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. 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array(NA,dim=c(8,237),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','gender '),1:237)) > 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' > #'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] "pageviews" > 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 > 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]) 141 151 186 207 301 371 387 391 449 463 473 474 496 530 568 572 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 592 602 613 616 620 628 675 689 705 721 728 740 742 744 761 766 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 771 799 800 807 829 834 861 865 869 870 872 874 893 901 910 918 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 937 946 956 961 979 995 1002 1018 1035 1036 1050 1054 1059 1060 1105 1109 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1111 1112 1126 1138 1161 1179 1186 1192 1204 1205 1206 1210 1220 1222 1226 1230 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1232 1241 1249 1254 1255 1268 1272 1281 1289 1290 1314 1317 1318 1324 1335 1369 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1373 1376 1381 1383 1386 1389 1391 1401 1403 1407 1416 1417 1418 1420 1421 1424 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1425 1438 1439 1441 1451 1459 1463 1468 1491 1495 1496 1499 1502 1521 1530 1544 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1559 1577 1579 1583 1587 1590 1597 1605 1606 1613 1626 1638 1639 1641 1644 1645 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1654 1659 1677 1684 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] "pageviews" "time_in_rfc" "logins" [4] "compendium_views_info" "compendium_views_pr" "shared_compendiums" [7] "blogged_computations" "gender." > colnames(x)[par1] [1] "pageviews" > 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 > 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/1wbo21323872773.tab") + } + } > m Conditional inference tree with 12 terminal nodes Response: pageviews Inputs: time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, gender. Number of observations: 237 1) compendium_views_info <= 565; criterion = 1, statistic = 217.004 2) compendium_views_info <= 276; criterion = 1, statistic = 124.956 3) compendium_views_info <= 154; criterion = 1, statistic = 31.982 4)* weights = 10 3) compendium_views_info > 154 5) time_in_rfc <= 81872; criterion = 1, statistic = 20.965 6)* weights = 25 5) time_in_rfc > 81872 7)* weights = 9 2) compendium_views_info > 276 8) compendium_views_info <= 462; criterion = 1, statistic = 54.534 9) compendium_views_info <= 400; criterion = 0.998, statistic = 13.344 10)* weights = 36 9) compendium_views_info > 400 11)* weights = 25 8) compendium_views_info > 462 12) logins <= 58; criterion = 0.954, statistic = 7.354 13)* weights = 24 12) logins > 58 14)* weights = 17 1) compendium_views_info > 565 15) compendium_views_info <= 992; criterion = 1, statistic = 71.659 16) compendium_views_info <= 818; criterion = 1, statistic = 46.046 17) logins <= 67; criterion = 0.997, statistic = 12.352 18)* weights = 28 17) logins > 67 19)* weights = 23 16) compendium_views_info > 818 20) logins <= 89; criterion = 0.975, statistic = 8.475 21)* weights = 13 20) logins > 89 22)* weights = 12 15) compendium_views_info > 992 23)* weights = 15 > postscript(file="/var/www/rcomp/tmp/2y2p11323872773.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/301mu1323872773.