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
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Type 'q()' to quit R.
> x <- array(list(8
+ ,6.5
+ ,8.3
+ ,9.3
+ ,9.8
+ ,9.9
+ ,8.5
+ ,6.6
+ ,8
+ ,8.3
+ ,9.3
+ ,9.8
+ ,10.4
+ ,7.6
+ ,8.5
+ ,8
+ ,8.3
+ ,9.3
+ ,11.1
+ ,8
+ ,10.4
+ ,8.5
+ ,8
+ ,8.3
+ ,10.9
+ ,8.1
+ ,11.1
+ ,10.4
+ ,8.5
+ ,8
+ ,10
+ ,7.7
+ ,10.9
+ ,11.1
+ ,10.4
+ ,8.5
+ ,9.2
+ ,7.5
+ ,10
+ ,10.9
+ ,11.1
+ ,10.4
+ ,9.2
+ ,7.6
+ ,9.2
+ ,10
+ ,10.9
+ ,11.1
+ ,9.5
+ ,7.8
+ ,9.2
+ ,9.2
+ ,10
+ ,10.9
+ ,9.6
+ ,7.8
+ ,9.5
+ ,9.2
+ ,9.2
+ ,10
+ ,9.5
+ ,7.8
+ ,9.6
+ ,9.5
+ ,9.2
+ ,9.2
+ ,9.1
+ ,7.5
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9.2
+ ,8.9
+ ,7.5
+ ,9.1
+ ,9.5
+ ,9.6
+ ,9.5
+ ,9
+ ,7.1
+ ,8.9
+ ,9.1
+ ,9.5
+ ,9.6
+ ,10.1
+ ,7.5
+ ,9
+ ,8.9
+ ,9.1
+ ,9.5
+ ,10.3
+ ,7.5
+ ,10.1
+ ,9
+ ,8.9
+ ,9.1
+ ,10.2
+ ,7.6
+ ,10.3
+ ,10.1
+ ,9
+ ,8.9
+ ,9.6
+ ,7.7
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9
+ ,9.2
+ ,7.7
+ ,9.6
+ ,10.2
+ ,10.3
+ ,10.1
+ ,9.3
+ ,7.9
+ ,9.2
+ ,9.6
+ ,10.2
+ ,10.3
+ ,9.4
+ ,8.1
+ ,9.3
+ ,9.2
+ ,9.6
+ ,10.2
+ ,9.4
+ ,8.2
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9.6
+ ,9.2
+ ,8.2
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9.2
+ ,9
+ ,8.2
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9.3
+ ,9
+ ,7.9
+ ,9
+ ,9.2
+ ,9.4
+ ,9.4
+ ,9
+ ,7.3
+ ,9
+ ,9
+ ,9.2
+ ,9.4
+ ,9.8
+ ,6.9
+ ,9
+ ,9
+ ,9
+ ,9.2
+ ,10
+ ,6.6
+ ,9.8
+ ,9
+ ,9
+ ,9
+ ,9.8
+ ,6.7
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,9.3
+ ,6.9
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,9
+ ,7
+ ,9.3
+ ,9.8
+ ,10
+ ,9.8
+ ,9
+ ,7.1
+ ,9
+ ,9.3
+ ,9.8
+ ,10
+ ,9.1
+ ,7.2
+ ,9
+ ,9
+ ,9.3
+ ,9.8
+ ,9.1
+ ,7.1
+ ,9.1
+ ,9
+ ,9
+ ,9.3
+ ,9.1
+ ,6.9
+ ,9.1
+ ,9.1
+ ,9
+ ,9
+ ,9.2
+ ,7
+ ,9.1
+ ,9.1
+ ,9.1
+ ,9
+ ,8.8
+ ,6.8
+ ,9.2
+ ,9.1
+ ,9.1
+ ,9.1
+ ,8.3
+ ,6.4
+ ,8.8
+ ,9.2
+ ,9.1
+ ,9.1
+ ,8.4
+ ,6.7
+ ,8.3
+ ,8.8
+ ,9.2
+ ,9.1
+ ,8.1
+ ,6.6
+ ,8.4
+ ,8.3
+ ,8.8
+ ,9.2
+ ,7.7
+ ,6.4
+ ,8.1
+ ,8.4
+ ,8.3
+ ,8.8
+ ,7.9
+ ,6.3
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8.3
+ ,7.9
+ ,6.2
+ ,7.9
+ ,7.7
+ ,8.1
+ ,8.4
+ ,8
+ ,6.5
+ ,7.9
+ ,7.9
+ ,7.7
+ ,8.1
+ ,7.9
+ ,6.8
+ ,8
+ ,7.9
+ ,7.9
+ ,7.7
+ ,7.6
+ ,6.8
+ ,7.9
+ ,8
+ ,7.9
+ ,7.9
+ ,7.1
+ ,6.4
+ ,7.6
+ ,7.9
+ ,8
+ ,7.9
+ ,6.8
+ ,6.1
+ ,7.1
+ ,7.6
+ ,7.9
+ ,8
+ ,6.5
+ ,5.8
+ ,6.8
+ ,7.1
+ ,7.6
+ ,7.9
+ ,6.9
+ ,6.1
+ ,6.5
+ ,6.8
+ ,7.1
+ ,7.6
+ ,8.2
+ ,7.2
+ ,6.9
+ ,6.5
+ ,6.8
+ ,7.1
+ ,8.7
+ ,7.3
+ ,8.2
+ ,6.9
+ ,6.5
+ ,6.8
+ ,8.3
+ ,6.9
+ ,8.7
+ ,8.2
+ ,6.9
+ ,6.5
+ ,7.9
+ ,6.1
+ ,8.3
+ ,8.7
+ ,8.2
+ ,6.9
+ ,7.5
+ ,5.8
+ ,7.9
+ ,8.3
+ ,8.7
+ ,8.2
+ ,7.8
+ ,6.2
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.7)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('WLVrouw'
