ws 10

*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, 14 Dec 2010 18:38:26 +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/14/t1292351793erdu495p94fy8vy.htm/, Retrieved Tue, 14 Dec 2010 19:36:34 +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/14/t1292351793erdu495p94fy8vy.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 «

14544,5 94,6 -3,0 14097,8 15116,3 95,9 -3,7 14776,8 17413,2 104,7 -4,7 16833,3 16181,5 102,8 -6,4 15385,5 15607,4 98,1 -7,5 15172,6 17160,9 113,9 -7,8 16858,9 14915,8 80,9 -7,7 14143,5 13768 95,7 -6,6 14731,8 17487,5 113,2 -4,2 16471,6 16198,1 105,9 -2,0 15214 17535,2 108,8 -0,7 17637,4 16571,8 102,3 0,1 17972,4 16198,9 99 0,9 16896,2 16554,2 100,7 2,1 16698 19554,2 115,5 3,5 19691,6 15903,8 100,7 4,9 15930,7 18003,8 109,9 5,7 17444,6 18329,6 114,6 6,2 17699,4 16260,7 85,4 6,5 15189,8 14851,9 100,5 6,5 15672,7 18174,1 114,8 6,3 17180,8 18406,6 116,5 6,2 17664,9 18466,5 112,9 6,4 17862,9 16016,5 102 6,3 16162,3 17428,5 106 5,8 17463,6 17167,2 105,3 5,1 16772,1 19630 118,8 5,1 19106,9 17183,6 106,1 5,8 16721,3 18344,7 109,3 6,7 18161,3 19301,4 117,2 7,1 18509,9 18147,5 92,5 6,7 17802,7 16192,9 104,2 5,5 16409,9 18374,4 112,5 4,2 17967,7 20515,2 122,4 3,0 20286,6 18957,2 113,3 2,2 19537,3 16471,5 100 2,0 18021,9 18746,8 110,7 1,8 20194,3 19009,5 112,8 1,8 19049,6 19211,2 109 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 4 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.7989 R-squared 0.6383 RMSE 5.9402 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 94.6 91.5384615384615 3.06153846153846 2 95.9 91.5384615384615 4.36153846153847 3 104.7 100.924242424242 3.77575757575758 4 102.8 100.924242424242 1.87575757575758 5 98.1 100.924242424242 -2.82424242424243 6 113.9 100.924242424242 12.9757575757576 7 80.9 91.5384615384615 -10.6384615384615 8 95.7 91.5384615384615 4.16153846153847 9 113.2 100.924242424242 12.2757575757576 10 105.9 100.924242424242 4.97575757575758 11 108.8 100.924242424242 7.87575757575758 12 102.3 100.924242424242 1.37575757575758 13 99 100.924242424242 -1.92424242424242 14 100.7 100.924242424242 -0.224242424242419 15 115.5 113.413636363636 2.08636363636364 16 100.7 100.924242424242 -0.224242424242419 17 109.9 100.924242424242 8.97575757575758 18 114.6 113.413636363636 1.18636363636364 19 85.4 100.924242424242 -15.5242424242424 20 100.5 91.5384615384615 8.96153846153847 21 114.8 113.413636363636 1.38636363636364 22 116.5 113.413636363636 3.08636363636364 23 112.9 113.413636363636 -0.513636363636351 24 102 100.924242424242 1.07575757575758 25 106 100.924242424242 5.07575757575758 26 105.3 100.924242424242 4.37575757575758 27 118.8 113.413636363636 5.38636363636364 28 106.1 100.924242424242 5.17575757575757 29 109.3 113.413636363636 -4.11363636363636 30 117.2 113.413636363636 3.78636363636365 31 92.5 100.924242424242 -8.42424242424242 32 104.2 100.924242424242 3.27575757575758 33 112.5 113.413636363636 -0.913636363636357 34 122.4 113.413636363636 8.98636363636365 35 113.3 113.413636363636 -0.11363636363636 36 100 100.924242424242 -0.924242424242422 37 110.7 113.413636363636 -2.71363636363635 38 112.8 113.413636363636 -0.61363636363636 39 109.8 113.413636363636 -3.61363636363636 40 117.3 113.413636363636 3.88636363636364 41 109.1 113.413636363636 -4.31363636363636 42 115.9 113.413636363636 2.48636363636365 43 96 113.413636363636 -17.4136363636364 44 99.8 100.924242424242 -1.12424242424242 45 116.8 113.413636363636 3.38636363636364 46 115.7 113.413636363636 2.28636363636365 47 99.4 100.924242424242 -1.52424242424242 48 94.3 91.5384615384615 2.76153846153846 49 91 91.5384615384615 -0.538461538461533 50 93.2 91.5384615384615 1.66153846153847 51 103.1 100.924242424242 2.17575757575757 52 94.1 91.5384615384615 2.56153846153846 53 91.8 91.5384615384615 0.261538461538464 54 102.7 100.924242424242 1.77575757575758 55 82.6 91.5384615384615 -8.93846153846154 56 89.1 91.5384615384615 -2.43846153846154 57 104.5 100.924242424242 3.57575757575758 58 105.1 100.924242424242 4.17575757575757 59 95.1 100.924242424242 -5.82424242424243 60 88.7 100.924242424242 -12.2242424242424 61 86.3 91.5384615384615 -5.23846153846154 62 91.8 100.924242424242 -9.12424242424242 63 111.5 113.413636363636 -1.91363636363636 64 99.7 100.924242424242 -1.22424242424242 65 97.5 100.924242424242 -3.42424242424242 66 111.7 113.413636363636 -1.71363636363635 67 86.2 100.924242424242 -14.7242424242424 68 95.4 100.924242424242 -5.52424242424242

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/2gm881292351900.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/2gm881292351900.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/3gm881292351900.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/3gm881292351900.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/49wqt1292351900.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292351793erdu495p94fy8vy/49wqt1292351900.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; 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