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(463
+ ,1802
+ ,461
+ ,461
+ ,455
+ ,462
+ ,462
+ ,1863
+ ,463
+ ,461
+ ,461
+ ,455
+ ,456
+ ,1989
+ ,462
+ ,463
+ ,461
+ ,461
+ ,455
+ ,2197
+ ,456
+ ,462
+ ,463
+ ,461
+ ,456
+ ,2409
+ ,455
+ ,456
+ ,462
+ ,463
+ ,472
+ ,2502
+ ,456
+ ,455
+ ,456
+ ,462
+ ,472
+ ,2593
+ ,472
+ ,456
+ ,455
+ ,456
+ ,471
+ ,2598
+ ,472
+ ,472
+ ,456
+ ,455
+ ,465
+ ,2053
+ ,471
+ ,472
+ ,472
+ ,456
+ ,459
+ ,2213
+ ,465
+ ,471
+ ,472
+ ,472
+ ,465
+ ,2238
+ ,459
+ ,465
+ ,471
+ ,472
+ ,468
+ ,2359
+ ,465
+ ,459
+ ,465
+ ,471
+ ,467
+ ,2151
+ ,468
+ ,465
+ ,459
+ ,465
+ ,463
+ ,2474
+ ,467
+ ,468
+ ,465
+ ,459
+ ,460
+ ,3079
+ ,463
+ ,467
+ ,468
+ ,465
+ ,462
+ ,2312
+ ,460
+ ,463
+ ,467
+ ,468
+ ,461
+ ,2565
+ ,462
+ ,460
+ ,463
+ ,467
+ ,476
+ ,1972
+ ,461
+ ,462
+ ,460
+ ,463
+ ,476
+ ,2484
+ ,476
+ ,461
+ ,462
+ ,460
+ ,471
+ ,2202
+ ,476
+ ,476
+ ,461
+ ,462
+ ,453
+ ,2151
+ ,471
+ ,476
+ ,476
+ ,461
+ ,443
+ ,1976
+ ,453
+ ,471
+ ,476
+ ,476
+ ,442
+ ,2012
+ ,443
+ ,453
+ ,471
+ ,476
+ ,444
+ ,2114
+ ,442
+ ,443
+ ,453
+ ,471
+ ,438
+ ,1772
+ ,444
+ ,442
+ ,443
+ ,453
+ ,427
+ ,1957
+ ,438
+ ,444
+ ,442
+ ,443
+ ,424
+ ,2070
+ ,427
+ ,438
+ ,444
+ ,442
+ ,416
+ ,1990
+ ,424
+ ,427
+ ,438
+ ,444
+ ,406
+ ,2182
+ ,416
+ ,424
+ ,427
+ ,438
+ ,431
+ ,2008
+ ,406
+ ,416
+ ,424
+ ,427
+ ,434
+ ,1916
+ ,431
+ ,406
+ ,416
+ ,424
+ ,418
+ ,2397
+ ,434
+ ,431
+ ,406
+ ,416
+ ,412
+ ,2114
+ ,418
+ ,434
+ ,431
+ ,406
+ ,404
+ ,1778
+ ,412
+ ,418
+ ,434
+ ,431
+ ,409
+ ,1641
+ ,404
+ ,412
+ ,418
+ ,434
+ ,412
+ ,2186
+ ,409
+ ,404
+ ,412
+ ,418
+ ,406
+ ,1773
+ ,412
+ ,409
+ ,404
+ ,412
+ ,398
+ ,1785
+ ,406
+ ,412
+ ,409
+ ,404
+ ,397
+ ,2217
+ ,398
+ ,406
+ ,412
+ ,409
+ ,385
+ ,2153
+ ,397
+ ,398
+ ,406
+ ,412
+ ,390
+ ,1895
+ ,385
+ ,397
+ ,398
+ ,406
+ ,413
+ ,2475
+ ,390
+ ,385
+ ,397
+ ,398
+ ,413
+ ,1793
+ ,413
+ ,390
+ ,385
+ ,397
+ ,401
+ ,2308
+ ,413
+ ,413
+ ,390
+ ,385
+ ,397
+ ,2051
+ ,401
+ ,413
+ ,413
+ ,390
+ ,397
+ ,1898
+ ,397
+ ,401
+ ,413
+ ,413
+ ,409
+ ,2142
+ ,397
+ ,397
+ ,401
+ ,413
+ ,419
+ ,1874
+ ,409
+ ,397
+ ,397
+ ,401
+ ,424
+ ,1560
+ ,419
+ ,409
+ ,397
+ ,397
+ ,428
+ ,1808
+ ,424
+ ,419
+ ,409
+ ,397
+ ,430
+ ,1575
+ ,428
+ ,424
+ ,419
+ ,409
+ ,424
+ ,1525
+ ,430
+ ,428
+ ,424
+ ,419
+ ,433
+ ,1997
+ ,424
+ ,430
+ ,428
+ ,424
+ ,456
+ ,1753
+ ,433
+ ,424
+ ,430
+ ,428
+ ,459
+ ,1623
+ ,456
+ ,433
+ ,424
+ ,430
+ ,446
+ ,2251
+ ,459
+ ,456
+ ,433
+ ,424
+ ,441
+ ,1890
+ ,446
+ ,459
+ ,456
+ ,433)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('wkl'
+ ,'bvg'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('wkl','bvg','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
wkl bvg Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 463 1802 461 461 455 462 1 0 0 0 0 0 0 0 0 0 0 1
2 462 1863 463 461 461 455 0 1 0 0 0 0 0 0 0 0 0 2
3 456 1989 462 463 461 461 0 0 1 0 0 0 0 0 0 0 0 3
