R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'contributors()' for more information and
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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(-0.03086
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+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,0.01116
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,0.02343
+ ,0.05928
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,0.02343
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,0.06617
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.04485
+ ,0.06617
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,0.01545
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,-0.00665
+ ,0.04485
+ ,0.06617
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,0.01140
+ ,0.01545
+ ,0.02985
+ ,0.03171
+ ,-0.02212)
+ ,dim=c(11
+ ,95)
+ ,dimnames=list(c('(1-B)lnYt'
+ ,'(1-B)lnY_[t-1]'
+ ,'(1-B)lnY_[t-2]'
+ ,'(1-B)lnY_[t-3]'
+ ,'(1-B)lnY_[t-4]'
+ ,'(1-B)lnY_[t-5]'
+ ,'(1-B)lnX_[t-1]'
+ ,'(1-B)lnX_[t-2]'
+ ,'(1-B)lnX_[t-3]'
+ ,'(1-B)lnX_[t-4]'
+ ,'(1-B)lnX_[t-5]')
+ ,1:95))
> y <- array(NA,dim=c(11,95),dimnames=list(c('(1-B)lnYt','(1-B)lnY_[t-1]','(1-B)lnY_[t-2]','(1-B)lnY_[t-3]','(1-B)lnY_[t-4]','(1-B)lnY_[t-5]','(1-B)lnX_[t-1]','(1-B)lnX_[t-2]','(1-B)lnX_[t-3]','(1-B)lnX_[t-4]','(1-B)lnX_[t-5]'),1:95))
> 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 = 'Do not include Seasonal 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
(1-B)lnYt (1-B)lnY_[t-1] (1-B)lnY_[t-2] (1-B)lnY_[t-3] (1-B)lnY_[t-4]
1 -0.03086 -0.01025 0.04860 0.04399 -0.03429
2 0.04033 -0.03086 -0.01025 0.04860 0.04399
3 -0.02352 0.04033 -0.03086 -0.01025 0.04860
4 0.00573 -0.02352 0.04033 -0.03086 -0.01025
5 0.01805 0.00573 -0.02352 0.04033 -0.03086
6 -0.01887 0.01805 0.00573 -0.02352 0.04033
7 0.04363 -0.01887 0.01805 0.00573 -0.02352
8 0.02875 0.04363 -0.01887 0.01805 0.00573
9 -0.00393 0.02875 0.04363 -0.01887 0.01805
10 0.05280 -0.00393 0.02875 0.04363 -0.01887
11 -0.00351 0.05280 -0.00393 0.02875 0.04363
12 0.05407 -0.00351 0.05280 -0.00393 0.02875
13 -0.01299 0.05407 -0.00351 0.05280 -0.00393
14 0.00747 -0.01299 0.05407 -0.00351 0.05280
15 -0.03288 0.00747 -0.01299 0.05407 -0.00351
16 -0.05013 -0.03288 0.00747 -0.01299 0.05407
17 0.03715 -0.05013 -0.03288 0.00747 -0.01299
18 0.00205 0.03715 -0.05013 -0.03288 0.00747
19 0.02912 0.00205 0.03715 -0.05013 -0.03288
20 -0.00832 0.02912 0.00205 0.03715 -0.05013
21 0.02908 -0.00832 0.02912 0.00205 0.03715
22 -0.00942 0.02908 -0.00832 0.02912 0.00205
23 0.04381 -0.00942 0.02908 -0.00832 0.02912
24 0.00603 0.04381 -0.00942 0.02908 -0.00832
25 0.02253 0.00603 0.04381 -0.00942 0.02908
26 0.05789 0.02253 0.00603 0.04381 -0.00942
27 -0.03783 0.05789 0.02253 0.00603 0.04381
28 -0.03176 -0.03783 0.05789 0.02253 0.00603
29 -0.00572 -0.03176 -0.03783 0.05789 0.02253
30 0.01040 -0.00572 -0.03176 -0.03783 0.05789
31 0.03662 0.01040 -0.00572 -0.03176 -0.03783
32 0.03771 0.03662 0.01040 -0.00572 -0.03176
33 0.05981 0.03771 0.03662 0.01040 -0.00572
