R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(97687
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+ ,106972
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+ ,28905
+ ,19441
+ ,30973
+ ,46726
+ ,104495
+ ,82590
+ ,30485
+ ,13854
+ ,30982)
+ ,dim=c(11
+ ,82)
+ ,dimnames=list(c('Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST'
+ ,'Werkloosheid_ANTWERPEN'
+ ,'Werkloosheid_VLAAMS-BRABANT'
+ ,'Werkloosheid_WAALS-BRABANT'
+ ,'Werkloosheid_WEST-VLAANDEREN'
+ ,'Werkloosheid_OOST-VLAANDEREN'
+ ,'Werkloosheid_HENEGOUWEN'
+ ,'Werkloosheid_LUIK'
+ ,'Werkloosheid_LIMBURG'
+ ,'Werkloosheid_LUXEMBURG'
+ ,'Werkloosheid_NAMEN')
+ ,1:82))
> y <- array(NA,dim=c(11,82),dimnames=list(c('Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST','Werkloosheid_ANTWERPEN','Werkloosheid_VLAAMS-BRABANT','Werkloosheid_WAALS-BRABANT','Werkloosheid_WEST-VLAANDEREN','Werkloosheid_OOST-VLAANDEREN','Werkloosheid_HENEGOUWEN','Werkloosheid_LUIK','Werkloosheid_LIMBURG','Werkloosheid_LUXEMBURG','Werkloosheid_NAMEN'),1:82))
> 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'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) 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
Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST Werkloosheid_ANTWERPEN
1 97687 70863
2 98512 70806
3 98673 69484
4 96028 70150
5 98014 69210
6 95580 68733
7 97838 75930
8 97760 76162
9 99913 73891
10 97588 67348
11 93942 64297
12 93656 63111
13 93365 63263
14 92881 60733
15 93120 58521
16 91063 56734
17 90930 55327
18 91946 55257
19 94624 64301
20 95484 64261
21 95862 59119
22 95530 56530
23 94574 54445
24 94677 55462
25 93845 55333
26 91533 54048
27 91214 53213
28 90922 52764
29 89563 49933
30 89945 51515
31 91850 59302
32 92505 59681
33 92437 56195
34 93876 55210
35 93561 54698
36 94119 57875
37 95264 60611
38 96089 61857
39 97160 62885
40 98644 62313
41 96266 62056
42 97938 64702
43 99757 72334
44 101550 73577
45 102449 70290
46 102416 68633
47 102491 68311
48 102495 73335
49 104552 71257
50 104798 70743
51 104947 68932
52 103950 68045
53 102858 66338
54 106952 67339
55 110901 75744
56 107706 76098
57 111267 71483
58 107643 69240
59 105387 66421
60 105718 67840
61 106039 69663
62 106203 68564
63 105558 67149
64 105230 65656
65 104864 64412
66 104374 63910
67 107450 71415
68 108173 71369
69 108629 68474
70 107847 66073
71 107394 64685
72 106278 66445
73 107733 70281
74 107573 70149
75 107500 68677
76 106382 67404
77 104412 66627
78 105871 66856
79 108767 73889
80 109728 76518
81 109769 74592
82 109609 73417
Werkloosheid_VLAAMS-BRABANT Werkloosheid_WAALS-BRABANT
1 28779 19459
2 28802 19266
3 28027 18661
4 28551 18153
5 28159 18151
6 28354 18431
7 32439 19867
8 33368 20508
9 31846 20761
10 28765 20390
11 27107 19781
12 26368 19147
13 26444 19359
14 25326 19110
15 24375 18179
16 23899 18342
17 23065 17765
18 23279 16691
19 28134 18529
20 28438 19177
21 25717 18764
22 24125 18448
23 23050 17574
24 23489 17561
25 23238 17784
26 22625 17786
27 22223 16748
28 22036 16788
