R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9.3
+ ,8.1
+ ,10.9
+ ,25.6
+ ,8.7
+ ,7.7
+ ,10
+ ,23.7
+ ,8.2
+ ,7.5
+ ,9.2
+ ,22
+ ,8.3
+ ,7.6
+ ,9.2
+ ,21.3
+ ,8.5
+ ,7.8
+ ,9.5
+ ,20.7
+ ,8.6
+ ,7.8
+ ,9.6
+ ,20.4
+ ,8.5
+ ,7.8
+ ,9.5
+ ,20.3
+ ,8.2
+ ,7.5
+ ,9.1
+ ,20.4
+ ,8.1
+ ,7.5
+ ,8.9
+ ,19.8
+ ,7.9
+ ,7.1
+ ,9
+ ,19.5
+ ,8.6
+ ,7.5
+ ,10.1
+ ,23.1
+ ,8.7
+ ,7.5
+ ,10.3
+ ,23.5
+ ,8.7
+ ,7.6
+ ,10.2
+ ,23.5
+ ,8.5
+ ,7.7
+ ,9.6
+ ,22.9
+ ,8.4
+ ,7.7
+ ,9.2
+ ,21.9
+ ,8.5
+ ,7.9
+ ,9.3
+ ,21.5
+ ,8.7
+ ,8.1
+ ,9.4
+ ,20.5
+ ,8.7
+ ,8.2
+ ,9.4
+ ,20.2
+ ,8.6
+ ,8.2
+ ,9.2
+ ,19.4
+ ,8.5
+ ,8.2
+ ,9
+ ,19.2
+ ,8.3
+ ,7.9
+ ,9
+ ,18.8
+ ,8
+ ,7.3
+ ,9
+ ,18.8
+ ,8.2
+ ,6.9
+ ,9.8
+ ,22.6
+ ,8.1
+ ,6.6
+ ,10
+ ,23.3
+ ,8.1
+ ,6.7
+ ,9.8
+ ,23
+ ,8
+ ,6.9
+ ,9.3
+ ,21.4
+ ,7.9
+ ,7
+ ,9
+ ,19.9
+ ,7.9
+ ,7.1
+ ,9
+ ,18.8
+ ,8
+ ,7.2
+ ,9.1
+ ,18.6
+ ,8
+ ,7.1
+ ,9.1
+ ,18.4
+ ,7.9
+ ,6.9
+ ,9.1
+ ,18.6
+ ,8
+ ,7
+ ,9.2
+ ,19.9
+ ,7.7
+ ,6.8
+ ,8.8
+ ,19.2
+ ,7.2
+ ,6.4
+ ,8.3
+ ,18.4
+ ,7.5
+ ,6.7
+ ,8.4
+ ,21.1
+ ,7.3
+ ,6.6
+ ,8.1
+ ,20.5
+ ,7
+ ,6.4
+ ,7.7
+ ,19.1
+ ,7
+ ,6.3
+ ,7.9
+ ,18.1
+ ,7
+ ,6.2
+ ,7.9
+ ,17
+ ,7.2
+ ,6.5
+ ,8
+ ,17.1
+ ,7.3
+ ,6.8
+ ,7.9
+ ,17.4
+ ,7.1
+ ,6.8
+ ,7.6
+ ,16.8
+ ,6.8
+ ,6.4
+ ,7.1
+ ,15.3
+ ,6.4
+ ,6.1
+ ,6.8
+ ,14.3
+ ,6.1
+ ,5.8
+ ,6.5
+ ,13.4
+ ,6.5
+ ,6.1
+ ,6.9
+ ,15.3
+ ,7.7
+ ,7.2
+ ,8.2
+ ,22.1
+ ,7.9
+ ,7.3
+ ,8.7
+ ,23.7
+ ,7.5
+ ,6.9
+ ,8.3
+ ,22.2
+ ,6.9
+ ,6.1
+ ,7.9
+ ,19.5
+ ,6.6
+ ,5.8
+ ,7.5
+ ,16.6
+ ,6.9
+ ,6.2
+ ,7.8
+ ,17.3
+ ,7.7
+ ,7.1
+ ,8.3
+ ,19.8
+ ,8
+ ,7.7
+ ,8.4
+ ,21.2
+ ,8
+ ,7.9
+ ,8.2
+ ,21.5
+ ,7.7
+ ,7.7
+ ,7.7
+ ,20.6
+ ,7.3
+ ,7.4
+ ,7.2
+ ,19.1
+ ,7.4
+ ,7.5
+ ,7.3
+ ,19.6
+ ,8.1
+ ,8
+ ,8.1
+ ,23.5
+ ,8.3
+ ,8.1
+ ,8.5
+ ,24
+ ,8.2
+ ,8
+ ,8.4
+ ,23.2)
+ ,dim=c(4
+ ,61)
+ ,dimnames=list(c('TW'
+ ,'WM'
+ ,'WV'
+ ,'WJ')
+ ,1:61))
> y <- array(NA,dim=c(4,61),dimnames=list(c('TW','WM','WV','WJ'),1:61))
> 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
TW WM WV WJ M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.3 8.1 10.9 25.6 1 0 0 0 0 0 0 0 0 0 0 1
2 8.7 7.7 10.0 23.7 0 1 0 0 0 0 0 0 0 0 0 2
3 8.2 7.5 9.2 22.0 0 0 1 0 0 0 0 0 0 0 0 3
4 8.3 7.6 9.2 21.3 0 0 0 1 0 0 0 0 0 0 0 4
5 8.5 7.8 9.5 20.7 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 7.8 9.6 20.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.5 7.8 9.5 20.3 0 0 0 0 0 0 1 0 0 0 0 7
8 8.2 7.5 9.1 20.4 0 0 0 0 0 0 0 1 0 0 0 8
9 8.1 7.5 8.9 19.8 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 7.1 9.0 19.5 0 0 0 0 0 0 0 0 0 1 0 10
11 8.6 7.5 10.1 23.1 0 0 0 0 0 0 0 0 0 0 1 11
12 8.7 7.5 10.3 23.5 0 0 0 0 0 0 0 0 0 0 0 12
13 8.7 7.6 10.2 23.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 7.7 9.6 22.9 0 1 0 0 0 0 0 0 0 0 0 14
15 8.4 7.7 9.2 21.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 7.9 9.3 21.5 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 8.1 9.4 20.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.7 8.2 9.4 20.2 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 8.2 9.2 19.4 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 8.2 9.0 19.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.3 7.9 9.0 18.8 0 0 0 0 0 0 0 0 1 0 0 21