34 -0.03204 0.05981 0.03771 0.03662 0.01040
35 0.02837 -0.03204 0.05981 0.03771 0.03662
36 0.05003 0.02837 -0.03204 0.05981 0.03771
37 0.04980 0.05003 0.02837 -0.03204 0.05981
38 -0.02299 0.04980 0.05003 0.02837 -0.03204
39 0.04030 -0.02299 0.04980 0.05003 0.02837
40 0.03176 0.04030 -0.02299 0.04980 0.05003
41 -0.00135 0.03176 0.04030 -0.02299 0.04980
42 -0.02473 -0.00135 0.03176 0.04030 -0.02299
43 -0.00171 -0.02473 -0.00135 0.03176 0.04030
44 -0.01575 -0.00171 -0.02473 -0.00135 0.03176
45 -0.02624 -0.01575 -0.00171 -0.02473 -0.00135
46 0.06724 -0.02624 -0.01575 -0.00171 -0.02473
47 -0.01362 0.06724 -0.02624 -0.01575 -0.00171
48 -0.00422 -0.01362 0.06724 -0.02624 -0.01575
49 0.00754 -0.00422 -0.01362 0.06724 -0.02624
50 0.00087 0.00754 -0.00422 -0.01362 0.06724
51 0.02715 0.00087 0.00754 -0.00422 -0.01362
52 0.02976 0.02715 0.00087 0.00754 -0.00422
53 0.07946 0.02976 0.02715 0.00087 0.00754
54 0.01909 0.07946 0.02976 0.02715 0.00087
55 -0.02483 0.01909 0.07946 0.02976 0.02715
56 -0.01870 -0.02483 0.01909 0.07946 0.02976
57 0.09682 -0.01870 -0.02483 0.01909 0.07946
58 0.03823 0.09682 -0.01870 -0.02483 0.01909
59 0.09571 0.03823 0.09682 -0.01870 -0.02483
60 -0.04663 0.09571 0.03823 0.09682 -0.01870
61 -0.01359 -0.04663 0.09571 0.03823 0.09682
62 0.05114 -0.01359 -0.04663 0.09571 0.03823
63 -0.04275 0.05114 -0.01359 -0.04663 0.09571
64 0.05739 -0.04275 0.05114 -0.01359 -0.04663
65 0.01186 0.05739 -0.04275 0.05114 -0.01359
66 0.01066 0.01186 0.05739 -0.04275 0.05114
67 -0.07387 0.01066 0.01186 0.05739 -0.04275
68 -0.04131 -0.07387 0.01066 0.01186 0.05739
69 -0.17889 -0.04131 -0.07387 0.01066 0.01186
70 -0.12781 -0.17889 -0.04131 -0.07387 0.01066
71 -0.26933 -0.12781 -0.17889 -0.04131 -0.07387
72 -0.05095 -0.26933 -0.12781 -0.17889 -0.04131
73 -0.01074 -0.05095 -0.26933 -0.12781 -0.17889
74 0.08172 -0.01074 -0.05095 -0.26933 -0.12781
75 0.11870 0.08172 -0.01074 -0.05095 -0.26933
76 0.08475 0.11870 0.08172 -0.01074 -0.05095
77 0.04663 0.08475 0.11870 0.08172 -0.01074
78 -0.04415 0.04663 0.08475 0.11870 0.08172
79 0.00970 -0.04415 0.04663 0.08475 0.11870
80 -0.03341 0.00970 -0.04415 0.04663 0.08475
81 0.04031 -0.03341 0.00970 -0.04415 0.04663
82 0.01938 0.04031 -0.03341 0.00970 -0.04415
83 0.05928 0.01938 0.04031 -0.03341 0.00970
84 0.02343 0.05928 0.01938 0.04031 -0.03341
85 -0.04536 0.02343 0.05928 0.01938 0.04031
86 0.03355 -0.04536 0.02343 0.05928 0.01938
87 0.05659 0.03355 -0.04536 0.02343 0.05928
88 -0.06579 0.05659 0.03355 -0.04536 0.02343
89 -0.04267 -0.06579 0.05659 0.03355 -0.04536
90 -0.02422 -0.04267 -0.06579 0.05659 0.03355
91 0.07584 -0.02422 -0.04267 -0.06579 0.05659
92 -0.00903 0.07584 -0.02422 -0.04267 -0.06579
93 0.06617 -0.00903 0.07584 -0.02422 -0.04267
94 0.04485 0.06617 -0.00903 0.07584 -0.02422
95 -0.00665 0.04485 0.06617 -0.00903 0.07584
(1-B)lnY_[t-5] (1-B)lnX_[t-1] (1-B)lnX_[t-2] (1-B)lnX_[t-3] (1-B)lnX_[t-4]
1 0.00779 0.00149 0.01848 0.00338 0.00099
2 -0.03429 0.01244 0.00149 0.01848 0.00338
3 0.04399 0.01150 0.01244 0.00149 0.01848
4 0.04860 -0.00793 0.01150 0.01244 0.00149
