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(357
+ ,15.5
+ ,358
+ ,363
+ ,364
+ ,363
+ ,357
+ ,15.1
+ ,357
+ ,358
+ ,363
+ ,364
+ ,380
+ ,15
+ ,357
+ ,357
+ ,358
+ ,363
+ ,378
+ ,12.1
+ ,380
+ ,357
+ ,357
+ ,358
+ ,376
+ ,15.8
+ ,378
+ ,380
+ ,357
+ ,357
+ ,380
+ ,16.9
+ ,376
+ ,378
+ ,380
+ ,357
+ ,379
+ ,15.1
+ ,380
+ ,376
+ ,378
+ ,380
+ ,384
+ ,13.7
+ ,379
+ ,380
+ ,376
+ ,378
+ ,392
+ ,14.8
+ ,384
+ ,379
+ ,380
+ ,376
+ ,394
+ ,14.7
+ ,392
+ ,384
+ ,379
+ ,380
+ ,392
+ ,16
+ ,394
+ ,392
+ ,384
+ ,379
+ ,396
+ ,15.4
+ ,392
+ ,394
+ ,392
+ ,384
+ ,392
+ ,15
+ ,396
+ ,392
+ ,394
+ ,392
+ ,396
+ ,15.5
+ ,392
+ ,396
+ ,392
+ ,394
+ ,419
+ ,15.1
+ ,396
+ ,392
+ ,396
+ ,392
+ ,421
+ ,11.7
+ ,419
+ ,396
+ ,392
+ ,396
+ ,420
+ ,16.3
+ ,421
+ ,419
+ ,396
+ ,392
+ ,418
+ ,16.7
+ ,420
+ ,421
+ ,419
+ ,396
+ ,410
+ ,15
+ ,418
+ ,420
+ ,421
+ ,419
+ ,418
+ ,14.9
+ ,410
+ ,418
+ ,420
+ ,421
+ ,426
+ ,14.6
+ ,418
+ ,410
+ ,418
+ ,420
+ ,428
+ ,15.3
+ ,426
+ ,418
+ ,410
+ ,418
+ ,430
+ ,17.9
+ ,428
+ ,426
+ ,418
+ ,410
+ ,424
+ ,16.4
+ ,430
+ ,428
+ ,426
+ ,418
+ ,423
+ ,15.4
+ ,424
+ ,430
+ ,428
+ ,426
+ ,427
+ ,17.9
+ ,423
+ ,424
+ ,430
+ ,428
+ ,441
+ ,15.9
+ ,427
+ ,423
+ ,424
+ ,430
+ ,449
+ ,13.9
+ ,441
+ ,427
+ ,423
+ ,424
+ ,452
+ ,17.8
+ ,449
+ ,441
+ ,427
+ ,423
+ ,462
+ ,17.9
+ ,452
+ ,449
+ ,441
+ ,427
+ ,455
+ ,17.4
+ ,462
+ ,452
+ ,449
+ ,441
+ ,461
+ ,16.7
+ ,455
+ ,462
+ ,452
+ ,449
+ ,461
+ ,16
+ ,461
+ ,455
+ ,462
+ ,452
+ ,463
+ ,16.6
+ ,461
+ ,461
+ ,455
+ ,462
+ ,462
+ ,19.1
+ ,463
+ ,461
+ ,461
+ ,455
+ ,456
+ ,17.8
+ ,462
+ ,463
+ ,461
+ ,461
+ ,455
+ ,17.2
+ ,456
+ ,462
+ ,463
+ ,461
+ ,456
+ ,18.6
+ ,455
+ ,456
+ ,462
+ ,463
+ ,472
+ ,16.3
+ ,456
+ ,455
+ ,456
+ ,462
+ ,472
+ ,15.1
+ ,472
+ ,456
+ ,455
+ ,456
+ ,471
+ ,19.2
+ ,472
+ ,472
+ ,456
+ ,455
+ ,465
+ ,17.7
+ ,471
+ ,472
+ ,472
+ ,456
+ ,459
+ ,19.1
+ ,465
+ ,471
+ ,472
+ ,472
+ ,465
+ ,18
+ ,459
+ ,465
+ ,471
+ ,472
+ ,468
+ ,17.5
+ ,465
+ ,459
+ ,465
+ ,471
+ ,467
+ ,17.8
+ ,468
+ ,465
+ ,459
+ ,465
+ ,463
+ ,21.1
+ ,467
+ ,468
+ ,465
+ ,459
+ ,460
+ ,17.2
+ ,463
+ ,467
+ ,468
+ ,465
+ ,462
+ ,19.4
+ ,460
+ ,463
+ ,467
+ ,468
+ ,461
+ ,19.8
+ ,462
+ ,460
+ ,463
+ ,467
+ ,476
+ ,17.6
+ ,461
+ ,462
+ ,460
+ ,463
+ ,476
+ ,16.2
+ ,476
+ ,461
+ ,462
+ ,460
+ ,471
+ ,19.5
+ ,476
+ ,476
+ ,461
+ ,462
+ ,453
+ ,19.9
+ ,471
+ ,476
+ ,476
+ ,461
+ ,443
+ ,20
+ ,453
+ ,471
+ ,476
+ ,476
+ ,442
+ ,17.3
+ ,443
+ ,453
+ ,471
+ ,476)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y-1'
+ ,'Y-2'
+ ,'Y-3'
+ ,'Y-4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y-1','Y-2','Y-3','Y-4'),1:56))
> 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 Y-1 Y-2 Y-3 Y-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 357 15.5 358 363 364 363 1 0 0 0 0 0 0 0 0 0 0 1
2 357 15.1 357 358 363 364 0 1 0 0 0 0 0 0 0 0 0 2
3 380 15.0 357 357 358 363 0 0 1 0 0 0 0 0 0 0 0 3
4 378 12.1 380 357 357 358 0 0 0 1 0 0 0 0 0 0 0 4
5 376 15.8 378 380 357 357 0 0 0 0 1 0 0 0 0 0 0 5
6 380 16.9 376 378 380 357 0 0 0 0 0 1 0 0 0 0 0 6
