R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.3,7.9,7.6,9.1,7.5,9.4,7.6,9.4,7.9,9.1,7.9,9,8.1,9.3,8.2,9.9,8,9.8,7.5,9.3,6.8,8.3,6.5,8,6.6,8.5,7.6,10.4,8,11.1,8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3),dim=c(2,73),dimnames=list(c('WGM','WGV'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('WGM','WGV'),1:73))
> 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
WGM WGV M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.3 7.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 9.1 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 9.4 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 9.4 0 0 0 1 0 0 0 0 0 0 0 4
5 7.9 9.1 0 0 0 0 1 0 0 0 0 0 0 5
6 7.9 9.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.1 9.3 0 0 0 0 0 0 1 0 0 0 0 7
8 8.2 9.9 0 0 0 0 0 0 0 1 0 0 0 8
9 8.0 9.8 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 9.3 0 0 0 0 0 0 0 0 0 1 0 10
11 6.8 8.3 0 0 0 0 0 0 0 0 0 0 1 11
12 6.5 8.0 0 0 0 0 0 0 0 0 0 0 0 12
13 6.6 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.6 10.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.0 11.1 0 0 1 0 0 0 0 0 0 0 0 15
16 8.1 10.9 0 0 0 1 0 0 0 0 0 0 0 16
17 7.7 10.0 0 0 0 0 1 0 0 0 0 0 0 17
18 7.5 9.2 0 0 0 0 0 1 0 0 0 0 0 18
19 7.6 9.2 0 0 0 0 0 0 1 0 0 0 0 19
20 7.8 9.5 0 0 0 0 0 0 0 1 0 0 0 20
21 7.8 9.6 0 0 0 0 0 0 0 0 1 0 0 21
22 7.8 9.5 0 0 0 0 0 0 0 0 0 1 0 22
23 7.5 9.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.5 8.9 0 0 0 0 0 0 0 0 0 0 0 24
25 7.1 9.0 1 0 0 0 0 0 0 0 0 0 0 25
26 7.5 10.1 0 1 0 0 0 0 0 0 0 0 0 26
27 7.5 10.3 0 0 1 0 0 0 0 0 0 0 0 27
28 7.6 10.2 0 0 0 1 0 0 0 0 0 0 0 28
29 7.7 9.6 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 9.2 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 9.3 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 9.4 0 0 0 0 0 0 0 1 0 0 0 32
33 8.2 9.4 0 0 0 0 0 0 0 0 1 0 0 33
34 8.2 9.2 0 0 0 0 0 0 0 0 0 1 0 34
35 8.2 9.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 9.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.3 9.0 1 0 0 0 0 0 0 0 0 0 0 37
38 6.9 9.8 0 1 0 0 0 0 0 0 0 0 0 38
39 6.6 10.0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.7 9.8 0 0 0 1 0 0 0 0 0 0 0 40
41 6.9 9.3 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 9.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 9.0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 9.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.1 9.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 9.1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 9.2 0 0 0 0 0 0 0 0 0 0 1 47
48 6.8 8.8 0 0 0 0 0 0 0 0 0 0 0 48
49 6.4 8.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.7 8.4 0 1 0 0 0 0 0 0 0 0 0 50
51 6.6 8.1 0 0 1 0 0 0 0 0 0 0 0 51
52 6.4 7.7 0 0 0 1 0 0 0 0 0 0 0 52
53 6.3 7.9 0 0 0 0 1 0 0 0 0 0 0 53
54 6.2 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 6.5 8.0 0 0 0 0 0 0 1 0 0 0 0 55
56 6.8 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 6.8 7.6 0 0 0 0 0 0 0 0 1 0 0 57
58 6.4 7.1 0 0 0 0 0 0 0 0 0 1 0 58
