R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1687
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+ ,4.173884316)
+ ,dim=c(3
+ ,192)
+ ,dimnames=list(c('x'
+ ,'y'
+ ,'z')
+ ,1:192))
> y <- array(NA,dim=c(3,192),dimnames=list(c('x','y','z'),1:192))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
x y z M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1687 0 -183.9235445 1 0 0 0 0 0 0 0 0 0 0 1
2 1508 0 -177.0726091 0 1 0 0 0 0 0 0 0 0 0 2
3 1507 0 -228.6351091 0 0 1 0 0 0 0 0 0 0 0 3
4 1385 0 -237.4476091 0 0 0 1 0 0 0 0 0 0 0 4
5 1632 0 -127.7601091 0 0 0 0 1 0 0 0 0 0 0 5
6 1511 0 -193.0101091 0 0 0 0 0 1 0 0 0 0 0 6
7 1559 0 -220.6351091 0 0 0 0 0 0 1 0 0 0 0 7
8 1630 0 -164.5101091 0 0 0 0 0 0 0 1 0 0 0 8
9 1579 0 -268.3226091 0 0 0 0 0 0 0 0 1 0 0 9
10 1653 0 -333.6976091 0 0 0 0 0 0 0 0 0 1 0 10
11 2152 0 -34.2601091 0 0 0 0 0 0 0 0 0 0 1 11
12 2148 0 -154.8851091 0 0 0 0 0 0 0 0 0 0 0 12
13 1752 0 -97.7452805 1 0 0 0 0 0 0 0 0 0 0 13
14 1765 0 101.1056549 0 1 0 0 0 0 0 0 0 0 0 14
15 1717 0 2.5431549 0 0 1 0 0 0 0 0 0 0 0 15
16 1558 0 -43.2693451 0 0 0 1 0 0 0 0 0 0 0 16
17 1575 0 -163.5818451 0 0 0 0 1 0 0 0 0 0 0 17
18 1520 0 -162.8318451 0 0 0 0 0 1 0 0 0 0 0 18
19 1805 0 46.5431549 0 0 0 0 0 0 1 0 0 0 0 19
20 1800 0 26.6681549 0 0 0 0 0 0 0 1 0 0 0 20
21 1719 0 -107.1443451 0 0 0 0 0 0 0 0 1 0 0 21
22 2008 0 42.4806549 0 0 0 0 0 0 0 0 0 1 0 22
23 2242 0 76.9181549 0 0 0 0 0 0 0 0 0 0 1 23
24 2478 0 196.2931549 0 0 0 0 0 0 0 0 0 0 0 24
25 2030 0 201.4329835 1 0 0 0 0 0 0 0 0 0 0 25
26 1655 0 12.2839189 0 1 0 0 0 0 0 0 0 0 0 26
27 1693 0 -0.2785811 0 0 1 0 0 0 0 0 0 0 0 27
28 1623 0 42.9089189 0 0 0 1 0 0 0 0 0 0 0 28
29 1805 0 87.5964189 0 0 0 0 1 0 0 0 0 0 0 29
30 1746 0 84.3464189 0 0 0 0 0 1 0 0 0 0 0 30
31 1795 0 57.7214189 0 0 0 0 0 0 1 0 0 0 0 31
32 1926 0 173.8464189 0 0 0 0 0 0 0 1 0 0 0 32
33 1619 0 -185.9660811 0 0 0 0 0 0 0 0 1 0 0 33
34 1992 0 47.6589189 0 0 0 0 0 0 0 0 0 1 0 34
35 2233 0 89.0964189 0 0 0 0 0 0 0 0 0 0 1 35
36 2192 0 -68.5285811 0 0 0 0 0 0 0 0 0 0 0 36
37 2080 0 272.6112475 1 0 0 0 0 0 0 0 0 0 0 37
38 1768 0 146.4621829 0 1 0 0 0 0 0 0 0 0 0 38
39 1835 0 162.8996829 0 0 1 0 0 0 0 0 0 0 0 39
40 1569 0 10.0871828 0 0 0 1 0 0 0 0 0 0 0 40
41 1976 0 279.7746829 0 0 0 0 1 0 0 0 0 0 0 41
42 1853 0 212.5246829 0 0 0 0 0 1 0 0 0 0 0 42
43 1965 0 248.8996829 0 0 0 0 0 0 1 0 0 0 0 43
44 1689 0 -41.9753172 0 0 0 0 0 0 0 1 0 0 0 44
45 1778 0 -5.7878171 0 0 0 0 0 0 0 0 1 0 0 45
46 1976 0 52.8371828 0 0 0 0 0 0 0 0 0 1 0 46
47 2397 0 274.2746829 0 0 0 0 0 0 0 0 0 0 1 47
48 2654 0 414.6496829 0 0 0 0 0 0 0 0 0 0 0 48
49 2097 0 310.7895114 1 0 0 0 0 0 0 0 0 0 0 49
50 1963 0 362.6404468 0 1 0 0 0 0 0 0 0 0 0 50
51 1677 0 26.0779468 0 0 1 0 0 0 0 0 0 0 0 51
52 1941 0 403.2654468 0 0 0 1 0 0 0 0 0 0 0 52
53 2003 0 327.9529468 0 0 0 0 1 0 0 0 0 0 0 53
54 1813 0 193.7029468 0 0 0 0 0 1 0 0 0 0 0 54
55 2012 0 317.0779468 0 0 0 0 0 0 1 0 0 0 0 55
56 1912 0 202.2029468 0 0 0 0 0 0 0 1 0 0 0 56
57 2084 0 321.3904468 0 0 0 0 0 0 0 0 1 0 0 57
58 2080 0 178.0154468 0 0 0 0 0 0 0 0 0 1 0 58
59 2118 0 16.4529468 0 0 0 0 0 0 0 0 0 0 1 59
60 2150 0 -68.1720532 0 0 0 0 0 0 0 0 0 0 0 60
61 1608 0 -157.0322246 1 0 0 0 0 0 0 0 0 0 0 61
62 1503 0 -76.1812892 0 1 0 0 0 0 0 0 0 0 0 62
63 1548 0 -81.7437892 0 0 1 0 0 0 0 0 0 0 0 63
64 1382 0 -134.5562892 0 0 0 1 0 0 0 0 0 0 0 64
65 1731 0 77.1312108 0 0 0 0 1 0 0 0 0 0 0 65
66 1798 0 199.8812108 0 0 0 0 0 1 0 0 0 0 0 66
67 1779 0 105.2562108 0 0 0 0 0 0 1 0 0 0 0 67
68 1887 0 198.3812108 0 0 0 0 0 0 0 1 0 0 0 68
69 2004 0 262.5687108 0 0 0 0 0 0 0 0 1 0 0 69
70 2077 0 196.1937108 0 0 0 0 0 0 0 0 0 1 0 70
71 2092 0 11.6312108 0 0 0 0 0 0 0 0 0 0 1 71
72 2051 0 -145.9937892 0 0 0 0 0 0 0 0 0 0 0 72
73 1577 0 -166.8539606 1 0 0 0 0 0 0 0 0 0 0 73
74 1356 0 -202.0030252 0 1 0 0 0 0 0 0 0 0 0 74
75 1652 0 43.4344748 0 0 1 0 0 0 0 0 0 0 0 75
76 1382 0 -113.3780252 0 0 0 1 0 0 0 0 0 0 0 76
77 1519 0 -113.6905252 0 0 0 0 1 0 0 0 0 0 0 77
78 1421 0 -155.9405252 0 0 0 0 0 1 0 0 0 0 0 78
79 1442 0 -210.5655252 0 0 0 0 0 0 1 0 0 0 0 79
80 1543 0 -124.4405252 0 0 0 0 0 0 0 1 0 0 0 80
81 1656 0 -64.2530252 0 0 0 0 0 0 0 0 1 0 0 81
82 1561 0 -298.6280252 0 0 0 0 0 0 0 0 0 1 0 82
83 1905 0 -154.1905252 0 0 0 0 0 0 0 0 0 0 1 83
84 2199 0 23.1844748 0 0 0 0 0 0 0 0 0 0 0 84
85 1473 0 -249.6756966 1 0 0 0 0 0 0 0 0 0 0 85
86 1655 0 118.1752388 0 1 0 0 0 0 0 0 0 0 0 86
87 1407 0 -180.3872612 0 0 1 0 0 0 0 0 0 0 0 87
88 1395 0 -79.1997612 0 0 0 1 0 0 0 0 0 0 0 88
89 1530 0 -81.5122612 0 0 0 0 1 0 0 0 0 0 0 89
90 1309 0 -246.7622612 0 0 0 0 0 1 0 0 0 0 0 90
91 1526 0 -105.3872612 0 0 0 0 0 0 1 0 0 0 0 91
92 1327 0 -319.2622612 0 0 0 0 0 0 0 1 0 0 0 92
93 1627 0 -72.0747612 0 0 0 0 0 0 0 0 1 0 0 93
94 1748 0 -90.4497612 0 0 0 0 0 0 0 0 0 1 0 94
95 1958 0 -80.0122612 0 0 0 0 0 0 0 0 0 0 1 95
96 2274 0 119.3627388 0 0 0 0 0 0 0 0 0 0 0 96
97 1648 0 -53.4974326 1 0 0 0 0 0 0 0 0 0 0 97
98 1401 0 -114.6464972 0 1 0 0 0 0 0 0 0 0 0 98
99 1411 0 -155.2089972 0 0 1 0 0 0 0 0 0 0 0 99
100 1403 0 -50.0214972 0 0 0 1 0 0 0 0 0 0 0 100
101 1394 0 -196.3339972 0 0 0 0 1 0 0 0 0 0 0 101
