WS10 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: Mon, 13 Dec 2010 14:00:29 +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/13/t1292248752ivwnacdlz97mnii.htm/, Retrieved Mon, 13 Dec 2010 14:59:16 +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/13/t1292248752ivwnacdlz97mnii.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 24563400 -0,2643 24.45 2772.73 0,0373 115.7 5,98 9.12 14163200 -0,2643 23.62 2151.83 0,0353 109.2 5,49 11.03 18184800 -0,2643 21.90 1840.26 0,0292 116.9 5,31 12.74 20810300 -0,1918 27.12 2116.24 0,0327 109.9 4,8 9.98 12843000 -0,1918 27.70 2110.49 0,0362 116.1 4,21 11.62 13866700 -0,1918 29.23 2160.54 0,0325 118.9 3,97 9.40 15119200 -0,2246 26.50 2027.13 0,0272 116.3 3,77 9.27 8301600 -0,2246 22.84 1805.43 0,0272 114.0 3,65 7.76 14039600 -0,2246 20.49 1498.80 0,0265 97.0 3,07 8.78 12139700 0,3654 23.28 1690.20 0,0213 85.3 2,49 10.65 9649000 0,3654 25.71 1930.58 0,019 84.9 2,09 10.95 8513600 0,3654 26.52 1950.40 0,0155 94.6 1,82 12.36 15278600 0,0447 25.51 1934.03 0,0114 97.8 1,73 10.85 15590900 0,0447 23.36 1731.49 0,0114 95.0 1,74 11.84 9691100 0,0447 24.15 1845.35 0,0148 110.7 1,73 12.14 10882700 -0,0312 20.92 1688.23 0,0164 108.5 1,75 11.65 10294800 -0,0312 20.38 1615.73 0,0118 110.3 1,75 8.86 16031900 -0,0312 21.90 1463.21 0,0107 106.3 1,75 7.63 13683600 -0,0048 1 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 13 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.935 R-squared 0.8743 RMSE 26.8206 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 10.81 60.485 -49.675 2 9.12 11.3089285714286 -2.18892857142857 3 11.03 11.3089285714286 -0.278928571428569 4 12.74 11.3089285714286 1.43107142857143 5 9.98 11.3089285714286 -1.32892857142857 6 11.62 11.3089285714286 0.311071428571431 7 9.4 11.3089285714286 -1.90892857142857 8 9.27 11.3089285714286 -2.03892857142857 9 7.76 7.71666666666667 0.0433333333333321 10 8.78 11.3089285714286 -2.52892857142857 11 10.65 11.3089285714286 -0.658928571428568 12 10.95 11.3089285714286 -0.358928571428569 13 12.36 11.3089285714286 1.05107142857143 14 10.85 11.3089285714286 -0.458928571428569 15 11.84 11.3089285714286 0.531071428571432 16 12.14 11.3089285714286 0.831071428571432 17 11.65 11.3089285714286 0.341071428571432 18 8.86 7.71666666666667 1.14333333333333 19 7.63 7.71666666666667 -0.0866666666666678 20 7.38 7.71666666666667 -0.336666666666668 21 7.25 7.71666666666667 -0.466666666666668 22 8.03 7.71666666666667 0.313333333333332 23 7.75 7.71666666666667 0.0333333333333323 24 7.16 7.71666666666667 -0.556666666666668 25 7.18 7.71666666666667 -0.536666666666668 26 7.51 7.71666666666667 -0.206666666666668 27 7.07 98.131 -91.061 28 7.11 7.71666666666667 -0.606666666666667 29 8.98 7.71666666666667 1.26333333333333 30 9.53 11.3089285714286 -1.77892857142857 31 10.54 11.3089285714286 -0.768928571428569 32 11.31 11.3089285714286 0.00107142857143216 33 10.36 11.3089285714286 -0.948928571428569 34 11.44 11.3089285714286 0.131071428571431 35 10.45 11.3089285714286 -0.858928571428569 36 10.69 11.3089285714286 -0.618928571428569 37 11.28 11.3089285714286 -0.0289285714285690 38 11.96 11.3089285714286 0.651071428571433 39 13.52 11.3089285714286 2.21107142857143 40 12.89 11.3089285714286 1.58107142857143 41 