paper - RP - nieuwe leermogelijkheden

*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 08:31:35 +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/t12929201758qilm0yk73om4fp.htm/, Retrieved Tue, 21 Dec 2010 09:29:38 +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/t12929201758qilm0yk73om4fp.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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.5318 R-squared 0.2828 RMSE 6.6065 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 52 38.3636363636364 13.6363636363636 2 39 38.3636363636364 0.636363636363633 3 42 38.3636363636364 3.63636363636363 4 35 38.3636363636364 -3.36363636363637 5 32 38.3636363636364 -6.36363636363637 6 49 48.2380952380952 0.76190476190476 7 33 38.3636363636364 -5.36363636363637 8 47 38.3636363636364 8.63636363636363 9 46 38.3636363636364 7.63636363636363 10 40 38.3636363636364 1.63636363636363 11 33 38.3636363636364 -5.36363636363637 12 39 38.3636363636364 0.636363636363633 13 37 32.2666666666667 4.73333333333333 14 56 48.2380952380952 7.76190476190476 15 36 38.3636363636364 -2.36363636363637 16 24 38.3636363636364 -14.3636363636364 17 56 38.3636363636364 17.6363636363636 18 32 38.3636363636364 -6.36363636363637 19 41 38.3636363636364 2.63636363636363 20 24 38.3636363636364 -14.3636363636364 21 42 38.3636363636364 3.63636363636363 22 47 48.2380952380952 -1.23809523809524 23 25 32.2666666666667 -7.26666666666667 24 33 38.3636363636364 -5.36363636363637 25 43 38.3636363636364 4.63636363636363 26 45 48.2380952380952 -3.23809523809524 27 44 38.3636363636364 5.63636363636363 28 46 38.3636363636364 7.63636363636363 29 31 32.2666666666667 -1.26666666666667 30 31 38.3636363636364 -7.36363636363637 31 42 38.3636363636364 3.63636363636363 32 28 38.3636363636364 -10.3636363636364 33 38 38.3636363636364 -0.363636363636367 34 59 48.2380952380952 10.7619047619048 35 43 38.3636363636364 4.63636363636363 36 29 38.3636363636364 -9.36363636363637 37 38 38.3636363636364 -0.363636363636367 38 39 48.2380952380952 -9.23809523809524 39 50 38.3636363636364 11.6363636363636 40 44 38.3636363636364 5.63636363636363 41 29 38.3636363636364 -9.36363636363637 42 29 32.2666666666667 -3.26666666666667 43 36 38.3636363636364 -2.36363636363637 44 43 38.3636363636364 4.63636363636363 45 28 38.3636363636364 -10.3636363636364 46 39 38.3636363636364 0.636363636363633 47 35 32.2666666666667 2.73333333333333 48 43 48.2380952380952 -5.23809523809524 49 28 32.2666666666667 -4.26666666666667 50 49 38.3636363636364 10.6363636363636 51 33 38.3636363636364 -5.36363636363637 52 39 38.3636363636364 0.636363636363633 53 36 38.3636363636364 -2.36363636363637 54 24 38.3636363636364 -14.3636363636364 55 47 48.2380952380952 -1.23809523809524 56 34 38.3636363636364 -4.36363636363637 57 33 38.3636363636364 -5.36363636363637 58 43 38.3636363636364 4.63636363636363 59 41 48.2380952380952 -7.23809523809524 60 40 38.3636363636364 1.63636363636363 61 39 38.3636363636364 0.636363636363633 62 54 48.2380952380952 5.76190476190476 63 43 38.3636363636364 4.63636363636363 64 45 38.3636363636364 6.63636363636363 65 29 38.3636363636364 -9.36363636363637 66 45 38.3636363636364 6.63636363636363 67 47 38.3636363636364 8.63636363636363 68 38 38.3636363636364 -0.363636363636367 69 52 48.2380952380952 3.76190476190476 70 34 38.3636363636364 -4.36363636363637 71 56 48.2380952380952 7.76190476190476 72 26 38.3636363636364 -12.3636363636364 73 42 48.2380952380952 -6.23809523809524 74 32 32.2666666666667 -0.266666666666666 75 39 38.3636363636364 0.636363636363633 