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Type 'q()' to quit R. > x <- array(list(1687 + ,0 + ,-183.9235445 + ,1508 + ,0 + ,-177.0726091 + ,1507 + ,0 + ,-228.6351091 + ,1385 + ,0 + ,-237.4476091 + ,1632 + ,0 + ,-127.7601091 + ,1511 + ,0 + ,-193.0101091 + ,1559 + ,0 + ,-220.6351091 + ,1630 + ,0 + ,-164.5101091 + ,1579 + ,0 + ,-268.3226091 + ,1653 + ,0 + ,-333.6976091 + ,2152 + ,0 + ,-34.26010911 + ,2148 + ,0 + ,-154.8851091 + ,1752 + ,0 + ,-97.74528053 + ,1765 + ,0 + ,101.1056549 + ,1717 + ,0 + ,2.543154874 + ,1558 + ,0 + ,-43.26934513 + ,1575 + ,0 + ,-163.5818451 + ,1520 + ,0 + ,-162.8318451 + ,1805 + ,0 + ,46.54315487 + ,1800 + ,0 + ,26.66815487 + ,1719 + ,0 + ,-107.1443451 + ,2008 + ,0 + ,42.48065487 + ,2242 + ,0 + ,76.91815487 + ,2478 + ,0 + ,196.2931549 + ,2030 + ,0 + ,201.4329835 + ,1655 + ,0 + ,12.28391886 + ,1693 + ,0 + ,-0.278581137 + ,1623 + ,0 + ,42.90891886 + ,1805 + ,0 + ,87.59641886 + ,1746 + ,0 + ,84.34641886 + ,1795 + ,0 + ,57.72141886 + ,1926 + ,0 + ,173.8464189 + ,1619 + ,0 + ,-185.9660811 + ,1992 + ,0 + ,47.65891886 + ,2233 + ,0 + ,89.09641886 + ,2192 + ,0 + ,-68.52858114 + ,2080 + ,0 + ,272.6112475 + ,1768 + ,0 + ,146.4621829 + ,1835 + ,0 + ,162.8996829 + ,1569 + ,0 + ,10.08718285 + ,1976 + ,0 + ,279.7746829 + ,1853 + ,0 + ,212.5246829 + ,1965 + ,0 + ,248.8996829 + ,1689 + ,0 + ,-41.97531715 + ,1778 + ,0 + ,-5.787817149 + ,1976 + ,0 + ,52.83718285 + ,2397 + ,0 + ,274.2746829 + ,2654 + ,0 + ,414.6496829 + ,2097 + ,0 + ,310.7895114 + ,1963 + ,0 + ,362.6404468 + ,1677 + ,0 + ,26.07794684 + ,1941 + ,0 + ,403.2654468 + ,2003 + ,0 + ,327.9529468 + ,1813 + ,0 + ,193.7029468 + ,2012 + ,0 + ,317.0779468 + ,1912 + ,0 + ,202.2029468 + ,2084 + ,0 + ,321.3904468 + ,2080 + ,0 + ,178.0154468 + ,2118 + ,0 + ,16.45294684 + ,2150 + ,0 + ,-68.17205316 + ,1608 + ,0 + ,-157.0322246 + ,1503 + ,0 + ,-76.18128917 + ,1548 + ,0 + ,-81.74378917 + ,1382 + ,0 + ,-134.5562892 + ,1731 + ,0 + ,77.13121083 + ,1798 + ,0 + ,199.8812108 + ,1779 + ,0 + ,105.2562108 + ,1887 + ,0 + ,198.3812108 + ,2004 + ,0 + ,262.5687108 + ,2077 + ,0 + ,196.1937108 + ,2092 + ,0 + ,11.63121083 + ,2051 + ,0 + ,-145.9937892 + ,1577 + ,0 + ,-166.8539606 + ,1356 + ,0 + ,-202.0030252 + ,1652 + ,0 + ,43.43447482 + ,1382 + ,0 + ,-113.3780252 + ,1519 + ,0 + ,-113.6905252 + ,1421 + ,0 + ,-155.9405252 + ,1442 + ,0 + ,-210.5655252 + ,1543 + ,0 + ,-124.4405252 + ,1656 + ,0 + ,-64.25302518 + ,1561 + ,0 + ,-298.6280252 + ,1905 + ,0 + ,-154.1905252 + ,2199 + ,0 + ,23.18447482 + ,1473 + ,0 + ,-249.6756966 + ,1655 + ,0 + ,118.1752388 + ,1407 + ,0 + ,-180.3872612 + ,1395 + ,0 + ,-79.19976119 + ,1530 + ,0 + ,-81.51226119 + ,1309 + ,0 + ,-246.7622612 + ,1526 + ,0 + ,-105.3872612 + ,1327 + ,0 + ,-319.2622612 + ,1627 + ,0 + ,-72.07476119 + ,1748 + ,0 + ,-90.44976119 + ,1958 + ,0 + ,-80.01226119 + ,2274 + ,0 + ,119.3627388 + ,1648 + ,0 + ,-53.49743261 + ,1401 + ,0 + ,-114.6464972 + ,1411 + ,0 + ,-155.2089972 + ,1403 + ,0 + ,-50.02149721 + ,1394 + ,0 + ,-196.3339972 + ,1520 + ,0 + ,-14.58399721 + ,1528 + ,0 + ,-82.20899721 + ,1643 + ,0 + ,17.91600279 + ,1515 + ,0 + ,-162.8964972 + ,1685 + ,0 + ,-132.2714972 + ,2000 + ,0 + ,-16.83399721 + ,2215 + ,0 + ,81.54100279 + ,1956 + ,0 + ,275.6808314 + ,1462 + ,0 + ,-32.46823322 + ,1563 + ,0 + ,17.96926678 + ,1459 + ,0 + ,27.15676678 + ,1446 + ,0 + ,-123.1557332 + ,1622 + ,0 + ,108.5942668 + ,1657 + ,0 + ,67.96926678 + ,1638 + ,0 + ,34.09426678 + ,1643 + ,0 + ,-13.71823322 + ,1683 + ,0 + ,-113.0932332 + ,2050 + ,0 + ,54.34426678 + ,2262 + ,0 + ,149.7192668 + ,1813 + ,0 + ,153.8590954 + ,1445 + ,0 + ,-28.28996923 + ,1762 + ,0 + ,238.1475308 + ,1461 + ,0 + ,50.33503077 + ,1556 + ,0 + ,8.022530771 + ,1431 + ,0 + ,-61.22746923 + ,1427 + ,0 + ,-140.8524692 + ,1554 + ,0 + ,-28.72746923 + ,1645 + ,0 + ,9.460030771 + ,1653 + ,0 + ,-121.9149692 + ,2016 + ,0 + ,41.52253077 + ,2207 + ,0 + ,115.8975308 + ,1665 + ,0 + ,27.03735936 + ,1361 + ,0 + ,-91.11170524 + ,1506 + ,0 + ,3.325794759 + ,1360 + ,0 + ,-29.48670524 + ,1453 + ,0 + ,-73.79920524 + ,1522 + ,0 + ,50.95079476 + ,1460 + ,0 + ,-86.67420524 + ,1552 + ,0 + ,-9.54920524 + ,1548 + ,0 + ,-66.36170524 + ,1827 + ,0 + ,73.26329476 + ,1737 + ,0 + ,-216.2992052 + ,1941 + ,0 + ,-128.9242052 + ,1474 + ,0 + ,-142.7843767 + ,1458 + ,0 + ,27.06655875 + ,1542 + ,0 + ,60.50405875 + ,1404 + ,0 + ,35.69155875 + ,1522 + ,0 + ,16.37905875 + ,1385 + ,0 + ,-64.87094125 + ,1641 + ,0 + ,115.5040587 + ,1510 + ,0 + ,-30.37094125 + ,1681 + ,0 + ,87.81655875 + ,1938 + ,0 + ,205.4415587 + ,1868 + ,0 + ,-64.12094125 + ,1726 + ,0 + ,-322.7459413 + ,1456 + ,0 + ,-139.6061127 + ,1445 + ,0 + ,35.24482274 + ,1456 + ,0 + ,-4.317677263 + ,1365 + ,0 + ,17.86982274 + ,1487 + ,0 + ,2.557322737 + ,1558 + ,0 + ,129.3073227 + ,1488 + ,0 + ,-16.31767726 + ,1684 + ,0 + ,164.8073227 + ,1594 + ,0 + ,21.99482274 + ,1850 + ,0 + ,138.6198227 + ,1998 + ,0 + ,87.05732274 + ,2079 + ,0 + ,51.43232274 + ,1494 + ,0 + ,-80.42784867 + ,1057 + ,1 + ,-105.1918797 + ,1218 + ,1 + ,5.245620328 + ,1168 + ,1 + ,68.43312033 + ,1236 + ,1 + ,-0.879379672 + ,1076 + ,1 + ,-105.1293797 + ,1174 + ,1 + ,-82.75437967 + ,1139 + ,1 + ,-132.6293797 + ,1427 + ,1 + ,102.5581203 + ,1487 + ,1 + ,23.18312033 + ,1483 + ,1 + ,-180.3793797 + ,1513 + ,1 + ,-267.0043797 + ,1357 + ,1 + ,30.13544892 + ,1165 + ,1 + ,23.98638432 + ,1282 + ,1 + ,90.42388432 + ,1110 + ,1 + ,31.61138432 + ,1297 + ,1 + ,81.29888432 + ,1185 + ,1 + ,25.04888432 + ,1222 + ,1 + ,-13.57611568 + ,1284 + ,1 + ,33.54888432 + ,1444 + ,1 + ,140.7363843 + ,1575 + ,1 + ,132.3613843 + ,1737 + ,1 + ,94.79888432 + ,1763 + ,1 + ,4.173884316) + ,dim=c(3 + ,192) + ,dimnames=list(c('Deaths' + ,'seatbeltlaw' + ,'predictionerrors') + ,1:192)) > y <- array(NA,dim=c(3,192),dimnames=list(c('Deaths','seatbeltlaw','predictionerrors'),1:192)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), 