7 379 15.1 380 376 378 380 0 0 0 0 0 0 1 0 0 0 0 7
8 384 13.7 379 380 376 378 0 0 0 0 0 0 0 1 0 0 0 8
9 392 14.8 384 379 380 376 0 0 0 0 0 0 0 0 1 0 0 9
10 394 14.7 392 384 379 380 0 0 0 0 0 0 0 0 0 1 0 10
11 392 16.0 394 392 384 379 0 0 0 0 0 0 0 0 0 0 1 11
12 396 15.4 392 394 392 384 0 0 0 0 0 0 0 0 0 0 0 12
13 392 15.0 396 392 394 392 1 0 0 0 0 0 0 0 0 0 0 13
14 396 15.5 392 396 392 394 0 1 0 0 0 0 0 0 0 0 0 14
15 419 15.1 396 392 396 392 0 0 1 0 0 0 0 0 0 0 0 15
16 421 11.7 419 396 392 396 0 0 0 1 0 0 0 0 0 0 0 16
17 420 16.3 421 419 396 392 0 0 0 0 1 0 0 0 0 0 0 17
18 418 16.7 420 421 419 396 0 0 0 0 0 1 0 0 0 0 0 18
19 410 15.0 418 420 421 419 0 0 0 0 0 0 1 0 0 0 0 19
20 418 14.9 410 418 420 421 0 0 0 0 0 0 0 1 0 0 0 20
21 426 14.6 418 410 418 420 0 0 0 0 0 0 0 0 1 0 0 21
22 428 15.3 426 418 410 418 0 0 0 0 0 0 0 0 0 1 0 22
23 430 17.9 428 426 418 410 0 0 0 0 0 0 0 0 0 0 1 23
24 424 16.4 430 428 426 418 0 0 0 0 0 0 0 0 0 0 0 24
25 423 15.4 424 430 428 426 1 0 0 0 0 0 0 0 0 0 0 25
26 427 17.9 423 424 430 428 0 1 0 0 0 0 0 0 0 0 0 26
27 441 15.9 427 423 424 430 0 0 1 0 0 0 0 0 0 0 0 27
28 449 13.9 441 427 423 424 0 0 0 1 0 0 0 0 0 0 0 28
29 452 17.8 449 441 427 423 0 0 0 0 1 0 0 0 0 0 0 29
30 462 17.9 452 449 441 427 0 0 0 0 0 1 0 0 0 0 0 30
31 455 17.4 462 452 449 441 0 0 0 0 0 0 1 0 0 0 0 31
32 461 16.7 455 462 452 449 0 0 0 0 0 0 0 1 0 0 0 32
33 461 16.0 461 455 462 452 0 0 0 0 0 0 0 0 1 0 0 33
34 463 16.6 461 461 455 462 0 0 0 0 0 0 0 0 0 1 0 34
35 462 19.1 463 461 461 455 0 0 0 0 0 0 0 0 0 0 1 35
36 456 17.8 462 463 461 461 0 0 0 0 0 0 0 0 0 0 0 36
37 455 17.2 456 462 463 461 1 0 0 0 0 0 0 0 0 0 0 37
38 456 18.6 455 456 462 463 0 1 0 0 0 0 0 0 0 0 0 38
39 472 16.3 456 455 456 462 0 0 1 0 0 0 0 0 0 0 0 39
40 472 15.1 472 456 455 456 0 0 0 1 0 0 0 0 0 0 0 40
41 471 19.2 472 472 456 455 0 0 0 0 1 0 0 0 0 0 0 41
42 465 17.7 471 472 472 456 0 0 0 0 0 1 0 0 0 0 0 42
43 459 19.1 465 471 472 472 0 0 0 0 0 0 1 0 0 0 0 43
44 465 18.0 459 465 471 472 0 0 0 0 0 0 0 1 0 0 0 44
45 468 17.5 465 459 465 471 0 0 0 0 0 0 0 0 1 0 0 45
46 467 17.8 468 465 459 465 0 0 0 0 0 0 0 0 0 1 0 46
47 463 21.1 467 468 465 459 0 0 0 0 0 0 0 0 0 0 1 47
48 460 17.2 463 467 468 465 0 0 0 0 0 0 0 0 0 0 0 48
49 462 19.4 460 463 467 468 1 0 0 0 0 0 0 0 0 0 0 49
50 461 19.8 462 460 463 467 0 1 0 0 0 0 0 0 0 0 0 50
51 476 17.6 461 462 460 463 0 0 1 0 0 0 0 0 0 0 0 51
52 476 16.2 476 461 462 460 0 0 0 1 0 0 0 0 0 0 0 52
53 471 19.5 476 476 461 462 0 0 0 0 1 0 0 0 0 0 0 53
54 453 19.9 471 476 476 461 0 0 0 0 0 1 0 0 0 0 0 54
55 443 20.0 453 471 476 476 0 0 0 0 0 0 1 0 0 0 0 55
56 442 17.3 443 453 471 476 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Y-1` `Y-2` `Y-3` `Y-4`
-50.76900 1.50144 1.02890 0.06930 -0.09624 0.07961
M1 M2 M3 M4 M5 M6
1.10543 2.83667 21.76218 8.36985 -0.97677 -0.36604
M7 M8 M9 M10 M11 t
-4.34480 9.17821 8.36124 3.49196 -1.56210 -0.41287
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.933869 -1.604313 -0.008833 1.514402 10.918391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -50.76900 27.79173 -1.827 0.0756 .
