R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.04
+ ,493
+ ,9
+ ,3.030
+ ,9.026
+ ,25.64
+ ,104.8
+ ,3.28
+ ,481
+ ,11
+ ,2.803
+ ,9.787
+ ,27.97
+ ,105.2
+ ,3.51
+ ,462
+ ,13
+ ,2.768
+ ,9.536
+ ,27.62
+ ,105.6
+ ,3.69
+ ,457
+ ,12
+ ,2.883
+ ,9.490
+ ,23.31
+ ,105.8
+ ,3.92
+ ,442
+ ,13
+ ,2.863
+ ,9.736
+ ,29.07
+ ,106.1
+ ,4.29
+ ,439
+ ,15
+ ,2.897
+ ,9.694
+ ,29.58
+ ,106.5
+ ,4.31
+ ,488
+ ,13
+ ,3.013
+ ,9.647
+ ,28.63
+ ,106.71
+ ,4.42
+ ,521
+ ,16
+ ,3.143
+ ,9.753
+ ,29.92
+ ,106.68
+ ,4.59
+ ,501
+ ,10
+ ,3.033
+ ,10.070
+ ,32.68
+ ,107.41
+ ,4.76
+ ,485
+ ,14
+ ,3.046
+ ,10.137
+ ,31.54
+ ,107.15
+ ,4.83
+ ,464
+ ,14
+ ,3.111
+ ,9.984
+ ,32.43
+ ,107.5
+ ,4.83
+ ,460
+ ,45
+ ,3.013
+ ,9.732
+ ,26.54
+ ,107.22
+ ,4.76
+ ,467
+ ,13
+ ,2.987
+ ,9.103
+ ,25.85
+ ,107.11
+ ,4.99
+ ,460
+ ,8
+ ,2.996
+ ,9.155
+ ,27.60
+ ,107.57
+ ,4.78
+ ,448
+ ,7
+ ,2.833
+ ,9.308
+ ,25.71
+ ,107.81
+ ,5.06
+ ,443
+ ,3
+ ,2.849
+ ,9.394
+ ,25.38
+ ,108.75
+ ,4.65
+ ,436
+ ,3
+ ,2.795
+ ,9.948
+ ,28.57
+ ,109.43
+ ,4.54
+ ,431
+ ,4
+ ,2.845
+ ,10.177
+ ,27.64
+ ,109.62
+ ,4.51
+ ,484
+ ,4
+ ,2.915
+ ,10.002
+ ,25.36
+ ,109.54
+ ,4.49
+ ,510
+ ,0
+ ,2.893
+ ,9.728
+ ,25.90
+ ,109.53
+ ,3.99
+ ,513
+ ,-4
+ ,2.604
+ ,10.002
+ ,26.29
+ ,109.84
+ ,3.97
+ ,503
+ ,-14
+ ,2.642
+ ,10.063
+ ,21.74
+ ,109.67
+ ,3.51
+ ,471
+ ,-18
+ ,2.660
+ ,10.018
+ ,19.20
+ ,109.79
+ ,3.34
+ ,471
+ ,-8
+ ,2.639
+ ,9.960
+ ,19.32
+ ,109.56
+ ,3.29
+ ,476
+ ,-1
+ ,2.720
+ ,10.236
+ ,19.82
+ ,110.22
+ ,3.28
+ ,475
+ ,1
+ ,2.746
+ ,10.893
+ ,20.36
+ ,110.4
+ ,3.26
+ ,470
+ ,2
+ ,2.736
+ ,10.756
+ ,24.31
+ ,110.69
+ ,3.32
+ ,461
+ ,0
+ ,2.812
+ ,10.940
+ ,25.97
+ ,110.72
+ ,3.31
+ ,455
+ ,1
+ ,2.799
+ ,10.997
+ ,25.61
+ ,110.89
+ ,3.35
+ ,456
+ ,0
+ ,2.555
+ ,10.827
+ ,24.67
+ ,110.58
+ ,3.30
+ ,517
+ ,-1
+ ,2.305
+ ,10.166
+ ,25.59
+ ,110.94
+ ,3.29
+ ,525
+ ,-3
+ ,2.215
+ ,10.186
+ ,26.09
+ ,110.91
+ ,3.32
+ ,523
+ ,-3
+ ,2.066
+ ,10.457
+ ,28.37
+ ,111.22
+ ,3.30
+ ,519
+ ,-3
+ ,1.940
+ ,10.368
+ ,27.34
+ ,111.09
+ ,3.30
+ ,509
+ ,-4
+ ,2.042
+ ,10.244
+ ,24.46
+ ,111
+ ,3.09
+ ,512
+ ,-8
+ ,1.995
+ ,10.511
+ ,27.46
+ ,111.06
+ ,2.79
+ ,519
+ ,-9
+ ,1.947
+ ,10.812
+ ,30.23
+ ,111.55
+ ,2.76
+ ,517
+ ,-13
+ ,1.766
+ ,10.738
+ ,32.33
+ ,112.32
+ ,2.75
+ ,510
+ ,-18
+ ,1.635
+ ,10.171
+ ,29.87
+ ,112.64
+ ,2.56
+ ,509
+ ,-11
+ ,1.833
+ ,9.721
+ ,24.87
+ ,112.36
+ ,2.56
+ ,501
+ ,-9
+ ,1.910
+ ,9.897
+ ,25.48
+ ,112.04
+ ,2.21
+ ,507
+ ,-10
+ ,1.960
+ ,9.828
+ ,27.28
+ ,112.37
+ ,2.08
+ ,569
+ ,-13
+ ,1.970
+ ,9.924
+ ,28.24
+ ,112.59
+ ,2.10
+ ,580
+ ,-11
+ ,2.061
+ ,10.371
+ ,29.58
+ ,112.89
+ ,2.02
+ ,578
+ ,-5
+ ,2.093
+ ,10.846
+ ,26.95
+ ,113.22
+ ,2.01
+ ,565
+ ,-15
+ ,2.121
+ ,10.413
+ ,29.08
+ ,112.85
+ ,1.97
+ ,547
+ ,-6
+ ,2.175
+ ,10.709
+ ,28.76
+ ,113.06
+ ,2.06
+ ,555
+ ,-6
+ ,2.197
+ ,10.662
+ ,29.59
+ ,112.99
+ ,2.02
+ ,562
+ ,-3
+ ,2.350
+ ,10.570
+ ,30.70
+ ,113.32
+ ,2.03
+ ,561
+ ,-1
+ ,2.440
+ ,10.297
+ ,30.52
+ ,113.74
+ ,2.01
+ ,555
+ ,-3
+ ,2.409
+ ,10.635
+ ,32.67
+ ,113.91
+ ,2.08
+ ,544
+ ,-4
+ ,2.473
+ ,10.872
+ ,33.19
+ ,114.52
+ ,2.02
+ ,537
+ ,-6
+ ,2.408
+ ,10.296
+ ,37.13
+ ,114.96
+ ,2.03
+ ,543
+ ,0
+ ,2.455
+ ,10.383
+ ,35.54
+ ,114.91
+ ,2.07
+ ,594
+ ,-4
+ ,2.448
+ ,10.431
+ ,37.75
+ ,115.3
+ ,2.04
+ ,611
+ ,-2
+ ,2.498
+ ,10.574
+ ,41.84
+ ,115.44
+ ,2.05
+ ,613
+ ,-2
+ ,2.646
+ ,10.653
+ ,42.94
+ ,115.52
+ ,2.11
+ ,611
+ ,-6
+ ,2.757
+ ,10.805
+ ,49.14
+ ,116.08
+ ,2.09
+ ,594
+ ,-7
+ ,2.849
+ ,10.872
+ ,44.61
+ ,115.94
+ ,2.05
+ ,595
+ ,-6
+ ,2.921
+ ,10.625
+ ,40.22
+ ,115.56
+ ,2.08
+ ,591
+ ,-6
+ ,2.982
+ ,10.407
+ ,44.23
+ ,115.88
+ ,2.06
+ ,589
+ ,-3
+ ,3.081
+ ,10.463
+ ,45.85
+ ,116.66
+ ,2.06
+ ,584
+ ,-2
+ ,3.106
+ ,10.556
+ ,53.38
+ ,117.41
+ ,2.08
+ ,573
+ ,-5
+ ,3.119
+ ,10.646
+ ,53.26
+ ,117.68
+ ,2.07
+ ,567
+ ,-11
+ ,3.061
+ ,10.702
+ ,51.80
+ ,117.85
+ ,2.06
+ ,569
+ ,-11
+ ,3.097
+ ,11.353
+ ,55.30
+ ,118.21
+ ,2.07
+ ,621
+ ,-11
+ ,3.162
+ ,11.346
+ ,57.81
+ ,118.92
+ ,2.06
+ ,629
+ ,-10
+ ,3.257
+ ,11.451
+ ,63.96
+ ,119.03
+ ,2.09
+ ,628
+ ,-14
+ ,3.277
+ ,11.964
+ ,63.77
+ ,119.17
+ ,2.07
+ ,612
+ ,-8
+ ,3.295
+ ,12.574
+ ,59.15
+ ,118.95
+ ,2.09
+ ,595
+ ,-9
+ ,3.364
+ ,13.031
+ ,56.12
+ ,118.92
+ ,2.28
+ ,597
+ ,-5
+ ,3.494
+ ,13.812
+ ,57.42
+ ,118.9
+ ,2.33
+ ,593
+ ,-1
+ ,3.667
+ ,14.544
+ ,63.52
+ ,118.92
+ ,2.35
+ ,590
+ ,-2
+ ,3.813
+ ,14.931
+ ,61.71
+ ,119.44
+ ,2.52
+ ,580
+ ,-5
+ ,3.918
+ ,14.886
