Recursive Participation - werkloosheid en consumentenvertrouwen

*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, 10 Dec 2010 13:40:23 +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/10/t1291988318ilo8t1gvrlrwg5q.htm/, Retrieved Fri, 10 Dec 2010 14:38:38 +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/10/t1291988318ilo8t1gvrlrwg5q.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 «

235.1 1 280.7 1 264.6 2 240.7 0 201.4 1 240.8 0 241.1 -1 223.8 -3 206.1 -3 174.7 -3 203.3 -4 220.5 -8 299.5 -9 347.4 -13 338.3 -18 327.7 -11 351.6 -9 396.6 -10 438.8 -13 395.6 -11 363.5 -5 378.8 -15 357 -6 369 -6 464.8 -3 479.1 -1 431.3 -3 366.5 -4 326.3 -6 355.1 0 331.6 -4 261.3 -2 249 -2 205.5 -6 235.6 -7 240.9 -6 264.9 -6 253.8 -3 232.3 -2 193.8 -5 177 -11 213.2 -11 207.2 -11 180.6 -10 188.6 -14 175.4 -8 199 -9 179.6 -5 225.8 -1 234 -2 200.2 -5 183.6 -4 178.2 -6 203.2 -2 208.5 -2 191.8 -2 172.8 -2 148 2 159.4 1 154.5 -8 213.2 -1 196.4 1 182.8 -1 176.4 2 153.6 2 173.2 1 171 -1 151.2 -2 161.9 -2 157.2 -1 201.7 -8 236.4 -4 356.1 -6 398.3 -3 403.7 -3 384.6 -7 365.8 -9 368.1 -11 367.9 -13 347 -11 343.3 -9 292.9 -17 311.5 -22 300.9 -25 366.9 -20 356.9 -24 329.7 -24 316.2 -22 269 -19 289.3 -18 266.2 -17 253.6 -11 233.8 -11 228.4 -12 253.6 -10 260.1 -15 306.6 -15 309.2 -15 309.5 -13 271 -8 279.9 -13 317.9 -9 298.4 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 0.3972 R-squared 0.1578 RMSE 73.3205 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 235.1 218.409677419355 16.6903225806452 2 280.7 218.409677419355 62.2903225806452 3 264.6 218.409677419355 46.1903225806452 4 240.7 218.409677419355 22.2903225806452 5 201.4 218.409677419355 -17.0096774193548 6 240.8 218.409677419355 22.3903225806452 7 241.1 218.409677419355 22.6903225806452 8 223.8 288.169333333333 -64.3693333333333 9 206.1 288.169333333333 -82.0693333333333 10 174.7 288.169333333333 -113.469333333333 11 203.3 288.169333333333 -84.8693333333333 12 220.5 288.169333333333 -67.6693333333333 13 299.5 288.169333333333 11.3306666666667 14 347.4 288.169333333333 59.2306666666667 15 338.3 288.169333333333 50.1306666666667 16 327.7 288.169333333333 39.5306666666667 17 351.6 288.169333333333 63.4306666666667 18 396.6 288.169333333333 108.430666666667 19 438.8 288.169333333333 150.630666666667 20 395.6 288.169333333333 107.430666666667 21 363.5 288.169333333333 75.3306666666667 22 378.8 288.169333333333 90.6306666666667 23 357 288.169333333333 68.8306666666667 24 369 288.169333333333 80.8306666666667 25 464.8 288.169333333333 176.630666666667 26 479.1 218.409677419355 260.690322580645 27 431.3 288.169333333333 143.130666666667 28 366.5 288.169333333333 78.3306666666667 29 326.3 288.169333333333 38.1306666666667 30 355.1 218.409677419355 136.690322580645 31 331.6 288.169333333333 43.4306666666667 32 261.3 218.409677419355 42.8903225806452 33 249 218.409677419355 30.5903225806452 34 205.5 288.169333333333 -82.6693333333333 35 235.6 288.169333333333 -52.5693333333333 36 240.9 288.169333333333 -47.2693333333333 37 264.9 288.169333333333 -23.2693333333333 38 253.8 288.169333333333 -34.3693333333333 39 232.3 218.409677419355 13.8903225806452 40 193.8 288.169333333333 -94.3693333333333 41 177 288.169333333333 -111.169333333333 42 213.2 288.169333333333 -74.9693333333333 43 207.2 288.169333333333 -80.9693333333333 44 180.6 288.169333333333 -107.569333333333 45 188.6 288.169333333333 -99.5693333333333 46 175.4 288.169333333333 -112.769333333333 47 199 288.169333333333 -89.1693333333333 48 179.6 288.169333333333 -108.569333333333 49 225.8 218.409677419355 7.39032258064518 50 234 218.409677419355 15.5903225806452 51 200.2 288.169333333333 -87.9693333333333 52 183.6 288.169333333333 -104.569333333333 53 178.2 288.169333333333 -109.969333333333 54 203.2 218.409677419355 -15.2096774193548 55 208.5 218.409677419355 -9.90967741935484 56 191.8 218.409677419355 -26.6096774193548 57 172.8 218.409677419355 -45.6096774193548 58 148 218.409677419355 -70.4096774193548 59 159.4 218.409677419355 -59.0096774193548 60 154.5 288.169333333333 -133.669333333333 61 213.2 218.409677419355 -5.20967741935485 62 196.4 218.409677419355 -22.0096774193548 63 182.8 218.409677419355 -35.6096774193548 64 176.4 218.409677419355 -42.0096774193548 65 153.6 218.409677419355 -64.8096774193548 66 173.2 218.409677419355 -45.2096774193548 67 171 218.409677419355 -47.4096774193548 68 151.2 218.409677419355 -67.2096774193548 69 161.9 218.409677419355 -56.5096774193548 70 157.2 218.409677419355 -61.2096774193548 71 201.7 288.169333333333 -86.4693333333333 72 236.4 288.169333333333 -51.7693333333333 73 356.1 288.169333333333 67.9306666666667 74 398.3 288.169333333333 110.130666666667 75 403.7 288.169333333333 115.530666666667 76 384.6 288.169333333333 96.4306666666667 77 365.8 288.169333333333 77.6306666666667 78 368.1 288.169333333333 79.9306666666667 79 367.9 288.169333333333 79.7306666666667 80 347 288.169333333333 58.8306666666667 81 343.3 288.169333333333 55.1306666666667 82 292.9 288.169333333333 4.73066666666665 83 311.5 288.169333333333 23.3306666666667 84 300.9 288.169333333333 12.7306666666667 85 366.9 288.169333333333 78.7306666666667 86 356.9 288.169333333333 68.7306666666667 87 329.7 288.169333333333 41.5306666666667 88 316.2 288.169333333333 28.0306666666667 89 269 288.169333333333 -19.1693333333333 90 289.3 288.169333333333 1.13066666666668 91 266.2 288.169333333333 -21.9693333333333 92 253.6 288.169333333333 -34.5693333333333 93 233.8 288.169333333333 -54.3693333333333 94 228.4 288.169333333333 -59.7693333333333 95 253.6 288.169333333333 -34.5693333333333 96 260.1 288.169333333333 -28.0693333333333 97 306.6 288.169333333333 18.4306666666667 98 309.2 288.169333333333 21.0306666666667 99 309.5 288.169333333333 21.3306666666667 100 271 288.169333333333 -17.1693333333333 101 279.9 288.169333333333 -8.26933333333335 102 317.9 288.169333333333 29.7306666666667 103 298.4 288.169333333333 10.2306666666667 104 246.7 288.169333333333 -41.4693333333333 105 227.3 288.169333333333 -60.8693333333333 106 209.1 218.409677419355 -9.30967741935484

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/2lr0n1291988416.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/2lr0n1291988416.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/3lr0n1291988416.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/3lr0n1291988416.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/4w0z81291988416.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291988318ilo8t1gvrlrwg5q/4w0z81291988416.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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') }

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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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