5 -0.01025 -0.01514 -0.00793 0.01150 0.01244
6 -0.03086 0.01778 -0.01514 -0.00793 0.01150
7 0.04033 0.00634 0.01778 -0.01514 -0.00793
8 -0.02352 0.00770 0.00634 0.01778 -0.01514
9 0.00573 0.00692 0.00770 0.00634 0.01778
10 0.01805 0.00029 0.00692 0.00770 0.00634
11 -0.01887 0.02487 0.00029 0.00692 0.00770
12 0.04363 0.01708 0.02487 0.00029 0.00692
13 0.02875 0.02540 0.01708 0.02487 0.00029
14 -0.00393 0.02935 0.02540 0.01708 0.02487
15 0.05280 0.02615 0.02935 0.02540 0.01708
16 -0.00351 0.00424 0.02615 0.02935 0.02540
17 0.05407 -0.00032 0.00424 0.02615 0.02935
18 -0.01299 -0.02353 -0.00032 0.00424 0.02615
19 0.00747 0.01387 -0.02353 -0.00032 0.00424
20 -0.03288 0.01286 0.01387 -0.02353 -0.00032
21 -0.05013 -0.00609 0.01286 0.01387 -0.02353
22 0.03715 0.00635 -0.00609 0.01286 0.01387
23 0.00205 0.02049 0.00635 -0.00609 0.01286
24 0.02912 0.00332 0.02049 0.00635 -0.00609
25 -0.00832 0.00409 0.00332 0.02049 0.00635
26 0.02908 0.02753 0.00409 0.00332 0.02049
27 -0.00942 0.01205 0.02753 0.00409 0.00332
28 0.04381 0.01773 0.01205 0.02753 0.00409
29 0.00603 -0.00897 0.01773 0.01205 0.02753
30 0.02253 -0.01226 -0.00897 0.01773 0.01205
31 0.05789 0.00644 -0.01226 -0.00897 0.01773
32 -0.03783 -0.00059 0.00644 -0.01226 -0.00897
33 -0.03176 0.01707 -0.00059 0.00644 -0.01226
34 -0.00572 -0.00104 0.01707 -0.00059 0.00644
35 0.01040 0.01272 -0.00104 0.01707 -0.00059
36 0.03662 0.01859 0.01272 -0.00104 0.01707
37 0.03771 0.03238 0.01859 0.01272 -0.00104
38 0.05981 0.03132 0.03238 0.01859 0.01272
39 -0.03204 0.01412 0.03132 0.03238 0.01859
40 0.02837 0.00588 0.01412 0.03132 0.03238
41 0.05003 0.05686 0.00588 0.01412 0.03132
42 0.04980 0.05681 0.05686 0.00588 0.01412
43 -0.02299 -0.04078 0.05681 0.05686 0.00588
44 0.04030 0.02507 -0.04078 0.05681 0.05686
45 0.03176 0.00600 0.02507 -0.04078 0.05681
46 -0.00135 0.00249 0.00600 0.02507 -0.04078
47 -0.02473 0.01885 0.00249 0.00600 0.02507
48 -0.00171 0.00125 0.01885 0.00249 0.00600
49 -0.01575 0.00695 0.00125 0.01885 0.00249
50 -0.02624 -0.01563 0.00695 0.00125 0.01885
51 0.06724 0.00814 -0.01563 0.00695 0.00125
52 -0.01362 0.02368 0.00814 -0.01563 0.00695
53 -0.00422 0.04099 0.02368 0.00814 -0.01563
54 0.00754 0.00731 0.04099 0.02368 0.00814
55 0.00087 -0.01730 0.00731 0.04099 0.02368
56 0.02715 -0.00183 -0.01730 0.00731 0.04099
57 0.02976 -0.03830 -0.00183 -0.01730 0.00731
58 0.07946 -0.01249 -0.03830 -0.00183 -0.01730
59 0.01909 0.01229 -0.01249 -0.03830 -0.00183
60 -0.02483 -0.01747 0.01229 -0.01249 -0.03830
61 -0.01870 -0.02645 -0.01747 0.01229 -0.01249
62 0.09682 0.04038 -0.02645 -0.01747 0.01229
63 0.03823 0.02925 0.04038 -0.02645 -0.01747
64 0.09571 0.02270 0.02925 0.04038 -0.02645
65 -0.04663 -0.00460 0.02270 0.02925 0.04038
66 -0.01359 -0.01894 -0.00460 0.02270 0.02925
67 0.05114 -0.00966 -0.01894 -0.00460 0.02270
68 -0.04275 0.00392 -0.00966 -0.01894 -0.00460
69 0.05739 -0.03105 0.00392 -0.00966 -0.01894
70 0.01186 -0.02790 -0.03105 0.00392 -0.00966
71 0.01066 -0.09625 -0.02790 -0.03105 0.00392
72 -0.07387 -0.05388 -0.09625 -0.02790 -0.03105
