R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(9190
+ ,2514
+ ,2550
+ ,1512
+ ,1591
+ ,472
+ ,551
+ ,9251
+ ,2537
+ ,2572
+ ,1517
+ ,1595
+ ,476
+ ,554
+ ,9328
+ ,2564
+ ,2597
+ ,1525
+ ,1602
+ ,483
+ ,558
+ ,9428
+ ,2595
+ ,2623
+ ,1540
+ ,1613
+ ,493
+ ,565
+ ,9499
+ ,2617
+ ,2647
+ ,1547
+ ,1622
+ ,498
+ ,568
+ ,9556
+ ,2638
+ ,2670
+ ,1547
+ ,1627
+ ,502
+ ,572
+ ,9606
+ ,2657
+ ,2690
+ ,1547
+ ,1632
+ ,504
+ ,575
+ ,9632
+ ,2668
+ ,2705
+ ,1547
+ ,1634
+ ,503
+ ,574
+ ,9660
+ ,2683
+ ,2721
+ ,1546
+ ,1637
+ ,501
+ ,572
+ ,9651
+ ,2687
+ ,2729
+ ,1533
+ ,1627
+ ,502
+ ,573
+ ,9695
+ ,2705
+ ,2747
+ ,1538
+ ,1632
+ ,502
+ ,572
+ ,9727
+ ,2717
+ ,2761
+ ,1543
+ ,1637
+ ,500
+ ,569
+ ,9757
+ ,2728
+ ,2773
+ ,1549
+ ,1643
+ ,498
+ ,566
+ ,9788
+ ,2741
+ ,2786
+ ,1556
+ ,1650
+ ,495
+ ,560
+ ,9813
+ ,2752
+ ,2796
+ ,1559
+ ,1654
+ ,494
+ ,557
+ ,9823
+ ,2759
+ ,2807
+ ,1559
+ ,1656
+ ,490
+ ,552
+ ,9837
+ ,2767
+ ,2817
+ ,1563
+ ,1661
+ ,484
+ ,545
+ ,9842
+ ,2774
+ ,2827
+ ,1563
+ ,1662
+ ,477
+ ,539
+ ,9855
+ ,2781
+ ,2838
+ ,1564
+ ,1664
+ ,474
+ ,535
+ ,9863
+ ,2788
+ ,2847
+ ,1564
+ ,1665
+ ,469
+ ,531
+ ,9855
+ ,2789
+ ,2853
+ ,1557
+ ,1661
+ ,466
+ ,528
+ ,9858
+ ,2795
+ ,2860
+ ,1554
+ ,1659
+ ,464
+ ,526
+ ,9853
+ ,2798
+ ,2864
+ ,1552
+ ,1656
+ ,460
+ ,523
+ ,9858
+ ,2801
+ ,2869
+ ,1552
+ ,1656
+ ,458
+ ,521
+ ,9859
+ ,2803
+ ,2873
+ ,1551
+ ,1655
+ ,457
+ ,519
+ ,9865
+ ,2808
+ ,2877
+ ,1552
+ ,1654
+ ,456
+ ,517
+ ,9876
+ ,2813
+ ,2883
+ ,1554
+ ,1656
+ ,455
+ ,515
+ ,9928
+ ,2826
+ ,2896
+ ,1567
+ ,1668
+ ,456
+ ,514
+ ,9948
+ ,2835
+ ,2905
+ ,1572
+ ,1672
+ ,453
+ ,511
+ ,9987
+ ,2849
+ ,2919
+ ,1579
+ ,1680
+ ,453
+ ,508
+ ,10022
+ ,2862
+ ,2933
+ ,1588
+ ,1688
+ ,449
+ ,502
+ ,10068
+ ,2877
+ ,2948
+ ,1597
+ ,1696
+ ,449
+ ,501
+ ,10101
+ ,2888
+ ,2959
+ ,1603
+ ,1702
+ ,449
+ ,500
+ ,10131
+ ,2897
+ ,2969
+ ,1607
+ ,1706
+ ,452
+ ,500
+ ,10143
+ ,2902
+ ,2978
+ ,1607
+ ,1708
+ ,450
+ ,498
+ ,10170
+ ,2911
+ ,2988
+ ,1609
+ ,1711
+ ,452
+ ,499
+ ,10192
+ ,2917
+ ,2996
+ ,1612
+ ,1714
+ ,454
+ ,499
+ ,10214
+ ,2924
+ ,3003
+ ,1615
+ ,1717
+ ,455
+ ,500
+ ,10239
+ ,2930
+ ,3011
+ ,1619
+ ,1721
+ ,458
+ ,501
+ ,10263
+ ,2935
+ ,3018
+ ,1622
+ ,1724
+ ,461
+ ,503
+ ,10310
+ ,2945
+ ,3028
+ ,1628
+ ,1730
+ ,469
+ ,510
+ ,10355
+ ,2957
+ ,3038
+ ,1634
+ ,1735
+ ,477
+ ,515
+ ,10396
+ ,2967
+ ,3049
+ ,1640
+ ,1740
+ ,480
+ ,520
+ ,10446
+ ,2980
+ ,3063
+ ,1648
+ ,1748
+ ,484
+ ,523
+ ,10511
+ ,2997
+ ,3081
+ ,1657
+ ,1757
+ ,490
+ ,529
+ ,10585
+ ,3017
+ ,3100
+ ,1668
+ ,1768
+ ,497
+ ,534
+ ,10667
+ ,3040
+ ,3122
+ ,1678
+ ,1778
+ ,506
+ ,543
+ ,10753
+ ,3064
+ ,3145
+ ,1687
+ ,1789
+ ,516
+ ,553
+ ,10840
+ ,3085
+ ,3167
+ ,1700
+ ,1798
+ ,527
+ ,563
+ ,10951
+ ,3113
+ ,3193
+ ,1714
+ ,1811
+ ,542
+ ,577)
+ ,dim=c(7
+ ,50)
+ ,dimnames=list(c('totaal'
+ ,'vlaams_man'
+ ,'vlaams_vrouw'
+ ,'waals_man'
+ ,'waals_vrouw'
