R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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> x <- array(list(1687
+ ,0
+ ,NA
+ ,1508
+ ,0
+ ,1687
+ ,1507
+ ,0
+ ,1508
+ ,1385
+ ,0
+ ,1507
+ ,1632
+ ,0
+ ,1385
+ ,1511
+ ,0
+ ,1632
+ ,1559
+ ,0
+ ,1511
+ ,1630
+ ,0
+ ,1559
+ ,1579
+ ,0
+ ,1630
+ ,1653
+ ,0
+ ,1579
+ ,2152
+ ,0
+ ,1653
+ ,2148
+ ,0
+ ,2152
+ ,1752
+ ,0
+ ,2148
+ ,1765
+ ,0
+ ,1752
+ ,1717
+ ,0
+ ,1765
+ ,1558
+ ,0
+ ,1717
+ ,1575
+ ,0
+ ,1558
+ ,1520
+ ,0
+ ,1575
+ ,1805
+ ,0
+ ,1520
+ ,1800
+ ,0
+ ,1805
+ ,1719
+ ,0
+ ,1800
+ ,2008
+ ,0
+ ,1719
+ ,2242
+ ,0
+ ,2008
+ ,2478
+ ,0
+ ,2242
+ ,2030
+ ,0
+ ,2478
+ ,1655
+ ,0
+ ,2030
+ ,1693
+ ,0
+ ,1655
+ ,1623
+ ,0
+ ,1693
+ ,1805
+ ,0
+ ,1623
+ ,1746
+ ,0
+ ,1805
+ ,1795
+ ,0
+ ,1746
+ ,1926
+ ,0
+ ,1795
+ ,1619
+ ,0
+ ,1926
+ ,1992
+ ,0
+ ,1619
+ ,2233
+ ,0
+ ,1992
+ ,2192
+ ,0
+ ,2233
+ ,2080
+ ,0
+ ,2192
+ ,1768
+ ,0
+ ,2080
+ ,1835
+ ,0
+ ,1768
+ ,1569
+ ,0
+ ,1835
+ ,1976
+ ,0
+ ,1569
+ ,1853
+ ,0
+ ,1976
+ ,1965
+ ,0
+ ,1853
+ ,1689
+ ,0
+ ,1965
+ ,1778
+ ,0
+ ,1689
+ ,1976
+ ,0
+ ,1778
+ ,2397
+ ,0
+ ,1976
+ ,2654
+ ,0
+ ,2397
+ ,2097
+ ,0
+ ,2654
+ ,1963
+ ,0
+ ,2097
+ ,1677
+ ,0
+ ,1963
+ ,1941
+ ,0
+ ,1677
+ ,2003
+ ,0
+ ,1941
+ ,1813
+ ,0
+ ,2003
+ ,2012
+ ,0
+ ,1813
+ ,1912
+ ,0
+ ,2012
+ ,2084
+ ,0
+ ,1912
+ ,2080
+ ,0
+ ,2084
+ ,2118
+ ,0
+ ,2080
+ ,2150
+ ,0
+ ,2118
+ ,1608
+ ,0
+ ,2150
+ ,1503
+ ,0
+ ,1608
+ ,1548
+ ,0
+ ,1503
+ ,1382
+ ,0
+ ,1548
+ ,1731
+ ,0
+ ,1382
+ ,1798
+ ,0
+ ,1731
+ ,1779
+ ,0
+ ,1798
+ ,1887
+ ,0
+ ,1779
+ ,2004
+ ,0
+ ,1887
+ ,2077
+ ,0
+ ,2004
+ ,2092
+ ,0
+ ,2077
+ ,2051
+ ,0
+ ,2092
+ ,1577
+ ,0
+ ,2051
+ ,1356
+ ,0
+ ,1577
+ ,1652
+ ,0
+ ,1356
+ ,1382
+ ,0
+ ,1652
+ ,1519
+ ,0
+ ,1382
+ ,1421
+ ,0
+ ,1519
+ ,1442
+ ,0
+ ,1421
+ ,1543
+ ,0
+ ,1442
+ ,1656
+ ,0
+ ,1543
+ ,1561
+ ,0
+ ,1656
+ ,1905
+ ,0
+ ,1561
+ ,2199
+ ,0
+ ,1905
+ ,1473
+ ,0
+ ,2199
+ ,1655
+ ,0
+ ,1473
+ ,1407
+ ,0
+ ,1655
+ ,1395
+ ,0
+ ,1407
+ ,1530
+ ,0
+ ,1395
+ ,1309
+ ,0
+ ,1530
+ ,1526
+ ,0
+ ,1309
+ ,1327
+ ,0
+ ,1526
+ ,1627
+ ,0
+ ,1327
+ ,1748
+ ,0
+ ,1627
+ ,1958
+ ,0
+ ,1748
+ ,2274
+ ,0
+ ,1958
+ ,1648
+ ,0
+ ,2274
+ ,1401
+ ,0
+ ,1648
+ ,1411
+ ,0
+ ,1401
+ ,1403
+ ,0
+ ,1411
+ ,1394
+ ,0
+ ,1403
+ ,1520
+ ,0
+ ,1394
+ ,1528
+ ,0
+ ,1520
+ ,1643
+ ,0
+ ,1528
+ ,1515
+ ,0
+ ,1643
+ ,1685
+ ,0
+ ,1515
+ ,2000
+ ,0
+ ,1685
+ ,2215
+ ,0
+ ,2000
+ ,1956
+ ,0
+ ,2215
+ ,1462
+ ,0
+ ,1956
+ ,1563
+ ,0
+ ,1462
+ ,1459
+ ,0
+ ,1563
+ ,1446
+ ,0
+ ,1459
+ ,1622
+ ,0
+ ,1446
+ ,1657
+ ,0
+ ,1622
+ ,1638
+ ,0
+ ,1657
+ ,1643
+ ,0
+ ,1638
+ ,1683
+ ,0
+ ,1643
+ ,2050
+ ,0
+ ,1683
+ ,2262
+ ,0
+ ,2050
+ ,1813
+ ,0
+ ,2262
+ ,1445
+ ,0
+ ,1813
+ ,1762
+ ,0
+ ,1445
+ ,1461
+ ,0
+ ,1762
+ ,1556
+ ,0
+ ,1461
+ ,1431
+ ,0
+ ,1556
+ ,1427
+ ,0
+ ,1431
+ ,1554
+ ,0
+ ,1427
+ ,1645
+ ,0
+ ,1554
+ ,1653
+ ,0
+ ,1645
+ ,2016
+ ,0
+ ,1653
+ ,2207
+ ,0
+ ,2016
+ ,1665
+ ,0
+ ,2207
+ ,1361
+ ,0
+ ,1665
+ ,1506
+ ,0
+ ,1361
+ ,1360
+ ,0
+ ,1506
+ ,1453
+ ,0
+ ,1360
+ ,1522
+ ,0
+ ,1453
+ ,1460
+ ,0
+ ,1522
+ ,1552
+ ,0
+ ,1460
+ ,1548
+ ,0
+ ,1552
+ ,1827
+ ,0
+ ,1548
+ ,1737
+ ,0
+ ,1827
+ ,1941
+ ,0
+ ,1737
+ ,1474
+ ,0
+ ,1941
+ ,1458
+ ,0
+ ,1474
+ ,1542
+ ,0
+ ,1458
+ ,1404
+ ,0
+ ,1542
+ ,1522
+ ,0
+ ,1404
+ ,1385
+ ,0
+ ,1522
+ ,1641
+ ,0
+ ,1385
+ ,1510
+ ,0
+ ,1641
+ ,1681
+ ,0
+ ,1510
+ ,1938
+ ,0
+ ,1681
+ ,1868
+ ,0
+ ,1938
+ ,1726
+ ,0
+ ,1868
+ ,1456
+ ,0
+ ,1726
+ ,1445
+ ,0
+ ,1456
+ ,1456
+ ,0
+ ,1445
+ ,1365
+ ,0
+ ,1456
+ ,1487
+ ,0
+ ,1365
+ ,1558
+ ,0
+ ,1487
+ ,1488
+ ,0
+ ,1558
+ ,1684
+ ,0
+ ,1488
+ ,1594
+ ,0
+ ,1684
+ ,1850
+ ,0
+ ,1594
+ ,1998
+ ,0
+ ,1850
+ ,2079
+ ,0
+ ,1998
+ ,1494
+ ,0
+ ,2079
+ ,1057
+ ,1
+ ,1494
+ ,1218
+ ,1
+ ,1057
+ ,1168
+ ,1
+ ,1218
+ ,1236
+ ,1
+ ,1168
+ ,1076
+ ,1
+ ,1236
+ ,1174
+ ,1
+ ,1076
+ ,1139
+ ,1
+ ,1174
+ ,1427
+ ,1
+ ,1139
+ ,1487
+ ,1
+ ,1427
+ ,1483
+ ,1
+ ,1487
+ ,1513
+ ,1
+ ,1483
+ ,1357
+ ,1
+ ,1513
+ ,1165
+ ,1
+ ,1357
+ ,1282
+ ,1
+ ,1165
+ ,1110
+ ,1
+ ,1282
+ ,1297
+ ,1
+ ,1110
+ ,1185
+ ,1
+ ,1297
+ ,1222
+ ,1
+ ,1185
+ ,1284
+ ,1
+ ,1222
+ ,1444
+ ,1
+ ,1284
+ ,1575
+ ,1
+ ,1444
+ ,1737
+ ,1
+ ,1575
+ ,1763
+ ,1
+ ,1737)
+ ,dim=c(3
+ ,192)
+ ,dimnames=list(c('Accidents'
