R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,82206 + ,1182 + ,91005 + ,29 + ,3 + ,29 + ,11 + ,32073 + ,528 + ,40248 + ,16 + ,1 + ,8 + ,4 + ,5444 + ,642 + ,64187 + ,27 + ,0 + ,10 + ,16 + ,20154 + ,947 + ,50857 + ,21 + ,0 + ,15 + ,20 + ,36944 + ,819 + ,56613 + ,19 + ,1 + ,15 + ,12 + ,8019 + ,757 + ,62792 + ,35 + ,0 + ,28 + ,15 + ,30884 + ,894 + ,72535 + ,14 + ,0 + ,17 + ,16 + ,19540) + ,dim=c(7 + ,289) + ,dimnames=list(c('pageviews' + ,'time' + ,'logins' + ,'shared' + ,'blogged' + ,'reviewed' + ,'totsize') + ,1:289)) > y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','time','logins','shared','blogged','reviewed','totsize'),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 = '7' > #'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] "pageviews" "time" "logins" "shared" "blogged" "reviewed" [7] "totsize" > 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/1a4tb1323614239.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: totsize Inputs: pageviews, time, logins, shared, blogged, reviewed Number of observations: 289 1) blogged <= 57; criterion = 1, statistic = 166.55 2) reviewed <= 22; criterion = 1, statistic = 71.958 3) time <= 62215; criterion = 1, statistic = 50.231 4) reviewed <= 5; criterion = 0.999, statistic = 14.4 5)* weights = 13 4) reviewed > 5 6)* weights = 36 3) time > 62215 7) time <= 136084; criterion = 0.996, statistic = 11.401 8)* weights = 85 7) time > 136084 9)* weights = 9 2) reviewed > 22 10)* weights = 38 1) blogged > 57 11) blogged <= 109; criterion = 1, statistic = 17.746 12)* weights = 79 11) blogged > 109 13)* weights = 29 > postscript(file="/var/wessaorg/rcomp/tmp/23zj01323614239.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/3h8pf1323614239.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 87420.810 24864.18987 2 84786 87420.810 -2634.81013 3 83123 87420.810 -4297.81013 4 101193 87420.810 13772.18987 5 38361 31752.235 6608.76471 6 68504 65840.026 2663.97368 7 119182 115134.724 4047.27586 8 22807 20440.944 2366.05556 9 17140 31752.235 -14612.23529 10 116174 65840.026 50333.97368 11 57635 87420.810 -29785.81013 12 66198 87420.810 -21222.81013 13 71701 87420.810 -15719.81013 14 57793 65840.026 -8047.02632 15 80444 87420.810 -6976.81013 16 53855 87420.810 -33565.81013 17 97668 115134.724 -17466.72414 18 133824 87420.810 46403.18987 19 101481 87420.810 14060.18987 20 99645 87420.810 12224.18987 21 114789 115134.724 -345.72414 22 99052 87420.810 11631.18987 23 67654 87420.810 -19766.81013 24 65553 65840.026 -287.02632 25 97500 87420.810 10079.18987 26 69112 115134.724 -46022.72414 27 82753 87420.810 -4667.81013 28 85323 87420.810 -2097.81013 29 72654 87420.810 -14766.81013 30 30727 65840.026 -35113.02632 31 77873 87420.810 -9547.81013 32 117478 87420.810 30057.18987 33 74007 65840.026 8166.97368 34 90183 115134.724 -24951.72414 35 61542 65840.026 -4298.02632 36 101494 87420.810 14073.18987 37 27570 31752.235 -4182.23529 38 55813 87420.810 -31607.81013 39 79215 87420.810 -8205.81013 40 1423 3643.462 -2220.46154 41 55461 87420.810 -31959.81013 42 31081 31752.235 -671.23529 43 22996 31752.235 -8756.23529 44 83122 87420.810 -4298.81013 45 70106 87420.810 -17314.81013 46 60578 65840.026 -5262.02632 47 39992 48650.111 -8658.11111 48 79892 87420.810 -7528.81013 49 49810 65840.026 -16030.02632 50 71570 65840.026 5729.97368 51 100708 87420.810 13287.18987 52 33032 65840.026 -32808.02632 53 82875 87420.810 -4545.81013 54 139077 115134.724 23942.27586 55 71595 87420.810 -15825.81013 56 72260 87420.810 -15160.81013 57 5950 3643.462 2306.53846 58 115762 87420.810 28341.18987 59 32551 31752.235 798.76471 60 31701 31752.235 -51.23529 61 80670 87420.810 -6750.81013 62 143558 87420.810 56137.18987 63 117105 87420.810 29684.18987 64 23789 31752.235 -7963.23529 65 120733 87420.810 33312.18987 66 105195 115134.724 -9939.72414 67 73107 87420.810 -14313.81013 