+ ,63.01
+ ,119.40
+ ,2.63
+ ,574
+ ,-4
+ ,3.896
+ ,16.005
+ ,68.18
+ ,119.98
+ ,2.58
+ ,573
+ ,-6
+ ,3.801
+ ,17.064
+ ,72.03
+ ,120.43
+ ,2.70
+ ,573
+ ,-2
+ ,3.570
+ ,15.168
+ ,69.75
+ ,120.41
+ ,2.81
+ ,620
+ ,-2
+ ,3.702
+ ,16.050
+ ,74.41
+ ,120.82
+ ,2.97
+ ,626
+ ,-2
+ ,3.862
+ ,15.839
+ ,74.33
+ ,120.97
+ ,3.04
+ ,620
+ ,-2
+ ,3.970
+ ,15.137
+ ,64.24
+ ,120.63
+ ,3.28
+ ,588
+ ,2
+ ,4.139
+ ,14.954
+ ,60.03
+ ,120.38
+ ,3.33
+ ,566
+ ,1
+ ,4.200
+ ,15.648
+ ,59.44
+ ,120.68
+ ,3.50
+ ,557
+ ,-8
+ ,4.291
+ ,15.305
+ ,62.50
+ ,120.84
+ ,3.56
+ ,561
+ ,-1
+ ,4.444
+ ,15.579
+ ,55.04
+ ,120.90
+ ,3.57
+ ,549
+ ,1
+ ,4.503
+ ,16.348
+ ,58.34
+ ,121.56
+ ,3.69
+ ,532
+ ,-1
+ ,4.357
+ ,15.928
+ ,61.92
+ ,121.57
+ ,3.82
+ ,526
+ ,2
+ ,4.591
+ ,16.171
+ ,67.65
+ ,122.12
+ ,3.79
+ ,511
+ ,2
+ ,4.697
+ ,15.937
+ ,67.68
+ ,121.97
+ ,3.96
+ ,499
+ ,1
+ ,4.621
+ ,15.713
+ ,70.30
+ ,121.96
+ ,4.06
+ ,555
+ ,-1
+ ,4.563
+ ,15.594
+ ,75.26
+ ,122.48
+ ,4.05
+ ,565
+ ,-2
+ ,4.203
+ ,15.683
+ ,71.44
+ ,122.33
+ ,4.03
+ ,542
+ ,-2
+ ,4.296
+ ,16.438
+ ,76.36
+ ,122.44
+ ,3.94
+ ,527
+ ,-1
+ ,4.435
+ ,17.032
+ ,81.71
+ ,123.08
+ ,4.02
+ ,510
+ ,-8
+ ,4.105
+ ,17.696
+ ,92.60
+ ,124.23
+ ,3.88
+ ,514
+ ,-4
+ ,4.117
+ ,17.745
+ ,90.60
+ ,124.58
+ ,4.02
+ ,517
+ ,-6
+ ,3.844
+ ,19.394
+ ,92.23
+ ,125.08
+ ,4.03
+ ,508
+ ,-3
+ ,3.721
+ ,20.148
+ ,94.09
+ ,125.98
+ ,4.09
+ ,493
+ ,-3
+ ,3.674
+ ,20.108
+ ,102.79
+ ,126.90
+ ,3.99
+ ,490
+ ,-7
+ ,3.858
+ ,18.584
+ ,109.65
+ ,127.19
+ ,4.01
+ ,469
+ ,-9
+ ,3.801
+ ,18.441
+ ,124.05
+ ,128.33
+ ,4.01
+ ,478
+ ,-11
+ ,3.504
+ ,18.391
+ ,132.69
+ ,129.04
+ ,4.19
+ ,528
+ ,-13
+ ,3.033
+ ,19.178
+ ,135.81
+ ,129.72
+ ,4.30
+ ,534
+ ,-11
+ ,3.047
+ ,18.079
+ ,116.07
+ ,128.92
+ ,4.27
+ ,518
+ ,-9
+ ,2.962
+ ,18.483
+ ,101.42
+ ,129.13
+ ,3.82
+ ,506
+ ,-17
+ ,2.198
+ ,19.644
+ ,75.73
+ ,128.90
+ ,3.15
+ ,502
+ ,-22
+ ,2.014
+ ,19.195
+ ,55.48
+ ,128.13
+ ,2.49
+ ,516
+ ,-25
+ ,1.863
+ ,19.650
+ ,43.80
+ ,127.85
+ ,1.81
+ ,528
+ ,-20
+ ,1.905
+ ,20.830
+ ,45.29
+ ,127.98
+ ,1.26
+ ,533
+ ,-24
+ ,1.811
+ ,23.595
+ ,44.01
+ ,128.42
+ ,1.06
+ ,536
+ ,-24
+ ,1.670
+ ,22.937
+ ,47.48
+ ,127.68
+ ,0.84
+ ,537
+ ,-22
+ ,1.864
+ ,21.814
+ ,51.07
+ ,127.95
+ ,0.78
+ ,524
+ ,-19
+ ,2.052
+ ,21.928
+ ,57.84
+ ,127.85
+ ,0.70
+ ,536
+ ,-18
+ ,2.030
+ ,21.777
+ ,69.04
+ ,127.61
+ ,0.36
+ ,587
+ ,-17
+ ,2.071
+ ,21.383
+ ,65.61
+ ,127.53
+ ,0.35
+ ,597
+ ,-11
+ ,2.293
+ ,21.467
+ ,72.87
+ ,127.92
+ ,0.36
+ ,581
+ ,-11
+ ,2.443
+ ,22.052
+ ,68.41
+ ,127.59
+ ,0.36
+ ,564
+ ,-12
+ ,2.513
+ ,22.680
+ ,73.25
+ ,127.65
+ ,0.36
+ ,558
+ ,-10
+ ,2.467
+ ,24.320
+ ,77.43
+ ,127.98
+ ,0.35
+ ,575
+ ,-15
+ ,2.503
+ ,24.977
+ ,75.28
+ ,128.19
+ ,0.34
+ ,580
+ ,-15
+ ,2.540
+ ,25.204
+ ,77.33
+ ,128.77
+ ,0.34
+ ,575
+ ,-15
+ ,2.483
+ ,25.739
+ ,74.31
+ ,129.31
+ ,0.35
+ ,563
+ ,-13
+ ,2.626
+ ,26.434
+ ,79.70
+ ,129.80
+ ,0.35
+ ,552
+ ,-8
+ ,2.656
+ ,27.525
+ ,85.47
+ ,130.24
+ ,0.34
+ ,537
+ ,-13
+ ,2.447
+ ,30.695
+ ,77.98
+ ,130.76
+ ,0.35
+ ,545
+ ,-9
+ ,2.467
+ ,32.436
+ ,75.69
+ ,130.75
+ ,0.48
+ ,601
+ ,-7
+ ,2.462
+ ,30.160
+ ,75.20
+ ,130.81
+ ,0.43
+ ,604
+ ,-4
+ ,2.505
+ ,30.236
+ ,77.21
+ ,130.89
+ ,0.45
+ ,586
+ ,-4
+ ,2.579
+ ,31.293
+ ,77.85
+ ,131.30
+ ,0.70
+ ,564
+ ,-2
+ ,2.649
+ ,31.077
+ ,83.53
+ ,131.49
+ ,0.59
+ ,549
+ ,0
+ ,2.637
+ ,32.226
+ ,85.99
+ ,131.65)
+ ,dim=c(7
+ ,131)
+ ,dimnames=list(c('Eonia'
+ ,'Werkloosheid'
+ ,'Consumentenvertrouwen'
+ ,'BEL20'
+ ,'Goudprijs'
+ ,'Olieprijs'
+ ,'CPI')
+ ,1:131))
> y <- array(NA,dim=c(7,131),dimnames=list(c('Eonia','Werkloosheid','Consumentenvertrouwen','BEL20','Goudprijs','Olieprijs','CPI'),1:131))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'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
> 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
BEL20 Eonia Werkloosheid Consumentenvertrouwen Goudprijs Olieprijs CPI
1 3.030 3.04 493 9 9.026 25.64 104.80
2 2.803 3.28 481 11 9.787 27.97 105.20
3 2.768 3.51 462 13 9.536 27.62 105.60
4 2.883 3.69 457 12 9.490 23.31 105.80
5 2.863 3.92 442 13 9.736 29.07 106.10
6 2.897 4.29 439 15 9.694 29.58 106.50
7 3.013 4.31 488 13 9.647 28.63 106.71
8 3.143 4.42 521 16 9.753 29.92 106.68
9 3.033 4.59 501 10 10.070 32.68 107.41
10 3.046 4.76 485 14 10.137 31.54 107.15
11 3.111 4.83 464 14 9.984 32.43 107.50
12 3.013 4.83 460 45 9.732 26.54 107.22
13 2.987 4.76 467 13 9.103 25.85 107.11
14 2.996 4.99 460 8 9.155 27.60 107.57
