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(106.2
+ ,431
+ ,436
+ ,460
+ ,467
+ ,81
+ ,484
+ ,431
+ ,448
+ ,460
+ ,94.7
+ ,510
+ ,484
+ ,443
+ ,448
+ ,101
+ ,513
+ ,510
+ ,436
+ ,443
+ ,109.4
+ ,503
+ ,513
+ ,431
+ ,436
+ ,102.3
+ ,471
+ ,503
+ ,484
+ ,431
+ ,90.7
+ ,471
+ ,471
+ ,510
+ ,484
+ ,96.2
+ ,476
+ ,471
+ ,513
+ ,510
+ ,96.1
+ ,475
+ ,476
+ ,503
+ ,513
+ ,106
+ ,470
+ ,475
+ ,471
+ ,503
+ ,103.1
+ ,461
+ ,470
+ ,471
+ ,471
+ ,102
+ ,455
+ ,461
+ ,476
+ ,471
+ ,104.7
+ ,456
+ ,455
+ ,475
+ ,476
+ ,86
+ ,517
+ ,456
+ ,470
+ ,475
+ ,92.1
+ ,525
+ ,517
+ ,461
+ ,470
+ ,106.9
+ ,523
+ ,525
+ ,455
+ ,461
+ ,112.6
+ ,519
+ ,523
+ ,456
+ ,455
+ ,101.7
+ ,509
+ ,519
+ ,517
+ ,456
+ ,92
+ ,512
+ ,509
+ ,525
+ ,517
+ ,97.4
+ ,519
+ ,512
+ ,523
+ ,525
+ ,97
+ ,517
+ ,519
+ ,519
+ ,523
+ ,105.4
+ ,510
+ ,517
+ ,509
+ ,519
+ ,102.7
+ ,509
+ ,510
+ ,512
+ ,509
+ ,98.1
+ ,501
+ ,509
+ ,519
+ ,512
+ ,104.5
+ ,507
+ ,501
+ ,517
+ ,519
+ ,87.4
+ ,569
+ ,507
+ ,510
+ ,517
+ ,89.9
+ ,580
+ ,569
+ ,509
+ ,510
+ ,109.8
+ ,578
+ ,580
+ ,501
+ ,509
+ ,111.7
+ ,565
+ ,578
+ ,507
+ ,501
+ ,98.6
+ ,547
+ ,565
+ ,569
+ ,507
+ ,96.9
+ ,555
+ ,547
+ ,580
+ ,569
+ ,95.1
+ ,562
+ ,555
+ ,578
+ ,580
+ ,97
+ ,561
+ ,562
+ ,565
+ ,578
+ ,112.7
+ ,555
+ ,561
+ ,547
+ ,565
+ ,102.9
+ ,544
+ ,555
+ ,555
+ ,547
+ ,97.4
+ ,537
+ ,544
+ ,562
+ ,555
+ ,111.4
+ ,543
+ ,537
+ ,561
+ ,562
+ ,87.4
+ ,594
+ ,543
+ ,555
+ ,561
+ ,96.8
+ ,611
+ ,594
+ ,544
+ ,555
+ ,114.1
+ ,613
+ ,611
+ ,537
+ ,544
+ ,110.3
+ ,611
+ ,613
+ ,543
+ ,537
+ ,103.9
+ ,594
+ ,611
+ ,594
+ ,543
+ ,101.6
+ ,595
+ ,594
+ ,611
+ ,594
+ ,94.6
+ ,591
+ ,595
+ ,613
+ ,611
+ ,95.9
+ ,589
+ ,591
+ ,611
+ ,613
+ ,104.7
+ ,584
+ ,589
+ ,594
+ ,611
+ ,102.8
+ ,573
+ ,584
+ ,595
+ ,594
+ ,98.1
+ ,567
+ ,573
+ ,591
+ ,595
+ ,113.9
+ ,569
+ ,567
+ ,589
+ ,591
+ ,80.9
+ ,621
+ ,569
+ ,584
+ ,589
+ ,95.7
+ ,629
+ ,621
+ ,573
+ ,584
+ ,113.2
+ ,628
+ ,629
+ ,567
+ ,573
+ ,105.9
+ ,612
+ ,628
+ ,569
+ ,567
+ ,108.8
+ ,595
+ ,612
+ ,621
+ ,569
+ ,102.3
+ ,597
+ ,595
+ ,629
+ ,621
+ ,99
+ ,593
+ ,597
+ ,628
+ ,629
+ ,100.7
+ ,590
+ ,593
+ ,612
+ ,628
+ ,115.5
+ ,580
+ ,590
+ ,595
+ ,612
+ ,100.7
+ ,574
+ ,580
+ ,597
+ ,595
+ ,109.9
+ ,573
+ ,574
+ ,593
+ ,597
+ ,114.6
+ ,573
+ ,573
+ ,590
+ ,593
+ ,85.4
+ ,620
+ ,573
+ ,580
+ ,590
+ ,100.5
+ ,626
+ ,620
+ ,574
+ ,580
