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 'license()' or 'licence()' for distribution details.
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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(7.8
+ ,9.5
+ ,7.6
+ ,7.5
+ ,7.7
+ ,8.1
+ ,7.8
+ ,9.6
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.7
+ ,7.8
+ ,9.5
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.5
+ ,9.1
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,8.9
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.1
+ ,9
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.5
+ ,10.1
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.5
+ ,10.3
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.6
+ ,10.2
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.7
+ ,9.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.7
+ ,9.2
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.9
+ ,9.3
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,8.1
+ ,9.4
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,8.2
+ ,9.4
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,8.2
+ ,9.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,8.2
+ ,9
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.9
+ ,9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.3
+ ,9
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,6.9
+ ,9.8
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,6.6
+ ,10
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,6.7
+ ,9.8
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,6.9
+ ,9.3
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7
+ ,9
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.1
+ ,9
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,7.2
+ ,9.1
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,7.1
+ ,9.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.9
+ ,9.1
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,7
+ ,9.2
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.8
+ ,8.8
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.4
+ ,8.3
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,6.7
+ ,8.4
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,6.6
+ ,8.1
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.4
+ ,7.7
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,6.3
+ ,7.9
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.2
+ ,7.9
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.5
+ ,8
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.8
+ ,7.9
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.8
+ ,7.6
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,7.1
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.1
+ ,6.8
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,5.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.1
+ ,6.9
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.2
+ ,8.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.3
+ ,8.7
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.9
+ ,8.3
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,7.9
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,7.5
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.2
+ ,7.8
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.1
+ ,8.3
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.7
+ ,8.4
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,7.9
+ ,8.2
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,7.7
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,7.4
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,7.5
+ ,7.3
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,8
+ ,8.1
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8
+ ,7.5
+ ,7.4
+ ,7.7)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> 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 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.8 9.5 7.6 7.5 7.7 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 7.8 9.6 7.8 7.6 7.5 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 9.5 7.8 7.8 7.6 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 9.1 7.8 7.8 7.8 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 8.9 7.5 7.8 7.8 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.1 9.0 7.5 7.5 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 10.1 7.1 7.5 7.5 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 10.3 7.5 