R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,78 + ,37238 + ,2805 + ,2312 + ,254488 + ,120 + ,117 + ,103772 + ,29402 + ,1369 + ,104389 + ,135 + ,148 + ,123969 + ,16440 + ,2201 + ,199476 + ,87 + ,105 + ,135400 + ,28732 + ,1900 + ,224330 + ,131 + ,132 + ,130115 + ,28608 + ,207 + ,14688 + ,4 + ,0 + ,6023 + ,2065 + ,1645 + ,181633 + ,47 + ,73 + ,64466 + ,14817 + ,2429 + ,271856 + ,109 + ,86 + ,54990 + ,16714 + ,151 + ,7199 + ,7 + ,0 + ,1644 + ,556 + ,474 + ,46660 + ,12 + ,13 + ,6179 + ,2089 + ,141 + ,17547 + ,0 + ,4 + ,3926 + ,2658 + ,872 + ,95227 + ,37 + ,48 + ,34777 + ,1669 + ,1318 + ,152601 + ,46 + ,46 + ,73224 + ,16267) + ,dim=c(6 + ,156) + ,dimnames=list(c('pageviews' + ,'TineInRFC' + ,'BloggedComp' + ,'LongFBM' + ,'Totsize' + ,'Totrevision') + ,1:156)) > y <- array(NA,dim=c(6,156),dimnames=list(c('pageviews','TineInRFC','BloggedComp','LongFBM','Totsize','Totrevision'),1:156)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '' > par2 = 'none' > par1 = '4' > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "LongFBM" > x[,par1] [1] 94 103 93 103 51 70 91 22 93 60 123 148 90 124 70 168 115 71 [19] 66 134 117 108 84 156 120 114 94 120 81 110 133 122 158 109 124 92 [37] 126 70 37 120 93 95 90 80 31 110 66 138 133 113 100 7 140 61 [55] 41 96 164 102 124 99 129 62 73 114 99 70 104 116 91 74 138 67 [73] 151 72 120 115 105 104 108 98 69 111 99 71 27 69 107 107 93 129 [91] 69 118 73 119 104 107 99 90 197 85 139 106 50 64 31 63 92 106 [109] 69 93 114 110 0 83 30 98 82 0 60 9 115 140 120 66 21 124 [127] 152 139 144 120 160 114 78 119 141 101 133 83 116 90 36 97 98 78 [145] 117 148 105 132 0 73 86 0 13 4 48 46 > 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]) 0 4 7 9 13 21 22 27 30 31 36 37 41 46 48 50 51 60 61 62 4 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 63 64 66 67 69 70 71 72 73 74 78 80 81 82 83 84 85 86 90 91 1 1 3 1 4 4 2 1 3 1 2 1 1 1 2 1 1 1 4 2 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 2 5 2 1 1 1 3 4 1 1 1 2 3 2 2 3 2 1 3 1 113 114 115 116 117 118 119 120 122 123 124 126 129 132 133 134 138 139 140 141 1 4 3 2 2 1 2 6 1 1 4 1 2 1 3 1 2 2 2 1 144 148 151 152 156 158 160 164 168 197 1 2 1 1 1 1 1 1 1 1 > colnames(x) [1] "pageviews" "TineInRFC" "BloggedComp" "LongFBM" "Totsize" [6] "Totrevision" > colnames(x)[par1] [1] "LongFBM" > x[,par1] [1] 94 103 93 103 51 70 91 22 93 60 123 148 90 124 70 168 115 71 [19] 66 134 117 108 84 156 120 114 94 120 81 110 133 122 158 109 124 92 [37] 126 70 37 120 93 95 90 80 31 110 66 138 133 113 100 7 140 61 [55] 41 96 164 102 124 99 129 62 73 114 99 70 104 116 91 74 138 67 [73] 151 72 120 115 105 104 108 98 69 111 99 71 27 69 107 107 93 129 [91] 69 118 73 119 104 107 99 90 197 85 139 106 50 64 31 63 92 106 [109] 69 93 114 110 0 83 30 98 82 0 60 9 115 140 120 66 21 124 [127] 152 139 144 120 160 114 78 119 141 101 133 83 116 90 36 97 98 78 [145] 117 148 105 132 0 73 86 0 13 4 48 46 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/1wmkn1324041701.