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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Type 'contributors()' for more information and
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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(6.3
+ ,3.1
+ ,6.3
+ ,6.1
+ ,6.1
+ ,6.3
+ ,6
+ ,3
+ ,6.3
+ ,6.3
+ ,6.1
+ ,6.1
+ ,6.2
+ ,2.8
+ ,6
+ ,6.3
+ ,6.3
+ ,6.1
+ ,6.4
+ ,2.5
+ ,6.2
+ ,6
+ ,6.3
+ ,6.3
+ ,6.8
+ ,1.9
+ ,6.4
+ ,6.2
+ ,6
+ ,6.3
+ ,7.5
+ ,1.9
+ ,6.8
+ ,6.4
+ ,6.2
+ ,6
+ ,7.5
+ ,1.8
+ ,7.5
+ ,6.8
+ ,6.4
+ ,6.2
+ ,7.6
+ ,2
+ ,7.5
+ ,7.5
+ ,6.8
+ ,6.4
+ ,7.6
+ ,2.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,6.8
+ ,7.4
+ ,2.5
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.3
+ ,2.5
+ ,7.4
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.1
+ ,1.6
+ ,7.3
+ ,7.4
+ ,7.6
+ ,7.6
+ ,6.9
+ ,1.4
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.6
+ ,6.8
+ ,0.8
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.5
+ ,1.1
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.6
+ ,1.3
+ ,7.5
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.8
+ ,1.2
+ ,7.6
+ ,7.5
+ ,6.8
+ ,6.9
+ ,8
+ ,1.3
+ ,7.8
+ ,7.6
+ ,7.5
+ ,6.8
+ ,8.1
+ ,1.1
+ ,8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,8.2
+ ,1.3
+ ,8.1
+ ,8
+ ,7.8
+ ,7.6
+ ,8.3
+ ,1.2
+ ,8.2
+ ,8.1
+ ,8
+ ,7.8
+ ,8.2
+ ,1.6
+ ,8.3
+ ,8.2
+ ,8.1
+ ,8
+ ,8
+ ,1.7
+ ,8.2
+ ,8.3
+ ,8.2
+ ,8.1
+ ,7.9
+ ,1.5
+ ,8
+ ,8.2
+ ,8.3
+ ,8.2
+ ,7.6
+ ,0.9
+ ,7.9
+ ,8
+ ,8.2
+ ,8.3
+ ,7.6
+ ,1.5
+ ,7.6
+ ,7.9
+ ,8
+ ,8.2
+ ,8.3
+ ,1.4
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8
+ ,8.4
+ ,1.6
+ ,8.3
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8.4
+ ,1.7
+ ,8.4
+ ,8.3
+ ,7.6
+ ,7.6
+ ,8.4
+ ,1.4
+ ,8.4
+ ,8.4
+ ,8.3
+ ,7.6
+ ,8.4
+ ,1.8
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.3
+ ,8.6
+ ,1.7
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.9
+ ,1.4
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.8
+ ,1.2
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.3
+ ,1
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,7.5
+ ,1.7
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,7.2
+ ,2.4
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,2
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,2.1
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,2
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,1.8
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,2.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,2.3
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,2
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,2.3
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,2.8
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,2.4
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,2.7
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,2.7
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,2.9
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,3
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,2.3
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Werkl'
+ ,'Infl'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Werkl','Infl','Yt-1','Yt-2','Yt-3','Yt-4'),1:57))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Werkl Infl Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 3.1 6.3 6.1 6.1 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.0 3.0 6.3 6.3 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 2
3 6.2 2.8 6.0 6.3 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.4 2.5 6.2 6.0 6.3 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.8 1.9 6.4 6.2 6.0 6.3 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 1.9 6.8 6.4 6.2 6.0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 1.8 7.5 6.8 6.4 6.2 0 0 0 0 0 0 1 0 0 0 0 7
8 7.6 2.0 7.5 7.5 6.8 6.4 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 2.6 7.6 7.5 7.5 6.8 0 0 0 0 0 0 0 0 1 0 0 9
10 7.4 2.5 7.6 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.3 2.5 7.4 7.6 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.1 1.6 7.3 7.4 7.6 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 6.9 1.4 7.1 7.3 7.4 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 6.8 0.8 6.9 7.1 7.3 7.4 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 1.1 6.8 6.9 7.1 7.3 0 0 1 0 0 0 0 0 0 0 0 15
