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(6.3,2.7,6.1,2.5,6.1,2.2,6.3,2.9,6.3,3.1,6,3,6.2,2.8,6.4,2.5,6.8,1.9,7.5,1.9,7.5,1.8,7.6,2,7.6,2.6,7.4,2.5,7.3,2.5,7.1,1.6,6.9,1.4,6.8,0.8,7.5,1.1,7.6,1.3,7.8,1.2,8,1.3,8.1,1.1,8.2,1.3,8.3,1.2,8.2,1.6,8,1.7,7.9,1.5,7.6,0.9,7.6,1.5,8.3,1.4,8.4,1.6,8.4,1.7,8.4,1.4,8.4,1.8,8.6,1.7,8.9,1.4,8.8,1.2,8.3,1,7.5,1.7,7.2,2.4,7.4,2,8.8,2.1,9.3,2,9.3,1.8,8.7,2.7,8.2,2.3,8.3,1.9,8.5,2,8.6,2.3,8.5,2.8,8.2,2.4,8.1,2.3,7.9,2.7,8.6,2.7,8.7,2.9,8.7,3,8.5,2.2,8.4,2.3,8.5,2.8,8.7,2.8),dim=c(2,61),dimnames=list(c('Werkl','Infl'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werkl','Infl'),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 = '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
Werkl Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 2.7 1 0 0 0 0 0 0 0 0 0 0 1
2 6.1 2.5 0 1 0 0 0 0 0 0 0 0 0 2
3 6.1 2.2 0 0 1 0 0 0 0 0 0 0 0 3
4 6.3 2.9 0 0 0 1 0 0 0 0 0 0 0 4
5 6.3 3.1 0 0 0 0 1 0 0 0 0 0 0 5
6 6.0 3.0 0 0 0 0 0 1 0 0 0 0 0 6
7 6.2 2.8 0 0 0 0 0 0 1 0 0 0 0 7
8 6.4 2.5 0 0 0 0 0 0 0 1 0 0 0 8
9 6.8 1.9 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 1.9 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 1.8 0 0 0 0 0 0 0 0 0 0 1 11
12 7.6 2.0 0 0 0 0 0 0 0 0 0 0 0 12
13 7.6 2.6 1 0 0 0 0 0 0 0 0 0 0 13
14 7.4 2.5 0 1 0 0 0 0 0 0 0 0 0 14
15 7.3 2.5 0 0 1 0 0 0 0 0 0 0 0 15
16 7.1 1.6 0 0 0 1 0 0 0 0 0 0 0 16
17 6.9 1.4 0 0 0 0 1 0 0 0 0 0 0 17
18 6.8 0.8 0 0 0 0 0 1 0 0 0 0 0 18
19 7.5 1.1 0 0 0 0 0 0 1 0 0 0 0 19
20 7.6 1.3 0 0 0 0 0 0 0 1 0 0 0 20
21 7.8 1.2 0 0 0 0 0 0 0 0 1 0 0 21
22 8.0 1.3 0 0 0 0 0 0 0 0 0 1 0 22
23 8.1 1.1 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 1.3 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 1.2 1 0 0 0 0 0 0 0 0 0 0 25
26 8.2 1.6 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 1.7 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 1.5 0 0 0 1 0 0 0 0 0 0 0 28
29 7.6 0.9 0 0 0 0 1 0 0 0 0 0 0 29
30 7.6 1.5 0 0 0 0 0 1 0 0 0 0 0 30
31 8.3 1.4 0 0 0 0 0 0 1 0 0 0 0 31
32 8.4 1.6 0 0 0 0 0 0 0 1 0 0 0 32
33 8.4 1.7 0 0 0 0 0 0 0 0 1 0 0 33
34 8.4 1.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.4 1.8 0 0 0 0 0 0 0 0 0 0 1 35
36 8.6 1.7 0 0 0 0 0 0 0 0 0 0 0 36
37 8.9 1.4 1 0 0 0 0 0 0 0 0 0 0 37
38 8.8 1.2 0 1 0 0 0 0 0 0 0 0 0 38
39 8.3 1.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.5 1.7 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 2.4 0 0 0 0 1 0 0 0 0 0 0 41
42 7.4 2.0 0 0 0 0 0 1 0 0 0 0 0 42
43 8.8 2.1 0 0 0 0 0 0 1 0 0 0 0 43
44 9.3 2.0 0 0 0 0 0 0 0 1 0 0 0 44
45 9.3 1.8 0 0 0 0 0 0 0 0 1 0 0 45
46 8.7 2.7 0 0 0 0 0 0 0 0 0 1 0 46
47 8.2 2.3 0 0 0 0 0 0 0 0 0 0 1 47
48 8.3 1.9 0 0 0 0 0 0 0 0 0 0 0 48
49 8.5 2.0 1 0 0 0 0 0 0 0 0 0 0 49
50 8.6 2.3 0 1 0 0 0 0 0 0 0 0 0 50
51 8.5 2.8 0 0 1 0 0 0 0 0 0 0 0 51
52 8.2 2.4 0 0 0 1 0 0 0 0 0 0 0 52
53 8.1 2.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.9 2.7 0 0 0 0 0 1 0 0 0 0 0 54
55 8.6 2.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.7 2.9 0 0 0 0 0 0 0 1 0 0 0 56
57 8.7 3.0 0 0 0 0 0 0 0 0 1 0 0 57
58 8.5 2.2 0 0 0 0 0 0 0 0 0 1 0 58
59 8.4 2.3 0 0 0 0 0 0 0 0 0 0 1 59
60 8.5 2.8 0 0 0 0 0 0 0 0 0 0 0 60
61 8.7 2.8 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl M1 M2 M3 M4
7.621779 -0.406930 0.077400 0.003573 -0.207391 -0.494631
M5 M6 M7 M8 M9 M10
-0.713733 -0.840973 -0.131936 0.045239 0.069167 0.041926