4 455 2197 456 462 463 461 0 0 0 1 0 0 0 0 0 0 0 4
5 456 2409 455 456 462 463 0 0 0 0 1 0 0 0 0 0 0 5
6 472 2502 456 455 456 462 0 0 0 0 0 1 0 0 0 0 0 6
7 472 2593 472 456 455 456 0 0 0 0 0 0 1 0 0 0 0 7
8 471 2598 472 472 456 455 0 0 0 0 0 0 0 1 0 0 0 8
9 465 2053 471 472 472 456 0 0 0 0 0 0 0 0 1 0 0 9
10 459 2213 465 471 472 472 0 0 0 0 0 0 0 0 0 1 0 10
11 465 2238 459 465 471 472 0 0 0 0 0 0 0 0 0 0 1 11
12 468 2359 465 459 465 471 0 0 0 0 0 0 0 0 0 0 0 12
13 467 2151 468 465 459 465 1 0 0 0 0 0 0 0 0 0 0 13
14 463 2474 467 468 465 459 0 1 0 0 0 0 0 0 0 0 0 14
15 460 3079 463 467 468 465 0 0 1 0 0 0 0 0 0 0 0 15
16 462 2312 460 463 467 468 0 0 0 1 0 0 0 0 0 0 0 16
17 461 2565 462 460 463 467 0 0 0 0 1 0 0 0 0 0 0 17
18 476 1972 461 462 460 463 0 0 0 0 0 1 0 0 0 0 0 18
19 476 2484 476 461 462 460 0 0 0 0 0 0 1 0 0 0 0 19
20 471 2202 476 476 461 462 0 0 0 0 0 0 0 1 0 0 0 20
21 453 2151 471 476 476 461 0 0 0 0 0 0 0 0 1 0 0 21
22 443 1976 453 471 476 476 0 0 0 0 0 0 0 0 0 1 0 22
23 442 2012 443 453 471 476 0 0 0 0 0 0 0 0 0 0 1 23
24 444 2114 442 443 453 471 0 0 0 0 0 0 0 0 0 0 0 24
25 438 1772 444 442 443 453 1 0 0 0 0 0 0 0 0 0 0 25
26 427 1957 438 444 442 443 0 1 0 0 0 0 0 0 0 0 0 26
27 424 2070 427 438 444 442 0 0 1 0 0 0 0 0 0 0 0 27
28 416 1990 424 427 438 444 0 0 0 1 0 0 0 0 0 0 0 28
29 406 2182 416 424 427 438 0 0 0 0 1 0 0 0 0 0 0 29
30 431 2008 406 416 424 427 0 0 0 0 0 1 0 0 0 0 0 30
31 434 1916 431 406 416 424 0 0 0 0 0 0 1 0 0 0 0 31
32 418 2397 434 431 406 416 0 0 0 0 0 0 0 1 0 0 0 32
33 412 2114 418 434 431 406 0 0 0 0 0 0 0 0 1 0 0 33
34 404 1778 412 418 434 431 0 0 0 0 0 0 0 0 0 1 0 34
35 409 1641 404 412 418 434 0 0 0 0 0 0 0 0 0 0 1 35
36 412 2186 409 404 412 418 0 0 0 0 0 0 0 0 0 0 0 36
37 406 1773 412 409 404 412 1 0 0 0 0 0 0 0 0 0 0 37
38 398 1785 406 412 409 404 0 1 0 0 0 0 0 0 0 0 0 38
39 397 2217 398 406 412 409 0 0 1 0 0 0 0 0 0 0 0 39
40 385 2153 397 398 406 412 0 0 0 1 0 0 0 0 0 0 0 40
41 390 1895 385 397 398 406 0 0 0 0 1 0 0 0 0 0 0 41
42 413 2475 390 385 397 398 0 0 0 0 0 1 0 0 0 0 0 42
43 413 1793 413 390 385 397 0 0 0 0 0 0 1 0 0 0 0 43
44 401 2308 413 413 390 385 0 0 0 0 0 0 0 1 0 0 0 44
45 397 2051 401 413 413 390 0 0 0 0 0 0 0 0 1 0 0 45
46 397 1898 397 401 413 413 0 0 0 0 0 0 0 0 0 1 0 46
47 409 2142 397 397 401 413 0 0 0 0 0 0 0 0 0 0 1 47
48 419 1874 409 397 397 401 0 0 0 0 0 0 0 0 0 0 0 48
49 424 1560 419 409 397 397 1 0 0 0 0 0 0 0 0 0 0 49
50 428 1808 424 419 409 397 0 1 0 0 0 0 0 0 0 0 0 50
51 430 1575 428 424 419 409 0 0 1 0 0 0 0 0 0 0 0 51
52 424 1525 430 428 424 419 0 0 0 1 0 0 0 0 0 0 0 52
53 433 1997 424 430 428 424 0 0 0 0 1 0 0 0 0 0 0 53
54 456 1753 433 424 430 428 0 0 0 0 0 1 0 0 0 0 0 54
55 459 1623 456 433 424 430 0 0 0 0 0 0 1 0 0 0 0 55
56 446 2251 459 456 433 424 0 0 0 0 0 0 0 1 0 0 0 56
57 441 1890 446 459 456 433 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) bvg Y1 Y2 Y3 Y4
38.521096 -0.001521 1.098984 -0.082395 0.409148 -0.483747
