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(7.5
+ ,20.3
+ ,8
+ ,8.2
+ ,6.8
+ ,15.8
+ ,7.5
+ ,8
+ ,6.5
+ ,15.8
+ ,6.8
+ ,7.5
+ ,6.6
+ ,15.8
+ ,6.5
+ ,6.8
+ ,7.6
+ ,23.2
+ ,6.6
+ ,6.5
+ ,8
+ ,23.2
+ ,7.6
+ ,6.6
+ ,8.1
+ ,23.2
+ ,8
+ ,7.6
+ ,7.7
+ ,20.9
+ ,8.1
+ ,8
+ ,7.5
+ ,20.9
+ ,7.7
+ ,8.1
+ ,7.6
+ ,20.9
+ ,7.5
+ ,7.7
+ ,7.8
+ ,19.8
+ ,7.6
+ ,7.5
+ ,7.8
+ ,19.8
+ ,7.8
+ ,7.6
+ ,7.8
+ ,19.8
+ ,7.8
+ ,7.8
+ ,7.5
+ ,20.6
+ ,7.8
+ ,7.8
+ ,7.5
+ ,20.6
+ ,7.5
+ ,7.8
+ ,7.1
+ ,20.6
+ ,7.5
+ ,7.5
+ ,7.5
+ ,21.1
+ ,7.1
+ ,7.5
+ ,7.5
+ ,21.1
+ ,7.5
+ ,7.1
+ ,7.6
+ ,21.1
+ ,7.5
+ ,7.5
+ ,7.7
+ ,22.4
+ ,7.6
+ ,7.5
+ ,7.7
+ ,22.4
+ ,7.7
+ ,7.6
+ ,7.9
+ ,22.4
+ ,7.7
+ ,7.7
+ ,8.1
+ ,20.5
+ ,7.9
+ ,7.7
+ ,8.2
+ ,20.5
+ ,8.1
+ ,7.9
+ ,8.2
+ ,20.5
+ ,8.2
+ ,8.1
+ ,8.2
+ ,18.4
+ ,8.2
+ ,8.2
+ ,7.9
+ ,18.4
+ ,8.2
+ ,8.2
+ ,7.3
+ ,18.4
+ ,7.9
+ ,8.2
+ ,6.9
+ ,17.6
+ ,7.3
+ ,7.9
+ ,6.6
+ ,17.6
+ ,6.9
+ ,7.3
+ ,6.7
+ ,17.6
+ ,6.6
+ ,6.9
+ ,6.9
+ ,18.5
+ ,6.7
+ ,6.6
+ ,7
+ ,18.5
+ ,6.9
+ ,6.7
+ ,7.1
+ ,18.5
+ ,7
+ ,6.9
+ ,7.2
+ ,17.3
+ ,7.1
+ ,7
+ ,7.1
+ ,17.3
+ ,7.2
+ ,7.1
+ ,6.9
+ ,17.3
+ ,7.1
+ ,7.2
+ ,7
+ ,16.2
+ ,6.9
+ ,7.1
+ ,6.8
+ ,16.2
+ ,7
+ ,6.9
+ ,6.4
+ ,16.2
+ ,6.8
+ ,7
+ ,6.7
+ ,18.5
+ ,6.4
+ ,6.8
+ ,6.6
+ ,18.5
+ ,6.7
+ ,6.4
+ ,6.4
+ ,18.5
+ ,6.6
+ ,6.7
+ ,6.3
+ ,16.3
+ ,6.4
+ ,6.6
+ ,6.2
+ ,16.3
+ ,6.3
+ ,6.4
+ ,6.5
+ ,16.3
+ ,6.2
+ ,6.3
+ ,6.8
+ ,16.8
+ ,6.5
+ ,6.2
+ ,6.8
+ ,16.8
+ ,6.8
+ ,6.5
+ ,6.4
+ ,16.8
+ ,6.8
+ ,6.8
+ ,6.1
+ ,14.8
+ ,6.4
+ ,6.8
+ ,5.8
+ ,14.8
+ ,6.1
+ ,6.4
+ ,6.1
+ ,14.8
+ ,5.8
+ ,6.1
+ ,7.2
+ ,21.4
+ ,6.1
+ ,5.8
+ ,7.3
+ ,21.4
+ ,7.2
+ ,6.1
+ ,6.9
+ ,21.4
+ ,7.3
+ ,7.2
+ ,6.1
+ ,16.1
+ ,6.9
+ ,7.3
+ ,5.8
+ ,16.1
+ ,6.1
+ ,6.9
+ ,6.2
+ ,16.1
+ ,5.8
+ ,6.1
+ ,7.1
+ ,19.6
+ ,6.2
+ ,5.8
+ ,7.7
+ ,19.6
+ ,7.1
+ ,6.2
+ ,7.9
+ ,19.6
+ ,7.7
+ ,7.1
+ ,7.7
+ ,18.9
+ ,7.9
+ ,7.7
+ ,7.4
+ ,18.9
+ ,7.7
+ ,7.9
+ ,7.5
+ ,18.9
+ ,7.4
+ ,7.7
+ ,8
+ ,21.9
+ ,7.5
+ ,7.4
+ ,8.1
+ ,21.9
+ ,8
+ ,7.5
+ ,8
+ ,21.9
+ ,8.1
+ ,8)
+ ,dim=c(4
+ ,67)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'y(t-1)'
+ ,'y(t-2)')
+ ,1:67))
> y <- array(NA,dim=c(4,67),dimnames=list(c('Y','X','y(t-1)','y(t-2)'),1:67))
> 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 y(t-1) y(t-2) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.5 20.3 8.0 8.2 1 0 0 0 0 0 0 0 0 0 0 1
