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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,22818 + ,21 + ,21 + ,616 + ,53117 + ,155 + ,3 + ,12 + ,32689 + ,4 + ,4 + ,285 + ,19764 + ,75 + ,10 + ,2 + ,5752 + ,10 + ,10 + ,1145 + ,86680 + ,361 + ,37 + ,14 + ,22197 + ,43 + ,43 + ,733 + ,84105 + ,261 + ,17 + ,17 + ,20055 + ,34 + ,34 + ,888 + ,77945 + ,299 + ,28 + ,19 + ,25272 + ,32 + ,31 + ,849 + ,89113 + ,300 + ,19 + ,14 + ,82206 + ,20 + ,19 + ,1182 + ,91005 + ,450 + ,29 + ,11 + ,32073 + ,34 + ,34 + ,528 + ,40248 + ,183 + ,8 + ,4 + ,5444 + ,6 + ,6 + ,642 + ,64187 + ,238 + ,10 + ,16 + ,20154 + ,12 + ,11 + ,947 + ,50857 + ,165 + ,15 + ,20 + ,36944 + ,24 + ,24 + ,819 + ,56613 + ,234 + ,15 + ,12 + ,8019 + ,16 + ,16 + ,757 + ,62792 + ,176 + ,28 + ,15 + ,30884 + ,72 + ,72 + ,894 + ,72535 + ,329 + ,17 + ,16 + ,19540 + ,27 + ,21) + ,dim=c(8 + ,289) + ,dimnames=list(c('time_in_rfc' + ,'pageviews' + ,'compendium_views_info' + ,'blogged_computations' + ,'compendiums_reviewed' + ,'totale_size' + ,'totale_hyperlinks' + ,'totale_blogs') + ,1:289)) > y <- array(NA,dim=c(8,289),dimnames=list(c('time_in_rfc','pageviews','compendium_views_info','blogged_computations','compendiums_reviewed','totale_size','totale_hyperlinks','totale_blogs'),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 = '1' > 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] "time_in_rfc" > x[,par1] [1] 1418 869 1530 2172 901 463 3201 371 1192 1583 1439 1764 1495 1373 2187 [16] 1491 4041 1706 2152 1036 1882 1929 2242 1220 1289 2515 2147 2352 1638 1222 [31] 1812 1677 1579 1731 807 2452 829 1940 2662 186 1499 865 1793 2527 2747 [46] 1324 2702 1383 1179 2099 4308 918 1831 3373 1713 1438 496 2253 744 1161 [61] 2352 2144 4691 1112 2694 1973 1769 3148 2474 2084 1954 1226 1389 1496 2269 [76] 1833 1268 1943 893 1762 1403 1425 1857 1840 1502 1441 1420 1416 2970 1317 [91] 1644 870 1654 1054 937 3004 2008 2547 1885 1626 1468 2445 1964 1381 1369 [106] 1659 2888 1290 2845 1982 1904 1391 602 1743 1559 2014 2143 2146 874 1590 [121] 1590 1210 2072 1281 1401 834 1105 1272 1944 391 761 1605 530 1988 1386 [136] 2395 387 1742 620 449 800 1684 1050 2699 1606 1502 1204 1138 568 1459 [151] 2158 1111 1421 2833 1955 2922 1002 1060 956 2186 3604 1035 1417 3261 1587 [166] 1424 1701 1249 946 1926 3352 1641 2035 2312 1369 1577 2201 961 1900 1254 [181] 1335 1597 207 1645 2429 151 474 141 1639 872 1318 1018 1383 1314 1335 [196] 1403 910 616 1407 771 766 473 1376 1232 1521 572 1059 1544 1230 1206 [211] 1205 1255 613 721 1109 740 1126 728 689 592 995 1613 2048 705 301 [226] 1803 799 861 1186 1451 628 1161 1463 742 979 675 1241 676 1049 620 [241] 1081 1688 736 617 812 1051 1656 705 945 554 1597 982 222 1212 1143 [256] 435 532 882 608 459 578 826 509 717 637 857 830 652 707 954 [271] 1461 672 778 1141 680 1090 616 285 1145 733 888 849 1182 528 642 [286] 947 819 757 894 > 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]) 141 151 186 207 222 285 301 371 387 391 435 449 459 463 473 474 