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 1120.3056 297.6944444 2 869 1120.3056 -251.3055556 3 1530 1424.5833 105.4166667 4 2172 2226.4615 -54.4615385 5 901 873.8889 27.1111111 6 463 307.1000 155.9000000 7 3201 3353.3333 -152.3333333 8 371 307.1000 63.9000000 9 1192 1120.3056 71.6944444 10 1583 1753.8571 -170.8571429 11 1439 1424.5833 14.4166667 12 1764 1979.2174 -215.2173913 13 1495 1590.9412 -95.9411765 14 1373 1424.5833 -51.5833333 15 2187 2226.4615 -39.4615385 16 1491 1590.9412 -99.9411765 17 4041 3353.3333 687.6666667 18 1706 1753.8571 -47.8571429 19 2152 2553.4167 -401.4166667 20 1036 1120.3056 -84.3055556 21 1882 1753.8571 128.1428571 22 1929 1753.8571 175.1428571 23 2242 2226.4615 15.5384615 24 1220 1120.3056 99.6944444 25 1289 1295.2800 -6.2800000 26 2515 2553.4167 -38.4166667 27 2147 1979.2174 167.7826087 28 2352 2553.4167 -201.4166667 29 1638 1590.9412 47.0588235 30 1222 1424.5833 -202.5833333 31 1812 1753.8571 58.1428571 32 1677 1753.8571 -76.8571429 33 1579 1590.9412 -11.9411765 34 1731 1979.2174 -248.2173913 35 807 873.8889 -66.8888889 36 2452 2226.4615 225.5384615 37 829 873.8889 -44.8888889 38 1940 1753.8571 186.1428571 39 2662 2553.4167 108.5833333 40 186 307.1000 -121.1000000 41 1499 1590.9412 -91.9411765 42 865 873.8889 -8.8888889 43 1793 1590.9412 202.0588235 44 2527 2553.4167 -26.4166667 45 2747 3353.3333 -606.3333333 46 1324 1424.5833 -100.5833333 47 2702 2553.4167 148.5833333 48 1383 1424.5833 -41.5833333 49 1179 1120.3056 58.6944444 50 2099 2226.4615 -127.4615385 51 4308 3353.3333 954.6666667 52 918 1120.3056 -202.3055556 53 1831 1590.9412 240.0588235 54 3373 3353.3333 19.6666667 55 1713 1753.8571 -40.8571429 56 1438 1295.2800 142.7200000 57 496 656.0400 -160.0400000 58 2253 1979.2174 273.7826087 59 744 656.0400 87.9600000 60 1161 1295.2800 -134.2800000 61 2352 2226.4615 125.5384615 62 2144 1979.2174 164.7826087 63 4691 3353.3333 1337.6666667 64 1112 1120.3056 -8.3055556 65 2694 2553.4167 140.5833333 66 1973 1979.2174 -6.2173913 67 1769 1979.2174 -210.2173913 68 3148 3353.3333 -205.3333333 69 2474 2553.4167 -79.4166667 70 2084 2226.4615 -142.4615385 71 1954 1979.2174 -25.2173913 72 1226 1424.5833 -198.5833333 73 1389 1120.3056 268.6944444 74 1496 1590.9412 -94.9411765 75 2269 1979.2174 289.7826087 76 1833 1753.8571 79.1428571 77 1268 1295.2800 -27.2800000 78 1943 1753.8571 189.1428571 79 893 1120.3056 -227.3055556 80 1762 1753.8571 8.1428571 81 1403 1753.8571 -350.8571429 82 1425 1424.5833 0.4166667 83 1857 1753.8571 103.1428571 84 1840 1979.2174 -139.2173913 85 1502 1295.2800 206.7200000 86 1441 1295.2800 145.7200000 87 1420 1424.5833 -4.5833333 88 1416 1424.5833 -8.5833333 89 2970 3353.3333 -383.3333333 90 1317 1753.8571 -436.8571429 91 1644 1590.9412 53.0588235 92 870 1295.2800 -425.2800000 93 1654 1753.8571 -99.8571429 94 1054 1120.3056 -66.3055556 95 937 873.8889 63.1111111 96 3004 3353.3333 -349.3333333 97 2008 1979.2174 28.7826087 98 2547 2553.4167 -6.4166667 99 1885 1979.2174 -94.2173913 100 1626 1590.9412 35.0588235 101 1468 1424.5833 43.4166667 102 2445 2226.4615 218.5384615 103 1964 1753.8571 210.1428571 104 1381 1424.5833 -43.5833333 105 1369 1424.5833 -55.5833333 106 1659 1753.8571 -94.8571429 107 2888 2553.4167 334.5833333 108 1290 1120.3056 169.6944444 109 2845 3353.3333 -508.3333333 110 1982 1979.2174 2.7826087 111 1904 1753.8571 150.1428571 112 1391 1295.2800 95.7200000 113 602 656.0400 -54.0400000 114 1743 1753.8571 -10.8571429 115 1559 1979.2174 -420.2173913 116 2014 2226.4615 -212.4615385 117 2143 2226.4615 -83.4615385 118 2146 1979.2174 166.7826087 119 874 873.8889 0.1111111 120 1590 1590.9412 -0.9411765 121 1590 1424.5833 165.4166667 122 1210 1295.2800 -85.2800000 123 2072 1979.2174 92.7826087 124 1281 1295.2800 -14.2800000 125 1401 1424.5833 -23.5833333 126 834 873.8889 -39.8888889 127 1105 1120.3056 -15.3055556 128 1272 1120.3056 151.6944444 129 1944 1979.2174 -35.2173913 130 391 307.1000 83.9000000 131 761 656.0400 104.9600000 132 1605 1753.8571 -148.8571429 133 530 656.0400 -126.0400000 134 1988 