7.1 7.5 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 10.2 7.5 7.5 7.1 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.7 9.6 7.6 7.5 7.5 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 7.7 9.2 7.7 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.9 9.3 7.7 7.7 7.6 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 9.4 7.9 7.7 7.7 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 8.2 9.4 8.1 7.9 7.7 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 9.2 8.2 8.1 7.9 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 9.0 8.2 8.2 8.1 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 7.9 9.0 8.2 8.2 8.2 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 7.3 9.0 7.9 8.2 8.2 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 6.9 9.8 7.3 7.9 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 6.6 10.0 6.9 7.3 7.9 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.7 9.8 6.6 6.9 7.3 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 6.9 9.3 6.7 6.6 6.9 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 7.0 9.0 6.9 6.7 6.6 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 7.1 9.0 7.0 6.9 6.7 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 7.2 9.1 7.1 7.0 6.9 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 7.1 9.1 7.2 7.1 7.0 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 6.9 9.1 7.1 7.2 7.1 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.0 9.2 6.9 7.1 7.2 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 6.8 8.8 7.0 6.9 7.1 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 6.4 8.3 6.8 7.0 6.9 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 6.7 8.4 6.4 6.8 7.0 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 6.6 8.1 6.7 6.4 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 6.4 7.7 6.6 6.7 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 6.3 7.9 6.4 6.6 6.7 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 6.2 7.9 6.3 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 6.5 8.0 6.2 6.3 6.4 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 6.8 7.9 6.5 6.2 6.3 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 6.8 7.6 6.8 6.5 6.2 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 6.4 7.1 6.8 6.8 6.5 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.1 6.8 6.4 6.8 6.8 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.8 6.5 6.1 6.4 6.8 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 6.9 5.8 6.1 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.2 8.2 6.1 5.8 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 8.7 7.2 6.1 5.8 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.9 8.3 7.3 7.2 6.1 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 7.9 6.9 7.3 7.2 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 5.8 7.5 6.1 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 6.2 7.8 5.8 6.1 6.9 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.1 8.3 6.2 5.8 6.1 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 8.4 7.1 6.2 5.8 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.9 8.2 7.7 7.1 6.2 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 7.7 7.9 7.7 7.1 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 7.4 7.2 7.7 7.9 7.7 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 7.3 7.4 7.7 7.9 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 8.0 8.1 7.5 7.4 7.7 7.9 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 8.5 8.0 7.5 7.4 7.7 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-0.229570 0.047914 1.509569 -0.717207 -0.234146 0.425498
M1 M2 M3 M4 M5 M6
-0.082265 -0.260023 -0.178610 -0.118255 -0.292381 -0.336368
M7 M8 M9 M10 M11 t
0.144797 -0.618739 -0.331452 -0.174684 -0.253212 0.004933
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.50152 -0.11266 0.01887 0.11035 0.37487
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.229570 0.673358 -0.341 0.735032
X 0.047914 0.070483 0.680 0.500759
Y1 1.509569 0.154082 9.797 6.01e-12 ***
Y2 -0.717207 0.286072 -2.507 0.016567 *
Y3 -0.234146 0.288790 -0.811 0.422540
Y4 0.425498 0.161585 2.633 0.012161 *
M1 -0.082265 0.143541 -0.573 0.569944
M2 -0.260023 0.148693 -1.749 0.088414 .
M3 -0.178610 0.149902 -1.192 0.240843
M4 -0.118255 0.149977 -0.788 0.435304
M5 -0.292381 0.152041 -1.923 0.061992 .
M6 -0.336368 0.150705 -2.232 0.031590 *
