R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(537
+ ,20.1
+ ,544
+ ,555
+ ,561
+ ,562
+ ,543
+ ,19.9
+ ,537
+ ,544
+ ,555
+ ,561
+ ,594
+ ,20
+ ,543
+ ,537
+ ,544
+ ,555
+ ,611
+ ,22.6
+ ,594
+ ,543
+ ,537
+ ,544
+ ,613
+ ,20.6
+ ,611
+ ,594
+ ,543
+ ,537
+ ,611
+ ,20.1
+ ,613
+ ,611
+ ,594
+ ,543
+ ,594
+ ,20.2
+ ,611
+ ,613
+ ,611
+ ,594
+ ,595
+ ,21.8
+ ,594
+ ,611
+ ,613
+ ,611
+ ,591
+ ,22
+ ,595
+ ,594
+ ,611
+ ,613
+ ,589
+ ,19.5
+ ,591
+ ,595
+ ,594
+ ,611
+ ,584
+ ,17.5
+ ,589
+ ,591
+ ,595
+ ,594
+ ,573
+ ,18.2
+ ,584
+ ,589
+ ,591
+ ,595
+ ,567
+ ,18.8
+ ,573
+ ,584
+ ,589
+ ,591
+ ,569
+ ,19.7
+ ,567
+ ,573
+ ,584
+ ,589
+ ,621
+ ,18.8
+ ,569
+ ,567
+ ,573
+ ,584
+ ,629
+ ,18.5
+ ,621
+ ,569
+ ,567
+ ,573
+ ,628
+ ,18.7
+ ,629
+ ,621
+ ,569
+ ,567
+ ,612
+ ,18.5
+ ,628
+ ,629
+ ,621
+ ,569
+ ,595
+ ,19.3
+ ,612
+ ,628
+ ,629
+ ,621
+ ,597
+ ,18.9
+ ,595
+ ,612
+ ,628
+ ,629
+ ,593
+ ,21.4
+ ,597
+ ,595
+ ,612
+ ,628
+ ,590
+ ,22.5
+ ,593
+ ,597
+ ,595
+ ,612
+ ,580
+ ,25
+ ,590
+ ,593
+ ,597
+ ,595
+ ,574
+ ,22.9
+ ,580
+ ,590
+ ,593
+ ,597
+ ,573
+ ,22.9
+ ,574
+ ,580
+ ,590
+ ,593
+ ,573
+ ,21.3
+ ,573
+ ,574
+ ,580
+ ,590
+ ,620
+ ,22.3
+ ,573
+ ,573
+ ,574
+ ,580
+ ,626
+ ,20.9
+ ,620
+ ,573
+ ,573
+ ,574
+ ,620
+ ,19.9
+ ,626
+ ,620
+ ,573
+ ,573
+ ,588
+ ,20.2
+ ,620
+ ,626
+ ,620
+ ,573
+ ,566
+ ,19.8
+ ,588
+ ,620
+ ,626
+ ,620
+ ,557
+ ,17.7
+ ,566
+ ,588
+ ,620
+ ,626
+ ,561
+ ,18.1
+ ,557
+ ,566
+ ,588
+ ,620
+ ,549
+ ,17.6
+ ,561
+ ,557
+ ,566
+ ,588
+ ,532
+ ,18.2
+ ,549
+ ,561
+ ,557
+ ,566
+ ,526
+ ,16
+ ,532
+ ,549
+ ,561
+ ,557
+ ,511
+ ,16.3
+ ,526
+ ,532
+ ,549
+ ,561
+ ,499
+ ,17.3
+ ,511
+ ,526
+ ,532
+ ,549
+ ,555
+ ,19
+ ,499
+ ,511
+ ,526
+ ,532
+ ,565
+ ,18.6
+ ,555
+ ,499
+ ,511
+ ,526
+ ,542
+ ,18
+ ,565
+ ,555
+ ,499
+ ,511
+ ,527
+ ,17.9
+ ,542
+ ,565
+ ,555
+ ,499
+ ,510
+ ,17.8
+ ,527
+ ,542
+ ,565
+ ,555
+ ,514
+ ,18.5
+ ,510
+ ,527
+ ,542
+ ,565
+ ,517
+ ,17.4
+ ,514
+ ,510
+ ,527
+ ,542
+ ,508
+ ,19
+ ,517
+ ,514
+ ,510
+ ,527
+ ,493
+ ,17.4
+ ,508
+ ,517
+ ,514
+ ,510
+ ,490
+ ,20.6
+ ,493
+ ,508
+ ,517
+ ,514
+ ,469
+ ,18.5
+ ,490
+ ,493
+ ,508
+ ,517
+ ,478
+ ,20
+ ,469
+ ,490
+ ,493
+ ,508
+ ,528
+ ,18.8
+ ,478
+ ,469
+ ,490
+ ,493
+ ,534
+ ,18.8
+ ,528
+ ,478
+ ,469
+ ,490
+ ,518
+ ,19.7
+ ,534
+ ,528
+ ,478
+ ,469
+ ,506
+ ,15.3
+ ,518
+ ,534
+ ,528
+ ,478
+ ,502
+ ,10.6
+ ,506
+ ,518
+ ,534
+ ,528
+ ,516
+ ,6.1
+ ,502
+ ,506
+ ,518
+ ,534
+ ,528
+ ,0.9
+ ,516
+ ,502
+ ,506
+ ,518)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:57))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 537 20.1 544 555 561 562 1 0 0 0 0 0 0 0 0 0 0 1
2 543 19.9 537 544 555 561 0 1 0 0 0 0 0 0 0 0 0 2
3 594 20.0 543 537 544 555 0 0 1 0 0 0 0 0 0 0 0 3
4 611 22.6 594 543 537 544 0 0 0 1 0 0 0 0 0 0 0 4
5 613 20.6 611 594 543 537 0 0 0 0 1 0 0 0 0 0 0 5
6 611 20.1 613 611 594 543 0 0 0 0 0 1 0 0 0 0 0 6
7 594 20.2 611 613 611 594 0 0 0 0 0 0 1 0 0 0 0 7