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: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Deaths seatbeltlaw predictionerrors M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 1 1687 0 -183.9235445 1 0 0 0 0 0 0 0 0 0 0 2 1508 0 -177.0726091 0 1 0 0 0 0 0 0 0 0 0 3 1507 0 -228.6351091 0 0 1 0 0 0 0 0 0 0 0 4 1385 0 -237.4476091 0 0 0 1 0 0 0 0 0 0 0 5 1632 0 -127.7601091 0 0 0 0 1 0 0 0 0 0 0 6 1511 0 -193.0101091 0 0 0 0 0 1 0 0 0 0 0 7 1559 0 -220.6351091 0 0 0 0 0 0 1 0 0 0 0 8 1630 0 -164.5101091 0 0 0 0 0 0 0 1 0 0 0 9 1579 0 -268.3226091 0 0 0 0 0 0 0 0 1 0 0 10 1653 0 -333.6976091 0 0 0 0 0 0 0 0 0 1 0 11 2152 0 -34.2601091 0 0 0 0 0 0 0 0 0 0 1 12 2148 0 -154.8851091 0 0 0 0 0 0 0 0 0 0 0 13 1752 0 -97.7452805 1 0 0 0 0 0 0 0 0 0 0 14 1765 0 101.1056549 0 1 0 0 0 0 0 0 0 0 0 15 1717 0 2.5431549 0 0 1 0 0 0 0 0 0 0 0 16 1558 0 -43.2693451 0 0 0 1 0 0 0 0 0 0 0 17 1575 0 -163.5818451 0 0 0 0 1 0 0 0 0 0 0 18 1520 0 -162.8318451 0 0 0 0 0 1 0 0 0 0 0 19 1805 0 46.5431549 0 0 0 0 0 0 1 0 0 0 0 20 1800 0 26.6681549 0 0 0 0 0 0 0 1 0 0 0 21 1719 0 -107.1443451 0 0 0 0 0 0 0 0 1 0 0 22 2008 0 42.4806549 0 0 0 0 0 0 0 0 0 1 0 23 2242 0 76.9181549 0 0 0 0 0 0 0 0 0 0 1 24 2478 0 196.2931549 0 0 0 0 0 0 0 0 0 0 0 25 2030 0 201.4329835 1 0 0 0 0 0 0 0 0 0 0 26 1655 0 12.2839189 0 1 0 0 0 0 0 0 0 0 0 27 1693 0 -0.2785811 0 0 1 0 0 0 0 0 0 0 0 28 1623 0 42.9089189 0 0 0 1 0 0 0 0 0 0 0 29 1805 0 87.5964189 0 0 0 0 1 0 0 0 0 0 0 30 1746 0 84.3464189 0 0 0 0 0 1 0 0 0 0 0 31 1795 0 57.7214189 0 0 0 0 0 0 1 0 0 0 0 32 1926 0 173.8464189 0 0 0 0 0 0 0 1 0 0 0 33 1619 0 -185.9660811 0 0 0 0 0 0 0 0 1 0 0 34 1992 0 47.6589189 0 0 0 0 0 0 0 0 0 1 0 35 2233 0 89.0964189 0 0 0 0 0 0 0 0 0 0 1 36 2192 0 -68.5285811 0 0 0 0 0 0 0 0 0 0 0 37 2080 0 272.6112475 1 0 0 0 0 0 0 0 0 0 0 38 1768 0 146.4621829 0 1 0 0 0 0 0 0 0 0 0 39 1835 0 162.8996829 0 0 1 0 0 0 0 0 0 0 0 40 1569 0 10.0871828 0 0 0 1 0 0 0 0 0 0 0 41 1976 0 279.7746829 0 0 0 0 1 0 0 0 0 0 0 42 1853 0 212.5246829 0 0 0 0 0 1 0 0 0 0 0 43 1965 0 248.8996829 0 0 0 0 0 0 1 0 0 0 0 44 1689 0 -41.9753172 0 0 0 0 0 0 0 1 0 0 0 45 1778 0 -5.7878171 0 0 0 0 0 0 0 0 1 0 0 46 1976 0 52.8371828 0 0 0 0 0 0 0 0 0 1 0 47 2397 0 274.2746829 0 0 0 0 0 0 0 0 0 0 1 48 2654 0 414.6496829 0 0 0 0 0 0 0 0 0 0 0 49 2097 0 310.7895114 1 0 0 0 0 0 0 0 0 0 0 50 1963 0 362.6404468 0 1 0 0 0 0 0 0 0 0 0 51 1677 0 26.0779468 0 0 1 0 0 0 0 0 0 0 0 52 1941 0 403.2654468 0 0 0 1 0 0 0 0 0 0 0 53 2003 0 327.9529468 0 0 0 0 1 0 0 0 0 0 0 54 1813 0 193.7029468 0 0 0 0 0 1 0 0 0 0 0 55 2012 0 317.0779468 0 0 0 0 0 0 1 0 0 0 0 56 1912 0 202.2029468 0 0 0 0 0 0 0 1 0 0 0 57 2084 0 321.3904468 0 0 0 0 0 0 0 0 1 0 0 58 2080 0 178.0154468 0 0 0 0 0 0 0 0 0 1 0 59 2118 0 16.4529468 0 0 0 0 0 0 0 0 0 0 1 60 2150 0 -68.1720532 0 0 0 0 0 0 0 0 0 0 0 61 1608 0 -157.0322246 1 0 0 0 0 0 0 0 0 0 0 62 1503 0 -76.1812892 0 1 0 0 0 0 0 0 0 0 0 63 1548 0 -81.7437892 0 0 1 0 0 0 0 0 0 0 0 64 1382 0 -134.5562892 0 0 0 1 0 0 0 0 0 0 0 65 1731 0 77.1312108 0 0 0 0 1 0 0 0 0 0 0 66 1798 0 199.8812108 0 0 0 0 0 1 0 0 0 0 0 67 1779 0 105.2562108 0 0 0 0 0 0 1 0 0 0 0 68 1887 0 198.3812108 0 0 0 0 0 0 0 1 0 0 0 69 2004 0 262.5687108 0 0 0 0 0 0 0 0 1 0 0 70 2077 0 196.1937108 0 0 0 0 0 0 0 0 0 1 0 71 2092 0 11.6312108 0 0 0 0 0 0 0 0 0 0 1 72 2051 0 -145.9937892 0 0 0 0 0 0 0 0 0 0 0 73 1577 0 -166.8539606 1 0 0 0 0 0 0 0 0 0 0 74 1356 0 -202.0030252 0 1 0 0 0 0 0 0 0 0 0 75 1652 0 43.4344748 0 0 1 0 0 0 0 0 0 0 0 76 1382 0 -113.3780252 0 0 0 1 0 0 0 0 0 0 0 77 1519 0 -113.6905252 0 0 0 0 1 0 0 0 0 0 0 78 1421 0 -155.9405252 0 0 0 0 0 1 0 0 0 0 0 79 1442 0 -210.5655252 0 0 0 0 0 0 1 0 0 0 0 80 1543 0 -124.4405252 0 0 0 0 0 0 0 1 0 0 0 81 1656 0 -64.2530252 0 0 0 0 0 0 0 0 1 0 0 82 1561 0 -298.6280252 0 0 0 0 0 0 0 0 0 1 0 83 1905 0 -154.1905252 0 0 0 0 0 0 0 0 0 0 1 84 2199 0 23.1844748 0 0 0 0 0 0 0 0 0 0 0 85 1473 0 -249.6756966 1 0 0 0 0 0 0 0 0 0 0 86 1655 0 118.1752388 0 1 0 0 0 0 0 0 0 0 0 87 1407 0 -180.3872612 0 0 1 0 0 0 0 0 0 0 0 88 1395 0 -79.1997612 0 0 0 1 0 0 0 0 0 0 0 89 1530 0 -81.5122612 0 0 0 0 1 0 0 0 0 0 0 90 1309 0 -246.7622612 0 0 0 0 0 1 0 0 0 0 0 91 1526 0 -105.3872612 0 0 0 0 0 0 1 0 0 0 0 92 1327 0 -319.2622612 0 0 0 0 0 0 0 1 0 0 0 93 1627 0 -72.0747612 0 0 0 0 0 0 0 0 1 0 0 94 1748 0 -90.4497612 0 0 0 0 0 0 0 0 0 1 0 95 1958 0 -80.0122612 0 0 0 0 0 0 0 0 0 0 1 96 2274 0 119.3627388 0 0 0 0 0 0 0 0 0 0 0 97 1648 0 -53.4974326 1 0 0 0 0 0 0 0 0 0 0 98 1401 0 -114.6464972 0 1 0 0 0 0 0 0 0 0 0 99 1411 0 -155.2089972 0 0 1 0 0 0 0 0 0 0 0 100 1403 0 -50.0214972 0 0 0 1 0 0 0 0 0 0 0 101 1394 0 -196.3339972 0 0 0 0 1 0 0 0 0 0 0 102 1520 0 -14.5839972 0 0 0 0 0 1 0 0 0 0 0 103 1528 0 -82.2089972 0 0 0 0 0 0 1 0 0 0 0 104 1643 0 17.9160028 0 0 0 0 0 0 0 1 0 0 0 105 1515 0 -162.8964972 0 0 0 0 0 0 0 0 1 0 0 106 1685 0 -132.2714972 0 0 0 0 0 0 0 0 0 1 0 107 2000 0 -16.8339972 0 0 0 0 0 0 0 0 0 0 1 108 2215 0 81.5410028 0 0 0 0 0 0 0 0 0 0 0 109 1956 0 275.6808314 1 0 0 0 0 0 0 0 0 0 0 110 1462 0 -32.4682332 0 1 0 0 0 0 0 0 0 0 0 111 1563 0 17.9692668 0 0 1 0 0 0 0 0 0 0 0 112 1459 0 27.1567668 0 0 0 1 0 0 0 0 0 0 0 113 1446 0 -123.1557332 0 0 0 0 1 0 0 0 0 0 0 114 1622 0 108.5942668 0 0 0 0 0 1 0 0 0 0 0 115 1657 0 67.9692668 0 0 0 0 0 0 1 0 0 0 0 116 1638 0 34.0942668 0 0 0 0 0 0 0 1 0 0 0 117 1643 0 -13.7182332 0 0 0 0 0 0 0 0 1 0 0 118 1683 0 -113.0932332 0 0 0 0 0 0 0 0 0 1 0 119 2050 0 54.3442668 0 0 0 0 0 0 0 0 0 0 1 120 2262 0 149.7192668 0 0 0 0 0 0 0 0 0 0 0 121 1813 0 153.8590954 1 0 0 0 0 0 0 0 0 0 0 122 1445 0 -28.2899692 0 1 0 0 0 0 0 0 0 0 0 123 1762 0 238.1475308 0 0 1 0 0 0 0 0 0 0 0 124 1461 0 50.3350308 0 0 0 1 0 0 0 0 0 0 0 125 1556 0 8.0225308 0 