recursive huwelijken

*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: Sat, 18 Dec 2010 19:02:00 +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/18/t1292699035a07i1zkmhhl67cf.htm/, Retrieved Sat, 18 Dec 2010 20:03:56 +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/18/t1292699035a07i1zkmhhl67cf.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 «

3111 5140 17153 2.5 766 332 2.4 3995 4749 15579 1.8 294 369 2.4 5245 3635 16755 7.3 235 384 2.4 5588 4305 16585 9.9 462 373 2.1 10681 5805 16572 13.2 919 378 2 10516 4260 16325 17.8 346 426 2 7496 3869 17913 18.8 298 423 2.1 9935 7325 17572 19.3 92 397 2.1 10249 9280 17338 13.9 516 422 2 6271 6222 17087 7.5 843 409 2 3616 3272 15864 8 395 430 2 3724 7598 15554 4 961 412 1.7 2886 1345 16229 3.6 1231 470 1.3 3318 1900 15180 4.8 794 491 1.2 4166 1480 16215 5.9 420 504 1.1 6401 1472 15801 10.4 331 484 1.4 9209 3823 15751 12.3 312 474 1.5 9820 4454 16477 15.5 692 508 1.4 7470 3357 17324 16.7 1221 492 1.1 8207 5393 16919 18.8 1272 452 1.1 9564 8329 16438 15.2 622 457 1 5309 4152 16239 11.3 479 457 1.4 3385 4042 15613 6.3 757 471 1.3 3706 7747 15821 3.2 463 451 1.2 2733 1451 15678 5.3 534 493 1.5 3045 911 14671 2.4 731 514 1.6 3449 406 15876 6.5 498 522 1.8 5542 1387 15563 10.4 629 490 1.5 10072 2150 15711 12.6 542 484 1.3 9418 1577 15583 16.8 519 506 1.6 7516 2642 16405 17.7 1585 501 1 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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.8826 R-squared 0.779 RMSE 1326.5405 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 3111 3385.1 -274.1 2 3995 3385.1 609.9 3 5245 5145.125 99.875 4 5588 5145.125 442.875 5 10681 8842.76315789474 1838.23684210526 6 10516 8842.76315789474 1673.23684210526 7 7496 8842.76315789474 -1346.76315789474 8 9935 8842.76315789474 1092.23684210526 9 10249 8842.76315789474 1406.23684210526 10 6271 5145.125 1125.875 11 3616 5145.125 -1529.125 12 3724 3385.1 338.9 13 2886 3385.1 -499.1 14 3318 3385.1 -67.0999999999999 15 4166 3385.1 780.9 16 6401 5145.125 1255.875 17 9209 8842.76315789474 366.236842105263 18 9820 8842.76315789474 977.236842105263 19 7470 8842.76315789474 -1372.76315789474 20 8207 8842.76315789474 -635.763157894737 21 9564 8842.76315789474 721.236842105263 22 5309 5145.125 163.875 23 3385 3385.1 -0.099999999999909 24 3706 3385.1 320.9 25 2733 3385.1 -652.1 26 3045 3385.1 -340.1 27 3449 3385.1 63.9000000000001 28 5542 5145.125 396.875 29 10072 8842.76315789474 1229.23684210526 30 9418 8842.76315789474 575.236842105263 31 7516 8842.76315789474 -1326.76315789474 32 7840 8842.76315789474 -1002.76315789474 33 10081 8842.76315789474 1238.23684210526 34 4956 8842.76315789474 -3886.76315789474 35 3641 3385.1 255.9 36 3970 3385.1 584.9 37 2931 3385.1 -454.1 38 3170 3385.1 -215.1 39 3889 3385.1 503.9 40 4850 5145.125 -295.125 41 8037 8842.76315789474 -805.763157894737 42 12370 8842.76315789474 3527.23684210526 43 6712 8842.76315789474 -2130.76315789474 44 7297 8842.76315789474 -1545.76315789474 45 10613 8842.76315789474 1770.23684210526 46 5184 8842.76315789474 -3658.76315789474 47 3506 5145.125 -1639.125 48 3810 3385.1 424.9 49 2692 3385.1 -693.1 50 3073 3385.1 -312.1 51 3713 5145.125 -1432.125 52 4555 8842.76315789474 -4287.76315789474 53 7807 8842.76315789474 -1035.76315789474 54 10869 8842.76315789474 2026.23684210526 55 9682 8842.76315789474 839.236842105263 56 7704 8842.76315789474 -1138.76315789474 57 9826 8842.76315789474 983.236842105263 58 5456 5145.125 310.875 59 3677 3385.1 291.9 60 3431 3385.1 45.9000000000001 61 2765 3385.1 -620.1 62 3483 3385.1 97.9 63 3445 3385.1 59.9000000000001 64 6081 5145.125 935.875 65 8767 8842.76315789474 -75.7631578947367 66 9407 8842.76315789474 564.236842105263 67 6551 8842.76315789474 -2291.76315789474 68 12480 8842.76315789474 3637.23684210526 69 9530 8842.76315789474 687.236842105263 70 5960 5145.125 814.875 71 3252 3385.1 -133.1 72 3717 3385.1 331.9 73 2642 3385.1 -743.1 74 2989 3385.1 -396.1 75 3607 3385.1 221.9 76 5366 5145.125 220.875 77 8898 8842.76315789474 55.2368421052633 78 9435 8842.76315789474 592.236842105263 79 7328 8842.76315789474 -1514.76315789474 80 8594 8842.76315789474 -248.763157894737 81 11349 8842.76315789474 2506.23684210526 82 5797 5145.125 651.875 83 3621 5145.125 -1524.125 84 3851 3385.1 465.9

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/2ym0r1292698914.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/2ym0r1292698914.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/3ym0r1292698914.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/3ym0r1292698914.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/4reid1292698914.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699035a07i1zkmhhl67cf/4reid1292698914.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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