102 1520 0 -14.5839972 0 0 0 0 0 1 0 0 0 0 0 102
103 1528 0 -82.2089972 0 0 0 0 0 0 1 0 0 0 0 103
104 1643 0 17.9160028 0 0 0 0 0 0 0 1 0 0 0 104
105 1515 0 -162.8964972 0 0 0 0 0 0 0 0 1 0 0 105
106 1685 0 -132.2714972 0 0 0 0 0 0 0 0 0 1 0 106
107 2000 0 -16.8339972 0 0 0 0 0 0 0 0 0 0 1 107
108 2215 0 81.5410028 0 0 0 0 0 0 0 0 0 0 0 108
109 1956 0 275.6808314 1 0 0 0 0 0 0 0 0 0 0 109
110 1462 0 -32.4682332 0 1 0 0 0 0 0 0 0 0 0 110
111 1563 0 17.9692668 0 0 1 0 0 0 0 0 0 0 0 111
112 1459 0 27.1567668 0 0 0 1 0 0 0 0 0 0 0 112
113 1446 0 -123.1557332 0 0 0 0 1 0 0 0 0 0 0 113
114 1622 0 108.5942668 0 0 0 0 0 1 0 0 0 0 0 114
115 1657 0 67.9692668 0 0 0 0 0 0 1 0 0 0 0 115
116 1638 0 34.0942668 0 0 0 0 0 0 0 1 0 0 0 116
117 1643 0 -13.7182332 0 0 0 0 0 0 0 0 1 0 0 117
118 1683 0 -113.0932332 0 0 0 0 0 0 0 0 0 1 0 118
119 2050 0 54.3442668 0 0 0 0 0 0 0 0 0 0 1 119
120 2262 0 149.7192668 0 0 0 0 0 0 0 0 0 0 0 120
121 1813 0 153.8590954 1 0 0 0 0 0 0 0 0 0 0 121
122 1445 0 -28.2899692 0 1 0 0 0 0 0 0 0 0 0 122
123 1762 0 238.1475308 0 0 1 0 0 0 0 0 0 0 0 123
124 1461 0 50.3350308 0 0 0 1 0 0 0 0 0 0 0 124
125 1556 0 8.0225308 0 0 0 0 1 0 0 0 0 0 0 125
126 1431 0 -61.2274692 0 0 0 0 0 1 0 0 0 0 0 126
127 1427 0 -140.8524692 0 0 0 0 0 0 1 0 0 0 0 127
128 1554 0 -28.7274692 0 0 0 0 0 0 0 1 0 0 0 128
129 1645 0 9.4600308 0 0 0 0 0 0 0 0 1 0 0 129
130 1653 0 -121.9149692 0 0 0 0 0 0 0 0 0 1 0 130
131 2016 0 41.5225308 0 0 0 0 0 0 0 0 0 0 1 131
132 2207 0 115.8975308 0 0 0 0 0 0 0 0 0 0 0 132
133 1665 0 27.0373594 1 0 0 0 0 0 0 0 0 0 0 133
134 1361 0 -91.1117052 0 1 0 0 0 0 0 0 0 0 0 134
135 1506 0 3.3257948 0 0 1 0 0 0 0 0 0 0 0 135
136 1360 0 -29.4867052 0 0 0 1 0 0 0 0 0 0 0 136
137 1453 0 -73.7992052 0 0 0 0 1 0 0 0 0 0 0 137
138 1522 0 50.9507948 0 0 0 0 0 1 0 0 0 0 0 138
139 1460 0 -86.6742052 0 0 0 0 0 0 1 0 0 0 0 139
140 1552 0 -9.5492052 0 0 0 0 0 0 0 1 0 0 0 140
141 1548 0 -66.3617052 0 0 0 0 0 0 0 0 1 0 0 141
142 1827 0 73.2632948 0 0 0 0 0 0 0 0 0 1 0 142
143 1737 0 -216.2992052 0 0 0 0 0 0 0 0 0 0 1 143
144 1941 0 -128.9242052 0 0 0 0 0 0 0 0 0 0 0 144
145 1474 0 -142.7843767 1 0 0 0 0 0 0 0 0 0 0 145
146 1458 0 27.0665587 0 1 0 0 0 0 0 0 0 0 0 146
147 1542 0 60.5040587 0 0 1 0 0 0 0 0 0 0 0 147
148 1404 0 35.6915587 0 0 0 1 0 0 0 0 0 0 0 148
149 1522 0 16.3790587 0 0 0 0 1 0 0 0 0 0 0 149
150 1385 0 -64.8709413 0 0 0 0 0 1 0 0 0 0 0 150
151 1641 0 115.5040587 0 0 0 0 0 0 1 0 0 0 0 151
152 1510 0 -30.3709413 0 0 0 0 0 0 0 1 0 0 0 152
153 1681 0 87.8165587 0 0 0 0 0 0 0 0 1 0 0 153
154 1938 0 205.4415587 0 0 0 0 0 0 0 0 0 1 0 154
155 1868 0 -64.1209413 0 0 0 0 0 0 0 0 0 0 1 155
156 1726 0 -322.7459413 0 0 0 0 0 0 0 0 0 0 0 156
157 1456 0 -139.6061127 1 0 0 0 0 0 0 0 0 0 0 157
158 1445 0 35.2448227 0 1 0 0 0 0 0 0 0 0 0 158
159 1456 0 -4.3176773 0 0 1 0 0 0 0 0 0 0 0 159
160 1365 0 17.8698227 0 0 0 1 0 0 0 0 0 0 0 160
161 1487 0 2.5573227 0 0 0 0 1 0 0 0 0 0 0 161
162 1558 0 129.3073227 0 0 0 0 0 1 0 0 0 0 0 162
163 1488 0 -16.3176773 0 0 0 0 0 0 1 0 0 0 0 163
164 1684 0 164.8073227 0 0 0 0 0 0 0 1 0 0 0 164
165 1594 0 21.9948227 0 0 0 0 0 0 0 0 1 0 0 165
166 1850 0 138.6198227 0 0 0 0 0 0 0 0 0 1 0 166
167 1998 0 87.0573227 0 0 0 0 0 0 0 0 0 0 1 167
168 2079 0 51.4323227 0 0 0 0 0 0 0 0 0 0 0 168
169 1494 0 -80.4278487 1 0 0 0 0 0 0 0 0 0 0 169
170 1057 1 -105.1918797 0 1 0 0 0 0 0 0 0 0 0 170
171 1218 1 5.2456203 0 0 1 0 0 0 0 0 0 0 0 171
172 1168 1 68.4331203 0 0 0 1 0 0 0 0 0 0 0 172
173 1236 1 -0.8793797 0 0 0 0 1 0 0 0 0 0 0 173
174 1076 1 -105.1293797 0 0 0 0 0 1 0 0 0 0 0 174
175 1174 1 -82.7543797 0 0 0 0 0 0 1 0 0 0 0 175
176 1139 1 -132.6293797 0 0 0 0 0 0 0 1 0 0 0 176
177 1427 1 102.5581203 0 0 0 0 0 0 0 0 1 0 0 177
178 1487 1 23.1831203 0 0 0 0 0 0 0 0 0 1 0 178
179 1483 1 -180.3793797 0 0 0 0 0 0 0 0 0 0 1 179
180 1513 1 -267.0043797 0 0 0 0 0 0 0 0 0 0 0 180
181 1357 1 30.1354489 1 0 0 0 0 0 0 0 0 0 0 181
182 1165 1 23.9863843 0 1 0 0 0 0 0 0 0 0 0 182
183 1282 1 90.4238843 0 0 1 0 0 0 0 0 0 0 0 183
184 1110 1 31.6113843 0 0 0 1 0 0 0 0 0 0 0 184
185 1297 1 81.2988843 0 0 0 0 1 0 0 0 0 0 0 185
186 1185 1 25.0488843 0 0 0 0 0 1 0 0 0 0 0 186
187 1222 1 -13.5761157 0 0 0 0 0 0 1 0 0 0 0 187
188 1284 1 33.5488843 0 0 0 0 0 0 0 1 0 0 0 188
189 1444 1 140.7363843 0 0 0 0 0 0 0 0 1 0 0 189
190 1575 1 132.3613843 0 0 0 0 0 0 0 0 0 1 0 190
191 1737 1 94.7988843 0 0 0 0 0 0 0 0 0 0 1 191
192 1763 1 4.1738843 0 0 0 0 0 0 0 0 0 0 0 192
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y z M1 M2 M3
2324.063 -226.385 1.000 -451.375 -635.461 -583.134
M4 M5 M6 M7 M8 M9
-694.556 -555.479 -609.464 -532.074 -515.434 -460.857
M10 M11 t
-319.717 -118.390 -1.765
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.858e-08 -1.016e-08 -5.283e-10 9.344e-09 4.904e-08
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.324e+03 5.929e-09 3.920e+11 <2e-16 ***
y -2.264e+02 5.526e-09 -4.097e+10 <2e-16 ***
z 1.000e+00 1.009e-11 9.908e+10 <2e-16 ***
M1 -4.514e+02 7.264e-09 -6.214e+10 <2e-16 ***
M2 -6.355e+02 7.263e-09 -8.749e+10 <2e-16 ***
M3 -5.831e+02 7.262e-09 -8.030e+10 <2e-16 ***
M4 -6.946e+02 7.261e-09 -9.566e+10 <2e-16 ***