0 0 0 1 0 0 0 0 0 0 126 1431 0 -61.2274692 0 0 0 0 0 1 0 0 0 0 0 127 1427 0 -140.8524692 0 0 0 0 0 0 1 0 0 0 0 128 1554 0 -28.7274692 0 0 0 0 0 0 0 1 0 0 0 129 1645 0 9.4600308 0 0 0 0 0 0 0 0 1 0 0 130 1653 0 -121.9149692 0 0 0 0 0 0 0 0 0 1 0 131 2016 0 41.5225308 0 0 0 0 0 0 0 0 0 0 1 132 2207 0 115.8975308 0 0 0 0 0 0 0 0 0 0 0 133 1665 0 27.0373594 1 0 0 0 0 0 0 0 0 0 0 134 1361 0 -91.1117052 0 1 0 0 0 0 0 0 0 0 0 135 1506 0 3.3257948 0 0 1 0 0 0 0 0 0 0 0 136 1360 0 -29.4867052 0 0 0 1 0 0 0 0 0 0 0 137 1453 0 -73.7992052 0 0 0 0 1 0 0 0 0 0 0 138 1522 0 50.9507948 0 0 0 0 0 1 0 0 0 0 0 139 1460 0 -86.6742052 0 0 0 0 0 0 1 0 0 0 0 140 1552 0 -9.5492052 0 0 0 0 0 0 0 1 0 0 0 141 1548 0 -66.3617052 0 0 0 0 0 0 0 0 1 0 0 142 1827 0 73.2632948 0 0 0 0 0 0 0 0 0 1 0 143 1737 0 -216.2992052 0 0 0 0 0 0 0 0 0 0 1 144 1941 0 -128.9242052 0 0 0 0 0 0 0 0 0 0 0 145 1474 0 -142.7843767 1 0 0 0 0 0 0 0 0 0 0 146 1458 0 27.0665587 0 1 0 0 0 0 0 0 0 0 0 147 1542 0 60.5040587 0 0 1 0 0 0 0 0 0 0 0 148 1404 0 35.6915587 0 0 0 1 0 0 0 0 0 0 0 149 1522 0 16.3790587 0 0 0 0 1 0 0 0 0 0 0 150 1385 0 -64.8709413 0 0 0 0 0 1 0 0 0 0 0 151 1641 0 115.5040587 0 0 0 0 0 0 1 0 0 0 0 152 1510 0 -30.3709413 0 0 0 0 0 0 0 1 0 0 0 153 1681 0 87.8165587 0 0 0 0 0 0 0 0 1 0 0 154 1938 0 205.4415587 0 0 0 0 0 0 0 0 0 1 0 155 1868 0 -64.1209413 0 0 0 0 0 0 0 0 0 0 1 156 1726 0 -322.7459413 0 0 0 0 0 0 0 0 0 0 0 157 1456 0 -139.6061127 1 0 0 0 0 0 0 0 0 0 0 158 1445 0 35.2448227 0 1 0 0 0 0 0 0 0 0 0 159 1456 0 -4.3176773 0 0 1 0 0 0 0 0 0 0 0 160 1365 0 17.8698227 0 0 0 1 0 0 0 0 0 0 0 161 1487 0 2.5573227 0 0 0 0 1 0 0 0 0 0 0 162 1558 0 129.3073227 0 0 0 0 0 1 0 0 0 0 0 163 1488 0 -16.3176773 0 0 0 0 0 0 1 0 0 0 0 164 1684 0 164.8073227 0 0 0 0 0 0 0 1 0 0 0 165 1594 0 21.9948227 0 0 0 0 0 0 0 0 1 0 0 166 1850 0 138.6198227 0 0 0 0 0 0 0 0 0 1 0 167 1998 0 87.0573227 0 0 0 0 0 0 0 0 0 0 1 168 2079 0 51.4323227 0 0 0 0 0 0 0 0 0 0 0 169 1494 0 -80.4278487 1 0 0 0 0 0 0 0 0 0 0 170 1057 1 -105.1918797 0 1 0 0 0 0 0 0 0 0 0 171 1218 1 5.2456203 0 0 1 0 0 0 0 0 0 0 0 172 1168 1 68.4331203 0 0 0 1 0 0 0 0 0 0 0 173 1236 1 -0.8793797 0 0 0 0 1 0 0 0 0 0 0 174 1076 1 -105.1293797 0 0 0 0 0 1 0 0 0 0 0 175 1174 1 -82.7543797 0 0 0 0 0 0 1 0 0 0 0 176 1139 1 -132.6293797 0 0 0 0 0 0 0 1 0 0 0 177 1427 1 102.5581203 0 0 0 0 0 0 0 0 1 0 0 178 1487 1 23.1831203 0 0 0 0 0 0 0 0 0 1 0 179 1483 1 -180.3793797 0 0 0 0 0 0 0 0 0 0 1 180 1513 1 -267.0043797 0 0 0 0 0 0 0 0 0 0 0 181 1357 1 30.1354489 1 0 0 0 0 0 0 0 0 0 0 182 1165 1 23.9863843 0 1 0 0 0 0 0 0 0 0 0 183 1282 1 90.4238843 0 0 1 0 0 0 0 0 0 0 0 184 1110 1 31.6113843 0 0 0 1 0 0 0 0 0 0 0 185 1297 1 81.2988843 0 0 0 0 1 0 0 0 0 0 0 186 1185 1 25.0488843 0 0 0 0 0 1 0 0 0 0 0 187 1222 1 -13.5761157 0 0 0 0 0 0 1 0 0 0 0 188 1284 1 33.5488843 0 0 0 0 0 0 0 1 0 0 0 189 1444 1 140.7363843 0 0 0 0 0 0 0 0 1 0 0 190 1575 1 132.3613843 0 0 0 0 0 0 0 0 0 1 0 191 1737 1 94.7988843 0 0 0 0 0 0 0 0 0 0 1 192 1763 1 4.1738843 0 0 0 0 0 0 0 0 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) seatbeltlaw predictionerrors M1 2165.2 -395.8 1.0 -442.6 M2 M3 M4 M5 -617.8 -567.3 -680.4 -543.1 M6 M7 M8 M9 -598.9 -523.2 -508.4 -455.6 M10 M11 -316.2 -116.6 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.482e+02 -6.618e+01 -1.995e-09 6.618e+01 1.482e+02 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2165.22639 21.08334 102.698 < 2e-16 *** seatbeltlaw -395.81115 18.65458 -21.218 < 2e-16 *** predictionerrors 1.00000 0.04122 24.262 < 2e-16 *** M1 -442.55070 29.65635 -14.923 < 2e-16 *** M2 -617.81250 29.63342 -20.849 < 2e-16 *** M3 -567.25000 29.63342 -19.142 < 2e-16 *** M4 -680.43750 29.63342 -22.962 < 2e-16 *** M5 -543.12500 29.63342 -18.328 < 2e-16 *** M6 -598.87500 29.63342 -20.209 < 2e-16 *** M7 -523.25000 29.63342 -17.657 < 2e-16 *** M8 -508.37500 29.63342 -17.155 < 2e-16 *** M9 -455.56250 29.63342 -15.373 < 2e-16 *** M10 -316.18750 29.63342 -10.670 < 2e-16 *** M11 -116.62500 29.63342 -3.936 0.000119 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 83.82 on 178 degrees of freedom Multiple R-squared: 0.9219, Adjusted R-squared: 0.9162 F-statistic: 161.7 on 13 and 178 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 5.298645e-03 1.059729e-02 9.947014e-01 [2,] 1.422279e-03 2.844559e-03 9.985777e-01 [3,] 2.116982e-04 4.233964e-04 9.997883e-01 [4,] 3.021849e-05 6.043699e-05 9.999698e-01 [5,] 4.515164e-06 9.030328e-06 9.999955e-01 [6,] 9.143563e-07 1.828713e-06 9.999991e-01 [7,] 1.833315e-07 3.666631e-07 9.999998e-01 [8,] 2.926373e-08 5.852747e-08 1.000000e+00 [9,] 4.010743e-09 8.021486e-09 1.000000e+00 [10,] 9.578554e-09 1.915711e-08 1.000000e+00 [11,] 6.290415e-09 1.258083e-08 1.000000e+00 [12,] 2.010692e-09 4.021385e-09 1.000000e+00 [13,] 4.565757e-10 9.131514e-10 1.000000e+00 [14,] 9.062848e-11 1.812570e-10 1.000000e+00 [15,] 3.749591e-11 7.499181e-11 1.000000e+00 [16,] 7.675004e-12 1.535001e-11 1.000000e+00 [17,] 2.120551e-11 4.241102e-11 1.000000e+00 [18,] 6.212005e-12 1.242401e-11 1.000000e+00 [19,] 5.139617e-12 1.027923e-11 1.000000e+00 [20,] 3.176093e-11 6.352186e-11 1.000000e+00 [21,] 9.664994e-12 1.932999e-11 1.000000e+00 [22,] 6.883251e-12 1.376650e-11 1.000000e+00 [23,] 2.701388e-12 5.402776e-12 1.000000e+00 [24,] 4.340873e-12 8.681746e-12 1.000000e+00 [25,] 1.206631e-12 2.413263e-12 1.000000e+00 [26,] 3.643813e-13 7.287626e-13 1.000000e+00 [27,] 1.128602e-13 2.257205e-13 1.000000e+00 [28,] 1.580571e-12 3.161143e-12 1.000000e+00 [29,] 1.022486e-12 2.044972e-12 1.000000e+00 [30,] 9.113128e-13 1.822626e-12 1.000000e+00 [31,] 3.950867e-13 7.901735e-13 1.000000e+00 [32,] 1.655977e-13 3.311954e-13 1.000000e+00 [33,] 1.051934e-13 2.103868e-13 1.000000e+00 [34,] 4.171732e-14 8.343464e-14 1.000000e+00 [35,] 3.439912e-13 