R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1632
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+ ,dim=c(6
+ ,188)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:188))
> y <- array(NA,dim=c(6,188),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:188))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1632 0 1385 1507 1508 1687 1 0 0 0 0 0 0 0 0 0 0 1
2 1511 0 1632 1385 1507 1508 0 1 0 0 0 0 0 0 0 0 0 2
3 1559 0 1511 1632 1385 1507 0 0 1 0 0 0 0 0 0 0 0 3
4 1630 0 1559 1511 1632 1385 0 0 0 1 0 0 0 0 0 0 0 4
5 1579 0 1630 1559 1511 1632 0 0 0 0 1 0 0 0 0 0 0 5
6 1653 0 1579 1630 1559 1511 0 0 0 0 0 1 0 0 0 0 0 6
7 2152 0 1653 1579 1630 1559 0 0 0 0 0 0 1 0 0 0 0 7
8 2148 0 2152 1653 1579 1630 0 0 0 0 0 0 0 1 0 0 0 8
9 1752 0 2148 2152 1653 1579 0 0 0 0 0 0 0 0 1 0 0 9
10 1765 0 1752 2148 2152 1653 0 0 0 0 0 0 0 0 0 1 0 10
11 1717 0 1765 1752 2148 2152 0 0 0 0 0 0 0 0 0 0 1 11
12 1558 0 1717 1765 1752 2148 0 0 0 0 0 0 0 0 0 0 0 12
13 1575 0 1558 1717 1765 1752 1 0 0 0 0 0 0 0 0 0 0 13
14 1520 0 1575 1558 1717 1765 0 1 0 0 0 0 0 0 0 0 0 14
15 1805 0 1520 1575 1558 1717 0 0 1 0 0 0 0 0 0 0 0 15
16 1800 0 1805 1520 1575 1558 0 0 0 1 0 0 0 0 0 0 0 16
17 1719 0 1800 1805 1520 1575 0 0 0 0 1 0 0 0 0 0 0 17
18 2008 0 1719 1800 1805 1520 0 0 0 0 0 1 0 0 0 0 0 18
19 2242 0 2008 1719 1800 1805 0 0 0 0 0 0 1 0 0 0 0 19
20 2478 0 2242 2008 1719 1800 0 0 0 0 0 0 0 1 0 0 0 20
21 2030 0 2478 2242 2008 1719 0 0 0 0 0 0 0 0 1 0 0 21
22 1655 0 2030 2478 2242 2008 0 0 0 0 0 0 0 0 0 1 0 22
23 1693 0 1655 2030 2478 2242 0 0 0 0 0 0 0 0 0 0 1 23
24 1623 0 1693 1655 2030 2478 0 0 0 0 0 0 0 0 0 0 0 24
25 1805 0 1623 1693 1655 2030 1 0 0 0 0 0 0 0 0 0 0 25
26 1746 0 1805 1623 1693 1655 0 1 0 0 0 0 0 0 0 0 0 26
27 1795 0 1746 1805 1623 1693 0 0 1 0 0 0 0 0 0 0 0 27
28 1926 0 1795 1746 1805 1623 0 0 0 1 0 0 0 0 0 0 0 28
29 1619 0 1926 1795 1746 1805 0 0 0 0 1 0 0 0 0 0 0 29
30 1992 0 1619 1926 1795 1746 0 0 0 0 0 1 0 0 0 0 0 30
31 2233 0 1992 1619 1926 1795 0 0 0 0 0 0 1 0 0 0 0 31
32 2192 0 2233 1992 1619 1926 0 0 0 0 0 0 0 1 0 0 0 32
33 2080 0 2192 2233 1992 1619 0 0 0 0 0 0 0 0 1 0 0 33
34 1768 0 2080 2192 2233 1992 0 0 0 0 0 0 0 0 0 1 0 34
35 1835 0 1768 2080 2192 2233 0 0 0 0 0 0 0 0 0 0 1 35
36 1569 0 1835 1768 2080 2192 0 0 0 0 0 0 0 0 0 0 0 36
37 1976 0 1569 1835 1768 2080 1 0 0 0 0 0 0 0 0 0 0 37
38 1853 0 1976 1569 1835 1768 0 1 0 0 0 0 0 0 0 0 0 38
39 1965 0 1853 1976 1569 1835 0 0 1 0 0 0 0 0 0 0 0 39
40 1689 0 1965 1853 1976 1569 0 0 0 1 0 0 0 0 0 0 0 40
41 1778 0 1689 1965 1853 1976 0 0 0 0 1 0 0 0 0 0 0 41
42 1976 0 1778 1689 1965 1853 0 0 0 0 0 1 0 0 0 0 0 42
43 2397 0 1976 1778 1689 1965 0 0 0 0 0 0 1 0 0 0 0 43
44 2654 0 2397 1976 1778 1689 0 0 0 0 0 0 0 1 0 0 0 44
45 2097 0 2654 2397 1976 1778 0 0 0 0 0 0 0 0 1 0 0 45
46 1963 0 2097 2654 2397 1976 0 0 0 0 0 0 0 0 0 1 0 46
47 1677 0 1963 2097 2654 2397 0 0 0 0 0 0 0 0 0 0 1 47
48 1941 0 1677 1963 2097 2654 0 0 0 0 0 0 0 0 0 0 0 48
49 2003 0 1941 1677 1963 2097 1 0 0 0 0 0 0 0 0 0 0 49
50 1813 0 2003 1941 1677 1963 0 1 0 0 0 0 0 0 0 0 0 50
51 2012 0 1813 2003 1941 1677 0 0 1 0 0 0 0 0 0 0 0 51
52 1912 0 2012 1813 2003 1941 0 0 0 1 0 0 0 0 0 0 0 52
53 2084 0 1912 2012 1813 2003 0 0 0 0 1 0 0 0 0 0 0 53
54 2080 0 2084 1912 2012 1813 0 0 0 0 0 1 0 0 0 0 0 54
55 2118 0 2080 2084 1912 2012 0 0 0 0 0 0 1 0 0 0 0 55
56 2150 0 2118 2080 2084 1912 0 0 0 0 0 0 0 1 0 0 0 56
57 1608 0 2150 2118 2080 2084 0 0 0 0 0 0 0 0 1 0 0 57
58 1503 0 1608 2150 2118 2080 0 0 0 0 0 0 0 0 0 1 0 58
59 1548 0 1503 1608 2150 2118 0 0 0 0 0 0 0 0 0 0 1 59
60 1382 0 1548 1503 1608 2150 0 0 0 0 0 0 0 0 0 0 0 60
61 1731 0 1382 1548 1503 1608 1 0 0 0 0 0 0 0 0 0 0 61
62 1798 0 1731 1382 1548 1503 0 1 0 0 0 0 0 0 0 0 0 62
63 1779 0 1798 1731 1382 1548 0 0 1 0 0 0 0 0 0 0 0 63
64 1887 0 1779 1798 1731 1382 0 0 0 1 0 0 0 0 0 0 0 64
65 2004 0 1887 1779 1798 1731 0 0 0 0 1 0 0 0 0 0 0 65
66 2077 0 2004 1887 1779 1798 0 0 0 0 0 1 0 0 0 0 0 66
67 2092 0 2077 2004 1887 1779 0 0 0 0 0 0 1 0 0 0 0 67
68 2051 0 2092 2077 2004 1887 0 0 0 0 0 0 0 1 0 0 0 68
69 1577 0 2051 2092 2077 2004 0 0 0 0 0 0 0 0 1 0 0 69
70 1356 0 1577 2051 2092 2077 0 0 0 0 0 0 0 0 0 1 0 70
71 1652 0 1356 1577 2051 2092 0 0 0 0 0 0 0 0 0 0 1 71
72 1382 0 1652 1356 1577 2051 0 0 0 0 0 0 0 0 0 0 0 72
73 1519 0 1382 1652 1356 1577 1 0 0 0 0 0 0 0 0 0 0 73
74 1421 0 1519 1382 1652 1356 0 1 0 0 0 0 0 0 0 0 0 74
75 1442 0 1421 1519 1382 1652 0 0 1 0 0 0 0 0 0 0 0 75
76 1543 0 1442 1421 1519 1382 0 0 0 1 0 0 0 0 0 0 0 76
77 1656 0 1543 1442 1421 1519 0 0 0 0 1 0 0 0 0 0 0 77
78 1561 0 1656 1543 1442 1421 0 0 0 0 0 1 0 0 0 0 0 78
79 1905 0 1561 1656 1543 1442 0 0 0 0 0 0 1 0 0 0 0 79
80 2199 0 1905 1561 1656 1543 0 0 0 0 0 0 0 1 0 0 0 80
