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*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: Sun, 12 Dec 2010 16:02:19 +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/12/t1292169618g9fdjzjpg262ulv.htm/, Retrieved Sun, 12 Dec 2010 17:00:21 +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/12/t1292169618g9fdjzjpg262ulv.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 «

1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.5794 R-squared 0.3357 RMSE 3.4145 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 25 25.2380952380952 -0.238095238095237 2 25 21.325 3.675 3 19 21.325 -2.325 4 18 25.2380952380952 -7.23809523809524 5 18 17.1538461538462 0.846153846153847 6 22 21.325 0.675000000000001 7 29 25.2380952380952 3.76190476190476 8 26 21.325 4.675 9 25 21.325 3.675 10 23 25.2380952380952 -2.23809523809524 11 23 21.325 1.675 12 23 21.325 1.675 13 24 21.325 2.675 14 30 25 5 15 19 21.325 -2.325 16 24 21.325 2.675 17 32 25.2380952380952 6.76190476190476 18 30 25.2380952380952 4.76190476190476 19 29 25.2380952380952 3.76190476190476 20 17 25.2380952380952 -8.23809523809524 21 25 25.2380952380952 -0.238095238095237 22 26 25.2380952380952 0.761904761904763 23 26 25.2380952380952 0.761904761904763 24 25 25.2380952380952 -0.238095238095237 25 23 21.325 1.675 26 21 17.1538461538462 3.84615384615385 27 19 21.325 -2.325 28 35 25.2380952380952 9.76190476190476 29 19 21.325 -2.325 30 20 21.325 -1.325 31 21 21.325 -0.324999999999999 32 21 21.325 -0.324999999999999 33 24 21.325 2.675 34 23 25.2380952380952 -2.23809523809524 35 19 21.325 -2.325 36 17 25 -8 37 24 25.2380952380952 -1.23809523809524 38 15 21.325 -6.325 39 25 25.2380952380952 -0.238095238095237 40 27 25 2 41 27 25.2380952380952 1.76190476190476 42 18 21.325 -3.325 43 25 21.325 3.675 44 22 21.325 0.675000000000001 45 26 21.325 4.675 46 23 25.2380952380952 -2.23809523809524 47 16 21.325 -5.325 48 27 21.325 5.675 49 25 21.325 3.675 50 14 17.1538461538462 -3.15384615384615 51 19 17.1538461538462 1.84615384615385 52 20 25.2380952380952 -5.23809523809524 53 16 21.325 -5.325 54 18 21.325 -3.325 55 22 21.325 0.675000000000001 56 21 21.325 -0.324999999999999 57 22 21.325 0.675000000000001 58 22 21.325 0.675000000000001 59 32 25.2380952380952 6.76190476190476 60 23 25.2380952380952 -2.23809523809524 61 31 25.2380952380952 5.76190476190476 62 18 21.325 -3.325 63 23 21.325 1.675 64 26 25.2380952380952 0.761904761904763 65 24 21.325 2.675 66 19 21.325 -2.325 67 14 17.1538461538462 -3.15384615384615 68 20 21.325 -1.325 69 22 25 -3 70 24 21.325 2.675 71 25 21.325 3.675 72 21 25.2380952380952 -4.23809523809524 73 28 25.2380952380952 2.76190476190476 74 24 21.325 2.675 75 20 21.325 -1.325 76 21 21.325 -0.324999999999999 77 23 21.325 1.675 78 13 17.1538461538462 -4.15384615384615 79 24 21.325 2.675 80 21 21.325 -0.324999999999999 81 21 25.2380952380952 -4.23809523809524 82 17 21.325 -4.325 83 14 21.325 -7.325 84 29 25 4 85 25 21.325 3.675 86 16 17.1538461538462 -1.15384615384615 87 25 21.325 3.675 88 25 21.325 3.675 89 21 21.325 -0.324999999999999 90 23 21.325 1.675 91 22 25.2380952380952 -3.23809523809524 92 19 25 -6 93 24 25.2380952380952 -1.23809523809524 94 26 25.2380952380952 0.761904761904763 95 25 21.325 3.675 96 20 21.325 -1.325 97 22 25.2380952380952 -3.23809523809524 98 14 17.1538461538462 -3.15384615384615 99 20 21.325 -1.325 100 32 25.2380952380952 6.76190476190476 101 21 17.1538461538462 3.84615384615385 102 22 21.325 0.675000000000001 103 28 25.2380952380952 2.76190476190476 104 25 25.2380952380952 -0.238095238095237 105 17 21.325 -4.325 106 21 21.325 -0.324999999999999 107 23 21.325 1.675 108 27 25.2380952380952 1.76190476190476 109 22 21.325 0.675000000000001 110 19 21.325 -2.325 111 20 21.325 -1.325 112 17 25.2380952380952 -8.23809523809524 113 24 21.325 2.675 114 21 21.325 -0.324999999999999 115 21 21.325 -0.324999999999999 116 24 25.2380952380952 -1.23809523809524 117 19 21.325 -2.325 118 22 21.325 0.675000000000001 119 26 25 1 120 17 17.1538461538462 -0.153846153846153 121 17 21.325 -4.325 122 19 21.325 -2.325 123 15 17.1538461538462 -2.15384615384615 124 17 21.325 -4.325 125 27 25.2380952380952 1.76190476190476 126 19 21.325 -2.325 127 21 21.325 -0.324999999999999 128 25 21.325 3.675 129 19 25.2380952380952 -6.23809523809524 130 22 25.2380952380952 -3.23809523809524 131 20 21.325 -1.325 132 15 21.325 -6.325 133 20 21.325 -1.325 134 29 25.2380952380952 3.76190476190476 135 19 17.1538461538462 1.84615384615385 136 29 25.2380952380952 3.76190476190476 137 24 21.325 2.675 138 23 21.325 1.675 139 22 21.325 0.675000000000001 140 23 25.2380952380952 -2.23809523809524 141 22 17.1538461538462 4.84615384615385 142 29 25 4 143 26 25 1 144 21 21.325 -0.324999999999999

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/2r2iv1292169728.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/2r2iv1292169728.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/3r2iv1292169728.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/3r2iv1292169728.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/4jbzy1292169728.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292169618g9fdjzjpg262ulv/4jbzy1292169728.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 6 ; 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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