15 2.833 4.78 448 7 9.308 25.71 107.81
16 2.849 5.06 443 3 9.394 25.38 108.75
17 2.795 4.65 436 3 9.948 28.57 109.43
18 2.845 4.54 431 4 10.177 27.64 109.62
19 2.915 4.51 484 4 10.002 25.36 109.54
20 2.893 4.49 510 0 9.728 25.90 109.53
21 2.604 3.99 513 -4 10.002 26.29 109.84
22 2.642 3.97 503 -14 10.063 21.74 109.67
23 2.660 3.51 471 -18 10.018 19.20 109.79
24 2.639 3.34 471 -8 9.960 19.32 109.56
25 2.720 3.29 476 -1 10.236 19.82 110.22
26 2.746 3.28 475 1 10.893 20.36 110.40
27 2.736 3.26 470 2 10.756 24.31 110.69
28 2.812 3.32 461 0 10.940 25.97 110.72
29 2.799 3.31 455 1 10.997 25.61 110.89
30 2.555 3.35 456 0 10.827 24.67 110.58
31 2.305 3.30 517 -1 10.166 25.59 110.94
32 2.215 3.29 525 -3 10.186 26.09 110.91
33 2.066 3.32 523 -3 10.457 28.37 111.22
34 1.940 3.30 519 -3 10.368 27.34 111.09
35 2.042 3.30 509 -4 10.244 24.46 111.00
36 1.995 3.09 512 -8 10.511 27.46 111.06
37 1.947 2.79 519 -9 10.812 30.23 111.55
38 1.766 2.76 517 -13 10.738 32.33 112.32
39 1.635 2.75 510 -18 10.171 29.87 112.64
40 1.833 2.56 509 -11 9.721 24.87 112.36
41 1.910 2.56 501 -9 9.897 25.48 112.04
42 1.960 2.21 507 -10 9.828 27.28 112.37
43 1.970 2.08 569 -13 9.924 28.24 112.59
44 2.061 2.10 580 -11 10.371 29.58 112.89
45 2.093 2.02 578 -5 10.846 26.95 113.22
46 2.121 2.01 565 -15 10.413 29.08 112.85
47 2.175 1.97 547 -6 10.709 28.76 113.06
48 2.197 2.06 555 -6 10.662 29.59 112.99
49 2.350 2.02 562 -3 10.570 30.70 113.32
50 2.440 2.03 561 -1 10.297 30.52 113.74
51 2.409 2.01 555 -3 10.635 32.67 113.91
52 2.473 2.08 544 -4 10.872 33.19 114.52
53 2.408 2.02 537 -6 10.296 37.13 114.96
54 2.455 2.03 543 0 10.383 35.54 114.91
55 2.448 2.07 594 -4 10.431 37.75 115.30
56 2.498 2.04 611 -2 10.574 41.84 115.44
57 2.646 2.05 613 -2 10.653 42.94 115.52
58 2.757 2.11 611 -6 10.805 49.14 116.08
59 2.849 2.09 594 -7 10.872 44.61 115.94
60 2.921 2.05 595 -6 10.625 40.22 115.56
61 2.982 2.08 591 -6 10.407 44.23 115.88
62 3.081 2.06 589 -3 10.463 45.85 116.66
63 3.106 2.06 584 -2 10.556 53.38 117.41
64 3.119 2.08 573 -5 10.646 53.26 117.68
65 3.061 2.07 567 -11 10.702 51.80 117.85
66 3.097 2.06 569 -11 11.353 55.30 118.21
67 3.162 2.07 621 -11 11.346 57.81 118.92
68 3.257 2.06 629 -10 11.451 63.96 119.03
69 3.277 2.09 628 -14 11.964 63.77 119.17
70 3.295 2.07 612 -8 12.574 59.15 118.95
71 3.364 2.09 595 -9 13.031 56.12 118.92
72 3.494 2.28 597 -5 13.812 57.42 118.90
73 3.667 2.33 593 -1 14.544 63.52 118.92
74 3.813 2.35 590 -2 14.931 61.71 119.44
75 3.918 2.52 580 -5 14.886 63.01 119.40
76 3.896 2.63 574 -4 16.005 68.18 119.98
77 3.801 2.58 573 -6 17.064 72.03 120.43
78 3.570 2.70 573 -2 15.168 69.75 120.41
79 3.702 2.81 620 -2 16.050 74.41 120.82
80 3.862 2.97 626 -2 15.839 74.33 120.97
81 3.970 3.04 620 -2 15.137 64.24 120.63
82 4.139 3.28 588 2 14.954 60.03 120.38
83 4.200 3.33 566 1 15.648 59.44 120.68
84 4.291 3.50 557 -8 15.305 62.50 120.84
85 4.444 3.56 561 -1 15.579 55.04 120.90
86 4.503 3.57 549 1 16.348 58.34 121.56
87 4.357 3.69 532 -1 15.928 61.92 121.57
88 4.591 3.82 526 2 16.171 67.65 122.12
89 4.697 3.79 511 2 15.937 67.68 121.97
90 4.621 3.96 499 1 15.713 70.30 121.96
91 4.563 4.06 555 -1 15.594 75.26 122.48
92 4.203 4.05 565 -2 15.683 71.44 122.33
93 4.296 4.03 542 -2 16.438 76.36 122.44
94 4.435 3.94 527 -1 17.032 81.71 123.08
95 4.105 4.02 510 -8 17.696 92.60 124.23
96 4.117 3.88 514 -4 17.745 90.60 124.58
97 3.844 4.02 517 -6 19.394 92.23 125.08
98 3.721 4.03 508 -3 20.148 94.09 125.98
99 3.674 4.09 493 -3 20.108 102.79 126.90
100 3.858 3.99 490 -7 18.584 109.65 127.19
101 3.801 4.01 469 -9 18.441 124.05 128.33
102 3.504 4.01 478 -11 18.391 132.69 129.04
103 3.033 4.19 528 -13 19.178 135.81 129.72
104 3.047 4.30 534 -11 18.079 116.07 128.92
105 2.962 4.27 518 -9 18.483 101.42 129.13
106 2.198 3.82 506 -17 19.644 75.73 128.90
107 2.014 3.15 502 -22 19.195 55.48 128.13
108 1.863 2.49 516 -25 19.650 43.80 127.85
109 1.905 1.81 528 -20 20.830 45.29 127.98
110 1.811 1.26 533 -24 23.595 44.01 128.42
111 1.670 1.06 536 -24 22.937 47.48 127.68
112 1.864 0.84 537 -22 21.814 51.07 127.95
113 2.052 0.78 524 -19 21.928 57.84 127.85
114 2.030 0.70 536 -18 21.777 69.04 127.61
115 2.071 0.36 587 -17 21.383 65.61 127.53
116 2.293 0.35 597 -11 21.467 72.87 127.92
117 2.443 0.36 581 -11 22.052 68.41 127.59
118 2.513 0.36 564 -12 22.680 73.25 127.65
119 2.467 0.36 558 -10 24.320 77.43 127.98
120 2.503 0.35 575 -15 24.977 75.28 128.19
121 2.540 0.34 580 -15 25.204 77.33 128.77
