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Type 'q()' to quit R. > x <- array(list(1687 + ,0 + ,1508 + ,0 + ,1507 + ,0 + ,1385 + ,0 + ,1632 + ,0 + ,1511 + ,0 + ,1559 + ,0 + ,1630 + ,0 + ,1579 + ,0 + ,1653 + ,0 + ,2152 + ,0 + ,2148 + ,0 + ,1752 + ,0 + ,1765 + ,0 + ,1717 + ,0 + ,1558 + ,0 + ,1575 + ,0 + ,1520 + ,0 + ,1805 + ,0 + ,1800 + ,0 + ,1719 + ,0 + ,2008 + ,0 + ,2242 + ,0 + ,2478 + ,0 + ,2030 + ,0 + ,1655 + ,0 + ,1693 + ,0 + ,1623 + ,0 + ,1805 + ,0 + ,1746 + ,0 + ,1795 + ,0 + ,1926 + ,0 + ,1619 + ,0 + ,1992 + ,0 + ,2233 + ,0 + ,2192 + ,0 + ,2080 + ,0 + ,1768 + ,0 + ,1835 + ,0 + ,1569 + ,0 + ,1976 + ,0 + ,1853 + ,0 + ,1965 + ,0 + ,1689 + ,0 + ,1778 + ,0 + ,1976 + ,0 + ,2397 + ,0 + ,2654 + ,0 + ,2097 + ,0 + ,1963 + ,0 + ,1677 + ,0 + ,1941 + ,0 + ,2003 + ,0 + ,1813 + ,0 + ,2012 + ,0 + ,1912 + ,0 + ,2084 + ,0 + ,2080 + ,0 + ,2118 + ,0 + ,2150 + ,0 + ,1608 + ,0 + ,1503 + ,0 + ,1548 + ,0 + ,1382 + ,0 + ,1731 + ,0 + ,1798 + ,0 + ,1779 + ,0 + ,1887 + ,0 + ,2004 + ,0 + ,2077 + ,0 + ,2092 + ,0 + ,2051 + ,0 + ,1577 + ,0 + ,1356 + ,0 + ,1652 + ,0 + ,1382 + ,0 + ,1519 + ,0 + ,1421 + ,0 + ,1442 + ,0 + ,1543 + ,0 + ,1656 + ,0 + ,1561 + ,0 + ,1905 + ,0 + ,2199 + ,0 + ,1473 + ,0 + ,1655 + ,0 + ,1407 + ,0 + ,1395 + ,0 + ,1530 + ,0 + ,1309 + ,0 + ,1526 + ,0 + ,1327 + ,0 + ,1627 + ,0 + ,1748 + ,0 + ,1958 + ,0 + ,2274 + ,0 + ,1648 + ,0 + ,1401 + ,0 + ,1411 + ,0 + ,1403 + ,0 + ,1394 + ,0 + ,1520 + ,0 + ,1528 + ,0 + ,1643 + ,0 + ,1515 + ,0 + ,1685 + ,0 + ,2000 + ,0 + ,2215 + ,0 + ,1956 + ,0 + ,1462 + ,0 + ,1563 + ,0 + ,1459 + ,0 + ,1446 + ,0 + ,1622 + ,0 + ,1657 + ,0 + ,1638 + ,0 + ,1643 + ,0 + ,1683 + ,0 + ,2050 + ,0 + ,2262 + ,0 + ,1813 + ,0 + ,1445 + ,0 + ,1762 + ,0 + ,1461 + ,0 + ,1556 + ,0 + ,1431 + ,0 + ,1427 + ,0 + ,1554 + ,0 + ,1645 + ,0 + ,1653 + ,0 + ,2016 + ,0 + ,2207 + ,0 + ,1665 + ,0 + ,1361 + ,0 + ,1506 + ,0 + ,1360 + ,0 + ,1453 + ,0 + ,1522 + ,0 + ,1460 + ,0 + ,1552 + ,0 + ,1548 + ,0 + ,1827 + ,0 + ,1737 + ,0 + ,1941 + ,0 + ,1474 + ,0 + ,1458 + ,0 + ,1542 + ,0 + ,1404 + ,0 + ,1522 + ,0 + ,1385 + ,0 + ,1641 + ,0 + ,1510 + ,0 + ,1681 + ,0 + ,1938 + ,0 + ,1868 + ,0 + ,1726 + ,0 + ,1456 + ,0 + ,1445 + ,0 + ,1456 + ,0 + ,1365 + ,0 + ,1487 + ,0 + ,1558 + ,0 + ,1488 + ,0 + ,1684 + ,0 + ,1594 + ,0 + ,1850 + ,0 + ,1998 + ,0 + ,2079 + ,0 + ,1494 + ,0 + ,1057 + ,1 + ,1218 + ,1 + ,1168 + ,1 + ,1236 + ,1 + ,1076 + ,1 + ,1174 + ,1 + ,1139 + ,1 + ,1427 + ,1 + ,1487 + ,1 + ,1483 + ,1 + ,1513 + ,1 + ,1357 + ,1 + ,1165 + ,1 + ,1282 + ,1 + ,1110 + ,1 + ,1297 + ,1 + ,1185 + ,1 + ,1222 + ,1 + ,1284 + ,1 + ,1444 + ,1 + ,1575 + ,1 + ,1737 + ,1 + ,1763 + ,1) + ,dim=c(2 + ,192) + ,dimnames=list(c('Y' + ,'X') + ,1:192)) > y <- array(NA,dim=c(2,192),dimnames=list(c('Y','X'),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 Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 1687 0 1 0 0 0 0 0 0 0 0 0 0 1 2 1508 0 0 1 0 0 0 0 0 0 0 0 0 2 3 1507 0 0 0 1 0 0 0 0 0 0 0 0 3 4 1385 0 0 0 0 1 0 0 0 0 0 0 0 4 5 1632 0 0 0 0 0 1 0 0 0 0 0 0 5 6 1511 0 0 0 0 0 0 1 0 0 0 0 0 6 7 1559 0 0 0 0 0 0 0 1 0 0 0 0 7 8 1630 0 0 0 0 0 0 0 0 1 0 0 0 8 9 1579 0 0 0 0 0 0 0 0 0 1 0 0 9 10 1653 0 0 0 0 0 0 0 0 0 0 1 0 10 11 2152 0 0 0 0 0 0 0 0 0 0 0 1 11 12 2148 0 0 0 0 0 0 0 0 0 0 0 0 12 13 1752 0 1 0 0 0 0 0 0 0 0 0 0 13 14 1765 0 0 1 0 0 0 0 0 0 0 0 0 14 15 1717 0 0 0 1 0 0 0 0 0 0 0 0 15 16 1558 0 0 0 0 1 0 0 0 0 0 0 0 16 17 1575 0 0 0 0 0 1 0 0 0 0 0 0 17 18 1520 0 0 0 0 0 0 1 0 0 0 0 0 18 19 1805 0 0 0 0 0 0 0 1 0 0 0 0 19 20 1800 0 0 0 0 0 0 0 0 1 0 0 0 20 21 1719 0 0 0 0 0 0 0 0 0 1 0 0 21 22 2008 0 0 0 0 0 0 0 0 0 0 1 0 22 23 2242 0 0 0 0 0 0 0 0 0 0 0 1 23 24 2478 0 0 0 0 0 0 0 0 0 0 0 0 24 25 2030 0 1 0 0 0 0 0 0 0 0 0 0 25 26 1655 0 0 1 0 0 0 0 0 0 0 0 0 26 27 1693 0 0 0 1 0 0 0 0 0 0 0 0 27 28 1623 0 0 0 0 1 0 0 0 0 0 0 0 28 29 1805 0 0 0 0 0 1 0 0 0 0 0 0 29 30 1746 0 0 0 0 0 0 1 0 0 0 0 0 30 31 1795 0 0 0 0 0 0 0 1 0 0 0 0 31 32 1926 0 0 0 0 0 0 0 0 1 0 0 0 32 33 1619 0 0 0 0 0 0 0 0 0 1 0 0 33 34 1992 0 0 0 0 0 0 0 0 0 0 1 0 34 35 2233 0 0 0 0 0 0 0 0 0 0 0 1 35 36 2192 0 0 0 0 0 0 0 0 0 0 0 0 36 37 2080 0 1 0 0 0 0 0 0 0 0 0 0 37 38 1768 0 0 1 0 0 0 0 0 0 0 0 0 38 39 1835 0 0 0 1 0 0 0 0 0 0 0 0 39 40 1569 0 0 0 0 1 0 0 0 0 0 0 0 40 41 1976 0 0 0 0 0 1 0 0 0 0 0 0 41 42 1853 0 0 0 0 0 0 1 0 0 0 0 0 42 43 1965 0 0 0 0 0 0 0 1 0 0 0 0 43 44 1689 0 0 0 0 0 0 0 0 1 0 0 0 44 45 1778 0 0 0 0 0 0 0 0 0 1 0 0 45 46 1976 0 0 0 0 0 0 0 0 0 0 1 0 46 47 2397 0 0 0 0 0 0 0 0 0 0 0 1 47 48 2654 0 0 0 0 0 0 0 0 0 0 0 0 48 49 2097 0 1 0 0 0 0 0 0 0 0 0 0 49 50 1963 0 0 1 0 0 0 0 0 0 0 0 0 50 51 1677 0 0 0 1 0 0 0 0 0 0 0 0 51 52 1941 0 0 0 0 1 0 0 0 0 0 0 0 52 53 2003 0 0 0 0 0 1 0 0 0 0 0 0 53 54 1813 0 0 0 0 0 0 1 0 0 0 0 0 54 55 2012 0 0 0 0 0 0 0 1 0 0 0 0 55 56 1912 0 0 0 0 0 0 0 0 1 0 0 0 56 57 2084 0 0 0 0 0 0 0 0 0 1 0 0 57 58 2080 0 0 0 0 0 0 0 0 0 0 1 0 58 59 2118 0 0 0 0 0 0 0 0 0 0 0 1 59 60 2150 0 0 0 0 0 