16 7.6 1.3 7.5 6.8 6.9 7.1 0 0 0 1 0 0 0 0 0 0 0 16
17 7.8 1.2 7.6 7.5 6.8 6.9 0 0 0 0 1 0 0 0 0 0 0 17
18 8.0 1.3 7.8 7.6 7.5 6.8 0 0 0 0 0 1 0 0 0 0 0 18
19 8.1 1.1 8.0 7.8 7.6 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 1.3 8.1 8.0 7.8 7.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.3 1.2 8.2 8.1 8.0 7.8 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 1.6 8.3 8.2 8.1 8.0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 1.7 8.2 8.3 8.2 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 1.5 8.0 8.2 8.3 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 7.6 0.9 7.9 8.0 8.2 8.3 1 0 0 0 0 0 0 0 0 0 0 25
26 7.6 1.5 7.6 7.9 8.0 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 8.3 1.4 7.6 7.6 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.4 1.6 8.3 7.6 7.6 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.4 1.7 8.4 8.3 7.6 7.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.4 1.4 8.4 8.4 8.3 7.6 0 0 0 0 0 1 0 0 0 0 0 30
31 8.4 1.8 8.4 8.4 8.4 8.3 0 0 0 0 0 0 1 0 0 0 0 31
32 8.6 1.7 8.4 8.4 8.4 8.4 0 0 0 0 0 0 0 1 0 0 0 32
33 8.9 1.4 8.6 8.4 8.4 8.4 0 0 0 0 0 0 0 0 1 0 0 33
34 8.8 1.2 8.9 8.6 8.4 8.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.3 1.0 8.8 8.9 8.6 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 7.5 1.7 8.3 8.8 8.9 8.6 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 2.4 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 37
38 7.4 2.0 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 38
39 8.8 2.1 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 39
40 9.3 2.0 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 40
41 9.3 1.8 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 8.7 2.7 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 42
43 8.2 2.3 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 43
44 8.3 1.9 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 44
45 8.5 2.0 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 45
46 8.6 2.3 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 46
47 8.5 2.8 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 8.2 2.4 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 48
49 8.1 2.3 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 2.7 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.6 2.7 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 51
52 8.7 2.9 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.7 3.0 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.5 2.2 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 8.4 2.3 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 55
56 8.5 2.8 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 8.7 2.8 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl `Yt-1` `Yt-2` `Yt-3` `Yt-4`
0.578720 -0.028898 1.301432 -0.500216 -0.406060 0.510606
M1 M2 M3 M4 M5 M6
-0.009013 0.001340 0.749204 -0.004507 0.279501 0.582882
M7 M8 M9 M10 M11 t
0.217843 0.425664 0.326809 0.053531 0.104635 0.001244
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.319759 -0.068924 0.003627 0.077367 0.267841
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.578720 0.838499 0.690 0.494164
Infl -0.028898 0.053355 -0.542 0.591165
`Yt-1` 1.301432 0.142453 9.136 3.10e-11 ***
`Yt-2` -0.500216 0.239085 -2.092 0.042978 *
`Yt-3` -0.406060 0.239948 -1.692 0.098568 .
`Yt-4` 0.510606 0.150148 3.401 0.001564 **
M1 -0.009013 0.107011 -0.084 0.933309
M2 0.001340 0.111548 0.012 0.990473
M3 0.749204 0.118082 6.345 1.72e-07 ***
M4 -0.004507 0.145622 -0.031 0.975469
M5 0.279501 0.145553 1.920 0.062159 .
M6 0.582882 0.141407 4.122 0.000190 ***
M7 0.217843 0.105199 2.071 0.045044 *
M8 0.425664 0.103074 4.130 0.000185 ***
M9 0.326809 0.110542 2.956 0.005260 **
M10 0.053531 0.122196 0.438 0.663746
M11 0.104635 0.112827 0.927 0.359429
t 0.001244 0.004874 0.255 0.799830
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1523 on 39 degrees of freedom
Multiple R-squared: 0.9717, Adjusted R-squared: 0.9594
F-statistic: 78.89 on 17 and 39 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,] 0.7890893 0.4218214 0.2109107
