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 = '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
TW WM WV WJ M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9.3 8.1 10.9 25.6 1 0 0 0 0 0 0 0 0 0 0
2 8.7 7.7 10.0 23.7 0 1 0 0 0 0 0 0 0 0 0
3 8.2 7.5 9.2 22.0 0 0 1 0 0 0 0 0 0 0 0
4 8.3 7.6 9.2 21.3 0 0 0 1 0 0 0 0 0 0 0
5 8.5 7.8 9.5 20.7 0 0 0 0 1 0 0 0 0 0 0
6 8.6 7.8 9.6 20.4 0 0 0 0 0 1 0 0 0 0 0
7 8.5 7.8 9.5 20.3 0 0 0 0 0 0 1 0 0 0 0
8 8.2 7.5 9.1 20.4 0 0 0 0 0 0 0 1 0 0 0
9 8.1 7.5 8.9 19.8 0 0 0 0 0 0 0 0 1 0 0
10 7.9 7.1 9.0 19.5 0 0 0 0 0 0 0 0 0 1 0
11 8.6 7.5 10.1 23.1 0 0 0 0 0 0 0 0 0 0 1
12 8.7 7.5 10.3 23.5 0 0 0 0 0 0 0 0 0 0 0
13 8.7 7.6 10.2 23.5 1 0 0 0 0 0 0 0 0 0 0
14 8.5 7.7 9.6 22.9 0 1 0 0 0 0 0 0 0 0 0
15 8.4 7.7 9.2 21.9 0 0 1 0 0 0 0 0 0 0 0
16 8.5 7.9 9.3 21.5 0 0 0 1 0 0 0 0 0 0 0
17 8.7 8.1 9.4 20.5 0 0 0 0 1 0 0 0 0 0 0
18 8.7 8.2 9.4 20.2 0 0 0 0 0 1 0 0 0 0 0
19 8.6 8.2 9.2 19.4 0 0 0 0 0 0 1 0 0 0 0
20 8.5 8.2 9.0 19.2 0 0 0 0 0 0 0 1 0 0 0
21 8.3 7.9 9.0 18.8 0 0 0 0 0 0 0 0 1 0 0
22 8.0 7.3 9.0 18.8 0 0 0 0 0 0 0 0 0 1 0
23 8.2 6.9 9.8 22.6 0 0 0 0 0 0 0 0 0 0 1
24 8.1 6.6 10.0 23.3 0 0 0 0 0 0 0 0 0 0 0
25 8.1 6.7 9.8 23.0 1 0 0 0 0 0 0 0 0 0 0
26 8.0 6.9 9.3 21.4 0 1 0 0 0 0 0 0 0 0 0
27 7.9 7.0 9.0 19.9 0 0 1 0 0 0 0 0 0 0 0
28 7.9 7.1 9.0 18.8 0 0 0 1 0 0 0 0 0 0 0
29 8.0 7.2 9.1 18.6 0 0 0 0 1 0 0 0 0 0 0
30 8.0 7.1 9.1 18.4 0 0 0 0 0 1 0 0 0 0 0
31 7.9 6.9 9.1 18.6 0 0 0 0 0 0 1 0 0 0 0
32 8.0 7.0 9.2 19.9 0 0 0 0 0 0 0 1 0 0 0
33 7.7 6.8 8.8 19.2 0 0 0 0 0 0 0 0 1 0 0
34 7.2 6.4 8.3 18.4 0 0 0 0 0 0 0 0 0 1 0
35 7.5 6.7 8.4 21.1 0 0 0 0 0 0 0 0 0 0 1
36 7.3 6.6 8.1 20.5 0 0 0 0 0 0 0 0 0 0 0
37 7.0 6.4 7.7 19.1 1 0 0 0 0 0 0 0 0 0 0
38 7.0 6.3 7.9 18.1 0 1 0 0 0 0 0 0 0 0 0
39 7.0 6.2 7.9 17.0 0 0 1 0 0 0 0 0 0 0 0
40 7.2 6.5 8.0 17.1 0 0 0 1 0 0 0 0 0 0 0
41 7.3 6.8 7.9 17.4 0 0 0 0 1 0 0 0 0 0 0
42 7.1 6.8 7.6 16.8 0 0 0 0 0 1 0 0 0 0 0
43 6.8 6.4 7.1 15.3 0 0 0 0 0 0 1 0 0 0 0
44 6.4 6.1 6.8 14.3 0 0 0 0 0 0 0 1 0 0 0
45 6.1 5.8 6.5 13.4 0 0 0 0 0 0 0 0 1 0 0
46 6.5 6.1 6.9 15.3 0 0 0 0 0 0 0 0 0 1 0
47 7.7 7.2 8.2 22.1 0 0 0 0 0 0 0 0 0 0 1
48 7.9 7.3 8.7 23.7 0 0 0 0 0 0 0 0 0 0 0
49 7.5 6.9 8.3 22.2 1 0 0 0 0 0 0 0 0 0 0
50 6.9 6.1 7.9 19.5 0 1 0 0 0 0 0 0 0 0 0
51 6.6 5.8 7.5 16.6 0 0 1 0 0 0 0 0 0 0 0
52 6.9 6.2 7.8 17.3 0 0 0 1 0 0 0 0 0 0 0
53 7.7 7.1 8.3 19.8 0 0 0 0 1 0 0 0 0 0 0
54 8.0 7.7 8.4 21.2 0 0 0 0 0 1 0 0 0 0 0
55 8.0 7.9 8.2 21.5 0 0 0 0 0 0 1 0 0 0 0
56 7.7 7.7 7.7 20.6 0 0 0 0 0 0 0 1 0 0 0
57 7.3 7.4 7.2 19.1 0 0 0 0 0 0 0 0 1 0 0
58 7.4 7.5 7.3 19.6 0 0 0 0 0 0 0 0 0 1 0
59 8.1 8.0 8.1 23.5 0 0 0 0 0 0 0 0 0 0 1
60 8.3 8.1 8.5 24.0 0 0 0 0 0 0 0 0 0 0 0