22 8.0 7.3 9.0 18.8 0 0 0 0 0 0 0 0 0 1 0 22
23 8.2 6.9 9.8 22.6 0 0 0 0 0 0 0 0 0 0 1 23
24 8.1 6.6 10.0 23.3 0 0 0 0 0 0 0 0 0 0 0 24
25 8.1 6.7 9.8 23.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 6.9 9.3 21.4 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 7.0 9.0 19.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 7.1 9.0 18.8 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 7.2 9.1 18.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 7.1 9.1 18.4 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 6.9 9.1 18.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.0 7.0 9.2 19.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.7 6.8 8.8 19.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.2 6.4 8.3 18.4 0 0 0 0 0 0 0 0 0 1 0 34
35 7.5 6.7 8.4 21.1 0 0 0 0 0 0 0 0 0 0 1 35
36 7.3 6.6 8.1 20.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.0 6.4 7.7 19.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 6.3 7.9 18.1 0 1 0 0 0 0 0 0 0 0 0 38
39 7.0 6.2 7.9 17.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.2 6.5 8.0 17.1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.3 6.8 7.9 17.4 0 0 0 0 1 0 0 0 0 0 0 41
42 7.1 6.8 7.6 16.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 6.4 7.1 15.3 0 0 0 0 0 0 1 0 0 0 0 43
44 6.4 6.1 6.8 14.3 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 5.8 6.5 13.4 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 6.1 6.9 15.3 0 0 0 0 0 0 0 0 0 1 0 46
47 7.7 7.2 8.2 22.1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.9 7.3 8.7 23.7 0 0 0 0 0 0 0 0 0 0 0 48
49 7.5 6.9 8.3 22.2 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 6.1 7.9 19.5 0 1 0 0 0 0 0 0 0 0 0 50
51 6.6 5.8 7.5 16.6 0 0 1 0 0 0 0 0 0 0 0 51
52 6.9 6.2 7.8 17.3 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 7.1 8.3 19.8 0 0 0 0 1 0 0 0 0 0 0 53
54 8.0 7.7 8.4 21.2 0 0 0 0 0 1 0 0 0 0 0 54
55 8.0 7.9 8.2 21.5 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 7.7 7.7 20.6 0 0 0 0 0 0 0 1 0 0 0 56
57 7.3 7.4 7.2 19.1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.4 7.5 7.3 19.6 0 0 0 0 0 0 0 0 0 1 0 58
59 8.1 8.0 8.1 23.5 0 0 0 0 0 0 0 0 0 0 1 59
60 8.3 8.1 8.5 24.0 0 0 0 0 0 0 0 0 0 0 0 60
61 8.2 8.0 8.4 23.2 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WM WV WJ M1 M2
0.1374788 0.5305573 0.4315834 0.0060575 0.0017217 0.0022223
M3 M4 M5 M6 M7 M8
0.0287547 0.0101105 0.0303652 0.0148707 0.0254744 0.0123528
M9 M10 M11 t
-0.0055734 -0.0101279 0.0287036 0.0004593
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.061230 -0.021908 -0.002456 0.023610 0.064808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1374788 0.1024796 1.342 0.186
WM 0.5305573 0.0135786 39.073 <2e-16 ***
WV 0.4315834 0.0135530 31.844 <2e-16 ***
WJ 0.0060575 0.0057986 1.045 0.302
M1 0.0017217 0.0200528 0.086 0.932
M2 0.0022223 0.0220193 0.101 0.920
M3 0.0287547 0.0245110 1.173 0.247
M4 0.0101105 0.0265237 0.381 0.705
M5 0.0303652 0.0287602 1.056 0.297
M6 0.0148707 0.0297433 0.500 0.620
M7 0.0254744 0.0299847 0.850 0.400
M8 0.0123528 0.0288207 0.429 0.670
