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 '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(6.5
+ ,1.9
+ ,6.3
+ ,6.1
+ ,6.2
+ ,6.3
+ ,6.6
+ ,2
+ ,6.5
+ ,6.3
+ ,6.1
+ ,6.2
+ ,6.5
+ ,2.3
+ ,6.6
+ ,6.5
+ ,6.3
+ ,6.1
+ ,6.2
+ ,2.8
+ ,6.5
+ ,6.6
+ ,6.5
+ ,6.3
+ ,6.2
+ ,2.4
+ ,6.2
+ ,6.5
+ ,6.6
+ ,6.5
+ ,5.9
+ ,2.3
+ ,6.2
+ ,6.2
+ ,6.5
+ ,6.6
+ ,6.1
+ ,2.7
+ ,5.9
+ ,6.2
+ ,6.2
+ ,6.5
+ ,6.1
+ ,2.7
+ ,6.1
+ ,5.9
+ ,6.2
+ ,6.2
+ ,6.1
+ ,2.9
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6.2
+ ,6.1
+ ,3
+ ,6.1
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6.1
+ ,2.2
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.4
+ ,2.3
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.7
+ ,2.8
+ ,6.4
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.9
+ ,2.8
+ ,6.7
+ ,6.4
+ ,6.1
+ ,6.1
+ ,7
+ ,2.8
+ ,6.9
+ ,6.7
+ ,6.4
+ ,6.1
+ ,7
+ ,2.2
+ ,7
+ ,6.9
+ ,6.7
+ ,6.4
+ ,6.8
+ ,2.6
+ ,7
+ ,7
+ ,6.9
+ ,6.7
+ ,6.4
+ ,2.8
+ ,6.8
+ ,7
+ ,7
+ ,6.9
+ ,5.9
+ ,2.5
+ ,6.4
+ ,6.8
+ ,7
+ ,7
+ ,5.5
+ ,2.4
+ ,5.9
+ ,6.4
+ ,6.8
+ ,7
+ ,5.5
+ ,2.3
+ ,5.5
+ ,5.9
+ ,6.4
+ ,6.8
+ ,5.6
+ ,1.9
+ ,5.5
+ ,5.5
+ ,5.9
+ ,6.4
+ ,5.8
+ ,1.7
+ ,5.6
+ ,5.5
+ ,5.5
+ ,5.9
+ ,5.9
+ ,2
+ ,5.8
+ ,5.6
+ ,5.5
+ ,5.5
+ ,6.1
+ ,2.1
+ ,5.9
+ ,5.8
+ ,5.6
+ ,5.5
+ ,6.1
+ ,1.7
+ ,6.1
+ ,5.9
+ ,5.8
+ ,5.6
+ ,6
+ ,1.8
+ ,6.1
+ ,6.1
+ ,5.9
+ ,5.8
+ ,6
+ ,1.8
+ ,6
+ ,6.1
+ ,6.1
+ ,5.9
+ ,5.9
+ ,1.8
+ ,6
+ ,6
+ ,6.1
+ ,6.1
+ ,5.5
+ ,1.3
+ ,5.9
+ ,6
+ ,6
+ ,6.1
+ ,5.6
+ ,1.3
+ ,5.5
+ ,5.9
+ ,6
+ ,6
+ ,5.4
+ ,1.3
+ ,5.6
+ ,5.5
+ ,5.9
+ ,6
+ ,5.2
+ ,1.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,5.9
+ ,5.2
+ ,1.4
+ ,5.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,5.2
+ ,2.2
+ ,5.2
+ ,5.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,2.9
+ ,5.2
+ ,5.2
+ ,5.2
+ ,5.4
+ ,5.8
+ ,3.1
+ ,5.5
+ ,5.2
+ ,5.2
+ ,5.2
+ ,5.8
+ ,3.5
+ ,5.8
+ ,5.5
+ ,5.2
+ ,5.2
+ ,5.5
+ ,3.6
+ ,5.8
+ ,5.8
+ ,5.5
+ ,5.2
+ ,5.3
+ ,4.4
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.5
+ ,5.1
+ ,4.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.9
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.4
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5
+ ,5.5
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.2
+ ,4.9
+ ,4.8
+ ,5
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.3
+ ,3.2
+ ,4.9
+ ,5
+ ,5.5
+ ,5.8
+ ,6.1
+ ,2.7
+ ,5.3
+ ,4.9
+ ,5
+ ,5.5
+ ,6.5
+ ,2.1
+ ,6.1
+ ,5.3
+ ,4.9
+ ,5
+ ,6.8
+ ,1.9
+ ,6.5
+ ,6.1
+ ,5.3
+ ,4.9
+ ,6.6
+ ,0.6
+ ,6.8
+ ,6.5
+ ,6.1
+ ,5.3
+ ,6.4
+ ,0.7
+ ,6.6
+ ,6.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,-0.2
+ ,6.4
+ ,6.6
+ ,6.8
+ ,6.5
+ ,6.6
+ ,-1
+ ,6.4
+ ,6.4
+ ,6.6
+ ,6.8
+ ,6.7
+ ,-1.7
+ ,6.6
+ ,6.4
+ ,6.4
+ ,6.6)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('WMan>25'
+ ,'Infl'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('WMan>25','Infl','Yt-1','Yt-2','Yt-3','Yt-4'),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
WMan>25 Infl Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.5 1.9 6.3 6.1 6.2 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.6 2.0 6.5 6.3 6.1 6.2 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 2.3 6.6 6.5 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.2 2.8 6.5 6.6 6.5 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.2 2.4 6.2 6.5 6.6 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 5.9 2.3 6.2 6.2 6.5 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 6.1 2.7 5.9 6.2 6.2 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 6.1 2.7 6.1 5.9 6.2 6.2 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 2.9 6.1 6.1 5.9 6.2 0 0 0 0 0 0 0 0 1 0 0 9