M1 M2 M3 M4 M5 M6
-7.658921 -15.013007 -11.293830 -12.133417 -4.629920 13.410054
M7 M8 M9 M10 M11 t
-6.784556 -18.090000 -23.806852 -12.952858 2.413947 -0.068808
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.6444 -2.9062 0.4764 2.5887 8.7736
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.521096 22.308104 1.727 0.092122 .
bvg -0.001521 0.002951 -0.515 0.609190
Y1 1.098984 0.143791 7.643 2.85e-09 ***
Y2 -0.082395 0.220760 -0.373 0.710998
Y3 0.409148 0.226494 1.806 0.078570 .
Y4 -0.483747 0.157295 -3.075 0.003830 **
M1 -7.658921 3.784777 -2.024 0.049901 *
M2 -15.013007 4.295187 -3.495 0.001196 **
M3 -11.293830 4.014019 -2.814 0.007636 **
M4 -12.133417 3.601294 -3.369 0.001709 **
M5 -4.629920 3.654506 -1.267 0.212702
M6 13.410054 3.392235 3.953 0.000315 ***
M7 -6.784556 3.848867 -1.763 0.085780 .
M8 -18.090000 5.252776 -3.444 0.001384 **
M9 -23.806852 5.934347 -4.012 0.000264 ***
M10 -12.952858 4.268398 -3.035 0.004274 **
M11 2.413947 3.746094 0.644 0.523095
t -0.068808 0.075876 -0.907 0.370056
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.711 on 39 degrees of freedom
Multiple R-squared: 0.9779, Adjusted R-squared: 0.9683
F-statistic: 101.6 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,] 0.5072768 0.98544631 0.49272315
[2,] 0.4130923 0.82618470 0.58690765
[3,] 0.3597732 0.71954644 0.64022678
[4,] 0.4014801 0.80296021 0.59851989
[5,] 0.3258122 0.65162447 0.67418776
[6,] 0.2158548 0.43170953 0.78414523
[7,] 0.2537657 0.50753133 0.74623434
[8,] 0.3138463 0.62769251 0.68615375
[9,] 0.4846393 0.96927870 0.51536065
[10,] 0.8955566 0.20888672 0.10444336
[11,] 0.9898372 0.02032566 0.01016283
[12,] 0.9809801 0.03803985 0.01901993
[13,] 0.9544439 0.09111221 0.04555611
[14,] 0.9223831 0.15523383 0.07761692
[15,] 0.9562595 0.08748096 0.04374048
[16,] 0.9502743 0.09945144 0.04972572
> postscript(file="/var/www/html/rcomp/tmp/1lr1f1258744456.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j0pg1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/363rb1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qn781258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50f661258744456.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 7
3.6281262 2.1047099 -3.1877729 2.7301811 -1.4008201 -2.4407790 -2.0336413
8 9 10 11 12 13 14
8.7736353 2.7667654 0.4763797 -2.2749106 -1.7252565 1.4359517 1.3388930
15 16 17 18 19 20 21
1.5972426 8.1668720 -1.1753360 -4.4921620 -2.2867372 6.2711843 -7.1467721
22 23 24 25 26 27 28
-1.5721749 -6.2629425 3.5959162 -2.0928219 -3.0581879 0.7557172 -0.6445663
29 30 31 32 33 34 35
-7.6444046 5.3567073 2.0036011 -2.9061723 -0.7861709 -3.9405654 1.8481909
36 37 38 39 40 41 42
-3.2793012 -4.6939747 -4.3274570 1.1679937 -5.6749928 4.9740467 0.9405183
43 44 45 46 47 48 49
-0.2719417 -6.0700523 1.5208693 5.0363606 6.6896622 1.4086415 1.7227186
50 51 52 53 54 55 56
3.9420421 -0.3331805 -4.5774941 5.2465140 0.6357154 2.5887191 -6.0685950
