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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('x' + ,'y' + ,'z') + ,1:192)) > y <- array(NA,dim=c(3,192),dimnames=list(c('x','y','z'),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 = '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 x y z M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 1687 0 -183.9235445 1 0 0 0 0 0 0 0 0 0 0 1 2 1508 0 -177.0726091 0 1 0 0 0 0 0 0 0 0 0 2 3 1507 0 -228.6351091 0 0 1 0 0 0 0 0 0 0 0 3 4 1385 0 -237.4476091 0 0 0 1 0 0 0 0 0 0 0 4 5 1632 0 -127.7601091 0 0 0 0 1 0 0 0 0 0 0 5 6 1511 0 -193.0101091 0 0 0 0 0 1 0 0 0 0 0 6 7 1559 0 -220.6351091 0 0 0 0 0 0 1 0 0 0 0 7 8 1630 0 -164.5101091 0 0 0 0 0 0 0 1 0 0 0 8 9 1579 0 -268.3226091 0 0 0 0 0 0 0 0 1 0 0 9 10 1653 0 -333.6976091 0 0 0 0 0 0 0 0 0 1 0 10 11 2152 0 -34.2601091 0 0 0 0 0 0 0 0 0 0 1 11 12 2148 0 -154.8851091 0 0 0 0 0 0 0 0 0 0 0 12 13 1752 0 -97.7452805 1 0 0 0 0 0 0 0 0 0 0 13 14 1765 0 101.1056549 0 1 0 0 0 0 0 0 0 0 0 14 15 1717 0 2.5431549 0 0 1 0 0 0 0 0 0 0 0 15 16 1558 0 -43.2693451 0 0 0 1 0 0 0 0 0 0 0 16 17 1575 0 -163.5818451 0 0 0 0 1 0 0 0 0 0 0 17 18 1520 0 -162.8318451 0 0 0 0 0 1 0 0 0 0 0 18 19 1805 0 46.5431549 0 0 0 0 0 0 1 0 0 0 0 19 20 1800 0 26.6681549 0 0 0 0 0 0 0 1 0 0 0 20 21 1719 0 -107.1443451 0 0 0 0 0 0 0 0 1 0 0 21 22 2008 0 42.4806549 0 0 0 0 0 0 0 0 0 1 0 22 23 2242 0 76.9181549 0 0 0 0 0 0 0 0 0 0 1 23 24 2478 0 196.2931549 0 0 0 0 0 0 0 0 0 0 0 24 25 2030 0 201.4329835 1 0 0 0 0 0 0 0 0 0 0 25 26 1655 0 12.2839189 0 1 0 0 0 0 0 0 0 0 0 26 27 1693 0 -0.2785811 0 0 1 0 0 0 0 0 0 0 0 27 28 1623 0 42.9089189 0 0 0 1 0 0 0 0 0 0 0 28 29 1805 0 87.5964189 0 0 0 0 1 0 0 0 0 0 0 29 30 1746 0 84.3464189 0 0 0 0 0 1 0 0 0 0 0 30 31 1795 0 57.7214189 0 0 0 0 0 0 1 0 0 0 0 31 32 1926 0 173.8464189 0 0 0 0 0 0 0 1 0 0 0 32 33 1619 0 -185.9660811 0 0 0 0 0 0 0 0 1 0 0 33 34 1992 0 47.6589189 0 0 0 0 0 0 0 0 0 1 0 34 35 2233 0 89.0964189 0 0 0 0 0 0 0 0 0 0 1 35 36 2192 0 -68.5285811 0 0 0 0 0 0 0 0 0 0 0 36 37 2080 0 272.6112475 1 0 0 0 0 0 0 0 0 0 0 37 38 1768 0 146.4621829 0 1 0 0 0 0 0 0 0 0 0 38 39 1835 0 162.8996829 0 0 1 0 0 0 0 0 0 0 0 39 40 1569 0 10.0871828 0 0 0 1 0 0 0 0 0 0 0 40 41 1976 0 279.7746829 0 0 0 0 1 0 0 0 0 0 0 41 42 1853 0 212.5246829 0 0 0 0 0 1 0 0 0 0 0 42 43 1965 0 248.8996829 0 0 0 0 0 0 1 0 0 0 0 43 44 1689 0 -41.9753172 0 0 0 0 0 0 0 1 0 0 0 44 45 1778 0 -5.7878171 0 0 0 0 0 0 0 0 1 0 0 45 46 1976 0 52.8371828 0 0 0 0 0 0 0 0 0 1 0 46 47 2397 0 274.2746829 0 0 0 0 0 0 0 0 0 0 1 47 48 2654 0 414.6496829 0 0 0 0 0 0 0 0 0 0 0 48 49 2097 0 310.7895114 1 0 0 0 0 0 0 0 0 0 0 49 50 1963 0 362.6404468 0 1 0 0 0 0 0 0 0 0 0 50 51 1677 0 26.0779468 0 0 1 0 0 0 0 0 0 0 0 51 52 1941 0 403.2654468 0 0 0 1 0 0 0 0 0 0 0 52 53 2003 0 327.9529468 0 0 0 0 1 0 0 0 0 0 0 53 54 1813 0 193.7029468 0 0 0 0 0 1 0 0 0 0 0 54 55 2012 0 317.0779468 0 0 0 0 0 0 1 0 0 0 0 55 56 1912 0 202.2029468 0 0 0 0 0 0 0 1 0 0 0 56 57 2084 0 321.3904468 0 0 0 0 0 0 0 0 1 0 0 57 58 2080 0 178.0154468 0 0 0 0 0 0 0 0 0 1 0 58 59 2118 0 16.4529468 0 0 0 0 0 0 0 0 0 0 1 59 60 2150 0 -68.1720532 0 0 0 0 0 0 0 0 0 0 0 60 61 1608 0 -157.0322246 1 0 0 0 0 0 0 0 0 0 0 61 62 1503 0 -76.1812892 0 1 0 0 0 0 0 0 0 0 0 62 63 1548 0 -81.7437892 0 0 1 0 0 0 0 0 0 0 0 63 64 1382 0 -134.5562892 0 0 0 1 0 0 0 0 0 0 0 64 65 1731 0 77.1312108 0 0 0 0 1 0 0 0 0 0 0 65 66 1798 0 199.8812108 0 0 0 0 0 1 0 0 0 0 0 66 67 1779 0 105.2562108 0 0 0 0 0 0 1 0 0 0 0 67 68 1887 0 198.3812108 0 0 0 0 0 0 0 1 0 0 0 68 69 2004 0 262.5687108 0 0 0 0 0 0 0 0 1 0 0 69 70 2077 0 196.1937108 0 0 0 0 0 0 0 0 0 1 0 70 71 2092 0 11.6312108 0 0 0 0 0 0 0 0 0 0 1 71 72 2051 0 -145.9937892 0 0 0 0 0 0 0 0 0 0 0 72 73 1577 0 -166.8539606 1 0 0 0 0 0 0 0 0 0 0 73 74 1356 0 -202.0030252 0 1 0 0 0 0 0 0 0 0 0 74 75 1652 0 43.4344748 0 0 1 0 0 0 0 0 0 0 0 75 76 1382 0 -113.3780252 0 0 0 1 0 0 0 0 0 0 0 76 77 1519 0 -113.6905252 0 0 0 0 1 0 0 0 0 0 0 77 78 1421 0 -155.9405252 0 0 0 0 0 1 0 0 0 0 0 78 79 1442 0 -210.5655252 0 0 0 0 0 0 1 0 0 0 0 79 80 1543 0 -124.4405252 0 0 0 0 0 0 0 1 0 0 0 80 81 1656 0 -64.2530252 0 0 0 0 0 0 0 0 1 0 0 81 82 1561 0 -298.6280252 0 0 0 0 0 0 0 0 0 1 0 82 83 1905 0 -154.1905252 0 0 0 0 0 0 0 0 0 0 1 83 84 2199 0 23.1844748 0 0 0 0 0 0 0 0 0 0 0 84 85 1473 0 -249.6756966 1 0 0 0 0 0 0 0 0 0 0 85 86 1655 0 118.1752388 0 1 0 0 0 0 0 0 0 0 0 86 87 1407 0 -180.3872612 0 0 1 0 0 0 0 0 0 0 0 87 88 1395 0 -79.1997612 0 0 0 1 0 0 0 0 0 0 0 88 89 1530 0 -81.5122612 0 0 0 0 1 0 0 0 0 0 0 89 90 1309 0 -246.7622612 0 0 0 0 0 1 0 0 0 0 0 90 91 1526 0 -105.3872612 0 0 0 0 0 0 1 0 0 0 0 91 92 1327 0 -319.2622612 0 0 0 0 0 0 0 1 0 0 0 92 93 1627 0 -72.0747612 0 0 0 0 0 0 0 0 1 0 0 93 94 1748 0 -90.4497612 0 0 0 0 0 0 0 0 0 1 0 94 95 1958 0 -80.0122612 0 0 0 0 0 0 0 0 0 0 1 95 96 2274 0 119.3627388 0 0 0 0 0 0 0 0 0 0 0 96 97 1648 0 -53.4974326 1 0 0 0 0 0 0 0 0 0 0 97 98 1401 0 -114.6464972 0 1 0 0 0 0 0 0 0 0 0 98 99 1411 0 -155.2089972 0 0 1 0 0 0 0 0 0 0 0 99 100 1403 0 -50.0214972 0 0 0 1 0 0 0 0 0 0 0 100 101 1394 0 -196.3339972 0 0 0 0 1 0 0 0 0 0 0 101 