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,119308 + ,30 + ,16 + ,37 + ,0 + ,76702 + ,49 + ,21 + ,51 + ,NA + ,103425 + ,67 + ,19 + ,45 + ,NA + ,70344 + ,28 + ,16 + ,21 + ,NA + ,43410 + ,19 + ,1 + ,1 + ,NA + ,104838 + ,49 + ,16 + ,42 + ,NA + ,62215 + ,27 + ,10 + ,26 + ,NA + ,69304 + ,30 + ,19 + ,21 + ,NA + ,53117 + ,22 + ,12 + ,4 + ,NA + ,19764 + ,12 + ,2 + ,10 + ,NA + ,86680 + ,31 + ,14 + ,43 + ,NA + ,84105 + ,20 + ,17 + ,34 + ,0 + ,77945 + ,20 + ,19 + ,31 + ,NA + ,89113 + ,39 + ,14 + ,19 + ,NA + ,91005 + ,29 + ,11 + ,34 + ,NA + ,40248 + ,16 + ,4 + ,6 + ,NA + ,64187 + ,27 + ,16 + ,11 + ,0 + ,50857 + ,21 + ,20 + ,24 + ,NA + ,56613 + ,19 + ,12 + ,16 + ,NA + ,62792 + ,35 + ,15 + ,72 + ,NA + ,72535 + ,14 + ,16 + ,21 + ,NA) + ,dim=c(5 + ,289) + ,dimnames=list(c('timeinrfc' + ,'logins' + ,'compendiumsreviewed' + ,'totblogs' + ,'gender') + ,1:289)) > y <- array(NA,dim=c(5,289),dimnames=list(c('timeinrfc','logins','compendiumsreviewed','totblogs','gender'),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 = '' > 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] "timeinrfc" > 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] "timeinrfc" "logins" "compendiumsreviewed" [4] "totblogs" "gender" > colnames(x)[par1] [1] "timeinrfc" > 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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/www/rcomp/tmp/15k871324648186.tab") + } + } > m Conditional inference tree with 11 terminal nodes Response: timeinrfc Inputs: logins, compendiumsreviewed, totblogs, gender Number of observations: 289 1) totblogs <= 59; criterion = 1, statistic = 190.952 2) logins <= 28; criterion = 1, statistic = 64.903 3) totblogs <= 14; criterion = 1, statistic = 19.678 4) compendiumsreviewed <= 4; criterion = 0.976, statistic = 7.568 5)* weights = 12 4) compendiumsreviewed > 4 6)* weights = 10 3) totblogs > 14 7)* weights = 29 2) logins > 28 8) totblogs <= 48; criterion = 1, statistic = 21.368 9) logins <= 52; criterion = 0.995, statistic = 10.436 10)* weights = 62 9) logins > 52 11)* weights = 25 8) totblogs > 48 12)* weights = 17 1) totblogs > 59 13) logins <= 70; criterion = 1, statistic = 37.036 14) compendiumsreviewed <= 23; criterion = 1, statistic = 26.289 15)* weights = 14 14) compendiumsreviewed > 23 16) totblogs <= 101; criterion = 1, statistic = 15.091 17)* weights = 33 16) totblogs > 101 18)* weights = 31 13) logins > 70 19) totblogs <= 117; criterion = 0.986, statistic = 8.471 20)* weights = 24 19) totblogs > 117 21)* weights = 32 > postscript(file="/var/www/rcomp/tmp/2ntp31324648186.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/www/rcomp/tmp/31tbm1324648186.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 209753.16 1153.839 2 120982 164010.79 -43028.788 3 176508 164010.79 12497.212 4 179321 272321.38 -93000.375 5 123185 112183.07 11001.929 6 52746 59934.76 -7188.759 7 385534 272321.38 113212.625 8 33170 45023.60 -11853.600 9 101645 109181.80 -7536.800 10 149061 164010.79 -14949.788 11 165446 164010.79 1435.212 12 237213 272321.38 -35108.375 13 173326 212530.88 -39204.875 14 133131 164010.79 -30879.788 15 258873 209753.16 49119.839 16 180083 164010.79 16072.212 17 324799 272321.38 52477.625 18 230964 209753.16 21210.839 19 236785 212530.88 24254.125 20 135473 112183.07 23289.929 21 202925 209753.16 -6828.161 22 215147 209753.16 5393.839 23 344297 272321.38 71975.625 24 153935 164010.79 -10075.788 25 132943 209753.16 -76810.161 26 174724 272321.38 -97597.375 27 174415 212530.88 -38115.875 28 225548 212530.88 13017.125 29 223632 212530.88 11101.125 30 124817 136000.35 -11183.353 31 221698 209753.16 11944.839 32 210767 209753.16 1013.839 33 170266 209753.16 -39487.161 34 260561 272321.38 -11760.375 35 84853 82401.00 2452.000 36 294424 272321.38 22102.625 37 101011 82401.00 18610.000 38 215641 164010.79 51630.212 39 325107 212530.88 112576.125 40 7176 22366.50 -15190.500 41 167542 164010.79 3531.212 42 106408 82401.00 24007.000 43 96560 109181.80 -12621.800 44 265769 272321.38 -6552.375 45 269651 