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(95.1
+ ,93.8
+ ,96.9
+ ,98.6
+ ,111.7
+ ,97
+ ,93.8
+ ,95.1
+ ,96.9
+ ,98.6
+ ,112.7
+ ,107.6
+ ,97
+ ,95.1
+ ,96.9
+ ,102.9
+ ,101
+ ,112.7
+ ,97
+ ,95.1
+ ,97.4
+ ,95.4
+ ,102.9
+ ,112.7
+ ,97
+ ,111.4
+ ,96.5
+ ,97.4
+ ,102.9
+ ,112.7
+ ,87.4
+ ,89.2
+ ,111.4
+ ,97.4
+ ,102.9
+ ,96.8
+ ,87.1
+ ,87.4
+ ,111.4
+ ,97.4
+ ,114.1
+ ,110.5
+ ,96.8
+ ,87.4
+ ,111.4
+ ,110.3
+ ,110.8
+ ,114.1
+ ,96.8
+ ,87.4
+ ,103.9
+ ,104.2
+ ,110.3
+ ,114.1
+ ,96.8
+ ,101.6
+ ,88.9
+ ,103.9
+ ,110.3
+ ,114.1
+ ,94.6
+ ,89.8
+ ,101.6
+ ,103.9
+ ,110.3
+ ,95.9
+ ,90
+ ,94.6
+ ,101.6
+ ,103.9
+ ,104.7
+ ,93.9
+ ,95.9
+ ,94.6
+ ,101.6
+ ,102.8
+ ,91.3
+ ,104.7
+ ,95.9
+ ,94.6
+ ,98.1
+ ,87.8
+ ,102.8
+ ,104.7
+ ,95.9
+ ,113.9
+ ,99.7
+ ,98.1
+ ,102.8
+ ,104.7
+ ,80.9
+ ,73.5
+ ,113.9
+ ,98.1
+ ,102.8
+ ,95.7
+ ,79.2
+ ,80.9
+ ,113.9
+ ,98.1
+ ,113.2
+ ,96.9
+ ,95.7
+ ,80.9
+ ,113.9
+ ,105.9
+ ,95.2
+ ,113.2
+ ,95.7
+ ,80.9
+ ,108.8
+ ,95.6
+ ,105.9
+ ,113.2
+ ,95.7
+ ,102.3
+ ,89.7
+ ,108.8
+ ,105.9
+ ,113.2
+ ,99
+ ,92.8
+ ,102.3
+ ,108.8
+ ,105.9
+ ,100.7
+ ,88
+ ,99
+ ,102.3
+ ,108.8
+ ,115.5
+ ,101.1
+ ,100.7
+ ,99
+ ,102.3
+ ,100.7
+ ,92.7
+ ,115.5
+ ,100.7
+ ,99
+ ,109.9
+ ,95.8
+ ,100.7
+ ,115.5
+ ,100.7
+ ,114.6
+ ,103.8
+ ,109.9
+ ,100.7
+ ,115.5
+ ,85.4
+ ,81.8
+ ,114.6
+ ,109.9
+ ,100.7
+ ,100.5
+ ,87.1
+ ,85.4
+ ,114.6
+ ,109.9
+ ,114.8
+ ,105.9
+ ,100.5
+ ,85.4
+ ,114.6
+ ,116.5
+ ,108.1
+ ,114.8
+ ,100.5
+ ,85.4
+ ,112.9
+ ,102.6
+ ,116.5
+ ,114.8
+ ,100.5
+ ,102
+ ,93.7
+ ,112.9
+ ,116.5
+ ,114.8
+ ,106
+ ,103.5
+ ,102
+ ,112.9
+ ,116.5
+ ,105.3
+ ,100.6
+ ,106
+ ,102
+ ,112.9
+ ,118.8
+ ,113.3
+ ,105.3
+ ,106
+ ,102
+ ,106.1
+ ,102.4
+ ,118.8
+ ,105.3
+ ,106
+ ,109.3
+ ,102.1
+ ,106.1
+ ,118.8
+ ,105.3
+ ,117.2
+ ,106.9
+ ,109.3
+ ,106.1
+ ,118.8
+ ,92.5
+ ,87.3
+ ,117.2
+ ,109.3
+ ,106.1
+ ,104.2
+ ,93.1
+ ,92.5
+ ,117.2
+ ,109.3
+ ,112.5
+ ,109.1
+ ,104.2
+ ,92.5
+ ,117.2
+ ,122.4
+ ,120.3
+ ,112.5
+ ,104.2
+ ,92.5
+ ,113.3
+ ,104.9
+ ,122.4
+ ,112.5
+ ,104.2
+ ,100
+ ,92.6
+ ,113.3
+ ,122.4
+ ,112.5
+ ,110.7
+ ,109.8
+ ,100
+ ,113.3
+ ,122.4
+ ,112.8
+ ,111.4
+ ,110.7
+ ,100
+ ,113.3
+ ,109.8
+ ,117.9
+ ,112.8
+ ,110.7
+ ,100
+ ,117.3
+ ,121.6
+ ,109.8
+ ,112.8
+ ,110.7
+ ,109.1
+ ,117.8
+ ,117.3
+ ,109.8
+ ,112.8
+ ,115.9
+ ,124.2
+ ,109.1
+ ,117.3