+ ,114.8
+ ,620
+ ,626
+ ,573
+ ,574
+ ,116.5
+ ,588
+ ,620
+ ,573
+ ,573
+ ,112.9
+ ,566
+ ,588
+ ,620
+ ,573
+ ,102
+ ,557
+ ,566
+ ,626
+ ,620)
+ ,dim=c(5
+ ,67)
+ ,dimnames=list(c('X'
+ ,'Y'
+ ,'Y(t-1)'
+ ,'Y(t-4)'
+ ,'Y(t-5)')
+ ,1:67))
> y <- array(NA,dim=c(5,67),dimnames=list(c('X','Y','Y(t-1)','Y(t-4)','Y(t-5)'),1:67))
> 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 = '2'
> #'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 Y(t-1) Y(t-4) Y(t-5) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 431 106.2 436 460 467 1 0 0 0 0 0 0 0 0 0 0 1
2 484 81.0 431 448 460 0 1 0 0 0 0 0 0 0 0 0 2
3 510 94.7 484 443 448 0 0 1 0 0 0 0 0 0 0 0 3
4 513 101.0 510 436 443 0 0 0 1 0 0 0 0 0 0 0 4
5 503 109.4 513 431 436 0 0 0 0 1 0 0 0 0 0 0 5
6 471 102.3 503 484 431 0 0 0 0 0 1 0 0 0 0 0 6
7 471 90.7 471 510 484 0 0 0 0 0 0 1 0 0 0 0 7
8 476 96.2 471 513 510 0 0 0 0 0 0 0 1 0 0 0 8
9 475 96.1 476 503 513 0 0 0 0 0 0 0 0 1 0 0 9
10 470 106.0 475 471 503 0 0 0 0 0 0 0 0 0 1 0 10
11 461 103.1 470 471 471 0 0 0 0 0 0 0 0 0 0 1 11
12 455 102.0 461 476 471 0 0 0 0 0 0 0 0 0 0 0 12
13 456 104.7 455 475 476 1 0 0 0 0 0 0 0 0 0 0 13
14 517 86.0 456 470 475 0 1 0 0 0 0 0 0 0 0 0 14
15 525 92.1 517 461 470 0 0 1 0 0 0 0 0 0 0 0 15
16 523 106.9 525 455 461 0 0 0 1 0 0 0 0 0 0 0 16
17 519 112.6 523 456 455 0 0 0 0 1 0 0 0 0 0 0 17
18 509 101.7 519 517 456 0 0 0 0 0 1 0 0 0 0 0 18
19 512 92.0 509 525 517 0 0 0 0 0 0 1 0 0 0 0 19
20 519 97.4 512 523 525 0 0 0 0 0 0 0 1 0 0 0 20
21 517 97.0 519 519 523 0 0 0 0 0 0 0 0 1 0 0 21
22 510 105.4 517 509 519 0 0 0 0 0 0 0 0 0 1 0 22
23 509 102.7 510 512 509 0 0 0 0 0 0 0 0 0 0 1 23
24 501 98.1 509 519 512 0 0 0 0 0 0 0 0 0 0 0 24
25 507 104.5 501 517 519 1 0 0 0 0 0 0 0 0 0 0 25
26 569 87.4 507 510 517 0 1 0 0 0 0 0 0 0 0 0 26
27 580 89.9 569 509 510 0 0 1 0 0 0 0 0 0 0 0 27
28 578 109.8 580 501 509 0 0 0 1 0 0 0 0 0 0 0 28
29 565 111.7 578 507 501 0 0 0 0 1 0 0 0 0 0 0 29
30 547 98.6 565 569 507 0 0 0 0 0 1 0 0 0 0 0 30
31 555 96.9 547 580 569 0 0 0 0 0 0 1 0 0 0 0 31
32 562 95.1 555 578 580 0 0 0 0 0 0 0 1 0 0 0 32
33 561 97.0 562 565 578 0 0 0 0 0 0 0 0 1 0 0 33
34 555 112.7 561 547 565 0 0 0 0 0 0 0 0 0 1 0 34
35 544 102.9 555 555 547 0 0 0 0 0 0 0 0 0 0 1 35
36 537 97.4 544 562 555 0 0 0 0 0 0 0 0 0 0 0 36
37 543 111.4 537 561 562 1 0 0 0 0 0 0 0 0 0 0 37