81 1473 0 2199 1905 1561 1656 0 0 0 0 0 0 0 0 1 0 0 81
82 1655 0 1473 2199 1905 1561 0 0 0 0 0 0 0 0 0 1 0 82
83 1407 0 1655 1473 2199 1905 0 0 0 0 0 0 0 0 0 0 1 83
84 1395 0 1407 1655 1473 2199 0 0 0 0 0 0 0 0 0 0 0 84
85 1530 0 1395 1407 1655 1473 1 0 0 0 0 0 0 0 0 0 0 85
86 1309 0 1530 1395 1407 1655 0 1 0 0 0 0 0 0 0 0 0 86
87 1526 0 1309 1530 1395 1407 0 0 1 0 0 0 0 0 0 0 0 87
88 1327 0 1526 1309 1530 1395 0 0 0 1 0 0 0 0 0 0 0 88
89 1627 0 1327 1526 1309 1530 0 0 0 0 1 0 0 0 0 0 0 89
90 1748 0 1627 1327 1526 1309 0 0 0 0 0 1 0 0 0 0 0 90
91 1958 0 1748 1627 1327 1526 0 0 0 0 0 0 1 0 0 0 0 91
92 2274 0 1958 1748 1627 1327 0 0 0 0 0 0 0 1 0 0 0 92
93 1648 0 2274 1958 1748 1627 0 0 0 0 0 0 0 0 1 0 0 93
94 1401 0 1648 2274 1958 1748 0 0 0 0 0 0 0 0 0 1 0 94
95 1411 0 1401 1648 2274 1958 0 0 0 0 0 0 0 0 0 0 1 95
96 1403 0 1411 1401 1648 2274 0 0 0 0 0 0 0 0 0 0 0 96
97 1394 0 1403 1411 1401 1648 1 0 0 0 0 0 0 0 0 0 0 97
98 1520 0 1394 1403 1411 1401 0 1 0 0 0 0 0 0 0 0 0 98
99 1528 0 1520 1394 1403 1411 0 0 1 0 0 0 0 0 0 0 0 99
100 1643 0 1528 1520 1394 1403 0 0 0 1 0 0 0 0 0 0 0 100
101 1515 0 1643 1528 1520 1394 0 0 0 0 1 0 0 0 0 0 0 101
102 1685 0 1515 1643 1528 1520 0 0 0 0 0 1 0 0 0 0 0 102
103 2000 0 1685 1515 1643 1528 0 0 0 0 0 0 1 0 0 0 0 103
104 2215 0 2000 1685 1515 1643 0 0 0 0 0 0 0 1 0 0 0 104
105 1956 0 2215 2000 1685 1515 0 0 0 0 0 0 0 0 1 0 0 105
106 1462 0 1956 2215 2000 1685 0 0 0 0 0 0 0 0 0 1 0 106
107 1563 0 1462 1956 2215 2000 0 0 0 0 0 0 0 0 0 0 1 107
108 1459 0 1563 1462 1956 2215 0 0 0 0 0 0 0 0 0 0 0 108
109 1446 0 1459 1563 1462 1956 1 0 0 0 0 0 0 0 0 0 0 109
110 1622 0 1446 1459 1563 1462 0 1 0 0 0 0 0 0 0 0 0 110
111 1657 0 1622 1446 1459 1563 0 0 1 0 0 0 0 0 0 0 0 111
112 1638 0 1657 1622 1446 1459 0 0 0 1 0 0 0 0 0 0 0 112
113 1643 0 1638 1657 1622 1446 0 0 0 0 1 0 0 0 0 0 0 113
114 1683 0 1643 1638 1657 1622 0 0 0 0 0 1 0 0 0 0 0 114
115 2050 0 1683 1643 1638 1657 0 0 0 0 0 0 1 0 0 0 0 115
116 2262 0 2050 1683 1643 1638 0 0 0 0 0 0 0 1 0 0 0 116
117 1813 0 2262 2050 1683 1643 0 0 0 0 0 0 0 0 1 0 0 117
118 1445 0 1813 2262 2050 1683 0 0 0 0 0 0 0 0 0 1 0 118
119 1762 0 1445 1813 2262 2050 0 0 0 0 0 0 0 0 0 0 1 119
120 1461 0 1762 1445 1813 2262 0 0 0 0 0 0 0 0 0 0 0 120
121 1556 0 1461 1762 1445 1813 1 0 0 0 0 0 0 0 0 0 0 121
122 1431 0 1556 1461 1762 1445 0 1 0 0 0 0 0 0 0 0 0 122
123 1427 0 1431 1556 1461 1762 0 0 1 0 0 0 0 0 0 0 0 123
124 1554 0 1427 1431 1556 1461 0 0 0 1 0 0 0 0 0 0 0 124
125 1645 0 1554 1427 1431 1556 0 0 0 0 1 0 0 0 0 0 0 125
126 1653 0 1645 1554 1427 1431 0 0 0 0 0 1 0 0 0 0 0 126
127 2016 0 1653 1645 1554 1427 0 0 0 0 0 0 1 0 0 0 0 127
128 2207 0 2016 1653 1645 1554 0 0 0 0 0 0 0 1 0 0 0 128
129 1665 0 2207 2016 1653 1645 0 0 0 0 0 0 0 0 1 0 0 129
130 1361 0 1665 2207 2016 1653 0 0 0 0 0 0 0 0 0 1 0 130
131 1506 0 1361 1665 2207 2016 0 0 0 0 0 0 0 0 0 0 1 131
132 1360 0 1506 1361 1665 2207 0 0 0 0 0 0 0 0 0 0 0 132
133 1453 0 1360 1506 1361 1665 1 0 0 0 0 0 0 0 0 0 0 133
134 1522 0 1453 1360 1506 1361 0 1 0 0 0 0 0 0 0 0 0 134
135 1460 0 1522 1453 1360 1506 0 0 1 0 0 0 0 0 0 0 0 135
136 1552 0 1460 1522 1453 1360 0 0 0 1 0 0 0 0 0 0 0 136
137 1548 0 1552 1460 1522 1453 0 0 0 0 1 0 0 0 0 0 0 137
138 1827 0 1548 1552 1460 1522 0 0 0 0 0 1 0 0 0 0 0 138
139 1737 0 1827 1548 1552 1460 0 0 0 0 0 0 1 0 0 0 0 139
140 1941 0 1737 1827 1548 1552 0 0 0 0 0 0 0 1 0 0 0 140
141 1474 0 1941 1737 1827 1548 0 0 0 0 0 0 0 0 1 0 0 141
142 1458 0 1474 1941 1737 1827 0 0 0 0 0 0 0 0 0 1 0 142
143 1542 0 1458 1474 1941 1737 0 0 0 0 0 0 0 0 0 0 1 143
144 1404 0 1542 1458 1474 1941 0 0 0 0 0 0 0 0 0 0 0 144
145 1522 0 1404 1542 1458 1474 1 0 0 0 0 0 0 0 0 0 0 145
146 1385 0 1522 1404 1542 1458 0 1 0 0 0 0 0 0 0 0 0 146
147 1641 0 1385 1522 1404 1542 0 0 1 0 0 0 0 0 0 0 0 147
148 1510 0 1641 1385 1522 1404 0 0 0 1 0 0 0 0 0 0 0 148
149 1681 0 1510 1641 1385 1522 0 0 0 0 1 0 0 0 0 0 0 149
150 1938 0 1681 1510 1641 1385 0 0 0 0 0 1 0 0 0 0 0 150
151 1868 0 1938 1681 1510 1641 0 0 0 0 0 0 1 0 0 0 0 151
152 1726 0 1868 1938 1681 1510 0 0 0 0 0 0 0 1 0 0 0 152
153 1456 0 1726 1868 1938 1681 0 0 0 0 0 0 0 0 1 0 0 153
154 1445 0 1456 1726 1868 1938 0 0 0 0 0 0 0 0 0 1 0 154
155 1456 0 1445 1456 1726 1868 0 0 0 0 0 0 0 0 0 0 1 155
156 1365 0 1456 1445 1456 1726 0 0 0 0 0 0 0 0 0 0 0 156
157 1487 0 1365 1456 1445 1456 1 0 0 0 0 0 0 0 0 0 0 157
158 1558 0 1487 1365 1456 1445 0 1 0 0 0 0 0 0 0 0 0 158
159 1488 0 1558 1487 1365 1456 0 0 1 0 0 0 0 0 0 0 0 159
160 1684 0 1488 1558 1487 1365 0 0 0 1 0 0 0 0 0 0 0 160
161 1594 0 1684 1488 1558 1487 0 0 0 0 1 0 0 0 0 0 0 161
162 1850 0 1594 1684 1488 1558 0 0 0 0 0 1 0 0 0 0 0 162
163 1998 0 1850 1594 1684 1488 0 0 0 0 0 0 1 0 0 0 0 163
164 2079 0 1998 1850 1594 1684 0 0 0 0 0 0 0 1 0 0 0 164