8 595 21.8 594 611 613 611 0 0 0 0 0 0 0 1 0 0 0 8
9 591 22.0 595 594 611 613 0 0 0 0 0 0 0 0 1 0 0 9
10 589 19.5 591 595 594 611 0 0 0 0 0 0 0 0 0 1 0 10
11 584 17.5 589 591 595 594 0 0 0 0 0 0 0 0 0 0 1 11
12 573 18.2 584 589 591 595 0 0 0 0 0 0 0 0 0 0 0 12
13 567 18.8 573 584 589 591 1 0 0 0 0 0 0 0 0 0 0 13
14 569 19.7 567 573 584 589 0 1 0 0 0 0 0 0 0 0 0 14
15 621 18.8 569 567 573 584 0 0 1 0 0 0 0 0 0 0 0 15
16 629 18.5 621 569 567 573 0 0 0 1 0 0 0 0 0 0 0 16
17 628 18.7 629 621 569 567 0 0 0 0 1 0 0 0 0 0 0 17
18 612 18.5 628 629 621 569 0 0 0 0 0 1 0 0 0 0 0 18
19 595 19.3 612 628 629 621 0 0 0 0 0 0 1 0 0 0 0 19
20 597 18.9 595 612 628 629 0 0 0 0 0 0 0 1 0 0 0 20
21 593 21.4 597 595 612 628 0 0 0 0 0 0 0 0 1 0 0 21
22 590 22.5 593 597 595 612 0 0 0 0 0 0 0 0 0 1 0 22
23 580 25.0 590 593 597 595 0 0 0 0 0 0 0 0 0 0 1 23
24 574 22.9 580 590 593 597 0 0 0 0 0 0 0 0 0 0 0 24
25 573 22.9 574 580 590 593 1 0 0 0 0 0 0 0 0 0 0 25
26 573 21.3 573 574 580 590 0 1 0 0 0 0 0 0 0 0 0 26
27 620 22.3 573 573 574 580 0 0 1 0 0 0 0 0 0 0 0 27
28 626 20.9 620 573 573 574 0 0 0 1 0 0 0 0 0 0 0 28
29 620 19.9 626 620 573 573 0 0 0 0 1 0 0 0 0 0 0 29
30 588 20.2 620 626 620 573 0 0 0 0 0 1 0 0 0 0 0 30
31 566 19.8 588 620 626 620 0 0 0 0 0 0 1 0 0 0 0 31
32 557 17.7 566 588 620 626 0 0 0 0 0 0 0 1 0 0 0 32
33 561 18.1 557 566 588 620 0 0 0 0 0 0 0 0 1 0 0 33
34 549 17.6 561 557 566 588 0 0 0 0 0 0 0 0 0 1 0 34
35 532 18.2 549 561 557 566 0 0 0 0 0 0 0 0 0 0 1 35
36 526 16.0 532 549 561 557 0 0 0 0 0 0 0 0 0 0 0 36
37 511 16.3 526 532 549 561 1 0 0 0 0 0 0 0 0 0 0 37
38 499 17.3 511 526 532 549 0 1 0 0 0 0 0 0 0 0 0 38
39 555 19.0 499 511 526 532 0 0 1 0 0 0 0 0 0 0 0 39
40 565 18.6 555 499 511 526 0 0 0 1 0 0 0 0 0 0 0 40
41 542 18.0 565 555 499 511 0 0 0 0 1 0 0 0 0 0 0 41
42 527 17.9 542 565 555 499 0 0 0 0 0 1 0 0 0 0 0 42
43 510 17.8 527 542 565 555 0 0 0 0 0 0 1 0 0 0 0 43
44 514 18.5 510 527 542 565 0 0 0 0 0 0 0 1 0 0 0 44
45 517 17.4 514 510 527 542 0 0 0 0 0 0 0 0 1 0 0 45
46 508 19.0 517 514 510 527 0 0 0 0 0 0 0 0 0 1 0 46
47 493 17.4 508 517 514 510 0 0 0 0 0 0 0 0 0 0 1 47
48 490 20.6 493 508 517 514 0 0 0 0 0 0 0 0 0 0 0 48
49 469 18.5 490 493 508 517 1 0 0 0 0 0 0 0 0 0 0 49
50 478 20.0 469 490 493 508 0 1 0 0 0 0 0 0 0 0 0 50
51 528 18.8 478 469 490 493 0 0 1 0 0 0 0 0 0 0 0 51
52 534 18.8 528 478 469 490 0 0 0 1 0 0 0 0 0 0 0 52
53 518 19.7 534 528 478 469 0 0 0 0 1 0 0 0 0 0 0 53
54 506 15.3 518 534 528 478 0 0 0 0 0 1 0 0 0 0 0 54
55 502 10.6 506 518 534 528 0 0 0 0 0 0 1 0 0 0 0 55
56 516 6.1 502 506 518 534 0 0 0 0 0 0 0 1 0 0 0 56
57 528 0.9 516 502 506 518 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
58.29981 -0.66864 0.96117 0.03917 0.04788 -0.12710
M1 M2 M3 M4 M5 M6
-4.49727 6.69352 56.67885 16.68721 -4.55220 -14.58744
M7 M8 M9 M10 M11 t
-9.10435 9.94246 9.88391 2.43365 -5.18400 -0.25414
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.0338 -3.1899 -0.1632 4.0114 13.5567