102 1520 0 -14.5839972 0 0 0 0 0 1 0 0 0 0 0 102 103 1528 0 -82.2089972 0 0 0 0 0 0 1 0 0 0 0 103 104 1643 0 17.9160028 0 0 0 0 0 0 0 1 0 0 0 104 105 1515 0 -162.8964972 0 0 0 0 0 0 0 0 1 0 0 105 106 1685 0 -132.2714972 0 0 0 0 0 0 0 0 0 1 0 106 107 2000 0 -16.8339972 0 0 0 0 0 0 0 0 0 0 1 107 108 2215 0 81.5410028 0 0 0 0 0 0 0 0 0 0 0 108 109 1956 0 275.6808314 1 0 0 0 0 0 0 0 0 0 0 109 110 1462 0 -32.4682332 0 1 0 0 0 0 0 0 0 0 0 110 111 1563 0 17.9692668 0 0 1 0 0 0 0 0 0 0 0 111 112 1459 0 27.1567668 0 0 0 1 0 0 0 0 0 0 0 112 113 1446 0 -123.1557332 0 0 0 0 1 0 0 0 0 0 0 113 114 1622 0 108.5942668 0 0 0 0 0 1 0 0 0 0 0 114 115 1657 0 67.9692668 0 0 0 0 0 0 1 0 0 0 0 115 116 1638 0 34.0942668 0 0 0 0 0 0 0 1 0 0 0 116 117 1643 0 -13.7182332 0 0 0 0 0 0 0 0 1 0 0 117 118 1683 0 -113.0932332 0 0 0 0 0 0 0 0 0 1 0 118 119 2050 0 54.3442668 0 0 0 0 0 0 0 0 0 0 1 119 120 2262 0 149.7192668 0 0 0 0 0 0 0 0 0 0 0 120 121 1813 0 153.8590954 1 0 0 0 0 0 0 0 0 0 0 121 122 1445 0 -28.2899692 0 1 0 0 0 0 0 0 0 0 0 122 123 1762 0 238.1475308 0 0 1 0 0 0 0 0 0 0 0 123 124 1461 0 50.3350308 0 0 0 1 0 0 0 0 0 0 0 124 125 1556 0 8.0225308 0 0 0 0 1 0 0 0 0 0 0 125 126 1431 0 -61.2274692 0 0 0 0 0 1 0 0 0 0 0 126 127 1427 0 -140.8524692 0 0 0 0 0 0 1 0 0 0 0 127 128 1554 0 -28.7274692 0 0 0 0 0 0 0 1 0 0 0 128 129 1645 0 9.4600308 0 0 0 0 0 0 0 0 1 0 0 129 130 1653 0 -121.9149692 0 0 0 0 0 0 0 0 0 1 0 130 131 2016 0 41.5225308 0 0 0 0 0 0 0 0 0 0 1 131 132 2207 0 115.8975308 0 0 0 0 0 0 0 0 0 0 0 132 133 1665 0 27.0373594 1 0 0 0 0 0 0 0 0 0 0 133 134 1361 0 -91.1117052 0 1 0 0 0 0 0 0 0 0 0 134 135 1506 0 3.3257948 0 0 1 0 0 0 0 0 0 0 0 135 136 1360 0 -29.4867052 0 0 0 1 0 0 0 0 0 0 0 136 137 1453 0 -73.7992052 0 0 0 0 1 0 0 0 0 0 0 137 138 1522 0 50.9507948 0 0 0 0 0 1 0 0 0 0 0 138 139 1460 0 -86.6742052 0 0 0 0 0 0 1 0 0 0 0 139 140 1552 0 -9.5492052 0 0 0 0 0 0 0 1 0 0 0 140 141 1548 0 -66.3617052 0 0 0 0 0 0 0 0 1 0 0 141 142 1827 0 73.2632948 0 0 0 0 0 0 0 0 0 1 0 142 143 1737 0 -216.2992052 0 0 0 0 0 0 0 0 0 0 1 143 144 1941 0 -128.9242052 0 0 0 0 0 0 0 0 0 0 0 144 145 1474 0 -142.7843767 1 0 0 0 0 0 0 0 0 0 0 145 146 1458 0 27.0665587 0 1 0 0 0 0 0 0 0 0 0 146 147 1542 0 60.5040587 0 0 1 0 0 0 0 0 0 0 0 147 148 1404 0 35.6915587 0 0 0 1 0 0 0 0 0 0 0 148 149 1522 0 16.3790587 0 0 0 0 1 0 0 0 0 0 0 149 150 1385 0 -64.8709413 0 0 0 0 0 1 0 0 0 0 0 150 151 1641 0 115.5040587 0 0 0 0 0 0 1 0 0 0 0 151 152 1510 0 -30.3709413 0 0 0 0 0 0 0 1 0 0 0 152 153 1681 0 87.8165587 0 0 0 0 0 0 0 0 1 0 0 153 154 1938 0 205.4415587 0 0 0 0 0 0 0 0 0 1 0 154 155 1868 0 -64.1209413 0 0 0 0 0 0 0 0 0 0 1 155 156 1726 0 -322.7459413 0 0 0 0 0 0 0 0 0 0 0 156 157 1456 0 -139.6061127 1 0 0 0 0 0 0 0 0 0 0 157 158 1445 0 35.2448227 0 1 0 0 0 0 0 0 0 0 0 158 159 1456 0 -4.3176773 0 0 1 0 0 0 0 0 0 0 0 159 160 1365 0 17.8698227 0 0 0 1 0 0 0 0 0 0 0 160 161 1487 0 2.5573227 0 0 0 0 1 0 0 0 0 0 0 161 162 1558 0 129.3073227 0 0 0 0 0 1 0 0 0 0 0 162 163 1488 0 -16.3176773 0 0 0 0 0 0 1 0 0 0 0 163 164 1684 0 164.8073227 0 0 0 0 0 0 0 1 0 0 0 164 165 1594 0 21.9948227 0 0 0 0 0 0 0 0 1 0 0 165 166 1850 0 138.6198227 0 0 0 0 0 0 0 0 0 1 0 166 167 1998 0 87.0573227 0 0 0 0 0 0 0 0 0 0 1 167 168 2079 0 51.4323227 0 0 0 0 0 0 0 0 0 0 0 168 169 1494 0 -80.4278487 1 0 0 0 0 0 0 0 0 0 0 169 170 1057 1 -105.1918797 0 1 0 0 0 0 0 0 0 0 0 170 171 1218 1 5.2456203 0 0 1 0 0 0 0 0 0 0 0 171 172 1168 1 68.4331203 0 0 0 1 0 0 0 0 0 0 0 172 173 1236 1 -0.8793797 0 0 0 0 1 0 0 0 0 0 0 173 174 1076 1 -105.1293797 0 0 0 0 0 1 0 0 0 0 0 174 175 1174 1 -82.7543797 0 0 0 0 0 0 1 0 0 0 0 175 176 1139 1 -132.6293797 0 0 0 0 0 0 0 1 0 0 0 176 177 1427 1 102.5581203 0 0 0 0 0 0 0 0 1 0 0 177 178 1487 1 23.1831203 0 0 0 0 0 0 0 0 0 1 0 178 179 1483 1 -180.3793797 0 0 0 0 0 0 0 0 0 0 1 179 180 1513 1 -267.0043797 0 0 0 0 0 0 0 0 0 0 0 180 181 1357 1 30.1354489 1 0 0 0 0 0 0 0 0 0 0 181 182 1165 1 23.9863843 0 1 0 0 0 0 0 0 0 0 0 182 183 1282 1 90.4238843 0 0 1 0 0 0 0 0 0 0 0 183 184 1110 1 31.6113843 0 0 0 1 0 0 0 0 0 0 0 184 185 1297 1 81.2988843 0 0 0 0 1 0 0 0 0 0 0 185 186 1185 1 25.0488843 0 0 0 0 0 1 0 0 0 0 0 186 187 1222 1 -13.5761157 0 0 0 0 0 0 1 0 0 0 0 187 188 1284 1 33.5488843 0 0 0 0 0 0 0 1 0 0 0 188 189 1444 1 140.7363843 0 0 0 0 0 0 0 0 1 0 0 189 190 1575 1 132.3613843 0 0 0 0 0 0 0 0 0 1 0 190 191 1737 1 94.7988843 0 0 0 0 0 0 0 0 0 0 1 191 192 1763 1 4.1738843 0 0 0 0 0 0 0 0 0 0 0 192 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) y z M1 M2 M3 2324.063 -226.385 1.000 -451.375 -635.461 -583.134 M4 M5 M6 M7 M8 M9 -694.556 -555.479 -609.464 -532.074 -515.434 -460.857 M10 M11 t -319.717 -118.390 -1.765 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.858e-08 -1.016e-08 -5.283e-10 9.344e-09 4.904e-08 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.324e+03 5.929e-09 3.920e+11 <2e-16 *** y -2.264e+02 5.526e-09 -4.097e+10 <2e-16 *** z 1.000e+00 1.009e-11 9.908e+10 <2e-16 *** M1 -4.514e+02 7.264e-09 -6.214e+10 <2e-16 *** M2 -6.355e+02 7.263e-09 -8.749e+10 <2e-16 *** M3 -5.831e+02 7.262e-09 -8.030e+10 <2e-16 *** M4 -6.946e+02 7.261e-09 -9.566e+10 <2e-16 *** M5 -5.555e+02 7.260e-09 -7.651e+10 <2e-16 *** M6 -6.095e+02 7.259e-09 -8.396e+10 <2e-16 *** M7 -5.321e+02 7.258e-09 -7.331e+10 <2e-16 *** M8 -5.154e+02 7.257e-09 -7.102e+10 <2e-16 *** M9 -4.609e+02 7.257e-09 -6.351e+10 <2e-16 *** M10 -3.197e+02 7.257e-09 -4.406e+10 <2e-16 *** M11 -1.184e+02 7.256e-09 -1.632e+10 <2e-16 *** t -1.765e+00 3.239e-11 -5.449e+10 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.052e-08 on 177 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 2.717e+21 on 14 and 177 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,] 0.25441464 5.088293e-01 7.455854e-01 [2,] 0.13936362 2.787272e-01 8.606364e-01 [3,] 0.07772007 1.554401e-01 9.222799e-01 [4,] 0.06010402 1.202080e-01 9.398960e-01 [5,] 0.02814250 5.628500e-02 9.718575e-01 [6,] 0.01341571 2.683141e-02 9.865843e-01 [7,] 0.01784511 3.569022e-02 9.821549e-01 [8,] 0.07591454 1.518291e-01 9.240855e-01 [9,] 0.07292422 1.458484e-01 9.270758e-01 [10,] 0.04452607 8.905214e-02 9.554739e-01 [11,] 0.02636956 5.273911e-02 9.736304e-01 [12,] 0.02245384 4.490769e-02 9.775462e-01 [13,] 0.01791762 3.583525e-02 9.820824e-01 [14,] 0.01024889 2.049777e-02 9.897511e-01 [15,] 0.03571659 7.143317e-02 9.642834e-01 [16,] 0.04097777 8.195554e-02 9.590222e-01 [17,] 0.02715659 5.431319e-02 9.728434e-01 [18,] 0.01727014 3.454028e-02 9.827299e-01 [19,] 0.01463135 2.926269e-02 9.853687e-01 [20,] 0.04303146 8.606291e-02 9.569685e-01 [21,] 0.10226777 2.045355e-01 8.977322e-01 [22,] 0.20464895 4.092979e-01 7.953510e-01 [23,] 0.16651083 3.330217e-01 8.334892e-01 [24,] 0.22632246 4.526449e-01 7.736775e-01 [25,] 0.31860546 6.372109e-01 6.813945e-01 [26,] 0.48832088 9.766418e-01 5.116791e-01 [27,] 0.46896201 9.379240e-01 5.310380e-01 [28,] 0.53038721 9.392256e-01 4.696128e-01 [29,] 0.48686281 9.737256e-01 5.131372e-01 [30,] 0.69282267 6.143547e-01 3.071773e-01 [31,] 0.79885021 4.022996e-01 2.011498e-01 [32,] 0.97217527 5.564946e-02 2.782473e-02 [33,] 0.99642788 7.144233e-03 3.572116e-03 [34,] 0.99492313 1.015374e-02 5.076870e-03 [35,] 0.99798603 4.027948e-03 2.013974e-03 [36,] 0.99957904 8.419120e-04 4.209560e-04 [37,] 0.99985434 2.913129e-04 1.456564e-04 [38,] 0.99993097 1.380597e-04 6.902985e-05 [39,] 0.99995271 9.458446e-05 4.729223e-05 [40,] 0.99998608 2.783516e-05 1.391758e-05 [41,] 0.99998819 2.361376e-05 1.180688e-05 [42,] 0.99998207 3.586878e-05 1.793439e-05 [43,] 0.99997100 5.800084e-05 2.900042e-05 [44,] 0.99996078 7.844828e-05 3.922414e-05 [45,] 0.99995039 9.922334e-05 4.961167e-05 [46,] 0.99992644 1.471215e-04 7.356077e-05 [47,] 0.99991278 1.744439e-04 8.722197e-05 [48,] 0.99987389 2.522220e-04 1.261110e-04 [49,] 0.99985740 2.852034e-04 1.426017e-04 [50,] 0.99983701 3.259801e-04 1.629901e-04 [51,] 0.99980194 3.961156e-04 1.980578e-04 [52,] 0.99982363 3.527346e-04 1.763673e-04 [53,] 0.99982049 3.590164e-04 1.795082e-04 [54,] 0.99975273 4.945313e-04 2.472657e-04 [55,] 0.99979502 4.099588e-04 2.049794e-04 [56,] 0.99972167 5.566537e-04 2.783268e-04 [57,] 0.99962750 7.449997e-04 3.724998e-04 [58,] 0.99951361 9.727900e-04 4.863950e-04 [59,] 0.99949201 1.015981e-03 5.079906e-04 [60,] 0.99944724 1.105512e-03 5.527562e-04 [61,] 0.99927579 1.448424e-03 7.242119e-04 [62,] 0.99913335 1.733297e-03 8.666487e-04 [63,] 0.99890798 2.184037e-03 1.092018e-03 [64,] 0.99871757 2.564865e-03 1.282432e-03 [65,] 0.99846933 3.061331e-03 1.530666e-03 [66,] 0.99839984 3.200316e-03 1.600158e-03 [67,] 0.99792071 4.158582e-03 2.079291e-03 [68,] 0.99734963 5.300731e-03 2.650365e-03 [69,] 0.99679927 6.401469e-03 3.200735e-03 [70,] 0.99607460 7.850796e-03 3.925398e-03 [71,] 0.99647236 7.055290e-03 3.527645e-03 [72,] 0.99578495 8.430107e-03 4.215054e-03 [73,] 0.99453763 1.092475e-02 5.462373e-03 [74,] 0.99356765 1.286469e-02 6.432347e-03 [75,] 0.99190329 1.619341e-02 8.096707e-03 [76,] 0.99021625 1.956751e-02 9.783754e-03 [77,] 0.99000560 1.998881e-02 9.994404e-03 [78,] 0.98764566 2.470868e-02 1.235434e-02 [79,] 0.98609446 2.781107e-02 1.390554e-02 [80,] 0.98295301 3.409399e-02 1.704699e-02 [81,] 0.97939277 4.121447e-02 2.060723e-02 [82,] 0.97459000 5.082000e-02 2.541000e-02 [83,] 0.97255384 5.489232e-02 2.744616e-02 [84,] 0.96669067 6.661867e-02 3.330933e-02 [85,] 0.95988528 8.022945e-02 4.011472e-02 [86,] 0.95405572 9.188856e-02 4.594428e-02 [87,] 0.94616366 1.076727e-01 5.383634e-02 [88,] 0.93581593 1.283681e-01 6.418407e-02 [89,] 0.93023993 1.395201e-01 6.976007e-02 [90,] 0.92371372 1.525726e-01 7.628628e-02 [91,] 0.91962629 1.607474e-01 8.037371e-02 [92,] 0.91947236 1.610553e-01 8.052764e-02 [93,] 0.90554373 1.889125e-01 9.445627e-02 [94,] 0.89825921 2.034816e-01 1.017408e-01 [95,] 0.89377791 2.124442e-01 1.062221e-01 [96,] 0.88142166 2.371567e-01 1.185783e-01 [97,] 0.87876230 2.424754e-01 1.212377e-01 [98,] 0.86766900 2.646620e-01 1.323310e-01 [99,] 0.84778541 3.044292e-01 1.522146e-01 [100,] 0.82456538 3.508692e-01 1.754346e-01 [101,] 0.83057869 3.388426e-01 1.694213e-01 [102,] 0.82589184 3.482163e-01 1.741082e-01 [103,] 0.80571352 3.885730e-01 1.942865e-01 [104,] 0.82945131 3.410974e-01 1.705487e-01 [105,] 0.80201878 3.959624e-01 1.979812e-01 [106,] 0.79857136 4.028573e-01 