0 0 0 0 0 0 0 60 61 1608 0 1 0 0 0 0 0 0 0 0 0 0 61 62 1503 0 0 1 0 0 0 0 0 0 0 0 0 62 63 1548 0 0 0 1 0 0 0 0 0 0 0 0 63 64 1382 0 0 0 0 1 0 0 0 0 0 0 0 64 65 1731 0 0 0 0 0 1 0 0 0 0 0 0 65 66 1798 0 0 0 0 0 0 1 0 0 0 0 0 66 67 1779 0 0 0 0 0 0 0 1 0 0 0 0 67 68 1887 0 0 0 0 0 0 0 0 1 0 0 0 68 69 2004 0 0 0 0 0 0 0 0 0 1 0 0 69 70 2077 0 0 0 0 0 0 0 0 0 0 1 0 70 71 2092 0 0 0 0 0 0 0 0 0 0 0 1 71 72 2051 0 0 0 0 0 0 0 0 0 0 0 0 72 73 1577 0 1 0 0 0 0 0 0 0 0 0 0 73 74 1356 0 0 1 0 0 0 0 0 0 0 0 0 74 75 1652 0 0 0 1 0 0 0 0 0 0 0 0 75 76 1382 0 0 0 0 1 0 0 0 0 0 0 0 76 77 1519 0 0 0 0 0 1 0 0 0 0 0 0 77 78 1421 0 0 0 0 0 0 1 0 0 0 0 0 78 79 1442 0 0 0 0 0 0 0 1 0 0 0 0 79 80 1543 0 0 0 0 0 0 0 0 1 0 0 0 80 81 1656 0 0 0 0 0 0 0 0 0 1 0 0 81 82 1561 0 0 0 0 0 0 0 0 0 0 1 0 82 83 1905 0 0 0 0 0 0 0 0 0 0 0 1 83 84 2199 0 0 0 0 0 0 0 0 0 0 0 0 84 85 1473 0 1 0 0 0 0 0 0 0 0 0 0 85 86 1655 0 0 1 0 0 0 0 0 0 0 0 0 86 87 1407 0 0 0 1 0 0 0 0 0 0 0 0 87 88 1395 0 0 0 0 1 0 0 0 0 0 0 0 88 89 1530 0 0 0 0 0 1 0 0 0 0 0 0 89 90 1309 0 0 0 0 0 0 1 0 0 0 0 0 90 91 1526 0 0 0 0 0 0 0 1 0 0 0 0 91 92 1327 0 0 0 0 0 0 0 0 1 0 0 0 92 93 1627 0 0 0 0 0 0 0 0 0 1 0 0 93 94 1748 0 0 0 0 0 0 0 0 0 0 1 0 94 95 1958 0 0 0 0 0 0 0 0 0 0 0 1 95 96 2274 0 0 0 0 0 0 0 0 0 0 0 0 96 97 1648 0 1 0 0 0 0 0 0 0 0 0 0 97 98 1401 0 0 1 0 0 0 0 0 0 0 0 0 98 99 1411 0 0 0 1 0 0 0 0 0 0 0 0 99 100 1403 0 0 0 0 1 0 0 0 0 0 0 0 100 101 1394 0 0 0 0 0 1 0 0 0 0 0 0 101 102 1520 0 0 0 0 0 0 1 0 0 0 0 0 102 103 1528 0 0 0 0 0 0 0 1 0 0 0 0 103 104 1643 0 0 0 0 0 0 0 0 1 0 0 0 104 105 1515 0 0 0 0 0 0 0 0 0 1 0 0 105 106 1685 0 0 0 0 0 0 0 0 0 0 1 0 106 107 2000 0 0 0 0 0 0 0 0 0 0 0 1 107 108 2215 0 0 0 0 0 0 0 0 0 0 0 0 108 109 1956 0 1 0 0 0 0 0 0 0 0 0 0 109 110 1462 0 0 1 0 0 0 0 0 0 0 0 0 110 111 1563 0 0 0 1 0 0 0 0 0 0 0 0 111 112 1459 0 0 0 0 1 0 0 0 0 0 0 0 112 113 1446 0 0 0 0 0 1 0 0 0 0 0 0 113 114 1622 0 0 0 0 0 0 1 0 0 0 0 0 114 115 1657 0 0 0 0 0 0 0 1 0 0 0 0 115 116 1638 0 0 0 0 0 0 0 0 1 0 0 0 116 117 1643 0 0 0 0 0 0 0 0 0 1 0 0 117 118 1683 0 0 0 0 0 0 0 0 0 0 1 0 118 119 2050 0 0 0 0 0 0 0 0 0 0 0 1 119 120 2262 0 0 0 0 0 0 0 0 0 0 0 0 120 121 1813 0 1 0 0 0 0 0 0 0 0 0 0 121 122 1445 0 0 1 0 0 0 0 0 0 0 0 0 122 123 1762 0 0 0 1 0 0 0 0 0 0 0 0 123 124 1461 0 0 0 0 1 0 0 0 0 0 0 0 124 125 1556 0 0 0 0 0 1 0 0 0 0 0 0 125 126 1431 0 0 0 0 0 0 1 0 0 0 0 0 126 127 1427 0 0 0 0 0 0 0 1 0 0 0 0 127 128 1554 0 0 0 0 0 0 0 0 1 0 0 0 128 129 1645 0 0 0 0 0 0 0 0 0 1 0 0 129 130 1653 0 0 0 0 0 0 0 0 0 0 1 0 130 131 2016 0 0 0 0 0 0 0 0 0 0 0 1 131 132 2207 0 0 0 0 0 0 0 0 0 0 0 0 132 133 1665 0 1 0 0 0 0 0 0 0 0 0 0 133 134 1361 0 0 1 0 0 0 0 0 0 0 0 0 134 135 1506 0 0 0 1 0 0 0 0 0 0 0 0 135 136 1360 0 0 0 0 1 0 0 0 0 0 0 0 136 137 1453 0 0 0 0 0 1 0 0 0 0 0 0 137 138 1522 0 0 0 0 0 0 1 0 0 0 0 0 138 139 1460 0 0 0 0 0 0 0 1 0 0 0 0 139 140 1552 0 0 0 0 0 0 0 0 1 0 0 0 140 141 1548 0 0 0 0 0 0 0 0 0 1 0 0 141 142 1827 0 0 0 0 0 0 0 0 0 0 1 0 142 143 1737 0 0 0 0 0 0 0 0 0 0 0 1 143 144 1941 0 0 0 0 0 0 0 0 0 0 0 0 144 145 1474 0 1 0 0 0 0 0 0 0 0 0 0 145 146 1458 0 0 1 0 0 0 0 0 0 0 0 0 146 147 1542 0 0 0 1 0 0 0 0 0 0 0 0 147 148 1404 0 0 0 0 1 0 0 0 0 0 0 0 148 149 1522 0 0 0 0 0 1 0 0 0 0 0 0 149 150 1385 0 0 0 0 0 0 1 0 0 0 0 0 150 151 1641 0 0 0 0 0 0 0 1 0 0 0 0 151 152 1510 0 0 0 0 0 0 0 0 1 0 0 0 152 153 1681 0 0 0 0 0 0 0 0 0 1 0 0 153 154 1938 0 0 0 0 0 0 0 0 0 0 1 0 154 155 1868 0 0 0 0 0 0 0 0 0 0 0 1 155 156 1726 0 0 0 0 0 0 0 0 0 0 0 0 156 157 1456 0 1 0 0 0 0 0 0 0 0 0 0 157 158 1445 0 0 1 0 0 0 0 0 0 0 0 0 158 159 1456 0 0 0 1 0 0 0 0 0 0 0 0 159 160 1365 0 0 0 0 1 0 0 0 0 0 0 0 160 161 1487 0 0 0 0 0 1 0 0 0 0 0 0 161 162 1558 0 0 0 0 0 0 1 0 0 0 0 0 162 163 1488 0 0 0 0 0 0 0 1 0 0 0 0 163 164 1684 0 0 0 0 0 0 0 0 1 0 0 0 164 165 1594 0 0 0 0 0 0 0 0 0 1 0 0 165 166 1850 0 0 0 0 0 0 0 0 0 0 1 0 166 167 1998 0 0 0 0 0 0 0 0 0 0 0 1 167 168 2079 0 0 0 0 0 0 0 0 0 0 0 0 168 169 1494 0 1 0 0 0 0 0 0 0 0 0 0 169 170 1057 1 0 1 0 0 0 0 0 0 0 0 0 170 171 1218 1 0 0 1 0 0 0 0 0 0 0 0 171 172 1168 1 0 0 0 1 0 0 0 0 0 0 0 172 173 1236 1 0 0 0 0 1 0 0 0 0 0 0 173 174 1076 1 0 0 0 0 0 1 0 0 0 0 0 174 175 1174 1 0 0 0 0 0 0 1 0 0 0 0 175 176 1139 1 0 0 0 0 0 0 0 1 0 0 0 176 177 1427 1 0 0 0 0 0 0 0 0 1 0 0 177 178 1487 1 0 0 0 0 0 0 0 0 0 1 0 178 179 1483 1 0 0 0 0 0 0 0 0 0 0 1 179 180 1513 1 0 0 0 0 0 0 0 0 0 0 0 180 181 1357 1 1 0 0 0 0 0 0 0 0 0 0 181 182 1165 1 0 1 0 0 0 0 0 0 0 0 0 182 183 1282 1 0 0 1 0 0 0 0 0 0 0 0 183 184 1110 1 0 0 0 1 0 0 0 0 0 0 0 184 185 1297 1 0 0 0 0 1 0 0 0 0 0 0 185 186 1185 1 0 0 0 0 0 1 0 0 0 0 0 186 187 1222 1 0 0 0 0 0 0 1 0 0 0 0 187 188 1284 1 0 0 0 0 0 0 0 1 0 0 0 188 189 1444 1 0 0 0 0 0 0 0 0 1 0 0 189 190 1575 1 0 0 0 0 0 0 0 0 0 1 0 190 191 1737 1 0 0 0 0 0 0 0 0 0 0 1 191 192 1763 1 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) X M1 M2 M3 M4 2324.063 -226.385 -451.375 -635.461 -583.134 -694.556 M5 M6 M7 M8 M9 M10 -555.479 -609.464 -532.074 -515.434 -460.857 -319.717 M11 t -118.390 -1.765 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -333.70 -105.83 4.71 85.02 414.65 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2324.0634 44.0299 52.784 < 2e-16 *** X -226.3850 41.0372 -5.517 1.20e-07 *** M1 -451.3750 53.9429 -8.368 1.67e-14 *** M2 -635.4611 53.9415 -11.781 < 2e-16 *** M3 -583.1337 53.9313 -10.813 < 2e-16 *** M4 -694.5563 53.9222 -12.881 < 2e-16 *** M5 -555.4790 53.9141 -10.303 < 2e-16 *** M6 -609.4641 53.9071 -11.306 < 2e-16 *** M7 -532.0743 53.9012 -9.871 < 2e-16 *** M8 -515.4344 53.8964 -9.563 < 2e-16 *** M9 -460.8571 53.8926 -8.551 5.44e-15 *** M10 -319.7172 53.8900 -5.933 1.52e-08 *** M11 -118.3899 53.8884 -2.197 0.0293 * t -1.7649 0.2406 -7.337 7.47e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 152.4 on 178 degrees of freedom Multiple R-squared: 0.7419, Adjusted R-squared: 0.723 F-statistic: 39.35 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,] 0.322030229 6.440605e-01 6.779698e-01 [2,] 0.233521089 4.670422e-01 7.664789e-01 [3,] 0.180685578 3.613712e-01 8.193144e-01 [4,] 0.103668911 2.073378e-01 8.963311e-01 [5,] 0.055942538 1.118851e-01 9.440575e-01 [6,] 0.085842192 1.716844e-01 9.141578e-01 [7,] 0.052985211 1.059704e-01 9.470148e-01 [8,] 0.056850078 1.137002e-01 9.431499e-01 [9,] 0.036119292 7.223858e-02 9.638807e-01 [10,] 0.070561974 1.411239e-01 9.294380e-01 [11,] 0.062764515 1.255290e-01 9.372355e-01 [12,] 0.041106672 8.221334e-02 9.588933e-01 [13,] 0.025003522 5.000704e-02 9.749965e-01 [14,] 0.015172774 3.034555e-02 9.848272e-01 [15,] 0.010486251 2.097250e-02 9.895137e-01 [16,] 0.006139054 1.227811e-02 9.938609e-01 [17,] 0.013744830 2.748966e-02 9.862552e-01 [18,] 0.008287862 1.657572e-02 9.917121e-01 [19,] 0.007661080 1.532216e-02 9.923389e-01 [20,] 0.024077598 4.815520e-02 9.759224e-01 [21,] 0.018334199 3.666840e-02 9.816658e-01 [22,] 0.013477336 2.695467e-02 9.865227e-01 [23,] 0.008735154 1.747031e-02 9.912648e-01 [24,] 0.008726025 1.745205e-02 9.912740e-01 [25,] 0.008445244 1.689049e-02 9.915548e-01 [26,] 0.006249498 1.249900e-02 9.937505e-01 [27,] 0.004631341 9.262681e-03 9.953687e-01 [28,] 0.015859797 3.171959e-02 9.841402e-01 [29,] 0.011273509 2.254702e-02 9.887265e-01 [30,] 0.008556565 1.711313e-02 9.914434e-01 [31,] 0.006822711 1.364542e-02 9.931773e-01 [32,] 0.015023389 3.004678e-02 9.849766e-01 [33,] 0.014269097 2.853819e-02 9.857309e-01 [34,] 0.016533715 3.306743e-02 9.834663e-01 [35,] 0.029390380 5.878076e-02 9.706096e-01 [36,] 0.056643108 1.132862e-01 9.433569e-01 [37,] 0.064679720 1.293594e-01 9.353203e-01 [38,] 0.061423283 1.228466e-01 9.385767e-01 [39,] 0.074444241 1.488885e-01 9.255558e-01 [40,] 0.076168409 1.523368e-01 9.238316e-01 [41,] 0.126954884 2.539098e-01 8.730451e-01 [42,] 0.124807889 2.496158e-01 8.751921e-01 [43,] 0.308376884 6.167538e-01 6.916231e-01 [44,] 0.600261199 7.994776e-01 3.997388e-01 [45,] 0.910334590 1.793308e-01 8.966541e-02 [46,] 0.965959463 6.808107e-02 3.404054e-02 [47,] 0.977600612 4.479878e-02 2.239939e-02 [48,] 0.990218319 1.956336e-02 9.781681e-03 [49,] 0.991795631 1.640874e-02 8.204369e-03 [50,] 0.993423777 1.315245e-02 6.576223e-03 [51,] 0.994886610 1.022678e-02 5.113390e-03 [52,] 0.996511940 6.976120e-03 3.488060e-03 [53,] 0.998414653 3.170695e-03 1.585347e-03 [54,] 0.999071134 1.857732e-03 9.288658e-04 [55,] 0.999416963 1.166075e-03 5.830374e-04 [56,] 0.999768813 4.623747e-04 2.311874e-04 [57,] 0.999924721 1.505581e-04 7.527903e-05 [58,] 0.999979498 4.100324e-05 2.050162e-05 [59,] 0.999974507 5.098531e-05 2.549265e-05 [60,] 0.999977926 4.414876e-05 2.207438e-05 [61,] 0.999985409 2.918214e-05 1.459107e-05 [62,] 0.999990594 1.881281e-05 9.406403e-06 [63,] 0.999996307 7.386808e-06 3.693404e-06 [64,] 0.999996658 6.684040e-06 3.342020e-06 [65,] 0.999995281 9.437882e-06 4.718941e-06 [66,] 0.999998977 2.045492e-06 1.022746e-06 [67,] 0.999999085 1.829989e-06 9.149945e-07 [68,] 0.999998766 2.468953e-06 1.234477e-06 [69,] 0.999999467 1.065212e-06 5.326062e-07 [70,] 0.999999579 8.426334e-07 4.213167e-07 [71,] 0.999999643 7.144278e-07 3.572139e-07 [72,] 0.999999436 1.128474e-06 5.642371e-07 [73,] 0.999999195 1.610432e-06 8.052162e-07 [74,] 0.999999624 7.521880e-07 3.760940e-07 [75,] 0.999999464 1.071920e-06 5.359602e-07 [76,] 0.999999914 1.726455e-07 8.632276e-08 [77,] 0.999999854 2.916550e-07 1.458275e-07 [78,] 0.999999776 4.473434e-07 2.236717e-07 [79,] 0.999999637 7.268184e-07 3.634092e-07 [80,] 0.999999707 5.861344e-07 2.930672e-07 [81,] 0.999999491 1.017428e-06 5.087142e-07 [82,] 0.999999260 1.479839e-06 7.399197e-07 [83,] 0.999999383 1.234951e-06 6.174754e-07 [84,] 0.999998974 2.051313e-06 1.025656e-06 [85,] 0.999999204 1.592511e-06 7.962553e-07 [86,] 0.999998599 2.802193e-06 1.401096e-06 [87,] 0.999997773 4.454689e-06 2.227344e-06 [88,] 