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 496 509 528 530 532 554 568 572 578 592 602 608 613 616 617 620 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 628 637 642 652 672 675 676 680 689 705 707 717 721 728 733 736 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 740 742 744 757 761 766 771 778 799 800 807 812 819 826 829 830 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 834 849 857 861 865 869 870 872 874 882 888 893 894 901 910 918 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 937 945 946 947 954 956 961 979 982 995 1002 1018 1035 1036 1049 1050 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1051 1054 1059 1060 1081 1090 1105 1109 1111 1112 1126 1138 1141 1143 1145 1161 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1179 1182 1186 1192 1204 1205 1206 1210 1212 1220 1222 1226 1230 1232 1241 1249 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1254 1255 1268 1272 1281 1289 1290 1314 1317 1318 1324 1335 1369 1373 1376 1381 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1383 1386 1389 1391 1401 1403 1407 1416 1417 1418 1420 1421 1424 1425 1438 1439 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1441 1451 1459 1461 1463 1468 1491 1495 1496 1499 1502 1521 1530 1544 1559 1577 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1579 1583 1587 1590 1597 1605 1606 1613 1626 1638 1639 1641 1644 1645 1654 1656 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1659 1677 1684 1688 1701 1706 1713 1731 1742 1743 1762 1764 1769 1793 1803 1812 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1831 1833 1840 1857 1882 1885 1900 1904 1926 1929 1940 1943 1944 1954 1955 1964 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1973 1982 1988 2008 2014 2035 2048 2072 2084 2099 2143 2144 2146 2147 2152 2158 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2172 2186 2187 2201 2242 2253 2269 2312 2352 2395 2429 2445 2452 2474 2515 2527 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2547 2662 2694 2699 2702 2747 2833 2845 2888 2922 2970 3004 3148 3201 3261 3352 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3373 3604 4041 4308 4691 1 1 1 1 1 > colnames(x) [1] "time_in_rfc" "pageviews" "compendium_views_info" [4] "blogged_computations" "compendiums_reviewed" "totale_size" [7] "totale_hyperlinks" "totale_blogs" > colnames(x)[par1] [1] "time_in_rfc" > x[,par1] [1] 1418 869 1530 2172 901 463 3201 371 1192 1583 1439 1764 1495 1373 2187 [16] 1491 4041 1706 2152 1036 1882 1929 2242 1220 1289 2515 2147 2352 1638 1222 [31] 1812 1677 1579 1731 807 2452 829 1940 2662 186 1499 865 1793 2527 2747 [46] 1324 2702 1383 1179 2099 4308 918 1831 3373 1713 1438 496 2253 744 1161 [61] 2352 2144 4691 1112 2694 1973 1769 3148 2474 2084 1954 1226 1389 1496 2269 [76] 1833 1268 1943 893 1762 1403 1425 1857 1840 1502 1441 1420 1416 2970 1317 [91] 1644 870 1654 1054 937 3004 2008 2547 1885 1626 1468 2445 1964 1381 1369 [106] 1659 2888 1290 2845 1982 1904 1391 602 1743 1559 2014 2143 2146 