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 '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(7.3
+ ,20.9
+ ,7.4
+ ,8.1
+ ,8.3
+ ,8.2
+ ,7.7
+ ,20.9
+ ,7.3
+ ,7.4
+ ,8.1
+ ,8.3
+ ,8
+ ,22.3
+ ,7.7
+ ,7.3
+ ,7.4
+ ,8.1
+ ,8
+ ,22.3
+ ,8
+ ,7.7
+ ,7.3
+ ,7.4
+ ,7.7
+ ,22.3
+ ,8
+ ,8
+ ,7.7
+ ,7.3
+ ,6.9
+ ,19.9
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,6.6
+ ,19.9
+ ,6.9
+ ,7.7
+ ,8
+ ,8
+ ,6.9
+ ,19.9
+ ,6.6
+ ,6.9
+ ,7.7
+ ,8
+ ,7.5
+ ,24.1
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.7
+ ,7.9
+ ,24.1
+ ,7.5
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.7
+ ,24.1
+ ,7.9
+ ,7.5
+ ,6.9
+ ,6.6
+ ,6.5
+ ,13.8
+ ,7.7
+ ,7.9
+ ,7.5
+ ,6.9
+ ,6.1
+ ,13.8
+ ,6.5
+ ,7.7
+ ,7.9
+ ,7.5
+ ,6.4
+ ,13.8
+ ,6.1
+ ,6.5
+ ,7.7
+ ,7.9
+ ,6.8
+ ,16.2
+ ,6.4
+ ,6.1
+ ,6.5
+ ,7.7
+ ,7.1
+ ,16.2
+ ,6.8
+ ,6.4
+ ,6.1
+ ,6.5
+ ,7.3
+ ,16.2
+ ,7.1
+ ,6.8
+ ,6.4
+ ,6.1
+ ,7.2
+ ,18.6
+ ,7.3
+ ,7.1
+ ,6.8
+ ,6.4
+ ,7
+ ,18.6
+ ,7.2
+ ,7.3
+ ,7.1
+ ,6.8
+ ,7
+ ,18.6
+ ,7
+ ,7.2
+ ,7.3
+ ,7.1
+ ,7
+ ,22.4
+ ,7
+ ,7
+ ,7.2
+ ,7.3
+ ,7.3
+ ,22.4
+ ,7
+ ,7
+ ,7
+ ,7.2
+ ,7.5
+ ,22.4
+ ,7.3
+ ,7
+ ,7
+ ,7
+ ,7.2
+ ,22.6
+ ,7.5
+ ,7.3
+ ,7
+ ,7
+ ,7.7
+ ,22.6
+ ,7.2
+ ,7.5
+ ,7.3
+ ,7
+ ,8
+ ,22.6
+ ,7.7
+ ,7.2
+ ,7.5
+ ,7.3
+ ,7.9
+ ,20
+ ,8
+ ,7.7
+ ,7.2
+ ,7.5
+ ,8
+ ,20
+ ,7.9
+ ,8
+ ,7.7
+ ,7.2
+ ,8
+ ,20
+ ,8
+ ,7.9
+ ,8
+ ,7.7
+ ,7.9
+ ,21.8
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,7.9
+ ,21.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,21.8
+ ,7.9
+ ,7.9
+ ,8
+ ,8
+ ,8.1
+ ,28.7
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,28.7
+ ,8.1
+ ,8
+ ,7.9
+ ,7.9
+ ,8.2
+ ,28.7
+ ,8.1
+ ,8.1
+ ,8
+ ,7.9
+ ,8
+ ,19.5
+ ,8.2
+ ,8.1
+ ,8.1
+ ,8
+ ,8.3
+ ,19.5
+ ,8
+ ,8.2
+ ,8.1
+ ,8.1
+ ,8.5
+ ,19.5
+ ,8.3
+ ,8
+ ,8.2
+ ,8.1
+ ,8.6
+ ,19.4
+ ,8.5
+ ,8.3
+ ,8
+ ,8.2
+ ,8.7
+ ,19.4
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8
+ ,8.7
+ ,19.4
+ ,8.7
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.5
+ ,21.7
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,8.4
+ ,21.7
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,21.7
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,26.2
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,26.2
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,26.2
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,7.9
+ ,19.1
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.1
+ ,19.1
+ ,7.9
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.2
+ ,19.1
+ ,8.1
+ ,7.9
+ ,8.6
+ ,8.7
+ ,8.5
+ ,21.3
+ ,8.2
+ ,8.1
+ ,7.9
+ ,8.6
+ ,8.6
+ ,21.3
+ ,8.5
+ ,8.2
+ ,8.1
+ ,7.9
+ ,8.5
+ ,21.3
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.1
+ ,8.3
+ ,24.1
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.2
+ ,24.1
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.7
+ ,24.1
+ ,8.2
+ ,8.3
+ ,8.5
+ ,8.6)
+ ,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 7.3 20.9 7.4 8.1 8.3 8.2 1 0 0 0 0 0 0 0 0 0 0 1
2 7.7 20.9 7.3 7.4 8.1 8.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.0 22.3 7.7 7.3 7.4 8.1 0 0 1 0 0 0 0 0 0 0 0 3
4 8.0 22.3 8.0 7.7 7.3 7.4 0 0 0 1 0 0 0 0 0 0 0 4
5 7.7 22.3 8.0 8.0 7.7 7.3 0 0 0 0 1 0 0 0 0 0 0 5
