R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(31/01/2006
+ ,-1
+ ,-3
+ ,24
+ ,6
+ ,17
+ ,28/02/2006
+ ,-2
+ ,-4
+ ,24
+ ,6
+ ,13
+ ,31/03/2006
+ ,-5
+ ,-7
+ ,31
+ ,5
+ ,12
+ ,30/04/2006
+ ,-4
+ ,-7
+ ,25
+ ,5
+ ,13
+ ,31/05/2006
+ ,-6
+ ,-7
+ ,28
+ ,3
+ ,10
+ ,30/06/2006
+ ,-2
+ ,-3
+ ,24
+ ,5
+ ,14
+ ,31/07/2006
+ ,-2
+ ,0
+ ,25
+ ,5
+ ,13
+ ,31/08/2006
+ ,-2
+ ,-5
+ ,16
+ ,5
+ ,10
+ ,30/09/2006
+ ,-2
+ ,-3
+ ,17
+ ,3
+ ,11
+ ,31/10/2006
+ ,2
+ ,3
+ ,11
+ ,6
+ ,12
+ ,30/11/2006
+ ,1
+ ,2
+ ,12
+ ,6
+ ,7
+ ,31/12/2006
+ ,-8
+ ,-7
+ ,39
+ ,4
+ ,11
+ ,31/01/2007
+ ,-1
+ ,-1
+ ,19
+ ,6
+ ,9
+ ,28/02/2007
+ ,1
+ ,0
+ ,14
+ ,5
+ ,13
+ ,31/03/2007
+ ,-1
+ ,-3
+ ,15
+ ,4
+ ,12
+ ,30/04/2007
+ ,2
+ ,4
+ ,7
+ ,5
+ ,5
+ ,31/05/2007
+ ,2
+ ,2
+ ,12
+ ,5
+ ,13
+ ,30/06/2007
+ ,1
+ ,3
+ ,12
+ ,4
+ ,11
+ ,31/07/2007
+ ,-1
+ ,0
+ ,14
+ ,3
+ ,8
+ ,31/08/2007
+ ,-2
+ ,-10
+ ,9
+ ,2
+ ,8
+ ,30/09/2007
+ ,-2
+ ,-10
+ ,8
+ ,3
+ ,8
+ ,31/10/2007
+ ,-1
+ ,-9
+ ,4
+ ,2
+ ,8
+ ,30/11/2007
+ ,-8
+ ,-22
+ ,7
+ ,-1
+ ,0
+ ,31/12/2007
+ ,-4
+ ,-16
+ ,3
+ ,0
+ ,3
+ ,31/01/2008
+ ,-6
+ ,-18
+ ,5
+ ,-2
+ ,0
+ ,29/02/2008
+ ,-3
+ ,-14
+ ,0
+ ,1
+ ,-1
+ ,31/03/2008
+ ,-3
+ ,-12
+ ,-2
+ ,-2
+ ,-1
+ ,30/04/2008
+ ,-7
+ ,-17
+ ,6
+ ,-2
+ ,-4
+ ,31/05/2008
+ ,-9
+ ,-23
+ ,11
+ ,-2
+ ,1
+ ,30/06/2008
+ ,-11
+ ,-28
+ ,9
+ ,-6
+ ,-1
+ ,31/07/2008
+ ,-13
+ ,-31
+ ,17
+ ,-4
+ ,0
+ ,31/08/2008
+ ,-11
+ ,-21
+ ,21
+ ,-2
+ ,-1
+ ,30/09/2008
+ ,-9
+ ,-19
+ ,21
+ ,0
+ ,6
+ ,31/10/2008
+ ,-17
+ ,-22
+ ,41
+ ,-5
+ ,0
+ ,30/11/2008
+ ,-22
+ ,-22
+ ,57
+ ,-4
+ ,-3
+ ,31/12/2008
+ ,-25
+ ,-25
+ ,65
+ ,-5
+ ,-3
+ ,31/01/2009
+ ,-20
+ ,-16
+ ,68
+ ,-1
+ ,4
+ ,28/02/2009
+ ,-24
+ ,-22
+ ,73
+ ,-2
+ ,1
+ ,31/03/2009
+ ,-24
+ ,-21
+ ,71
+ ,-4
+ ,0
+ ,30/04/2009
+ ,-22
+ ,-10
+ ,71
+ ,-1
+ ,-4
+ ,31/05/2009
+ ,-19
+ ,-7
+ ,70
+ ,1
+ ,-2
+ ,30/06/2009
+ ,-18
+ ,-5
+ ,69
+ ,1
+ ,3
+ ,31/07/2009
+ ,-17
+ ,-4
+ ,65
+ ,-2
+ ,2
+ ,31/08/2009
+ ,-11
+ ,7
+ ,57
+ ,1
+ ,5
+ ,30/09/2009
+ ,-11
+ ,6
+ ,57
+ ,1
+ ,6
+ ,31/10/2009
+ ,-12
+ ,3
+ ,57
+ ,3
+ ,6
+ ,30/11/2009
+ ,-10
+ ,10
+ ,55
+ ,3
+ ,3
+ ,31/12/2009
+ ,-15
+ ,0
+ ,65
+ ,1
+ ,4
+ ,31/01/2010
+ ,-15
+ ,-2
+ ,65
+ ,1
+ ,7
+ ,28/02/2010
+ ,-15
+ ,-1
+ ,64
+ ,0
+ ,5
+ ,31/03/2010
+ ,-13
+ ,2
+ ,60
+ ,2
+ ,6
+ ,30/04/2010
+ ,-8
+ ,8
+ ,43
+ ,2
+ ,1
+ ,31/05/2010
+ ,-13
+ ,-6
+ ,47
+ ,-1
+ ,3
+ ,30/06/2010
+ ,-9
+ ,-4
+ ,40
+ ,1
+ ,6
+ ,31/07/2010
+ ,-7
+ ,4
+ ,31
+ ,0
+ ,0
+ ,31/08/2010
+ ,-4
+ ,7
+ ,27
+ ,1
+ ,3
+ ,30/09/2010