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.29981 27.45851 2.123 0.0401 *
X -0.66864 0.34484 -1.939 0.0598 .
Y1 0.96117 0.16139 5.955 5.97e-07 ***
Y2 0.03917 0.22445 0.175 0.8624
Y3 0.04788 0.22299 0.215 0.8311
Y4 -0.12710 0.16501 -0.770 0.4458
M1 -4.49727 4.84525 -0.928 0.3590
M2 6.69352 5.06339 1.322 0.1939
M3 56.67885 5.33530 10.623 4.49e-13 ***
M4 16.68721 11.21265 1.488 0.1447
M5 -4.55220 10.65494 -0.427 0.6716
M6 -14.58744 10.20374 -1.430 0.1608
M7 -9.10435 5.14718 -1.769 0.0847 .
M8 9.94246 5.18011 1.919 0.0623 .
M9 9.88391 5.87144 1.683 0.1003
M10 2.43365 6.06390 0.401 0.6904
M11 -5.18400 4.99563 -1.038 0.3058
t -0.25414 0.11043 -2.301 0.0268 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.822 on 39 degrees of freedom
Multiple R-squared: 0.9827, Adjusted R-squared: 0.9751
F-statistic: 130 on 17 and 39 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,] 0.21092631 0.42185262 0.7890737
[2,] 0.12034405 0.24068810 0.8796560
[3,] 0.06040299 0.12080597 0.9395970
[4,] 0.03903244 0.07806489 0.9609676
[5,] 0.08629661 0.17259322 0.9137034
[6,] 0.08099944 0.16199888 0.9190006
[7,] 0.04458464 0.08916928 0.9554154
[8,] 0.02450777 0.04901553 0.9754922
[9,] 0.18227805 0.36455610 0.8177219
[10,] 0.62655640 0.74688721 0.3734436
[11,] 0.71382905 0.57234191 0.2861710
[12,] 0.62022318 0.75955365 0.3797768
[13,] 0.54308005 0.91383990 0.4569200
[14,] 0.41845108 0.83690217 0.5815489
[15,] 0.47930071 0.95860142 0.5206993
[16,] 0.43516171 0.87032342 0.5648383
> postscript(file="/var/www/html/rcomp/tmp/16h3u1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21zqm1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3di8m1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4x10f1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zddx1258758247.ps",horizontal=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 = 57
Frequency = 1
1 2 3 4 5 6
-3.15221883 -0.90341073 -5.29649007 3.37031433 6.01223284 9.69996182
7 8 9 10 11 12
-4.95008025 -3.18986418 -6.68880935 1.70921795 3.11421080 -7.14482659
13 14 15 16 17 18
2.36380433 0.21197284 0.08285109 -3.04176283 7.00109313 -0.43083754
19 20 21 22 23 24
-0.48100723 0.49004796 -2.14321154 5.84338234 6.17053248 4.01140877
25 26 27 28 29 30
13.55674330 2.84393215 -0.16315771 -0.74300279 6.34690255 -11.88134947
31 32 33 34 35 36
-2.69910933 -8.44702096 6.41480092 -4.72107406 -4.43602127 -1.36241196
37 38 39 40 41 42
-3.89463840 -12.22139620 5.43223528 2.01082637 -13.03382994 -0.30243716
43 44 45 46 47 48
-0.64120936 4.33366628 1.52696650 -2.83152623 -4.84872201 4.49582977
49 50 51 52 53 54
-8.87369041 10.06890194 -0.05543858 -1.59637508 -6.32639858 2.91466235
55 56 57
8.77140618 6.81317089 0.89025347
> postscript(file="/var/www/html/rcomp/tmp/6ue7k1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.15221883 NA
1 -0.90341073 -3.15221883
2 -5.29649007 -0.90341073
3 3.37031433 -5.29649007
4 6.01223284 3.37031433
5 9.69996182 6.01223284
6 -4.95008025 9.69996182