+ ,'Belt'
+ ,'A1')
+ ,1:192))
> y <- array(NA,dim=c(3,192),dimnames=list(c('Accidents','Belt','A1'),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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '3'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, 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
A1 Accidents Belt M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 NA 1687 0 1 0 0 0 0 0 0 0 0 0 0
2 1687 1508 0 0 1 0 0 0 0 0 0 0 0 0
3 1508 1507 0 0 0 1 0 0 0 0 0 0 0 0
4 1507 1385 0 0 0 0 1 0 0 0 0 0 0 0
5 1385 1632 0 0 0 0 0 1 0 0 0 0 0 0
6 1632 1511 0 0 0 0 0 0 1 0 0 0 0 0
7 1511 1559 0 0 0 0 0 0 0 1 0 0 0 0
8 1559 1630 0 0 0 0 0 0 0 0 1 0 0 0
9 1630 1579 0 0 0 0 0 0 0 0 0 1 0 0
10 1579 1653 0 0 0 0 0 0 0 0 0 0 1 0
11 1653 2152 0 0 0 0 0 0 0 0 0 0 0 1
12 2152 2148 0 0 0 0 0 0 0 0 0 0 0 0
13 2148 1752 0 1 0 0 0 0 0 0 0 0 0 0
14 1752 1765 0 0 1 0 0 0 0 0 0 0 0 0
15 1765 1717 0 0 0 1 0 0 0 0 0 0 0 0
16 1717 1558 0 0 0 0 1 0 0 0 0 0 0 0
17 1558 1575 0 0 0 0 0 1 0 0 0 0 0 0
18 1575 1520 0 0 0 0 0 0 1 0 0 0 0 0
19 1520 1805 0 0 0 0 0 0 0 1 0 0 0 0
20 1805 1800 0 0 0 0 0 0 0 0 1 0 0 0
21 1800 1719 0 0 0 0 0 0 0 0 0 1 0 0
22 1719 2008 0 0 0 0 0 0 0 0 0 0 1 0
23 2008 2242 0 0 0 0 0 0 0 0 0 0 0 1
24 2242 2478 0 0 0 0 0 0 0 0 0 0 0 0
25 2478 2030 0 1 0 0 0 0 0 0 0 0 0 0
26 2030 1655 0 0 1 0 0 0 0 0 0 0 0 0
27 1655 1693 0 0 0 1 0 0 0 0 0 0 0 0
28 1693 1623 0 0 0 0 1 0 0 0 0 0 0 0
29 1623 1805 0 0 0 0 0 1 0 0 0 0 0 0
30 1805 1746 0 0 0 0 0 0 1 0 0 0 0 0
31 1746 1795 0 0 0 0 0 0 0 1 0 0 0 0
32 1795 1926 0 0 0 0 0 0 0 0 1 0 0 0
33 1926 1619 0 0 0 0 0 0 0 0 0 1 0 0
34 1619 1992 0 0 0 0 0 0 0 0 0 0 1 0
35 1992 2233 0 0 0 0 0 0 0 0 0 0 0 1
36 2233 2192 0 0 0 0 0 0 0 0 0 0 0 0
37 2192 2080 0 1 0 0 0 0 0 0 0 0 0 0
38 2080 1768 0 0 1 0 0 0 0 0 0 0 0 0
39 1768 1835 0 0 0 1 0 0 0 0 0 0 0 0
40 1835 1569 0 0 0 0 1 0 0 0 0 0 0 0
41 1569 1976 0 0 0 0 0 1 0 0 0 0 0 0
42 1976 1853 0 0 0 0 0 0 1 0 0 0 0 0
43 1853 1965 0 0 0 0 0 0 0 1 0 0 0 0
44 1965 1689 0 0 0 0 0 0 0 0 1 0 0 0
45 1689 1778 0 0 0 0 0 0 0 0 0 1 0 0
46 1778 1976 0 0 0 0 0 0 0 0 0 0 1 0
47 1976 2397 0 0 0 0 0 0 0 0 0 0 0 1
48 2397 2654 0 0 0 0 0 0 0 0 0 0 0 0
49 2654 2097 0 1 0 0 0 0 0 0 0 0 0 0
50 2097 1963 0 0 1 0 0 0 0 0 0 0 0 0
51 1963 1677 0 0 0 1 0 0 0 0 0 0 0 0
52 1677 1941 0 0 0 0 1 0 0 0 0 0 0 0
53 1941 2003 0 0 0 0 0 1 0 0 0 0 0 0
54 2003 1813 0 0 0 0 0 0 1 0 0 0 0 0
55 1813 2012 0 0 0 0 0 0 0 1 0 0 0 0
56 2012 1912 0 0 0 0 0 0 0 0 1 0 0 0
57 1912 2084 0 0 0 0 0 0 0 0 0 1 0 0
58 2084 2080 0 0 0 0 0 0 0 0 0 0 1 0
59 2080 2118 0 0 0 0 0 0 0 0 0 0 0 1
60 2118 2150 0 0 0 0 0 0 0 0 0 0 0 0
61 2150 1608 0 1 0 0 0 0 0 0 0 0 0 0
62 1608 1503 0 0 1 0 0 0 0 0 0 0 0 0
63 1503 1548 0 0 0 1 0 0 0 0 0 0 0 0
64 1548 1382 0 0 0 0 1 0 0 0 0 0 0 0
65 1382 1731 0 0 0 0 0 1 0 0 0 0 0 0
66 1731 1798 0 0 0 0 0 0 1 0 0 0 0 0
67 1798 1779 0 0 0 0 0 0 0 1 0 0 0 0
68 1779 1887 0 0 0 0 0 0 0 0 1 0 0 0
69 1887 2004 0 0 0 0 0 0 0 0 0 1 0 0
70 2004 2077 0 0 0 0 0 0 0 0 0 0 1 0
71 2077 2092 0 0 0 0 0 0 0 0 0 0 0 1
72 2092 2051 0 0 0 0 0 0 0 0 0 0 0 0
73 2051 1577 0 1 0 0 0 0 0 0 0 0 0 0
74 1577 1356 0 0 1 0 0 0 0 0 0 0 0 0
75 1356 1652 0 0 0 1 0 0 0 0 0 0 0 0
76 1652 1382 0 0 0 0 1 0 0 0 0 0 0 0
77 1382 1519 0 0 0 0 0 1 0 0 0 0 0 0
78 1519 1421 0 0 0 0 0 0 1 0 0 0 0 0
79 1421 1442 0 0 0 0 0 0 0 1 0 0 0 0
80 1442 1543 0 0 0 0 0 0 0 0 1 0 0 0
81 1543 1656 0 0 0 0 0 0 0 0 0 1 0 0
82 1656 1561 0 0 0 0 0 0 0 0 0 0 1 0
83 1561 1905 0 0 0 0 0 0 0 0 0 0 0 1
84 1905 2199 0 0 0 0 0 0 0 0 0 0 0 0
85 2199 1473 0 1 0 0 0 0 0 0 0 0 0 0
86 1473 1655 0 0 1 0 0 0 0 0 0 0 0 0
87 1655 1407 0 0 0 1 0 0 0 0 0 0 0 0
88 1407 1395 0 0 0 0 1 0 0 0 0 0 0 0
89 1395 1530 0 0 0 0 0 1 0 0 0 0 0 0
90 1530 1309 0 0 0 0 0 0 1 0 0 0 0 0
91 1309 1526 0 0 0 0 0 0 0 1 0 0 0 0
92 1526 1327 0 0 0 0 0 0 0 0 1 0 0 0
93 1327 1627 0 0 0 0 0 0 0 0 0 1 0 0
94 1627 1748 0 0 0 0 0 0 0 0 0 0 1 0
95 1748 1958 0 0 0 0 0 0 0 0 0 0 0 1
96 1958 2274 0 0 0 0 0 0 0 0 0 0 0 0
97 2274 1648 0 1 0 0 0 0 0 0 0 0 0 0
98 1648 1401 0 0 1 0 0 0 0 0 0 0 0 0
99 1401 1411 0 0 0 1 0 0 0 0 0 0 0 0
100 1411 1403 0 0 0 0 1 0 0 0 0 0 0 0
101 1403 1394 0 0 0 0 0 1 0 0 0 0 0 0
102 1394 1520 0 0 0 0 0 0 1 0 0 0 0 0
103 1520 1528 0 0 0 0 0 0 0 1 0 0 0 0
