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Type 'q()' to quit R. > x <- array(list(1826 + ,161442 + ,592 + ,48 + ,93 + ,1728 + ,189695 + ,524 + ,53 + ,60 + ,192 + ,7215 + ,72 + ,0 + ,18 + ,2295 + ,129098 + ,645 + ,51 + ,95 + ,3509 + ,245678 + ,1185 + ,79 + ,137 + ,6861 + ,515038 + ,1945 + ,136 + ,263 + ,1801 + ,183078 + ,585 + ,62 + ,57 + ,1681 + ,185559 + ,470 + ,83 + ,59 + ,1897 + ,154581 + ,612 + ,55 + ,44 + ,2974 + ,298001 + ,992 + ,67 + ,96 + ,1946 + ,121844 + ,634 + ,50 + ,75 + ,2363 + ,203796 + ,741 + ,88 + ,71 + ,1850 + ,104738 + ,674 + ,47 + ,101 + ,3189 + ,220490 + ,1081 + ,79 + ,120 + ,1486 + ,170952 + ,419 + ,56 + ,61 + ,1567 + ,154647 + ,469 + ,54 + ,88 + ,1759 + ,142025 + ,432 + ,81 + ,58 + ,1247 + ,79030 + ,361 + ,6 + ,61 + ,2779 + ,167047 + ,877 + ,74 + ,87 + ,727 + ,27997 + ,221 + ,13 + ,25 + ,1117 + ,84588 + ,377 + ,31 + ,61 + ,2809 + ,241227 + ,847 + ,99 + ,101 + ,1760 + ,195820 + ,642 + ,38 + ,72 + ,2279 + ,142530 + ,693 + ,59 + ,56 + ,1937 + ,157178 + ,611 + ,54 + ,87 + ,1800 + ,204256 + ,654 + ,63 + ,33 + 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+ ,29 + ,37 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1082 + ,91529 + ,332 + ,46 + ,32 + ,1135 + ,107205 + ,371 + ,25 + ,67 + ,1367 + ,144664 + ,465 + ,51 + ,45 + ,1506 + ,146445 + ,447 + ,60 + ,63 + ,910 + ,84940 + ,301 + ,36 + ,61 + ,78 + ,3616 + ,14 + ,0 + ,5 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1130 + ,183088 + ,388 + ,40 + ,44 + ,1635 + ,153780 + ,589 + ,74 + ,90 + ,2122 + ,176586 + ,591 + ,30 + ,101 + ,970 + ,128944 + ,299 + ,41 + ,39 + ,778 + ,43410 + ,292 + ,7 + ,19 + ,1752 + ,175774 + ,530 + ,70 + ,73 + ,1050 + ,108656 + ,297 + ,32 + ,43 + ,2180 + ,140243 + ,614 + ,81 + ,56 + ,731 + ,60493 + ,174 + ,3 + ,40 + ,285 + ,19764 + ,75 + ,10 + ,12 + ,1834 + ,164062 + ,565 + ,46 + ,56 + ,1167 + ,138469 + ,382 + ,35 + ,34 + ,1646 + ,155367 + ,544 + ,54 + ,54 + ,256 + ,11796 + ,79 + ,1 + ,9 + ,98 + ,10674 + ,33 + ,0 + ,9 + ,1409 + ,144927 + ,480 + ,39 + ,58 + ,41 + ,6836 + ,11 + ,0 + ,3 + ,1824 + ,162563 + ,626 + ,48 + ,63 + ,42 + ,5118 + ,6 + ,5 + ,3 + ,528 + ,40248 + ,183 + ,8 + ,16 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1114 + ,127476 + ,342 + ,38 + ,50 + ,1305 + ,88837 + ,269 + ,21 + ,38 + ,81 + ,7131 + ,27 + ,0 + ,4 + ,261 + ,9056 + ,99 + ,0 + ,14 + ,1062 + ,87305 + ,291 + ,18 + ,26 + ,1279 + ,142829 + ,324 + ,53 + ,53 + ,1148 + ,100681 + ,414 + ,17 + ,20) + ,dim=c(5 + ,144) + ,dimnames=list(c('Pageviews' + ,'TimeinRFC' + ,'CompendiumViews' + ,'BloggedComputations' + ,'Logins') + ,1:144)) > y <- array(NA,dim=c(5,144),dimnames=list(c('Pageviews','TimeinRFC','CompendiumViews','BloggedComputations','Logins'),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 = '3' > 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] "TimeinRFC" > x[,par1] [1] 161442 189695 7215 129098 245678 515038 183078 185559 154581 298001 [11] 121844 203796 104738 220490 170952 154647 142025 79030 167047 27997 [21] 84588 241227 195820 142530 157178 204256 212298 201403 354924 192399 [31] 182286 181590 134868 235002 228872 0 230360 100129 145864 252386 [41] 242379 