paper - RP no categorisation. Carrièremogelijkheden

*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, 20 Dec 2010 12:17:02 +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/20/t12928473210ect6thmlbwen03.htm/, Retrieved Mon, 20 Dec 2010 13:15:25 +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/20/t12928473210ect6thmlbwen03.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 «

46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.2715 R-squared 0.0737 RMSE 5.4265 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 46 49.3684210526316 -3.36842105263158 2 44 44.8188976377953 -0.818897637795274 3 42 44.8188976377953 -2.81889763779527 4 41 44.8188976377953 -3.81889763779527 5 48 44.8188976377953 3.18110236220473 6 49 49.3684210526316 -0.368421052631582 7 51 44.8188976377953 6.18110236220473 8 47 44.8188976377953 2.18110236220473 9 49 44.8188976377953 4.18110236220473 10 46 44.8188976377953 1.18110236220473 11 51 44.8188976377953 6.18110236220473 12 54 44.8188976377953 9.18110236220473 13 52 44.8188976377953 7.18110236220473 14 52 49.3684210526316 2.63157894736842 15 45 44.8188976377953 0.181102362204726 16 52 44.8188976377953 7.18110236220473 17 56 49.3684210526316 6.63157894736842 18 54 44.8188976377953 9.18110236220473 19 50 44.8188976377953 5.18110236220473 20 35 44.8188976377953 -9.81889763779527 21 48 44.8188976377953 3.18110236220473 22 37 44.8188976377953 -7.81889763779527 23 47 44.8188976377953 2.18110236220473 24 31 44.8188976377953 -13.8188976377953 25 45 44.8188976377953 0.181102362204726 26 47 44.8188976377953 2.18110236220473 27 44 44.8188976377953 -0.818897637795274 28 30 44.8188976377953 -14.8188976377953 29 40 44.8188976377953 -4.81889763779527 30 44 44.8188976377953 -0.818897637795274 31 43 44.8188976377953 -1.81889763779527 32 51 44.8188976377953 6.18110236220473 33 48 44.8188976377953 3.18110236220473 34 55 49.3684210526316 5.63157894736842 35 48 44.8188976377953 3.18110236220473 36 53 44.8188976377953 8.18110236220473 37 49 44.8188976377953 4.18110236220473 38 44 44.8188976377953 -0.818897637795274 39 45 49.3684210526316 -4.36842105263158 40 40 44.8188976377953 -4.81889763779527 41 44 44.8188976377953 -0.818897637795274 42 41 44.8188976377953 -3.81889763779527 43 46 44.8188976377953 1.18110236220473 44 47 44.8188976377953 2.18110236220473 45 48 44.8188976377953 3.18110236220473 46 43 44.8188976377953 -1.81889763779527 47 46 44.8188976377953 1.18110236220473 48 53 44.8188976377953 8.18110236220473 49 33 44.8188976377953 -11.8188976377953 50 47 49.3684210526316 -2.36842105263158 51 43 44.8188976377953 -1.81889763779527 52 45 44.8188976377953 0.181102362204726 53 49 44.8188976377953 4.18110236220473 54 45 44.8188976377953 0.181102362204726 55 37 44.8188976377953 -7.81889763779527 56 42 44.8188976377953 -2.81889763779527 57 43 44.8188976377953 -1.81889763779527 58 44 44.8188976377953 -0.818897637795274 59 39 44.8188976377953 -5.81889763779527 60 37 44.8188976377953 -7.81889763779527 61 53 44.8188976377953 8.18110236220473 62 48 49.3684210526316 -1.36842105263158 63 47 44.8188976377953 2.18110236220473 64 49 44.8188976377953 4.18110236220473 65 47 44.8188976377953 2.18110236220473 66 56 44.8188976377953 11.1811023622047 67 51 44.8188976377953 6.18110236220473 68 43 44.8188976377953 -1.81889763779527 69 51 49.3684210526316 1.63157894736842 70 36 44.8188976377953 -8.81889763779527 71 55 49.3684210526316 5.63157894736842 72 33 44.8188976377953 -11.8188976377953 73 42 44.8188976377953 -2.81889763779527 74 43 44.8188976377953 -1.81889763779527 75 44 44.8188976377953 -0.818897637795274 76 47 44.8188976377953 2.18110236220473 77 43 44.8188976377953 -1.81889763779527 78 47 49.3684210526316 -2.36842105263158 79 41 44.8188976377953 -3.81889763779527 80 53 44.8188976377953 8.18110236220473 81 47 44.8188976377953 2.18110236220473 82 23 44.8188976377953 -21.8188976377953 83 43 49.3684210526316 -6.36842105263158 84 47 44.8188976377953 2.18110236220473 85 47 44.8188976377953 2.18110236220473 86 49 44.8188976377953 4.18110236220473 87 50 44.8188976377953 5.18110236220473 88 43 44.8188976377953 -1.81889763779527 89 44 44.8188976377953 -0.818897637795274 90 49 49.3684210526316 -0.368421052631582 91 47 44.8188976377953 2.18110236220473 92 39 44.8188976377953 -5.81889763779527 93 49 44.8188976377953 4.18110236220473 94 41 44.8188976377953 -3.81889763779527 95 40 44.8188976377953 -4.81889763779527 96 38 44.8188976377953 -6.81889763779527 97 43 44.8188976377953 -1.81889763779527 98 55 44.8188976377953 10.1811023622047 99 46 44.8188976377953 1.18110236220473 100 54 44.8188976377953 9.18110236220473 101 47 44.8188976377953 2.18110236220473 102 35 44.8188976377953 -9.81889763779527 103 41 44.8188976377953 -3.81889763779527 104 53 49.3684210526316 3.63157894736842 105 44 44.8188976377953 -0.818897637795274 106 48 44.8188976377953 3.18110236220473 107 49 44.8188976377953 4.18110236220473 108 39 44.8188976377953 -5.81889763779527 109 45 44.8188976377953 0.181102362204726 110 34 44.8188976377953 -10.8188976377953 111 46 44.8188976377953 1.18110236220473 112 45 44.8188976377953 0.181102362204726 113 53 44.8188976377953 8.18110236220473 114 51 44.8188976377953 6.18110236220473 115 45 49.3684210526316 -4.36842105263158 116 50 44.8188976377953 5.18110236220473 117 41 44.8188976377953 -3.81889763779527 118 44 44.8188976377953 -0.818897637795274 119 43 44.8188976377953 -1.81889763779527 120 42 44.8188976377953 -2.81889763779527 121 48 49.3684210526316 -1.36842105263158 122 45 44.8188976377953 0.181102362204726 123 48 44.8188976377953 3.18110236220473 124 48 44.8188976377953 3.18110236220473 125 53 49.3684210526316 3.63157894736842 126 45 44.8188976377953 0.181102362204726 127 45 44.8188976377953 0.181102362204726 128 50 44.8188976377953 5.18110236220473 129 48 44.8188976377953 3.18110236220473 130 41 44.8188976377953 -3.81889763779527 131 53 49.3684210526316 3.63157894736842 132 40 44.8188976377953 -4.81889763779527 133 49 44.8188976377953 4.18110236220473 134 46 44.8188976377953 1.18110236220473 135 48 44.8188976377953 3.18110236220473 136 43 49.3684210526316 -6.36842105263158 137 53 44.8188976377953 8.18110236220473 138 51 44.8188976377953 6.18110236220473 139 41 44.8188976377953 -3.81889763779527 140 45 44.8188976377953 0.181102362204726 141 44 44.8188976377953 -0.818897637795274 142 43 44.8188976377953 -1.81889763779527 143 34 44.8188976377953 -10.8188976377953 144 38 44.8188976377953 -6.81889763779527 145 40 44.8188976377953 -4.81889763779527 146 48 44.8188976377953 3.18110236220473

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/2ekb81292847415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/2ekb81292847415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/3ekb81292847415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/3ekb81292847415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/4ptaa1292847415.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928473210ect6thmlbwen03/4ptaa1292847415.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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