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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
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
+ ,0
+ ,1508
+ ,0
+ ,1507
+ ,0
+ ,1385
+ ,0
+ ,1632
+ ,0
+ ,1511
+ ,0
+ ,1559
+ ,0
+ ,1630
+ ,0
+ ,1579
+ ,0
+ ,1653
+ ,0
+ ,2152
+ ,0
+ ,2148
+ ,0
+ ,1752
+ ,0
+ ,1765
+ ,0
+ ,1717
+ ,0
+ ,1558
+ ,0
+ ,1575
+ ,0
+ ,1520
+ ,0
+ ,1805
+ ,0
+ ,1800
+ ,0
+ ,1719
+ ,0
+ ,2008
+ ,0
+ ,2242
+ ,0
+ ,2478
+ ,0
+ ,2030
+ ,0
+ ,1655
+ ,0
+ ,1693
+ ,0
+ ,1623
+ ,0
+ ,1805
+ ,0
+ ,1746
+ ,0
+ ,1795
+ ,0
+ ,1926
+ ,0
+ ,1619
+ ,0
+ ,1992
+ ,0
+ ,2233
+ ,0
+ ,2192
+ ,0
+ ,2080
+ ,0
+ ,1768
+ ,0
+ ,1835
+ ,0
+ ,1569
+ ,0
+ ,1976
+ ,0
+ ,1853
+ ,0
+ ,1965
+ ,0
+ ,1689
+ ,0
+ ,1778
+ ,0
+ ,1976
+ ,0
+ ,2397
+ ,0
+ ,2654
+ ,0
+ ,2097
+ ,0
+ ,1963
+ ,0
+ ,1677
+ ,0
+ ,1941
+ ,0
+ ,2003
+ ,0
+ ,1813
+ ,0
+ ,2012
+ ,0
+ ,1912
+ ,0
+ ,2084
+ ,0
+ ,2080
+ ,0
+ ,2118
+ ,0
+ ,2150
+ ,0
+ ,1608
+ ,0
+ ,1503
+ ,0
+ ,1548
+ ,0
+ ,1382
+ ,0
+ ,1731
+ ,0
+ ,1798
+ ,0
+ ,1779
+ ,0
+ ,1887
+ ,0
+ ,2004
+ ,0
+ ,2077
+ ,0
+ ,2092
+ ,0
+ ,2051
+ ,0
+ ,1577
+ ,0
+ ,1356
+ ,0
+ ,1652
+ ,0
+ ,1382
+ ,0
+ ,1519
+ ,0
+ ,1421
+ ,0
+ ,1442
+ ,0
+ ,1543
+ ,0
+ ,1656
+ ,0
+ ,1561
+ ,0
+ ,1905
+ ,0
+ ,2199
+ ,0
+ ,1473
+ ,0
+ ,1655
+ ,0
+ ,1407
+ ,0
+ ,1395
+ ,0
+ ,1530
+ ,0
+ ,1309
+ ,0
+ ,1526
+ ,0
+ ,1327
+ ,0
+ ,1627
+ ,0
+ ,1748
+ ,0
+ ,1958
+ ,0
+ ,2274
+ ,0
+ ,1648
+ ,0
+ ,1401
+ ,0
+ ,1411
+ ,0
+ ,1403
+ ,0
+ ,1394
+ ,0
+ ,1520
+ ,0
+ ,1528
+ ,0
+ ,1643
+ ,0
+ ,1515
+ ,0
+ ,1685
+ ,0
+ ,2000
+ ,0
+ ,2215
+ ,0
+ ,1956
+ ,0
+ ,1462
+ ,0
+ ,1563
+ ,0
+ ,1459
+ ,0
+ ,1446
+ ,0
+ ,1622
+ ,0
+ ,1657
+ ,0
+ ,1638
+ ,0
+ ,1643
+ ,0
+ ,1683
+ ,0
+ ,2050
+ ,0
+ ,2262
+ ,0
+ ,1813
+ ,0
+ ,1445
+ ,0
+ ,1762
+ ,0
+ ,1461
+ ,0
+ ,1556
+ ,0
+ ,1431
+ ,0
+ ,1427
+ ,0
+ ,1554
+ ,0
+ ,1645
+ ,0
+ ,1653
+ ,0
+ ,2016
+ ,0
+ ,2207
+ ,0
+ ,1665
+ ,0
+ ,1361
+ ,0
+ ,1506
+ ,0
+ ,1360
+ ,0
+ ,1453
+ ,0
+ ,1522
+ ,0
+ ,1460
+ ,0
+ ,1552
+ ,0
+ ,1548
+ ,0
+ ,1827
+ ,0
+ ,1737
+ ,0
+ ,1941
+ ,0
+ ,1474
+ ,0
+ ,1458
+ ,0
+ ,1542
+ ,0
+ ,1404
+ ,0
+ ,1522
+ ,0
+ ,1385
+ ,0
+ ,1641
+ ,0
+ ,1510
+ ,0
+ ,1681
+ ,0
+ ,1938
+ ,0
+ ,1868
+ ,0
+ ,1726
+ ,0
+ ,1456
+ ,0
+ ,1445
+ ,0
+ ,1456
+ ,0
+ ,1365
+ ,0
+ ,1487
+ ,0
+ ,1558
+ ,0
+ ,1488
+ ,0
+ ,1684
+ ,0
+ ,1594
+ ,0
+ ,1850
+ ,0
+ ,1998
+ ,0
+ ,2079
+ ,0
+ ,1494
+ ,0
+ ,1057
+ ,1
+ ,1218
+ ,1
+ ,1168
+ ,1
+ ,1236
+ ,1
+ ,1076
+ ,1
+ ,1174
+ ,1
+ ,1139
+ ,1
+ ,1427
+ ,1
+ ,1487
+ ,1
+ ,1483
+ ,1
+ ,1513
+ ,1
+ ,1357
+ ,1
+ ,1165
+ ,1
+ ,1282
+ ,1
+ ,1110
+ ,1
+ ,1297
+ ,1
+ ,1185
+ ,1
+ ,1222
+ ,1
+ ,1284
+ ,1
+ ,1444
+ ,1
+ ,1575
+ ,1
+ ,1737
+ ,1
+ ,1763
+ ,1)
+ ,dim=c(2
+ ,192)
+ ,dimnames=list(c('death'
+ ,'seatbelt')
+ ,1:192))
> y <- array(NA,dim=c(2,192),dimnames=list(c('death','seatbelt'),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
death seatbelt M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1687 0 1 0 0 0 0 0 0 0 0 0 0
2 1508 0 0 1 0 0 0 0 0 0 0 0 0
3 1507 0 0 0 1 0 0 0 0 0 0 0 0
4 1385 0 0 0 0 1 0 0 0 0 0 0 0
5 1632 0 0 0 0 0 1 0 0 0 0 0 0
6 1511 0 0 0 0 0 0 1 0 0 0 0 0
7 1559 0 0 0 0 0 0 0 1 0 0 0 0
8 1630 0 0 0 0 0 0 0 0 1 0 0 0
9 1579 0 0 0 0 0 0 0 0 0 1 0 0
10 1653 0 0 0 0 0 0 0 0 0 0 1 0
11 2152 0 0 0 0 0 0 0 0 0 0 0 1
12 2148 0 0 0 0 0 0 0 0 0 0 0 0
13 1752 0 1 0 0 0 0 0 0 0 0 0 0
14 1765 0 0 1 0 0 0 0 0 0 0 0 0
15 1717 0 0 0 1 0 0 0 0 0 0 0 0
16 1558 0 0 0 0 1 0 0 0 0 0 0 0
17 1575 0 0 0 0 0 1 0 0 0 0 0 0
18 1520 0 0 0 0 0 0 1 0 0 0 0 0
19 1805 0 0 0 0 0 0 0 1 0 0 0 0
20 1800 0 0 0 0 0 0 0 0 1 0 0 0
21 1719 0 0 0 0 0 0 0 0 0 1 0 0
22 2008 0 0 0 0 0 0 0 0 0 0 1 0
23 2242 0 0 0 0 0 0 0 0 0 0 0 1
24 2478 0 0 0 0 0 0 0 0 0 0 0 0
25 2030 0 1 0 0 0 0 0 0 0 0 0 0
26 1655 0 0 1 0 0 0 0 0 0 0 0 0
27 1693 0 0 0 1 0 0 0 0 0 0 0 0
28 1623 0 0 0 0 1 0 0 0 0 0 0 0
29 1805 0 0 0 0 0 1 0 0 0 0 0 0
30 1746 0 0 0 0 0 0 1 0 0 0 0 0
31 1795 0 0 0 0 0 0 0 1 0 0 0 0
32 1926 0 0 0 0 0 0 0 0 1 0 0 0
33 1619 0 0 0 0 0 0 0 0 0 1 0 0
34 1992 0 0 0 0 0 0 0 0 0 0 1 0
35 2233 0 0 0 0 0 0 0 0 0 0 0 1
36 2192 0 0 0 0 0 0 0 0 0 0 0 0
37 2080 0 1 0 0 0 0 0 0 0 0 0 0
38 1768 0 0 1 0 0 0 0 0 0 0 0 0
39 1835 0 0 0 1 0 0 0 0 0 0 0 0
40 1569 0 0 0 0 1 0 0 0 0 0 0 0
41 1976 0 0 0 0 0 1 0 0 0 0 0 0
42 1853 0 0 0 0 0 0 1 0 0 0 0 0
43 1965 0 0 0 0 0 0 0 1 0 0 0 0
44 1689 0 0 0 0 0 0 0 0 1 0 0 0
45 1778 0 0 0 0 0 0 0 0 0 1 0 0
46 1976 0 0 0 0 0 0 0 0 0 0 1 0
47 2397 0 0 0 0 0 0 0 0 0 0 0 1
48 2654 0 0 0 0 0 0 0 0 0 0 0 0
49 2097 0 1 0 0 0 0 0 0 0 0 0 0
50 1963 0 0 1 0 0 0 0 0 0 0 0 0
51 1677 0 0 0 1 0 0 0 0 0 0 0 0
52 1941 0 0 0 0 1 0 0 0 0 0 0 0
53 2003 0 0 0 0 0 1 0 0 0 0 0 0
54 1813 0 0 0 0 0 0 1 0 0 0 0 0
55 2012 0 0 0 0 0 0 0 1 0 0 0 0
56 1912 0 0 0 0 0 0 0 0 1 0 0 0
57 2084 0 0 0 0 0 0 0 0 0 1 0 0
58 2080 0 0 0 0 0 0 0 0 0 0 1 0
59 2118 0 0 0 0 0 0 0 0 0 0 0 1
60 2150 0 0 0 0 0 0 0 0 0 0 0 0