104 1528 1643 0 0 0 0 0 0 0 0 1 0 0 0
105 1643 1515 0 0 0 0 0 0 0 0 0 1 0 0
106 1515 1685 0 0 0 0 0 0 0 0 0 0 1 0
107 1685 2000 0 0 0 0 0 0 0 0 0 0 0 1
108 2000 2215 0 0 0 0 0 0 0 0 0 0 0 0
109 2215 1956 0 1 0 0 0 0 0 0 0 0 0 0
110 1956 1462 0 0 1 0 0 0 0 0 0 0 0 0
111 1462 1563 0 0 0 1 0 0 0 0 0 0 0 0
112 1563 1459 0 0 0 0 1 0 0 0 0 0 0 0
113 1459 1446 0 0 0 0 0 1 0 0 0 0 0 0
114 1446 1622 0 0 0 0 0 0 1 0 0 0 0 0
115 1622 1657 0 0 0 0 0 0 0 1 0 0 0 0
116 1657 1638 0 0 0 0 0 0 0 0 1 0 0 0
117 1638 1643 0 0 0 0 0 0 0 0 0 1 0 0
118 1643 1683 0 0 0 0 0 0 0 0 0 0 1 0
119 1683 2050 0 0 0 0 0 0 0 0 0 0 0 1
120 2050 2262 0 0 0 0 0 0 0 0 0 0 0 0
121 2262 1813 0 1 0 0 0 0 0 0 0 0 0 0
122 1813 1445 0 0 1 0 0 0 0 0 0 0 0 0
123 1445 1762 0 0 0 1 0 0 0 0 0 0 0 0
124 1762 1461 0 0 0 0 1 0 0 0 0 0 0 0
125 1461 1556 0 0 0 0 0 1 0 0 0 0 0 0
126 1556 1431 0 0 0 0 0 0 1 0 0 0 0 0
127 1431 1427 0 0 0 0 0 0 0 1 0 0 0 0
128 1427 1554 0 0 0 0 0 0 0 0 1 0 0 0
129 1554 1645 0 0 0 0 0 0 0 0 0 1 0 0
130 1645 1653 0 0 0 0 0 0 0 0 0 0 1 0
131 1653 2016 0 0 0 0 0 0 0 0 0 0 0 1
132 2016 2207 0 0 0 0 0 0 0 0 0 0 0 0
133 2207 1665 0 1 0 0 0 0 0 0 0 0 0 0
134 1665 1361 0 0 1 0 0 0 0 0 0 0 0 0
135 1361 1506 0 0 0 1 0 0 0 0 0 0 0 0
136 1506 1360 0 0 0 0 1 0 0 0 0 0 0 0
137 1360 1453 0 0 0 0 0 1 0 0 0 0 0 0
138 1453 1522 0 0 0 0 0 0 1 0 0 0 0 0
139 1522 1460 0 0 0 0 0 0 0 1 0 0 0 0
140 1460 1552 0 0 0 0 0 0 0 0 1 0 0 0
141 1552 1548 0 0 0 0 0 0 0 0 0 1 0 0
142 1548 1827 0 0 0 0 0 0 0 0 0 0 1 0
143 1827 1737 0 0 0 0 0 0 0 0 0 0 0 1
144 1737 1941 0 0 0 0 0 0 0 0 0 0 0 0
145 1941 1474 0 1 0 0 0 0 0 0 0 0 0 0
146 1474 1458 0 0 1 0 0 0 0 0 0 0 0 0
147 1458 1542 0 0 0 1 0 0 0 0 0 0 0 0
148 1542 1404 0 0 0 0 1 0 0 0 0 0 0 0
149 1404 1522 0 0 0 0 0 1 0 0 0 0 0 0
150 1522 1385 0 0 0 0 0 0 1 0 0 0 0 0
151 1385 1641 0 0 0 0 0 0 0 1 0 0 0 0
152 1641 1510 0 0 0 0 0 0 0 0 1 0 0 0
153 1510 1681 0 0 0 0 0 0 0 0 0 1 0 0
154 1681 1938 0 0 0 0 0 0 0 0 0 0 1 0
155 1938 1868 0 0 0 0 0 0 0 0 0 0 0 1
156 1868 1726 0 0 0 0 0 0 0 0 0 0 0 0
157 1726 1456 0 1 0 0 0 0 0 0 0 0 0 0
158 1456 1445 0 0 1 0 0 0 0 0 0 0 0 0
159 1445 1456 0 0 0 1 0 0 0 0 0 0 0 0
160 1456 1365 0 0 0 0 1 0 0 0 0 0 0 0
161 1365 1487 0 0 0 0 0 1 0 0 0 0 0 0
162 1487 1558 0 0 0 0 0 0 1 0 0 0 0 0
163 1558 1488 0 0 0 0 0 0 0 1 0 0 0 0
164 1488 1684 0 0 0 0 0 0 0 0 1 0 0 0
165 1684 1594 0 0 0 0 0 0 0 0 0 1 0 0
166 1594 1850 0 0 0 0 0 0 0 0 0 0 1 0
167 1850 1998 0 0 0 0 0 0 0 0 0 0 0 1
168 1998 2079 0 0 0 0 0 0 0 0 0 0 0 0
169 2079 1494 0 1 0 0 0 0 0 0 0 0 0 0
170 1494 1057 1 0 1 0 0 0 0 0 0 0 0 0
171 1057 1218 1 0 0 1 0 0 0 0 0 0 0 0
172 1218 1168 1 0 0 0 1 0 0 0 0 0 0 0
173 1168 1236 1 0 0 0 0 1 0 0 0 0 0 0
174 1236 1076 1 0 0 0 0 0 1 0 0 0 0 0
175 1076 1174 1 0 0 0 0 0 0 1 0 0 0 0
176 1174 1139 1 0 0 0 0 0 0 0 1 0 0 0
177 1139 1427 1 0 0 0 0 0 0 0 0 1 0 0
178 1427 1487 1 0 0 0 0 0 0 0 0 0 1 0
179 1487 1483 1 0 0 0 0 0 0 0 0 0 0 1
180 1483 1513 1 0 0 0 0 0 0 0 0 0 0 0
181 1513 1357 1 1 0 0 0 0 0 0 0 0 0 0
182 1357 1165 1 0 1 0 0 0 0 0 0 0 0 0
183 1165 1282 1 0 0 1 0 0 0 0 0 0 0 0
184 1282 1110 1 0 0 0 1 0 0 0 0 0 0 0
185 1110 1297 1 0 0 0 0 1 0 0 0 0 0 0
186 1297 1185 1 0 0 0 0 0 1 0 0 0 0 0
187 1185 1222 1 0 0 0 0 0 0 1 0 0 0 0
188 1222 1284 1 0 0 0 0 0 0 0 1 0 0 0
189 1284 1444 1 0 0 0 0 0 0 0 0 1 0 0
190 1444 1575 1 0 0 0 0 0 0 0 0 0 1 0
191 1575 1737 1 0 0 0 0 0 0 0 0 0 0 1
192 1737 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) Accidents Belt M1 M2 M3
663.9895 0.6391 -136.6393 398.7368 93.6680 -138.6474
M4 M5 M6 M7 M8 M9
-15.7447 -216.6911 -43.7478 -147.8311 -81.7130 -100.5915
M10 M11
-136.8561 -125.0253
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-342.40 -86.90 -3.57 80.02 365.86
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 663.9895 128.9546 5.149 6.92e-07 ***
Accidents 0.6391 0.0575 11.114 < 2e-16 ***
Belt -136.6393 37.4230 -3.651 0.000343 ***
M1 398.7368 54.2532 7.350 7.06e-12 ***
M2 93.6680 59.0565 1.586 0.114507
M3 -138.6474 57.3545 -2.417 0.016648 *
M4 -15.7447 61.2904 -0.257 0.797565
M5 -216.6911 56.5770 -3.830 0.000178 ***
M6 -43.7478 58.4079 -0.749 0.454849
M7 -147.8311 55.9542 -2.642 0.008980 **
M8 -81.7130 55.4989 -1.472 0.142706
M9 -100.5915 53.9610 -1.864 0.063956 .
