R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'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('Deaths'
+ ,'seatbeltlaw'
+ ,'predictionerrors')
+ ,1:192))
> y <- array(NA,dim=c(3,192),dimnames=list(c('Deaths','seatbeltlaw','predictionerrors'),1:192))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Deaths seatbeltlaw predictionerrors M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1687 0 -183.9235445 1 0 0 0 0 0 0 0 0 0 0
2 1508 0 -177.0726091 0 1 0 0 0 0 0 0 0 0 0
3 1507 0 -228.6351091 0 0 1 0 0 0 0 0 0 0 0
4 1385 0 -237.4476091 0 0 0 1 0 0 0 0 0 0 0
5 1632 0 -127.7601091 0 0 0 0 1 0 0 0 0 0 0
6 1511 0 -193.0101091 0 0 0 0 0 1 0 0 0 0 0
7 1559 0 -220.6351091 0 0 0 0 0 0 1 0 0 0 0
8 1630 0 -164.5101091 0 0 0 0 0 0 0 1 0 0 0
9 1579 0 -268.3226091 0 0 0 0 0 0 0 0 1 0 0
10 1653 0 -333.6976091 0 0 0 0 0 0 0 0 0 1 0
11 2152 0 -34.2601091 0 0 0 0 0 0 0 0 0 0 1
12 2148 0 -154.8851091 0 0 0 0 0 0 0 0 0 0 0
13 1752 0 -97.7452805 1 0 0 0 0 0 0 0 0 0 0
14 1765 0 101.1056549 0 1 0 0 0 0 0 0 0 0 0
15 1717 0 2.5431549 0 0 1 0 0 0 0 0 0 0 0
16 1558 0 -43.2693451 0 0 0 1 0 0 0 0 0 0 0
17 1575 0 -163.5818451 0 0 0 0 1 0 0 0 0 0 0
18 1520 0 -162.8318451 0 0 0 0 0 1 0 0 0 0 0
19 1805 0 46.5431549 0 0 0 0 0 0 1 0 0 0 0
20 1800 0 26.6681549 0 0 0 0 0 0 0 1 0 0 0
21 1719 0 -107.1443451 0 0 0 0 0 0 0 0 1 0 0
22 2008 0 42.4806549 0 0 0 0 0 0 0 0 0 1 0
23 2242 0 76.9181549 0 0 0 0 0 0 0 0 0 0 1
24 2478 0 196.2931549 0 0 0 0 0 0 0 0 0 0 0
25 2030 0 201.4329835 1 0 0 0 0 0 0 0 0 0 0
26 1655 0 12.2839189 0 1 0 0 0 0 0 0 0 0 0
27 1693 0 -0.2785811 0 0 1 0 0 0 0 0 0 0 0
28 1623 0 42.9089189 0 0 0 1 0 0 0 0 0 0 0
29 1805 0 87.5964189 0 0 0 0 1 0 0 0 0 0 0
30 1746 0 84.3464189 0 0 0 0 0 1 0 0 0 0 0
31 1795 0 57.7214189 0 0 0 0 0 0 1 0 0 0 0
32 1926 0 173.8464189 0 0 0 0 0 0 0 1 0 0 0
33 1619 0 -185.9660811 0 0 0 0 0 0 0 0 1 0 0
34 1992 0 47.6589189 0 0 0 0 0 0 0 0 0 1 0
35 2233 0 89.0964189 0 0 0 0 0 0 0 0 0 0 1
36 2192 0 -68.5285811 0 0 0 0 0 0 0 0 0 0 0
37 2080 0 272.6112475 1 0 0 0 0 0 0 0 0 0 0
38 1768 0 146.4621829 0 1 0 0 0 0 0 0 0 0 0
39 1835 0 162.8996829 0 0 1 0 0 0 0 0 0 0 0
40 1569 0 10.0871828 0 0 0 1 0 0 0 0 0 0 0
41 1976 0 279.7746829 0 0 0 0 1 0 0 0 0 0 0
42 1853 0 212.5246829 0 0 0 0 0 1 0 0 0 0 0
43 1965 0 248.8996829 0 0 0 0 0 0 1 0 0 0 0
44 1689 0 -41.9753172 0 0 0 0 0 0 0 1 0 0 0
45 1778 0 -5.7878171 0 0 0 0 0 0 0 0 1 0 0
46 1976 0 52.8371828 0 0 0 0 0 0 0 0 0 1 0
47 2397 0 274.2746829 0 0 0 0 0 0 0 0 0 0 1
48 2654 0 414.6496829 0 0 0 0 0 0 0 0 0 0 0
49 2097 0 310.7895114 1 0 0 0 0 0 0 0 0 0 0
50 1963 0 362.6404468 0 1 0 0 0 0 0 0 0 0 0
51 1677 0 26.0779468 0 0 1 0 0 0 0 0 0 0 0
52 1941 0 403.2654468 0 0 0 1 0 0 0 0 0 0 0
53 2003 0 327.9529468 0 0 0 0 1 0 0 0 0 0 0
54 1813 0 193.7029468 0 0 0 0 0 1 0 0 0 0 0
55 2012 0 317.0779468 0 0 0 0 0 0 1 0 0 0 0
56 1912 0 202.2029468 0 0 0 0 0 0 0 1 0 0 0
57 2084 0 321.3904468 0 0 0 0 0 0 0 0 1 0 0
58 2080 0 178.0154468 0 0 0 0 0 0 0 0 0 1 0
59 2118 0 16.4529468 0 0 0 0 0 0 0 0 0 0 1
60 2150 0 -68.1720532 0 0 0 0 0 0 0 0 0 0 0
61 1608 0 -157.0322246 1 0 0 0 0 0 0 0 0 0 0
62 1503 0 -76.1812892 0 1 0 0 0 0 0 0 0 0 0
63 1548 0 -81.7437892 0 0 1 0 0 0 0 0 0 0 0
64 1382 0 -134.5562892 0 0 0 1 0 0 0 0 0 0 0
65 1731 0 77.1312108 0 0 0 0 1 0 0 0 0 0 0
66 1798 0 199.8812108 0 0 0 0 0 1 0 0 0 0 0
67 1779 0 105.2562108 0 0 0 0 0 0 1 0 0 0 0
68 1887 0 198.3812108 0 0 0 0 0 0 0 1 0 0 0
69 2004 0 262.5687108 0 0 0 0 0 0 0 0 1 0 0
70 2077 0 196.1937108 0 0 0 0 0 0 0 0 0 1 0
71 2092 0 11.6312108 0 0 0 0 0 0 0 0 0 0 1
72 2051 0 -145.9937892 0 0 0 0 0 0 0 0 0 0 0
73 1577 0 -166.8539606 1 0 0 0 0 0 0 0 0 0 0
74 1356 0 -202.0030252 0 1 0 0 0 0 0 0 0 0 0
75 1652 0 43.4344748 0 0 1 0 0 0 0 0 0 0 0
76 1382 0 -113.3780252 0 0 0 1 0 0 0 0 0 0 0
77 1519 0 -113.6905252 0 0 0 0 1 0 0 0 0 0 0
78 1421 0 -155.9405252 0 0 0 0 0 1 0 0 0 0 0
79 1442 0 -210.5655252 0 0 0 0 0 0 1 0 0 0 0
80 1543 0 -124.4405252 0 0 0 0 0 0 0 1 0 0 0
81 1656 0 -64.2530252 0 0 0 0 0 0 0 0 1 0 0
82 1561 0 -298.6280252 0 0 0 0 0 0 0 0 0 1 0
83 1905 0 -154.1905252 0 0 0 0 0 0 0 0 0 0 1
84 2199 0 23.1844748 0 0 0 0 0 0 0 0 0 0 0
85 1473 0 -249.6756966 1 0 0 0 0 0 0 0 0 0 0
86 1655 0 118.1752388 0 1 0 0 0 0 0 0 0 0 0
87 1407 0 -180.3872612 0 0 1 0 0 0 0 0 0 0 0
88 1395 0 -79.1997612 0 0 0 1 0 0 0 0 0 0 0
89 1530 0 -81.5122612 0 0 0 0 1 0 0 0 0 0 0
90 1309 0 -246.7622612 0 0 0 0 0 1 0 0 0 0 0
91 1526 0 -105.3872612 0 0 0 0 0 0 1 0 0 0 0
92 1327 0 -319.2622612 0 0 0 0 0 0 0 1 0 0 0
93 1627 0 -72.0747612 0 0 0 0 0 0 0 0 1 0 0
94 1748 0 -90.4497612 0 0 0 0 0 0 0 0 0 1 0
95 1958 0 -80.0122612 0 0 0 0 0 0 0 0 0 0 1
96 2274 0 119.3627388 0 0 0 0 0 0 0 0 0 0 0
97 1648 0 -53.4974326 1 0 0 0 0 0 0 0 0 0 0
98 1401 0 -114.6464972 0 1 0 0 0 0 0 0 0 0 0
99 1411 0 -155.2089972 0 0 1 0 0 0 0 0 0 0 0
100 1403 0 -50.0214972 0 0 0 1 0 0 0 0 0 0 0
101 1394 0 -196.3339972 0 0 0 0 1 0 0 0 0 0 0
102 1520 0 -14.5839972 0 0 0 0 0 1 0 0 0 0 0
103 1528 0 -82.2089972 0 0 0 0 0 0 1 0 0 0 0
104 1643 0 17.9160028 0 0 0 0 0 0 0 1 0 0 0
105 1515 0 -162.8964972 0 0 0 0 0 0 0 0 1 0 0
106 1685 0 -132.2714972 0 0 0 0 0 0 0 0 0 1 0