156399 103623 195891 139654 167934 81293 246211 233155 160344 [51] 48188 161922 311044 235223 195583 155574 208834 101687 151985 201027 [61] 172600 144556 129561 122204 160930 109798 192811 138708 114408 31970 [71] 245432 142907 113612 119537 162215 100098 174768 158459 90743 84971 [81] 80545 287191 67006 134091 95803 173833 241469 115367 115603 155537 [91] 153133 179228 151517 133686 61350 245196 195576 19349 245422 157961 [101] 66802 91762 151077 136847 85338 27676 162934 122417 0 91529 [111] 107205 144664 146445 84940 3616 0 183088 153780 176586 128944 [121] 43410 175774 108656 140243 60493 19764 164062 138469 155367 11796 [131] 10674 144927 6836 162563 5118 40248 0 127476 88837 7131 [141] 9056 87305 142829 100681 > 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 3616 5118 6836 7131 7215 9056 10674 11796 19349 19764 4 1 1 1 1 1 1 1 1 1 1 27676 27997 31970 40248 43410 48188 60493 61350 66802 67006 79030 1 1 1 1 1 1 1 1 1 1 1 80545 81293 84588 84940 84971 85338 87305 88837 90743 91529 91762 1 1 1 1 1 1 1 1 1 1 1 95803 100098 100129 100681 101687 103623 104738 107205 108656 109798 113612 1 1 1 1 1 1 1 1 1 1 1 114408 115367 115603 119537 121844 122204 122417 127476 128944 129098 129561 1 1 1 1 1 1 1 1 1 1 1 133686 134091 134868 136847 138469 138708 139654 140243 142025 142530 142829 1 1 1 1 1 1 1 1 1 1 1 142907 144556 144664 144927 145864 146445 151077 151517 151985 153133 153780 1 1 1 1 1 1 1 1 1 1 1 154581 154647 155367 155537 155574 156399 157178 157961 158459 160344 160930 1 1 1 1 1 1 1 1 1 1 1 161442 161922 162215 162563 162934 164062 167047 167934 170952 172600 173833 1 1 1 1 1 1 1 1 1 1 1 174768 175774 176586 179228 181590 182286 183078 183088 185559 189695 192399 1 1 1 1 1 1 1 1 1 1 1 192811 195576 195583 195820 195891 201027 201403 203796 204256 208834 212298 1 1 1 1 1 1 1 1 1 1 1 220490 228872 230360 233155 235002 235223 241227 241469 242379 245196 245422 1 1 1 1 1 1 1 1 1 1 1 245432 245678 246211 252386 287191 298001 311044 354924 515038 1 1 1 1 1 1 1 1 1 > colnames(x) [1] "Pageviews" "TimeinRFC" "CompendiumViews" [4] "BloggedComputations" "Logins" > colnames(x)[par1] [1] "TimeinRFC" > x[,par1] [1] 161442 189695 7215 129098 245678 515038 183078 185559 154581 298001 [11] 121844 203796 104738 220490 170952 154647 142025 79030 167047 27997 [21] 84588 241227 195820 142530 157178 204256 212298 201403 354924 192399 [31] 182286 181590 134868 235002 228872 0 230360 100129 145864 252386 [41] 242379 156399 103623 195891 139654 167934 81293 246211 233155 160344 [51] 48188 161922 311044 235223 195583 155574 208834 101687 151985 201027 [61] 172600 144556 129561 122204 160930 109798 192811 138708 114408 31970 [71] 245432 142907 113612 119537 162215 100098 174768 158459 90743 84971 [81] 80545 287191 67006 134091 95803 173833 241469 115367 115603 155537 [91] 153133 179228 151517 133686 61350 245196 195576 19349 245422 157961 [101] 66802 91762 151077 136847 85338 27676 162934 122417 0 91529 [111] 107205 144664 146445 84940 3616 0 183088 153780 176586 128944 [121] 43410 175774 108656 140243 60493 19764 164062 138469 155367 11796 [131] 10674 144927 6836 162563 5118 40248 