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(8.3,0,8.5,8.6,7.8,0,8.3,8.5,7.8,0,7.8,8.3,8,0,7.8,7.8,8.6,0,8,7.8,8.9,0,8.6,8,8.9,0,8.9,8.6,8.6,0,8.9,8.9,8.3,0,8.6,8.9,8.3,0,8.3,8.6,8.3,0,8.3,8.3,8.4,0,8.3,8.3,8.5,0,8.4,8.3,8.4,0,8.5,8.4,8.6,0,8.4,8.5,8.5,0,8.6,8.4,8.5,0,8.5,8.6,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.5,0,8.5,8.5,8.6,0,8.5,8.5,8.4,0,8.6,8.5,8.1,0,8.4,8.6,8,0,8.1,8.4,8,0,8,8.1,8,0,8,8,8,0,8,8,7.9,0,8,8,7.8,0,7.9,8,7.8,0,7.8,7.9,7.9,0,7.8,7.8,8.1,0,7.9,7.8,8,0,8.1,7.9,7.6,0,8,8.1,7.3,0,7.6,8,7,0,7.3,7.6,6.8,0,7,7.3,7,0,6.8,7,7.1,0,7,6.8,7.2,0,7.1,7,7.1,1,7.2,7.1,6.9,1,7.1,7.2,6.7,1,6.9,7.1,6.7,1,6.7,6.9,6.6,1,6.7,6.7,6.9,1,6.6,6.7,7.3,1,6.9,6.6,7.5,1,7.3,6.9,7.3,1,7.5,7.3,7.1,1,7.3,7.5,6.9,1,7.1,7.3,7.1,1,6.9,7.1),dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> 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
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.3 0 8.5 8.6 1 0 0 0 0 0 0 0 0 0 0 1
2 7.8 0 8.3 8.5 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 0 7.8 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.0 0 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 4
5 8.6 0 8.0 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 8.9 0 8.6 8.0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.9 0 8.9 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.6 0 8.9 8.9 0 0 0 0 0 0 0 1 0 0 0 8
9 8.3 0 8.6 8.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.3 0 8.3 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.3 0 8.3 8.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 0 8.3 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 0 8.4 8.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.4 0 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 0 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 0 8.6 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 0 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 0 8.5 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 0 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 0 8.5 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 0 8.5 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 0 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 0 8.5 8.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 0 8.5 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.5 0 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 0 8.5 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.6 0 8.5 8.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.4 0 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 0 8.4 8.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 0 8.1 8.4 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 0 8.0 8.1 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 0 8.0 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 0 8.0 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.9 0 8.0 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.8 0 7.9 8.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.8 0 7.8 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 0 7.8 7.8 1 0 0 0 0 0 0 0 0 0 0 37
38 8.1 0 7.9 7.8 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 0 8.1 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.6 0 8.0 8.1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.3 0 7.6 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 0 7.3 7.6 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 0 7.0 7.3 0 0 0 0 0 0 1 0 0 0 0 43