X 1.50144 0.94263 1.593 0.1195
`Y-1` 1.02890 0.15665 6.568 9.50e-08 ***
`Y-2` 0.06930 0.22037 0.314 0.7549
`Y-3` -0.09624 0.24688 -0.390 0.6988
`Y-4` 0.07961 0.19973 0.399 0.6924
M1 1.10543 2.91911 0.379 0.7070
M2 2.83667 3.28292 0.864 0.3930
M3 21.76218 3.20982 6.780 4.89e-08 ***
M4 8.36985 4.80300 1.743 0.0895 .
M5 -0.97677 4.24287 -0.230 0.8192
M6 -0.36604 3.83191 -0.096 0.9244
M7 -4.34480 2.91387 -1.491 0.1442
M8 9.17821 3.15971 2.905 0.0061 **
M9 8.36124 3.40790 2.453 0.0188 *
M10 3.49196 3.70108 0.943 0.3514
M11 -1.56210 3.50382 -0.446 0.6583
t -0.41287 0.18090 -2.282 0.0282 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.104 on 38 degrees of freedom
Multiple R-squared: 0.9902, Adjusted R-squared: 0.9858
F-statistic: 225.7 on 17 and 38 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,] 0.2910247 0.5820494 0.7089753
[2,] 0.2513236 0.5026472 0.7486764
[3,] 0.1413652 0.2827303 0.8586348
[4,] 0.3285211 0.6570421 0.6714789
[5,] 0.2720425 0.5440850 0.7279575
[6,] 0.1688772 0.3377543 0.8311228
[7,] 0.3452781 0.6905562 0.6547219
[8,] 0.2722054 0.5444108 0.7277946
[9,] 0.2350097 0.4700195 0.7649903
[10,] 0.8304528 0.3390944 0.1695472
[11,] 0.7349248 0.5301503 0.2650752
[12,] 0.6779456 0.6441089 0.3220544
[13,] 0.5944120 0.8111760 0.4055880
[14,] 0.4833131 0.9666262 0.5166869
[15,] 0.3185382 0.6370763 0.6814618
> postscript(file="/var/www/html/rcomp/tmp/1aijj1258794512.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xfm91258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lofh1258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4g3ot1258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mqz01258794512.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 = 56
Frequency = 1
1 2 3 4 5
-3.562529952 -3.080783454 1.224430237 -5.979080153 -3.231337152
6 7 8 9 10
3.329135129 3.422913021 -1.866760731 1.180477961 -0.379576348
11 12 13 14 15
-0.915878459 5.126848457 -3.386509258 2.031118109 3.824825908
16 17 18 19 20
0.089663348 -1.005703597 -1.018739169 -1.586048914 1.568292360
21 22 23 24 25
3.458881833 0.293747639 2.651543852 -4.308850691 1.090456174
26 27 28 29 30
1.496438310 -4.796270041 5.711433246 3.878532500 10.918391137
31 32 33 34 35
-1.780717344 -1.678658476 -4.362541185 0.133208832 -0.076561726
36 37 38 39 40
-4.861252995 0.782225941 -0.448928506 -0.965687044 -1.509041969
41 42 43 44 45
0.161646704 -1.294918476 -0.036324998 0.998060178 -0.276818609
46 47 48 49 50
-0.047380123 -1.659103667 4.043255230 5.076357096 0.002155542
51 52 53 54 55
0.712700941 1.687025527 0.196861545 -11.933868620 -0.019821765
56
0.979066669
> postscript(file="/var/www/html/rcomp/tmp/66viq1258794512.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.562529952 NA
1 -3.080783454 -3.562529952
2 1.224430237 -3.080783454
3 -5.979080153 1.224430237
4 -3.231337152 -5.979080153
5 3.329135129 -3.231337152
6 3.422913021 3.329135129
7 -1.866760731 3.422913021
8 1.180477961 -1.866760731
9 -0.379576348 1.180477961
