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(8.4
+ ,410
+ ,8.4
+ ,8.4
+ ,8.6
+ ,418
+ ,8.4
+ ,8.4
+ ,8.9
+ ,426
+ ,8.6
+ ,8.4
+ ,8.8
+ ,428
+ ,8.9
+ ,8.6
+ ,8.3
+ ,430
+ ,8.8
+ ,8.9
+ ,7.5
+ ,424
+ ,8.3
+ ,8.8
+ ,7.2
+ ,423
+ ,7.5
+ ,8.3
+ ,7.4
+ ,427
+ ,7.2
+ ,7.5
+ ,8.8
+ ,441
+ ,7.4
+ ,7.2
+ ,9.3
+ ,449
+ ,8.8
+ ,7.4
+ ,9.3
+ ,452
+ ,9.3
+ ,8.8
+ ,8.7
+ ,462
+ ,9.3
+ ,9.3
+ ,8.2
+ ,455
+ ,8.7
+ ,9.3
+ ,8.3
+ ,461
+ ,8.2
+ ,8.7
+ ,8.5
+ ,461
+ ,8.3
+ ,8.2
+ ,8.6
+ ,463
+ ,8.5
+ ,8.3
+ ,8.5
+ ,462
+ ,8.6
+ ,8.5
+ ,8.2
+ ,456
+ ,8.5
+ ,8.6
+ ,8.1
+ ,455
+ ,8.2
+ ,8.5
+ ,7.9
+ ,456
+ ,8.1
+ ,8.2
+ ,8.6
+ ,472
+ ,7.9
+ ,8.1
+ ,8.7
+ ,472
+ ,8.6
+ ,7.9
+ ,8.7
+ ,471
+ ,8.7
+ ,8.6
+ ,8.5
+ ,465
+ ,8.7
+ ,8.7
+ ,8.4
+ ,459
+ ,8.5
+ ,8.7
+ ,8.5
+ ,465
+ ,8.4
+ ,8.5
+ ,8.7
+ ,468
+ ,8.5
+ ,8.4
+ ,8.7
+ ,467
+ ,8.7
+ ,8.5
+ ,8.6
+ ,463
+ ,8.7
+ ,8.7
+ ,8.5
+ ,460
+ ,8.6
+ ,8.7
+ ,8.3
+ ,462
+ ,8.5
+ ,8.6
+ ,8.00
+ ,461
+ ,8.3
+ ,8.5
+ ,8.2
+ ,476
+ ,8.00
+ ,8.3
+ ,8.1
+ ,476
+ ,8.2
+ ,8.00
+ ,8.1
+ ,471
+ ,8.1
+ ,8.2
+ ,8.00
+ ,453
+ ,8.1
+ ,8.1
+ ,7.9
+ ,443
+ ,8.00
+ ,8.1
+ ,7.9
+ ,442
+ ,7.9
+ ,8.00
+ ,8.00
+ ,444
+ ,7.9
+ ,7.9
+ ,8.00
+ ,438
+ ,8.00
+ ,7.9
+ ,7.9
+ ,427
+ ,8.00
+ ,8.00
+ ,8.00
+ ,424
+ ,7.9
+ ,8.00
+ ,7.7
+ ,416
+ ,8.00
+ ,7.9
+ ,7.2
+ ,406
+ ,7.7
+ ,8.00
+ ,7.5
+ ,431
+ ,7.2
+ ,7.7
+ ,7.3
+ ,434
+ ,7.5
+ ,7.2
+ ,7.00
+ ,418
+ ,7.3
+ ,7.5
+ ,7.00
+ ,412
+ ,7.00
+ ,7.3
+ ,7.00
+ ,404
+ ,7.00
+ ,7.00
+ ,7.2
+ ,409
+ ,7.00
+ ,7.00
+ ,7.3
+ ,412
+ ,7.2
+ ,7.00
+ ,7.1
+ ,406
+ ,7.3
+ ,7.2
+ ,6.8
+ ,398
+ ,7.1
+ ,7.3
+ ,6.4
+ ,397
+ ,6.8
+ ,7.1
+ ,6.1
+ ,385
+ ,6.4
+ ,6.8
+ ,6.5
+ ,390
+ ,6.1
+ ,6.4
+ ,7.7
+ ,413
+ ,6.5
+ ,6.1
+ ,7.9
+ ,413
+ ,7.7
+ ,6.5
+ ,7.5
+ ,401
+ ,7.9
+ ,7.7
+ ,6.9
+ ,397
+ ,7.5
+ ,7.9
+ ,6.6
+ ,397
+ ,6.9
+ ,7.5
+ ,6.9
+ ,409
+ ,6.6
+ ,6.9
+ ,7.7
+ ,419
+ ,6.9
+ ,6.6
+ ,8.00
+ ,424
+ ,7.7
+ ,6.9
+ ,8.00
+ ,428
+ ,8.00
+ ,7.7
+ ,7.7
+ ,430
+ ,8.00
+ ,8.00
+ ,7.3
+ ,424
+ ,7.7
+ ,8.00
+ ,7.4
+ ,433
+ ,7.3
+ ,7.7
+ ,8.1
+ ,456
+ ,7.4
+ ,7.3
+ ,8.3
+ ,459
+ ,8.1
+ ,7.4
+ ,8.2
+ ,446
+ ,8.3
+ ,8.1)
+ ,dim=c(4
+ ,71)
+ ,dimnames=list(c('wgb'
+ ,'nwwz'
+ ,'Y1'
+ ,'Y2')
+ ,1:71))
> y <- array(NA,dim=c(4,71),dimnames=list(c('wgb','nwwz','Y1','Y2'),1:71))
> 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
wgb nwwz Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.4 410 8.4 8.4 1 0 0 0 0 0 0 0 0 0 0 1
2 8.6 418 8.4 8.4 0 1 0 0 0 0 0 0 0 0 0 2
3 8.9 426 8.6 8.4 0 0 1 0 0 0 0 0 0 0 0 3
4 8.8 428 8.9 8.6 0 0 0 1 0 0 0 0 0 0 0 4
5 8.3 430 8.8 8.9 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 424 8.3 8.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.2 423 7.5 8.3 0 0 0 0 0 0 1 0 0 0 0 7
8 7.4 427 7.2 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.8 441 7.4 7.2 0 0 0 0 0 0 0 0 1 0 0 9
10 9.3 449 8.8 7.4 0 0 0 0 0 0 0 0 0 1 0 10
11 9.3 452 9.3 8.8 0 0 0 0 0 0 0 0 0 0 1 11