2.014286e-01 [107,] 0.78032198 4.393560e-01 2.196780e-01 [108,] 0.75394370 4.921126e-01 2.460563e-01 [109,] 0.71501068 5.699786e-01 2.849893e-01 [110,] 0.74882676 5.023465e-01 2.511732e-01 [111,] 0.71067926 5.786415e-01 2.893207e-01 [112,] 0.66842345 6.631531e-01 3.315765e-01 [113,] 0.77485626 4.502875e-01 2.251437e-01 [114,] 0.75679090 4.864182e-01 2.432091e-01 [115,] 0.75082197 4.983561e-01 2.491780e-01 [116,] 0.71538009 5.692398e-01 2.846199e-01 [117,] 0.67102276 6.579545e-01 3.289772e-01 [118,] 0.62635118 7.472976e-01 3.736488e-01 [119,] 0.58324524 8.335095e-01 4.167548e-01 [120,] 0.53361577 9.327685e-01 4.663842e-01 [121,] 0.48796808 9.759362e-01 5.120319e-01 [122,] 0.44023946 8.804789e-01 5.597605e-01 [123,] 0.40102517 8.020503e-01 5.989748e-01 [124,] 0.35738931 7.147786e-01 6.426107e-01 [125,] 0.35293640 7.058728e-01 6.470636e-01 [126,] 0.54781427 9.043715e-01 4.521857e-01 [127,] 0.88685194 2.262961e-01 1.131481e-01 [128,] 0.90859998 1.828000e-01 9.140002e-02 [129,] 0.89769664 2.046067e-01 1.023034e-01 [130,] 0.87498058 2.500388e-01 1.250194e-01 [131,] 0.84939280 3.012144e-01 1.506072e-01 [132,] 0.82180093 3.563981e-01 1.781991e-01 [133,] 0.86725886 2.654823e-01 1.327411e-01 [134,] 0.93457541 1.308492e-01 6.542459e-02 [135,] 0.96074373 7.851254e-02 3.925627e-02 [136,] 0.97367981 5.264039e-02 2.632019e-02 [137,] 0.97411306 5.177389e-02 2.588694e-02 [138,] 0.97994426 4.011148e-02 2.005574e-02 [139,] 0.98275599 3.448803e-02 1.724401e-02 [140,] 0.98832561 2.334877e-02 1.167439e-02 [141,] 0.98593462 2.813076e-02 1.406538e-02 [142,] 0.97717143 4.565713e-02 2.282857e-02 [143,] 0.96491669 7.016662e-02 3.508331e-02 [144,] 0.94599797 1.080041e-01 5.400203e-02 [145,] 0.95278031 9.443938e-02 4.721969e-02 [146,] 0.92792655 1.441469e-01 7.207345e-02 [147,] 0.96149305 7.701391e-02 3.850695e-02 [148,] 0.98558336 2.883328e-02 1.441664e-02 [149,] 0.99765812 4.683757e-03 2.341878e-03 [150,] 0.99450648 1.098703e-02 5.493516e-03 [151,] 0.98957012 2.085977e-02 1.042988e-02 [152,] 0.97734847 4.530306e-02 2.265153e-02 [153,] 0.97029662 5.940676e-02 2.970338e-02 [154,] 0.94043187 1.191363e-01 5.956813e-02 [155,] 0.88955661 2.208868e-01 1.104434e-01 [156,] 0.79435460 4.112908e-01 2.056454e-01 [157,] 0.73481182 5.303764e-01 2.651882e-01 > postscript(file="/var/www/html/rcomp/tmp/18bq71227119165.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2j3uo1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3usa01227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4fal91227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5qj9x1227119165.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 -8.078410e-09 -5.839902e-09 5.442616e-11 -1.059637e-08 -2.533044e-09 6 7 8 9 10 -9.523306e-09 -9.415816e-09 -1.338624e-08 -7.223528e-09 -1.333857e-08 11 12 13 14 15 6.096565e-09 -2.424932e-09 9.121680e-09 -1.937490e-08 1.270367e-08 16 17 18 19 20 6.197827e-09 -1.482440e-08 -2.207741e-08 7.091446e-09 3.420170e-09 21 22 23 24 25 -2.029849e-08 2.732400e-09 1.322083e-08 -1.625320e-08 -3.449967e-08 26 27 28 29 30 8.543836e-09 1.128114e-08 3.422055e-09 1.174467e-08 4.507499e-09 31 32 33 34 35 4.613436e-09 -3.959784e-08 -3.241947e-08 2.781602e-10 1.073858e-08 36 37 38 39 40 1.236435e-08 -4.721592e-08 -4.442263e-08 -3.880065e-08 1.118448e-09 41 42 43 44 45 -4.145215e-08 -4.843518e-08 -4.857931e-08 -1.174711e-09 3.431565e-09 46 47 48 49 50 -2.176285e-09 -4.243033e-08 -4.198753e-08 4.019866e-08 4.228539e-08 51 52 53 54 55 9.308972e-09 3.712380e-08 4.592290e-08 3.920575e-08 3.871622e-08 56 57 58 59 60 3.542194e-08 4.069888e-08 3.489306e-08 6.159526e-09 7.495511e-09 61 62 63 64 65 2.962222e-08 1.593758e-09 7.303206e-09 2.682531e-08 4.484916e-09 66 67 68 69 70 2.674769e-08 2.712330e-08 2.300338e-08 2.849864e-08 2.238705e-08 71 72 73 74 75 3.744885e-09 3.537071e-08 1.722740e-08 1.965951e-08 4.372436e-09 76 77 78 79 80 1.430738e-08 2.280869e-08 1.572638e-08 1.594341e-08 1.185137e-08 81 82 83 84 85 -2.637620e-09 1.191792e-08 2.196938e-08 2.265361e-09 5.122485e-09 86 87 88 89 90 5.954542e-09 1.282729e-08 -8.262047e-09 2.471861e-10 3.653265e-09 91 92 93 94 95 3.092120e-09 1.910713e-10 -5.040357e-09 -1.134251e-08 -7.587460e-10 96 97 98 99 100 9.449601e-09 1.909885e-09 -5.554737e-09 2.935065e-10 -8.116233e-10 101 102 103 104 105 -1.730759e-09 2.977468e-10 5.664224e-10 -3.581312e-09 -7.113596e-09 106 107 108 109 110 -1.361017e-08 6.556636e-09 7.166119e-09 -2.183079e-08 1.685140e-09 111 112 113 114 115 7.172185e-09 -3.551770e-09 -1.445504e-08 -2.262511e-08 -2.463683e-09 116 117 118 119 120 -6.079331e-09 -1.395920e-10 -2.612008e-08 3.840322e-09 -1.553836e-08 121 122 123 124 125 -3.378089e-08 -7.652053e-10 -2.613568e-08 -6.077659e-09 1.590524e-09 126 127 128 129 130 -4.384584e-09 -3.406852e-08 -8.263735e-09 -3.665229e-09 -3.851881e-08 131 132 133 134 135 1.457376e-09 -2.783792e-08 -5.711119e-09 -2.949631e-09 3.362908e-09 136 137 138 139 140 -8.194389e-09 4.816172e-10 -7.263821e-09 -6.717292e-09 -1.077369e-08 141 142 143 144 145 -4.798132e-09 -1.172741e-08 -3.995279e-08 -3.929965e-08 4.252930e-08 146 147 148 149 150 -5.852581e-09 -2.979099e-10 -1.088700e-08 -2.310120e-09 -9.237622e-09 151 152 153 154 155 4.004636e-08 -1.312465e-08 -7.843882e-09 3.531396e-08 -2.990760e-09 156 157 158 159 160 4.903606e-08 3.008300e-08 -8.318699e-09 5.257424e-10 -1.325000e-08 161 162 163 164 165 -1.689110e-09 2.755757e-08 -1.186418e-08 2.366685e-08 -1.001647e-08 166 167 168 169 170 2.314559e-08 -6.024660e-09 -4.883105e-09 -1.258571e-08 2.315130e-08 171 172 173 174 175 4.003447e-10 -1.253815e-08 -1.762883e-09 1.940079e-08 -1.068786e-08 176 177 178 179 180 1.576010e-08 2.057630e-08 -1.548350e-08 2.594973e-08 2.729364e-08 181 182 183 184 185 -1.211213e-08 -9.795188e-09 -4.371582e-09 -1.482580e-08 -6.522989e-09 186 187 188 189 190 -1.354965e-08 -1.339605e-08 -1.733338e-08 7.990972e-09 1.649194e-09 191 192 -7.576552e-09 -2.216650e-09 > postscript(file="/var/www/html/rcomp/tmp/6fbmq1227119165.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 -8.078410e-09 NA 1 -5.839902e-09 -8.078410e-09 2 5.442616e-11 -5.839902e-09 3 -1.059637e-08 5.442616e-11 4 -2.533044e-09 -1.059637e-08 5 -9.523306e-09 -2.533044e-09 6 -9.415816e-09 -9.523306e-09 7 -1.338624e-08 -9.415816e-09 8 -7.223528e-09 -1.338624e-08 9 -1.333857e-08 -7.223528e-09 10 6.096565e-09 -1.333857e-08 11 -2.424932e-09 6.096565e-09 12 9.121680e-09 -2.424932e-09 13 -1.937490e-08 9.121680e-09 14 1.270367e-08 -1.937490e-08 15 6.197827e-09 1.270367e-08 16 -1.482440e-08 6.197827e-09 17 -2.207741e-08 -1.482440e-08 18 7.091446e-09 -2.207741e-08 19 3.420170e-09 7.091446e-09 20 -2.029849e-08 3.420170e-09 21 2.732400e-09 -2.029849e-08 22 1.322083e-08 2.732400e-09 23 -1.625320e-08 1.322083e-08 24 -3.449967e-08 -1.625320e-08 25 8.543836e-09 -3.449967e-08 26 1.128114e-08 8.543836e-09 27 3.422055e-09 1.128114e-08 28 1.174467e-08 3.422055e-09 29 4.507499e-09 1.174467e-08 30 4.613436e-09 4.507499e-09 31 -3.959784e-08 4.613436e-09 32 -3.241947e-08 -3.959784e-08 33 2.781602e-10 -3.241947e-08 34 1.073858e-08 2.781602e-10 35 1.236435e-08 1.073858e-08 36 -4.721592e-08 1.236435e-08 37 -4.442263e-08 -4.721592e-08 38 -3.880065e-08 -4.442263e-08 39 1.118448e-09 -3.880065e-08 40 -4.145215e-08 1.118448e-09 41 -4.843518e-08 -4.145215e-08 42 -4.857931e-08 -4.843518e-08 43 -1.174711e-09 -4.857931e-08 44 3.431565e-09 -1.174711e-09 45 -2.176285e-09 3.431565e-09 46 -4.243033e-08 -2.176285e-09 47 -4.198753e-08 -4.243033e-08 48 4.019866e-08 -4.198753e-08 49 4.228539e-08 4.019866e-08 50 9.308972e-09 4.228539e-08 51 3.712380e-08 9.308972e-09 52 4.592290e-08 3.712380e-08 53 3.920575e-08 4.592290e-08 54 3.871622e-08 3.920575e-08 55 3.542194e-08 3.871622e-08 56 4.069888e-08 3.542194e-08 57 3.489306e-08 4.069888e-08 58 6.159526e-09 3.489306e-08 59 7.495511e-09 6.159526e-09 60 2.962222e-08 7.495511e-09 61 1.593758e-09 2.962222e-08 62 7.303206e-09 1.593758e-09 63 2.682531e-08 7.303206e-09 64 4.484916e-09 2.682531e-08 65 2.674769e-08 4.484916e-09 66 2.712330e-08 2.674769e-08 67 2.300338e-08 2.712330e-08 68 2.849864e-08 2.300338e-08 69 2.238705e-08 2.849864e-08 70 3.744885e-09 2.238705e-08 71 3.537071e-08 3.744885e-09 72 1.722740e-08 3.537071e-08 73 1.965951e-08 1.722740e-08 74 4.372436e-09 1.965951e-08 75 1.430738e-08 4.372436e-09 76 2.280869e-08 1.430738e-08 77 1.572638e-08 2.280869e-08 78 1.594341e-08 1.572638e-08 79 1.185137e-08 1.594341e-08 80 -2.637620e-09 1.185137e-08 81 1.191792e-08 -2.637620e-09 82 2.196938e-08 1.191792e-08 83 2.265361e-09 2.196938e-08 84 5.122485e-09 2.265361e-09 85 5.954542e-09 5.122485e-09 86 1.282729e-08 5.954542e-09 87 -8.262047e-09 1.282729e-08 88 2.471861e-10 -8.262047e-09 89 3.653265e-09 2.471861e-10 90 3.092120e-09 3.653265e-09 91 1.910713e-10 3.092120e-09 92 -5.040357e-09 1.910713e-10 93 -1.134251e-08 -5.040357e-09 94 -7.587460e-10 -1.134251e-08 95 9.449601e-09 -7.587460e-10 96 1.909885e-09 9.449601e-09 97 -5.554737e-09 1.909885e-09 98 2.935065e-10 -5.554737e-09 99 -8.116233e-10 2.935065e-10 100 -1.730759e-09 -8.116233e-10 101 2.977468e-10 -1.730759e-09 102 5.664224e-10 2.977468e-10 103 -3.581312e-09 5.664224e-10 104 -7.113596e-09 -3.581312e-09 105 -1.361017e-08 -7.113596e-09 106 6.556636e-09 -1.361017e-08 107 7.166119e-09 6.556636e-09 108 -2.183079e-08 7.166119e-09 109 1.685140e-09 -2.183079e-08 110 7.172185e-09 1.685140e-09 111 -3.551770e-09 7.172185e-09 112 -1.445504e-08 -3.551770e-09 113 -2.262511e-08 -1.445504e-08 114 -2.463683e-09 -2.262511e-08 115 -6.079331e-09 -2.463683e-09 116 -1.395920e-10 -6.079331e-09 117 -2.612008e-08 -1.395920e-10 118 3.840322e-09 -2.612008e-08 119 -1.553836e-08 3.840322e-09 120 -3.378089e-08 -1.553836e-08 121 -7.652053e-10 -3.378089e-08 122 -2.613568e-08 -7.652053e-10 123 -6.077659e-09 -2.613568e-08 124 1.590524e-09 -6.077659e-09 125 -4.384584e-09 1.590524e-09 126 -3.406852e-08 -4.384584e-09 127 -8.263735e-09 -3.406852e-08 128 -3.665229e-09 -8.263735e-09 129 -3.851881e-08 -3.665229e-09 130 1.457376e-09 -3.851881e-08 131 -2.783792e-08 1.457376e-09 132 -5.711119e-09 -2.783792e-08 133 -2.949631e-09 -5.711119e-09 134 3.362908e-09 -2.949631e-09 135 -8.194389e-09 3.362908e-09 136 4.816172e-10 -8.194389e-09 137 -7.263821e-09 4.816172e-10 138 -6.717292e-09 -7.263821e-09 139 -1.077369e-08 -6.717292e-09 140 -4.798132e-09 -1.077369e-08 