209753.16 59897.839 46 149112 164010.79 -14898.788 47 175824 212530.88 -36706.875 48 152871 164010.79 -11139.788 49 111665 136000.35 -24335.353 50 116408 109181.80 7226.200 51 362301 212530.88 149770.125 52 78800 82401.00 -3601.000 53 183167 209753.16 -26586.161 54 277965 272321.38 5643.625 55 150629 209753.16 -59124.161 56 168809 164010.79 4798.212 57 24188 22366.50 1821.500 58 329267 272321.38 56945.625 59 65029 59934.76 5094.241 60 101097 109181.80 -8084.800 61 218946 209753.16 9192.839 62 244052 209753.16 34298.839 63 341570 272321.38 69248.625 64 103597 82401.00 21196.000 65 233328 272321.38 -38993.375 66 256462 272321.38 -15859.375 67 206161 212530.88 -6369.875 68 311473 272321.38 39151.625 69 235800 272321.38 -36521.375 70 177939 212530.88 -34591.875 71 207176 164010.79 43165.212 72 196553 209753.16 -13200.161 73 174184 164010.79 10173.212 74 143246 212530.88 -69284.875 75 187559 212530.88 -24971.875 76 187681 209753.16 -22072.161 77 119016 112183.07 6832.929 78 182192 164010.79 18181.212 79 73566 82401.00 -8835.000 80 194979 164010.79 30968.212 81 167488 209753.16 -42265.161 82 143756 209753.16 -65997.161 83 275541 209753.16 65787.839 84 243199 272321.38 -29122.375 85 182999 136000.35 46998.647 86 135649 164010.79 -28361.788 87 152299 164010.79 -11711.788 88 120221 136000.35 -15779.353 89 346485 272321.38 74163.625 90 145790 109181.80 36608.200 91 193339 272321.38 -78982.375 92 80953 59934.76 21018.241 93 122774 82401.00 40373.000 94 130585 164010.79 -33425.788 95 112611 82401.00 30210.000 96 286468 212530.88 73937.125 97 241066 136000.35 105065.647 98 148446 272321.38 -123875.375 99 204713 212530.88 -7817.875 100 182079 209753.16 -27674.161 101 140344 164010.79 -23666.788 102 220516 164010.79 56505.212 103 243060 209753.16 33306.839 104 162765 164010.79 -1245.788 105 182613 209753.16 -27140.161 106 232138 209753.16 22384.839 107 265318 272321.38 -7003.375 108 85574 82401.00 3173.000 109 310839 272321.38 38517.625 110 225060 212530.88 12529.125 111 232317 209753.16 22563.839 112 144966 136000.35 8965.647 113 43287 59934.76 -16647.759 114 155754 112183.07 43570.929 115 164709 272321.38 -107612.375 116 201940 164010.79 37929.212 117 235454 212530.88 22923.125 118 220801 212530.88 8270.125 119 99466 136000.35 -36534.353 120 92661 109181.80 -16520.800 121 133328 112183.07 21144.929 122 61361 109181.80 -47820.800 123 125930 212530.88 -86600.875 124 100750 136000.35 -35250.353 125 224549 164010.79 60538.212 126 82316 82401.00 -85.000 127 102010 109181.80 -7171.800 128 101523 112183.07 -10660.071 129 243511 272321.38 -28810.375 130 22938 22366.50 571.500 131 41566 82401.00 -40835.000 132 152474 164010.79 -11536.788 133 61857 59934.76 1922.241 134 99923 109181.80 -9258.800 135 132487 164010.79 -31523.788 136 317394 272321.38 45072.625 137 21054 22366.50 -1312.500 138 209641 164010.79 45630.212 139 22648 59934.76 -37286.759 140 31414 59934.76 -28520.759 141 46698 82401.00 -35703.000 142 131698 112183.07 19514.929 143 91735 82401.00 9334.000 144 244749 212530.88 32218.125 145 184510 164010.79 20499.212 146 79863 82401.00 -2538.000 147 128423 109181.80 19241.200 148 97839 164010.79 -66171.788 149 38214 82401.00 -44187.000 150 151101 136000.35 15100.647 151 272458 209753.16 62704.839 152 172494 209753.16 -37259.161 153 108043 109181.80 -1138.800 154 328107 209753.16 118353.839 155 250579 272321.38 -21742.375 156 351067 272321.38 78745.625 157 158015 164010.79 -5995.788 158 98866 59934.76 38931.241 159 85439 82401.00 3038.000 160 229242 212530.88 16711.125 161 351619 272321.38 79297.625 162 84207 82401.00 1806.000 163 120445 136000.35 -15555.353 164 324598 272321.38 52276.625 165 131069 164010.79 -32941.788 166 204271 209753.16 -5482.161 167 165543 209753.16 -44210.161 168 141722 212530.88 -70808.875 169 116048 112183.07 3864.929 170 250047 136000.35 114046.647 171 299775 212530.88 87244.125 172 195838 209753.16 -13915.161 173 173260 109181.80 64078.200 174 254488 272321.38 -17833.375 175 104389 164010.79 -59621.788 176 136084 82401.00 53683.000 