M11 t
-0.113453 0.039102
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.5862 -0.2852 0.0555 0.2308 0.7264
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.621779 0.230460 33.072 < 2e-16 ***
Infl -0.406930 0.076462 -5.322 2.81e-06 ***
M1 0.077400 0.219341 0.353 0.725759
M2 0.003573 0.229868 0.016 0.987666
M3 -0.207391 0.229628 -0.903 0.371047
M4 -0.494631 0.229279 -2.157 0.036124 *
M5 -0.713733 0.229031 -3.116 0.003120 **
M6 -0.840973 0.228762 -3.676 0.000607 ***
M7 -0.131936 0.228629 -0.577 0.566642
M8 0.045239 0.228600 0.198 0.843980
M9 0.069167 0.228237 0.303 0.763190
M10 0.041926 0.228170 0.184 0.854999
M11 -0.113453 0.228184 -0.497 0.621367
t 0.039102 0.002692 14.526 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3607 on 47 degrees of freedom
Multiple R-squared: 0.8596, Adjusted R-squared: 0.8208
F-statistic: 22.14 on 13 and 47 DF, p-value: 1.011e-15
> 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,] 0.01767432 0.03534864 0.9823257
[2,] 0.02016667 0.04033334 0.9798333
[3,] 0.09623674 0.19247348 0.9037633
[4,] 0.08967379 0.17934758 0.9103262
[5,] 0.07436974 0.14873947 0.9256303
[6,] 0.21865159 0.43730318 0.7813484
[7,] 0.23221613 0.46443226 0.7677839
[8,] 0.22037462 0.44074925 0.7796254
[9,] 0.14903222 0.29806444 0.8509678
[10,] 0.09931368 0.19862735 0.9006863
[11,] 0.06965699 0.13931399 0.9303430
[12,] 0.05452932 0.10905864 0.9454707
[13,] 0.04968200 0.09936400 0.9503180
[14,] 0.03352021 0.06704041 0.9664798
[15,] 0.02171942 0.04343884 0.9782806
[16,] 0.01835338 0.03670675 0.9816466
[17,] 0.02661642 0.05323285 0.9733836
[18,] 0.07032597 0.14065195 0.9296740
[19,] 0.10010279 0.20020558 0.8998972
[20,] 0.10226526 0.20453051 0.8977347
[21,] 0.08693018 0.17386035 0.9130698
[22,] 0.05658257 0.11316514 0.9434174
[23,] 0.05727093 0.11454186 0.9427291
[24,] 0.25282359 0.50564717 0.7471764
[25,] 0.50078831 0.99842337 0.4992117
[26,] 0.62942426 0.74115149 0.3705757
[27,] 0.50421314 0.99157373 0.4957869
[28,] 0.54218137 0.91563725 0.4578186
> postscript(file="/var/www/html/rcomp/tmp/1m53p1260025089.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cg8c1260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31skm1260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ejwe1260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53giu1260025089.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 = 61
Frequency = 1
1 2 3 4 5 6
-0.339569489 -0.586229857 -0.536447475 0.196542164 0.457928175 0.205373771
7 8 9 10 11 12
-0.424150841 -0.562507059 -0.469694884 0.218443717 0.294027913 0.322859520
13 14 15 16 17 18
0.450515647 0.244548284 0.316409683 -0.001688764 -0.103074775 -0.359094207
19 20 21 22 23 24
-0.285153792 -0.320044983 -0.223767781 0.005063826 0.139955017 0.168786624
25 26 27 28 29 30
0.111591713 0.209089377 0.221643782 0.288396372 -0.075761661 0.256534973
31 32 33 34 35 36
0.167703366 0.132812175 0.110475388 -0.023465027 0.255584197 0.262336787
37 38 39 40 41 42
0.323755865 0.177095497 -0.232429115 -0.499439476 -0.334588438 -0.209221858
43 44 45 46 47 48
0.483332546 0.726362338 0.581946535 0.336322185 -0.210172634 -0.425499061
49 50 51 52 53 54
-0.301307961 -0.044503301 0.230823125 0.016189704 0.055496699 0.106407321
55 56 57 58 59 60
0.058268720 0.023377529 0.001040742 -0.536364700 -0.479394493 -0.328483870
61
-0.244985775
