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+ ,12645 + ,24 + ,56613 + ,44 + ,8019 + ,3080 + ,11017 + ,16 + ,62792 + ,60 + ,30884 + ,10205 + ,37623 + ,72 + ,72535 + ,64 + ,19540 + ,6095 + ,35873 + ,27) + ,dim=c(6 + ,289) + ,dimnames=list(c('time_in_rfc' + ,'feedback_messages_p1' + ,'totsize' + ,'totrevisions' + ,'totseconds' + ,'tothyperlinks ') + ,1:289)) > y <- array(NA,dim=c(6,289),dimnames=list(c('time_in_rfc','feedback_messages_p1','totsize','totrevisions','totseconds','tothyperlinks '),1:289)) > 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 = '3' > par2 = 'none' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > 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] "time_in_rfc" > x[,par1] [1] 210907 120982 176508 179321 123185 52746 385534 33170 101645 149061 [11] 165446 237213 173326 133131 258873 180083 324799 230964 236785 135473 [21] 202925 215147 344297 153935 132943 174724 174415 225548 223632 124817 [31] 221698 210767 170266 260561 84853 294424 101011 215641 325107 7176 [41] 167542 106408 96560 265769 269651 149112 175824 152871 111665 116408 [51] 362301 78800 183167 277965 150629 168809 24188 329267 65029 101097 [61] 218946 244052 341570 103597 233328 256462 206161 311473 235800 177939 [71] 207176 196553 174184 143246 187559 187681 119016 182192 73566 194979 [81] 167488 143756 275541 243199 182999 135649 152299 120221 346485 145790 [91] 193339 80953 122774 130585 112611 286468 241066 148446 204713 182079 [101] 140344 220516 243060 162765 182613 232138 265318 85574 310839 225060 [111] 232317 144966 43287 155754 164709 201940 235454 220801 99466 92661 [121] 133328 61361 125930 100750 224549 82316 102010 101523 243511 22938 [131] 41566 152474 61857 99923 132487 317394 21054 209641 22648 31414 [141] 46698 131698 91735 244749 184510 79863 128423 97839 38214 151101 [151] 272458 172494 108043 328107 250579 351067 158015 98866 85439 229242 [161] 351619 84207 120445 324598 131069 204271 165543 141722 116048 250047 [171] 299775 195838 173260 254488 104389 136084 199476 92499 224330 135781 [181] 74408 81240 14688 181633 271856 7199 46660 17547 133368 95227 [191] 152601 98146 79619 59194 139942 118612 72880 65475 99643 71965 [201] 77272 49289 135131 108446 89746 44296 77648 181528 134019 124064 [211] 92630 121848 52915 81872 58981 53515 60812 56375 65490 80949 [221] 76302 104011 98104 67989 30989 135458 73504 63123 61254 74914 [231] 31774 81437 87186 50090 65745 56653 158399 46455 73624 38395 [241] 91899 139526 52164 51567 70551 84856 102538 86678 85709 34662 [251] 150580 99611 19349 99373 86230 30837 31706 89806 62088 40151 [261] 27634 76990 37460 54157 49862 84337 64175 59382 119308 76702 [271] 103425 70344 43410 104838 62215 69304 53117 19764 86680 84105 [281] 77945 89113 91005 40248 64187 50857 56613 62792 72535 > 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]) 7176 7199 14688 17547 19349 19764 21054 22648 22938 24188 27634 1 1 1 1 1 1 1 1 1 1 1 30837 30989 31414 31706 31774 33170 34662 37460 38214 38395 40151 1 1 1 1 1 1 1 1 1 1 1 40248 41566 43287 43410 44296 46455 46660 46698 49289 49862 50090 1 1 1 1 1 1 1 1 1 1 1 50857 51567 52164 52746 52915 53117 53515 