+ ,109.8
+ ,96
+ ,106.8
+ ,115.9
+ ,109.1
+ ,117.3
+ ,99.8
+ ,102.7
+ ,96
+ ,115.9
+ ,109.1
+ ,116.8
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,115.7
+ ,113.6
+ ,116.8
+ ,99.8
+ ,96
+ ,99.4
+ ,96.1
+ ,115.7
+ ,116.8
+ ,99.8
+ ,94.3
+ ,85
+ ,99.4
+ ,115.7
+ ,116.8)
+ ,dim=c(5
+ ,60)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)')
+ ,1:60))
> y <- array(NA,dim=c(5,60),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-3)'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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(t) X(t) Y(t-1) Y(t-2) Y(t-3) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 95.1 93.8 96.9 98.6 111.7 1 0 0 0 0 0 0 0 0 0 0
2 97.0 93.8 95.1 96.9 98.6 0 1 0 0 0 0 0 0 0 0 0
3 112.7 107.6 97.0 95.1 96.9 0 0 1 0 0 0 0 0 0 0 0
4 102.9 101.0 112.7 97.0 95.1 0 0 0 1 0 0 0 0 0 0 0
5 97.4 95.4 102.9 112.7 97.0 0 0 0 0 1 0 0 0 0 0 0
6 111.4 96.5 97.4 102.9 112.7 0 0 0 0 0 1 0 0 0 0 0
7 87.4 89.2 111.4 97.4 102.9 0 0 0 0 0 0 1 0 0 0 0
8 96.8 87.1 87.4 111.4 97.4 0 0 0 0 0 0 0 1 0 0 0
9 114.1 110.5 96.8 87.4 111.4 0 0 0 0 0 0 0 0 1 0 0
10 110.3 110.8 114.1 96.8 87.4 0 0 0 0 0 0 0 0 0 1 0
11 103.9 104.2 110.3 114.1 96.8 0 0 0 0 0 0 0 0 0 0 1
12 101.6 88.9 103.9 110.3 114.1 0 0 0 0 0 0 0 0 0 0 0
13 94.6 89.8 101.6 103.9 110.3 1 0 0 0 0 0 0 0 0 0 0
14 95.9 90.0 94.6 101.6 103.9 0 1 0 0 0 0 0 0 0 0 0
15 104.7 93.9 95.9 94.6 101.6 0 0 1 0 0 0 0 0 0 0 0
16 102.8 91.3 104.7 95.9 94.6 0 0 0 1 0 0 0 0 0 0 0
17 98.1 87.8 102.8 104.7 95.9 0 0 0 0 1 0 0 0 0 0 0
18 113.9 99.7 98.1 102.8 104.7 0 0 0 0 0 1 0 0 0 0 0
19 80.9 73.5 113.9 98.1 102.8 0 0 0 0 0 0 1 0 0 0 0
20 95.7 79.2 80.9 113.9 98.1 0 0 0 0 0 0 0 1 0 0 0
21 113.2 96.9 95.7 80.9 113.9 0 0 0 0 0 0 0 0 1 0 0
22 105.9 95.2 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 1 0
23 108.8 95.6 105.9 113.2 95.7 0 0 0 0 0 0 0 0 0 0 1
24 102.3 89.7 108.8 105.9 113.2 0 0 0 0 0 0 0 0 0 0 0
25 99.0 92.8 102.3 108.8 105.9 1 0 0 0 0 0 0 0 0 0 0
26 100.7 88.0 99.0 102.3 108.8 0 1 0 0 0 0 0 0 0 0 0
27 115.5 101.1 100.7 99.0 102.3 0 0 1 0 0 0 0 0 0 0 0
28 100.7 92.7 115.5 100.7 99.0 0 0 0 1 0 0 0 0 0 0 0
29 109.9 95.8 100.7 115.5 100.7 0 0 0 0 1 0 0 0 0 0 0
30 114.6 103.8 109.9 100.7 115.5 0 0 0 0 0 1 0 0 0 0 0
31 85.4 81.8 114.6 109.9 100.7 0 0 0 0 0 0 1 0 0 0 0
32 100.5 87.1 85.4 114.6 109.9 0 0 0 0 0 0 0 1 0 0 0