M5 -5.555e+02 7.260e-09 -7.651e+10 <2e-16 ***
M6 -6.095e+02 7.259e-09 -8.396e+10 <2e-16 ***
M7 -5.321e+02 7.258e-09 -7.331e+10 <2e-16 ***
M8 -5.154e+02 7.257e-09 -7.102e+10 <2e-16 ***
M9 -4.609e+02 7.257e-09 -6.351e+10 <2e-16 ***
M10 -3.197e+02 7.257e-09 -4.406e+10 <2e-16 ***
M11 -1.184e+02 7.256e-09 -1.632e+10 <2e-16 ***
t -1.765e+00 3.239e-11 -5.449e+10 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.052e-08 on 177 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.717e+21 on 14 and 177 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.25441464 5.088293e-01 7.455854e-01
[2,] 0.13936362 2.787272e-01 8.606364e-01
[3,] 0.07772007 1.554401e-01 9.222799e-01
[4,] 0.06010402 1.202080e-01 9.398960e-01
[5,] 0.02814250 5.628500e-02 9.718575e-01
[6,] 0.01341571 2.683141e-02 9.865843e-01
[7,] 0.01784511 3.569022e-02 9.821549e-01
[8,] 0.07591454 1.518291e-01 9.240855e-01
[9,] 0.07292422 1.458484e-01 9.270758e-01
[10,] 0.04452607 8.905214e-02 9.554739e-01
[11,] 0.02636956 5.273911e-02 9.736304e-01
[12,] 0.02245384 4.490769e-02 9.775462e-01
[13,] 0.01791762 3.583525e-02 9.820824e-01
[14,] 0.01024889 2.049777e-02 9.897511e-01
[15,] 0.03571659 7.143317e-02 9.642834e-01
[16,] 0.04097777 8.195554e-02 9.590222e-01
[17,] 0.02715659 5.431319e-02 9.728434e-01
[18,] 0.01727014 3.454028e-02 9.827299e-01
[19,] 0.01463135 2.926269e-02 9.853687e-01
[20,] 0.04303146 8.606291e-02 9.569685e-01
[21,] 0.10226777 2.045355e-01 8.977322e-01
[22,] 0.20464895 4.092979e-01 7.953510e-01
[23,] 0.16651083 3.330217e-01 8.334892e-01
[24,] 0.22632246 4.526449e-01 7.736775e-01
[25,] 0.31860546 6.372109e-01 6.813945e-01
[26,] 0.48832088 9.766418e-01 5.116791e-01
[27,] 0.46896201 9.379240e-01 5.310380e-01
[28,] 0.53038721 9.392256e-01 4.696128e-01
[29,] 0.48686281 9.737256e-01 5.131372e-01
[30,] 0.69282267 6.143547e-01 3.071773e-01
[31,] 0.79885021 4.022996e-01 2.011498e-01
[32,] 0.97217527 5.564946e-02 2.782473e-02
[33,] 0.99642788 7.144233e-03 3.572116e-03
[34,] 0.99492313 1.015374e-02 5.076870e-03
[35,] 0.99798603 4.027948e-03 2.013974e-03
[36,] 0.99957904 8.419120e-04 4.209560e-04
[37,] 0.99985434 2.913129e-04 1.456564e-04
[38,] 0.99993097 1.380597e-04 6.902985e-05
[39,] 0.99995271 9.458446e-05 4.729223e-05
[40,] 0.99998608 2.783516e-05 1.391758e-05
[41,] 0.99998819 2.361376e-05 1.180688e-05
[42,] 0.99998207 3.586878e-05 1.793439e-05
[43,] 0.99997100 5.800084e-05 2.900042e-05
[44,] 0.99996078 7.844828e-05 3.922414e-05
[45,] 0.99995039 9.922334e-05 4.961167e-05
[46,] 0.99992644 1.471215e-04 7.356077e-05
[47,] 0.99991278 1.744439e-04 8.722197e-05
[48,] 0.99987389 2.522220e-04 1.261110e-04
[49,] 0.99985740 2.852034e-04 1.426017e-04
[50,] 0.99983701 3.259801e-04 1.629901e-04
[51,] 0.99980194 3.961156e-04 1.980578e-04
[52,] 0.99982363 3.527346e-04 1.763673e-04
[53,] 0.99982049 3.590164e-04 1.795082e-04
[54,] 0.99975273 4.945313e-04 2.472657e-04
[55,] 0.99979502 4.099588e-04 2.049794e-04
[56,] 0.99972167 5.566537e-04 2.783268e-04
[57,] 0.99962750 7.449997e-04 3.724998e-04
[58,] 0.99951361 9.727900e-04 4.863950e-04
[59,] 0.99949201 1.015981e-03 5.079906e-04
[60,] 0.99944724 1.105512e-03 5.527562e-04
[61,] 0.99927579 1.448424e-03 7.242119e-04
[62,] 0.99913335 1.733297e-03 8.666487e-04
[63,] 0.99890798 2.184037e-03 1.092018e-03
[64,] 0.99871757 2.564865e-03 1.282432e-03
[65,] 0.99846933 3.061331e-03 1.530666e-03
[66,] 0.99839984 3.200316e-03 1.600158e-03
[67,] 0.99792071 4.158582e-03 2.079291e-03
[68,] 0.99734963 5.300731e-03 2.650365e-03
[69,] 0.99679927 6.401469e-03 3.200735e-03
[70,] 0.99607460 7.850796e-03 3.925398e-03
[71,] 0.99647236 7.055290e-03 3.527645e-03
[72,] 0.99578495 8.430107e-03 4.215054e-03
[73,] 0.99453763 1.092475e-02 5.462373e-03
[74,] 0.99356765 1.286469e-02 6.432347e-03
[75,] 0.99190329 1.619341e-02 8.096707e-03
[76,] 0.99021625 1.956751e-02 9.783754e-03
[77,] 0.99000560 1.998881e-02 9.994404e-03
[78,] 0.98764566 2.470868e-02 1.235434e-02
[79,] 0.98609446 2.781107e-02 1.390554e-02
[80,] 0.98295301 3.409399e-02 1.704699e-02
[81,] 0.97939277 4.121447e-02 2.060723e-02
[82,] 0.97459000 5.082000e-02 2.541000e-02
[83,] 0.97255384 5.489232e-02 2.744616e-02
[84,] 0.96669067 6.661867e-02 3.330933e-02
[85,] 0.95988528 8.022945e-02 4.011472e-02
[86,] 0.95405572 9.188856e-02 4.594428e-02
[87,] 0.94616366 1.076727e-01 5.383634e-02
[88,] 0.93581593 1.283681e-01 6.418407e-02
[89,] 0.93023993 1.395201e-01 6.976007e-02
[90,] 0.92371372 1.525726e-01 7.628628e-02
[91,] 0.91962629 1.607474e-01 8.037371e-02
[92,] 0.91947236 1.610553e-01 8.052764e-02
[93,] 0.90554373 1.889125e-01 9.445627e-02
[94,] 0.89825921 2.034816e-01 1.017408e-01
[95,] 0.89377791 2.124442e-01 1.062221e-01
[96,] 0.88142166 2.371567e-01 1.185783e-01
[97,] 0.87876230 2.424754e-01 1.212377e-01
[98,] 0.86766900 2.646620e-01 1.323310e-01
[99,] 0.84778541 3.044292e-01 1.522146e-01
[100,] 0.82456538 3.508692e-01 1.754346e-01
[101,] 0.83057869 3.388426e-01 1.694213e-01
[102,] 0.82589184 3.482163e-01 1.741082e-01
[103,] 0.80571352 3.885730e-01 1.942865e-01
[104,] 0.82945131 3.410974e-01 1.705487e-01
[105,] 0.80201878 3.959624e-01 1.979812e-01
[106,] 0.79857136 4.028573e-01 2.014286e-01