61 1608 0 1 0 0 0 0 0 0 0 0 0 0
62 1503 0 0 1 0 0 0 0 0 0 0 0 0
63 1548 0 0 0 1 0 0 0 0 0 0 0 0
64 1382 0 0 0 0 1 0 0 0 0 0 0 0
65 1731 0 0 0 0 0 1 0 0 0 0 0 0
66 1798 0 0 0 0 0 0 1 0 0 0 0 0
67 1779 0 0 0 0 0 0 0 1 0 0 0 0
68 1887 0 0 0 0 0 0 0 0 1 0 0 0
69 2004 0 0 0 0 0 0 0 0 0 1 0 0
70 2077 0 0 0 0 0 0 0 0 0 0 1 0
71 2092 0 0 0 0 0 0 0 0 0 0 0 1
72 2051 0 0 0 0 0 0 0 0 0 0 0 0
73 1577 0 1 0 0 0 0 0 0 0 0 0 0
74 1356 0 0 1 0 0 0 0 0 0 0 0 0
75 1652 0 0 0 1 0 0 0 0 0 0 0 0
76 1382 0 0 0 0 1 0 0 0 0 0 0 0
77 1519 0 0 0 0 0 1 0 0 0 0 0 0
78 1421 0 0 0 0 0 0 1 0 0 0 0 0
79 1442 0 0 0 0 0 0 0 1 0 0 0 0
80 1543 0 0 0 0 0 0 0 0 1 0 0 0
81 1656 0 0 0 0 0 0 0 0 0 1 0 0
82 1561 0 0 0 0 0 0 0 0 0 0 1 0
83 1905 0 0 0 0 0 0 0 0 0 0 0 1
84 2199 0 0 0 0 0 0 0 0 0 0 0 0
85 1473 0 1 0 0 0 0 0 0 0 0 0 0
86 1655 0 0 1 0 0 0 0 0 0 0 0 0
87 1407 0 0 0 1 0 0 0 0 0 0 0 0
88 1395 0 0 0 0 1 0 0 0 0 0 0 0
89 1530 0 0 0 0 0 1 0 0 0 0 0 0
90 1309 0 0 0 0 0 0 1 0 0 0 0 0
91 1526 0 0 0 0 0 0 0 1 0 0 0 0
92 1327 0 0 0 0 0 0 0 0 1 0 0 0
93 1627 0 0 0 0 0 0 0 0 0 1 0 0
94 1748 0 0 0 0 0 0 0 0 0 0 1 0
95 1958 0 0 0 0 0 0 0 0 0 0 0 1
96 2274 0 0 0 0 0 0 0 0 0 0 0 0
97 1648 0 1 0 0 0 0 0 0 0 0 0 0
98 1401 0 0 1 0 0 0 0 0 0 0 0 0
99 1411 0 0 0 1 0 0 0 0 0 0 0 0
100 1403 0 0 0 0 1 0 0 0 0 0 0 0
101 1394 0 0 0 0 0 1 0 0 0 0 0 0
102 1520 0 0 0 0 0 0 1 0 0 0 0 0
103 1528 0 0 0 0 0 0 0 1 0 0 0 0
104 1643 0 0 0 0 0 0 0 0 1 0 0 0
105 1515 0 0 0 0 0 0 0 0 0 1 0 0
106 1685 0 0 0 0 0 0 0 0 0 0 1 0
107 2000 0 0 0 0 0 0 0 0 0 0 0 1
108 2215 0 0 0 0 0 0 0 0 0 0 0 0
109 1956 0 1 0 0 0 0 0 0 0 0 0 0
110 1462 0 0 1 0 0 0 0 0 0 0 0 0
111 1563 0 0 0 1 0 0 0 0 0 0 0 0
112 1459 0 0 0 0 1 0 0 0 0 0 0 0
113 1446 0 0 0 0 0 1 0 0 0 0 0 0
114 1622 0 0 0 0 0 0 1 0 0 0 0 0
115 1657 0 0 0 0 0 0 0 1 0 0 0 0
116 1638 0 0 0 0 0 0 0 0 1 0 0 0
117 1643 0 0 0 0 0 0 0 0 0 1 0 0
118 1683 0 0 0 0 0 0 0 0 0 0 1 0
119 2050 0 0 0 0 0 0 0 0 0 0 0 1
120 2262 0 0 0 0 0 0 0 0 0 0 0 0
121 1813 0 1 0 0 0 0 0 0 0 0 0 0
122 1445 0 0 1 0 0 0 0 0 0 0 0 0
123 1762 0 0 0 1 0 0 0 0 0 0 0 0
124 1461 0 0 0 0 1 0 0 0 0 0 0 0
125 1556 0 0 0 0 0 1 0 0 0 0 0 0
126 1431 0 0 0 0 0 0 1 0 0 0 0 0
127 1427 0 0 0 0 0 0 0 1 0 0 0 0
128 1554 0 0 0 0 0 0 0 0 1 0 0 0
129 1645 0 0 0 0 0 0 0 0 0 1 0 0
130 1653 0 0 0 0 0 0 0 0 0 0 1 0
131 2016 0 0 0 0 0 0 0 0 0 0 0 1
132 2207 0 0 0 0 0 0 0 0 0 0 0 0
133 1665 0 1 0 0 0 0 0 0 0 0 0 0
134 1361 0 0 1 0 0 0 0 0 0 0 0 0
135 1506 0 0 0 1 0 0 0 0 0 0 0 0
136 1360 0 0 0 0 1 0 0 0 0 0 0 0
137 1453 0 0 0 0 0 1 0 0 0 0 0 0
138 1522 0 0 0 0 0 0 1 0 0 0 0 0
139 1460 0 0 0 0 0 0 0 1 0 0 0 0
140 1552 0 0 0 0 0 0 0 0 1 0 0 0
141 1548 0 0 0 0 0 0 0 0 0 1 0 0
142 1827 0 0 0 0 0 0 0 0 0 0 1 0
143 1737 0 0 0 0 0 0 0 0 0 0 0 1
144 1941 0 0 0 0 0 0 0 0 0 0 0 0
145 1474 0 1 0 0 0 0 0 0 0 0 0 0
146 1458 0 0 1 0 0 0 0 0 0 0 0 0
147 1542 0 0 0 1 0 0 0 0 0 0 0 0
148 1404 0 0 0 0 1 0 0 0 0 0 0 0
149 1522 0 0 0 0 0 1 0 0 0 0 0 0
150 1385 0 0 0 0 0 0 1 0 0 0 0 0
151 1641 0 0 0 0 0 0 0 1 0 0 0 0
152 1510 0 0 0 0 0 0 0 0 1 0 0 0
153 1681 0 0 0 0 0 0 0 0 0 1 0 0
154 1938 0 0 0 0 0 0 0 0 0 0 1 0
155 1868 0 0 0 0 0 0 0 0 0 0 0 1
156 1726 0 0 0 0 0 0 0 0 0 0 0 0
157 1456 0 1 0 0 0 0 0 0 0 0 0 0
158 1445 0 0 1 0 0 0 0 0 0 0 0 0
159 1456 0 0 0 1 0 0 0 0 0 0 0 0
160 1365 0 0 0 0 1 0 0 0 0 0 0 0
161 1487 0 0 0 0 0 1 0 0 0 0 0 0
162 1558 0 0 0 0 0 0 1 0 0 0 0 0
163 1488 0 0 0 0 0 0 0 1 0 0 0 0
164 1684 0 0 0 0 0 0 0 0 1 0 0 0
165 1594 0 0 0 0 0 0 0 0 0 1 0 0
166 1850 0 0 0 0 0 0 0 0 0 0 1 0
167 1998 0 0 0 0 0 0 0 0 0 0 0 1
168 2079 0 0 0 0 0 0 0 0 0 0 0 0
169 1494 0 1 0 0 0 0 0 0 0 0 0 0
170 1057 1 0 1 0 0 0 0 0 0 0 0 0
171 1218 1 0 0 1 0 0 0 0 0 0 0 0
172 1168 1 0 0 0 1 0 0 0 0 0 0 0
173 1236 1 0 0 0 0 1 0 0 0 0 0 0
174 1076 1 0 0 0 0 0 1 0 0 0 0 0
175 1174 1 0 0 0 0 0 0 1 0 0 0 0
176 1139 1 0 0 0 0 0 0 0 1 0 0 0
177 1427 1 0 0 0 0 0 0 0 0 1 0 0
178 1487 1 0 0 0 0 0 0 0 0 0 1 0
179 1483 1 0 0 0 0 0 0 0 0 0 0 1
180 1513 1 0 0 0 0 0 0 0 0 0 0 0
181 1357 1 1 0 0 0 0 0 0 0 0 0 0
182 1165 1 0 1 0 0 0 0 0 0 0 0 0
183 1282 1 0 0 1 0 0 0 0 0 0 0 0
184 1110 1 0 0 0 1 0 0 0 0 0 0 0
185 1297 1 0 0 0 0 1 0 0 0 0 0 0
186 1185 1 0 0 0 0 0 1 0 0 0 0 0
187 1222 1 0 0 0 0 0 0 1 0 0 0 0
188 1284 1 0 0 0 0 0 0 0 1 0 0 0
189 1444 1 0 0 0 0 0 0 0 0 1 0 0
190 1575 1 0 0 0 0 0 0 0 0 0 1 0
191 1737 1 0 0 0 0 0 0 0 0 0 0 1
192 1763 1 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) seatbelt M1 M2 M3 M4
2165.2 -395.8 -442.6 -617.8 -567.2 -680.4
M5 M6 M7 M8 M9 M10
-543.1 -598.9 -523.3 -508.4 -455.6 -316.2
M11
-116.6
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-439.23 -113.88 -27.76 98.43 488.77
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2165.23 43.63 49.625 < 2e-16 ***
seatbelt -395.81 38.61 -10.253 < 2e-16 ***
M1 -442.55 61.37 -7.211 1.51e-11 ***
M2 -617.81 61.33 -10.074 < 2e-16 ***
M3 -567.25 61.33 -9.250 < 2e-16 ***