M7 0.144797 0.142972 1.013 0.317578
M8 -0.618739 0.156864 -3.944 0.000333 ***
M9 -0.331452 0.178646 -1.855 0.071314 .
M10 -0.174684 0.158005 -1.106 0.275867
M11 -0.253212 0.147162 -1.721 0.093451 .
t 0.004933 0.003294 1.498 0.142450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2061 on 38 degrees of freedom
Multiple R-squared: 0.9332, Adjusted R-squared: 0.9034
F-statistic: 31.24 on 17 and 38 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,] 0.3254148 0.65082951 0.67458525
[2,] 0.8202538 0.35949240 0.17974620
[3,] 0.7667928 0.46641438 0.23320719
[4,] 0.7116263 0.57674738 0.28837369
[5,] 0.5997373 0.80052541 0.40026270
[6,] 0.4708849 0.94176989 0.52911506
[7,] 0.3442818 0.68856352 0.65571824
[8,] 0.3603916 0.72078318 0.63960841
[9,] 0.2662926 0.53258526 0.73370737
[10,] 0.6128497 0.77430059 0.38715029
[11,] 0.9006140 0.19877190 0.09938595
[12,] 0.9621778 0.07564432 0.03782216
[13,] 0.9307515 0.13849702 0.06924851
[14,] 0.9372334 0.12553328 0.06276664
[15,] 0.8664630 0.26707408 0.13353704
> postscript(file="/var/www/html/rcomp/tmp/1uohv1259258300.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m5ff1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hx6u1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42ndp1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gwmb1259258300.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 = 56
Frequency = 1
1 2 3 4 5 6
-0.085560533 -0.024350372 0.146049799 -0.195793288 0.350753416 -0.230146505
7 8 9 10 11 12
0.164633722 0.150592776 0.156387919 0.236335581 0.079660073 0.111858889
13 14 15 16 17 18
0.063350564 0.135152917 0.097702548 0.075447578 -0.117044536 -0.267670099
19 20 21 22 23 24
-0.501520262 0.050759143 0.021270873 -0.120952212 -0.063221217 -0.077817711
25 26 27 28 29 30
0.019766172 -0.048330521 -0.131135039 0.109843393 -0.262160883 -0.229795257
31 32 33 34 35 36
0.148215483 -0.007940167 -0.123435046 0.075916863 -0.094037344 0.017982929
37 38 39 40 41 42
-0.062799966 -0.094174586 -0.228608766 -0.033100740 -0.111195332 0.352742811
43 44 45 46 47 48
0.336279230 -0.217032895 -0.054223747 -0.191300232 0.077598487 -0.052024107
49 50 51 52 53 54
0.065243762 0.031702561 0.115991457 0.043603055 0.139647335 0.374869050
55 56
-0.147608173 0.023621143
> postscript(file="/var/www/html/rcomp/tmp/6mk9y1259258300.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.085560533 NA
1 -0.024350372 -0.085560533
2 0.146049799 -0.024350372
3 -0.195793288 0.146049799
4 0.350753416 -0.195793288
5 -0.230146505 0.350753416
6 0.164633722 -0.230146505
7 0.150592776 0.164633722
8 0.156387919 0.150592776
9 0.236335581 0.156387919
10 0.079660073 0.236335581
11 0.111858889 0.079660073
12 0.063350564 0.111858889
13 0.135152917 0.063350564
14 0.097702548 0.135152917
15 0.075447578 0.097702548
16 -0.117044536 0.075447578
17 -0.267670099 -0.117044536
18 -0.501520262 -0.267670099
19 0.050759143 -0.501520262
20 0.021270873 0.050759143
21 -0.120952212 0.021270873
22 -0.063221217 -0.120952212
23 -0.077817711 -0.063221217
24 0.019766172 -0.077817711
25 -0.048330521 0.019766172
26 -0.131135039 -0.048330521
27 0.109843393 -0.131135039
28 -0.262160883 0.109843393
29 -0.229795257 -0.262160883
30 0.148215483 -0.229795257
31 -0.007940167 0.148215483
32 -0.123435046 -0.007940167
33 0.075916863 -0.123435046
34 -0.094037344 0.075916863
35 0.017982929 -0.094037344
36 -0.062799966 0.017982929
37 -0.094174586 -0.062799966
38 -0.228608766 -0.094174586
39 -0.033100740 -0.228608766
40 -0.111195332 -0.033100740
41 0.352742811 -0.111195332
42 0.336279230 0.352742811
43 -0.217032895 0.336279230
44 -0.054223747 -0.217032895
45 -0.191300232 -0.054223747
46 0.077598487 -0.191300232
47 -0.052024107 0.077598487
48 0.065243762 -0.052024107
49 0.031702561 0.065243762
50 0.115991457 0.031702561
51 0.043603055 0.115991457
52 0.139647335 0.043603055
53 0.374869050 0.139647335
54 -0.147608173 0.374869050
55 0.023621143 -0.147608173
56 NA 0.023621143
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.024350372 -0.085560533
[2,] 0.146049799 -0.024350372
[3,] -0.195793288 0.146049799
[4,] 0.350753416 -0.195793288
[5,] -0.230146505 0.350753416
[6,] 0.164633722 -0.230146505
[7,] 0.150592776 0.164633722
[8,] 0.156387919 0.150592776
[9,] 0.236335581 0.156387919
[10,] 0.079660073 0.236335581
[11,] 0.111858889 0.079660073
[12,] 0.063350564 0.111858889