874 1590 [121] 1590 1210 2072 1281 1401 834 1105 1272 1944 391 761 1605 530 1988 1386 [136] 2395 387 1742 620 449 800 1684 1050 2699 1606 1502 1204 1138 568 1459 [151] 2158 1111 1421 2833 1955 2922 1002 1060 956 2186 3604 1035 1417 3261 1587 [166] 1424 1701 1249 946 1926 3352 1641 2035 2312 1369 1577 2201 961 1900 1254 [181] 1335 1597 207 1645 2429 151 474 141 1639 872 1318 1018 1383 1314 1335 [196] 1403 910 616 1407 771 766 473 1376 1232 1521 572 1059 1544 1230 1206 [211] 1205 1255 613 721 1109 740 1126 728 689 592 995 1613 2048 705 301 [226] 1803 799 861 1186 1451 628 1161 1463 742 979 675 1241 676 1049 620 [241] 1081 1688 736 617 812 1051 1656 705 945 554 1597 982 222 1212 1143 [256] 435 532 882 608 459 578 826 509 717 637 857 830 652 707 954 [271] 1461 672 778 1141 680 1090 616 285 1145 733 888 849 1182 528 642 [286] 947 819 757 894 > 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/1f8541323895151.tab") + } + } > m Conditional inference tree with 12 terminal nodes Response: time_in_rfc Inputs: pageviews, compendium_views_info, blogged_computations, compendiums_reviewed, totale_size, totale_hyperlinks, totale_blogs Number of observations: 289 1) compendium_views_info <= 555; criterion = 1, statistic = 266.8 2) compendium_views_info <= 304; criterion = 1, statistic = 164.668 3) compendium_views_info <= 154; criterion = 1, statistic = 57.696 4)* weights = 15 3) compendium_views_info > 154 5) pageviews <= 46660; criterion = 1, statistic = 27.859 6)* weights = 17 5) pageviews > 46660 7) compendium_views_info <= 264; criterion = 1, statistic = 19.113 8) blogged_computations <= 32; criterion = 0.99, statistic = 10.188 9)* weights = 30 8) blogged_computations > 32 10)* weights = 9 7) compendium_views_info > 264 11)* weights = 14 2) compendium_views_info > 304 12) compendium_views_info <= 462; criterion = 1, statistic = 51.023 13) pageviews <= 98866; criterion = 0.999, statistic = 15.229 14)* weights = 33 13) pageviews > 98866 15)* weights = 36 12) compendium_views_info > 462 16)* weights = 39 1) compendium_views_info > 555 17) compendium_views_info <= 992; criterion = 1, statistic = 76.433 18) compendium_views_info <= 818; criterion = 1, statistic = 51.209 19) compendium_views_info <= 711; criterion = 0.999, statistic = 14.803 20)* weights = 42 19) compendium_views_info > 711 21)* weights = 14 18) compendium_views_info > 818 22)* weights = 25 17) compendium_views_info > 992 23)* weights = 15 > postscript(file="/var/wessaorg/rcomp/tmp/22ees1323895151.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/3c3pb1323895151.