141 -1.172741e-08 -4.798132e-09 142 -3.995279e-08 -1.172741e-08 143 -3.929965e-08 -3.995279e-08 144 4.252930e-08 -3.929965e-08 145 -5.852581e-09 4.252930e-08 146 -2.979099e-10 -5.852581e-09 147 -1.088700e-08 -2.979099e-10 148 -2.310120e-09 -1.088700e-08 149 -9.237622e-09 -2.310120e-09 150 4.004636e-08 -9.237622e-09 151 -1.312465e-08 4.004636e-08 152 -7.843882e-09 -1.312465e-08 153 3.531396e-08 -7.843882e-09 154 -2.990760e-09 3.531396e-08 155 4.903606e-08 -2.990760e-09 156 3.008300e-08 4.903606e-08 157 -8.318699e-09 3.008300e-08 158 5.257424e-10 -8.318699e-09 159 -1.325000e-08 5.257424e-10 160 -1.689110e-09 -1.325000e-08 161 2.755757e-08 -1.689110e-09 162 -1.186418e-08 2.755757e-08 163 2.366685e-08 -1.186418e-08 164 -1.001647e-08 2.366685e-08 165 2.314559e-08 -1.001647e-08 166 -6.024660e-09 2.314559e-08 167 -4.883105e-09 -6.024660e-09 168 -1.258571e-08 -4.883105e-09 169 2.315130e-08 -1.258571e-08 170 4.003447e-10 2.315130e-08 171 -1.253815e-08 4.003447e-10 172 -1.762883e-09 -1.253815e-08 173 1.940079e-08 -1.762883e-09 174 -1.068786e-08 1.940079e-08 175 1.576010e-08 -1.068786e-08 176 2.057630e-08 1.576010e-08 177 -1.548350e-08 2.057630e-08 178 2.594973e-08 -1.548350e-08 179 2.729364e-08 2.594973e-08 180 -1.211213e-08 2.729364e-08 181 -9.795188e-09 -1.211213e-08 182 -4.371582e-09 -9.795188e-09 183 -1.482580e-08 -4.371582e-09 184 -6.522989e-09 -1.482580e-08 185 -1.354965e-08 -6.522989e-09 186 -1.339605e-08 -1.354965e-08 187 -1.733338e-08 -1.339605e-08 188 7.990972e-09 -1.733338e-08 189 1.649194e-09 7.990972e-09 190 -7.576552e-09 1.649194e-09 191 -2.216650e-09 -7.576552e-09 192 NA -2.216650e-09 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -5.839902e-09 -8.078410e-09 [2,] 5.442616e-11 -5.839902e-09 [3,] -1.059637e-08 5.442616e-11 [4,] -2.533044e-09 -1.059637e-08 [5,] -9.523306e-09 -2.533044e-09 [6,] -9.415816e-09 -9.523306e-09 [7,] -1.338624e-08 -9.415816e-09 [8,] -7.223528e-09 -1.338624e-08 [9,] -1.333857e-08 -7.223528e-09 [10,] 6.096565e-09 -1.333857e-08 [11,] -2.424932e-09 6.096565e-09 [12,] 9.121680e-09 -2.424932e-09 [13,] -1.937490e-08 9.121680e-09 [14,] 1.270367e-08 -1.937490e-08 [15,] 6.197827e-09 1.270367e-08 [16,] -1.482440e-08 6.197827e-09 [17,] -2.207741e-08 -1.482440e-08 [18,] 7.091446e-09 -2.207741e-08 [19,] 3.420170e-09 7.091446e-09 [20,] -2.029849e-08 3.420170e-09 [21,] 2.732400e-09 -2.029849e-08 [22,] 1.322083e-08 2.732400e-09 [23,] -1.625320e-08 1.322083e-08 [24,] -3.449967e-08 -1.625320e-08 [25,] 8.543836e-09 -3.449967e-08 [26,] 1.128114e-08 8.543836e-09 [27,] 3.422055e-09 1.128114e-08 [28,] 1.174467e-08 3.422055e-09 [29,] 4.507499e-09 1.174467e-08 [30,] 4.613436e-09 4.507499e-09 [31,] -3.959784e-08 4.613436e-09 [32,] -3.241947e-08 -3.959784e-08 [33,] 2.781602e-10 -3.241947e-08 [34,] 1.073858e-08 2.781602e-10 [35,] 1.236435e-08 1.073858e-08 [36,] -4.721592e-08 1.236435e-08 [37,] -4.442263e-08 -4.721592e-08 [38,] -3.880065e-08 -4.442263e-08 [39,] 1.118448e-09 -3.880065e-08 [40,] -4.145215e-08 1.118448e-09 [41,] -4.843518e-08 -4.145215e-08 [42,] -4.857931e-08 -4.843518e-08 [43,] -1.174711e-09 -4.857931e-08 [44,] 3.431565e-09 -1.174711e-09 [45,] -2.176285e-09 3.431565e-09 [46,] -4.243033e-08 -2.176285e-09 [47,] -4.198753e-08 -4.243033e-08 [48,] 4.019866e-08 -4.198753e-08 [49,] 4.228539e-08 4.019866e-08 [50,] 9.308972e-09 4.228539e-08 [51,] 3.712380e-08 9.308972e-09 [52,] 4.592290e-08 3.712380e-08 [53,] 3.920575e-08 4.592290e-08 [54,] 3.871622e-08 3.920575e-08 [55,] 3.542194e-08 3.871622e-08 [56,] 4.069888e-08 3.542194e-08 [57,] 3.489306e-08 4.069888e-08 [58,] 6.159526e-09 3.489306e-08 [59,] 7.495511e-09 6.159526e-09 [60,] 2.962222e-08 7.495511e-09 [61,] 1.593758e-09 2.962222e-08 [62,] 7.303206e-09 1.593758e-09 [63,] 2.682531e-08 7.303206e-09 [64,] 4.484916e-09 2.682531e-08 [65,] 2.674769e-08 4.484916e-09 [66,] 2.712330e-08 2.674769e-08 [67,] 2.300338e-08 2.712330e-08 [68,] 2.849864e-08 2.300338e-08 [69,] 2.238705e-08 2.849864e-08 [70,] 3.744885e-09 2.238705e-08 [71,] 3.537071e-08 3.744885e-09 [72,] 1.722740e-08 3.537071e-08 [73,] 1.965951e-08 1.722740e-08 [74,] 4.372436e-09 1.965951e-08 [75,] 1.430738e-08 4.372436e-09 [76,] 2.280869e-08 1.430738e-08 [77,] 1.572638e-08 2.280869e-08 [78,] 1.594341e-08 1.572638e-08 [79,] 1.185137e-08 1.594341e-08 [80,] -2.637620e-09 1.185137e-08 [81,] 1.191792e-08 -2.637620e-09 [82,] 2.196938e-08 1.191792e-08 [83,] 2.265361e-09 2.196938e-08 [84,] 5.122485e-09 2.265361e-09 [85,] 5.954542e-09 5.122485e-09 [86,] 1.282729e-08 5.954542e-09 [87,] -8.262047e-09 1.282729e-08 [88,] 2.471861e-10 -8.262047e-09 [89,] 3.653265e-09 2.471861e-10 [90,] 3.092120e-09 3.653265e-09 [91,] 1.910713e-10 3.092120e-09 [92,] -5.040357e-09 1.910713e-10 [93,] -1.134251e-08 -5.040357e-09 [94,] -7.587460e-10 -1.134251e-08 [95,] 9.449601e-09 -7.587460e-10 [96,] 1.909885e-09 9.449601e-09 [97,] -5.554737e-09 1.909885e-09 [98,] 2.935065e-10 -5.554737e-09 [99,] -8.116233e-10 2.935065e-10 [100,] -1.730759e-09 -8.116233e-10 [101,] 2.977468e-10 -1.730759e-09 [102,] 5.664224e-10 2.977468e-10 [103,] -3.581312e-09 5.664224e-10 [104,] -7.113596e-09 -3.581312e-09 [105,] -1.361017e-08 -7.113596e-09 [106,] 6.556636e-09 -1.361017e-08 [107,] 7.166119e-09 6.556636e-09 [108,] -2.183079e-08 7.166119e-09 [109,] 1.685140e-09 -2.183079e-08 [110,] 7.172185e-09 1.685140e-09 [111,] -3.551770e-09 7.172185e-09 [112,] -1.445504e-08 -3.551770e-09 [113,] -2.262511e-08 -1.445504e-08 [114,] -2.463683e-09 -2.262511e-08 [115,] -6.079331e-09 -2.463683e-09 [116,] -1.395920e-10 -6.079331e-09 [117,] -2.612008e-08 -1.395920e-10 [118,] 3.840322e-09 -2.612008e-08 [119,] -1.553836e-08 3.840322e-09 [120,] -3.378089e-08 -1.553836e-08 [121,] -7.652053e-10 -3.378089e-08 [122,] -2.613568e-08 -7.652053e-10 [123,] -6.077659e-09 -2.613568e-08 [124,] 1.590524e-09 -6.077659e-09 [125,] -4.384584e-09 1.590524e-09 [126,] -3.406852e-08 -4.384584e-09 [127,] -8.263735e-09 -3.406852e-08 [128,] -3.665229e-09 -8.263735e-09 [129,] -3.851881e-08 -3.665229e-09 [130,] 1.457376e-09 -3.851881e-08 [131,] -2.783792e-08 1.457376e-09 [132,] -5.711119e-09 -2.783792e-08 [133,] -2.949631e-09 -5.711119e-09 [134,] 3.362908e-09 -2.949631e-09 [135,] -8.194389e-09 3.362908e-09 [136,] 4.816172e-10 -8.194389e-09 [137,] -7.263821e-09 4.816172e-10 [138,] -6.717292e-09 -7.263821e-09 [139,] -1.077369e-08 -6.717292e-09 [140,] -4.798132e-09 -1.077369e-08 [141,] -1.172741e-08 -4.798132e-09 [142,] -3.995279e-08 -1.172741e-08 [143,] -3.929965e-08 -3.995279e-08 [144,] 4.252930e-08 -3.929965e-08 [145,] -5.852581e-09 4.252930e-08 [146,] -2.979099e-10 -5.852581e-09 [147,] -1.088700e-08 -2.979099e-10 [148,] -2.310120e-09 -1.088700e-08 [149,] -9.237622e-09 -2.310120e-09 [150,] 4.004636e-08 -9.237622e-09 [151,] -1.312465e-08 4.004636e-08 [152,] -7.843882e-09 -1.312465e-08 [153,] 3.531396e-08 -7.843882e-09 [154,] -2.990760e-09 3.531396e-08 [155,] 4.903606e-08 -2.990760e-09 [156,] 3.008300e-08 4.903606e-08 [157,] -8.318699e-09 3.008300e-08 [158,] 5.257424e-10 -8.318699e-09 [159,] -1.325000e-08 5.257424e-10 [160,] -1.689110e-09 -1.325000e-08 [161,] 2.755757e-08 -1.689110e-09 [162,] -1.186418e-08 2.755757e-08 [163,] 2.366685e-08 -1.186418e-08 [164,] -1.001647e-08 2.366685e-08 [165,] 2.314559e-08 -1.001647e-08 [166,] -6.024660e-09 2.314559e-08 [167,] -4.883105e-09 -6.024660e-09 [168,] -1.258571e-08 -4.883105e-09 [169,] 2.315130e-08 -1.258571e-08 [170,] 4.003447e-10 2.315130e-08 [171,] -1.253815e-08 4.003447e-10 [172,] -1.762883e-09 -1.253815e-08 [173,] 1.940079e-08 -1.762883e-09 [174,] -1.068786e-08 1.940079e-08 [175,] 1.576010e-08 -1.068786e-08 [176,] 2.057630e-08 1.576010e-08 [177,] -1.548350e-08 2.057630e-08 [178,] 2.594973e-08 -1.548350e-08 [179,] 2.729364e-08 2.594973e-08 [180,] -1.211213e-08 2.729364e-08 [181,] -9.795188e-09 -1.211213e-08 [182,] -4.371582e-09 -9.795188e-09 [183,] -1.482580e-08 -4.371582e-09 [184,] -6.522989e-09 -1.482580e-08 [185,] -1.354965e-08 -6.522989e-09 [186,] -1.339605e-08 -1.354965e-08 [187,] -1.733338e-08 -1.339605e-08 [188,] 7.990972e-09 -1.733338e-08 [189,] 1.649194e-09 7.990972e-09 [190,] -7.576552e-09 1.649194e-09 [191,] -2.216650e-09 -7.576552e-09 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -5.839902e-09 -8.078410e-09 2 5.442616e-11 -5.839902e-09 3 -1.059637e-08 5.442616e-11 4 -2.533044e-09 -1.059637e-08 5 -9.523306e-09 -2.533044e-09 6 -9.415816e-09 -9.523306e-09 7 -1.338624e-08 -9.415816e-09 8 -7.223528e-09 -1.338624e-08 9 -1.333857e-08 -7.223528e-09 10 6.096565e-09 -1.333857e-08 11 -2.424932e-09 6.096565e-09 12 9.121680e-09 -2.424932e-09 13 -1.937490e-08 9.121680e-09 14 1.270367e-08 -1.937490e-08 15 6.197827e-09 1.270367e-08 16 -1.482440e-08 6.197827e-09 17 -2.207741e-08 -1.482440e-08 18 7.091446e-09 -2.207741e-08 19 3.420170e-09 7.091446e-09 20 -2.029849e-08 3.420170e-09 21 2.732400e-09 -2.029849e-08 22 1.322083e-08 2.732400e-09 23 -1.625320e-08 1.322083e-08 24 -3.449967e-08 -1.625320e-08 25 8.543836e-09 -3.449967e-08 26 1.128114e-08 8.543836e-09 27 3.422055e-09 1.128114e-08 28 1.174467e-08 3.422055e-09 29 4.507499e-09 1.174467e-08 30 4.613436e-09 4.507499e-09 31 -3.959784e-08 4.613436e-09 32 -3.241947e-08 -3.959784e-08 33 2.781602e-10 -3.241947e-08 34 1.073858e-08 2.781602e-10 35 1.236435e-08 1.073858e-08 36 -4.721592e-08 1.236435e-08 37 -4.442263e-08 -4.721592e-08 38 -3.880065e-08 -4.442263e-08 39 1.118448e-09 -3.880065e-08 40 -4.145215e-08 1.118448e-09 41 -4.843518e-08 -4.145215e-08 42 -4.857931e-08 -4.843518e-08 43 -1.174711e-09 -4.857931e-08 44 3.431565e-09 -1.174711e-09 45 -2.176285e-09 3.431565e-09 46 -4.243033e-08 -2.176285e-09 47 -4.198753e-08 -4.243033e-08 48 4.019866e-08 -4.198753e-08 49 4.228539e-08 4.019866e-08 50 9.308972e-09 4.228539e-08 51 3.712380e-08 9.308972e-09 52 4.592290e-08 3.712380e-08 53 3.920575e-08 4.592290e-08 54 3.871622e-08 3.920575e-08 55 3.542194e-08 3.871622e-08 56 4.069888e-08 3.542194e-08 57 3.489306e-08 4.069888e-08 58 6.159526e-09 3.489306e-08 59 7.495511e-09 6.159526e-09 60 2.962222e-08 7.495511e-09 61 1.593758e-09 2.962222e-08 62 7.303206e-09 1.593758e-09 63 2.682531e-08 7.303206e-09 64 4.484916e-09 2.682531e-08 65 2.674769e-08 4.484916e-09 66 2.712330e-08 2.674769e-08 67 2.300338e-08 2.712330e-08 68 2.849864e-08 2.300338e-08 69 2.238705e-08 2.849864e-08 70 3.744885e-09 2.238705e-08 71 3.537071e-08 3.744885e-09 72 1.722740e-08 