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.
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Type 'license()' or 'licence()' for distribution details.
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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
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Type 'q()' to quit R.
> x <- array(list(8.3,10,8.2,7,8,5,7.9,9,7.6,10,7.6,9,8.3,8,8.4,7,8.4,10,8.4,9,8.4,11,8.6,12,8.9,12,8.8,12,8.3,12,7.5,12,7.2,11,7.4,12,8.8,11,9.3,12,9.3,11,8.7,13,8.2,10,8.3,11,8.5,12,8.6,12,8.5,11,8.2,9,8.1,8,7.9,9,8.6,9,8.7,8,8.7,6,8.5,10,8.4,10,8.5,11,8.7,12,8.7,12,8.6,11,8.5,11,8.3,9,8,11,8.2,11,8.1,11,8.1,9,8,12,7.9,12,7.9,10,8,12,8,11,7.9,10,8,11,7.7,11,7.2,10,7.5,9,7.3,8,7,9,7,8,7,5,7.2,6,7.3,4,7.1,7,6.8,4,6.4,4,6.1,4,6.5,0,7.7,2,7.9,4,7.5,6,6.9,1,6.6,2,6.9,1),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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 = '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
Y X
1 8.3 10
2 8.2 7
3 8.0 5
4 7.9 9
5 7.6 10
6 7.6 9
7 8.3 8
8 8.4 7
9 8.4 10
10 8.4 9
11 8.4 11
12 8.6 12
13 8.9 12
14 8.8 12
15 8.3 12
16 7.5 12
17 7.2 11
18 7.4 12
19 8.8 11
20 9.3 12
21 9.3 11
22 8.7 13
23 8.2 10
24 8.3 11
25 8.5 12
26 8.6 12
27 8.5 11
28 8.2 9
29 8.1 8
30 7.9 9
31 8.6 9
32 8.7 8
33 8.7 6
34 8.5 10
35 8.4 10
36 8.5 11
37 8.7 12
38 8.7 12
39 8.6 11
40 8.5 11
41 8.3 9
42 8.0 11
43 8.2 11
44 8.1 11
45 8.1 9
46 8.0 12
47 7.9 12
48 7.9 10
49 8.0 12
50 8.0 11
51 7.9 10
52 8.0 11
53 7.7 11
54 7.2 10
55 7.5 9
56 7.3 8
57 7.0 9
58 7.0 8
59 7.0 5
60 7.2 6
61 7.3 4
62 7.1 7
63 6.8 4
64 6.4 4
65 6.1 4
66 6.5 0
67 7.7 2
68 7.9 4
69 7.5 6
70 6.9 1
71 6.6 2
72 6.9 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
6.660 0.145
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.14046 -0.37048 0.09203 0.30825 1.16953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.66044 0.18553 35.899 < 2e-16 ***
X 0.14500 0.01964 7.383 2.49e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5264 on 70 degrees of freedom
Multiple R-squared: 0.4378, Adjusted R-squared: 0.4298
F-statistic: 54.51 on 1 and 70 DF, p-value: 2.490e-10
> 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,] 0.20163493 0.40326986 0.7983651
[2,] 0.15868069 0.31736137 0.8413193
[3,] 0.12337892 0.24675783 0.8766211
[4,] 0.10027514 0.20055028 0.8997249
[5,] 0.10001857 0.20003714 0.8999814
[6,] 0.07942617 0.15885235 0.9205738
[7,] 0.05837185 0.11674370 0.9416282
[8,] 0.04997589 0.09995177 0.9500241
[9,] 0.06504022 0.13008044 0.9349598
[10,] 0.05239205 0.10478409 0.9476080
[11,] 0.03378593 0.06757186 0.9662141
[12,] 0.12712367 0.25424733 0.8728763
[13,] 0.35488719 0.70977438 0.6451128
[14,] 0.49254787 0.98509573 0.5074521
[15,] 0.52749830 0.94500340 0.4725017
[16,] 0.72385103 0.55229793 0.2761490
[17,] 0.86693571 0.26612857 0.1330643
[18,] 0.83160123 0.33679754 0.1683988
[19,] 0.78281568 0.43436863 0.2171843
[20,] 0.72583624 0.54832752 0.2741638
[21,] 0.66573157 0.66853685 0.3342684
[22,] 0.61043414 0.77913171 0.3895659
[23,] 0.55446505 0.89106991 0.4455350
[24,] 0.49380941 0.98761883 0.5061906
[25,] 0.43757323 0.87514646 0.5624268
[26,] 0.38352280 0.76704560 0.6164772
[27,] 0.39778232 0.79556464 0.6022177
[28,] 0.49074483 0.98148965 0.5092552
[29,] 0.70229408 0.59541184 0.2977059
[30,] 0.69127661 0.61744679 0.3087234
[31,] 0.66576494 0.66847012 0.3342351
