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
+ ,0
+ ,544
+ ,555
+ ,561
+ ,562
+ ,543
+ ,0
+ ,537
+ ,544
+ ,555
+ ,561
+ ,594
+ ,0
+ ,543
+ ,537
+ ,544
+ ,555
+ ,611
+ ,0
+ ,594
+ ,543
+ ,537
+ ,544
+ ,613
+ ,0
+ ,611
+ ,594
+ ,543
+ ,537
+ ,611
+ ,0
+ ,613
+ ,611
+ ,594
+ ,543
+ ,594
+ ,0
+ ,611
+ ,613
+ ,611
+ ,594
+ ,595
+ ,0
+ ,594
+ ,611
+ ,613
+ ,611
+ ,591
+ ,0
+ ,595
+ ,594
+ ,611
+ ,613
+ ,589
+ ,0
+ ,591
+ ,595
+ ,594
+ ,611
+ ,584
+ ,0
+ ,589
+ ,591
+ ,595
+ ,594
+ ,573
+ ,0
+ ,584
+ ,589
+ ,591
+ ,595
+ ,567
+ ,0
+ ,573
+ ,584
+ ,589
+ ,591
+ ,569
+ ,0
+ ,567
+ ,573
+ ,584
+ ,589
+ ,621
+ ,0
+ ,569
+ ,567
+ ,573
+ ,584
+ ,629
+ ,0
+ ,621
+ ,569
+ ,567
+ ,573
+ ,628
+ ,0
+ ,629
+ ,621
+ ,569
+ ,567
+ ,612
+ ,0
+ ,628
+ ,629
+ ,621
+ ,569
+ ,595
+ ,0
+ ,612
+ ,628
+ ,629
+ ,621
+ ,597
+ ,0
+ ,595
+ ,612
+ ,628
+ ,629
+ ,593
+ ,0
+ ,597
+ ,595
+ ,612
+ ,628
+ ,590
+ ,0
+ ,593
+ ,597
+ ,595
+ ,612
+ ,580
+ ,0
+ ,590
+ ,593
+ ,597
+ ,595
+ ,574
+ ,0
+ ,580
+ ,590
+ ,593
+ ,597
+ ,573
+ ,0
+ ,574
+ ,580
+ ,590
+ ,593
+ ,573
+ ,0
+ ,573
+ ,574
+ ,580
+ ,590
+ ,620
+ ,0
+ ,573
+ ,573
+ ,574
+ ,580
+ ,626
+ ,0
+ ,620
+ ,573
+ ,573
+ ,574
+ ,620
+ ,0
+ ,626
+ ,620
+ ,573
+ ,573
+ ,588
+ ,0
+ ,620
+ ,626
+ ,620
+ ,573
+ ,566
+ ,0
+ ,588
+ ,620
+ ,626
+ ,620
+ ,557
+ ,0
+ ,566
+ ,588
+ ,620
+ ,626
+ ,561
+ ,0
+ ,557
+ ,566
+ ,588
+ ,620
+ ,549
+ ,0
+ ,561
+ ,557
+ ,566
+ ,588
+ ,532
+ ,0
+ ,549
+ ,561
+ ,557
+ ,566
+ ,526
+ ,0
+ ,532
+ ,549
+ ,561
+ ,557
+ ,511
+ ,0
+ ,526
+ ,532
+ ,549
+ ,561
+ ,499
+ ,0
+ ,511
+ ,526
+ ,532
+ ,549
+ ,555
+ ,0
+ ,499
+ ,511
+ ,526
+ ,532
+ ,565
+ ,0
+ ,555
+ ,499
+ ,511
+ ,526
+ ,542
+ ,0
+ ,565
+ ,555
+ ,499
+ ,511
+ ,527
+ ,1
+ ,542
+ ,565
+ ,555
+ ,499
+ ,510
+ ,1
+ ,527
+ ,542
+ ,565
+ ,555
+ ,514
+ ,1
+ ,510
+ ,527
+ ,542
+ ,565
+ ,517
+ ,1
+ ,514
+ ,510
+ ,527
+ ,542
+ ,508
+ ,1
+ ,517
+ ,514
+ ,510
+ ,527
+ ,493
+ ,1
+ ,508
+ ,517
+ ,514
+ ,510
+ ,490
+ ,1
+ ,493
+ ,508
+ ,517
+ ,514
+ ,469
+ ,1
+ ,490
+ ,493
+ ,508
+ ,517
+ ,478
+ ,1
+ ,469
+ ,490
+ ,493
+ ,508
+ ,528
+ ,1
+ ,478
+ ,469
+ ,490
+ ,493
+ ,534
+ ,1
+ ,528
+ ,478
+ ,469
+ ,490
+ ,518
+ ,1
+ ,534
+ ,528
+ ,478
+ ,469
+ ,506
+ ,1
+ ,518
+ ,534
+ ,528
+ ,478
+ ,502
+ ,1
+ ,506
+ ,518
+ ,534
+ ,528
+ ,516
+ ,1
+ ,502
+ ,506
+ ,518
+ ,534
+ ,528
+ ,1
+ ,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 0 544 555 561 562 1 0 0 0 0 0 0 0 0 0 0 1
2 543 0 537 544 555 561 0 1 0 0 0 0 0 0 0 0 0 2
3 594 0 543 537 544 555 0 0 1 0 0 0 0 0 0 0 0 3
4 611 0 594 543 537 544 0 0 0 1 0 0 0 0 0 0 0 4
5 613 0 611 594 543 537 0 0 0 0 1 0 0 0 0 0 0 5
6 611 0 613 611 594 543 0 0 0 0 0 1 0 0 0 0 0 6
7 594 0 611 613 611 594 0 0 0 0 0 0 1 0 0 0 0 7
8 595 0 594 611 613 611 0 0 0 0 0 0 0 1 0 0 0 8
9 591 0 595 594 611 613 0 0 0 0 0 0 0 0 1 0 0 9
10 589 0 591 595 594 611 0 0 0 0 0 0 0 0 0 1 0 10
11 584 0 589 591 595 594 0 0 0 0 0 0 0 0 0 0 1 11