38 594 87.4 543 555 561 0 1 0 0 0 0 0 0 0 0 0 38
39 611 96.8 594 544 555 0 0 1 0 0 0 0 0 0 0 0 39
40 613 114.1 611 537 544 0 0 0 1 0 0 0 0 0 0 0 40
41 611 110.3 613 543 537 0 0 0 0 1 0 0 0 0 0 0 41
42 594 103.9 611 594 543 0 0 0 0 0 1 0 0 0 0 0 42
43 595 101.6 594 611 594 0 0 0 0 0 0 1 0 0 0 0 43
44 591 94.6 595 613 611 0 0 0 0 0 0 0 1 0 0 0 44
45 589 95.9 591 611 613 0 0 0 0 0 0 0 0 1 0 0 45
46 584 104.7 589 594 611 0 0 0 0 0 0 0 0 0 1 0 46
47 573 102.8 584 595 594 0 0 0 0 0 0 0 0 0 0 1 47
48 567 98.1 573 591 595 0 0 0 0 0 0 0 0 0 0 0 48
49 569 113.9 567 589 591 1 0 0 0 0 0 0 0 0 0 0 49
50 621 80.9 569 584 589 0 1 0 0 0 0 0 0 0 0 0 50
51 629 95.7 621 573 584 0 0 1 0 0 0 0 0 0 0 0 51
52 628 113.2 629 567 573 0 0 0 1 0 0 0 0 0 0 0 52
53 612 105.9 628 569 567 0 0 0 0 1 0 0 0 0 0 0 53
54 595 108.8 612 621 569 0 0 0 0 0 1 0 0 0 0 0 54
55 597 102.3 595 629 621 0 0 0 0 0 0 1 0 0 0 0 55
56 593 99.0 597 628 629 0 0 0 0 0 0 0 1 0 0 0 56
57 590 100.7 593 612 628 0 0 0 0 0 0 0 0 1 0 0 57
58 580 115.5 590 595 612 0 0 0 0 0 0 0 0 0 1 0 58
59 574 100.7 580 597 595 0 0 0 0 0 0 0 0 0 0 1 59
60 573 109.9 574 593 597 0 0 0 0 0 0 0 0 0 0 0 60
61 573 114.6 573 590 593 1 0 0 0 0 0 0 0 0 0 0 61
62 620 85.4 573 580 590 0 1 0 0 0 0 0 0 0 0 0 62
63 626 100.5 620 574 580 0 0 1 0 0 0 0 0 0 0 0 63
64 620 114.8 626 573 574 0 0 0 1 0 0 0 0 0 0 0 64
65 588 116.5 620 573 573 0 0 0 0 1 0 0 0 0 0 0 65
66 566 112.9 588 620 573 0 0 0 0 0 1 0 0 0 0 0 66
67 557 102.0 566 626 620 0 0 0 0 0 0 1 0 0 0 0 67
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Y(t-1)` `Y(t-4)` `Y(t-5)` M1
-99.756682 0.382756 1.023540 0.104099 0.005104 4.041204
M2 M3 M4 M5 M6 M7
67.323331 21.702405 3.103729 -8.800759 -17.756279 4.463578
M8 M9 M10 M11 t
4.869827 1.888165 -4.805750 -2.918548 -0.470059
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.2473 -2.5617 -0.2233 2.5754 9.9149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -99.756682 41.611218 -2.397 0.02029 *
X 0.382756 0.240011 1.595 0.11707
`Y(t-1)` 1.023540 0.074896 13.666 < 2e-16 ***
`Y(t-4)` 0.104099 0.174031 0.598 0.55243
`Y(t-5)` 0.005104 0.159946 0.032 0.97467
M1 4.041204 3.873252 1.043 0.30180
M2 67.323331 5.603693 12.014 2.36e-16 ***
M3 21.702405 6.548458 3.314 0.00172 **
M4 3.103729 7.725819 0.402 0.68959
M5 -8.800759 7.692180 -1.144 0.25802
M6 -17.756279 9.010969 -1.971 0.05433 .
