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Type 'q()' to quit R. > x <- array(list(115 + ,112285 + ,24188 + ,146283 + ,109 + ,84786 + ,18273 + ,98364 + ,146 + ,83123 + ,14130 + ,86146 + ,116 + ,101193 + ,32287 + ,96933 + ,68 + ,38361 + ,8654 + ,79234 + ,101 + ,68504 + ,9245 + ,42551 + ,96 + ,119182 + ,33251 + ,195663 + ,67 + ,22807 + ,1271 + ,6853 + ,44 + ,17140 + ,5279 + ,21529 + ,100 + ,116174 + ,27101 + ,95757 + ,93 + ,57635 + ,16373 + ,85584 + ,140 + ,66198 + ,19716 + ,143983 + ,166 + ,71701 + ,17753 + ,75851 + ,99 + ,57793 + ,9028 + ,59238 + ,139 + ,80444 + ,18653 + ,93163 + ,130 + ,53855 + ,8828 + ,96037 + ,181 + ,97668 + ,29498 + ,151511 + ,116 + ,133824 + ,27563 + ,136368 + ,116 + ,101481 + ,18293 + ,112642 + ,88 + ,99645 + ,22530 + ,94728 + ,139 + ,114789 + ,15977 + ,105499 + ,135 + ,99052 + ,35082 + ,121527 + ,108 + ,67654 + ,16116 + ,127766 + ,89 + ,65553 + ,15849 + ,98958 + ,156 + ,97500 + ,16026 + ,77900 + ,129 + ,69112 + ,26569 + ,85646 + ,118 + ,82753 + ,24785 + ,98579 + ,118 + ,85323 + ,17569 + ,130767 + ,125 + 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,64 + ,19499 + ,6909 + ,32378 + ,63 + ,16380 + ,3189 + ,10824 + ,59 + ,36874 + ,6745 + ,39613 + ,84 + ,48259 + ,16724 + ,60865 + ,64 + ,16734 + ,4850 + ,19787 + ,56 + ,28207 + ,7025 + ,20107 + ,54 + ,30143 + ,6047 + ,36605 + ,67 + ,41369 + ,7377 + ,40961 + ,58 + ,45833 + ,9078 + ,48231 + ,59 + ,29156 + ,4605 + ,39725 + ,40 + ,35944 + ,3238 + ,21455 + ,22 + ,36278 + ,8100 + ,23430 + ,83 + ,45588 + ,9653 + ,62991 + ,81 + ,45097 + ,8914 + ,49363 + ,2 + ,3895 + ,786 + ,9604 + ,72 + ,28394 + ,6700 + ,24552 + ,61 + ,18632 + ,5788 + ,31493 + ,15 + ,2325 + ,593 + ,3439 + ,32 + ,25139 + ,4506 + ,19555 + ,62 + ,27975 + ,6382 + ,21228 + ,58 + ,14483 + ,5621 + ,23177 + ,36 + ,13127 + ,3997 + ,22094 + ,59 + ,5839 + ,520 + ,2342 + ,68 + ,24069 + ,8891 + ,38798 + ,21 + ,3738 + ,999 + ,3255 + ,55 + ,18625 + ,7067 + ,24261 + ,54 + ,36341 + ,4639 + ,18511 + ,55 + ,24548 + ,5654 + ,40798 + ,72 + ,21792 + ,6928 + ,28893 + ,41 + ,26263 + ,1514 + ,21425 + ,61 + ,23686 + ,9238 + ,50276 + ,67 + ,49303 + ,8204 + ,37643 + ,76 + ,25659 + ,5926 + ,30377 + ,64 + ,28904 + ,5785 + ,27126 + ,3 + ,2781 + ,4 + ,13 + ,63 + ,29236 + ,5930 + ,42097 + ,40 + ,19546 + ,3710 + ,24451 + ,69 + ,22818 + ,705 + ,14335 + ,48 + ,32689 + ,443 + ,5084 + ,8 + ,5752 + ,2416 + ,9927 + ,52 + ,22197 + ,7747 + ,43527 + ,66 + ,20055 + ,5432 + ,27184 + ,76 + ,25272 + ,4913 + ,21610 + ,43 + ,82206 + ,2650 + ,20484 + ,39 + ,32073 + ,2370 + ,20156 + ,14 + ,5444 + ,775 + ,6012 + ,61 + ,20154 + ,5576 + ,18475 + ,71 + ,36944 + ,1352 + ,12645 + ,44 + ,8019 + ,3080 + ,11017 + ,60 + ,30884 + ,10205 + ,37623 + ,64 + ,19540 + ,6095 + ,35873) + ,dim=c(4 + ,289) + ,dimnames=list(c('feedback_messages_p1' + ,'totsize' + ,'totrevisions' + ,'totseconds ') + ,1:289)) > y <- array(NA,dim=c(4,289),dimnames=list(c('feedback_messages_p1','totsize','totrevisions','totseconds '),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 = '2' > #'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] "totsize" > x[,par1] [1] 112285 84786 83123 101193 38361 68504 119182 22807 17140 116174 [11] 57635 66198 71701 57793 80444 53855 97668 133824 101481 99645 [21] 114789 99052 67654 65553 97500 69112 82753 85323 72654 30727 [31] 77873 117478 74007 90183 61542 101494 27570 55813 79215 1423 [41] 55461 31081 22996 83122 70106 60578 39992 79892 49810 71570 [51] 100708 33032 82875 139077 71595 72260 5950 115762 32551 31701 [61] 80670 143558 117105 23789 120733 105195 73107 132068 149193 46821 [71] 87011 95260 55183 106671 73511 92945 78664 70054 22618 74011 [81] 83737 69094 93133 95536 225920 62133 61370 43836 106117 38692 [91] 84651 56622 15986 95364 26706 89691 67267 126846 41140 102860 [101] 51715 55801 111813 120293 138599 161647 115929 24266 162901 109825 [111] 129838 37510 43750 40652 87771 85872 89275 44418 192565 35232 [121] 40909 13294 32387 140867 120662 21233 44332 61056 101338 1168 [131] 13497 65567 25162 32334 40735 91413 855 97068 44339 14116 [141] 10288 65622 16563 76643 110681 29011 92696 94785 8773 83209 [151] 93815 86687 34553 105547 103487 213688 71220 23517 56926 91721 [161] 115168 111194 51009 135777 51513 74163 51633 75345 33416 83305 [171] 98952 102372 37238 103772 123969 27142 135400 21399 130115 24874 [181] 34988 45549 6023 64466 54990 1644 6179 3926 32755 34777 [191] 73224 27114 20760 37636 65461 30080 24094 69008 54968 46090 [201] 27507 10672 34029 46300 24760 18779 21280 40662 28987 22827 [211] 18513 30594 24006 27913 42744 12934 22574 41385 18653 18472 [221] 30976 63339 25568 33747 4154 19474 35130 39067 13310 65892 [231] 4143 28579 51776 21152 38084 27717 32928 11342 19499 16380 [241] 36874 48259 16734 28207 30143 41369 45833 29156 35944 36278 [251] 45588 45097 3895 28394 18632 2325 25139 27975 14483 13127 [261] 5839 24069 3738 18625 36341 24548 21792 26263 23686 49303 [271] 25659 28904 2781 29236 19546 22818 32689 5752 22197 20055 [281] 25272 82206 32073 5444 20154 36944 8019 30884 19540 > 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]) 855 1168 1423 1644 2325 2781 3738 3895 3926 4143 4154 1 1 1 1 1 1 1 1 1 1 1 5444 5752 5839 5950 6023 6179 8019 8773 10288 10672 11342 1 1 1 1 1 1 1 1 1 1 1 12934 13127 13294 13310 13497 14116 14483 15986 16380 16563 16734 1 1 1 1 1 1 1 1 1 1 1 17140 18472 18513 18625 18632 18653 18779 19474 19499 19540 19546 1 1 1 1 1 1 1 1 1 1 1 20055 20154 20760 21152 21233 21280 21399 21792 22197 22574 22618 1 1 1 1 1 1 1 1 1 1 1 22807 22818 22827 22996 23517 23686 23789 24006 24069 24094 24266 1 1 1 1 1 1 1 1 1 1 1 24548 24760 24874 25139 25162 25272 25568 25659 26263 26706 27114 1 1 1 1 1 1 1 1 1 1 1 27142 27507 27570 27717 27913 27975 28207 28394 28579 28904 28987 1 1 1 1 1 1 1 1 1 1 1 29011 29156 29236 30080 30143 30594 30727 30884 30976 31081 31701 1 1 1 1 1 1 1 1 1 1 1 32073 32334 32387 32551 32689 32755 32928 33032 33416 33747 34029 1 1 1 1 1 1 