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(2006
+ ,70863
+ ,28779
+ ,19459
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+ ,90582
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+ ,19781
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+ ,33047
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+ ,92881
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+ ,40994
+ ,112999
+ ,84371
+ ,27313
+ ,12957
+ ,31941
+ ,0
+ ,93120
+ ,531673
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+ ,56734
+ ,23899
+ ,18342
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+ ,40001
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+ ,85368
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+ ,31951
+ ,0
+ ,91063
+ ,525919
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+ ,55327
+ ,23065
+ ,17765
+ ,24488
+ ,38675
+ ,110388
+ ,81981
+ ,25747
+ ,12147
+ ,30525
+ ,0
+ ,90930
+ ,511038
+ ,2007
+ ,55257
+ ,23279
+ ,16691
+ ,25011
+ ,38933
+ ,104055
+ ,76861
+ ,25573
+ ,11735
+ ,29321
+ ,0
+ ,91946
+ ,498662
+ ,2007
+ ,64301
+ ,28134
+ ,18529
+ ,31224
+ ,47441
+ ,112205
+ ,82785
+ ,31200
+ ,12766
+ ,32153
+ ,0
+ ,94624
+ ,555362
+ ,2007
+ ,64261
+ ,28438
+ ,19177
+ ,31192
+ ,47431
+ ,115302
+ ,85314
+ ,31066
+ ,13444
+ ,33482
+ ,0
+ ,95484
+ ,564591
+ ,2007
+ ,59119
+ ,25717
+ ,18764
+ ,27630
+ ,42799
+ ,113290
+ ,84691
+ ,27251
+ ,13584
+ ,32950
+ ,0
+ ,95862
+ ,541657
+ ,2007
+ ,56530
+ ,24125
+ ,18448
+ ,26423
+ ,40844
+ ,111036
+ ,82758
+ ,25554
+ ,13355
+ ,32467
+ ,0
+ ,95530
+ ,527070
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+ ,54445
+ ,23050
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+ ,79645
+ ,24193
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+ ,31506
+ ,0
+ ,94574
+ ,509846
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+ ,55462
+ ,23489
+ ,17561
+ ,26834
+ ,40408
+ ,107007
+ ,79663
+ ,25104
+ ,12649
+ ,31404
+ ,0
+ ,94677
+ ,514258
+ ,2008
+ ,55333
+ ,23238
+ ,17784
+ ,26563
+ ,40033
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+ ,81661
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+ ,91533
+ ,507561
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+ ,53213
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+ ,49933
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+ ,17691
+ ,27446
+ ,41585
+ ,107473
+ ,81450
+ ,24936
+ ,13053
+ ,31511
+ ,0
+ ,92437
+ ,517945
+ ,2008
+ ,55210
+ ,23333
+ ,17095
+ ,26148
+ ,40133
+ ,104079
+ ,78725
+ ,24538
+ ,12527
+ ,30510
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+ ,93876
+ ,506174
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+ ,54698
+ ,22695
+ ,17007
+ ,26303
+ ,39012
+ ,103497
+ ,78109
+ ,24119
+ ,12522
+ ,30343
+ ,0
+ ,93561
+ ,501866
+ ,2008
+ ,57875
+ ,23884
+ ,16992
+ ,28112
+ ,41902
+ ,104741
+ ,79089
+ ,26264
+ ,12722
+ ,30441
+ ,0
+ ,94119
+ ,516141
+ ,2009
+ ,60611
+ ,24835
+ ,17118
+ ,29610
+ ,43440
+ ,105625
+ ,79831
+ ,27916
+ ,13060
+ ,30912
+ ,0
+ ,95264
+ ,528222
+ ,2009
+ ,61857
+ ,24930
+ ,17349
+ ,29902
+ ,44214
+ ,105908
+ ,80080
+ ,28323
+ ,13006
+ ,30980