M4 -680.44 61.33 -11.095 < 2e-16 ***
M5 -543.13 61.33 -8.856 8.00e-16 ***
M6 -598.87 61.33 -9.765 < 2e-16 ***
M7 -523.25 61.33 -8.532 5.95e-15 ***
M8 -508.38 61.33 -8.290 2.62e-14 ***
M9 -455.56 61.33 -7.429 4.33e-12 ***
M10 -316.19 61.33 -5.156 6.64e-07 ***
M11 -116.63 61.33 -1.902 0.0588 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 173.5 on 179 degrees of freedom
Multiple R-squared: 0.6638, Adjusted R-squared: 0.6413
F-statistic: 29.45 on 12 and 179 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,] 0.47741239 9.548248e-01 5.225876e-01
[2,] 0.31778306 6.355661e-01 6.822169e-01
[3,] 0.19188503 3.837701e-01 8.081150e-01
[4,] 0.21949644 4.389929e-01 7.805036e-01
[5,] 0.18287133 3.657427e-01 8.171287e-01
[6,] 0.13885064 2.777013e-01 8.611494e-01
[7,] 0.24706682 4.941336e-01 7.529332e-01
[8,] 0.18657308 3.731462e-01 8.134269e-01
[9,] 0.26817432 5.363486e-01 7.318257e-01
[10,] 0.37531735 7.506347e-01 6.246827e-01
[11,] 0.29847123 5.969425e-01 7.015288e-01
[12,] 0.23992527 4.798505e-01 7.600747e-01
[13,] 0.21304504 4.260901e-01 7.869550e-01
[14,] 0.21376887 4.275377e-01 7.862311e-01
[15,] 0.22977523 4.595505e-01 7.702248e-01
[16,] 0.19477154 3.895431e-01 8.052285e-01
[17,] 0.20776511 4.155302e-01 7.922349e-01
[18,] 0.16215652 3.243130e-01 8.378435e-01
[19,] 0.14811643 2.962329e-01 8.518836e-01
[20,] 0.11783130 2.356626e-01 8.821687e-01
[21,] 0.09738212 1.947642e-01 9.026179e-01
[22,] 0.14140096 2.828019e-01 8.585990e-01
[23,] 0.12978229 2.595646e-01 8.702177e-01
[24,] 0.13977283 2.795457e-01 8.602272e-01
[25,] 0.11058541 2.211708e-01 8.894146e-01
[26,] 0.18412472 3.682494e-01 8.158753e-01
[27,] 0.23489933 4.697987e-01 7.651007e-01
[28,] 0.29165015 5.833003e-01 7.083499e-01
[29,] 0.25525438 5.105088e-01 7.447456e-01
[30,] 0.23193398 4.638680e-01 7.680660e-01
[31,] 0.20390963 4.078193e-01 7.960904e-01
[32,] 0.24314771 4.862954e-01 7.568523e-01
[33,] 0.47388776 9.477755e-01 5.261122e-01
[34,] 0.56919853 8.616029e-01 4.308015e-01
[35,] 0.72641888 5.471622e-01 2.735811e-01
[36,] 0.68839676 6.232065e-01 3.116032e-01
[37,] 0.88824239 2.235152e-01 1.117576e-01
[38,] 0.94487746 1.102451e-01 5.512254e-02
[39,] 0.95465457 9.069086e-02 4.534543e-02
[40,] 0.98111521 3.776959e-02 1.888479e-02
[41,] 0.98651396 2.697207e-02 1.348604e-02
[42,] 0.99784800 4.304002e-03 2.152001e-03
[43,] 0.99856289 2.874213e-03 1.437106e-03
[44,] 0.99850029 2.999419e-03 1.499709e-03
[45,] 0.99854322 2.913559e-03 1.456780e-03
[46,] 0.99907173 1.856538e-03 9.282688e-04
[47,] 0.99911510 1.769792e-03 8.848961e-04
[48,] 0.99887191 2.256189e-03 1.128095e-03
[49,] 0.99891035 2.179308e-03 1.089654e-03
[50,] 0.99893062 2.138754e-03 1.069377e-03
[51,] 0.99939751 1.204988e-03 6.024940e-04
[52,] 0.99950476 9.904806e-04 4.952403e-04
[53,] 0.99976071 4.785728e-04 2.392864e-04
[54,] 0.99994846 1.030889e-04 5.154444e-05
[55,] 0.99997998 4.004880e-05 2.002440e-05
[56,] 0.99998031 3.938805e-05 1.969402e-05
[57,] 0.99998395 3.209181e-05 1.604590e-05
[58,] 0.99998878 2.244154e-05 1.122077e-05
[59,] 0.99999410 1.180842e-05 5.904212e-06
[60,] 0.99999214 1.572156e-05 7.860778e-06
[61,] 0.99999076 1.848517e-05 9.242585e-06
[62,] 0.99999171 1.658808e-05 8.294040e-06
[63,] 0.99999320 1.360741e-05 6.803704e-06
[64,] 0.99999671 6.582016e-06 3.291008e-06
[65,] 0.99999678 6.434503e-06 3.217251e-06
[66,] 0.99999538 9.241981e-06 4.620990e-06
[67,] 0.99999891 2.170476e-06 1.085238e-06
[68,] 0.99999906 1.889264e-06 9.446319e-07
[69,] 0.99999883 2.340760e-06 1.170380e-06
[70,] 0.99999951 9.725548e-07 4.862774e-07
[71,] 0.99999965 6.921906e-07 3.460953e-07
[72,] 0.99999972 5.554639e-07 2.777320e-07
[73,] 0.99999959 8.276925e-07 4.138463e-07
[74,] 0.99999948 1.031699e-06 5.158494e-07
[75,] 0.99999978 4.341386e-07 2.170693e-07
[76,] 0.99999973 5.317610e-07 2.658805e-07
[77,] 0.99999996 8.132188e-08 4.066094e-08
[78,] 0.99999993 1.302404e-07 6.512019e-08
[79,] 0.99999990 1.979434e-07 9.897170e-08
[80,] 0.99999986 2.775578e-07 1.387789e-07
[81,] 0.99999991 1.718401e-07 8.592003e-08
[82,] 0.99999986 2.797564e-07 1.398782e-07
[83,] 0.99999983 3.499577e-07 1.749788e-07
[84,] 0.99999985 3.060981e-07 1.530491e-07
[85,] 0.99999975 5.056908e-07 2.528454e-07
[86,] 0.99999981 3.748334e-07 1.874167e-07
[87,] 0.99999969 6.296961e-07 3.148481e-07
[88,] 0.99999955 9.022465e-07 4.511233e-07
[89,] 0.99999929 1.415548e-06 7.077740e-07
[90,] 0.99999936 1.270947e-06 6.354733e-07
[91,] 0.99999930 1.405501e-06 7.027503e-07
[92,] 0.99999896 2.082544e-06 1.041272e-06
[93,] 0.99999915 1.695707e-06 8.478537e-07
[94,] 0.99999992 1.602973e-07 8.014864e-08
[95,] 0.99999987 2.578258e-07 1.289129e-07
[96,] 0.99999976 4.787881e-07 2.393941e-07
[97,] 0.99999958 8.320678e-07 4.160339e-07
[98,] 0.99999949 1.010353e-06 5.051766e-07
[99,] 0.99999948 1.039465e-06 5.197324e-07
[100,] 0.99999945 1.095198e-06 5.475992e-07