6.879825e-13 1.000000e+00 [36,] 1.549009e-13 3.098018e-13 1.000000e+00 [37,] 8.419360e-14 1.683872e-13 1.000000e+00 [38,] 8.720270e-14 1.744054e-13 1.000000e+00 [39,] 5.963572e-14 1.192714e-13 1.000000e+00 [40,] 6.985579e-14 1.397116e-13 1.000000e+00 [41,] 5.207873e-14 1.041575e-13 1.000000e+00 [42,] 5.886904e-14 1.177381e-13 1.000000e+00 [43,] 3.345223e-12 6.690446e-12 1.000000e+00 [44,] 2.286701e-10 4.573401e-10 1.000000e+00 [45,] 4.520976e-08 9.041952e-08 1.000000e+00 [46,] 7.279701e-07 1.455940e-06 9.999993e-01 [47,] 3.690733e-06 7.381466e-06 9.999963e-01 [48,] 2.339046e-05 4.678092e-05 9.999766e-01 [49,] 6.665606e-05 1.333121e-04 9.999333e-01 [50,] 1.205097e-04 2.410195e-04 9.998795e-01 [51,] 3.176509e-04 6.353017e-04 9.996823e-01 [52,] 6.327087e-04 1.265417e-03 9.993673e-01 [53,] 1.089669e-03 2.179338e-03 9.989103e-01 [54,] 2.228091e-03 4.456183e-03 9.977719e-01 [55,] 6.449904e-03 1.289981e-02 9.935501e-01 [56,] 1.802717e-02 3.605433e-02 9.819728e-01 [57,] 5.086851e-02 1.017370e-01 9.491315e-01 [58,] 1.080329e-01 2.160658e-01 8.919671e-01 [59,] 1.590517e-01 3.181034e-01 8.409483e-01 [60,] 2.343250e-01 4.686501e-01 7.656750e-01 [61,] 3.435654e-01 6.871308e-01 6.564346e-01 [62,] 4.528831e-01 9.057663e-01 5.471169e-01 [63,] 5.680512e-01 8.638976e-01 4.319488e-01 [64,] 6.644567e-01 6.710866e-01 3.355433e-01 [65,] 7.402058e-01 5.195885e-01 2.597942e-01 [66,] 8.012476e-01 3.975049e-01 1.987524e-01 [67,] 8.579183e-01 2.841634e-01 1.420817e-01 [68,] 9.074911e-01 1.850179e-01 9.250895e-02 [69,] 9.395079e-01 1.209842e-01 6.049209e-02 [70,] 9.658366e-01 6.832674e-02 3.416337e-02 [71,] 9.767224e-01 4.655518e-02 2.327759e-02 [72,] 9.852637e-01 2.947252e-02 1.473626e-02 [73,] 9.915291e-01 1.694175e-02 8.470874e-03 [74,] 9.944654e-01 1.106913e-02 5.534565e-03 [75,] 9.968762e-01 6.247574e-03 3.123787e-03 [76,] 9.979309e-01 4.138144e-03 2.069072e-03 [77,] 9.988718e-01 2.256466e-03 1.128233e-03 [78,] 9.993966e-01 1.206886e-03 6.034430e-04 [79,] 9.997077e-01 5.846509e-04 2.923255e-04 [80,] 9.998942e-01 2.116113e-04 1.058057e-04 [81,] 9.999575e-01 8.505495e-05 4.252748e-05 [82,] 9.999784e-01 4.326348e-05 2.163174e-05 [83,] 9.999881e-01 2.387123e-05 1.193562e-05 [84,] 9.999943e-01 1.139803e-05 5.699016e-06 [85,] 9.999971e-01 5.792668e-06 2.896334e-06 [86,] 9.999988e-01 2.498931e-06 1.249465e-06 [87,] 9.999995e-01 1.080637e-06 5.403185e-07 [88,] 9.999998e-01 4.201408e-07 2.100704e-07 [89,] 9.999999e-01 1.765685e-07 8.828425e-08 [90,] 1.000000e+00 6.494483e-08 3.247242e-08 [91,] 1.000000e+00 2.219504e-08 1.109752e-08 [92,] 1.000000e+00 6.947713e-09 3.473856e-09 [93,] 1.000000e+00 1.509631e-09 7.548154e-10 [94,] 1.000000e+00 6.724245e-10 3.362123e-10 [95,] 1.000000e+00 2.983456e-10 1.491728e-10 [96,] 1.000000e+00 1.255427e-10 6.277136e-11 [97,] 1.000000e+00 5.849986e-11 2.924993e-11 [98,] 1.000000e+00 2.111616e-11 1.055808e-11 [99,] 1.000000e+00 7.165318e-12 3.582659e-12 [100,] 1.000000e+00 2.499159e-12 1.249580e-12 [101,] 1.000000e+00 9.579240e-13 4.789620e-13 [102,] 1.000000e+00 3.951362e-13 1.975681e-13 [103,] 1.000000e+00 1.097529e-13 5.487643e-14 [104,] 1.000000e+00 1.780779e-14 8.903894e-15 [105,] 1.000000e+00 2.019271e-15 1.009635e-15 [106,] 1.000000e+00 1.111541e-15 5.557704e-16 [107,] 1.000000e+00 2.144797e-16 1.072399e-16 [108,] 1.000000e+00 9.184599e-17 4.592299e-17 [109,] 1.000000e+00 4.201699e-17 2.100850e-17 [110,] 1.000000e+00 2.658138e-17 1.329069e-17 [111,] 1.000000e+00 2.079781e-17 1.039890e-17 [112,] 1.000000e+00 1.020732e-17 5.103661e-18 [113,] 1.000000e+00 5.014013e-18 2.507006e-18 [114,] 1.000000e+00 4.980736e-18 2.490368e-18 [115,] 1.000000e+00 9.273638e-19 4.636819e-19 [116,] 1.000000e+00 1.704464e-20 8.522321e-21 [117,] 1.000000e+00 8.048142e-22 4.024071e-22 [118,] 1.000000e+00 1.287271e-21 6.436356e-22 [119,] 1.000000e+00 1.382179e-21 6.910894e-22 [120,] 1.000000e+00 1.881113e-21 9.405566e-22 [121,] 1.000000e+00 3.097281e-21 1.548640e-21 [122,] 1.000000e+00 1.172633e-21 5.863167e-22 [123,] 1.000000e+00 1.669792e-21 8.348960e-22 [124,] 1.000000e+00 1.153974e-21 5.769869e-22 [125,] 1.000000e+00 2.383593e-21 1.191796e-21 [126,] 1.000000e+00 1.903238e-21 9.516190e-22 [127,] 1.000000e+00 4.387684e-21 2.193842e-21 [128,] 1.000000e+00 1.433986e-21 7.169928e-22 [129,] 1.000000e+00 1.569855e-21 7.849275e-22 [130,] 1.000000e+00 3.514624e-21 1.757312e-21 [131,] 1.000000e+00 8.191253e-21 4.095627e-21 [132,] 1.000000e+00 2.286260e-20 1.143130e-20 [133,] 1.000000e+00 6.538230e-20 3.269115e-20 [134,] 1.000000e+00 2.604337e-19 1.302168e-19 [135,] 1.000000e+00 1.618393e-19 8.091963e-20 [136,] 1.000000e+00 5.405147e-19 2.702573e-19 [137,] 1.000000e+00 1.142705e-18 5.713525e-19 [138,] 1.000000e+00 5.254061e-19 2.627031e-19 [139,] 1.000000e+00 1.506236e-18 7.531181e-19 [140,] 1.000000e+00 1.172646e-17 5.863232e-18 [141,] 1.000000e+00 7.301310e-17 3.650655e-17 [142,] 1.000000e+00 5.476553e-16 2.738276e-16 [143,] 1.000000e+00 3.732924e-15 1.866462e-15 [144,] 1.000000e+00 2.613194e-14 1.306597e-14 [145,] 1.000000e+00 1.749457e-13 8.747285e-14 [146,] 1.000000e+00 1.089273e-12 5.446364e-13 [147,] 1.000000e+00 8.345512e-12 4.172756e-12 [148,] 1.000000e+00 3.657317e-11 1.828658e-11 [149,] 1.000000e+00 1.947312e-10 9.736559e-11 [150,] 1.000000e+00 1.522161e-09 7.610805e-10 [151,] 1.000000e+00 9.527728e-09 4.763864e-09 [152,] 1.000000e+00 8.748942e-09 4.374471e-09 [153,] 1.000000e+00 7.907326e-08 3.953663e-08 [154,] 9.999997e-01 6.730215e-07 3.365108e-07 [155,] 9.999978e-01 4.468867e-06 2.234434e-06 [156,] 9.999959e-01 8.265055e-06 4.132527e-06 [157,] 9.999719e-01 5.620773e-05 2.810386e-05 [158,] 9.997446e-01 5.107218e-04 2.553609e-04 [159,] 9.984700e-01 3.059959e-03 1.529979e-03 > postscript(file="/var/www/html/rcomp/tmp/1k0zw1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/219t01227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3p1ns1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4h79u1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5geb21227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 192 Frequency = 1 1 2 3 4 5 1.482478e+02 1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02 6 7 8 9 10 1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02 11 12 13 14 15 1.376587e+02 1.376587e+02 1.270696e+02 1.164805e+02 1.164805e+02 16 17 18 19 20 1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02 21 22 23 24 25 1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02 1.058913e+02 26 27 28 29 30 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 31 32 33 34 35 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 36 37 38 39 40 9.530219e+01 8.471306e+01 7.412392e+01 7.412392e+01 7.412392e+01 41 42 43 44 45 7.412392e+01 7.412392e+01 7.412392e+01 7.412392e+01 7.412392e+01 46 47 48 49 50 7.412392e+01 7.412392e+01 7.412392e+01 6.353479e+01 5.294566e+01 51 52 53 54 55 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 56 57 58 59 60 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 61 62 63 64 65 4.235653e+01 3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01 66 67 68 69 70 3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01 71 72 73 74 75 3.176740e+01 3.176740e+01 2.117826e+01 1.058913e+01 1.058913e+01 76 77 78 79 80 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 81 82 83 84 85 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 8.532245e-09 86 87 88 89 90 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 91 92 93 94 95 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 96 97 98 99 100 -1.058913e+01 -2.117826e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01 101 102 103 104 105 -3.176740e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01 106 107 108 109 110 -3.176740e+01 -3.176740e+01 -3.176740e+01 -4.235653e+01 -5.294566e+01 111 112 113 114 115 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 116 117 118 119 120 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 121 122 123 124 125 -6.353479e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 126 127 128 129 130 -7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 131 132 133 134 135 -7.412392e+01 -7.412392e+01 -8.471306e+01 -9.530219e+01 -9.530219e+01 136 137 138 139 140 -9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01 141 142 143 144 145 -9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01 -1.058913e+02 146 147 148 149 150 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 151 152 153 154 155 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 156 157 158 159 160 -1.164805e+02 -1.270696e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02 161 162 163 164 165 -1.376587e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02 166 167 168 169 170 -1.376587e+02 -1.376587e+02 -1.376587e+02 -1.482478e+02 1.058913e+01 171 172 173 174 175 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 176 177 178 179 180 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 181 182 183 184 185 -1.252250e-08 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 186 187 188 189 190 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 191 192 -1.058913e+01 -1.058913e+01 > postscript(file="/var/www/html/rcomp/tmp/6odnr1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 192 Frequency = 1 lag(myerror, k = 1) myerror 0 1.482478e+02 NA 1 1.376587e+02 1.482478e+02 2 1.376587e+02 1.376587e+02 3 1.376587e+02 1.376587e+02 4 1.376587e+02 1.376587e+02 5 1.376587e+02 1.376587e+02 6 1.376587e+02 1.376587e+02 7 1.376587e+02 1.376587e+02 8 1.376587e+02 1.376587e+02 9 1.376587e+02 1.376587e+02 10 1.376587e+02 1.376587e+02 11 1.376587e+02 1.376587e+02 12 1.270696e+02 1.376587e+02 13 1.164805e+02 1.270696e+02 14 1.164805e+02 1.164805e+02 15 1.164805e+02 1.164805e+02 16 1.164805e+02 1.164805e+02 17 1.164805e+02 1.164805e+02 18 1.164805e+02 1.164805e+02 19 1.164805e+02 1.164805e+02 20 1.164805e+02 1.164805e+02 21 1.164805e+02 1.164805e+02 22 1.164805e+02 1.164805e+02 23 1.164805e+02 1.164805e+02 24 1.058913e+02 1.164805e+02 25 9.530219e+01 1.058913e+02 26 9.530219e+01 9.530219e+01 27 9.530219e+01 9.530219e+01 28 9.530219e+01 9.530219e+01 29 9.530219e+01 9.530219e+01 30 9.530219e+01 9.530219e+01 31 9.530219e+01 9.530219e+01 32 9.530219e+01 9.530219e+01 33 9.530219e+01 9.530219e+01 34 9.530219e+01 9.530219e+01 35 9.530219e+01 9.530219e+01 36 8.471306e+01 9.530219e+01 37 7.412392e+01 8.471306e+01 38 7.412392e+01 7.412392e+01 39 7.412392e+01 7.412392e+01 40 7.412392e+01 7.412392e+01 41 7.412392e+01 7.412392e+01 42 7.412392e+01 7.412392e+01 43 7.412392e+01 7.412392e+01 44 7.412392e+01 7.412392e+01 45 7.412392e+01 7.412392e+01 46 7.412392e+01 7.412392e+01 47 7.412392e+01 7.412392e+01 48 6.353479e+01 7.412392e+01 49 5.294566e+01 6.353479e+01 50 5.294566e+01 5.294566e+01 51 5.294566e+01 5.294566e+01 52 5.294566e+01 5.294566e+01 53 5.294566e+01 5.294566e+01 54 5.294566e+01 5.294566e+01 55 5.294566e+01 5.294566e+01 56 5.294566e+01 5.294566e+01 57 5.294566e+01 5.294566e+01 58 5.294566e+01 5.294566e+01 59 5.294566e+01 5.294566e+01 60 4.235653e+01 5.294566e+01 61 3.176740e+01 4.235653e+01 62 3.176740e+01 3.176740e+01 63 3.176740e+01 3.176740e+01 64 3.176740e+01 3.176740e+01 65 3.176740e+01 3.176740e+01 66 3.176740e+01 3.176740e+01 67 3.176740e+01 3.176740e+01 68 3.176740e+01 3.176740e+01 69 3.176740e+01 3.176740e+01 70 3.176740e+01 3.176740e+01 71 3.176740e+01 3.176740e+01 72 2.117826e+01 3.176740e+01 73 1.058913e+01 2.117826e+01 74 1.058913e+01 1.058913e+01 75 1.058913e+01 1.058913e+01 76 1.058913e+01 1.058913e+01 77 1.058913e+01 1.058913e+01 78 1.058913e+01 1.058913e+01 79 1.058913e+01 