122 2.483 0.34 575 -15 25.739 74.31 129.31
123 2.626 0.35 563 -13 26.434 79.70 129.80
124 2.656 0.35 552 -8 27.525 85.47 130.24
125 2.447 0.34 537 -13 30.695 77.98 130.76
126 2.467 0.35 545 -9 32.436 75.69 130.75
127 2.462 0.48 601 -7 30.160 75.20 130.81
128 2.505 0.43 604 -4 30.236 77.21 130.89
129 2.579 0.45 586 -4 31.293 77.85 131.30
130 2.649 0.70 564 -2 31.077 83.53 131.49
131 2.637 0.59 549 0 32.226 85.99 131.65
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Eonia Werkloosheid
33.6449842 0.4642603 0.0034927
Consumentenvertrouwen Goudprijs Olieprijs
0.0201134 -0.0005438 0.0181403
CPI t
-0.3340700 0.0719137
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.90625 -0.26449 -0.02716 0.30197 0.78311
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.6449842 5.7394120 5.862 3.93e-08 ***
Eonia 0.4642603 0.0546817 8.490 5.59e-14 ***
Werkloosheid 0.0034927 0.0011744 2.974 0.00354 **
Consumentenvertrouwen 0.0201134 0.0060964 3.299 0.00127 **
Goudprijs -0.0005438 0.0186282 -0.029 0.97676
Olieprijs 0.0181403 0.0035240 5.148 1.01e-06 ***
CPI -0.3340700 0.0543041 -6.152 9.88e-09 ***
t 0.0719137 0.0096391 7.461 1.35e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3697 on 123 degrees of freedom
Multiple R-squared: 0.7756, Adjusted R-squared: 0.7628
F-statistic: 60.73 on 7 and 123 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,] 2.268653e-03 4.537306e-03 9.977313e-01
[2,] 3.238139e-04 6.476279e-04 9.996762e-01
[3,] 2.252716e-03 4.505431e-03 9.977473e-01
[4,] 5.438542e-04 1.087708e-03 9.994561e-01
[5,] 1.124369e-04 2.248738e-04 9.998876e-01
[6,] 2.196662e-05 4.393323e-05 9.999780e-01
[7,] 6.819833e-06 1.363967e-05 9.999932e-01
[8,] 3.620370e-06 7.240741e-06 9.999964e-01
[9,] 6.917858e-07 1.383572e-06 9.999993e-01
[10,] 2.259537e-07 4.519075e-07 9.999998e-01
[11,] 5.779948e-07 1.155990e-06 9.999994e-01
[12,] 1.477108e-07 2.954216e-07 9.999999e-01
[13,] 3.259213e-07 6.518425e-07 9.999997e-01
[14,] 1.609481e-07 3.218963e-07 9.999998e-01
[15,] 1.187961e-07 2.375922e-07 9.999999e-01
[16,] 6.167289e-08 1.233458e-07 9.999999e-01
[17,] 2.427074e-08 4.854148e-08 1.000000e+00
[18,] 1.476818e-08 2.953636e-08 1.000000e+00
[19,] 8.953889e-09 1.790778e-08 1.000000e+00
[20,] 2.645492e-08 5.290983e-08 1.000000e+00
[21,] 4.945832e-07 9.891664e-07 9.999995e-01
[22,] 1.014946e-06 2.029893e-06 9.999990e-01
[23,] 3.249748e-06 6.499496e-06 9.999968e-01
[24,] 1.086398e-05 2.172797e-05 9.999891e-01
[25,] 8.892193e-06 1.778439e-05 9.999911e-01
[26,] 4.897319e-06 9.794638e-06 9.999951e-01
[27,] 2.296949e-06 4.593897e-06 9.999977e-01
[28,] 1.062491e-06 2.124982e-06 9.999989e-01
[29,] 4.508420e-07 9.016839e-07 9.999995e-01
[30,] 3.663686e-07 7.327371e-07 9.999996e-01
[31,] 4.325883e-07 8.651766e-07 9.999996e-01
[32,] 1.874195e-06 3.748389e-06 9.999981e-01
[33,] 6.471149e-06 1.294230e-05 9.999935e-01
[34,] 1.477170e-05 2.954340e-05 9.999852e-01
[35,] 1.095391e-05 2.190781e-05 9.999890e-01
[36,] 5.153156e-05 1.030631e-04 9.999485e-01
[37,] 1.200639e-04 2.401279e-04 9.998799e-01
[38,] 4.383291e-04 8.766582e-04 9.995617e-01
[39,] 2.261766e-03 4.523531e-03 9.977382e-01
[40,] 7.869992e-03 1.573998e-02 9.921300e-01
[41,] 2.095749e-02 4.191499e-02 9.790425e-01
[42,] 3.618868e-02 7.237736e-02 9.638113e-01
[43,] 4.938853e-02 9.877706e-02 9.506115e-01
[44,] 7.113149e-02 1.422630e-01 9.288685e-01
[45,] 1.049862e-01 2.099724e-01 8.950138e-01
[46,] 1.886268e-01 3.772535e-01 8.113732e-01
[47,] 3.497443e-01 6.994885e-01 6.502557e-01
[48,] 4.774185e-01 9.548371e-01 5.225815e-01
[49,] 6.710386e-01 6.579227e-01 3.289614e-01
[50,] 9.150732e-01 1.698537e-01 8.492685e-02
[51,] 9.856068e-01 2.878639e-02 1.439319e-02
[52,] 9.936972e-01 1.260565e-02 6.302823e-03
[53,] 9.954256e-01 9.148793e-03 4.574397e-03
[54,] 9.963346e-01 7.330820e-03 3.665410e-03
[55,] 9.963358e-01 7.328376e-03 3.664188e-03
[56,] 9.960581e-01 7.883744e-03 3.941872e-03
[57,] 9.947425e-01 1.051499e-02 5.257496e-03
[58,] 9.932761e-01 1.344780e-02 6.723900e-03
[59,] 9.941729e-01 1.165416e-02 5.827082e-03
[60,] 9.921416e-01 1.571687e-02 7.858435e-03
[61,] 9.911013e-01 1.779732e-02 8.898658e-03
[62,] 9.899747e-01 2.005068e-02 1.002534e-02
[63,] 9.920987e-01 1.580254e-02 7.901270e-03
[64,] 9.893231e-01 2.135372e-02 1.067686e-02
[65,] 9.866246e-01 2.675080e-02 1.337540e-02
[66,] 9.821146e-01 3.577078e-02 1.788539e-02
[67,] 9.812116e-01 3.757676e-02 1.878838e-02
[68,] 9.944618e-01 1.107645e-02 5.538226e-03