[101,] 0.99999916 1.674251e-06 8.371256e-07
[102,] 0.99999853 2.938252e-06 1.469126e-06
[103,] 0.99999849 3.014493e-06 1.507246e-06
[104,] 0.99999832 3.354157e-06 1.677079e-06
[105,] 0.99999962 7.642658e-07 3.821329e-07
[106,] 0.99999989 2.132533e-07 1.066267e-07
[107,] 0.99999982 3.627772e-07 1.813886e-07
[108,] 0.99999995 1.012869e-07 5.064345e-08
[109,] 0.99999991 1.758676e-07 8.793382e-08
[110,] 0.99999984 3.154519e-07 1.577259e-07
[111,] 0.99999973 5.337449e-07 2.668724e-07
[112,] 0.99999969 6.215975e-07 3.107987e-07
[113,] 0.99999944 1.122448e-06 5.612242e-07
[114,] 0.99999894 2.128644e-06 1.064322e-06
[115,] 0.99999943 1.144548e-06 5.722738e-07
[116,] 0.99999939 1.218006e-06 6.090030e-07
[117,] 0.99999994 1.285110e-07 6.425551e-08
[118,] 0.99999995 1.086202e-07 5.431012e-08
[119,] 0.99999992 1.688809e-07 8.444044e-08
[120,] 0.99999983 3.453907e-07 1.726954e-07
[121,] 0.99999968 6.377744e-07 3.188872e-07
[122,] 0.99999952 9.673572e-07 4.836786e-07
[123,] 0.99999922 1.563660e-06 7.818302e-07
[124,] 0.99999879 2.429792e-06 1.214896e-06
[125,] 0.99999765 4.708264e-06 2.354132e-06
[126,] 0.99999751 4.975224e-06 2.487612e-06
[127,] 0.99999506 9.877835e-06 4.938918e-06
[128,] 0.99999774 4.524341e-06 2.262171e-06
[129,] 0.99999647 7.061571e-06 3.530785e-06
[130,] 0.99999486 1.027521e-05 5.137605e-06
[131,] 0.99999027 1.945645e-05 9.728226e-06
[132,] 0.99998095 3.810912e-05 1.905456e-05
[133,] 0.99996251 7.497375e-05 3.748687e-05
[134,] 0.99992843 1.431343e-04 7.156713e-05
[135,] 0.99989400 2.119945e-04 1.059973e-04
[136,] 0.99989402 2.119584e-04 1.059792e-04
[137,] 0.99981724 3.655169e-04 1.827585e-04
[138,] 0.99964997 7.000595e-04 3.500297e-04
[139,] 0.99953118 9.376331e-04 4.688166e-04
[140,] 0.99925210 1.495796e-03 7.478982e-04
[141,] 0.99984849 3.030268e-04 1.515134e-04
[142,] 0.99982177 3.564521e-04 1.782261e-04
[143,] 0.99964666 7.066722e-04 3.533361e-04
[144,] 0.99947225 1.055507e-03 5.277534e-04
[145,] 0.99916194 1.676116e-03 8.380581e-04
[146,] 0.99879443 2.411139e-03 1.205569e-03
[147,] 0.99813242 3.735163e-03 1.867581e-03
[148,] 0.99654132 6.917365e-03 3.458682e-03
[149,] 0.99643064 7.138715e-03 3.569358e-03
[150,] 0.99630291 7.394174e-03 3.697087e-03
[151,] 0.99262482 1.475036e-02 7.375179e-03
[152,] 0.98694900 2.610199e-02 1.305100e-02
[153,] 0.99091472 1.817056e-02 9.085279e-03
[154,] 0.98227105 3.545791e-02 1.772895e-02
[155,] 0.97187716 5.624568e-02 2.812284e-02
[156,] 0.94975516 1.004897e-01 5.024484e-02
[157,] 0.91220104 1.755979e-01 8.779896e-02
[158,] 0.85246186 2.950763e-01 1.475381e-01
[159,] 0.78235732 4.352854e-01 2.176427e-01
[160,] 0.65694282 6.861144e-01 3.430572e-01
[161,] 0.55007285 8.998543e-01 4.499271e-01
> postscript(file="/var/www/html/rcomp/tmp/15lrt1227713666.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lqwg1227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xjh41227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4l00t1227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52p5w1227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 192
Frequency = 1
1 2 3 4 5 6
-35.6756966 -39.4138932 -90.9763932 -99.7888932 9.8986068 -55.3513932
7 8 9 10 11 12
-82.9763932 -26.8513932 -130.6638932 -196.0388932 103.3986068 -17.2263932
13 14 15 16 17 18
29.3243034 217.5861068 119.0236068 73.2111068 -47.1013932 -46.3513932
19 20 21 22 23 24
163.0236068 143.1486068 9.3361068 158.9611068 193.3986068 312.7736068
25 26 27 28 29 30
307.3243034 107.5861068 95.0236068 138.2111068 182.8986068 179.6486068
31 32 33 34 35 36
153.0236068 269.1486068 -90.6638932 142.9611068 184.3986068 26.7736068
37 38 39 40 41 42
357.3243034 220.5861068 237.0236068 84.2111068 353.8986068 286.6486068
43 44 45 46 47 48
323.0236068 32.1486068 68.3361068 126.9611068 348.3986068 488.7736068
49 50 51 52 53 54
374.3243034 415.5861068 79.0236068 456.2111068 380.8986068 246.6486068
55 56 57 58 59 60
370.0236068 255.1486068 374.3361068 230.9611068 69.3986068 -15.2263932
61 62 63 64 65 66
-114.6756966 -44.4138932 -49.9763932 -102.7888932 108.8986068 231.6486068
67 68 69 70 71 72
137.0236068 230.1486068 294.3361068 227.9611068 43.3986068 -114.2263932
73 74 75 76 77 78
-145.6756966 -191.4138932 54.0236068 -102.7888932 -103.1013932 -145.3513932
79 80 81 82 83 84
-199.9763932 -113.8513932 -53.6638932 -288.0388932 -143.6013932 33.7736068
85 86 87 88 89 90
-249.6756966 107.5861068 -190.9763932 -89.7888932 -92.1013932 -257.3513932
91 92 93 94 95 96
-115.9763932 -329.8513932 -82.6638932 -101.0388932 -90.6013932 108.7736068
97 98 99 100 101 102
-74.6756966 -146.4138932 -186.9763932 -81.7888932 -228.1013932 -46.3513932
103 104 105 106 107 108
-113.9763932 -13.8513932 -194.6638932 -164.0388932 -48.6013932 49.7736068
109 110 111 112 113 114
233.3243034 -85.4138932 -34.9763932 -25.7888932 -176.1013932 55.6486068
115 116 117 118 119 120