61 8.2 8.0 8.4 23.2 1 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) WM WV WJ M1 M2
0.2132482 0.5278236 0.4213843 0.0083844 0.0011271 -0.0005976
M3 M4 M5 M6 M7 M8
0.0260612 0.0101492 0.0331632 0.0182520 0.0279408 0.0125699
M9 M10 M11
-0.0064462 -0.0114891 0.0275238
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0640788 -0.0209872 -0.0008752 0.0262817 0.0606306
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2132482 0.0555940 3.836 0.000379 ***
WM 0.5278236 0.0131872 40.026 < 2e-16 ***
WV 0.4213843 0.0070305 59.936 < 2e-16 ***
WJ 0.0083844 0.0051496 1.628 0.110320
M1 0.0011271 0.0199926 0.056 0.955288
M2 -0.0005976 0.0217323 -0.027 0.978182
M3 0.0260612 0.0242604 1.074 0.288323
M4 0.0101492 0.0264590 0.384 0.703055
M5 0.0331632 0.0285147 1.163 0.250819
M6 0.0182520 0.0294228 0.620 0.538098
M7 0.0279408 0.0297810 0.938 0.353037
M8 0.0125699 0.0287495 0.437 0.663997
M9 -0.0064462 0.0295195 -0.218 0.828107
M10 -0.0114891 0.0272390 -0.422 0.675145
M11 0.0275238 0.0209304 1.315 0.195023
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03281 on 46 degrees of freedom
Multiple R-squared: 0.9982, Adjusted R-squared: 0.9977
F-statistic: 1843 on 14 and 46 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,] 0.70493163 0.5901367 0.2950684
[2,] 0.65779475 0.6844105 0.3422052
[3,] 0.74297013 0.5140597 0.2570299
[4,] 0.68154748 0.6369050 0.3184525
[5,] 0.58882900 0.8223420 0.4111710
[6,] 0.59517944 0.8096411 0.4048206
[7,] 0.48685086 0.9737017 0.5131491
[8,] 0.41436339 0.8287268 0.5856366
[9,] 0.43652900 0.8730580 0.5634710
[10,] 0.34282617 0.6856523 0.6571738
[11,] 0.31867826 0.6373565 0.6813217
[12,] 0.49059019 0.9811804 0.5094098
[13,] 0.40719129 0.8143826 0.5928087
[14,] 0.36520703 0.7304141 0.6347930
[15,] 0.31773785 0.6354757 0.6822622
[16,] 0.34742781 0.6948556 0.6525722
[17,] 0.32896614 0.6579323 0.6710339
[18,] 0.24617083 0.4923417 0.7538292
[19,] 0.18340221 0.3668044 0.8165978
[20,] 0.12732780 0.2546556 0.8726722
[21,] 0.10209414 0.2041883 0.8979059
[22,] 0.06713543 0.1342709 0.9328646
[23,] 0.04709460 0.0941892 0.9529054
[24,] 0.06443179 0.1288636 0.9355682
[25,] 0.26555274 0.5311055 0.7344473
[26,] 0.68334622 0.6333076 0.3166538
> postscript(file="/var/www/html/rcomp/tmp/1j6891258887121.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2i5z51258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yl7b1258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ksl61258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58p221258887121.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
0.0025247793 0.0105550223 -0.0591781631 0.0098205381 -0.0401427593
6 7 8 9 10
0.0351452758 -0.0315666673 0.0098666496 0.0181901538 -0.0052606119