29 20921 15966
30 21982 16291
31 25828 17939
32 26099 18171
33 24168 17691
34 23333 17095
35 22695 17007
36 23884 16992
37 24835 17118
38 24930 17349
39 25283 17399
40 25056 17547
41 24583 16962
42 25967 17125
43 30042 19119
44 31011 19691
45 29404 19274
46 28233 18743
47 27552 18577
48 29009 18629
49 28645 19245
50 28472 18998
51 27613 18662
52 27078 17937
53 26260 17421
54 27078 17708
55 31018 19608
56 31546 20209
57 29293 19983
58 28528 19256
59 27151 18582
60 27241 18430
61 27640 18154
62 27106 18023
63 26457 17821
64 25897 17482
65 25227 17243
66 25405 17097
67 29466 18885
68 29824 19738
69 28357 19359
70 27117 18854
71 26136 18670
72 26481 18338
73 27876 19102
74 27531 19070
75 26899 18232
76 26335 17990
77 26044 17740
78 26429 17649
79 29970 19729
80 31450 20370
81 29910 20060
82 28905 19441
Werkloosheid_WEST-VLAANDEREN Werkloosheid_OOST-VLAANDEREN
1 35054 49638
2 34984 49566
3 32996 48268
4 32864 49060
5 31943 48473
6 32032 49063
7 37740 55813
8 37430 55878
9 35681 53075
10 32042 47957
11 30623 45030
12 30335 44401
13 30294 44364
14 28507 42489
15 26903 40994
16 25504 40001
17 24488 38675
18 25011 38933
19 31224 47441
20 31192 47431
21 27630 42799
22 26423 40844
23 25703 39053
24 26834 40408
25 26563 40033
26 25515 38550
27 24583 38694
28 23834 38156
29 22274 36027
30 23943 37659
31 29226 44630
32 29528 44467
33 27446 41585
34 26148 40133
35 26303 39012
36 28112 41902
37 29610 43440
38 29902 44214
39 30065 44529
40 29027 44052
41 28238 43318
42 29823 45333
43 35004 52043
44 35596 52545
45 33112 49331
46 31710 47736
47 31794 46786
48 34412 50367
49 33735 48695
50 33143 48439
51 31682 46993
52 30483 46454
53 29281 44895
54 29589 45313
55 35155 52826
56 35198 52560
57 32032 48224
58 30642 46029
59 30011 44262
60 30464 45453
61 30981 45671
62 30010 44620
63 28403 43467
64 26988 42542
65 25903 41161
66 25893 41407
67 31220 48444
68 31486 47924
69 29343 45206
70 27972 42923
71 27699 41532
72 28746 42860
73 30786 45173
74 30055 45079
75 28534 43751
76 27189 43087
77 26378 42257
78 26523 42563
79 30999 48299
80 33356 50385
81 31794 48600
82 30973 46726
Werkloosheid_HENEGOUWEN Werkloosheid_LUIK Werkloosheid_LIMBURG
1 119087 90582 34943
2 117267 89214 35155
3 116417 87633 33835
4 114582 86279 34146
5 114804 86370 33357
6 115956 87056 33275
7 121919 91972 38126
8 124049 93651 37798
9 124286 94551 36087
10 121491 91188 32683
11 118314 88686 30865
12 116786 86821 30381
13 118038 88490 30216
14 116710 88003 28631
15 112999 84371 27313
16 113754 85368 26470
17 110388 81981 25747
18 104055 76861 25573
19 112205 82785 31200
20 115302 85314 31066
21 113290 84691 27251
22 111036 82758 25554
23 107273 79645 24193
24 107007 79663 25104
25 108862 81661 24534
26 108383 81269 23444
27 103508 77079 23201
28 103459 77499 22822
29 99384 73724 21846
30 99649 73841 23015
31 107542 80755 27544
32 108831 81806 27294
33 107473 81450 24936
34 104079 78725 24538
35 103497 78109 24119
36 104741 79089 26264
37 105625 79831 27916
38 105908 80080 28323
39 106028 80377 28801