[32,] 0.63941788 0.72116423 0.3605821
[33,] 0.63870387 0.72259227 0.3612961
[34,] 0.64765928 0.70468144 0.3523407
[35,] 0.67206187 0.65587626 0.3279381
[36,] 0.68231299 0.63537402 0.3176870
[37,] 0.71229824 0.57540351 0.2877018
[38,] 0.67614448 0.64771104 0.3238555
[39,] 0.64965417 0.70069167 0.3503458
[40,] 0.61624460 0.76751080 0.3837554
[41,] 0.62659023 0.74681954 0.3734098
[42,] 0.58606269 0.82787462 0.4139373
[43,] 0.54674474 0.90651052 0.4532553
[44,] 0.51779893 0.96440213 0.4822011
[45,] 0.47600838 0.95201676 0.5239916
[46,] 0.45331743 0.90663486 0.5466826
[47,] 0.44367375 0.88734750 0.5563262
[48,] 0.45742631 0.91485263 0.5425737
[49,] 0.44745728 0.89491457 0.5525427
[50,] 0.47788263 0.95576526 0.5221174
[51,] 0.45958979 0.91917958 0.5404102
[52,] 0.43905875 0.87811751 0.5609412
[53,] 0.46042395 0.92084790 0.5395761
[54,] 0.45713469 0.91426938 0.5428653
[55,] 0.40086704 0.80173409 0.5991330
[56,] 0.32767475 0.65534949 0.6723253
[57,] 0.26410873 0.52821746 0.7358913
[58,] 0.20631325 0.41262650 0.7936867
[59,] 0.15615369 0.31230737 0.8438463
[60,] 0.19785737 0.39571475 0.8021426
[61,] 0.64691558 0.70616884 0.3530844
[62,] 0.53800048 0.92399905 0.4619995
[63,] 0.62418026 0.75163948 0.3758197
> postscript(file="/var/www/html/rcomp/tmp/13klw1258722468.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bhzq1258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/356r51258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40b761258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qhkj1258722468.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 = 72
Frequency = 1
1 2 3 4 5 6
0.18950869 0.52452290 0.61453238 -0.06548657 -0.51049131 -0.36548657
7 8 9 10 11 12
0.47951817 0.72452290 0.28950869 0.43451343 0.14450395 0.19949922
13 14 15 16 17 18
0.49949922 0.39949922 -0.10050078 -0.90050078 -1.05549605 -1.00050078
19 20 21 22 23 24
0.54450395 0.89949922 1.04450395 0.15449448 0.08950869 0.04450395
25 26 27 28 29 30
0.09949922 0.19949922 0.24450395 0.23451343 0.27951817 -0.06548657
31 32 33 34 35 36
0.63451343 0.87951817 1.16952764 0.38950869 0.28950869 0.24450395
37 38 39 40 41 42
0.29949922 0.29949922 0.34450395 0.24450395 0.33451343 -0.25549605
43 44 45 46 47 48
-0.05549605 -0.15549605 0.13451343 -0.40050078 -0.50050078 -0.21049131
49 50 51 52 53 54
-0.40050078 -0.25549605 -0.21049131 -0.25549605 -0.55549605 -0.91049131
55 56 57 58 59 60
-0.46548657 -0.52048183 -0.96548657 -0.82048183 -0.38546762 -0.33047236
61 62 63 64 65 66
0.05953711 -0.57547710 -0.44046289 -0.84046289 -1.14046289 -0.16044394
67 68 69 70 71 72
0.74954659 0.65953711 -0.03047236 0.09455133 -0.35045341 0.09455133
> postscript(file="/var/www/html/rcomp/tmp/6yyru1258722468.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.18950869 NA
1 0.52452290 0.18950869
2 0.61453238 0.52452290
3 -0.06548657 0.61453238
4 -0.51049131 -0.06548657
5 -0.36548657 -0.51049131
6 0.47951817 -0.36548657
7 0.72452290 0.47951817
8 0.28950869 0.72452290
9 0.43451343 0.28950869
10 0.14450395 0.43451343
11 0.19949922 0.14450395
12 0.49949922 0.19949922
13 0.39949922 0.49949922
14 -0.10050078 0.39949922
15 -0.90050078 -0.10050078
16 -1.05549605 -0.90050078
17 -1.00050078 -1.05549605
18 0.54450395 -1.00050078
19 0.89949922 0.54450395
20 1.04450395 0.89949922
21 0.15449448 1.04450395
22 0.08950869 0.15449448