33 114.8 105.9 100.5 85.4 114.6 0 0 0 0 0 0 0 0 1 0 0
34 116.5 108.1 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 1 0
35 112.9 102.6 116.5 114.8 100.5 0 0 0 0 0 0 0 0 0 0 1
36 102.0 93.7 112.9 116.5 114.8 0 0 0 0 0 0 0 0 0 0 0
37 106.0 103.5 102.0 112.9 116.5 1 0 0 0 0 0 0 0 0 0 0
38 105.3 100.6 106.0 102.0 112.9 0 1 0 0 0 0 0 0 0 0 0
39 118.8 113.3 105.3 106.0 102.0 0 0 1 0 0 0 0 0 0 0 0
40 106.1 102.4 118.8 105.3 106.0 0 0 0 1 0 0 0 0 0 0 0
41 109.3 102.1 106.1 118.8 105.3 0 0 0 0 1 0 0 0 0 0 0
42 117.2 106.9 109.3 106.1 118.8 0 0 0 0 0 1 0 0 0 0 0
43 92.5 87.3 117.2 109.3 106.1 0 0 0 0 0 0 1 0 0 0 0
44 104.2 93.1 92.5 117.2 109.3 0 0 0 0 0 0 0 1 0 0 0
45 112.5 109.1 104.2 92.5 117.2 0 0 0 0 0 0 0 0 1 0 0
46 122.4 120.3 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 1 0
47 113.3 104.9 122.4 112.5 104.2 0 0 0 0 0 0 0 0 0 0 1
48 100.0 92.6 113.3 122.4 112.5 0 0 0 0 0 0 0 0 0 0 0
49 110.7 109.8 100.0 113.3 122.4 1 0 0 0 0 0 0 0 0 0 0
50 112.8 111.4 110.7 100.0 113.3 0 1 0 0 0 0 0 0 0 0 0
51 109.8 117.9 112.8 110.7 100.0 0 0 1 0 0 0 0 0 0 0 0
52 117.3 121.6 109.8 112.8 110.7 0 0 0 1 0 0 0 0 0 0 0
53 109.1 117.8 117.3 109.8 112.8 0 0 0 0 1 0 0 0 0 0 0
54 115.9 124.2 109.1 117.3 109.8 0 0 0 0 0 1 0 0 0 0 0
55 96.0 106.8 115.9 109.1 117.3 0 0 0 0 0 0 1 0 0 0 0
56 99.8 102.7 96.0 115.9 109.1 0 0 0 0 0 0 0 1 0 0 0
57 116.8 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 1 0 0
58 115.7 113.6 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0 1 0
59 99.4 96.1 115.7 116.8 99.8 0 0 0 0 0 0 0 0 0 0 1
60 94.3 85.0 99.4 115.7 116.8 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(t)` `Y(t-1)` `Y(t-2)` `Y(t-3)` M1
38.18167 0.31175 -0.12622 0.05106 0.36373 -1.66303
M2 M3 M4 M5 M6 M7
2.51624 12.01548 8.22541 5.88814 10.26995 -6.51336
M8 M9 M10 M11
0.41800 8.82630 18.99128 10.51080
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9120 -2.2479 0.3065 2.3983 6.1500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.18167 16.48338 2.316 0.025257 *
`X(t)` 0.31175 0.08704 3.582 0.000848 ***
`Y(t-1)` -0.12622 0.11688 -1.080 0.286066
`Y(t-2)` 0.05106 0.11773 0.434 0.666652
`Y(t-3)` 0.36373 0.12578 2.892 0.005933 **
M1 -1.66303 2.76684 -0.601 0.550887
M2 2.51624 3.25014 0.774 0.442955
M3 12.01548 4.17929 2.875 0.006203 **
M4 8.22541 3.74889 2.194 0.033554 *
M5 5.88814 3.07150 1.917 0.061743 .