54157 56375 56613 56653 1 1 1 1 1 1 1 1 1 1 1 58981 59194 59382 60812 61254 61361 61857 62088 62215 62792 63123 1 1 1 1 1 1 1 1 1 1 1 64175 64187 65029 65475 65490 65745 67989 69304 70344 70551 71965 1 1 1 1 1 1 1 1 1 1 1 72535 72880 73504 73566 73624 74408 74914 76302 76702 76990 77272 1 1 1 1 1 1 1 1 1 1 1 77648 77945 78800 79619 79863 80949 80953 81240 81437 81872 82316 1 1 1 1 1 1 1 1 1 1 1 84105 84207 84337 84853 84856 85439 85574 85709 86230 86678 86680 1 1 1 1 1 1 1 1 1 1 1 87186 89113 89746 89806 91005 91735 91899 92499 92630 92661 95227 1 1 1 1 1 1 1 1 1 1 1 96560 97839 98104 98146 98866 99373 99466 99611 99643 99923 100750 1 1 1 1 1 1 1 1 1 1 1 101011 101097 101523 101645 102010 102538 103425 103597 104011 104389 104838 1 1 1 1 1 1 1 1 1 1 1 106408 108043 108446 111665 112611 116048 116408 118612 119016 119308 120221 1 1 1 1 1 1 1 1 1 1 1 120445 120982 121848 122774 123185 124064 124817 125930 128423 130585 131069 1 1 1 1 1 1 1 1 1 1 1 131698 132487 132943 133131 133328 133368 134019 135131 135458 135473 135649 1 1 1 1 1 1 1 1 1 1 1 135781 136084 139526 139942 140344 141722 143246 143756 144966 145790 148446 1 1 1 1 1 1 1 1 1 1 1 149061 149112 150580 150629 151101 152299 152474 152601 152871 153935 155754 1 1 1 1 1 1 1 1 1 1 1 158015 158399 162765 164709 165446 165543 167488 167542 168809 170266 172494 1 1 1 1 1 1 1 1 1 1 1 173260 173326 174184 174415 174724 175824 176508 177939 179321 180083 181528 1 1 1 1 1 1 1 1 1 1 1 181633 182079 182192 182613 182999 183167 184510 187559 187681 193339 194979 1 1 1 1 1 1 1 1 1 1 1 195838 196553 199476 201940 202925 204271 204713 206161 207176 209641 210767 1 1 1 1 1 1 1 1 1 1 1 210907 215147 215641 218946 220516 220801 221698 223632 224330 224549 225060 1 1 1 1 1 1 1 1 1 1 1 225548 229242 230964 232138 232317 233328 235454 235800 236785 237213 241066 1 1 1 1 1 1 1 1 1 1 1 243060 243199 243511 244052 244749 250047 250579 254488 256462 258873 260561 1 1 1 1 1 1 1 1 1 1 1 265318 265769 269651 271856 272458 275541 277965 286468 294424 299775 310839 1 1 1 1 1 1 1 1 1 1 1 311473 317394 324598 324799 325107 328107 329267 341570 344297 346485 351067 1 1 1 1 1 1 1 1 1 1 1 351619 362301 385534 1 1 1 > colnames(x) [1] "time_in_rfc" "feedback_messages_p1" "totsize" [4] "totrevisions" "totseconds" "tothyperlinks." > colnames(x)[par1] [1] "time_in_rfc" > x[,par1] [1] 210907 120982 176508 179321 123185 52746 385534 33170 101645 149061 [11] 165446 237213 173326 133131 258873 180083 324799 230964 236785 135473 [21] 202925 215147 344297 153935 132943 174724 174415 225548 223632 124817 [31] 221698 210767 170266 260561 84853 294424 101011 215641 325107 7176 [41] 167542 106408 96560 265769 269651 149112 175824 152871 111665 116408 [51] 362301 78800 183167 277965 150629 168809 24188 329267 65029 101097 [61] 218946 244052 341570 103597 233328 256462 206161 311473 235800 177939 [71] 207176 196553 174184 143246 187559 187681 119016 182192 73566 194979 [81] 167488 143756 275541 243199 182999 135649 152299 120221 346485 145790 [91] 193339 80953 122774 130585 112611 286468 241066 148446 204713 182079 [101] 140344 220516 243060 162765 182613 232138 265318 85574 310839 