2 6.8 15.8 7.5 8.0 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 15.8 6.8 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 6.6 15.8 6.5 6.8 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 23.2 6.6 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 8.0 23.2 7.6 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 8.1 23.2 8.0 7.6 0 0 0 0 0 0 1 0 0 0 0 7
8 7.7 20.9 8.1 8.0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 20.9 7.7 8.1 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 20.9 7.5 7.7 0 0 0 0 0 0 0 0 0 1 0 10
11 7.8 19.8 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.8 19.8 7.8 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 7.8 19.8 7.8 7.8 1 0 0 0 0 0 0 0 0 0 0 13
14 7.5 20.6 7.8 7.8 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 20.6 7.5 7.8 0 0 1 0 0 0 0 0 0 0 0 15
16 7.1 20.6 7.5 7.5 0 0 0 1 0 0 0 0 0 0 0 16
17 7.5 21.1 7.1 7.5 0 0 0 0 1 0 0 0 0 0 0 17
18 7.5 21.1 7.5 7.1 0 0 0 0 0 1 0 0 0 0 0 18
19 7.6 21.1 7.5 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 7.7 22.4 7.6 7.5 0 0 0 0 0 0 0 1 0 0 0 20
21 7.7 22.4 7.7 7.6 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 22.4 7.7 7.7 0 0 0 0 0 0 0 0 0 1 0 22
23 8.1 20.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 20.5 8.1 7.9 0 0 0 0 0 0 0 0 0 0 0 24
25 8.2 20.5 8.2 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 8.2 18.4 8.2 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 18.4 8.2 8.2 0 0 1 0 0 0 0 0 0 0 0 27
28 7.3 18.4 7.9 8.2 0 0 0 1 0 0 0 0 0 0 0 28
29 6.9 17.6 7.3 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 6.6 17.6 6.9 7.3 0 0 0 0 0 1 0 0 0 0 0 30
31 6.7 17.6 6.6 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 6.9 18.5 6.7 6.6 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 18.5 6.9 6.7 0 0 0 0 0 0 0 0 1 0 0 33
34 7.1 18.5 7.0 6.9 0 0 0 0 0 0 0 0 0 1 0 34
35 7.2 17.3 7.1 7.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.1 17.3 7.2 7.1 0 0 0 0 0 0 0 0 0 0 0 36
37 6.9 17.3 7.1 7.2 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 16.2 6.9 7.1 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 16.2 7.0 6.9 0 0 1 0 0 0 0 0 0 0 0 39