73 -0.04131 -0.05034 -0.05388 -0.09625 -0.02790
74 -0.17889 -0.02846 -0.05034 -0.05388 -0.09625
75 -0.12781 -0.01454 -0.02846 -0.05034 -0.05388
76 -0.26933 0.01284 -0.01454 -0.02846 -0.05034
77 -0.05095 0.03762 0.01284 -0.01454 -0.02846
78 -0.01074 0.01973 0.03762 0.01284 -0.01454
79 0.08172 0.03178 0.01973 0.03762 0.01284
80 0.11870 0.01329 0.03178 0.01973 0.03762
81 0.08475 0.05094 0.01329 0.03178 0.01973
82 0.04663 -0.00804 0.05094 0.01329 0.03178
83 -0.04415 0.01116 -0.00804 0.05094 0.01329
84 0.00970 0.01128 0.01116 -0.00804 0.05094
85 -0.03341 0.02227 0.01128 0.01116 -0.00804
86 0.04031 0.01494 0.02227 0.01128 0.01116
87 0.01938 -0.02514 0.01494 0.02227 0.01128
88 0.05928 0.02975 -0.02514 0.01494 0.02227
89 0.02343 0.05216 0.02975 -0.02514 0.01494
90 -0.04536 -0.04459 0.05216 0.02975 -0.02514
91 0.03355 -0.02212 -0.04459 0.05216 0.02975
92 0.05659 0.03171 -0.02212 -0.04459 0.05216
93 -0.06579 0.02985 0.03171 -0.02212 -0.04459
94 -0.04267 0.01545 0.02985 0.03171 -0.02212
95 -0.02422 0.01140 0.01545 0.02985 0.03171
(1-B)lnX_[t-5]
1 -0.01826
2 0.00099
3 0.00338
4 0.01848
5 0.00149
6 0.01244
7 0.01150
8 -0.00793
9 -0.01514
10 0.01778
11 0.00634
12 0.00770
13 0.00692
14 0.00029
15 0.02487
16 0.01708
17 0.02540
18 0.02935
19 0.02615
20 0.00424
21 -0.00032
22 -0.02353
23 0.01387
24 0.01286
25 -0.00609
26 0.00635
27 0.02049
28 0.00332
29 0.00409
30 0.02753
31 0.01205
32 0.01773
33 -0.00897
34 -0.01226
35 0.00644
36 -0.00059
37 0.01707
38 -0.00104
39 0.01272
40 0.01859
41 0.03238
42 0.03132
43 0.01412
44 0.00588
45 0.05686
46 0.05681
47 -0.04078
48 0.02507
49 0.00600
50 0.00249
51 0.01885
52 0.00125
53 0.00695
54 -0.01563
55 0.00814
56 0.02368
57 0.04099
58 0.00731
59 -0.01730
60 -0.00183
61 -0.03830
62 -0.01249
63 0.01229
64 -0.01747
65 -0.02645
66 0.04038
67 0.02925
68 0.02270
69 -0.00460
70 -0.01894
71 -0.00966
72 0.00392
73 -0.03105
74 -0.02790
75 -0.09625
76 -0.05388
77 -0.05034
78 -0.02846
79 -0.01454
80 0.01284
81 0.03762
82 0.01973
83 0.03178
84 0.01329
85 0.05094
86 -0.00804
87 0.01116
88 0.01128
89 0.02227
90 0.01494
91 -0.02514
92 0.02975
93 0.05216
94 -0.04459
95 -0.02212
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `(1-B)lnY_[t-1]` `(1-B)lnY_[t-2]` `(1-B)lnY_[t-3]`
0.002987 0.279842 0.092460 -0.099940
`(1-B)lnY_[t-4]` `(1-B)lnY_[t-5]` `(1-B)lnX_[t-1]` `(1-B)lnX_[t-2]`
-0.133172 -0.131647 0.630661 -0.338976
`(1-B)lnX_[t-3]` `(1-B)lnX_[t-4]` `(1-B)lnX_[t-5]`
0.510545 -0.405092 0.148230
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.162457 -0.029780 0.006978 0.026954 0.147021
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.002987 0.005339 0.559 0.5774
`(1-B)lnY_[t-1]` 0.279842 0.111326 2.514 0.0139 *
`(1-B)lnY_[t-2]` 0.092460 0.127783 0.724 0.4713
`(1-B)lnY_[t-3]` -0.099940 0.125216 -0.798 0.4270
`(1-B)lnY_[t-4]` -0.133172 0.121284 -1.098 0.2753
`(1-B)lnY_[t-5]` -0.131647 0.125205 -1.051 0.2961
`(1-B)lnX_[t-1]` 0.630661 0.274683 2.296 0.0242 *
`(1-B)lnX_[t-2]` -0.338976 0.270783 -1.252 0.2141
`(1-B)lnX_[t-3]` 0.510545 0.264283 1.932 0.0568 .