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 1418 1286.5833 131.4166667 2 869 928.7143 -59.7142857 3 1530 1765.5952 -235.5952381 4 2172 2383.4000 -211.4000000 5 901 928.7143 -27.7142857 6 463 343.6000 119.4000000 7 3201 3353.3333 -152.3333333 8 371 343.6000 27.4000000 9 1192 1286.5833 -94.5833333 10 1583 1765.5952 -182.5952381 11 1439 1473.4359 -34.4358974 12 1764 1765.5952 -1.5952381 13 1495 1473.4359 21.5641026 14 1373 1473.4359 -100.4358974 15 2187 2383.4000 -196.4000000 16 1491 1473.4359 17.5641026 17 4041 3353.3333 687.6666667 18 1706 1765.5952 -59.5952381 19 2152 2383.4000 -231.4000000 20 1036 1286.5833 -250.5833333 21 1882 1765.5952 116.4047619 22 1929 1765.5952 163.4047619 23 2242 2383.4000 -141.4000000 24 1220 1286.5833 -66.5833333 25 1289 1286.5833 2.4166667 26 2515 2383.4000 131.6000000 27 2147 2045.9286 101.0714286 28 2352 2383.4000 -31.4000000 29 1638 1473.4359 164.5641026 30 1222 1473.4359 -251.4358974 31 1812 1765.5952 46.4047619 32 1677 1765.5952 -88.5952381 33 1579 1473.4359 105.5641026 34 1731 1765.5952 -34.5952381 35 807 830.5556 -23.5555556 36 2452 2383.4000 68.6000000 37 829 711.3000 117.7000000 38 1940 2045.9286 -105.9285714 39 2662 2383.4000 278.6000000 40 186 343.6000 -157.6000000 41 1499 1473.4359 25.5641026 42 865 830.5556 34.4444444 43 1793 1473.4359 319.5641026 44 2527 2383.4000 143.6000000 45 2747 3353.3333 -606.3333333 46 1324 1473.4359 -149.4358974 47 2702 2383.4000 318.6000000 48 1383 1473.4359 -90.4358974 49 1179 1286.5833 -107.5833333 50 2099 2383.4000 -284.4000000 51 4308 3353.3333 954.6666667 52 918 1094.9091 -176.9090909 53 1831 1765.5952 65.4047619 54 3373 3353.3333 19.6666667 55 1713 2045.9286 -332.9285714 56 1438 1286.5833 151.4166667 57 496 580.6471 -84.6470588 58 2253 2045.9286 207.0714286 59 744 711.3000 32.7000000 60 1161 1286.5833 -125.5833333 61 2352 2383.4000 -31.4000000 62 2144 1765.5952 378.4047619 63 4691 3353.3333 1337.6666667 64 1112 1286.5833 -174.5833333 65 2694 2383.4000 310.6000000 66 1973 2045.9286 -72.9285714 67 1769 1765.5952 3.4047619 68 3148 3353.3333 -205.3333333 69 2474 2383.4000 90.6000000 70 2084 2383.4000 -299.4000000 71 1954 1765.5952 188.4047619 72 1226 1473.4359 -247.4358974 73 1389 1286.5833 102.4166667 74 1496 1473.4359 22.5641026 75 2269 2045.9286 223.0714286 76 1833 1765.5952 67.4047619 77 1268 1286.5833 -18.5833333 78 1943 1765.5952 177.4047619 79 893 1094.9091 -201.9090909 80 1762 1765.5952 -3.5952381 81 1403 1765.5952 -362.5952381 82 1425 1473.4359 -48.4358974 83 1857 2045.9286 -188.9285714 84 1840 2045.9286 -205.9285714 85 1502 1286.5833 215.4166667 86 1441 1286.5833 154.4166667 87 1420 1473.4359 -53.4358974 88 1416 1473.4359 -57.4358974 89 2970 3353.3333 -383.3333333 90 1317 1765.5952 -448.5952381 91 1644 1473.4359 170.5641026 92 870 1094.9091 -224.9090909 93 1654 1765.5952 -111.5952381 94 1054 928.7143 125.2857143 95 937 830.5556 106.4444444 96 3004 3353.3333 -349.3333333 97 2008 2045.9286 -37.9285714 98 2547 2383.4000 163.6000000 99 1885 1765.5952 119.4047619 100 1626 1473.4359 152.5641026 101 1468 1473.4359 -5.4358974 102 2445 2383.4000 61.6000000 103 1964 2045.9286 -81.9285714 104 1381 1473.4359 -92.4358974 105 1369 1473.4359 -104.4358974 106 1659 1765.5952 -106.5952381 107 2888 2383.4000 504.6000000 108 1290 1094.9091 195.0909091 109 2845 3353.3333 -508.3333333 110 1982 1765.5952 216.4047619 111 1904 1765.5952 138.4047619 112 1391 1286.5833 