15.0236068 -18.8513932 -66.6638932 -166.0388932 1.3986068 96.7736068
121 122 123 124 125 126
90.3243034 -102.4138932 164.0236068 -23.7888932 -66.1013932 -135.3513932
127 128 129 130 131 132
-214.9763932 -102.8513932 -64.6638932 -196.0388932 -32.6013932 41.7736068
133 134 135 136 137 138
-57.6756966 -186.4138932 -91.9763932 -124.7888932 -169.1013932 -44.3513932
139 140 141 142 143 144
-181.9763932 -104.8513932 -161.6638932 -22.0388932 -311.6013932 -224.2263932
145 146 147 148 149 150
-248.6756966 -89.4138932 -55.9763932 -80.7888932 -100.1013932 -181.3513932
151 152 153 154 155 156
-0.9763932 -146.8513932 -28.6638932 88.9611068 -180.6013932 -439.2263932
157 158 159 160 161 162
-266.6756966 -102.4138932 -141.9763932 -119.7888932 -135.1013932 -8.3513932
163 164 165 166 167 168
-153.9763932 27.1486068 -115.6638932 0.9611068 -50.6013932 -86.2263932
169 170 171 172 173 174
-228.6756966 -94.6027477 15.8347523 79.0222523 9.7097523 -94.5402477
175 176 177 178 179 180
-72.1652477 -122.0402477 113.1472523 33.7722523 -169.7902477 -256.4152477
181 182 183 184 185 186
30.1354489 13.3972523 79.8347523 21.0222523 70.7097523 14.4597523
187 188 189 190 191 192
-24.1652477 22.9597523 130.1472523 121.7722523 84.2097523 -6.4152477
> postscript(file="/var/www/html/rcomp/tmp/62d0v1227713666.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 -35.6756966 NA
1 -39.4138932 -35.6756966
2 -90.9763932 -39.4138932
3 -99.7888932 -90.9763932
4 9.8986068 -99.7888932
5 -55.3513932 9.8986068
6 -82.9763932 -55.3513932
7 -26.8513932 -82.9763932
8 -130.6638932 -26.8513932
9 -196.0388932 -130.6638932
10 103.3986068 -196.0388932
11 -17.2263932 103.3986068
12 29.3243034 -17.2263932
13 217.5861068 29.3243034
14 119.0236068 217.5861068
15 73.2111068 119.0236068
16 -47.1013932 73.2111068
17 -46.3513932 -47.1013932
18 163.0236068 -46.3513932
19 143.1486068 163.0236068
20 9.3361068 143.1486068
21 158.9611068 9.3361068
22 193.3986068 158.9611068
23 312.7736068 193.3986068
24 307.3243034 312.7736068
25 107.5861068 307.3243034
26 95.0236068 107.5861068
27 138.2111068 95.0236068
28 182.8986068 138.2111068
29 179.6486068 182.8986068
30 153.0236068 179.6486068
31 269.1486068 153.0236068
32 -90.6638932 269.1486068
33 142.9611068 -90.6638932
34 184.3986068 142.9611068
35 26.7736068 184.3986068
36 357.3243034 26.7736068
37 220.5861068 357.3243034
38 237.0236068 220.5861068
39 84.2111068 237.0236068
40 353.8986068 84.2111068
41 286.6486068 353.8986068
42 323.0236068 286.6486068
43 32.1486068 323.0236068
44 68.3361068 32.1486068
45 126.9611068 68.3361068
46 348.3986068 126.9611068
47 488.7736068 348.3986068
48 374.3243034 488.7736068
49 415.5861068 374.3243034
50 79.0236068 415.5861068
51 456.2111068 79.0236068
52 380.8986068 456.2111068
53 246.6486068 380.8986068
54 370.0236068 246.6486068
55 255.1486068 370.0236068
56 374.3361068 255.1486068
57 230.9611068 374.3361068
58 69.3986068 230.9611068
59 -15.2263932 69.3986068
60 -114.6756966 -15.2263932
61 -44.4138932 -114.6756966
62 -49.9763932 -44.4138932
63 -102.7888932 -49.9763932
64 108.8986068 -102.7888932
65 231.6486068 108.8986068
66 137.0236068 231.6486068
67 230.1486068 137.0236068
68 294.3361068 230.1486068
69 227.9611068 294.3361068
70 43.3986068 227.9611068
71 -114.2263932 43.3986068
72 -145.6756966 -114.2263932
73 -191.4138932 -145.6756966
74 54.0236068 -191.4138932
75 -102.7888932 54.0236068
76 -103.1013932 -102.7888932
77 -145.3513932 -103.1013932
78 -199.9763932 -145.3513932
79 -113.8513932 -199.9763932
80 -53.6638932 -113.8513932
81 -288.0388932 -53.6638932
82 -143.6013932 -288.0388932
83 33.7736068 -143.6013932
84 -249.6756966 33.7736068
85 107.5861068 -249.6756966
86 -190.9763932 107.5861068
87 -89.7888932 -190.9763932
88 -92.1013932 -89.7888932
89 -257.3513932 -92.1013932
90 -115.9763932 -257.3513932
91 -329.8513932 -115.9763932
92 -82.6638932 -329.8513932
93 -101.0388932 -82.6638932
94 -90.6013932 -101.0388932
95 108.7736068 -90.6013932
96 -74.6756966 108.7736068
97 -146.4138932 -74.6756966
98 -186.9763932 -146.4138932
99 -81.7888932 -186.9763932
100 -228.1013932 -81.7888932
101 -46.3513932 -228.1013932
102 -113.9763932 -46.3513932
103 -13.8513932 -113.9763932
104 -194.6638932 -13.8513932
105 -164.0388932 -194.6638932
106 -48.6013932 -164.0388932
107 49.7736068 -48.6013932
108 233.3243034 49.7736068
109 -85.4138932 233.3243034
110 -34.9763932 -85.4138932
111 -25.7888932 -34.9763932
112 -176.1013932 -25.7888932
113 55.6486068 -176.1013932
114 15.0236068 55.6486068
115 -18.8513932 15.0236068
116 -66.6638932 -18.8513932
117 -166.0388932 -66.6638932
118 1.3986068 -166.0388932
119 96.7736068 1.3986068
120 90.3243034 96.7736068
121 -102.4138932 90.3243034
122 164.0236068 -102.4138932
123 -23.7888932 164.0236068
124 -66.1013932 -23.7888932
125 -135.3513932 -66.1013932
126 -214.9763932 -135.3513932
127 -102.8513932 -214.9763932