M7 4.463578 4.085768 1.092 0.27986
M8 4.869827 4.134005 1.178 0.24438
M9 1.888165 4.263069 0.443 0.65974
M10 -4.805750 5.161882 -0.931 0.35632
M11 -2.918548 3.500481 -0.834 0.40838
t -0.470059 0.172063 -2.732 0.00868 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.381 on 50 degrees of freedom
Multiple R-squared: 0.9915, Adjusted R-squared: 0.9888
F-statistic: 363.9 on 16 and 50 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,] 0.9910311 0.01793782 0.00896891
[2,] 0.9804807 0.03903852 0.01951926
[3,] 0.9799652 0.04006960 0.02003480
[4,] 0.9631254 0.07374920 0.03687460
[5,] 0.9472837 0.10543251 0.05271625
[6,] 0.9137580 0.17248393 0.08624196
[7,] 0.8902891 0.21942175 0.10971087
[8,] 0.8726307 0.25473863 0.12736931
[9,] 0.8655235 0.26895300 0.13447650
[10,] 0.8574054 0.28518930 0.14259465
[11,] 0.7978452 0.40430956 0.20215478
[12,] 0.7410274 0.51794523 0.25897262
[13,] 0.7158319 0.56833619 0.28416810
[14,] 0.6308183 0.73836346 0.36918173
[15,] 0.5428664 0.91426719 0.45713359
[16,] 0.5341598 0.93168042 0.46584021
[17,] 0.5321923 0.93561530 0.46780765
[18,] 0.4328330 0.86566608 0.56716696
[19,] 0.4626849 0.92536975 0.53731512
[20,] 0.3693018 0.73860366 0.63069817
[21,] 0.2869743 0.57394868 0.71302566
[22,] 0.8020598 0.39588040 0.19794020
[23,] 0.7217725 0.55645496 0.27822748
[24,] 0.6093160 0.78136793 0.39068397
[25,] 0.5428563 0.91428735 0.45714368
[26,] 0.4349576 0.86991516 0.56504242
[27,] 0.2987467 0.59749342 0.70125329
[28,] 0.3335033 0.66700666 0.66649667
> postscript(file="/var/www/html/rcomp/tmp/1xgyk1260894021.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gkbj1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y2o81260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40eum1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53a9d1260894021.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 = 67
Frequency = 1
1 2 3 4 5 6
-9.99598625 -3.75998290 9.42136337 3.22090672 -0.13409022 -15.24727185
7 8 9 10 11 12
-2.78092245 -0.26728067 -1.86930629 0.91113872 -3.11497164 -2.45106211
13 14 15 16 17 18
0.16417071 5.01169930 -4.70566455 -0.81950541 7.34694075 8.68357180
19 20 21 22 23 24
2.73774981 4.83142053 -0.30193148 -0.24461865 5.27520705 -3.13307022
25 26 27 28 29 30
5.20693624 5.53765692 -1.64790969 -2.61706320 -2.50643113 0.75450383
31 32 33 34 35 36
4.61755383 4.33405360 0.25725474 -1.62435924 -4.89016696 -1.74408380
37 38 39 40 41 42
2.55933797 -5.57812858 2.89011722 0.72183260 9.91490865 1.49752668
43 44 45 46 47 48
-3.00175661 -5.57716703 -0.33087782 2.29181760 -4.29771315 0.72298580
49 50 51 52 53 54
-0.52584636 -0.22334315 -3.85062416 0.01230256 0.02693712 2.29580543
55 56 57 58 59 60
1.33588479 -3.32102643 2.24486085 -1.33397843 7.02764470 6.60523034
61 62 63 64 65 66
2.59138769 -0.98790159 -2.10728220 -0.51847327 -14.64826516 2.01586411
67
-2.90850937
> postscript(file="/var/www/html/rcomp/tmp/679qg1260894021.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -9.99598625 NA
1 -3.75998290 -9.99598625
2 9.42136337 -3.75998290
3 3.22090672 9.42136337
4 -0.13409022 3.22090672
5 -15.24727185 -0.13409022
6 -2.78092245 -15.24727185
7 -0.26728067 -2.78092245
8 -1.86930629 -0.26728067
9 0.91113872 -1.86930629
10 -3.11497164 0.91113872
11 -2.45106211 -3.11497164
12 0.16417071 -2.45106211
13 5.01169930 0.16417071
14 -4.70566455 5.01169930