68 132068 115134.724 16933.27586 69 149193 87420.810 61772.18987 70 46821 65840.026 -19019.02632 71 87011 65840.026 21170.97368 72 95260 65840.026 29419.97368 73 55183 87420.810 -32237.81013 74 106671 87420.810 19250.18987 75 73511 87420.810 -13909.81013 76 92945 115134.724 -22189.72414 77 78664 115134.724 -36470.72414 78 70054 87420.810 -17366.81013 79 22618 65840.026 -43222.02632 80 74011 87420.810 -13409.81013 81 83737 87420.810 -3683.81013 82 69094 87420.810 -18326.81013 83 93133 115134.724 -22001.72414 84 95536 87420.810 8115.18987 85 225920 87420.810 138499.18987 86 62133 87420.810 -25287.81013 87 61370 87420.810 -26050.81013 88 43836 31752.235 12083.76471 89 106117 115134.724 -9017.72414 90 38692 65840.026 -27148.02632 91 84651 87420.810 -2769.81013 92 56622 31752.235 24869.76471 93 15986 65840.026 -49854.02632 94 95364 87420.810 7943.18987 95 26706 31752.235 -5046.23529 96 89691 65840.026 23850.97368 97 67267 87420.810 -20153.81013 98 126846 115134.724 11711.27586 99 41140 87420.810 -46280.81013 100 102860 115134.724 -12274.72414 101 51715 65840.026 -14125.02632 102 55801 87420.810 -31619.81013 103 111813 87420.810 24392.18987 104 120293 87420.810 32872.18987 105 138599 87420.810 51178.18987 106 161647 115134.724 46512.27586 107 115929 115134.724 794.27586 108 24266 31752.235 -7486.23529 109 162901 115134.724 47766.27586 110 109825 87420.810 22404.18987 111 129838 115134.724 14703.27586 112 37510 65840.026 -28330.02632 113 43750 20440.944 23309.05556 114 40652 87420.810 -46768.81013 115 87771 87420.810 350.18987 116 85872 87420.810 -1548.81013 117 89275 115134.724 -25859.72414 118 44418 48650.111 -4232.11111 119 192565 65840.026 126724.97368 120 35232 31752.235 3479.76471 121 40909 31752.235 9156.76471 122 13294 20440.944 -7146.94444 123 32387 31752.235 634.76471 124 140867 87420.810 53446.18987 125 120662 65840.026 54821.97368 126 21233 31752.235 -10519.23529 127 44332 31752.235 12579.76471 128 61056 87420.810 -26364.81013 129 101338 115134.724 -13796.72414 130 1168 3643.462 -2475.46154 131 13497 20440.944 -6943.94444 132 65567 87420.810 -21853.81013 133 25162 20440.944 4721.05556 134 32334 65840.026 -33506.02632 135 40735 87420.810 -46685.81013 136 91413 115134.724 -23721.72414 137 855 3643.462 -2788.46154 138 97068 87420.810 9647.18987 139 44339 20440.944 23898.05556 140 14116 20440.944 -6324.94444 141 10288 20440.944 -10152.94444 142 65622 87420.810 -21798.81013 143 16563 31752.235 -15189.23529 144 76643 87420.810 -10777.81013 145 110681 87420.810 23260.18987 146 29011 31752.235 -2741.23529 147 92696 65840.026 26855.97368 148 94785 65840.026 28944.97368 149 8773 20440.944 -11667.94444 150 83209 65840.026 17368.97368 151 93815 87420.810 6394.18987 152 86687 65840.026 20846.97368 153 34553 31752.235 2800.76471 154 105547 115134.724 -9587.72414 155 103487 115134.724 -11647.72414 156 213688 115134.724 98553.27586 157 71220 87420.810 -16200.81013 158 23517 31752.235 -8235.23529 159 56926 65840.026 -8914.02632 160 91721 87420.810 4300.18987 161 115168 87420.810 27747.18987 162 111194 65840.026 45353.97368 163 51009 31752.235 19256.76471 164 135777 115134.724 20642.27586 165 51513 65840.026 -14327.02632 166 74163 87420.810 -13257.81013 167 51633 87420.810 -35787.81013 168 75345 65840.026 9504.97368 169 33416 31752.235 1663.76471 170 83305 48650.111 34654.88889 171 98952 87420.810 11531.18987 172 102372 115134.724 -12762.72414 173 37238 48650.111 -11412.11111 174 103772 115134.724 -11362.72414 175 123969 115134.724 8834.27586 176 27142 31752.235 -4610.23529 177 135400 87420.810 47979.18987 178 21399 31752.235 -10353.23529 179 130115 115134.724 14980.27586 180 24874 31752.235 -6878.23529 181 34988 31752.235 3235.76471 182 45549 87420.810 -41871.81013 183 6023 3643.462 2379.53846 184 64466 65840.026 -1374.02632 185 54990 87420.810 -32430.81013 186 1644 3643.462 -1999.46154 187 6179 3643.462 