R version 2.12.0 (2010-10-15) Copyright (C) 2010 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. Type 'q()' to quit R. > x <- array(list(33024 + ,31086 + ,19828 + ,18932 + ,32526 + ,30839 + ,19967 + ,18927 + ,31455 + ,30051 + ,19814 + ,19124 + ,31524 + ,29976 + ,20053 + ,19066 + ,31856 + ,30463 + ,20719 + ,19971 + ,32696 + ,31422 + ,21174 + ,20165 + ,32584 + ,31588 + ,20648 + ,19705 + ,33498 + ,31900 + ,20659 + ,19718 + ,34175 + ,32878 + ,20733 + ,19938 + ,34172 + ,33010 + ,21069 + ,20039 + ,34379 + ,32954 + ,20566 + ,19721 + ,34988 + ,33076 + ,20839 + ,19777 + ,36158 + ,35057 + ,21615 + ,20505 + ,37411 + ,35906 + ,22739 + ,21763 + ,38015 + ,36100 + ,23222 + ,22404 + ,37577 + ,35824 + ,23031 + ,22038 + ,36354 + ,34579 + ,23014 + ,22038 + ,36030 + ,34484 + ,22868 + ,21874 + ,35636 + ,33920 + ,22182 + ,21269 + ,35669 + ,34059 + ,22177 + ,21127 + ,34635 + ,33812 + ,21216 + ,20609 + ,35496 + ,34594 + ,21031 + ,20565 + ,36376 + ,36083 + ,20968 + ,19791 + ,37635 + ,36563 + ,21049 + ,20672 + ,38875 + ,37416 + ,21033 + ,20938 + ,38372 + ,37953 + ,21078 + ,20675 + ,38897 + ,37517 + ,20702 + ,19992 + ,38018 + ,37467 + ,20309 + ,19801 + ,37325 + ,36963 + ,20449 + ,20050 + ,36893 + ,36019 + ,20737 + ,20427 + ,36117 + ,35232 + ,20849 + ,20815 + ,37599 + ,36857 + ,21966 + ,21666 + ,39037 + ,37978 + ,23100 + ,22720 + ,40809 + ,40160 + ,23975 + ,23650 + ,42508 + ,42165 + ,24350 + ,24244 + ,44021 + ,43069 + ,24020 + ,23669 + ,44088 + ,43021 + ,24005 + ,23881 + ,44510 + ,43376 + ,23602 + ,23857 + ,45786 + ,43978 + ,24120 + ,23999 + ,47349 + ,45911 + ,24847 + ,24780 + ,48696 + ,47107 + ,25702 + ,25426 + ,50598 + ,49168 + ,26312 + ,26229 + ,50066 + ,48390 + ,25891 + ,25973 + ,49367 + ,47678 + ,25172 + ,25375 + ,48784 + ,47822 + ,25698 + ,25966 + ,47841 + ,46695 + ,25833 + ,25391 + ,48300 + ,47185 + ,25658 + ,26046 + ,47518 + ,45684 + ,25269 + ,25572 + ,46504 + ,44884 + ,24846 + ,24900 + ,45147 + ,44256 + ,24390 + ,24744 + ,44404 + ,43637 + ,23954 + ,24526 + ,43455 + ,42368 + ,23828 + ,24274 + ,42299 + ,40892 + ,23507 + ,23774 + ,42105 + ,40616 + ,23144 + ,23414 + ,40152 + ,39026 + ,22302 + ,23002 + ,39519 + ,38921 + ,23028 + ,23137 + ,39633 + ,38512 + ,22741 + ,22947 + ,39376 + ,38884 + ,23129 + ,23733 + ,38850 + ,38406 + ,22911 + ,23234 + ,39657 + ,38804 + ,22071 + ,22969 + ,34804 + ,34871 + ,16466 + ,17708 + ,34372 + ,34660 + ,16370 + ,17377 + ,32678 + ,33104 + ,15049 + ,16273 + ,28420 + ,28952 + ,13174 + ,14342 + ,25420 + ,26488 + ,12231 + ,13522 + ,27683 + ,29418 + ,13620 + ,15210 + ,29904 + ,32315 + ,14317 + ,16493 + ,30546 + ,32885 + ,14039 + ,16701 + ,29142 + ,31565 + ,13526 + ,15662 + ,27724 + ,30782 + ,12826 + ,15526 + ,27069 + ,30442 + ,12360 + ,15413 + ,26665 + ,30851 + ,12592 + ,15805 + ,26004 + ,30432 + ,12381 + ,15802 + ,25767 + ,31260 + ,12554 + ,16753 + ,24915 + ,30737 + ,12338 + ,16906 + ,23689 + ,30129 + ,11768 + ,16891 + ,20915 + ,27672 + ,10687 + ,15703 + ,19414 + ,26469 + ,9964 + ,15429 + ,17824 + ,24895 + ,9338 + ,14762 + ,16348 + ,24427 + ,8697 + ,14426 + ,15571 + ,23252 + ,8068 + ,14250 + ,13929 + ,21815 + ,7295 + ,13267 + ,12480 + ,20837 + ,6372 + ,12397 + ,10837 + ,18537 + ,5649 + ,11586 + ,9473 + ,17237 + ,4926 + ,10888 + ,8051 + ,15476 + ,4199 + ,9841 + ,5278 + ,10709 + ,2568 + ,6443 + ,3008 + ,6776 + ,1461 + ,4019 + ,2404 + ,5810 + ,1173 + ,3449 + ,2298 + ,5765 + ,1084 + ,3179 + ,2260 + ,5775 + ,978 + ,3341 + ,1938 + ,5589 + ,947 + ,3325 + ,1371 + ,4687 + ,679 + ,2478 + ,1009 + ,3630 + ,457 + ,1982 + ,686 + ,2552 + ,262 + ,1405 + ,493 + ,1928 + ,218 + ,1059 + ,285 + ,1323 + ,132 + ,740 + ,192 + ,1005 + ,70 + ,533 + ,129 + ,678 + ,44 + ,366 + ,60 + ,397 + ,24 + ,224 + ,54 + ,286 + ,20 + ,147 + ,26 + ,166 + ,4 + ,75 + ,11 + ,80 + ,4 + ,54 + ,3 + ,53 + ,1 + ,23 + ,0 + ,32 + ,0 + ,16 + ,2 + ,11 + ,0 + ,6 + ,1 + ,6 + ,0 + ,7 + ,0 + ,4 + ,0 + ,2 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0) + ,dim=c(4 + ,111) + ,dimnames=list(c('MVG' + ,'VVG' + ,'MWG' + ,'VWG') + ,1:111)) > y <- array(NA,dim=c(4,111),dimnames=list(c('MVG','VVG','MWG','VWG'),1:111)) > 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 = '1' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "MVG" > x[,par1] [1] 33024 32526 31455 31524 31856 32696 32584 33498 34175 34172 34379 34988 [13] 36158 37411 38015 37577 36354 36030 35636 35669 34635 35496 36376 37635 [25] 38875 38372 38897 38018 37325 36893 36117 37599 39037 40809 42508 44021 [37] 44088 44510 45786 47349 48696 50598 50066 49367 48784 47841 48300 47518 [49] 46504 45147 44404 43455 42299 42105 40152 39519 39633 39376 38850 39657 [61] 34804 34372 32678 28420 25420 27683 29904 30546 29142 27724 27069 26665 [73] 26004 25767 24915 23689 20915 19414 17824 16348 15571 13929 12480 10837 [85] 9473 8051 5278 3008 2404 2298 2260 1938 1371 1009 686 493 [97] 285 192 129 60 54 26 11 3 0 2 1 0 [109] 0 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 0 1 2 3 11 26 54 60 129 192 285 493 686 5 1 1 1 1 1 1 1 1 1 1 1 1 1009 1371 1938 2260 2298 2404 3008 5278 8051 9473 10837 12480 13929 1 1 1 1 1 1 1 1 1 1 1 1 1 15571 16348 17824 19414 20915 23689 24915 25420 25767 26004 26665 27069 27683 1 1 1 1 1 1 1 1 1 1 1 1 1 27724 28420 29142 29904 30546 31455 31524 31856 32526 32584 32678 32696 33024 1 1 1 1 1 1 1 1 1 1 1 1 1 33498 34172 34175 34372 34379 34635 34804 34988 35496 35636 35669 36030 36117 1 1 1 1 1 1 1 1 1 1 1 1 1 36158 36354 36376 36893 37325 37411 37577 37599 37635 38015 38018 38372 38850 1 1 1 1 1 1 1 1 1 1 1 1 1 38875 38897 39037 39376 39519 39633 39657 40152 40809 42105 42299 42508 43455 1 1 1 1 1 1 1 1 1 1 1 1 1 44021 44088 44404 44510 45147 45786 46504 47349 47518 47841 48300 48696 48784 1 1 1 1 1 1 1 1 1 1 1 1 1 49367 50066 50598 