R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(103.8
+ ,122.5
+ ,80.2
+ ,19
+ ,103.5
+ ,122.4
+ ,74.8
+ ,18
+ ,104.1
+ ,121.9
+ ,77.8
+ ,19
+ ,101.9
+ ,122.2
+ ,73
+ ,19
+ ,102
+ ,123.7
+ ,72
+ ,22
+ ,100.7
+ ,122.6
+ ,75.8
+ ,23
+ ,99
+ ,115.7
+ ,72.6
+ ,20
+ ,96.5
+ ,116.1
+ ,71.9
+ ,14
+ ,101.8
+ ,120.5
+ ,74.8
+ ,14
+ ,100.5
+ ,122.6
+ ,72.9
+ ,14
+ ,103.3
+ ,119.9
+ ,72.9
+ ,15
+ ,102.3
+ ,120.7
+ ,79.9
+ ,11
+ ,100.4
+ ,120.2
+ ,74
+ ,17
+ ,103
+ ,122.1
+ ,76
+ ,16
+ ,99
+ ,119.3
+ ,69.6
+ ,20
+ ,104.8
+ ,121.7
+ ,77.3
+ ,24
+ ,104.5
+ ,113.5
+ ,75.2
+ ,23
+ ,104.8
+ ,123.7
+ ,75.8
+ ,20
+ ,103.8
+ ,123.4
+ ,77.6
+ ,21
+ ,106.3
+ ,126.4
+ ,76.7
+ ,19
+ ,105.2
+ ,124.1
+ ,77
+ ,23
+ ,108.2
+ ,125.6
+ ,77.9
+ ,23
+ ,106.2
+ ,124.8
+ ,76.7
+ ,23
+ ,103.9
+ ,123
+ ,71.9
+ ,23
+ ,104.9
+ ,126.9
+ ,73.4
+ ,27
+ ,106.2
+ ,127.3
+ ,72.5
+ ,26
+ ,107.9
+ ,129
+ ,73.7
+ ,17
+ ,106.9
+ ,126.2
+ ,69.5
+ ,24
+ ,110.3
+ ,125.4
+ ,74.7
+ ,26
+ ,109.8
+ ,126.3
+ ,72.5
+ ,24
+ ,108.3
+ ,126.3
+ ,72.1
+ ,27
+ ,110.9
+ ,128.4
+ ,70.7
+ ,27
+ ,109.8
+ ,127.2
+ ,71.4
+ ,26
+ ,109.3
+ ,128.5
+ ,69.5
+ ,24
+ ,109
+ ,129
+ ,73.5
+ ,23
+ ,107.9
+ ,128.9
+ ,72.4
+ ,23
+ ,108.4
+ ,128.3
+ ,74.5
+ ,24
+ ,107.2
+ ,124.6
+ ,72.2
+ ,17
+ ,109.5
+ ,126.2
+ ,73
+ ,21
+ ,109.9
+ ,129.1
+ ,73.3
+ ,19
+ ,108
+ ,127.3
+ ,71.3
+ ,22
+ ,114.7
+ ,129.2
+ ,73.6
+ ,22
+ ,115.6
+ ,130.4
+ ,71.3
+ ,18
+ ,107.6
+ ,125.9
+ ,71.2
+ ,16
+ ,115.9
+ ,135.8
+ ,81.4
+ ,14
+ ,111.8
+ ,126.4
+ ,76.1
+ ,12
+ ,110
+ ,129.5
+ ,71.1
+ ,14
+ ,109.2
+ ,128.4
+ ,75.7
+ ,16
+ ,108
+ ,125.6
+ ,70
+ ,8
+ ,105.6
+ ,127.7
+ ,68.5
+ ,3
+ ,103
+ ,126.4
+ ,56.7
+ ,0
+ ,99.6
+ ,124.2
+ ,57.9
+ ,5
+ ,97.9
+ ,126.4
+ ,58.8
+ ,1
+ ,97.6
+ ,123.7
+ ,59.3
+ ,1
+ ,96.2
+ ,121.8
+ ,61.3
+ ,3
+ ,97.9
+ ,124
+ ,62.9
+ ,6
+ ,94.5
+ ,122.7
+ ,61.4
+ ,7
+ ,95.4
+ ,122.9
+ ,64.5
+ ,8
+ ,94.4
+ ,121
+ ,63.8
+ ,14
+ ,96.3
+ ,122.8
+ ,61.6
+ ,14
+ ,95.1
+ ,122.9
+ ,64.7
+ ,13)
+ ,dim=c(4
+ ,61)
+ ,dimnames=list(c('totid'
+ ,'ndzcg'
+ ,'dzcg'
+ ,'indc
')
+ ,1:61))
> y <- array(NA,dim=c(4,61),dimnames=list(c('totid','ndzcg','dzcg','indc
'),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 = 'Include Monthly Dummies'
> par1 = '3'
> #'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
dzcg totid ndzcg indc\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 80.2 103.8 122.5 19 1 0 0 0 0 0 0 0 0 0 0
2 74.8 103.5 122.4 18 0 1 0 0 0 0 0 0 0 0 0
3 77.8 104.1 121.9 19 0 0 1 0 0 0 0 0 0 0 0