12 8.7 462 9.3 9.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.2 455 8.7 9.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.3 461 8.2 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 461 8.3 8.2 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 463 8.5 8.3 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 462 8.6 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 456 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.1 455 8.2 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 7.9 456 8.1 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.6 472 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 472 8.6 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.7 471 8.7 8.6 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 465 8.7 8.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.4 459 8.5 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 465 8.4 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.7 468 8.5 8.4 0 0 1 0 0 0 0 0 0 0 0 27
28 8.7 467 8.7 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.6 463 8.7 8.7 0 0 0 0 1 0 0 0 0 0 0 29
30 8.5 460 8.6 8.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.3 462 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 461 8.3 8.5 0 0 0 0 0 0 0 1 0 0 0 32
33 8.2 476 8.0 8.3 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 476 8.2 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 8.1 471 8.1 8.2 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 453 8.1 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 443 8.0 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 442 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 444 7.9 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 438 8.0 7.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 427 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 8.0 424 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.7 416 8.0 7.9 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 406 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.5 431 7.2 7.7 0 0 0 0 0 0 0 0 1 0 0 45
46 7.3 434 7.5 7.2 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 418 7.3 7.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.0 412 7.0 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.0 404 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 7.2 409 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 412 7.2 7.0 0 0 1 0 0 0 0 0 0 0 0 51
52 7.1 406 7.3 7.2 0 0 0 1 0 0 0 0 0 0 0 52
53 6.8 398 7.1 7.3 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 397 6.8 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.1 385 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.5 390 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 413 6.5 6.1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.9 413 7.7 6.5 0 0 0 0 0 0 0 0 0 1 0 58
59 7.5 401 7.9 7.7 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 397 7.5 7.9 0 0 0 0 0 0 0 0 0 0 0 60
61 6.6 397 6.9 7.5 1 0 0 0 0 0 0 0 0 0 0 61
62 6.9 409 6.6 6.9 0 1 0 0 0 0 0 0 0 0 0 62
63 7.7 419 6.9 6.6 0 0 1 0 0 0 0 0 0 0 0 63
64 8.0 424 7.7 6.9 0 0 0 1 0 0 0 0 0 0 0 64
65 8.0 428 8.0 7.7 0 0 0 0 1 0 0 0 0 0 0 65
