Recursive Partioning Concern mistakes

*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 12:54:06 +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/t1292245110ysuadi22xuoqh8o.htm/, Retrieved Mon, 13 Dec 2010 14:00:33 +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/t1292245110ysuadi22xuoqh8o.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 69 26 9 15 6 25 25 1 53 20 9 15 6 25 24 1 43 21 9 14 13 19 21 0 60 31 14 10 8 18 23 1 49 21 8 10 7 18 17 1 62 18 8 12 9 22 19 1 45 26 11 18 5 29 18 1 50 22 10 12 8 26 27 1 75 22 9 14 9 25 23 1 82 29 15 18 11 23 23 0 60 15 14 9 8 23 29 1 59 16 11 11 11 23 21 1 21 24 14 11 12 24 26 1 62 17 6 17 8 30 25 0 54 19 20 8 7 19 25 1 47 22 9 16 9 24 23 1 59 31 10 21 12 32 26 0 37 28 8 24 20 30 20 0 43 38 11 21 7 29 29 1 48 26 14 14 8 17 24 0 79 25 11 7 8 25 23 0 62 25 16 18 16 26 24 1 16 29 14 18 10 26 30 0 38 28 11 13 6 25 22 1 58 15 11 11 8 23 22 0 60 18 12 13 9 21 13 0 67 21 9 13 9 19 24 0 55 25 7 18 11 35 17 1 47 23 13 14 12 19 24 0 59 23 10 12 8 20 21 1 49 19 9 9 7 21 23 0 47 18 9 12 8 21 24 1 57 18 13 8 9 24 24 0 39 26 16 5 4 23 24 1 49 18 12 10 8 19 23 1 26 18 6 11 8 17 26 0 53 28 14 11 8 24 24 0 75 17 14 12 6 15 21 1 65 29 10 12 8 25 23 1 49 12 4 15 4 27 28 0 48 25 12 12 7 29 23 0 45 28 12 16 14 27 22 0 31 20 14 14 10 18 24 1 61 17 9 17 9 25 21 1 49 17 9 13 6 22 23 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 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.5743 R-squared 0.3298 RMSE 3.5965 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 25 25.1777777777778 -0.177777777777777 2 25 21.6413043478261 3.35869565217391 3 19 21.6413043478261 -2.64130434782609 4 18 25.1777777777778 -7.17777777777778 5 18 16.2 1.8 6 22 21.6413043478261 0.358695652173914 7 29 25.1777777777778 3.82222222222222 8 26 21.6413043478261 4.35869565217391 9 25 21.6413043478261 3.35869565217391 10 23 25.1777777777778 -2.17777777777778 11 23 21.6413043478261 1.35869565217391 12 23 21.6413043478261 1.35869565217391 13 24 21.6413043478261 2.35869565217391 14 30 21.6413043478261 8.35869565217391 15 19 21.6413043478261 -2.64130434782609 16 24 21.6413043478261 2.35869565217391 17 32 25.1777777777778 6.82222222222222 18 30 25.1777777777778 4.82222222222222 19 29 25.1777777777778 3.82222222222222 20 17 25.1777777777778 -8.17777777777778 21 25 25.1777777777778 -0.177777777777777 22 26 25.1777777777778 0.822222222222223 23 26 25.1777777777778 0.822222222222223 24 25 25.1777777777778 -0.177777777777777 25 23 21.6413043478261 1.35869565217391 26 21 16.2 4.8 27 19 21.6413043478261 -2.64130434782609 28 35 25.1777777777778 9.82222222222222 29 19 21.6413043478261 -2.64130434782609 30 20 21.6413043478261 -1.64130434782609 31 21 21.6413043478261 -0.641304347826086 32 21 21.6413043478261 -0.641304347826086 33 24 21.6413043478261 2.35869565217391 34 23 25.1777777777778 -2.17777777777778 35 19 21.6413043478261 -2.64130434782609 36 17 21.6413043478261 -4.64130434782609 37 24 25.1777777777778 -1.17777777777778 38 15 21.6413043478261 -6.64130434782609 39 25 25.1777777777778 -0.177777777777777 40 27 21.6413043478261 5.35869565217391 41 29 25.1777777777778 3.82222222222222 42 27 25.1777777777778 1.82222222222222 43 18 21.6413043478261 -3.64130434782609 44 25 21.6413043478261 3.35869565217391 45 22 21.6413043478261 0.358695652173914 46 26 21.6413043478261 4.35869565217391 47 23 25.1777777777778 -2.17777777777778 48 16 21.6413043478261 -5.64130434782609 49 27 21.6413043478261 5.35869565217391 50 25 21.6413043478261 3.35869565217391 51 14 16.2 -2.2 52 19 16.2 2.8 53 20 25.1777777777778 -5.17777777777778 54 16 21.6413043478261 -5.64130434782609 55 18 21.6413043478261 -3.64130434782609 56 22 21.6413043478261 0.358695652173914 57 21 21.6413043478261 -0.641304347826086 58 22 21.6413043478261 0.358695652173914 59 22 21.6413043478261 0.358695652173914 60 32 25.1777777777778 6.82222222222222 61 23 25.1777777777778 -2.17777777777778 62 31 25.1777777777778 5.82222222222222 63 18 21.6413043478261 -3.64130434782609 64 23 21.6413043478261 