6 6.9 19.9 7.7 8.0 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 6
7 6.6 19.9 6.9 7.7 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 7
8 6.9 19.9 6.6 6.9 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 24.1 6.9 6.6 6.9 7.7 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 24.1 7.5 6.9 6.6 6.9 0 0 0 0 0 0 0 0 0 1 0 10
11 7.7 24.1 7.9 7.5 6.9 6.6 0 0 0 0 0 0 0 0 0 0 1 11
12 6.5 13.8 7.7 7.9 7.5 6.9 0 0 0 0 0 0 0 0 0 0 0 12
13 6.1 13.8 6.5 7.7 7.9 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 6.4 13.8 6.1 6.5 7.7 7.9 0 1 0 0 0 0 0 0 0 0 0 14
15 6.8 16.2 6.4 6.1 6.5 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 7.1 16.2 6.8 6.4 6.1 6.5 0 0 0 1 0 0 0 0 0 0 0 16
17 7.3 16.2 7.1 6.8 6.4 6.1 0 0 0 0 1 0 0 0 0 0 0 17
18 7.2 18.6 7.3 7.1 6.8 6.4 0 0 0 0 0 1 0 0 0 0 0 18
19 7.0 18.6 7.2 7.3 7.1 6.8 0 0 0 0 0 0 1 0 0 0 0 19
20 7.0 18.6 7.0 7.2 7.3 7.1 0 0 0 0 0 0 0 1 0 0 0 20
21 7.0 22.4 7.0 7.0 7.2 7.3 0 0 0 0 0 0 0 0 1 0 0 21
22 7.3 22.4 7.0 7.0 7.0 7.2 0 0 0 0 0 0 0 0 0 1 0 22
23 7.5 22.4 7.3 7.0 7.0 7.0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.2 22.6 7.5 7.3 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 24
25 7.7 22.6 7.2 7.5 7.3 7.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 22.6 7.7 7.2 7.5 7.3 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 20.0 8.0 7.7 7.2 7.5 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 20.0 7.9 8.0 7.7 7.2 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 20.0 8.0 7.9 8.0 7.7 0 0 0 0 1 0 0 0 0 0 0 29
30 7.9 21.8 8.0 8.0 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 21.8 7.9 8.0 8.0 7.9 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 21.8 7.9 7.9 8.0 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 28.7 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 28.7 8.1 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 1 0 34
35 8.2 28.7 8.1 8.1 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 19.5 8.2 8.1 8.1 8.0 0 0 0 0 0 0 0 0 0 0 0 36
37 8.3 19.5 8.0 8.2 8.1 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 8.5 19.5 8.3 8.0 8.2 8.1 0 1 0 0 0 0 0 0 0 0 0 38
39 8.6 19.4 8.5 8.3 8.0 8.2 0 0 1 0 0 0 0 0 0 0 0 39
40 8.7 19.4 8.6 8.5 8.3 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 8.7 19.4 8.7 8.6 8.5 8.3 0 0 0 0 1 0 0 0 0 0 0 41
42 8.5 21.7 8.7 8.7 8.6 8.5 0 0 0 0 0 1 0 0 0 0 0 42
43 8.4 21.7 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 43
44 8.5 21.7 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 44
45 8.7 26.2 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 45
46 8.7 26.2 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 46
47 8.6 26.2 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 47
48 7.9 19.1 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 48
49 8.1 19.1 7.9 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 49
50 8.2 19.1 8.1 7.9 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 50
51 8.5 21.3 8.2 8.1 7.9 8.6 0 0 1 0 0 0 0 0 0 0 0 51
52 8.6 21.3 8.5 8.2 8.1 7.9 0 0 0 1 0 0 0 0 0 0 0 52
53 8.5 21.3 8.6 8.5 8.2 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.3 24.1 8.5 8.6 8.5 8.2 0 0 0 0 0 1 0 0 0 0 0 54