1 1 1 1 1 34553 34777 34988 35130 35232 35944 36278 36341 36874 36944 37238 1 1 1 1 1 1 1 1 1 1 1 37510 37636 38084 38361 38692 39067 39992 40652 40662 40735 40909 1 1 1 1 1 1 1 1 1 1 1 41140 41369 41385 42744 43750 43836 44332 44339 44418 45097 45549 1 1 1 1 1 1 1 1 1 1 1 45588 45833 46090 46300 46821 48259 49303 49810 51009 51513 51633 1 1 1 1 1 1 1 1 1 1 1 51715 51776 53855 54968 54990 55183 55461 55801 55813 56622 56926 1 1 1 1 1 1 1 1 1 1 1 57635 57793 60578 61056 61370 61542 62133 63339 64466 65461 65553 1 1 1 1 1 1 1 1 1 1 1 65567 65622 65892 66198 67267 67654 68504 69008 69094 69112 70054 1 1 1 1 1 1 1 1 1 1 1 70106 71220 71570 71595 71701 72260 72654 73107 73224 73511 74007 1 1 1 1 1 1 1 1 1 1 1 74011 74163 75345 76643 77873 78664 79215 79892 80444 80670 82206 1 1 1 1 1 1 1 1 1 1 1 82753 82875 83122 83123 83209 83305 83737 84651 84786 85323 85872 1 1 1 1 1 1 1 1 1 1 1 86687 87011 87771 89275 89691 90183 91413 91721 92696 92945 93133 1 1 1 1 1 1 1 1 1 1 1 93815 94785 95260 95364 95536 97068 97500 97668 98952 99052 99645 1 1 1 1 1 1 1 1 1 1 1 100708 101193 101338 101481 101494 102372 102860 103487 103772 105195 105547 1 1 1 1 1 1 1 1 1 1 1 106117 106671 109825 110681 111194 111813 112285 114789 115168 115762 115929 1 1 1 1 1 1 1 1 1 1 1 116174 117105 117478 119182 120293 120662 120733 123969 126846 129838 130115 1 1 1 1 1 1 1 1 1 1 1 132068 133824 135400 135777 138599 139077 140867 143558 149193 161647 162901 1 1 1 1 1 1 1 1 1 1 1 192565 213688 225920 1 1 1 > colnames(x) [1] "feedback_messages_p1" "totsize" "totrevisions" [4] "totseconds." > colnames(x)[par1] [1] "totsize" > x[,par1] [1] 112285 84786 83123 101193 38361 68504 119182 22807 17140 116174 [11] 57635 66198 71701 57793 80444 53855 97668 133824 101481 99645 [21] 114789 99052 67654 65553 97500 69112 82753 85323 72654 30727 [31] 77873 117478 74007 90183 61542 101494 27570 55813 79215 1423 [41] 55461 31081 22996 83122 70106 60578 39992 79892 49810 71570 [51] 100708 33032 82875 139077 71595 72260 5950 115762 32551 31701 [61] 80670 143558 117105 23789 120733 105195 73107 132068 149193 46821 [71] 87011 95260 55183 106671 73511 92945 78664 70054 22618 74011 [81] 83737 69094 93133 95536 225920 62133 61370 43836 106117 38692 [91] 84651 56622 15986 95364 26706 89691 67267 126846 41140 102860 [101] 51715 55801 111813 120293 138599 161647 115929 24266 162901 109825 [111] 129838 37510 43750 40652 87771 85872 89275 44418 192565 35232 [121] 40909 13294 32387 140867 120662 21233 44332 61056 101338 1168 [131] 13497 65567 25162 32334 40735 91413 855 97068 44339 14116 [141] 10288 65622 16563 76643 110681 29011 92696 94785 8773 83209 [151] 93815 86687 34553 105547 103487 213688 71220 23517 56926 91721 [161] 115168 111194 51009 135777 51513 74163 51633 75345 33416 83305 [171] 98952 102372 37238 103772 123969 27142 135400 21399 130115 24874 [181] 34988 45549 6023 64466 54990 1644 6179 3926 32755 34777 [191] 73224 27114 20760 37636 65461 30080 24094 69008 