107 2000 0 -16.8339972 0 0 0 0 0 0 0 0 0 0 1
108 2215 0 81.5410028 0 0 0 0 0 0 0 0 0 0 0
109 1956 0 275.6808314 1 0 0 0 0 0 0 0 0 0 0
110 1462 0 -32.4682332 0 1 0 0 0 0 0 0 0 0 0
111 1563 0 17.9692668 0 0 1 0 0 0 0 0 0 0 0
112 1459 0 27.1567668 0 0 0 1 0 0 0 0 0 0 0
113 1446 0 -123.1557332 0 0 0 0 1 0 0 0 0 0 0
114 1622 0 108.5942668 0 0 0 0 0 1 0 0 0 0 0
115 1657 0 67.9692668 0 0 0 0 0 0 1 0 0 0 0
116 1638 0 34.0942668 0 0 0 0 0 0 0 1 0 0 0
117 1643 0 -13.7182332 0 0 0 0 0 0 0 0 1 0 0
118 1683 0 -113.0932332 0 0 0 0 0 0 0 0 0 1 0
119 2050 0 54.3442668 0 0 0 0 0 0 0 0 0 0 1
120 2262 0 149.7192668 0 0 0 0 0 0 0 0 0 0 0
121 1813 0 153.8590954 1 0 0 0 0 0 0 0 0 0 0
122 1445 0 -28.2899692 0 1 0 0 0 0 0 0 0 0 0
123 1762 0 238.1475308 0 0 1 0 0 0 0 0 0 0 0
124 1461 0 50.3350308 0 0 0 1 0 0 0 0 0 0 0
125 1556 0 8.0225308 0 0 0 0 1 0 0 0 0 0 0
126 1431 0 -61.2274692 0 0 0 0 0 1 0 0 0 0 0
127 1427 0 -140.8524692 0 0 0 0 0 0 1 0 0 0 0
128 1554 0 -28.7274692 0 0 0 0 0 0 0 1 0 0 0
129 1645 0 9.4600308 0 0 0 0 0 0 0 0 1 0 0
130 1653 0 -121.9149692 0 0 0 0 0 0 0 0 0 1 0
131 2016 0 41.5225308 0 0 0 0 0 0 0 0 0 0 1
132 2207 0 115.8975308 0 0 0 0 0 0 0 0 0 0 0
133 1665 0 27.0373594 1 0 0 0 0 0 0 0 0 0 0
134 1361 0 -91.1117052 0 1 0 0 0 0 0 0 0 0 0
135 1506 0 3.3257948 0 0 1 0 0 0 0 0 0 0 0
136 1360 0 -29.4867052 0 0 0 1 0 0 0 0 0 0 0
137 1453 0 -73.7992052 0 0 0 0 1 0 0 0 0 0 0
138 1522 0 50.9507948 0 0 0 0 0 1 0 0 0 0 0
139 1460 0 -86.6742052 0 0 0 0 0 0 1 0 0 0 0
140 1552 0 -9.5492052 0 0 0 0 0 0 0 1 0 0 0
141 1548 0 -66.3617052 0 0 0 0 0 0 0 0 1 0 0
142 1827 0 73.2632948 0 0 0 0 0 0 0 0 0 1 0
143 1737 0 -216.2992052 0 0 0 0 0 0 0 0 0 0 1
144 1941 0 -128.9242052 0 0 0 0 0 0 0 0 0 0 0
145 1474 0 -142.7843767 1 0 0 0 0 0 0 0 0 0 0
146 1458 0 27.0665587 0 1 0 0 0 0 0 0 0 0 0
147 1542 0 60.5040587 0 0 1 0 0 0 0 0 0 0 0
148 1404 0 35.6915587 0 0 0 1 0 0 0 0 0 0 0
149 1522 0 16.3790587 0 0 0 0 1 0 0 0 0 0 0
150 1385 0 -64.8709413 0 0 0 0 0 1 0 0 0 0 0
151 1641 0 115.5040587 0 0 0 0 0 0 1 0 0 0 0
152 1510 0 -30.3709413 0 0 0 0 0 0 0 1 0 0 0
153 1681 0 87.8165587 0 0 0 0 0 0 0 0 1 0 0
154 1938 0 205.4415587 0 0 0 0 0 0 0 0 0 1 0
155 1868 0 -64.1209413 0 0 0 0 0 0 0 0 0 0 1
156 1726 0 -322.7459413 0 0 0 0 0 0 0 0 0 0 0
157 1456 0 -139.6061127 1 0 0 0 0 0 0 0 0 0 0
158 1445 0 35.2448227 0 1 0 0 0 0 0 0 0 0 0
159 1456 0 -4.3176773 0 0 1 0 0 0 0 0 0 0 0
160 1365 0 17.8698227 0 0 0 1 0 0 0 0 0 0 0
161 1487 0 2.5573227 0 0 0 0 1 0 0 0 0 0 0
162 1558 0 129.3073227 0 0 0 0 0 1 0 0 0 0 0
163 1488 0 -16.3176773 0 0 0 0 0 0 1 0 0 0 0
164 1684 0 164.8073227 0 0 0 0 0 0 0 1 0 0 0
165 1594 0 21.9948227 0 0 0 0 0 0 0 0 1 0 0
166 1850 0 138.6198227 0 0 0 0 0 0 0 0 0 1 0
167 1998 0 87.0573227 0 0 0 0 0 0 0 0 0 0 1
168 2079 0 51.4323227 0 0 0 0 0 0 0 0 0 0 0
169 1494 0 -80.4278487 1 0 0 0 0 0 0 0 0 0 0
170 1057 1 -105.1918797 0 1 0 0 0 0 0 0 0 0 0
171 1218 1 5.2456203 0 0 1 0 0 0 0 0 0 0 0
172 1168 1 68.4331203 0 0 0 1 0 0 0 0 0 0 0
173 1236 1 -0.8793797 0 0 0 0 1 0 0 0 0 0 0
174 1076 1 -105.1293797 0 0 0 0 0 1 0 0 0 0 0
175 1174 1 -82.7543797 0 0 0 0 0 0 1 0 0 0 0
176 1139 1 -132.6293797 0 0 0 0 0 0 0 1 0 0 0
177 1427 1 102.5581203 0 0 0 0 0 0 0 0 1 0 0
178 1487 1 23.1831203 0 0 0 0 0 0 0 0 0 1 0
179 1483 1 -180.3793797 0 0 0 0 0 0 0 0 0 0 1
180 1513 1 -267.0043797 0 0 0 0 0 0 0 0 0 0 0
181 1357 1 30.1354489 1 0 0 0 0 0 0 0 0 0 0
182 1165 1 23.9863843 0 1 0 0 0 0 0 0 0 0 0
183 1282 1 90.4238843 0 0 1 0 0 0 0 0 0 0 0
184 1110 1 31.6113843 0 0 0 1 0 0 0 0 0 0 0
185 1297 1 81.2988843 0 0 0 0 1 0 0 0 0 0 0
186 1185 1 25.0488843 0 0 0 0 0 1 0 0 0 0 0
187 1222 1 -13.5761157 0 0 0 0 0 0 1 0 0 0 0
188 1284 1 33.5488843 0 0 0 0 0 0 0 1 0 0 0
189 1444 1 140.7363843 0 0 0 0 0 0 0 0 1 0 0
190 1575 1 132.3613843 0 0 0 0 0 0 0 0 0 1 0
191 1737 1 94.7988843 0 0 0 0 0 0 0 0 0 0 1
192 1763 1 4.1738843 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) seatbeltlaw predictionerrors M1
2165.2 -395.8 1.0 -442.6
M2 M3 M4 M5
-617.8 -567.3 -680.4 -543.1
M6 M7 M8 M9
-598.9 -523.2 -508.4 -455.6
M10 M11
-316.2 -116.6
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.482e+02 -6.618e+01 -1.995e-09 6.618e+01 1.482e+02
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2165.22639 21.08334 102.698 < 2e-16 ***
seatbeltlaw -395.81115 18.65458 -21.218 < 2e-16 ***
predictionerrors 1.00000 0.04122 24.262 < 2e-16 ***
M1 -442.55070 29.65635 -14.923 < 2e-16 ***
M2 -617.81250 29.63342 -20.849 < 2e-16 ***
M3 -567.25000 29.63342 -19.142 < 2e-16 ***
M4 -680.43750 29.63342 -22.962 < 2e-16 ***
M5 -543.12500 29.63342 -18.328 < 2e-16 ***
M6 -598.87500 29.63342 -20.209 < 2e-16 ***
M7 -523.25000 29.63342 -17.657 < 2e-16 ***
M8 -508.37500 29.63342 -17.155 < 2e-16 ***
M9 -455.56250 29.63342 -15.373 < 2e-16 ***
M10 -316.18750 29.63342 -10.670 < 2e-16 ***
M11 -116.62500 29.63342 -3.936 0.000119 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 83.82 on 178 degrees of freedom
Multiple R-squared: 0.9219, Adjusted R-squared: 0.9162
F-statistic: 161.7 on 13 and 178 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.298645e-03 1.059729e-02 9.947014e-01