[2,] 0.6973423 0.6053154 0.3026577
[3,] 0.5719719 0.8560562 0.4280281
[4,] 0.5963005 0.8073991 0.4036995
[5,] 0.6653031 0.6693939 0.3346969
[6,] 0.6135987 0.7728026 0.3864013
[7,] 0.7570470 0.4859059 0.2429530
[8,] 0.6734682 0.6530635 0.3265318
[9,] 0.6689183 0.6621633 0.3310817
[10,] 0.7021279 0.5957442 0.2978721
[11,] 0.6546975 0.6906050 0.3453025
[12,] 0.5704624 0.8590752 0.4295376
[13,] 0.5221234 0.9557531 0.4778766
[14,] 0.5641893 0.8716214 0.4358107
[15,] 0.4644785 0.9289571 0.5355215
[16,] 0.8279396 0.3441207 0.1720604
> postscript(file="/var/www/html/rcomp/tmp/18pu81260026268.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2arbw1260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32yin1260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4it071260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5joxj1260026268.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 = 57
Frequency = 1
1 2 3 4 5 6
-0.068923915 -0.181247225 -0.264493365 0.166831717 -0.017819916 0.191417808
7 8 9 10 11 12
-0.179502533 0.127665496 0.192471759 -0.045787189 0.102757650 -0.040820575
13 14 15 16 17 18
-0.109778784 -0.016956801 -0.057447164 -0.139315988 0.054065413 0.077366693
19 20 21 22 23 24
0.058320753 -0.044913768 0.048776770 0.090732199 0.010984613 0.208405550
25 26 27 28 29 30
-0.162731058 0.153266355 0.012718694 -0.110795113 -0.019967622 0.001000472
31 32 33 34 35 36
0.059536412 -0.003479356 0.125175946 0.001043015 -0.195664164 -0.251654221
37 38 39 40 41 42
0.073592818 0.088723525 0.262608569 0.076889959 0.069838325 -0.092309137
43 44 45 46 47 48
0.028968135 0.003627495 -0.319758836 -0.045988026 0.081921902 0.084069246
49 50 51 52 53 54
0.267840939 -0.043785854 0.046613266 0.006389425 -0.086116199 -0.177475836
55 56 57
0.032677234 -0.082899868 -0.046665640
> postscript(file="/var/www/html/rcomp/tmp/64unn1260026268.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.068923915 NA
1 -0.181247225 -0.068923915
2 -0.264493365 -0.181247225
3 0.166831717 -0.264493365
4 -0.017819916 0.166831717
5 0.191417808 -0.017819916
6 -0.179502533 0.191417808
7 0.127665496 -0.179502533
8 0.192471759 0.127665496
9 -0.045787189 0.192471759
10 0.102757650 -0.045787189
11 -0.040820575 0.102757650
12 -0.109778784 -0.040820575
13 -0.016956801 -0.109778784
14 -0.057447164 -0.016956801
15 -0.139315988 -0.057447164
16 0.054065413 -0.139315988
17 0.077366693 0.054065413
18 0.058320753 0.077366693
19 -0.044913768 0.058320753
20 0.048776770 -0.044913768
21 0.090732199 0.048776770
22 0.010984613 0.090732199
23 0.208405550 0.010984613
24 -0.162731058 0.208405550
25 0.153266355 -0.162731058
26 0.012718694 0.153266355
27 -0.110795113 0.012718694
28 -0.019967622 -0.110795113
29 0.001000472 -0.019967622
30 0.059536412 0.001000472
31 -0.003479356 0.059536412
32 0.125175946 -0.003479356
33 0.001043015 0.125175946
34 -0.195664164 0.001043015
35 -0.251654221 -0.195664164
36 0.073592818 -0.251654221
37 0.088723525 0.073592818
38 0.262608569 0.088723525
39 0.076889959 0.262608569
40 0.069838325 0.076889959
41 -0.092309137 0.069838325
42 0.028968135 -0.092309137
43 0.003627495 0.028968135
44 -0.319758836 0.003627495
45 -0.045988026 -0.319758836
46 0.081921902 -0.045988026
47 0.084069246 0.081921902
48 0.267840939 0.084069246
49 -0.043785854 0.267840939
50 0.046613266 -0.043785854
51 0.006389425 0.046613266
52 -0.086116199 0.006389425
53 -0.177475836 -0.086116199
54 0.032677234 -0.177475836
55 -0.082899868 0.032677234
56 -0.046665640 -0.082899868
57 NA -0.046665640
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.181247225 -0.068923915
[2,] -0.264493365 -0.181247225
[3,] 0.166831717 -0.264493365
[4,] -0.017819916 0.166831717
[5,] 0.191417808 -0.017819916
[6,] -0.179502533 0.191417808
[7,] 0.127665496 -0.179502533
[8,] 0.192471759 0.127665496
[9,] -0.045787189 0.192471759
[10,] 0.102757650 -0.045787189
[11,] -0.040820575 0.102757650
[12,] -0.109778784 -0.040820575
[13,] -0.016956801 -0.109778784
[14,] -0.057447164 -0.016956801
[15,] -0.139315988 -0.057447164
[16,] 0.054065413 -0.139315988
[17,] 0.077366693 0.054065413
[18,] 0.058320753 0.077366693
[19,] -0.044913768 0.058320753