54968 46090 [201] 27507 10672 34029 46300 24760 18779 21280 40662 28987 22827 [211] 18513 30594 24006 27913 42744 12934 22574 41385 18653 18472 [221] 30976 63339 25568 33747 4154 19474 35130 39067 13310 65892 [231] 4143 28579 51776 21152 38084 27717 32928 11342 19499 16380 [241] 36874 48259 16734 28207 30143 41369 45833 29156 35944 36278 [251] 45588 45097 3895 28394 18632 2325 25139 27975 14483 13127 [261] 5839 24069 3738 18625 36341 24548 21792 26263 23686 49303 [271] 25659 28904 2781 29236 19546 22818 32689 5752 22197 20055 [281] 25272 82206 32073 5444 20154 36944 8019 30884 19540 > 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/1fkiy1324146769.tab") + } + } > m Conditional inference tree with 10 terminal nodes Response: totsize Inputs: feedback_messages_p1, totrevisions, totseconds. Number of observations: 289 1) totseconds. <= 67808; criterion = 1, statistic = 195.162 2) feedback_messages_p1 <= 82; criterion = 1, statistic = 72.247 3) totseconds. <= 10824; criterion = 1, statistic = 39.34 4) feedback_messages_p1 <= 21; criterion = 0.972, statistic = 6.747 5)* weights = 12 4) feedback_messages_p1 > 21 6)* weights = 8 3) totseconds. > 10824 7) totseconds. <= 35873; criterion = 0.998, statistic = 11.905 8)* weights = 68 7) totseconds. > 35873 9)* weights = 48 2) feedback_messages_p1 > 82 10) feedback_messages_p1 <= 111; criterion = 0.986, statistic = 7.965 11)* weights = 22 10) feedback_messages_p1 > 111 12)* weights = 9 1) totseconds. > 67808 13) totseconds. <= 120642; criterion = 1, statistic = 36.68 14) totrevisions <= 15849; criterion = 1, statistic = 17.72 15)* weights = 26 14) totrevisions > 15849 16) totrevisions <= 26569; criterion = 0.972, statistic = 6.758 17)* weights = 39 16) totrevisions > 26569 18)* weights = 8 13) totseconds. > 120642 19)* weights = 49 > postscript(file="/var/wessaorg/rcomp/tmp/2ju9v1324146769.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/3668d1324146769.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 112285 113150.510 -865.51020 2 84786 84518.513 267.48718 3 83123 65058.808 18064.19231 4 101193 104357.250 -3164.25000 5 38361 65058.808 -26697.80769 6 68504 49117.500 19386.50000 7 119182 113150.510 6031.48980 8 22807 14509.000 8298.00000 9 17140 25858.515 -8718.51471 10 116174 104357.250 11816.75000 11 57635 84518.513 -26883.51282 12 66198 113150.510 -46952.51020 13 71701 84518.513 -12817.51282 14 57793 49117.500 8675.50000 15 80444 84518.513 -4074.51282 16 53855 65058.808 -11203.80769 17 97668 113150.510 -15482.51020 18 133824 113150.510 20673.48980 19 101481 84518.513 16962.48718 20 99645 84518.513 15126.48718 21 114789 84518.513 30270.48718 22 99052 113150.510 -14098.51020 23 67654 113150.510 -45496.51020 24 65553 65058.808 494.19231 25 97500 84518.513 12981.48718 26 69112 84518.513 -15406.51282 27 82753 84518.513 -1765.51282 28 85323 113150.510 -27827.51020 29 72654 113150.510 -40496.51020 30 30727 49117.500 -18390.50000 31 77873 113150.510 -35277.51020 32 117478 113150.510 4327.48980 33 74007 65058.808 8948.19231 34 90183 113150.510 -22967.51020 35 61542 74928.667 -13386.66667 36 101494 113150.510 -11656.51020 37 27570 33994.792 -6424.79167 38 55813 65058.808 -9245.80769 39 79215 113150.510 -33935.51020 40 1423 3247.833 -1824.83333 41 55461 84518.513 -29057.51282 42 31081 33994.792 -2913.79167 43 22996 25858.515 -2862.51471 44 83122 113150.510 -30028.51020 45 70106 84518.513 -14412.51282 46 60578 65058.808 -4480.80769 47 39992 33994.792 5997.20833 48 79892 84518.513 -4626.51282 49 49810 49117.500 692.50000 50 71570 65058.808 6511.19231 51 100708 65058.808 35649.19231 52 33032 33994.792 -962.79167 53 82875 84518.513 -1643.51282 54 139077 113150.510 25926.48980 55 71595 84518.513 -12923.51282 56 72260 113150.510 -40890.51020 57 5950 25858.515 -19908.51471 58 115762 113150.510 2611.48980 59 32551 25858.515 6692.48529 60 31701 33994.792 -2293.79167 61 80670 84518.513 -3848.51282 62 143558 113150.510 30407.48980 63 117105 113150.510 3954.48980 64 23789 33994.792 -10205.79167 65 120733 113150.510 7582.48980 66 105195 113150.510 -7955.51020 67 73107 49117.500 23989.50000 68 132068 113150.510 18917.48980 69 149193 113150.510 36042.48980 70 46821 65058.808 -18237.80769 71 87011 84518.513 2492.48718 72 95260 113150.510 -17890.51020 73 55183 84518.513 -29335.51282 74 106671 84518.513 22152.48718 75 73511 84518.513 -11007.51282 76 92945 84518.513 8426.48718 77 78664 84518.513 -5854.51282 78 70054 65058.808 4995.19231 79 22618 49117.500 -26499.50000 80 74011 84518.513 -10507.51282 81 83737 84518.513 -781.51282 82 69094 84518.513 -15424.51282 83 93133 84518.513 8614.48718 84 95536 113150.510 -17614.51020 85 225920 113150.510 112769.48980 86 62133 74928.667 -12795.66667 87 61370 74928.667 -13558.66667 88 43836 49117.500 -5281.50000 89 106117 113150.510 -7033.51020 90 38692 65058.808 -26366.80769 91 84651 84518.513 132.48718 92 56622 33994.792 22627.20833 93 15986 49117.500 -33131.50000 94 95364 104357.250 -8993.25000 95 26706 33994.792 -7288.79167 96 89691 84518.513 5172.48718 97 67267 113150.510 -45883.51020 98 126846 104357.250 22488.75000 99 41140 65058.808 -23918.80769 100 102860 84518.513 18341.48718 101 51715 65058.808 -13343.80769 102 55801 65058.808 -9257.80769 103 111813 104357.250 7455.75000 104 120293 84518.513 35774.48718 105 138599 113150.510 25448.48980 106 161647 113150.510 48496.48980 107 115929 113150.510 2778.48980 108 24266 25858.515 -1592.51471 109 162901 113150.510 49750.48980 110 109825 104357.250 5467.75000 111 129838 113150.510 16687.48980 112 37510 74928.667 -37418.66667 113 43750 25858.515 17891.48529 114 40652 65058.808 -24406.80769 115 87771 104357.250 -16586.25000 116 85872 104357.250 -18485.25000 117 89275 84518.513 4756.48718 118 44418 65058.808 -20640.80769 119 192565 113150.510 79414.48980 120 35232 25858.515 9373.48529 121 40909 33994.792 6914.20833 122 13294 25858.515 -12564.51471 123 32387 33994.792 -1607.79167 124 140867 