225060 [111] 232317 144966 43287 155754 164709 201940 235454 220801 99466 92661 [121] 133328 61361 125930 100750 224549 82316 102010 101523 243511 22938 [131] 41566 152474 61857 99923 132487 317394 21054 209641 22648 31414 [141] 46698 131698 91735 244749 184510 79863 128423 97839 38214 151101 [151] 272458 172494 108043 328107 250579 351067 158015 98866 85439 229242 [161] 351619 84207 120445 324598 131069 204271 165543 141722 116048 250047 [171] 299775 195838 173260 254488 104389 136084 199476 92499 224330 135781 [181] 74408 81240 14688 181633 271856 7199 46660 17547 133368 95227 [191] 152601 98146 79619 59194 139942 118612 72880 65475 99643 71965 [201] 77272 49289 135131 108446 89746 44296 77648 181528 134019 124064 [211] 92630 121848 52915 81872 58981 53515 60812 56375 65490 80949 [221] 76302 104011 98104 67989 30989 135458 73504 63123 61254 74914 [231] 31774 81437 87186 50090 65745 56653 158399 46455 73624 38395 [241] 91899 139526 52164 51567 70551 84856 102538 86678 85709 34662 [251] 150580 99611 19349 99373 86230 30837 31706 89806 62088 40151 [261] 27634 76990 37460 54157 49862 84337 64175 59382 119308 76702 [271] 103425 70344 43410 104838 62215 69304 53117 19764 86680 84105 [281] 77945 89113 91005 40248 64187 50857 56613 62792 72535 > 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/1bpma1324144138.tab") + } + } > m Conditional inference tree with 13 terminal nodes Response: time_in_rfc Inputs: feedback_messages_p1, totsize, totrevisions, totseconds, tothyperlinks. Number of observations: 289 1) totseconds <= 77395; criterion = 1, statistic = 230.649 2) totseconds <= 43527; criterion = 1, statistic = 115.918 3) totseconds <= 20107; criterion = 1, statistic = 41.665 4) feedback_messages_p1 <= 67; criterion = 1, statistic = 18.912 5) feedback_messages_p1 <= 12; criterion = 0.998, statistic = 13.007 6)* weights = 10 5) feedback_messages_p1 > 12 7)* weights = 20 4) feedback_messages_p1 > 67 8)* weights = 13 3) totseconds > 20107 9) totseconds <= 29788; criterion = 0.974, statistic = 7.772 10) feedback_messages_p1 <= 68; criterion = 0.986, statistic = 8.866 11)* weights = 27 10) feedback_messages_p1 > 68 12)* weights = 14 9) totseconds > 29788 13)* weights = 37 2) totseconds > 43527 14) feedback_messages_p1 <= 104; criterion = 0.999, statistic = 13.62 15)* weights = 43 14) feedback_messages_p1 > 104 16)* weights = 18 1) totseconds > 77395 17) totseconds <= 117286; criterion = 1, statistic = 38.902 18)* weights = 55 17) totseconds > 117286 19) totseconds <= 153197; criterion = 0.985, statistic = 8.771 20) feedback_messages_p1 <= 135; criterion = 0.958, statistic = 6.92 21) totsize <= 111813; criterion = 0.965, statistic = 7.252 22)* weights = 13 21) totsize > 111813 23)* weights = 8 20) feedback_messages_p1 > 135 24)* weights = 10 19) totseconds > 153197 25)* weights = 21 > postscript(file="/var/wessaorg/rcomp/tmp/2nosu1324144138.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/3819w1324144138.