1.058913e+01 80 1.058913e+01 1.058913e+01 81 1.058913e+01 1.058913e+01 82 1.058913e+01 1.058913e+01 83 1.058913e+01 1.058913e+01 84 8.532245e-09 1.058913e+01 85 -1.058913e+01 8.532245e-09 86 -1.058913e+01 -1.058913e+01 87 -1.058913e+01 -1.058913e+01 88 -1.058913e+01 -1.058913e+01 89 -1.058913e+01 -1.058913e+01 90 -1.058913e+01 -1.058913e+01 91 -1.058913e+01 -1.058913e+01 92 -1.058913e+01 -1.058913e+01 93 -1.058913e+01 -1.058913e+01 94 -1.058913e+01 -1.058913e+01 95 -1.058913e+01 -1.058913e+01 96 -2.117826e+01 -1.058913e+01 97 -3.176740e+01 -2.117826e+01 98 -3.176740e+01 -3.176740e+01 99 -3.176740e+01 -3.176740e+01 100 -3.176740e+01 -3.176740e+01 101 -3.176740e+01 -3.176740e+01 102 -3.176740e+01 -3.176740e+01 103 -3.176740e+01 -3.176740e+01 104 -3.176740e+01 -3.176740e+01 105 -3.176740e+01 -3.176740e+01 106 -3.176740e+01 -3.176740e+01 107 -3.176740e+01 -3.176740e+01 108 -4.235653e+01 -3.176740e+01 109 -5.294566e+01 -4.235653e+01 110 -5.294566e+01 -5.294566e+01 111 -5.294566e+01 -5.294566e+01 112 -5.294566e+01 -5.294566e+01 113 -5.294566e+01 -5.294566e+01 114 -5.294566e+01 -5.294566e+01 115 -5.294566e+01 -5.294566e+01 116 -5.294566e+01 -5.294566e+01 117 -5.294566e+01 -5.294566e+01 118 -5.294566e+01 -5.294566e+01 119 -5.294566e+01 -5.294566e+01 120 -6.353479e+01 -5.294566e+01 121 -7.412392e+01 -6.353479e+01 122 -7.412392e+01 -7.412392e+01 123 -7.412392e+01 -7.412392e+01 124 -7.412392e+01 -7.412392e+01 125 -7.412392e+01 -7.412392e+01 126 -7.412392e+01 -7.412392e+01 127 -7.412392e+01 -7.412392e+01 128 -7.412392e+01 -7.412392e+01 129 -7.412392e+01 -7.412392e+01 130 -7.412392e+01 -7.412392e+01 131 -7.412392e+01 -7.412392e+01 132 -8.471306e+01 -7.412392e+01 133 -9.530219e+01 -8.471306e+01 134 -9.530219e+01 -9.530219e+01 135 -9.530219e+01 -9.530219e+01 136 -9.530219e+01 -9.530219e+01 137 -9.530219e+01 -9.530219e+01 138 -9.530219e+01 -9.530219e+01 139 -9.530219e+01 -9.530219e+01 140 -9.530219e+01 -9.530219e+01 141 -9.530219e+01 -9.530219e+01 142 -9.530219e+01 -9.530219e+01 143 -9.530219e+01 -9.530219e+01 144 -1.058913e+02 -9.530219e+01 145 -1.164805e+02 -1.058913e+02 146 -1.164805e+02 -1.164805e+02 147 -1.164805e+02 -1.164805e+02 148 -1.164805e+02 -1.164805e+02 149 -1.164805e+02 -1.164805e+02 150 -1.164805e+02 -1.164805e+02 151 -1.164805e+02 -1.164805e+02 152 -1.164805e+02 -1.164805e+02 153 -1.164805e+02 -1.164805e+02 154 -1.164805e+02 -1.164805e+02 155 -1.164805e+02 -1.164805e+02 156 -1.270696e+02 -1.164805e+02 157 -1.376587e+02 -1.270696e+02 158 -1.376587e+02 -1.376587e+02 159 -1.376587e+02 -1.376587e+02 160 -1.376587e+02 -1.376587e+02 161 -1.376587e+02 -1.376587e+02 162 -1.376587e+02 -1.376587e+02 163 -1.376587e+02 -1.376587e+02 164 -1.376587e+02 -1.376587e+02 165 -1.376587e+02 -1.376587e+02 166 -1.376587e+02 -1.376587e+02 167 -1.376587e+02 -1.376587e+02 168 -1.482478e+02 -1.376587e+02 169 1.058913e+01 -1.482478e+02 170 1.058913e+01 1.058913e+01 171 1.058913e+01 1.058913e+01 172 1.058913e+01 1.058913e+01 173 1.058913e+01 1.058913e+01 174 1.058913e+01 1.058913e+01 175 1.058913e+01 1.058913e+01 176 1.058913e+01 1.058913e+01 177 1.058913e+01 1.058913e+01 178 1.058913e+01 1.058913e+01 179 1.058913e+01 1.058913e+01 180 -1.252250e-08 1.058913e+01 181 -1.058913e+01 -1.252250e-08 182 -1.058913e+01 -1.058913e+01 183 -1.058913e+01 -1.058913e+01 184 -1.058913e+01 -1.058913e+01 185 -1.058913e+01 -1.058913e+01 186 -1.058913e+01 -1.058913e+01 187 -1.058913e+01 -1.058913e+01 188 -1.058913e+01 -1.058913e+01 189 -1.058913e+01 -1.058913e+01 190 -1.058913e+01 -1.058913e+01 191 -1.058913e+01 -1.058913e+01 192 NA -1.058913e+01 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.376587e+02 1.482478e+02 [2,] 1.376587e+02 1.376587e+02 [3,] 1.376587e+02 1.376587e+02 [4,] 1.376587e+02 1.376587e+02 [5,] 1.376587e+02 1.376587e+02 [6,] 1.376587e+02 1.376587e+02 [7,] 1.376587e+02 1.376587e+02 [8,] 1.376587e+02 1.376587e+02 [9,] 1.376587e+02 1.376587e+02 [10,] 1.376587e+02 1.376587e+02 [11,] 1.376587e+02 1.376587e+02 [12,] 1.270696e+02 1.376587e+02 [13,] 1.164805e+02 1.270696e+02 [14,] 1.164805e+02 1.164805e+02 [15,] 1.164805e+02 1.164805e+02 [16,] 1.164805e+02 1.164805e+02 [17,] 1.164805e+02 1.164805e+02 [18,] 1.164805e+02 1.164805e+02 [19,] 1.164805e+02 1.164805e+02 [20,] 1.164805e+02 1.164805e+02 [21,] 1.164805e+02 1.164805e+02 [22,] 1.164805e+02 1.164805e+02 [23,] 1.164805e+02 1.164805e+02 [24,] 1.058913e+02 1.164805e+02 [25,] 9.530219e+01 1.058913e+02 [26,] 9.530219e+01 9.530219e+01 [27,] 9.530219e+01 9.530219e+01 [28,] 9.530219e+01 9.530219e+01 [29,] 9.530219e+01 9.530219e+01 [30,] 9.530219e+01 9.530219e+01 [31,] 9.530219e+01 9.530219e+01 [32,] 9.530219e+01 9.530219e+01 [33,] 9.530219e+01 9.530219e+01 [34,] 9.530219e+01 9.530219e+01 [35,] 9.530219e+01 9.530219e+01 [36,] 8.471306e+01 9.530219e+01 [37,] 7.412392e+01 8.471306e+01 [38,] 7.412392e+01 7.412392e+01 [39,] 7.412392e+01 7.412392e+01 [40,] 7.412392e+01 7.412392e+01 [41,] 7.412392e+01 7.412392e+01 [42,] 7.412392e+01 7.412392e+01 [43,] 7.412392e+01 7.412392e+01 [44,] 7.412392e+01 7.412392e+01 [45,] 7.412392e+01 7.412392e+01 [46,] 7.412392e+01 7.412392e+01 [47,] 7.412392e+01 7.412392e+01 [48,] 6.353479e+01 7.412392e+01 [49,] 5.294566e+01 6.353479e+01 [50,] 5.294566e+01 5.294566e+01 [51,] 5.294566e+01 5.294566e+01 [52,] 5.294566e+01 5.294566e+01 [53,] 5.294566e+01 5.294566e+01 [54,] 5.294566e+01 5.294566e+01 [55,] 5.294566e+01 5.294566e+01 [56,] 5.294566e+01 5.294566e+01 [57,] 5.294566e+01 5.294566e+01 [58,] 5.294566e+01 5.294566e+01 [59,] 5.294566e+01 5.294566e+01 [60,] 4.235653e+01 5.294566e+01 [61,] 3.176740e+01 4.235653e+01 [62,] 3.176740e+01 3.176740e+01 [63,] 3.176740e+01 3.176740e+01 [64,] 3.176740e+01 3.176740e+01 [65,] 3.176740e+01 3.176740e+01 [66,] 3.176740e+01 3.176740e+01 [67,] 3.176740e+01 3.176740e+01 [68,] 3.176740e+01 