66 7.7 430 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 7.3 424 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 67
68 7.4 433 7.3 7.7 0 0 0 0 0 0 0 1 0 0 0 68
69 8.1 456 7.4 7.3 0 0 0 0 0 0 0 0 1 0 0 69
70 8.3 459 8.1 7.4 0 0 0 0 0 0 0 0 0 1 0 70
71 8.2 446 8.3 8.1 0 0 0 0 0 0 0 0 0 0 1 71
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) nwwz Y1 Y2 M1 M2
0.653201 0.007022 1.348625 -0.805560 0.169468 0.306393
M3 M4 M5 M6 M7 M8
0.228421 -0.005831 0.043343 0.029589 0.055929 0.120209
M9 M10 M11 t
0.592741 -0.353047 0.017621 -0.005677
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.30395 -0.10717 0.00221 0.08651 0.36863
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.653201 0.453948 1.439 0.155836
nwwz 0.007022 0.001412 4.973 6.81e-06 ***
Y1 1.348625 0.086747 15.547 < 2e-16 ***
Y2 -0.805560 0.090653 -8.886 3.21e-12 ***
M1 0.169468 0.100487 1.686 0.097371 .
M2 0.306393 0.102123 3.000 0.004048 **
M3 0.228421 0.106125 2.152 0.035772 *
M4 -0.005831 0.106016 -0.055 0.956335
M5 0.043343 0.100087 0.433 0.666670
M6 0.029589 0.099418 0.298 0.767111
M7 0.055929 0.100908 0.554 0.581647
M8 0.120209 0.104058 1.155 0.252998
M9 0.592741 0.116655 5.081 4.64e-06 ***
M10 -0.353047 0.131114 -2.693 0.009376 **
M11 0.017621 0.101819 0.173 0.863236
t -0.005677 0.001411 -4.023 0.000177 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1638 on 55 degrees of freedom
Multiple R-squared: 0.957, Adjusted R-squared: 0.9453
F-statistic: 81.61 on 15 and 55 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,] 0.14614353 0.29228706 0.8538565
[2,] 0.18278616 0.36557233 0.8172138
[3,] 0.17647870 0.35295740 0.8235213
[4,] 0.31934228 0.63868457 0.6806577
[5,] 0.22199503 0.44399005 0.7780050
[6,] 0.15911889 0.31823779 0.8408811
[7,] 0.10293582 0.20587164 0.8970642
[8,] 0.09020510 0.18041020 0.9097949
[9,] 0.05488917 0.10977834 0.9451108
[10,] 0.03588741 0.07177483 0.9641126
[11,] 0.04969284 0.09938568 0.9503072
[12,] 0.15033966 0.30067933 0.8496603
[13,] 0.12423975 0.24847950 0.8757603
[14,] 0.11050255 0.22100511 0.8894974
[15,] 0.34164702 0.68329403 0.6583530
[16,] 0.27515110 0.55030220 0.7248489
[17,] 0.24513331 0.49026662 0.7548667
[18,] 0.23496372 0.46992744 0.7650363
[19,] 0.24222445 0.48444889 0.7577756
[20,] 0.25338255 0.50676509 0.7466175
[21,] 0.19939754 0.39879509 0.8006025
[22,] 0.16152094 0.32304189 0.8384791
[23,] 0.13260985 0.26521970 0.8673902
[24,] 0.76836004 0.46327992 0.2316400
[25,] 0.83606091 0.32787818 0.1639391
[26,] 0.76624519 0.46750963 0.2337548
[27,] 0.85885206 0.28229589 0.1411479
[28,] 0.81579369 0.36841263 0.1842063
[29,] 0.76189697 0.47620606 0.2381030
[30,] 0.77155955 0.45688091 0.2284405
[31,] 0.70949139 0.58101721 0.2905086
[32,] 0.68937640 0.62124721 0.3106236
[33,] 0.71316012 0.57367975 0.2868399
[34,] 0.60331726 0.79336548 0.3966827
> postscript(file="/var/www/html/rcomp/tmp/156s31258627174.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qi101258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e5gs1258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ddkh1258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5b4ed1258627174.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 = 71