44 7.0 0 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.1 0 7.0 6.8 0 0 0 0 0 0 0 0 1 0 0 45
46 7.2 0 7.1 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 7.1 1 7.2 7.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.9 1 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.7 1 6.9 7.1 1 0 0 0 0 0 0 0 0 0 0 49
50 6.7 1 6.7 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 6.6 1 6.7 6.7 0 0 1 0 0 0 0 0 0 0 0 51
52 6.9 1 6.6 6.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 1 6.9 6.6 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 1 7.3 6.9 0 0 0 0 0 1 0 0 0 0 0 54
55 7.3 1 7.5 7.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.1 1 7.3 7.5 0 0 0 0 0 0 0 1 0 0 0 56
57 6.9 1 7.1 7.3 0 0 0 0 0 0 0 0 1 0 0 57
58 7.1 1 6.9 7.1 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
2.069273 -0.079702 1.398817 -0.633441 -0.033577 -0.076853
M3 M4 M5 M6 M7 M8
0.035825 -0.076718 0.078013 -0.033121 -0.084312 -0.013629
M9 M10 M11 t
-0.054266 0.065743 -0.051050 -0.006109
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.40613 -0.12107 0.01553 0.10355 0.31669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.069273 0.587125 3.524 0.00104 **
X -0.079702 0.084142 -0.947 0.34894
Y1 1.398817 0.121775 11.487 1.53e-14 ***
Y2 -0.633441 0.124017 -5.108 7.50e-06 ***
M1 -0.033577 0.113042 -0.297 0.76791
M2 -0.076853 0.112984 -0.680 0.50011
M3 0.035825 0.113091 0.317 0.75298
M4 -0.076718 0.113350 -0.677 0.50223
M5 0.078013 0.112986 0.690 0.49370
M6 -0.033121 0.113996 -0.291 0.77283
M7 -0.084312 0.113241 -0.745 0.46070
M8 -0.013629 0.113018 -0.121 0.90459
M9 -0.054266 0.113129 -0.480 0.63395
M10 0.065743 0.113410 0.580 0.56522
M11 -0.051050 0.119009 -0.429 0.67014
t -0.006109 0.002450 -2.494 0.01665 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1681 on 42 degrees of freedom
Multiple R-squared: 0.9518, Adjusted R-squared: 0.9346
F-statistic: 55.31 on 15 and 42 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,] 0.25087902 0.50175804 0.7491210
[2,] 0.13877887 0.27755774 0.8612211
[3,] 0.10264859 0.20529718 0.8973514
[4,] 0.13037513 0.26075025 0.8696249
[5,] 0.07408068 0.14816135 0.9259193
[6,] 0.03817957 0.07635915 0.9618204
[7,] 0.01764408 0.03528817 0.9823559
[8,] 0.02626797 0.05253593 0.9737320
[9,] 0.03255804 0.06511608 0.9674420
[10,] 0.02234775 0.04469550 0.9776523
[11,] 0.08406543 0.16813086 0.9159346
[12,] 0.05661653 0.11323306 0.9433835
[13,] 0.07879018 0.15758036 0.9212098
[14,] 0.07052608 0.14105216 0.9294739
[15,] 0.08144326 0.16288653 0.9185567
[16,] 0.10112101 0.20224202 0.8988790
[17,] 0.08378836 0.16757672 0.9162116
[18,] 0.08058352 0.16116705 0.9194165
[19,] 0.09231527 0.18463054 0.9076847
[20,] 0.14692464 0.29384927 0.8530754
[21,] 0.74033089 0.51933822 0.2596691
> postscript(file="/var/www/html/rcomp/tmp/19ef11258721265.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jzb51258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d65c1258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xj9z1258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h5uy1258721265.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 = 58
Frequency = 1
1 2 3 4 5 6
-0.171938330 -0.406134058 0.060018551 0.061949573 0.233564629 -0.061794398
7 8 9 10 11 12
-0.044073758 -0.218615583 -0.052224164 0.063489714 -0.003640599 0.051418542
13 14 15 16 17 18
0.051223042 -0.075929587 0.220728530 -0.103727384 0.014221069 0.068119968
19 20 21 22 23 24
0.125421012 0.060846814 0.107593083 -0.006305815 0.116596244 0.071655386
25 26 27 28 29 30
0.111341602 0.160726565 0.154158842 -0.067071232 -0.172585186 0.137614737