+ ,'WLMan'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('WLVrouw','WLMan','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 = '2'
> #'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
WLMan WLVrouw Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.5 8.0 8.3 9.3 9.8 9.9 1 0 0 0 0 0 0 0 0 0 0 1
2 6.6 8.5 8.0 8.3 9.3 9.8 0 1 0 0 0 0 0 0 0 0 0 2
3 7.6 10.4 8.5 8.0 8.3 9.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.0 11.1 10.4 8.5 8.0 8.3 0 0 0 1 0 0 0 0 0 0 0 4
5 8.1 10.9 11.1 10.4 8.5 8.0 0 0 0 0 1 0 0 0 0 0 0 5
6 7.7 10.0 10.9 11.1 10.4 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 9.2 10.0 10.9 11.1 10.4 0 0 0 0 0 0 1 0 0 0 0 7
8 7.6 9.2 9.2 10.0 10.9 11.1 0 0 0 0 0 0 0 1 0 0 0 8
9 7.8 9.5 9.2 9.2 10.0 10.9 0 0 0 0 0 0 0 0 1 0 0 9
10 7.8 9.6 9.5 9.2 9.2 10.0 0 0 0 0 0 0 0 0 0 1 0 10
11 7.8 9.5 9.6 9.5 9.2 9.2 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 9.1 9.5 9.6 9.5 9.2 0 0 0 0 0 0 0 0 0 0 0 12
13 7.5 8.9 9.1 9.5 9.6 9.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.1 9.0 8.9 9.1 9.5 9.6 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 10.1 9.0 8.9 9.1 9.5 0 0 1 0 0 0 0 0 0 0 0 15
16 7.5 10.3 10.1 9.0 8.9 9.1 0 0 0 1 0 0 0 0 0 0 0 16
17 7.6 10.2 10.3 10.1 9.0 8.9 0 0 0 0 1 0 0 0 0 0 0 17
18 7.7 9.6 10.2 10.3 10.1 9.0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.7 9.2 9.6 10.2 10.3 10.1 0 0 0 0 0 0 1 0 0 0 0 19
20 7.9 9.3 9.2 9.6 10.2 10.3 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 9.4 9.3 9.2 9.6 10.2 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 9.4 9.4 9.3 9.2 9.6 0 0 0 0 0 0 0 0 0 1 0 22
23 8.2 9.2 9.4 9.4 9.3 9.2 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 9.0 9.2 9.4 9.4 9.3 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 9.0 9.0 9.2 9.4 9.4 1 0 0 0 0 0 0 0 0 0 0 25
26 7.3 9.0 9.0 9.0 9.2 9.4 0 1 0 0 0 0 0 0 0 0 0 26
27 6.9 9.8 9.0 9.0 9.0 9.2 0 0 1 0 0 0 0 0 0 0 0 27
28 6.6 10.0 9.8 9.0 9.0 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 6.7 9.8 10.0 9.8 9.0 9.0 0 0 0 0 1 0 0 0 0 0 0 29
30 6.9 9.3 9.8 10.0 9.8 9.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.0 9.0 9.3 9.8 10.0 9.8 0 0 0 0 0 0 1 0 0 0 0 31
32 7.1 9.0 9.0 9.3 9.8 10.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.2 9.1 9.0 9.0 9.3 9.8 0 0 0 0 0 0 0 0 1 0 0 33
34 7.1 9.1 9.1 9.0 9.0 9.3 0 0 0 0 0 0 0 0 0 1 0 34
35 6.9 9.1 9.1 9.1 9.0 9.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 9.2 9.1 9.1 9.1 9.0 0 0 0 0 0 0 0 0 0 0 0 36
37 6.8 8.8 9.2 9.1 9.1 9.1 1 0 0 0 0 0 0 0 0 0 0 37
38 6.4 8.3 8.8 9.2 9.1 9.1 0 1 0 0 0 0 0 0 0 0 0 38
39 6.7 8.4 8.3 8.8 9.2 9.1 0 0 1 0 0 0 0 0 0 0 0 39
