Recursive partitioning: affiliatie

*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, 27 Dec 2010 16:33:37 +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/27/t1293467493nwmctopo0faaldo.htm/, Retrieved Mon, 27 Dec 2010 17:31:37 +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/27/t1293467493nwmctopo0faaldo.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 «

22 1 27 5 26 49 35 23 1 36 4 25 45 34 27 1 25 4 17 54 13 19 1 27 3 37 36 35 15 2 25 3 35 36 28 29 2 44 3 15 53 32 25 1 50 4 27 46 35 25 1 41 4 36 42 36 21 1 48 5 25 41 27 22 2 43 4 30 45 29 22 2 47 2 27 47 27 24 2 41 3 33 42 28 22 1 44 2 29 45 29 23 2 47 5 30 40 28 19 2 40 3 25 45 30 19 2 46 3 23 40 25 21 1 28 3 26 42 15 20 1 56 3 24 45 33 23 2 49 4 35 47 31 11 2 25 4 39 31 37 21 2 41 4 23 46 37 19 2 26 3 32 34 34 21 1 50 5 29 43 32 23 1 47 4 26 45 21 19 1 52 2 21 42 25 22 2 37 5 35 51 32 19 2 41 3 23 44 28 23 1 45 4 21 47 22 29 2 26 4 28 47 25 27 1 NA 3 30 41 26 18 1 52 4 21 44 34 30 1 46 2 29 51 34 26 1 58 3 28 46 36 20 1 54 5 19 47 36 22 1 29 3 26 46 26 20 2 50 3 33 38 26 21 1 43 2 34 50 34 18 2 30 3 33 48 33 21 2 47 2 40 36 31 27 1 45 3 24 51 33 NA 2 48 1 35 35 22 18 2 48 3 35 49 29 24 2 26 4 32 38 24 24 1 46 5 20 47 37 17 2 NA 3 35 36 32 22 2 50 3 35 47 23 21 1 25 4 21 46 29 23 1 47 2 33 43 35 19 2 47 2 40 53 20 22 1 41 3 22 55 28 19 2 45 2 35 39 26 24 2 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.4744 R-squared 0.2251 RMSE 3.2339 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 22 21.6293706293706 0.37062937062937 2 23 21.6293706293706 1.37062937062937 3 27 24.4 2.6 4 19 17.6923076923077 1.30769230769231 5 15 17.6923076923077 -2.69230769230769 6 29 24.4 4.6 7 25 21.6293706293706 3.37062937062937 8 25 21.6293706293706 3.37062937062937 9 21 21.6293706293706 -0.62937062937063 10 22 21.6293706293706 0.37062937062937 11 22 21.6293706293706 0.37062937062937 12 24 21.6293706293706 2.37062937062937 13 22 21.6293706293706 0.37062937062937 14 23 21.6293706293706 1.37062937062937 15 19 21.6293706293706 -2.62937062937063 16 19 21.6293706293706 -2.62937062937063 17 21 21.6293706293706 -0.62937062937063 18 20 21.6293706293706 -1.62937062937063 19 23 21.6293706293706 1.37062937062937 20 11 17.6923076923077 -6.6923076923077 21 21 21.6293706293706 -0.62937062937063 22 19 17.6923076923077 1.30769230769231 23 21 21.6293706293706 -0.62937062937063 24 23 21.6293706293706 1.37062937062937 25 19 21.6293706293706 -2.62937062937063 26 22 24.4 -2.4 27 19 21.6293706293706 -2.62937062937063 28 23 21.6293706293706 1.37062937062937 29 29 21.6293706293706 7.37062937062937 30 27 21.6293706293706 5.37062937062937 31 18 21.6293706293706 -3.62937062937063 32 30 24.4 5.6 33 26 21.6293706293706 4.37062937062937 34 20 21.6293706293706 -1.62937062937063 35 22 21.6293706293706 0.37062937062937 36 20 21.6293706293706 -1.62937062937063 37 21 21.6293706293706 -0.62937062937063 38 18 21.6293706293706 -3.62937062937063 39 21 17.6923076923077 3.30769230769231 40 27 24.4 2.6 41 18 21.6293706293706 -3.62937062937063 42 24 21.6293706293706 2.37062937062937 43 24 21.6293706293706 2.37062937062937 44 17 17.6923076923077 -0.692307692307693 45 22 21.6293706293706 0.37062937062937 46 21 21.6293706293706 -0.62937062937063 