M10 -136.8562 50.5580 -2.707 0.007456 **
M11 -125.0253 47.6498 -2.624 0.009454 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 133.4 on 177 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.8032, Adjusted R-squared: 0.7887
F-statistic: 55.56 on 13 and 177 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.4689551 0.937910283 0.5310448587
[2,] 0.3866851 0.773370296 0.6133148519
[3,] 0.3452550 0.690509985 0.6547450075
[4,] 0.2542084 0.508416781 0.7457916097
[5,] 0.1791529 0.358305863 0.8208470684
[6,] 0.3525359 0.705071712 0.6474641441
[7,] 0.2874453 0.574890614 0.7125546929
[8,] 0.2713968 0.542793668 0.7286031659
[9,] 0.4804989 0.960997790 0.5195011048
[10,] 0.3964418 0.792883548 0.6035582261
[11,] 0.3157617 0.631523377 0.6842383116
[12,] 0.2453068 0.490613690 0.7546931548
[13,] 0.1909595 0.381919012 0.8090404940
[14,] 0.1867513 0.373502516 0.8132487420
[15,] 0.1404561 0.280912164 0.8595439181
[16,] 0.2232493 0.446498601 0.7767506995
[17,] 0.2180661 0.436132164 0.7819339181
[18,] 0.2032833 0.406566625 0.7967166873
[19,] 0.1876894 0.375378784 0.8123106082
[20,] 0.2527922 0.505584321 0.7472078394
[21,] 0.2870834 0.574166888 0.7129165560
[22,] 0.2367721 0.473544130 0.7632279350
[23,] 0.2574632 0.514926301 0.7425368493
[24,] 0.2505865 0.501173048 0.7494134759
[25,] 0.2605424 0.521084853 0.7394575735
[26,] 0.2366334 0.473266897 0.7633665515
[27,] 0.4207276 0.841455203 0.5792723985
[28,] 0.4301889 0.860377832 0.5698110841
[29,] 0.3892502 0.778500322 0.6107498391
[30,] 0.3429772 0.685954340 0.6570228302
[31,] 0.2963919 0.592783750 0.7036081248
[32,] 0.4491427 0.898285371 0.5508573146
[33,] 0.4091512 0.818302327 0.5908488363
[34,] 0.6429416 0.714116835 0.3570584176
[35,] 0.7122030 0.575594073 0.2877970363
[36,] 0.8030823 0.393835488 0.1969177441
[37,] 0.8447707 0.310458646 0.1552293228
[38,] 0.8160602 0.367879679 0.1839398394
[39,] 0.8530053 0.293989441 0.1469947206
[40,] 0.8442820 0.311436036 0.1557180182
[41,] 0.9261820 0.147635960 0.0738179799
[42,] 0.9547924 0.090415204 0.0452076020
[43,] 0.9471735 0.105652996 0.0528264978
[44,] 0.9346802 0.130639631 0.0653198153
[45,] 0.9364805 0.127038959 0.0635194797
[46,] 0.9318020 0.136396098 0.0681980491
[47,] 0.9148196 0.170360768 0.0851803839
[48,] 0.9215988 0.156802366 0.0784011828
[49,] 0.9179677 0.164064596 0.0820322982
[50,] 0.9324578 0.135084405 0.0675422023
[51,] 0.9309804 0.138039199 0.0690195995
[52,] 0.9376431 0.124713848 0.0623569240
[53,] 0.9614201 0.077159806 0.0385799031
[54,] 0.9808881 0.038223729 0.0191118647
[55,] 0.9794420 0.041116016 0.0205580079
[56,] 0.9740580 0.051883964 0.0259419819
[57,] 0.9692811 0.061437817 0.0307189085
[58,] 0.9845036 0.030992706 0.0154963531
[59,] 0.9837276 0.032544702 0.0162723511
[60,] 0.9788278 0.042344451 0.0211722253
[61,] 0.9739904 0.052019199 0.0260095994
[62,] 0.9670844 0.065831176 0.0329155881
[63,] 0.9697410 0.060517928 0.0302589642
[64,] 0.9679928 0.064014476 0.0320072379
[65,] 0.9639114 0.072177298 0.0360886491
[66,] 0.9750177 0.049964504 0.0249822519
[67,] 0.9788462 0.042307610 0.0211538050
[68,] 0.9820016 0.035996804 0.0179984022
[69,] 0.9949471 0.010105810 0.0050529052
[70,] 0.9973944 0.005211118 0.0026055590
[71,] 0.9974844 0.005031196 0.0025155979
[72,] 0.9964992 0.007001573 0.0035007867
[73,] 0.9954566 0.009086719 0.0045433597
[74,] 0.9967109 0.006578109 0.0032890546
[75,] 0.9957499 0.008500256 0.0042501280
[76,] 0.9988039 0.002392158 0.0011960790
[77,] 0.9982988 0.003402375 0.0017011875
[78,] 0.9976458 0.004708473 0.0023542364
[79,] 0.9975952 0.004809593 0.0024047967
[80,] 0.9985139 0.002972263 0.0014861316
[81,] 0.9979082 0.004183530 0.0020917651
[82,] 0.9972299 0.005540296 0.0027701480
[83,] 0.9973793 0.005241377 0.0026206885
[84,] 0.9965306 0.006938884 0.0034694421
[85,] 0.9974719 0.005056248 0.0025281240
[86,] 0.9965185 0.006963077 0.0034815384
[87,] 0.9958372 0.008325681 0.0041628407
[88,] 0.9953905 0.009218939 0.0046094693
[89,] 0.9947982 0.010403520 0.0052017599
[90,] 0.9947650 0.010470013 0.0052350064
[91,] 0.9931658 0.013668479 0.0068342396
[92,] 0.9930372 0.013925543 0.0069627713
[93,] 0.9983003 0.003399498 0.0016997489
[94,] 0.9978238 0.004352429 0.0021762144
[95,] 0.9968850 0.006230075 0.0031150376
[96,] 0.9960968 0.007806401 0.0039032005
[97,] 0.9967888 0.006422417 0.0032112083
[98,] 0.9963724 0.007255169 0.0036275843
[99,] 0.9960799 0.007840208 0.0039201041
[100,] 0.9950735 0.009853046 0.0049265228
[101,] 0.9931723 0.013655394 0.0068276972
[102,] 0.9943957 0.011208520 0.0056042598
[103,] 0.9928331 0.014333770 0.0071668851
[104,] 0.9962991 0.007401897 0.0037009484
[105,] 0.9975852 0.004829686 0.0024148429
[106,] 0.9975065 0.004986904 0.0024934519
[107,] 0.9989790 0.002042013 0.0010210065
[108,] 0.9985782 0.002843578 0.0014217891
[109,] 0.9979710 0.004057954 0.0020289771
[110,] 0.9970114 0.005977282 0.0029886408
[111,] 0.9966958 0.006608360 0.0033041798
[112,] 0.9953713 0.009257435 0.0046287174
[113,] 0.9934209 0.013158229 0.0065791146
[114,] 0.9961860 0.007627937 0.0038139686
[115,] 0.9953938 0.009212458 0.0046062291
[116,] 0.9991687 0.001662653 0.0008313267
[117,] 0.9989010 0.002198058 0.0010990292
[118,] 0.9985796 0.002840854 0.0014204271
[119,] 0.9978044 0.004391172 0.0021955858
[120,] 0.9967634 0.006473225 0.0032366127
[121,] 0.9960222 0.007955625 0.0039778125
[122,] 0.9949755 0.010048967 0.0050244835
[123,] 0.9934955 0.013008927 0.0065044637
[124,] 0.9904601 0.019079815 0.0095399077
[125,] 0.9908178 0.018364392 0.0091821958
[126,] 0.9888773 0.022245367 0.0111226835
[127,] 0.9922106 0.015578851 0.0077894256
[128,] 0.9899764 0.020047253 0.0100236264
[129,] 0.9924065 0.015186921 0.0075934604
[130,] 0.9890319 0.021936221 0.0109681103
[131,] 0.9839608 0.032078446 0.0160392229