[13,] 0.135152917 0.063350564
[14,] 0.097702548 0.135152917
[15,] 0.075447578 0.097702548
[16,] -0.117044536 0.075447578
[17,] -0.267670099 -0.117044536
[18,] -0.501520262 -0.267670099
[19,] 0.050759143 -0.501520262
[20,] 0.021270873 0.050759143
[21,] -0.120952212 0.021270873
[22,] -0.063221217 -0.120952212
[23,] -0.077817711 -0.063221217
[24,] 0.019766172 -0.077817711
[25,] -0.048330521 0.019766172
[26,] -0.131135039 -0.048330521
[27,] 0.109843393 -0.131135039
[28,] -0.262160883 0.109843393
[29,] -0.229795257 -0.262160883
[30,] 0.148215483 -0.229795257
[31,] -0.007940167 0.148215483
[32,] -0.123435046 -0.007940167
[33,] 0.075916863 -0.123435046
[34,] -0.094037344 0.075916863
[35,] 0.017982929 -0.094037344
[36,] -0.062799966 0.017982929
[37,] -0.094174586 -0.062799966
[38,] -0.228608766 -0.094174586
[39,] -0.033100740 -0.228608766
[40,] -0.111195332 -0.033100740
[41,] 0.352742811 -0.111195332
[42,] 0.336279230 0.352742811
[43,] -0.217032895 0.336279230
[44,] -0.054223747 -0.217032895
[45,] -0.191300232 -0.054223747
[46,] 0.077598487 -0.191300232
[47,] -0.052024107 0.077598487
[48,] 0.065243762 -0.052024107
[49,] 0.031702561 0.065243762
[50,] 0.115991457 0.031702561
[51,] 0.043603055 0.115991457
[52,] 0.139647335 0.043603055
[53,] 0.374869050 0.139647335
[54,] -0.147608173 0.374869050
[55,] 0.023621143 -0.147608173
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.024350372 -0.085560533
2 0.146049799 -0.024350372
3 -0.195793288 0.146049799
4 0.350753416 -0.195793288
5 -0.230146505 0.350753416
6 0.164633722 -0.230146505
7 0.150592776 0.164633722
8 0.156387919 0.150592776
9 0.236335581 0.156387919
10 0.079660073 0.236335581
11 0.111858889 0.079660073
12 0.063350564 0.111858889
13 0.135152917 0.063350564
14 0.097702548 0.135152917
15 0.075447578 0.097702548
16 -0.117044536 0.075447578
17 -0.267670099 -0.117044536
18 -0.501520262 -0.267670099
19 0.050759143 -0.501520262
20 0.021270873 0.050759143
21 -0.120952212 0.021270873
22 -0.063221217 -0.120952212
23 -0.077817711 -0.063221217
24 0.019766172 -0.077817711
25 -0.048330521 0.019766172
26 -0.131135039 -0.048330521
27 0.109843393 -0.131135039
28 -0.262160883 0.109843393
29 -0.229795257 -0.262160883
30 0.148215483 -0.229795257
31 -0.007940167 0.148215483
32 -0.123435046 -0.007940167
33 0.075916863 -0.123435046
34 -0.094037344 0.075916863
35 0.017982929 -0.094037344
36 -0.062799966 0.017982929
37 -0.094174586 -0.062799966
38 -0.228608766 -0.094174586
39 -0.033100740 -0.228608766
40 -0.111195332 -0.033100740
41 0.352742811 -0.111195332
42 0.336279230 0.352742811
43 -0.217032895 0.336279230
44 -0.054223747 -0.217032895
45 -0.191300232 -0.054223747
46 0.077598487 -0.191300232
47 -0.052024107 0.077598487
48 0.065243762 -0.052024107
49 0.031702561 0.065243762
50 0.115991457 0.031702561
51 0.043603055 0.115991457
52 0.139647335 0.043603055
53 0.374869050 0.139647335
54 -0.147608173 0.374869050
55 0.023621143 -0.147608173
> 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/7z4sz1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hsdu1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93fa81259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ta3a1259258300.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ggnq1259258300.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/127e3w1259258300.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/13qkvm1259258300.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/142y971259258300.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/15gj461259258300.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/16hgyd1259258300.tab")
+ }
>
> system("convert tmp/1uohv1259258300.ps tmp/1uohv1259258300.png")
> system("convert tmp/2m5ff1259258300.ps tmp/2m5ff1259258300.png")
> system("convert tmp/3hx6u1259258300.ps tmp/3hx6u1259258300.png")
> system("convert tmp/42ndp1259258300.ps tmp/42ndp1259258300.png")
> system("convert tmp/5gwmb1259258300.ps tmp/5gwmb1259258300.png")
> system("convert tmp/6mk9y1259258300.ps tmp/6mk9y1259258300.png")
> system("convert tmp/7z4sz1259258300.ps tmp/7z4sz1259258300.png")
> system("convert tmp/8hsdu1259258300.ps tmp/8hsdu1259258300.png")
> system("convert tmp/93fa81259258300.ps tmp/93fa81259258300.png")
> system("convert tmp/10ta3a1259258300.ps tmp/10ta3a1259258300.png")
>
>
> proc.time()
user system elapsed
2.345 1.546 3.745