+ ,'brussel_man'
+ ,'brussel_vrouw')
+ ,1:50))
> y <- array(NA,dim=c(7,50),dimnames=list(c('totaal','vlaams_man','vlaams_vrouw','waals_man','waals_vrouw','brussel_man','brussel_vrouw'),1:50))
> 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
totaal vlaams_man vlaams_vrouw waals_man waals_vrouw brussel_man
1 9190 2514 2550 1512 1591 472
2 9251 2537 2572 1517 1595 476
3 9328 2564 2597 1525 1602 483
4 9428 2595 2623 1540 1613 493
5 9499 2617 2647 1547 1622 498
6 9556 2638 2670 1547 1627 502
7 9606 2657 2690 1547 1632 504
8 9632 2668 2705 1547 1634 503
9 9660 2683 2721 1546 1637 501
10 9651 2687 2729 1533 1627 502
11 9695 2705 2747 1538 1632 502
12 9727 2717 2761 1543 1637 500
13 9757 2728 2773 1549 1643 498
14 9788 2741 2786 1556 1650 495
15 9813 2752 2796 1559 1654 494
16 9823 2759 2807 1559 1656 490
17 9837 2767 2817 1563 1661 484
18 9842 2774 2827 1563 1662 477
19 9855 2781 2838 1564 1664 474
20 9863 2788 2847 1564 1665 469
21 9855 2789 2853 1557 1661 466
22 9858 2795 2860 1554 1659 464
23 9853 2798 2864 1552 1656 460
24 9858 2801 2869 1552 1656 458
25 9859 2803 2873 1551 1655 457
26 9865 2808 2877 1552 1654 456
27 9876 2813 2883 1554 1656 455
28 9928 2826 2896 1567 1668 456
29 9948 2835 2905 1572 1672 453
30 9987 2849 2919 1579 1680 453
31 10022 2862 2933 1588 1688 449
32 10068 2877 2948 1597 1696 449
33 10101 2888 2959 1603 1702 449
34 10131 2897 2969 1607 1706 452
35 10143 2902 2978 1607 1708 450
36 10170 2911 2988 1609 1711 452
37 10192 2917 2996 1612 1714 454
38 10214 2924 3003 1615 1717 455
39 10239 2930 3011 1619 1721 458
40 10263 2935 3018 1622 1724 461
41 10310 2945 3028 1628 1730 469
42 10355 2957 3038 1634 1735 477
43 10396 2967 3049 1640 1740 480
44 10446 2980 3063 1648 1748 484
45 10511 2997 3081 1657 1757 490
46 10585 3017 3100 1668 1768 497
47 10667 3040 3122 1678 1778 506
48 10753 3064 3145 1687 1789 516
49 10840 3085 3167 1700 1798 527
50 10951 3113 3193 1714 1811 542
brussel_vrouw
1 551
2 554
3 558
4 565
5 568
6 572
7 575
8 574
9 572
10 573
11 572
12 569
13 566
14 560
15 557
16 552
17 545
18 539
19 535
20 531
21 528
22 526
23 523
24 521
25 519
26 517
27 515
28 514
29 511
30 508
31 502
32 501
33 500
34 500
35 498
36 499
37 499
38 500
39 501
40 503
41 510
42 515
43 520
44 523
45 529
46 534
47 543
48 553
49 563
50 577
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) vlaams_man vlaams_vrouw waals_man waals_vrouw
-15.6002 0.9765 1.0300 1.0199 0.9740
brussel_man brussel_vrouw
0.9301 1.0769
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.23005 -0.27032 0.08218 0.21548 1.15485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -15.60018 22.71234 -0.687 0.496
vlaams_man 0.97647 0.03002 32.530 <2e-16 ***
vlaams_vrouw 1.02997 0.02795 36.847 <2e-16 ***
waals_man 1.01993 0.03544 28.780 <2e-16 ***
waals_vrouw 0.97402 0.05014 19.426 <2e-16 ***
brussel_man 0.93010 0.05695 16.331 <2e-16 ***