12 573 0 584 589 591 595 0 0 0 0 0 0 0 0 0 0 0 12
13 567 0 573 584 589 591 1 0 0 0 0 0 0 0 0 0 0 13
14 569 0 567 573 584 589 0 1 0 0 0 0 0 0 0 0 0 14
15 621 0 569 567 573 584 0 0 1 0 0 0 0 0 0 0 0 15
16 629 0 621 569 567 573 0 0 0 1 0 0 0 0 0 0 0 16
17 628 0 629 621 569 567 0 0 0 0 1 0 0 0 0 0 0 17
18 612 0 628 629 621 569 0 0 0 0 0 1 0 0 0 0 0 18
19 595 0 612 628 629 621 0 0 0 0 0 0 1 0 0 0 0 19
20 597 0 595 612 628 629 0 0 0 0 0 0 0 1 0 0 0 20
21 593 0 597 595 612 628 0 0 0 0 0 0 0 0 1 0 0 21
22 590 0 593 597 595 612 0 0 0 0 0 0 0 0 0 1 0 22
23 580 0 590 593 597 595 0 0 0 0 0 0 0 0 0 0 1 23
24 574 0 580 590 593 597 0 0 0 0 0 0 0 0 0 0 0 24
25 573 0 574 580 590 593 1 0 0 0 0 0 0 0 0 0 0 25
26 573 0 573 574 580 590 0 1 0 0 0 0 0 0 0 0 0 26
27 620 0 573 573 574 580 0 0 1 0 0 0 0 0 0 0 0 27
28 626 0 620 573 573 574 0 0 0 1 0 0 0 0 0 0 0 28
29 620 0 626 620 573 573 0 0 0 0 1 0 0 0 0 0 0 29
30 588 0 620 626 620 573 0 0 0 0 0 1 0 0 0 0 0 30
31 566 0 588 620 626 620 0 0 0 0 0 0 1 0 0 0 0 31
32 557 0 566 588 620 626 0 0 0 0 0 0 0 1 0 0 0 32
33 561 0 557 566 588 620 0 0 0 0 0 0 0 0 1 0 0 33
34 549 0 561 557 566 588 0 0 0 0 0 0 0 0 0 1 0 34
35 532 0 549 561 557 566 0 0 0 0 0 0 0 0 0 0 1 35
36 526 0 532 549 561 557 0 0 0 0 0 0 0 0 0 0 0 36
37 511 0 526 532 549 561 1 0 0 0 0 0 0 0 0 0 0 37
38 499 0 511 526 532 549 0 1 0 0 0 0 0 0 0 0 0 38
39 555 0 499 511 526 532 0 0 1 0 0 0 0 0 0 0 0 39
40 565 0 555 499 511 526 0 0 0 1 0 0 0 0 0 0 0 40
41 542 0 565 555 499 511 0 0 0 0 1 0 0 0 0 0 0 41
42 527 1 542 565 555 499 0 0 0 0 0 1 0 0 0 0 0 42
43 510 1 527 542 565 555 0 0 0 0 0 0 1 0 0 0 0 43
44 514 1 510 527 542 565 0 0 0 0 0 0 0 1 0 0 0 44
45 517 1 514 510 527 542 0 0 0 0 0 0 0 0 1 0 0 45
46 508 1 517 514 510 527 0 0 0 0 0 0 0 0 0 1 0 46
47 493 1 508 517 514 510 0 0 0 0 0 0 0 0 0 0 1 47
48 490 1 493 508 517 514 0 0 0 0 0 0 0 0 0 0 0 48
49 469 1 490 493 508 517 1 0 0 0 0 0 0 0 0 0 0 49
50 478 1 469 490 493 508 0 1 0 0 0 0 0 0 0 0 0 50
51 528 1 478 469 490 493 0 0 1 0 0 0 0 0 0 0 0 51
52 534 1 528 478 469 490 0 0 0 1 0 0 0 0 0 0 0 52
53 518 1 534 528 478 469 0 0 0 0 1 0 0 0 0 0 0 53
54 506 1 518 534 528 478 0 0 0 0 0 1 0 0 0 0 0 54
55 502 1 506 518 534 528 0 0 0 0 0 0 1 0 0 0 0 55
56 516 1 502 506 518 534 0 0 0 0 0 0 0 1 0 0 0 56
57 528 1 516 502 506 518 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
29.23366 5.23705 1.06339 -0.05155 -0.03463 -0.02814
M1 M2 M3 M4 M5 M6
-4.86620 6.11415 55.42773 10.12636 -6.02994 -10.58658
M7 M8 M9 M10 M11 t
-8.05078 10.13826 8.45831 -0.74929 -5.88401 -0.24701
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.6702 -4.1277 -0.2089 3.6572 11.4426
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.23366 38.40366 0.761 0.4511
X 5.23705 5.28454 0.991 0.3278
Y1 1.06339 0.15747 6.753 4.69e-08 ***
Y2 -0.05155 0.22858 -0.226 0.8228
Y3 -0.03463 0.22941 -0.151 0.8808
Y4 -0.02814 0.18869 -0.149 0.8822