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+ ,532638
+ ,2009
+ ,62885
+ ,25283
+ ,17399
+ ,30065
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+ ,106028
+ ,80377
+ ,28801
+ ,12870
+ ,30925
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+ ,97160
+ ,536322
+ ,2009
+ ,62313
+ ,25056
+ ,17547
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+ ,44052
+ ,106619
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+ ,110832
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+ ,32816
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+ ,600991
+ ,2010
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+ ,112899
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+ ,14887
+ ,33843
+ ,0
+ ,107706
+ ,604378
+ ,2010
+ ,71483
+ ,29293
+ ,19983
+ ,32032
+ ,48224
+ ,110949
+ ,84348
+ ,30836
+ ,14661
+ ,33035
+ ,0
+ ,111267
+ ,586111
+ ,2010
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+ ,28528
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+ ,46029
+ ,106594
+ ,81247
+ ,29116
+ ,13827
+ ,31546
+ ,0
+ ,107643
+ ,563668
+ ,2010
+ ,66421
+ ,27151
+ ,18582
+ ,30011
+ ,44262
+ ,104743
+ ,79685
+ ,27925
+ ,13530
+ ,30907
+ ,0
+ ,105387
+ ,548604
+ ,2010
+ ,67840
+ ,27241
+ ,18430
+ ,30464
+ ,45453
+ ,103932
+ ,79365
+ ,28836
+ ,13383
+ ,30512
+ ,0
+ ,105718
+ ,551174)
+ ,dim=c(14
+ ,60)
+ ,dimnames=list(c('jaar'
+ ,'Antwerpen'
+ ,'Vlaams_Brabant'
+ ,'Waals_Brabant'
+ ,'West_vlaanderen'
+ ,'Oost_Vlaanderen'
+ ,'Henehouwen'
+ ,'Luik'
+ ,'Limburg'
+ ,'Luxemburg'
+ ,'Namen'
+ ,'Buitenland'
+ ,'Brussel'
+ ,'LAND')
+ ,1:60))
> y <- array(NA,dim=c(14,60),dimnames=list(c('jaar','Antwerpen','Vlaams_Brabant','Waals_Brabant','West_vlaanderen','Oost_Vlaanderen','Henehouwen','Luik','Limburg','Luxemburg','Namen','Buitenland','Brussel','LAND'),1:60))
> 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 = '14'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '14'
> #'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, 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
LAND jaar Antwerpen Vlaams_Brabant Waals_Brabant West_vlaanderen
1 593408 2006 70863 28779 19459 35054
2 590072 2006 70806 28802 19266 34984
3 579799 2006 69484 28027 18661 32996
4 574205 2006 70150 28551 18153 32864
5 572775 2006 69210 28159 18151 31943
6 572942 2006 68733 28354 18431 32032
7 619567 2006 75930 32439 19867 37740
8 625809 2006 76162 33368 20508 37430
9 619916 2006 73891 31846 20761 35681
10 587625 2006 67348 28765 20390 32042
11 565742 2006 64297 27107 19781 30623
12 557274 2006 63111 26368 19147 30335
13 560576 2007 63263 26444 19359 30294
14 548854 2007 60733 25326 19110 28507
15 531673 2007 58521 24375 18179 26903
16 525919 2007 56734 23899 18342 25504
17 511038 2007 55327 23065 17765 24488
18 498662 2007 55257 23279 16691 25011
19 555362 2007 64301 28134 18529 31224
20 564591 2007 64261 28438 19177 31192
21 541657 2007 59119 25717 18764 27630
22 527070 2007 56530 24125 18448 26423
23 509846 2007 54445 23050 17574 25703
24 514258 2007 55462 23489 17561 26834
25 516922 2008 55333 23238 17784 26563
26 507561 2008 54048 22625 17786 25515
27 492622 2008 53213 22223 16748 24583