59 6.1 6.8 0 0 0 0 0 0 0 0 0 0 1 59
60 5.8 6.5 0 0 0 0 0 0 0 0 0 0 0 60
61 6.1 6.9 1 0 0 0 0 0 0 0 0 0 0 61
62 7.2 8.2 0 1 0 0 0 0 0 0 0 0 0 62
63 7.3 8.7 0 0 1 0 0 0 0 0 0 0 0 63
64 6.9 8.3 0 0 0 1 0 0 0 0 0 0 0 64
65 6.1 7.9 0 0 0 0 1 0 0 0 0 0 0 65
66 5.8 7.5 0 0 0 0 0 1 0 0 0 0 0 66
67 6.2 7.8 0 0 0 0 0 0 1 0 0 0 0 67
68 7.1 8.3 0 0 0 0 0 0 0 1 0 0 0 68
69 7.7 8.4 0 0 0 0 0 0 0 0 1 0 0 69
70 7.9 8.2 0 0 0 0 0 0 0 0 0 1 0 70
71 7.7 7.7 0 0 0 0 0 0 0 0 0 0 1 71
72 7.4 7.2 0 0 0 0 0 0 0 0 0 0 0 72
73 7.5 7.3 1 0 0 0 0 0 0 0 0 0 0 73
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WGV M1 M2 M3 M4
3.779336 0.421916 -0.133197 -0.315250 -0.423012 -0.360181
M5 M6 M7 M8 M9 M10
-0.296300 -0.234246 -0.069086 0.130184 0.215664 0.175892
M11 t
0.109042 -0.004749
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.79027 -0.33155 -0.05895 0.30598 1.12055
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.779336 0.891344 4.240 7.98e-05 ***
WGV 0.421916 0.093426 4.516 3.07e-05 ***
M1 -0.133197 0.267267 -0.498 0.620
M2 -0.315250 0.293183 -1.075 0.287
M3 -0.423012 0.302757 -1.397 0.168
M4 -0.360181 0.295994 -1.217 0.229
M5 -0.296300 0.285283 -1.039 0.303
M6 -0.234246 0.279891 -0.837 0.406
M7 -0.069086 0.282155 -0.245 0.807
M8 0.130184 0.287996 0.452 0.653
M9 0.215664 0.287764 0.749 0.457
M10 0.175892 0.282786 0.622 0.536
M11 0.109042 0.277947 0.392 0.696
t -0.004749 0.003732 -1.273 0.208
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4796 on 59 degrees of freedom
Multiple R-squared: 0.5475, Adjusted R-squared: 0.4478
F-statistic: 5.492 on 13 and 59 DF, p-value: 2.188e-06
> 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,] 0.010856508 0.021713017 0.9891435
[2,] 0.026938251 0.053876503 0.9730617
[3,] 0.015643355 0.031286710 0.9843566
[4,] 0.012319539 0.024639077 0.9876805
[5,] 0.007993089 0.015986178 0.9920069
[6,] 0.013168455 0.026336911 0.9868315
[7,] 0.023124400 0.046248800 0.9768756
[8,] 0.049998787 0.099997575 0.9500012
[9,] 0.027989017 0.055978034 0.9720110
[10,] 0.014455924 0.028911848 0.9855441
[11,] 0.007081067 0.014162134 0.9929189
[12,] 0.003350712 0.006701425 0.9966493
[13,] 0.002055069 0.004110139 0.9979449
[14,] 0.001680043 0.003360087 0.9983200
[15,] 0.001613699 0.003227398 0.9983863
[16,] 0.001932157 0.003864314 0.9980678
[17,] 0.003268914 0.006537827 0.9967311
[18,] 0.010619071 0.021238142 0.9893809
[19,] 0.090066674 0.180133347 0.9099333
[20,] 0.227486550 0.454973100 0.7725134
[21,] 0.219400352 0.438800704 0.7805996
[22,] 0.260183803 0.520367606 0.7398162
[23,] 0.421429995 0.842859991 0.5785700
[24,] 0.480168876 0.960337752 0.5198311
[25,] 0.478762681 0.957525362 0.5212373
[26,] 0.591469486 0.817061027 0.4085305
[27,] 0.710962949 0.578074103 0.2890371
[28,] 0.705404768 0.589190464 0.2945952
[29,] 0.632537259 0.734925482 0.3674627
[30,] 0.585242499 0.829515001 0.4147575
[31,] 0.529137055 0.941725889 0.4708629
[32,] 0.476244669 0.952489339 0.5237553
[33,] 0.741394605 0.517210790 0.2586054
[34,] 0.812475668 0.375048665 0.1875243
[35,] 0.767253433 0.465493135 0.2327466