+ ,-4
+ ,3
+ ,24
+ ,1
+ ,4
+ ,31/10/2010
+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,30/11/2010
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,31/12/2010
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,31/01/2011
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6)
+ ,dim=c(6
+ ,61)
+ ,dimnames=list(c('Maand'
+ ,'CVI'
+ ,'Econ.Sit.'
+ ,'Werkloos'
+ ,'Fin.Sit.'
+ ,'Spaarverm.')
+ ,1:61))
> y <- array(NA,dim=c(6,61),dimnames=list(c('Maand','CVI','Econ.Sit.','Werkloos','Fin.Sit.','Spaarverm.'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
CVI Maand Econ.Sit. Werkloos Fin.Sit. Spaarverm.
1 -1 0.015453639 -3 24 6 17
2 -2 0.006979063 -4 24 6 13
3 -5 0.005151213 -7 31 5 12
4 -4 0.003738784 -7 25 5 13
5 -6 0.003090728 -7 28 3 10
6 -2 0.002492522 -3 24 5 14
7 -2 0.002207663 0 25 5 13
8 -2 0.001931705 -5 16 5 10
9 -2 0.001661682 -3 17 3 11
10 2 0.001545364 3 11 6 12
11 1 0.001359558 2 12 6 7
12 -8 0.001287803 -7 39 4 11
13 -1 0.015445939 -1 19 6 9
14 1 0.006975585 0 14 5 13
15 -1 0.005148646 -3 15 4 12
16 2 0.003736921 4 7 5 5
17 2 0.003089188 2 12 5 13
18 1 0.002491281 3 12 4 11
19 -1 0.002206563 0 14 3 8
20 -2 0.001930742 -10 9 2 8
21 -2 0.001660854 -10 8 3 8
22 -1 0.001544594 -9 4 2 8
23 -8 0.001358880 -22 7 -1 0
24 -4 0.001287162 -16 3 0 3
25 -6 0.015438247 -18 5 -2 0
26 -3 0.007221116 -14 0 1 -1
27 -3 0.005146082 -12 -2 -2 -1
28 -7 0.003735060 -17 6 -2 -4
29 -9 0.003087649 -23 11 -2 1
30 -11 0.002490040 -28 9 -6 -1
31 -13 0.002205464 -31 17 -4 0
32 -11 0.001929781 -21 21 -2 -1
33 -9 0.001660027 -19 21 0 6
34 -17 0.001543825 -22 41 -5 0
35 -22 0.001358204 -22 57 -4 -3
36 -25 0.001286521 -25 65 -5 -3
37 -20 0.015430562 -16 68 -1 4
38 -24 0.006968641 -22 73 -2 1
39 -24 0.005143521 -21 71 -4 0
40 -22 0.003733201 -10 71 -1 -4
41 -19 0.003086112 -7 70 1 -2
42 -18 0.002488800 -5 69 1 3
43 -17 0.002204366 -4 65 -2 2
44 -11 0.001928820 7 57 1 5
45 -11 0.001659200 6 57 1 6
46 -12 0.001543056 3 57 3 6
47 -10 0.001357527 10 55 3 3
48 -15 0.001285880 0 65 1 4
49 -15 0.015422886 -2 65 1 7
50 -15 0.006965174 -1 64 0 5
51 -13 0.005140962 2 60 2 6
52 -8 0.003731343 8 43 2 1
53 -13 0.003084577 -6 47 -1 3
54 -9 0.002487562 -4 40 1 6
55 -7 0.002203269 4 31 0 0
56 -4 0.001927861 7 27 1 3
57 -4 0.001658375 3 24 1 4
58 -2 0.001542289 3 23 3 7
59 0 0.001356852 8 17 2 6
60 -2 0.001285240 3 16 0 6
61 -3 0.015415216 -3 15 0 6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand Econ.Sit. Werkloos Fin.Sit. Spaarverm.
