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 'q()' to quit R.
> x <- array(list(1.8
+ ,0.8
+ ,2.9
+ ,1.8
+ ,2.3
+ ,0.8
+ ,2.6
+ ,1.7
+ ,-0.1
+ ,2.9
+ ,1.7
+ ,2.2
+ ,1
+ ,2.2
+ ,1.4
+ ,-1.5
+ ,2.9
+ ,1.6
+ ,2.1
+ ,0.6
+ ,2.3
+ ,1.2
+ ,-4.4
+ ,1.4
+ ,1.8
+ ,2.4
+ ,0.9
+ ,2.4
+ ,1
+ ,-4.2
+ ,1.1
+ ,1.6
+ ,2.5
+ ,0.6
+ ,2.1
+ ,1.7
+ ,3.5
+ ,1.9
+ ,1.5
+ ,2.4
+ ,0.6
+ ,1.9
+ ,2.4
+ ,10
+ ,2.8
+ ,1.5
+ ,2.3
+ ,0.4
+ ,2.2
+ ,2
+ ,8.6
+ ,1.4
+ ,1.3
+ ,2.1
+ ,0.3
+ ,1.9
+ ,2.1
+ ,9.5
+ ,0.7
+ ,1.4
+ ,2.3
+ ,0
+ ,2.3
+ ,2
+ ,9.9
+ ,-0.8
+ ,1.4
+ ,2.2
+ ,0.3
+ ,2.1
+ ,1.8
+ ,10.4
+ ,-3.1
+ ,1.3
+ ,2.1
+ ,0.1
+ ,2.2
+ ,2.7
+ ,16
+ ,0.1
+ ,1.3
+ ,2
+ ,0
+ ,2.3
+ ,2.3
+ ,12.7
+ ,1
+ ,1.2
+ ,2.1
+ ,0
+ ,1.9
+ ,1.9
+ ,10.2
+ ,1.9
+ ,1.1
+ ,2.1
+ ,0
+ ,1.7
+ ,2
+ ,8.9
+ ,-0.5
+ ,1.4
+ ,2.5
+ ,-0.2
+ ,2.5
+ ,2.3
+ ,12.6
+ ,1.5
+ ,1.2
+ ,2.2
+ ,-0.3
+ ,2.1
+ ,2.8
+ ,13.6
+ ,3.9
+ ,1.5
+ ,2.3
+ ,0.1
+ ,2.4
+ ,2.4
+ ,14.8
+ ,1.9
+ ,1.1
+ ,2.3
+ ,0.1
+ ,1.5
+ ,2.3
+ ,9.5
+ ,2.6
+ ,1.3
+ ,2.2
+ ,0.4
+ ,1.9
+ ,2.7
+ ,13.7
+ ,1.7
+ ,1.5
+ ,2.2
+ ,0.4
+ ,2.1
+ ,2.7
+ ,17
+ ,1.4
+ ,1.1
+ ,1.6
+ ,-0.5
+ ,2.2
+ ,2.9
+ ,14.7
+ ,2.8
+ ,1.4
+ ,1.8
+ ,0.5
+ ,2
+ ,3
+ ,17.4
+ ,0.5
+ ,1.3
+ ,1.7
+ ,0.4
+ ,2
+ ,2.2
+ ,9
+ ,1
+ ,1.5
+ ,1.9
+ ,0.7
+ ,2.2
+ ,2.3
+ ,9.1
+ ,1.5
+ ,1.6
+ ,1.8
+ ,0.8
+ ,2.3
+ ,2.8
+ ,12.2
+ ,1.8
+ ,1.7
+ ,1.9
+ ,0.8
+ ,2.3
+ ,2.8
+ ,15.9
+ ,2.7
+ ,1.1
+ ,1.5
+ ,0
+ ,2
+ ,2.8
+ ,12.9
+ ,3
+ ,1.6
+ ,1
+ ,1.1
+ ,2.2
+ ,2.2
+ ,10.9
+ ,-0.3
+ ,1.3
+ ,0.8
+ ,0.9
+ ,1.9
+ ,2.6
+ ,10.6
+ ,1.1
+ ,1.7
+ ,1.1
+ ,1.1
+ ,2.3
+ ,2.8
+ ,13.2
+ ,1.7
+ ,1.6
+ ,1.5
+ ,1
+ ,2.2
+ ,2.5
+ ,9.6
+ ,1.6
+ ,1.7
+ ,1.7
+ ,1.1
+ ,2.3
+ ,2.4
+ ,6.4
+ ,3
+ ,1.9
+ ,2.3
+ ,1.5
+ ,2.1
+ ,2.3