[2,] 1.422279e-03 2.844559e-03 9.985777e-01
[3,] 2.116982e-04 4.233964e-04 9.997883e-01
[4,] 3.021849e-05 6.043699e-05 9.999698e-01
[5,] 4.515164e-06 9.030328e-06 9.999955e-01
[6,] 9.143563e-07 1.828713e-06 9.999991e-01
[7,] 1.833315e-07 3.666631e-07 9.999998e-01
[8,] 2.926373e-08 5.852747e-08 1.000000e+00
[9,] 4.010743e-09 8.021486e-09 1.000000e+00
[10,] 9.578554e-09 1.915711e-08 1.000000e+00
[11,] 6.290415e-09 1.258083e-08 1.000000e+00
[12,] 2.010692e-09 4.021385e-09 1.000000e+00
[13,] 4.565757e-10 9.131514e-10 1.000000e+00
[14,] 9.062848e-11 1.812570e-10 1.000000e+00
[15,] 3.749591e-11 7.499181e-11 1.000000e+00
[16,] 7.675004e-12 1.535001e-11 1.000000e+00
[17,] 2.120551e-11 4.241102e-11 1.000000e+00
[18,] 6.212005e-12 1.242401e-11 1.000000e+00
[19,] 5.139617e-12 1.027923e-11 1.000000e+00
[20,] 3.176093e-11 6.352186e-11 1.000000e+00
[21,] 9.664994e-12 1.932999e-11 1.000000e+00
[22,] 6.883251e-12 1.376650e-11 1.000000e+00
[23,] 2.701388e-12 5.402776e-12 1.000000e+00
[24,] 4.340873e-12 8.681746e-12 1.000000e+00
[25,] 1.206631e-12 2.413263e-12 1.000000e+00
[26,] 3.643813e-13 7.287626e-13 1.000000e+00
[27,] 1.128602e-13 2.257205e-13 1.000000e+00
[28,] 1.580571e-12 3.161143e-12 1.000000e+00
[29,] 1.022486e-12 2.044972e-12 1.000000e+00
[30,] 9.113128e-13 1.822626e-12 1.000000e+00
[31,] 3.950867e-13 7.901735e-13 1.000000e+00
[32,] 1.655977e-13 3.311954e-13 1.000000e+00
[33,] 1.051934e-13 2.103868e-13 1.000000e+00
[34,] 4.171732e-14 8.343464e-14 1.000000e+00
[35,] 3.439912e-13 6.879825e-13 1.000000e+00
[36,] 1.549009e-13 3.098018e-13 1.000000e+00
[37,] 8.419360e-14 1.683872e-13 1.000000e+00
[38,] 8.720270e-14 1.744054e-13 1.000000e+00
[39,] 5.963572e-14 1.192714e-13 1.000000e+00
[40,] 6.985579e-14 1.397116e-13 1.000000e+00
[41,] 5.207873e-14 1.041575e-13 1.000000e+00
[42,] 5.886904e-14 1.177381e-13 1.000000e+00
[43,] 3.345223e-12 6.690446e-12 1.000000e+00
[44,] 2.286701e-10 4.573401e-10 1.000000e+00
[45,] 4.520976e-08 9.041952e-08 1.000000e+00
[46,] 7.279701e-07 1.455940e-06 9.999993e-01
[47,] 3.690733e-06 7.381466e-06 9.999963e-01
[48,] 2.339046e-05 4.678092e-05 9.999766e-01
[49,] 6.665606e-05 1.333121e-04 9.999333e-01
[50,] 1.205097e-04 2.410195e-04 9.998795e-01
[51,] 3.176509e-04 6.353017e-04 9.996823e-01
[52,] 6.327087e-04 1.265417e-03 9.993673e-01
[53,] 1.089669e-03 2.179338e-03 9.989103e-01
[54,] 2.228091e-03 4.456183e-03 9.977719e-01
[55,] 6.449904e-03 1.289981e-02 9.935501e-01
[56,] 1.802717e-02 3.605433e-02 9.819728e-01
[57,] 5.086851e-02 1.017370e-01 9.491315e-01
[58,] 1.080329e-01 2.160658e-01 8.919671e-01
[59,] 1.590517e-01 3.181034e-01 8.409483e-01
[60,] 2.343250e-01 4.686501e-01 7.656750e-01
[61,] 3.435654e-01 6.871308e-01 6.564346e-01
[62,] 4.528831e-01 9.057663e-01 5.471169e-01
[63,] 5.680512e-01 8.638976e-01 4.319488e-01
[64,] 6.644567e-01 6.710866e-01 3.355433e-01
[65,] 7.402058e-01 5.195885e-01 2.597942e-01
[66,] 8.012476e-01 3.975049e-01 1.987524e-01
[67,] 8.579183e-01 2.841634e-01 1.420817e-01
[68,] 9.074911e-01 1.850179e-01 9.250895e-02
[69,] 9.395079e-01 1.209842e-01 6.049209e-02
[70,] 9.658366e-01 6.832674e-02 3.416337e-02
[71,] 9.767224e-01 4.655518e-02 2.327759e-02
[72,] 9.852637e-01 2.947252e-02 1.473626e-02
[73,] 9.915291e-01 1.694175e-02 8.470874e-03
[74,] 9.944654e-01 1.106913e-02 5.534565e-03
[75,] 9.968762e-01 6.247574e-03 3.123787e-03
[76,] 9.979309e-01 4.138144e-03 2.069072e-03
[77,] 9.988718e-01 2.256466e-03 1.128233e-03
[78,] 9.993966e-01 1.206886e-03 6.034430e-04
[79,] 9.997077e-01 5.846509e-04 2.923255e-04
[80,] 9.998942e-01 2.116113e-04 1.058057e-04
[81,] 9.999575e-01 8.505495e-05 4.252748e-05
[82,] 9.999784e-01 4.326348e-05 2.163174e-05
[83,] 9.999881e-01 2.387123e-05 1.193562e-05
[84,] 9.999943e-01 1.139803e-05 5.699016e-06
[85,] 9.999971e-01 5.792668e-06 2.896334e-06
[86,] 9.999988e-01 2.498931e-06 1.249465e-06
[87,] 9.999995e-01 1.080637e-06 5.403185e-07
[88,] 9.999998e-01 4.201408e-07 2.100704e-07
[89,] 9.999999e-01 1.765685e-07 8.828425e-08
[90,] 1.000000e+00 6.494483e-08 3.247242e-08
[91,] 1.000000e+00 2.219504e-08 1.109752e-08
[92,] 1.000000e+00 6.947713e-09 3.473856e-09
[93,] 1.000000e+00 1.509631e-09 7.548154e-10
[94,] 1.000000e+00 6.724245e-10 3.362123e-10
[95,] 1.000000e+00 2.983456e-10 1.491728e-10
[96,] 1.000000e+00 1.255427e-10 6.277136e-11
[97,] 1.000000e+00 5.849986e-11 2.924993e-11
[98,] 1.000000e+00 2.111616e-11 1.055808e-11
[99,] 1.000000e+00 7.165318e-12 3.582659e-12
[100,] 1.000000e+00 2.499159e-12 1.249580e-12
[101,] 1.000000e+00 9.579240e-13 4.789620e-13
[102,] 1.000000e+00 3.951362e-13 1.975681e-13
[103,] 1.000000e+00 1.097529e-13 5.487643e-14
[104,] 1.000000e+00 1.780779e-14 8.903894e-15
[105,] 1.000000e+00 2.019271e-15 1.009635e-15
[106,] 1.000000e+00 1.111541e-15 5.557704e-16
[107,] 1.000000e+00 2.144797e-16 1.072399e-16
[108,] 1.000000e+00 9.184599e-17 4.592299e-17
[109,] 1.000000e+00 4.201699e-17 2.100850e-17
[110,] 1.000000e+00 2.658138e-17 1.329069e-17
[111,] 1.000000e+00 2.079781e-17 1.039890e-17
[112,] 1.000000e+00 1.020732e-17 5.103661e-18
[113,] 1.000000e+00 5.014013e-18 2.507006e-18
[114,] 1.000000e+00 4.980736e-18 2.490368e-18