10 6.1 3.0 6.1 6.1 6.1 5.9 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 2.2 6.1 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 6.4 2.3 6.1 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 6.7 2.8 6.4 6.1 6.1 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 6.9 2.8 6.7 6.4 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 14
15 7.0 2.8 6.9 6.7 6.4 6.1 0 0 1 0 0 0 0 0 0 0 0 15
16 7.0 2.2 7.0 6.9 6.7 6.4 0 0 0 1 0 0 0 0 0 0 0 16
17 6.8 2.6 7.0 7.0 6.9 6.7 0 0 0 0 1 0 0 0 0 0 0 17
18 6.4 2.8 6.8 7.0 7.0 6.9 0 0 0 0 0 1 0 0 0 0 0 18
19 5.9 2.5 6.4 6.8 7.0 7.0 0 0 0 0 0 0 1 0 0 0 0 19
20 5.5 2.4 5.9 6.4 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 20
21 5.5 2.3 5.5 5.9 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 21
22 5.6 1.9 5.5 5.5 5.9 6.4 0 0 0 0 0 0 0 0 0 1 0 22
23 5.8 1.7 5.6 5.5 5.5 5.9 0 0 0 0 0 0 0 0 0 0 1 23
24 5.9 2.0 5.8 5.6 5.5 5.5 0 0 0 0 0 0 0 0 0 0 0 24
25 6.1 2.1 5.9 5.8 5.6 5.5 1 0 0 0 0 0 0 0 0 0 0 25
26 6.1 1.7 6.1 5.9 5.8 5.6 0 1 0 0 0 0 0 0 0 0 0 26
27 6.0 1.8 6.1 6.1 5.9 5.8 0 0 1 0 0 0 0 0 0 0 0 27
28 6.0 1.8 6.0 6.1 6.1 5.9 0 0 0 1 0 0 0 0 0 0 0 28
29 5.9 1.8 6.0 6.0 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 29
30 5.5 1.3 5.9 6.0 6.0 6.1 0 0 0 0 0 1 0 0 0 0 0 30
31 5.6 1.3 5.5 5.9 6.0 6.0 0 0 0 0 0 0 1 0 0 0 0 31
32 5.4 1.3 5.6 5.5 5.9 6.0 0 0 0 0 0 0 0 1 0 0 0 32
33 5.2 1.2 5.4 5.6 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 33
34 5.2 1.4 5.2 5.4 5.6 5.5 0 0 0 0 0 0 0 0 0 1 0 34
35 5.2 2.2 5.2 5.2 5.4 5.6 0 0 0 0 0 0 0 0 0 0 1 35
36 5.5 2.9 5.2 5.2 5.2 5.4 0 0 0 0 0 0 0 0 0 0 0 36
37 5.8 3.1 5.5 5.2 5.2 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.8 3.5 5.8 5.5 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0 38
39 5.5 3.6 5.8 5.8 5.5 5.2 0 0 1 0 0 0 0 0 0 0 0 39
40 5.3 4.4 5.5 5.8 5.8 5.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.1 4.1 5.3 5.5 5.8 5.8 0 0 0 0 1 0 0 0 0 0 0 41
42 5.2 5.1 5.1 5.3 5.5 5.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 5.8 5.2 5.1 5.3 5.5 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 5.9 5.8 5.2 5.1 5.3 0 0 0 0 0 0 0 1 0 0 0 44
45 5.5 5.4 5.8 5.8 5.2 5.1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.0 5.5 5.5 5.8 5.8 5.2 0 0 0 0 0 0 0 0 0 1 0 46
47 4.9 4.8 5.0 5.5 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 5.3 3.2 4.9 5.0 5.5 5.8 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 2.7 5.3 4.9 5.0 5.5 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 2.1 6.1 5.3 4.9 5.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 1.9 6.5 6.1 5.3 4.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.6 0.6 6.8 6.5 6.1 5.3 0 0 0 1 0 0 0 0 0 0 0 52
53 6.4 0.7 6.6 6.8 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 -0.2 6.4 6.6 6.8 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 -1.0 6.4 6.4 6.6 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.7 -1.7 6.6 6.4 6.4 6.6 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) Infl `Yt-1` `Yt-2` `Yt-3` `Yt-4`