[132,] 0.9765715 0.046857054 0.0234285269
[133,] 0.9664321 0.067135705 0.0335678524
[134,] 0.9625478 0.074904485 0.0374522424
[135,] 0.9657973 0.068405453 0.0342027267
[136,] 0.9556469 0.088706285 0.0443531424
[137,] 0.9388548 0.122290488 0.0611452438
[138,] 0.9387464 0.122507235 0.0612536175
[139,] 0.9177024 0.164595178 0.0822975892
[140,] 0.9466472 0.106705643 0.0533528216
[141,] 0.9838935 0.032212938 0.0161064690
[142,] 0.9746501 0.050699823 0.0253499114
[143,] 0.9705292 0.058941515 0.0294707576
[144,] 0.9651053 0.069789484 0.0348947419
[145,] 0.9651945 0.069610974 0.0348054869
[146,] 0.9510473 0.097905424 0.0489527120
[147,] 0.9383459 0.123308121 0.0616540605
[148,] 0.9378280 0.124344076 0.0621720380
[149,] 0.9690257 0.061948599 0.0309742997
[150,] 0.9683784 0.063243122 0.0316215611
[151,] 0.9856879 0.028624219 0.0143121097
[152,] 0.9713583 0.057283474 0.0286417368
[153,] 0.9867435 0.026513001 0.0132565006
[154,] 0.9773395 0.045320919 0.0226604593
[155,] 0.9671407 0.065718528 0.0328592638
[156,] 0.9509143 0.098171343 0.0490856713
[157,] 0.8884566 0.223086785 0.1115433923
[158,] 0.8083864 0.383227170 0.1916135850
[159,] 0.6317653 0.736469433 0.3682347164
> postscript(file="/var/fisher/rcomp/tmp/18xl11384957268.ps",horizontal=F,onefile=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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2fvvq1384957268.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/32e2c1384957268.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/459gv1384957268.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51mev1384957268.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 191
Frequency = 1
2 3 4 5 6 7
-34.4485500 19.5060093 -26.4242569 -105.3400922 46.0499047 -1.5444405
8 9 10 11 12 13
-65.0399790 57.4335338 -4.5965794 -261.3476932 115.1835032 -34.4622883
14 15 16 17 18 19
-133.7020628 142.2910767 73.0082034 104.0896752 -16.7021639 -149.7676473
20 21 22 23 24 25
72.3098374 137.9569120 -91.4837275 36.1316214 -5.7256766 117.8627057
26 27 28 29 30 31
214.6009971 47.6299261 7.4654861 22.0923680 68.8570039 82.6235400
32 33 34 35 36 37
-18.2191222 327.8687847 -181.2578278 25.8836899 168.0622792 -200.0932307
38 39 40 41 42 43
192.3805810 69.8750669 183.9778974 -141.1969343 171.4713001 80.9733564
44 45 46 47 48 49
303.2520161 -10.7510928 -12.0319282 -94.9317813 36.7894275 251.0417510
50 51 52 53 54 55
84.7524293 365.8558258 -211.7742690 213.5468601 224.0360492 10.9347763
56 57 58 59 60 61
207.7285400 16.6785768 227.4997242 187.3823435 79.9052658 59.5708084
62 63 64 65 66 67
-110.2529564 -11.6978585 16.4930993 -171.6128462 -38.3771699 144.8494396
68 69 70 71 72 73
-9.2934918 42.8080749 149.4170804 200.9994304 117.1780197 -19.6165111
74 75 76 77 78 79
-47.3025036 -225.1662061 120.4930993 -36.1196761 -9.4294099 -16.7675495
80 81 82 83 84 85
-126.4366498 -78.7786082 131.2023434 -195.4853677 -164.4115518 194.8518365
86 87 88 89 90 91
-342.3990029 230.4178820 -132.8154442 -30.1499821 73.1518875 -182.4535225
92 93 94 95 96 97
95.6129952 -276.2441651 -17.3128585 -42.3586602 -159.3454564 158.0060593
98 99 100 101 102 103
-5.0628463 -26.1385929 -133.9283940 64.7701647 -197.7021639 27.2682400
104 105 106 107 108 109
-104.3485225 111.3371323 -89.0483787 -132.2016467 -79.6374515 -97.8425085
110 111 112 113 114 115
263.9509114 -62.2846394 -17.7190427 87.5359909 -210.8922740 46.8219243
116 117 118 119 120 121
27.8470711 24.5299353 40.2298588 -166.1575831 -59.6760316 40.5514694
122 123 124 125 126 127
131.8159298 -206.4692660 180.0027199 19.2329310 21.1794028 2.8192314
128 129 130 131 132 133
-148.4669558 -60.7483022 61.4034206 -174.4275464 -58.5245017 80.1410410
134 135 136 137 138 139
37.5019028 -126.8548720 -11.4462887 -15.9378401 -139.9804013 72.7283135
140 141 142 143 144 145
-114.1887183 -0.7537857 -146.8032379 177.8865784 -167.5189203 -63.7872822
146 147 148 149 150 151
-215.4926137 -52.8631461 -3.5675127 -16.0370323 16.5788643 -179.9521761
152 153 154 155 156 157
93.6542682 -127.7565763 -84.7454166 205.1620252 100.8916059 -267.2831451
158 159 160 161 162 163
-225.1840702 -10.8989356 -64.6418824 -32.6678769 -128.9886755 90.8329891
164 165 166 167 168 169
-170.5523903 101.8467529 -115.5029686 34.0765907 5.2826954 61.4303432
170 171 172 173 174 175
197.4333385 -110.1493359 -40.0961504 67.3902663 64.7058936 -53.8443879
176 177 178 179 180 181
0.4066586 -199.7810770 86.1364719 136.8620778 -11.3367625 -280.3710485
182 183 184 185 186 187
-8.5914840 -43.0529344 60.9727357 -29.5959760 56.0419524 24.4779132
188 189 190 191 192
-44.2655568 -65.6460954 46.8940240 62.5259212 82.8835558
> postscript(file="/var/fisher/rcomp/tmp/6r1pw1384957268.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 191
Frequency = 1
lag(myerror, k = 1) myerror
0 -34.4485500 NA
1 19.5060093 -34.4485500
2 -26.4242569 19.5060093
3 -105.3400922 -26.4242569
4 46.0499047 -105.3400922
5 -1.5444405 46.0499047
6 -65.0399790 -1.5444405
7 57.4335338 -65.0399790
8 -4.5965794 57.4335338
9 -261.3476932 -4.5965794
10 115.1835032 -261.3476932
11 -34.4622883 115.1835032
12 -133.7020628 -34.4622883
13 142.2910767 -133.7020628
14 73.0082034 142.2910767
15 104.0896752 73.0082034
16 -16.7021639 104.0896752
17 -149.7676473 -16.7021639
18 72.3098374 -149.7676473
19 137.9569120 72.3098374
20 -91.4837275 137.9569120
21 36.1316214 -91.4837275
22 -5.7256766 36.1316214
23 117.8627057 -5.7256766
24 214.6009971 117.8627057
25 47.6299261 214.6009971
26 7.4654861 47.6299261
27 22.0923680 7.4654861
28 68.8570039 22.0923680
29 82.6235400 68.8570039
30 -18.2191222 82.6235400
31 327.8687847 -18.2191222
32 -181.2578278 327.8687847
33 25.8836899 -181.2578278
34 168.0622792 25.8836899
35 -200.0932307 168.0622792