28 490243 2008 52764 22036 16788 23834
29 469357 2008 49933 20921 15966 22274
30 477580 2008 51515 21982 16291 23943
31 528379 2008 59302 25828 17939 29226
32 533590 2008 59681 26099 18171 29528
33 517945 2008 56195 24168 17691 27446
34 506174 2008 55210 23333 17095 26148
35 501866 2008 54698 22695 17007 26303
36 516141 2008 57875 23884 16992 28112
37 528222 2009 60611 24835 17118 29610
38 532638 2009 61857 24930 17349 29902
39 536322 2009 62885 25283 17399 30065
40 536535 2009 62313 25056 17547 29027
41 523597 2009 62056 24583 16962 28238
42 536214 2009 64702 25967 17125 29823
43 586570 2009 72334 30042 19119 35004
44 596594 2009 73577 31011 19691 35596
45 580523 2009 70290 29404 19274 33112
46 564478 2009 68633 28233 18743 31710
47 557560 2009 68311 27552 18577 31794
48 575093 2009 73335 29009 18629 34412
49 580112 2010 71257 28645 19245 33735
50 574761 2010 70743 28472 18998 33143
51 563250 2010 68932 27613 18662 31682
52 551531 2010 68045 27078 17937 30483
53 537034 2010 66338 26260 17421 29281
54 544686 2010 67339 27078 17708 29589
55 600991 2010 75744 31018 19608 35155
56 604378 2010 76098 31546 20209 35198
57 586111 2010 71483 29293 19983 32032
58 563668 2010 69240 28528 19256 30642
59 548604 2010 66421 27151 18582 30011
60 551174 2010 67840 27241 18430 30464
Oost_Vlaanderen Henehouwen Luik Limburg Luxemburg Namen Buitenland Brussel
1 49638 119087 90582 34943 13292 33932 92 97687
2 49566 117267 89214 35155 13124 33287 89 98512
3 48268 116417 87633 33835 12934 32871 0 98673
4 49060 114582 86279 34146 12654 31738 0 96028
5 48473 114804 86370 33357 12649 31645 0 98014
6 49063 115956 87056 33275 12828 31634 0 95580
7 55813 121919 91972 38126 13997 33926 0 97838
8 55878 124049 93651 37798 14484 34721 0 97760
9 53075 124286 94551 36087 14733 35092 0 99913
10 47957 121491 91188 32683 14207 33966 0 97588
11 45030 118314 88686 30865 13854 33243 0 93942
12 44401 116786 86821 30381 13619 32649 0 93656
13 44364 118038 88490 30216 13679 33064 0 93365
14 42489 116710 88003 28631 13417 33047 0 92881
15 40994 112999 84371 27313 12957 31941 0 93120
16 40001 113754 85368 26470 12833 31951 0 91063
17 38675 110388 81981 25747 12147 30525 0 90930
18 38933 104055 76861 25573 11735 29321 0 91946
19 47441 112205 82785 31200 12766 32153 0 94624
20 47431 115302 85314 31066 13444 33482 0 95484
21 42799 113290 84691 27251 13584 32950 0 95862
22 40844 111036 82758 25554 13355 32467 0 95530
23 39053 107273 79645 24193 12830 31506 0 94574
24 40408 107007 79663 25104 12649 31404 0 94677
25 40033 108862 81661 24534 13072 31997 0 93845
26 38550 108383 81269 23444 12803 31605 0 91533
27 38694 103508 77079 23201 12217 29942 0 91214
28 38156 103459 77499 22822 12041 29922 0 90922
29 36027 99384 73724 21846 11233 28486 0 89563
30 37659 99649 73841 23015 11224 28516 0 89945
31 44630 107542 80755 27544 12593 31170 0 91850
32 44467 108831 81806 27294 13126 32082 0 92505
33 41585 107473 81450 24936 13053 31511 0 92437
34 40133 104079 78725 24538 12527 30510 0 93876
35 39012 103497 78109 24119 12522 30343 0 93561
36 41902 104741 79089 26264 12722 30441 0 94119
37 43440 105625 79831 27916 13060 30912 0 95264