7 -3.18986418 -4.95008025
8 -6.68880935 -3.18986418
9 1.70921795 -6.68880935
10 3.11421080 1.70921795
11 -7.14482659 3.11421080
12 2.36380433 -7.14482659
13 0.21197284 2.36380433
14 0.08285109 0.21197284
15 -3.04176283 0.08285109
16 7.00109313 -3.04176283
17 -0.43083754 7.00109313
18 -0.48100723 -0.43083754
19 0.49004796 -0.48100723
20 -2.14321154 0.49004796
21 5.84338234 -2.14321154
22 6.17053248 5.84338234
23 4.01140877 6.17053248
24 13.55674330 4.01140877
25 2.84393215 13.55674330
26 -0.16315771 2.84393215
27 -0.74300279 -0.16315771
28 6.34690255 -0.74300279
29 -11.88134947 6.34690255
30 -2.69910933 -11.88134947
31 -8.44702096 -2.69910933
32 6.41480092 -8.44702096
33 -4.72107406 6.41480092
34 -4.43602127 -4.72107406
35 -1.36241196 -4.43602127
36 -3.89463840 -1.36241196
37 -12.22139620 -3.89463840
38 5.43223528 -12.22139620
39 2.01082637 5.43223528
40 -13.03382994 2.01082637
41 -0.30243716 -13.03382994
42 -0.64120936 -0.30243716
43 4.33366628 -0.64120936
44 1.52696650 4.33366628
45 -2.83152623 1.52696650
46 -4.84872201 -2.83152623
47 4.49582977 -4.84872201
48 -8.87369041 4.49582977
49 10.06890194 -8.87369041
50 -0.05543858 10.06890194
51 -1.59637508 -0.05543858
52 -6.32639858 -1.59637508
53 2.91466235 -6.32639858
54 8.77140618 2.91466235
55 6.81317089 8.77140618
56 0.89025347 6.81317089
57 NA 0.89025347
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.90341073 -3.15221883
[2,] -5.29649007 -0.90341073
[3,] 3.37031433 -5.29649007
[4,] 6.01223284 3.37031433
[5,] 9.69996182 6.01223284
[6,] -4.95008025 9.69996182
[7,] -3.18986418 -4.95008025
[8,] -6.68880935 -3.18986418
[9,] 1.70921795 -6.68880935
[10,] 3.11421080 1.70921795
[11,] -7.14482659 3.11421080
[12,] 2.36380433 -7.14482659
[13,] 0.21197284 2.36380433
[14,] 0.08285109 0.21197284
[15,] -3.04176283 0.08285109
[16,] 7.00109313 -3.04176283
[17,] -0.43083754 7.00109313
[18,] -0.48100723 -0.43083754
[19,] 0.49004796 -0.48100723
[20,] -2.14321154 0.49004796
[21,] 5.84338234 -2.14321154
[22,] 6.17053248 5.84338234
[23,] 4.01140877 6.17053248
[24,] 13.55674330 4.01140877
[25,] 2.84393215 13.55674330
[26,] -0.16315771 2.84393215
[27,] -0.74300279 -0.16315771
[28,] 6.34690255 -0.74300279
[29,] -11.88134947 6.34690255
[30,] -2.69910933 -11.88134947
[31,] -8.44702096 -2.69910933
[32,] 6.41480092 -8.44702096
[33,] -4.72107406 6.41480092
[34,] -4.43602127 -4.72107406
[35,] -1.36241196 -4.43602127
[36,] -3.89463840 -1.36241196
[37,] -12.22139620 -3.89463840
[38,] 5.43223528 -12.22139620
[39,] 2.01082637 5.43223528
[40,] -13.03382994 2.01082637
[41,] -0.30243716 -13.03382994
[42,] -0.64120936 -0.30243716
[43,] 4.33366628 -0.64120936
[44,] 1.52696650 4.33366628
[45,] -2.83152623 1.52696650
[46,] -4.84872201 -2.83152623
[47,] 4.49582977 -4.84872201
[48,] -8.87369041 4.49582977
[49,] 10.06890194 -8.87369041
[50,] -0.05543858 10.06890194
[51,] -1.59637508 -0.05543858
[52,] -6.32639858 -1.59637508
[53,] 2.91466235 -6.32639858
[54,] 8.77140618 2.91466235
[55,] 6.81317089 8.77140618