1.35869565217391 65 26 25.1777777777778 0.822222222222223 66 24 21.6413043478261 2.35869565217391 67 19 21.6413043478261 -2.64130434782609 68 14 16.2 -2.2 69 20 21.6413043478261 -1.64130434782609 70 22 21.6413043478261 0.358695652173914 71 24 21.6413043478261 2.35869565217391 72 25 21.6413043478261 3.35869565217391 73 21 25.1777777777778 -4.17777777777778 74 28 25.1777777777778 2.82222222222222 75 24 21.6413043478261 2.35869565217391 76 20 21.6413043478261 -1.64130434782609 77 21 21.6413043478261 -0.641304347826086 78 23 21.6413043478261 1.35869565217391 79 13 16.2 -3.2 80 24 21.6413043478261 2.35869565217391 81 21 21.6413043478261 -0.641304347826086 82 21 25.1777777777778 -4.17777777777778 83 17 21.6413043478261 -4.64130434782609 84 14 21.6413043478261 -7.64130434782609 85 29 21.6413043478261 7.35869565217391 86 25 21.6413043478261 3.35869565217391 87 16 16.2 -0.199999999999999 88 25 21.6413043478261 3.35869565217391 89 25 21.6413043478261 3.35869565217391 90 21 21.6413043478261 -0.641304347826086 91 23 21.6413043478261 1.35869565217391 92 22 25.1777777777778 -3.17777777777778 93 19 21.6413043478261 -2.64130434782609 94 24 25.1777777777778 -1.17777777777778 95 26 25.1777777777778 0.822222222222223 96 25 21.6413043478261 3.35869565217391 97 20 21.6413043478261 -1.64130434782609 98 22 25.1777777777778 -3.17777777777778 99 14 16.2 -2.2 100 20 21.6413043478261 -1.64130434782609 101 32 25.1777777777778 6.82222222222222 102 21 16.2 4.8 103 22 21.6413043478261 0.358695652173914 104 28 25.1777777777778 2.82222222222222 105 25 25.1777777777778 -0.177777777777777 106 17 21.6413043478261 -4.64130434782609 107 21 21.6413043478261 -0.641304347826086 108 23 21.6413043478261 1.35869565217391 109 27 25.1777777777778 1.82222222222222 110 22 21.6413043478261 0.358695652173914 111 19 21.6413043478261 -2.64130434782609 112 20 21.6413043478261 -1.64130434782609 113 17 25.1777777777778 -8.17777777777778 114 24 21.6413043478261 2.35869565217391 115 21 21.6413043478261 -0.641304347826086 116 21 21.6413043478261 -0.641304347826086 117 23 21.6413043478261 1.35869565217391 118 24 25.1777777777778 -1.17777777777778 119 19 21.6413043478261 -2.64130434782609 120 22 21.6413043478261 0.358695652173914 121 26 21.6413043478261 4.35869565217391 122 17 16.2 0.8 123 17 21.6413043478261 -4.64130434782609 124 19 21.6413043478261 -2.64130434782609 125 15 16.2 -1.2 126 17 21.6413043478261 -4.64130434782609 127 27 25.1777777777778 1.82222222222222 128 19 21.6413043478261 -2.64130434782609 129 21 21.6413043478261 -0.641304347826086 130 25 21.6413043478261 3.35869565217391 131 19 25.1777777777778 -6.17777777777778 132 22 25.1777777777778 -3.17777777777778 133 18 25.1777777777778 -7.17777777777778 134 20 21.6413043478261 -1.64130434782609 135 15 21.6413043478261 -6.64130434782609 136 20 21.6413043478261 -1.64130434782609 137 29 25.1777777777778 3.82222222222222 138 19 16.2 2.8 139 29 25.1777777777778 3.82222222222222 140 24 21.6413043478261 2.35869565217391 141 23 21.6413043478261 1.35869565217391 142 22 21.6413043478261 0.358695652173914 143 23 25.1777777777778 -2.17777777777778 144 22 16.2 5.8 145 29 21.6413043478261 7.35869565217391 146 26 25.1777777777778 0.822222222222223 147 26 21.6413043478261 4.35869565217391 148 21 21.6413043478261 -0.641304347826086 149 18 21.6413043478261 -3.64130434782609 150 10 16.2 -6.2 151 19 21.6413043478261 -2.64130434782609 152 10 16.2 -6.2

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/2mje61292244839.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/2mje61292244839.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/3fbe91292244839.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/3fbe91292244839.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/4p2dc1292244839.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292245110ysuadi22xuoqh8o/4p2dc1292244839.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 7 ; par2 = none ; par3 = 0 ; 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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