40 6.6 8.1 8.4 8.3 8.8 9.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 7.7 8.1 8.4 8.3 8.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.3 7.9 7.7 8.1 8.4 8.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.2 7.9 7.9 7.7 8.1 8.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.5 8.0 7.9 7.9 7.7 8.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.8 7.9 8.0 7.9 7.9 7.7 0 0 0 0 0 0 0 0 1 0 0 45
46 6.8 7.6 7.9 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 1 0 46
47 6.4 7.1 7.6 7.9 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.1 6.8 7.1 7.6 7.9 8.0 0 0 0 0 0 0 0 0 0 0 0 48
49 5.8 6.5 6.8 7.1 7.6 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 6.9 6.5 6.8 7.1 7.6 0 1 0 0 0 0 0 0 0 0 0 50
51 7.2 8.2 6.9 6.5 6.8 7.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.3 8.7 8.2 6.9 6.5 6.8 0 0 0 1 0 0 0 0 0 0 0 52
53 6.9 8.3 8.7 8.2 6.9 6.5 0 0 0 0 1 0 0 0 0 0 0 53
54 6.1 7.9 8.3 8.7 8.2 6.9 0 0 0 0 0 1 0 0 0 0 0 54
55 5.8 7.5 7.9 8.3 8.7 8.2 0 0 0 0 0 0 1 0 0 0 0 55
56 6.2 7.8 7.5 7.9 8.3 8.7 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) WLVrouw `Yt-1` `Yt-2` `Yt-3` `Yt-4`
4.74589 0.25298 0.28673 -0.14028 0.03077 -0.09424
M1 M2 M3 M4 M5 M6
-0.21190 -0.40347 -0.24927 -0.59269 -0.52820 -0.51537
M7 M8 M9 M10 M11 t
-0.32648 -0.03897 0.13103 0.10422 0.00937 -0.01307
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6933 -0.2070 -0.0708 0.2191 0.7588
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.745894 1.484975 3.196 0.00280 **
WLVrouw 0.252978 0.358825 0.705 0.48510
`Yt-1` 0.286731 0.576107 0.498 0.62156
`Yt-2` -0.140282 0.572234 -0.245 0.80766
`Yt-3` 0.030768 0.555972 0.055 0.95616
`Yt-4` -0.094237 0.316676 -0.298 0.76764
M1 -0.211903 0.286808 -0.739 0.46455
M2 -0.403474 0.291209 -1.386 0.17398
M3 -0.249269 0.414358 -0.602 0.55103
M4 -0.592689 0.402130 -1.474 0.14875
M5 -0.528198 0.394484 -1.339 0.18854
M6 -0.515371 0.350840 -1.469 0.15007
M7 -0.326476 0.292308 -1.117 0.27105
M8 -0.038971 0.308159 -0.126 0.90003
M9 0.131030 0.325835 0.402 0.68984
M10 0.104219 0.328711 0.317 0.75294
M11 0.009370 0.298661 0.031 0.97514
t -0.013068 0.007412 -1.763 0.08595 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4162 on 38 degrees of freedom
Multiple R-squared: 0.7288, Adjusted R-squared: 0.6075
F-statistic: 6.007 on 17 and 38 DF, p-value: 2.251e-06
> 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,] 0.007501103 0.01500221 0.992498897
[2,] 0.007983054 0.01596611 0.992016946
[3,] 0.007403455 0.01480691 0.992596545
[4,] 0.022257561 0.04451512 0.977742439
[5,] 0.029404743 0.05880949 0.970595257
[6,] 0.016685449 0.03337090 0.983314551
[7,] 0.060772977 0.12154595 0.939227023
[8,] 0.565485636 0.86902873 0.434514364
[9,] 0.921740492 0.15651902 0.078259508
[10,] 0.902006687 0.19598663 0.097993313
[11,] 0.992898210 0.01420358 0.007101790
