*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: Fri, 24 Dec 2010 11:01:47 +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/24/t12931887429jddmf5rns0h355.htm/, Retrieved Fri, 24 Dec 2010 12:05:43 +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/24/t12931887429jddmf5rns0h355.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 «

9,1 4,5 1,0 -1,0 1989,3 9,0 4,3 1,0 3,0 2097,8 9,0 4,3 1,3 2,0 2154,9 8,9 4,2 1,1 3,0 2152,2 8,8 4,0 0,8 5,0 2250,3 8,7 3,8 0,7 5,0 2346,9 8,5 4,1 0,7 3,0 2525,6 8,3 4,2 0,9 2,0 2409,4 8,1 4,0 1,3 1,0 2394,4 7,9 4,3 1,4 -4,0 2401,3 7,8 4,7 1,6 1,0 2354,3 7,6 5,0 2,1 1,0 2450,4 7,4 5,1 0,3 6,0 2504,7 7,2 5,4 2,1 3,0 2661,4 7,0 5,4 2,5 2,0 2880,4 7,0 5,4 2,3 2,0 3064,4 6,8 5,5 2,4 2,0 3141,1 6,8 5,8 3,0 -8,0 3327,7 6,7 5,7 1,7 0,0 3565,0 6,8 5,5 3,5 -2,0 3403,1 6,7 5,6 4,0 3,0 3149,9 6,7 5,6 3,7 5,0 3006,8 6,7 5,5 3,7 8,0 3230,7 6,5 5,5 3,0 8,0 3361,1 6,3 5,7 2,7 9,0 3484,7 6,3 5,6 2,5 11,0 3411,1 6,3 5,6 2,2 13,0 3288,2 6,5 5,4 2,9 12,0 3280,4 6,6 5,2 3,1 13,0 3174,0 6,5 5,1 3,0 15,0 3165,3 6,3 5,1 2,8 13,0 3092,7 6,3 5,0 2,5 16,0 3053,1 6,5 5,3 1,9 10,0 3182,0 7,0 5,4 1,9 14,0 2999,9 7,1 5,3 1,8 14,0 3249,6 7,3 5,1 2,0 15,0 3210,5 7,3 5,0 2,6 13,0 3030,3 7,4 5,0 2,5 8,0 2803,5 7,4 4,6 2,5 7,0 2767,6 7,3 4,8 1,6 3,0 2882,6 7,4 5,1 1,4 3,0 2863,4 7,5 5,1 0,8 4,0 2897,1 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 0.9236 R-squared 0.8531 RMSE 0.2726 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 9.1 7.73157894736842 1.36842105263158 2 9 8.78571428571429 0.214285714285714 3 9 8.78571428571429 0.214285714285714 4 8.9 8.78571428571429 0.114285714285714 5 8.8 8.78571428571429 0.0142857142857142 6 8.7 8.78571428571429 -0.0857142857142872 7 8.5 8.27 0.23 8 8.3 8.27 0.0300000000000011 9 8.1 8.27 -0.17 10 7.9 8.27 -0.369999999999999 11 7.8 7.73157894736842 0.0684210526315789 12 7.6 7.1875 0.4125 13 7.4 7.73157894736842 -0.331578947368421 14 7.2 6.70952380952381 0.490476190476191 15 7 6.70952380952381 0.29047619047619 16 7 6.70952380952381 0.29047619047619 17 6.8 6.70952380952381 0.0904761904761902 18 6.8 6.70952380952381 0.0904761904761902 19 6.7 7.73157894736842 -1.03157894736842 20 6.8 6.70952380952381 0.0904761904761902 21 6.7 6.70952380952381 -0.0095238095238095 22 6.7 6.70952380952381 -0.0095238095238095 23 6.7 6.70952380952381 -0.0095238095238095 24 6.5 6.70952380952381 -0.20952380952381 25 6.3 6.70952380952381 -0.40952380952381 26 6.3 6.70952380952381 -0.40952380952381 27 6.3 6.70952380952381 -0.40952380952381 28 6.5 6.70952380952381 -0.20952380952381 29 6.6 6.70952380952381 -0.10952380952381 30 6.5 6.70952380952381 -0.20952380952381 31 6.3 6.70952380952381 -0.40952380952381 32 6.3 7.1875 -0.8875 33 6.5 6.70952380952381 -0.20952380952381 34 7 6.70952380952381 0.29047619047619 35 7.1 6.70952380952381 0.39047619047619 36 7.3 6.70952380952381 0.59047619047619 37 7.3 7.1875 0.1125 38 7.4 7.1875 0.2125 39 7.4 7.1875 0.2125 40 7.3 7.73157894736842 -0.431578947368421 41 7.4 7.73157894736842 -0.331578947368421 42 7.5 7.73157894736842 -0.231578947368421 43 7.7 7.73157894736842 -0.0315789473684207 44 7.7 7.73157894736842 -0.0315789473684207 45 7.7 7.73157894736842 -0.0315789473684207 46 7.7 7.73157894736842 -0.0315789473684207 47 7.7 7.73157894736842 -0.0315789473684207 48 7.8 7.73157894736842 0.0684210526315789 49 8 7.73157894736842 0.268421052631579 50 8.1 8.06428571428571 0.0357142857142847 51 8.1 7.73157894736842 0.368421052631579 52 8.2 7.73157894736842 0.468421052631578 53 8.2 8.27 -0.0700000000000003 54 8.2 8.27 -0.0700000000000003 55 8.1 8.27 -0.17 56 8.1 8.06428571428571 0.0357142857142847 57 8.2 8.06428571428571 0.135714285714284 58 8.3 8.47272727272727 -0.172727272727272 