23 0.04450395 0.08950869
24 0.09949922 0.04450395
25 0.19949922 0.09949922
26 0.24450395 0.19949922
27 0.23451343 0.24450395
28 0.27951817 0.23451343
29 -0.06548657 0.27951817
30 0.63451343 -0.06548657
31 0.87951817 0.63451343
32 1.16952764 0.87951817
33 0.38950869 1.16952764
34 0.28950869 0.38950869
35 0.24450395 0.28950869
36 0.29949922 0.24450395
37 0.29949922 0.29949922
38 0.34450395 0.29949922
39 0.24450395 0.34450395
40 0.33451343 0.24450395
41 -0.25549605 0.33451343
42 -0.05549605 -0.25549605
43 -0.15549605 -0.05549605
44 0.13451343 -0.15549605
45 -0.40050078 0.13451343
46 -0.50050078 -0.40050078
47 -0.21049131 -0.50050078
48 -0.40050078 -0.21049131
49 -0.25549605 -0.40050078
50 -0.21049131 -0.25549605
51 -0.25549605 -0.21049131
52 -0.55549605 -0.25549605
53 -0.91049131 -0.55549605
54 -0.46548657 -0.91049131
55 -0.52048183 -0.46548657
56 -0.96548657 -0.52048183
57 -0.82048183 -0.96548657
58 -0.38546762 -0.82048183
59 -0.33047236 -0.38546762
60 0.05953711 -0.33047236
61 -0.57547710 0.05953711
62 -0.44046289 -0.57547710
63 -0.84046289 -0.44046289
64 -1.14046289 -0.84046289
65 -0.16044394 -1.14046289
66 0.74954659 -0.16044394
67 0.65953711 0.74954659
68 -0.03047236 0.65953711
69 0.09455133 -0.03047236
70 -0.35045341 0.09455133
71 0.09455133 -0.35045341
72 NA 0.09455133
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.52452290 0.18950869
[2,] 0.61453238 0.52452290
[3,] -0.06548657 0.61453238
[4,] -0.51049131 -0.06548657
[5,] -0.36548657 -0.51049131
[6,] 0.47951817 -0.36548657
[7,] 0.72452290 0.47951817
[8,] 0.28950869 0.72452290
[9,] 0.43451343 0.28950869
[10,] 0.14450395 0.43451343
[11,] 0.19949922 0.14450395
[12,] 0.49949922 0.19949922
[13,] 0.39949922 0.49949922
[14,] -0.10050078 0.39949922
[15,] -0.90050078 -0.10050078
[16,] -1.05549605 -0.90050078
[17,] -1.00050078 -1.05549605
[18,] 0.54450395 -1.00050078
[19,] 0.89949922 0.54450395
[20,] 1.04450395 0.89949922
[21,] 0.15449448 1.04450395
[22,] 0.08950869 0.15449448
[23,] 0.04450395 0.08950869
[24,] 0.09949922 0.04450395
[25,] 0.19949922 0.09949922
[26,] 0.24450395 0.19949922
[27,] 0.23451343 0.24450395
[28,] 0.27951817 0.23451343
[29,] -0.06548657 0.27951817
[30,] 0.63451343 -0.06548657
[31,] 0.87951817 0.63451343
[32,] 1.16952764 0.87951817
[33,] 0.38950869 1.16952764
[34,] 0.28950869 0.38950869
[35,] 0.24450395 0.28950869
[36,] 0.29949922 0.24450395
[37,] 0.29949922 0.29949922
[38,] 0.34450395 0.29949922
[39,] 0.24450395 0.34450395
[40,] 0.33451343 0.24450395
[41,] -0.25549605 0.33451343
[42,] -0.05549605 -0.25549605
[43,] -0.15549605 -0.05549605
[44,] 0.13451343 -0.15549605
[45,] -0.40050078 0.13451343
[46,] -0.50050078 -0.40050078
[47,] -0.21049131 -0.50050078
[48,] -0.40050078 -0.21049131
[49,] -0.25549605 -0.40050078
[50,] -0.21049131 -0.25549605
[51,] -0.25549605 -0.21049131
[52,] -0.55549605 -0.25549605
[53,] -0.91049131 -0.55549605
[54,] -0.46548657 -0.91049131
[55,] -0.52048183 -0.46548657
[56,] -0.96548657 -0.52048183
[57,] -0.82048183 -0.96548657
[58,] -0.38546762 -0.82048183
[59,] -0.33047236 -0.38546762
[60,] 0.05953711 -0.33047236
[61,] -0.57547710 0.05953711
[62,] -0.44046289 -0.57547710
[63,] -0.84046289 -0.44046289
[64,] -1.14046289 -0.84046289
[65,] -0.16044394 -1.14046289
[66,] 0.74954659 -0.16044394
[67,] 0.65953711 0.74954659
[68,] -0.03047236 0.65953711
[69,] 0.09455133 -0.03047236
[70,] -0.35045341 0.09455133
