Recursive Partitioning no categorization

*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: Mon, 13 Dec 2010 21:09:30 +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/13/t12922744695c05wdw5wpk3bsr.htm/, Retrieved Mon, 13 Dec 2010 22:07:49 +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/13/t12922744695c05wdw5wpk3bsr.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 «

2649.2 31077 2579.4 31293 2504.6 30236 2462.3 30160 2467.4 32436 2446.7 30695 2656.3 27525 2626.2 26434 2482.6 25739 2539.9 25204 2502.7 24977 2466.9 24320 2513.2 22680 2443.3 22052 2293.4 21467 2070.8 21383 2029.6 21777 2052 21928 1864.4 21814 1670.1 22937 1811 23595 1905.4 20830 1862.8 19650 2014.5 19195 2197.8 19644 2962.3 18483 3047 18079 3032.6 19178 3504.4 18391 3801.1 18441 3857.6 18584 3674.4 20108 3721 20148 3844.5 19394 4116.7 17745 4105.2 17696 4435.2 17032 4296.5 16438 4202.5 15683 4562.8 15594 4621.4 15713 4697 15937 4591.3 16171 4357 15928 4502.6 16348 4443.9 15579 4290.9 15305 4199.8 15648 4138.5 14954 3970.1 15137 3862.3 15839 3701.6 16050 3570.12 15168 3801.06 17064 3895.51 16005 3917.96 14886 3813.06 14931 3667.03 14544 3494.17 13812 3364 13031 3295.3 12574 3277.0 11964 3257.2 11451 3161.7 11346 3097.3 11353 3061.3 10702 3119.3 10646 310 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.8764 R-squared 0.7681 RMSE 405.7911 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 2649.2 2461.55172413793 187.648275862069 2 2579.4 2461.55172413793 117.848275862069 3 2504.6 2461.55172413793 43.0482758620687 4 2462.3 2461.55172413793 0.748275862069022 5 2467.4 2461.55172413793 5.84827586206893 6 2446.7 2461.55172413793 -14.8517241379313 7 2656.3 2461.55172413793 194.748275862069 8 2626.2 2461.55172413793 164.648275862069 9 2482.6 2461.55172413793 21.0482758620687 10 2539.9 2461.55172413793 78.348275862069 11 2502.7 2461.55172413793 41.1482758620687 12 2466.9 2461.55172413793 5.34827586206893 13 2513.2 2461.55172413793 51.6482758620687 14 2443.3 2461.55172413793 -18.2517241379310 15 2293.4 2461.55172413793 -168.151724137931 16 2070.8 2461.55172413793 -390.751724137931 17 2029.6 2461.55172413793 -431.951724137931 18 2052 2461.55172413793 -409.551724137931 19 1864.4 2461.55172413793 -597.151724137931 20 1670.1 2461.55172413793 -791.451724137931 21 1811 2461.55172413793 -650.551724137931 22 1905.4 2461.55172413793 -556.151724137931 23 1862.8 2461.55172413793 -598.751724137931 24 2014.5 2461.55172413793 -447.051724137931 25 2197.8 2461.55172413793 -263.751724137931 26 2962.3 3649.42 -687.12 27 3047 3649.42 -602.42 28 3032.6 2461.55172413793 571.048275862069 29 3504.4 3649.42 -145.02 30 3801.1 3649.42 151.680000000000 31 3857.6 3649.42 208.18 32 3674.4 2461.55172413793 1212.84827586207 33 3721 2461.55172413793 1259.44827586207 34 3844.5 2461.55172413793 1382.94827586207 35 4116.7 3649.42 467.28 36 4105.2 3649.42 455.78 37 4435.2 4312.10071428571 123.099285714286 38 4296.5 4312.10071428571 -15.6007142857143 39 4202.5 4312.10071428571 -109.600714285714 40 4562.8 4312.10071428571 250.699285714286 41 4621.4 4312.10071428571 309.299285714285 42 4697 4312.10071428571 384.899285714286 43 4591.3 4312.10071428571 279.199285714286 44 4357 4312.10071428571 44.8992857142857 45 4502.6 4312.10071428571 190.499285714286 46 4443.9 4312.10071428571 131.799285714285 47 4290.9 3909.66714285714 381.232857142857 48 4199.8 4312.10071428571 -112.300714285714 49 4138.5 3909.66714285714 228.832857142857 50 3970.1 3909.66714285714 60.4328571428573 51 3862.3 4312.10071428571 -449.800714285714 52 3701.6 4312.10071428571 -610.500714285714 53 3570.12 3909.66714285714 -339.547142857143 54 3801.06 3649.42 151.64 55 3895.51 4312.10071428571 -416.590714285714 56 3917.96 3909.66714285714 8.29285714285743 57 3813.06 3909.66714285714 -96.6071428571427 58 3667.03 3909.66714285714 -242.637142857142 59 3494.17 3191.32666666667 302.843333333333 60 3364 3191.32666666667 172.673333333333 61 3295.3 3191.32666666667 103.973333333333 62 3277 3191.32666666667 85.6733333333332 63 3257.2 3191.32666666667 65.873333333333 64 3161.7 3191.32666666667 -29.626666666667 65 3097.3 3191.32666666667 -94.0266666666666 66 3061.3 3191.32666666667 -130.026666666667 67 3119.3 3191.32666666667 -72.0266666666666 68 3106.22 3191.32666666667 -85.106666666667 69 3080.58 3191.32666666667 -110.746666666667 70 2981.85 3191.32666666667 -209.476666666667

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/2962x1292274563.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/2962x1292274563.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/3962x1292274563.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/3962x1292274563.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/4ky101292274563.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t12922744695c05wdw5wpk3bsr/4ky101292274563.ps (open in new window)

Parameters (Session):

par1 = pearson ;

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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