R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-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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array(NA,dim=c(7,289),dimnames=list(c('Time','Logins','Views','Shared','Reviewed','TotalSize','Pop'),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' > 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 Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). 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" > 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" "Logins" "Views" "Shared" "Reviewed" "TotalSize" [7] "Pop" > colnames(x)[par1] [1] "Time" > 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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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/fisher/rcomp/tmp/1nkt31355219403.tab") + } + } > m Conditional inference tree with 10 terminal nodes Response: Time Inputs: Logins, Views, Shared, Reviewed, TotalSize, Pop Number of observations: 289 1) Views <= 555; criterion = 1, statistic = 229.425 2) Views <= 361; criterion = 1, statistic = 117.828 3) Views <= 229; criterion = 1, statistic = 51.517 4) Reviewed <= 8; criterion = 1, statistic = 20.867 5)* weights = 13 4) Reviewed > 8 6)* weights = 36 3) Views > 229 7) Reviewed <= 12; criterion = 0.999, statistic = 14.707 8)* weights = 8 7) Reviewed > 12 9)* weights = 47 2) Views > 361 10) Reviewed <= 24; criterion = 1, statistic = 40.126 11)* weights = 51 10) Reviewed > 24 12)* weights = 38 1) Views > 555 13) Views <= 967; criterion = 1, statistic = 47.205 14) TotalSize <= 51633; criterion = 1, statistic = 20.072 15)* weights = 17 14) TotalSize > 51633 16) Logins <= 70; criterion = 0.972, statistic = 7.988 17)* weights = 31 16) Logins > 70 18)* weights = 31 13) Views > 967 19)* weights = 17 > postscript(file="/var/fisher/rcomp/tmp/2gvaj1355219403.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/fisher/rcomp/tmp/398tz1355219403.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 161692.71 49214.28947 2 120982 87325.87 33656.12766 3 176508 204881.74 -28373.74194 4 179321 237560.35 -58239.35484 5 123185 87325.87 35859.12766 6 52746 51607.69 1138.30556 7 385534 322592.29 62941.70588 8 33170 51607.69 -18437.69444 9 101645 104038.57 -2393.56863 10 149061 204881.74 -55820.74194 11 165446 161692.71 3753.28947 12 237213 237560.35 -347.35484 13 173326 161692.71 11633.28947 14 133131 161692.71 -28561.71053 15 258873 204881.74 53991.25806 16 180083 161692.71 18390.28947 17 324799 322592.29 2206.70588 18 230964 204881.74 26082.25806 19 236785 237560.35 -775.35484 20 135473 104038.57 31434.43137 21 202925 204881.74 -1956.74194 22 215147 204881.74 10265.25806 23 344297 237560.35 106736.64516 24 153935 161692.71 -7757.71053 25 132943 161692.71 -28749.71053 26 174724 237560.35 -62836.35484 27 174415 237560.35 -63145.35484 28 225548 237560.35 -12012.35484 29 223632 161692.71 61939.28947 30 124817 161692.71 -36875.71053 31 221698 204881.74 16816.25806 32 210767 204881.74 5885.25806 33 170266 161692.71 8573.28947 34 260561 237560.35 23000.64516 35 84853 87325.87 -2472.87234 36 294424 322592.29 -28168.29412 37 101011 87325.87 13685.12766 38 215641 204881.74 10759.25806 39 325107 237560.35 87546.64516 40 7176 22604.77 -15428.76923 41 167542 161692.71 5849.28947 42 106408 87325.87 19082.12766 43 96560 104038.57 -7478.56863 44 265769 237560.35 28208.64516 45 269651 322592.29 -52941.29412 46 149112 161692.71 -12580.71053 47 175824 149425.88 26398.11765 48 152871 161692.71 -8821.71053 49 111665 87325.87 24339.12766 50 116408 204881.74 -88473.74194 51 362301 322592.29 39708.70588 52 78800 87325.87 -8525.87234 53 183167 204881.74 -21714.74194 54 277965 322592.29 -44627.29412 55 150629 204881.74 -54252.74194 56 168809 161692.71 7116.28947 57 24188 22604.77 1583.23077 58 329267 237560.35 91706.64516 59 65029 87325.87 -22296.87234 60 101097 104038.57 -2941.56863 61 218946 204881.74 14064.25806 62 244052 204881.74 39170.25806 63 341570 322592.29 