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Type 'q()' to quit R. > x <- array(list(158258 + ,0 + ,48 + ,18 + ,20465 + ,23975 + ,186930 + ,1 + ,53 + ,20 + ,33629 + ,85634 + ,7215 + ,0 + ,0 + ,0 + ,1423 + ,1929 + ,129098 + ,0 + ,51 + ,27 + ,25629 + ,36294 + ,230632 + ,0 + ,76 + ,31 + ,54002 + ,72255 + ,508313 + ,1 + ,128 + ,36 + ,151036 + ,189748 + ,180745 + ,1 + ,62 + ,23 + ,33287 + ,61834 + ,185559 + ,0 + ,83 + ,30 + ,31172 + ,68167 + ,154581 + ,0 + ,55 + ,30 + ,28113 + ,38462 + ,290658 + ,1 + ,67 + ,26 + ,57803 + ,101219 + ,121844 + ,2 + ,50 + ,24 + ,49830 + ,43270 + ,184039 + ,0 + ,77 + ,30 + ,52143 + ,76183 + ,100324 + ,0 + ,46 + ,22 + ,21055 + ,31476 + ,209427 + ,4 + ,79 + ,25 + ,47007 + ,62157 + ,168265 + ,4 + ,56 + ,18 + ,28735 + ,46261 + ,154593 + ,3 + ,54 + ,22 + ,59147 + ,50063 + ,142018 + ,0 + ,81 + ,33 + ,78950 + ,64483 + ,78604 + ,5 + ,6 + ,15 + ,13497 + ,2341 + ,167047 + ,0 + ,74 + ,34 + ,46154 + ,48149 + ,27997 + ,0 + ,13 + ,18 + ,53249 + ,12743 + ,73019 + ,0 + ,22 + ,15 + ,10726 + ,18743 + ,241082 + ,0 + ,99 + ,30 + ,83700 + ,97057 + ,195820 + ,0 + ,38 + ,25 + ,40400 + ,17675 + ,141899 + ,1 + ,59 + ,34 + ,33797 + ,33106 + ,145433 + ,1 + ,50 + ,21 + ,36205 + ,53311 + ,183744 + ,0 + ,50 + ,21 + ,30165 + ,42754 + ,202232 + ,0 + ,61 + ,25 + ,58534 + ,59056 + ,190230 + ,0 + ,81 + ,31 + ,44663 + ,101621 + ,354924 + ,0 + ,60 + ,31 + ,92556 + ,118120 + ,192399 + ,0 + ,52 + ,20 + ,40078 + ,79572 + ,182286 + ,0 + ,61 + ,28 + ,34711 + ,42744 + ,181590 + ,2 + ,60 + ,22 + ,31076 + ,65931 + ,133801 + ,4 + ,53 + ,17 + ,74608 + ,38575 + ,233686 + ,0 + ,76 + ,25 + ,58092 + ,28795 + ,219428 + ,1 + ,63 + ,24 + ,42009 + ,94440 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,223044 + ,0 + ,54 + ,28 + ,36022 + ,38229 + ,100129 + ,3 + ,44 + ,14 + ,23333 + ,31972 + ,136733 + ,9 + ,36 + ,35 + ,53349 + ,40071 + ,249965 + ,0 + ,83 + ,34 + ,92596 + ,132480 + ,242379 + ,2 + ,105 + ,22 + ,49598 + ,62797 + ,145794 + ,0 + ,37 + ,34 + ,44093 + ,40429 + ,96404 + ,2 + ,25 + ,23 + ,84205 + ,45545 + ,195891 + ,1 + ,64 + ,24 + ,63369 + ,57568 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,8 + ,69 + ,36 + ,74990 + ,110600 + ,138708 + ,2 + ,93 + ,25 + ,29653 + ,52235 + ,114408 + ,0 + ,59 + ,24 + ,64622 + ,53986 + ,31970 + ,0 + ,5 + ,21 + ,4157 + ,4105 + ,225558 + ,3 + ,53 + ,19 + ,29245 + ,59331 + ,137011 + ,1 + ,40 + ,12 + ,50008 + ,47796 + ,113612 + ,2 + ,72 + ,30 + ,52338 + ,38302 + ,108641 + ,1 + ,51 + ,21 + ,13310 + ,14063 + ,162203 + ,0 + ,81 + ,34 + ,92901 + ,54414 + ,100098 + ,2 + ,27 + ,32 + ,10956 + ,9903 + ,174768 + ,1 + ,94 + ,28 + ,34241 + ,53987 + ,158459 + ,0 + ,71 + ,28 + ,75043 + ,88937 + ,80934 + ,0 + ,20 + ,21 + ,21152 + ,21928 + ,84971 + ,0 + ,34 + ,31 + ,42249 + ,29487 + ,80545 + ,0 + ,54 + ,26 + ,42005 + ,35334 + ,287191 + ,0 + ,49 + ,29 + ,41152 + ,57596 + ,62974 + ,1 + ,26 + ,23 + ,14399 + ,29750 + ,130982 + ,0 + ,47 + ,25 + ,28263 + ,41029 + ,75555 + ,0 + ,35 + ,22 + ,17215 + ,12416 + ,162154 + ,0 + ,32 + ,26 + ,48140 + ,51158 + ,226638 + ,0 + ,55 + ,33 + ,62897 + ,79935 + ,115019 + ,0 + ,58 + ,24 + ,22883 + ,26552 + ,105038 + ,7 + ,44 + ,24 + ,41622 + ,25807 + ,155537 + ,0 + ,45 + ,21 + ,40715 + ,50620 + ,153133 + ,5 + ,49 + ,28 + ,65897 + ,61467 + ,165577 + ,1 + ,72 + ,27 + ,76542 + ,65292 + ,151517 + ,0 + ,39 + ,25 + ,37477 + ,55516 + ,133686 + ,0 + ,28 + ,15 + ,53216 + ,42006 + ,58128 + ,0 + ,24 + ,13 + ,40911 + ,26273 + ,245196 + ,0 + ,52 + ,36 + ,57021 + ,90248 + ,195576 + ,0 + ,96 + ,24 + ,73116 + ,61476 + ,19349 + ,0 + ,13 + ,1 + ,3895 + ,9604 + ,225371 + ,3 + ,38 + ,24 + ,46609 + ,45108 + ,152796 + ,0 + ,41 + ,31 + ,29351 + ,47232 + ,59117 + ,0 + ,24 + ,4 + ,2325 + ,3439 + ,91762 + ,0 + ,54 + ,21 + ,31747 + ,30553 + ,127987 + ,0 + ,59 + ,23 + ,32665 + ,24751 + ,113552 + ,1 + ,28 + ,23 + ,19249 + ,34458 + ,85338 + ,1 + ,36 + ,12 + ,15292 + ,24649 + ,27676 + ,0 + ,2 + ,16 + ,5842 + ,2342 + ,147984 + ,0 + ,83 + ,29 + ,33994 + ,52739 + ,122417 + ,0 + ,29 + ,26 + ,13018 + ,6245 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,91529 + ,0 + ,46 + ,25 + ,98177 + ,35381 + ,107205 + ,0 + ,25 + ,21 + ,37941 + ,19595 + ,144664 + ,0 + ,51 + ,23 + ,31032 + ,50848 + ,136540 + ,0 + ,59 + ,21 + ,32683 + ,39443 + ,76656 + ,0 + ,36 + ,21 + ,34545 + ,27023 + ,3616 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,183065 + ,0 + ,40 + ,23 + ,27525 + ,61022 + ,144636 + ,0 + ,68 + ,33 + ,66856 + ,63528 + ,159104 + ,2 + ,28 + ,30 + ,28549 + ,34835 + ,113273 + ,0 + ,36 + ,23 + ,38610 + ,37172 + ,43410 + ,0 + ,7 + ,1 + ,2781 + ,13 + ,175774 + ,1 + ,70 + ,29 + ,41211 + ,62548 + ,95401 + ,0 + ,30 + ,18 + ,22698 + ,31334 + ,118893 + ,8 + ,59 + ,32 + ,41194 + ,20839 + ,60493 + ,3 + ,3 + ,12 + ,32689 + ,5084 + ,19764 + ,1 + ,10 + ,2 + ,5752 + ,9927 + ,164062 + ,3 + ,46 + ,21 + ,26757 + ,53229 + ,132696 + ,0 + ,34 + ,28 + ,22527 + ,29877 + ,155367 + ,0 + ,54 + ,29 + ,44810 + ,37310 + ,11796 + ,0 + ,1 + ,2 + ,0 + ,0 + ,10674 + ,0 + ,0 + ,0 + ,0 + ,0 + ,142261 + ,0 + ,39 + ,18 + ,100674 + ,50067 + ,6836 + ,0 + ,0 + ,1 + ,0 + ,0 + ,154206 + ,6 + ,48 + ,21 + ,57786 + ,47708 + ,5118 + ,0 + ,5 + ,0 + ,0 + ,0 + ,40248 + ,1 + ,8 + ,4 + ,5444 + ,6012 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,122641 + ,0 + ,38 + ,25 + ,28470 + ,27749 + ,88837 + ,0 + ,21 + ,26 + ,61849 + ,47555 + ,7131 + ,1 + ,0 + ,0 + ,0 + ,0 + ,9056 + ,0 + ,0 + ,4 + ,2179 + ,1336 + ,76611 + ,1 + ,15 + ,17 + ,8019 + ,11017 + ,132697 + ,0 + ,50 + ,21 + ,39644 + ,55184 + ,100681 + ,1 + ,17 + ,22 + ,23494 + ,43485) + ,dim=c(6 + ,144) + ,dimnames=list(c('Time' + ,'shared' + ,'computations' + ,'reviewed' + ,'characters' + ,'seconds') + ,1:144)) > y <- array(NA,dim=c(6,144),dimnames=list(c('Time','shared','computations','reviewed','characters','seconds'),1:144)) > 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 = '5' > #'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] "characters" > x[,par1] [1] 