104.4166667 113 602 580.6471 21.3529412 114 1743 1765.5952 -22.5952381 115 1559 1765.5952 -206.5952381 116 2014 2383.4000 -369.4000000 117 2143 2383.4000 -240.4000000 118 2146 2045.9286 100.0714286 119 874 928.7143 -54.7142857 120 1590 1473.4359 116.5641026 121 1590 1473.4359 116.5641026 122 1210 1094.9091 115.0909091 123 2072 1765.5952 306.4047619 124 1281 1286.5833 -5.5833333 125 1401 1473.4359 -72.4358974 126 834 711.3000 122.7000000 127 1105 1286.5833 -181.5833333 128 1272 1286.5833 -14.5833333 129 1944 1765.5952 178.4047619 130 391 343.6000 47.4000000 131 761 580.6471 180.3529412 132 1605 1765.5952 -160.5952381 133 530 711.3000 -181.3000000 134 1988 1765.5952 222.4047619 135 1386 1286.5833 99.4166667 136 2395 2383.4000 11.6000000 137 387 343.6000 43.4000000 138 1742 1765.5952 -23.5952381 139 620 580.6471 39.3529412 140 449 580.6471 -131.6470588 141 800 928.7143 -128.7142857 142 1684 1473.4359 210.5641026 143 1050 1094.9091 -44.9090909 144 2699 2383.4000 315.6000000 145 1606 1473.4359 132.5641026 146 1502 1094.9091 407.0909091 147 1204 1286.5833 -82.5833333 148 1138 1094.9091 43.0909091 149 568 580.6471 -12.6470588 150 1459 1473.4359 -14.4358974 151 2158 2383.4000 -225.4000000 152 1111 1286.5833 -175.5833333 153 1421 1473.4359 -52.4358974 154 2833 3353.3333 -520.3333333 155 1955 1765.5952 189.4047619 156 2922 3353.3333 -431.3333333 157 1002 1286.5833 -284.5833333 158 1060 1094.9091 -34.9090909 159 956 1094.9091 -138.9090909 160 2186 2045.9286 140.0714286 161 3604 3353.3333 250.6666667 162 1035 1094.9091 -59.9090909 163 1417 1286.5833 130.4166667 164 3261 3353.3333 -92.3333333 165 1587 1765.5952 -178.5952381 166 1424 1473.4359 -49.4358974 167 1701 1765.5952 -64.5952381 168 1249 1286.5833 -37.5833333 169 946 830.5556 115.4444444 170 1926 1765.5952 160.4047619 171 3352 3353.3333 -1.3333333 172 1641 1765.5952 -124.5952381 173 2035 2045.9286 -10.9285714 174 2312 2045.9286 266.0714286 175 1369 1473.4359 -104.4358974 176 1577 1765.5952 -188.5952381 177 2201 2383.4000 -182.4000000 178 961 1094.9091 -133.9090909 179 1900 1765.5952 134.4047619 180 1254 1286.5833 -32.5833333 181 1335 1094.9091 240.0909091 182 1597 1473.4359 123.5641026 183 207 343.6000 -136.6000000 184 1645 1765.5952 -120.5952381 185 2429 2383.4000 45.6000000 186 151 343.6000 -192.6000000 187 474 580.6471 -106.6470588 188 141 343.6000 -202.6000000 189 1639 1473.4359 165.5641026 190 872 830.5556 41.4444444 191 1318 1286.5833 31.4166667 192 1018 1094.9091 -76.9090909 193 1383 1094.9091 288.0909091 194 1314 928.7143 385.2857143 195 1335 1473.4359 -138.4358974 196 1403 1286.5833 116.4166667 197 910 1094.9091 -184.9090909 198 616 711.3000 -95.3000000 199 1407 1473.4359 -66.4358974 200 771 711.3000 59.7000000 201 766 711.3000 54.7000000 202 473 343.6000 129.4000000 203 1376 1473.4359 -97.4358974 204 1232 1286.5833 -54.5833333 205 1521 1765.5952 -244.5952381 206 572 580.6471 -8.6470588 207 1059 928.7143 130.2857143 208 1544 1765.5952 -221.5952381 209 1230 1286.5833 -56.5833333 