M1 -4.86620 5.01083 -0.971 0.3375
M2 6.11415 5.22691 1.170 0.2492
M3 55.42773 5.46250 10.147 1.69e-12 ***
M4 10.12636 10.90557 0.929 0.3588
M5 -6.02994 11.11804 -0.542 0.5907
M6 -10.58658 10.89105 -0.972 0.3370
M7 -8.05078 5.32993 -1.510 0.1390
M8 10.13826 5.70115 1.778 0.0832 .
M9 8.45831 6.42891 1.316 0.1960
M10 -0.74929 6.37745 -0.117 0.9071
M11 -5.88401 5.15202 -1.142 0.2604
t -0.24701 0.11873 -2.080 0.0441 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.055 on 39 degrees of freedom
Multiple R-squared: 0.9815, Adjusted R-squared: 0.9734
F-statistic: 121.4 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,] 0.21158532 0.42317064 0.7884147
[2,] 0.11637235 0.23274471 0.8836276
[3,] 0.05830109 0.11660218 0.9416989
[4,] 0.03853487 0.07706975 0.9614651
[5,] 0.06762158 0.13524315 0.9323784
[6,] 0.05650000 0.11300001 0.9435000
[7,] 0.02825253 0.05650505 0.9717475
[8,] 0.01427580 0.02855160 0.9857242
[9,] 0.17744995 0.35489990 0.8225501
[10,] 0.54377048 0.91245905 0.4562295
[11,] 0.43721268 0.87442536 0.5627873
[12,] 0.49622278 0.99244555 0.5037772
[13,] 0.39204740 0.78409479 0.6079526
[14,] 0.28473551 0.56947102 0.7152645
[15,] 0.24060979 0.48121957 0.7593902
[16,] 0.18988096 0.37976193 0.8101190
> postscript(file="/var/www/html/rcomp/tmp/1ye7o1258571299.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27zy81258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fll91258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4evkd1258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/518jb1258571299.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
-1.74948560 0.15790968 -5.19962006 2.87340105 5.83894386 9.32731793
7 8 9 10 11 12
-5.70774305 -4.12767611 -8.15341778 2.96123919 4.81982043 -6.71373785
13 14 15 16 17 18
3.65715448 0.50765897 0.48336054 -1.67856344 7.79860483 -0.06471836
19 20 21 22 23 24
-0.65058689 0.85065317 -4.80776761 4.96449476 2.92109612 1.68105869
25 26 27 28 29 30
11.44264529 1.03265175 -1.57465684 -0.20888288 6.20874196 -12.67019523
31 32 33 34 35 36
-1.70959000 -6.94563969 6.14060624 -2.78464154 -2.36684788 3.34042862
37 38 39 40 41 42
-1.34541890 -9.36369484 8.87098220 3.56282564 -11.61871086 -0.47689917
43 44 45 46 47 48
-3.07839243 -0.23127567 -1.60088761 -5.14109241 -5.37406867 1.69225054
49 50 51 52 53 54
-12.00489527 7.66547443 -2.58006584 -4.54878037 -8.22757979 3.88449483
55 56 57
11.14631237 10.45393831 8.42146676
> postscript(file="/var/www/html/rcomp/tmp/6vpbp1258571299.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 -1.74948560 NA
1 0.15790968 -1.74948560
2 -5.19962006 0.15790968
3 2.87340105 -5.19962006
4 5.83894386 2.87340105
5 9.32731793 5.83894386
6 -5.70774305 9.32731793
7 -4.12767611 -5.70774305
8 -8.15341778 -4.12767611
9 2.96123919 -8.15341778
10 4.81982043 2.96123919
11 -6.71373785 4.81982043
12 3.65715448 -6.71373785
13 0.50765897 3.65715448