0.205529 -0.006702 1.464243 -0.572214 -0.306649 0.404540
M1 M2 M3 M4 M5 M6
0.008417 -0.192488 -0.133692 -0.162225 -0.213985 -0.354608
M7 M8 M9 M10 M11 t
-0.059047 -0.438770 -0.336610 -0.209949 -0.200348 0.002337
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.439478 -0.107005 -0.001852 0.108155 0.296381
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.205529 0.627458 0.328 0.74504
Infl -0.006702 0.020618 -0.325 0.74693
`Yt-1` 1.464243 0.157152 9.317 2.35e-11 ***
`Yt-2` -0.572214 0.281900 -2.030 0.04942 *
`Yt-3` -0.306649 0.283438 -1.082 0.28612
`Yt-4` 0.404540 0.170919 2.367 0.02314 *
M1 0.008417 0.120462 0.070 0.94466
M2 -0.192488 0.127934 -1.505 0.14070
M3 -0.133692 0.132805 -1.007 0.32046
M4 -0.162225 0.133475 -1.215 0.23171
M5 -0.213985 0.130341 -1.642 0.10890
M6 -0.354608 0.129565 -2.737 0.00938 **
M7 -0.059047 0.129365 -0.456 0.65067
M8 -0.438770 0.127628 -3.438 0.00144 **
M9 -0.336610 0.140883 -2.389 0.02195 *
M10 -0.209949 0.127155 -1.651 0.10695
M11 -0.200348 0.123495 -1.622 0.11300
t 0.002337 0.002109 1.108 0.27463
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1725 on 38 degrees of freedom
Multiple R-squared: 0.9321, Adjusted R-squared: 0.9017
F-statistic: 30.68 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,] 0.5314189 0.9371621 0.4685811
[2,] 0.4207358 0.8414715 0.5792642
[3,] 0.6633057 0.6733887 0.3366943
[4,] 0.7567983 0.4864033 0.2432017
[5,] 0.6470193 0.7059613 0.3529807
[6,] 0.5815462 0.8369075 0.4184538
[7,] 0.4711390 0.9422779 0.5288610
[8,] 0.5772035 0.8455929 0.4227965
[9,] 0.8565748 0.2868504 0.1434252
[10,] 0.7918554 0.4162892 0.2081446
[11,] 0.8833156 0.2333689 0.1166844
[12,] 0.8312029 0.3375941 0.1687971
[13,] 0.7349408 0.5301183 0.2650592
[14,] 0.6645585 0.6708829 0.3354415
[15,] 0.6781850 0.6436300 0.3218150
> postscript(file="/var/www/html/rcomp/tmp/1znxg1258896881.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pc1r1258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39zpb1258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48lb81258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xosw1258896881.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
-0.0851590875 0.0454624517 -0.0438585388 -0.1302439223 0.2483057521
6 7 8 9 10
-0.1568608826 0.1356535719 0.1698882236 0.0891798550 0.1435436420
11 12 13 14 15
0.0453358399 0.1433212216 -0.0033549049 0.1276043740 0.1372807765
16 17 18 19 20
0.0981070886 -0.0526011439 -0.0703687270 -0.4394775333 -0.0208561469
21 22 23 24 25
0.1318155391 -0.1202571435 -0.0003493086 -0.1748345980 0.0137648902
26 27 28 29 30
-0.0050993803 -0.1013634035 0.0921329783 -0.0965744422 -0.2458791660
31 32 33 34 35
0.1251520573 -0.1034373925 -0.1407398831 0.1024890597 -0.1203144640
36 37 38 39 40
0.0012703409 -0.0665082113 -0.1328682392 -0.2296730417 0.0117901094
41 42 43 44 45
-0.1409760872 0.1904235578 0.2963814079 -0.1273091070 -0.0802555110
46 47 48 49 50
-0.1257755581 0.0753279326 0.0302430355 0.1412573135 -0.0350992062
51 52 53 54 55
0.2376142076 -0.0717862540 0.0418459213 0.2826852178 -0.1177095038
56
0.0817144227
> postscript(file="/var/www/html/rcomp/tmp/6poet1258896881.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 -0.0851590875 NA
1 0.0454624517 -0.0851590875
2 -0.0438585388 0.0454624517