[36,] 0.753463937 0.493072126 0.2465361
[37,] 0.692967896 0.614064208 0.3070321
[38,] 0.573465736 0.853068527 0.4265343
[39,] 0.484324858 0.968649716 0.5156751
[40,] 0.744724299 0.510551403 0.2552757
> postscript(file="/var/www/html/rcomp/tmp/1mfku1258737966.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ruka1258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ktbv1258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nah01258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kjmt1258737966.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 = 73
Frequency = 1
1 2 3 4 5 6
0.32547505 0.30597798 0.19191412 0.23383238 0.60127554 0.58616187
7 8 9 10 11 12
0.49917591 0.15150541 -0.08703354 -0.33155461 -0.53803991 -0.59767464
13 14 15 16 17 18
-0.57068678 -0.18552487 0.03164498 0.15794638 0.07853899 0.15876633
19 20 21 22 23 24
0.09835510 -0.02274069 -0.14566279 -0.05895015 -0.11858488 0.07958880
25 26 27 28 29 30
-0.22465704 -0.10196254 -0.07383482 0.01027501 0.30429290 0.41575394
31 32 33 34 35 36
0.41315114 0.37643850 0.39570797 0.52461218 0.68059430 0.49438484
37 38 39 40 41 42
0.03233057 -0.51840020 -0.79027249 -0.66397108 -0.31214477 -0.14287530
43 44 45 46 47 48
-0.20328653 -0.33999917 -0.52072970 -0.67620863 -0.54680124 -0.46424440
49 50 51 52 53 54
-0.51534080 -0.07073056 0.06835502 -0.02096043 -0.26447513 -0.42178038
55 56 57 58 59 60
-0.32438319 -0.17671268 -0.13086848 -0.27538955 -0.37721586 -0.43685060
61 62 63 64 65 66
-0.16767116 0.57064019 0.57219319 0.28287774 -0.40748752 -0.59602647
67 68 69 70 71 72
-0.48301243 0.01150864 0.48858654 0.81749075 0.90004759 0.92479600
73
1.12055016
> postscript(file="/var/www/html/rcomp/tmp/6og6q1258737966.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 0.32547505 NA
1 0.30597798 0.32547505
2 0.19191412 0.30597798
3 0.23383238 0.19191412
4 0.60127554 0.23383238
5 0.58616187 0.60127554
6 0.49917591 0.58616187
7 0.15150541 0.49917591
8 -0.08703354 0.15150541
9 -0.33155461 -0.08703354
10 -0.53803991 -0.33155461
11 -0.59767464 -0.53803991
12 -0.57068678 -0.59767464
13 -0.18552487 -0.57068678
14 0.03164498 -0.18552487
15 0.15794638 0.03164498
16 0.07853899 0.15794638
17 0.15876633 0.07853899
18 0.09835510 0.15876633
19 -0.02274069 0.09835510
20 -0.14566279 -0.02274069
21 -0.05895015 -0.14566279
22 -0.11858488 -0.05895015
23 0.07958880 -0.11858488
24 -0.22465704 0.07958880
25 -0.10196254 -0.22465704
26 -0.07383482 -0.10196254
27 0.01027501 -0.07383482
28 0.30429290 0.01027501
29 0.41575394 0.30429290
30 0.41315114 0.41575394
31 0.37643850 0.41315114
32 0.39570797 0.37643850
33 0.52461218 0.39570797
34 0.68059430 0.52461218
35 0.49438484 0.68059430
36 0.03233057 0.49438484
37 -0.51840020 0.03233057
38 -0.79027249 -0.51840020
39 -0.66397108 -0.79027249
40 -0.31214477 -0.66397108
41 -0.14287530 -0.31214477
42 -0.20328653 -0.14287530
43 -0.33999917 -0.20328653
44 -0.52072970 -0.33999917
45 -0.67620863 -0.52072970
46 -0.54680124 -0.67620863
47 -0.46424440 -0.54680124
48 -0.51534080 -0.46424440
49 -0.07073056 -0.51534080