3.176740e+01 [69,] 3.176740e+01 3.176740e+01 [70,] 3.176740e+01 3.176740e+01 [71,] 3.176740e+01 3.176740e+01 [72,] 2.117826e+01 3.176740e+01 [73,] 1.058913e+01 2.117826e+01 [74,] 1.058913e+01 1.058913e+01 [75,] 1.058913e+01 1.058913e+01 [76,] 1.058913e+01 1.058913e+01 [77,] 1.058913e+01 1.058913e+01 [78,] 1.058913e+01 1.058913e+01 [79,] 1.058913e+01 1.058913e+01 [80,] 1.058913e+01 1.058913e+01 [81,] 1.058913e+01 1.058913e+01 [82,] 1.058913e+01 1.058913e+01 [83,] 1.058913e+01 1.058913e+01 [84,] 8.532245e-09 1.058913e+01 [85,] -1.058913e+01 8.532245e-09 [86,] -1.058913e+01 -1.058913e+01 [87,] -1.058913e+01 -1.058913e+01 [88,] -1.058913e+01 -1.058913e+01 [89,] -1.058913e+01 -1.058913e+01 [90,] -1.058913e+01 -1.058913e+01 [91,] -1.058913e+01 -1.058913e+01 [92,] -1.058913e+01 -1.058913e+01 [93,] -1.058913e+01 -1.058913e+01 [94,] -1.058913e+01 -1.058913e+01 [95,] -1.058913e+01 -1.058913e+01 [96,] -2.117826e+01 -1.058913e+01 [97,] -3.176740e+01 -2.117826e+01 [98,] -3.176740e+01 -3.176740e+01 [99,] -3.176740e+01 -3.176740e+01 [100,] -3.176740e+01 -3.176740e+01 [101,] -3.176740e+01 -3.176740e+01 [102,] -3.176740e+01 -3.176740e+01 [103,] -3.176740e+01 -3.176740e+01 [104,] -3.176740e+01 -3.176740e+01 [105,] -3.176740e+01 -3.176740e+01 [106,] -3.176740e+01 -3.176740e+01 [107,] -3.176740e+01 -3.176740e+01 [108,] -4.235653e+01 -3.176740e+01 [109,] -5.294566e+01 -4.235653e+01 [110,] -5.294566e+01 -5.294566e+01 [111,] -5.294566e+01 -5.294566e+01 [112,] -5.294566e+01 -5.294566e+01 [113,] -5.294566e+01 -5.294566e+01 [114,] -5.294566e+01 -5.294566e+01 [115,] -5.294566e+01 -5.294566e+01 [116,] -5.294566e+01 -5.294566e+01 [117,] -5.294566e+01 -5.294566e+01 [118,] -5.294566e+01 -5.294566e+01 [119,] -5.294566e+01 -5.294566e+01 [120,] -6.353479e+01 -5.294566e+01 [121,] -7.412392e+01 -6.353479e+01 [122,] -7.412392e+01 -7.412392e+01 [123,] -7.412392e+01 -7.412392e+01 [124,] -7.412392e+01 -7.412392e+01 [125,] -7.412392e+01 -7.412392e+01 [126,] -7.412392e+01 -7.412392e+01 [127,] -7.412392e+01 -7.412392e+01 [128,] -7.412392e+01 -7.412392e+01 [129,] -7.412392e+01 -7.412392e+01 [130,] -7.412392e+01 -7.412392e+01 [131,] -7.412392e+01 -7.412392e+01 [132,] -8.471306e+01 -7.412392e+01 [133,] -9.530219e+01 -8.471306e+01 [134,] -9.530219e+01 -9.530219e+01 [135,] -9.530219e+01 -9.530219e+01 [136,] -9.530219e+01 -9.530219e+01 [137,] -9.530219e+01 -9.530219e+01 [138,] -9.530219e+01 -9.530219e+01 [139,] -9.530219e+01 -9.530219e+01 [140,] -9.530219e+01 -9.530219e+01 [141,] -9.530219e+01 -9.530219e+01 [142,] -9.530219e+01 -9.530219e+01 [143,] -9.530219e+01 -9.530219e+01 [144,] -1.058913e+02 -9.530219e+01 [145,] -1.164805e+02 -1.058913e+02 [146,] -1.164805e+02 -1.164805e+02 [147,] -1.164805e+02 -1.164805e+02 [148,] -1.164805e+02 -1.164805e+02 [149,] -1.164805e+02 -1.164805e+02 [150,] -1.164805e+02 -1.164805e+02 [151,] -1.164805e+02 -1.164805e+02 [152,] -1.164805e+02 -1.164805e+02 [153,] -1.164805e+02 -1.164805e+02 [154,] -1.164805e+02 -1.164805e+02 [155,] -1.164805e+02 -1.164805e+02 [156,] -1.270696e+02 -1.164805e+02 [157,] -1.376587e+02 -1.270696e+02 [158,] -1.376587e+02 -1.376587e+02 [159,] -1.376587e+02 -1.376587e+02 [160,] -1.376587e+02 -1.376587e+02 [161,] -1.376587e+02 -1.376587e+02 [162,] -1.376587e+02 -1.376587e+02 [163,] -1.376587e+02 -1.376587e+02 [164,] -1.376587e+02 -1.376587e+02 [165,] -1.376587e+02 -1.376587e+02 [166,] -1.376587e+02 -1.376587e+02 [167,] -1.376587e+02 -1.376587e+02 [168,] -1.482478e+02 -1.376587e+02 [169,] 1.058913e+01 -1.482478e+02 [170,] 1.058913e+01 1.058913e+01 [171,] 1.058913e+01 1.058913e+01 [172,] 1.058913e+01 1.058913e+01 [173,] 1.058913e+01 1.058913e+01 [174,] 1.058913e+01 1.058913e+01 [175,] 1.058913e+01 1.058913e+01 [176,] 1.058913e+01 1.058913e+01 [177,] 1.058913e+01 1.058913e+01 [178,] 1.058913e+01 1.058913e+01 [179,] 1.058913e+01 1.058913e+01 [180,] -1.252250e-08 1.058913e+01 [181,] -1.058913e+01 -1.252250e-08 [182,] -1.058913e+01 -1.058913e+01 [183,] -1.058913e+01 -1.058913e+01 [184,] -1.058913e+01 -1.058913e+01 [185,] -1.058913e+01 -1.058913e+01 [186,] -1.058913e+01 -1.058913e+01 [187,] -1.058913e+01 -1.058913e+01 [188,] -1.058913e+01 -1.058913e+01 [189,] -1.058913e+01 -1.058913e+01 [190,] -1.058913e+01 -1.058913e+01 [191,] -1.058913e+01 -1.058913e+01 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.376587e+02 1.482478e+02 2 1.376587e+02 1.376587e+02 3 1.376587e+02 1.376587e+02 4 1.376587e+02 1.376587e+02 5 1.376587e+02 1.376587e+02 6 1.376587e+02 1.376587e+02 7 1.376587e+02 1.376587e+02 8 1.376587e+02 1.376587e+02 9 1.376587e+02 1.376587e+02 10 1.376587e+02 1.376587e+02 11 1.376587e+02 1.376587e+02 12 1.270696e+02 1.376587e+02 13 1.164805e+02 1.270696e+02 14 1.164805e+02 1.164805e+02 15 1.164805e+02 1.164805e+02 16 1.164805e+02 1.164805e+02 17 1.164805e+02 1.164805e+02 18 1.164805e+02 1.164805e+02 19 1.164805e+02 1.164805e+02 20 1.164805e+02 1.164805e+02 21 1.164805e+02 1.164805e+02 22 1.164805e+02 1.164805e+02 23 1.164805e+02 1.164805e+02 24 1.058913e+02 1.164805e+02 25 9.530219e+01 1.058913e+02 26 9.530219e+01 9.530219e+01 27 9.530219e+01 9.530219e+01 28 9.530219e+01 9.530219e+01 29 9.530219e+01 9.530219e+01 30 9.530219e+01 9.530219e+01 31 9.530219e+01 9.530219e+01 32 9.530219e+01 9.530219e+01 33 9.530219e+01 9.530219e+01 34 9.530219e+01 9.530219e+01 35 9.530219e+01 9.530219e+01 36 8.471306e+01 9.530219e+01 37 7.412392e+01 8.471306e+01 38 7.412392e+01 7.412392e+01 39 7.412392e+01 7.412392e+01 40 7.412392e+01 7.412392e+01 41 7.412392e+01 7.412392e+01 42 7.412392e+01 7.412392e+01 43 7.412392e+01 7.412392e+01 44 7.412392e+01 7.412392e+01 45 7.412392e+01 7.412392e+01 46 7.412392e+01 7.412392e+01 47 7.412392e+01 7.412392e+01 48 6.353479e+01 7.412392e+01 49 5.294566e+01 6.353479e+01 50 5.294566e+01 5.294566e+01 51 5.294566e+01 5.294566e+01 