40 106619 81034 28458
41 103930 78207 27810
42 104216 79197 29484
43 112086 85448 34109
44 113824 86899 34170
45 111904 85899 31989
46 108435 82824 30591
47 106798 80785 29999
48 107841 81061 33253
49 111377 84209 31988
50 109589 82931 31791
51 107481 81327 30596
52 105055 78790 30136
53 102265 76645 28948
54 102323 76614 29244
55 110832 83558 34396
56 112899 85307 34125
57 110949 84348 30836
58 106594 81247 29116
59 104743 79685 27925
60 103932 79365 28836
61 104727 79577 29134
62 103163 78666 28180
63 102364 78790 27208
64 100650 77396 26744
65 99513 75712 25711
66 98565 75456 25895
67 106846 82648 30979
68 110051 84929 30848
69 106968 82731 28760
70 104773 80655 27483
71 103209 79635 26372
72 102176 78882 27455
73 105190 81507 29467
74 104718 81284 29106
75 101671 79593 28117
76 100434 78122 27380
77 98870 77192 26916
78 98374 77669 27051
79 107670 84926 31262
80 110188 86563 32616
81 106972 84766 31326
82 104495 82590 30485
Werkloosheid_LUXEMBURG Werkloosheid_NAMEN
1 13292 33932
2 13124 33287
3 12934 32871
4 12654 31738
5 12649 31645
6 12828 31634
7 13997 33926
8 14484 34721
9 14733 35092
10 14207 33966
11 13854 33243
12 13619 32649
13 13679 33064
14 13417 33047
15 12957 31941
16 12833 31951
17 12147 30525
18 11735 29321
19 12766 32153
20 13444 33482
21 13584 32950
22 13355 32467
23 12830 31506
24 12649 31404
25 13072 31997
26 12803 31605
27 12217 29942
28 12041 29922
29 11233 28486
30 11224 28516
31 12593 31170
32 13126 32082
33 13053 31511
34 12527 30510
35 12522 30343
36 12722 30441
37 13060 30912
38 13006 30980
39 12870 30925
40 12929 30856
41 12365 29862
42 12384 30045
43 13801 32827
44 14421 33310
45 14097 32774
46 13656 31501
47 13375 31092
48 13493 31198
49 13885 32524
50 13788 32069
51 13529 31488
52 13090 30513
53 12529 29594
54 12690 29836
55 14137 32816
56 14887 33843
57 14661 33035
58 13827 31546
59 13530 30907
60 13383 30512
61 13569 30499
62 13324 30111
63 13166 29941
64 12777 29215
65 12390 28413
66 12225 28427
67 13706 31214
68 14431 32529
69 13860 31593
70 13303 30612
71 13075 30305
72 13096 29978
73 13652 30882
74 13568 30552
75 13034 29724
76 12804 29225
77 12520 28720
78 12622 28848
79 14285 31948
80 14767 32773
81 14377 31609
82 13854 30982
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Werkloosheid_ANTWERPEN
62563.6644 1.5746
`Werkloosheid_VLAAMS-BRABANT` `Werkloosheid_WAALS-BRABANT`
0.3901 0.1905
`Werkloosheid_WEST-VLAANDEREN` `Werkloosheid_OOST-VLAANDEREN`
-0.5329 -1.0605
Werkloosheid_HENEGOUWEN Werkloosheid_LUIK
-0.1787 -0.8563
Werkloosheid_LIMBURG Werkloosheid_LUXEMBURG
-0.3267 0.4554
Werkloosheid_NAMEN
2.4801
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2690.23 -762.00 -5.11 659.98 2752.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62563.6644 8977.5010 6.969 1.34e-09 ***
Werkloosheid_ANTWERPEN 1.5746 0.1965 8.014 1.57e-11 ***
`Werkloosheid_VLAAMS-BRABANT` 0.3901 0.5235 0.745 0.458550
`Werkloosheid_WAALS-BRABANT` 0.1905 0.7932 0.240 0.810883
`Werkloosheid_WEST-VLAANDEREN` -0.5329 0.2964 -1.798 0.076416 .