36 192.3805810 -200.0932307
37 69.8750669 192.3805810
38 183.9778974 69.8750669
39 -141.1969343 183.9778974
40 171.4713001 -141.1969343
41 80.9733564 171.4713001
42 303.2520161 80.9733564
43 -10.7510928 303.2520161
44 -12.0319282 -10.7510928
45 -94.9317813 -12.0319282
46 36.7894275 -94.9317813
47 251.0417510 36.7894275
48 84.7524293 251.0417510
49 365.8558258 84.7524293
50 -211.7742690 365.8558258
51 213.5468601 -211.7742690
52 224.0360492 213.5468601
53 10.9347763 224.0360492
54 207.7285400 10.9347763
55 16.6785768 207.7285400
56 227.4997242 16.6785768
57 187.3823435 227.4997242
58 79.9052658 187.3823435
59 59.5708084 79.9052658
60 -110.2529564 59.5708084
61 -11.6978585 -110.2529564
62 16.4930993 -11.6978585
63 -171.6128462 16.4930993
64 -38.3771699 -171.6128462
65 144.8494396 -38.3771699
66 -9.2934918 144.8494396
67 42.8080749 -9.2934918
68 149.4170804 42.8080749
69 200.9994304 149.4170804
70 117.1780197 200.9994304
71 -19.6165111 117.1780197
72 -47.3025036 -19.6165111
73 -225.1662061 -47.3025036
74 120.4930993 -225.1662061
75 -36.1196761 120.4930993
76 -9.4294099 -36.1196761
77 -16.7675495 -9.4294099
78 -126.4366498 -16.7675495
79 -78.7786082 -126.4366498
80 131.2023434 -78.7786082
81 -195.4853677 131.2023434
82 -164.4115518 -195.4853677
83 194.8518365 -164.4115518
84 -342.3990029 194.8518365
85 230.4178820 -342.3990029
86 -132.8154442 230.4178820
87 -30.1499821 -132.8154442
88 73.1518875 -30.1499821
89 -182.4535225 73.1518875
90 95.6129952 -182.4535225
91 -276.2441651 95.6129952
92 -17.3128585 -276.2441651
93 -42.3586602 -17.3128585
94 -159.3454564 -42.3586602
95 158.0060593 -159.3454564
96 -5.0628463 158.0060593
97 -26.1385929 -5.0628463
98 -133.9283940 -26.1385929
99 64.7701647 -133.9283940
100 -197.7021639 64.7701647
101 27.2682400 -197.7021639
102 -104.3485225 27.2682400
103 111.3371323 -104.3485225
104 -89.0483787 111.3371323
105 -132.2016467 -89.0483787
106 -79.6374515 -132.2016467
107 -97.8425085 -79.6374515
108 263.9509114 -97.8425085
109 -62.2846394 263.9509114
110 -17.7190427 -62.2846394
111 87.5359909 -17.7190427
112 -210.8922740 87.5359909
113 46.8219243 -210.8922740
114 27.8470711 46.8219243
115 24.5299353 27.8470711
116 40.2298588 24.5299353
117 -166.1575831 40.2298588
118 -59.6760316 -166.1575831
119 40.5514694 -59.6760316
120 131.8159298 40.5514694
121 -206.4692660 131.8159298
122 180.0027199 -206.4692660
123 19.2329310 180.0027199
124 21.1794028 19.2329310
125 2.8192314 21.1794028
126 -148.4669558 2.8192314
127 -60.7483022 -148.4669558
128 61.4034206 -60.7483022
129 -174.4275464 61.4034206
130 -58.5245017 -174.4275464
131 80.1410410 -58.5245017
132 37.5019028 80.1410410
133 -126.8548720 37.5019028
134 -11.4462887 -126.8548720
135 -15.9378401 -11.4462887
136 -139.9804013 -15.9378401
137 72.7283135 -139.9804013
138 -114.1887183 72.7283135
139 -0.7537857 -114.1887183
140 -146.8032379 -0.7537857
141 177.8865784 -146.8032379
142 -167.5189203 177.8865784
143 -63.7872822 -167.5189203
144 -215.4926137 -63.7872822
145 -52.8631461 -215.4926137
146 -3.5675127 -52.8631461
147 -16.0370323 -3.5675127
148 16.5788643 -16.0370323
149 -179.9521761 16.5788643
150 93.6542682 -179.9521761
151 -127.7565763 93.6542682
152 -84.7454166 -127.7565763
153 205.1620252 -84.7454166
154 100.8916059 205.1620252
155 -267.2831451 100.8916059
156 -225.1840702 -267.2831451
157 -10.8989356 -225.1840702
158 -64.6418824 -10.8989356
159 -32.6678769 -64.6418824
160 -128.9886755 -32.6678769
161 90.8329891 -128.9886755
162 -170.5523903 90.8329891
163 101.8467529 -170.5523903
164 -115.5029686 101.8467529
165 34.0765907 -115.5029686
166 5.2826954 34.0765907
167 61.4303432 5.2826954
168 197.4333385 61.4303432
169 -110.1493359 197.4333385
170 -40.0961504 -110.1493359
171 67.3902663 -40.0961504
172 64.7058936 67.3902663
173 -53.8443879 64.7058936
174 0.4066586 -53.8443879
175 -199.7810770 0.4066586
176 86.1364719 -199.7810770
177 136.8620778 86.1364719
178 -11.3367625 136.8620778
179 -280.3710485 -11.3367625
180 -8.5914840 -280.3710485
181 -43.0529344 -8.5914840
182 60.9727357 -43.0529344
183 -29.5959760 60.9727357
184 56.0419524 -29.5959760
185 24.4779132 56.0419524
186 -44.2655568 24.4779132
187 -65.6460954 -44.2655568
188 46.8940240 -65.6460954
189 62.5259212 46.8940240
190 82.8835558 62.5259212
191 NA 82.8835558
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 19.5060093 -34.4485500
[2,] -26.4242569 19.5060093
[3,] -105.3400922 -26.4242569
[4,] 46.0499047 -105.3400922
[5,] -1.5444405 46.0499047
[6,] -65.0399790 -1.5444405
[7,] 57.4335338 -65.0399790
[8,] -4.5965794 57.4335338
[9,] -261.3476932 -4.5965794
[10,] 115.1835032 -261.3476932
[11,] -34.4622883 115.1835032
[12,] -133.7020628 -34.4622883
[13,] 142.2910767 -133.7020628
[14,] 73.0082034 142.2910767
[15,] 104.0896752 73.0082034
[16,] -16.7021639 104.0896752
[17,] -149.7676473 -16.7021639
[18,] 72.3098374 -149.7676473
[19,] 137.9569120 72.3098374
[20,] -91.4837275 137.9569120
[21,] 36.1316214 -91.4837275
[22,] -5.7256766 36.1316214
[23,] 117.8627057 -5.7256766
[24,] 214.6009971 117.8627057
[25,] 47.6299261 214.6009971
[26,] 7.4654861 47.6299261
[27,] 22.0923680 7.4654861
[28,] 68.8570039 22.0923680
[29,] 82.6235400 68.8570039
[30,] -18.2191222 82.6235400
[31,] 327.8687847 -18.2191222
[32,] -181.2578278 327.8687847
[33,] 25.8836899 -181.2578278
[34,] 168.0622792 25.8836899
[35,] -200.0932307 168.0622792
[36,] 192.3805810 -200.0932307
[37,] 69.8750669 192.3805810
[38,] 183.9778974 69.8750669
[39,] -141.1969343 183.9778974
[40,] 171.4713001 -141.1969343
[41,] 80.9733564 171.4713001
[42,] 303.2520161 80.9733564
[43,] -10.7510928 303.2520161
[44,] -12.0319282 -10.7510928
[45,] -94.9317813 -12.0319282
[46,] 36.7894275 -94.9317813
[47,] 251.0417510 36.7894275