128 -64.6638932 -102.8513932
129 -196.0388932 -64.6638932
130 -32.6013932 -196.0388932
131 41.7736068 -32.6013932
132 -57.6756966 41.7736068
133 -186.4138932 -57.6756966
134 -91.9763932 -186.4138932
135 -124.7888932 -91.9763932
136 -169.1013932 -124.7888932
137 -44.3513932 -169.1013932
138 -181.9763932 -44.3513932
139 -104.8513932 -181.9763932
140 -161.6638932 -104.8513932
141 -22.0388932 -161.6638932
142 -311.6013932 -22.0388932
143 -224.2263932 -311.6013932
144 -248.6756966 -224.2263932
145 -89.4138932 -248.6756966
146 -55.9763932 -89.4138932
147 -80.7888932 -55.9763932
148 -100.1013932 -80.7888932
149 -181.3513932 -100.1013932
150 -0.9763932 -181.3513932
151 -146.8513932 -0.9763932
152 -28.6638932 -146.8513932
153 88.9611068 -28.6638932
154 -180.6013932 88.9611068
155 -439.2263932 -180.6013932
156 -266.6756966 -439.2263932
157 -102.4138932 -266.6756966
158 -141.9763932 -102.4138932
159 -119.7888932 -141.9763932
160 -135.1013932 -119.7888932
161 -8.3513932 -135.1013932
162 -153.9763932 -8.3513932
163 27.1486068 -153.9763932
164 -115.6638932 27.1486068
165 0.9611068 -115.6638932
166 -50.6013932 0.9611068
167 -86.2263932 -50.6013932
168 -228.6756966 -86.2263932
169 -94.6027477 -228.6756966
170 15.8347523 -94.6027477
171 79.0222523 15.8347523
172 9.7097523 79.0222523
173 -94.5402477 9.7097523
174 -72.1652477 -94.5402477
175 -122.0402477 -72.1652477
176 113.1472523 -122.0402477
177 33.7722523 113.1472523
178 -169.7902477 33.7722523
179 -256.4152477 -169.7902477
180 30.1354489 -256.4152477
181 13.3972523 30.1354489
182 79.8347523 13.3972523
183 21.0222523 79.8347523
184 70.7097523 21.0222523
185 14.4597523 70.7097523
186 -24.1652477 14.4597523
187 22.9597523 -24.1652477
188 130.1472523 22.9597523
189 121.7722523 130.1472523
190 84.2097523 121.7722523
191 -6.4152477 84.2097523
192 NA -6.4152477
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -39.4138932 -35.6756966
[2,] -90.9763932 -39.4138932
[3,] -99.7888932 -90.9763932
[4,] 9.8986068 -99.7888932
[5,] -55.3513932 9.8986068
[6,] -82.9763932 -55.3513932
[7,] -26.8513932 -82.9763932
[8,] -130.6638932 -26.8513932
[9,] -196.0388932 -130.6638932
[10,] 103.3986068 -196.0388932
[11,] -17.2263932 103.3986068
[12,] 29.3243034 -17.2263932
[13,] 217.5861068 29.3243034
[14,] 119.0236068 217.5861068
[15,] 73.2111068 119.0236068
[16,] -47.1013932 73.2111068
[17,] -46.3513932 -47.1013932
[18,] 163.0236068 -46.3513932
[19,] 143.1486068 163.0236068
[20,] 9.3361068 143.1486068
[21,] 158.9611068 9.3361068
[22,] 193.3986068 158.9611068
[23,] 312.7736068 193.3986068
[24,] 307.3243034 312.7736068
[25,] 107.5861068 307.3243034
[26,] 95.0236068 107.5861068
[27,] 138.2111068 95.0236068
[28,] 182.8986068 138.2111068
[29,] 179.6486068 182.8986068
[30,] 153.0236068 179.6486068
[31,] 269.1486068 153.0236068
[32,] -90.6638932 269.1486068
[33,] 142.9611068 -90.6638932
[34,] 184.3986068 142.9611068
[35,] 26.7736068 184.3986068
[36,] 357.3243034 26.7736068
[37,] 220.5861068 357.3243034
[38,] 237.0236068 220.5861068
[39,] 84.2111068 237.0236068
[40,] 353.8986068 84.2111068
[41,] 286.6486068 353.8986068
[42,] 323.0236068 286.6486068
[43,] 32.1486068 323.0236068
[44,] 68.3361068 32.1486068
[45,] 126.9611068 68.3361068
[46,] 348.3986068 126.9611068
[47,] 488.7736068 348.3986068
[48,] 374.3243034 488.7736068
[49,] 415.5861068 374.3243034
[50,] 79.0236068 415.5861068
[51,] 456.2111068 79.0236068
[52,] 380.8986068 456.2111068
[53,] 246.6486068 380.8986068
[54,] 370.0236068 246.6486068
[55,] 255.1486068 370.0236068
[56,] 374.3361068 255.1486068
[57,] 230.9611068 374.3361068
[58,] 69.3986068 230.9611068
[59,] -15.2263932 69.3986068
[60,] -114.6756966 -15.2263932
[61,] -44.4138932 -114.6756966
[62,] -49.9763932 -44.4138932
[63,] -102.7888932 -49.9763932
[64,] 108.8986068 -102.7888932
[65,] 231.6486068 108.8986068
[66,] 137.0236068 231.6486068
[67,] 230.1486068 137.0236068
[68,] 294.3361068 230.1486068
[69,] 227.9611068 294.3361068
[70,] 43.3986068 227.9611068
[71,] -114.2263932 43.3986068
[72,] -145.6756966 -114.2263932
[73,] -191.4138932 -145.6756966
[74,] 54.0236068 -191.4138932
[75,] -102.7888932 54.0236068
[76,] -103.1013932 -102.7888932
[77,] -145.3513932 -103.1013932
[78,] -199.9763932 -145.3513932
[79,] -113.8513932 -199.9763932
[80,] -53.6638932 -113.8513932
[81,] -288.0388932 -53.6638932
[82,] -143.6013932 -288.0388932
[83,] 33.7736068 -143.6013932
[84,] -249.6756966 33.7736068
[85,] 107.5861068 -249.6756966
[86,] -190.9763932 107.5861068
[87,] -89.7888932 -190.9763932
[88,] -92.1013932 -89.7888932
[89,] -257.3513932 -92.1013932
[90,] -115.9763932 -257.3513932
[91,] -329.8513932 -115.9763932
[92,] -82.6638932 -329.8513932
[93,] -101.0388932 -82.6638932
[94,] -90.6013932 -101.0388932
[95,] 108.7736068 -90.6013932
[96,] -74.6756966 108.7736068
[97,] -146.4138932 -74.6756966
[98,] -186.9763932 -146.4138932