`Werkloosheid_OOST-VLAANDEREN` -1.0605 0.3835 -2.765 0.007239 **
Werkloosheid_HENEGOUWEN -0.1787 0.1893 -0.944 0.348406
Werkloosheid_LUIK -0.8563 0.1920 -4.461 3.00e-05 ***
Werkloosheid_LIMBURG -0.3267 0.4979 -0.656 0.513853
Werkloosheid_LUXEMBURG 0.4554 0.8895 0.512 0.610288
Werkloosheid_NAMEN 2.4801 0.6068 4.088 0.000113 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1253 on 71 degrees of freedom
Multiple R-squared: 0.9656, Adjusted R-squared: 0.9607
F-statistic: 199 on 10 and 71 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,] 0.56153888 0.876922238 0.438461119
[2,] 0.48129715 0.962594308 0.518702846
[3,] 0.33302803 0.666056061 0.666971969
[4,] 0.26263112 0.525262233 0.737368884
[5,] 0.17510444 0.350208882 0.824895559
[6,] 0.18689491 0.373789824 0.813105088
[7,] 0.13578197 0.271563933 0.864218033
[8,] 0.08644464 0.172889290 0.913555355
[9,] 0.06055572 0.121111444 0.939444278
[10,] 0.04941591 0.098831813 0.950584093
[11,] 0.03395892 0.067917839 0.966041081
[12,] 0.02533163 0.050663261 0.974668369
[13,] 0.02870515 0.057410308 0.971294846
[14,] 0.10082977 0.201659534 0.899170233
[15,] 0.13604286 0.272085712 0.863957144
[16,] 0.12911826 0.258236523 0.870881738
[17,] 0.14120933 0.282418656 0.858790672
[18,] 0.12016253 0.240325050 0.879837475
[19,] 0.20355559 0.407111174 0.796444413
[20,] 0.17835282 0.356705633 0.821647183
[21,] 0.14279343 0.285586863 0.857206569
[22,] 0.14295360 0.285907204 0.857046398
[23,] 0.11509890 0.230197803 0.884901099
[24,] 0.14299081 0.285981623 0.857009188
[25,] 0.17701628 0.354032562 0.822983719
[26,] 0.14236109 0.284722181 0.857638910
[27,] 0.14796016 0.295920314 0.852039843
[28,] 0.18720652 0.374413038 0.812793481
[29,] 0.14528932 0.290578643 0.854710679
[30,] 0.19273512 0.385470245 0.807264877
[31,] 0.17945838 0.358916765 0.820541617
[32,] 0.17655883 0.353117660 0.823441170
[33,] 0.18154913 0.363098251 0.818450875
[34,] 0.21609974 0.432199487 0.783900257
[35,] 0.31343287 0.626865736 0.686567132
[36,] 0.35032854 0.700657080 0.649671460
[37,] 0.36184458 0.723689168 0.638155416
[38,] 0.37099439 0.741988770 0.629005615
[39,] 0.34957418 0.699148367 0.650425817
[40,] 0.58219217 0.835615658 0.417807829
[41,] 0.73868777 0.522624455 0.261312228
[42,] 0.93301857 0.133962868 0.066981434
[43,] 0.97920581 0.041588379 0.020794190
[44,] 0.99773879 0.004522424 0.002261212
[45,] 0.99593738 0.008125233 0.004062616
[46,] 0.99468328 0.010633448 0.005316724
[47,] 0.98899316 0.022013683 0.011006841
[48,] 0.97839545 0.043209107 0.021604554
[49,] 0.96289338 0.074213234 0.037106617
[50,] 0.94726593 0.105468142 0.052734071
[51,] 0.90400660 0.191986805 0.095993403
[52,] 0.85091433 0.298171337 0.149085669
[53,] 0.78349306 0.433013882 0.216506941
[54,] 0.85391189 0.292176213 0.146088106
[55,] 0.73060700 0.538785995 0.269392997
> postscript(file="/var/fisher/rcomp/tmp/1bwjy1353433296.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/fisher/rcomp/tmp/2w8kn1353433297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/31riq1353433297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4kxtn1353433297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/58wpr1353433297.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 82
Frequency = 1
1 2 3 4 5
-24.1931457 1053.5852422 459.2899709 -1020.5929474 1578.2974561
6 7 8 9 10
1151.3137454 1052.6479434 -453.8617540 1136.4882429 1560.9493756
11 12 13 14 15