[115,] 1.000000e+00 9.273638e-19 4.636819e-19
[116,] 1.000000e+00 1.704464e-20 8.522321e-21
[117,] 1.000000e+00 8.048142e-22 4.024071e-22
[118,] 1.000000e+00 1.287271e-21 6.436356e-22
[119,] 1.000000e+00 1.382179e-21 6.910894e-22
[120,] 1.000000e+00 1.881113e-21 9.405566e-22
[121,] 1.000000e+00 3.097281e-21 1.548640e-21
[122,] 1.000000e+00 1.172633e-21 5.863167e-22
[123,] 1.000000e+00 1.669792e-21 8.348960e-22
[124,] 1.000000e+00 1.153974e-21 5.769869e-22
[125,] 1.000000e+00 2.383593e-21 1.191796e-21
[126,] 1.000000e+00 1.903238e-21 9.516190e-22
[127,] 1.000000e+00 4.387684e-21 2.193842e-21
[128,] 1.000000e+00 1.433986e-21 7.169928e-22
[129,] 1.000000e+00 1.569855e-21 7.849275e-22
[130,] 1.000000e+00 3.514624e-21 1.757312e-21
[131,] 1.000000e+00 8.191253e-21 4.095627e-21
[132,] 1.000000e+00 2.286260e-20 1.143130e-20
[133,] 1.000000e+00 6.538230e-20 3.269115e-20
[134,] 1.000000e+00 2.604337e-19 1.302168e-19
[135,] 1.000000e+00 1.618393e-19 8.091963e-20
[136,] 1.000000e+00 5.405147e-19 2.702573e-19
[137,] 1.000000e+00 1.142705e-18 5.713525e-19
[138,] 1.000000e+00 5.254061e-19 2.627031e-19
[139,] 1.000000e+00 1.506236e-18 7.531181e-19
[140,] 1.000000e+00 1.172646e-17 5.863232e-18
[141,] 1.000000e+00 7.301310e-17 3.650655e-17
[142,] 1.000000e+00 5.476553e-16 2.738276e-16
[143,] 1.000000e+00 3.732924e-15 1.866462e-15
[144,] 1.000000e+00 2.613194e-14 1.306597e-14
[145,] 1.000000e+00 1.749457e-13 8.747285e-14
[146,] 1.000000e+00 1.089273e-12 5.446364e-13
[147,] 1.000000e+00 8.345512e-12 4.172756e-12
[148,] 1.000000e+00 3.657317e-11 1.828658e-11
[149,] 1.000000e+00 1.947312e-10 9.736559e-11
[150,] 1.000000e+00 1.522161e-09 7.610805e-10
[151,] 1.000000e+00 9.527728e-09 4.763864e-09
[152,] 1.000000e+00 8.748942e-09 4.374471e-09
[153,] 1.000000e+00 7.907326e-08 3.953663e-08
[154,] 9.999997e-01 6.730215e-07 3.365108e-07
[155,] 9.999978e-01 4.468867e-06 2.234434e-06
[156,] 9.999959e-01 8.265055e-06 4.132527e-06
[157,] 9.999719e-01 5.620773e-05 2.810386e-05
[158,] 9.997446e-01 5.107218e-04 2.553609e-04
[159,] 9.984700e-01 3.059959e-03 1.529979e-03
> postscript(file="/var/www/html/rcomp/tmp/1k0zw1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/219t01227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3p1ns1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h79u1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5geb21227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 192
Frequency = 1
1 2 3 4 5
1.482478e+02 1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02
6 7 8 9 10
1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02 1.376587e+02
11 12 13 14 15
1.376587e+02 1.376587e+02 1.270696e+02 1.164805e+02 1.164805e+02
16 17 18 19 20
1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02
21 22 23 24 25
1.164805e+02 1.164805e+02 1.164805e+02 1.164805e+02 1.058913e+02
26 27 28 29 30
9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01
31 32 33 34 35
9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01 9.530219e+01
36 37 38 39 40
9.530219e+01 8.471306e+01 7.412392e+01 7.412392e+01 7.412392e+01
41 42 43 44 45
7.412392e+01 7.412392e+01 7.412392e+01 7.412392e+01 7.412392e+01
46 47 48 49 50
7.412392e+01 7.412392e+01 7.412392e+01 6.353479e+01 5.294566e+01
51 52 53 54 55
5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01
56 57 58 59 60
5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01 5.294566e+01
61 62 63 64 65
4.235653e+01 3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01
66 67 68 69 70
3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01 3.176740e+01
71 72 73 74 75
3.176740e+01 3.176740e+01 2.117826e+01 1.058913e+01 1.058913e+01
76 77 78 79 80
1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01
81 82 83 84 85
1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 8.532245e-09
86 87 88 89 90
-1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01
91 92 93 94 95
-1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01
96 97 98 99 100
-1.058913e+01 -2.117826e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01
101 102 103 104 105
-3.176740e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01 -3.176740e+01
106 107 108 109 110
-3.176740e+01 -3.176740e+01 -3.176740e+01 -4.235653e+01 -5.294566e+01
111 112 113 114 115
-5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01
116 117 118 119 120
-5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01 -5.294566e+01
121 122 123 124 125
-6.353479e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01
126 127 128 129 130
-7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01 -7.412392e+01
131 132 133 134 135
-7.412392e+01 -7.412392e+01 -8.471306e+01 -9.530219e+01 -9.530219e+01
136 137 138 139 140
-9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01
141 142 143 144 145
-9.530219e+01 -9.530219e+01 -9.530219e+01 -9.530219e+01 -1.058913e+02
146 147 148 149 150
-1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02
151 152 153 154 155
-1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02 -1.164805e+02
156 157 158 159 160
-1.164805e+02 -1.270696e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02