[69,] 9.968227e-01 6.354668e-03 3.177334e-03
[70,] 9.966713e-01 6.657302e-03 3.328651e-03
[71,] 9.966344e-01 6.731207e-03 3.365604e-03
[72,] 9.989424e-01 2.115197e-03 1.057599e-03
[73,] 9.997092e-01 5.815950e-04 2.907975e-04
[74,] 9.997264e-01 5.471430e-04 2.735715e-04
[75,] 9.997779e-01 4.441024e-04 2.220512e-04
[76,] 9.997096e-01 5.808370e-04 2.904185e-04
[77,] 9.995217e-01 9.566874e-04 4.783437e-04
[78,] 9.992513e-01 1.497429e-03 7.487144e-04
[79,] 9.991862e-01 1.627696e-03 8.138481e-04
[80,] 9.988386e-01 2.322773e-03 1.161386e-03
[81,] 9.994036e-01 1.192715e-03 5.963575e-04
[82,] 9.991320e-01 1.735940e-03 8.679702e-04
[83,] 9.987328e-01 2.534356e-03 1.267178e-03
[84,] 9.988266e-01 2.346895e-03 1.173447e-03
[85,] 9.993410e-01 1.317965e-03 6.589825e-04
[86,] 9.997079e-01 5.842880e-04 2.921440e-04
[87,] 9.998485e-01 3.029388e-04 1.514694e-04
[88,] 9.999172e-01 1.655912e-04 8.279562e-05
[89,] 9.999362e-01 1.275849e-04 6.379243e-05
[90,] 9.999779e-01 4.417828e-05 2.208914e-05
[91,] 9.999993e-01 1.416234e-06 7.081171e-07
[92,] 9.999999e-01 2.735009e-07 1.367504e-07
[93,] 9.999999e-01 1.465005e-07 7.325027e-08
[94,] 9.999999e-01 2.890000e-07 1.445000e-07
[95,] 9.999999e-01 1.020391e-07 5.101956e-08
[96,] 9.999999e-01 2.350607e-07 1.175304e-07
[97,] 9.999996e-01 8.045374e-07 4.022687e-07
[98,] 9.999985e-01 2.923376e-06 1.461688e-06
[99,] 9.999985e-01 2.962253e-06 1.481127e-06
[100,] 9.999999e-01 1.208765e-07 6.043827e-08
[101,] 9.999998e-01 4.338365e-07 2.169183e-07
[102,] 9.999990e-01 1.922525e-06 9.612623e-07
[103,] 9.999994e-01 1.180380e-06 5.901898e-07
[104,] 9.999970e-01 6.061981e-06 3.030990e-06
[105,] 9.999991e-01 1.754317e-06 8.771587e-07
[106,] 9.999984e-01 3.245621e-06 1.622810e-06
[107,] 9.999860e-01 2.803139e-05 1.401569e-05
[108,] 9.999179e-01 1.641089e-04 8.205444e-05
[109,] 9.995450e-01 9.100289e-04 4.550145e-04
[110,] 9.961194e-01 7.761272e-03 3.880636e-03
> postscript(file="/var/www/rcomp/tmp/1hkf71293038783.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2hkf71293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3hkf71293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4abws1293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5abws1293038783.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 = 131
Frequency = 1
1 2 3 4 5 6
0.549138387 0.232263108 0.184545402 0.326615758 0.156066213 0.040981305
7 8 9 10 11 12
0.032227730 -0.169720295 -0.046047363 -0.274596759 -0.139964892 -0.906251536
13 14 15 16 17 18
-0.377061745 -0.299784540 -0.260632181 -0.028562804 0.229920488 0.336894086
19 20 21 22 23 24
0.178332656 0.070061085 0.107888709 0.345100913 0.783108198 0.488940773
25 26 27 28 29 30
0.574549084 0.547238094 0.497111368 0.525012540 0.508937920 0.104472239
31 32 33 34 35 36
-0.283955062 -0.448022687 -0.613529644 -0.812980127 -0.705742665 -0.691418129
37 38 39 40 41 42
-0.562780286 -0.495228355 -0.417264211 -0.343351727 -0.468422561 -0.251135225
43 44 45 46 47 48
-0.352771523 -0.345461170 -0.303718185 -0.258930507 -0.300304206 -0.458410108
49 50 51 52 53 54
-0.353485265 -0.233349305 -0.247820738 -0.035220650 -0.004398251 -0.163404079
55 56 57 58 59 60
-0.268340855 -0.403276328 -0.332003898 -0.158642618 -0.014338901 -0.066733040
61 62 63 64 65 66
-0.043562525 0.170672094 0.235114985 0.358100884 0.457773562 0.476644994
67 68 69 70 71 72
0.475120274 0.380035660 0.448635721 0.349855534 0.462338085 0.314936905
73 74 75 76 77 78
0.222751053 0.524904569 0.537364299 0.493808498 0.474894270 0.069463582
79 80 81 82 83 84
-0.032762850 0.011532429 0.105147494 0.114879211 0.289007137 0.439379198
85 86 87 88 89 90
0.493365676 0.638536674 0.402684795 0.544960064 0.594583225 0.378781228
91 92 93 94 95 96
0.130750814 -0.292100215 -0.233488121 0.024736189 -0.027155227 0.036735031
97 98 99 100 101 102
-0.205063699 -0.166193509 -0.111070188 0.109982613 0.204898864 -0.074792776
103 104 105 106 107 108
-0.664685547 -0.744614113 -0.535812544 -0.570168831 -0.290626483 -0.077099525
109 110 111 112 113 114
0.083245858 0.407378996 -0.033677403 0.171291168 0.144143417 -0.258086346
115 116 117 118 119 120
-0.294112662 -0.296357102 -0.196048735 -0.185886215 -0.287761997 -0.168329071
121 122 123 124 125 126
-0.059367488 0.064655038 0.199080579 0.137934431 0.325932370 0.200128191
127 128 129 130 131
-0.145263628 -0.231477713 -0.049873801 -0.170920465 -0.182150517