3 -0.1302439223 -0.0438585388
4 0.2483057521 -0.1302439223
5 -0.1568608826 0.2483057521
6 0.1356535719 -0.1568608826
7 0.1698882236 0.1356535719
8 0.0891798550 0.1698882236
9 0.1435436420 0.0891798550
10 0.0453358399 0.1435436420
11 0.1433212216 0.0453358399
12 -0.0033549049 0.1433212216
13 0.1276043740 -0.0033549049
14 0.1372807765 0.1276043740
15 0.0981070886 0.1372807765
16 -0.0526011439 0.0981070886
17 -0.0703687270 -0.0526011439
18 -0.4394775333 -0.0703687270
19 -0.0208561469 -0.4394775333
20 0.1318155391 -0.0208561469
21 -0.1202571435 0.1318155391
22 -0.0003493086 -0.1202571435
23 -0.1748345980 -0.0003493086
24 0.0137648902 -0.1748345980
25 -0.0050993803 0.0137648902
26 -0.1013634035 -0.0050993803
27 0.0921329783 -0.1013634035
28 -0.0965744422 0.0921329783
29 -0.2458791660 -0.0965744422
30 0.1251520573 -0.2458791660
31 -0.1034373925 0.1251520573
32 -0.1407398831 -0.1034373925
33 0.1024890597 -0.1407398831
34 -0.1203144640 0.1024890597
35 0.0012703409 -0.1203144640
36 -0.0665082113 0.0012703409
37 -0.1328682392 -0.0665082113
38 -0.2296730417 -0.1328682392
39 0.0117901094 -0.2296730417
40 -0.1409760872 0.0117901094
41 0.1904235578 -0.1409760872
42 0.2963814079 0.1904235578
43 -0.1273091070 0.2963814079
44 -0.0802555110 -0.1273091070
45 -0.1257755581 -0.0802555110
46 0.0753279326 -0.1257755581
47 0.0302430355 0.0753279326
48 0.1412573135 0.0302430355
49 -0.0350992062 0.1412573135
50 0.2376142076 -0.0350992062
51 -0.0717862540 0.2376142076
52 0.0418459213 -0.0717862540
53 0.2826852178 0.0418459213
54 -0.1177095038 0.2826852178
55 0.0817144227 -0.1177095038
56 NA 0.0817144227
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0454624517 -0.0851590875
[2,] -0.0438585388 0.0454624517
[3,] -0.1302439223 -0.0438585388
[4,] 0.2483057521 -0.1302439223
[5,] -0.1568608826 0.2483057521
[6,] 0.1356535719 -0.1568608826
[7,] 0.1698882236 0.1356535719
[8,] 0.0891798550 0.1698882236
[9,] 0.1435436420 0.0891798550
[10,] 0.0453358399 0.1435436420
[11,] 0.1433212216 0.0453358399
[12,] -0.0033549049 0.1433212216
[13,] 0.1276043740 -0.0033549049
[14,] 0.1372807765 0.1276043740
[15,] 0.0981070886 0.1372807765
[16,] -0.0526011439 0.0981070886
[17,] -0.0703687270 -0.0526011439
[18,] -0.4394775333 -0.0703687270
[19,] -0.0208561469 -0.4394775333
[20,] 0.1318155391 -0.0208561469
[21,] -0.1202571435 0.1318155391
[22,] -0.0003493086 -0.1202571435
[23,] -0.1748345980 -0.0003493086
[24,] 0.0137648902 -0.1748345980
[25,] -0.0050993803 0.0137648902
[26,] -0.1013634035 -0.0050993803
[27,] 0.0921329783 -0.1013634035
[28,] -0.0965744422 0.0921329783
[29,] -0.2458791660 -0.0965744422
[30,] 0.1251520573 -0.2458791660
[31,] -0.1034373925 0.1251520573
[32,] -0.1407398831 -0.1034373925
[33,] 0.1024890597 -0.1407398831
[34,] -0.1203144640 0.1024890597
[35,] 0.0012703409 -0.1203144640
[36,] -0.0665082113 0.0012703409
[37,] -0.1328682392 -0.0665082113
[38,] -0.2296730417 -0.1328682392
[39,] 0.0117901094 -0.2296730417
[40,] -0.1409760872 0.0117901094
[41,] 0.1904235578 -0.1409760872
[42,] 0.2963814079 0.1904235578
[43,] -0.1273091070 0.2963814079
[44,] -0.0802555110 -0.1273091070
[45,] -0.1257755581 -0.0802555110
[46,] 0.0753279326 -0.1257755581
[47,] 0.0302430355 0.0753279326
[48,] 0.1412573135 0.0302430355
[49,] -0.0350992062 0.1412573135
[50,] 0.2376142076 -0.0350992062