18977.70588 64 103597 104038.57 -441.56863 65 233328 237560.35 -4232.35484 66 256462 237560.35 18901.64516 67 206161 237560.35 -31399.35484 68 311473 322592.29 -11119.29412 69 235800 237560.35 -1760.35484 70 177939 149425.88 28513.11765 71 207176 204881.74 2294.25806 72 196553 161692.71 34860.28947 73 174184 161692.71 12491.28947 74 143246 161692.71 -18446.71053 75 187559 237560.35 -50001.35484 76 187681 204881.74 -17200.74194 77 119016 104038.57 14977.43137 78 182192 204881.74 -22689.74194 79 73566 104038.57 -30472.56863 80 194979 204881.74 -9902.74194 81 167488 204881.74 -37393.74194 82 143756 161692.71 -17936.71053 83 275541 204881.74 70659.25806 84 243199 237560.35 5638.64516 85 182999 161692.71 21306.28947 86 135649 161692.71 -26043.71053 87 152299 161692.71 -9393.71053 88 120221 104038.57 16182.43137 89 346485 322592.29 23892.70588 90 145790 149425.88 -3635.88235 91 193339 161692.71 31646.28947 92 80953 104038.57 -23085.56863 93 122774 149425.88 -26651.88235 94 130585 87325.87 43259.12766 95 112611 87325.87 25285.12766 96 286468 322592.29 -36124.29412 97 241066 237560.35 3505.64516 98 148446 237560.35 -89114.35484 99 204713 149425.88 55287.11765 100 182079 161692.71 20386.28947 101 140344 161692.71 -21348.71053 102 220516 204881.74 15634.25806 103 243060 204881.74 38178.25806 104 162765 161692.71 1072.28947 105 182613 161692.71 20920.28947 106 232138 204881.74 27256.25806 107 265318 237560.35 27757.64516 108 85574 87325.87 -1751.87234 109 310839 322592.29 -11753.29412 110 225060 237560.35 -12500.35484 111 232317 204881.74 27435.25806 112 144966 161692.71 -16726.71053 113 43287 51607.69 -8320.69444 114 155754 149425.88 6328.11765 115 164709 237560.35 -72851.35484 116 201940 204881.74 -2941.74194 117 235454 237560.35 -2106.35484 118 220801 149425.88 71375.11765 119 99466 87325.87 12140.12766 120 92661 104038.57 -11377.56863 121 133328 104038.57 29289.43137 122 61361 104038.57 -42677.56863 123 125930 149425.88 -23495.88235 124 100750 161692.71 -60942.71053 125 224549 161692.71 62856.28947 126 82316 57636.00 24680.00000 127 102010 104038.57 -2028.56863 128 101523 87325.87 14197.12766 129 243511 237560.35 5950.64516 130 22938 22604.77 333.23077 131 41566 51607.69 -10041.69444 132 152474 204881.74 -52407.74194 133 61857 51607.69 10249.30556 134 99923 149425.88 -49502.88235 135 132487 161692.71 -29205.71053 136 317394 322592.29 -5198.29412 137 21054 22604.77 -1550.76923 138 209641 204881.74 4759.25806 139 22648 51607.69 -28959.69444 140 31414 22604.77 8809.23077 141 46698 87325.87 -40627.87234 142 131698 104038.57 27659.43137 143 91735 104038.57 -12303.56863 144 244749 237560.35 7188.64516 145 184510 161692.71 22817.28947 146 79863 104038.57 -24175.56863 147 128423 161692.71 -33269.71053 148 97839 104038.57 -6199.56863 149 38214 57636.00 -19422.00000 150 151101 161692.71 -10591.71053 151 272458 204881.74 67576.25806 152 172494 161692.71 10801.28947 153 108043 104038.57 4004.43137 154 328107 322592.29 5514.70588 155 250579 237560.35 13018.64516 156 351067 322592.29 28474.70588 157 158015 161692.71 -3677.71053 158 98866 104038.57 -5172.56863 159 85439 87325.87 -1886.87234 160 229242 237560.35 -8318.35484 161 351619 322592.29 29026.70588 162 84207 87325.87 -3118.87234 163 120445 104038.57 16406.43137 164 324598 322592.29 2005.70588 165 131069 149425.88 -18356.88235 166 204271 161692.71 42578.28947 167 165543 149425.88 16117.11765 168 141722 161692.71 -19970.71053 169 116048 87325.87 28722.12766 170 250047 237560.35 12486.64516 171 299775 322592.29 -22817.29412 172 195838 204881.74 -9043.74194 173 173260 149425.88 23834.11765 174 254488 237560.35 16927.64516 175 104389 161692.71 -57303.71053 176 136084 149425.88 -13341.88235 177 199476 204881.74 -5405.74194 178 92499 87325.87 5173.12766 179 224330 237560.35 -13230.35484 180 135781 104038.57 31742.43137 181 74408 104038.57 -29630.56863 182 81240 104038.57 -22798.56863 183 14688 22604.77 -7916.76923 184 181633 204881.74 -23248.74194 185 271856 237560.35 