1 1 1 > colnames(x) [1] "MVG" "VVG" "MWG" "VWG" > colnames(x)[par1] [1] "MVG" > x[,par1] [1] 33024 32526 31455 31524 31856 32696 32584 33498 34175 34172 34379 34988 [13] 36158 37411 38015 37577 36354 36030 35636 35669 34635 35496 36376 37635 [25] 38875 38372 38897 38018 37325 36893 36117 37599 39037 40809 42508 44021 [37] 44088 44510 45786 47349 48696 50598 50066 49367 48784 47841 48300 47518 [49] 46504 45147 44404 43455 42299 42105 40152 39519 39633 39376 38850 39657 [61] 34804 34372 32678 28420 25420 27683 29904 30546 29142 27724 27069 26665 [73] 26004 25767 24915 23689 20915 19414 17824 16348 15571 13929 12480 10837 [85] 9473 8051 5278 3008 2404 2298 2260 1938 1371 1009 686 493 [97] 285 192 129 60 54 26 11 3 0 2 1 0 [109] 0 0 0 > 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/1qyzz1292088573.tab") + } + } > m Conditional inference tree with 8 terminal nodes Response: MVG Inputs: VVG, MWG, VWG Number of observations: 111 1) MWG <= 9964; criterion = 1, statistic = 107.276 2) MWG <= 2568; criterion = 1, statistic = 32.984 3) MWG <= 457; criterion = 1, statistic = 23.954 4)* weights = 18 3) MWG > 457 5)* weights = 7 2) MWG > 2568 6)* weights = 9 1) MWG > 9964 7) VVG <= 36963; criterion = 1, statistic = 70.888 8) MWG <= 14317; criterion = 1, statistic = 36.382 9)* weights = 14 8) MWG > 14317 10) VVG <= 33812; criterion = 1, statistic = 27.232 11)* weights = 14 10) VVG > 33812 12)* weights = 17 7) VVG > 36963 13) VVG <= 42368; criterion = 1, statistic = 30.736 14)* weights = 16 13) VVG > 42368 15)* weights = 16 > postscript(file="/var/www/rcomp/tmp/2qyzz1292088573.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/3qyzz1292088573.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 33024 33156.4286 -132.42857 2 32526 33156.4286 -630.42857 3 31455 33156.4286 -1701.42857 4 31524 33156.4286 -1632.42857 5 31856 33156.4286 -1300.42857 6 32696 33156.4286 -460.42857 7 32584 33156.4286 -572.42857 8 33498 33156.4286 341.57143 9 34175 33156.4286 1018.57143 10 34172 33156.4286 1015.57143 11 34379 33156.4286 1222.57143 12 34988 33156.4286 1831.57143 13 36158 36439.2353 -281.23529 14 37411 36439.2353 971.76471 15 38015 36439.2353 1575.76471 16 37577 36439.2353 1137.76471 17 36354 36439.2353 -85.23529 18 36030 36439.2353 -409.23529 19 35636 36439.2353 -803.23529 20 35669 36439.2353 -770.23529 21 34635 33156.4286 1478.57143 22 35496 36439.2353 -943.23529 23 36376 36439.2353 -63.23529 24 37635 36439.2353 1195.76471 25 38875 40097.6250 -1222.62500 26 38372 40097.6250 -1725.62500 27 38897 40097.6250 -1200.62500 28 38018 40097.6250 -2079.62500 29 37325 36439.2353 885.76471 30 36893 36439.2353 453.76471 31 36117 36439.2353 -322.23529 32 37599 36439.2353 1159.76471 33 39037 40097.6250 -1060.62500 34 40809 40097.6250 711.37500 35 42508 40097.6250 2410.37500 36 44021 47061.1875 -3040.18750 37 44088 