[20,] 0.048776770 -0.044913768
[21,] 0.090732199 0.048776770
[22,] 0.010984613 0.090732199
[23,] 0.208405550 0.010984613
[24,] -0.162731058 0.208405550
[25,] 0.153266355 -0.162731058
[26,] 0.012718694 0.153266355
[27,] -0.110795113 0.012718694
[28,] -0.019967622 -0.110795113
[29,] 0.001000472 -0.019967622
[30,] 0.059536412 0.001000472
[31,] -0.003479356 0.059536412
[32,] 0.125175946 -0.003479356
[33,] 0.001043015 0.125175946
[34,] -0.195664164 0.001043015
[35,] -0.251654221 -0.195664164
[36,] 0.073592818 -0.251654221
[37,] 0.088723525 0.073592818
[38,] 0.262608569 0.088723525
[39,] 0.076889959 0.262608569
[40,] 0.069838325 0.076889959
[41,] -0.092309137 0.069838325
[42,] 0.028968135 -0.092309137
[43,] 0.003627495 0.028968135
[44,] -0.319758836 0.003627495
[45,] -0.045988026 -0.319758836
[46,] 0.081921902 -0.045988026
[47,] 0.084069246 0.081921902
[48,] 0.267840939 0.084069246
[49,] -0.043785854 0.267840939
[50,] 0.046613266 -0.043785854
[51,] 0.006389425 0.046613266
[52,] -0.086116199 0.006389425
[53,] -0.177475836 -0.086116199
[54,] 0.032677234 -0.177475836
[55,] -0.082899868 0.032677234
[56,] -0.046665640 -0.082899868
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.181247225 -0.068923915
2 -0.264493365 -0.181247225
3 0.166831717 -0.264493365
4 -0.017819916 0.166831717
5 0.191417808 -0.017819916
6 -0.179502533 0.191417808
7 0.127665496 -0.179502533
8 0.192471759 0.127665496
9 -0.045787189 0.192471759
10 0.102757650 -0.045787189
11 -0.040820575 0.102757650
12 -0.109778784 -0.040820575
13 -0.016956801 -0.109778784
14 -0.057447164 -0.016956801
15 -0.139315988 -0.057447164
16 0.054065413 -0.139315988
17 0.077366693 0.054065413
18 0.058320753 0.077366693
19 -0.044913768 0.058320753
20 0.048776770 -0.044913768
21 0.090732199 0.048776770
22 0.010984613 0.090732199
23 0.208405550 0.010984613
24 -0.162731058 0.208405550
25 0.153266355 -0.162731058
26 0.012718694 0.153266355
27 -0.110795113 0.012718694
28 -0.019967622 -0.110795113
29 0.001000472 -0.019967622
30 0.059536412 0.001000472
31 -0.003479356 0.059536412
32 0.125175946 -0.003479356
33 0.001043015 0.125175946
34 -0.195664164 0.001043015
35 -0.251654221 -0.195664164
36 0.073592818 -0.251654221
37 0.088723525 0.073592818
38 0.262608569 0.088723525
39 0.076889959 0.262608569
40 0.069838325 0.076889959
41 -0.092309137 0.069838325
42 0.028968135 -0.092309137
43 0.003627495 0.028968135
44 -0.319758836 0.003627495
45 -0.045988026 -0.319758836
46 0.081921902 -0.045988026
47 0.084069246 0.081921902
48 0.267840939 0.084069246
49 -0.043785854 0.267840939
50 0.046613266 -0.043785854
51 0.006389425 0.046613266
52 -0.086116199 0.006389425
53 -0.177475836 -0.086116199
54 0.032677234 -0.177475836
55 -0.082899868 0.032677234
56 -0.046665640 -0.082899868
> 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/771xv1260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vip81260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y5ow1260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ckp21260026268.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11agsf1260026268.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/129tr01260026268.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/135p6p1260026268.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/142mms1260026268.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/15y89f1260026268.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/16860v1260026268.tab")
+ }
>
> system("convert tmp/18pu81260026268.ps tmp/18pu81260026268.png")
> system("convert tmp/2arbw1260026268.ps tmp/2arbw1260026268.png")
> system("convert tmp/32yin1260026268.ps tmp/32yin1260026268.png")
> system("convert tmp/4it071260026268.ps tmp/4it071260026268.png")
> system("convert tmp/5joxj1260026268.ps tmp/5joxj1260026268.png")
> system("convert tmp/64unn1260026268.ps tmp/64unn1260026268.png")
> system("convert tmp/771xv1260026268.ps tmp/771xv1260026268.png")
> system("convert tmp/8vip81260026268.ps tmp/8vip81260026268.png")
> system("convert tmp/9y5ow1260026268.ps tmp/9y5ow1260026268.png")
> system("convert tmp/10ckp21260026268.ps tmp/10ckp21260026268.png")
>
>
> proc.time()
user system elapsed
2.306 1.553 2.882