0.999996233 7.534737e-06 3.767368e-06 [89,] 0.999997128 5.744777e-06 2.872388e-06 [90,] 0.999997517 4.965157e-06 2.482578e-06 [91,] 0.999995704 8.592372e-06 4.296186e-06 [92,] 0.999994940 1.011926e-05 5.059628e-06 [93,] 0.999999306 1.388117e-06 6.940584e-07 [94,] 0.999998759 2.482348e-06 1.241174e-06 [95,] 0.999997911 4.178654e-06 2.089327e-06 [96,] 0.999996402 7.195198e-06 3.597599e-06 [97,] 0.999995632 8.736546e-06 4.368273e-06 [98,] 0.999994964 1.007175e-05 5.035874e-06 [99,] 0.999993790 1.241960e-05 6.209801e-06 [100,] 0.999990094 1.981135e-05 9.905675e-06 [101,] 0.999983602 3.279668e-05 1.639834e-05 [102,] 0.999985073 2.985496e-05 1.492748e-05 [103,] 0.999980317 3.936545e-05 1.968273e-05 [104,] 0.999993816 1.236891e-05 6.184455e-06 [105,] 0.999998229 3.542868e-06 1.771434e-06 [106,] 0.999996906 6.187668e-06 3.093834e-06 [107,] 0.999999405 1.189585e-06 5.947923e-07 [108,] 0.999999120 1.760731e-06 8.803656e-07 [109,] 0.999998561 2.877304e-06 1.438652e-06 [110,] 0.999997360 5.279578e-06 2.639789e-06 [111,] 0.999996029 7.941917e-06 3.970958e-06 [112,] 0.999992949 1.410232e-05 7.051160e-06 [113,] 0.999987450 2.509972e-05 1.254986e-05 [114,] 0.999990330 1.933970e-05 9.669848e-06 [115,] 0.999991466 1.706757e-05 8.533785e-06 [116,] 0.999999671 6.588261e-07 3.294131e-07 [117,] 0.999999908 1.846120e-07 9.230599e-08 [118,] 0.999999811 3.774246e-07 1.887123e-07 [119,] 0.999999640 7.203513e-07 3.601757e-07 [120,] 0.999999259 1.481194e-06 7.405972e-07 [121,] 0.999998520 2.960269e-06 1.480134e-06 [122,] 0.999998728 2.543501e-06 1.271750e-06 [123,] 0.999997479 5.042960e-06 2.521480e-06 [124,] 0.999996084 7.832483e-06 3.916241e-06 [125,] 0.999993020 1.396089e-05 6.980444e-06 [126,] 0.999988065 2.386909e-05 1.193455e-05 [127,] 0.999986986 2.602708e-05 1.301354e-05 [128,] 0.999984428 3.114480e-05 1.557240e-05 [129,] 0.999971704 5.659151e-05 2.829576e-05 [130,] 0.999956782 8.643508e-05 4.321754e-05 [131,] 0.999932679 1.346425e-04 6.732126e-05 [132,] 0.999880892 2.382157e-04 1.191079e-04 [133,] 0.999787840 4.243193e-04 2.121597e-04 [134,] 0.999617514 7.649710e-04 3.824855e-04 [135,] 0.999849348 3.013047e-04 1.506524e-04 [136,] 0.999712456 5.750887e-04 2.875443e-04 [137,] 0.999566514 8.669717e-04 4.334858e-04 [138,] 0.999832416 3.351673e-04 1.675837e-04 [139,] 0.999689039 6.219227e-04 3.109614e-04 [140,] 0.999814008 3.719845e-04 1.859923e-04 [141,] 0.999663751 6.724982e-04 3.362491e-04 [142,] 0.999343456 1.313088e-03 6.565440e-04 [143,] 0.999054436 1.891127e-03 9.455636e-04 [144,] 0.998550855 2.898290e-03 1.449145e-03 [145,] 0.998007267 3.985465e-03 1.992733e-03 [146,] 0.997326909 5.346182e-03 2.673091e-03 [147,] 0.994901009 1.019798e-02 5.098991e-03 [148,] 0.996192980 7.614041e-03 3.807020e-03 [149,] 0.996594748 6.810505e-03 3.405252e-03 [150,] 0.993103233 1.379353e-02 6.896767e-03 [151,] 0.988736805 2.252639e-02 1.126320e-02 [152,] 0.997981315 4.037371e-03 2.018685e-03 [153,] 0.994934054 1.013189e-02 5.065946e-03 [154,] 0.987866899 2.426620e-02 1.213310e-02 [155,] 0.973650040 5.269992e-02 2.634996e-02 [156,] 0.979706390 4.058722e-02 2.029361e-02 [157,] 0.957667776 8.466445e-02 4.233222e-02 [158,] 0.902218060 1.955639e-01 9.778194e-02 [159,] 0.832347654 3.353047e-01 1.676523e-01 > postscript(file="/var/www/html/rcomp/tmp/1e2ot1259074035.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2bpq91259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3r1ln1259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4z5bz1259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5zngj1259074035.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 6 -183.9235445 -177.0726091 -228.6351091 -237.4476091 -127.7601091 -193.0101091 7 8 9 10 11 12 -220.6351091 -164.5101091 -268.3226091 -333.6976091 -34.2601091 -154.8851091 13 14 15 16 17 18 -97.7452805 101.1056549 2.5431549 -43.2693451 -163.5818451 -162.8318451 19 20 21 22 23 24 46.5431549 26.6681549 -107.1443451 42.4806549 76.9181549 196.2931549 25 26 27 28 29 30 201.4329835 12.2839189 -0.2785811 42.9089189 87.5964189 84.3464189 31 32 33 34 35 36 57.7214189 173.8464189 -185.9660811 47.6589189 89.0964189 -68.5285811 37 38 39 40 41 42 272.6112475 146.4621829 162.8996829 10.0871829 279.7746829 212.5246829 43 44 45 46 47 48 248.8996829 -41.9753171 -5.7878171 52.8371829 274.2746829 414.6496829 49 50 51 52 53 54 310.7895114 362.6404468 26.0779468 403.2654468 327.9529468 193.7029468 55 56 57 58 59 60 317.0779468 202.2029468 321.3904468 178.0154468 16.4529468 -68.1720532 61 62 63 64 65 66 -157.0322246 -76.1812892 -81.7437892 -134.5562892 77.1312108 199.8812108 67 68 69 70 71 72 105.2562108 198.3812108 262.5687108 196.1937108 11.6312108 -145.9937892 73 74 75 76 77 78 -166.8539606 -202.0030252 43.4344748 -113.3780252 -113.6905252 -155.9405252 79 80 81 82 83 84 -210.5655252 -124.4405252 -64.2530252 -298.6280252 -154.1905252 23.1844748 85 86 87 88 89 90 -249.6756966 118.1752388 -180.3872612 -79.1997612 -81.5122612 -246.7622612 91 92 93 94 95 96 -105.3872612 -319.2622612 -72.0747612 -90.4497612 -80.0122612 119.3627388 97 98 99 100 101 102 -53.4974326 -114.6464972 -155.2089972 -50.0214972 -196.3339972 -14.5839972 103 104 105 106 107 108 -82.2089972 17.9160028 -162.8964972 -132.2714972 -16.8339972 81.5410028 109 110 111 112 113 114 275.6808314 -32.4682332 17.9692668 27.1567668 -123.1557332 108.5942668 115 116 117 118 119 120 67.9692668 34.0942668 -13.7182332 -113.0932332 54.3442668 149.7192668 121 122 123 124 125 126 153.8590954 -28.2899692 238.1475308 50.3350308 8.0225308 -61.2274692 127 128 129 130 131 132 -140.8524692 -28.7274692 9.4600308 -121.9149692 41.5225308 115.8975308 133 134 135 136 137 138 27.0373594 -91.1117052 3.3257948 -29.4867052 -73.7992052 50.9507948 139 140 141 142 143 144 -86.6742052 -9.5492052 -66.3617052 73.2632948 -216.2992052 -128.9242052 145 146 147 148 149 150 -142.7843767 27.0665587 60.5040587 35.6915587 16.3790587 -64.8709413 151 152 153 154 155 156 115.5040587 -30.3709413 87.8165587 205.4415587 -64.1209413 -322.7459413 157 158 159 160 161 162 -139.6061127 35.2448227 -4.3176773 17.8698227 2.5573227 129.3073227 163 164 165 166 167 168 -16.3176773 164.8073227 21.9948227 138.6198227 87.0573227 51.4323227 169 170 171 172 173 174 -80.4278487 -105.1918797 5.2456203 68.4331203 -0.8793797 -105.1293797 175 176 177 178 179 180 -82.7543797 -132.6293797 102.5581203 23.1831203 -180.3793797 -267.0043797 181 182 183 184 185 186 30.1354489 23.9863843 90.4238843 31.6113843 81.2988843 25.0488843 187 188 189 190 191 192 -13.5761157 33.5488843 140.7363843 132.3613843 94.7988843 4.1738843 > postscript(file="/var/www/html/rcomp/tmp/6dhn91259074035.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 -183.9235445 NA 1 -177.0726091 -183.9235445 2 -228.6351091 -177.0726091 3 -237.4476091 -228.6351091 4 -127.7601091 -237.4476091 5 -193.0101091 -127.7601091 6 -220.6351091 -193.0101091 7 -164.5101091 -220.6351091 8 -268.3226091 -164.5101091 9 -333.6976091 -268.3226091 10 -34.2601091 -333.6976091 11 -154.8851091 -34.2601091 12 -97.7452805 -154.8851091 13 101.1056549 -97.7452805 14 2.5431549 101.1056549 15 -43.2693451 2.5431549 16 -163.5818451 -43.2693451 17 -162.8318451 -163.5818451 18 46.5431549 -162.8318451 19 26.6681549 46.5431549 20 -107.1443451 26.6681549 21 42.4806549 -107.1443451 22 76.9181549 42.4806549 23 196.2931549 76.9181549 24 201.4329835 196.2931549 25 12.2839189 201.4329835 26 -0.2785811 12.2839189 27 42.9089189 -0.2785811 28 87.5964189 42.9089189 29 84.3464189 87.5964189 30 57.7214189 84.3464189 31 173.8464189 57.7214189 32 -185.9660811 173.8464189 33 47.6589189 -185.9660811 34 89.0964189 47.6589189 35 -68.5285811 89.0964189 36 272.6112475 -68.5285811 37 146.4621829 272.6112475 38 162.8996829 146.4621829 39 10.0871829 162.8996829 40 279.7746829 10.0871829 41 212.5246829 279.7746829 42 248.8996829 212.5246829 43 -41.9753171 248.8996829 44 -5.7878171 -41.9753171 45 52.8371829 -5.7878171 46 274.2746829 52.8371829 47 414.6496829 274.2746829 48 310.7895114 414.6496829 49 362.6404468 310.7895114 50 26.0779468 362.6404468 51 403.2654468 26.0779468 52 327.9529468 403.2654468 53 193.7029468 327.9529468 54 317.0779468 193.7029468 55 202.2029468 317.0779468 56 321.3904468 202.2029468 57 178.0154468 321.3904468 58 16.4529468 178.0154468 59 -68.1720532 16.4529468 60 -157.0322246 -68.1720532 61 -76.1812892 -157.0322246 62 -81.7437892 -76.1812892 63 -134.5562892 -81.7437892 64 77.1312108 -134.5562892 65 199.8812108 77.1312108 66 105.2562108 199.8812108 67 198.3812108 105.2562108 68 262.5687108 198.3812108 69 196.1937108 262.5687108 70 11.6312108 196.1937108 71 -145.9937892 11.6312108 72 -166.8539606 -145.9937892 73 -202.0030252 -166.8539606 74 43.4344748 -202.0030252 75 -113.3780252 43.4344748 76 -113.6905252 -113.3780252 77 -155.9405252 -113.6905252 78 -210.5655252 -155.9405252 79 -124.4405252 -210.5655252 80 -64.2530252 -124.4405252 81 -298.6280252 -64.2530252 82 -154.1905252 -298.6280252 83 23.1844748 -154.1905252 84 -249.6756966 23.1844748 85 118.1752388 -249.6756966 86 -180.3872612 118.1752388 87 -79.1997612 -180.3872612 88 -81.5122612 -79.1997612 89 -246.7622612 -81.5122612 90 -105.3872612 -246.7622612 91 -319.2622612 -105.3872612 92 -72.0747612 -319.2622612 93 -90.4497612 -72.0747612 94 -80.0122612 -90.4497612 95 119.3627388 -80.0122612 96 -53.4974326 119.3627388 97 -114.6464972 -53.4974326 98 -155.2089972 -114.6464972 99 -50.0214972 -155.2089972 100 -196.3339972 -50.0214972 101 -14.5839972 -196.3339972 102 -82.2089972 -14.5839972 103 17.9160028 -82.2089972 104 -162.8964972 17.9160028 105 -132.2714972 -162.8964972 106 -16.8339972 -132.2714972 107 81.5410028 -16.8339972 108 275.6808314 81.5410028 109 -32.4682332 275.6808314 110 17.9692668 -32.4682332 111 27.1567668 17.9692668 112 -123.1557332 27.1567668 113 108.5942668 -123.1557332 114 67.9692668 