[99,] -81.7888932 -186.9763932
[100,] -228.1013932 -81.7888932
[101,] -46.3513932 -228.1013932
[102,] -113.9763932 -46.3513932
[103,] -13.8513932 -113.9763932
[104,] -194.6638932 -13.8513932
[105,] -164.0388932 -194.6638932
[106,] -48.6013932 -164.0388932
[107,] 49.7736068 -48.6013932
[108,] 233.3243034 49.7736068
[109,] -85.4138932 233.3243034
[110,] -34.9763932 -85.4138932
[111,] -25.7888932 -34.9763932
[112,] -176.1013932 -25.7888932
[113,] 55.6486068 -176.1013932
[114,] 15.0236068 55.6486068
[115,] -18.8513932 15.0236068
[116,] -66.6638932 -18.8513932
[117,] -166.0388932 -66.6638932
[118,] 1.3986068 -166.0388932
[119,] 96.7736068 1.3986068
[120,] 90.3243034 96.7736068
[121,] -102.4138932 90.3243034
[122,] 164.0236068 -102.4138932
[123,] -23.7888932 164.0236068
[124,] -66.1013932 -23.7888932
[125,] -135.3513932 -66.1013932
[126,] -214.9763932 -135.3513932
[127,] -102.8513932 -214.9763932
[128,] -64.6638932 -102.8513932
[129,] -196.0388932 -64.6638932
[130,] -32.6013932 -196.0388932
[131,] 41.7736068 -32.6013932
[132,] -57.6756966 41.7736068
[133,] -186.4138932 -57.6756966
[134,] -91.9763932 -186.4138932
[135,] -124.7888932 -91.9763932
[136,] -169.1013932 -124.7888932
[137,] -44.3513932 -169.1013932
[138,] -181.9763932 -44.3513932
[139,] -104.8513932 -181.9763932
[140,] -161.6638932 -104.8513932
[141,] -22.0388932 -161.6638932
[142,] -311.6013932 -22.0388932
[143,] -224.2263932 -311.6013932
[144,] -248.6756966 -224.2263932
[145,] -89.4138932 -248.6756966
[146,] -55.9763932 -89.4138932
[147,] -80.7888932 -55.9763932
[148,] -100.1013932 -80.7888932
[149,] -181.3513932 -100.1013932
[150,] -0.9763932 -181.3513932
[151,] -146.8513932 -0.9763932
[152,] -28.6638932 -146.8513932
[153,] 88.9611068 -28.6638932
[154,] -180.6013932 88.9611068
[155,] -439.2263932 -180.6013932
[156,] -266.6756966 -439.2263932
[157,] -102.4138932 -266.6756966
[158,] -141.9763932 -102.4138932
[159,] -119.7888932 -141.9763932
[160,] -135.1013932 -119.7888932
[161,] -8.3513932 -135.1013932
[162,] -153.9763932 -8.3513932
[163,] 27.1486068 -153.9763932
[164,] -115.6638932 27.1486068
[165,] 0.9611068 -115.6638932
[166,] -50.6013932 0.9611068
[167,] -86.2263932 -50.6013932
[168,] -228.6756966 -86.2263932
[169,] -94.6027477 -228.6756966
[170,] 15.8347523 -94.6027477
[171,] 79.0222523 15.8347523
[172,] 9.7097523 79.0222523
[173,] -94.5402477 9.7097523
[174,] -72.1652477 -94.5402477
[175,] -122.0402477 -72.1652477
[176,] 113.1472523 -122.0402477
[177,] 33.7722523 113.1472523
[178,] -169.7902477 33.7722523
[179,] -256.4152477 -169.7902477
[180,] 30.1354489 -256.4152477
[181,] 13.3972523 30.1354489
[182,] 79.8347523 13.3972523
[183,] 21.0222523 79.8347523
[184,] 70.7097523 21.0222523
[185,] 14.4597523 70.7097523
[186,] -24.1652477 14.4597523
[187,] 22.9597523 -24.1652477
[188,] 130.1472523 22.9597523
[189,] 121.7722523 130.1472523
[190,] 84.2097523 121.7722523
[191,] -6.4152477 84.2097523
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -39.4138932 -35.6756966
2 -90.9763932 -39.4138932
3 -99.7888932 -90.9763932
4 9.8986068 -99.7888932
5 -55.3513932 9.8986068
6 -82.9763932 -55.3513932
7 -26.8513932 -82.9763932
8 -130.6638932 -26.8513932
9 -196.0388932 -130.6638932
10 103.3986068 -196.0388932
11 -17.2263932 103.3986068
12 29.3243034 -17.2263932
13 217.5861068 29.3243034
14 119.0236068 217.5861068
15 73.2111068 119.0236068
16 -47.1013932 73.2111068
17 -46.3513932 -47.1013932
18 163.0236068 -46.3513932
19 143.1486068 163.0236068
20 9.3361068 143.1486068
21 158.9611068 9.3361068
22 193.3986068 158.9611068
23 312.7736068 193.3986068
24 307.3243034 312.7736068
25 107.5861068 307.3243034
26 95.0236068 107.5861068
27 138.2111068 95.0236068
28 182.8986068 138.2111068
29 179.6486068 182.8986068
30 153.0236068 179.6486068
31 269.1486068 153.0236068
32 -90.6638932 269.1486068
33 142.9611068 -90.6638932
34 184.3986068 142.9611068
35 26.7736068 184.3986068
36 357.3243034 26.7736068
37 220.5861068 357.3243034
38 237.0236068 220.5861068
39 84.2111068 237.0236068
40 353.8986068 84.2111068
41 286.6486068 353.8986068
42 323.0236068 286.6486068
43 32.1486068 323.0236068
44 68.3361068 32.1486068
45 126.9611068 68.3361068
46 348.3986068 126.9611068
47 488.7736068 348.3986068
48 374.3243034 488.7736068
49 415.5861068 374.3243034
50 79.0236068 415.5861068
51 456.2111068 79.0236068
52 380.8986068 456.2111068
53 246.6486068 380.8986068
54 370.0236068 246.6486068
55 255.1486068 370.0236068
56 374.3361068 255.1486068
57 230.9611068 374.3361068
58 69.3986068 230.9611068
59 -15.2263932 69.3986068
60 -114.6756966 -15.2263932
61 -44.4138932 -114.6756966
62 -49.9763932 -44.4138932
63 -102.7888932 -49.9763932
64 108.8986068 -102.7888932
65 231.6486068 108.8986068
66 137.0236068 231.6486068
67 230.1486068 137.0236068
68 294.3361068 230.1486068
69 227.9611068 294.3361068
70 43.3986068 227.9611068