-1728.4478970 -1006.2712812 -1125.4037951 -1093.3905991 -514.2979898
16 17 18 19 20
-656.5692242 24.7973237 -574.2936146 -1173.7796808 -1450.2822181
21 22 23 24 25
470.5264272 869.8676416 339.7016601 1313.1432287 390.3103882
26 27 28 29 30
-1473.3987209 -613.7911161 -746.3350147 -495.1295444 -0.7219207
31 32 33 34 35
-359.0518742 -1918.7130030 312.8703454 1162.9718442 461.7065536
36 37 38 39 40
1013.1146463 -104.7590030 -93.6523458 262.5826111 2348.9896968
41 42 43 44 45
-917.9505859 -18.6030083 -1585.0634809 -1295.7954314 318.1791196
46 47 48 49 50
662.1605193 -510.7353722 -2633.9574656 32.6398316 310.0810531
51 52 53 54 55
816.1858627 214.1096008 -255.0722244 1903.3758960 2752.0957254
56 57 58 59 60
-2690.2301097 2636.8703842 -9.4942208 291.4908472 811.7660702
61 62 63 64 65
-965.6656217 -767.2234117 -832.1431278 65.0377928 104.7175711
66 67 68 69 70
331.0482631 1678.4546859 653.4341550 1754.3532352 2280.8577343
71 72 73 74 75
2159.9626496 493.5338172 -293.0783911 -130.2725201 283.1464855
76 77 78 79 80
-364.1893842 -2107.5270998 -740.1589281 -1422.8907479 -1804.4495407
81 82
-266.5560317 -1278.6622498
> postscript(file="/var/fisher/rcomp/tmp/63vio1353433297.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 = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 -24.1931457 NA
1 1053.5852422 -24.1931457
2 459.2899709 1053.5852422
3 -1020.5929474 459.2899709
4 1578.2974561 -1020.5929474
5 1151.3137454 1578.2974561
6 1052.6479434 1151.3137454
7 -453.8617540 1052.6479434
8 1136.4882429 -453.8617540
9 1560.9493756 1136.4882429
10 -1728.4478970 1560.9493756
11 -1006.2712812 -1728.4478970
12 -1125.4037951 -1006.2712812
13 -1093.3905991 -1125.4037951
14 -514.2979898 -1093.3905991
15 -656.5692242 -514.2979898
16 24.7973237 -656.5692242
17 -574.2936146 24.7973237
18 -1173.7796808 -574.2936146
19 -1450.2822181 -1173.7796808
20 470.5264272 -1450.2822181
21 869.8676416 470.5264272
22 339.7016601 869.8676416
23 1313.1432287 339.7016601
24 390.3103882 1313.1432287
25 -1473.3987209 390.3103882
26 -613.7911161 -1473.3987209
27 -746.3350147 -613.7911161
28 -495.1295444 -746.3350147
29 -0.7219207 -495.1295444
30 -359.0518742 -0.7219207
31 -1918.7130030 -359.0518742
32 312.8703454 -1918.7130030
33 1162.9718442 312.8703454
34 461.7065536 1162.9718442
35 1013.1146463 461.7065536
36 -104.7590030 1013.1146463
37 -93.6523458 -104.7590030
38 262.5826111 -93.6523458
39 2348.9896968 262.5826111
40 -917.9505859 2348.9896968
41 -18.6030083 -917.9505859
42 -1585.0634809 -18.6030083
43 -1295.7954314 -1585.0634809
44 318.1791196 -1295.7954314
45 662.1605193 318.1791196
46 -510.7353722 662.1605193
47 -2633.9574656 -510.7353722
48 32.6398316 -2633.9574656
49 310.0810531 32.6398316
50 816.1858627 310.0810531
51 214.1096008 816.1858627
52 -255.0722244 214.1096008
53 1903.3758960 -255.0722244
54 2752.0957254 1903.3758960
55 -2690.2301097 2752.0957254
56 2636.8703842 -2690.2301097
57 -9.4942208 2636.8703842
58 291.4908472 -9.4942208
59 811.7660702 291.4908472
60 -965.6656217 811.7660702
61 -767.2234117 -965.6656217
62 -832.1431278 -767.2234117
63 65.0377928 -832.1431278
64 104.7175711 65.0377928
65 331.0482631 104.7175711
66 1678.4546859 331.0482631
67 653.4341550 1678.4546859
68 1754.3532352 653.4341550
69 2280.8577343 1754.3532352
70 2159.9626496 2280.8577343
71 493.5338172 2159.9626496
72 -293.0783911 493.5338172
73 -130.2725201 -293.0783911
74 283.1464855 -130.2725201
75 -364.1893842 283.1464855