210 1206 1286.5833 -80.5833333 211 1205 1094.9091 110.0909091 212 1255 1286.5833 -31.5833333 213 613 711.3000 -98.3000000 214 721 711.3000 9.7000000 215 1109 1094.9091 14.0909091 216 740 711.3000 28.7000000 217 1126 1094.9091 31.0909091 218 728 711.3000 16.7000000 219 689 711.3000 -22.3000000 220 592 711.3000 -119.3000000 221 995 1094.9091 -99.9090909 222 1613 1286.5833 326.4166667 223 2048 1765.5952 282.4047619 224 705 711.3000 -6.3000000 225 301 343.6000 -42.6000000 226 1803 1765.5952 37.4047619 227 799 711.3000 87.7000000 228 861 928.7143 -67.7142857 229 1186 1094.9091 91.0909091 230 1451 1094.9091 356.0909091 231 628 580.6471 47.3529412 232 1161 1094.9091 66.0909091 233 1463 1473.4359 -10.4358974 234 742 711.3000 30.7000000 235 979 1094.9091 -115.9090909 236 675 830.5556 -155.5555556 237 1241 1286.5833 -45.5833333 238 676 580.6471 95.3529412 239 1049 1094.9091 -45.9090909 240 620 580.6471 39.3529412 241 1081 1094.9091 -13.9090909 242 1688 1286.5833 401.4166667 243 736 711.3000 24.7000000 244 617 711.3000 -94.3000000 245 812 928.7143 -116.7142857 246 1051 1094.9091 -43.9090909 247 1656 1473.4359 182.5641026 248 705 711.3000 -6.3000000 249 945 1094.9091 -149.9090909 250 554 580.6471 -26.6470588 251 1597 1473.4359 123.5641026 252 982 928.7143 53.2857143 253 222 343.6000 -121.6000000 254 1212 1286.5833 -74.5833333 255 1143 1473.4359 -330.4358974 256 435 580.6471 -145.6470588 257 532 580.6471 -48.6470588 258 882 928.7143 -46.7142857 259 608 343.6000 264.4000000 260 459 343.6000 115.4000000 261 578 580.6471 -2.6470588 262 826 830.5556 -4.5555556 263 509 343.6000 165.4000000 264 717 830.5556 -113.5555556 265 637 711.3000 -74.3000000 266 857 928.7143 -71.7142857 267 830 830.5556 -0.5555556 268 652 711.3000 -59.3000000 269 707 711.3000 -4.3000000 270 954 1094.9091 -140.9090909 271 1461 1286.5833 174.4166667 272 672 711.3000 -39.3000000 273 778 580.6471 197.3529412 274 1141 1286.5833 -145.5833333 275 680 711.3000 -31.3000000 276 1090 1094.9091 -4.9090909 277 616 711.3000 -95.3000000 278 285 343.6000 -58.6000000 279 1145 1094.9091 50.0909091 280 733 711.3000 21.7000000 281 888 928.7143 -40.7142857 282 849 928.7143 -79.7142857 283 1182 1094.9091 87.0909091 284 528 580.6471 -52.6470588 285 642 711.3000 -69.3000000 286 947 711.3000 235.7000000 287 819 711.3000 107.7000000 288 757 711.3000 45.7000000 289 894 1094.9091 -200.9090909 > 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/4fnfx1323895151.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5i7t41323895151.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6h5wh1323895151.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/72ka11323895151.tab") + } > > try(system("convert tmp/22ees1323895151.ps tmp/22ees1323895151.png",intern=TRUE)) character(0) > try(system("convert tmp/3c3pb1323895151.ps tmp/3c3pb1323895151.png",intern=TRUE)) character(0) > try(system("convert tmp/4fnfx1323895151.ps tmp/4fnfx1323895151.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.284 0.333 6.627