Frequency = 1
1 2 3 4 5 6
0.142229010 0.154804054 0.212551993 0.094960827 -0.086050393 -0.230731377
7 8 9 10 11 12
0.131747116 0.005196046 0.328638622 -0.003035816 0.064377482 -0.179765161
13 14 15 16 17 18
0.014772493 0.132368257 -0.121624944 0.015090418 -0.095135683 -0.118154710
19 20 21 22 23 24
0.092235179 -0.280194970 0.029765466 -0.023919179 0.047140429 -0.006873683
25 26 27 28 29 30
0.041191970 -0.058438376 -0.011273785 0.046507652 0.092210121 0.167569052
31 32 33 34 35 36
-0.012832123 -0.175243794 -0.303954805 0.036117055 0.002209916 -0.028651829
37 38 39 40 41 42
-0.087360570 -0.157280775 -0.068231663 0.078966426 0.093267102 0.368626034
43 44 45 46 47 48
-0.111279880 -0.114518897 -0.024281136 -0.101249780 -0.142496182 0.166409289
49 50 51 52 53 54
-0.182873898 -0.149232780 -0.256374715 -0.148064692 -0.085105102 -0.215177166
55 56 57 58 59 60
-0.153794441 0.234856331 0.025375693 -0.119285622 -0.103066400 0.048881384
61 62 63 64 65 66
0.072040995 0.077779620 0.244953114 -0.087460631 0.080813955 0.027868168
67 68 69 70 71
0.053924149 0.329905283 -0.055543839 0.211373341 0.131834754
> postscript(file="/var/www/html/rcomp/tmp/6n67k1258627174.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 = 71
Frequency = 1
lag(myerror, k = 1) myerror
0 0.142229010 NA
1 0.154804054 0.142229010
2 0.212551993 0.154804054
3 0.094960827 0.212551993
4 -0.086050393 0.094960827
5 -0.230731377 -0.086050393
6 0.131747116 -0.230731377
7 0.005196046 0.131747116
8 0.328638622 0.005196046
9 -0.003035816 0.328638622
10 0.064377482 -0.003035816
11 -0.179765161 0.064377482
12 0.014772493 -0.179765161
13 0.132368257 0.014772493
14 -0.121624944 0.132368257
15 0.015090418 -0.121624944
16 -0.095135683 0.015090418
17 -0.118154710 -0.095135683
18 0.092235179 -0.118154710
19 -0.280194970 0.092235179
20 0.029765466 -0.280194970
21 -0.023919179 0.029765466
22 0.047140429 -0.023919179
23 -0.006873683 0.047140429
24 0.041191970 -0.006873683
25 -0.058438376 0.041191970
26 -0.011273785 -0.058438376
27 0.046507652 -0.011273785
28 0.092210121 0.046507652
29 0.167569052 0.092210121
30 -0.012832123 0.167569052
31 -0.175243794 -0.012832123
32 -0.303954805 -0.175243794
33 0.036117055 -0.303954805
34 0.002209916 0.036117055
35 -0.028651829 0.002209916
36 -0.087360570 -0.028651829
37 -0.157280775 -0.087360570
38 -0.068231663 -0.157280775
39 0.078966426 -0.068231663
40 0.093267102 0.078966426
41 0.368626034 0.093267102
42 -0.111279880 0.368626034
43 -0.114518897 -0.111279880
44 -0.024281136 -0.114518897
45 -0.101249780 -0.024281136
46 -0.142496182 -0.101249780
47 0.166409289 -0.142496182
48 -0.182873898 0.166409289
49 -0.149232780 -0.182873898
50 -0.256374715 -0.149232780
51 -0.148064692 -0.256374715
52 -0.085105102 -0.148064692
53 -0.215177166 -0.085105102
54 -0.153794441 -0.215177166
55 0.234856331 -0.153794441
56 0.025375693 0.234856331