4 73.0 101.9 122.2 19 0 0 0 1 0 0 0 0 0 0 0
5 72.0 102.0 123.7 22 0 0 0 0 1 0 0 0 0 0 0
6 75.8 100.7 122.6 23 0 0 0 0 0 1 0 0 0 0 0
7 72.6 99.0 115.7 20 0 0 0 0 0 0 1 0 0 0 0
8 71.9 96.5 116.1 14 0 0 0 0 0 0 0 1 0 0 0
9 74.8 101.8 120.5 14 0 0 0 0 0 0 0 0 1 0 0
10 72.9 100.5 122.6 14 0 0 0 0 0 0 0 0 0 1 0
11 72.9 103.3 119.9 15 0 0 0 0 0 0 0 0 0 0 1
12 79.9 102.3 120.7 11 0 0 0 0 0 0 0 0 0 0 0
13 74.0 100.4 120.2 17 1 0 0 0 0 0 0 0 0 0 0
14 76.0 103.0 122.1 16 0 1 0 0 0 0 0 0 0 0 0
15 69.6 99.0 119.3 20 0 0 1 0 0 0 0 0 0 0 0
16 77.3 104.8 121.7 24 0 0 0 1 0 0 0 0 0 0 0
17 75.2 104.5 113.5 23 0 0 0 0 1 0 0 0 0 0 0
18 75.8 104.8 123.7 20 0 0 0 0 0 1 0 0 0 0 0
19 77.6 103.8 123.4 21 0 0 0 0 0 0 1 0 0 0 0
20 76.7 106.3 126.4 19 0 0 0 0 0 0 0 1 0 0 0
21 77.0 105.2 124.1 23 0 0 0 0 0 0 0 0 1 0 0
22 77.9 108.2 125.6 23 0 0 0 0 0 0 0 0 0 1 0
23 76.7 106.2 124.8 23 0 0 0 0 0 0 0 0 0 0 1
24 71.9 103.9 123.0 23 0 0 0 0 0 0 0 0 0 0 0
25 73.4 104.9 126.9 27 1 0 0 0 0 0 0 0 0 0 0
26 72.5 106.2 127.3 26 0 1 0 0 0 0 0 0 0 0 0
27 73.7 107.9 129.0 17 0 0 1 0 0 0 0 0 0 0 0
28 69.5 106.9 126.2 24 0 0 0 1 0 0 0 0 0 0 0
29 74.7 110.3 125.4 26 0 0 0 0 1 0 0 0 0 0 0
30 72.5 109.8 126.3 24 0 0 0 0 0 1 0 0 0 0 0
31 72.1 108.3 126.3 27 0 0 0 0 0 0 1 0 0 0 0
32 70.7 110.9 128.4 27 0 0 0 0 0 0 0 1 0 0 0
33 71.4 109.8 127.2 26 0 0 0 0 0 0 0 0 1 0 0
34 69.5 109.3 128.5 24 0 0 0 0 0 0 0 0 0 1 0
35 73.5 109.0 129.0 23 0 0 0 0 0 0 0 0 0 0 1
36 72.4 107.9 128.9 23 0 0 0 0 0 0 0 0 0 0 0
37 74.5 108.4 128.3 24 1 0 0 0 0 0 0 0 0 0 0
38 72.2 107.2 124.6 17 0 1 0 0 0 0 0 0 0 0 0
39 73.0 109.5 126.2 21 0 0 1 0 0 0 0 0 0 0 0
40 73.3 109.9 129.1 19 0 0 0 1 0 0 0 0 0 0 0
41 71.3 108.0 127.3 22 0 0 0 0 1 0 0 0 0 0 0
42 73.6 114.7 129.2 22 0 0 0 0 0 1 0 0 0 0 0
43 71.3 115.6 130.4 18 0 0 0 0 0 0 1 0 0 0 0
44 71.2 107.6 125.9 16 0 0 0 0 0 0 0 1 0 0 0
45 81.4 115.9 135.8 14 0 0 0 0 0 0 0 0 1 0 0
46 76.1 111.8 126.4 12 0 0 0 0 0 0 0 0 0 1 0
47 71.1 110.0 129.5 14 0 0 0 0 0 0 0 0 0 0 1
48 75.7 109.2 128.4 16 0 0 0 0 0 0 0 0 0 0 0
49 70.0 108.0 125.6 8 1 0 0 0 0 0 0 0 0 0 0
50 68.5 105.6 127.7 3 0 1 0 0 0 0 0 0 0 0 0
51 56.7 103.0 126.4 0 0 0 1 0 0 0 0 0 0 0 0
52 57.9 99.6 124.2 5 0 0 0 1 0 0 0 0 0 0 0
53 58.8 97.9 126.4 1 0 0 0 0 1 0 0 0 0 0 0
54 59.3 97.6 123.7 1 0 0 0 0 0 1 0 0 0 0 0
55 61.3 96.2 121.8 3 0 0 0 0 0 0 1 0 0 0 0
56 62.9 97.9 124.0 6 0 0 0 0 0 0 0 1 0 0 0
57 61.4 94.5 122.7 7 0 0 0 0 0 0 0 0 1 0 0
58 64.5 95.4 122.9 8 0 0 0 0 0 0 0 0 0 1 0
59 63.8 94.4 121.0 14 0 0 0 0 0 0 0 0 0 0 1