> postscript(file="/var/www/html/rcomp/tmp/6ldvo1260025089.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.339569489 NA
1 -0.586229857 -0.339569489
2 -0.536447475 -0.586229857
3 0.196542164 -0.536447475
4 0.457928175 0.196542164
5 0.205373771 0.457928175
6 -0.424150841 0.205373771
7 -0.562507059 -0.424150841
8 -0.469694884 -0.562507059
9 0.218443717 -0.469694884
10 0.294027913 0.218443717
11 0.322859520 0.294027913
12 0.450515647 0.322859520
13 0.244548284 0.450515647
14 0.316409683 0.244548284
15 -0.001688764 0.316409683
16 -0.103074775 -0.001688764
17 -0.359094207 -0.103074775
18 -0.285153792 -0.359094207
19 -0.320044983 -0.285153792
20 -0.223767781 -0.320044983
21 0.005063826 -0.223767781
22 0.139955017 0.005063826
23 0.168786624 0.139955017
24 0.111591713 0.168786624
25 0.209089377 0.111591713
26 0.221643782 0.209089377
27 0.288396372 0.221643782
28 -0.075761661 0.288396372
29 0.256534973 -0.075761661
30 0.167703366 0.256534973
31 0.132812175 0.167703366
32 0.110475388 0.132812175
33 -0.023465027 0.110475388
34 0.255584197 -0.023465027
35 0.262336787 0.255584197
36 0.323755865 0.262336787
37 0.177095497 0.323755865
38 -0.232429115 0.177095497
39 -0.499439476 -0.232429115
40 -0.334588438 -0.499439476
41 -0.209221858 -0.334588438
42 0.483332546 -0.209221858
43 0.726362338 0.483332546
44 0.581946535 0.726362338
45 0.336322185 0.581946535
46 -0.210172634 0.336322185
47 -0.425499061 -0.210172634
48 -0.301307961 -0.425499061
49 -0.044503301 -0.301307961
50 0.230823125 -0.044503301
51 0.016189704 0.230823125
52 0.055496699 0.016189704
53 0.106407321 0.055496699
54 0.058268720 0.106407321
55 0.023377529 0.058268720
56 0.001040742 0.023377529
57 -0.536364700 0.001040742
58 -0.479394493 -0.536364700
59 -0.328483870 -0.479394493
60 -0.244985775 -0.328483870
61 NA -0.244985775
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.586229857 -0.339569489
[2,] -0.536447475 -0.586229857
[3,] 0.196542164 -0.536447475
[4,] 0.457928175 0.196542164
[5,] 0.205373771 0.457928175
[6,] -0.424150841 0.205373771
[7,] -0.562507059 -0.424150841
[8,] -0.469694884 -0.562507059
[9,] 0.218443717 -0.469694884
[10,] 0.294027913 0.218443717
[11,] 0.322859520 0.294027913
[12,] 0.450515647 0.322859520
[13,] 0.244548284 0.450515647
[14,] 0.316409683 0.244548284
[15,] -0.001688764 0.316409683
[16,] -0.103074775 -0.001688764
[17,] -0.359094207 -0.103074775
[18,] -0.285153792 -0.359094207
[19,] -0.320044983 -0.285153792
[20,] -0.223767781 -0.320044983
[21,] 0.005063826 -0.223767781
[22,] 0.139955017 0.005063826
[23,] 0.168786624 0.139955017
[24,] 0.111591713 0.168786624
[25,] 0.209089377 0.111591713
[26,] 0.221643782 0.209089377
[27,] 0.288396372 0.221643782
[28,] -0.075761661 0.288396372
[29,] 0.256534973 -0.075761661
[30,] 0.167703366 0.256534973
[31,] 0.132812175 0.167703366
[32,] 0.110475388 0.132812175
[33,] -0.023465027 0.110475388
[34,] 0.255584197 -0.023465027
[35,] 0.262336787 0.255584197
[36,] 0.323755865 0.262336787
[37,] 0.177095497 0.323755865
[38,] -0.232429115 0.177095497
[39,] -0.499439476 -0.232429115
[40,] -0.334588438 -0.499439476
[41,] -0.209221858 -0.334588438
[42,] 0.483332546 -0.209221858
[43,] 0.726362338 0.483332546
[44,] 0.581946535 0.726362338
[45,] 0.336322185 0.581946535
[46,] -0.210172634 0.336322185
[47,] -0.425499061 -0.210172634
[48,] -0.301307961 -0.425499061
[49,] -0.044503301 -0.301307961
[50,] 0.230823125 -0.044503301
[51,] 0.016189704 0.230823125
[52,] 0.055496699 0.016189704