165 1494 0 2079 1998 1850 1594 0 0 0 0 0 0 0 0 1 0 0 165
166 1057 1 1494 2079 1998 1850 0 0 0 0 0 0 0 0 0 1 0 166
167 1218 1 1057 1494 2079 1998 0 0 0 0 0 0 0 0 0 0 1 167
168 1168 1 1218 1057 1494 2079 0 0 0 0 0 0 0 0 0 0 0 168
169 1236 1 1168 1218 1057 1494 1 0 0 0 0 0 0 0 0 0 0 169
170 1076 1 1236 1168 1218 1057 0 1 0 0 0 0 0 0 0 0 0 170
171 1174 1 1076 1236 1168 1218 0 0 1 0 0 0 0 0 0 0 0 171
172 1139 1 1174 1076 1236 1168 0 0 0 1 0 0 0 0 0 0 0 172
173 1427 1 1139 1174 1076 1236 0 0 0 0 1 0 0 0 0 0 0 173
174 1487 1 1427 1139 1174 1076 0 0 0 0 0 1 0 0 0 0 0 174
175 1483 1 1487 1427 1139 1174 0 0 0 0 0 0 1 0 0 0 0 175
176 1513 1 1483 1487 1427 1139 0 0 0 0 0 0 0 1 0 0 0 176
177 1357 1 1513 1483 1487 1427 0 0 0 0 0 0 0 0 1 0 0 177
178 1165 1 1357 1513 1483 1487 0 0 0 0 0 0 0 0 0 1 0 178
179 1282 1 1165 1357 1513 1483 0 0 0 0 0 0 0 0 0 0 1 179
180 1110 1 1282 1165 1357 1513 0 0 0 0 0 0 0 0 0 0 0 180
181 1297 1 1110 1282 1165 1357 1 0 0 0 0 0 0 0 0 0 0 181
182 1185 1 1297 1110 1282 1165 0 1 0 0 0 0 0 0 0 0 0 182
183 1222 1 1185 1297 1110 1282 0 0 1 0 0 0 0 0 0 0 0 183
184 1284 1 1222 1185 1297 1110 0 0 0 1 0 0 0 0 0 0 0 184
185 1444 1 1284 1222 1185 1297 0 0 0 0 1 0 0 0 0 0 0 185
186 1575 1 1444 1284 1222 1185 0 0 0 0 0 1 0 0 0 0 0 186
187 1737 1 1575 1444 1284 1222 0 0 0 0 0 0 1 0 0 0 0 187
188 1763 1 1737 1575 1444 1284 0 0 0 0 0 0 0 1 0 0 0 188
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
522.91809 -80.79823 0.38975 0.17272 0.03596 0.03546
M1 M2 M3 M4 M5 M6
188.66231 104.98735 181.66732 176.54103 208.39566 324.64288
M7 M8 M9 M10 M11 t
458.31248 471.42553 -54.24817 -111.18751 86.60035 -0.76712
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-328.526 -92.222 1.110 86.948 294.049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 522.91809 164.86157 3.172 0.00180 **
X -80.79823 38.14953 -2.118 0.03563 *
Y1 0.38975 0.07672 5.080 9.85e-07 ***
Y2 0.17272 0.08213 2.103 0.03693 *
Y3 0.03596 0.08190 0.439 0.66121
Y4 0.03546 0.07517 0.472 0.63770
M1 188.66231 56.52126 3.338 0.00104 **
M2 104.98735 63.65157 1.649 0.10091
M3 181.66732 63.23377 2.873 0.00459 **
M4 176.54103 68.28950 2.585 0.01057 *
M5 208.39566 62.64060 3.327 0.00108 **
M6 324.64288 66.37528 4.891 2.31e-06 ***
M7 458.31248 65.93663 6.951 7.44e-11 ***
M8 471.42553 73.30544 6.431 1.24e-09 ***
M9 -54.24817 80.31085 -0.675 0.50029
M10 -111.18751 77.36123 -1.437 0.15248
M11 86.60035 62.44811 1.387 0.16733
t -0.76712 0.25623 -2.994 0.00317 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 127.3 on 170 degrees of freedom
Multiple R-squared: 0.8265, Adjusted R-squared: 0.8091
F-statistic: 47.63 on 17 and 170 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,] 0.8016493 0.3967014664 0.1983507332
[2,] 0.7228218 0.5543564493 0.2771782247
[3,] 0.6785324 0.6429352848 0.3214676424
[4,] 0.5679789 0.8640421545 0.4320210772
[5,] 0.4490784 0.8981568557 0.5509215722
[6,] 0.3686116 0.7372231802 0.6313884099
[7,] 0.3344754 0.6689507354 0.6655246323
[8,] 0.2534019 0.5068038610 0.7465980695
[9,] 0.3846916 0.7693832782 0.6153083609
[10,] 0.3034947 0.6069893318 0.6965053341
[11,] 0.3156192 0.6312383584 0.6843808208
[12,] 0.4108516 0.8217031808 0.5891484096
[13,] 0.3675716 0.7351431649 0.6324284176
[14,] 0.3084594 0.6169187054 0.6915406473
[15,] 0.2451489 0.4902978135 0.7548510933
[16,] 0.2898302 0.5796603183 0.7101698409
[17,] 0.3175729 0.6351458797 0.6824270601
[18,] 0.2748645 0.5497290438 0.7251354781
[19,] 0.2312151 0.4624301453 0.7687849273
[20,] 0.3948002 0.7896004623 0.6051997688
[21,] 0.3387800 0.6775599137 0.6612200431
[22,] 0.2910086 0.5820172325 0.7089913837
[23,] 0.2741187 0.5482374094 0.7258812953
[24,] 0.3721914 0.7443828411 0.6278085795
[25,] 0.3300696 0.6601391897 0.6699304052
[26,] 0.3042312 0.6084623970 0.6957688015
[27,] 0.3820380 0.7640760041 0.6179619980
[28,] 0.4841640 0.9683279935 0.5158360032
[29,] 0.4755416 0.9510832085 0.5244583957
[30,] 0.4426661 0.8853321692 0.5573339154
[31,] 0.4378598 0.8757195681 0.5621402159
[32,] 0.3965444 0.7930887113 0.6034556444
[33,] 0.4639825 0.9279650268 0.5360174866
[34,] 0.4228630 0.8457259890 0.5771370055
[35,] 0.6614372 0.6771255951 0.3385627976
[36,] 0.8400852 0.3198295952 0.1599147976
[37,] 0.9508020 0.0983959562 0.0491979781
[38,] 0.9500359 0.0999281912 0.0499640956
[39,] 0.9364451 0.1271097981 0.0635548991
[40,] 0.9357217 0.1285565208 0.0642782604
[41,] 0.9341031 0.1317938678 0.0658969339
[42,] 0.9494317 0.1011365869 0.0505682934
[43,] 0.9451728 0.1096544542 0.0548272271
[44,] 0.9477001 0.1045998094 0.0522999047
[45,] 0.9658922 0.0682155469 0.0341077735
[46,] 0.9688296 0.0623408796 0.0311704398
[47,] 0.9814587 0.0370825356 0.0185412678
[48,] 0.9864259 0.0271482768 0.0135741384
[49,] 0.9861492 0.0277015160 0.0138507580
