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 '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(9.5
+ ,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
+ ,8.3
+ ,7.1
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.3
+ ,8.4
+ ,7.7
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.9
+ ,8.2
+ ,7.9
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.5
+ ,7.7
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.8
+ ,7.2
+ ,7.4
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.3
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8.2
+ ,8.4
+ ,8.1
+ ,8
+ ,7.3
+ ,7.2
+ ,7.7
+ ,8.2
+ ,8.5
+ ,8.1
+ ,8.1
+ ,7.3
+ ,7.2
+ ,7.7)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y[t]'
+ ,'X[t]'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y[t]','X[t]','Y1','Y2','Y3','Y4'),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
Y[t] X[t] Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.5 7.8 9.2 9.2 10.0 10.9 1 0 0 0 0 0 0 0 0 0 0 1
2 9.6 7.8 9.5 9.2 9.2 10.0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.5 7.8 9.6 9.5 9.2 9.2 0 0 1 0 0 0 0 0 0 0 0 3
4 9.1 7.5 9.5 9.6 9.5 9.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 7.5 9.1 9.5 9.6 9.5 0 0 0 0 1 0 0 0 0 0 0 5
6 9.0 7.1 8.9 9.1 9.5 9.6 0 0 0 0 0 1 0 0 0 0 0 6
7 10.1 7.5 9.0 8.9 9.1 9.5 0 0 0 0 0 0 1 0 0 0 0 7
8 10.3 7.5 10.1 9.0 8.9 9.1 0 0 0 0 0 0 0 1 0 0 0 8
9 10.2 7.6 10.3 10.1 9.0 8.9 0 0 0 0 0 0 0 0 1 0 0 9
10 9.6 7.7 10.2 10.3 10.1 9.0 0 0 0 0 0 0 0 0 0 1 0 10
11 9.2 7.7 9.6 10.2 10.3 10.1 0 0 0 0 0 0 0 0 0 0 1 11
12 9.3 7.9 9.2 9.6 10.2 10.3 0 0 0 0 0 0 0 0 0 0 0 12
13 9.4 8.1 9.3 9.2 9.6 10.2 1 0 0 0 0 0 0 0 0 0 0 13
14 9.4 8.2 9.4 9.3 9.2 9.6 0 1 0 0 0 0 0 0 0 0 0 14
15 9.2 8.2 9.4 9.4 9.3 9.2 0 0 1 0 0 0 0 0 0 0 0 15
16 9.0 8.2 9.2 9.4 9.4 9.3 0 0 0 1 0 0 0 0 0 0 0 16
17 9.0 7.9 9.0 9.2 9.4 9.4 0 0 0 0 1 0 0 0 0 0 0 17
18 9.0 7.3 9.0 9.0 9.2 9.4 0 0 0 0 0 1 0 0 0 0 0 18
19 9.8 6.9 9.0 9.0 9.0 9.2 0 0 0 0 0 0 1 0 0 0 0 19
20 10.0 6.6 9.8 9.0 9.0 9.0 0 0 0 0 0 0 0 1 0 0 0 20
21 9.8 6.7 10.0 9.8 9.0 9.0 0 0 0 0 0 0 0 0 1 0 0 21
22 9.3 6.9 9.8 10.0 9.8 9.0 0 0 0 0 0 0 0 0 0 1 0 22
23 9.0 7.0 9.3 9.8 10.0 9.8 0 0 0 0 0 0 0 0 0 0 1 23
24 9.0 7.1 9.0 9.3 9.8 10.0 0 0 0 0 0 0 0 0 0 0 0 24
25 9.1 7.2 9.0 9.0 9.3 9.8 1 0 0 0 0 0 0 0 0 0 0 25
26 9.1 7.1 9.1 9.0 9.0 9.3 0 1 0 0 0 0 0 0 0 0 0 26
27 9.1 6.9 9.1 9.1 9.0 9.0 0 0 1 0 0 0 0 0 0 0 0 27
28 9.2 7.0 9.1 9.1 9.1 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.8 6.8 9.2 9.1 9.1 9.1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.3 6.4 8.8 9.2 9.1 9.1 0 0 0 0 0 1 0 0 0 0 0 30
31 8.4 6.7 8.3 8.8 9.2 9.1 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 6.6 8.4 8.3 8.8 9.2 0 0 0 0 0 0 0 1 0 0 0 32
33 7.7 6.4 8.1 8.4 8.3 8.8 0 0 0 0 0 0 0 0 1 0 0 33
34 7.9 6.3 7.7 8.1 8.4 8.3 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 6.2 7.9 7.7 8.1 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 6.5 7.9 7.9 7.7 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 6.8 8.0 7.9 7.9 7.7 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 6.8 7.9 8.0 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 6.4 7.6 7.9 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 6.1 7.1 7.6 7.9 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.5 5.8 6.8 7.1 7.6 7.9 0 0 0 0 1 0 0 0 0 0 0 41
42 6.9 6.1 6.5 6.8 7.1 7.6 0 0 0 0 0 1 0 0 0 0 0 42