10 -0.915878459 -0.379576348
11 5.126848457 -0.915878459
12 -3.386509258 5.126848457
13 2.031118109 -3.386509258
14 3.824825908 2.031118109
15 0.089663348 3.824825908
16 -1.005703597 0.089663348
17 -1.018739169 -1.005703597
18 -1.586048914 -1.018739169
19 1.568292360 -1.586048914
20 3.458881833 1.568292360
21 0.293747639 3.458881833
22 2.651543852 0.293747639
23 -4.308850691 2.651543852
24 1.090456174 -4.308850691
25 1.496438310 1.090456174
26 -4.796270041 1.496438310
27 5.711433246 -4.796270041
28 3.878532500 5.711433246
29 10.918391137 3.878532500
30 -1.780717344 10.918391137
31 -1.678658476 -1.780717344
32 -4.362541185 -1.678658476
33 0.133208832 -4.362541185
34 -0.076561726 0.133208832
35 -4.861252995 -0.076561726
36 0.782225941 -4.861252995
37 -0.448928506 0.782225941
38 -0.965687044 -0.448928506
39 -1.509041969 -0.965687044
40 0.161646704 -1.509041969
41 -1.294918476 0.161646704
42 -0.036324998 -1.294918476
43 0.998060178 -0.036324998
44 -0.276818609 0.998060178
45 -0.047380123 -0.276818609
46 -1.659103667 -0.047380123
47 4.043255230 -1.659103667
48 5.076357096 4.043255230
49 0.002155542 5.076357096
50 0.712700941 0.002155542
51 1.687025527 0.712700941
52 0.196861545 1.687025527
53 -11.933868620 0.196861545
54 -0.019821765 -11.933868620
55 0.979066669 -0.019821765
56 NA 0.979066669
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.080783454 -3.562529952
[2,] 1.224430237 -3.080783454
[3,] -5.979080153 1.224430237
[4,] -3.231337152 -5.979080153
[5,] 3.329135129 -3.231337152
[6,] 3.422913021 3.329135129
[7,] -1.866760731 3.422913021
[8,] 1.180477961 -1.866760731
[9,] -0.379576348 1.180477961
[10,] -0.915878459 -0.379576348
[11,] 5.126848457 -0.915878459
[12,] -3.386509258 5.126848457
[13,] 2.031118109 -3.386509258
[14,] 3.824825908 2.031118109
[15,] 0.089663348 3.824825908
[16,] -1.005703597 0.089663348
[17,] -1.018739169 -1.005703597
[18,] -1.586048914 -1.018739169
[19,] 1.568292360 -1.586048914
[20,] 3.458881833 1.568292360
[21,] 0.293747639 3.458881833
[22,] 2.651543852 0.293747639
[23,] -4.308850691 2.651543852
[24,] 1.090456174 -4.308850691
[25,] 1.496438310 1.090456174
[26,] -4.796270041 1.496438310
[27,] 5.711433246 -4.796270041
[28,] 3.878532500 5.711433246
[29,] 10.918391137 3.878532500
[30,] -1.780717344 10.918391137
[31,] -1.678658476 -1.780717344
[32,] -4.362541185 -1.678658476
[33,] 0.133208832 -4.362541185
[34,] -0.076561726 0.133208832
[35,] -4.861252995 -0.076561726
[36,] 0.782225941 -4.861252995
[37,] -0.448928506 0.782225941
[38,] -0.965687044 -0.448928506
[39,] -1.509041969 -0.965687044
[40,] 0.161646704 -1.509041969
[41,] -1.294918476 0.161646704
[42,] -0.036324998 -1.294918476
[43,] 0.998060178 -0.036324998
[44,] -0.276818609 0.998060178
[45,] -0.047380123 -0.276818609
[46,] -1.659103667 -0.047380123
[47,] 4.043255230 -1.659103667
[48,] 5.076357096 4.043255230
[49,] 0.002155542 5.076357096
[50,] 0.712700941 0.002155542
[51,] 1.687025527 0.712700941
[52,] 0.196861545 1.687025527
[53,] -11.933868620 0.196861545
[54,] -0.019821765 -11.933868620