108.5942668 115 34.0942668 67.9692668 116 -13.7182332 34.0942668 117 -113.0932332 -13.7182332 118 54.3442668 -113.0932332 119 149.7192668 54.3442668 120 153.8590954 149.7192668 121 -28.2899692 153.8590954 122 238.1475308 -28.2899692 123 50.3350308 238.1475308 124 8.0225308 50.3350308 125 -61.2274692 8.0225308 126 -140.8524692 -61.2274692 127 -28.7274692 -140.8524692 128 9.4600308 -28.7274692 129 -121.9149692 9.4600308 130 41.5225308 -121.9149692 131 115.8975308 41.5225308 132 27.0373594 115.8975308 133 -91.1117052 27.0373594 134 3.3257948 -91.1117052 135 -29.4867052 3.3257948 136 -73.7992052 -29.4867052 137 50.9507948 -73.7992052 138 -86.6742052 50.9507948 139 -9.5492052 -86.6742052 140 -66.3617052 -9.5492052 141 73.2632948 -66.3617052 142 -216.2992052 73.2632948 143 -128.9242052 -216.2992052 144 -142.7843767 -128.9242052 145 27.0665587 -142.7843767 146 60.5040587 27.0665587 147 35.6915587 60.5040587 148 16.3790587 35.6915587 149 -64.8709413 16.3790587 150 115.5040587 -64.8709413 151 -30.3709413 115.5040587 152 87.8165587 -30.3709413 153 205.4415587 87.8165587 154 -64.1209413 205.4415587 155 -322.7459413 -64.1209413 156 -139.6061127 -322.7459413 157 35.2448227 -139.6061127 158 -4.3176773 35.2448227 159 17.8698227 -4.3176773 160 2.5573227 17.8698227 161 129.3073227 2.5573227 162 -16.3176773 129.3073227 163 164.8073227 -16.3176773 164 21.9948227 164.8073227 165 138.6198227 21.9948227 166 87.0573227 138.6198227 167 51.4323227 87.0573227 168 -80.4278487 51.4323227 169 -105.1918797 -80.4278487 170 5.2456203 -105.1918797 171 68.4331203 5.2456203 172 -0.8793797 68.4331203 173 -105.1293797 -0.8793797 174 -82.7543797 -105.1293797 175 -132.6293797 -82.7543797 176 102.5581203 -132.6293797 177 23.1831203 102.5581203 178 -180.3793797 23.1831203 179 -267.0043797 -180.3793797 180 30.1354489 -267.0043797 181 23.9863843 30.1354489 182 90.4238843 23.9863843 183 31.6113843 90.4238843 184 81.2988843 31.6113843 185 25.0488843 81.2988843 186 -13.5761157 25.0488843 187 33.5488843 -13.5761157 188 140.7363843 33.5488843 189 132.3613843 140.7363843 190 94.7988843 132.3613843 191 4.1738843 94.7988843 192 NA 4.1738843 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -177.0726091 -183.9235445 [2,] -228.6351091 -177.0726091 [3,] -237.4476091 -228.6351091 [4,] -127.7601091 -237.4476091 [5,] -193.0101091 -127.7601091 [6,] -220.6351091 -193.0101091 [7,] -164.5101091 -220.6351091 [8,] -268.3226091 -164.5101091 [9,] -333.6976091 -268.3226091 [10,] -34.2601091 -333.6976091 [11,] -154.8851091 -34.2601091 [12,] -97.7452805 -154.8851091 [13,] 101.1056549 -97.7452805 [14,] 2.5431549 101.1056549 [15,] -43.2693451 2.5431549 [16,] -163.5818451 -43.2693451 [17,] -162.8318451 -163.5818451 [18,] 46.5431549 -162.8318451 [19,] 26.6681549 46.5431549 [20,] -107.1443451 26.6681549 [21,] 42.4806549 -107.1443451 [22,] 76.9181549 42.4806549 [23,] 196.2931549 76.9181549 [24,] 201.4329835 196.2931549 [25,] 12.2839189 201.4329835 [26,] -0.2785811 12.2839189 [27,] 42.9089189 -0.2785811 [28,] 87.5964189 42.9089189 [29,] 84.3464189 87.5964189 [30,] 57.7214189 84.3464189 [31,] 173.8464189 57.7214189 [32,] -185.9660811 173.8464189 [33,] 47.6589189 -185.9660811 [34,] 89.0964189 47.6589189 [35,] -68.5285811 89.0964189 [36,] 272.6112475 -68.5285811 [37,] 146.4621829 272.6112475 [38,] 162.8996829 146.4621829 [39,] 10.0871829 162.8996829 [40,] 279.7746829 10.0871829 [41,] 212.5246829 279.7746829 [42,] 248.8996829 212.5246829 [43,] -41.9753171 248.8996829 [44,] -5.7878171 -41.9753171 [45,] 52.8371829 -5.7878171 [46,] 274.2746829 52.8371829 [47,] 414.6496829 274.2746829 [48,] 310.7895114 414.6496829 [49,] 362.6404468 310.7895114 [50,] 26.0779468 362.6404468 [51,] 403.2654468 26.0779468 [52,] 327.9529468 403.2654468 [53,] 193.7029468 327.9529468 [54,] 317.0779468 193.7029468 [55,] 202.2029468 317.0779468 [56,] 321.3904468 202.2029468 [57,] 178.0154468 321.3904468 [58,] 16.4529468 178.0154468 [59,] -68.1720532 16.4529468 [60,] -157.0322246 -68.1720532 [61,] -76.1812892 -157.0322246 [62,] -81.7437892 -76.1812892 [63,] -134.5562892 -81.7437892 [64,] 77.1312108 -134.5562892 [65,] 199.8812108 77.1312108 [66,] 105.2562108 199.8812108 [67,] 198.3812108 105.2562108 [68,] 262.5687108 198.3812108 [69,] 196.1937108 262.5687108 [70,] 11.6312108 196.1937108 [71,] -145.9937892 11.6312108 [72,] -166.8539606 -145.9937892 [73,] -202.0030252 -166.8539606 [74,] 43.4344748 -202.0030252 [75,] -113.3780252 43.4344748 [76,] -113.6905252 -113.3780252 [77,] -155.9405252 -113.6905252 [78,] -210.5655252 -155.9405252 [79,] -124.4405252 -210.5655252 [80,] -64.2530252 -124.4405252 [81,] -298.6280252 -64.2530252 [82,] -154.1905252 -298.6280252 [83,] 23.1844748 -154.1905252 [84,] -249.6756966 23.1844748 [85,] 118.1752388 -249.6756966 [86,] -180.3872612 118.1752388 [87,] -79.1997612 -180.3872612 [88,] -81.5122612 -79.1997612 [89,] -246.7622612 -81.5122612 [90,] -105.3872612 -246.7622612 [91,] -319.2622612 -105.3872612 [92,] -72.0747612 -319.2622612 [93,] -90.4497612 -72.0747612 [94,] -80.0122612 -90.4497612 [95,] 119.3627388 -80.0122612 [96,] -53.4974326 119.3627388 [97,] -114.6464972 -53.4974326 [98,] -155.2089972 -114.6464972 [99,] -50.0214972 -155.2089972 [100,] -196.3339972 -50.0214972 [101,] -14.5839972 -196.3339972 [102,] -82.2089972 -14.5839972 [103,] 17.9160028 -82.2089972 [104,] -162.8964972 17.9160028 [105,] -132.2714972 -162.8964972 [106,] -16.8339972 -132.2714972 [107,] 81.5410028 -16.8339972 [108,] 275.6808314 81.5410028 [109,] -32.4682332 275.6808314 [110,] 17.9692668 -32.4682332 [111,] 27.1567668 17.9692668 [112,] -123.1557332 27.1567668 [113,] 108.5942668 -123.1557332 [114,] 67.9692668 108.5942668 [115,] 34.0942668 67.9692668 [116,] -13.7182332 34.0942668 [117,] -113.0932332 -13.7182332 [118,] 54.3442668 -113.0932332 [119,] 149.7192668 54.3442668 [120,] 153.8590954 149.7192668 [121,] -28.2899692 153.8590954 [122,] 238.1475308 -28.2899692 [123,] 50.3350308 238.1475308 [124,] 8.0225308 50.3350308 [125,] -61.2274692 8.0225308 [126,] -140.8524692 -61.2274692 [127,] -28.7274692 -140.8524692 [128,] 9.4600308 -28.7274692 [129,] -121.9149692 9.4600308 [130,] 41.5225308 -121.9149692 [131,] 115.8975308 41.5225308 [132,] 27.0373594 115.8975308 [133,] -91.1117052 27.0373594 [134,] 3.3257948 -91.1117052 [135,] -29.4867052 3.3257948 [136,] -73.7992052 -29.4867052 [137,] 50.9507948 -73.7992052 [138,] -86.6742052 50.9507948 [139,] -9.5492052 -86.6742052 [140,] -66.3617052 -9.5492052 [141,] 73.2632948 -66.3617052 [142,] -216.2992052 73.2632948 [143,] -128.9242052 -216.2992052 [144,] -142.7843767 -128.9242052 [145,] 27.0665587 -142.7843767 [146,] 60.5040587 27.0665587 [147,] 35.6915587 60.5040587 [148,] 16.3790587 35.6915587 [149,] -64.8709413 16.3790587 [150,] 115.5040587 -64.8709413 [151,] -30.3709413 115.5040587 [152,] 87.8165587 -30.3709413 [153,] 205.4415587 87.8165587 [154,] -64.1209413 205.4415587 [155,] -322.7459413 -64.1209413 [156,] -139.6061127 -322.7459413 [157,] 35.2448227 -139.6061127 [158,] -4.3176773 35.2448227 [159,] 17.8698227 -4.3176773 [160,] 2.5573227 17.8698227 [161,] 129.3073227 2.5573227 [162,] -16.3176773 129.3073227 [163,] 164.8073227 -16.3176773 [164,] 21.9948227 164.8073227 [165,] 138.6198227 21.9948227 [166,] 87.0573227 138.6198227 [167,] 51.4323227 87.0573227 [168,] -80.4278487 51.4323227 [169,] -105.1918797 -80.4278487 [170,] 5.2456203 -105.1918797 [171,] 68.4331203 5.2456203 [172,] -0.8793797 68.4331203 [173,] -105.1293797 -0.8793797 [174,] -82.7543797 -105.1293797 [175,] -132.6293797 -82.7543797 [176,] 102.5581203 -132.6293797 [177,] 23.1831203 102.5581203 [178,] -180.3793797 23.1831203 [179,] -267.0043797 -180.3793797 [180,] 30.1354489 -267.0043797 [181,] 23.9863843 30.1354489 [182,] 90.4238843 23.9863843 [183,] 31.6113843 90.4238843 [184,] 81.2988843 31.6113843 [185,] 25.0488843 81.2988843 [186,] -13.5761157 25.0488843 [187,] 33.5488843 -13.5761157 [188,] 140.7363843 33.5488843 [189,] 132.3613843 140.7363843 [190,] 94.7988843 132.3613843 [191,] 4.1738843 94.7988843 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -177.0726091 -183.9235445 2 -228.6351091 -177.0726091 3 -237.4476091 -228.6351091 4 -127.7601091 -237.4476091 5 -193.0101091 -127.7601091 6 -220.6351091 -193.0101091 7 -164.5101091 -220.6351091 8 -268.3226091 -164.5101091 9 -333.6976091 -268.3226091 10 -34.2601091 -333.6976091 11 -154.8851091 -34.2601091 12 -97.7452805 -154.8851091 13 101.1056549 -97.7452805 14 2.5431549 101.1056549 15 -43.2693451 2.5431549 16 -163.5818451 -43.2693451 17 -162.8318451 -163.5818451 18 46.5431549 -162.8318451 19 26.6681549 46.5431549 20 -107.1443451 26.6681549 21 42.4806549 -107.1443451 22 76.9181549 42.4806549 23 196.2931549 76.9181549 24 201.4329835 196.2931549 25 12.2839189 201.4329835 26 -0.2785811 12.2839189 27 42.9089189 -0.2785811 28 87.5964189 42.9089189 29 84.3464189 87.5964189 30 57.7214189 84.3464189 31 173.8464189 57.7214189 32 -185.9660811 173.8464189 33 47.6589189 -185.9660811 34 89.0964189 47.6589189 35 -68.5285811 89.0964189 36 272.6112475 -68.5285811 37 146.4621829 272.6112475 38 162.8996829 146.4621829 39 10.0871829 162.8996829 40 279.7746829 10.0871829 41 212.5246829 279.7746829 42 248.8996829 212.5246829 43 -41.9753171 248.8996829 44 -5.7878171 -41.9753171 45 52.8371829 -5.7878171 46 274.2746829 52.8371829 47 414.6496829 274.2746829 48 310.7895114 414.6496829 49 362.6404468 310.7895114 50 26.0779468 362.6404468 51 403.2654468 26.0779468 52 327.9529468 403.2654468 53 193.7029468 327.9529468 54 317.0779468 193.7029468 55 202.2029468 317.0779468 56 321.3904468 202.2029468 57 178.0154468 321.3904468 58 16.4529468 178.0154468 59 -68.1720532 16.4529468 60 -157.0322246 -68.1720532 61 -76.1812892 -157.0322246 62 -81.7437892 -76.1812892 63 -134.5562892 -81.7437892 64 77.1312108 -134.5562892 65 199.8812108 77.1312108 66 105.2562108 