15 -0.81950541 -4.70566455
16 7.34694075 -0.81950541
17 8.68357180 7.34694075
18 2.73774981 8.68357180
19 4.83142053 2.73774981
20 -0.30193148 4.83142053
21 -0.24461865 -0.30193148
22 5.27520705 -0.24461865
23 -3.13307022 5.27520705
24 5.20693624 -3.13307022
25 5.53765692 5.20693624
26 -1.64790969 5.53765692
27 -2.61706320 -1.64790969
28 -2.50643113 -2.61706320
29 0.75450383 -2.50643113
30 4.61755383 0.75450383
31 4.33405360 4.61755383
32 0.25725474 4.33405360
33 -1.62435924 0.25725474
34 -4.89016696 -1.62435924
35 -1.74408380 -4.89016696
36 2.55933797 -1.74408380
37 -5.57812858 2.55933797
38 2.89011722 -5.57812858
39 0.72183260 2.89011722
40 9.91490865 0.72183260
41 1.49752668 9.91490865
42 -3.00175661 1.49752668
43 -5.57716703 -3.00175661
44 -0.33087782 -5.57716703
45 2.29181760 -0.33087782
46 -4.29771315 2.29181760
47 0.72298580 -4.29771315
48 -0.52584636 0.72298580
49 -0.22334315 -0.52584636
50 -3.85062416 -0.22334315
51 0.01230256 -3.85062416
52 0.02693712 0.01230256
53 2.29580543 0.02693712
54 1.33588479 2.29580543
55 -3.32102643 1.33588479
56 2.24486085 -3.32102643
57 -1.33397843 2.24486085
58 7.02764470 -1.33397843
59 6.60523034 7.02764470
60 2.59138769 6.60523034
61 -0.98790159 2.59138769
62 -2.10728220 -0.98790159
63 -0.51847327 -2.10728220
64 -14.64826516 -0.51847327
65 2.01586411 -14.64826516
66 -2.90850937 2.01586411
67 NA -2.90850937
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.75998290 -9.99598625
[2,] 9.42136337 -3.75998290
[3,] 3.22090672 9.42136337
[4,] -0.13409022 3.22090672
[5,] -15.24727185 -0.13409022
[6,] -2.78092245 -15.24727185
[7,] -0.26728067 -2.78092245
[8,] -1.86930629 -0.26728067
[9,] 0.91113872 -1.86930629
[10,] -3.11497164 0.91113872
[11,] -2.45106211 -3.11497164
[12,] 0.16417071 -2.45106211
[13,] 5.01169930 0.16417071
[14,] -4.70566455 5.01169930
[15,] -0.81950541 -4.70566455
[16,] 7.34694075 -0.81950541
[17,] 8.68357180 7.34694075
[18,] 2.73774981 8.68357180
[19,] 4.83142053 2.73774981
[20,] -0.30193148 4.83142053
[21,] -0.24461865 -0.30193148
[22,] 5.27520705 -0.24461865
[23,] -3.13307022 5.27520705
[24,] 5.20693624 -3.13307022
[25,] 5.53765692 5.20693624
[26,] -1.64790969 5.53765692
[27,] -2.61706320 -1.64790969
[28,] -2.50643113 -2.61706320
[29,] 0.75450383 -2.50643113
[30,] 4.61755383 0.75450383
[31,] 4.33405360 4.61755383
[32,] 0.25725474 4.33405360
[33,] -1.62435924 0.25725474
[34,] -4.89016696 -1.62435924
[35,] -1.74408380 -4.89016696
[36,] 2.55933797 -1.74408380
[37,] -5.57812858 2.55933797
[38,] 2.89011722 -5.57812858
[39,] 0.72183260 2.89011722
[40,] 9.91490865 0.72183260
[41,] 1.49752668 9.91490865
[42,] -3.00175661 1.49752668
[43,] -5.57716703 -3.00175661
[44,] -0.33087782 -5.57716703
[45,] 2.29181760 -0.33087782
[46,] -4.29771315 2.29181760
[47,] 0.72298580 -4.29771315
[48,] -0.52584636 0.72298580
[49,] -0.22334315 -0.52584636
[50,] -3.85062416 -0.22334315
[51,] 0.01230256 -3.85062416
[52,] 0.02693712 0.01230256
[53,] 2.29580543 0.02693712
[54,] 1.33588479 2.29580543
[55,] -3.32102643 1.33588479
[56,] 2.24486085 -3.32102643
[57,] -1.33397843 2.24486085
[58,] 7.02764470 -1.33397843
[59,] 6.60523034 7.02764470
[60,] 2.59138769 6.60523034
[61,] -0.98790159 2.59138769
[62,] -2.10728220 -0.98790159
[63,] -0.51847327 -2.10728220
[64,] -14.64826516 -0.51847327
[65,] 2.01586411 -14.64826516
[66,] -2.90850937 2.01586411