> postscript(file="/var/www/rcomp/tmp/6abws1293038783.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 = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 0.549138387 NA
1 0.232263108 0.549138387
2 0.184545402 0.232263108
3 0.326615758 0.184545402
4 0.156066213 0.326615758
5 0.040981305 0.156066213
6 0.032227730 0.040981305
7 -0.169720295 0.032227730
8 -0.046047363 -0.169720295
9 -0.274596759 -0.046047363
10 -0.139964892 -0.274596759
11 -0.906251536 -0.139964892
12 -0.377061745 -0.906251536
13 -0.299784540 -0.377061745
14 -0.260632181 -0.299784540
15 -0.028562804 -0.260632181
16 0.229920488 -0.028562804
17 0.336894086 0.229920488
18 0.178332656 0.336894086
19 0.070061085 0.178332656
20 0.107888709 0.070061085
21 0.345100913 0.107888709
22 0.783108198 0.345100913
23 0.488940773 0.783108198
24 0.574549084 0.488940773
25 0.547238094 0.574549084
26 0.497111368 0.547238094
27 0.525012540 0.497111368
28 0.508937920 0.525012540
29 0.104472239 0.508937920
30 -0.283955062 0.104472239
31 -0.448022687 -0.283955062
32 -0.613529644 -0.448022687
33 -0.812980127 -0.613529644
34 -0.705742665 -0.812980127
35 -0.691418129 -0.705742665
36 -0.562780286 -0.691418129
37 -0.495228355 -0.562780286
38 -0.417264211 -0.495228355
39 -0.343351727 -0.417264211
40 -0.468422561 -0.343351727
41 -0.251135225 -0.468422561
42 -0.352771523 -0.251135225
43 -0.345461170 -0.352771523
44 -0.303718185 -0.345461170
45 -0.258930507 -0.303718185
46 -0.300304206 -0.258930507
47 -0.458410108 -0.300304206
48 -0.353485265 -0.458410108
49 -0.233349305 -0.353485265
50 -0.247820738 -0.233349305
51 -0.035220650 -0.247820738
52 -0.004398251 -0.035220650
53 -0.163404079 -0.004398251
54 -0.268340855 -0.163404079
55 -0.403276328 -0.268340855
56 -0.332003898 -0.403276328
57 -0.158642618 -0.332003898
58 -0.014338901 -0.158642618
59 -0.066733040 -0.014338901
60 -0.043562525 -0.066733040
61 0.170672094 -0.043562525
62 0.235114985 0.170672094
63 0.358100884 0.235114985
64 0.457773562 0.358100884
65 0.476644994 0.457773562
66 0.475120274 0.476644994
67 0.380035660 0.475120274
68 0.448635721 0.380035660
69 0.349855534 0.448635721
70 0.462338085 0.349855534
71 0.314936905 0.462338085
72 0.222751053 0.314936905
73 0.524904569 0.222751053
74 0.537364299 0.524904569
75 0.493808498 0.537364299
76 0.474894270 0.493808498
77 0.069463582 0.474894270
78 -0.032762850 0.069463582
79 0.011532429 -0.032762850
80 0.105147494 0.011532429
81 0.114879211 0.105147494
82 0.289007137 0.114879211
83 0.439379198 0.289007137
84 0.493365676 0.439379198
85 0.638536674 0.493365676
86 0.402684795 0.638536674
87 0.544960064 0.402684795
88 0.594583225 0.544960064
89 0.378781228 0.594583225
90 0.130750814 0.378781228
91 -0.292100215 0.130750814
92 -0.233488121 -0.292100215
93 0.024736189 -0.233488121
94 -0.027155227 0.024736189
95 0.036735031 -0.027155227
96 -0.205063699 0.036735031
97 -0.166193509 -0.205063699
98 -0.111070188 -0.166193509
99 0.109982613 -0.111070188
100 0.204898864 0.109982613
101 -0.074792776 0.204898864
102 -0.664685547 -0.074792776
103 -0.744614113 -0.664685547
104 -0.535812544 -0.744614113
105 -0.570168831 -0.535812544
106 -0.290626483 -0.570168831
107 -0.077099525 -0.290626483
108 0.083245858 -0.077099525
109 0.407378996 0.083245858
110 -0.033677403 0.407378996
111 0.171291168 -0.033677403
112 0.144143417 0.171291168
113 -0.258086346 0.144143417
114 -0.294112662 -0.258086346
115 -0.296357102 -0.294112662
116 -0.196048735 -0.296357102
117 -0.185886215 -0.196048735
118 -0.287761997 -0.185886215
119 -0.168329071 -0.287761997
120 -0.059367488 -0.168329071
121 0.064655038 -0.059367488
122 0.199080579 0.064655038
123 0.137934431 0.199080579
124 0.325932370 0.137934431
125 0.200128191 0.325932370
126 -0.145263628 0.200128191
127 -0.231477713 -0.145263628
128 -0.049873801 -0.231477713
129 -0.170920465 -0.049873801
130 -0.182150517 -0.170920465
131 NA -0.182150517
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.232263108 0.549138387
[2,] 0.184545402 0.232263108
[3,] 0.326615758 0.184545402
[4,] 0.156066213 0.326615758
[5,] 0.040981305 0.156066213
[6,] 0.032227730 0.040981305
[7,] -0.169720295 0.032227730
[8,] -0.046047363 -0.169720295
[9,] -0.274596759 -0.046047363
[10,] -0.139964892 -0.274596759
[11,] -0.906251536 -0.139964892
[12,] -0.377061745 -0.906251536
[13,] -0.299784540 -0.377061745
[14,] -0.260632181 -0.299784540
[15,] -0.028562804 -0.260632181
[16,] 0.229920488 -0.028562804
[17,] 0.336894086 0.229920488
[18,] 0.178332656 0.336894086
[19,] 0.070061085 0.178332656