60 61.6 96.3 122.8 14 0 0 0 0 0 0 0 0 0 0 0
61 64.7 95.1 122.9 13 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) totid ndzcg `indc\r` M1 M2
86.4486 0.8204 -0.8344 0.2700 0.4369 -0.0400
M3 M4 M5 M6 M7 M8
-2.4068 -2.9571 -4.0384 -2.0910 -3.0042 -1.7850
M9 M10 M11
0.8998 -0.3477 -1.2827
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9100 -2.1801 -0.2320 2.3334 8.5019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.44863 16.94844 5.101 6.27e-06 ***
totid 0.82042 0.16820 4.878 1.32e-05 ***
ndzcg -0.83444 0.20597 -4.051 0.000194 ***
`indc\r` 0.27002 0.08829 3.058 0.003702 **
M1 0.43686 2.32707 0.188 0.851915
M2 -0.04000 2.44511 -0.016 0.987019
M3 -2.40677 2.44768 -0.983 0.330609
M4 -2.95707 2.43105 -1.216 0.230046
M5 -4.03836 2.45427 -1.645 0.106696
M6 -2.09098 2.43641 -0.858 0.395220
M7 -3.00420 2.45130 -1.226 0.226605
M8 -1.78501 2.43453 -0.733 0.467153
M9 0.89975 2.43718 0.369 0.713690
M10 -0.34769 2.43812 -0.143 0.887225
M11 -1.28273 2.43017 -0.528 0.600149
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.84 on 46 degrees of freedom
Multiple R-squared: 0.6505, Adjusted R-squared: 0.5442
F-statistic: 6.116 on 14 and 46 DF, p-value: 1.304e-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,] 0.2300968 0.4601936 0.7699032
[2,] 0.1575753 0.3151507 0.8424247
[3,] 0.2706477 0.5412954 0.7293523
[4,] 0.1846653 0.3693305 0.8153347
[5,] 0.1288536 0.2577071 0.8711464
[6,] 0.1007260 0.2014520 0.8992740
[7,] 0.2770375 0.5540749 0.7229625
[8,] 0.2307549 0.4615098 0.7692451
[9,] 0.1674792 0.3349583 0.8325208
[10,] 0.3091459 0.6182918 0.6908541
[11,] 0.4346696 0.8693392 0.5653304
[12,] 0.3588418 0.7176836 0.6411582
[13,] 0.4412665 0.8825329 0.5587335
[14,] 0.3942774 0.7885548 0.6057226
[15,] 0.3996350 0.7992700 0.6003650
[16,] 0.4863440 0.9726880 0.5136560
[17,] 0.7331853 0.5336293 0.2668147
[18,] 0.6421658 0.7156683 0.3578342
[19,] 0.5689223 0.8621553 0.4310777
[20,] 0.4843998 0.9687996 0.5156002
[21,] 0.4195663 0.8391326 0.5804337
[22,] 0.4795949 0.9591897 0.5204051
[23,] 0.5052147 0.9895705 0.4947853
[24,] 0.4408491 0.8816982 0.5591509
[25,] 0.3168644 0.6337289 0.6831356
[26,] 0.5140807 0.9718387 0.4859193
> postscript(file="/var/www/html/rcomp/tmp/1lk2f1258746295.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gwxq1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r85a1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aczi1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58hze1258746295.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