38 44214 105908 80080 28323 13006 30980 0 96089
39 44529 106028 80377 28801 12870 30925 0 97160
40 44052 106619 81034 28458 12929 30856 0 98644
41 43318 103930 78207 27810 12365 29862 0 96266
42 45333 104216 79197 29484 12384 30045 0 97938
43 52043 112086 85448 34109 13801 32827 0 99757
44 52545 113824 86899 34170 14421 33310 0 101550
45 49331 111904 85899 31989 14097 32774 0 102449
46 47736 108435 82824 30591 13656 31501 0 102416
47 46786 106798 80785 29999 13375 31092 0 102491
48 50367 107841 81061 33253 13493 31198 0 102495
49 48695 111377 84209 31988 13885 32524 0 104552
50 48439 109589 82931 31791 13788 32069 0 104798
51 46993 107481 81327 30596 13529 31488 0 104947
52 46454 105055 78790 30136 13090 30513 0 103950
53 44895 102265 76645 28948 12529 29594 0 102858
54 45313 102323 76614 29244 12690 29836 0 106952
55 52826 110832 83558 34396 14137 32816 0 110901
56 52560 112899 85307 34125 14887 33843 0 107706
57 48224 110949 84348 30836 14661 33035 0 111267
58 46029 106594 81247 29116 13827 31546 0 107643
59 44262 104743 79685 27925 13530 30907 0 105387
60 45453 103932 79365 28836 13383 30512 0 105718
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jaar Antwerpen Vlaams_Brabant
2.492e-09 -1.283e-12 1.000e+00 1.000e+00
Waals_Brabant West_vlaanderen Oost_Vlaanderen Henehouwen
1.000e+00 1.000e+00 1.000e+00 1.000e+00
Luik Limburg Luxemburg Namen
1.000e+00 1.000e+00 1.000e+00 1.000e+00
Buitenland Brussel
1.000e+00 1.000e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.573e-11 -2.568e-12 4.400e-13 2.685e-12 7.326e-11
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.492e-09 1.145e-08 2.180e-01 0.829
jaar -1.283e-12 5.665e-12 -2.270e-01 0.822
Antwerpen 1.000e+00 4.705e-15 2.126e+14 <2e-16 ***
Vlaams_Brabant 1.000e+00 9.303e-15 1.075e+14 <2e-16 ***
Waals_Brabant 1.000e+00 1.583e-14 6.319e+13 <2e-16 ***
West_vlaanderen 1.000e+00 6.274e-15 1.594e+14 <2e-16 ***
Oost_Vlaanderen 1.000e+00 6.400e-15 1.562e+14 <2e-16 ***
Henehouwen 1.000e+00 6.064e-15 1.649e+14 <2e-16 ***
Luik 1.000e+00 4.733e-15 2.113e+14 <2e-16 ***
Limburg 1.000e+00 8.320e-15 1.202e+14 <2e-16 ***
Luxemburg 1.000e+00 1.564e-14 6.396e+13 <2e-16 ***
Namen 1.000e+00 1.100e-14 9.091e+13 <2e-16 ***
Buitenland 1.000e+00 2.265e-13 4.416e+12 <2e-16 ***
Brussel 1.000e+00 1.966e-15 5.085e+14 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.607e-11 on 46 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.295e+31 on 13 and 46 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,] 4.630502e-01 9.261005e-01 5.369498e-01
[2,] 4.686499e-01 9.372998e-01 5.313501e-01
[3,] 6.120525e-02 1.224105e-01 9.387947e-01
[4,] 6.552543e-01 6.894914e-01 3.447457e-01
[5,] 3.772658e-01 7.545317e-01 6.227342e-01
[6,] 2.286902e-01 4.573804e-01 7.713098e-01
[7,] 9.999993e-01 1.498893e-06 7.494467e-07
[8,] 9.994530e-01 1.094017e-03 5.470087e-04
[9,] 8.317952e-11 1.663590e-10 1.000000e+00
[10,] 6.197626e-03 1.239525e-02 9.938024e-01
[11,] 4.077831e-01 8.155661e-01 5.922169e-01
[12,] 9.998720e-01 2.559964e-04 1.279982e-04
[13,] 5.526353e-01 8.947293e-01 4.473647e-01
[14,] 4.266022e-15 8.532044e-15 1.000000e+00