161 162 163 164 165
-1.376587e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02 -1.376587e+02
166 167 168 169 170
-1.376587e+02 -1.376587e+02 -1.376587e+02 -1.482478e+02 1.058913e+01
171 172 173 174 175
1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01
176 177 178 179 180
1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01 1.058913e+01
181 182 183 184 185
-1.252250e-08 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01
186 187 188 189 190
-1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01 -1.058913e+01
191 192
-1.058913e+01 -1.058913e+01
> postscript(file="/var/www/html/rcomp/tmp/6odnr1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 192
Frequency = 1
lag(myerror, k = 1) myerror
0 1.482478e+02 NA
1 1.376587e+02 1.482478e+02
2 1.376587e+02 1.376587e+02
3 1.376587e+02 1.376587e+02
4 1.376587e+02 1.376587e+02
5 1.376587e+02 1.376587e+02
6 1.376587e+02 1.376587e+02
7 1.376587e+02 1.376587e+02
8 1.376587e+02 1.376587e+02
9 1.376587e+02 1.376587e+02
10 1.376587e+02 1.376587e+02
11 1.376587e+02 1.376587e+02
12 1.270696e+02 1.376587e+02
13 1.164805e+02 1.270696e+02
14 1.164805e+02 1.164805e+02
15 1.164805e+02 1.164805e+02
16 1.164805e+02 1.164805e+02
17 1.164805e+02 1.164805e+02
18 1.164805e+02 1.164805e+02
19 1.164805e+02 1.164805e+02
20 1.164805e+02 1.164805e+02
21 1.164805e+02 1.164805e+02
22 1.164805e+02 1.164805e+02
23 1.164805e+02 1.164805e+02
24 1.058913e+02 1.164805e+02
25 9.530219e+01 1.058913e+02
26 9.530219e+01 9.530219e+01
27 9.530219e+01 9.530219e+01
28 9.530219e+01 9.530219e+01
29 9.530219e+01 9.530219e+01
30 9.530219e+01 9.530219e+01
31 9.530219e+01 9.530219e+01
32 9.530219e+01 9.530219e+01
33 9.530219e+01 9.530219e+01
34 9.530219e+01 9.530219e+01
35 9.530219e+01 9.530219e+01
36 8.471306e+01 9.530219e+01
37 7.412392e+01 8.471306e+01
38 7.412392e+01 7.412392e+01
39 7.412392e+01 7.412392e+01
40 7.412392e+01 7.412392e+01
41 7.412392e+01 7.412392e+01
42 7.412392e+01 7.412392e+01
43 7.412392e+01 7.412392e+01
44 7.412392e+01 7.412392e+01
45 7.412392e+01 7.412392e+01
46 7.412392e+01 7.412392e+01
47 7.412392e+01 7.412392e+01
48 6.353479e+01 7.412392e+01
49 5.294566e+01 6.353479e+01
50 5.294566e+01 5.294566e+01
51 5.294566e+01 5.294566e+01
52 5.294566e+01 5.294566e+01
53 5.294566e+01 5.294566e+01
54 5.294566e+01 5.294566e+01
55 5.294566e+01 5.294566e+01
56 5.294566e+01 5.294566e+01
57 5.294566e+01 5.294566e+01
58 5.294566e+01 5.294566e+01
59 5.294566e+01 5.294566e+01
60 4.235653e+01 5.294566e+01
61 3.176740e+01 4.235653e+01
62 3.176740e+01 3.176740e+01
63 3.176740e+01 3.176740e+01
64 3.176740e+01 3.176740e+01
65 3.176740e+01 3.176740e+01
66 3.176740e+01 3.176740e+01
67 3.176740e+01 3.176740e+01
68 3.176740e+01 3.176740e+01
69 3.176740e+01 3.176740e+01
70 3.176740e+01 3.176740e+01
71 3.176740e+01 3.176740e+01
72 2.117826e+01 3.176740e+01
73 1.058913e+01 2.117826e+01
74 1.058913e+01 1.058913e+01
75 1.058913e+01 1.058913e+01
76 1.058913e+01 1.058913e+01
77 1.058913e+01 1.058913e+01
78 1.058913e+01 1.058913e+01
79 1.058913e+01 1.058913e+01
80 1.058913e+01 1.058913e+01
81 1.058913e+01 1.058913e+01
82 1.058913e+01 1.058913e+01
83 1.058913e+01 1.058913e+01
84 8.532245e-09 1.058913e+01
85 -1.058913e+01 8.532245e-09
86 -1.058913e+01 -1.058913e+01
87 -1.058913e+01 -1.058913e+01
88 -1.058913e+01 -1.058913e+01
89 -1.058913e+01 -1.058913e+01
90 -1.058913e+01 -1.058913e+01
91 -1.058913e+01 -1.058913e+01
92 -1.058913e+01 -1.058913e+01
93 -1.058913e+01 -1.058913e+01
94 -1.058913e+01 -1.058913e+01
95 -1.058913e+01 -1.058913e+01
96 -2.117826e+01 -1.058913e+01
97 -3.176740e+01 -2.117826e+01
98 -3.176740e+01 -3.176740e+01
99 -3.176740e+01 -3.176740e+01
100 -3.176740e+01 -3.176740e+01
101 -3.176740e+01 -3.176740e+01
102 -3.176740e+01 -3.176740e+01
103 -3.176740e+01 -3.176740e+01
104 -3.176740e+01 -3.176740e+01
105 -3.176740e+01 -3.176740e+01
106 -3.176740e+01 -3.176740e+01
107 -3.176740e+01 -3.176740e+01
108 -4.235653e+01 -3.176740e+01
109 -5.294566e+01 -4.235653e+01
110 -5.294566e+01 -5.294566e+01
111 -5.294566e+01 -5.294566e+01
112 -5.294566e+01 -5.294566e+01
113 -5.294566e+01 -5.294566e+01
114 -5.294566e+01 -5.294566e+01
115 -5.294566e+01 -5.294566e+01
116 -5.294566e+01 -5.294566e+01
117 -5.294566e+01 -5.294566e+01
118 -5.294566e+01 -5.294566e+01
119 -5.294566e+01 -5.294566e+01
120 -6.353479e+01 -5.294566e+01
121 -7.412392e+01 -6.353479e+01
122 -7.412392e+01 -7.412392e+01
123 -7.412392e+01 -7.412392e+01
124 -7.412392e+01 -7.412392e+01
125 -7.412392e+01 -7.412392e+01
126 -7.412392e+01 -7.412392e+01
127 -7.412392e+01 -7.412392e+01
128 -7.412392e+01 -7.412392e+01
129 -7.412392e+01 -7.412392e+01
130 -7.412392e+01 -7.412392e+01
131 -7.412392e+01 -7.412392e+01
132 -8.471306e+01 -7.412392e+01
133 -9.530219e+01 -8.471306e+01
134 -9.530219e+01 -9.530219e+01
135 -9.530219e+01 -9.530219e+01
136 -9.530219e+01 -9.530219e+01
137 -9.530219e+01 -9.530219e+01
138 -9.530219e+01 -9.530219e+01
139 -9.530219e+01 -9.530219e+01
140 -9.530219e+01 -9.530219e+01
141 -9.530219e+01 -9.530219e+01
142 -9.530219e+01 -9.530219e+01
143 -9.530219e+01 -9.530219e+01