55 8.2 24.1 8.3 8.5 8.6 8.5 0 0 0 0 0 0 1 0 0 0 0 55
56 8.7 24.1 8.2 8.3 8.5 8.6 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
0.550380 0.031912 1.299066 -0.702880 -0.105447 0.276468
M1 M2 M3 M4 M5 M6
0.855906 0.494903 0.345195 0.563227 0.500826 0.255446
M7 M8 M9 M10 M11 t
0.401571 0.542381 0.274629 0.305464 0.284068 0.006858
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.265825 -0.101292 -0.006086 0.085408 0.263802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.550380 0.398630 1.381 0.17545
X 0.031912 0.011769 2.712 0.01000 *
Y1 1.299066 0.154282 8.420 3.23e-10 ***
Y2 -0.702880 0.267159 -2.631 0.01223 *
Y3 -0.105447 0.265330 -0.397 0.69328
Y4 0.276468 0.138013 2.003 0.05233 .
M1 0.855906 0.128063 6.683 6.61e-08 ***
M2 0.494903 0.150247 3.294 0.00214 **
M3 0.345195 0.137688 2.507 0.01657 *
M4 0.563227 0.103924 5.420 3.56e-06 ***
M5 0.500826 0.102889 4.868 2.01e-05 ***
M6 0.255446 0.105050 2.432 0.01985 *
M7 0.401571 0.113590 3.535 0.00109 **
M8 0.542381 0.117994 4.597 4.64e-05 ***
M9 0.274629 0.142074 1.933 0.06071 .
M10 0.305464 0.134773 2.267 0.02920 *
M11 0.284068 0.130173 2.182 0.03534 *
t 0.006858 0.002036 3.369 0.00174 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1508 on 38 degrees of freedom
Multiple R-squared: 0.9655, Adjusted R-squared: 0.9501
F-statistic: 62.6 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,] 0.1038322 0.2076644 0.8961678
[2,] 0.4311217 0.8622435 0.5688783
[3,] 0.5122365 0.9755269 0.4877635
[4,] 0.5714274 0.8571453 0.4285726
[5,] 0.4896938 0.9793875 0.5103062
[6,] 0.5204650 0.9590699 0.4795350
[7,] 0.6036198 0.7927603 0.3963802
[8,] 0.7424601 0.5150797 0.2575399
[9,] 0.6334957 0.7330086 0.3665043
[10,] 0.5469276 0.9061448 0.4530724
[11,] 0.4440257 0.8880515 0.5559743
[12,] 0.4635217 0.9270434 0.5364783
[13,] 0.4495294 0.8990588 0.5504706
[14,] 0.4855633 0.9711267 0.5144367
[15,] 0.5491250 0.9017500 0.4508750
> postscript(file="/var/www/html/rcomp/tmp/1ic4b1258581204.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hxpc1258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hn5i1258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45kwj1258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cg2n1258581204.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
-0.091693072 0.251606300 0.041345246 -0.109130222 -0.072897964 -0.247019789
7 8 9 10 11 12
0.045445401 -0.006441748 0.118421034 0.101692139 -0.067094245 -0.139897737
13 14 15 16 17 18
-0.108060552 0.090578252 -0.185275896 -0.129346134 0.159849934 0.132072942
19 20 21 22 23 24
-0.029380899 -0.049375400 -0.116160792 0.152703122 0.032814296 -0.045307665
25 26 27 28 29 30
0.153857979 -0.114245123 -0.113631860 0.237912409 -0.013339663 0.044544077
31 32 33 34 35 36
0.059658847 -0.085944777 -0.085694328 -0.155359556 0.040010636 0.263801942
37 38 39 40 41 42
0.003491883 0.037885781 0.186241267 0.158947942 0.093020774 0.083684834
43 44 45 46 47 48
0.073412598 -0.012572392 0.083434086 -0.099035704 -0.005730687 -0.078596540
49 50 51 52 53 54
0.042403762 -0.265825210 0.071321244 -0.158383995 -0.166633081 -0.013282064
55 56
-0.149135947 0.154334317
> postscript(file="/var/www/html/rcomp/tmp/68ksz1258581204.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 -0.091693072 NA