[15,] 4.472946e-01 8.945892e-01 5.527054e-01
[16,] 9.999853e-01 2.940302e-05 1.470151e-05
[17,] 2.029232e-05 4.058465e-05 9.999797e-01
[18,] 9.708330e-01 5.833394e-02 2.916697e-02
[19,] 1.759749e-11 3.519499e-11 1.000000e+00
[20,] 9.999990e-01 2.089794e-06 1.044897e-06
[21,] 3.737388e-01 7.474777e-01 6.262612e-01
[22,] 9.081090e-01 1.837820e-01 9.189098e-02
[23,] 1.892843e-03 3.785686e-03 9.981072e-01
[24,] 5.841289e-01 8.317423e-01 4.158711e-01
[25,] 3.264797e-14 6.529595e-14 1.000000e+00
[26,] 1.348939e-05 2.697877e-05 9.999865e-01
[27,] 7.986689e-18 1.597338e-17 1.000000e+00
> postscript(file="/var/wessaorg/rcomp/tmp/1m5cj1352042350.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2zmze1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3xyin1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4l4gx1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5vhrd1352042350.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 = 60
Frequency = 1
1 2 3 4 5
7.325621e-11 -7.572552e-11 6.128195e-12 -4.293661e-12 -6.525006e-12
6 7 8 9 10
-1.815881e-12 3.646845e-12 -4.392207e-12 1.942926e-12 6.813038e-12
11 12 13 14 15
2.777429e-12 2.108830e-12 -2.211244e-12 -4.207001e-12 -4.186244e-12
16 17 18 19 20
-3.423883e-12 5.866670e-12 9.809580e-12 -2.224783e-13 -1.844664e-12
21 22 23 24 25
-1.446659e-12 -1.514030e-12 6.489188e-13 3.046400e-12 -5.286183e-12
26 27 28 29 30
-1.069837e-11 -2.755750e-12 -3.902497e-12 1.412169e-12 4.647414e-12
31 32 33 34 35
2.773751e-12 -2.808025e-12 -2.888372e-12 1.453462e-12 6.188876e-13
36 37 38 39 40
2.655900e-12 4.265398e-12 2.347690e-13 -1.700136e-13 4.730078e-12
41 42 43 44 45
-8.996992e-13 -3.390114e-13 4.912353e-13 3.890085e-12 2.867622e-12
46 47 48 49 50
1.620716e-12 -2.504975e-12 -1.261872e-12 -5.427350e-12 2.079086e-12
51 52 53 54 55
-1.371197e-12 3.894179e-13 -1.126375e-12 6.735331e-13 1.121844e-12
56 57 58 59 60
-5.537690e-12 1.464536e-12 1.027146e-12 9.006606e-13 3.423105e-12
> postscript(file="/var/wessaorg/rcomp/tmp/6fg021352042350.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 7.325621e-11 NA
1 -7.572552e-11 7.325621e-11
2 6.128195e-12 -7.572552e-11
3 -4.293661e-12 6.128195e-12
4 -6.525006e-12 -4.293661e-12
5 -1.815881e-12 -6.525006e-12
6 3.646845e-12 -1.815881e-12
7 -4.392207e-12 3.646845e-12
8 1.942926e-12 -4.392207e-12
9 6.813038e-12 1.942926e-12
10 2.777429e-12 6.813038e-12
11 2.108830e-12 2.777429e-12
12 -2.211244e-12 2.108830e-12
13 -4.207001e-12 -2.211244e-12
14 -4.186244e-12 -4.207001e-12
15 -3.423883e-12 -4.186244e-12
16 5.866670e-12 -3.423883e-12
17 9.809580e-12 5.866670e-12
18 -2.224783e-13 9.809580e-12
19 -1.844664e-12 -2.224783e-13
20 -1.446659e-12 -1.844664e-12
21 -1.514030e-12 -1.446659e-12
22 6.489188e-13 -1.514030e-12
23 3.046400e-12 6.489188e-13
24 -5.286183e-12 3.046400e-12
25 -1.069837e-11 -5.286183e-12
26 -2.755750e-12 -1.069837e-11
27 -3.902497e-12 -2.755750e-12
28 1.412169e-12 -3.902497e-12
29 4.647414e-12 1.412169e-12
30 2.773751e-12 4.647414e-12
31 -2.808025e-12 2.773751e-12
32 -2.888372e-12 -2.808025e-12
33 1.453462e-12 -2.888372e-12
34 6.188876e-13 1.453462e-12