[51,] -0.0717862540 0.2376142076
[52,] 0.0418459213 -0.0717862540
[53,] 0.2826852178 0.0418459213
[54,] -0.1177095038 0.2826852178
[55,] 0.0817144227 -0.1177095038
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0454624517 -0.0851590875
2 -0.0438585388 0.0454624517
3 -0.1302439223 -0.0438585388
4 0.2483057521 -0.1302439223
5 -0.1568608826 0.2483057521
6 0.1356535719 -0.1568608826
7 0.1698882236 0.1356535719
8 0.0891798550 0.1698882236
9 0.1435436420 0.0891798550
10 0.0453358399 0.1435436420
11 0.1433212216 0.0453358399
12 -0.0033549049 0.1433212216
13 0.1276043740 -0.0033549049
14 0.1372807765 0.1276043740
15 0.0981070886 0.1372807765
16 -0.0526011439 0.0981070886
17 -0.0703687270 -0.0526011439
18 -0.4394775333 -0.0703687270
19 -0.0208561469 -0.4394775333
20 0.1318155391 -0.0208561469
21 -0.1202571435 0.1318155391
22 -0.0003493086 -0.1202571435
23 -0.1748345980 -0.0003493086
24 0.0137648902 -0.1748345980
25 -0.0050993803 0.0137648902
26 -0.1013634035 -0.0050993803
27 0.0921329783 -0.1013634035
28 -0.0965744422 0.0921329783
29 -0.2458791660 -0.0965744422
30 0.1251520573 -0.2458791660
31 -0.1034373925 0.1251520573
32 -0.1407398831 -0.1034373925
33 0.1024890597 -0.1407398831
34 -0.1203144640 0.1024890597
35 0.0012703409 -0.1203144640
36 -0.0665082113 0.0012703409
37 -0.1328682392 -0.0665082113
38 -0.2296730417 -0.1328682392
39 0.0117901094 -0.2296730417
40 -0.1409760872 0.0117901094
41 0.1904235578 -0.1409760872
42 0.2963814079 0.1904235578
43 -0.1273091070 0.2963814079
44 -0.0802555110 -0.1273091070
45 -0.1257755581 -0.0802555110
46 0.0753279326 -0.1257755581
47 0.0302430355 0.0753279326
48 0.1412573135 0.0302430355
49 -0.0350992062 0.1412573135
50 0.2376142076 -0.0350992062
51 -0.0717862540 0.2376142076
52 0.0418459213 -0.0717862540
53 0.2826852178 0.0418459213
54 -0.1177095038 0.2826852178
55 0.0817144227 -0.1177095038
> 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/7awr11258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ggfm1258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cx0t1258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101x8d1258896881.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m91p1258896881.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/123n681258896881.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/13zgm81258896881.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/147d2u1258896881.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/15tqdl1258896881.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/16c93j1258896881.tab")
+ }
>
> system("convert tmp/1znxg1258896881.ps tmp/1znxg1258896881.png")
> system("convert tmp/2pc1r1258896881.ps tmp/2pc1r1258896881.png")
> system("convert tmp/39zpb1258896881.ps tmp/39zpb1258896881.png")
> system("convert tmp/48lb81258896881.ps tmp/48lb81258896881.png")
> system("convert tmp/5xosw1258896881.ps tmp/5xosw1258896881.png")
> system("convert tmp/6poet1258896881.ps tmp/6poet1258896881.png")
> system("convert tmp/7awr11258896881.ps tmp/7awr11258896881.png")
> system("convert tmp/8ggfm1258896881.ps tmp/8ggfm1258896881.png")
> system("convert tmp/9cx0t1258896881.ps tmp/9cx0t1258896881.png")
> system("convert tmp/101x8d1258896881.ps tmp/101x8d1258896881.png")
>
>
> proc.time()
user system elapsed
2.227 1.553 2.966