`(1-B)lnX_[t-4]` -0.405092 0.268436 -1.509 0.1350
`(1-B)lnX_[t-5]` 0.148230 0.243634 0.608 0.5446
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04873 on 84 degrees of freedom
Multiple R-squared: 0.2921, Adjusted R-squared: 0.2078
F-statistic: 3.466 on 10 and 84 DF, p-value: 0.0007522
> 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,] 3.191502e-01 6.383004e-01 0.6808498
[2,] 1.988411e-01 3.976822e-01 0.8011589
[3,] 2.655635e-01 5.311271e-01 0.7344365
[4,] 2.479599e-01 4.959197e-01 0.7520401
[5,] 2.165562e-01 4.331125e-01 0.7834438
[6,] 1.374572e-01 2.749144e-01 0.8625428
[7,] 8.367198e-02 1.673440e-01 0.9163280
[8,] 4.864329e-02 9.728658e-02 0.9513567
[9,] 2.867067e-02 5.734134e-02 0.9713293
[10,] 1.986286e-02 3.972572e-02 0.9801371
[11,] 1.040060e-02 2.080120e-02 0.9895994
[12,] 5.732891e-03 1.146578e-02 0.9942671
[13,] 8.527400e-03 1.705480e-02 0.9914726
[14,] 6.396508e-03 1.279302e-02 0.9936035
[15,] 1.174148e-02 2.348296e-02 0.9882585
[16,] 7.220313e-03 1.444063e-02 0.9927797
[17,] 4.186189e-03 8.372378e-03 0.9958138
[18,] 2.403455e-03 4.806911e-03 0.9975965
[19,] 1.478109e-03 2.956219e-03 0.9985219
[20,] 1.448566e-03 2.897133e-03 0.9985514
[21,] 9.110173e-04 1.822035e-03 0.9990890
[22,] 4.806981e-04 9.613962e-04 0.9995193
[23,] 5.064331e-04 1.012866e-03 0.9994936
[24,] 4.805524e-04 9.611048e-04 0.9995194
[25,] 2.782701e-04 5.565403e-04 0.9997217
[26,] 4.669169e-04 9.338338e-04 0.9995331
[27,] 4.277752e-04 8.555503e-04 0.9995722
[28,] 2.812696e-04 5.625393e-04 0.9997187
[29,] 1.779231e-04 3.558461e-04 0.9998221
[30,] 1.170411e-04 2.340822e-04 0.9998830
[31,] 7.451147e-05 1.490229e-04 0.9999255
[32,] 4.389806e-05 8.779613e-05 0.9999561
[33,] 2.327526e-05 4.655051e-05 0.9999767
[34,] 1.617989e-05 3.235978e-05 0.9999838
[35,] 7.771604e-06 1.554321e-05 0.9999922
[36,] 3.904325e-06 7.808649e-06 0.9999961
[37,] 1.800643e-06 3.601287e-06 0.9999982
[38,] 8.250665e-07 1.650133e-06 0.9999992
[39,] 3.945517e-07 7.891034e-07 0.9999996
[40,] 5.957792e-07 1.191558e-06 0.9999994
[41,] 4.770119e-07 9.540239e-07 0.9999995
[42,] 2.191375e-07 4.382751e-07 0.9999998
[43,] 1.027396e-07 2.054793e-07 0.9999999
[44,] 1.081922e-05 2.163844e-05 0.9999892
[45,] 6.419243e-06 1.283849e-05 0.9999936
[46,] 6.216102e-05 1.243220e-04 0.9999378
[47,] 1.436707e-04 2.873414e-04 0.9998563
[48,] 3.177439e-04 6.354879e-04 0.9996823
[49,] 4.391837e-04 8.783675e-04 0.9995608
[50,] 7.642347e-04 1.528469e-03 0.9992358
[51,] 4.638446e-04 9.276892e-04 0.9995362
[52,] 4.993563e-04 9.987126e-04 0.9995006
[53,] 4.578574e-04 9.157147e-04 0.9995421
[54,] 8.086458e-04 1.617292e-03 0.9991914
[55,] 1.188907e-03 2.377813e-03 0.9988111
[56,] 2.864620e-02 5.729240e-02 0.9713538
[57,] 4.200764e-02 8.401528e-02 0.9579924
[58,] 4.936763e-01 9.873527e-01 0.5063237
[59,] 4.943617e-01 9.887234e-01 0.5056383
[60,] 4.957595e-01 9.915190e-01 0.5042405
[61,] 4.238387e-01 8.476775e-01 0.5761613
[62,] 3.701807e-01 7.403614e-01 0.6298193
[63,] 2.942704e-01 5.885408e-01 0.7057296
[64,] 2.503789e-01 5.007579e-01 0.7496211
[65,] 1.782534e-01 3.565068e-01 0.8217466
[66,] 1.341278e-01 2.682555e-01 0.8658722
[67,] 7.581091e-02 1.516218e-01 0.9241891
[68,] 1.310764e-01 2.621527e-01 0.8689236
> postscript(file="/var/www/html/rcomp/tmp/13adl1293047476.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v1u61293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56su91293047476.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 = 95
Frequency = 1
1 2 3 4 5
-0.0279099565 0.0375751724 -0.0205120194 0.0079574506 0.0200131597
6 7 8 9 10
-0.0379622576 0.0518458295 -0.0019730894 -0.0132683120 0.0506427135
11 12 13 14 15
-0.0316495364 0.0555356121 -0.0460796671 0.0006939173 -0.0411471282
16 17 18 19 20
-0.0402027358 0.0537792346 0.0081095683 -0.0049993585 -0.0190908212
21 22 23 24 25
0.0159160413 -0.0152296393 0.0395798438 -0.0054773361 0.0072008993
26 27 28 29 30