47061.1875 -2973.18750 38 44510 47061.1875 -2551.18750 39 45786 47061.1875 -1275.18750 40 47349 47061.1875 287.81250 41 48696 47061.1875 1634.81250 42 50598 47061.1875 3536.81250 43 50066 47061.1875 3004.81250 44 49367 47061.1875 2305.81250 45 48784 47061.1875 1722.81250 46 47841 47061.1875 779.81250 47 48300 47061.1875 1238.81250 48 47518 47061.1875 456.81250 49 46504 47061.1875 -557.18750 50 45147 47061.1875 -1914.18750 51 44404 47061.1875 -2657.18750 52 43455 40097.6250 3357.37500 53 42299 40097.6250 2201.37500 54 42105 40097.6250 2007.37500 55 40152 40097.6250 54.37500 56 39519 40097.6250 -578.62500 57 39633 40097.6250 -464.62500 58 39376 40097.6250 -721.62500 59 38850 40097.6250 -1247.62500 60 39657 40097.6250 -440.62500 61 34804 36439.2353 -1635.23529 62 34372 36439.2353 -2067.23529 63 32678 33156.4286 -478.42857 64 28420 26704.5000 1715.50000 65 25420 26704.5000 -1284.50000 66 27683 26704.5000 978.50000 67 29904 26704.5000 3199.50000 68 30546 26704.5000 3841.50000 69 29142 26704.5000 2437.50000 70 27724 26704.5000 1019.50000 71 27069 26704.5000 364.50000 72 26665 26704.5000 -39.50000 73 26004 26704.5000 -700.50000 74 25767 26704.5000 -937.50000 75 24915 26704.5000 -1789.50000 76 23689 26704.5000 -3015.50000 77 20915 26704.5000 -5789.50000 78 19414 13769.6667 5644.33333 79 17824 13769.6667 4054.33333 80 16348 13769.6667 2578.33333 81 15571 13769.6667 1801.33333 82 13929 13769.6667 159.33333 83 12480 13769.6667 -1289.66667 84 10837 13769.6667 -2932.66667 85 9473 13769.6667 -4296.66667 86 8051 13769.6667 -5718.66667 87 5278 2651.0000 2627.00000 88 3008 2651.0000 357.00000 89 2404 2651.0000 -247.00000 90 2298 2651.0000 -353.00000 91 2260 2651.0000 -391.00000 92 1938 2651.0000 -713.00000 93 1371 2651.0000 -1280.00000 94 1009 163.9444 845.05556 95 686 163.9444 522.05556 96 493 163.9444 329.05556 97 285 163.9444 121.05556 98 192 163.9444 28.05556 99 129 163.9444 -34.94444 100 60 163.9444 -103.94444 101 54 163.9444 -109.94444 102 26 163.9444 -137.94444 103 11 163.9444 -152.94444 104 3 163.9444 -160.94444 105 0 163.9444 -163.94444 106 2 163.9444 -161.94444 107 1 163.9444 -162.94444 108 0 163.9444 -163.94444 109 0 163.9444 -163.94444 110 0 163.9444 -163.94444 111 0 163.9444 -163.94444 > 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/417y21292088573.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/5xhwt1292088573.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/6p8we1292088573.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/7trc21292088573.tab") + } > > try(system("convert tmp/2qyzz1292088573.ps tmp/2qyzz1292088573.png",intern=TRUE)) character(0) > try(system("convert tmp/3qyzz1292088573.ps tmp/3qyzz1292088573.png",intern=TRUE)) character(0) > try(system("convert tmp/417y21292088573.ps tmp/417y21292088573.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.520 0.490 3.017