57
3.6453083
> postscript(file="/var/www/html/rcomp/tmp/67cf01258744456.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 3.6281262 NA
1 2.1047099 3.6281262
2 -3.1877729 2.1047099
3 2.7301811 -3.1877729
4 -1.4008201 2.7301811
5 -2.4407790 -1.4008201
6 -2.0336413 -2.4407790
7 8.7736353 -2.0336413
8 2.7667654 8.7736353
9 0.4763797 2.7667654
10 -2.2749106 0.4763797
11 -1.7252565 -2.2749106
12 1.4359517 -1.7252565
13 1.3388930 1.4359517
14 1.5972426 1.3388930
15 8.1668720 1.5972426
16 -1.1753360 8.1668720
17 -4.4921620 -1.1753360
18 -2.2867372 -4.4921620
19 6.2711843 -2.2867372
20 -7.1467721 6.2711843
21 -1.5721749 -7.1467721
22 -6.2629425 -1.5721749
23 3.5959162 -6.2629425
24 -2.0928219 3.5959162
25 -3.0581879 -2.0928219
26 0.7557172 -3.0581879
27 -0.6445663 0.7557172
28 -7.6444046 -0.6445663
29 5.3567073 -7.6444046
30 2.0036011 5.3567073
31 -2.9061723 2.0036011
32 -0.7861709 -2.9061723
33 -3.9405654 -0.7861709
34 1.8481909 -3.9405654
35 -3.2793012 1.8481909
36 -4.6939747 -3.2793012
37 -4.3274570 -4.6939747
38 1.1679937 -4.3274570
39 -5.6749928 1.1679937
40 4.9740467 -5.6749928
41 0.9405183 4.9740467
42 -0.2719417 0.9405183
43 -6.0700523 -0.2719417
44 1.5208693 -6.0700523
45 5.0363606 1.5208693
46 6.6896622 5.0363606
47 1.4086415 6.6896622
48 1.7227186 1.4086415
49 3.9420421 1.7227186
50 -0.3331805 3.9420421
51 -4.5774941 -0.3331805
52 5.2465140 -4.5774941
53 0.6357154 5.2465140
54 2.5887191 0.6357154
55 -6.0685950 2.5887191
56 3.6453083 -6.0685950
57 NA 3.6453083
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.1047099 3.6281262
[2,] -3.1877729 2.1047099
[3,] 2.7301811 -3.1877729
[4,] -1.4008201 2.7301811
[5,] -2.4407790 -1.4008201
[6,] -2.0336413 -2.4407790
[7,] 8.7736353 -2.0336413
[8,] 2.7667654 8.7736353
[9,] 0.4763797 2.7667654
[10,] -2.2749106 0.4763797
[11,] -1.7252565 -2.2749106
[12,] 1.4359517 -1.7252565
[13,] 1.3388930 1.4359517
[14,] 1.5972426 1.3388930
[15,] 8.1668720 1.5972426
[16,] -1.1753360 8.1668720
[17,] -4.4921620 -1.1753360
[18,] -2.2867372 -4.4921620
[19,] 6.2711843 -2.2867372
[20,] -7.1467721 6.2711843
[21,] -1.5721749 -7.1467721
[22,] -6.2629425 -1.5721749
[23,] 3.5959162 -6.2629425
[24,] -2.0928219 3.5959162
[25,] -3.0581879 -2.0928219
[26,] 0.7557172 -3.0581879
[27,] -0.6445663 0.7557172
[28,] -7.6444046 -0.6445663
[29,] 5.3567073 -7.6444046
[30,] 2.0036011 5.3567073
[31,] -2.9061723 2.0036011
[32,] -0.7861709 -2.9061723
[33,] -3.9405654 -0.7861709
[34,] 1.8481909 -3.9405654
[35,] -3.2793012 1.8481909
[36,] -4.6939747 -3.2793012
[37,] -4.3274570 -4.6939747
[38,] 1.1679937 -4.3274570
[39,] -5.6749928 1.1679937
[40,] 4.9740467 -5.6749928
[41,] 0.9405183 4.9740467
[42,] -0.2719417 0.9405183
[43,] -6.0700523 -0.2719417
[44,] 1.5208693 -6.0700523
[45,] 5.0363606 1.5208693
[46,] 6.6896622 5.0363606
[47,] 1.4086415 6.6896622
[48,] 1.7227186 1.4086415
[49,] 3.9420421 1.7227186
[50,] -0.3331805 3.9420421
[51,] -4.5774941 -0.3331805