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.75998290 -9.99598625
2 9.42136337 -3.75998290
3 3.22090672 9.42136337
4 -0.13409022 3.22090672
5 -15.24727185 -0.13409022
6 -2.78092245 -15.24727185
7 -0.26728067 -2.78092245
8 -1.86930629 -0.26728067
9 0.91113872 -1.86930629
10 -3.11497164 0.91113872
11 -2.45106211 -3.11497164
12 0.16417071 -2.45106211
13 5.01169930 0.16417071
14 -4.70566455 5.01169930
15 -0.81950541 -4.70566455
16 7.34694075 -0.81950541
17 8.68357180 7.34694075
18 2.73774981 8.68357180
19 4.83142053 2.73774981
20 -0.30193148 4.83142053
21 -0.24461865 -0.30193148
22 5.27520705 -0.24461865
23 -3.13307022 5.27520705
24 5.20693624 -3.13307022
25 5.53765692 5.20693624
26 -1.64790969 5.53765692
27 -2.61706320 -1.64790969
28 -2.50643113 -2.61706320
29 0.75450383 -2.50643113
30 4.61755383 0.75450383
31 4.33405360 4.61755383
32 0.25725474 4.33405360
33 -1.62435924 0.25725474
34 -4.89016696 -1.62435924
35 -1.74408380 -4.89016696
36 2.55933797 -1.74408380
37 -5.57812858 2.55933797
38 2.89011722 -5.57812858
39 0.72183260 2.89011722
40 9.91490865 0.72183260
41 1.49752668 9.91490865
42 -3.00175661 1.49752668
43 -5.57716703 -3.00175661
44 -0.33087782 -5.57716703
45 2.29181760 -0.33087782
46 -4.29771315 2.29181760
47 0.72298580 -4.29771315
48 -0.52584636 0.72298580
49 -0.22334315 -0.52584636
50 -3.85062416 -0.22334315
51 0.01230256 -3.85062416
52 0.02693712 0.01230256
53 2.29580543 0.02693712
54 1.33588479 2.29580543
55 -3.32102643 1.33588479
56 2.24486085 -3.32102643
57 -1.33397843 2.24486085
58 7.02764470 -1.33397843
59 6.60523034 7.02764470
60 2.59138769 6.60523034
61 -0.98790159 2.59138769
62 -2.10728220 -0.98790159
63 -0.51847327 -2.10728220
64 -14.64826516 -0.51847327
65 2.01586411 -14.64826516
66 -2.90850937 2.01586411
> 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/7r4js1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z8ob1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9297g1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10twcr1260894021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115qka1260894021.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/12ff731260894021.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/13pbrh1260894021.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/14kzz81260894021.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/15r22h1260894021.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/160eij1260894021.tab")
+ }
>
> try(system("convert tmp/1xgyk1260894021.ps tmp/1xgyk1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gkbj1260894021.ps tmp/2gkbj1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y2o81260894021.ps tmp/3y2o81260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/40eum1260894021.ps tmp/40eum1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/53a9d1260894021.ps tmp/53a9d1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/679qg1260894021.ps tmp/679qg1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/7r4js1260894021.ps tmp/7r4js1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z8ob1260894021.ps tmp/8z8ob1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/9297g1260894021.ps tmp/9297g1260894021.png",intern=TRUE))
character(0)
> try(system("convert tmp/10twcr1260894021.ps tmp/10twcr1260894021.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.532 1.588 3.328