3.537071e-08 73 1.965951e-08 1.722740e-08 74 4.372436e-09 1.965951e-08 75 1.430738e-08 4.372436e-09 76 2.280869e-08 1.430738e-08 77 1.572638e-08 2.280869e-08 78 1.594341e-08 1.572638e-08 79 1.185137e-08 1.594341e-08 80 -2.637620e-09 1.185137e-08 81 1.191792e-08 -2.637620e-09 82 2.196938e-08 1.191792e-08 83 2.265361e-09 2.196938e-08 84 5.122485e-09 2.265361e-09 85 5.954542e-09 5.122485e-09 86 1.282729e-08 5.954542e-09 87 -8.262047e-09 1.282729e-08 88 2.471861e-10 -8.262047e-09 89 3.653265e-09 2.471861e-10 90 3.092120e-09 3.653265e-09 91 1.910713e-10 3.092120e-09 92 -5.040357e-09 1.910713e-10 93 -1.134251e-08 -5.040357e-09 94 -7.587460e-10 -1.134251e-08 95 9.449601e-09 -7.587460e-10 96 1.909885e-09 9.449601e-09 97 -5.554737e-09 1.909885e-09 98 2.935065e-10 -5.554737e-09 99 -8.116233e-10 2.935065e-10 100 -1.730759e-09 -8.116233e-10 101 2.977468e-10 -1.730759e-09 102 5.664224e-10 2.977468e-10 103 -3.581312e-09 5.664224e-10 104 -7.113596e-09 -3.581312e-09 105 -1.361017e-08 -7.113596e-09 106 6.556636e-09 -1.361017e-08 107 7.166119e-09 6.556636e-09 108 -2.183079e-08 7.166119e-09 109 1.685140e-09 -2.183079e-08 110 7.172185e-09 1.685140e-09 111 -3.551770e-09 7.172185e-09 112 -1.445504e-08 -3.551770e-09 113 -2.262511e-08 -1.445504e-08 114 -2.463683e-09 -2.262511e-08 115 -6.079331e-09 -2.463683e-09 116 -1.395920e-10 -6.079331e-09 117 -2.612008e-08 -1.395920e-10 118 3.840322e-09 -2.612008e-08 119 -1.553836e-08 3.840322e-09 120 -3.378089e-08 -1.553836e-08 121 -7.652053e-10 -3.378089e-08 122 -2.613568e-08 -7.652053e-10 123 -6.077659e-09 -2.613568e-08 124 1.590524e-09 -6.077659e-09 125 -4.384584e-09 1.590524e-09 126 -3.406852e-08 -4.384584e-09 127 -8.263735e-09 -3.406852e-08 128 -3.665229e-09 -8.263735e-09 129 -3.851881e-08 -3.665229e-09 130 1.457376e-09 -3.851881e-08 131 -2.783792e-08 1.457376e-09 132 -5.711119e-09 -2.783792e-08 133 -2.949631e-09 -5.711119e-09 134 3.362908e-09 -2.949631e-09 135 -8.194389e-09 3.362908e-09 136 4.816172e-10 -8.194389e-09 137 -7.263821e-09 4.816172e-10 138 -6.717292e-09 -7.263821e-09 139 -1.077369e-08 -6.717292e-09 140 -4.798132e-09 -1.077369e-08 141 -1.172741e-08 -4.798132e-09 142 -3.995279e-08 -1.172741e-08 143 -3.929965e-08 -3.995279e-08 144 4.252930e-08 -3.929965e-08 145 -5.852581e-09 4.252930e-08 146 -2.979099e-10 -5.852581e-09 147 -1.088700e-08 -2.979099e-10 148 -2.310120e-09 -1.088700e-08 149 -9.237622e-09 -2.310120e-09 150 4.004636e-08 -9.237622e-09 151 -1.312465e-08 4.004636e-08 152 -7.843882e-09 -1.312465e-08 153 3.531396e-08 -7.843882e-09 154 -2.990760e-09 3.531396e-08 155 4.903606e-08 -2.990760e-09 156 3.008300e-08 4.903606e-08 157 -8.318699e-09 3.008300e-08 158 5.257424e-10 -8.318699e-09 159 -1.325000e-08 5.257424e-10 160 -1.689110e-09 -1.325000e-08 161 2.755757e-08 -1.689110e-09 162 -1.186418e-08 2.755757e-08 163 2.366685e-08 -1.186418e-08 164 -1.001647e-08 2.366685e-08 165 2.314559e-08 -1.001647e-08 166 -6.024660e-09 2.314559e-08 167 -4.883105e-09 -6.024660e-09 168 -1.258571e-08 -4.883105e-09 169 2.315130e-08 -1.258571e-08 170 4.003447e-10 2.315130e-08 171 -1.253815e-08 4.003447e-10 172 -1.762883e-09 -1.253815e-08 173 1.940079e-08 -1.762883e-09 174 -1.068786e-08 1.940079e-08 175 1.576010e-08 -1.068786e-08 176 2.057630e-08 1.576010e-08 177 -1.548350e-08 2.057630e-08 178 2.594973e-08 -1.548350e-08 179 2.729364e-08 2.594973e-08 180 -1.211213e-08 2.729364e-08 181 -9.795188e-09 -1.211213e-08 182 -4.371582e-09 -9.795188e-09 183 -1.482580e-08 -4.371582e-09 184 -6.522989e-09 -1.482580e-08 185 -1.354965e-08 -6.522989e-09 186 -1.339605e-08 -1.354965e-08 187 -1.733338e-08 -1.339605e-08 188 7.990972e-09 -1.733338e-08 189 1.649194e-09 7.990972e-09 190 -7.576552e-09 1.649194e-09 191 -2.216650e-09 -7.576552e-09 > 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/7ji7r1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8ljf01227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9rp551227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10ycup1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/1118k31227119165.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/12s7jn1227119165.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/133gjz1227119165.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/1443u41227119165.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/154fjh1227119165.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/16zmw71227119165.tab") + } > > system("convert tmp/18bq71227119165.ps tmp/18bq71227119165.png") > system("convert tmp/2j3uo1227119165.ps tmp/2j3uo1227119165.png") > system("convert tmp/3usa01227119165.ps tmp/3usa01227119165.png") > system("convert tmp/4fal91227119165.ps tmp/4fal91227119165.png") > system("convert tmp/5qj9x1227119165.ps tmp/5qj9x1227119165.png") > system("convert tmp/6fbmq1227119165.ps tmp/6fbmq1227119165.png") > system("convert tmp/7ji7r1227119165.ps tmp/7ji7r1227119165.png") > system("convert tmp/8ljf01227119165.ps tmp/8ljf01227119165.png") > system("convert tmp/9rp551227119165.ps tmp/9rp551227119165.png") > system("convert tmp/10ycup1227119165.ps tmp/10ycup1227119165.png") > > > proc.time() user system elapsed 5.225 1.791 6.214