test

*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 12:52:41 +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/t1292763038rdc2an0i24rqi96.htm/, Retrieved Sun, 19 Dec 2010 13:50:40 +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/t1292763038rdc2an0i24rqi96.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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation NA R-squared NA RMSE 41.3567 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 631.923 636.72825 -4.80525000000023 2 654.294 636.72825 17.5657499999998 3 671.833 636.72825 35.1047499999997 4 586.84 636.72825 -49.8882500000002 5 600.969 636.72825 -35.7592500000002 6 625.568 636.72825 -11.1602500000002 7 558.11 636.72825 -78.6182500000002 8 630.577 636.72825 -6.15125000000023 9 628.654 636.72825 -8.07425000000023 10 603.184 636.72825 -33.5442500000003 11 656.255 636.72825 19.5267499999998 12 600.73 636.72825 -35.9982500000002 13 670.326 636.72825 33.5977499999998 14 678.423 636.72825 41.6947499999998 15 641.502 636.72825 4.77374999999972 16 625.311 636.72825 -11.4172500000002 17 628.177 636.72825 -8.55125000000021 18 589.767 636.72825 -46.9612500000002 19 582.471 636.72825 -54.2572500000002 20 636.248 636.72825 -0.480250000000183 21 599.885 636.72825 -36.8432500000002 22 621.694 636.72825 -15.0342500000003 23 637.406 636.72825 0.677749999999719 24 595.994 636.72825 -40.7342500000002 25 696.308 636.72825 59.5797499999998 26 674.201 636.72825 37.4727499999998 27 648.861 636.72825 12.1327499999998 28 649.605 636.72825 12.8767499999998 29 672.392 636.72825 35.6637499999998 30 598.396 636.72825 -38.3322500000003 31 613.177 636.72825 -23.5512500000002 32 638.104 636.72825 1.37574999999981 33 615.632 636.72825 -21.0962500000003 34 634.465 636.72825 -2.2632500000002 35 638.686 636.72825 1.95774999999981 36 604.243 636.72825 -32.4852500000002 37 706.669 636.72825 69.9407499999998 38 677.185 636.72825 40.4567499999997 39 644.328 636.72825 7.59974999999974 40 664.825 636.72825 28.0967499999998 41 605.707 636.72825 -31.0212500000002 42 600.136 636.72825 -36.5922500000003 43 612.166 636.72825 -24.5622500000002 44 599.659 636.72825 -37.0692500000002 45 634.21 636.72825 -2.51825000000019 46 618.234 636.72825 -18.4942500000002 47 613.576 636.72825 -23.1522500000002 48 627.2 636.72825 -9.52825000000018 49 668.973 636.72825 32.2447499999997 50 651.479 636.72825 14.7507499999998 51 619.661 636.72825 -17.0672500000003 52 644.26 636.72825 7.53174999999976 53 579.936 636.72825 -56.7922500000002 54 601.752 636.72825 -34.9762500000003 55 595.376 636.72825 -41.3522500000003 56 588.902 636.72825 -47.8262500000002 57 634.341 636.72825 -2.38725000000022 58 594.305 636.72825 -42.4232500000003 59 606.2 636.72825 -30.5282500000002 60 610.926 636.72825 -25.8022500000002 61 633.685 636.72825 -3.04325000000028 62 639.696 636.72825 2.96774999999980 63 659.451 636.72825 22.7227499999998 64 593.248 636.72825 -43.4802500000002 65 606.677 636.72825 -30.0512500000002 66 599.434 636.72825 -37.2942500000003 67 569.578 636.72825 -67.1502500000003 68 629.873 636.72825 -6.85525000000018 69 613.438 636.72825 -23.2902500000002 70 604.172 636.72825 -32.5562500000002 71 658.328 636.72825 21.5997499999997 72 612.633 636.72825 -24.0952500000002 73 707.372 636.72825 70.6437499999997 74 739.77 636.72825 103.041750000000 75 777.535 636.72825 140.806750000000 76 685.03 636.72825 48.3017499999997 77 730.234 636.72825 93.5057499999998 78 714.154 636.72825 77.4257499999998 79 630.872 636.72825 -5.85625000000027 80 719.492 636.72825 82.7637499999997 81 677.023 636.72825 40.2947499999998 82 679.272 636.72825 42.5437499999998 83 718.317 636.72825 81.5887499999998 84 645.672 636.72825 8.9437499999998

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/26jh01292763148.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/26jh01292763148.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/3ysg31292763148.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/3ysg31292763148.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/4r2f61292763148.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292763038rdc2an0i24rqi96/4r2f61292763148.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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

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