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 12:41:50 +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/t1292244015spe7wfjdeml61cr.htm/, Retrieved Mon, 13 Dec 2010 13:40:16 +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/t1292244015spe7wfjdeml61cr.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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25.94 23688100 39.18 3940.35 0.0274 144.7 5.45 28.66 13741000 35.78 4696.69 0.0322 140.8 5.73 33.95 14143500 42.54 4572.83 0.0376 137.1 5.85 31.01 16763800 27.92 3860.66 0.0307 137.7 6.02 21.00 16634600 25.05 3400.91 0.0319 144.7 6.27 26.19 13693300 32.03 3966.11 0.0373 139.2 6.53 25.41 10545800 27.95 3766.99 0.0366 143.0 6.54 30.47 9409900 27.95 4206.35 0.0341 140.8 6.5 12.88 39182200 24.15 3672.82 0.0345 142.5 6.52 9.78 37005800 27.57 3369.63 0.0345 135.8 6.51 8.25 15818500 22.97 2597.93 0.0345 132.6 6.51 7.44 16952000 17.37 2470.52 0.0339 128.6 6.4 10.81 24563400 24.45 2772.73 0.0373 115.7 5.98 9.12 14163200 23.62 2151.83 0.0353 109.2 5.49 11.03 18184800 21.90 1840.26 0.0292 116.9 5.31 12.74 20810300 27.12 2116.24 0.0327 109.9 4.8 9.98 12843000 27.70 2110.49 0.0362 116.1 4.21 11.62 13866700 29.23 2160.54 0.0325 118.9 3.97 9.40 15119200 26.50 2027.13 0.0272 116.3 3.77 9.27 8301600 22.84 1805.43 0.0272 114.0 3.65 7.76 14039600 20.49 1498.80 0.0265 97.0 3.07 8.78 1213 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.9268 R-squared 0.859 RMSE 28.6691 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 25.94 49.0725 -23.1325 2 28.66 26.1283333333333 2.53166666666667 3 33.95 26.1283333333333 7.82166666666667 4 31.01 26.1283333333333 4.88166666666667 5 21 26.1283333333333 -5.12833333333333 6 26.19 26.1283333333333 0.0616666666666674 7 25.41 26.1283333333333 -0.718333333333334 8 30.47 26.1283333333333 4.34166666666667 9 12.88 50.825 -37.945 10 9.78 119.258421052632 -109.478421052632 11 8.25 26.1283333333333 -17.8783333333333 12 7.44 26.1283333333333 -18.6883333333333 13 10.81 49.0725 -38.2625 14 9.12 13.5443478260870 -4.42434782608696 15 11.03 13.5443478260870 -2.51434782608696 16 12.74 13.5443478260870 -0.804347826086957 17 9.98 13.5443478260870 -3.56434782608696 18 11.62 26.1283333333333 -14.5083333333333 19 9.4 13.5443478260870 -4.14434782608696 20 9.27 10.39 -1.12 21 7.76 7.71666666666667 0.043333333333333 22 8.78 10.39 -1.61 23 10.65 13.5443478260870 -2.89434782608696 24 10.95 13.5443478260870 -2.59434782608696 25 12.36 13.5443478260870 -1.18434782608696 26 10.85 10.39 0.459999999999999 27 11.84 13.5443478260870 -1.70434782608696 28 12.14 10.39 1.75 29 11.65 10.39 1.26 30 8.86 7.71666666666667 1.14333333333333 31 7.63 7.71666666666667 -0.0866666666666669 32 7.38 7.71666666666667 -0.336666666666667 33 7.25 7.71666666666667 -0.466666666666667 34 8.03 7.71666666666667 0.313333333333333 35 7.75 7.71666666666667 0.0333333333333332 36 7.16 7.71666666666667 -0.556666666666667 37 7.18 7.71666666666667 -0.536666666666667 38 7.51 7.71666666666667 -0.206666666666667 39 7.07 115.362142857143 -108.292142857143 40 7.11 7.71666666666667 -0.606666666666666 41 8.98 7.71666666666667 1.26333333333333 42 9.53 10.39 -0.860000000000001 43 10.54 10.39 0.149999999999999 44 11.31 13.5443478260870 -2.23434782608696 45 10.36 10.39 -0.0300000000000011 46 11.44 13.5443478260870 -2.10434782608696 47 10.45 13.5443478260870 -3.09434782608696 48 10.69 13.5443478260870 -2.85434782608696 49 11.28 13.5443478260870 -2.26434782608696 50 11.96 13.5443478260870 -1.58434782608696 51 13.52 13.5443478260870 -0.0243478260869576 52 12.89 13.5443478260870 -0.654347826086957 53 14.03 13.5443478260870 0.485652173913042 