14 0.48336054 0.50765897
15 -1.67856344 0.48336054
16 7.79860483 -1.67856344
17 -0.06471836 7.79860483
18 -0.65058689 -0.06471836
19 0.85065317 -0.65058689
20 -4.80776761 0.85065317
21 4.96449476 -4.80776761
22 2.92109612 4.96449476
23 1.68105869 2.92109612
24 11.44264529 1.68105869
25 1.03265175 11.44264529
26 -1.57465684 1.03265175
27 -0.20888288 -1.57465684
28 6.20874196 -0.20888288
29 -12.67019523 6.20874196
30 -1.70959000 -12.67019523
31 -6.94563969 -1.70959000
32 6.14060624 -6.94563969
33 -2.78464154 6.14060624
34 -2.36684788 -2.78464154
35 3.34042862 -2.36684788
36 -1.34541890 3.34042862
37 -9.36369484 -1.34541890
38 8.87098220 -9.36369484
39 3.56282564 8.87098220
40 -11.61871086 3.56282564
41 -0.47689917 -11.61871086
42 -3.07839243 -0.47689917
43 -0.23127567 -3.07839243
44 -1.60088761 -0.23127567
45 -5.14109241 -1.60088761
46 -5.37406867 -5.14109241
47 1.69225054 -5.37406867
48 -12.00489527 1.69225054
49 7.66547443 -12.00489527
50 -2.58006584 7.66547443
51 -4.54878037 -2.58006584
52 -8.22757979 -4.54878037
53 3.88449483 -8.22757979
54 11.14631237 3.88449483
55 10.45393831 11.14631237
56 8.42146676 10.45393831
57 NA 8.42146676
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.15790968 -1.74948560
[2,] -5.19962006 0.15790968
[3,] 2.87340105 -5.19962006
[4,] 5.83894386 2.87340105
[5,] 9.32731793 5.83894386
[6,] -5.70774305 9.32731793
[7,] -4.12767611 -5.70774305
[8,] -8.15341778 -4.12767611
[9,] 2.96123919 -8.15341778
[10,] 4.81982043 2.96123919
[11,] -6.71373785 4.81982043
[12,] 3.65715448 -6.71373785
[13,] 0.50765897 3.65715448
[14,] 0.48336054 0.50765897
[15,] -1.67856344 0.48336054
[16,] 7.79860483 -1.67856344
[17,] -0.06471836 7.79860483
[18,] -0.65058689 -0.06471836
[19,] 0.85065317 -0.65058689
[20,] -4.80776761 0.85065317
[21,] 4.96449476 -4.80776761
[22,] 2.92109612 4.96449476
[23,] 1.68105869 2.92109612
[24,] 11.44264529 1.68105869
[25,] 1.03265175 11.44264529
[26,] -1.57465684 1.03265175
[27,] -0.20888288 -1.57465684
[28,] 6.20874196 -0.20888288
[29,] -12.67019523 6.20874196
[30,] -1.70959000 -12.67019523
[31,] -6.94563969 -1.70959000
[32,] 6.14060624 -6.94563969
[33,] -2.78464154 6.14060624
[34,] -2.36684788 -2.78464154
[35,] 3.34042862 -2.36684788
[36,] -1.34541890 3.34042862
[37,] -9.36369484 -1.34541890
[38,] 8.87098220 -9.36369484
[39,] 3.56282564 8.87098220
[40,] -11.61871086 3.56282564
[41,] -0.47689917 -11.61871086
[42,] -3.07839243 -0.47689917
[43,] -0.23127567 -3.07839243
[44,] -1.60088761 -0.23127567
[45,] -5.14109241 -1.60088761
[46,] -5.37406867 -5.14109241
[47,] 1.69225054 -5.37406867
[48,] -12.00489527 1.69225054
[49,] 7.66547443 -12.00489527
[50,] -2.58006584 7.66547443
[51,] -4.54878037 -2.58006584
[52,] -8.22757979 -4.54878037
[53,] 3.88449483 -8.22757979
[54,] 11.14631237 3.88449483
[55,] 10.45393831 11.14631237
[56,] 8.42146676 10.45393831
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.15790968 -1.74948560