[12,] 0.992791368 0.01441726 0.007208632
[13,] 0.986118559 0.02776288 0.013881441
[14,] 0.983446380 0.03310724 0.016553620
[15,] 0.978707989 0.04258402 0.021292011
> postscript(file="/var/www/html/rcomp/tmp/1lhzd1258733196.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25q9c1258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37d3y1258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gyf71258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ctgr1258733196.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
-0.4885699187 -0.3587229091 -0.1823189280 -0.1625709091 -0.0412301188
6 7 8 9 10
-0.0691054459 0.1449646293 0.1457780641 0.0095688485 -0.1220685930
11 12 13 14 15
-0.0508320265 -0.1937326469 0.2076920791 0.0007666543 -0.0724921171
16 17 18 19 20
-0.0995174997 0.0493963018 0.3337328020 0.5146147431 0.4673272161
21 22 23 24 25
0.4093466542 0.4903454943 0.6221146272 0.7588405748 0.7225246399
26 27 28 29 30
0.3052602153 -0.4509528950 -0.6932932315 -0.5392420555 -0.1517236400
31 32 33 34 35
0.0328880906 -0.1006698084 -0.2284495184 -0.3551319341 -0.4614579228
36 37 38 39 40
-0.3673952963 -0.2604830361 -0.2006349558 0.0171064252 0.2724040819
41 42 43 44 45
0.1999080989 0.0719660715 -0.2986659058 -0.2863085811 -0.1904659844
46 47 48 49 50
-0.0131449673 -0.1098246778 -0.1977126316 -0.1811637642 0.2533309953
51 52 53 54 55
0.6886575149 0.6829775583 0.3311677736 -0.1848697877 -0.3938015573
56
-0.2261268907
> postscript(file="/var/www/html/rcomp/tmp/6fbcn1258733196.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 -0.4885699187 NA
1 -0.3587229091 -0.4885699187
2 -0.1823189280 -0.3587229091
3 -0.1625709091 -0.1823189280
4 -0.0412301188 -0.1625709091
5 -0.0691054459 -0.0412301188
6 0.1449646293 -0.0691054459
7 0.1457780641 0.1449646293
8 0.0095688485 0.1457780641
9 -0.1220685930 0.0095688485
10 -0.0508320265 -0.1220685930
11 -0.1937326469 -0.0508320265
12 0.2076920791 -0.1937326469
13 0.0007666543 0.2076920791
14 -0.0724921171 0.0007666543
15 -0.0995174997 -0.0724921171
16 0.0493963018 -0.0995174997
17 0.3337328020 0.0493963018
18 0.5146147431 0.3337328020
19 0.4673272161 0.5146147431
20 0.4093466542 0.4673272161
21 0.4903454943 0.4093466542
22 0.6221146272 0.4903454943
23 0.7588405748 0.6221146272
24 0.7225246399 0.7588405748
25 0.3052602153 0.7225246399
26 -0.4509528950 0.3052602153
27 -0.6932932315 -0.4509528950
28 -0.5392420555 -0.6932932315
29 -0.1517236400 -0.5392420555
30 0.0328880906 -0.1517236400
31 -0.1006698084 0.0328880906
32 -0.2284495184 -0.1006698084
33 -0.3551319341 -0.2284495184
34 -0.4614579228 -0.3551319341
35 -0.3673952963 -0.4614579228
36 -0.2604830361 -0.3673952963
37 -0.2006349558 -0.2604830361
38 0.0171064252 -0.2006349558
39 0.2724040819 0.0171064252