31 32 33 34 35 36
0.144765124 0.016846802 0.063593072 -0.150305827 0.012477949 0.044074683
37 38 39 40 41 42
0.120416774 0.229920021 -0.193067011 -0.207845403 -0.160284173 -0.176772499
43 44 45 46 47 48
-0.089858679 0.135298183 -0.124407228 -0.151499595 -0.125433594 -0.167148612
49 50 51 52 53 54
-0.111043088 0.091417060 -0.241838912 0.316694447 0.085083662 0.032832192
55 56 57 58
-0.136253699 0.005623784 0.005445238 0.244621523
> postscript(file="/var/www/html/rcomp/tmp/6xzbp1258721265.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.171938330 NA
1 -0.406134058 -0.171938330
2 0.060018551 -0.406134058
3 0.061949573 0.060018551
4 0.233564629 0.061949573
5 -0.061794398 0.233564629
6 -0.044073758 -0.061794398
7 -0.218615583 -0.044073758
8 -0.052224164 -0.218615583
9 0.063489714 -0.052224164
10 -0.003640599 0.063489714
11 0.051418542 -0.003640599
12 0.051223042 0.051418542
13 -0.075929587 0.051223042
14 0.220728530 -0.075929587
15 -0.103727384 0.220728530
16 0.014221069 -0.103727384
17 0.068119968 0.014221069
18 0.125421012 0.068119968
19 0.060846814 0.125421012
20 0.107593083 0.060846814
21 -0.006305815 0.107593083
22 0.116596244 -0.006305815
23 0.071655386 0.116596244
24 0.111341602 0.071655386
25 0.160726565 0.111341602
26 0.154158842 0.160726565
27 -0.067071232 0.154158842
28 -0.172585186 -0.067071232
29 0.137614737 -0.172585186
30 0.144765124 0.137614737
31 0.016846802 0.144765124
32 0.063593072 0.016846802
33 -0.150305827 0.063593072
34 0.012477949 -0.150305827
35 0.044074683 0.012477949
36 0.120416774 0.044074683
37 0.229920021 0.120416774
38 -0.193067011 0.229920021
39 -0.207845403 -0.193067011
40 -0.160284173 -0.207845403
41 -0.176772499 -0.160284173
42 -0.089858679 -0.176772499
43 0.135298183 -0.089858679
44 -0.124407228 0.135298183
45 -0.151499595 -0.124407228
46 -0.125433594 -0.151499595
47 -0.167148612 -0.125433594
48 -0.111043088 -0.167148612
49 0.091417060 -0.111043088
50 -0.241838912 0.091417060
51 0.316694447 -0.241838912
52 0.085083662 0.316694447
53 0.032832192 0.085083662
54 -0.136253699 0.032832192
55 0.005623784 -0.136253699
56 0.005445238 0.005623784
57 0.244621523 0.005445238
58 NA 0.244621523
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.406134058 -0.171938330
[2,] 0.060018551 -0.406134058
[3,] 0.061949573 0.060018551
[4,] 0.233564629 0.061949573
[5,] -0.061794398 0.233564629
[6,] -0.044073758 -0.061794398
[7,] -0.218615583 -0.044073758
[8,] -0.052224164 -0.218615583
[9,] 0.063489714 -0.052224164
[10,] -0.003640599 0.063489714
[11,] 0.051418542 -0.003640599
[12,] 0.051223042 0.051418542
[13,] -0.075929587 0.051223042
[14,] 0.220728530 -0.075929587
[15,] -0.103727384 0.220728530
[16,] 0.014221069 -0.103727384
[17,] 0.068119968 0.014221069
[18,] 0.125421012 0.068119968
[19,] 0.060846814 0.125421012
[20,] 0.107593083 0.060846814
[21,] -0.006305815 0.107593083
[22,] 0.116596244 -0.006305815
[23,] 0.071655386 0.116596244
[24,] 0.111341602 0.071655386
[25,] 0.160726565 0.111341602
[26,] 0.154158842 0.160726565
[27,] -0.067071232 0.154158842
[28,] -0.172585186 -0.067071232
[29,] 0.137614737 -0.172585186
[30,] 0.144765124 0.137614737
[31,] 0.016846802 0.144765124
[32,] 0.063593072 0.016846802
[33,] -0.150305827 0.063593072
[34,] 0.012477949 -0.150305827
[35,] 0.044074683 0.012477949
[36,] 0.120416774 0.044074683
[37,] 0.229920021 0.120416774
[38,] -0.193067011 0.229920021
[39,] -0.207845403 -0.193067011
[40,] -0.160284173 -0.207845403
[41,] -0.176772499 -0.160284173
[42,] -0.089858679 -0.176772499