[50,] 0.9847405 0.0305189212 0.0152594606
[51,] 0.9873049 0.0253901109 0.0126950554
[52,] 0.9861943 0.0276114104 0.0138057052
[53,] 0.9852918 0.0294163808 0.0147081904
[54,] 0.9819955 0.0360090021 0.0180045011
[55,] 0.9808924 0.0382152827 0.0191076413
[56,] 0.9748088 0.0503824552 0.0251912276
[57,] 0.9692512 0.0614975483 0.0307487741
[58,] 0.9829441 0.0341117922 0.0170558961
[59,] 0.9775147 0.0449705791 0.0224852896
[60,] 0.9800118 0.0399764782 0.0199882391
[61,] 0.9881644 0.0236711258 0.0118355629
[62,] 0.9939948 0.0120104973 0.0060052486
[63,] 0.9951509 0.0096982799 0.0048491400
[64,] 0.9933259 0.0133481559 0.0066740780
[65,] 0.9914256 0.0171488313 0.0085744157
[66,] 0.9948704 0.0102592490 0.0051296245
[67,] 0.9930189 0.0139621240 0.0069810620
[68,] 0.9967869 0.0064262478 0.0032131239
[69,] 0.9963755 0.0072490453 0.0036245226
[70,] 0.9953764 0.0092472380 0.0046236190
[71,] 0.9937424 0.0125151321 0.0062575660
[72,] 0.9961200 0.0077599795 0.0038799898
[73,] 0.9952936 0.0094128391 0.0047064196
[74,] 0.9944863 0.0110274244 0.0055137122
[75,] 0.9941030 0.0117940685 0.0058970342
[76,] 0.9923674 0.0152651433 0.0076325717
[77,] 0.9934006 0.0131987545 0.0065993772
[78,] 0.9917749 0.0164501593 0.0082250796
[79,] 0.9890276 0.0219448618 0.0109724309
[80,] 0.9863444 0.0273112790 0.0136556395
[81,] 0.9890796 0.0218407109 0.0109203554
[82,] 0.9880509 0.0238982999 0.0119491499
[83,] 0.9845631 0.0308737535 0.0154368768
[84,] 0.9829052 0.0341896680 0.0170948340
[85,] 0.9944455 0.0111089810 0.0055544905
[86,] 0.9937232 0.0125536436 0.0062768218
[87,] 0.9912654 0.0174691545 0.0087345773
[88,] 0.9882464 0.0235072555 0.0117536278
[89,] 0.9894139 0.0211721133 0.0105860567
[90,] 0.9908651 0.0182697572 0.0091348786
[91,] 0.9890051 0.0219898609 0.0109949305
[92,] 0.9850517 0.0298965652 0.0149482826
[93,] 0.9798696 0.0402607589 0.0201303795
[94,] 0.9813057 0.0373885078 0.0186942539
[95,] 0.9812146 0.0375708725 0.0187854363
[96,] 0.9891047 0.0217905789 0.0108952895
[97,] 0.9924982 0.0150036883 0.0075018442
[98,] 0.9908077 0.0183845103 0.0091922552
[99,] 0.9976445 0.0047110273 0.0023555136
[100,] 0.9966494 0.0067011165 0.0033505582
[101,] 0.9955072 0.0089856901 0.0044928451
[102,] 0.9939457 0.0121086563 0.0060543282
[103,] 0.9927710 0.0144579349 0.0072289675
[104,] 0.9901267 0.0197465223 0.0098732611
[105,] 0.9869431 0.0261137948 0.0130568974
[106,] 0.9898951 0.0202098384 0.0101049192
[107,] 0.9937901 0.0124198759 0.0062099379
[108,] 0.9992685 0.0014630720 0.0007315360
[109,] 0.9996383 0.0007234945 0.0003617473
[110,] 0.9995328 0.0009343615 0.0004671807
[111,] 0.9993232 0.0013536472 0.0006768236
[112,] 0.9989107 0.0021786965 0.0010893483
[113,] 0.9983243 0.0033514340 0.0016757170
[114,] 0.9988109 0.0023782989 0.0011891495
[115,] 0.9981331 0.0037338469 0.0018669235
[116,] 0.9977032 0.0045935557 0.0022967778
[117,] 0.9963997 0.0072006977 0.0036003489
[118,] 0.9950807 0.0098385038 0.0049192519
[119,] 0.9954274 0.0091452920 0.0045726460
[120,] 0.9946397 0.0107205477 0.0053602738
[121,] 0.9919943 0.0160113317 0.0080056659
[122,] 0.9911504 0.0176991376 0.0088495688
[123,] 0.9912683 0.0174633189 0.0087316594
[124,] 0.9875383 0.0249234474 0.0124617237
[125,] 0.9851346 0.0297307207 0.0148653603
[126,] 0.9781628 0.0436744712 0.0218372356
[127,] 0.9908347 0.0183305008 0.0091652504
[128,] 0.9858162 0.0283675641 0.0141837820
[129,] 0.9843032 0.0313935882 0.0156967941
[130,] 0.9982786 0.0034428974 0.0017214487
[131,] 0.9970684 0.0058631884 0.0029315942
[132,] 0.9981093 0.0037814473 0.0018907236
[133,] 0.9968034 0.0063931029 0.0031965515
[134,] 0.9952664 0.0094672627 0.0047336313
[135,] 0.9955493 0.0089014767 0.0044507383
[136,] 0.9918216 0.0163568720 0.0081784360
[137,] 0.9852646 0.0294708030 0.0147354015
[138,] 0.9752156 0.0495687529 0.0247843764
[139,] 0.9566489 0.0867022840 0.0433511420
[140,] 0.9800517 0.0398965156 0.0199482578
[141,] 0.9755125 0.0489749665 0.0244874833
[142,] 0.9528973 0.0942054814 0.0471027407
[143,] 0.9350420 0.1299160934 0.0649580467
[144,] 0.9595679 0.0808642235 0.0404321118
[145,] 0.9137468 0.1725064682 0.0862532341
[146,] 0.8268313 0.3463374579 0.1731687289
[147,] 0.7572851 0.4854297076 0.2427148538
> postscript(file="/var/www/html/rcomp/tmp/1dziy1258751152.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2op4n1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p8rf1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4e9ir1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5twv31258751152.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 = 188
Frequency = 1
1 2 3 4 5 6
7.037835 -98.331247 -117.325401 -42.794803 -165.254264 -196.555340
7 8 9 10 11 12
145.254550 -79.041426 -34.083291 171.087420 -28.154237 -68.943562
13 14 15 16 17 18