40 6.4 16.2 6.8 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.7 18.5 6.4 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.6 18.5 6.7 6.4 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 18.5 6.6 6.7 0 0 0 0 0 0 1 0 0 0 0 43
44 6.3 16.3 6.4 6.6 0 0 0 0 0 0 0 1 0 0 0 44
45 6.2 16.3 6.3 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 16.3 6.2 6.3 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 16.8 6.5 6.2 0 0 0 0 0 0 0 0 0 0 1 47
48 6.8 16.8 6.8 6.5 0 0 0 0 0 0 0 0 0 0 0 48
49 6.4 16.8 6.8 6.8 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 14.8 6.4 6.8 0 1 0 0 0 0 0 0 0 0 0 50
51 5.8 14.8 6.1 6.4 0 0 1 0 0 0 0 0 0 0 0 51
52 6.1 14.8 5.8 6.1 0 0 0 1 0 0 0 0 0 0 0 52
53 7.2 21.4 6.1 5.8 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 21.4 7.2 6.1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 21.4 7.3 7.2 0 0 0 0 0 0 1 0 0 0 0 55
56 6.1 16.1 6.9 7.3 0 0 0 0 0 0 0 1 0 0 0 56
57 5.8 16.1 6.1 6.9 0 0 0 0 0 0 0 0 1 0 0 57
58 6.2 16.1 5.8 6.1 0 0 0 0 0 0 0 0 0 1 0 58
59 7.1 19.6 6.2 5.8 0 0 0 0 0 0 0 0 0 0 1 59
60 7.7 19.6 7.1 6.2 0 0 0 0 0 0 0 0 0 0 0 60
61 7.9 19.6 7.7 7.1 1 0 0 0 0 0 0 0 0 0 0 61
62 7.7 18.9 7.9 7.7 0 1 0 0 0 0 0 0 0 0 0 62
63 7.4 18.9 7.7 7.9 0 0 1 0 0 0 0 0 0 0 0 63
64 7.5 18.9 7.4 7.7 0 0 0 1 0 0 0 0 0 0 0 64
65 8.0 21.9 7.5 7.4 0 0 0 0 1 0 0 0 0 0 0 65
66 8.1 21.9 8.0 7.5 0 0 0 0 0 1 0 0 0 0 0 66
67 8.0 21.9 8.1 8.0 0 0 0 0 0 0 1 0 0 0 0 67
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `y(t-1)` `y(t-2)` M1 M2
1.0053417 0.1041257 0.9537922 -0.3536087 -0.1200388 -0.0200035
M3 M4 M5 M6 M7 M8
-0.0837018 -0.0935343 0.1207524 -0.3598304 -0.2872545 -0.2871728
M9 M10 M11 t
-0.2175061 0.0272761 0.1263707 -0.0001247
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.45622 -0.09080 0.01686 0.11634 0.38049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0053417 0.3806068 2.641 0.01093 *
X 0.1041257 0.0246444 4.225 9.87e-05 ***
`y(t-1)` 0.9537922 0.1544336 6.176 1.09e-07 ***
`y(t-2)` -0.3536087 0.1209396 -2.924 0.00515 **
M1 -0.1200388 0.1251247 -0.959 0.34191
M2 -0.0200035 0.1294144 -0.155 0.87777
M3 -0.0837018 0.1326666 -0.631 0.53091
M4 -0.0935343 0.1346792 -0.694 0.49052
M5 0.1207524 0.1691828 0.714 0.47864
M6 -0.3598304 0.1277647 -2.816 0.00689 **
M7 -0.2872545 0.1430987 -2.007 0.05002 .
