 150.437272727273 -11.9572727272728 81 153.47 150.437272727273 3.03272727272724 82 189.95 150.437272727273 39.5127272727272 83 182.22 150.437272727273 31.7827272727272 84 198.08 218.733125 -20.653125 85 135.36 150.437272727273 -15.0772727272727 86 125.02 150.437272727273 -25.4172727272728 87 143.5 150.437272727273 -6.93727272727276 88 173.95 150.437272727273 23.5127272727272 89 188.75 218.733125 -29.983125 90 167.44 150.437272727273 17.0027272727272 91 158.95 218.733125 -59.783125 92 169.53 218.733125 -49.203125 93 113.66 150.437272727273 -36.7772727272728 94 107.59 124.094615384615 -16.5046153846154 95 92.67 124.094615384615 -31.4246153846154 96 85.35 124.094615384615 -38.7446153846154 97 90.13 124.094615384615 -33.9646153846154 98 89.31 124.094615384615 -34.7846153846154 99 105.12 124.094615384615 -18.9746153846154 100 125.83 124.094615384615 1.73538461538462 101 135.81 124.094615384615 11.7153846153846 102 142.43 124.094615384615 18.3353846153846 103 163.39 124.094615384615 39.2953846153846 104 168.21 124.094615384615 44.1153846153846 105 185.35 124.094615384615 61.2553846153846 106 188.5 218.733125 -30.233125 107 199.91 218.733125 -18.8231250000000 108 210.73 218.733125 -8.00312500000004 109 192.06 218.733125 -26.6731250000000 110 204.62 218.733125 -14.1131250000000 111 235 218.733125 16.2668750000000 112 261.09 218.733125 42.3568749999999 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 24.3668750000000 117 283.75 218.733125 65.016875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/2ig0y1293193895.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/2ig0y1293193895.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/3ig0y1293193895.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/3ig0y1293193895.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/4tph11293193895.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931941457snoryv3x9deqxl/4tph11293193895.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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') }

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