11 12 13 14 15
-0.0491094196 -0.0092162359 -0.0209872191 -0.0141837570 0.0360955595
16 17 18 19 20
0.0076581581 0.0453254710 0.0099695747 -0.0087348675 -0.0074101617
21 22 23 24 25
-0.0266933147 -0.0049562545 -0.0018077936 -0.0060828525 0.0267999086
26 27 28 29 30
0.0470669770 0.0066177041 -0.0210298381 -0.0372876801 0.0320827010
31 32 33 34 35
0.0262817302 0.0358322099 0.0348357235 -0.0315922853 0.0062714863
36 37 38 39 40
0.0180235540 0.0027530743 -0.0186324054 0.0167140011 0.0313020457
41 42 43 44 45
-0.0104358549 -0.0640787948 0.0606305507 -0.0308517287 -0.0195274053
46 47 48 49 50
0.0426843792 0.0182521572 -0.0311135678 -0.0399808918 -0.0248058369
51 52 53 54 55
-0.0002491017 -0.0277509040 0.0425408233 -0.0131187567 -0.0466107461
56 57 58 59 60
-0.0074369691 -0.0068051572 -0.0008752275 0.0263935697 0.0283891022
61
0.0288903487
> postscript(file="/var/www/html/rcomp/tmp/603d01258887121.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 0.0025247793 NA
1 0.0105550223 0.0025247793
2 -0.0591781631 0.0105550223
3 0.0098205381 -0.0591781631
4 -0.0401427593 0.0098205381
5 0.0351452758 -0.0401427593
6 -0.0315666673 0.0351452758
7 0.0098666496 -0.0315666673
8 0.0181901538 0.0098666496
9 -0.0052606119 0.0181901538
10 -0.0491094196 -0.0052606119
11 -0.0092162359 -0.0491094196
12 -0.0209872191 -0.0092162359
13 -0.0141837570 -0.0209872191
14 0.0360955595 -0.0141837570
15 0.0076581581 0.0360955595
16 0.0453254710 0.0076581581
17 0.0099695747 0.0453254710
18 -0.0087348675 0.0099695747
19 -0.0074101617 -0.0087348675
20 -0.0266933147 -0.0074101617
21 -0.0049562545 -0.0266933147
22 -0.0018077936 -0.0049562545
23 -0.0060828525 -0.0018077936
24 0.0267999086 -0.0060828525
25 0.0470669770 0.0267999086
26 0.0066177041 0.0470669770
27 -0.0210298381 0.0066177041
28 -0.0372876801 -0.0210298381
29 0.0320827010 -0.0372876801
30 0.0262817302 0.0320827010
31 0.0358322099 0.0262817302
32 0.0348357235 0.0358322099
33 -0.0315922853 0.0348357235
34 0.0062714863 -0.0315922853
35 0.0180235540 0.0062714863
36 0.0027530743 0.0180235540
37 -0.0186324054 0.0027530743
38 0.0167140011 -0.0186324054
39 0.0313020457 0.0167140011
40 -0.0104358549 0.0313020457
41 -0.0640787948 -0.0104358549
42 0.0606305507 -0.0640787948
43 -0.0308517287 0.0606305507
44 -0.0195274053 -0.0308517287
45 0.0426843792 -0.0195274053
46 0.0182521572 0.0426843792
47 -0.0311135678 0.0182521572
48 -0.0399808918 -0.0311135678
49 -0.0248058369 -0.0399808918
50 -0.0002491017 -0.0248058369
51 -0.0277509040 -0.0002491017
52 0.0425408233 -0.0277509040
53 -0.0131187567 0.0425408233
54 -0.0466107461 -0.0131187567
55 -0.0074369691 -0.0466107461
56 -0.0068051572 -0.0074369691
57 -0.0008752275 -0.0068051572