35 2.655900e-12 6.188876e-13
36 4.265398e-12 2.655900e-12
37 2.347690e-13 4.265398e-12
38 -1.700136e-13 2.347690e-13
39 4.730078e-12 -1.700136e-13
40 -8.996992e-13 4.730078e-12
41 -3.390114e-13 -8.996992e-13
42 4.912353e-13 -3.390114e-13
43 3.890085e-12 4.912353e-13
44 2.867622e-12 3.890085e-12
45 1.620716e-12 2.867622e-12
46 -2.504975e-12 1.620716e-12
47 -1.261872e-12 -2.504975e-12
48 -5.427350e-12 -1.261872e-12
49 2.079086e-12 -5.427350e-12
50 -1.371197e-12 2.079086e-12
51 3.894179e-13 -1.371197e-12
52 -1.126375e-12 3.894179e-13
53 6.735331e-13 -1.126375e-12
54 1.121844e-12 6.735331e-13
55 -5.537690e-12 1.121844e-12
56 1.464536e-12 -5.537690e-12
57 1.027146e-12 1.464536e-12
58 9.006606e-13 1.027146e-12
59 3.423105e-12 9.006606e-13
60 NA 3.423105e-12
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.572552e-11 7.325621e-11
[2,] 6.128195e-12 -7.572552e-11
[3,] -4.293661e-12 6.128195e-12
[4,] -6.525006e-12 -4.293661e-12
[5,] -1.815881e-12 -6.525006e-12
[6,] 3.646845e-12 -1.815881e-12
[7,] -4.392207e-12 3.646845e-12
[8,] 1.942926e-12 -4.392207e-12
[9,] 6.813038e-12 1.942926e-12
[10,] 2.777429e-12 6.813038e-12
[11,] 2.108830e-12 2.777429e-12
[12,] -2.211244e-12 2.108830e-12
[13,] -4.207001e-12 -2.211244e-12
[14,] -4.186244e-12 -4.207001e-12
[15,] -3.423883e-12 -4.186244e-12
[16,] 5.866670e-12 -3.423883e-12
[17,] 9.809580e-12 5.866670e-12
[18,] -2.224783e-13 9.809580e-12
[19,] -1.844664e-12 -2.224783e-13
[20,] -1.446659e-12 -1.844664e-12
[21,] -1.514030e-12 -1.446659e-12
[22,] 6.489188e-13 -1.514030e-12
[23,] 3.046400e-12 6.489188e-13
[24,] -5.286183e-12 3.046400e-12
[25,] -1.069837e-11 -5.286183e-12
[26,] -2.755750e-12 -1.069837e-11
[27,] -3.902497e-12 -2.755750e-12
[28,] 1.412169e-12 -3.902497e-12
[29,] 4.647414e-12 1.412169e-12
[30,] 2.773751e-12 4.647414e-12
[31,] -2.808025e-12 2.773751e-12
[32,] -2.888372e-12 -2.808025e-12
[33,] 1.453462e-12 -2.888372e-12
[34,] 6.188876e-13 1.453462e-12
[35,] 2.655900e-12 6.188876e-13
[36,] 4.265398e-12 2.655900e-12
[37,] 2.347690e-13 4.265398e-12
[38,] -1.700136e-13 2.347690e-13
[39,] 4.730078e-12 -1.700136e-13
[40,] -8.996992e-13 4.730078e-12
[41,] -3.390114e-13 -8.996992e-13
[42,] 4.912353e-13 -3.390114e-13
[43,] 3.890085e-12 4.912353e-13
[44,] 2.867622e-12 3.890085e-12
[45,] 1.620716e-12 2.867622e-12
[46,] -2.504975e-12 1.620716e-12
[47,] -1.261872e-12 -2.504975e-12
[48,] -5.427350e-12 -1.261872e-12
[49,] 2.079086e-12 -5.427350e-12
[50,] -1.371197e-12 2.079086e-12
[51,] 3.894179e-13 -1.371197e-12
[52,] -1.126375e-12 3.894179e-13
[53,] 6.735331e-13 -1.126375e-12
[54,] 1.121844e-12 6.735331e-13
[55,] -5.537690e-12 1.121844e-12
[56,] 1.464536e-12 -5.537690e-12
[57,] 1.027146e-12 1.464536e-12
[58,] 9.006606e-13 1.027146e-12
[59,] 3.423105e-12 9.006606e-13
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.572552e-11 7.325621e-11
2 6.128195e-12 -7.572552e-11
3 -4.293661e-12 6.128195e-12
4 -6.525006e-12 -4.293661e-12
5 -1.815881e-12 -6.525006e-12
6 3.646845e-12 -1.815881e-12
7 -4.392207e-12 3.646845e-12
8 1.942926e-12 -4.392207e-12
9 6.813038e-12 1.942926e-12
10 2.777429e-12 6.813038e-12
11 2.108830e-12 2.777429e-12