0.07421 25.71004 0.25439 -0.25338 0.26876 0.21970
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.55900 -0.26930 0.02529 0.21236 0.60650
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.074207 0.108860 0.682 0.49831
Maand 25.710039 9.412789 2.731 0.00846 **
Econ.Sit. 0.254386 0.005639 45.109 < 2e-16 ***
Werkloos -0.253377 0.001832 -138.333 < 2e-16 ***
Fin.Sit. 0.268756 0.028899 9.300 7.04e-13 ***
Spaarverm. 0.219698 0.013993 15.701 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2929 on 55 degrees of freedom
Multiple R-squared: 0.9986, Adjusted R-squared: 0.9985
F-statistic: 7760 on 5 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,] 0.67953043 0.640939149 0.320469575
[2,] 0.57434509 0.851309819 0.425654909
[3,] 0.52128019 0.957439618 0.478719809
[4,] 0.51693900 0.966122009 0.483061005
[5,] 0.44003210 0.880064191 0.559967904
[6,] 0.41689121 0.833782425 0.583108788
[7,] 0.33217193 0.664343866 0.667828067
[8,] 0.28639562 0.572791241 0.713604380
[9,] 0.27050227 0.541004531 0.729497735
[10,] 0.24957773 0.499155457 0.750422271
[11,] 0.19576489 0.391529772 0.804235114
[12,] 0.48988774 0.979775486 0.510112257
[13,] 0.42280019 0.845600387 0.577199807
[14,] 0.34272720 0.685454394 0.657272803
[15,] 0.39631160 0.792623203 0.603688398
[16,] 0.36924776 0.738495511 0.630752245
[17,] 0.30315308 0.606306164 0.696846918
[18,] 0.28087947 0.561758948 0.719120526
[19,] 0.25305432 0.506108639 0.746945681
[20,] 0.20275393 0.405507861 0.797246069
[21,] 0.16383340 0.327666796 0.836166602
[22,] 0.14484193 0.289683860 0.855158070
[23,] 0.10950062 0.219001248 0.890499376
[24,] 0.10260749 0.205214987 0.897392507
[25,] 0.11807159 0.236143190 0.881928405
[26,] 0.08576074 0.171521483 0.914239258
[27,] 0.12472579 0.249451572 0.875274214
[28,] 0.14289648 0.285792955 0.857103522
[29,] 0.13314903 0.266298055 0.866850972
[30,] 0.09593790 0.191875800 0.904062100
[31,] 0.07010093 0.140201862 0.929899069
[32,] 0.12465353 0.249307068 0.875346466
[33,] 0.32390293 0.647805854 0.676097073
[34,] 0.30179743 0.603594860 0.698202570
[35,] 0.39048994 0.780979872 0.609510064
[36,] 0.32587676 0.651753516 0.674123242
[37,] 0.28538482 0.570769642 0.714615179
[38,] 0.77941458 0.441170848 0.220585424
[39,] 0.73530113 0.529397738 0.264698869
[40,] 0.66264158 0.674716833 0.337358417
[41,] 0.56888708 0.862225836 0.431112918
[42,] 0.92174913 0.156501740 0.078250870
[43,] 0.98496978 0.030060444 0.015030222
[44,] 0.99847706 0.003045872 0.001522936
> postscript(file="/var/wessaorg/rcomp/tmp/1rb3g1322128996.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2w13k1322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3zvqg1322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4pdgv1322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52nm41322128996.ps",horizontal=F,onefile=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 = 61
Frequency = 1
1 2 3 4 5 6
0.025291003 0.376350351 0.448596236 -0.255049898 -0.281652818 0.286370792
7 8 9 10 11 12
0.003609869 -0.338662364 -0.269302647 -0.338857562 0.272171966 0.063393980
13 14 15 16 17 18
0.007413369 0.093879468 -0.354160019 0.143543105 0.178271943 -0.352591147
19 20 21 22 23 24
-0.147508625 0.405318228 -0.109875506 -0.106025330 -0.470246196 0.063921829
25 26 27 28 29 30
-0.087771612 0.252491266 0.096580265 0.090899468 -0.197740660 0.097220308
31 32 33 34 35 36
0.137503196 0.296420723 -0.280812588 0.214838997 -0.336019005 -0.275245037
37 38 39 40 41 42
0.218856767 0.157465956 0.200458373 -0.489009204 0.534184233 -0.311097780
43 44 45 46 47 48
0.454285147 0.170741983 0.212362620 -0.559003034 -0.182599102 0.214691145
49 50 51 52 53 54