[107,] 0.78032198 4.393560e-01 2.196780e-01
[108,] 0.75394370 4.921126e-01 2.460563e-01
[109,] 0.71501068 5.699786e-01 2.849893e-01
[110,] 0.74882676 5.023465e-01 2.511732e-01
[111,] 0.71067926 5.786415e-01 2.893207e-01
[112,] 0.66842345 6.631531e-01 3.315765e-01
[113,] 0.77485626 4.502875e-01 2.251437e-01
[114,] 0.75679090 4.864182e-01 2.432091e-01
[115,] 0.75082197 4.983561e-01 2.491780e-01
[116,] 0.71538009 5.692398e-01 2.846199e-01
[117,] 0.67102276 6.579545e-01 3.289772e-01
[118,] 0.62635118 7.472976e-01 3.736488e-01
[119,] 0.58324524 8.335095e-01 4.167548e-01
[120,] 0.53361577 9.327685e-01 4.663842e-01
[121,] 0.48796808 9.759362e-01 5.120319e-01
[122,] 0.44023946 8.804789e-01 5.597605e-01
[123,] 0.40102517 8.020503e-01 5.989748e-01
[124,] 0.35738931 7.147786e-01 6.426107e-01
[125,] 0.35293640 7.058728e-01 6.470636e-01
[126,] 0.54781427 9.043715e-01 4.521857e-01
[127,] 0.88685194 2.262961e-01 1.131481e-01
[128,] 0.90859998 1.828000e-01 9.140002e-02
[129,] 0.89769664 2.046067e-01 1.023034e-01
[130,] 0.87498058 2.500388e-01 1.250194e-01
[131,] 0.84939280 3.012144e-01 1.506072e-01
[132,] 0.82180093 3.563981e-01 1.781991e-01
[133,] 0.86725886 2.654823e-01 1.327411e-01
[134,] 0.93457541 1.308492e-01 6.542459e-02
[135,] 0.96074373 7.851254e-02 3.925627e-02
[136,] 0.97367981 5.264039e-02 2.632019e-02
[137,] 0.97411306 5.177389e-02 2.588694e-02
[138,] 0.97994426 4.011148e-02 2.005574e-02
[139,] 0.98275599 3.448803e-02 1.724401e-02
[140,] 0.98832561 2.334877e-02 1.167439e-02
[141,] 0.98593462 2.813076e-02 1.406538e-02
[142,] 0.97717143 4.565713e-02 2.282857e-02
[143,] 0.96491669 7.016662e-02 3.508331e-02
[144,] 0.94599797 1.080041e-01 5.400203e-02
[145,] 0.95278031 9.443938e-02 4.721969e-02
[146,] 0.92792655 1.441469e-01 7.207345e-02
[147,] 0.96149305 7.701391e-02 3.850695e-02
[148,] 0.98558336 2.883328e-02 1.441664e-02
[149,] 0.99765812 4.683757e-03 2.341878e-03
[150,] 0.99450648 1.098703e-02 5.493516e-03
[151,] 0.98957012 2.085977e-02 1.042988e-02
[152,] 0.97734847 4.530306e-02 2.265153e-02
[153,] 0.97029662 5.940676e-02 2.970338e-02
[154,] 0.94043187 1.191363e-01 5.956813e-02
[155,] 0.88955661 2.208868e-01 1.104434e-01
[156,] 0.79435460 4.112908e-01 2.056454e-01
[157,] 0.73481182 5.303764e-01 2.651882e-01
> postscript(file="/var/www/html/rcomp/tmp/18bq71227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j3uo1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3usa01227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fal91227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qj9x1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 192
Frequency = 1
1 2 3 4 5
-8.078410e-09 -5.839902e-09 5.442616e-11 -1.059637e-08 -2.533044e-09
6 7 8 9 10
-9.523306e-09 -9.415816e-09 -1.338624e-08 -7.223528e-09 -1.333857e-08
11 12 13 14 15
6.096565e-09 -2.424932e-09 9.121680e-09 -1.937490e-08 1.270367e-08
16 17 18 19 20
6.197827e-09 -1.482440e-08 -2.207741e-08 7.091446e-09 3.420170e-09
21 22 23 24 25
-2.029849e-08 2.732400e-09 1.322083e-08 -1.625320e-08 -3.449967e-08
26 27 28 29 30
8.543836e-09 1.128114e-08 3.422055e-09 1.174467e-08 4.507499e-09
31 32 33 34 35
4.613436e-09 -3.959784e-08 -3.241947e-08 2.781602e-10 1.073858e-08
36 37 38 39 40
1.236435e-08 -4.721592e-08 -4.442263e-08 -3.880065e-08 1.118448e-09
41 42 43 44 45
-4.145215e-08 -4.843518e-08 -4.857931e-08 -1.174711e-09 3.431565e-09
46 47 48 49 50
-2.176285e-09 -4.243033e-08 -4.198753e-08 4.019866e-08 4.228539e-08
51 52 53 54 55
9.308972e-09 3.712380e-08 4.592290e-08 3.920575e-08 3.871622e-08
56 57 58 59 60
3.542194e-08 4.069888e-08 3.489306e-08 6.159526e-09 7.495511e-09
61 62 63 64 65
2.962222e-08 1.593758e-09 7.303206e-09 2.682531e-08 4.484916e-09
66 67 68 69 70
2.674769e-08 2.712330e-08 2.300338e-08 2.849864e-08 2.238705e-08
71 72 73 74 75
3.744885e-09 3.537071e-08 1.722740e-08 1.965951e-08 4.372436e-09
76 77 78 79 80
1.430738e-08 2.280869e-08 1.572638e-08 1.594341e-08 1.185137e-08
81 82 83 84 85
-2.637620e-09 1.191792e-08 2.196938e-08 2.265361e-09 5.122485e-09
86 87 88 89 90
5.954542e-09 1.282729e-08 -8.262047e-09 2.471861e-10 3.653265e-09
91 92 93 94 95
3.092120e-09 1.910713e-10 -5.040357e-09 -1.134251e-08 -7.587460e-10
96 97 98 99 100
9.449601e-09 1.909885e-09 -5.554737e-09 2.935065e-10 -8.116233e-10
101 102 103 104 105
-1.730759e-09 2.977468e-10 5.664224e-10 -3.581312e-09 -7.113596e-09
106 107 108 109 110
-1.361017e-08 6.556636e-09 7.166119e-09 -2.183079e-08 1.685140e-09
111 112 113 114 115
7.172185e-09 -3.551770e-09 -1.445504e-08 -2.262511e-08 -2.463683e-09
116 117 118 119 120
-6.079331e-09 -1.395920e-10 -2.612008e-08 3.840322e-09 -1.553836e-08
121 122 123 124 125
-3.378089e-08 -7.652053e-10 -2.613568e-08 -6.077659e-09 1.590524e-09
126 127 128 129 130
-4.384584e-09 -3.406852e-08 -8.263735e-09 -3.665229e-09 -3.851881e-08
131 132 133 134 135
1.457376e-09 -2.783792e-08 -5.711119e-09 -2.949631e-09 3.362908e-09
136 137 138 139 140
-8.194389e-09 4.816172e-10 -7.263821e-09 -6.717292e-09 -1.077369e-08
141 142 143 144 145
-4.798132e-09 -1.172741e-08 -3.995279e-08 -3.929965e-08 4.252930e-08
146 147 148 149 150
-5.852581e-09 -2.979099e-10 -1.088700e-08 -2.310120e-09 -9.237622e-09