0.0446813302 -0.0559511804 -0.0406783852 0.0293193411 0.0152848751
31 32 33 34 35
0.0324190874 0.0162848933 0.0210908314 -0.0397895978 0.0205507056
36 37 38 39 40
0.0580078158 0.0163519325 -0.0510568609 0.0345278354 0.0304446762
41 42 43 44 45
-0.0392174526 -0.0412319872 0.0240977410 -0.0430479940 0.0169876321
46 47 48 49 50
0.0321340027 -0.0359613850 -0.0115129194 -0.0053183716 0.0191440098
51 52 53 54 55
0.0135710775 0.0159286369 0.0368287875 -0.0022537958 -0.0329544595
56 57 58 59 60
0.0036275762 0.1470211475 0.0081360612 0.0798026627 -0.0697016219
61 62 63 64 65
0.0069775144 0.0649872905 -0.0458266991 0.0335280433 0.0098687769
66 67 68 69 70
0.0044562793 -0.0672813363 -0.0227156837 -0.1344344852 -0.0773548452
71 72 73 74 75
-0.1624566962 0.0025845205 0.0393223981 0.0124997709 0.0537166620
76 77 78 79 80
-0.0132269068 -0.0070357871 -0.0546075537 0.0245879230 0.0021982278
81 82 83 84 85
0.0173003887 0.0349324189 0.0041855124 0.0223578106 -0.0842045708
86 87 88 89 90
0.0529830071 0.0736602514 -0.1088863816 -0.0393062530 0.0131838345
91 92 93 94 95
0.0769373609 -0.0245880915 0.0193560574 0.0047518547 -0.0233648458
> postscript(file="/var/www/html/rcomp/tmp/66su91293047476.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 = 95
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0279099565 NA
1 0.0375751724 -0.0279099565
2 -0.0205120194 0.0375751724
3 0.0079574506 -0.0205120194
4 0.0200131597 0.0079574506
5 -0.0379622576 0.0200131597
6 0.0518458295 -0.0379622576
7 -0.0019730894 0.0518458295
8 -0.0132683120 -0.0019730894
9 0.0506427135 -0.0132683120
10 -0.0316495364 0.0506427135
11 0.0555356121 -0.0316495364
12 -0.0460796671 0.0555356121
13 0.0006939173 -0.0460796671
14 -0.0411471282 0.0006939173
15 -0.0402027358 -0.0411471282
16 0.0537792346 -0.0402027358
17 0.0081095683 0.0537792346
18 -0.0049993585 0.0081095683
19 -0.0190908212 -0.0049993585
20 0.0159160413 -0.0190908212
21 -0.0152296393 0.0159160413
22 0.0395798438 -0.0152296393
23 -0.0054773361 0.0395798438
24 0.0072008993 -0.0054773361
25 0.0446813302 0.0072008993
26 -0.0559511804 0.0446813302
27 -0.0406783852 -0.0559511804
28 0.0293193411 -0.0406783852
29 0.0152848751 0.0293193411
30 0.0324190874 0.0152848751
31 0.0162848933 0.0324190874
32 0.0210908314 0.0162848933
33 -0.0397895978 0.0210908314
34 0.0205507056 -0.0397895978
35 0.0580078158 0.0205507056
36 0.0163519325 0.0580078158
37 -0.0510568609 0.0163519325
38 0.0345278354 -0.0510568609
39 0.0304446762 0.0345278354
40 -0.0392174526 0.0304446762
41 -0.0412319872 -0.0392174526
42 0.0240977410 -0.0412319872
43 -0.0430479940 0.0240977410
44 0.0169876321 -0.0430479940
45 0.0321340027 0.0169876321
46 -0.0359613850 0.0321340027
47 -0.0115129194 -0.0359613850
48 -0.0053183716 -0.0115129194
49 0.0191440098 -0.0053183716
50 0.0135710775 0.0191440098
51 0.0159286369 0.0135710775
52 0.0368287875 0.0159286369
53 -0.0022537958 0.0368287875
54 -0.0329544595 -0.0022537958
55 0.0036275762 -0.0329544595
56 0.1470211475 0.0036275762
57 0.0081360612 0.1470211475
58 0.0798026627 0.0081360612
59 -0.0697016219 0.0798026627
60 0.0069775144 -0.0697016219
61 0.0649872905 0.0069775144
62 -0.0458266991 0.0649872905
63 0.0335280433 -0.0458266991
64 0.0098687769 0.0335280433
65 0.0044562793 0.0098687769
66 -0.0672813363 0.0044562793
67 -0.0227156837 -0.0672813363
68 -0.1344344852 -0.0227156837
69 -0.0773548452 -0.1344344852
70 -0.1624566962 -0.0773548452
71 0.0025845205 -0.1624566962
72 0.0393223981 0.0025845205
73 0.0124997709 0.0393223981
74 0.0537166620 0.0124997709
75 -0.0132269068 0.0537166620
76 -0.0070357871 -0.0132269068
77 -0.0546075537 -0.0070357871
78 0.0245879230 -0.0546075537
79 0.0021982278 0.0245879230
80 0.0173003887 0.0021982278
81 0.0349324189 0.0173003887
82 0.0041855124 0.0349324189
83 0.0223578106 0.0041855124
84 -0.0842045708 0.0223578106
85 0.0529830071 -0.0842045708
86 0.0736602514 0.0529830071
87 -0.1088863816 0.0736602514