-156.001393 -104.456935 130.549461 34.892228 -123.098350 74.557655
19 20 21 22 23 24
67.081018 152.706477 91.230387 -110.882336 -63.151544 11.916144
25 26 27 28 29 30
56.111818 34.643474 0.459169 124.384106 -277.557055 77.319078
31 32 33 34 35 36
86.618978 -118.689669 267.580039 42.125992 45.978952 -99.395392
37 38 39 40 41 42
227.000384 84.415753 105.331312 -192.382642 -56.256421 39.582940
43 44 45 46 47 48
241.089876 294.048755 80.331486 154.577464 -204.181032 292.712867
49 50 51 52 53 54
137.895615 -22.390059 164.690640 14.249854 164.397885 -5.263454
55 56 57 58 59 60
-131.777414 -128.880938 -169.431718 -12.233581 -32.212320 -91.893747
61 62 63 64 65 66
149.134034 195.332137 18.398174 121.462520 153.777652 45.349818
67 68 69 70 71 72
-124.422532 -204.260726 -145.205342 -119.804957 148.122321 -93.205897
73 74 75 76 77 78
-65.238845 -88.362037 -129.532158 -9.246989 28.338810 -240.907524
79 80 81 82 83 84
-16.678224 139.666337 -234.487642 228.394802 -184.933176 -28.666193
85 86 87 88 89 90
-14.845319 -199.483853 13.646527 -230.291792 81.857962 4.860818
91 92 93 94 95 96
-17.559034 179.618783 -94.363138 -106.097640 -107.534979 21.900007
97 98 99 100 101 102
-142.522114 81.209751 -34.323815 62.295644 -147.206044 -67.417971
103 104 105 106 107 108
66.112415 117.156705 244.820327 -145.019708 -22.671434 8.345306
109 110 111 112 113 114
-142.513254 154.846929 47.741408 -5.250102 -35.845112 -117.492487
115 116 117 118 119 120
99.593403 149.795051 79.603783 -106.925290 213.396123 -51.597815
121 122 123 124 125 126
-52.776777 -76.717847 -124.740444 38.561582 50.792408 -109.513841
127 128 129 130 131 132
97.323112 125.340787 -30.874241 -112.250838 28.087050 -21.835884
133 134 135 136 137 138
-54.720134 93.260781 -87.500796 24.472888 -41.541758 107.428710
139 140 141 142 143 144
-224.631733 -49.209466 -63.622072 118.202235 87.936531 16.885729
145 146 147 148 149 150
3.404292 -73.760814 141.323268 -59.244249 88.248924 181.402623
151 152 153 154 155 156
-155.569657 -328.526166 -19.955793 149.911989 22.402527 31.127072
157 158 159 160 161 162
8.770015 132.375378 -59.400035 156.352869 -35.914159 105.827993
163 164 165 166 167 168
32.130288 -4.830850 -126.535940 -225.420030 1.761681 70.022432
169 170 171 172 173 174
-21.733975 -105.449405 -36.659879 -76.997632 179.970481 20.438967
175 176 177 178 179 180
-191.810150 -192.074837 154.993155 74.334477 95.153539 2.628934
181 182 183 184 185 186
60.997820 -7.132007 -32.657434 39.536517 135.289041 80.382017
187 188
27.245104 -52.818816
> postscript(file="/var/www/html/rcomp/tmp/6vow61258751152.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 = 188
Frequency = 1
lag(myerror, k = 1) myerror
0 7.037835 NA
1 -98.331247 7.037835
2 -117.325401 -98.331247
3 -42.794803 -117.325401
4 -165.254264 -42.794803
5 -196.555340 -165.254264
6 145.254550 -196.555340
7 -79.041426 145.254550
8 -34.083291 -79.041426
9 171.087420 -34.083291
10 -28.154237 171.087420
11 -68.943562 -28.154237
12 -156.001393 -68.943562
13 -104.456935 -156.001393
14 130.549461 -104.456935
15 34.892228 130.549461
16 -123.098350 34.892228
17 74.557655 -123.098350
18 67.081018 74.557655
19 152.706477 67.081018
20 91.230387 152.706477
21 -110.882336 91.230387
22 -63.151544 -110.882336
23 11.916144 -63.151544
24 56.111818 11.916144
25 34.643474 56.111818
26 0.459169 34.643474
27 124.384106 0.459169
28 -277.557055 124.384106
29 77.319078 -277.557055
30 86.618978 77.319078
31 -118.689669 86.618978
32 267.580039 -118.689669
33 42.125992 267.580039
34 45.978952 42.125992
35 -99.395392 45.978952
36 227.000384 -99.395392
37 84.415753 227.000384
38 105.331312 84.415753
39 -192.382642 105.331312
40 -56.256421 -192.382642
41 39.582940 -56.256421
42 241.089876 39.582940
43 294.048755 241.089876
44 80.331486 294.048755
45 154.577464 80.331486
46 -204.181032 154.577464
47 292.712867 -204.181032
48 137.895615 292.712867
49 -22.390059 137.895615
50 164.690640 -22.390059
51 14.249854 164.690640
52 164.397885 14.249854
53 -5.263454 164.397885
54 -131.777414 -5.263454
55 -128.880938 -131.777414
56 -169.431718 -128.880938
57 -12.233581 -169.431718
58 -32.212320 -12.233581
59 -91.893747 -32.212320
60 149.134034 -91.893747
61 195.332137 149.134034
62 18.398174 195.332137
63 121.462520 18.398174
64 153.777652 121.462520
65 45.349818 153.777652
66 -124.422532 45.349818
67 -204.260726 -124.422532
68 -145.205342 -204.260726
69 -119.804957 -145.205342
70 148.122321 -119.804957
71 -93.205897 148.122321
72 -65.238845 -93.205897
73 -88.362037 -65.238845
74 -129.532158 -88.362037
75 -9.246989 -129.532158
76 28.338810 -9.246989
77 -240.907524 28.338810
78 -16.678224 -240.907524
79 139.666337 -16.678224
80 -234.487642 139.666337
81 228.394802 -234.487642
82 -184.933176 228.394802