76 37 38.3636363636364 -1.36363636363637 77 37 38.3636363636364 -1.36363636363637 78 52 48.2380952380952 3.76190476190476 79 31 38.3636363636364 -7.36363636363637 80 34 32.2666666666667 1.73333333333333 81 38 38.3636363636364 -0.363636363636367 82 29 38.3636363636364 -9.36363636363637 83 52 38.3636363636364 13.6363636363636 84 40 38.3636363636364 1.63636363636363 85 47 38.3636363636364 8.63636363636363 86 34 38.3636363636364 -4.36363636363637 87 37 38.3636363636364 -1.36363636363637 88 43 38.3636363636364 4.63636363636363 89 37 38.3636363636364 -1.36363636363637 90 55 48.2380952380952 6.76190476190476 91 36 38.3636363636364 -2.36363636363637 92 28 38.3636363636364 -10.3636363636364 93 47 38.3636363636364 8.63636363636363 94 38 38.3636363636364 -0.363636363636367 95 37 38.3636363636364 -1.36363636363637 96 32 38.3636363636364 -6.36363636363637 97 47 38.3636363636364 8.63636363636363 98 40 32.2666666666667 7.73333333333333 99 45 38.3636363636364 6.63636363636363 100 37 38.3636363636364 -1.36363636363637 101 38 38.3636363636364 -0.363636363636367 102 37 38.3636363636364 -1.36363636363637 103 35 38.3636363636364 -3.36363636363637 104 50 38.3636363636364 11.6363636363636 105 32 32.2666666666667 -0.266666666666666 106 32 38.3636363636364 -6.36363636363637 107 38 38.3636363636364 -0.363636363636367 108 31 38.3636363636364 -7.36363636363637 109 27 38.3636363636364 -11.3636363636364 110 34 32.2666666666667 1.73333333333333 111 43 38.3636363636364 4.63636363636363 112 28 38.3636363636364 -10.3636363636364 113 44 48.2380952380952 -4.23809523809524 114 43 38.3636363636364 4.63636363636363 115 53 48.2380952380952 4.76190476190476 116 33 38.3636363636364 -5.36363636363637 117 36 38.3636363636364 -2.36363636363637 118 46 38.3636363636364 7.63636363636363 119 36 38.3636363636364 -2.36363636363637 120 24 32.2666666666667 -8.26666666666667 121 50 48.2380952380952 1.76190476190476 122 40 32.2666666666667 7.73333333333333 123 40 48.2380952380952 -8.23809523809524 124 32 38.3636363636364 -6.36363636363637 125 49 38.3636363636364 10.6363636363636 126 47 38.3636363636364 8.63636363636363 127 28 32.2666666666667 -4.26666666666667 128 41 38.3636363636364 2.63636363636363 129 25 38.3636363636364 -13.3636363636364 130 46 38.3636363636364 7.63636363636363 131 53 38.3636363636364 14.6363636363636 132 34 38.3636363636364 -4.36363636363637 133 40 38.3636363636364 1.63636363636363 134 46 38.3636363636364 7.63636363636363 135 38 38.3636363636364 -0.363636363636367 136 51 48.2380952380952 2.76190476190476 137 38 48.2380952380952 -10.2380952380952 138 45 38.3636363636364 6.63636363636363 139 41 38.3636363636364 2.63636363636363 140 42 38.3636363636364 3.63636363636363 141 36 38.3636363636364 -2.36363636363637 142 41 38.3636363636364 2.63636363636363 143 35 38.3636363636364 -3.36363636363637 144 42 38.3636363636364 3.63636363636363 145 35 32.2666666666667 2.73333333333333 146 32 38.3636363636364 -6.36363636363637

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/2rt2s1292920288.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/2rt2s1292920288.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/3rt2s1292920288.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/3rt2s1292920288.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/41kjd1292920288.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929201758qilm0yk73om4fp/41kjd1292920288.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 3 ; 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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