144 -1.058913e+02 -9.530219e+01
145 -1.164805e+02 -1.058913e+02
146 -1.164805e+02 -1.164805e+02
147 -1.164805e+02 -1.164805e+02
148 -1.164805e+02 -1.164805e+02
149 -1.164805e+02 -1.164805e+02
150 -1.164805e+02 -1.164805e+02
151 -1.164805e+02 -1.164805e+02
152 -1.164805e+02 -1.164805e+02
153 -1.164805e+02 -1.164805e+02
154 -1.164805e+02 -1.164805e+02
155 -1.164805e+02 -1.164805e+02
156 -1.270696e+02 -1.164805e+02
157 -1.376587e+02 -1.270696e+02
158 -1.376587e+02 -1.376587e+02
159 -1.376587e+02 -1.376587e+02
160 -1.376587e+02 -1.376587e+02
161 -1.376587e+02 -1.376587e+02
162 -1.376587e+02 -1.376587e+02
163 -1.376587e+02 -1.376587e+02
164 -1.376587e+02 -1.376587e+02
165 -1.376587e+02 -1.376587e+02
166 -1.376587e+02 -1.376587e+02
167 -1.376587e+02 -1.376587e+02
168 -1.482478e+02 -1.376587e+02
169 1.058913e+01 -1.482478e+02
170 1.058913e+01 1.058913e+01
171 1.058913e+01 1.058913e+01
172 1.058913e+01 1.058913e+01
173 1.058913e+01 1.058913e+01
174 1.058913e+01 1.058913e+01
175 1.058913e+01 1.058913e+01
176 1.058913e+01 1.058913e+01
177 1.058913e+01 1.058913e+01
178 1.058913e+01 1.058913e+01
179 1.058913e+01 1.058913e+01
180 -1.252250e-08 1.058913e+01
181 -1.058913e+01 -1.252250e-08
182 -1.058913e+01 -1.058913e+01
183 -1.058913e+01 -1.058913e+01
184 -1.058913e+01 -1.058913e+01
185 -1.058913e+01 -1.058913e+01
186 -1.058913e+01 -1.058913e+01
187 -1.058913e+01 -1.058913e+01
188 -1.058913e+01 -1.058913e+01
189 -1.058913e+01 -1.058913e+01
190 -1.058913e+01 -1.058913e+01
191 -1.058913e+01 -1.058913e+01
192 NA -1.058913e+01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.376587e+02 1.482478e+02
[2,] 1.376587e+02 1.376587e+02
[3,] 1.376587e+02 1.376587e+02
[4,] 1.376587e+02 1.376587e+02
[5,] 1.376587e+02 1.376587e+02
[6,] 1.376587e+02 1.376587e+02
[7,] 1.376587e+02 1.376587e+02
[8,] 1.376587e+02 1.376587e+02
[9,] 1.376587e+02 1.376587e+02
[10,] 1.376587e+02 1.376587e+02
[11,] 1.376587e+02 1.376587e+02
[12,] 1.270696e+02 1.376587e+02
[13,] 1.164805e+02 1.270696e+02
[14,] 1.164805e+02 1.164805e+02
[15,] 1.164805e+02 1.164805e+02
[16,] 1.164805e+02 1.164805e+02
[17,] 1.164805e+02 1.164805e+02
[18,] 1.164805e+02 1.164805e+02
[19,] 1.164805e+02 1.164805e+02
[20,] 1.164805e+02 1.164805e+02
[21,] 1.164805e+02 1.164805e+02
[22,] 1.164805e+02 1.164805e+02
[23,] 1.164805e+02 1.164805e+02
[24,] 1.058913e+02 1.164805e+02
[25,] 9.530219e+01 1.058913e+02
[26,] 9.530219e+01 9.530219e+01
[27,] 9.530219e+01 9.530219e+01
[28,] 9.530219e+01 9.530219e+01
[29,] 9.530219e+01 9.530219e+01
[30,] 9.530219e+01 9.530219e+01
[31,] 9.530219e+01 9.530219e+01
[32,] 9.530219e+01 9.530219e+01
[33,] 9.530219e+01 9.530219e+01
[34,] 9.530219e+01 9.530219e+01
[35,] 9.530219e+01 9.530219e+01
[36,] 8.471306e+01 9.530219e+01
[37,] 7.412392e+01 8.471306e+01
[38,] 7.412392e+01 7.412392e+01
[39,] 7.412392e+01 7.412392e+01
[40,] 7.412392e+01 7.412392e+01
[41,] 7.412392e+01 7.412392e+01
[42,] 7.412392e+01 7.412392e+01
[43,] 7.412392e+01 7.412392e+01
[44,] 7.412392e+01 7.412392e+01
[45,] 7.412392e+01 7.412392e+01
[46,] 7.412392e+01 7.412392e+01
[47,] 7.412392e+01 7.412392e+01
[48,] 6.353479e+01 7.412392e+01
[49,] 5.294566e+01 6.353479e+01
[50,] 5.294566e+01 5.294566e+01
[51,] 5.294566e+01 5.294566e+01
[52,] 5.294566e+01 5.294566e+01
[53,] 5.294566e+01 5.294566e+01
[54,] 5.294566e+01 5.294566e+01
[55,] 5.294566e+01 5.294566e+01
[56,] 5.294566e+01 5.294566e+01
[57,] 5.294566e+01 5.294566e+01
[58,] 5.294566e+01 5.294566e+01
[59,] 5.294566e+01 5.294566e+01
[60,] 4.235653e+01 5.294566e+01
[61,] 3.176740e+01 4.235653e+01
[62,] 3.176740e+01 3.176740e+01
[63,] 3.176740e+01 3.176740e+01
[64,] 3.176740e+01 3.176740e+01
[65,] 3.176740e+01 3.176740e+01
[66,] 3.176740e+01 3.176740e+01
[67,] 3.176740e+01 3.176740e+01
[68,] 3.176740e+01 3.176740e+01
[69,] 3.176740e+01 3.176740e+01
[70,] 3.176740e+01 3.176740e+01
[71,] 3.176740e+01 3.176740e+01
[72,] 2.117826e+01 3.176740e+01
[73,] 1.058913e+01 2.117826e+01
[74,] 1.058913e+01 1.058913e+01
[75,] 1.058913e+01 1.058913e+01
[76,] 1.058913e+01 1.058913e+01
[77,] 1.058913e+01 1.058913e+01
[78,] 1.058913e+01 1.058913e+01
[79,] 1.058913e+01 1.058913e+01
[80,] 1.058913e+01 1.058913e+01
[81,] 1.058913e+01 1.058913e+01
[82,] 1.058913e+01 1.058913e+01
[83,] 1.058913e+01 1.058913e+01
[84,] 8.532245e-09 1.058913e+01
[85,] -1.058913e+01 8.532245e-09
[86,] -1.058913e+01 -1.058913e+01
[87,] -1.058913e+01 -1.058913e+01
[88,] -1.058913e+01 -1.058913e+01
[89,] -1.058913e+01 -1.058913e+01
[90,] -1.058913e+01 -1.058913e+01
[91,] -1.058913e+01 -1.058913e+01
[92,] -1.058913e+01 -1.058913e+01
[93,] -1.058913e+01 -1.058913e+01
[94,] -1.058913e+01 -1.058913e+01
[95,] -1.058913e+01 -1.058913e+01
[96,] -2.117826e+01 -1.058913e+01
[97,] -3.176740e+01 -2.117826e+01
[98,] -3.176740e+01 -3.176740e+01
[99,] -3.176740e+01 -3.176740e+01
[100,] -3.176740e+01 -3.176740e+01
[101,] -3.176740e+01 -3.176740e+01
[102,] -3.176740e+01 -3.176740e+01
[103,] -3.176740e+01 -3.176740e+01
[104,] -3.176740e+01 -3.176740e+01
[105,] -3.176740e+01 -3.176740e+01
[106,] -3.176740e+01 -3.176740e+01
[107,] -3.176740e+01 -3.176740e+01
[108,] -4.235653e+01 -3.176740e+01
