W10 -RP

*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 19:55:30 +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/t1292183772g51nl09i71z1y5y.htm/, Retrieved Sun, 12 Dec 2010 20:56:13 +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/t1292183772g51nl09i71z1y5y.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 «

0 24 14 11 12 24 26 0 25 11 7 8 25 23 0 17 6 17 8 30 25 1 18 12 10 8 19 23 1 18 8 12 9 22 19 1 16 10 12 7 22 29 1 20 10 11 4 25 25 1 16 11 11 11 23 21 1 18 16 12 7 17 22 1 17 11 13 7 21 25 0 23 13 14 12 19 24 0 30 12 16 10 19 18 1 23 8 11 10 15 22 1 18 12 10 8 16 15 1 15 11 11 8 23 22 1 12 4 15 4 27 28 0 21 9 9 9 22 20 1 15 8 11 8 14 12 1 20 8 17 7 22 24 0 31 14 17 11 23 20 0 27 15 11 9 23 21 1 34 16 18 11 21 20 1 21 9 14 13 19 21 1 31 14 10 8 18 23 1 19 11 11 8 20 28 0 16 8 15 9 23 24 1 20 9 15 6 25 24 1 21 9 13 9 19 24 1 22 9 16 9 24 23 1 17 9 13 6 22 23 1 24 10 9 6 25 29 0 25 16 18 16 26 24 0 26 11 18 5 29 18 1 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 1 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 1 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 0 20 10 10 8 26 23 1 15 12 11 8 20 25 1 20 14 14 10 18 24 1 33 14 9 6 32 24 0 29 10 12 8 25 23 1 23 14 17 7 25 21 0 26 16 5 4 23 24 1 18 9 12 8 21 2 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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.6299 R-squared 0.3968 RMSE 4.4307 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 24 25.0714285714286 -1.07142857142857 2 25 20.4230769230769 4.57692307692308 3 17 21.4615384615385 -4.46153846153846 4 18 20.4230769230769 -2.42307692307692 5 18 20.4230769230769 -2.42307692307692 6 16 18.24 -2.24 7 20 18.24 1.76 8 16 20.4230769230769 -4.42307692307692 9 18 18.24 -0.239999999999998 10 17 18.24 -1.24 11 23 25.0714285714286 -2.07142857142857 12 30 20.4230769230769 9.57692307692308 13 23 20.4230769230769 2.57692307692308 14 18 20.4230769230769 -2.42307692307692 15 15 20.4230769230769 -5.42307692307692 16 12 21.4615384615385 -9.46153846153846 17 21 20.4230769230769 0.576923076923077 18 15 20.4230769230769 -5.42307692307692 19 20 18.24 1.76 20 31 25.0714285714286 5.92857142857143 21 27 25.0714285714286 1.92857142857143 22 34 25.0714285714286 8.92857142857143 23 21 20.4230769230769 0.576923076923077 24 31 25.0714285714286 5.92857142857143 25 19 20.4230769230769 -1.42307692307692 26 16 20.4230769230769 -4.42307692307692 27 20 18.24 1.76 28 21 20.4230769230769 0.576923076923077 29 22 20.4230769230769 1.57692307692308 30 17 18.24 -1.24 31 24 18.24 5.76 32 25 29.8125 -4.8125 33 26 29.8125 -3.8125 34 25 21.4615384615385 3.53846153846154 35 17 20.4230769230769 -3.42307692307692 36 32 29.8125 2.1875 37 33 29.8125 3.1875 38 13 18.24 -5.24 39 32 29.8125 2.1875 40 25 29.8125 -4.8125 41 29 29.8125 -0.8125 42 22 20.4230769230769 1.57692307692308 43 18 20.4230769230769 -2.42307692307692 44 17 18.24 -1.24 45 20 21.4615384615385 -1.46153846153846 46 15 20.4230769230769 -5.42307692307692 47 20 25.0714285714286 -5.07142857142857 48 33 29.8125 3.1875 49 29 20.4230769230769 8.57692307692308 50 23 18.24 4.76 51 26 18.24 7.76 52 18 20.4230769230769 -2.42307692307692 53 20 20.4230769230769 -0.423076923076923 54 11 18.24 -7.24 55 28 21.4615384615385 6.53846153846154 56 26 25.0714285714286 0.928571428571427 57 22 21.4615384615385 0.53846153846154 58 17 18.24 -1.24 59 12 18.24 -6.24 60 14 25.0714285714286 -11.0714285714286 61 17 20.4230769230769 -3.42307692307692 62 21 18.24 2.76 63 19 18.24 0.760000000000002 64 18 25.0714285714286 -7.07142857142857 65 10 18.24 -8.24 66 29 29.8125 -0.8125 67 