[20,] 0.107888709 0.070061085
[21,] 0.345100913 0.107888709
[22,] 0.783108198 0.345100913
[23,] 0.488940773 0.783108198
[24,] 0.574549084 0.488940773
[25,] 0.547238094 0.574549084
[26,] 0.497111368 0.547238094
[27,] 0.525012540 0.497111368
[28,] 0.508937920 0.525012540
[29,] 0.104472239 0.508937920
[30,] -0.283955062 0.104472239
[31,] -0.448022687 -0.283955062
[32,] -0.613529644 -0.448022687
[33,] -0.812980127 -0.613529644
[34,] -0.705742665 -0.812980127
[35,] -0.691418129 -0.705742665
[36,] -0.562780286 -0.691418129
[37,] -0.495228355 -0.562780286
[38,] -0.417264211 -0.495228355
[39,] -0.343351727 -0.417264211
[40,] -0.468422561 -0.343351727
[41,] -0.251135225 -0.468422561
[42,] -0.352771523 -0.251135225
[43,] -0.345461170 -0.352771523
[44,] -0.303718185 -0.345461170
[45,] -0.258930507 -0.303718185
[46,] -0.300304206 -0.258930507
[47,] -0.458410108 -0.300304206
[48,] -0.353485265 -0.458410108
[49,] -0.233349305 -0.353485265
[50,] -0.247820738 -0.233349305
[51,] -0.035220650 -0.247820738
[52,] -0.004398251 -0.035220650
[53,] -0.163404079 -0.004398251
[54,] -0.268340855 -0.163404079
[55,] -0.403276328 -0.268340855
[56,] -0.332003898 -0.403276328
[57,] -0.158642618 -0.332003898
[58,] -0.014338901 -0.158642618
[59,] -0.066733040 -0.014338901
[60,] -0.043562525 -0.066733040
[61,] 0.170672094 -0.043562525
[62,] 0.235114985 0.170672094
[63,] 0.358100884 0.235114985
[64,] 0.457773562 0.358100884
[65,] 0.476644994 0.457773562
[66,] 0.475120274 0.476644994
[67,] 0.380035660 0.475120274
[68,] 0.448635721 0.380035660
[69,] 0.349855534 0.448635721
[70,] 0.462338085 0.349855534
[71,] 0.314936905 0.462338085
[72,] 0.222751053 0.314936905
[73,] 0.524904569 0.222751053
[74,] 0.537364299 0.524904569
[75,] 0.493808498 0.537364299
[76,] 0.474894270 0.493808498
[77,] 0.069463582 0.474894270
[78,] -0.032762850 0.069463582
[79,] 0.011532429 -0.032762850
[80,] 0.105147494 0.011532429
[81,] 0.114879211 0.105147494
[82,] 0.289007137 0.114879211
[83,] 0.439379198 0.289007137
[84,] 0.493365676 0.439379198
[85,] 0.638536674 0.493365676
[86,] 0.402684795 0.638536674
[87,] 0.544960064 0.402684795
[88,] 0.594583225 0.544960064
[89,] 0.378781228 0.594583225
[90,] 0.130750814 0.378781228
[91,] -0.292100215 0.130750814
[92,] -0.233488121 -0.292100215
[93,] 0.024736189 -0.233488121
[94,] -0.027155227 0.024736189
[95,] 0.036735031 -0.027155227
[96,] -0.205063699 0.036735031
[97,] -0.166193509 -0.205063699
[98,] -0.111070188 -0.166193509
[99,] 0.109982613 -0.111070188
[100,] 0.204898864 0.109982613
[101,] -0.074792776 0.204898864
[102,] -0.664685547 -0.074792776
[103,] -0.744614113 -0.664685547
[104,] -0.535812544 -0.744614113
[105,] -0.570168831 -0.535812544
[106,] -0.290626483 -0.570168831
[107,] -0.077099525 -0.290626483
[108,] 0.083245858 -0.077099525
[109,] 0.407378996 0.083245858
[110,] -0.033677403 0.407378996
[111,] 0.171291168 -0.033677403
[112,] 0.144143417 0.171291168
[113,] -0.258086346 0.144143417
[114,] -0.294112662 -0.258086346
[115,] -0.296357102 -0.294112662
[116,] -0.196048735 -0.296357102
[117,] -0.185886215 -0.196048735
[118,] -0.287761997 -0.185886215
[119,] -0.168329071 -0.287761997
[120,] -0.059367488 -0.168329071
[121,] 0.064655038 -0.059367488
[122,] 0.199080579 0.064655038
[123,] 0.137934431 0.199080579
[124,] 0.325932370 0.137934431
[125,] 0.200128191 0.325932370
[126,] -0.145263628 0.200128191
[127,] -0.231477713 -0.145263628
[128,] -0.049873801 -0.231477713
[129,] -0.170920465 -0.049873801
[130,] -0.182150517 -0.170920465
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.232263108 0.549138387
2 0.184545402 0.232263108
3 0.326615758 0.184545402
4 0.156066213 0.326615758
5 0.040981305 0.156066213
6 0.032227730 0.040981305
7 -0.169720295 0.032227730
8 -0.046047363 -0.169720295
9 -0.274596759 -0.046047363
10 -0.139964892 -0.274596759
11 -0.906251536 -0.139964892
12 -0.377061745 -0.906251536
13 -0.299784540 -0.377061745
14 -0.260632181 -0.299784540
15 -0.028562804 -0.260632181
16 0.229920488 -0.028562804
17 0.336894086 0.229920488
18 0.178332656 0.336894086
19 0.070061085 0.178332656
20 0.107888709 0.070061085
21 0.345100913 0.107888709
22 0.783108198 0.345100913
23 0.488940773 0.783108198
24 0.574549084 0.488940773
25 0.547238094 0.574549084
26 0.497111368 0.547238094
27 0.525012540 0.497111368
28 0.508937920 0.525012540
29 0.104472239 0.508937920
30 -0.283955062 0.104472239
31 -0.448022687 -0.283955062
32 -0.613529644 -0.448022687
33 -0.812980127 -0.613529644