43 8.2 7.2 6.9 6.5 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 8.7 7.3 8.2 6.9 6.5 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 8.3 6.9 8.7 8.2 6.9 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 7.9 6.1 8.3 8.7 8.2 6.9 0 0 0 0 0 0 0 0 0 1 0 46
47 7.5 5.8 7.9 8.3 8.7 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 7.8 6.2 7.5 7.9 8.3 8.7 0 0 0 0 0 0 0 0 0 0 0 48
49 8.3 7.1 7.8 7.5 7.9 8.3 1 0 0 0 0 0 0 0 0 0 0 49
50 8.4 7.7 8.3 7.8 7.5 7.9 0 1 0 0 0 0 0 0 0 0 0 50
51 8.2 7.9 8.4 8.3 7.8 7.5 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 7.7 8.2 8.4 8.3 7.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.2 7.4 7.7 8.2 8.4 8.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 7.5 7.2 7.7 8.2 8.4 0 0 0 0 0 1 0 0 0 0 0 54
55 8.1 8.0 7.3 7.2 7.7 8.2 0 0 0 0 0 0 1 0 0 0 0 55
56 8.5 8.1 8.1 7.3 7.2 7.7 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) `X[t]` Y1 Y2 Y3 Y4
0.896551 0.067309 1.405000 -0.524001 -0.374099 0.366079
M1 M2 M3 M4 M5 M6
-0.223823 -0.429401 -0.320995 -0.266241 -0.340228 -0.113023
M7 M8 M9 M10 M11 t
0.486611 -0.457802 -0.434078 0.028706 -0.170540 -0.004081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26207 -0.15071 -0.01374 0.14605 0.30531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.896551 0.696565 1.287 0.20584
`X[t]` 0.067309 0.051976 1.295 0.20314
Y1 1.405000 0.156182 8.996 5.94e-11 ***
Y2 -0.524001 0.275272 -1.904 0.06456 .
Y3 -0.374099 0.272264 -1.374 0.17748
Y4 0.366079 0.145139 2.522 0.01597 *
M1 -0.223823 0.136939 -1.634 0.11042
M2 -0.429401 0.141424 -3.036 0.00431 **
M3 -0.320995 0.140445 -2.286 0.02795 *
M4 -0.266241 0.139002 -1.915 0.06299 .
M5 -0.340228 0.131747 -2.582 0.01379 *
M6 -0.113023 0.129688 -0.872 0.38895
M7 0.486611 0.135915 3.580 0.00096 ***
M8 -0.457802 0.179057 -2.557 0.01468 *
M9 -0.434078 0.191835 -2.263 0.02945 *
M10 0.028706 0.178020 0.161 0.87275
M11 -0.170540 0.142509 -1.197 0.23884
t -0.004081 0.003494 -1.168 0.25001
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1879 on 38 degrees of freedom
Multiple R-squared: 0.9708, Adjusted R-squared: 0.9578
F-statistic: 74.37 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,] 0.128972210 0.257944420 0.8710278
[2,] 0.067748594 0.135497187 0.9322514
[3,] 0.036693300 0.073386599 0.9633067
[4,] 0.020861164 0.041722328 0.9791388
[5,] 0.007653726 0.015307451 0.9923463
[6,] 0.002951051 0.005902101 0.9970489
[7,] 0.006215278 0.012430556 0.9937847
[8,] 0.222176822 0.444353645 0.7778232
[9,] 0.244266937 0.488533875 0.7557331
[10,] 0.189297328 0.378594657 0.8107027
[11,] 0.726418360 0.547163281 0.2735816
[12,] 0.768478472 0.463043056 0.2315215
[13,] 0.863494843 0.273010315 0.1365052
[14,] 0.858323830 0.283352341 0.1416762
[15,] 0.796954891 0.406090217 0.2030451
> postscript(file="/var/www/html/rcomp/tmp/1e3sc1258800914.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rlhu1258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wt0h1258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46e321258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57fho1258800914.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 6
-0.048112201 -0.129760447 -0.024521764 -0.149872414 0.165382069 0.066564168
7 8 9 10 11 12
0.185756397 -0.087236805 0.192416263 -0.252816538 0.013243631 0.070297074
13 14 15 16 17 18
-0.153211930 0.031624844 -0.036458375 -0.005329513 0.232523511 -0.129834292
19 20 21 22 23 24
0.099932916 0.217836158 0.129663251 -0.157422261 0.118830588 -0.042894802
25 26 27 28 29 30