151 152 153 154 155
4.004636e-08 -1.312465e-08 -7.843882e-09 3.531396e-08 -2.990760e-09
156 157 158 159 160
4.903606e-08 3.008300e-08 -8.318699e-09 5.257424e-10 -1.325000e-08
161 162 163 164 165
-1.689110e-09 2.755757e-08 -1.186418e-08 2.366685e-08 -1.001647e-08
166 167 168 169 170
2.314559e-08 -6.024660e-09 -4.883105e-09 -1.258571e-08 2.315130e-08
171 172 173 174 175
4.003447e-10 -1.253815e-08 -1.762883e-09 1.940079e-08 -1.068786e-08
176 177 178 179 180
1.576010e-08 2.057630e-08 -1.548350e-08 2.594973e-08 2.729364e-08
181 182 183 184 185
-1.211213e-08 -9.795188e-09 -4.371582e-09 -1.482580e-08 -6.522989e-09
186 187 188 189 190
-1.354965e-08 -1.339605e-08 -1.733338e-08 7.990972e-09 1.649194e-09
191 192
-7.576552e-09 -2.216650e-09
> postscript(file="/var/www/html/rcomp/tmp/6fbmq1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 192
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.078410e-09 NA
1 -5.839902e-09 -8.078410e-09
2 5.442616e-11 -5.839902e-09
3 -1.059637e-08 5.442616e-11
4 -2.533044e-09 -1.059637e-08
5 -9.523306e-09 -2.533044e-09
6 -9.415816e-09 -9.523306e-09
7 -1.338624e-08 -9.415816e-09
8 -7.223528e-09 -1.338624e-08
9 -1.333857e-08 -7.223528e-09
10 6.096565e-09 -1.333857e-08
11 -2.424932e-09 6.096565e-09
12 9.121680e-09 -2.424932e-09
13 -1.937490e-08 9.121680e-09
14 1.270367e-08 -1.937490e-08
15 6.197827e-09 1.270367e-08
16 -1.482440e-08 6.197827e-09
17 -2.207741e-08 -1.482440e-08
18 7.091446e-09 -2.207741e-08
19 3.420170e-09 7.091446e-09
20 -2.029849e-08 3.420170e-09
21 2.732400e-09 -2.029849e-08
22 1.322083e-08 2.732400e-09
23 -1.625320e-08 1.322083e-08
24 -3.449967e-08 -1.625320e-08
25 8.543836e-09 -3.449967e-08
26 1.128114e-08 8.543836e-09
27 3.422055e-09 1.128114e-08
28 1.174467e-08 3.422055e-09
29 4.507499e-09 1.174467e-08
30 4.613436e-09 4.507499e-09
31 -3.959784e-08 4.613436e-09
32 -3.241947e-08 -3.959784e-08
33 2.781602e-10 -3.241947e-08
34 1.073858e-08 2.781602e-10
35 1.236435e-08 1.073858e-08
36 -4.721592e-08 1.236435e-08
37 -4.442263e-08 -4.721592e-08
38 -3.880065e-08 -4.442263e-08
39 1.118448e-09 -3.880065e-08
40 -4.145215e-08 1.118448e-09
41 -4.843518e-08 -4.145215e-08
42 -4.857931e-08 -4.843518e-08
43 -1.174711e-09 -4.857931e-08
44 3.431565e-09 -1.174711e-09
45 -2.176285e-09 3.431565e-09
46 -4.243033e-08 -2.176285e-09
47 -4.198753e-08 -4.243033e-08
48 4.019866e-08 -4.198753e-08
49 4.228539e-08 4.019866e-08
50 9.308972e-09 4.228539e-08
51 3.712380e-08 9.308972e-09
52 4.592290e-08 3.712380e-08
53 3.920575e-08 4.592290e-08
54 3.871622e-08 3.920575e-08
55 3.542194e-08 3.871622e-08
56 4.069888e-08 3.542194e-08
57 3.489306e-08 4.069888e-08
58 6.159526e-09 3.489306e-08
59 7.495511e-09 6.159526e-09
60 2.962222e-08 7.495511e-09
61 1.593758e-09 2.962222e-08
62 7.303206e-09 1.593758e-09
63 2.682531e-08 7.303206e-09
64 4.484916e-09 2.682531e-08
65 2.674769e-08 4.484916e-09
66 2.712330e-08 2.674769e-08
67 2.300338e-08 2.712330e-08
68 2.849864e-08 2.300338e-08
69 2.238705e-08 2.849864e-08
70 3.744885e-09 2.238705e-08
71 3.537071e-08 3.744885e-09
72 1.722740e-08 3.537071e-08
73 1.965951e-08 1.722740e-08
74 4.372436e-09 1.965951e-08
75 1.430738e-08 4.372436e-09
76 2.280869e-08 1.430738e-08
77 1.572638e-08 2.280869e-08
78 1.594341e-08 1.572638e-08
79 1.185137e-08 1.594341e-08
80 -2.637620e-09 1.185137e-08
81 1.191792e-08 -2.637620e-09
82 2.196938e-08 1.191792e-08
83 2.265361e-09 2.196938e-08
84 5.122485e-09 2.265361e-09
85 5.954542e-09 5.122485e-09
86 1.282729e-08 5.954542e-09
87 -8.262047e-09 1.282729e-08
88 2.471861e-10 -8.262047e-09
89 3.653265e-09 2.471861e-10
90 3.092120e-09 3.653265e-09
91 1.910713e-10 3.092120e-09
92 -5.040357e-09 1.910713e-10
93 -1.134251e-08 -5.040357e-09
94 -7.587460e-10 -1.134251e-08
95 9.449601e-09 -7.587460e-10
96 1.909885e-09 9.449601e-09
97 -5.554737e-09 1.909885e-09
98 2.935065e-10 -5.554737e-09
99 -8.116233e-10 2.935065e-10
100 -1.730759e-09 -8.116233e-10
101 2.977468e-10 -1.730759e-09
102 5.664224e-10 2.977468e-10
103 -3.581312e-09 5.664224e-10
104 -7.113596e-09 -3.581312e-09
105 -1.361017e-08 -7.113596e-09
106 6.556636e-09 -1.361017e-08
107 7.166119e-09 6.556636e-09
108 -2.183079e-08 7.166119e-09
109 1.685140e-09 -2.183079e-08
110 7.172185e-09 1.685140e-09
111 -3.551770e-09 7.172185e-09
112 -1.445504e-08 -3.551770e-09
113 -2.262511e-08 -1.445504e-08
114 -2.463683e-09 -2.262511e-08
115 -6.079331e-09 -2.463683e-09
116 -1.395920e-10 -6.079331e-09
117 -2.612008e-08 -1.395920e-10
118 3.840322e-09 -2.612008e-08
119 -1.553836e-08 3.840322e-09
120 -3.378089e-08 -1.553836e-08
121 -7.652053e-10 -3.378089e-08
122 -2.613568e-08 -7.652053e-10
123 -6.077659e-09 -2.613568e-08
124 1.590524e-09 -6.077659e-09
125 -4.384584e-09 1.590524e-09
126 -3.406852e-08 -4.384584e-09
127 -8.263735e-09 -3.406852e-08
128 -3.665229e-09 -8.263735e-09
129 -3.851881e-08 -3.665229e-09
130 1.457376e-09 -3.851881e-08
131 -2.783792e-08 1.457376e-09
132 -5.711119e-09 -2.783792e-08
133 -2.949631e-09 -5.711119e-09
134 3.362908e-09 -2.949631e-09
135 -8.194389e-09 3.362908e-09
136 4.816172e-10 -8.194389e-09
137 -7.263821e-09 4.816172e-10
138 -6.717292e-09 -7.263821e-09
139 -1.077369e-08 -6.717292e-09