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: LongFBM Inputs: pageviews, TineInRFC, BloggedComp, Totsize, Totrevision Number of observations: 156 1) BloggedComp <= 37; criterion = 1, statistic = 69.776 2) TineInRFC <= 38214; criterion = 0.999, statistic = 14.47 3)* weights = 9 2) TineInRFC > 38214 4)* weights = 20 1) BloggedComp > 37 5) BloggedComp <= 82; criterion = 1, statistic = 23.027 6) Totsize <= 57635; criterion = 0.99, statistic = 9.558 7)* weights = 18 6) Totsize > 57635 8)* weights = 46 5) BloggedComp > 82 9)* weights = 63 > postscript(file="/var/wessaorg/rcomp/tmp/2291u1324041701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3g9r01324041701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > 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) + } Actuals Forecasts Residuals 1 94 102.1522 -8.15217391 2 103 102.1522 0.84782609 3 93 102.1522 -9.15217391 4 103 117.9841 -14.98412698 5 51 69.0000 -18.00000000 6 70 61.9500 8.05000000 7 91 117.9841 -26.98412698 8 22 7.0000 15.00000000 9 93 102.1522 -9.15217391 10 60 69.0000 -9.00000000 11 123 102.1522 20.84782609 12 148 117.9841 30.01587302 13 90 102.1522 -12.15217391 14 124 117.9841 6.01587302 15 70 69.0000 1.00000000 16 168 117.9841 50.01587302 17 115 117.9841 -2.98412698 18 71 102.1522 -31.15217391 19 66 102.1522 -36.15217391 20 134 117.9841 16.01587302 21 117 117.9841 -0.98412698 22 108 102.1522 5.84782609 23 84 102.1522 -18.15217391 24 156 117.9841 38.01587302 25 120 117.9841 2.01587302 26 114 102.1522 11.84782609 27 94 102.1522 -8.15217391 28 120 117.9841 2.01587302 29 81 69.0000 12.00000000 30 110 117.9841 -7.98412698 31 133 117.9841 15.01587302 32 122 102.1522 19.84782609 33 158 117.9841 40.01587302 34 109 102.1522 6.84782609 35 124 117.9841 6.01587302 36 92 69.0000 23.00000000 37 126 117.9841 8.01587302 38 70 69.0000 1.00000000 39 37 61.9500 -24.95000000 40 120 117.9841 2.01587302 41 93 117.9841 -24.98412698 42 95 102.1522 -7.15217391 43 90 102.1522 -12.15217391 44 80 69.0000 11.00000000 45 31 61.9500 -30.95000000 46 110 102.1522 7.84782609 47 66 61.9500 4.05000000 48 138 117.9841 20.01587302 49 133 117.9841 15.01587302 50 113 117.9841 -4.98412698 51 100 102.1522 -2.15217391 52 7 7.0000 0.00000000 53 140 102.1522 37.84782609 54 61 61.9500 -0.95000000 55 41 61.9500 -20.95000000 56 96 102.1522 -6.15217391 57 164 117.9841 46.01587302 58 102 117.9841 -15.98412698 59 124 117.9841 6.01587302 60 99 102.1522 -3.15217391 61 129 117.9841 11.01587302 62 62 117.9841 -55.98412698 63 73 69.0000 4.00000000 64 114 102.1522 11.84782609 65 99 102.1522 -3.15217391 66 70 69.0000 1.00000000 67 104 102.1522 1.84782609 68 116 102.1522 13.84782609 69 91 117.9841 -26.98412698 70 74 117.9841 -43.98412698 71 138 102.1522 35.84782609 72 67 61.9500 5.05000000 73 151 102.1522 48.84782609 74 72 102.1522 -30.15217391 75 120 117.9841 2.01587302 76 115 117.9841 -2.98412698 77 105 117.9841 -12.98412698 78 104 102.1522 1.84782609 79 108 117.9841 -9.98412698 80 98 102.1522 -4.15217391 81 69 69.0000 0.00000000 