[48,] 84.7524293 251.0417510
[49,] 365.8558258 84.7524293
[50,] -211.7742690 365.8558258
[51,] 213.5468601 -211.7742690
[52,] 224.0360492 213.5468601
[53,] 10.9347763 224.0360492
[54,] 207.7285400 10.9347763
[55,] 16.6785768 207.7285400
[56,] 227.4997242 16.6785768
[57,] 187.3823435 227.4997242
[58,] 79.9052658 187.3823435
[59,] 59.5708084 79.9052658
[60,] -110.2529564 59.5708084
[61,] -11.6978585 -110.2529564
[62,] 16.4930993 -11.6978585
[63,] -171.6128462 16.4930993
[64,] -38.3771699 -171.6128462
[65,] 144.8494396 -38.3771699
[66,] -9.2934918 144.8494396
[67,] 42.8080749 -9.2934918
[68,] 149.4170804 42.8080749
[69,] 200.9994304 149.4170804
[70,] 117.1780197 200.9994304
[71,] -19.6165111 117.1780197
[72,] -47.3025036 -19.6165111
[73,] -225.1662061 -47.3025036
[74,] 120.4930993 -225.1662061
[75,] -36.1196761 120.4930993
[76,] -9.4294099 -36.1196761
[77,] -16.7675495 -9.4294099
[78,] -126.4366498 -16.7675495
[79,] -78.7786082 -126.4366498
[80,] 131.2023434 -78.7786082
[81,] -195.4853677 131.2023434
[82,] -164.4115518 -195.4853677
[83,] 194.8518365 -164.4115518
[84,] -342.3990029 194.8518365
[85,] 230.4178820 -342.3990029
[86,] -132.8154442 230.4178820
[87,] -30.1499821 -132.8154442
[88,] 73.1518875 -30.1499821
[89,] -182.4535225 73.1518875
[90,] 95.6129952 -182.4535225
[91,] -276.2441651 95.6129952
[92,] -17.3128585 -276.2441651
[93,] -42.3586602 -17.3128585
[94,] -159.3454564 -42.3586602
[95,] 158.0060593 -159.3454564
[96,] -5.0628463 158.0060593
[97,] -26.1385929 -5.0628463
[98,] -133.9283940 -26.1385929
[99,] 64.7701647 -133.9283940
[100,] -197.7021639 64.7701647
[101,] 27.2682400 -197.7021639
[102,] -104.3485225 27.2682400
[103,] 111.3371323 -104.3485225
[104,] -89.0483787 111.3371323
[105,] -132.2016467 -89.0483787
[106,] -79.6374515 -132.2016467
[107,] -97.8425085 -79.6374515
[108,] 263.9509114 -97.8425085
[109,] -62.2846394 263.9509114
[110,] -17.7190427 -62.2846394
[111,] 87.5359909 -17.7190427
[112,] -210.8922740 87.5359909
[113,] 46.8219243 -210.8922740
[114,] 27.8470711 46.8219243
[115,] 24.5299353 27.8470711
[116,] 40.2298588 24.5299353
[117,] -166.1575831 40.2298588
[118,] -59.6760316 -166.1575831
[119,] 40.5514694 -59.6760316
[120,] 131.8159298 40.5514694
[121,] -206.4692660 131.8159298
[122,] 180.0027199 -206.4692660
[123,] 19.2329310 180.0027199
[124,] 21.1794028 19.2329310
[125,] 2.8192314 21.1794028
[126,] -148.4669558 2.8192314
[127,] -60.7483022 -148.4669558
[128,] 61.4034206 -60.7483022
[129,] -174.4275464 61.4034206
[130,] -58.5245017 -174.4275464
[131,] 80.1410410 -58.5245017
[132,] 37.5019028 80.1410410
[133,] -126.8548720 37.5019028
[134,] -11.4462887 -126.8548720
[135,] -15.9378401 -11.4462887
[136,] -139.9804013 -15.9378401
[137,] 72.7283135 -139.9804013
[138,] -114.1887183 72.7283135
[139,] -0.7537857 -114.1887183
[140,] -146.8032379 -0.7537857
[141,] 177.8865784 -146.8032379
[142,] -167.5189203 177.8865784
[143,] -63.7872822 -167.5189203
[144,] -215.4926137 -63.7872822
[145,] -52.8631461 -215.4926137
[146,] -3.5675127 -52.8631461
[147,] -16.0370323 -3.5675127
[148,] 16.5788643 -16.0370323
[149,] -179.9521761 16.5788643
[150,] 93.6542682 -179.9521761
[151,] -127.7565763 93.6542682
[152,] -84.7454166 -127.7565763
[153,] 205.1620252 -84.7454166
[154,] 100.8916059 205.1620252
[155,] -267.2831451 100.8916059
[156,] -225.1840702 -267.2831451
[157,] -10.8989356 -225.1840702
[158,] -64.6418824 -10.8989356
[159,] -32.6678769 -64.6418824
[160,] -128.9886755 -32.6678769
[161,] 90.8329891 -128.9886755
[162,] -170.5523903 90.8329891
[163,] 101.8467529 -170.5523903
[164,] -115.5029686 101.8467529
[165,] 34.0765907 -115.5029686
[166,] 5.2826954 34.0765907
[167,] 61.4303432 5.2826954
[168,] 197.4333385 61.4303432
[169,] -110.1493359 197.4333385
[170,] -40.0961504 -110.1493359
[171,] 67.3902663 -40.0961504
[172,] 64.7058936 67.3902663
[173,] -53.8443879 64.7058936
[174,] 0.4066586 -53.8443879
[175,] -199.7810770 0.4066586
[176,] 86.1364719 -199.7810770
[177,] 136.8620778 86.1364719
[178,] -11.3367625 136.8620778
[179,] -280.3710485 -11.3367625
[180,] -8.5914840 -280.3710485
[181,] -43.0529344 -8.5914840
[182,] 60.9727357 -43.0529344
[183,] -29.5959760 60.9727357
[184,] 56.0419524 -29.5959760
[185,] 24.4779132 56.0419524
[186,] -44.2655568 24.4779132
[187,] -65.6460954 -44.2655568
[188,] 46.8940240 -65.6460954
[189,] 62.5259212 46.8940240
[190,] 82.8835558 62.5259212
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 19.5060093 -34.4485500
2 -26.4242569 19.5060093
3 -105.3400922 -26.4242569
4 46.0499047 -105.3400922
5 -1.5444405 46.0499047
6 -65.0399790 -1.5444405
7 57.4335338 -65.0399790
8 -4.5965794 57.4335338
9 -261.3476932 -4.5965794
10 115.1835032 -261.3476932
11 -34.4622883 115.1835032
12 -133.7020628 -34.4622883
13 142.2910767 -133.7020628
14 73.0082034 142.2910767
15 104.0896752 73.0082034
16 -16.7021639 104.0896752
17 -149.7676473 -16.7021639
18 72.3098374 -149.7676473
19 137.9569120 72.3098374
20 -91.4837275 137.9569120
21 36.1316214 -91.4837275
22 -5.7256766 36.1316214
23 117.8627057 -5.7256766
24 214.6009971 117.8627057
25 47.6299261 214.6009971
26 7.4654861 47.6299261
27 22.0923680 7.4654861
28 68.8570039 22.0923680
29 82.6235400 68.8570039
30 -18.2191222 82.6235400
31 327.8687847 -18.2191222
32 -181.2578278 327.8687847
33 25.8836899 -181.2578278
34 168.0622792 25.8836899
35 -200.0932307 168.0622792
36 192.3805810 -200.0932307
37 69.8750669 192.3805810
38 183.9778974 69.8750669
39 -141.1969343 183.9778974
40 171.4713001 -141.1969343
41 80.9733564 171.4713001
42 303.2520161 80.9733564
43 -10.7510928 303.2520161
44 -12.0319282 -10.7510928
45 -94.9317813 -12.0319282
46 36.7894275 -94.9317813
47 251.0417510 36.7894275
48 84.7524293 251.0417510
49 365.8558258 84.7524293