140 -4.798132e-09 -1.077369e-08
141 -1.172741e-08 -4.798132e-09
142 -3.995279e-08 -1.172741e-08
143 -3.929965e-08 -3.995279e-08
144 4.252930e-08 -3.929965e-08
145 -5.852581e-09 4.252930e-08
146 -2.979099e-10 -5.852581e-09
147 -1.088700e-08 -2.979099e-10
148 -2.310120e-09 -1.088700e-08
149 -9.237622e-09 -2.310120e-09
150 4.004636e-08 -9.237622e-09
151 -1.312465e-08 4.004636e-08
152 -7.843882e-09 -1.312465e-08
153 3.531396e-08 -7.843882e-09
154 -2.990760e-09 3.531396e-08
155 4.903606e-08 -2.990760e-09
156 3.008300e-08 4.903606e-08
157 -8.318699e-09 3.008300e-08
158 5.257424e-10 -8.318699e-09
159 -1.325000e-08 5.257424e-10
160 -1.689110e-09 -1.325000e-08
161 2.755757e-08 -1.689110e-09
162 -1.186418e-08 2.755757e-08
163 2.366685e-08 -1.186418e-08
164 -1.001647e-08 2.366685e-08
165 2.314559e-08 -1.001647e-08
166 -6.024660e-09 2.314559e-08
167 -4.883105e-09 -6.024660e-09
168 -1.258571e-08 -4.883105e-09
169 2.315130e-08 -1.258571e-08
170 4.003447e-10 2.315130e-08
171 -1.253815e-08 4.003447e-10
172 -1.762883e-09 -1.253815e-08
173 1.940079e-08 -1.762883e-09
174 -1.068786e-08 1.940079e-08
175 1.576010e-08 -1.068786e-08
176 2.057630e-08 1.576010e-08
177 -1.548350e-08 2.057630e-08
178 2.594973e-08 -1.548350e-08
179 2.729364e-08 2.594973e-08
180 -1.211213e-08 2.729364e-08
181 -9.795188e-09 -1.211213e-08
182 -4.371582e-09 -9.795188e-09
183 -1.482580e-08 -4.371582e-09
184 -6.522989e-09 -1.482580e-08
185 -1.354965e-08 -6.522989e-09
186 -1.339605e-08 -1.354965e-08
187 -1.733338e-08 -1.339605e-08
188 7.990972e-09 -1.733338e-08
189 1.649194e-09 7.990972e-09
190 -7.576552e-09 1.649194e-09
191 -2.216650e-09 -7.576552e-09
192 NA -2.216650e-09
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.839902e-09 -8.078410e-09
[2,] 5.442616e-11 -5.839902e-09
[3,] -1.059637e-08 5.442616e-11
[4,] -2.533044e-09 -1.059637e-08
[5,] -9.523306e-09 -2.533044e-09
[6,] -9.415816e-09 -9.523306e-09
[7,] -1.338624e-08 -9.415816e-09
[8,] -7.223528e-09 -1.338624e-08
[9,] -1.333857e-08 -7.223528e-09
[10,] 6.096565e-09 -1.333857e-08
[11,] -2.424932e-09 6.096565e-09
[12,] 9.121680e-09 -2.424932e-09
[13,] -1.937490e-08 9.121680e-09
[14,] 1.270367e-08 -1.937490e-08
[15,] 6.197827e-09 1.270367e-08
[16,] -1.482440e-08 6.197827e-09
[17,] -2.207741e-08 -1.482440e-08
[18,] 7.091446e-09 -2.207741e-08
[19,] 3.420170e-09 7.091446e-09
[20,] -2.029849e-08 3.420170e-09
[21,] 2.732400e-09 -2.029849e-08
[22,] 1.322083e-08 2.732400e-09
[23,] -1.625320e-08 1.322083e-08
[24,] -3.449967e-08 -1.625320e-08
[25,] 8.543836e-09 -3.449967e-08
[26,] 1.128114e-08 8.543836e-09
[27,] 3.422055e-09 1.128114e-08
[28,] 1.174467e-08 3.422055e-09
[29,] 4.507499e-09 1.174467e-08
[30,] 4.613436e-09 4.507499e-09
[31,] -3.959784e-08 4.613436e-09
[32,] -3.241947e-08 -3.959784e-08
[33,] 2.781602e-10 -3.241947e-08
[34,] 1.073858e-08 2.781602e-10
[35,] 1.236435e-08 1.073858e-08
[36,] -4.721592e-08 1.236435e-08
[37,] -4.442263e-08 -4.721592e-08
[38,] -3.880065e-08 -4.442263e-08
[39,] 1.118448e-09 -3.880065e-08
[40,] -4.145215e-08 1.118448e-09
[41,] -4.843518e-08 -4.145215e-08
[42,] -4.857931e-08 -4.843518e-08
[43,] -1.174711e-09 -4.857931e-08
[44,] 3.431565e-09 -1.174711e-09
[45,] -2.176285e-09 3.431565e-09
[46,] -4.243033e-08 -2.176285e-09
[47,] -4.198753e-08 -4.243033e-08
[48,] 4.019866e-08 -4.198753e-08
[49,] 4.228539e-08 4.019866e-08
[50,] 9.308972e-09 4.228539e-08
[51,] 3.712380e-08 9.308972e-09
[52,] 4.592290e-08 3.712380e-08
[53,] 3.920575e-08 4.592290e-08
[54,] 3.871622e-08 3.920575e-08
[55,] 3.542194e-08 3.871622e-08
[56,] 4.069888e-08 3.542194e-08
[57,] 3.489306e-08 4.069888e-08
[58,] 6.159526e-09 3.489306e-08
[59,] 7.495511e-09 6.159526e-09
[60,] 2.962222e-08 7.495511e-09
[61,] 1.593758e-09 2.962222e-08
[62,] 7.303206e-09 1.593758e-09
[63,] 2.682531e-08 7.303206e-09
[64,] 4.484916e-09 2.682531e-08
[65,] 2.674769e-08 4.484916e-09
[66,] 2.712330e-08 2.674769e-08
[67,] 2.300338e-08 2.712330e-08
[68,] 2.849864e-08 2.300338e-08
[69,] 2.238705e-08 2.849864e-08
[70,] 3.744885e-09 2.238705e-08
[71,] 3.537071e-08 3.744885e-09
[72,] 1.722740e-08 3.537071e-08
[73,] 1.965951e-08 1.722740e-08
[74,] 4.372436e-09 1.965951e-08
[75,] 1.430738e-08 4.372436e-09
[76,] 2.280869e-08 1.430738e-08
[77,] 1.572638e-08 2.280869e-08
[78,] 1.594341e-08 1.572638e-08
[79,] 1.185137e-08 1.594341e-08
[80,] -2.637620e-09 1.185137e-08
[81,] 1.191792e-08 -2.637620e-09
[82,] 2.196938e-08 1.191792e-08
[83,] 2.265361e-09 2.196938e-08
[84,] 5.122485e-09 2.265361e-09
[85,] 5.954542e-09 5.122485e-09
[86,] 1.282729e-08 5.954542e-09
[87,] -8.262047e-09 1.282729e-08
[88,] 2.471861e-10 -8.262047e-09
[89,] 3.653265e-09 2.471861e-10
[90,] 3.092120e-09 3.653265e-09
[91,] 1.910713e-10 3.092120e-09
[92,] -5.040357e-09 1.910713e-10
[93,] -1.134251e-08 -5.040357e-09
[94,] -7.587460e-10 -1.134251e-08
[95,] 9.449601e-09 -7.587460e-10
[96,] 1.909885e-09 9.449601e-09
[97,] -5.554737e-09 1.909885e-09
[98,] 2.935065e-10 -5.554737e-09
[99,] -8.116233e-10 2.935065e-10
[100,] -1.730759e-09 -8.116233e-10
[101,] 2.977468e-10 -1.730759e-09
[102,] 5.664224e-10 2.977468e-10
[103,] -3.581312e-09 5.664224e-10
[104,] -7.113596e-09 -3.581312e-09
[105,] -1.361017e-08 -7.113596e-09