50 0.06835502 -0.07073056
51 -0.02096043 0.06835502
52 -0.26447513 -0.02096043
53 -0.42178038 -0.26447513
54 -0.32438319 -0.42178038
55 -0.17671268 -0.32438319
56 -0.13086848 -0.17671268
57 -0.27538955 -0.13086848
58 -0.37721586 -0.27538955
59 -0.43685060 -0.37721586
60 -0.16767116 -0.43685060
61 0.57064019 -0.16767116
62 0.57219319 0.57064019
63 0.28287774 0.57219319
64 -0.40748752 0.28287774
65 -0.59602647 -0.40748752
66 -0.48301243 -0.59602647
67 0.01150864 -0.48301243
68 0.48858654 0.01150864
69 0.81749075 0.48858654
70 0.90004759 0.81749075
71 0.92479600 0.90004759
72 1.12055016 0.92479600
73 NA 1.12055016
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.30597798 0.32547505
[2,] 0.19191412 0.30597798
[3,] 0.23383238 0.19191412
[4,] 0.60127554 0.23383238
[5,] 0.58616187 0.60127554
[6,] 0.49917591 0.58616187
[7,] 0.15150541 0.49917591
[8,] -0.08703354 0.15150541
[9,] -0.33155461 -0.08703354
[10,] -0.53803991 -0.33155461
[11,] -0.59767464 -0.53803991
[12,] -0.57068678 -0.59767464
[13,] -0.18552487 -0.57068678
[14,] 0.03164498 -0.18552487
[15,] 0.15794638 0.03164498
[16,] 0.07853899 0.15794638
[17,] 0.15876633 0.07853899
[18,] 0.09835510 0.15876633
[19,] -0.02274069 0.09835510
[20,] -0.14566279 -0.02274069
[21,] -0.05895015 -0.14566279
[22,] -0.11858488 -0.05895015
[23,] 0.07958880 -0.11858488
[24,] -0.22465704 0.07958880
[25,] -0.10196254 -0.22465704
[26,] -0.07383482 -0.10196254
[27,] 0.01027501 -0.07383482
[28,] 0.30429290 0.01027501
[29,] 0.41575394 0.30429290
[30,] 0.41315114 0.41575394
[31,] 0.37643850 0.41315114
[32,] 0.39570797 0.37643850
[33,] 0.52461218 0.39570797
[34,] 0.68059430 0.52461218
[35,] 0.49438484 0.68059430
[36,] 0.03233057 0.49438484
[37,] -0.51840020 0.03233057
[38,] -0.79027249 -0.51840020
[39,] -0.66397108 -0.79027249
[40,] -0.31214477 -0.66397108
[41,] -0.14287530 -0.31214477
[42,] -0.20328653 -0.14287530
[43,] -0.33999917 -0.20328653
[44,] -0.52072970 -0.33999917
[45,] -0.67620863 -0.52072970
[46,] -0.54680124 -0.67620863
[47,] -0.46424440 -0.54680124
[48,] -0.51534080 -0.46424440
[49,] -0.07073056 -0.51534080
[50,] 0.06835502 -0.07073056
[51,] -0.02096043 0.06835502
[52,] -0.26447513 -0.02096043
[53,] -0.42178038 -0.26447513
[54,] -0.32438319 -0.42178038
[55,] -0.17671268 -0.32438319
[56,] -0.13086848 -0.17671268
[57,] -0.27538955 -0.13086848
[58,] -0.37721586 -0.27538955
[59,] -0.43685060 -0.37721586
[60,] -0.16767116 -0.43685060
[61,] 0.57064019 -0.16767116
[62,] 0.57219319 0.57064019
[63,] 0.28287774 0.57219319
[64,] -0.40748752 0.28287774
[65,] -0.59602647 -0.40748752
[66,] -0.48301243 -0.59602647
[67,] 0.01150864 -0.48301243
[68,] 0.48858654 0.01150864
[69,] 0.81749075 0.48858654
[70,] 0.90004759 0.81749075
[71,] 0.92479600 0.90004759
[72,] 1.12055016 0.92479600
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.30597798 0.32547505
2 0.19191412 0.30597798
3 0.23383238 0.19191412
4 0.60127554 0.23383238
5 0.58616187 0.60127554
6 0.49917591 0.58616187
7 0.15150541 0.49917591
8 -0.08703354 0.15150541
9 -0.33155461 -0.08703354
10 -0.53803991 -0.33155461