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 210907 192717.62 18189.3750 2 120982 189887.02 -68905.0182 3 176508 189887.02 -13379.0182 4 179321 189887.02 -10566.0182 5 123185 189887.02 -66702.0182 6 52746 87820.68 -35074.6757 7 385534 290061.95 95472.0476 8 33170 42026.40 -8856.4000 9 101645 66406.56 35238.4444 10 149061 189887.02 -40826.0182 11 165446 189887.02 -24441.0182 12 237213 276261.10 -39048.1000 13 173326 147306.28 26019.7222 14 133131 119628.74 13502.2558 15 258873 189887.02 68985.9818 16 180083 189887.02 -9804.0182 17 324799 276261.10 48537.9000 18 230964 192717.62 38246.3750 19 236785 189887.02 46897.9818 20 135473 189887.02 -54414.0182 21 202925 189887.02 13037.9818 22 215147 236933.77 -21786.7692 23 344297 236933.77 107363.2308 24 153935 189887.02 -35952.0182 25 132943 189887.02 -56944.0182 26 174724 189887.02 -15163.0182 27 174415 189887.02 -15472.0182 28 225548 236933.77 -11385.7692 29 223632 236933.77 -13301.7692 30 124817 119628.74 5188.2558 31 221698 290061.95 -68363.9524 32 210767 192717.62 18049.3750 33 170266 189887.02 -19621.0182 34 260561 290061.95 -29500.9524 35 84853 82828.71 2024.2857 36 294424 290061.95 4362.0476 37 101011 119628.74 -18617.7442 38 215641 189887.02 25753.9818 39 325107 290061.95 35045.0476 40 7176 19731.30 -12555.3000 41 167542 189887.02 -22345.0182 42 106408 119628.74 -13220.7442 43 96560 87820.68 8739.3243 44 265769 236933.77 28835.2308 45 269651 236933.77 32717.2308 46 149112 147306.28 1805.7222 47 175824 119628.74 56195.2558 48 152871 189887.02 -37016.0182 49 111665 119628.74 -7963.7442 50 116408 189887.02 -73479.0182 51 362301 189887.02 172413.9818 52 78800 119628.74 -40828.7442 53 183167 189887.02 -6720.0182 54 277965 290061.95 -12096.9524 55 150629 189887.02 -39258.0182 56 168809 236933.77 -68124.7692 57 24188 19731.30 4456.7000 58 329267 290061.95 39205.0476 59 65029 66406.56 -1377.5556 60 101097 119628.74 -18531.7442 61 218946 189887.02 29058.9818 62 244052 290061.95 -46009.9524 63 341570 290061.95 51508.0476 64 103597 119628.74 -16031.7442 65 233328 290061.95 -56733.9524 66 256462 290061.95 -33599.9524 67 206161 147306.28 58854.7222 68 311473 290061.95 21411.0476 69 235800 290061.95 -54261.9524 70 177939 147306.28 30632.7222 71 207176 189887.02 17288.9818 72 196553 236933.77 -40380.7692 73 174184 189887.02 -15703.0182 74 143246 147306.28 -4060.2778 75 187559 189887.02 -2328.0182 76 187681 189887.02 -2206.0182 77 119016 119628.74 -612.7442 78 182192 189887.02 -7695.0182 79 73566 82828.71 -9262.7143 80 194979 189887.02 5091.9818 81 167488 189887.02 -22399.0182 82 143756 147306.28 -3550.2778 83 275541 189887.02 85653.9818 84 243199 236933.77 6265.2308 85 182999 192717.62 -9718.6250 86 135649 147306.28 -11657.2778 87 152299 147306.28 4992.7222 88 120221 119628.74 592.2558 89 346485 276261.10 70223.9000 90 145790 119628.74 26161.2558 91 193339 189887.02 3451.9818 92 80953 119628.74 -38675.7442 93 122774 82828.71 39945.2857 94 130585 147306.28 -16721.2778 95 112611 119628.74 -7017.7442 96 286468 189887.02 96580.9818 97 241066 276261.10 -35195.1000 98 148446 147306.28 1139.7222 99 204713 189887.02 14825.9818 100 182079 189887.02 -7808.0182 101 140344 119628.74 20715.2558 102 220516 189887.02 30628.9818 103 243060 236933.77 6126.2308 104 162765 189887.02 -27122.0182 105 182613 192717.62 -10104.6250 106 232138 290061.95 -57923.9524 