M8 -0.2871728 0.1378750 -2.083 0.04230 *
M9 -0.2175061 0.1493021 -1.457 0.15130
M10 0.0272761 0.1462929 0.186 0.85283
M11 0.1263707 0.1299644 0.972 0.33547
t -0.0001247 0.0014991 -0.083 0.93401
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1998 on 51 degrees of freedom
Multiple R-squared: 0.928, Adjusted R-squared: 0.9068
F-statistic: 43.79 on 15 and 51 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,] 0.33222988 0.6644598 0.66777012
[2,] 0.18823903 0.3764781 0.81176097
[3,] 0.28122620 0.5624524 0.71877380
[4,] 0.19803927 0.3960785 0.80196073
[5,] 0.12092508 0.2418502 0.87907492
[6,] 0.09852205 0.1970441 0.90147795
[7,] 0.07061062 0.1412212 0.92938938
[8,] 0.16966468 0.3393294 0.83033532
[9,] 0.18158568 0.3631714 0.81841432
[10,] 0.14569679 0.2913936 0.85430321
[11,] 0.35950090 0.7190018 0.64049910
[12,] 0.27876293 0.5575259 0.72123707
[13,] 0.44246503 0.8849301 0.55753497
[14,] 0.42768230 0.8553646 0.57231770
[15,] 0.42064032 0.8412806 0.57935968
[16,] 0.41992242 0.8398448 0.58007758
[17,] 0.35779467 0.7155893 0.64220533
[18,] 0.31199752 0.6239950 0.68800248
[19,] 0.25996949 0.5199390 0.74003051
[20,] 0.35958078 0.7191616 0.64041922
[21,] 0.36929773 0.7385955 0.63070227
[22,] 0.40375207 0.8075041 0.59624793
[23,] 0.30953760 0.6190752 0.69046240
[24,] 0.25772775 0.5154555 0.74227225
[25,] 0.27848201 0.5569640 0.72151799
[26,] 0.57490668 0.8501866 0.42509332
[27,] 0.49845831 0.9969166 0.50154169
[28,] 0.56611872 0.8677626 0.43388128
[29,] 0.55267243 0.8946551 0.44732757
[30,] 0.91950475 0.1609905 0.08049525
> postscript(file="/var/www/html/rcomp/tmp/1d0f71258669059.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23p101258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33o921258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w96v1258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vieu1258669059.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 = 67
Frequency = 1
1 2 3 4 5 6
-0.229677265 -0.154847654 0.099825681 0.248394434 0.062240158 0.024516340
7 8 9 10 11 12
0.024157015 -0.090246480 0.057089363 -0.038253154 0.011214330 -0.017687800
13 14 15 16 17 18
0.173197503 -0.310013691 0.039947099 -0.456178340 0.059113728 0.016860905
19 20 21 22 23 24
0.185853254 0.055153612 -0.074406670 -0.083703282 0.024407310 0.130866051
25 26 27 28 29 30
0.226372130 0.380486453 0.144309568 -0.159595584 -0.224264217 0.125795018
31 32 33 34 35 36
0.298038075 0.202906116 0.077966610 -0.091348357 -0.025385687 -0.058908592
37 38 39 40 41 42
-0.008004934 0.262020357 -0.040257495 -0.204181000 -0.047037006 -0.093910605
43 44 45 46 47 48
-0.164899901 0.119617351 -0.025267093 0.090093777 -0.082437502 -0.135997116
49 50 51 52 53 54
-0.309750941 -0.119893163 -0.211375857 0.278636379 0.085024246 -0.277357056
55 56 57 58 59 60
-0.456217836 -0.287430598 -0.035382210 0.123211015 0.072201549 0.081727457
61 62 63 64 65 66
0.147863507 -0.057752302 -0.032448995 0.292924111 0.064923092 0.204095398
67
0.113069393
> postscript(file="/var/www/html/rcomp/tmp/65s891258669059.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.229677265 NA
1 -0.154847654 -0.229677265
2 0.099825681 -0.154847654
3 0.248394434 0.099825681
4 0.062240158 0.248394434
5 0.024516340 0.062240158
6 0.024157015 0.024516340
7 -0.090246480 0.024157015
8 0.057089363 -0.090246480
9 -0.038253154 0.057089363
10 0.011214330 -0.038253154
11 -0.017687800 0.011214330
12 0.173197503 -0.017687800
13 -0.310013691 0.173197503