0.007244917 0.153945373 0.225306179 0.305312119 -0.180265394 -0.262065070
31 32 33 34 35 36
-0.247500903 -0.181023498 -0.153922622 0.219354118 -0.210025404 -0.231692040
37 38 39 40 41 42
-0.043228787 -0.013884715 -0.184776233 -0.043974923 -0.161836168 0.181921718
43 44 45 46 47 48
0.163939115 -0.013602323 -0.168156892 0.190884681 0.077951185 0.204289769
49 50 51 52 53 54
0.237308001 -0.041925054 0.020450193 -0.106135270 -0.055804019 0.143413476
55 56
-0.202127525 0.064026469
> postscript(file="/var/www/html/rcomp/tmp/6bdzy1258800914.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 -0.048112201 NA
1 -0.129760447 -0.048112201
2 -0.024521764 -0.129760447
3 -0.149872414 -0.024521764
4 0.165382069 -0.149872414
5 0.066564168 0.165382069
6 0.185756397 0.066564168
7 -0.087236805 0.185756397
8 0.192416263 -0.087236805
9 -0.252816538 0.192416263
10 0.013243631 -0.252816538
11 0.070297074 0.013243631
12 -0.153211930 0.070297074
13 0.031624844 -0.153211930
14 -0.036458375 0.031624844
15 -0.005329513 -0.036458375
16 0.232523511 -0.005329513
17 -0.129834292 0.232523511
18 0.099932916 -0.129834292
19 0.217836158 0.099932916
20 0.129663251 0.217836158
21 -0.157422261 0.129663251
22 0.118830588 -0.157422261
23 -0.042894802 0.118830588
24 0.007244917 -0.042894802
25 0.153945373 0.007244917
26 0.225306179 0.153945373
27 0.305312119 0.225306179
28 -0.180265394 0.305312119
29 -0.262065070 -0.180265394
30 -0.247500903 -0.262065070
31 -0.181023498 -0.247500903
32 -0.153922622 -0.181023498
33 0.219354118 -0.153922622
34 -0.210025404 0.219354118
35 -0.231692040 -0.210025404
36 -0.043228787 -0.231692040
37 -0.013884715 -0.043228787
38 -0.184776233 -0.013884715
39 -0.043974923 -0.184776233
40 -0.161836168 -0.043974923
41 0.181921718 -0.161836168
42 0.163939115 0.181921718
43 -0.013602323 0.163939115
44 -0.168156892 -0.013602323
45 0.190884681 -0.168156892
46 0.077951185 0.190884681
47 0.204289769 0.077951185
48 0.237308001 0.204289769
49 -0.041925054 0.237308001
50 0.020450193 -0.041925054
51 -0.106135270 0.020450193
52 -0.055804019 -0.106135270
53 0.143413476 -0.055804019
54 -0.202127525 0.143413476
55 0.064026469 -0.202127525
56 NA 0.064026469
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.129760447 -0.048112201
[2,] -0.024521764 -0.129760447
[3,] -0.149872414 -0.024521764
[4,] 0.165382069 -0.149872414
[5,] 0.066564168 0.165382069
[6,] 0.185756397 0.066564168
[7,] -0.087236805 0.185756397
[8,] 0.192416263 -0.087236805
[9,] -0.252816538 0.192416263
[10,] 0.013243631 -0.252816538
[11,] 0.070297074 0.013243631
[12,] -0.153211930 0.070297074
[13,] 0.031624844 -0.153211930
[14,] -0.036458375 0.031624844
[15,] -0.005329513 -0.036458375
[16,] 0.232523511 -0.005329513
[17,] -0.129834292 0.232523511
[18,] 0.099932916 -0.129834292
[19,] 0.217836158 0.099932916
[20,] 0.129663251 0.217836158
[21,] -0.157422261 0.129663251
[22,] 0.118830588 -0.157422261
[23,] -0.042894802 0.118830588
[24,] 0.007244917 -0.042894802
[25,] 0.153945373 0.007244917
[26,] 0.225306179 0.153945373
[27,] 0.305312119 0.225306179
[28,] -0.180265394 0.305312119
[29,] -0.262065070 -0.180265394
[30,] -0.247500903 -0.262065070
[31,] -0.181023498 -0.247500903
[32,] -0.153922622 -0.181023498
[33,] 0.219354118 -0.153922622
[34,] -0.210025404 0.219354118
[35,] -0.231692040 -0.210025404
[36,] -0.043228787 -0.231692040
[37,] -0.013884715 -0.043228787
[38,] -0.184776233 -0.013884715
[39,] -0.043974923 -0.184776233
[40,] -0.161836168 -0.043974923
[41,] 0.181921718 -0.161836168