20465 33629 1423 25629 54002 151036 33287 31172 28113 57803 [11] 49830 52143 21055 47007 28735 59147 78950 13497 46154 53249 [21] 10726 83700 40400 33797 36205 30165 58534 44663 92556 40078 [31] 34711 31076 74608 58092 42009 0 36022 23333 53349 92596 [41] 49598 44093 84205 63369 60132 37403 24460 46456 66616 41554 [51] 22346 30874 68701 35728 29010 23110 38844 27084 35139 57476 [61] 33277 31141 61281 25820 23284 35378 74990 29653 64622 4157 [71] 29245 50008 52338 13310 92901 10956 34241 75043 21152 42249 [81] 42005 41152 14399 28263 17215 48140 62897 22883 41622 40715 [91] 65897 76542 37477 53216 40911 57021 73116 3895 46609 29351 [101] 2325 31747 32665 19249 15292 5842 33994 13018 0 98177 [111] 37941 31032 32683 34545 0 0 27525 66856 28549 38610 [121] 2781 41211 22698 41194 32689 5752 26757 22527 44810 0 [131] 0 100674 0 57786 0 5444 0 28470 61849 0 [141] 2179 8019 39644 23494 > 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 1423 2179 2325 2781 3895 4157 5444 5752 5842 8019 10 1 1 1 1 1 1 1 1 1 1 10726 10956 13018 13310 13497 14399 15292 17215 19249 20465 21055 1 1 1 1 1 1 1 1 1 1 1 21152 22346 22527 22698 22883 23110 23284 23333 23494 24460 25629 1 1 1 1 1 1 1 1 1 1 1 25820 26757 27084 27525 28113 28263 28470 28549 28735 29010 29245 1 1 1 1 1 1 1 1 1 1 1 29351 29653 30165 30874 31032 31076 31141 31172 31747 32665 32683 1 1 1 1 1 1 1 1 1 1 1 32689 33277 33287 33629 33797 33994 34241 34545 34711 35139 35378 1 1 1 1 1 1 1 1 1 1 1 35728 36022 36205 37403 37477 37941 38610 38844 39644 40078 40400 1 1 1 1 1 1 1 1 1 1 1 40715 40911 41152 41194 41211 41554 41622 42005 42009 42249 44093 1 1 1 1 1 1 1 1 1 1 1 44663 44810 46154 46456 46609 47007 48140 49598 49830 50008 52143 1 1 1 1 1 1 1 1 1 1 1 52338 53216 53249 53349 54002 57021 57476 57786 57803 58092 58534 1 1 1 1 1 1 1 1 1 1 1 59147 60132 61281 61849 62897 63369 64622 65897 66616 66856 68701 1 1 1 1 1 1 1 1 1 1 1 73116 74608 74990 75043 76542 78950 83700 84205 92556 92596 92901 1 1 1 1 1 1 1 1 1 1 1 98177 100674 151036 1 1 1 > colnames(x) [1] "Time" "shared" "computations" "reviewed" "characters" [6] "seconds" > colnames(x)[par1] [1] "characters" > x[,par1] [1] 20465 33629 1423 25629 54002 151036 33287 31172 28113 57803 [11] 49830 52143 21055 47007 28735 59147 78950 13497 46154 53249 [21] 10726 83700 40400 33797 36205 30165 58534 44663 92556 40078 [31] 34711 31076 74608 58092 42009 0 36022 23333 53349 92596 [41] 49598 44093 84205 63369 60132 37403 24460 46456 66616 41554 [51] 22346 30874 68701 35728 29010 23110 38844 27084 35139 57476 [61] 33277 31141 61281 25820 23284 35378 74990 29653 64622 4157 [71] 29245 50008 52338 13310 92901 10956 34241 75043 21152 42249 [81] 42005 41152 14399 28263 17215 48140 62897 22883 41622 40715 [91] 65897 76542 37477 53216 40911 57021 73116 3895 46609 29351 [101] 2325 31747 32665 19249 15292 5842 33994 13018 0 98177 [111] 37941 31032 32683 34545 0 0 27525 66856 28549 38610 [121] 2781 41211 22698 41194 32689 5752 26757 22527 44810 0 [131] 0 100674 0 57786 0 5444 0 28470 61849 0 [141] 2179 8019 39644 23494 > 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/1yx101324477814.