M6 10.26995 3.15341 3.257 0.002173 **
M7 -6.51336 2.85289 -2.283 0.027313 *
M8 0.41800 3.23227 0.129 0.897694
M9 8.82630 4.74857 1.859 0.069762 .
M10 18.99128 5.50952 3.447 0.001259 **
M11 10.51080 3.41147 3.081 0.003551 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.535 on 44 degrees of freedom
Multiple R-squared: 0.8849, Adjusted R-squared: 0.8457
F-statistic: 22.56 on 15 and 44 DF, p-value: 7.966e-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,] 0.15550110 0.31100220 0.8444989
[2,] 0.06266270 0.12532540 0.9373373
[3,] 0.04573558 0.09147116 0.9542644
[4,] 0.02357337 0.04714675 0.9764266
[5,] 0.06731275 0.13462549 0.9326873
[6,] 0.03491224 0.06982447 0.9650878
[7,] 0.04145418 0.08290836 0.9585458
[8,] 0.12592326 0.25184651 0.8740767
[9,] 0.16907393 0.33814786 0.8309261
[10,] 0.14003820 0.28007640 0.8599618
[11,] 0.27681539 0.55363078 0.7231846
[12,] 0.20122783 0.40245566 0.7987722
[13,] 0.15143594 0.30287188 0.8485641
[14,] 0.09579524 0.19159049 0.9042048
[15,] 0.05685144 0.11370287 0.9431486
[16,] 0.05025443 0.10050886 0.9497456
[17,] 0.05531260 0.11062520 0.9446874
[18,] 0.04410165 0.08820330 0.9558984
[19,] 0.02463122 0.04926244 0.9753688
[20,] 0.01779728 0.03559456 0.9822027
[21,] 0.03373146 0.06746292 0.9662685
[22,] 0.02301897 0.04603795 0.9769810
[23,] 0.01464492 0.02928984 0.9853551
> postscript(file="/var/www/html/rcomp/tmp/198ke1261914350.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24bmd1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yktm1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ad0k1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vhou1261914350.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 = 60
Frequency = 1
1 2 3 4 5 6 7
-4.0931172 -1.7479450 1.1006879 -0.3123833 -4.4588612 -1.0879817 -0.4165114
8 9 10 11 12 13 14
0.9633512 -0.1203963 -3.7458010 -4.3896800 1.6846635 -2.5142650 -3.8941124
15 16 17 18 19 20 21
-4.4511377 1.8399085 -0.5936230 3.4176920 -1.7058257 1.1235410 2.5031357
22 23 24 25 26 27 28
-0.9756671 3.0820887 3.3057194 2.3890623 0.2667377 4.2308313 -1.1788774
29 30 31 32 33 34 35
6.1500139 0.5078128 0.4563592 -0.2990582 1.4188451 3.9228333 4.5101321
36 37 38 39 40 41 42
1.1530469 1.9506046 0.3462044 4.0597802 -1.1673110 2.4259112 0.5896504
43 44 45 46 47 48 49
4.2363857 2.5120568 -2.7199468 2.9577849 3.7094110 0.0818097 2.2677154
50 51 52 53 54 55 56
5.0291153 -4.9401618 0.8186632 -3.5234410 -3.4271735 -2.5704078 -4.2998909
57 58 59 60
-1.0816377 -2.1591501 -6.9119518 -6.2252395
> postscript(file="/var/www/html/rcomp/tmp/6su6b1261914350.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.0931172 NA
1 -1.7479450 -4.0931172
2 1.1006879 -1.7479450
3 -0.3123833 1.1006879
4 -4.4588612 -0.3123833
5 -1.0879817 -4.4588612
6 -0.4165114 -1.0879817
7 0.9633512 -0.4165114
8 -0.1203963 0.9633512
9 -3.7458010 -0.1203963
10 -4.3896800 -3.7458010
11 1.6846635 -4.3896800