52 5.294566e+01 5.294566e+01 53 5.294566e+01 5.294566e+01 54 5.294566e+01 5.294566e+01 55 5.294566e+01 5.294566e+01 56 5.294566e+01 5.294566e+01 57 5.294566e+01 5.294566e+01 58 5.294566e+01 5.294566e+01 59 5.294566e+01 5.294566e+01 60 4.235653e+01 5.294566e+01 61 3.176740e+01 4.235653e+01 62 3.176740e+01 3.176740e+01 63 3.176740e+01 3.176740e+01 64 3.176740e+01 3.176740e+01 65 3.176740e+01 3.176740e+01 66 3.176740e+01 3.176740e+01 67 3.176740e+01 3.176740e+01 68 3.176740e+01 3.176740e+01 69 3.176740e+01 3.176740e+01 70 3.176740e+01 3.176740e+01 71 3.176740e+01 3.176740e+01 72 2.117826e+01 3.176740e+01 73 1.058913e+01 2.117826e+01 74 1.058913e+01 1.058913e+01 75 1.058913e+01 1.058913e+01 76 1.058913e+01 1.058913e+01 77 1.058913e+01 1.058913e+01 78 1.058913e+01 1.058913e+01 79 1.058913e+01 1.058913e+01 80 1.058913e+01 1.058913e+01 81 1.058913e+01 1.058913e+01 82 1.058913e+01 1.058913e+01 83 1.058913e+01 1.058913e+01 84 8.532245e-09 1.058913e+01 85 -1.058913e+01 8.532245e-09 86 -1.058913e+01 -1.058913e+01 87 -1.058913e+01 -1.058913e+01 88 -1.058913e+01 -1.058913e+01 89 -1.058913e+01 -1.058913e+01 90 -1.058913e+01 -1.058913e+01 91 -1.058913e+01 -1.058913e+01 92 -1.058913e+01 -1.058913e+01 93 -1.058913e+01 -1.058913e+01 94 -1.058913e+01 -1.058913e+01 95 -1.058913e+01 -1.058913e+01 96 -2.117826e+01 -1.058913e+01 97 -3.176740e+01 -2.117826e+01 98 -3.176740e+01 -3.176740e+01 99 -3.176740e+01 -3.176740e+01 100 -3.176740e+01 -3.176740e+01 101 -3.176740e+01 -3.176740e+01 102 -3.176740e+01 -3.176740e+01 103 -3.176740e+01 -3.176740e+01 104 -3.176740e+01 -3.176740e+01 105 -3.176740e+01 -3.176740e+01 106 -3.176740e+01 -3.176740e+01 107 -3.176740e+01 -3.176740e+01 108 -4.235653e+01 -3.176740e+01 109 -5.294566e+01 -4.235653e+01 110 -5.294566e+01 -5.294566e+01 111 -5.294566e+01 -5.294566e+01 112 -5.294566e+01 -5.294566e+01 113 -5.294566e+01 -5.294566e+01 114 -5.294566e+01 -5.294566e+01 115 -5.294566e+01 -5.294566e+01 116 -5.294566e+01 -5.294566e+01 117 -5.294566e+01 -5.294566e+01 118 -5.294566e+01 -5.294566e+01 119 -5.294566e+01 -5.294566e+01 120 -6.353479e+01 -5.294566e+01 121 -7.412392e+01 -6.353479e+01 122 -7.412392e+01 -7.412392e+01 123 -7.412392e+01 -7.412392e+01 124 -7.412392e+01 -7.412392e+01 125 -7.412392e+01 -7.412392e+01 126 -7.412392e+01 -7.412392e+01 127 -7.412392e+01 -7.412392e+01 128 -7.412392e+01 -7.412392e+01 129 -7.412392e+01 -7.412392e+01 130 -7.412392e+01 -7.412392e+01 131 -7.412392e+01 -7.412392e+01 132 -8.471306e+01 -7.412392e+01 133 -9.530219e+01 -8.471306e+01 134 -9.530219e+01 -9.530219e+01 135 -9.530219e+01 -9.530219e+01 136 -9.530219e+01 -9.530219e+01 137 -9.530219e+01 -9.530219e+01 138 -9.530219e+01 -9.530219e+01 139 -9.530219e+01 -9.530219e+01 140 -9.530219e+01 -9.530219e+01 141 -9.530219e+01 -9.530219e+01 142 -9.530219e+01 -9.530219e+01 143 -9.530219e+01 -9.530219e+01 144 -1.058913e+02 -9.530219e+01 145 -1.164805e+02 -1.058913e+02 146 -1.164805e+02 -1.164805e+02 147 -1.164805e+02 -1.164805e+02 148 -1.164805e+02 -1.164805e+02 149 -1.164805e+02 -1.164805e+02 150 -1.164805e+02 -1.164805e+02 151 -1.164805e+02 -1.164805e+02 152 -1.164805e+02 -1.164805e+02 153 -1.164805e+02 -1.164805e+02 154 -1.164805e+02 -1.164805e+02 155 -1.164805e+02 -1.164805e+02 156 -1.270696e+02 -1.164805e+02 157 -1.376587e+02 -1.270696e+02 158 -1.376587e+02 -1.376587e+02 159 -1.376587e+02 -1.376587e+02 160 -1.376587e+02 -1.376587e+02 161 -1.376587e+02 -1.376587e+02 162 -1.376587e+02 -1.376587e+02 163 -1.376587e+02 -1.376587e+02 164 -1.376587e+02 -1.376587e+02 165 -1.376587e+02 -1.376587e+02 166 -1.376587e+02 -1.376587e+02 167 -1.376587e+02 -1.376587e+02 168 -1.482478e+02 -1.376587e+02 169 1.058913e+01 -1.482478e+02 170 1.058913e+01 1.058913e+01 171 1.058913e+01 1.058913e+01 172 1.058913e+01 1.058913e+01 173 1.058913e+01 1.058913e+01 174 1.058913e+01 1.058913e+01 175 1.058913e+01 1.058913e+01 176 1.058913e+01 1.058913e+01 177 1.058913e+01 1.058913e+01 178 1.058913e+01 1.058913e+01 179 1.058913e+01 1.058913e+01 180 -1.252250e-08 1.058913e+01 181 -1.058913e+01 -1.252250e-08 182 -1.058913e+01 -1.058913e+01 183 -1.058913e+01 -1.058913e+01 184 -1.058913e+01 -1.058913e+01 185 -1.058913e+01 -1.058913e+01 186 -1.058913e+01 -1.058913e+01 187 -1.058913e+01 -1.058913e+01 188 -1.058913e+01 -1.058913e+01 189 -1.058913e+01 -1.058913e+01 190 -1.058913e+01 -1.058913e+01 191 -1.058913e+01 -1.058913e+01 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7yvu01227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8fb5f1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9mm931227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10z95n1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/1187v31227552869.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12sudo1227552869.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13t4vx1227552869.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14rj321227552869.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/1533301227552869.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16rtju1227552869.tab") + } > > system("convert tmp/1k0zw1227552869.ps tmp/1k0zw1227552869.png") > system("convert tmp/219t01227552869.ps tmp/219t01227552869.png") > system("convert tmp/3p1ns1227552869.ps tmp/3p1ns1227552869.png") > system("convert tmp/4h79u1227552869.ps tmp/4h79u1227552869.png") > system("convert tmp/5geb21227552869.ps tmp/5geb21227552869.png") > system("convert tmp/6odnr1227552869.ps tmp/6odnr1227552869.png") > system("convert tmp/7yvu01227552869.ps tmp/7yvu01227552869.png") > system("convert tmp/8fb5f1227552869.ps tmp/8fb5f1227552869.png") > system("convert tmp/9mm931227552869.ps tmp/9mm931227552869.png") > system("convert tmp/10z95n1227552869.ps tmp/10z95n1227552869.png") > > > proc.time() user system elapsed 14.311 4.877 14.915