recursive partitioning (NC) gewesten

*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, 11 Dec 2010 17:29:40 +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/11/t1292088638iwo89qm4iw3gv1o.htm/, Retrieved Sat, 11 Dec 2010 18:30:39 +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/11/t1292088638iwo89qm4iw3gv1o.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 «

33024 31086 19828 18932 32526 30839 19967 18927 31455 30051 19814 19124 31524 29976 20053 19066 31856 30463 20719 19971 32696 31422 21174 20165 32584 31588 20648 19705 33498 31900 20659 19718 34175 32878 20733 19938 34172 33010 21069 20039 34379 32954 20566 19721 34988 33076 20839 19777 36158 35057 21615 20505 37411 35906 22739 21763 38015 36100 23222 22404 37577 35824 23031 22038 36354 34579 23014 22038 36030 34484 22868 21874 35636 33920 22182 21269 35669 34059 22177 21127 34635 33812 21216 20609 35496 34594 21031 20565 36376 36083 20968 19791 37635 36563 21049 20672 38875 37416 21033 20938 38372 37953 21078 20675 38897 37517 20702 19992 38018 37467 20309 19801 37325 36963 20449 20050 36893 36019 20737 20427 36117 35232 20849 20815 37599 36857 21966 21666 39037 37978 23100 22720 40809 40160 23975 23650 42508 42165 24350 24244 44021 43069 24020 23669 44088 43021 24005 23881 44510 43376 23602 23857 45786 43978 24120 23999 47349 45911 24847 24780 48696 47107 25702 25426 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 0.994 R-squared 0.988 RMSE 1810.3897 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 33024 33156.4285714286 -132.428571428572 2 32526 33156.4285714286 -630.428571428572 3 31455 33156.4285714286 -1701.42857142857 4 31524 33156.4285714286 -1632.42857142857 5 31856 33156.4285714286 -1300.42857142857 6 32696 33156.4285714286 -460.428571428572 7 32584 33156.4285714286 -572.428571428572 8 33498 33156.4285714286 341.571428571428 9 34175 33156.4285714286 1018.57142857143 10 34172 33156.4285714286 1015.57142857143 11 34379 33156.4285714286 1222.57142857143 12 34988 33156.4285714286 1831.57142857143 13 36158 36439.2352941177 -281.23529411765 14 37411 36439.2352941177 971.76470588235 15 38015 36439.2352941177 1575.76470588235 16 37577 36439.2352941177 1137.76470588235 17 36354 36439.2352941177 -85.2352941176505 18 36030 36439.2352941177 -409.23529411765 19 35636 36439.2352941177 -803.23529411765 20 35669 36439.2352941177 -770.23529411765 21 34635 33156.4285714286 1478.57142857143 22 35496 36439.2352941177 -943.23529411765 23 36376 36439.2352941177 -63.2352941176505 24 37635 36439.2352941177 1195.76470588235 25 38875 40097.625 -1222.625 26 38372 40097.625 -1725.625 27 38897 40097.625 -1200.625 28 38018 40097.625 -2079.625 29 37325 36439.2352941177 885.76470588235 30 36893 36439.2352941177 453.76470588235 31 36117 36439.2352941177 -322.23529411765 32 37599 36439.2352941177 1159.76470588235 33 39037 40097.625 -1060.625 34 40809 40097.625 711.375 35 42508 40097.625 2410.375 36 44021 47061.1875 -3040.1875 37 44088 47061.1875 -2973.1875 38 44510 47061.1875 -2551.1875 39 45786 47061.1875 -1275.1875 40 47349 47061.1875 287.8125 41 48696 47061.1875 1634.8125 42 50598 47061.1875 3536.8125 43 50066 47061.1875 3004.8125 44 49367 47061.1875 2305.8125 45 48784 47061.1875 1722.8125 46 47841 47061.1875 779.8125 47 48300 47061.1875 1238.8125 48 47518 47061.1875 456.8125 49 46504 47061.1875 -557.1875 50 45147 47061.1875 -1914.1875 51 44404 47061.1875 -2657.1875 52 43455 40097.625 3357.375 53 42299 40097.625 2201.375 54 42105 40097.625 2007.375 55 40152 40097.625 54.375 56 39519 40097.625 -578.625 57 39633 40097.625 -464.625 58 39376 40097.625 -721.625 59 38850 40097.625 -1247.625 60 39657 40097.625 -440.625 61 34804 36439.2352941177 -1635.23529411765 62 34372 36439.2352941177 -2067.23529411765 63 32678 33156.4285714286 -478.428571428572 64 28420 26704.5 1715.5 65 25420 26704.5 -1284.5 66 27683 26704.5 978.5 67 29904 26704.5 3199.5 68 30546 26704.5 3841.5 69 29142 26704.5 2437.5 70 27724 26704.5 1019.5 71 27069 26704.5 364.5 72 26665 26704.5 -39.5 73 26004 26704.5 -700.5 74 25767 26704.5 -937.5 75 24915 26704.5 -1789.5 76 23689 26704.5 -3015.5 77 20915 26704.5 -5789.5 78 19414 13769.6666666667 5644.33333333333 79 17824 13769.6666666667 4054.33333333333 80 16348 13769.6666666667 2578.33333333333 81 15571 13769.6666666667 1801.33333333333 82 13929 13769.6666666667 159.333333333334 83 12480 13769.6666666667 -1289.66666666667 84 10837 13769.6666666667 -2932.66666666667 85 9473 13769.6666666667 -4296.66666666667 86 8051 13769.6666666667 -5718.66666666667 87 5278 2651 2627 88 3008 2651 357 89 2404 2651 -247 90 2298 2651 -353 91 2260 2651 -391 92 1938 2651 -713 93 1371 2651 -1280 94 1009 163.944444444444 845.055555555556 95 686 163.944444444444 522.055555555556 96 493 163.944444444444 329.055555555556 97 285 163.944444444444 121.055555555556 98 192 163.944444444444 28.0555555555555 99 129 163.944444444444 -34.9444444444445 100 60 163.944444444444 -103.944444444444 101 54 163.944444444444 -109.944444444444 102 26 163.944444444444 -137.944444444444 103 11 163.944444444444 -152.944444444444 104 3 163.944444444444 -160.944444444444 105 0 163.944444444444 -163.944444444444 106 2 163.944444444444 -161.944444444444 107 1 163.944444444444 -162.944444444444 108 0 163.944444444444 -163.944444444444 109 0 163.944444444444 -163.944444444444 110 0 163.944444444444 -163.944444444444 111 0 163.944444444444 -163.944444444444

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/2qyzz1292088573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/2qyzz1292088573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/3qyzz1292088573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/3qyzz1292088573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/417y21292088573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292088638iwo89qm4iw3gv1o/417y21292088573.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 1 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 1 ; 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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