57 -0.119285622 0.025375693
58 -0.103066400 -0.119285622
59 0.048881384 -0.103066400
60 0.072040995 0.048881384
61 0.077779620 0.072040995
62 0.244953114 0.077779620
63 -0.087460631 0.244953114
64 0.080813955 -0.087460631
65 0.027868168 0.080813955
66 0.053924149 0.027868168
67 0.329905283 0.053924149
68 -0.055543839 0.329905283
69 0.211373341 -0.055543839
70 0.131834754 0.211373341
71 NA 0.131834754
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.154804054 0.142229010
[2,] 0.212551993 0.154804054
[3,] 0.094960827 0.212551993
[4,] -0.086050393 0.094960827
[5,] -0.230731377 -0.086050393
[6,] 0.131747116 -0.230731377
[7,] 0.005196046 0.131747116
[8,] 0.328638622 0.005196046
[9,] -0.003035816 0.328638622
[10,] 0.064377482 -0.003035816
[11,] -0.179765161 0.064377482
[12,] 0.014772493 -0.179765161
[13,] 0.132368257 0.014772493
[14,] -0.121624944 0.132368257
[15,] 0.015090418 -0.121624944
[16,] -0.095135683 0.015090418
[17,] -0.118154710 -0.095135683
[18,] 0.092235179 -0.118154710
[19,] -0.280194970 0.092235179
[20,] 0.029765466 -0.280194970
[21,] -0.023919179 0.029765466
[22,] 0.047140429 -0.023919179
[23,] -0.006873683 0.047140429
[24,] 0.041191970 -0.006873683
[25,] -0.058438376 0.041191970
[26,] -0.011273785 -0.058438376
[27,] 0.046507652 -0.011273785
[28,] 0.092210121 0.046507652
[29,] 0.167569052 0.092210121
[30,] -0.012832123 0.167569052
[31,] -0.175243794 -0.012832123
[32,] -0.303954805 -0.175243794
[33,] 0.036117055 -0.303954805
[34,] 0.002209916 0.036117055
[35,] -0.028651829 0.002209916
[36,] -0.087360570 -0.028651829
[37,] -0.157280775 -0.087360570
[38,] -0.068231663 -0.157280775
[39,] 0.078966426 -0.068231663
[40,] 0.093267102 0.078966426
[41,] 0.368626034 0.093267102
[42,] -0.111279880 0.368626034
[43,] -0.114518897 -0.111279880
[44,] -0.024281136 -0.114518897
[45,] -0.101249780 -0.024281136
[46,] -0.142496182 -0.101249780
[47,] 0.166409289 -0.142496182
[48,] -0.182873898 0.166409289
[49,] -0.149232780 -0.182873898
[50,] -0.256374715 -0.149232780
[51,] -0.148064692 -0.256374715
[52,] -0.085105102 -0.148064692
[53,] -0.215177166 -0.085105102
[54,] -0.153794441 -0.215177166
[55,] 0.234856331 -0.153794441
[56,] 0.025375693 0.234856331
[57,] -0.119285622 0.025375693
[58,] -0.103066400 -0.119285622
[59,] 0.048881384 -0.103066400
[60,] 0.072040995 0.048881384
[61,] 0.077779620 0.072040995
[62,] 0.244953114 0.077779620
[63,] -0.087460631 0.244953114
[64,] 0.080813955 -0.087460631
[65,] 0.027868168 0.080813955
[66,] 0.053924149 0.027868168
[67,] 0.329905283 0.053924149
[68,] -0.055543839 0.329905283
[69,] 0.211373341 -0.055543839
[70,] 0.131834754 0.211373341
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.154804054 0.142229010
2 0.212551993 0.154804054
3 0.094960827 0.212551993
4 -0.086050393 0.094960827
5 -0.230731377 -0.086050393
6 0.131747116 -0.230731377
7 0.005196046 0.131747116
8 0.328638622 0.005196046
9 -0.003035816 0.328638622
10 0.064377482 -0.003035816
11 -0.179765161 0.064377482
12 0.014772493 -0.179765161
13 0.132368257 0.014772493