83 -28.666193 -184.933176
84 -14.845319 -28.666193
85 -199.483853 -14.845319
86 13.646527 -199.483853
87 -230.291792 13.646527
88 81.857962 -230.291792
89 4.860818 81.857962
90 -17.559034 4.860818
91 179.618783 -17.559034
92 -94.363138 179.618783
93 -106.097640 -94.363138
94 -107.534979 -106.097640
95 21.900007 -107.534979
96 -142.522114 21.900007
97 81.209751 -142.522114
98 -34.323815 81.209751
99 62.295644 -34.323815
100 -147.206044 62.295644
101 -67.417971 -147.206044
102 66.112415 -67.417971
103 117.156705 66.112415
104 244.820327 117.156705
105 -145.019708 244.820327
106 -22.671434 -145.019708
107 8.345306 -22.671434
108 -142.513254 8.345306
109 154.846929 -142.513254
110 47.741408 154.846929
111 -5.250102 47.741408
112 -35.845112 -5.250102
113 -117.492487 -35.845112
114 99.593403 -117.492487
115 149.795051 99.593403
116 79.603783 149.795051
117 -106.925290 79.603783
118 213.396123 -106.925290
119 -51.597815 213.396123
120 -52.776777 -51.597815
121 -76.717847 -52.776777
122 -124.740444 -76.717847
123 38.561582 -124.740444
124 50.792408 38.561582
125 -109.513841 50.792408
126 97.323112 -109.513841
127 125.340787 97.323112
128 -30.874241 125.340787
129 -112.250838 -30.874241
130 28.087050 -112.250838
131 -21.835884 28.087050
132 -54.720134 -21.835884
133 93.260781 -54.720134
134 -87.500796 93.260781
135 24.472888 -87.500796
136 -41.541758 24.472888
137 107.428710 -41.541758
138 -224.631733 107.428710
139 -49.209466 -224.631733
140 -63.622072 -49.209466
141 118.202235 -63.622072
142 87.936531 118.202235
143 16.885729 87.936531
144 3.404292 16.885729
145 -73.760814 3.404292
146 141.323268 -73.760814
147 -59.244249 141.323268
148 88.248924 -59.244249
149 181.402623 88.248924
150 -155.569657 181.402623
151 -328.526166 -155.569657
152 -19.955793 -328.526166
153 149.911989 -19.955793
154 22.402527 149.911989
155 31.127072 22.402527
156 8.770015 31.127072
157 132.375378 8.770015
158 -59.400035 132.375378
159 156.352869 -59.400035
160 -35.914159 156.352869
161 105.827993 -35.914159
162 32.130288 105.827993
163 -4.830850 32.130288
164 -126.535940 -4.830850
165 -225.420030 -126.535940
166 1.761681 -225.420030
167 70.022432 1.761681
168 -21.733975 70.022432
169 -105.449405 -21.733975
170 -36.659879 -105.449405
171 -76.997632 -36.659879
172 179.970481 -76.997632
173 20.438967 179.970481
174 -191.810150 20.438967
175 -192.074837 -191.810150
176 154.993155 -192.074837
177 74.334477 154.993155
178 95.153539 74.334477
179 2.628934 95.153539
180 60.997820 2.628934
181 -7.132007 60.997820
182 -32.657434 -7.132007
183 39.536517 -32.657434
184 135.289041 39.536517
185 80.382017 135.289041
186 27.245104 80.382017
187 -52.818816 27.245104
188 NA -52.818816
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -98.331247 7.037835
[2,] -117.325401 -98.331247
[3,] -42.794803 -117.325401
[4,] -165.254264 -42.794803
[5,] -196.555340 -165.254264
[6,] 145.254550 -196.555340
[7,] -79.041426 145.254550
[8,] -34.083291 -79.041426
[9,] 171.087420 -34.083291
[10,] -28.154237 171.087420
[11,] -68.943562 -28.154237
[12,] -156.001393 -68.943562
[13,] -104.456935 -156.001393
[14,] 130.549461 -104.456935
[15,] 34.892228 130.549461
[16,] -123.098350 34.892228
[17,] 74.557655 -123.098350
[18,] 67.081018 74.557655
[19,] 152.706477 67.081018
[20,] 91.230387 152.706477
[21,] -110.882336 91.230387
[22,] -63.151544 -110.882336
[23,] 11.916144 -63.151544
[24,] 56.111818 11.916144
[25,] 34.643474 56.111818
[26,] 0.459169 34.643474
[27,] 124.384106 0.459169
[28,] -277.557055 124.384106
[29,] 77.319078 -277.557055
[30,] 86.618978 77.319078
[31,] -118.689669 86.618978
[32,] 267.580039 -118.689669
[33,] 42.125992 267.580039
[34,] 45.978952 42.125992
[35,] -99.395392 45.978952
[36,] 227.000384 -99.395392
[37,] 84.415753 227.000384
[38,] 105.331312 84.415753
[39,] -192.382642 105.331312
[40,] -56.256421 -192.382642
[41,] 39.582940 -56.256421
[42,] 241.089876 39.582940
[43,] 294.048755 241.089876
[44,] 80.331486 294.048755
[45,] 154.577464 80.331486
[46,] -204.181032 154.577464
[47,] 292.712867 -204.181032
[48,] 137.895615 292.712867
[49,] -22.390059 137.895615
[50,] 164.690640 -22.390059
[51,] 14.249854 164.690640
[52,] 164.397885 14.249854
[53,] -5.263454 164.397885
[54,] -131.777414 -5.263454
[55,] -128.880938 -131.777414
[56,] -169.431718 -128.880938
[57,] -12.233581 -169.431718
[58,] -32.212320 -12.233581
[59,] -91.893747 -32.212320
[60,] 149.134034 -91.893747
[61,] 195.332137 149.134034
[62,] 18.398174 195.332137
[63,] 121.462520 18.398174
[64,] 153.777652 121.462520
[65,] 45.349818 153.777652
[66,] -124.422532 45.349818
[67,] -204.260726 -124.422532
[68,] -145.205342 -204.260726
[69,] -119.804957 -145.205342
[70,] 148.122321 -119.804957
[71,] -93.205897 148.122321
[72,] -65.238845 -93.205897