brussel_vrouw 1.07691 0.05123 21.020 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6203 on 43 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.302e+06 on 6 and 43 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1876728 0.37534569 0.81232716
[2,] 0.1027518 0.20550351 0.89724824
[3,] 0.2659090 0.53181796 0.73409102
[4,] 0.1859825 0.37196491 0.81401755
[5,] 0.1812432 0.36248635 0.81875682
[6,] 0.3740532 0.74810631 0.62594685
[7,] 0.4033883 0.80677651 0.59661175
[8,] 0.4198873 0.83977457 0.58011271
[9,] 0.3922006 0.78440122 0.60779939
[10,] 0.4527544 0.90550883 0.54724558
[11,] 0.5030136 0.99397273 0.49698637
[12,] 0.8459622 0.30807558 0.15403779
[13,] 0.8095220 0.38095605 0.19047802
[14,] 0.8225168 0.35496647 0.17748324
[15,] 0.8729118 0.25417631 0.12708815
[16,] 0.8760811 0.24783787 0.12391893
[17,] 0.9048307 0.19033853 0.09516927
[18,] 0.8663301 0.26733971 0.13366985
[19,] 0.9540286 0.09194276 0.04597138
[20,] 0.9391667 0.12166669 0.06083335
[21,] 0.9411656 0.11766875 0.05883437
[22,] 0.9140364 0.17192721 0.08596361
[23,] 0.8630976 0.27380487 0.13690244
[24,] 0.8137327 0.37253468 0.18626734
[25,] 0.7629520 0.47409598 0.23704799
[26,] 0.7596821 0.48063583 0.24031791
[27,] 0.6898518 0.62029642 0.31014821
[28,] 0.5693342 0.86133162 0.43066581
[29,] 0.4614151 0.92283023 0.53858489
[30,] 0.5943776 0.81124484 0.40562242
[31,] 0.5149092 0.97018163 0.48509082
> postscript(file="/var/wessaorg/rcomp/tmp/1rpsm1351870971.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25alx1351870971.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3jo0i1351870971.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4osjo1351870971.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kxhb1351870971.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 50
Frequency = 1
1 2 3 4 5 6
0.150998776 0.085983989 -0.823871719 -0.726174890 0.285300117 0.191963126
7 8 9 10 11 12
1.078584289 0.946830002 -0.067872566 -0.221287156 -1.230046658 -0.246075228
13 14 15 16 17 18
-0.219640408 -0.009237233 1.154849226 0.146797034 0.204495157 0.067637581
19 20 21 22 23 24
-0.967349084 -1.088257339 0.812048948 -0.234732021 -0.370986545 0.563772909
25 26 27 28 29 30
0.568819760 0.604594115 -0.361551315 0.754253970 -0.278396962 -1.069482031
31 32 33 34 35 36
0.027182190 0.035985226 0.078369366 0.224351861 0.138254265 0.151296765
37 38 39 40 41 42
0.210680134 0.176746313 -0.764824642 0.217081087 0.209834186 -0.622520863
43 44 45 46 47 48
0.118600257 0.102207720 -0.024823157 1.047677082 -0.073039052 -1.161299916
49 50
-0.351932953 0.558206288
> postscript(file="/var/wessaorg/rcomp/tmp/6cpi31351870971.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 = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 0.150998776 NA
1 0.085983989 0.150998776
2 -0.823871719 0.085983989
3 -0.726174890 -0.823871719
4 0.285300117 -0.726174890
5 0.191963126 0.285300117
6 1.078584289 0.191963126