34 -0.705742665 -0.812980127
35 -0.691418129 -0.705742665
36 -0.562780286 -0.691418129
37 -0.495228355 -0.562780286
38 -0.417264211 -0.495228355
39 -0.343351727 -0.417264211
40 -0.468422561 -0.343351727
41 -0.251135225 -0.468422561
42 -0.352771523 -0.251135225
43 -0.345461170 -0.352771523
44 -0.303718185 -0.345461170
45 -0.258930507 -0.303718185
46 -0.300304206 -0.258930507
47 -0.458410108 -0.300304206
48 -0.353485265 -0.458410108
49 -0.233349305 -0.353485265
50 -0.247820738 -0.233349305
51 -0.035220650 -0.247820738
52 -0.004398251 -0.035220650
53 -0.163404079 -0.004398251
54 -0.268340855 -0.163404079
55 -0.403276328 -0.268340855
56 -0.332003898 -0.403276328
57 -0.158642618 -0.332003898
58 -0.014338901 -0.158642618
59 -0.066733040 -0.014338901
60 -0.043562525 -0.066733040
61 0.170672094 -0.043562525
62 0.235114985 0.170672094
63 0.358100884 0.235114985
64 0.457773562 0.358100884
65 0.476644994 0.457773562
66 0.475120274 0.476644994
67 0.380035660 0.475120274
68 0.448635721 0.380035660
69 0.349855534 0.448635721
70 0.462338085 0.349855534
71 0.314936905 0.462338085
72 0.222751053 0.314936905
73 0.524904569 0.222751053
74 0.537364299 0.524904569
75 0.493808498 0.537364299
76 0.474894270 0.493808498
77 0.069463582 0.474894270
78 -0.032762850 0.069463582
79 0.011532429 -0.032762850
80 0.105147494 0.011532429
81 0.114879211 0.105147494
82 0.289007137 0.114879211
83 0.439379198 0.289007137
84 0.493365676 0.439379198
85 0.638536674 0.493365676
86 0.402684795 0.638536674
87 0.544960064 0.402684795
88 0.594583225 0.544960064
89 0.378781228 0.594583225
90 0.130750814 0.378781228
91 -0.292100215 0.130750814
92 -0.233488121 -0.292100215
93 0.024736189 -0.233488121
94 -0.027155227 0.024736189
95 0.036735031 -0.027155227
96 -0.205063699 0.036735031
97 -0.166193509 -0.205063699
98 -0.111070188 -0.166193509
99 0.109982613 -0.111070188
100 0.204898864 0.109982613
101 -0.074792776 0.204898864
102 -0.664685547 -0.074792776
103 -0.744614113 -0.664685547
104 -0.535812544 -0.744614113
105 -0.570168831 -0.535812544
106 -0.290626483 -0.570168831
107 -0.077099525 -0.290626483
108 0.083245858 -0.077099525
109 0.407378996 0.083245858
110 -0.033677403 0.407378996
111 0.171291168 -0.033677403
112 0.144143417 0.171291168
113 -0.258086346 0.144143417
114 -0.294112662 -0.258086346
115 -0.296357102 -0.294112662
116 -0.196048735 -0.296357102
117 -0.185886215 -0.196048735
118 -0.287761997 -0.185886215
119 -0.168329071 -0.287761997
120 -0.059367488 -0.168329071
121 0.064655038 -0.059367488
122 0.199080579 0.064655038
123 0.137934431 0.199080579
124 0.325932370 0.137934431
125 0.200128191 0.325932370
126 -0.145263628 0.200128191
127 -0.231477713 -0.145263628
128 -0.049873801 -0.231477713
129 -0.170920465 -0.049873801
130 -0.182150517 -0.170920465
> 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/rcomp/tmp/732ev1293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8dcvy1293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9dcvy1293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10dcvy1293038783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11r3t71293038783.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/rcomp/tmp/12kvar1293038783.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/rcomp/tmp/13rw731293038783.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/rcomp/tmp/14jn6o1293038783.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/rcomp/tmp/15n54u1293038783.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/rcomp/tmp/161fkl1293038783.tab")
+ }
>
> try(system("convert tmp/1hkf71293038783.ps tmp/1hkf71293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hkf71293038783.ps tmp/2hkf71293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hkf71293038783.ps tmp/3hkf71293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/4abws1293038783.ps tmp/4abws1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/5abws1293038783.ps tmp/5abws1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/6abws1293038783.ps tmp/6abws1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/732ev1293038783.ps tmp/732ev1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dcvy1293038783.ps tmp/8dcvy1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dcvy1293038783.ps tmp/9dcvy1293038783.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dcvy1293038783.ps tmp/10dcvy1293038783.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.160 1.850 6.039