1753.8571 234.1428571 135 1386 1295.2800 90.7200000 136 2395 2226.4615 168.5384615 137 387 307.1000 79.9000000 138 1742 1753.8571 -11.8571429 139 620 656.0400 -36.0400000 140 449 656.0400 -207.0400000 141 800 656.0400 143.9600000 142 1684 1590.9412 93.0588235 143 1050 1120.3056 -70.3055556 144 2699 2553.4167 145.5833333 145 1606 1424.5833 181.4166667 146 1502 1295.2800 206.7200000 147 1204 1120.3056 83.6944444 148 1138 1295.2800 -157.2800000 149 568 656.0400 -88.0400000 150 1459 1424.5833 34.4166667 151 2158 2226.4615 -68.4615385 152 1111 1120.3056 -9.3055556 153 1421 1590.9412 -169.9411765 154 2833 3353.3333 -520.3333333 155 1955 1979.2174 -24.2173913 156 2922 3353.3333 -431.3333333 157 1002 1120.3056 -118.3055556 158 1060 1120.3056 -60.3055556 159 956 1120.3056 -164.3055556 160 2186 1979.2174 206.7826087 161 3604 3353.3333 250.6666667 162 1035 1120.3056 -85.3055556 163 1417 1295.2800 121.7200000 164 3261 3353.3333 -92.3333333 165 1587 1753.8571 -166.8571429 166 1424 1424.5833 -0.5833333 167 1701 1753.8571 -52.8571429 168 1249 1295.2800 -46.2800000 169 946 873.8889 72.1111111 170 1926 1979.2174 -53.2173913 171 3352 3353.3333 -1.3333333 172 1641 1590.9412 50.0588235 173 2035 1753.8571 281.1428571 174 2312 1979.2174 332.7826087 175 1369 1424.5833 -55.5833333 176 1577 1753.8571 -176.8571429 177 2201 2226.4615 -25.4615385 178 961 1120.3056 -159.3055556 179 1900 1979.2174 -79.2173913 180 1254 1295.2800 -41.2800000 181 1335 1295.2800 39.7200000 182 1597 1590.9412 6.0588235 183 207 307.1000 -100.1000000 184 1645 1590.9412 54.0588235 185 2429 2553.4167 -124.4166667 186 151 307.1000 -156.1000000 187 474 656.0400 -182.0400000 188 141 307.1000 -166.1000000 189 1639 1424.5833 214.4166667 190 872 873.8889 -1.8888889 191 1318 1295.2800 22.7200000 192 1018 1295.2800 -277.2800000 193 1383 1295.2800 87.7200000 194 1314 1120.3056 193.6944444 195 1335 1424.5833 -89.5833333 196 1403 1295.2800 107.7200000 197 910 1120.3056 -210.3055556 198 616 656.0400 -40.0400000 199 1407 1424.5833 -17.5833333 200 771 656.0400 114.9600000 201 766 656.0400 109.9600000 202 473 307.1000 165.9000000 203 1376 1590.9412 -214.9411765 204 1232 1120.3056 111.6944444 205 1521 1424.5833 96.4166667 206 572 656.0400 -84.0400000 207 1059 1120.3056 -61.3055556 208 1544 1753.8571 -209.8571429 209 1230 1295.2800 -65.2800000 210 1206 1295.2800 -89.2800000 211 1205 1120.3056 84.6944444 212 1255 1120.3056 134.6944444 213 613 656.0400 -43.0400000 214 721 656.0400 64.9600000 215 1109 1120.3056 -11.3055556 216 740 656.0400 83.9600000 217 1126 1120.3056 5.6944444 218 728 656.0400 71.9600000 219 689 656.0400 32.9600000 220 592 656.0400 -64.0400000 221 995 1120.3056 -125.3055556 222 1613 1120.3056 492.6944444 223 2048 1753.8571 294.1428571 224 705 656.0400 48.9600000 225 301 307.1000 -6.1000000 226 1803 1979.2174 -176.2173913 227 799 656.0400 142.9600000 228 861 1120.3056 -259.3055556 229 1186 1120.3056 65.6944444 230 1451 1295.2800 155.7200000 231 628 656.0400 -28.0400000 232 1161 1120.3056 40.6944444 233 1463 1424.5833 38.4166667 234 742 656.0400 85.9600000 235 979 1120.3056 -141.3055556 236 675 656.0400 18.9600000 237 1241 1295.2800 -54.2800000 > 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/4o9pp1323872773.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/5fhgh1323872773.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/65m7h1323872773.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/7sal51323872773.tab") + } > > try(system("convert tmp/2y2p11323872773.ps tmp/2y2p11323872773.png",intern=TRUE)) character(0) > try(system("convert tmp/301mu1323872773.ps tmp/301mu1323872773.png",intern=TRUE)) character(0) > try(system("convert tmp/4o9pp1323872773.ps tmp/4o9pp1323872773.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.330 0.090 4.405