M9 -0.0055734 0.0296082 -0.188 0.852
M10 -0.0101279 0.0273492 -0.370 0.713
M11 0.0287036 0.0210242 1.365 0.179
t 0.0004593 0.0005214 0.881 0.383
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03289 on 45 degrees of freedom
Multiple R-squared: 0.9983, Adjusted R-squared: 0.9977
F-statistic: 1711 on 15 and 45 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,] 0.72258867 0.55482265 0.2774113
[2,] 0.68742521 0.62514957 0.3125748
[3,] 0.68467445 0.63065109 0.3153255
[4,] 0.57107065 0.85785870 0.4289293
[5,] 0.46538917 0.93077834 0.5346108
[6,] 0.42171576 0.84343153 0.5782842
[7,] 0.33345501 0.66691003 0.6665450
[8,] 0.36284035 0.72568071 0.6371596
[9,] 0.26401468 0.52802936 0.7359853
[10,] 0.21285715 0.42571431 0.7871428
[11,] 0.36534958 0.73069915 0.6346504
[12,] 0.28098776 0.56197552 0.7190122
[13,] 0.21493199 0.42986397 0.7850680
[14,] 0.17567799 0.35135599 0.8243220
[15,] 0.18497885 0.36995770 0.8150212
[16,] 0.21430149 0.42860299 0.7856985
[17,] 0.15374219 0.30748438 0.8462578
[18,] 0.12226114 0.24452228 0.8777389
[19,] 0.09612098 0.19224197 0.9038790
[20,] 0.06263581 0.12527163 0.9373642
[21,] 0.04209736 0.08419472 0.9579026
[22,] 0.08013398 0.16026797 0.9198660
[23,] 0.04323644 0.08647289 0.9567636
[24,] 0.23223618 0.46447236 0.7677638
> postscript(file="/var/www/html/rcomp/tmp/1b0ka1258890520.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vfqg1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39tum1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ipdo1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tpgh1258890520.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 = 61
Frequency = 1
1 2 3 4 5 6
0.003496591 0.014693726 -0.050622085 0.018747257 -0.033918762 0.039775344
7 8 9 10 11 12
-0.027523663 0.016333380 0.023751471 -0.001271567 -0.049333878 -0.009829273
13 14 15 16 17 18
-0.021907684 -0.013338891 0.038360275 0.009698316 0.045771955 0.009568672
19 20 21 22 23 24
-0.010331776 -0.010141347 -0.031084243 -0.008654730 -0.004007712 -0.007153167
25 26 27 28 29 30
0.025743997 0.044156189 0.002670022 -0.025537651 -0.041254254 0.028048168
31 32 33 34 35 36
0.021885039 0.030458539 0.030910502 -0.032133785 0.009894715 0.024304192
37 38 39 40 41 42
0.009348416 -0.018815049 0.013912242 0.029165826 -0.009374286 -0.061229595
43 44 45 46 47 48
0.064808096 -0.027829990 -0.016269145 0.044516307 0.019363363 -0.030931722
49 50 51 52 53 54
-0.039170300 -0.026695975 -0.004320454 -0.032073747 0.038775347 -0.016162589
55 56 57 58 59 60
-0.048837696 -0.008820582 -0.007308586 -0.002456225 0.024083511 0.023609970
61
0.022488980
> postscript(file="/var/www/html/rcomp/tmp/6w4ay1258890520.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.003496591 NA
1 0.014693726 0.003496591
2 -0.050622085 0.014693726
3 0.018747257 -0.050622085
4 -0.033918762 0.018747257
5 0.039775344 -0.033918762
6 -0.027523663 0.039775344
7 0.016333380 -0.027523663
8 0.023751471 0.016333380
9 -0.001271567 0.023751471
10 -0.049333878 -0.001271567
11 -0.009829273 -0.049333878
12 -0.021907684 -0.009829273
13 -0.013338891 -0.021907684
14 0.038360275 -0.013338891
15 0.009698316 0.038360275
16 0.045771955 0.009698316