0 127476 88837 7131 [141] 9056 87305 142829 100681 > 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/1r02z1324564436.tab") + } + } > m Conditional inference tree with 8 terminal nodes Response: TimeinRFC Inputs: Pageviews, CompendiumViews, BloggedComputations, Logins Number of observations: 144 1) Pageviews <= 1117; criterion = 1, statistic = 113.795 2) Pageviews <= 727; criterion = 1, statistic = 33.37 3)* weights = 19 2) Pageviews > 727 4) Pageviews <= 953; criterion = 0.98, statistic = 7.855 5)* weights = 8 4) Pageviews > 953 6)* weights = 12 1) Pageviews > 1117 7) Pageviews <= 2311; criterion = 1, statistic = 63.03 8) CompendiumViews <= 674; criterion = 1, statistic = 25.116 9) BloggedComputations <= 34; criterion = 0.999, statistic = 14.612 10)* weights = 10 9) BloggedComputations > 34 11)* weights = 58 8) CompendiumViews > 674 12)* weights = 14 7) Pageviews > 2311 13) Pageviews <= 2878; criterion = 0.998, statistic = 11.893 14)* weights = 16 13) Pageviews > 2878 15)* weights = 7 > postscript(file="/var/wessaorg/rcomp/tmp/20ahi1324564436.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/3wanm1324564436.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 161442 153535.28 7906.7241 2 189695 153535.28 36159.7241 3 7215 14559.68 -7344.6842 4 129098 153535.28 -24437.2759 5 245678 271192.71 -25514.7143 6 515038 271192.71 243845.2857 7 183078 153535.28 29542.7241 8 185559 153535.28 32023.7241 9 154581 153535.28 1045.7241 10 298001 271192.71 26808.2857 11 121844 153535.28 -31691.2759 12 203796 224303.75 -20507.7500 13 104738 153535.28 -48797.2759 14 220490 271192.71 -50702.7143 15 170952 153535.28 17416.7241 16 154647 153535.28 1111.7241 17 142025 153535.28 -11510.2759 18 79030 115444.10 -36414.1000 19 167047 224303.75 -57256.7500 20 27997 14559.68 13437.3158 21 84588 99810.58 -15222.5833 22 241227 224303.75 16923.2500 23 195820 153535.28 42284.7241 24 142530 199226.36 -56696.3571 25 157178 153535.28 3642.7241 26 204256 153535.28 50720.7241 27 212298 199226.36 13071.6429 28 201403 153535.28 47867.7241 29 354924 224303.75 130620.2500 30 192399 199226.36 -6827.3571 31 182286 153535.28 28750.7241 32 181590 153535.28 28054.7241 33 134868 199226.36 -64358.3571 34 235002 271192.71 -36190.7143 35 228872 224303.75 4568.2500 36 0 14559.68 -14559.6842 37 230360 199226.36 31133.6429 38 100129 153535.28 -53406.2759 39 145864 153535.28 -7671.2759 40 252386 199226.36 53159.6429 41 242379 224303.75 18075.2500 42 156399 153535.28 2863.7241 43 103623 99810.58 3812.4167 44 195891 199226.36 -3335.3571 45 139654 153535.28 -13881.2759 46 167934 153535.28 14398.7241 47 81293 70521.38 10771.6250 48 246211 224303.75 21907.2500 49 233155 199226.36 33928.6429 50 160344 224303.75 -63959.7500 51 48188 14559.68 33628.3158 52 161922 153535.28 8386.7241 53 311044 224303.75 86740.2500 54 235223 199226.36 35996.6429 55 195583 199226.36 -3643.3571 56 155574 153535.28 2038.7241 57 208834 153535.28 55298.7241 58 101687 99810.58 1876.4167 59 151985 153535.28 -1550.2759 60 201027 224303.75 -23276.7500 61 172600 153535.28 19064.7241 62 144556 199226.36 -54670.3571 63 129561 153535.28 -23974.2759 