paper - RP - persoonlijke redenen

*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: Tue, 21 Dec 2010 08:35:54 +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/21/t1292920440azxy8sahz0ahrj5.htm/, Retrieved Tue, 21 Dec 2010 09:34:00 +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/21/t1292920440azxy8sahz0ahrj5.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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.4768 R-squared 0.2274 RMSE 5.0928 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 26 35.6315789473684 -9.63157894736842 2 25 27.9381443298969 -2.93814432989691 3 28 27.9381443298969 0.0618556701030926 4 30 27.9381443298969 2.06185567010309 5 28 27.9381443298969 0.0618556701030926 6 40 35.6315789473684 4.36842105263158 7 28 27.9381443298969 0.0618556701030926 8 27 31.9 -4.9 9 25 31.9 -6.9 10 27 27.9381443298969 -0.938144329896907 11 32 27.9381443298969 4.06185567010309 12 28 27.9381443298969 0.0618556701030926 13 21 27.9381443298969 -6.93814432989691 14 40 35.6315789473684 4.36842105263158 15 29 27.9381443298969 1.06185567010309 16 27 27.9381443298969 -0.938144329896907 17 31 35.6315789473684 -4.63157894736842 18 33 27.9381443298969 5.06185567010309 19 28 27.9381443298969 0.0618556701030926 20 26 27.9381443298969 -1.93814432989691 21 25 27.9381443298969 -2.93814432989691 22 37 31.9 5.1 23 13 27.9381443298969 -14.9381443298969 24 32 27.9381443298969 4.06185567010309 25 32 31.9 0.100000000000001 26 38 31.9 6.1 27 30 31.9 -1.9 28 33 31.9 1.10000000000000 29 22 27.9381443298969 -5.93814432989691 30 29 27.9381443298969 1.06185567010309 31 33 27.9381443298969 5.06185567010309 32 31 27.9381443298969 3.06185567010309 33 23 27.9381443298969 -4.93814432989691 34 42 35.6315789473684 6.36842105263158 35 35 31.9 3.1 36 31 27.9381443298969 3.06185567010309 37 31 27.9381443298969 3.06185567010309 38 38 27.9381443298969 10.0618556701031 39 34 35.6315789473684 -1.63157894736842 40 33 31.9 1.10000000000000 41 23 27.9381443298969 -4.93814432989691 42 18 27.9381443298969 -9.9381443298969 43 33 27.9381443298969 5.06185567010309 44 26 31.9 -5.9 45 29 27.9381443298969 1.06185567010309 46 23 27.9381443298969 -4.93814432989691 47 18 27.9381443298969 -9.9381443298969 48 36 31.9 4.1 49 21 27.9381443298969 -6.93814432989691 50 31 35.6315789473684 -4.63157894736842 51 31 27.9381443298969 3.06185567010309 52 29 27.9381443298969 1.06185567010309 53 24 27.9381443298969 -3.93814432989691 54 35 27.9381443298969 7.06185567010309 55 37 31.9 5.1 56 29 27.9381443298969 1.06185567010309 57 31 27.9381443298969 3.06185567010309 58 34 31.9 2.1 59 38 27.9381443298969 10.0618556701031 60 27 27.9381443298969 -0.938144329896907 61 33 27.9381443298969 5.06185567010309 62 36 35.6315789473684 0.368421052631582 63 27 31.9 -4.9 64 33 31.9 1.10000000000000 65 24 27.9381443298969 -3.93814432989691 66 31 31.9 -0.899999999999999 67 31 31.9 -0.899999999999999 68 23 27.9381443298969 -4.93814432989691 69 38 35.6315789473684 2.36842105263158 70 30 27.9381443298969 2.06185567010309 71 39 35.6315789473684 3.36842105263158 72 28 27.9381443298969 0.0618556701030926 73 39 27.9381443298969 11.0618556701031 74 19 27.9381443298969 -8.9381443298969 75 32 27.9381443298969 4.06185567010309 76 32 27.9381443298969 4.06185567010309 77 35 27.9381443298969 7.06185567010309 78 42 35.6315789473684 6.36842105263158 79 25 27.9381443298969 -2.93814432989691 80 11 27.9381443298969 -16.9381443298969 81 31 27.9381443298969 3.06185567010309 82 30 27.9381443298969 2.06185567010309 83 30 35.6315789473684 -5.63157894736842 84 31 27.9381443298969 3.06185567010309 85 28 31.9 -3.9 86 34 27.9381443298969 6.06185567010309 87 32 27.9381443298969 4.06185567010309 88 30 31.9 -1.9 89 27 27.9381443298969 -0.938144329896907 90 36 35.6315789473684 0.368421052631582 91 32 27.9381443298969 4.06185567010309 92 27 27.9381443298969 -0.938144329896907 93 35 31.9 3.1 94 34 27.9381443298969 6.06185567010309 95 32 27.9381443298969 4.06185567010309 96 28 27.9381443298969 0.0618556701030926 97 29 31.9 -2.9 98 18 27.9381443298969 -9.9381443298969 99 34 31.9 2.1 100 35 27.9381443298969 7.06185567010309 101 34 27.9381443298969 6.06185567010309 102 26 27.9381443298969 -1.93814432989691 103 30 27.9381443298969 2.06185567010309 104 35 35.6315789473684 -0.631578947368418 105 17 27.9381443298969 -10.9381443298969 106 34 27.9381443298969 6.06185567010309 107 30 27.9381443298969 2.06185567010309 108 31 27.9381443298969 3.06185567010309 109 25 27.9381443298969 -2.93814432989691 110 16 27.9381443298969 -11.9381443298969 111 35 31.9 3.1 112 28 27.9381443298969 0.0618556701030926 113 42 31.9 10.1 114 30 31.9 -1.9 115 37 35.6315789473684 1.36842105263158 116 26 27.9381443298969 -1.93814432989691 117 28 27.9381443298969 0.0618556701030926 118 33 31.9 1.10000000000000 119 29 27.9381443298969 1.06185567010309 120 21 27.9381443298969 -6.93814432989691 121 38 35.6315789473684 2.36842105263158 122 18 27.9381443298969 -9.9381443298969 123 38 27.9381443298969 10.0618556701031 124 30 27.9381443298969 2.06185567010309 125 35 35.6315789473684 -0.631578947368418 126 34 31.9 2.1 127 21 27.9381443298969 -6.93814432989691 128 30 27.9381443298969 2.06185567010309 129 32 27.9381443298969 4.06185567010309 130 23 31.9 -8.9 131 31 35.6315789473684 -4.63157894736842 132 26 27.9381443298969 -1.93814432989691 133 29 27.9381443298969 1.06185567010309 134 28 31.9 -3.9 135 29 27.9381443298969 1.06185567010309 136 36 35.6315789473684 0.368421052631582 137 36 27.9381443298969 8.0618556701031 138 31 31.9 -0.899999999999999 139 30 27.9381443298969 2.06185567010309 140 29 27.9381443298969 1.06185567010309 141 35 27.9381443298969 7.06185567010309 142 26 27.9381443298969 -1.93814432989691 143 25 27.9381443298969 -2.93814432989691 144 25 27.9381443298969 -2.93814432989691 145 20 27.9381443298969 -7.9381443298969 146 27 27.9381443298969 -0.938144329896907

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/2nsvm1292920547.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/2nsvm1292920547.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/3nsvm1292920547.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/3nsvm1292920547.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/4yjcp1292920547.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292920440azxy8sahz0ahrj5/4yjcp1292920547.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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') }

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Software written by Ed van Stee & Patrick Wessa

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