17 0.009568672 0.045771955
18 -0.010331776 0.009568672
19 -0.010141347 -0.010331776
20 -0.031084243 -0.010141347
21 -0.008654730 -0.031084243
22 -0.004007712 -0.008654730
23 -0.007153167 -0.004007712
24 0.025743997 -0.007153167
25 0.044156189 0.025743997
26 0.002670022 0.044156189
27 -0.025537651 0.002670022
28 -0.041254254 -0.025537651
29 0.028048168 -0.041254254
30 0.021885039 0.028048168
31 0.030458539 0.021885039
32 0.030910502 0.030458539
33 -0.032133785 0.030910502
34 0.009894715 -0.032133785
35 0.024304192 0.009894715
36 0.009348416 0.024304192
37 -0.018815049 0.009348416
38 0.013912242 -0.018815049
39 0.029165826 0.013912242
40 -0.009374286 0.029165826
41 -0.061229595 -0.009374286
42 0.064808096 -0.061229595
43 -0.027829990 0.064808096
44 -0.016269145 -0.027829990
45 0.044516307 -0.016269145
46 0.019363363 0.044516307
47 -0.030931722 0.019363363
48 -0.039170300 -0.030931722
49 -0.026695975 -0.039170300
50 -0.004320454 -0.026695975
51 -0.032073747 -0.004320454
52 0.038775347 -0.032073747
53 -0.016162589 0.038775347
54 -0.048837696 -0.016162589
55 -0.008820582 -0.048837696
56 -0.007308586 -0.008820582
57 -0.002456225 -0.007308586
58 0.024083511 -0.002456225
59 0.023609970 0.024083511
60 0.022488980 0.023609970
61 NA 0.022488980
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.014693726 0.003496591
[2,] -0.050622085 0.014693726
[3,] 0.018747257 -0.050622085
[4,] -0.033918762 0.018747257
[5,] 0.039775344 -0.033918762
[6,] -0.027523663 0.039775344
[7,] 0.016333380 -0.027523663
[8,] 0.023751471 0.016333380
[9,] -0.001271567 0.023751471
[10,] -0.049333878 -0.001271567
[11,] -0.009829273 -0.049333878
[12,] -0.021907684 -0.009829273
[13,] -0.013338891 -0.021907684
[14,] 0.038360275 -0.013338891
[15,] 0.009698316 0.038360275
[16,] 0.045771955 0.009698316
[17,] 0.009568672 0.045771955
[18,] -0.010331776 0.009568672
[19,] -0.010141347 -0.010331776
[20,] -0.031084243 -0.010141347
[21,] -0.008654730 -0.031084243
[22,] -0.004007712 -0.008654730
[23,] -0.007153167 -0.004007712
[24,] 0.025743997 -0.007153167
[25,] 0.044156189 0.025743997
[26,] 0.002670022 0.044156189
[27,] -0.025537651 0.002670022
[28,] -0.041254254 -0.025537651
[29,] 0.028048168 -0.041254254
[30,] 0.021885039 0.028048168
[31,] 0.030458539 0.021885039
[32,] 0.030910502 0.030458539
[33,] -0.032133785 0.030910502
[34,] 0.009894715 -0.032133785
[35,] 0.024304192 0.009894715
[36,] 0.009348416 0.024304192
[37,] -0.018815049 0.009348416
[38,] 0.013912242 -0.018815049
[39,] 0.029165826 0.013912242
[40,] -0.009374286 0.029165826
[41,] -0.061229595 -0.009374286
[42,] 0.064808096 -0.061229595
[43,] -0.027829990 0.064808096
[44,] -0.016269145 -0.027829990
[45,] 0.044516307 -0.016269145
[46,] 0.019363363 0.044516307
[47,] -0.030931722 0.019363363
[48,] -0.039170300 -0.030931722
[49,] -0.026695975 -0.039170300
[50,] -0.004320454 -0.026695975
[51,] -0.032073747 -0.004320454
[52,] 0.038775347 -0.032073747
[53,] -0.016162589 0.038775347
[54,] -0.048837696 -0.016162589
[55,] -0.008820582 -0.048837696
[56,] -0.007308586 -0.008820582
[57,] -0.002456225 -0.007308586
[58,] 0.024083511 -0.002456225
[59,] 0.023609970 0.024083511
[60,] 0.022488980 0.023609970