[73,] -88.362037 -65.238845
[74,] -129.532158 -88.362037
[75,] -9.246989 -129.532158
[76,] 28.338810 -9.246989
[77,] -240.907524 28.338810
[78,] -16.678224 -240.907524
[79,] 139.666337 -16.678224
[80,] -234.487642 139.666337
[81,] 228.394802 -234.487642
[82,] -184.933176 228.394802
[83,] -28.666193 -184.933176
[84,] -14.845319 -28.666193
[85,] -199.483853 -14.845319
[86,] 13.646527 -199.483853
[87,] -230.291792 13.646527
[88,] 81.857962 -230.291792
[89,] 4.860818 81.857962
[90,] -17.559034 4.860818
[91,] 179.618783 -17.559034
[92,] -94.363138 179.618783
[93,] -106.097640 -94.363138
[94,] -107.534979 -106.097640
[95,] 21.900007 -107.534979
[96,] -142.522114 21.900007
[97,] 81.209751 -142.522114
[98,] -34.323815 81.209751
[99,] 62.295644 -34.323815
[100,] -147.206044 62.295644
[101,] -67.417971 -147.206044
[102,] 66.112415 -67.417971
[103,] 117.156705 66.112415
[104,] 244.820327 117.156705
[105,] -145.019708 244.820327
[106,] -22.671434 -145.019708
[107,] 8.345306 -22.671434
[108,] -142.513254 8.345306
[109,] 154.846929 -142.513254
[110,] 47.741408 154.846929
[111,] -5.250102 47.741408
[112,] -35.845112 -5.250102
[113,] -117.492487 -35.845112
[114,] 99.593403 -117.492487
[115,] 149.795051 99.593403
[116,] 79.603783 149.795051
[117,] -106.925290 79.603783
[118,] 213.396123 -106.925290
[119,] -51.597815 213.396123
[120,] -52.776777 -51.597815
[121,] -76.717847 -52.776777
[122,] -124.740444 -76.717847
[123,] 38.561582 -124.740444
[124,] 50.792408 38.561582
[125,] -109.513841 50.792408
[126,] 97.323112 -109.513841
[127,] 125.340787 97.323112
[128,] -30.874241 125.340787
[129,] -112.250838 -30.874241
[130,] 28.087050 -112.250838
[131,] -21.835884 28.087050
[132,] -54.720134 -21.835884
[133,] 93.260781 -54.720134
[134,] -87.500796 93.260781
[135,] 24.472888 -87.500796
[136,] -41.541758 24.472888
[137,] 107.428710 -41.541758
[138,] -224.631733 107.428710
[139,] -49.209466 -224.631733
[140,] -63.622072 -49.209466
[141,] 118.202235 -63.622072
[142,] 87.936531 118.202235
[143,] 16.885729 87.936531
[144,] 3.404292 16.885729
[145,] -73.760814 3.404292
[146,] 141.323268 -73.760814
[147,] -59.244249 141.323268
[148,] 88.248924 -59.244249
[149,] 181.402623 88.248924
[150,] -155.569657 181.402623
[151,] -328.526166 -155.569657
[152,] -19.955793 -328.526166
[153,] 149.911989 -19.955793
[154,] 22.402527 149.911989
[155,] 31.127072 22.402527
[156,] 8.770015 31.127072
[157,] 132.375378 8.770015
[158,] -59.400035 132.375378
[159,] 156.352869 -59.400035
[160,] -35.914159 156.352869
[161,] 105.827993 -35.914159
[162,] 32.130288 105.827993
[163,] -4.830850 32.130288
[164,] -126.535940 -4.830850
[165,] -225.420030 -126.535940
[166,] 1.761681 -225.420030
[167,] 70.022432 1.761681
[168,] -21.733975 70.022432
[169,] -105.449405 -21.733975
[170,] -36.659879 -105.449405
[171,] -76.997632 -36.659879
[172,] 179.970481 -76.997632
[173,] 20.438967 179.970481
[174,] -191.810150 20.438967
[175,] -192.074837 -191.810150
[176,] 154.993155 -192.074837
[177,] 74.334477 154.993155
[178,] 95.153539 74.334477
[179,] 2.628934 95.153539
[180,] 60.997820 2.628934
[181,] -7.132007 60.997820
[182,] -32.657434 -7.132007
[183,] 39.536517 -32.657434
[184,] 135.289041 39.536517
[185,] 80.382017 135.289041
[186,] 27.245104 80.382017
[187,] -52.818816 27.245104
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -98.331247 7.037835
2 -117.325401 -98.331247
3 -42.794803 -117.325401
4 -165.254264 -42.794803
5 -196.555340 -165.254264
6 145.254550 -196.555340
7 -79.041426 145.254550
8 -34.083291 -79.041426
9 171.087420 -34.083291
10 -28.154237 171.087420
11 -68.943562 -28.154237
12 -156.001393 -68.943562
13 -104.456935 -156.001393
14 130.549461 -104.456935
15 34.892228 130.549461
16 -123.098350 34.892228
17 74.557655 -123.098350
18 67.081018 74.557655
19 152.706477 67.081018
20 91.230387 152.706477
21 -110.882336 91.230387
22 -63.151544 -110.882336
23 11.916144 -63.151544
24 56.111818 11.916144
25 34.643474 56.111818
26 0.459169 34.643474
27 124.384106 0.459169
28 -277.557055 124.384106
29 77.319078 -277.557055
30 86.618978 77.319078
31 -118.689669 86.618978
32 267.580039 -118.689669
33 42.125992 267.580039
34 45.978952 42.125992
35 -99.395392 45.978952
36 227.000384 -99.395392
37 84.415753 227.000384
38 105.331312 84.415753
39 -192.382642 105.331312
40 -56.256421 -192.382642
41 39.582940 -56.256421
42 241.089876 39.582940
43 294.048755 241.089876
44 80.331486 294.048755
45 154.577464 80.331486
46 -204.181032 154.577464
47 292.712867 -204.181032
48 137.895615 292.712867
49 -22.390059 137.895615
50 164.690640 -22.390059
51 14.249854 164.690640
52 164.397885 14.249854
53 -5.263454 164.397885
54 -131.777414 -5.263454
55 -128.880938 -131.777414
56 -169.431718 -128.880938
57 -12.233581 -169.431718