177 199476 209753.16 -10277.161 178 92499 82401.00 10098.000 179 224330 272321.38 -47991.375 180 135781 82401.00 53380.000 181 74408 109181.80 -34773.800 182 81240 109181.80 -27941.800 183 14688 22366.50 -7678.500 184 181633 164010.79 17622.212 185 271856 272321.38 -465.375 186 7199 22366.50 -15167.500 187 46660 45023.60 1636.400 188 17547 22366.50 -4819.500 189 133368 136000.35 -2632.353 190 95227 82401.00 12826.000 191 152601 136000.35 16600.647 192 98146 82401.00 15745.000 193 79619 82401.00 -2782.000 194 59194 82401.00 -23207.000 195 139942 112183.07 27758.929 196 118612 112183.07 6428.929 197 72880 82401.00 -9521.000 198 65475 59934.76 5540.241 199 99643 136000.35 -36357.353 200 71965 112183.07 -40218.071 201 77272 109181.80 -31909.800 202 49289 59934.76 -10645.759 203 135131 109181.80 25949.200 204 108446 109181.80 -735.800 205 89746 82401.00 7345.000 206 44296 45023.60 -727.600 207 77648 82401.00 -4753.000 208 181528 109181.80 72346.200 209 134019 109181.80 24837.200 210 124064 82401.00 41663.000 211 92630 82401.00 10229.000 212 121848 82401.00 39447.000 213 52915 59934.76 -7019.759 214 81872 82401.00 -529.000 215 58981 82401.00 -23420.000 216 53515 59934.76 -6419.759 217 60812 82401.00 -21589.000 218 56375 82401.00 -26026.000 219 65490 59934.76 5555.241 220 80949 45023.60 35925.400 221 76302 82401.00 -6099.000 222 104011 109181.80 -5170.800 223 98104 112183.07 -14079.071 224 67989 59934.76 8054.241 225 30989 45023.60 -14034.600 226 135458 212530.88 -77072.875 227 73504 82401.00 -8897.000 228 63123 112183.07 -49060.071 229 61254 82401.00 -21147.000 230 74914 82401.00 -7487.000 231 31774 45023.60 -13249.600 232 81437 82401.00 -964.000 233 87186 109181.80 -21995.800 234 50090 59934.76 -9844.759 235 65745 109181.80 -43436.800 236 56653 82401.00 -25748.000 237 158399 82401.00 75998.000 238 46455 59934.76 -13479.759 239 73624 59934.76 13689.241 240 38395 82401.00 -44006.000 241 91899 82401.00 9498.000 242 139526 212530.88 -73004.875 243 52164 82401.00 -30237.000 244 51567 82401.00 -30834.000 245 70551 82401.00 -11850.000 246 84856 82401.00 2455.000 247 102538 136000.35 -33462.353 248 86678 82401.00 4277.000 249 85709 82401.00 3308.000 250 34662 59934.76 -25272.759 251 150580 109181.80 41398.200 252 99611 136000.35 -36389.353 253 19349 22366.50 -3017.500 254 99373 109181.80 -9808.800 255 86230 82401.00 3829.000 256 30837 22366.50 8470.500 257 31706 59934.76 -28228.759 258 89806 82401.00 7405.000 259 62088 82401.00 -20313.000 260 40151 82401.00 -42250.000 261 27634 45023.60 -17389.600 262 76990 59934.76 17055.241 263 37460 45023.60 -7563.600 264 54157 59934.76 -5777.759 265 49862 82401.00 -32539.000 266 84337 59934.76 24402.241 267 64175 82401.00 -18226.000 268 59382 82401.00 -23019.000 269 119308 82401.00 36907.000 270 76702 136000.35 -59298.353 271 103425 109181.80 -5756.800 272 70344 59934.76 10409.241 273 43410 22366.50 21043.500 274 104838 82401.00 22437.000 275 62215 59934.76 2280.241 276 69304 82401.00 -13097.000 277 53117 45023.60 8093.400 278 19764 22366.50 -2602.500 279 86680 82401.00 4279.000 280 84105 59934.76 24170.241 281 77945 59934.76 18010.241 282 89113 82401.00 6712.000 283 91005 82401.00 8604.000 284 40248 22366.50 17881.500 285 64187 45023.60 19163.400 286 50857 59934.76 -9077.759 287 56613 59934.76 -3321.759 288 62792 112183.07 -49391.071 289 72535 59934.76 12600.241 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/rcomp/tmp/40g3f1324648186.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/www/rcomp/tmp/53far1324648186.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/www/rcomp/tmp/6pga91324648186.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/www/rcomp/tmp/75xc91324648186.tab") + } > > try(system("convert tmp/2ntp31324648186.ps tmp/2ntp31324648186.png",intern=TRUE)) character(0) > try(system("convert tmp/31tbm1324648186.ps tmp/31tbm1324648186.png",intern=TRUE)) character(0) > try(system("convert tmp/40g3f1324648186.ps tmp/40g3f1324648186.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.760 0.190 4.917