5.24371074 0.75327319 4.94054719 2.74610718 3.18695338 4.91820711
7 8 9 10 11 12
-0.92145179 1.16433137 0.70289011 2.86921182 -1.01595049 7.26938788
13 14 15 16 17 18
0.45397006 2.65319738 -1.51487898 2.89954456 -4.44545916 3.28243512
19 20 21 22 23 24
6.29572153 5.16885686 0.68724596 1.62508585 2.33341752 -3.36434406
25 26 27 28 29 30
-0.94738369 -1.83327438 4.18754566 -2.86834159 -0.58408750 -3.03021271
31 32 33 34 35 36
-2.09642838 -5.09638181 -6.90998753 -5.52751264 0.34090254 -1.22280992
37 38 39 40 41 42
-0.74056906 -2.77648748 -1.24166733 2.24039736 0.56842092 -2.99034552
43 44 45 46 47 48
-3.03407815 -1.00484442 8.50193926 0.50937752 -0.03208813 2.48358178
49 50 51 52 53 54
-2.84502849 1.20329130 -6.37154655 -5.01770751 1.27417237 -2.18008400
55 56 57 58 59 60
-0.24376320 -0.23196201 -2.98208780 0.52383744 -1.62628143 -5.16581567
61
-1.16469956
> postscript(file="/var/www/html/rcomp/tmp/6c5b91258746295.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 5.24371074 NA
1 0.75327319 5.24371074
2 4.94054719 0.75327319
3 2.74610718 4.94054719
4 3.18695338 2.74610718
5 4.91820711 3.18695338
6 -0.92145179 4.91820711
7 1.16433137 -0.92145179
8 0.70289011 1.16433137
9 2.86921182 0.70289011
10 -1.01595049 2.86921182
11 7.26938788 -1.01595049
12 0.45397006 7.26938788
13 2.65319738 0.45397006
14 -1.51487898 2.65319738
15 2.89954456 -1.51487898
16 -4.44545916 2.89954456
17 3.28243512 -4.44545916
18 6.29572153 3.28243512
19 5.16885686 6.29572153
20 0.68724596 5.16885686
21 1.62508585 0.68724596
22 2.33341752 1.62508585
23 -3.36434406 2.33341752
24 -0.94738369 -3.36434406
25 -1.83327438 -0.94738369
26 4.18754566 -1.83327438
27 -2.86834159 4.18754566
28 -0.58408750 -2.86834159
29 -3.03021271 -0.58408750
30 -2.09642838 -3.03021271
31 -5.09638181 -2.09642838
32 -6.90998753 -5.09638181
33 -5.52751264 -6.90998753
34 0.34090254 -5.52751264
35 -1.22280992 0.34090254
36 -0.74056906 -1.22280992
37 -2.77648748 -0.74056906
38 -1.24166733 -2.77648748
39 2.24039736 -1.24166733
40 0.56842092 2.24039736
41 -2.99034552 0.56842092
42 -3.03407815 -2.99034552
43 -1.00484442 -3.03407815
44 8.50193926 -1.00484442
45 0.50937752 8.50193926
46 -0.03208813 0.50937752
47 2.48358178 -0.03208813
48 -2.84502849 2.48358178
49 1.20329130 -2.84502849
50 -6.37154655 1.20329130
51 -5.01770751 -6.37154655
52 1.27417237 -5.01770751
53 -2.18008400 1.27417237
54 -0.24376320 -2.18008400
55 -0.23196201 -0.24376320