[106,] 6.556636e-09 -1.361017e-08
[107,] 7.166119e-09 6.556636e-09
[108,] -2.183079e-08 7.166119e-09
[109,] 1.685140e-09 -2.183079e-08
[110,] 7.172185e-09 1.685140e-09
[111,] -3.551770e-09 7.172185e-09
[112,] -1.445504e-08 -3.551770e-09
[113,] -2.262511e-08 -1.445504e-08
[114,] -2.463683e-09 -2.262511e-08
[115,] -6.079331e-09 -2.463683e-09
[116,] -1.395920e-10 -6.079331e-09
[117,] -2.612008e-08 -1.395920e-10
[118,] 3.840322e-09 -2.612008e-08
[119,] -1.553836e-08 3.840322e-09
[120,] -3.378089e-08 -1.553836e-08
[121,] -7.652053e-10 -3.378089e-08
[122,] -2.613568e-08 -7.652053e-10
[123,] -6.077659e-09 -2.613568e-08
[124,] 1.590524e-09 -6.077659e-09
[125,] -4.384584e-09 1.590524e-09
[126,] -3.406852e-08 -4.384584e-09
[127,] -8.263735e-09 -3.406852e-08
[128,] -3.665229e-09 -8.263735e-09
[129,] -3.851881e-08 -3.665229e-09
[130,] 1.457376e-09 -3.851881e-08
[131,] -2.783792e-08 1.457376e-09
[132,] -5.711119e-09 -2.783792e-08
[133,] -2.949631e-09 -5.711119e-09
[134,] 3.362908e-09 -2.949631e-09
[135,] -8.194389e-09 3.362908e-09
[136,] 4.816172e-10 -8.194389e-09
[137,] -7.263821e-09 4.816172e-10
[138,] -6.717292e-09 -7.263821e-09
[139,] -1.077369e-08 -6.717292e-09
[140,] -4.798132e-09 -1.077369e-08
[141,] -1.172741e-08 -4.798132e-09
[142,] -3.995279e-08 -1.172741e-08
[143,] -3.929965e-08 -3.995279e-08
[144,] 4.252930e-08 -3.929965e-08
[145,] -5.852581e-09 4.252930e-08
[146,] -2.979099e-10 -5.852581e-09
[147,] -1.088700e-08 -2.979099e-10
[148,] -2.310120e-09 -1.088700e-08
[149,] -9.237622e-09 -2.310120e-09
[150,] 4.004636e-08 -9.237622e-09
[151,] -1.312465e-08 4.004636e-08
[152,] -7.843882e-09 -1.312465e-08
[153,] 3.531396e-08 -7.843882e-09
[154,] -2.990760e-09 3.531396e-08
[155,] 4.903606e-08 -2.990760e-09
[156,] 3.008300e-08 4.903606e-08
[157,] -8.318699e-09 3.008300e-08
[158,] 5.257424e-10 -8.318699e-09
[159,] -1.325000e-08 5.257424e-10
[160,] -1.689110e-09 -1.325000e-08
[161,] 2.755757e-08 -1.689110e-09
[162,] -1.186418e-08 2.755757e-08
[163,] 2.366685e-08 -1.186418e-08
[164,] -1.001647e-08 2.366685e-08
[165,] 2.314559e-08 -1.001647e-08
[166,] -6.024660e-09 2.314559e-08
[167,] -4.883105e-09 -6.024660e-09
[168,] -1.258571e-08 -4.883105e-09
[169,] 2.315130e-08 -1.258571e-08
[170,] 4.003447e-10 2.315130e-08
[171,] -1.253815e-08 4.003447e-10
[172,] -1.762883e-09 -1.253815e-08
[173,] 1.940079e-08 -1.762883e-09
[174,] -1.068786e-08 1.940079e-08
[175,] 1.576010e-08 -1.068786e-08
[176,] 2.057630e-08 1.576010e-08
[177,] -1.548350e-08 2.057630e-08
[178,] 2.594973e-08 -1.548350e-08
[179,] 2.729364e-08 2.594973e-08
[180,] -1.211213e-08 2.729364e-08
[181,] -9.795188e-09 -1.211213e-08
[182,] -4.371582e-09 -9.795188e-09
[183,] -1.482580e-08 -4.371582e-09
[184,] -6.522989e-09 -1.482580e-08
[185,] -1.354965e-08 -6.522989e-09
[186,] -1.339605e-08 -1.354965e-08
[187,] -1.733338e-08 -1.339605e-08
[188,] 7.990972e-09 -1.733338e-08
[189,] 1.649194e-09 7.990972e-09
[190,] -7.576552e-09 1.649194e-09
[191,] -2.216650e-09 -7.576552e-09
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.839902e-09 -8.078410e-09
2 5.442616e-11 -5.839902e-09
3 -1.059637e-08 5.442616e-11
4 -2.533044e-09 -1.059637e-08
5 -9.523306e-09 -2.533044e-09
6 -9.415816e-09 -9.523306e-09
7 -1.338624e-08 -9.415816e-09
8 -7.223528e-09 -1.338624e-08
9 -1.333857e-08 -7.223528e-09
10 6.096565e-09 -1.333857e-08
11 -2.424932e-09 6.096565e-09
12 9.121680e-09 -2.424932e-09
13 -1.937490e-08 9.121680e-09
14 1.270367e-08 -1.937490e-08
15 6.197827e-09 1.270367e-08
16 -1.482440e-08 6.197827e-09
17 -2.207741e-08 -1.482440e-08
18 7.091446e-09 -2.207741e-08
19 3.420170e-09 7.091446e-09
20 -2.029849e-08 3.420170e-09
21 2.732400e-09 -2.029849e-08
22 1.322083e-08 2.732400e-09
23 -1.625320e-08 1.322083e-08
24 -3.449967e-08 -1.625320e-08
25 8.543836e-09 -3.449967e-08
26 1.128114e-08 8.543836e-09
27 3.422055e-09 1.128114e-08
28 1.174467e-08 3.422055e-09
29 4.507499e-09 1.174467e-08
30 4.613436e-09 4.507499e-09
31 -3.959784e-08 4.613436e-09
32 -3.241947e-08 -3.959784e-08
33 2.781602e-10 -3.241947e-08
34 1.073858e-08 2.781602e-10
35 1.236435e-08 1.073858e-08
36 -4.721592e-08 1.236435e-08
37 -4.442263e-08 -4.721592e-08
38 -3.880065e-08 -4.442263e-08
39 1.118448e-09 -3.880065e-08
40 -4.145215e-08 1.118448e-09
41 -4.843518e-08 -4.145215e-08
42 -4.857931e-08 -4.843518e-08
43 -1.174711e-09 -4.857931e-08
44 3.431565e-09 -1.174711e-09
45 -2.176285e-09 3.431565e-09
46 -4.243033e-08 -2.176285e-09
47 -4.198753e-08 -4.243033e-08
48 4.019866e-08 -4.198753e-08
49 4.228539e-08 4.019866e-08
50 9.308972e-09 4.228539e-08
51 3.712380e-08 9.308972e-09
52 4.592290e-08 3.712380e-08
53 3.920575e-08 4.592290e-08
54 3.871622e-08 3.920575e-08
55 3.542194e-08 3.871622e-08
56 4.069888e-08 3.542194e-08
57 3.489306e-08 4.069888e-08
58 6.159526e-09 3.489306e-08
59 7.495511e-09 6.159526e-09
60 2.962222e-08 7.495511e-09
61 1.593758e-09 2.962222e-08
62 7.303206e-09 1.593758e-09
63 2.682531e-08 7.303206e-09
64 4.484916e-09 2.682531e-08
65 2.674769e-08 4.484916e-09
66 2.712330e-08 2.674769e-08
67 2.300338e-08 2.712330e-08
68 2.849864e-08 2.300338e-08
69 2.238705e-08 2.849864e-08
70 3.744885e-09 2.238705e-08
71 3.537071e-08 3.744885e-09