-0.299092222 0.118743568 -0.368232271 -0.067229762 -0.108811394 0.427521399
55 56 57 58 59 60
-0.293711909 0.008852456 0.053498128 0.606501156 0.307527725 -0.134564510
61
-0.224904814
> postscript(file="/var/wessaorg/rcomp/tmp/6zxhe1322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.025291003 NA
1 0.376350351 0.025291003
2 0.448596236 0.376350351
3 -0.255049898 0.448596236
4 -0.281652818 -0.255049898
5 0.286370792 -0.281652818
6 0.003609869 0.286370792
7 -0.338662364 0.003609869
8 -0.269302647 -0.338662364
9 -0.338857562 -0.269302647
10 0.272171966 -0.338857562
11 0.063393980 0.272171966
12 0.007413369 0.063393980
13 0.093879468 0.007413369
14 -0.354160019 0.093879468
15 0.143543105 -0.354160019
16 0.178271943 0.143543105
17 -0.352591147 0.178271943
18 -0.147508625 -0.352591147
19 0.405318228 -0.147508625
20 -0.109875506 0.405318228
21 -0.106025330 -0.109875506
22 -0.470246196 -0.106025330
23 0.063921829 -0.470246196
24 -0.087771612 0.063921829
25 0.252491266 -0.087771612
26 0.096580265 0.252491266
27 0.090899468 0.096580265
28 -0.197740660 0.090899468
29 0.097220308 -0.197740660
30 0.137503196 0.097220308
31 0.296420723 0.137503196
32 -0.280812588 0.296420723
33 0.214838997 -0.280812588
34 -0.336019005 0.214838997
35 -0.275245037 -0.336019005
36 0.218856767 -0.275245037
37 0.157465956 0.218856767
38 0.200458373 0.157465956
39 -0.489009204 0.200458373
40 0.534184233 -0.489009204
41 -0.311097780 0.534184233
42 0.454285147 -0.311097780
43 0.170741983 0.454285147
44 0.212362620 0.170741983
45 -0.559003034 0.212362620
46 -0.182599102 -0.559003034
47 0.214691145 -0.182599102
48 -0.299092222 0.214691145
49 0.118743568 -0.299092222
50 -0.368232271 0.118743568
51 -0.067229762 -0.368232271
52 -0.108811394 -0.067229762
53 0.427521399 -0.108811394
54 -0.293711909 0.427521399
55 0.008852456 -0.293711909
56 0.053498128 0.008852456
57 0.606501156 0.053498128
58 0.307527725 0.606501156
59 -0.134564510 0.307527725
60 -0.224904814 -0.134564510
61 NA -0.224904814
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.376350351 0.025291003
[2,] 0.448596236 0.376350351
[3,] -0.255049898 0.448596236
[4,] -0.281652818 -0.255049898
[5,] 0.286370792 -0.281652818
[6,] 0.003609869 0.286370792
[7,] -0.338662364 0.003609869
[8,] -0.269302647 -0.338662364
[9,] -0.338857562 -0.269302647
[10,] 0.272171966 -0.338857562
[11,] 0.063393980 0.272171966
[12,] 0.007413369 0.063393980
[13,] 0.093879468 0.007413369
[14,] -0.354160019 0.093879468
[15,] 0.143543105 -0.354160019
[16,] 0.178271943 0.143543105
[17,] -0.352591147 0.178271943
[18,] -0.147508625 -0.352591147
[19,] 0.405318228 -0.147508625
[20,] -0.109875506 0.405318228
[21,] -0.106025330 -0.109875506
[22,] -0.470246196 -0.106025330
[23,] 0.063921829 -0.470246196
[24,] -0.087771612 0.063921829
[25,] 0.252491266 -0.087771612
[26,] 0.096580265 0.252491266
[27,] 0.090899468 0.096580265
[28,] -0.197740660 0.090899468
[29,] 0.097220308 -0.197740660
[30,] 0.137503196 0.097220308
[31,] 0.296420723 0.137503196
[32,] -0.280812588 0.296420723
[33,] 0.214838997 -0.280812588
[34,] -0.336019005 0.214838997
[35,] -0.275245037 -0.336019005
[36,] 0.218856767 -0.275245037
[37,] 0.157465956 0.218856767
[38,] 0.200458373 0.157465956
[39,] -0.489009204 0.200458373
[40,] 0.534184233 -0.489009204
[41,] -0.311097780 0.534184233
[42,] 0.454285147 -0.311097780
[43,] 0.170741983 0.454285147
[44,] 0.212362620 0.170741983
[45,] -0.559003034 0.212362620
[46,] -0.182599102 -0.559003034
[47,] 0.214691145 -0.182599102
[48,] -0.299092222 0.214691145
[49,] 0.118743568 -0.299092222
[50,] -0.368232271 0.118743568
[51,] -0.067229762 -0.368232271
[52,] -0.108811394 -0.067229762
[53,] 0.427521399 -0.108811394