[56,] 0.89025347 6.81317089
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.90341073 -3.15221883
2 -5.29649007 -0.90341073
3 3.37031433 -5.29649007
4 6.01223284 3.37031433
5 9.69996182 6.01223284
6 -4.95008025 9.69996182
7 -3.18986418 -4.95008025
8 -6.68880935 -3.18986418
9 1.70921795 -6.68880935
10 3.11421080 1.70921795
11 -7.14482659 3.11421080
12 2.36380433 -7.14482659
13 0.21197284 2.36380433
14 0.08285109 0.21197284
15 -3.04176283 0.08285109
16 7.00109313 -3.04176283
17 -0.43083754 7.00109313
18 -0.48100723 -0.43083754
19 0.49004796 -0.48100723
20 -2.14321154 0.49004796
21 5.84338234 -2.14321154
22 6.17053248 5.84338234
23 4.01140877 6.17053248
24 13.55674330 4.01140877
25 2.84393215 13.55674330
26 -0.16315771 2.84393215
27 -0.74300279 -0.16315771
28 6.34690255 -0.74300279
29 -11.88134947 6.34690255
30 -2.69910933 -11.88134947
31 -8.44702096 -2.69910933
32 6.41480092 -8.44702096
33 -4.72107406 6.41480092
34 -4.43602127 -4.72107406
35 -1.36241196 -4.43602127
36 -3.89463840 -1.36241196
37 -12.22139620 -3.89463840
38 5.43223528 -12.22139620
39 2.01082637 5.43223528
40 -13.03382994 2.01082637
41 -0.30243716 -13.03382994
42 -0.64120936 -0.30243716
43 4.33366628 -0.64120936
44 1.52696650 4.33366628
45 -2.83152623 1.52696650
46 -4.84872201 -2.83152623
47 4.49582977 -4.84872201
48 -8.87369041 4.49582977
49 10.06890194 -8.87369041
50 -0.05543858 10.06890194
51 -1.59637508 -0.05543858
52 -6.32639858 -1.59637508
53 2.91466235 -6.32639858
54 8.77140618 2.91466235
55 6.81317089 8.77140618
56 0.89025347 6.81317089
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7202a1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8l3wg1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/992oc1258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109k831258758247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11bcb61258758247.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12m53b1258758247.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/139jem1258758247.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14810t1258758247.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15iidx1258758247.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/161zcn1258758247.tab")
+ }
>
> system("convert tmp/16h3u1258758247.ps tmp/16h3u1258758247.png")
> system("convert tmp/21zqm1258758247.ps tmp/21zqm1258758247.png")
> system("convert tmp/3di8m1258758247.ps tmp/3di8m1258758247.png")
> system("convert tmp/4x10f1258758247.ps tmp/4x10f1258758247.png")
> system("convert tmp/5zddx1258758247.ps tmp/5zddx1258758247.png")
> system("convert tmp/6ue7k1258758247.ps tmp/6ue7k1258758247.png")
> system("convert tmp/7202a1258758247.ps tmp/7202a1258758247.png")
> system("convert tmp/8l3wg1258758247.ps tmp/8l3wg1258758247.png")
> system("convert tmp/992oc1258758247.ps tmp/992oc1258758247.png")
> system("convert tmp/109k831258758247.ps tmp/109k831258758247.png")
>
>
> proc.time()
user system elapsed
2.343 1.544 3.093