58 0.0263935697 -0.0008752275
59 0.0283891022 0.0263935697
60 0.0288903487 0.0283891022
61 NA 0.0288903487
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0105550223 0.0025247793
[2,] -0.0591781631 0.0105550223
[3,] 0.0098205381 -0.0591781631
[4,] -0.0401427593 0.0098205381
[5,] 0.0351452758 -0.0401427593
[6,] -0.0315666673 0.0351452758
[7,] 0.0098666496 -0.0315666673
[8,] 0.0181901538 0.0098666496
[9,] -0.0052606119 0.0181901538
[10,] -0.0491094196 -0.0052606119
[11,] -0.0092162359 -0.0491094196
[12,] -0.0209872191 -0.0092162359
[13,] -0.0141837570 -0.0209872191
[14,] 0.0360955595 -0.0141837570
[15,] 0.0076581581 0.0360955595
[16,] 0.0453254710 0.0076581581
[17,] 0.0099695747 0.0453254710
[18,] -0.0087348675 0.0099695747
[19,] -0.0074101617 -0.0087348675
[20,] -0.0266933147 -0.0074101617
[21,] -0.0049562545 -0.0266933147
[22,] -0.0018077936 -0.0049562545
[23,] -0.0060828525 -0.0018077936
[24,] 0.0267999086 -0.0060828525
[25,] 0.0470669770 0.0267999086
[26,] 0.0066177041 0.0470669770
[27,] -0.0210298381 0.0066177041
[28,] -0.0372876801 -0.0210298381
[29,] 0.0320827010 -0.0372876801
[30,] 0.0262817302 0.0320827010
[31,] 0.0358322099 0.0262817302
[32,] 0.0348357235 0.0358322099
[33,] -0.0315922853 0.0348357235
[34,] 0.0062714863 -0.0315922853
[35,] 0.0180235540 0.0062714863
[36,] 0.0027530743 0.0180235540
[37,] -0.0186324054 0.0027530743
[38,] 0.0167140011 -0.0186324054
[39,] 0.0313020457 0.0167140011
[40,] -0.0104358549 0.0313020457
[41,] -0.0640787948 -0.0104358549
[42,] 0.0606305507 -0.0640787948
[43,] -0.0308517287 0.0606305507
[44,] -0.0195274053 -0.0308517287
[45,] 0.0426843792 -0.0195274053
[46,] 0.0182521572 0.0426843792
[47,] -0.0311135678 0.0182521572
[48,] -0.0399808918 -0.0311135678
[49,] -0.0248058369 -0.0399808918
[50,] -0.0002491017 -0.0248058369
[51,] -0.0277509040 -0.0002491017
[52,] 0.0425408233 -0.0277509040
[53,] -0.0131187567 0.0425408233
[54,] -0.0466107461 -0.0131187567
[55,] -0.0074369691 -0.0466107461
[56,] -0.0068051572 -0.0074369691
[57,] -0.0008752275 -0.0068051572
[58,] 0.0263935697 -0.0008752275
[59,] 0.0283891022 0.0263935697
[60,] 0.0288903487 0.0283891022
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0105550223 0.0025247793
2 -0.0591781631 0.0105550223
3 0.0098205381 -0.0591781631
4 -0.0401427593 0.0098205381
5 0.0351452758 -0.0401427593
6 -0.0315666673 0.0351452758
7 0.0098666496 -0.0315666673
8 0.0181901538 0.0098666496
9 -0.0052606119 0.0181901538
10 -0.0491094196 -0.0052606119
11 -0.0092162359 -0.0491094196
12 -0.0209872191 -0.0092162359
13 -0.0141837570 -0.0209872191
14 0.0360955595 -0.0141837570
15 0.0076581581 0.0360955595
16 0.0453254710 0.0076581581
17 0.0099695747 0.0453254710
18 -0.0087348675 0.0099695747