47 23 21.6293706293706 1.37062937062937 48 19 24.4 -5.4 49 22 24.4 -2.4 50 19 21.6293706293706 -2.62937062937063 51 24 24.4 -0.399999999999999 52 22 21.6293706293706 0.37062937062937 53 26 17.6923076923077 8.3076923076923 54 22 24.4 -2.4 55 23 21.6293706293706 1.37062937062937 56 27 24.4 2.6 57 21 21.6293706293706 -0.62937062937063 58 16 17.6923076923077 -1.69230769230769 59 21 24.4 -3.4 60 18 21.6293706293706 -3.62937062937063 61 25 21.6293706293706 3.37062937062937 62 20 21.6293706293706 -1.62937062937063 63 24 21.6293706293706 2.37062937062937 64 20 24.4 -4.4 65 24 21.6293706293706 2.37062937062937 66 23 21.6293706293706 1.37062937062937 67 23 21.6293706293706 1.37062937062937 68 22 21.6293706293706 0.37062937062937 69 22 21.6293706293706 0.37062937062937 70 20 21.6293706293706 -1.62937062937063 71 14 17.6923076923077 -3.69230769230769 72 21 17.6923076923077 3.30769230769231 73 23 21.6293706293706 1.37062937062937 74 17 21.6293706293706 -4.62937062937063 75 25 24.4 0.600000000000001 76 10 17.6923076923077 -7.6923076923077 77 25 24.4 0.600000000000001 78 23 24.4 -1.400 79 27 21.6293706293706 5.37062937062937 80 16 21.6293706293706 -5.62937062937063 81 19 17.6923076923077 1.30769230769231 82 23 21.6293706293706 1.37062937062937 83 19 21.6293706293706 -2.62937062937063 84 19 21.6293706293706 -2.62937062937063 85 26 24.4 1.6 86 19 17.6923076923077 1.30769230769231 87 22 21.6293706293706 0.37062937062937 88 21 21.6293706293706 -0.62937062937063 89 22 21.6293706293706 0.37062937062937 90 20 21.6293706293706 -1.62937062937063 91 20 21.6293706293706 -1.62937062937063 92 20 17.6923076923077 2.30769230769231 93 21 21.6293706293706 -0.62937062937063 94 21 21.6293706293706 -0.62937062937063 95 14 21.6293706293706 -7.62937062937063 96 28 21.6293706293706 6.37062937062937 97 24 21.6293706293706 2.37062937062937 98 24 24.4 -0.399999999999999 99 24 21.6293706293706 2.37062937062937 100 19 21.6293706293706 -2.62937062937063 101 19 21.6293706293706 -2.62937062937063 102 14 17.6923076923077 -3.69230769230769 103 29 21.6293706293706 7.37062937062937 104 22 21.6293706293706 0.37062937062937 105 21 21.6293706293706 -0.62937062937063 106 15 21.6293706293706 -6.62937062937063 107 23 21.6293706293706 1.37062937062937 108 24 17.6923076923077 6.3076923076923 109 20 24.4 -4.4 110 25 21.6293706293706 3.37062937062937 111 25 21.6293706293706 3.37062937062937 112 19 17.6923076923077 1.30769230769231 113 23 21.6293706293706 1.37062937062937 114 22 21.6293706293706 0.37062937062937 115 19 21.6293706293706 -2.62937062937063 116 24 21.6293706293706 2.37062937062937 117 21 21.6293706293706 -0.62937062937063 118 19 21.6293706293706 -2.62937062937063 119 21 21.6293706293706 -0.62937062937063 120 18 17.6923076923077 0.307692307692307 121 24 21.6293706293706 2.37062937062937 122 7 17.6923076923077 -10.6923076923077 123 24 21.6293706293706 2.37062937062937 124 24 21.6293706293706 2.37062937062937 125 23 17.6923076923077 5.30769230769231 126 24 24.4 -0.399999999999999 127 27 24.4 2.6 128 20 21.6293706293706 -1.62937062937063 129 20 21.6293706293706 -1.62937062937063 130 22 21.6293706293706 0.37062937062937 131 19 21.6293706293706 -2.62937062937063 132 18 21.6293706293706 -3.62937062937063 133 14 