7 0.946830002 1.078584289
8 -0.067872566 0.946830002
9 -0.221287156 -0.067872566
10 -1.230046658 -0.221287156
11 -0.246075228 -1.230046658
12 -0.219640408 -0.246075228
13 -0.009237233 -0.219640408
14 1.154849226 -0.009237233
15 0.146797034 1.154849226
16 0.204495157 0.146797034
17 0.067637581 0.204495157
18 -0.967349084 0.067637581
19 -1.088257339 -0.967349084
20 0.812048948 -1.088257339
21 -0.234732021 0.812048948
22 -0.370986545 -0.234732021
23 0.563772909 -0.370986545
24 0.568819760 0.563772909
25 0.604594115 0.568819760
26 -0.361551315 0.604594115
27 0.754253970 -0.361551315
28 -0.278396962 0.754253970
29 -1.069482031 -0.278396962
30 0.027182190 -1.069482031
31 0.035985226 0.027182190
32 0.078369366 0.035985226
33 0.224351861 0.078369366
34 0.138254265 0.224351861
35 0.151296765 0.138254265
36 0.210680134 0.151296765
37 0.176746313 0.210680134
38 -0.764824642 0.176746313
39 0.217081087 -0.764824642
40 0.209834186 0.217081087
41 -0.622520863 0.209834186
42 0.118600257 -0.622520863
43 0.102207720 0.118600257
44 -0.024823157 0.102207720
45 1.047677082 -0.024823157
46 -0.073039052 1.047677082
47 -1.161299916 -0.073039052
48 -0.351932953 -1.161299916
49 0.558206288 -0.351932953
50 NA 0.558206288
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.085983989 0.150998776
[2,] -0.823871719 0.085983989
[3,] -0.726174890 -0.823871719
[4,] 0.285300117 -0.726174890
[5,] 0.191963126 0.285300117
[6,] 1.078584289 0.191963126
[7,] 0.946830002 1.078584289
[8,] -0.067872566 0.946830002
[9,] -0.221287156 -0.067872566
[10,] -1.230046658 -0.221287156
[11,] -0.246075228 -1.230046658
[12,] -0.219640408 -0.246075228
[13,] -0.009237233 -0.219640408
[14,] 1.154849226 -0.009237233
[15,] 0.146797034 1.154849226
[16,] 0.204495157 0.146797034
[17,] 0.067637581 0.204495157
[18,] -0.967349084 0.067637581
[19,] -1.088257339 -0.967349084
[20,] 0.812048948 -1.088257339
[21,] -0.234732021 0.812048948
[22,] -0.370986545 -0.234732021
[23,] 0.563772909 -0.370986545
[24,] 0.568819760 0.563772909
[25,] 0.604594115 0.568819760
[26,] -0.361551315 0.604594115
[27,] 0.754253970 -0.361551315
[28,] -0.278396962 0.754253970
[29,] -1.069482031 -0.278396962
[30,] 0.027182190 -1.069482031
[31,] 0.035985226 0.027182190
[32,] 0.078369366 0.035985226
[33,] 0.224351861 0.078369366
[34,] 0.138254265 0.224351861
[35,] 0.151296765 0.138254265
[36,] 0.210680134 0.151296765
[37,] 0.176746313 0.210680134
[38,] -0.764824642 0.176746313
[39,] 0.217081087 -0.764824642
[40,] 0.209834186 0.217081087
[41,] -0.622520863 0.209834186
[42,] 0.118600257 -0.622520863
[43,] 0.102207720 0.118600257
[44,] -0.024823157 0.102207720
[45,] 1.047677082 -0.024823157
[46,] -0.073039052 1.047677082
[47,] -1.161299916 -0.073039052
[48,] -0.351932953 -1.161299916
[49,] 0.558206288 -0.351932953
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.085983989 0.150998776
2 -0.823871719 0.085983989
3 -0.726174890 -0.823871719