19 -0.0074101617 -0.0087348675
20 -0.0266933147 -0.0074101617
21 -0.0049562545 -0.0266933147
22 -0.0018077936 -0.0049562545
23 -0.0060828525 -0.0018077936
24 0.0267999086 -0.0060828525
25 0.0470669770 0.0267999086
26 0.0066177041 0.0470669770
27 -0.0210298381 0.0066177041
28 -0.0372876801 -0.0210298381
29 0.0320827010 -0.0372876801
30 0.0262817302 0.0320827010
31 0.0358322099 0.0262817302
32 0.0348357235 0.0358322099
33 -0.0315922853 0.0348357235
34 0.0062714863 -0.0315922853
35 0.0180235540 0.0062714863
36 0.0027530743 0.0180235540
37 -0.0186324054 0.0027530743
38 0.0167140011 -0.0186324054
39 0.0313020457 0.0167140011
40 -0.0104358549 0.0313020457
41 -0.0640787948 -0.0104358549
42 0.0606305507 -0.0640787948
43 -0.0308517287 0.0606305507
44 -0.0195274053 -0.0308517287
45 0.0426843792 -0.0195274053
46 0.0182521572 0.0426843792
47 -0.0311135678 0.0182521572
48 -0.0399808918 -0.0311135678
49 -0.0248058369 -0.0399808918
50 -0.0002491017 -0.0248058369
51 -0.0277509040 -0.0002491017
52 0.0425408233 -0.0277509040
53 -0.0131187567 0.0425408233
54 -0.0466107461 -0.0131187567
55 -0.0074369691 -0.0466107461
56 -0.0068051572 -0.0074369691
57 -0.0008752275 -0.0068051572
58 0.0263935697 -0.0008752275
59 0.0283891022 0.0263935697
60 0.0288903487 0.0283891022
> 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/7lfmh1258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ed4i1258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ik2n1258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zm2q1258887121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1148og1258887121.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/12cixr1258887121.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/13mi1r1258887121.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/147tyt1258887122.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/15uiaf1258887122.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/16a3px1258887122.tab")
+ }
>
> system("convert tmp/1j6891258887121.ps tmp/1j6891258887121.png")
> system("convert tmp/2i5z51258887121.ps tmp/2i5z51258887121.png")
> system("convert tmp/3yl7b1258887121.ps tmp/3yl7b1258887121.png")
> system("convert tmp/4ksl61258887121.ps tmp/4ksl61258887121.png")
> system("convert tmp/58p221258887121.ps tmp/58p221258887121.png")
> system("convert tmp/603d01258887121.ps tmp/603d01258887121.png")
> system("convert tmp/7lfmh1258887121.ps tmp/7lfmh1258887121.png")
> system("convert tmp/8ed4i1258887121.ps tmp/8ed4i1258887121.png")
> system("convert tmp/9ik2n1258887121.ps tmp/9ik2n1258887121.png")
> system("convert tmp/10zm2q1258887121.ps tmp/10zm2q1258887121.png")
>
>
> proc.time()
user system elapsed
2.399 1.544 3.791