[71,] 0.09455133 -0.35045341
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.52452290 0.18950869
2 0.61453238 0.52452290
3 -0.06548657 0.61453238
4 -0.51049131 -0.06548657
5 -0.36548657 -0.51049131
6 0.47951817 -0.36548657
7 0.72452290 0.47951817
8 0.28950869 0.72452290
9 0.43451343 0.28950869
10 0.14450395 0.43451343
11 0.19949922 0.14450395
12 0.49949922 0.19949922
13 0.39949922 0.49949922
14 -0.10050078 0.39949922
15 -0.90050078 -0.10050078
16 -1.05549605 -0.90050078
17 -1.00050078 -1.05549605
18 0.54450395 -1.00050078
19 0.89949922 0.54450395
20 1.04450395 0.89949922
21 0.15449448 1.04450395
22 0.08950869 0.15449448
23 0.04450395 0.08950869
24 0.09949922 0.04450395
25 0.19949922 0.09949922
26 0.24450395 0.19949922
27 0.23451343 0.24450395
28 0.27951817 0.23451343
29 -0.06548657 0.27951817
30 0.63451343 -0.06548657
31 0.87951817 0.63451343
32 1.16952764 0.87951817
33 0.38950869 1.16952764
34 0.28950869 0.38950869
35 0.24450395 0.28950869
36 0.29949922 0.24450395
37 0.29949922 0.29949922
38 0.34450395 0.29949922
39 0.24450395 0.34450395
40 0.33451343 0.24450395
41 -0.25549605 0.33451343
42 -0.05549605 -0.25549605
43 -0.15549605 -0.05549605
44 0.13451343 -0.15549605
45 -0.40050078 0.13451343
46 -0.50050078 -0.40050078
47 -0.21049131 -0.50050078
48 -0.40050078 -0.21049131
49 -0.25549605 -0.40050078
50 -0.21049131 -0.25549605
51 -0.25549605 -0.21049131
52 -0.55549605 -0.25549605
53 -0.91049131 -0.55549605
54 -0.46548657 -0.91049131
55 -0.52048183 -0.46548657
56 -0.96548657 -0.52048183
57 -0.82048183 -0.96548657
58 -0.38546762 -0.82048183
59 -0.33047236 -0.38546762
60 0.05953711 -0.33047236
61 -0.57547710 0.05953711
62 -0.44046289 -0.57547710
63 -0.84046289 -0.44046289
64 -1.14046289 -0.84046289
65 -0.16044394 -1.14046289
66 0.74954659 -0.16044394
67 0.65953711 0.74954659
68 -0.03047236 0.65953711
69 0.09455133 -0.03047236
70 -0.35045341 0.09455133
71 0.09455133 -0.35045341
> 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/704t81258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89z761258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9muq21258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109d4u1258722468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11f6q71258722468.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/127pj01258722469.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/13dlmj1258722469.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/14r3xx1258722469.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/15m79e1258722469.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/1643q51258722469.tab")
+ }
> system("convert tmp/13klw1258722468.ps tmp/13klw1258722468.png")
> system("convert tmp/2bhzq1258722468.ps tmp/2bhzq1258722468.png")
> system("convert tmp/356r51258722468.ps tmp/356r51258722468.png")
> system("convert tmp/40b761258722468.ps tmp/40b761258722468.png")
> system("convert tmp/5qhkj1258722468.ps tmp/5qhkj1258722468.png")
> system("convert tmp/6yyru1258722468.ps tmp/6yyru1258722468.png")
> system("convert tmp/704t81258722468.ps tmp/704t81258722468.png")
> system("convert tmp/89z761258722468.ps tmp/89z761258722468.png")
> system("convert tmp/9muq21258722468.ps tmp/9muq21258722468.png")
> system("convert tmp/109d4u1258722468.ps tmp/109d4u1258722468.png")
>
>
> proc.time()
user system elapsed
2.641 1.601 5.408