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.014693726 0.003496591
2 -0.050622085 0.014693726
3 0.018747257 -0.050622085
4 -0.033918762 0.018747257
5 0.039775344 -0.033918762
6 -0.027523663 0.039775344
7 0.016333380 -0.027523663
8 0.023751471 0.016333380
9 -0.001271567 0.023751471
10 -0.049333878 -0.001271567
11 -0.009829273 -0.049333878
12 -0.021907684 -0.009829273
13 -0.013338891 -0.021907684
14 0.038360275 -0.013338891
15 0.009698316 0.038360275
16 0.045771955 0.009698316
17 0.009568672 0.045771955
18 -0.010331776 0.009568672
19 -0.010141347 -0.010331776
20 -0.031084243 -0.010141347
21 -0.008654730 -0.031084243
22 -0.004007712 -0.008654730
23 -0.007153167 -0.004007712
24 0.025743997 -0.007153167
25 0.044156189 0.025743997
26 0.002670022 0.044156189
27 -0.025537651 0.002670022
28 -0.041254254 -0.025537651
29 0.028048168 -0.041254254
30 0.021885039 0.028048168
31 0.030458539 0.021885039
32 0.030910502 0.030458539
33 -0.032133785 0.030910502
34 0.009894715 -0.032133785
35 0.024304192 0.009894715
36 0.009348416 0.024304192
37 -0.018815049 0.009348416
38 0.013912242 -0.018815049
39 0.029165826 0.013912242
40 -0.009374286 0.029165826
41 -0.061229595 -0.009374286
42 0.064808096 -0.061229595
43 -0.027829990 0.064808096
44 -0.016269145 -0.027829990
45 0.044516307 -0.016269145
46 0.019363363 0.044516307
47 -0.030931722 0.019363363
48 -0.039170300 -0.030931722
49 -0.026695975 -0.039170300
50 -0.004320454 -0.026695975
51 -0.032073747 -0.004320454
52 0.038775347 -0.032073747
53 -0.016162589 0.038775347
54 -0.048837696 -0.016162589
55 -0.008820582 -0.048837696
56 -0.007308586 -0.008820582
57 -0.002456225 -0.007308586
58 0.024083511 -0.002456225
59 0.023609970 0.024083511
60 0.022488980 0.023609970
> 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/7hjjn1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xsdr1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94zbs1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105uiq1258890520.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119xex1258890520.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/12vxod1258890520.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/1396qv1258890520.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/14o3xr1258890520.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/15d8bz1258890520.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/168tb11258890520.tab")
+ }
>
> system("convert tmp/1b0ka1258890520.ps tmp/1b0ka1258890520.png")
> system("convert tmp/2vfqg1258890520.ps tmp/2vfqg1258890520.png")
> system("convert tmp/39tum1258890520.ps tmp/39tum1258890520.png")
> system("convert tmp/4ipdo1258890520.ps tmp/4ipdo1258890520.png")
> system("convert tmp/5tpgh1258890520.ps tmp/5tpgh1258890520.png")
> system("convert tmp/6w4ay1258890520.ps tmp/6w4ay1258890520.png")
> system("convert tmp/7hjjn1258890520.ps tmp/7hjjn1258890520.png")
> system("convert tmp/8xsdr1258890520.ps tmp/8xsdr1258890520.png")
> system("convert tmp/94zbs1258890520.ps tmp/94zbs1258890520.png")
> system("convert tmp/105uiq1258890520.ps tmp/105uiq1258890520.png")
>
>
> proc.time()
user system elapsed
2.378 1.549 2.897