Paper Recursive Partitioning 2

*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: Sun, 19 Dec 2010 21:18:05 +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/19/t12927934926ihbl0f78heho1b.htm/, Retrieved Sun, 19 Dec 2010 22:18:12 +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/19/t12927934926ihbl0f78heho1b.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 «

97.06 21.454 631.923 130.678 97.73 23.899 654.294 120.877 98 24.939 671.833 137.114 97.76 23.580 586.840 134.406 97.48 24.562 600.969 120.262 97.77 24.696 625.568 130.846 97.96 23.785 558.110 120.343 98.22 23.812 630.577 98.881 98.51 21.917 628.654 115.678 98.19 19.713 603.184 120.796 98.37 19.282 656.255 94.261 98.31 18.788 600.730 89.151 98.6 21.453 670.326 119.880 98.96 24.482 678.423 131.468 99.11 27.474 641.502 155.089 99.64 27.264 625.311 149.581 100.02 27.349 628.177 122.788 99.98 30.632 589.767 143.900 100.32 29.429 582.471 112.115 100.44 30.084 636.248 109.600 100.51 26.290 599.885 117.446 101 24.379 621.694 118.456 100.88 23.335 637.406 101.901 100.55 21.346 595.994 89.940 100.82 21.106 696.308 129.143 101.5 24.514 674.201 126.102 102.15 28.353 648.861 143.048 102.39 30.805 649.605 142.258 102.54 31.348 672.392 131.011 102.85 34.556 598.396 146.471 103.47 33.855 613.177 114.073 103.56 34.787 638.104 114.642 103.69 32.529 615.632 118.226 103.49 29.998 634.465 111.338 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.9131 R-squared 0.8337 RMSE 3.4133 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 21.454 24.214 -2.76 2 23.899 24.214 -0.314999999999998 3 24.939 24.214 0.725000000000001 4 23.58 24.214 -0.634 5 24.562 24.214 0.348000000000003 6 24.696 24.214 0.482000000000003 7 23.785 24.214 -0.428999999999998 8 23.812 24.214 -0.401999999999997 9 21.917 24.214 -2.29700000000000 10 19.713 24.214 -4.501 11 19.282 24.214 -4.932 12 18.788 24.214 -5.426 13 21.453 24.214 -2.761 14 24.482 24.214 0.268000000000001 15 27.474 24.214 3.26 16 27.264 24.214 3.05 17 27.349 24.214 3.135 18 30.632 24.214 6.418 19 29.429 24.214 5.215 20 30.084 24.214 5.87 21 26.29 24.214 2.076 22 24.379 24.214 0.165000000000003 23 23.335 24.214 -0.878999999999998 24 21.346 24.214 -2.868 25 21.106 24.214 -3.108 26 24.514 24.214 0.300000000000001 27 28.353 30.7547777777778 -2.40177777777778 28 30.805 30.7547777777778 0.0502222222222208 29 31.348 30.7547777777778 0.59322222222222 30 34.556 30.7547777777778 3.80122222222222 31 33.855 30.7547777777778 3.10022222222222 32 34.787 38.0655483870968 -3.27854838709678 33 32.529 38.0655483870968 -5.53654838709677 34 29.998 30.7547777777778 -0.756777777777778 35 29.257 30.7547777777778 -1.49777777777778 36 28.155 30.7547777777778 -2.59977777777778 37 30.466 30.7547777777778 -0.288777777777778 38 35.704 38.0655483870968 -2.36154838709678 39 39.327 38.0655483870968 1.26145161290322 40 39.351 38.0655483870968 1.28545161290322 41 42.234 44.0426666666667 -1.80866666666667 42 43.63 44.0426666666667 -0.412666666666667 43 43.722 44.0426666666667 -0.320666666666668 44 43.121 44.0426666666667 -0.921666666666667 45 37.985 38.0655483870968 -0.0805483870967763 46 37.135 38.0655483870968 -0.930548387096778 47 34.646 38.0655483870968 -3.41954838709677 48 33.026 38.0655483870968 -5.03954838709677 49 35.087 38.0655483870968 -2.97854838709677 50 38.846 38.0655483870968 0.780451612903221 51 42.013 38.0655483870968 3.94745161290322 52 43.908 38.0655483870968 5.84245161290323 53 42.868 44.0426666666667 -1.17466666666667 54 44.423 44.0426666666667 0.380333333333333 55 44.167 44.0426666666667 0.124333333333333 56 43.636 44.0426666666667 -0.406666666666666 57 44.382 38.0655483870968 6.31645161290322 58 42.142 44.0426666666667 -1.90066666666667 59 43.452 44.0426666666667 -0.590666666666671 60 36.912 44.0426666666667 -7.13066666666667 61 42.413 38.0655483870968 4.34745161290322 62 45.344 38.0655483870968 7.27845161290323 63 44.873 38.0655483870968 6.80745161290322 64 47.51 44.0426666666667 3.46733333333333 65 49.554 44.0426666666667 5.51133333333333 66 47.369 44.0426666666667 3.32633333333333 67 45.998 44.0426666666667 1.95533333333333 68 48.14 38.0655483870968 10.0744516129032 69 48.441 44.0426666666667 4.39833333333333 70 44.928 44.0426666666667 0.885333333333328 71 40.454 38.0655483870968 2.38845161290322 72 38.661 44.0426666666667 -5.38166666666667 73 37.246 38.0655483870968 -0.819548387096773 74 36.843 38.0655483870968 -1.22254838709677 75 36.424 38.0655483870968 -1.64154838709678 76 37.594 38.0655483870968 -0.471548387096774 77 38.144 38.0655483870968 0.0784516129032227 78 38.737 38.0655483870968 0.671451612903226 79 34.56 38.0655483870968 -3.50554838709677 80 36.08 38.0655483870968 -1.98554838709678 81 33.508 38.0655483870968 -4.55754838709677 82 35.462 38.0655483870968 -2.60354838709677 83 33.374 38.0655483870968 -4.69154838709677 84 32.11 38.0655483870968 -5.95554838709678

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/2ce831292793477.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/2ce831292793477.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/3ce831292793477.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/3ce831292793477.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/4n5861292793477.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t12927934926ihbl0f78heho1b/4n5861292793477.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = equal ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 4 ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by