[42,] 0.163939115 0.181921718
[43,] -0.013602323 0.163939115
[44,] -0.168156892 -0.013602323
[45,] 0.190884681 -0.168156892
[46,] 0.077951185 0.190884681
[47,] 0.204289769 0.077951185
[48,] 0.237308001 0.204289769
[49,] -0.041925054 0.237308001
[50,] 0.020450193 -0.041925054
[51,] -0.106135270 0.020450193
[52,] -0.055804019 -0.106135270
[53,] 0.143413476 -0.055804019
[54,] -0.202127525 0.143413476
[55,] 0.064026469 -0.202127525
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.129760447 -0.048112201
2 -0.024521764 -0.129760447
3 -0.149872414 -0.024521764
4 0.165382069 -0.149872414
5 0.066564168 0.165382069
6 0.185756397 0.066564168
7 -0.087236805 0.185756397
8 0.192416263 -0.087236805
9 -0.252816538 0.192416263
10 0.013243631 -0.252816538
11 0.070297074 0.013243631
12 -0.153211930 0.070297074
13 0.031624844 -0.153211930
14 -0.036458375 0.031624844
15 -0.005329513 -0.036458375
16 0.232523511 -0.005329513
17 -0.129834292 0.232523511
18 0.099932916 -0.129834292
19 0.217836158 0.099932916
20 0.129663251 0.217836158
21 -0.157422261 0.129663251
22 0.118830588 -0.157422261
23 -0.042894802 0.118830588
24 0.007244917 -0.042894802
25 0.153945373 0.007244917
26 0.225306179 0.153945373
27 0.305312119 0.225306179
28 -0.180265394 0.305312119
29 -0.262065070 -0.180265394
30 -0.247500903 -0.262065070
31 -0.181023498 -0.247500903
32 -0.153922622 -0.181023498
33 0.219354118 -0.153922622
34 -0.210025404 0.219354118
35 -0.231692040 -0.210025404
36 -0.043228787 -0.231692040
37 -0.013884715 -0.043228787
38 -0.184776233 -0.013884715
39 -0.043974923 -0.184776233
40 -0.161836168 -0.043974923
41 0.181921718 -0.161836168
42 0.163939115 0.181921718
43 -0.013602323 0.163939115
44 -0.168156892 -0.013602323
45 0.190884681 -0.168156892
46 0.077951185 0.190884681
47 0.204289769 0.077951185
48 0.237308001 0.204289769
49 -0.041925054 0.237308001
50 0.020450193 -0.041925054
51 -0.106135270 0.020450193
52 -0.055804019 -0.106135270
53 0.143413476 -0.055804019
54 -0.202127525 0.143413476
55 0.064026469 -0.202127525
> 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/7tu591258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8j6nq1258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nr481258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10alti1258800914.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ps9l1258800914.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/120zku1258800914.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/134si21258800914.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/140mdn1258800914.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/15r7hj1258800915.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/16xzlj1258800915.tab")
+ }
>
> system("convert tmp/1e3sc1258800914.ps tmp/1e3sc1258800914.png")
> system("convert tmp/2rlhu1258800914.ps tmp/2rlhu1258800914.png")
> system("convert tmp/3wt0h1258800914.ps tmp/3wt0h1258800914.png")
> system("convert tmp/46e321258800914.ps tmp/46e321258800914.png")
> system("convert tmp/57fho1258800914.ps tmp/57fho1258800914.png")
> system("convert tmp/6bdzy1258800914.ps tmp/6bdzy1258800914.png")
> system("convert tmp/7tu591258800914.ps tmp/7tu591258800914.png")
> system("convert tmp/8j6nq1258800914.ps tmp/8j6nq1258800914.png")
> system("convert tmp/9nr481258800914.ps tmp/9nr481258800914.png")
> system("convert tmp/10alti1258800914.ps tmp/10alti1258800914.png")
>
>
> proc.time()
user system elapsed
2.303 1.536 3.803