14 -0.121624944 0.132368257
15 0.015090418 -0.121624944
16 -0.095135683 0.015090418
17 -0.118154710 -0.095135683
18 0.092235179 -0.118154710
19 -0.280194970 0.092235179
20 0.029765466 -0.280194970
21 -0.023919179 0.029765466
22 0.047140429 -0.023919179
23 -0.006873683 0.047140429
24 0.041191970 -0.006873683
25 -0.058438376 0.041191970
26 -0.011273785 -0.058438376
27 0.046507652 -0.011273785
28 0.092210121 0.046507652
29 0.167569052 0.092210121
30 -0.012832123 0.167569052
31 -0.175243794 -0.012832123
32 -0.303954805 -0.175243794
33 0.036117055 -0.303954805
34 0.002209916 0.036117055
35 -0.028651829 0.002209916
36 -0.087360570 -0.028651829
37 -0.157280775 -0.087360570
38 -0.068231663 -0.157280775
39 0.078966426 -0.068231663
40 0.093267102 0.078966426
41 0.368626034 0.093267102
42 -0.111279880 0.368626034
43 -0.114518897 -0.111279880
44 -0.024281136 -0.114518897
45 -0.101249780 -0.024281136
46 -0.142496182 -0.101249780
47 0.166409289 -0.142496182
48 -0.182873898 0.166409289
49 -0.149232780 -0.182873898
50 -0.256374715 -0.149232780
51 -0.148064692 -0.256374715
52 -0.085105102 -0.148064692
53 -0.215177166 -0.085105102
54 -0.153794441 -0.215177166
55 0.234856331 -0.153794441
56 0.025375693 0.234856331
57 -0.119285622 0.025375693
58 -0.103066400 -0.119285622
59 0.048881384 -0.103066400
60 0.072040995 0.048881384
61 0.077779620 0.072040995
62 0.244953114 0.077779620
63 -0.087460631 0.244953114
64 0.080813955 -0.087460631
65 0.027868168 0.080813955
66 0.053924149 0.027868168
67 0.329905283 0.053924149
68 -0.055543839 0.329905283
69 0.211373341 -0.055543839
70 0.131834754 0.211373341
> 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/7ms4x1258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/838051258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dea71258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102dw81258627174.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u2dk1258627174.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/12wu0m1258627175.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/13f7df1258627175.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/14zepd1258627175.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/15tg331258627175.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/16tlec1258627175.tab")
+ }
>
> system("convert tmp/156s31258627174.ps tmp/156s31258627174.png")
> system("convert tmp/2qi101258627174.ps tmp/2qi101258627174.png")
> system("convert tmp/3e5gs1258627174.ps tmp/3e5gs1258627174.png")
> system("convert tmp/4ddkh1258627174.ps tmp/4ddkh1258627174.png")
> system("convert tmp/5b4ed1258627174.ps tmp/5b4ed1258627174.png")
> system("convert tmp/6n67k1258627174.ps tmp/6n67k1258627174.png")
> system("convert tmp/7ms4x1258627174.ps tmp/7ms4x1258627174.png")
> system("convert tmp/838051258627174.ps tmp/838051258627174.png")
> system("convert tmp/9dea71258627174.ps tmp/9dea71258627174.png")
> system("convert tmp/102dw81258627174.ps tmp/102dw81258627174.png")
>
>
> proc.time()
user system elapsed
2.526 1.569 4.846