2 -5.19962006 0.15790968
3 2.87340105 -5.19962006
4 5.83894386 2.87340105
5 9.32731793 5.83894386
6 -5.70774305 9.32731793
7 -4.12767611 -5.70774305
8 -8.15341778 -4.12767611
9 2.96123919 -8.15341778
10 4.81982043 2.96123919
11 -6.71373785 4.81982043
12 3.65715448 -6.71373785
13 0.50765897 3.65715448
14 0.48336054 0.50765897
15 -1.67856344 0.48336054
16 7.79860483 -1.67856344
17 -0.06471836 7.79860483
18 -0.65058689 -0.06471836
19 0.85065317 -0.65058689
20 -4.80776761 0.85065317
21 4.96449476 -4.80776761
22 2.92109612 4.96449476
23 1.68105869 2.92109612
24 11.44264529 1.68105869
25 1.03265175 11.44264529
26 -1.57465684 1.03265175
27 -0.20888288 -1.57465684
28 6.20874196 -0.20888288
29 -12.67019523 6.20874196
30 -1.70959000 -12.67019523
31 -6.94563969 -1.70959000
32 6.14060624 -6.94563969
33 -2.78464154 6.14060624
34 -2.36684788 -2.78464154
35 3.34042862 -2.36684788
36 -1.34541890 3.34042862
37 -9.36369484 -1.34541890
38 8.87098220 -9.36369484
39 3.56282564 8.87098220
40 -11.61871086 3.56282564
41 -0.47689917 -11.61871086
42 -3.07839243 -0.47689917
43 -0.23127567 -3.07839243
44 -1.60088761 -0.23127567
45 -5.14109241 -1.60088761
46 -5.37406867 -5.14109241
47 1.69225054 -5.37406867
48 -12.00489527 1.69225054
49 7.66547443 -12.00489527
50 -2.58006584 7.66547443
51 -4.54878037 -2.58006584
52 -8.22757979 -4.54878037
53 3.88449483 -8.22757979
54 11.14631237 3.88449483
55 10.45393831 11.14631237
56 8.42146676 10.45393831
> 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/7ecrn1258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ptyz1258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sx7q1258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ik3p1258571299.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jsaj1258571299.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/12cagt1258571300.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/13dgdp1258571300.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/140irl1258571300.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/155umm1258571300.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/167pc41258571300.tab")
+ }
>
> system("convert tmp/1ye7o1258571299.ps tmp/1ye7o1258571299.png")
> system("convert tmp/27zy81258571299.ps tmp/27zy81258571299.png")
> system("convert tmp/3fll91258571299.ps tmp/3fll91258571299.png")
> system("convert tmp/4evkd1258571299.ps tmp/4evkd1258571299.png")
> system("convert tmp/518jb1258571299.ps tmp/518jb1258571299.png")
> system("convert tmp/6vpbp1258571299.ps tmp/6vpbp1258571299.png")
> system("convert tmp/7ecrn1258571299.ps tmp/7ecrn1258571299.png")
> system("convert tmp/8ptyz1258571299.ps tmp/8ptyz1258571299.png")
> system("convert tmp/9sx7q1258571299.ps tmp/9sx7q1258571299.png")
> system("convert tmp/10ik3p1258571299.ps tmp/10ik3p1258571299.png")
>
>
> proc.time()
user system elapsed
2.306 1.551 2.736