1 0.251606300 -0.091693072
2 0.041345246 0.251606300
3 -0.109130222 0.041345246
4 -0.072897964 -0.109130222
5 -0.247019789 -0.072897964
6 0.045445401 -0.247019789
7 -0.006441748 0.045445401
8 0.118421034 -0.006441748
9 0.101692139 0.118421034
10 -0.067094245 0.101692139
11 -0.139897737 -0.067094245
12 -0.108060552 -0.139897737
13 0.090578252 -0.108060552
14 -0.185275896 0.090578252
15 -0.129346134 -0.185275896
16 0.159849934 -0.129346134
17 0.132072942 0.159849934
18 -0.029380899 0.132072942
19 -0.049375400 -0.029380899
20 -0.116160792 -0.049375400
21 0.152703122 -0.116160792
22 0.032814296 0.152703122
23 -0.045307665 0.032814296
24 0.153857979 -0.045307665
25 -0.114245123 0.153857979
26 -0.113631860 -0.114245123
27 0.237912409 -0.113631860
28 -0.013339663 0.237912409
29 0.044544077 -0.013339663
30 0.059658847 0.044544077
31 -0.085944777 0.059658847
32 -0.085694328 -0.085944777
33 -0.155359556 -0.085694328
34 0.040010636 -0.155359556
35 0.263801942 0.040010636
36 0.003491883 0.263801942
37 0.037885781 0.003491883
38 0.186241267 0.037885781
39 0.158947942 0.186241267
40 0.093020774 0.158947942
41 0.083684834 0.093020774
42 0.073412598 0.083684834
43 -0.012572392 0.073412598
44 0.083434086 -0.012572392
45 -0.099035704 0.083434086
46 -0.005730687 -0.099035704
47 -0.078596540 -0.005730687
48 0.042403762 -0.078596540
49 -0.265825210 0.042403762
50 0.071321244 -0.265825210
51 -0.158383995 0.071321244
52 -0.166633081 -0.158383995
53 -0.013282064 -0.166633081
54 -0.149135947 -0.013282064
55 0.154334317 -0.149135947
56 NA 0.154334317
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.251606300 -0.091693072
[2,] 0.041345246 0.251606300
[3,] -0.109130222 0.041345246
[4,] -0.072897964 -0.109130222
[5,] -0.247019789 -0.072897964
[6,] 0.045445401 -0.247019789
[7,] -0.006441748 0.045445401
[8,] 0.118421034 -0.006441748
[9,] 0.101692139 0.118421034
[10,] -0.067094245 0.101692139
[11,] -0.139897737 -0.067094245
[12,] -0.108060552 -0.139897737
[13,] 0.090578252 -0.108060552
[14,] -0.185275896 0.090578252
[15,] -0.129346134 -0.185275896
[16,] 0.159849934 -0.129346134
[17,] 0.132072942 0.159849934
[18,] -0.029380899 0.132072942
[19,] -0.049375400 -0.029380899
[20,] -0.116160792 -0.049375400
[21,] 0.152703122 -0.116160792
[22,] 0.032814296 0.152703122
[23,] -0.045307665 0.032814296
[24,] 0.153857979 -0.045307665
[25,] -0.114245123 0.153857979
[26,] -0.113631860 -0.114245123
[27,] 0.237912409 -0.113631860
[28,] -0.013339663 0.237912409
[29,] 0.044544077 -0.013339663
[30,] 0.059658847 0.044544077
[31,] -0.085944777 0.059658847
[32,] -0.085694328 -0.085944777
[33,] -0.155359556 -0.085694328
[34,] 0.040010636 -0.155359556
[35,] 0.263801942 0.040010636
[36,] 0.003491883 0.263801942
[37,] 0.037885781 0.003491883
[38,] 0.186241267 0.037885781
[39,] 0.158947942 0.186241267
[40,] 0.093020774 0.158947942
[41,] 0.083684834 0.093020774
[42,] 0.073412598 0.083684834
[43,] -0.012572392 0.073412598
[44,] 0.083434086 -0.012572392
[45,] -0.099035704 0.083434086
[46,] -0.005730687 -0.099035704
[47,] -0.078596540 -0.005730687
[48,] 0.042403762 -0.078596540
[49,] -0.265825210 0.042403762
[50,] 0.071321244 -0.265825210