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: characters Inputs: Time, shared, computations, reviewed, seconds Number of observations: 144 1) seconds <= 12416; criterion = 1, statistic = 81.785 2) Time <= 43410; criterion = 0.996, statistic = 11.344 3)* weights = 18 2) Time > 43410 4)* weights = 7 1) seconds > 12416 5) seconds <= 94440; criterion = 1, statistic = 45.217 6) seconds <= 34835; criterion = 0.999, statistic = 14.452 7)* weights = 29 6) seconds > 34835 8)* weights = 82 5) seconds > 94440 9)* weights = 8 > postscript(file="/var/wessaorg/rcomp/tmp/252nh1324477814.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/39jwa1324477814.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 20465 30325.03 -9860.03448 2 33629 46108.30 -12479.30488 3 1423 1748.50 -325.50000 4 25629 46108.30 -20479.30488 5 54002 46108.30 7893.69512 6 151036 83255.62 67780.37500 7 33287 46108.30 -12821.30488 8 31172 46108.30 -14936.30488 9 28113 46108.30 -17995.30488 10 57803 83255.62 -25452.62500 11 49830 46108.30 3721.69512 12 52143 46108.30 6034.69512 13 21055 30325.03 -9270.03448 14 47007 46108.30 898.69512 15 28735 46108.30 -17373.30488 16 59147 46108.30 13038.69512 17 78950 46108.30 32841.69512 18 13497 13959.86 -462.85714 19 46154 46108.30 45.69512 20 53249 30325.03 22923.96552 21 10726 30325.03 -19599.03448 22 83700 83255.62 444.37500 23 40400 30325.03 10074.96552 24 33797 30325.03 3471.96552 25 36205 46108.30 -9903.30488 26 30165 46108.30 -15943.30488 27 58534 46108.30 12425.69512 28 44663 83255.62 -38592.62500 29 92556 83255.62 9300.37500 30 40078 46108.30 -6030.30488 31 34711 46108.30 -11397.30488 32 31076 46108.30 -15032.30488 33 74608 46108.30 28499.69512 34 58092 30325.03 27766.96552 35 42009 46108.30 -4099.30488 36 0 1748.50 -1748.50000 37 36022 46108.30 -10086.30488 38 23333 30325.03 -6992.03448 39 53349 46108.30 7240.69512 40 92596 83255.62 9340.37500 41 49598 46108.30 3489.69512 42 44093 46108.30 -2015.30488 43 84205 46108.30 38096.69512 44 63369 46108.30 17260.69512 45 60132 46108.30 14023.69512 46 37403 46108.30 -8705.30488 47 24460 46108.30 -21648.30488 48 46456 46108.30 347.69512 49 66616 46108.30 20507.69512 50 41554 46108.30 -4554.30488 51 22346 30325.03 -7979.03448 52 30874 46108.30 -15234.30488 53 68701 83255.62 -14554.62500 54 35728 46108.30 -10380.30488 55 29010 46108.30 -17098.30488 56 23110 46108.30 -22998.30488 57 38844 46108.30 -7264.30488 58 27084 30325.03 -3241.03448 59 35139 46108.30 -10969.30488 60 57476 30325.03 27150.96552 61 33277 46108.30 -12831.30488 62 31141 46108.30 -14967.30488 63 61281 46108.30 15172.69512 64 25820 46108.30 -20288.30488 65 23284 46108.30 -22824.30488 66 35378 46108.30 -10730.30488 67 74990 83255.62 -8265.62500 68 29653 46108.30 -16455.30488 69 64622 46108.30 18513.69512 70 4157 1748.50 2408.50000 71 29245 46108.30 -16863.30488 72 50008 46108.30 3899.69512 73 52338 46108.30 6229.69512 74 13310 30325.03 -17015.03448 75 92901 46108.30 46792.69512 76 10956 13959.86 -3003.85714 77 34241 46108.30 -11867.30488 78 75043 46108.30 28934.69512 79 21152 30325.03 -9173.03448 80 42249 30325.03 11923.96552 81 42005 46108.30 -4103.30488 82 41152 46108.30 -4956.30488 83 14399 30325.03 -15926.03448 84 28263 46108.30 -17845.30488 85 17215 13959.86 3255.14286 86 48140 46108.30 2031.69512 87 62897 46108.30 16788.69512 88 22883 30325.03 -7442.03448 89 41622 30325.03 11296.96552 90 40715 46108.30 -5393.30488 91 65897 46108.30 19788.69512 92 76542 46108.30 30433.69512 93 37477 46108.30 -8631.30488 94 53216 46108.30 7107.69512 95 40911 30325.03 10585.96552 96 57021 46108.30 10912.69512 97 73116 46108.30 27007.69512 98 3895 1748.50 2146.50000 99 46609 46108.30 500.69512 100 29351 46108.30 -16757.30488 101 2325 13959.86 -11634.85714 102 31747 30325.03 1421.96552 103 32665 30325.03 2339.96552 104 19249 30325.03 -11076.03448 105 15292 30325.03 -15033.03448 106 5842 1748.50 4093.50000 107 33994 46108.30 -12114.30488 108 13018 13959.86 -941.85714 109 0 1748.50 -1748.50000 110 98177 46108.30 52068.69512 111 37941 30325.03 7615.96552 112 31032 46108.30 -15076.30488 113 32683 46108.30 -13425.30488 114 34545 30325.03 4219.96552 115 0 1748.50 -1748.50000 116 0 1748.50 -1748.50000 117 27525 46108.30 -18583.30488 118 66856 46108.30 20747.69512 119 28549 30325.03 -1776.03448 120 38610 46108.30 -7498.30488 121 2781 1748.50 1032.50000 122 41211 46108.30 -4897.30488 123 22698 30325.03 -7627.03448 124 41194 30325.03 10868.96552 125 32689 13959.86 18729.14286 126 5752 1748.50 4003.50000 127 26757 46108.30 -19351.30488 128 22527 30325.03 -7798.03448 129 44810 46108.30 -1298.30488 130 0 1748.50 -1748.50000 131 0 1748.50 -1748.50000 132 100674 46108.30 54565.69512 133 0 1748.50 -1748.50000 134 57786 46108.30 11677.69512 135 0 1748.50 -1748.50000 136 5444 1748.50 3695.50000 137 0 1748.50 -1748.50000 138 28470 30325.03 -1855.03448 139 61849 46108.30 15740.69512 140 0 1748.50 -1748.50000 141 2179 1748.50 430.50000 142 8019 13959.86 -5940.85714 143 39644 46108.30 -6464.30488 144 23494 46108.30 -22614.30488 > 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/4upfl1324477814.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/55n0n1324477814.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/6mpe71324477814.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/7in1d1324477814.tab") + } > > try(system("convert tmp/252nh1324477814.ps tmp/252nh1324477814.png",intern=TRUE)) character(0) > try(system("convert tmp/39jwa1324477814.ps tmp/39jwa1324477814.png",intern=TRUE)) character(0) > try(system("convert tmp/4upfl1324477814.ps tmp/4upfl1324477814.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.399 0.295 3.687