40 0.1999080989 0.2724040819
41 0.0719660715 0.1999080989
42 -0.2986659058 0.0719660715
43 -0.2863085811 -0.2986659058
44 -0.1904659844 -0.2863085811
45 -0.0131449673 -0.1904659844
46 -0.1098246778 -0.0131449673
47 -0.1977126316 -0.1098246778
48 -0.1811637642 -0.1977126316
49 0.2533309953 -0.1811637642
50 0.6886575149 0.2533309953
51 0.6829775583 0.6886575149
52 0.3311677736 0.6829775583
53 -0.1848697877 0.3311677736
54 -0.3938015573 -0.1848697877
55 -0.2261268907 -0.3938015573
56 NA -0.2261268907
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3587229091 -0.4885699187
[2,] -0.1823189280 -0.3587229091
[3,] -0.1625709091 -0.1823189280
[4,] -0.0412301188 -0.1625709091
[5,] -0.0691054459 -0.0412301188
[6,] 0.1449646293 -0.0691054459
[7,] 0.1457780641 0.1449646293
[8,] 0.0095688485 0.1457780641
[9,] -0.1220685930 0.0095688485
[10,] -0.0508320265 -0.1220685930
[11,] -0.1937326469 -0.0508320265
[12,] 0.2076920791 -0.1937326469
[13,] 0.0007666543 0.2076920791
[14,] -0.0724921171 0.0007666543
[15,] -0.0995174997 -0.0724921171
[16,] 0.0493963018 -0.0995174997
[17,] 0.3337328020 0.0493963018
[18,] 0.5146147431 0.3337328020
[19,] 0.4673272161 0.5146147431
[20,] 0.4093466542 0.4673272161
[21,] 0.4903454943 0.4093466542
[22,] 0.6221146272 0.4903454943
[23,] 0.7588405748 0.6221146272
[24,] 0.7225246399 0.7588405748
[25,] 0.3052602153 0.7225246399
[26,] -0.4509528950 0.3052602153
[27,] -0.6932932315 -0.4509528950
[28,] -0.5392420555 -0.6932932315
[29,] -0.1517236400 -0.5392420555
[30,] 0.0328880906 -0.1517236400
[31,] -0.1006698084 0.0328880906
[32,] -0.2284495184 -0.1006698084
[33,] -0.3551319341 -0.2284495184
[34,] -0.4614579228 -0.3551319341
[35,] -0.3673952963 -0.4614579228
[36,] -0.2604830361 -0.3673952963
[37,] -0.2006349558 -0.2604830361
[38,] 0.0171064252 -0.2006349558
[39,] 0.2724040819 0.0171064252
[40,] 0.1999080989 0.2724040819
[41,] 0.0719660715 0.1999080989
[42,] -0.2986659058 0.0719660715
[43,] -0.2863085811 -0.2986659058
[44,] -0.1904659844 -0.2863085811
[45,] -0.0131449673 -0.1904659844
[46,] -0.1098246778 -0.0131449673
[47,] -0.1977126316 -0.1098246778
[48,] -0.1811637642 -0.1977126316
[49,] 0.2533309953 -0.1811637642
[50,] 0.6886575149 0.2533309953
[51,] 0.6829775583 0.6886575149
[52,] 0.3311677736 0.6829775583
[53,] -0.1848697877 0.3311677736
[54,] -0.3938015573 -0.1848697877
[55,] -0.2261268907 -0.3938015573
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3587229091 -0.4885699187
2 -0.1823189280 -0.3587229091
3 -0.1625709091 -0.1823189280
4 -0.0412301188 -0.1625709091
5 -0.0691054459 -0.0412301188
6 0.1449646293 -0.0691054459
7 0.1457780641 0.1449646293
8 0.0095688485 0.1457780641
9 -0.1220685930 0.0095688485
10 -0.0508320265 -0.1220685930
11 -0.1937326469 -0.0508320265