34295.64516 186 7199 22604.77 -15405.76923 187 46660 57636.00 -10976.00000 188 17547 22604.77 -5057.76923 189 133368 104038.57 29329.43137 190 95227 87325.87 7901.12766 191 152601 104038.57 48562.43137 192 98146 104038.57 -5892.56863 193 79619 104038.57 -24419.56863 194 59194 87325.87 -28131.87234 195 139942 104038.57 35903.43137 196 118612 104038.57 14573.43137 197 72880 104038.57 -31158.56863 198 65475 51607.69 13867.30556 199 99643 104038.57 -4395.56863 200 71965 87325.87 -15360.87234 201 77272 51607.69 25664.30556 202 49289 51607.69 -2318.69444 203 135131 104038.57 31092.43137 204 108446 104038.57 4407.43137 205 89746 149425.88 -59679.88235 206 44296 51607.69 -7311.69444 207 77648 87325.87 -9677.87234 208 181528 149425.88 32102.11765 209 134019 104038.57 29980.43137 210 124064 104038.57 20025.43137 211 92630 104038.57 -11408.56863 212 121848 87325.87 34522.12766 213 52915 51607.69 1307.30556 214 81872 87325.87 -5453.87234 215 58981 104038.57 -45057.56863 216 53515 51607.69 1907.30556 217 60812 104038.57 -43226.56863 218 56375 51607.69 4767.30556 219 65490 51607.69 13882.30556 220 80949 51607.69 29341.30556 221 76302 87325.87 -11023.87234 222 104011 104038.57 -27.56863 223 98104 149425.88 -51321.88235 224 67989 51607.69 16381.30556 225 30989 51607.69 -20618.69444 226 135458 149425.88 -13967.88235 227 73504 57636.00 15868.00000 228 63123 87325.87 -24202.87234 229 61254 87325.87 -26071.87234 230 74914 104038.57 -29124.56863 231 31774 51607.69 -19833.69444 232 81437 87325.87 -5888.87234 233 87186 104038.57 -16852.56863 234 50090 51607.69 -1517.69444 235 65745 87325.87 -21580.87234 236 56653 51607.69 5045.30556 237 158399 104038.57 54360.43137 238 46455 51607.69 -5152.69444 239 73624 104038.57 -30414.56863 240 38395 51607.69 -13212.69444 241 91899 87325.87 4573.12766 242 139526 104038.57 35487.43137 243 52164 51607.69 556.30556 244 51567 51607.69 -40.69444 245 70551 87325.87 -16774.87234 246 84856 104038.57 -19182.56863 247 102538 104038.57 -1500.56863 248 86678 87325.87 -647.87234 249 85709 57636.00 28073.00000 250 34662 57636.00 -22974.00000 251 150580 104038.57 46541.43137 252 99611 87325.87 12285.12766 253 19349 22604.77 -3255.76923 254 99373 104038.57 -4665.56863 255 86230 104038.57 -17808.56863 256 30837 22604.77 8232.23077 257 31706 51607.69 -19901.69444 258 89806 87325.87 2480.12766 259 62088 51607.69 10480.30556 260 40151 51607.69 -11456.69444 261 27634 51607.69 -23973.69444 262 76990 51607.69 25382.30556 263 37460 22604.77 14855.23077 264 54157 51607.69 2549.30556 265 49862 51607.69 -1745.69444 266 84337 87325.87 -2988.87234 267 64175 87325.87 -23150.87234 268 59382 51607.69 7774.30556 269 119308 87325.87 31982.12766 270 76702 87325.87 -10623.87234 271 103425 104038.57 -613.56863 272 70344 87325.87 -16981.87234 273 43410 57636.00 -14226.00000 274 104838 87325.87 17512.12766 275 62215 51607.69 10607.30556 276 69304 87325.87 -18021.87234 277 53117 51607.69 1509.30556 278 19764 22604.77 -2840.76923 279 86680 87325.87 -645.87234 280 84105 87325.87 -3220.87234 281 77945 87325.87 -9380.87234 282 89113 87325.87 1787.12766 283 91005 104038.57 -13033.56863 284 40248 22604.77 17643.23077 285 64187 87325.87 -23138.87234 286 50857 51607.69 -750.69444 287 56613 57636.00 -1023.00000 288 62792 51607.69 11184.30556 289 72535 87325.87 -14790.87234 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/fisher/rcomp/tmp/4p5mu1355219403.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/5s01j1355219403.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/fisher/rcomp/tmp/6djkn1355219403.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/fisher/rcomp/tmp/71ura1355219403.tab") + } > > try(system("convert tmp/2gvaj1355219403.ps tmp/2gvaj1355219403.png",intern=TRUE)) character(0) > try(system("convert tmp/398tz1355219403.ps tmp/398tz1355219403.png",intern=TRUE)) character(0) > try(system("convert tmp/4p5mu1355219403.ps tmp/4p5mu1355219403.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.637 0.738 10.363