[109,] -5.294566e+01 -4.235653e+01
[110,] -5.294566e+01 -5.294566e+01
[111,] -5.294566e+01 -5.294566e+01
[112,] -5.294566e+01 -5.294566e+01
[113,] -5.294566e+01 -5.294566e+01
[114,] -5.294566e+01 -5.294566e+01
[115,] -5.294566e+01 -5.294566e+01
[116,] -5.294566e+01 -5.294566e+01
[117,] -5.294566e+01 -5.294566e+01
[118,] -5.294566e+01 -5.294566e+01
[119,] -5.294566e+01 -5.294566e+01
[120,] -6.353479e+01 -5.294566e+01
[121,] -7.412392e+01 -6.353479e+01
[122,] -7.412392e+01 -7.412392e+01
[123,] -7.412392e+01 -7.412392e+01
[124,] -7.412392e+01 -7.412392e+01
[125,] -7.412392e+01 -7.412392e+01
[126,] -7.412392e+01 -7.412392e+01
[127,] -7.412392e+01 -7.412392e+01
[128,] -7.412392e+01 -7.412392e+01
[129,] -7.412392e+01 -7.412392e+01
[130,] -7.412392e+01 -7.412392e+01
[131,] -7.412392e+01 -7.412392e+01
[132,] -8.471306e+01 -7.412392e+01
[133,] -9.530219e+01 -8.471306e+01
[134,] -9.530219e+01 -9.530219e+01
[135,] -9.530219e+01 -9.530219e+01
[136,] -9.530219e+01 -9.530219e+01
[137,] -9.530219e+01 -9.530219e+01
[138,] -9.530219e+01 -9.530219e+01
[139,] -9.530219e+01 -9.530219e+01
[140,] -9.530219e+01 -9.530219e+01
[141,] -9.530219e+01 -9.530219e+01
[142,] -9.530219e+01 -9.530219e+01
[143,] -9.530219e+01 -9.530219e+01
[144,] -1.058913e+02 -9.530219e+01
[145,] -1.164805e+02 -1.058913e+02
[146,] -1.164805e+02 -1.164805e+02
[147,] -1.164805e+02 -1.164805e+02
[148,] -1.164805e+02 -1.164805e+02
[149,] -1.164805e+02 -1.164805e+02
[150,] -1.164805e+02 -1.164805e+02
[151,] -1.164805e+02 -1.164805e+02
[152,] -1.164805e+02 -1.164805e+02
[153,] -1.164805e+02 -1.164805e+02
[154,] -1.164805e+02 -1.164805e+02
[155,] -1.164805e+02 -1.164805e+02
[156,] -1.270696e+02 -1.164805e+02
[157,] -1.376587e+02 -1.270696e+02
[158,] -1.376587e+02 -1.376587e+02
[159,] -1.376587e+02 -1.376587e+02
[160,] -1.376587e+02 -1.376587e+02
[161,] -1.376587e+02 -1.376587e+02
[162,] -1.376587e+02 -1.376587e+02
[163,] -1.376587e+02 -1.376587e+02
[164,] -1.376587e+02 -1.376587e+02
[165,] -1.376587e+02 -1.376587e+02
[166,] -1.376587e+02 -1.376587e+02
[167,] -1.376587e+02 -1.376587e+02
[168,] -1.482478e+02 -1.376587e+02
[169,] 1.058913e+01 -1.482478e+02
[170,] 1.058913e+01 1.058913e+01
[171,] 1.058913e+01 1.058913e+01
[172,] 1.058913e+01 1.058913e+01
[173,] 1.058913e+01 1.058913e+01
[174,] 1.058913e+01 1.058913e+01
[175,] 1.058913e+01 1.058913e+01
[176,] 1.058913e+01 1.058913e+01
[177,] 1.058913e+01 1.058913e+01
[178,] 1.058913e+01 1.058913e+01
[179,] 1.058913e+01 1.058913e+01
[180,] -1.252250e-08 1.058913e+01
[181,] -1.058913e+01 -1.252250e-08
[182,] -1.058913e+01 -1.058913e+01
[183,] -1.058913e+01 -1.058913e+01
[184,] -1.058913e+01 -1.058913e+01
[185,] -1.058913e+01 -1.058913e+01
[186,] -1.058913e+01 -1.058913e+01
[187,] -1.058913e+01 -1.058913e+01
[188,] -1.058913e+01 -1.058913e+01
[189,] -1.058913e+01 -1.058913e+01
[190,] -1.058913e+01 -1.058913e+01
[191,] -1.058913e+01 -1.058913e+01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.376587e+02 1.482478e+02
2 1.376587e+02 1.376587e+02
3 1.376587e+02 1.376587e+02
4 1.376587e+02 1.376587e+02
5 1.376587e+02 1.376587e+02
6 1.376587e+02 1.376587e+02
7 1.376587e+02 1.376587e+02
8 1.376587e+02 1.376587e+02
9 1.376587e+02 1.376587e+02
10 1.376587e+02 1.376587e+02
11 1.376587e+02 1.376587e+02
12 1.270696e+02 1.376587e+02
13 1.164805e+02 1.270696e+02
14 1.164805e+02 1.164805e+02
15 1.164805e+02 1.164805e+02
16 1.164805e+02 1.164805e+02
17 1.164805e+02 1.164805e+02
18 1.164805e+02 1.164805e+02
19 1.164805e+02 1.164805e+02
20 1.164805e+02 1.164805e+02
21 1.164805e+02 1.164805e+02
22 1.164805e+02 1.164805e+02
23 1.164805e+02 1.164805e+02
24 1.058913e+02 1.164805e+02
25 9.530219e+01 1.058913e+02
26 9.530219e+01 9.530219e+01
27 9.530219e+01 9.530219e+01
28 9.530219e+01 9.530219e+01
29 9.530219e+01 9.530219e+01
30 9.530219e+01 9.530219e+01
31 9.530219e+01 9.530219e+01
32 9.530219e+01 9.530219e+01
33 9.530219e+01 9.530219e+01
34 9.530219e+01 9.530219e+01
35 9.530219e+01 9.530219e+01
36 8.471306e+01 9.530219e+01
37 7.412392e+01 8.471306e+01
38 7.412392e+01 7.412392e+01
39 7.412392e+01 7.412392e+01
40 7.412392e+01 7.412392e+01
41 7.412392e+01 7.412392e+01
42 7.412392e+01 7.412392e+01
43 7.412392e+01 7.412392e+01
44 7.412392e+01 7.412392e+01
45 7.412392e+01 7.412392e+01
46 7.412392e+01 7.412392e+01
47 7.412392e+01 7.412392e+01
48 6.353479e+01 7.412392e+01
49 5.294566e+01 6.353479e+01
50 5.294566e+01 5.294566e+01
51 5.294566e+01 5.294566e+01
52 5.294566e+01 5.294566e+01
53 5.294566e+01 5.294566e+01
54 5.294566e+01 5.294566e+01
55 5.294566e+01 5.294566e+01
56 5.294566e+01 5.294566e+01
57 5.294566e+01 5.294566e+01
58 5.294566e+01 5.294566e+01
59 5.294566e+01 5.294566e+01
60 4.235653e+01 5.294566e+01
61 3.176740e+01 4.235653e+01
62 3.176740e+01 3.176740e+01
63 3.176740e+01 3.176740e+01
64 3.176740e+01 3.176740e+01
65 3.176740e+01 3.176740e+01
66 3.176740e+01 3.176740e+01
67 3.176740e+01 3.176740e+01
68 3.176740e+01 3.176740e+01
69 3.176740e+01 3.176740e+01
70 3.176740e+01 3.176740e+01
71 3.176740e+01 3.176740e+01
72 2.117826e+01 3.176740e+01
73 1.058913e+01 2.117826e+01
74 1.058913e+01 1.058913e+01