12 -2.5142650 1.6846635
13 -3.8941124 -2.5142650
14 -4.4511377 -3.8941124
15 1.8399085 -4.4511377
16 -0.5936230 1.8399085
17 3.4176920 -0.5936230
18 -1.7058257 3.4176920
19 1.1235410 -1.7058257
20 2.5031357 1.1235410
21 -0.9756671 2.5031357
22 3.0820887 -0.9756671
23 3.3057194 3.0820887
24 2.3890623 3.3057194
25 0.2667377 2.3890623
26 4.2308313 0.2667377
27 -1.1788774 4.2308313
28 6.1500139 -1.1788774
29 0.5078128 6.1500139
30 0.4563592 0.5078128
31 -0.2990582 0.4563592
32 1.4188451 -0.2990582
33 3.9228333 1.4188451
34 4.5101321 3.9228333
35 1.1530469 4.5101321
36 1.9506046 1.1530469
37 0.3462044 1.9506046
38 4.0597802 0.3462044
39 -1.1673110 4.0597802
40 2.4259112 -1.1673110
41 0.5896504 2.4259112
42 4.2363857 0.5896504
43 2.5120568 4.2363857
44 -2.7199468 2.5120568
45 2.9577849 -2.7199468
46 3.7094110 2.9577849
47 0.0818097 3.7094110
48 2.2677154 0.0818097
49 5.0291153 2.2677154
50 -4.9401618 5.0291153
51 0.8186632 -4.9401618
52 -3.5234410 0.8186632
53 -3.4271735 -3.5234410
54 -2.5704078 -3.4271735
55 -4.2998909 -2.5704078
56 -1.0816377 -4.2998909
57 -2.1591501 -1.0816377
58 -6.9119518 -2.1591501
59 -6.2252395 -6.9119518
60 NA -6.2252395
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.7479450 -4.0931172
[2,] 1.1006879 -1.7479450
[3,] -0.3123833 1.1006879
[4,] -4.4588612 -0.3123833
[5,] -1.0879817 -4.4588612
[6,] -0.4165114 -1.0879817
[7,] 0.9633512 -0.4165114
[8,] -0.1203963 0.9633512
[9,] -3.7458010 -0.1203963
[10,] -4.3896800 -3.7458010
[11,] 1.6846635 -4.3896800
[12,] -2.5142650 1.6846635
[13,] -3.8941124 -2.5142650
[14,] -4.4511377 -3.8941124
[15,] 1.8399085 -4.4511377
[16,] -0.5936230 1.8399085
[17,] 3.4176920 -0.5936230
[18,] -1.7058257 3.4176920
[19,] 1.1235410 -1.7058257
[20,] 2.5031357 1.1235410
[21,] -0.9756671 2.5031357
[22,] 3.0820887 -0.9756671
[23,] 3.3057194 3.0820887
[24,] 2.3890623 3.3057194
[25,] 0.2667377 2.3890623
[26,] 4.2308313 0.2667377
[27,] -1.1788774 4.2308313
[28,] 6.1500139 -1.1788774
[29,] 0.5078128 6.1500139
[30,] 0.4563592 0.5078128
[31,] -0.2990582 0.4563592
[32,] 1.4188451 -0.2990582
[33,] 3.9228333 1.4188451
[34,] 4.5101321 3.9228333
[35,] 1.1530469 4.5101321
[36,] 1.9506046 1.1530469
[37,] 0.3462044 1.9506046
[38,] 4.0597802 0.3462044
[39,] -1.1673110 4.0597802
[40,] 2.4259112 -1.1673110
[41,] 0.5896504 2.4259112
[42,] 4.2363857 0.5896504
[43,] 2.5120568 4.2363857
[44,] -2.7199468 2.5120568
[45,] 2.9577849 -2.7199468
[46,] 3.7094110 2.9577849
[47,] 0.0818097 3.7094110
[48,] 2.2677154 0.0818097
[49,] 5.0291153 2.2677154
[50,] -4.9401618 5.0291153
[51,] 0.8186632 -4.9401618
[52,] -3.5234410 0.8186632
[53,] -3.4271735 -3.5234410
[54,] -2.5704078 -3.4271735
[55,] -4.2998909 -2.5704078
[56,] -1.0816377 -4.2998909
[57,] -2.1591501 -1.0816377
[58,] -6.9119518 -2.1591501
[59,] -6.2252395 -6.9119518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.7479450 -4.0931172
2 1.1006879 -1.7479450
3 -0.3123833 1.1006879