31 20.4230769230769 10.5769230769231 68 19 21.4615384615385 -2.46153846153846 69 9 18.24 -9.24 70 20 20.4230769230769 -0.423076923076923 71 28 18.24 9.76 72 19 18.24 0.760000000000002 73 30 21.4615384615385 8.53846153846154 74 29 25.0714285714286 3.92857142857143 75 26 18.24 7.76 76 23 20.4230769230769 2.57692307692308 77 13 18.24 -5.24 78 21 20.4230769230769 0.576923076923077 79 19 20.4230769230769 -1.42307692307692 80 28 18.24 9.76 81 23 25.0714285714286 -2.07142857142857 82 18 20.4230769230769 -2.42307692307692 83 21 20.4230769230769 0.576923076923077 84 20 20.4230769230769 -0.423076923076923 85 23 18.24 4.76 86 21 18.24 2.76 87 21 21.4615384615385 -0.46153846153846 88 15 25.0714285714286 -10.0714285714286 89 28 29.8125 -1.8125 90 19 18.24 0.760000000000002 91 26 25.0714285714286 0.928571428571427 92 10 18.24 -8.24 93 16 18.24 -2.24 94 22 18.24 3.76 95 19 20.4230769230769 -1.42307692307692 96 31 21.4615384615385 9.53846153846154 97 31 25.0714285714286 5.92857142857143 98 29 25.0714285714286 3.92857142857143 99 19 18.24 0.760000000000002 100 22 20.4230769230769 1.57692307692308 101 23 20.4230769230769 2.57692307692308 102 15 18.24 -3.24 103 20 20.4230769230769 -0.423076923076923 104 18 20.4230769230769 -2.42307692307692 105 23 25.0714285714286 -2.07142857142857 106 25 18.24 6.76 107 21 18.24 2.76 108 24 20.4230769230769 3.57692307692308 109 25 25.0714285714286 -0.071428571428573 110 17 18.24 -1.24 111 13 18.24 -5.24 112 28 20.4230769230769 7.57692307692308 113 21 20.4230769230769 0.576923076923077 114 25 21.4615384615385 3.53846153846154 115 9 21.4615384615385 -12.4615384615385 116 16 18.24 -2.24 117 19 20.4230769230769 -1.42307692307692 118 17 18.24 -1.24 119 25 25.0714285714286 -0.071428571428573 120 20 18.24 1.76 121 29 29.8125 -0.8125 122 14 18.24 -4.24 123 22 25.0714285714286 -3.07142857142857 124 15 18.24 -3.24 125 19 18.24 0.760000000000002 126 20 20.4230769230769 -0.423076923076923 127 15 18.24 -3.24 128 20 20.4230769230769 -0.423076923076923 129 18 20.4230769230769 -2.42307692307692 130 33 29.8125 3.1875 131 22 20.4230769230769 1.57692307692308 132 16 20.4230769230769 -4.42307692307692 133 17 18.24 -1.24 134 16 18.24 -2.24 135 21 20.4230769230769 0.576923076923077 136 26 29.8125 -3.8125 137 18 18.24 -0.239999999999998 138 18 20.4230769230769 -2.42307692307692 139 17 20.4230769230769 -3.42307692307692 140 22 25.0714285714286 -3.07142857142857 141 30 25.0714285714286 4.92857142857143 142 30 29.8125 0.1875 143 24 25.0714285714286 -1.07142857142857 144 21 18.24 2.76 145 21 25.0714285714286 -4.07142857142857 146 29 29.8125 -0.8125 147 31 20.4230769230769 10.5769230769231 148 20 18.24 1.76 149 16 20.4230769230769 -4.42307692307692 150 22 20.4230769230769 1.57692307692308 151 20 21.4615384615385 -1.46153846153846 152 28 25.0714285714286 2.92857142857143 153 38 29.8125 8.1875 154 22 18.24 3.76 155 20 20.4230769230769 -0.423076923076923 156 17 18.24 -1.24 157 28 25.0714285714286 2.92857142857143 158 22 25.0714285714286 -3.07142857142857 159 31 25.0714285714286 5.92857142857143

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/29e981292183723.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/29e981292183723.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/39e981292183723.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/39e981292183723.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/42oqb1292183723.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292183772g51nl09i71z1y5y/42oqb1292183723.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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