107 265318 276261.10 -10943.1000 108 85574 87820.68 -2246.6757 109 310839 290061.95 20777.0476 110 225060 189887.02 35172.9818 111 232317 290061.95 -57744.9524 112 144966 147306.28 -2340.2778 113 43287 73271.92 -29984.9231 114 155754 119628.74 36125.2558 115 164709 189887.02 -25178.0182 116 201940 189887.02 12052.9818 117 235454 236933.77 -1479.7692 118 220801 189887.02 30913.9818 119 99466 192717.62 -93251.6250 120 92661 87820.68 4840.3243 121 133328 119628.74 13699.2558 122 61361 66406.56 -5045.5556 123 125930 119628.74 6301.2558 124 100750 147306.28 -46556.2778 125 224549 192717.62 31831.3750 126 82316 87820.68 -5504.6757 127 102010 87820.68 14189.3243 128 101523 119628.74 -18105.7442 129 243511 189887.02 53623.9818 130 22938 19731.30 3206.7000 131 41566 42026.40 -460.4000 132 152474 189887.02 -37413.0182 133 61857 66406.56 -4549.5556 134 99923 87820.68 12102.3243 135 132487 147306.28 -14819.2778 136 317394 290061.95 27332.0476 137 21054 19731.30 1322.7000 138 209641 189887.02 19753.9818 139 22648 42026.40 -19378.4000 140 31414 42026.40 -10612.4000 141 46698 42026.40 4671.6000 142 131698 119628.74 12069.2558 143 91735 73271.92 18463.0769 144 244749 236933.77 7815.2308 145 184510 147306.28 37203.7222 146 79863 73271.92 6591.0769 147 128423 82828.71 45594.2857 148 97839 119628.74 -21789.7442 149 38214 42026.40 -3812.4000 150 151101 147306.28 3794.7222 151 272458 276261.10 -3803.1000 152 172494 189887.02 -17393.0182 153 108043 119628.74 -11585.7442 154 328107 290061.95 38045.0476 155 250579 290061.95 -39482.9524 156 351067 290061.95 61005.0476 157 158015 189887.02 -31872.0182 158 98866 87820.68 11045.3243 159 85439 119628.74 -34189.7442 160 229242 189887.02 39354.9818 161 351619 290061.95 61557.0476 162 84207 73271.92 10935.0769 163 120445 119628.74 816.2558 164 324598 276261.10 48336.9000 165 131069 147306.28 -16237.2778 166 204271 236933.77 -32662.7692 167 165543 189887.02 -24344.0182 168 141722 147306.28 -5584.2778 169 116048 119628.74 -3580.7442 170 250047 189887.02 60159.9818 171 299775 189887.02 109887.9818 172 195838 189887.02 5950.9818 173 173260 73271.92 99988.0769 174 254488 276261.10 -21773.1000 175 104389 147306.28 -42917.2778 176 136084 119628.74 16455.2558 177 199476 192717.62 6758.3750 178 92499 82828.71 9670.2857 179 224330 276261.10 -51931.1000 180 135781 119628.74 16152.2558 181 74408 66406.56 8001.4444 182 81240 73271.92 7968.0769 183 14688 19731.30 -5043.3000 184 181633 189887.02 -8254.0182 185 271856 276261.10 -4405.1000 186 7199 19731.30 -12532.3000 187 46660 66406.56 -19746.5556 188 17547 19731.30 -2184.3000 189 133368 119628.74 13739.2558 190 95227 73271.92 21955.0769 191 152601 119628.74 32972.2558 192 98146 87820.68 10325.3243 193 79619 66406.56 13212.4444 194 59194 82828.71 -23634.7143 195 139942 189887.02 -49945.0182 196 118612 87820.68 30791.3243 197 72880 82828.71 -9948.7143 198 65475 87820.68 -22345.6757 199 99643 87820.68 11822.3243 200 71965 66406.56 5558.4444 201 77272 87820.68 -10548.6757 202 49289 73271.92 -23982.9231 203 135131 87820.68 47310.3243 204 108446 119628.74 -11182.7442 205 89746 82828.71 6917.2857 206 44296 73271.92 -28975.9231 207 77648 