76 -2107.5270998 -364.1893842
77 -740.1589281 -2107.5270998
78 -1422.8907479 -740.1589281
79 -1804.4495407 -1422.8907479
80 -266.5560317 -1804.4495407
81 -1278.6622498 -266.5560317
82 NA -1278.6622498
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1053.5852422 -24.1931457
[2,] 459.2899709 1053.5852422
[3,] -1020.5929474 459.2899709
[4,] 1578.2974561 -1020.5929474
[5,] 1151.3137454 1578.2974561
[6,] 1052.6479434 1151.3137454
[7,] -453.8617540 1052.6479434
[8,] 1136.4882429 -453.8617540
[9,] 1560.9493756 1136.4882429
[10,] -1728.4478970 1560.9493756
[11,] -1006.2712812 -1728.4478970
[12,] -1125.4037951 -1006.2712812
[13,] -1093.3905991 -1125.4037951
[14,] -514.2979898 -1093.3905991
[15,] -656.5692242 -514.2979898
[16,] 24.7973237 -656.5692242
[17,] -574.2936146 24.7973237
[18,] -1173.7796808 -574.2936146
[19,] -1450.2822181 -1173.7796808
[20,] 470.5264272 -1450.2822181
[21,] 869.8676416 470.5264272
[22,] 339.7016601 869.8676416
[23,] 1313.1432287 339.7016601
[24,] 390.3103882 1313.1432287
[25,] -1473.3987209 390.3103882
[26,] -613.7911161 -1473.3987209
[27,] -746.3350147 -613.7911161
[28,] -495.1295444 -746.3350147
[29,] -0.7219207 -495.1295444
[30,] -359.0518742 -0.7219207
[31,] -1918.7130030 -359.0518742
[32,] 312.8703454 -1918.7130030
[33,] 1162.9718442 312.8703454
[34,] 461.7065536 1162.9718442
[35,] 1013.1146463 461.7065536
[36,] -104.7590030 1013.1146463
[37,] -93.6523458 -104.7590030
[38,] 262.5826111 -93.6523458
[39,] 2348.9896968 262.5826111
[40,] -917.9505859 2348.9896968
[41,] -18.6030083 -917.9505859
[42,] -1585.0634809 -18.6030083
[43,] -1295.7954314 -1585.0634809
[44,] 318.1791196 -1295.7954314
[45,] 662.1605193 318.1791196
[46,] -510.7353722 662.1605193
[47,] -2633.9574656 -510.7353722
[48,] 32.6398316 -2633.9574656
[49,] 310.0810531 32.6398316
[50,] 816.1858627 310.0810531
[51,] 214.1096008 816.1858627
[52,] -255.0722244 214.1096008
[53,] 1903.3758960 -255.0722244
[54,] 2752.0957254 1903.3758960
[55,] -2690.2301097 2752.0957254
[56,] 2636.8703842 -2690.2301097
[57,] -9.4942208 2636.8703842
[58,] 291.4908472 -9.4942208
[59,] 811.7660702 291.4908472
[60,] -965.6656217 811.7660702
[61,] -767.2234117 -965.6656217
[62,] -832.1431278 -767.2234117
[63,] 65.0377928 -832.1431278
[64,] 104.7175711 65.0377928
[65,] 331.0482631 104.7175711
[66,] 1678.4546859 331.0482631
[67,] 653.4341550 1678.4546859
[68,] 1754.3532352 653.4341550
[69,] 2280.8577343 1754.3532352
[70,] 2159.9626496 2280.8577343
[71,] 493.5338172 2159.9626496
[72,] -293.0783911 493.5338172
[73,] -130.2725201 -293.0783911
[74,] 283.1464855 -130.2725201
[75,] -364.1893842 283.1464855
[76,] -2107.5270998 -364.1893842
[77,] -740.1589281 -2107.5270998
[78,] -1422.8907479 -740.1589281
[79,] -1804.4495407 -1422.8907479
[80,] -266.5560317 -1804.4495407
[81,] -1278.6622498 -266.5560317
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1053.5852422 -24.1931457
2 459.2899709 1053.5852422
3 -1020.5929474 459.2899709
4 1578.2974561 -1020.5929474
5 1151.3137454 1578.2974561
6 1052.6479434 1151.3137454
7 -453.8617540 1052.6479434
8 1136.4882429 -453.8617540
9 1560.9493756 1136.4882429
10 -1728.4478970 1560.9493756
11 -1006.2712812 -1728.4478970
12 -1125.4037951 -1006.2712812
13 -1093.3905991 -1125.4037951
14 -514.2979898 -1093.3905991
15 -656.5692242 -514.2979898
16 24.7973237 -656.5692242
17 -574.2936146 24.7973237
18 -1173.7796808 -574.2936146
19 -1450.2822181 -1173.7796808