82 111 117.9841 -6.98412698 83 99 61.9500 37.05000000 84 71 117.9841 -46.98412698 85 27 69.0000 -42.00000000 86 69 61.9500 7.05000000 87 107 102.1522 4.84782609 88 107 102.1522 4.84782609 89 93 102.1522 -9.15217391 90 129 117.9841 11.01587302 91 69 69.0000 0.00000000 92 118 117.9841 0.01587302 93 73 61.9500 11.05000000 94 119 117.9841 1.01587302 95 104 102.1522 1.84782609 96 107 102.1522 4.84782609 97 99 102.1522 -3.15217391 98 90 117.9841 -27.98412698 99 197 117.9841 79.01587302 100 85 117.9841 -32.98412698 101 139 117.9841 21.01587302 102 106 117.9841 -11.98412698 103 50 69.0000 -19.00000000 104 64 61.9500 2.05000000 105 31 69.0000 -38.00000000 106 63 102.1522 -39.15217391 107 92 117.9841 -25.98412698 108 106 117.9841 -11.98412698 109 69 61.9500 7.05000000 110 93 117.9841 -24.98412698 111 114 102.1522 11.84782609 112 110 117.9841 -7.98412698 113 0 7.0000 -7.00000000 114 83 117.9841 -34.98412698 115 30 61.9500 -31.95000000 116 98 69.0000 29.00000000 117 82 117.9841 -35.98412698 118 0 7.0000 -7.00000000 119 60 102.1522 -42.15217391 120 9 7.0000 2.00000000 121 115 117.9841 -2.98412698 122 140 102.1522 37.84782609 123 120 61.9500 58.05000000 124 66 61.9500 4.05000000 125 21 7.0000 14.00000000 126 124 102.1522 21.84782609 127 152 117.9841 34.01587302 128 139 102.1522 36.84782609 129 144 117.9841 26.01587302 130 120 117.9841 2.01587302 131 160 117.9841 42.01587302 132 114 102.1522 11.84782609 133 78 61.9500 16.05000000 134 119 102.1522 16.84782609 135 141 117.9841 23.01587302 136 101 61.9500 39.05000000 137 133 117.9841 15.01587302 138 83 69.0000 14.00000000 139 116 117.9841 -1.98412698 140 90 69.0000 21.00000000 141 36 61.9500 -25.95000000 142 97 117.9841 -20.98412698 143 98 117.9841 -19.98412698 144 78 69.0000 9.00000000 145 117 117.9841 -0.98412698 146 148 117.9841 30.01587302 147 105 117.9841 -12.98412698 148 132 117.9841 14.01587302 149 0 7.0000 -7.00000000 150 73 102.1522 -29.15217391 151 86 117.9841 -31.98412698 152 0 7.0000 -7.00000000 153 13 61.9500 -48.95000000 154 4 7.0000 -3.00000000 155 48 61.9500 -13.95000000 156 46 102.1522 -56.15217391 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/4vekn1324041701.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > 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() null device 1 > 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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5dki61324041701.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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="/var/wessaorg/rcomp/tmp/6q1je1324041702.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="/var/wessaorg/rcomp/tmp/76mpi1324041702.tab") + } > > try(system("convert tmp/2291u1324041701.ps tmp/2291u1324041701.png",intern=TRUE)) character(0) > try(system("convert tmp/3g9r01324041701.ps tmp/3g9r01324041701.png",intern=TRUE)) character(0) > try(system("convert tmp/4vekn1324041701.ps tmp/4vekn1324041701.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.343 0.335 3.702