54 16.27 13.5443478260870 2.72565217391304 55 16.17 13.5443478260870 2.62565217391304 56 17.25 13.5443478260870 3.70565217391304 57 19.38 13.5443478260870 5.83565217391304 58 26.2 50.825 -24.625 59 33.53 50.825 -17.295 60 32.2 50.825 -18.625 61 38.45 50.825 -12.375 62 44.86 50.825 -5.965 63 41.67 49.0725 -7.4025 64 36.06 50.825 -14.765 65 39.76 49.0725 -9.3125 66 36.81 13.5443478260870 23.2656521739130 67 42.65 26.1283333333333 16.5216666666667 68 46.89 26.1283333333333 20.7616666666667 69 53.61 49.0725 4.5375 70 57.59 50.825 6.765 71 67.82 49.0725 18.7475 72 71.89 49.0725 22.8175 73 75.51 119.258421052632 -43.7484210526316 74 68.49 50.825 17.665 75 62.72 50.825 11.895 76 70.39 50.825 19.565 77 59.77 50.825 8.945 78 57.27 50.825 6.445 79 67.96 50.825 17.135 80 67.85 50.825 17.025 81 76.98 50.825 26.155 82 81.08 49.0725 32.0075 83 91.66 119.258421052632 -27.5984210526316 84 84.84 119.258421052632 -34.4184210526316 85 85.73 119.258421052632 -33.5284210526316 86 84.61 119.258421052632 -34.6484210526316 87 92.91 119.258421052632 -26.3484210526316 88 99.8 119.258421052632 -19.4584210526316 89 121.19 119.258421052632 1.93157894736842 90 122.04 119.258421052632 2.78157894736843 91 131.76 119.258421052632 12.5015789473684 92 138.48 119.258421052632 19.2215789473684 93 153.47 119.258421052632 34.2115789473684 94 189.95 119.258421052632 70.6915789473684 95 182.22 119.258421052632 62.9615789473684 96 198.08 119.258421052632 78.8215789473684 97 135.36 119.258421052632 16.1015789473684 98 125.02 119.258421052632 5.76157894736842 99 143.5 119.258421052632 24.2415789473684 100 173.95 218.937222222222 -44.9872222222222 101 188.75 218.937222222222 -30.1872222222222 102 167.44 218.937222222222 -51.4972222222222 103 158.95 218.937222222222 -59.9872222222222 104 169.53 218.937222222222 -49.4072222222222 105 113.66 115.362142857143 -1.70214285714286 106 107.59 115.362142857143 -7.77214285714285 107 92.67 115.362142857143 -22.6921428571429 108 85.35 115.362142857143 -30.0121428571429 109 90.13 115.362142857143 -25.2321428571429 110 89.31 115.362142857143 -26.0521428571429 111 105.12 115.362142857143 -10.2421428571429 112 125.83 115.362142857143 10.4678571428571 113 135.81 115.362142857143 20.4478571428571 114 142.43 115.362142857143 27.0678571428572 115 163.39 115.362142857143 48.0278571428571 116 168.21 115.362142857143 52.8478571428572 117 185.35 218.937222222222 -33.5872222222222 118 188.5 115.362142857143 73.1378571428571 119 199.91 218.937222222222 -19.0272222222222 120 210.73 218.937222222222 -8.20722222222224 121 192.06 218.937222222222 -26.8772222222222 122 204.62 218.937222222222 -14.3172222222222 123 235 218.937222222222 16.0627777777778 124 261.09 218.937222222222 42.1527777777777 125 256.88 218.937222222222 37.9427777777778 126 251.53 218.937222222222 32.5927777777778 127 257.25 218.937222222222 38.3127777777778 128 243.1 218.937222222222 24.1627777777778 129 283.75 218.937222222222 64.8127777777778 130 300.98 218.937222222222 82.0427777777778

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/20l8c1292244101.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/20l8c1292244101.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/30l8c1292244101.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/30l8c1292244101.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/4sv8x1292244101.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292244015spe7wfjdeml61cr/4sv8x1292244101.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') }

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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