65058.808 75808.19231 125 120662 113150.510 7511.48980 126 21233 33994.792 -12761.79167 127 44332 33994.792 10337.20833 128 61056 49117.500 11938.50000 129 101338 84518.513 16819.48718 130 1168 3247.833 -2079.83333 131 13497 14509.000 -1012.00000 132 65567 84518.513 -18951.51282 133 25162 25858.515 -696.51471 134 32334 49117.500 -16783.50000 135 40735 74928.667 -34193.66667 136 91413 113150.510 -21737.51020 137 855 3247.833 -2392.83333 138 97068 65058.808 32009.19231 139 44339 25858.515 18480.48529 140 14116 25858.515 -11742.51471 141 10288 25858.515 -15570.51471 142 65622 65058.808 563.19231 143 16563 14509.000 2054.00000 144 76643 113150.510 -36507.51020 145 110681 84518.513 26162.48718 146 29011 25858.515 3152.48529 147 92696 74928.667 17767.33333 148 94785 49117.500 45667.50000 149 8773 25858.515 -17085.51471 150 83209 74928.667 8280.33333 151 93815 113150.510 -19335.51020 152 86687 84518.513 2168.48718 153 34553 33994.792 558.20833 154 105547 113150.510 -7603.51020 155 103487 113150.510 -9663.51020 156 213688 113150.510 100537.48980 157 71220 84518.513 -13298.51282 158 23517 25858.515 -2341.51471 159 56926 49117.500 7808.50000 160 91721 84518.513 7202.48718 161 115168 113150.510 2017.48980 162 111194 74928.667 36265.33333 163 51009 33994.792 17014.20833 164 135777 113150.510 22626.48980 165 51513 49117.500 2395.50000 166 74163 113150.510 -38987.51020 167 51633 65058.808 -13425.80769 168 75345 49117.500 26227.50000 169 33416 65058.808 -31642.80769 170 83305 65058.808 18246.19231 171 98952 65058.808 33893.19231 172 102372 84518.513 17853.48718 173 37238 25858.515 11379.48529 174 103772 113150.510 -9378.51020 175 123969 74928.667 49040.33333 176 27142 33994.792 -6852.79167 177 135400 113150.510 22249.48980 178 21399 25858.515 -4459.51471 179 130115 113150.510 16964.48980 180 24874 33994.792 -9120.79167 181 34988 25858.515 9129.48529 182 45549 25858.515 19690.48529 183 6023 3247.833 2775.16667 184 64466 65058.808 -592.80769 185 54990 113150.510 -58160.51020 186 1644 3247.833 -1603.83333 187 6179 25858.515 -19679.51471 188 3926 3247.833 678.16667 189 32755 33994.792 -1239.79167 190 34777 49117.500 -14340.50000 191 73224 49117.500 24106.50000 192 27114 25858.515 1255.48529 193 20760 25858.515 -5098.51471 194 37636 25858.515 11777.48529 195 65461 84518.513 -19057.51282 196 30080 33994.792 -3914.79167 197 24094 25858.515 -1764.51471 198 69008 33994.792 35013.20833 199 54968 33994.792 20973.20833 200 46090 25858.515 20231.48529 201 27507 33994.792 -6487.79167 202 10672 25858.515 -15186.51471 203 34029 33994.792 34.20833 204 46300 33994.792 12305.20833 205 24760 25858.515 -1098.51471 206 18779 25858.515 -7079.51471 207 21280 25858.515 -4578.51471 208 40662 33994.792 6667.20833 209 28987 33994.792 -5007.79167 210 22827 49117.500 -26290.50000 211 18513 33994.792 -15481.79167 212 30594 25858.515 4735.48529 213 24006 25858.515 -1852.51471 214 27913 33994.792 -6081.79167 215 42744 49117.500 -6373.50000 216 