4 0.285300117 -0.726174890
5 0.191963126 0.285300117
6 1.078584289 0.191963126
7 0.946830002 1.078584289
8 -0.067872566 0.946830002
9 -0.221287156 -0.067872566
10 -1.230046658 -0.221287156
11 -0.246075228 -1.230046658
12 -0.219640408 -0.246075228
13 -0.009237233 -0.219640408
14 1.154849226 -0.009237233
15 0.146797034 1.154849226
16 0.204495157 0.146797034
17 0.067637581 0.204495157
18 -0.967349084 0.067637581
19 -1.088257339 -0.967349084
20 0.812048948 -1.088257339
21 -0.234732021 0.812048948
22 -0.370986545 -0.234732021
23 0.563772909 -0.370986545
24 0.568819760 0.563772909
25 0.604594115 0.568819760
26 -0.361551315 0.604594115
27 0.754253970 -0.361551315
28 -0.278396962 0.754253970
29 -1.069482031 -0.278396962
30 0.027182190 -1.069482031
31 0.035985226 0.027182190
32 0.078369366 0.035985226
33 0.224351861 0.078369366
34 0.138254265 0.224351861
35 0.151296765 0.138254265
36 0.210680134 0.151296765
37 0.176746313 0.210680134
38 -0.764824642 0.176746313
39 0.217081087 -0.764824642
40 0.209834186 0.217081087
41 -0.622520863 0.209834186
42 0.118600257 -0.622520863
43 0.102207720 0.118600257
44 -0.024823157 0.102207720
45 1.047677082 -0.024823157
46 -0.073039052 1.047677082
47 -1.161299916 -0.073039052
48 -0.351932953 -1.161299916
49 0.558206288 -0.351932953
> 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/7rpro1351870971.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/8gszg1351870971.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/9z2c81351870971.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/10zvtx1351870971.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1109we1351870972.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122rfi1351870972.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13orpw1351870972.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14k4yj1351870972.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/15e0s11351870972.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/16jblh1351870972.tab")
+ }
>
> try(system("convert tmp/1rpsm1351870971.ps tmp/1rpsm1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/25alx1351870971.ps tmp/25alx1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jo0i1351870971.ps tmp/3jo0i1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/4osjo1351870971.ps tmp/4osjo1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kxhb1351870971.ps tmp/5kxhb1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cpi31351870971.ps tmp/6cpi31351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rpro1351870971.ps tmp/7rpro1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gszg1351870971.ps tmp/8gszg1351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z2c81351870971.ps tmp/9z2c81351870971.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zvtx1351870971.ps tmp/10zvtx1351870971.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.828 1.098 6.915