[55,] 0.979066669 -0.019821765
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.080783454 -3.562529952
2 1.224430237 -3.080783454
3 -5.979080153 1.224430237
4 -3.231337152 -5.979080153
5 3.329135129 -3.231337152
6 3.422913021 3.329135129
7 -1.866760731 3.422913021
8 1.180477961 -1.866760731
9 -0.379576348 1.180477961
10 -0.915878459 -0.379576348
11 5.126848457 -0.915878459
12 -3.386509258 5.126848457
13 2.031118109 -3.386509258
14 3.824825908 2.031118109
15 0.089663348 3.824825908
16 -1.005703597 0.089663348
17 -1.018739169 -1.005703597
18 -1.586048914 -1.018739169
19 1.568292360 -1.586048914
20 3.458881833 1.568292360
21 0.293747639 3.458881833
22 2.651543852 0.293747639
23 -4.308850691 2.651543852
24 1.090456174 -4.308850691
25 1.496438310 1.090456174
26 -4.796270041 1.496438310
27 5.711433246 -4.796270041
28 3.878532500 5.711433246
29 10.918391137 3.878532500
30 -1.780717344 10.918391137
31 -1.678658476 -1.780717344
32 -4.362541185 -1.678658476
33 0.133208832 -4.362541185
34 -0.076561726 0.133208832
35 -4.861252995 -0.076561726
36 0.782225941 -4.861252995
37 -0.448928506 0.782225941
38 -0.965687044 -0.448928506
39 -1.509041969 -0.965687044
40 0.161646704 -1.509041969
41 -1.294918476 0.161646704
42 -0.036324998 -1.294918476
43 0.998060178 -0.036324998
44 -0.276818609 0.998060178
45 -0.047380123 -0.276818609
46 -1.659103667 -0.047380123
47 4.043255230 -1.659103667
48 5.076357096 4.043255230
49 0.002155542 5.076357096
50 0.712700941 0.002155542
51 1.687025527 0.712700941
52 0.196861545 1.687025527
53 -11.933868620 0.196861545
54 -0.019821765 -11.933868620
55 0.979066669 -0.019821765
> 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/703y01258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88y9m1258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/930ym1258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104kf31258794512.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119k9y1258794512.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/121x9s1258794512.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/139q6o1258794512.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/144a7n1258794512.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/15pk7i1258794512.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/16zf7x1258794513.tab")
+ }
>
> system("convert tmp/1aijj1258794512.ps tmp/1aijj1258794512.png")
> system("convert tmp/2xfm91258794512.ps tmp/2xfm91258794512.png")
> system("convert tmp/3lofh1258794512.ps tmp/3lofh1258794512.png")
> system("convert tmp/4g3ot1258794512.ps tmp/4g3ot1258794512.png")
> system("convert tmp/5mqz01258794512.ps tmp/5mqz01258794512.png")
> system("convert tmp/66viq1258794512.ps tmp/66viq1258794512.png")
> system("convert tmp/703y01258794512.ps tmp/703y01258794512.png")
> system("convert tmp/88y9m1258794512.ps tmp/88y9m1258794512.png")
> system("convert tmp/930ym1258794512.ps tmp/930ym1258794512.png")
> system("convert tmp/104kf31258794512.ps tmp/104kf31258794512.png")
>
>
> proc.time()
user system elapsed
2.302 1.498 3.057