66406.56 11241.4444 208 181528 119628.74 61899.2558 209 134019 119628.74 14390.2558 210 124064 119628.74 4435.2558 211 92630 119628.74 -26998.7442 212 121848 87820.68 34027.3243 213 52915 66406.56 -13491.5556 214 81872 119628.74 -37756.7442 215 58981 73271.92 -14290.9231 216 53515 82828.71 -29313.7143 217 60812 66406.56 -5594.5556 218 56375 87820.68 -31445.6757 219 65490 87820.68 -22330.6757 220 80949 87820.68 -6871.6757 221 76302 87820.68 -11518.6757 222 104011 189887.02 -85876.0182 223 98104 119628.74 -21524.7442 224 67989 87820.68 -19831.6757 225 30989 73271.92 -42282.9231 226 135458 119628.74 15829.2558 227 73504 87820.68 -14316.6757 228 63123 66406.56 -3283.5556 229 61254 42026.40 19227.6000 230 74914 82828.71 -7914.7143 231 31774 42026.40 -10252.4000 232 81437 87820.68 -6383.6757 233 87186 119628.74 -32442.7442 234 50090 66406.56 -16316.5556 235 65745 82828.71 -17083.7143 236 56653 66406.56 -9753.5556 237 158399 87820.68 70578.3243 238 46455 66406.56 -19951.5556 239 73624 87820.68 -14196.6757 240 38395 42026.40 -3631.4000 241 91899 87820.68 4078.3243 242 139526 119628.74 19897.2558 243 52164 42026.40 10137.6000 244 51567 42026.40 9540.6000 245 70551 87820.68 -17269.6757 246 84856 87820.68 -2964.6757 247 102538 119628.74 -17090.7442 248 86678 87820.68 -1142.6757 249 85709 66406.56 19302.4444 250 34662 66406.56 -31744.5556 251 150580 119628.74 30951.2558 252 99611 119628.74 -20017.7442 253 19349 19731.30 -382.3000 254 99373 82828.71 16544.2857 255 86230 87820.68 -1590.6757 256 30837 42026.40 -11189.4000 257 31706 42026.40 -10320.4000 258 89806 66406.56 23399.4444 259 62088 66406.56 -4318.5556 260 40151 66406.56 -26255.5556 261 27634 42026.40 -14392.4000 262 76990 87820.68 -10830.6757 263 37460 42026.40 -4566.4000 264 54157 66406.56 -12249.5556 265 49862 42026.40 7835.6000 266 84337 87820.68 -3483.6757 267 64175 82828.71 -18653.7143 268 59382 66406.56 -7024.5556 269 119308 119628.74 -320.7442 270 76702 87820.68 -11118.6757 271 103425 87820.68 15604.3243 272 70344 66406.56 3937.4444 273 43410 19731.30 23678.7000 274 104838 87820.68 17017.3243 275 62215 66406.56 -4191.5556 276 69304 73271.92 -3967.9231 277 53117 42026.40 11090.6000 278 19764 19731.30 32.7000 279 86680 87820.68 -1140.6757 280 84105 66406.56 17698.4444 281 77945 82828.71 -4883.7143 282 89113 66406.56 22706.4444 283 91005 66406.56 24598.4444 284 40248 42026.40 -1778.4000 285 64187 42026.40 22160.6000 286 50857 73271.92 -22414.9231 287 56613 42026.40 14586.6000 288 62792 87820.68 -25028.6757 289 72535 87820.68 -15285.6757 > 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/4pyvb1324144138.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/5ldpn1324144138.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/61tq31324144138.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/7udsr1324144138.tab") + } > > try(system("convert tmp/2nosu1324144138.ps tmp/2nosu1324144138.png",intern=TRUE)) character(0) > try(system("convert tmp/3819w1324144138.ps tmp/3819w1324144138.png",intern=TRUE)) character(0) > try(system("convert tmp/4pyvb1324144138.ps tmp/4pyvb1324144138.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.552 0.320 5.902