71 -114.2263932 43.3986068
72 -145.6756966 -114.2263932
73 -191.4138932 -145.6756966
74 54.0236068 -191.4138932
75 -102.7888932 54.0236068
76 -103.1013932 -102.7888932
77 -145.3513932 -103.1013932
78 -199.9763932 -145.3513932
79 -113.8513932 -199.9763932
80 -53.6638932 -113.8513932
81 -288.0388932 -53.6638932
82 -143.6013932 -288.0388932
83 33.7736068 -143.6013932
84 -249.6756966 33.7736068
85 107.5861068 -249.6756966
86 -190.9763932 107.5861068
87 -89.7888932 -190.9763932
88 -92.1013932 -89.7888932
89 -257.3513932 -92.1013932
90 -115.9763932 -257.3513932
91 -329.8513932 -115.9763932
92 -82.6638932 -329.8513932
93 -101.0388932 -82.6638932
94 -90.6013932 -101.0388932
95 108.7736068 -90.6013932
96 -74.6756966 108.7736068
97 -146.4138932 -74.6756966
98 -186.9763932 -146.4138932
99 -81.7888932 -186.9763932
100 -228.1013932 -81.7888932
101 -46.3513932 -228.1013932
102 -113.9763932 -46.3513932
103 -13.8513932 -113.9763932
104 -194.6638932 -13.8513932
105 -164.0388932 -194.6638932
106 -48.6013932 -164.0388932
107 49.7736068 -48.6013932
108 233.3243034 49.7736068
109 -85.4138932 233.3243034
110 -34.9763932 -85.4138932
111 -25.7888932 -34.9763932
112 -176.1013932 -25.7888932
113 55.6486068 -176.1013932
114 15.0236068 55.6486068
115 -18.8513932 15.0236068
116 -66.6638932 -18.8513932
117 -166.0388932 -66.6638932
118 1.3986068 -166.0388932
119 96.7736068 1.3986068
120 90.3243034 96.7736068
121 -102.4138932 90.3243034
122 164.0236068 -102.4138932
123 -23.7888932 164.0236068
124 -66.1013932 -23.7888932
125 -135.3513932 -66.1013932
126 -214.9763932 -135.3513932
127 -102.8513932 -214.9763932
128 -64.6638932 -102.8513932
129 -196.0388932 -64.6638932
130 -32.6013932 -196.0388932
131 41.7736068 -32.6013932
132 -57.6756966 41.7736068
133 -186.4138932 -57.6756966
134 -91.9763932 -186.4138932
135 -124.7888932 -91.9763932
136 -169.1013932 -124.7888932
137 -44.3513932 -169.1013932
138 -181.9763932 -44.3513932
139 -104.8513932 -181.9763932
140 -161.6638932 -104.8513932
141 -22.0388932 -161.6638932
142 -311.6013932 -22.0388932
143 -224.2263932 -311.6013932
144 -248.6756966 -224.2263932
145 -89.4138932 -248.6756966
146 -55.9763932 -89.4138932
147 -80.7888932 -55.9763932
148 -100.1013932 -80.7888932
149 -181.3513932 -100.1013932
150 -0.9763932 -181.3513932
151 -146.8513932 -0.9763932
152 -28.6638932 -146.8513932
153 88.9611068 -28.6638932
154 -180.6013932 88.9611068
155 -439.2263932 -180.6013932
156 -266.6756966 -439.2263932
157 -102.4138932 -266.6756966
158 -141.9763932 -102.4138932
159 -119.7888932 -141.9763932
160 -135.1013932 -119.7888932
161 -8.3513932 -135.1013932
162 -153.9763932 -8.3513932
163 27.1486068 -153.9763932
164 -115.6638932 27.1486068
165 0.9611068 -115.6638932
166 -50.6013932 0.9611068
167 -86.2263932 -50.6013932
168 -228.6756966 -86.2263932
169 -94.6027477 -228.6756966
170 15.8347523 -94.6027477
171 79.0222523 15.8347523
172 9.7097523 79.0222523
173 -94.5402477 9.7097523
174 -72.1652477 -94.5402477
175 -122.0402477 -72.1652477
176 113.1472523 -122.0402477
177 33.7722523 113.1472523
178 -169.7902477 33.7722523
179 -256.4152477 -169.7902477
180 30.1354489 -256.4152477
181 13.3972523 30.1354489
182 79.8347523 13.3972523
183 21.0222523 79.8347523
184 70.7097523 21.0222523
185 14.4597523 70.7097523
186 -24.1652477 14.4597523
187 22.9597523 -24.1652477
188 130.1472523 22.9597523
189 121.7722523 130.1472523
190 84.2097523 121.7722523
191 -6.4152477 84.2097523
> 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/74txa1227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82uv01227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nv711227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10miyb1227713666.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u3ul1227713666.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/12to5c1227713666.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/1356bo1227713666.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/14yde31227713666.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/15odo21227713666.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/164lj81227713666.tab")
+ }
>
> system("convert tmp/15lrt1227713666.ps tmp/15lrt1227713666.png")
> system("convert tmp/2lqwg1227713666.ps tmp/2lqwg1227713666.png")
> system("convert tmp/3xjh41227713666.ps tmp/3xjh41227713666.png")
> system("convert tmp/4l00t1227713666.ps tmp/4l00t1227713666.png")
> system("convert tmp/52p5w1227713666.ps tmp/52p5w1227713666.png")
> system("convert tmp/62d0v1227713666.ps tmp/62d0v1227713666.png")
> system("convert tmp/74txa1227713666.ps tmp/74txa1227713666.png")
> system("convert tmp/82uv01227713666.ps tmp/82uv01227713666.png")
> system("convert tmp/9nv711227713666.ps tmp/9nv711227713666.png")
> system("convert tmp/10miyb1227713666.ps tmp/10miyb1227713666.png")
>
>
> proc.time()
user system elapsed
4.696 1.681 5.537