14.03 11.3089285714286 2.72107142857143 42 16.27 11.3089285714286 4.96107142857143 43 16.17 34.065 -17.895 44 17.25 34.065 -16.815 45 19.38 34.065 -14.685 46 26.2 60.485 -34.285 47 33.53 60.485 -26.955 48 32.2 60.485 -28.285 49 38.45 60.485 -22.035 50 44.86 60.485 -15.625 51 41.67 60.485 -18.815 52 36.06 60.485 -24.425 53 39.76 34.065 5.69499999999999 54 36.81 34.065 2.745 55 42.65 34.065 8.585 56 46.89 34.065 12.825 57 53.61 34.065 19.545 58 57.59 60.485 -2.89499999999999 59 67.82 60.485 7.335 60 71.89 60.485 11.405 61 75.51 60.485 15.025 62 68.49 60.485 8.005 63 62.72 60.485 2.23500000000001 64 70.39 60.485 9.905 65 59.77 60.485 -0.714999999999989 66 57.27 60.485 -3.21499999999999 67 67.96 60.485 7.475 68 67.85 60.485 7.365 69 76.98 60.485 16.495 70 81.08 60.485 20.595 71 91.66 134.148571428571 -42.4885714285714 72 84.84 134.148571428571 -49.3085714285714 73 85.73 134.148571428571 -48.4185714285714 74 84.61 60.485 24.125 75 92.91 60.485 32.425 76 99.8 134.148571428571 -34.3485714285714 77 121.19 134.148571428571 -12.9585714285714 78 122.04 134.148571428571 -12.1085714285714 79 131.76 134.148571428571 -2.38857142857142 80 138.48 134.148571428571 4.33142857142857 81 153.47 134.148571428571 19.3214285714286 82 189.95 134.148571428571 55.8014285714286 83 182.22 134.148571428571 48.0714285714286 84 198.08 134.148571428571 63.9314285714286 85 135.36 134.148571428571 1.21142857142860 86 125.02 60.485 64.535 87 143.5 134.148571428571 9.35142857142858 88 173.95 203.507142857143 -29.5571428571428 89 188.75 203.507142857143 -14.7571428571428 90 167.44 203.507142857143 -36.0671428571428 91 158.95 203.507142857143 -44.5571428571428 92 169.53 203.507142857143 -33.9771428571428 93 113.66 203.507142857143 -89.8471428571428 94 107.59 98.131 9.45899999999999 95 92.67 98.131 -5.46100000000001 96 85.35 98.131 -12.7810000000000 97 90.13 98.131 -8.00100000000002 98 89.31 98.131 -8.82100000000001 99 105.12 98.131 6.98899999999999 100 125.83 98.131 27.699 101 135.81 98.131 37.679 102 142.43 98.131 44.299 103 163.39 203.507142857143 -40.1171428571429 104 168.21 203.507142857143 -35.2971428571428 105 185.35 203.507142857143 -18.1571428571428 106 188.5 203.507142857143 -15.0071428571428 107 199.91 203.507142857143 -3.59714285714284 108 210.73 203.507142857143 7.22285714285715 109 192.06 203.507142857143 -11.4471428571428 110 204.62 203.507142857143 1.11285714285717 111 235 203.507142857143 31.4928571428572 112 261.09 203.507142857143 57.5828571428571 113 256.88 203.507142857143 53.3728571428572 114 251.53 203.507142857143 48.0228571428572 115 257.25 203.507142857143 53.7428571428572 116 243.1 203.507142857143 39.5928571428572 117 283.75 203.507142857143 80.2428571428572

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/2dn3n1292248814.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/2dn3n1292248814.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/35e2p1292248814.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/35e2p1292248814.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/4gn1s1292248814.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292248752ivwnacdlz97mnii/4gn1s1292248814.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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