11 -0.59767464 -0.53803991
12 -0.57068678 -0.59767464
13 -0.18552487 -0.57068678
14 0.03164498 -0.18552487
15 0.15794638 0.03164498
16 0.07853899 0.15794638
17 0.15876633 0.07853899
18 0.09835510 0.15876633
19 -0.02274069 0.09835510
20 -0.14566279 -0.02274069
21 -0.05895015 -0.14566279
22 -0.11858488 -0.05895015
23 0.07958880 -0.11858488
24 -0.22465704 0.07958880
25 -0.10196254 -0.22465704
26 -0.07383482 -0.10196254
27 0.01027501 -0.07383482
28 0.30429290 0.01027501
29 0.41575394 0.30429290
30 0.41315114 0.41575394
31 0.37643850 0.41315114
32 0.39570797 0.37643850
33 0.52461218 0.39570797
34 0.68059430 0.52461218
35 0.49438484 0.68059430
36 0.03233057 0.49438484
37 -0.51840020 0.03233057
38 -0.79027249 -0.51840020
39 -0.66397108 -0.79027249
40 -0.31214477 -0.66397108
41 -0.14287530 -0.31214477
42 -0.20328653 -0.14287530
43 -0.33999917 -0.20328653
44 -0.52072970 -0.33999917
45 -0.67620863 -0.52072970
46 -0.54680124 -0.67620863
47 -0.46424440 -0.54680124
48 -0.51534080 -0.46424440
49 -0.07073056 -0.51534080
50 0.06835502 -0.07073056
51 -0.02096043 0.06835502
52 -0.26447513 -0.02096043
53 -0.42178038 -0.26447513
54 -0.32438319 -0.42178038
55 -0.17671268 -0.32438319
56 -0.13086848 -0.17671268
57 -0.27538955 -0.13086848
58 -0.37721586 -0.27538955
59 -0.43685060 -0.37721586
60 -0.16767116 -0.43685060
61 0.57064019 -0.16767116
62 0.57219319 0.57064019
63 0.28287774 0.57219319
64 -0.40748752 0.28287774
65 -0.59602647 -0.40748752
66 -0.48301243 -0.59602647
67 0.01150864 -0.48301243
68 0.48858654 0.01150864
69 0.81749075 0.48858654
70 0.90004759 0.81749075
71 0.92479600 0.90004759
72 1.12055016 0.92479600
> 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/7duw01258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f5yz1258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ypn21258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106o131258737966.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kspw1258737966.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/125rku1258737967.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/13qeow1258737967.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/14lobm1258737967.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/15wxcu1258737967.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/16h3hm1258737967.tab")
+ }
>
> system("convert tmp/1mfku1258737966.ps tmp/1mfku1258737966.png")
> system("convert tmp/2ruka1258737966.ps tmp/2ruka1258737966.png")
> system("convert tmp/3ktbv1258737966.ps tmp/3ktbv1258737966.png")
> system("convert tmp/4nah01258737966.ps tmp/4nah01258737966.png")
> system("convert tmp/5kjmt1258737966.ps tmp/5kjmt1258737966.png")
> system("convert tmp/6og6q1258737966.ps tmp/6og6q1258737966.png")
> system("convert tmp/7duw01258737966.ps tmp/7duw01258737966.png")
> system("convert tmp/8f5yz1258737966.ps tmp/8f5yz1258737966.png")
> system("convert tmp/9ypn21258737966.ps tmp/9ypn21258737966.png")
> system("convert tmp/106o131258737966.ps tmp/106o131258737966.png")
>
>
> proc.time()
user system elapsed
2.572 1.606 2.959