56 -2.98208780 -0.23196201
57 0.52383744 -2.98208780
58 -1.62628143 0.52383744
59 -5.16581567 -1.62628143
60 -1.16469956 -5.16581567
61 NA -1.16469956
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.75327319 5.24371074
[2,] 4.94054719 0.75327319
[3,] 2.74610718 4.94054719
[4,] 3.18695338 2.74610718
[5,] 4.91820711 3.18695338
[6,] -0.92145179 4.91820711
[7,] 1.16433137 -0.92145179
[8,] 0.70289011 1.16433137
[9,] 2.86921182 0.70289011
[10,] -1.01595049 2.86921182
[11,] 7.26938788 -1.01595049
[12,] 0.45397006 7.26938788
[13,] 2.65319738 0.45397006
[14,] -1.51487898 2.65319738
[15,] 2.89954456 -1.51487898
[16,] -4.44545916 2.89954456
[17,] 3.28243512 -4.44545916
[18,] 6.29572153 3.28243512
[19,] 5.16885686 6.29572153
[20,] 0.68724596 5.16885686
[21,] 1.62508585 0.68724596
[22,] 2.33341752 1.62508585
[23,] -3.36434406 2.33341752
[24,] -0.94738369 -3.36434406
[25,] -1.83327438 -0.94738369
[26,] 4.18754566 -1.83327438
[27,] -2.86834159 4.18754566
[28,] -0.58408750 -2.86834159
[29,] -3.03021271 -0.58408750
[30,] -2.09642838 -3.03021271
[31,] -5.09638181 -2.09642838
[32,] -6.90998753 -5.09638181
[33,] -5.52751264 -6.90998753
[34,] 0.34090254 -5.52751264
[35,] -1.22280992 0.34090254
[36,] -0.74056906 -1.22280992
[37,] -2.77648748 -0.74056906
[38,] -1.24166733 -2.77648748
[39,] 2.24039736 -1.24166733
[40,] 0.56842092 2.24039736
[41,] -2.99034552 0.56842092
[42,] -3.03407815 -2.99034552
[43,] -1.00484442 -3.03407815
[44,] 8.50193926 -1.00484442
[45,] 0.50937752 8.50193926
[46,] -0.03208813 0.50937752
[47,] 2.48358178 -0.03208813
[48,] -2.84502849 2.48358178
[49,] 1.20329130 -2.84502849
[50,] -6.37154655 1.20329130
[51,] -5.01770751 -6.37154655
[52,] 1.27417237 -5.01770751
[53,] -2.18008400 1.27417237
[54,] -0.24376320 -2.18008400
[55,] -0.23196201 -0.24376320
[56,] -2.98208780 -0.23196201
[57,] 0.52383744 -2.98208780
[58,] -1.62628143 0.52383744
[59,] -5.16581567 -1.62628143
[60,] -1.16469956 -5.16581567
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.75327319 5.24371074
2 4.94054719 0.75327319
3 2.74610718 4.94054719
4 3.18695338 2.74610718
5 4.91820711 3.18695338
6 -0.92145179 4.91820711
7 1.16433137 -0.92145179
8 0.70289011 1.16433137
9 2.86921182 0.70289011
10 -1.01595049 2.86921182
11 7.26938788 -1.01595049
12 0.45397006 7.26938788
13 2.65319738 0.45397006
14 -1.51487898 2.65319738
15 2.89954456 -1.51487898
16 -4.44545916 2.89954456
17 3.28243512 -4.44545916
18 6.29572153 3.28243512
19 5.16885686 6.29572153
20 0.68724596 5.16885686