4 -4.4588612 -0.3123833
5 -1.0879817 -4.4588612
6 -0.4165114 -1.0879817
7 0.9633512 -0.4165114
8 -0.1203963 0.9633512
9 -3.7458010 -0.1203963
10 -4.3896800 -3.7458010
11 1.6846635 -4.3896800
12 -2.5142650 1.6846635
13 -3.8941124 -2.5142650
14 -4.4511377 -3.8941124
15 1.8399085 -4.4511377
16 -0.5936230 1.8399085
17 3.4176920 -0.5936230
18 -1.7058257 3.4176920
19 1.1235410 -1.7058257
20 2.5031357 1.1235410
21 -0.9756671 2.5031357
22 3.0820887 -0.9756671
23 3.3057194 3.0820887
24 2.3890623 3.3057194
25 0.2667377 2.3890623
26 4.2308313 0.2667377
27 -1.1788774 4.2308313
28 6.1500139 -1.1788774
29 0.5078128 6.1500139
30 0.4563592 0.5078128
31 -0.2990582 0.4563592
32 1.4188451 -0.2990582
33 3.9228333 1.4188451
34 4.5101321 3.9228333
35 1.1530469 4.5101321
36 1.9506046 1.1530469
37 0.3462044 1.9506046
38 4.0597802 0.3462044
39 -1.1673110 4.0597802
40 2.4259112 -1.1673110
41 0.5896504 2.4259112
42 4.2363857 0.5896504
43 2.5120568 4.2363857
44 -2.7199468 2.5120568
45 2.9577849 -2.7199468
46 3.7094110 2.9577849
47 0.0818097 3.7094110
48 2.2677154 0.0818097
49 5.0291153 2.2677154
50 -4.9401618 5.0291153
51 0.8186632 -4.9401618
52 -3.5234410 0.8186632
53 -3.4271735 -3.5234410
54 -2.5704078 -3.4271735
55 -4.2998909 -2.5704078
56 -1.0816377 -4.2998909
57 -2.1591501 -1.0816377
58 -6.9119518 -2.1591501
59 -6.2252395 -6.9119518
> 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/7ark81261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88rmb1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9klai1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rchh1261914350.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u01q1261914351.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/122urg1261914351.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/1353sv1261914351.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/14ut6v1261914351.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/15hdia1261914351.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/16j7wp1261914351.tab")
+ }
>
> try(system("convert tmp/198ke1261914350.ps tmp/198ke1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/24bmd1261914350.ps tmp/24bmd1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yktm1261914350.ps tmp/3yktm1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ad0k1261914350.ps tmp/4ad0k1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vhou1261914350.ps tmp/5vhou1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/6su6b1261914350.ps tmp/6su6b1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ark81261914350.ps tmp/7ark81261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/88rmb1261914350.ps tmp/88rmb1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/9klai1261914350.ps tmp/9klai1261914350.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rchh1261914350.ps tmp/10rchh1261914350.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.425 1.555 3.604