[51,] -0.158383995 0.071321244
[52,] -0.166633081 -0.158383995
[53,] -0.013282064 -0.166633081
[54,] -0.149135947 -0.013282064
[55,] 0.154334317 -0.149135947
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.251606300 -0.091693072
2 0.041345246 0.251606300
3 -0.109130222 0.041345246
4 -0.072897964 -0.109130222
5 -0.247019789 -0.072897964
6 0.045445401 -0.247019789
7 -0.006441748 0.045445401
8 0.118421034 -0.006441748
9 0.101692139 0.118421034
10 -0.067094245 0.101692139
11 -0.139897737 -0.067094245
12 -0.108060552 -0.139897737
13 0.090578252 -0.108060552
14 -0.185275896 0.090578252
15 -0.129346134 -0.185275896
16 0.159849934 -0.129346134
17 0.132072942 0.159849934
18 -0.029380899 0.132072942
19 -0.049375400 -0.029380899
20 -0.116160792 -0.049375400
21 0.152703122 -0.116160792
22 0.032814296 0.152703122
23 -0.045307665 0.032814296
24 0.153857979 -0.045307665
25 -0.114245123 0.153857979
26 -0.113631860 -0.114245123
27 0.237912409 -0.113631860
28 -0.013339663 0.237912409
29 0.044544077 -0.013339663
30 0.059658847 0.044544077
31 -0.085944777 0.059658847
32 -0.085694328 -0.085944777
33 -0.155359556 -0.085694328
34 0.040010636 -0.155359556
35 0.263801942 0.040010636
36 0.003491883 0.263801942
37 0.037885781 0.003491883
38 0.186241267 0.037885781
39 0.158947942 0.186241267
40 0.093020774 0.158947942
41 0.083684834 0.093020774
42 0.073412598 0.083684834
43 -0.012572392 0.073412598
44 0.083434086 -0.012572392
45 -0.099035704 0.083434086
46 -0.005730687 -0.099035704
47 -0.078596540 -0.005730687
48 0.042403762 -0.078596540
49 -0.265825210 0.042403762
50 0.071321244 -0.265825210
51 -0.158383995 0.071321244
52 -0.166633081 -0.158383995
53 -0.013282064 -0.166633081
54 -0.149135947 -0.013282064
55 0.154334317 -0.149135947
> 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/7pon91258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8h5xg1258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9825t1258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10x3f71258581204.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110t9k1258581204.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/1228381258581204.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/13700t1258581204.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/14n19i1258581204.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/15ybvl1258581204.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/16l2t61258581204.tab")
+ }
>
> system("convert tmp/1ic4b1258581204.ps tmp/1ic4b1258581204.png")
> system("convert tmp/2hxpc1258581204.ps tmp/2hxpc1258581204.png")
> system("convert tmp/3hn5i1258581204.ps tmp/3hn5i1258581204.png")
> system("convert tmp/45kwj1258581204.ps tmp/45kwj1258581204.png")
> system("convert tmp/5cg2n1258581204.ps tmp/5cg2n1258581204.png")
> system("convert tmp/68ksz1258581204.ps tmp/68ksz1258581204.png")
> system("convert tmp/7pon91258581204.ps tmp/7pon91258581204.png")
> system("convert tmp/8h5xg1258581204.ps tmp/8h5xg1258581204.png")
> system("convert tmp/9825t1258581204.ps tmp/9825t1258581204.png")
> system("convert tmp/10x3f71258581204.ps tmp/10x3f71258581204.png")
>
>
> proc.time()
user system elapsed
2.320 1.561 2.873