[43,] 0.135298183 -0.089858679
[44,] -0.124407228 0.135298183
[45,] -0.151499595 -0.124407228
[46,] -0.125433594 -0.151499595
[47,] -0.167148612 -0.125433594
[48,] -0.111043088 -0.167148612
[49,] 0.091417060 -0.111043088
[50,] -0.241838912 0.091417060
[51,] 0.316694447 -0.241838912
[52,] 0.085083662 0.316694447
[53,] 0.032832192 0.085083662
[54,] -0.136253699 0.032832192
[55,] 0.005623784 -0.136253699
[56,] 0.005445238 0.005623784
[57,] 0.244621523 0.005445238
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.406134058 -0.171938330
2 0.060018551 -0.406134058
3 0.061949573 0.060018551
4 0.233564629 0.061949573
5 -0.061794398 0.233564629
6 -0.044073758 -0.061794398
7 -0.218615583 -0.044073758
8 -0.052224164 -0.218615583
9 0.063489714 -0.052224164
10 -0.003640599 0.063489714
11 0.051418542 -0.003640599
12 0.051223042 0.051418542
13 -0.075929587 0.051223042
14 0.220728530 -0.075929587
15 -0.103727384 0.220728530
16 0.014221069 -0.103727384
17 0.068119968 0.014221069
18 0.125421012 0.068119968
19 0.060846814 0.125421012
20 0.107593083 0.060846814
21 -0.006305815 0.107593083
22 0.116596244 -0.006305815
23 0.071655386 0.116596244
24 0.111341602 0.071655386
25 0.160726565 0.111341602
26 0.154158842 0.160726565
27 -0.067071232 0.154158842
28 -0.172585186 -0.067071232
29 0.137614737 -0.172585186
30 0.144765124 0.137614737
31 0.016846802 0.144765124
32 0.063593072 0.016846802
33 -0.150305827 0.063593072
34 0.012477949 -0.150305827
35 0.044074683 0.012477949
36 0.120416774 0.044074683
37 0.229920021 0.120416774
38 -0.193067011 0.229920021
39 -0.207845403 -0.193067011
40 -0.160284173 -0.207845403
41 -0.176772499 -0.160284173
42 -0.089858679 -0.176772499
43 0.135298183 -0.089858679
44 -0.124407228 0.135298183
45 -0.151499595 -0.124407228
46 -0.125433594 -0.151499595
47 -0.167148612 -0.125433594
48 -0.111043088 -0.167148612
49 0.091417060 -0.111043088
50 -0.241838912 0.091417060
51 0.316694447 -0.241838912
52 0.085083662 0.316694447
53 0.032832192 0.085083662
54 -0.136253699 0.032832192
55 0.005623784 -0.136253699
56 0.005445238 0.005623784
57 0.244621523 0.005445238
> 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/7gnaz1258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dhic1258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96zp61258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k33s1258721265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1199cy1258721265.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/12wx3l1258721265.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/132ukc1258721266.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/14aej61258721266.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/15ckft1258721266.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/16vp2s1258721266.tab")
+ }
>
> system("convert tmp/19ef11258721265.ps tmp/19ef11258721265.png")
> system("convert tmp/2jzb51258721265.ps tmp/2jzb51258721265.png")
> system("convert tmp/3d65c1258721265.ps tmp/3d65c1258721265.png")
> system("convert tmp/4xj9z1258721265.ps tmp/4xj9z1258721265.png")
> system("convert tmp/5h5uy1258721265.ps tmp/5h5uy1258721265.png")
> system("convert tmp/6xzbp1258721265.ps tmp/6xzbp1258721265.png")
> system("convert tmp/7gnaz1258721265.ps tmp/7gnaz1258721265.png")
> system("convert tmp/8dhic1258721265.ps tmp/8dhic1258721265.png")
> system("convert tmp/96zp61258721265.ps tmp/96zp61258721265.png")
> system("convert tmp/10k33s1258721265.ps tmp/10k33s1258721265.png")
>
>
> proc.time()
user system elapsed
2.321 1.528 2.753