[54,] -0.293711909 0.427521399
[55,] 0.008852456 -0.293711909
[56,] 0.053498128 0.008852456
[57,] 0.606501156 0.053498128
[58,] 0.307527725 0.606501156
[59,] -0.134564510 0.307527725
[60,] -0.224904814 -0.134564510
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.376350351 0.025291003
2 0.448596236 0.376350351
3 -0.255049898 0.448596236
4 -0.281652818 -0.255049898
5 0.286370792 -0.281652818
6 0.003609869 0.286370792
7 -0.338662364 0.003609869
8 -0.269302647 -0.338662364
9 -0.338857562 -0.269302647
10 0.272171966 -0.338857562
11 0.063393980 0.272171966
12 0.007413369 0.063393980
13 0.093879468 0.007413369
14 -0.354160019 0.093879468
15 0.143543105 -0.354160019
16 0.178271943 0.143543105
17 -0.352591147 0.178271943
18 -0.147508625 -0.352591147
19 0.405318228 -0.147508625
20 -0.109875506 0.405318228
21 -0.106025330 -0.109875506
22 -0.470246196 -0.106025330
23 0.063921829 -0.470246196
24 -0.087771612 0.063921829
25 0.252491266 -0.087771612
26 0.096580265 0.252491266
27 0.090899468 0.096580265
28 -0.197740660 0.090899468
29 0.097220308 -0.197740660
30 0.137503196 0.097220308
31 0.296420723 0.137503196
32 -0.280812588 0.296420723
33 0.214838997 -0.280812588
34 -0.336019005 0.214838997
35 -0.275245037 -0.336019005
36 0.218856767 -0.275245037
37 0.157465956 0.218856767
38 0.200458373 0.157465956
39 -0.489009204 0.200458373
40 0.534184233 -0.489009204
41 -0.311097780 0.534184233
42 0.454285147 -0.311097780
43 0.170741983 0.454285147
44 0.212362620 0.170741983
45 -0.559003034 0.212362620
46 -0.182599102 -0.559003034
47 0.214691145 -0.182599102
48 -0.299092222 0.214691145
49 0.118743568 -0.299092222
50 -0.368232271 0.118743568
51 -0.067229762 -0.368232271
52 -0.108811394 -0.067229762
53 0.427521399 -0.108811394
54 -0.293711909 0.427521399
55 0.008852456 -0.293711909
56 0.053498128 0.008852456
57 0.606501156 0.053498128
58 0.307527725 0.606501156
59 -0.134564510 0.307527725
60 -0.224904814 -0.134564510
> 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/wessaorg/rcomp/tmp/70qu31322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cg431322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9s8ko1322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/109o881322128996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11jzp91322128996.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/wessaorg/rcomp/tmp/12c65e1322128996.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/wessaorg/rcomp/tmp/13urjt1322128996.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/wessaorg/rcomp/tmp/14p08p1322128996.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/wessaorg/rcomp/tmp/15q3s21322128996.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/wessaorg/rcomp/tmp/16jx6c1322128996.tab")
+ }
>
> try(system("convert tmp/1rb3g1322128996.ps tmp/1rb3g1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w13k1322128996.ps tmp/2w13k1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zvqg1322128996.ps tmp/3zvqg1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pdgv1322128996.ps tmp/4pdgv1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/52nm41322128996.ps tmp/52nm41322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zxhe1322128996.ps tmp/6zxhe1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/70qu31322128996.ps tmp/70qu31322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cg431322128996.ps tmp/8cg431322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/9s8ko1322128996.ps tmp/9s8ko1322128996.png",intern=TRUE))
character(0)
> try(system("convert tmp/109o881322128996.ps tmp/109o881322128996.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.270 0.443 3.744