21.6293706293706 -7.62937062937063 134 24 21.6293706293706 2.37062937062937 135 29 24.4 4.6 136 25 21.6293706293706 3.37062937062937 137 24 21.6293706293706 2.37062937062937 138 20 21.6293706293706 -1.62937062937063 139 18 21.6293706293706 -3.62937062937063 140 25 21.6293706293706 3.37062937062937 141 21 21.6293706293706 -0.62937062937063 142 21 21.6293706293706 -0.62937062937063 143 21 21.6293706293706 -0.62937062937063 144 23 21.6293706293706 1.37062937062937 145 18 21.6293706293706 -3.62937062937063 146 23 21.6293706293706 1.37062937062937 147 13 17.6923076923077 -4.69230769230769 148 23 17.6923076923077 5.30769230769231 149 17 17.6923076923077 -0.692307692307693 150 24 21.6293706293706 2.37062937062937 151 16 21.6293706293706 -5.62937062937063 152 23 21.6293706293706 1.37062937062937 153 20 21.6293706293706 -1.62937062937063 154 24 21.6293706293706 2.37062937062937 155 15 17.6923076923077 -2.69230769230769 156 20 21.6293706293706 -1.62937062937063 157 27 21.6293706293706 5.37062937062937 158 27 21.6293706293706 5.37062937062937 159 19 21.6293706293706 -2.62937062937063 160 22 21.6293706293706 0.37062937062937 161 16 21.6293706293706 -5.62937062937063 162 21 21.6293706293706 -0.62937062937063 163 18 17.6923076923077 0.307692307692307 164 22 21.6293706293706 0.37062937062937 165 18 21.6293706293706 -3.62937062937063 166 24 24.4 -0.399999999999999 167 24 21.6293706293706 2.37062937062937 168 19 21.6293706293706 -2.62937062937063 169 26 21.6293706293706 4.37062937062937 170 28 24.4 3.6 171 23 21.6293706293706 1.37062937062937 172 22 17.6923076923077 4.30769230769231 173 20 21.6293706293706 -1.62937062937063 174 20 21.6293706293706 -1.62937062937063 175 27 21.6293706293706 5.37062937062937 176 19 21.6293706293706 -2.62937062937063 177 23 21.6293706293706 1.37062937062937 178 19 21.6293706293706 -2.62937062937063 179 21 21.6293706293706 -0.62937062937063 180 13 21.6293706293706 -8.62937062937063 181 18 21.6293706293706 -3.62937062937063 182 19 21.6293706293706 -2.62937062937063 183 23 21.6293706293706 1.37062937062937 184 30 21.6293706293706 8.37062937062937 185 22 24.4 -2.4 186 23 24.4 -1.400 187 22 21.6293706293706 0.37062937062937 188 22 21.6293706293706 0.37062937062937 189 23 21.6293706293706 1.37062937062937 190 27 21.6293706293706 5.37062937062937 191 23 21.6293706293706 1.37062937062937 192 18 21.6293706293706 -3.62937062937063 193 24 21.6293706293706 2.37062937062937 194 19 21.6293706293706 -2.62937062937063

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/2pw6h1293467606.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/2pw6h1293467606.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/3zn521293467606.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/3zn521293467606.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/4axmn1293467606.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293467493nwmctopo0faaldo/4axmn1293467606.ps (open in new window)

Parameters (Session):

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') }

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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We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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