[53,] 0.106407321 0.055496699
[54,] 0.058268720 0.106407321
[55,] 0.023377529 0.058268720
[56,] 0.001040742 0.023377529
[57,] -0.536364700 0.001040742
[58,] -0.479394493 -0.536364700
[59,] -0.328483870 -0.479394493
[60,] -0.244985775 -0.328483870
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.586229857 -0.339569489
2 -0.536447475 -0.586229857
3 0.196542164 -0.536447475
4 0.457928175 0.196542164
5 0.205373771 0.457928175
6 -0.424150841 0.205373771
7 -0.562507059 -0.424150841
8 -0.469694884 -0.562507059
9 0.218443717 -0.469694884
10 0.294027913 0.218443717
11 0.322859520 0.294027913
12 0.450515647 0.322859520
13 0.244548284 0.450515647
14 0.316409683 0.244548284
15 -0.001688764 0.316409683
16 -0.103074775 -0.001688764
17 -0.359094207 -0.103074775
18 -0.285153792 -0.359094207
19 -0.320044983 -0.285153792
20 -0.223767781 -0.320044983
21 0.005063826 -0.223767781
22 0.139955017 0.005063826
23 0.168786624 0.139955017
24 0.111591713 0.168786624
25 0.209089377 0.111591713
26 0.221643782 0.209089377
27 0.288396372 0.221643782
28 -0.075761661 0.288396372
29 0.256534973 -0.075761661
30 0.167703366 0.256534973
31 0.132812175 0.167703366
32 0.110475388 0.132812175
33 -0.023465027 0.110475388
34 0.255584197 -0.023465027
35 0.262336787 0.255584197
36 0.323755865 0.262336787
37 0.177095497 0.323755865
38 -0.232429115 0.177095497
39 -0.499439476 -0.232429115
40 -0.334588438 -0.499439476
41 -0.209221858 -0.334588438
42 0.483332546 -0.209221858
43 0.726362338 0.483332546
44 0.581946535 0.726362338
45 0.336322185 0.581946535
46 -0.210172634 0.336322185
47 -0.425499061 -0.210172634
48 -0.301307961 -0.425499061
49 -0.044503301 -0.301307961
50 0.230823125 -0.044503301
51 0.016189704 0.230823125
52 0.055496699 0.016189704
53 0.106407321 0.055496699
54 0.058268720 0.106407321
55 0.023377529 0.058268720
56 0.001040742 0.023377529
57 -0.536364700 0.001040742
58 -0.479394493 -0.536364700
59 -0.328483870 -0.479394493
60 -0.244985775 -0.328483870
> 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/7zwx41260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k1981260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9j5xe1260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pgmy1260025089.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pqu61260025089.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/12rn321260025089.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/13f9iz1260025089.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/14nm3z1260025089.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/1566hc1260025090.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/1656at1260025090.tab")
+ }
>
> system("convert tmp/1m53p1260025089.ps tmp/1m53p1260025089.png")
> system("convert tmp/2cg8c1260025089.ps tmp/2cg8c1260025089.png")
> system("convert tmp/31skm1260025089.ps tmp/31skm1260025089.png")
> system("convert tmp/4ejwe1260025089.ps tmp/4ejwe1260025089.png")
> system("convert tmp/53giu1260025089.ps tmp/53giu1260025089.png")
> system("convert tmp/6ldvo1260025089.ps tmp/6ldvo1260025089.png")
> system("convert tmp/7zwx41260025089.ps tmp/7zwx41260025089.png")
> system("convert tmp/8k1981260025089.ps tmp/8k1981260025089.png")
> system("convert tmp/9j5xe1260025089.ps tmp/9j5xe1260025089.png")
> system("convert tmp/10pgmy1260025089.ps tmp/10pgmy1260025089.png")
>
>
> proc.time()
user system elapsed
2.452 1.587 3.353