14 0.039947099 -0.310013691
15 -0.456178340 0.039947099
16 0.059113728 -0.456178340
17 0.016860905 0.059113728
18 0.185853254 0.016860905
19 0.055153612 0.185853254
20 -0.074406670 0.055153612
21 -0.083703282 -0.074406670
22 0.024407310 -0.083703282
23 0.130866051 0.024407310
24 0.226372130 0.130866051
25 0.380486453 0.226372130
26 0.144309568 0.380486453
27 -0.159595584 0.144309568
28 -0.224264217 -0.159595584
29 0.125795018 -0.224264217
30 0.298038075 0.125795018
31 0.202906116 0.298038075
32 0.077966610 0.202906116
33 -0.091348357 0.077966610
34 -0.025385687 -0.091348357
35 -0.058908592 -0.025385687
36 -0.008004934 -0.058908592
37 0.262020357 -0.008004934
38 -0.040257495 0.262020357
39 -0.204181000 -0.040257495
40 -0.047037006 -0.204181000
41 -0.093910605 -0.047037006
42 -0.164899901 -0.093910605
43 0.119617351 -0.164899901
44 -0.025267093 0.119617351
45 0.090093777 -0.025267093
46 -0.082437502 0.090093777
47 -0.135997116 -0.082437502
48 -0.309750941 -0.135997116
49 -0.119893163 -0.309750941
50 -0.211375857 -0.119893163
51 0.278636379 -0.211375857
52 0.085024246 0.278636379
53 -0.277357056 0.085024246
54 -0.456217836 -0.277357056
55 -0.287430598 -0.456217836
56 -0.035382210 -0.287430598
57 0.123211015 -0.035382210
58 0.072201549 0.123211015
59 0.081727457 0.072201549
60 0.147863507 0.081727457
61 -0.057752302 0.147863507
62 -0.032448995 -0.057752302
63 0.292924111 -0.032448995
64 0.064923092 0.292924111
65 0.204095398 0.064923092
66 0.113069393 0.204095398
67 NA 0.113069393
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.154847654 -0.229677265
[2,] 0.099825681 -0.154847654
[3,] 0.248394434 0.099825681
[4,] 0.062240158 0.248394434
[5,] 0.024516340 0.062240158
[6,] 0.024157015 0.024516340
[7,] -0.090246480 0.024157015
[8,] 0.057089363 -0.090246480
[9,] -0.038253154 0.057089363
[10,] 0.011214330 -0.038253154
[11,] -0.017687800 0.011214330
[12,] 0.173197503 -0.017687800
[13,] -0.310013691 0.173197503
[14,] 0.039947099 -0.310013691
[15,] -0.456178340 0.039947099
[16,] 0.059113728 -0.456178340
[17,] 0.016860905 0.059113728
[18,] 0.185853254 0.016860905
[19,] 0.055153612 0.185853254
[20,] -0.074406670 0.055153612
[21,] -0.083703282 -0.074406670
[22,] 0.024407310 -0.083703282
[23,] 0.130866051 0.024407310
[24,] 0.226372130 0.130866051
[25,] 0.380486453 0.226372130
[26,] 0.144309568 0.380486453
[27,] -0.159595584 0.144309568
[28,] -0.224264217 -0.159595584
[29,] 0.125795018 -0.224264217
[30,] 0.298038075 0.125795018
[31,] 0.202906116 0.298038075
[32,] 0.077966610 0.202906116
[33,] -0.091348357 0.077966610
[34,] -0.025385687 -0.091348357
[35,] -0.058908592 -0.025385687
[36,] -0.008004934 -0.058908592
[37,] 0.262020357 -0.008004934
[38,] -0.040257495 0.262020357
[39,] -0.204181000 -0.040257495
[40,] -0.047037006 -0.204181000
[41,] -0.093910605 -0.047037006
[42,] -0.164899901 -0.093910605
[43,] 0.119617351 -0.164899901
[44,] -0.025267093 0.119617351
[45,] 0.090093777 -0.025267093
[46,] -0.082437502 0.090093777
[47,] -0.135997116 -0.082437502
[48,] -0.309750941 -0.135997116
[49,] -0.119893163 -0.309750941
[50,] -0.211375857 -0.119893163
[51,] 0.278636379 -0.211375857
[52,] 0.085024246 0.278636379
[53,] -0.277357056 0.085024246
[54,] -0.456217836 -0.277357056
[55,] -0.287430598 -0.456217836
[56,] -0.035382210 -0.287430598
[57,] 0.123211015 -0.035382210
[58,] 0.072201549 0.123211015
[59,] 0.081727457 0.072201549
[60,] 0.147863507 0.081727457
[61,] -0.057752302 0.147863507
[62,] -0.032448995 -0.057752302