50 -211.7742690 365.8558258
51 213.5468601 -211.7742690
52 224.0360492 213.5468601
53 10.9347763 224.0360492
54 207.7285400 10.9347763
55 16.6785768 207.7285400
56 227.4997242 16.6785768
57 187.3823435 227.4997242
58 79.9052658 187.3823435
59 59.5708084 79.9052658
60 -110.2529564 59.5708084
61 -11.6978585 -110.2529564
62 16.4930993 -11.6978585
63 -171.6128462 16.4930993
64 -38.3771699 -171.6128462
65 144.8494396 -38.3771699
66 -9.2934918 144.8494396
67 42.8080749 -9.2934918
68 149.4170804 42.8080749
69 200.9994304 149.4170804
70 117.1780197 200.9994304
71 -19.6165111 117.1780197
72 -47.3025036 -19.6165111
73 -225.1662061 -47.3025036
74 120.4930993 -225.1662061
75 -36.1196761 120.4930993
76 -9.4294099 -36.1196761
77 -16.7675495 -9.4294099
78 -126.4366498 -16.7675495
79 -78.7786082 -126.4366498
80 131.2023434 -78.7786082
81 -195.4853677 131.2023434
82 -164.4115518 -195.4853677
83 194.8518365 -164.4115518
84 -342.3990029 194.8518365
85 230.4178820 -342.3990029
86 -132.8154442 230.4178820
87 -30.1499821 -132.8154442
88 73.1518875 -30.1499821
89 -182.4535225 73.1518875
90 95.6129952 -182.4535225
91 -276.2441651 95.6129952
92 -17.3128585 -276.2441651
93 -42.3586602 -17.3128585
94 -159.3454564 -42.3586602
95 158.0060593 -159.3454564
96 -5.0628463 158.0060593
97 -26.1385929 -5.0628463
98 -133.9283940 -26.1385929
99 64.7701647 -133.9283940
100 -197.7021639 64.7701647
101 27.2682400 -197.7021639
102 -104.3485225 27.2682400
103 111.3371323 -104.3485225
104 -89.0483787 111.3371323
105 -132.2016467 -89.0483787
106 -79.6374515 -132.2016467
107 -97.8425085 -79.6374515
108 263.9509114 -97.8425085
109 -62.2846394 263.9509114
110 -17.7190427 -62.2846394
111 87.5359909 -17.7190427
112 -210.8922740 87.5359909
113 46.8219243 -210.8922740
114 27.8470711 46.8219243
115 24.5299353 27.8470711
116 40.2298588 24.5299353
117 -166.1575831 40.2298588
118 -59.6760316 -166.1575831
119 40.5514694 -59.6760316
120 131.8159298 40.5514694
121 -206.4692660 131.8159298
122 180.0027199 -206.4692660
123 19.2329310 180.0027199
124 21.1794028 19.2329310
125 2.8192314 21.1794028
126 -148.4669558 2.8192314
127 -60.7483022 -148.4669558
128 61.4034206 -60.7483022
129 -174.4275464 61.4034206
130 -58.5245017 -174.4275464
131 80.1410410 -58.5245017
132 37.5019028 80.1410410
133 -126.8548720 37.5019028
134 -11.4462887 -126.8548720
135 -15.9378401 -11.4462887
136 -139.9804013 -15.9378401
137 72.7283135 -139.9804013
138 -114.1887183 72.7283135
139 -0.7537857 -114.1887183
140 -146.8032379 -0.7537857
141 177.8865784 -146.8032379
142 -167.5189203 177.8865784
143 -63.7872822 -167.5189203
144 -215.4926137 -63.7872822
145 -52.8631461 -215.4926137
146 -3.5675127 -52.8631461
147 -16.0370323 -3.5675127
148 16.5788643 -16.0370323
149 -179.9521761 16.5788643
150 93.6542682 -179.9521761
151 -127.7565763 93.6542682
152 -84.7454166 -127.7565763
153 205.1620252 -84.7454166
154 100.8916059 205.1620252
155 -267.2831451 100.8916059
156 -225.1840702 -267.2831451
157 -10.8989356 -225.1840702
158 -64.6418824 -10.8989356
159 -32.6678769 -64.6418824
160 -128.9886755 -32.6678769
161 90.8329891 -128.9886755
162 -170.5523903 90.8329891
163 101.8467529 -170.5523903
164 -115.5029686 101.8467529
165 34.0765907 -115.5029686
166 5.2826954 34.0765907
167 61.4303432 5.2826954
168 197.4333385 61.4303432
169 -110.1493359 197.4333385
170 -40.0961504 -110.1493359
171 67.3902663 -40.0961504
172 64.7058936 67.3902663
173 -53.8443879 64.7058936
174 0.4066586 -53.8443879
175 -199.7810770 0.4066586
176 86.1364719 -199.7810770
177 136.8620778 86.1364719
178 -11.3367625 136.8620778
179 -280.3710485 -11.3367625
180 -8.5914840 -280.3710485
181 -43.0529344 -8.5914840
182 60.9727357 -43.0529344
183 -29.5959760 60.9727357
184 56.0419524 -29.5959760
185 24.4779132 56.0419524
186 -44.2655568 24.4779132
187 -65.6460954 -44.2655568
188 46.8940240 -65.6460954
189 62.5259212 46.8940240
190 82.8835558 62.5259212
> 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/fisher/rcomp/tmp/7kjgj1384957268.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/8hxtg1384957268.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/9ocr61384957268.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/10shmh1384957268.ps",horizontal=F,onefile=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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11t0im1384957268.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12ngp01384957268.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> 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, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/138tln1384957268.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14uj8j1384957268.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15lamk1384957268.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/fisher/rcomp/tmp/16sz821384957268.tab")
+ }
>
> try(system("convert tmp/18xl11384957268.ps tmp/18xl11384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fvvq1384957268.ps tmp/2fvvq1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/32e2c1384957268.ps tmp/32e2c1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/459gv1384957268.ps tmp/459gv1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/51mev1384957268.ps tmp/51mev1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r1pw1384957268.ps tmp/6r1pw1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kjgj1384957268.ps tmp/7kjgj1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hxtg1384957268.ps tmp/8hxtg1384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ocr61384957268.ps tmp/9ocr61384957268.png",intern=TRUE))
character(0)
> try(system("convert tmp/10shmh1384957268.ps tmp/10shmh1384957268.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.767 1.424 10.185