21 1.62508585 0.68724596
22 2.33341752 1.62508585
23 -3.36434406 2.33341752
24 -0.94738369 -3.36434406
25 -1.83327438 -0.94738369
26 4.18754566 -1.83327438
27 -2.86834159 4.18754566
28 -0.58408750 -2.86834159
29 -3.03021271 -0.58408750
30 -2.09642838 -3.03021271
31 -5.09638181 -2.09642838
32 -6.90998753 -5.09638181
33 -5.52751264 -6.90998753
34 0.34090254 -5.52751264
35 -1.22280992 0.34090254
36 -0.74056906 -1.22280992
37 -2.77648748 -0.74056906
38 -1.24166733 -2.77648748
39 2.24039736 -1.24166733
40 0.56842092 2.24039736
41 -2.99034552 0.56842092
42 -3.03407815 -2.99034552
43 -1.00484442 -3.03407815
44 8.50193926 -1.00484442
45 0.50937752 8.50193926
46 -0.03208813 0.50937752
47 2.48358178 -0.03208813
48 -2.84502849 2.48358178
49 1.20329130 -2.84502849
50 -6.37154655 1.20329130
51 -5.01770751 -6.37154655
52 1.27417237 -5.01770751
53 -2.18008400 1.27417237
54 -0.24376320 -2.18008400
55 -0.23196201 -0.24376320
56 -2.98208780 -0.23196201
57 0.52383744 -2.98208780
58 -1.62628143 0.52383744
59 -5.16581567 -1.62628143
60 -1.16469956 -5.16581567
> 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/7noxo1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jl6h1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9az8j1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yedl1258746295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117uli1258746295.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/123wjk1258746295.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/13ac8f1258746296.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/143pao1258746296.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/15rt3v1258746296.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/168r0m1258746296.tab")
+ }
>
> system("convert tmp/1lk2f1258746295.ps tmp/1lk2f1258746295.png")
> system("convert tmp/2gwxq1258746295.ps tmp/2gwxq1258746295.png")
> system("convert tmp/3r85a1258746295.ps tmp/3r85a1258746295.png")
> system("convert tmp/4aczi1258746295.ps tmp/4aczi1258746295.png")
> system("convert tmp/58hze1258746295.ps tmp/58hze1258746295.png")
> system("convert tmp/6c5b91258746295.ps tmp/6c5b91258746295.png")
> system("convert tmp/7noxo1258746295.ps tmp/7noxo1258746295.png")
> system("convert tmp/8jl6h1258746295.ps tmp/8jl6h1258746295.png")
> system("convert tmp/9az8j1258746295.ps tmp/9az8j1258746295.png")
> system("convert tmp/10yedl1258746295.ps tmp/10yedl1258746295.png")
>
>
> proc.time()
user system elapsed
2.462 1.597 2.847