12 0.2076920791 -0.1937326469
13 0.0007666543 0.2076920791
14 -0.0724921171 0.0007666543
15 -0.0995174997 -0.0724921171
16 0.0493963018 -0.0995174997
17 0.3337328020 0.0493963018
18 0.5146147431 0.3337328020
19 0.4673272161 0.5146147431
20 0.4093466542 0.4673272161
21 0.4903454943 0.4093466542
22 0.6221146272 0.4903454943
23 0.7588405748 0.6221146272
24 0.7225246399 0.7588405748
25 0.3052602153 0.7225246399
26 -0.4509528950 0.3052602153
27 -0.6932932315 -0.4509528950
28 -0.5392420555 -0.6932932315
29 -0.1517236400 -0.5392420555
30 0.0328880906 -0.1517236400
31 -0.1006698084 0.0328880906
32 -0.2284495184 -0.1006698084
33 -0.3551319341 -0.2284495184
34 -0.4614579228 -0.3551319341
35 -0.3673952963 -0.4614579228
36 -0.2604830361 -0.3673952963
37 -0.2006349558 -0.2604830361
38 0.0171064252 -0.2006349558
39 0.2724040819 0.0171064252
40 0.1999080989 0.2724040819
41 0.0719660715 0.1999080989
42 -0.2986659058 0.0719660715
43 -0.2863085811 -0.2986659058
44 -0.1904659844 -0.2863085811
45 -0.0131449673 -0.1904659844
46 -0.1098246778 -0.0131449673
47 -0.1977126316 -0.1098246778
48 -0.1811637642 -0.1977126316
49 0.2533309953 -0.1811637642
50 0.6886575149 0.2533309953
51 0.6829775583 0.6886575149
52 0.3311677736 0.6829775583
53 -0.1848697877 0.3311677736
54 -0.3938015573 -0.1848697877
55 -0.2261268907 -0.3938015573
> 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/73sbq1258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8su2l1258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9lap21258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106x7r1258733196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mijh1258733196.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/12rbu31258733196.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/139i651258733196.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/14h3zf1258733196.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/152x4w1258733196.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/16hahd1258733196.tab")
+ }
>
> system("convert tmp/1lhzd1258733196.ps tmp/1lhzd1258733196.png")
> system("convert tmp/25q9c1258733196.ps tmp/25q9c1258733196.png")
> system("convert tmp/37d3y1258733196.ps tmp/37d3y1258733196.png")
> system("convert tmp/4gyf71258733196.ps tmp/4gyf71258733196.png")
> system("convert tmp/5ctgr1258733196.ps tmp/5ctgr1258733196.png")
> system("convert tmp/6fbcn1258733196.ps tmp/6fbcn1258733196.png")
> system("convert tmp/73sbq1258733196.ps tmp/73sbq1258733196.png")
> system("convert tmp/8su2l1258733196.ps tmp/8su2l1258733196.png")
> system("convert tmp/9lap21258733196.ps tmp/9lap21258733196.png")
> system("convert tmp/106x7r1258733196.ps tmp/106x7r1258733196.png")
>
>
> proc.time()
user system elapsed
2.351 1.524 2.762