12 -2.211244e-12 2.108830e-12
13 -4.207001e-12 -2.211244e-12
14 -4.186244e-12 -4.207001e-12
15 -3.423883e-12 -4.186244e-12
16 5.866670e-12 -3.423883e-12
17 9.809580e-12 5.866670e-12
18 -2.224783e-13 9.809580e-12
19 -1.844664e-12 -2.224783e-13
20 -1.446659e-12 -1.844664e-12
21 -1.514030e-12 -1.446659e-12
22 6.489188e-13 -1.514030e-12
23 3.046400e-12 6.489188e-13
24 -5.286183e-12 3.046400e-12
25 -1.069837e-11 -5.286183e-12
26 -2.755750e-12 -1.069837e-11
27 -3.902497e-12 -2.755750e-12
28 1.412169e-12 -3.902497e-12
29 4.647414e-12 1.412169e-12
30 2.773751e-12 4.647414e-12
31 -2.808025e-12 2.773751e-12
32 -2.888372e-12 -2.808025e-12
33 1.453462e-12 -2.888372e-12
34 6.188876e-13 1.453462e-12
35 2.655900e-12 6.188876e-13
36 4.265398e-12 2.655900e-12
37 2.347690e-13 4.265398e-12
38 -1.700136e-13 2.347690e-13
39 4.730078e-12 -1.700136e-13
40 -8.996992e-13 4.730078e-12
41 -3.390114e-13 -8.996992e-13
42 4.912353e-13 -3.390114e-13
43 3.890085e-12 4.912353e-13
44 2.867622e-12 3.890085e-12
45 1.620716e-12 2.867622e-12
46 -2.504975e-12 1.620716e-12
47 -1.261872e-12 -2.504975e-12
48 -5.427350e-12 -1.261872e-12
49 2.079086e-12 -5.427350e-12
50 -1.371197e-12 2.079086e-12
51 3.894179e-13 -1.371197e-12
52 -1.126375e-12 3.894179e-13
53 6.735331e-13 -1.126375e-12
54 1.121844e-12 6.735331e-13
55 -5.537690e-12 1.121844e-12
56 1.464536e-12 -5.537690e-12
57 1.027146e-12 1.464536e-12
58 9.006606e-13 1.027146e-12
59 3.423105e-12 9.006606e-13
> 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/wessaorg/rcomp/tmp/7vlcg1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8222v1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9cnkh1352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10tpx21352042350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11gum21352042350.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/wessaorg/rcomp/tmp/12l3631352042350.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/wessaorg/rcomp/tmp/13k6ht1352042350.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/wessaorg/rcomp/tmp/143ba51352042350.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/wessaorg/rcomp/tmp/15bo1u1352042350.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/wessaorg/rcomp/tmp/162th71352042350.tab")
+ }
>
> try(system("convert tmp/1m5cj1352042350.ps tmp/1m5cj1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zmze1352042350.ps tmp/2zmze1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xyin1352042350.ps tmp/3xyin1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l4gx1352042350.ps tmp/4l4gx1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vhrd1352042350.ps tmp/5vhrd1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fg021352042350.ps tmp/6fg021352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vlcg1352042350.ps tmp/7vlcg1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/8222v1352042350.ps tmp/8222v1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cnkh1352042350.ps tmp/9cnkh1352042350.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tpx21352042350.ps tmp/10tpx21352042350.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.498 1.121 7.608