20 470.5264272 -1450.2822181
21 869.8676416 470.5264272
22 339.7016601 869.8676416
23 1313.1432287 339.7016601
24 390.3103882 1313.1432287
25 -1473.3987209 390.3103882
26 -613.7911161 -1473.3987209
27 -746.3350147 -613.7911161
28 -495.1295444 -746.3350147
29 -0.7219207 -495.1295444
30 -359.0518742 -0.7219207
31 -1918.7130030 -359.0518742
32 312.8703454 -1918.7130030
33 1162.9718442 312.8703454
34 461.7065536 1162.9718442
35 1013.1146463 461.7065536
36 -104.7590030 1013.1146463
37 -93.6523458 -104.7590030
38 262.5826111 -93.6523458
39 2348.9896968 262.5826111
40 -917.9505859 2348.9896968
41 -18.6030083 -917.9505859
42 -1585.0634809 -18.6030083
43 -1295.7954314 -1585.0634809
44 318.1791196 -1295.7954314
45 662.1605193 318.1791196
46 -510.7353722 662.1605193
47 -2633.9574656 -510.7353722
48 32.6398316 -2633.9574656
49 310.0810531 32.6398316
50 816.1858627 310.0810531
51 214.1096008 816.1858627
52 -255.0722244 214.1096008
53 1903.3758960 -255.0722244
54 2752.0957254 1903.3758960
55 -2690.2301097 2752.0957254
56 2636.8703842 -2690.2301097
57 -9.4942208 2636.8703842
58 291.4908472 -9.4942208
59 811.7660702 291.4908472
60 -965.6656217 811.7660702
61 -767.2234117 -965.6656217
62 -832.1431278 -767.2234117
63 65.0377928 -832.1431278
64 104.7175711 65.0377928
65 331.0482631 104.7175711
66 1678.4546859 331.0482631
67 653.4341550 1678.4546859
68 1754.3532352 653.4341550
69 2280.8577343 1754.3532352
70 2159.9626496 2280.8577343
71 493.5338172 2159.9626496
72 -293.0783911 493.5338172
73 -130.2725201 -293.0783911
74 283.1464855 -130.2725201
75 -364.1893842 283.1464855
76 -2107.5270998 -364.1893842
77 -740.1589281 -2107.5270998
78 -1422.8907479 -740.1589281
79 -1804.4495407 -1422.8907479
80 -266.5560317 -1804.4495407
81 -1278.6622498 -266.5560317
> 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/75eqq1353433297.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/844kc1353433297.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/94tsg1353433297.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/10v4zg1353433297.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ttru1353433297.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12no841353433297.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ydee1353433297.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14xjs41353433297.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/fisher/rcomp/tmp/15vys61353433297.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/fisher/rcomp/tmp/16roft1353433297.tab")
+ }
>
> try(system("convert tmp/1bwjy1353433296.ps tmp/1bwjy1353433296.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w8kn1353433297.ps tmp/2w8kn1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/31riq1353433297.ps tmp/31riq1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kxtn1353433297.ps tmp/4kxtn1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/58wpr1353433297.ps tmp/58wpr1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/63vio1353433297.ps tmp/63vio1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/75eqq1353433297.ps tmp/75eqq1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/844kc1353433297.ps tmp/844kc1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/94tsg1353433297.ps tmp/94tsg1353433297.png",intern=TRUE))
character(0)
> try(system("convert tmp/10v4zg1353433297.ps tmp/10v4zg1353433297.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.484 1.337 7.834