[63,] 0.292924111 -0.032448995
[64,] 0.064923092 0.292924111
[65,] 0.204095398 0.064923092
[66,] 0.113069393 0.204095398
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.154847654 -0.229677265
2 0.099825681 -0.154847654
3 0.248394434 0.099825681
4 0.062240158 0.248394434
5 0.024516340 0.062240158
6 0.024157015 0.024516340
7 -0.090246480 0.024157015
8 0.057089363 -0.090246480
9 -0.038253154 0.057089363
10 0.011214330 -0.038253154
11 -0.017687800 0.011214330
12 0.173197503 -0.017687800
13 -0.310013691 0.173197503
14 0.039947099 -0.310013691
15 -0.456178340 0.039947099
16 0.059113728 -0.456178340
17 0.016860905 0.059113728
18 0.185853254 0.016860905
19 0.055153612 0.185853254
20 -0.074406670 0.055153612
21 -0.083703282 -0.074406670
22 0.024407310 -0.083703282
23 0.130866051 0.024407310
24 0.226372130 0.130866051
25 0.380486453 0.226372130
26 0.144309568 0.380486453
27 -0.159595584 0.144309568
28 -0.224264217 -0.159595584
29 0.125795018 -0.224264217
30 0.298038075 0.125795018
31 0.202906116 0.298038075
32 0.077966610 0.202906116
33 -0.091348357 0.077966610
34 -0.025385687 -0.091348357
35 -0.058908592 -0.025385687
36 -0.008004934 -0.058908592
37 0.262020357 -0.008004934
38 -0.040257495 0.262020357
39 -0.204181000 -0.040257495
40 -0.047037006 -0.204181000
41 -0.093910605 -0.047037006
42 -0.164899901 -0.093910605
43 0.119617351 -0.164899901
44 -0.025267093 0.119617351
45 0.090093777 -0.025267093
46 -0.082437502 0.090093777
47 -0.135997116 -0.082437502
48 -0.309750941 -0.135997116
49 -0.119893163 -0.309750941
50 -0.211375857 -0.119893163
51 0.278636379 -0.211375857
52 0.085024246 0.278636379
53 -0.277357056 0.085024246
54 -0.456217836 -0.277357056
55 -0.287430598 -0.456217836
56 -0.035382210 -0.287430598
57 0.123211015 -0.035382210
58 0.072201549 0.123211015
59 0.081727457 0.072201549
60 0.147863507 0.081727457
61 -0.057752302 0.147863507
62 -0.032448995 -0.057752302
63 0.292924111 -0.032448995
64 0.064923092 0.292924111
65 0.204095398 0.064923092
66 0.113069393 0.204095398
> 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/71z181258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a24e1258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92fp51258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w8jf1258669059.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1186ws1258669059.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/12azn11258669059.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/13rm871258669059.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/14ttuy1258669059.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/15ug7u1258669059.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/164q0p1258669059.tab")
+ }
>
> system("convert tmp/1d0f71258669059.ps tmp/1d0f71258669059.png")
> system("convert tmp/23p101258669059.ps tmp/23p101258669059.png")
> system("convert tmp/33o921258669059.ps tmp/33o921258669059.png")
> system("convert tmp/4w96v1258669059.ps tmp/4w96v1258669059.png")
> system("convert tmp/5vieu1258669059.ps tmp/5vieu1258669059.png")
> system("convert tmp/65s891258669059.ps tmp/65s891258669059.png")
> system("convert tmp/71z181258669059.ps tmp/71z181258669059.png")
> system("convert tmp/8a24e1258669059.ps tmp/8a24e1258669059.png")
> system("convert tmp/92fp51258669059.ps tmp/92fp51258669059.png")
> system("convert tmp/10w8jf1258669059.ps tmp/10w8jf1258669059.png")
>
>
> proc.time()
user system elapsed
2.498 1.571 2.889