58 -32.212320 -12.233581
59 -91.893747 -32.212320
60 149.134034 -91.893747
61 195.332137 149.134034
62 18.398174 195.332137
63 121.462520 18.398174
64 153.777652 121.462520
65 45.349818 153.777652
66 -124.422532 45.349818
67 -204.260726 -124.422532
68 -145.205342 -204.260726
69 -119.804957 -145.205342
70 148.122321 -119.804957
71 -93.205897 148.122321
72 -65.238845 -93.205897
73 -88.362037 -65.238845
74 -129.532158 -88.362037
75 -9.246989 -129.532158
76 28.338810 -9.246989
77 -240.907524 28.338810
78 -16.678224 -240.907524
79 139.666337 -16.678224
80 -234.487642 139.666337
81 228.394802 -234.487642
82 -184.933176 228.394802
83 -28.666193 -184.933176
84 -14.845319 -28.666193
85 -199.483853 -14.845319
86 13.646527 -199.483853
87 -230.291792 13.646527
88 81.857962 -230.291792
89 4.860818 81.857962
90 -17.559034 4.860818
91 179.618783 -17.559034
92 -94.363138 179.618783
93 -106.097640 -94.363138
94 -107.534979 -106.097640
95 21.900007 -107.534979
96 -142.522114 21.900007
97 81.209751 -142.522114
98 -34.323815 81.209751
99 62.295644 -34.323815
100 -147.206044 62.295644
101 -67.417971 -147.206044
102 66.112415 -67.417971
103 117.156705 66.112415
104 244.820327 117.156705
105 -145.019708 244.820327
106 -22.671434 -145.019708
107 8.345306 -22.671434
108 -142.513254 8.345306
109 154.846929 -142.513254
110 47.741408 154.846929
111 -5.250102 47.741408
112 -35.845112 -5.250102
113 -117.492487 -35.845112
114 99.593403 -117.492487
115 149.795051 99.593403
116 79.603783 149.795051
117 -106.925290 79.603783
118 213.396123 -106.925290
119 -51.597815 213.396123
120 -52.776777 -51.597815
121 -76.717847 -52.776777
122 -124.740444 -76.717847
123 38.561582 -124.740444
124 50.792408 38.561582
125 -109.513841 50.792408
126 97.323112 -109.513841
127 125.340787 97.323112
128 -30.874241 125.340787
129 -112.250838 -30.874241
130 28.087050 -112.250838
131 -21.835884 28.087050
132 -54.720134 -21.835884
133 93.260781 -54.720134
134 -87.500796 93.260781
135 24.472888 -87.500796
136 -41.541758 24.472888
137 107.428710 -41.541758
138 -224.631733 107.428710
139 -49.209466 -224.631733
140 -63.622072 -49.209466
141 118.202235 -63.622072
142 87.936531 118.202235
143 16.885729 87.936531
144 3.404292 16.885729
145 -73.760814 3.404292
146 141.323268 -73.760814
147 -59.244249 141.323268
148 88.248924 -59.244249
149 181.402623 88.248924
150 -155.569657 181.402623
151 -328.526166 -155.569657
152 -19.955793 -328.526166
153 149.911989 -19.955793
154 22.402527 149.911989
155 31.127072 22.402527
156 8.770015 31.127072
157 132.375378 8.770015
158 -59.400035 132.375378
159 156.352869 -59.400035
160 -35.914159 156.352869
161 105.827993 -35.914159
162 32.130288 105.827993
163 -4.830850 32.130288
164 -126.535940 -4.830850
165 -225.420030 -126.535940
166 1.761681 -225.420030
167 70.022432 1.761681
168 -21.733975 70.022432
169 -105.449405 -21.733975
170 -36.659879 -105.449405
171 -76.997632 -36.659879
172 179.970481 -76.997632
173 20.438967 179.970481
174 -191.810150 20.438967
175 -192.074837 -191.810150
176 154.993155 -192.074837
177 74.334477 154.993155
178 95.153539 74.334477
179 2.628934 95.153539
180 60.997820 2.628934
181 -7.132007 60.997820
182 -32.657434 -7.132007
183 39.536517 -32.657434
184 135.289041 39.536517
185 80.382017 135.289041
186 27.245104 80.382017
187 -52.818816 27.245104
> 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/7tb3w1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/860xm1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zukw1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10obow1258751152.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rha31258751152.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/12p1as1258751152.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/136e3d1258751152.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/14v0w41258751152.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/15jfv81258751152.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/16a65s1258751152.tab")
+ }
>
> system("convert tmp/1dziy1258751152.ps tmp/1dziy1258751152.png")
> system("convert tmp/2op4n1258751152.ps tmp/2op4n1258751152.png")
> system("convert tmp/3p8rf1258751152.ps tmp/3p8rf1258751152.png")
> system("convert tmp/4e9ir1258751152.ps tmp/4e9ir1258751152.png")
> system("convert tmp/5twv31258751152.ps tmp/5twv31258751152.png")
> system("convert tmp/6vow61258751152.ps tmp/6vow61258751152.png")
> system("convert tmp/7tb3w1258751152.ps tmp/7tb3w1258751152.png")
> system("convert tmp/860xm1258751152.ps tmp/860xm1258751152.png")
> system("convert tmp/9zukw1258751152.ps tmp/9zukw1258751152.png")
> system("convert tmp/10obow1258751152.ps tmp/10obow1258751152.png")
>
>
> proc.time()
user system elapsed
4.969 1.812 5.469