88 -0.0393062530 -0.1088863816
89 0.0131838345 -0.0393062530
90 0.0769373609 0.0131838345
91 -0.0245880915 0.0769373609
92 0.0193560574 -0.0245880915
93 0.0047518547 0.0193560574
94 -0.0233648458 0.0047518547
95 NA -0.0233648458
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0375751724 -0.0279099565
[2,] -0.0205120194 0.0375751724
[3,] 0.0079574506 -0.0205120194
[4,] 0.0200131597 0.0079574506
[5,] -0.0379622576 0.0200131597
[6,] 0.0518458295 -0.0379622576
[7,] -0.0019730894 0.0518458295
[8,] -0.0132683120 -0.0019730894
[9,] 0.0506427135 -0.0132683120
[10,] -0.0316495364 0.0506427135
[11,] 0.0555356121 -0.0316495364
[12,] -0.0460796671 0.0555356121
[13,] 0.0006939173 -0.0460796671
[14,] -0.0411471282 0.0006939173
[15,] -0.0402027358 -0.0411471282
[16,] 0.0537792346 -0.0402027358
[17,] 0.0081095683 0.0537792346
[18,] -0.0049993585 0.0081095683
[19,] -0.0190908212 -0.0049993585
[20,] 0.0159160413 -0.0190908212
[21,] -0.0152296393 0.0159160413
[22,] 0.0395798438 -0.0152296393
[23,] -0.0054773361 0.0395798438
[24,] 0.0072008993 -0.0054773361
[25,] 0.0446813302 0.0072008993
[26,] -0.0559511804 0.0446813302
[27,] -0.0406783852 -0.0559511804
[28,] 0.0293193411 -0.0406783852
[29,] 0.0152848751 0.0293193411
[30,] 0.0324190874 0.0152848751
[31,] 0.0162848933 0.0324190874
[32,] 0.0210908314 0.0162848933
[33,] -0.0397895978 0.0210908314
[34,] 0.0205507056 -0.0397895978
[35,] 0.0580078158 0.0205507056
[36,] 0.0163519325 0.0580078158
[37,] -0.0510568609 0.0163519325
[38,] 0.0345278354 -0.0510568609
[39,] 0.0304446762 0.0345278354
[40,] -0.0392174526 0.0304446762
[41,] -0.0412319872 -0.0392174526
[42,] 0.0240977410 -0.0412319872
[43,] -0.0430479940 0.0240977410
[44,] 0.0169876321 -0.0430479940
[45,] 0.0321340027 0.0169876321
[46,] -0.0359613850 0.0321340027
[47,] -0.0115129194 -0.0359613850
[48,] -0.0053183716 -0.0115129194
[49,] 0.0191440098 -0.0053183716
[50,] 0.0135710775 0.0191440098
[51,] 0.0159286369 0.0135710775
[52,] 0.0368287875 0.0159286369
[53,] -0.0022537958 0.0368287875
[54,] -0.0329544595 -0.0022537958
[55,] 0.0036275762 -0.0329544595
[56,] 0.1470211475 0.0036275762
[57,] 0.0081360612 0.1470211475
[58,] 0.0798026627 0.0081360612
[59,] -0.0697016219 0.0798026627
[60,] 0.0069775144 -0.0697016219
[61,] 0.0649872905 0.0069775144
[62,] -0.0458266991 0.0649872905
[63,] 0.0335280433 -0.0458266991
[64,] 0.0098687769 0.0335280433
[65,] 0.0044562793 0.0098687769
[66,] -0.0672813363 0.0044562793
[67,] -0.0227156837 -0.0672813363
[68,] -0.1344344852 -0.0227156837
[69,] -0.0773548452 -0.1344344852
[70,] -0.1624566962 -0.0773548452
[71,] 0.0025845205 -0.1624566962
[72,] 0.0393223981 0.0025845205
[73,] 0.0124997709 0.0393223981
[74,] 0.0537166620 0.0124997709
[75,] -0.0132269068 0.0537166620
[76,] -0.0070357871 -0.0132269068
[77,] -0.0546075537 -0.0070357871
[78,] 0.0245879230 -0.0546075537
[79,] 0.0021982278 0.0245879230
[80,] 0.0173003887 0.0021982278
[81,] 0.0349324189 0.0173003887
[82,] 0.0041855124 0.0349324189
[83,] 0.0223578106 0.0041855124
[84,] -0.0842045708 0.0223578106
[85,] 0.0529830071 -0.0842045708
[86,] 0.0736602514 0.0529830071
[87,] -0.1088863816 0.0736602514
[88,] -0.0393062530 -0.1088863816
[89,] 0.0131838345 -0.0393062530
[90,] 0.0769373609 0.0131838345
[91,] -0.0245880915 0.0769373609
[92,] 0.0193560574 -0.0245880915
[93,] 0.0047518547 0.0193560574
[94,] -0.0233648458 0.0047518547
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0375751724 -0.0279099565
2 -0.0205120194 0.0375751724
3 0.0079574506 -0.0205120194
4 0.0200131597 0.0079574506
5 -0.0379622576 0.0200131597
6 0.0518458295 -0.0379622576
7 -0.0019730894 0.0518458295
8 -0.0132683120 -0.0019730894
9 0.0506427135 -0.0132683120
10 -0.0316495364 0.0506427135
11 0.0555356121 -0.0316495364
12 -0.0460796671 0.0555356121
13 0.0006939173 -0.0460796671
14 -0.0411471282 0.0006939173
15 -0.0402027358 -0.0411471282
16 0.0537792346 -0.0402027358
17 0.0081095683 0.0537792346