59 8.3 8.06428571428571 0.235714285714286 60 8.4 8.06428571428571 0.335714285714285 61 8.5 8.27 0.23 62 8.5 8.27 0.23 63 8.4 8.27 0.130000000000001 64 8 8.06428571428571 -0.064285714285715 65 7.9 8.06428571428571 -0.164285714285715 66 8.1 8.06428571428571 0.0357142857142847 67 8.5 8.50588235294118 -0.00588235294117645 68 8.8 8.50588235294118 0.294117647058824 69 8.8 8.78571428571429 0.0142857142857142 70 8.6 8.50588235294118 0.0941176470588232 71 8.3 8.50588235294118 -0.205882352941176 72 8.3 8.78571428571429 -0.485714285714286 73 8.3 8.50588235294118 -0.205882352941176 74 8.4 8.50588235294118 -0.105882352941176 75 8.4 8.50588235294118 -0.105882352941176 76 8.5 8.50588235294118 -0.00588235294117645 77 8.6 8.50588235294118 0.0941176470588232 78 8.6 8.50588235294118 0.0941176470588232 79 8.6 8.50588235294118 0.0941176470588232 80 8.6 8.50588235294118 0.0941176470588232 81 8.6 8.50588235294118 0.0941176470588232 82 8.5 8.50588235294118 -0.00588235294117645 83 8.4 8.50588235294118 -0.105882352941176 84 8.4 8.50588235294118 -0.105882352941176 85 8.4 8.47272727272727 -0.0727272727272723 86 8.5 8.47272727272727 0.0272727272727273 87 8.5 8.47272727272727 0.0272727272727273 88 8.6 8.47272727272727 0.127272727272727 89 8.6 8.47272727272727 0.127272727272727 90 8.4 8.47272727272727 -0.0727272727272723 91 8.2 8.47272727272727 -0.272727272727273 92 8 8.06428571428571 -0.064285714285715 93 8 8.06428571428571 -0.064285714285715 94 8 8.06428571428571 -0.064285714285715 95 8 8.06428571428571 -0.064285714285715 96 7.9 8.06428571428571 -0.164285714285715 97 7.9 8.06428571428571 -0.164285714285715 98 7.8 7.13636363636364 0.663636363636363 99 7.8 8.15 -0.350000000000001 100 8 8.15 -0.15 101 7.8 8.15 -0.350000000000001 102 7.4 7.13636363636364 0.263636363636364 103 7.2 7.13636363636364 0.0636363636363635 104 7 7.13636363636364 -0.136363636363637 105 7 7.13636363636364 -0.136363636363637 106 7.2 7.1875 0.0125000000000002 107 7.2 7.1875 0.0125000000000002 108 7.2 7.1875 0.0125000000000002 109 7 7.1875 -0.1875 110 6.9 7.1875 -0.2875 111 6.8 7.13636363636364 -0.336363636363637 112 6.8 7.13636363636364 -0.336363636363637 113 6.8 7.1875 -0.3875 114 6.9 7.13636363636364 -0.236363636363636 115 7.2 7.13636363636364 0.0636363636363635 116 7.2 7.13636363636364 0.0636363636363635 117 7.2 7.13636363636364 0.0636363636363635 118 7.1 7.1875 -0.0875000000000004 119 7.2 7.1875 0.0125000000000002 120 7.3 7.1875 0.1125 121 7.5 7.1875 0.3125 122 7.6 7.1875 0.4125 123 7.7 7.73157894736842 -0.0315789473684207 124 7.7 7.73157894736842 -0.0315789473684207 125 7.7 7.73157894736842 -0.0315789473684207 126 7.8 8.15 -0.350000000000001 127 8 8.15 -0.15 128 8.1 8.15 -0.0500000000000007 129 8.1 8.15 -0.0500000000000007 130 8 8.15 -0.15 131 8.1 8.15 -0.0500000000000007 132 8.2 8.15 0.0499999999999989 133 8.3 8.15 0.15 134 8.4 8.15 0.25 135 8.4 8.15 0.25 136 8.4 8.15 0.25 137 8.5 8.15 0.35 138 8.5 8.15 0.35 139 8.6 8.47272727272727 0.127272727272727 140 8.6 8.47272727272727 0.127272727272727 141 8.5 8.47272727272727 0.0272727272727273 142 8.5 8.50588235294118 -0.00588235294117645

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/244bv1293188500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/244bv1293188500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/344bv1293188500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/344bv1293188500.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/4fwtg1293188500.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931887429jddmf5rns0h355/4fwtg1293188500.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = yes ;

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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