199.8812108 67 198.3812108 105.2562108 68 262.5687108 198.3812108 69 196.1937108 262.5687108 70 11.6312108 196.1937108 71 -145.9937892 11.6312108 72 -166.8539606 -145.9937892 73 -202.0030252 -166.8539606 74 43.4344748 -202.0030252 75 -113.3780252 43.4344748 76 -113.6905252 -113.3780252 77 -155.9405252 -113.6905252 78 -210.5655252 -155.9405252 79 -124.4405252 -210.5655252 80 -64.2530252 -124.4405252 81 -298.6280252 -64.2530252 82 -154.1905252 -298.6280252 83 23.1844748 -154.1905252 84 -249.6756966 23.1844748 85 118.1752388 -249.6756966 86 -180.3872612 118.1752388 87 -79.1997612 -180.3872612 88 -81.5122612 -79.1997612 89 -246.7622612 -81.5122612 90 -105.3872612 -246.7622612 91 -319.2622612 -105.3872612 92 -72.0747612 -319.2622612 93 -90.4497612 -72.0747612 94 -80.0122612 -90.4497612 95 119.3627388 -80.0122612 96 -53.4974326 119.3627388 97 -114.6464972 -53.4974326 98 -155.2089972 -114.6464972 99 -50.0214972 -155.2089972 100 -196.3339972 -50.0214972 101 -14.5839972 -196.3339972 102 -82.2089972 -14.5839972 103 17.9160028 -82.2089972 104 -162.8964972 17.9160028 105 -132.2714972 -162.8964972 106 -16.8339972 -132.2714972 107 81.5410028 -16.8339972 108 275.6808314 81.5410028 109 -32.4682332 275.6808314 110 17.9692668 -32.4682332 111 27.1567668 17.9692668 112 -123.1557332 27.1567668 113 108.5942668 -123.1557332 114 67.9692668 108.5942668 115 34.0942668 67.9692668 116 -13.7182332 34.0942668 117 -113.0932332 -13.7182332 118 54.3442668 -113.0932332 119 149.7192668 54.3442668 120 153.8590954 149.7192668 121 -28.2899692 153.8590954 122 238.1475308 -28.2899692 123 50.3350308 238.1475308 124 8.0225308 50.3350308 125 -61.2274692 8.0225308 126 -140.8524692 -61.2274692 127 -28.7274692 -140.8524692 128 9.4600308 -28.7274692 129 -121.9149692 9.4600308 130 41.5225308 -121.9149692 131 115.8975308 41.5225308 132 27.0373594 115.8975308 133 -91.1117052 27.0373594 134 3.3257948 -91.1117052 135 -29.4867052 3.3257948 136 -73.7992052 -29.4867052 137 50.9507948 -73.7992052 138 -86.6742052 50.9507948 139 -9.5492052 -86.6742052 140 -66.3617052 -9.5492052 141 73.2632948 -66.3617052 142 -216.2992052 73.2632948 143 -128.9242052 -216.2992052 144 -142.7843767 -128.9242052 145 27.0665587 -142.7843767 146 60.5040587 27.0665587 147 35.6915587 60.5040587 148 16.3790587 35.6915587 149 -64.8709413 16.3790587 150 115.5040587 -64.8709413 151 -30.3709413 115.5040587 152 87.8165587 -30.3709413 153 205.4415587 87.8165587 154 -64.1209413 205.4415587 155 -322.7459413 -64.1209413 156 -139.6061127 -322.7459413 157 35.2448227 -139.6061127 158 -4.3176773 35.2448227 159 17.8698227 -4.3176773 160 2.5573227 17.8698227 161 129.3073227 2.5573227 162 -16.3176773 129.3073227 163 164.8073227 -16.3176773 164 21.9948227 164.8073227 165 138.6198227 21.9948227 166 87.0573227 138.6198227 167 51.4323227 87.0573227 168 -80.4278487 51.4323227 169 -105.1918797 -80.4278487 170 5.2456203 -105.1918797 171 68.4331203 5.2456203 172 -0.8793797 68.4331203 173 -105.1293797 -0.8793797 174 -82.7543797 -105.1293797 175 -132.6293797 -82.7543797 176 102.5581203 -132.6293797 177 23.1831203 102.5581203 178 -180.3793797 23.1831203 179 -267.0043797 -180.3793797 180 30.1354489 -267.0043797 181 23.9863843 30.1354489 182 90.4238843 23.9863843 183 31.6113843 90.4238843 184 81.2988843 31.6113843 185 25.0488843 81.2988843 186 -13.5761157 25.0488843 187 33.5488843 -13.5761157 188 140.7363843 33.5488843 189 132.3613843 140.7363843 190 94.7988843 132.3613843 191 4.1738843 94.7988843 > 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/73bdb1259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8l6731259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9sbc71259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10v3jc1259074035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11duym1259074035.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/12kbp71259074035.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/13l8wq1259074035.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/14q6tu1259074035.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/15ltu11259074035.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/165wic1259074035.tab") + } > system("convert tmp/1e2ot1259074035.ps tmp/1e2ot1259074035.png") > system("convert tmp/2bpq91259074035.ps tmp/2bpq91259074035.png") > system("convert tmp/3r1ln1259074035.ps tmp/3r1ln1259074035.png") > system("convert tmp/4z5bz1259074035.ps tmp/4z5bz1259074035.png") > system("convert tmp/5zngj1259074035.ps tmp/5zngj1259074035.png") > system("convert tmp/6dhn91259074035.ps tmp/6dhn91259074035.png") > system("convert tmp/73bdb1259074035.ps tmp/73bdb1259074035.png") > system("convert tmp/8l6731259074035.ps tmp/8l6731259074035.png") > system("convert tmp/9sbc71259074035.ps tmp/9sbc71259074035.png") > system("convert tmp/10v3jc1259074035.ps tmp/10v3jc1259074035.png") > > > proc.time() user system elapsed 4.765 1.755 5.522