72 1.722740e-08 3.537071e-08
73 1.965951e-08 1.722740e-08
74 4.372436e-09 1.965951e-08
75 1.430738e-08 4.372436e-09
76 2.280869e-08 1.430738e-08
77 1.572638e-08 2.280869e-08
78 1.594341e-08 1.572638e-08
79 1.185137e-08 1.594341e-08
80 -2.637620e-09 1.185137e-08
81 1.191792e-08 -2.637620e-09
82 2.196938e-08 1.191792e-08
83 2.265361e-09 2.196938e-08
84 5.122485e-09 2.265361e-09
85 5.954542e-09 5.122485e-09
86 1.282729e-08 5.954542e-09
87 -8.262047e-09 1.282729e-08
88 2.471861e-10 -8.262047e-09
89 3.653265e-09 2.471861e-10
90 3.092120e-09 3.653265e-09
91 1.910713e-10 3.092120e-09
92 -5.040357e-09 1.910713e-10
93 -1.134251e-08 -5.040357e-09
94 -7.587460e-10 -1.134251e-08
95 9.449601e-09 -7.587460e-10
96 1.909885e-09 9.449601e-09
97 -5.554737e-09 1.909885e-09
98 2.935065e-10 -5.554737e-09
99 -8.116233e-10 2.935065e-10
100 -1.730759e-09 -8.116233e-10
101 2.977468e-10 -1.730759e-09
102 5.664224e-10 2.977468e-10
103 -3.581312e-09 5.664224e-10
104 -7.113596e-09 -3.581312e-09
105 -1.361017e-08 -7.113596e-09
106 6.556636e-09 -1.361017e-08
107 7.166119e-09 6.556636e-09
108 -2.183079e-08 7.166119e-09
109 1.685140e-09 -2.183079e-08
110 7.172185e-09 1.685140e-09
111 -3.551770e-09 7.172185e-09
112 -1.445504e-08 -3.551770e-09
113 -2.262511e-08 -1.445504e-08
114 -2.463683e-09 -2.262511e-08
115 -6.079331e-09 -2.463683e-09
116 -1.395920e-10 -6.079331e-09
117 -2.612008e-08 -1.395920e-10
118 3.840322e-09 -2.612008e-08
119 -1.553836e-08 3.840322e-09
120 -3.378089e-08 -1.553836e-08
121 -7.652053e-10 -3.378089e-08
122 -2.613568e-08 -7.652053e-10
123 -6.077659e-09 -2.613568e-08
124 1.590524e-09 -6.077659e-09
125 -4.384584e-09 1.590524e-09
126 -3.406852e-08 -4.384584e-09
127 -8.263735e-09 -3.406852e-08
128 -3.665229e-09 -8.263735e-09
129 -3.851881e-08 -3.665229e-09
130 1.457376e-09 -3.851881e-08
131 -2.783792e-08 1.457376e-09
132 -5.711119e-09 -2.783792e-08
133 -2.949631e-09 -5.711119e-09
134 3.362908e-09 -2.949631e-09
135 -8.194389e-09 3.362908e-09
136 4.816172e-10 -8.194389e-09
137 -7.263821e-09 4.816172e-10
138 -6.717292e-09 -7.263821e-09
139 -1.077369e-08 -6.717292e-09
140 -4.798132e-09 -1.077369e-08
141 -1.172741e-08 -4.798132e-09
142 -3.995279e-08 -1.172741e-08
143 -3.929965e-08 -3.995279e-08
144 4.252930e-08 -3.929965e-08
145 -5.852581e-09 4.252930e-08
146 -2.979099e-10 -5.852581e-09
147 -1.088700e-08 -2.979099e-10
148 -2.310120e-09 -1.088700e-08
149 -9.237622e-09 -2.310120e-09
150 4.004636e-08 -9.237622e-09
151 -1.312465e-08 4.004636e-08
152 -7.843882e-09 -1.312465e-08
153 3.531396e-08 -7.843882e-09
154 -2.990760e-09 3.531396e-08
155 4.903606e-08 -2.990760e-09
156 3.008300e-08 4.903606e-08
157 -8.318699e-09 3.008300e-08
158 5.257424e-10 -8.318699e-09
159 -1.325000e-08 5.257424e-10
160 -1.689110e-09 -1.325000e-08
161 2.755757e-08 -1.689110e-09
162 -1.186418e-08 2.755757e-08
163 2.366685e-08 -1.186418e-08
164 -1.001647e-08 2.366685e-08
165 2.314559e-08 -1.001647e-08
166 -6.024660e-09 2.314559e-08
167 -4.883105e-09 -6.024660e-09
168 -1.258571e-08 -4.883105e-09
169 2.315130e-08 -1.258571e-08
170 4.003447e-10 2.315130e-08
171 -1.253815e-08 4.003447e-10
172 -1.762883e-09 -1.253815e-08
173 1.940079e-08 -1.762883e-09
174 -1.068786e-08 1.940079e-08
175 1.576010e-08 -1.068786e-08
176 2.057630e-08 1.576010e-08
177 -1.548350e-08 2.057630e-08
178 2.594973e-08 -1.548350e-08
179 2.729364e-08 2.594973e-08
180 -1.211213e-08 2.729364e-08
181 -9.795188e-09 -1.211213e-08
182 -4.371582e-09 -9.795188e-09
183 -1.482580e-08 -4.371582e-09
184 -6.522989e-09 -1.482580e-08
185 -1.354965e-08 -6.522989e-09
186 -1.339605e-08 -1.354965e-08
187 -1.733338e-08 -1.339605e-08
188 7.990972e-09 -1.733338e-08
189 1.649194e-09 7.990972e-09
190 -7.576552e-09 1.649194e-09
191 -2.216650e-09 -7.576552e-09
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7ji7r1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ljf01227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rp551227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ycup1227119165.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1118k31227119165.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12s7jn1227119165.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/133gjz1227119165.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1443u41227119165.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/154fjh1227119165.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16zmw71227119165.tab")
+ }
>
> system("convert tmp/18bq71227119165.ps tmp/18bq71227119165.png")
> system("convert tmp/2j3uo1227119165.ps tmp/2j3uo1227119165.png")
> system("convert tmp/3usa01227119165.ps tmp/3usa01227119165.png")
> system("convert tmp/4fal91227119165.ps tmp/4fal91227119165.png")
> system("convert tmp/5qj9x1227119165.ps tmp/5qj9x1227119165.png")
> system("convert tmp/6fbmq1227119165.ps tmp/6fbmq1227119165.png")
> system("convert tmp/7ji7r1227119165.ps tmp/7ji7r1227119165.png")
> system("convert tmp/8ljf01227119165.ps tmp/8ljf01227119165.png")
> system("convert tmp/9rp551227119165.ps tmp/9rp551227119165.png")
> system("convert tmp/10ycup1227119165.ps tmp/10ycup1227119165.png")
>
>
> proc.time()
user system elapsed
5.225 1.791 6.214