75 1.058913e+01 1.058913e+01
76 1.058913e+01 1.058913e+01
77 1.058913e+01 1.058913e+01
78 1.058913e+01 1.058913e+01
79 1.058913e+01 1.058913e+01
80 1.058913e+01 1.058913e+01
81 1.058913e+01 1.058913e+01
82 1.058913e+01 1.058913e+01
83 1.058913e+01 1.058913e+01
84 8.532245e-09 1.058913e+01
85 -1.058913e+01 8.532245e-09
86 -1.058913e+01 -1.058913e+01
87 -1.058913e+01 -1.058913e+01
88 -1.058913e+01 -1.058913e+01
89 -1.058913e+01 -1.058913e+01
90 -1.058913e+01 -1.058913e+01
91 -1.058913e+01 -1.058913e+01
92 -1.058913e+01 -1.058913e+01
93 -1.058913e+01 -1.058913e+01
94 -1.058913e+01 -1.058913e+01
95 -1.058913e+01 -1.058913e+01
96 -2.117826e+01 -1.058913e+01
97 -3.176740e+01 -2.117826e+01
98 -3.176740e+01 -3.176740e+01
99 -3.176740e+01 -3.176740e+01
100 -3.176740e+01 -3.176740e+01
101 -3.176740e+01 -3.176740e+01
102 -3.176740e+01 -3.176740e+01
103 -3.176740e+01 -3.176740e+01
104 -3.176740e+01 -3.176740e+01
105 -3.176740e+01 -3.176740e+01
106 -3.176740e+01 -3.176740e+01
107 -3.176740e+01 -3.176740e+01
108 -4.235653e+01 -3.176740e+01
109 -5.294566e+01 -4.235653e+01
110 -5.294566e+01 -5.294566e+01
111 -5.294566e+01 -5.294566e+01
112 -5.294566e+01 -5.294566e+01
113 -5.294566e+01 -5.294566e+01
114 -5.294566e+01 -5.294566e+01
115 -5.294566e+01 -5.294566e+01
116 -5.294566e+01 -5.294566e+01
117 -5.294566e+01 -5.294566e+01
118 -5.294566e+01 -5.294566e+01
119 -5.294566e+01 -5.294566e+01
120 -6.353479e+01 -5.294566e+01
121 -7.412392e+01 -6.353479e+01
122 -7.412392e+01 -7.412392e+01
123 -7.412392e+01 -7.412392e+01
124 -7.412392e+01 -7.412392e+01
125 -7.412392e+01 -7.412392e+01
126 -7.412392e+01 -7.412392e+01
127 -7.412392e+01 -7.412392e+01
128 -7.412392e+01 -7.412392e+01
129 -7.412392e+01 -7.412392e+01
130 -7.412392e+01 -7.412392e+01
131 -7.412392e+01 -7.412392e+01
132 -8.471306e+01 -7.412392e+01
133 -9.530219e+01 -8.471306e+01
134 -9.530219e+01 -9.530219e+01
135 -9.530219e+01 -9.530219e+01
136 -9.530219e+01 -9.530219e+01
137 -9.530219e+01 -9.530219e+01
138 -9.530219e+01 -9.530219e+01
139 -9.530219e+01 -9.530219e+01
140 -9.530219e+01 -9.530219e+01
141 -9.530219e+01 -9.530219e+01
142 -9.530219e+01 -9.530219e+01
143 -9.530219e+01 -9.530219e+01
144 -1.058913e+02 -9.530219e+01
145 -1.164805e+02 -1.058913e+02
146 -1.164805e+02 -1.164805e+02
147 -1.164805e+02 -1.164805e+02
148 -1.164805e+02 -1.164805e+02
149 -1.164805e+02 -1.164805e+02
150 -1.164805e+02 -1.164805e+02
151 -1.164805e+02 -1.164805e+02
152 -1.164805e+02 -1.164805e+02
153 -1.164805e+02 -1.164805e+02
154 -1.164805e+02 -1.164805e+02
155 -1.164805e+02 -1.164805e+02
156 -1.270696e+02 -1.164805e+02
157 -1.376587e+02 -1.270696e+02
158 -1.376587e+02 -1.376587e+02
159 -1.376587e+02 -1.376587e+02
160 -1.376587e+02 -1.376587e+02
161 -1.376587e+02 -1.376587e+02
162 -1.376587e+02 -1.376587e+02
163 -1.376587e+02 -1.376587e+02
164 -1.376587e+02 -1.376587e+02
165 -1.376587e+02 -1.376587e+02
166 -1.376587e+02 -1.376587e+02
167 -1.376587e+02 -1.376587e+02
168 -1.482478e+02 -1.376587e+02
169 1.058913e+01 -1.482478e+02
170 1.058913e+01 1.058913e+01
171 1.058913e+01 1.058913e+01
172 1.058913e+01 1.058913e+01
173 1.058913e+01 1.058913e+01
174 1.058913e+01 1.058913e+01
175 1.058913e+01 1.058913e+01
176 1.058913e+01 1.058913e+01
177 1.058913e+01 1.058913e+01
178 1.058913e+01 1.058913e+01
179 1.058913e+01 1.058913e+01
180 -1.252250e-08 1.058913e+01
181 -1.058913e+01 -1.252250e-08
182 -1.058913e+01 -1.058913e+01
183 -1.058913e+01 -1.058913e+01
184 -1.058913e+01 -1.058913e+01
185 -1.058913e+01 -1.058913e+01
186 -1.058913e+01 -1.058913e+01
187 -1.058913e+01 -1.058913e+01
188 -1.058913e+01 -1.058913e+01
189 -1.058913e+01 -1.058913e+01
190 -1.058913e+01 -1.058913e+01
191 -1.058913e+01 -1.058913e+01
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7yvu01227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fb5f1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mm931227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10z95n1227552869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1187v31227552869.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12sudo1227552869.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13t4vx1227552869.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14rj321227552869.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1533301227552869.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16rtju1227552869.tab")
+ }
>
> system("convert tmp/1k0zw1227552869.ps tmp/1k0zw1227552869.png")
> system("convert tmp/219t01227552869.ps tmp/219t01227552869.png")
> system("convert tmp/3p1ns1227552869.ps tmp/3p1ns1227552869.png")
> system("convert tmp/4h79u1227552869.ps tmp/4h79u1227552869.png")
> system("convert tmp/5geb21227552869.ps tmp/5geb21227552869.png")
> system("convert tmp/6odnr1227552869.ps tmp/6odnr1227552869.png")
> system("convert tmp/7yvu01227552869.ps tmp/7yvu01227552869.png")
> system("convert tmp/8fb5f1227552869.ps tmp/8fb5f1227552869.png")
> system("convert tmp/9mm931227552869.ps tmp/9mm931227552869.png")
> system("convert tmp/10z95n1227552869.ps tmp/10z95n1227552869.png")
>
>
> proc.time()
user system elapsed
14.311 4.877 14.915