[52,] 5.2465140 -4.5774941
[53,] 0.6357154 5.2465140
[54,] 2.5887191 0.6357154
[55,] -6.0685950 2.5887191
[56,] 3.6453083 -6.0685950
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.1047099 3.6281262
2 -3.1877729 2.1047099
3 2.7301811 -3.1877729
4 -1.4008201 2.7301811
5 -2.4407790 -1.4008201
6 -2.0336413 -2.4407790
7 8.7736353 -2.0336413
8 2.7667654 8.7736353
9 0.4763797 2.7667654
10 -2.2749106 0.4763797
11 -1.7252565 -2.2749106
12 1.4359517 -1.7252565
13 1.3388930 1.4359517
14 1.5972426 1.3388930
15 8.1668720 1.5972426
16 -1.1753360 8.1668720
17 -4.4921620 -1.1753360
18 -2.2867372 -4.4921620
19 6.2711843 -2.2867372
20 -7.1467721 6.2711843
21 -1.5721749 -7.1467721
22 -6.2629425 -1.5721749
23 3.5959162 -6.2629425
24 -2.0928219 3.5959162
25 -3.0581879 -2.0928219
26 0.7557172 -3.0581879
27 -0.6445663 0.7557172
28 -7.6444046 -0.6445663
29 5.3567073 -7.6444046
30 2.0036011 5.3567073
31 -2.9061723 2.0036011
32 -0.7861709 -2.9061723
33 -3.9405654 -0.7861709
34 1.8481909 -3.9405654
35 -3.2793012 1.8481909
36 -4.6939747 -3.2793012
37 -4.3274570 -4.6939747
38 1.1679937 -4.3274570
39 -5.6749928 1.1679937
40 4.9740467 -5.6749928
41 0.9405183 4.9740467
42 -0.2719417 0.9405183
43 -6.0700523 -0.2719417
44 1.5208693 -6.0700523
45 5.0363606 1.5208693
46 6.6896622 5.0363606
47 1.4086415 6.6896622
48 1.7227186 1.4086415
49 3.9420421 1.7227186
50 -0.3331805 3.9420421
51 -4.5774941 -0.3331805
52 5.2465140 -4.5774941
53 0.6357154 5.2465140
54 2.5887191 0.6357154
55 -6.0685950 2.5887191
56 3.6453083 -6.0685950
> 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/75gte1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ihaw1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/905yi1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hagw1258744456.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11s5m51258744456.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/122siw1258744456.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/13sktt1258744456.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/14ftzf1258744456.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/15g7hq1258744456.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/166itp1258744456.tab")
+ }
>
> system("convert tmp/1lr1f1258744456.ps tmp/1lr1f1258744456.png")
> system("convert tmp/2j0pg1258744456.ps tmp/2j0pg1258744456.png")
> system("convert tmp/363rb1258744456.ps tmp/363rb1258744456.png")
> system("convert tmp/4qn781258744456.ps tmp/4qn781258744456.png")
> system("convert tmp/50f661258744456.ps tmp/50f661258744456.png")
> system("convert tmp/67cf01258744456.ps tmp/67cf01258744456.png")
> system("convert tmp/75gte1258744456.ps tmp/75gte1258744456.png")
> system("convert tmp/8ihaw1258744456.ps tmp/8ihaw1258744456.png")
> system("convert tmp/905yi1258744456.ps tmp/905yi1258744456.png")
> system("convert tmp/10hagw1258744456.ps tmp/10hagw1258744456.png")
>
>
> proc.time()
user system elapsed
2.358 1.553 2.762