12934 49117.500 -36183.50000 217 22574 25858.515 -3284.51471 218 41385 25858.515 15526.48529 219 18653 33994.792 -15341.79167 220 18472 25858.515 -7386.51471 221 30976 33994.792 -3018.79167 222 63339 65058.808 -1719.80769 223 25568 33994.792 -8426.79167 224 33747 33994.792 -247.79167 225 4154 14509.000 -10355.00000 226 19474 33994.792 -14520.79167 227 35130 25858.515 9271.48529 228 39067 25858.515 13208.48529 229 13310 25858.515 -12548.51471 230 65892 49117.500 16774.50000 231 4143 14509.000 -10366.00000 232 28579 33994.792 -5415.79167 233 51776 33994.792 17781.20833 234 21152 25858.515 -4706.51471 235 38084 25858.515 12225.48529 236 27717 25858.515 1858.48529 237 32928 33994.792 -1066.79167 238 11342 25858.515 -14516.51471 239 19499 25858.515 -6359.51471 240 16380 14509.000 1871.00000 241 36874 33994.792 2879.20833 242 48259 49117.500 -858.50000 243 16734 25858.515 -9124.51471 244 28207 25858.515 2348.48529 245 30143 33994.792 -3851.79167 246 41369 33994.792 7374.20833 247 45833 33994.792 11838.20833 248 29156 33994.792 -4838.79167 249 35944 25858.515 10085.48529 250 36278 25858.515 10419.48529 251 45588 49117.500 -3529.50000 252 45097 33994.792 11102.20833 253 3895 3247.833 647.16667 254 28394 25858.515 2535.48529 255 18632 25858.515 -7226.51471 256 2325 3247.833 -922.83333 257 25139 25858.515 -719.51471 258 27975 25858.515 2116.48529 259 14483 25858.515 -11375.51471 260 13127 25858.515 -12731.51471 261 5839 14509.000 -8670.00000 262 24069 33994.792 -9925.79167 263 3738 3247.833 490.16667 264 18625 25858.515 -7233.51471 265 36341 25858.515 10482.48529 266 24548 33994.792 -9446.79167 267 21792 25858.515 -4066.51471 268 26263 25858.515 404.48529 269 23686 33994.792 -10308.79167 270 49303 33994.792 15308.20833 271 25659 25858.515 -199.51471 272 28904 25858.515 3045.48529 273 2781 3247.833 -466.83333 274 29236 33994.792 -4758.79167 275 19546 25858.515 -6312.51471 276 22818 25858.515 -3040.51471 277 32689 14509.000 18180.00000 278 5752 3247.833 2504.16667 279 22197 33994.792 -11797.79167 280 20055 25858.515 -5803.51471 281 25272 25858.515 -586.51471 282 82206 25858.515 56347.48529 283 32073 25858.515 6214.48529 284 5444 3247.833 2196.16667 285 20154 25858.515 -5704.51471 286 36944 25858.515 11085.48529 287 8019 25858.515 -17839.51471 288 30884 33994.792 -3110.79167 289 19540 25858.515 -6318.51471 > 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/4v2zo1324146769.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/5kgmd1324146769.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/6no2q1324146769.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/7v71j1324146769.tab") + } > > try(system("convert tmp/2ju9v1324146769.ps tmp/2ju9v1324146769.png",intern=TRUE)) character(0) > try(system("convert tmp/3668d1324146769.ps tmp/3668d1324146769.png",intern=TRUE)) character(0) > try(system("convert tmp/4v2zo1324146769.ps tmp/4v2zo1324146769.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.850 0.278 5.218