18 -0.0049993585 0.0081095683
19 -0.0190908212 -0.0049993585
20 0.0159160413 -0.0190908212
21 -0.0152296393 0.0159160413
22 0.0395798438 -0.0152296393
23 -0.0054773361 0.0395798438
24 0.0072008993 -0.0054773361
25 0.0446813302 0.0072008993
26 -0.0559511804 0.0446813302
27 -0.0406783852 -0.0559511804
28 0.0293193411 -0.0406783852
29 0.0152848751 0.0293193411
30 0.0324190874 0.0152848751
31 0.0162848933 0.0324190874
32 0.0210908314 0.0162848933
33 -0.0397895978 0.0210908314
34 0.0205507056 -0.0397895978
35 0.0580078158 0.0205507056
36 0.0163519325 0.0580078158
37 -0.0510568609 0.0163519325
38 0.0345278354 -0.0510568609
39 0.0304446762 0.0345278354
40 -0.0392174526 0.0304446762
41 -0.0412319872 -0.0392174526
42 0.0240977410 -0.0412319872
43 -0.0430479940 0.0240977410
44 0.0169876321 -0.0430479940
45 0.0321340027 0.0169876321
46 -0.0359613850 0.0321340027
47 -0.0115129194 -0.0359613850
48 -0.0053183716 -0.0115129194
49 0.0191440098 -0.0053183716
50 0.0135710775 0.0191440098
51 0.0159286369 0.0135710775
52 0.0368287875 0.0159286369
53 -0.0022537958 0.0368287875
54 -0.0329544595 -0.0022537958
55 0.0036275762 -0.0329544595
56 0.1470211475 0.0036275762
57 0.0081360612 0.1470211475
58 0.0798026627 0.0081360612
59 -0.0697016219 0.0798026627
60 0.0069775144 -0.0697016219
61 0.0649872905 0.0069775144
62 -0.0458266991 0.0649872905
63 0.0335280433 -0.0458266991
64 0.0098687769 0.0335280433
65 0.0044562793 0.0098687769
66 -0.0672813363 0.0044562793
67 -0.0227156837 -0.0672813363
68 -0.1344344852 -0.0227156837
69 -0.0773548452 -0.1344344852
70 -0.1624566962 -0.0773548452
71 0.0025845205 -0.1624566962
72 0.0393223981 0.0025845205
73 0.0124997709 0.0393223981
74 0.0537166620 0.0124997709
75 -0.0132269068 0.0537166620
76 -0.0070357871 -0.0132269068
77 -0.0546075537 -0.0070357871
78 0.0245879230 -0.0546075537
79 0.0021982278 0.0245879230
80 0.0173003887 0.0021982278
81 0.0349324189 0.0173003887
82 0.0041855124 0.0349324189
83 0.0223578106 0.0041855124
84 -0.0842045708 0.0223578106
85 0.0529830071 -0.0842045708
86 0.0736602514 0.0529830071
87 -0.1088863816 0.0736602514
88 -0.0393062530 -0.1088863816
89 0.0131838345 -0.0393062530
90 0.0769373609 0.0131838345
91 -0.0245880915 0.0769373609
92 0.0193560574 -0.0245880915
93 0.0047518547 0.0193560574
94 -0.0233648458 0.0047518547
> 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/7zjbt1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zjbt1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rtaf1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rtaf1293047476.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dtr21293047476.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/123xex1293047476.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/13ndm21293047476.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/14ym351293047476.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/15152t1293047476.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/16fei11293047476.tab")
+ }
>
> try(system("convert tmp/13adl1293047476.ps tmp/13adl1293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v1u61293047476.ps tmp/2v1u61293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v1u61293047476.ps tmp/3v1u61293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v1u61293047476.ps tmp/4v1u61293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/56su91293047476.ps tmp/56su91293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/66su91293047476.ps tmp/66su91293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zjbt1293047476.ps tmp/7zjbt1293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zjbt1293047476.ps tmp/8zjbt1293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rtaf1293047476.ps tmp/9rtaf1293047476.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rtaf1293047476.ps tmp/10rtaf1293047476.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.249 1.696 8.518