64 122204 153535.28 -31331.2759 65 160930 115444.10 45485.9000 66 109798 153535.28 -43737.2759 67 192811 153535.28 39275.7241 68 138708 271192.71 -132484.7143 69 114408 99810.58 14597.4167 70 31970 14559.68 17410.3158 71 245432 271192.71 -25760.7143 72 142907 153535.28 -10628.2759 73 113612 153535.28 -39923.2759 74 119537 153535.28 -33998.2759 75 162215 224303.75 -62088.7500 76 100098 115444.10 -15346.1000 77 174768 199226.36 -24458.3571 78 158459 224303.75 -65844.7500 79 90743 99810.58 -9067.5833 80 84971 115444.10 -30473.1000 81 80545 70521.38 10023.6250 82 287191 199226.36 87964.6429 83 67006 99810.58 -32804.5833 84 134091 153535.28 -19444.2759 85 95803 153535.28 -57732.2759 86 173833 153535.28 20297.7241 87 241469 224303.75 17165.2500 88 115367 153535.28 -38168.2759 89 115603 153535.28 -37932.2759 90 155537 153535.28 2001.7241 91 153133 153535.28 -402.2759 92 179228 224303.75 -45075.7500 93 151517 153535.28 -2018.2759 94 133686 115444.10 18241.9000 95 61350 70521.38 -9171.3750 96 245196 224303.75 20892.2500 97 195576 153535.28 42040.7241 98 19349 14559.68 4789.3158 99 245422 224303.75 21118.2500 100 157961 199226.36 -41265.3571 101 66802 70521.38 -3719.3750 102 91762 99810.58 -8048.5833 103 151077 153535.28 -2458.2759 104 136847 153535.28 -16688.2759 105 85338 70521.38 14816.6250 106 27676 14559.68 13116.3158 107 162934 153535.28 9398.7241 108 122417 115444.10 6972.9000 109 0 14559.68 -14559.6842 110 91529 99810.58 -8281.5833 111 107205 115444.10 -8239.1000 112 144664 153535.28 -8871.2759 113 146445 153535.28 -7090.2759 114 84940 70521.38 14418.6250 115 3616 14559.68 -10943.6842 116 0 14559.68 -14559.6842 117 183088 153535.28 29552.7241 118 153780 153535.28 244.7241 119 176586 115444.10 61141.9000 120 128944 99810.58 29133.4167 121 43410 70521.38 -27111.3750 122 175774 153535.28 22238.7241 123 108656 99810.58 8845.4167 124 140243 153535.28 -13292.2759 125 60493 70521.38 -10028.3750 126 19764 14559.68 5204.3158 127 164062 153535.28 10526.7241 128 138469 153535.28 -15066.2759 129 155367 153535.28 1831.7241 130 11796 14559.68 -2763.6842 131 10674 14559.68 -3885.6842 132 144927 153535.28 -8608.2759 133 6836 14559.68 -7723.6842 134 162563 153535.28 9027.7241 135 5118 14559.68 -9441.6842 136 40248 14559.68 25688.3158 137 0 14559.68 -14559.6842 138 127476 99810.58 27665.4167 139 88837 115444.10 -26607.1000 140 7131 14559.68 -7428.6842 141 9056 14559.68 -5503.6842 142 87305 99810.58 -12505.5833 143 142829 153535.28 -10706.2759 144 100681 115444.10 -14763.1000 > 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/4ewlh1324564436.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/5ujkh1324564436.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/63hri1324564437.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/7gbcv1324564437.tab") + } > > try(system("convert tmp/20ahi1324564436.ps tmp/20ahi1324564436.png",intern=TRUE)) character(0) > try(system("convert tmp/3wanm1324564436.ps tmp/3wanm1324564436.png",intern=TRUE)) character(0) > try(system("convert tmp/4ewlh1324564436.ps tmp/4ewlh1324564436.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.745 0.415 4.202