2535.53846 188 3926 3643.462 282.53846 189 32755 31752.235 1002.76471 190 34777 65840.026 -31063.02632 191 73224 65840.026 7383.97368 192 27114 31752.235 -4638.23529 193 20760 31752.235 -10992.23529 194 37636 65840.026 -28204.02632 195 65461 48650.111 16810.88889 196 30080 31752.235 -1672.23529 197 24094 31752.235 -7658.23529 198 69008 31752.235 37255.76471 199 54968 31752.235 23215.76471 200 46090 31752.235 14337.76471 201 27507 31752.235 -4245.23529 202 10672 65840.026 -55168.02632 203 34029 31752.235 2276.76471 204 46300 31752.235 14547.76471 205 24760 31752.235 -6992.23529 206 18779 20440.944 -1661.94444 207 21280 31752.235 -10472.23529 208 40662 48650.111 -7988.11111 209 28987 31752.235 -2765.23529 210 22827 31752.235 -8925.23529 211 18513 31752.235 -13239.23529 212 30594 31752.235 -1158.23529 213 24006 20440.944 3565.05556 214 27913 31752.235 -3839.23529 215 42744 65840.026 -23096.02632 216 12934 20440.944 -7506.94444 217 22574 20440.944 2133.05556 218 41385 20440.944 20944.05556 219 18653 31752.235 -13099.23529 220 18472 31752.235 -13280.23529 221 30976 31752.235 -776.23529 222 63339 31752.235 31586.76471 223 25568 31752.235 -6184.23529 224 33747 31752.235 1994.76471 225 4154 20440.944 -16286.94444 226 19474 31752.235 -12278.23529 227 35130 31752.235 3377.76471 228 39067 31752.235 7314.76471 229 13310 20440.944 -7130.94444 230 65892 65840.026 51.97368 231 4143 20440.944 -16297.94444 232 28579 31752.235 -3173.23529 233 51776 31752.235 20023.76471 234 21152 20440.944 711.05556 235 38084 31752.235 6331.76471 236 27717 20440.944 7276.05556 237 32928 48650.111 -15722.11111 238 11342 20440.944 -9098.94444 239 19499 31752.235 -12253.23529 240 16380 20440.944 -4060.94444 241 36874 31752.235 5121.76471 242 48259 48650.111 -391.11111 243 16734 20440.944 -3706.94444 244 28207 20440.944 7766.05556 245 30143 31752.235 -1609.23529 246 41369 31752.235 9616.76471 247 45833 31752.235 14080.76471 248 29156 31752.235 -2596.23529 249 35944 31752.235 4191.76471 250 36278 20440.944 15837.05556 251 45588 48650.111 -3062.11111 252 45097 31752.235 13344.76471 253 3895 3643.462 251.53846 254 28394 31752.235 -3358.23529 255 18632 31752.235 -13120.23529 256 2325 3643.462 -1318.46154 257 25139 20440.944 4698.05556 258 27975 31752.235 -3777.23529 259 14483 20440.944 -5957.94444 260 13127 20440.944 -7313.94444 261 5839 20440.944 -14601.94444 262 24069 31752.235 -7683.23529 263 3738 20440.944 -16702.94444 264 18625 20440.944 -1815.94444 265 36341 20440.944 15900.05556 266 24548 31752.235 -7204.23529 267 21792 31752.235 -9960.23529 268 26263 20440.944 5822.05556 269 23686 31752.235 -8066.23529 270 49303 31752.235 17550.76471 271 25659 31752.235 -6093.23529 272 28904 31752.235 -2848.23529 273 2781 3643.462 -862.46154 274 29236 31752.235 -2516.23529 275 19546 20440.944 -894.94444 276 22818 31752.235 -8934.23529 277 32689 20440.944 12248.05556 278 5752 3643.462 2108.53846 279 22197 31752.235 -9555.23529 280 20055 31752.235 -11697.23529 281 25272 31752.235 -6480.23529 282 82206 31752.235 50453.76471 283 32073 31752.235 320.76471 284 5444 3643.462 1800.53846 285 20154 31752.235 -11598.23529 286 36944 20440.944 16503.05556 287 8019 20440.944 -12421.94444 288 30884 31752.235 -868.23529 289 19540 31752.235 -12212.23529 > 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/4cj0g1323614239.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/5555y1323614239.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/65ipr1323614239.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/7kwqi1323614239.tab") + } > > try(system("convert tmp/23zj01323614239.ps tmp/23zj01323614239.png",intern=TRUE)) character(0) > try(system("convert tmp/3h8pf1323614239.ps tmp/3h8pf1323614239.png",intern=TRUE)) character(0) > try(system("convert tmp/4cj0g1323614239.ps tmp/4cj0g1323614239.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.536 0.216 5.821