+ ,5.8
+ ,3.3
+ ,1.8
+ ,2.4
+ ,1
+ ,2.4
+ ,1.9
+ ,-1
+ ,6.7
+ ,1.9
+ ,3
+ ,1
+ ,2.3
+ ,1.7
+ ,-0.2
+ ,5.6
+ ,1.6
+ ,3
+ ,0.9
+ ,1.9
+ ,2
+ ,2.7
+ ,6
+ ,1.5
+ ,3.2
+ ,0.8
+ ,1.6
+ ,2.1
+ ,3.6
+ ,4.8
+ ,1.6
+ ,3.2
+ ,0.8
+ ,1.8
+ ,1.7
+ ,-0.9
+ ,5.9
+ ,1.6
+ ,3.2
+ ,0.8
+ ,1.8
+ ,1.8
+ ,0.3
+ ,4.3
+ ,1.7
+ ,3.5
+ ,0.8
+ ,2
+ ,1.8
+ ,-1.1
+ ,3.7
+ ,2
+ ,4
+ ,0.9
+ ,2.3
+ ,1.8
+ ,-2.5
+ ,5.6
+ ,2
+ ,4.3
+ ,0.8
+ ,2.2
+ ,1.3
+ ,-3.4
+ ,1.7
+ ,1.9
+ ,4.1
+ ,0.7
+ ,2.2
+ ,1.3
+ ,-3.5
+ ,3.2
+ ,1.7
+ ,4
+ ,0.6
+ ,2
+ ,1.3
+ ,-3.9
+ ,3.6
+ ,1.8
+ ,4.1
+ ,0.6
+ ,2
+ ,1.2
+ ,-4.6
+ ,1.7
+ ,1.9
+ ,4.2
+ ,1
+ ,1.9
+ ,1.4
+ ,-0.1
+ ,0.5
+ ,1.7
+ ,4.5
+ ,1
+ ,1.5
+ ,2.2
+ ,4.3
+ ,2.1
+ ,2
+ ,5.6
+ ,1
+ ,1.6
+ ,2.9
+ ,10.2
+ ,1.5
+ ,2.1
+ ,6.5
+ ,1.1
+ ,1.5
+ ,3.1
+ ,8.7
+ ,2.7
+ ,2.4
+ ,7.6
+ ,1.1
+ ,2
+ ,3.5
+ ,13.3
+ ,1.4
+ ,2.5
+ ,8.5
+ ,1.4
+ ,1.5
+ ,3.6
+ ,15
+ ,1.2
+ ,2.5
+ ,8.7
+ ,1.2
+ ,1.5
+ ,4.4
+ ,20.7
+ ,2.3
+ ,2.6
+ ,8.3
+ ,1.2
+ ,1.9
+ ,4.1
+ ,20.7
+ ,1.6
+ ,2.2
+ ,8.3
+ ,1.3
+ ,1.1
+ ,5.1
+ ,26.4
+ ,4.7
+ ,2.5
+ ,8.5
+ ,1.4
+ ,1.5
+ ,5.8
+ ,31.2
+ ,3.5
+ ,2.8
+ ,8.7
+ ,1.4
+ ,2.1
+ ,5.9
+ ,31.4
+ ,4.4
+ ,2.8
+ ,8.7
+ ,1.1
+ ,2.3
+ ,5.4
+ ,26.6
+ ,3.9
+ ,2.9
+ ,8.5
+ ,1.1
+ ,2.6
+ ,5.5
+ ,26.6
+ ,3.5
+ ,3
+ ,7.9
+ ,1.3
+ ,2.9
+ ,4.8
+ ,19.2
+ ,3
+ ,3.1
+ ,7
+ ,1.5
+ ,3.2
+ ,3.2
+ ,6.5
+ ,1.6
+ ,2.9
+ ,5.8
+ ,1.5
+ ,3.2)
+ ,dim=c(7
+ ,61)
+ ,dimnames=list(c('HCPI'
+ ,'ED'
+ ,'NBL'
+ ,'IT'
+ ,'BL'
+ ,'NEI'
+ ,'D')
+ ,1:61))
> y <- array(NA,dim=c(7,61),dimnames=list(c('HCPI','ED','NBL','IT','BL','NEI','D'),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 = 'Do not include Seasonal 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
HCPI ED NBL IT BL NEI D
1 1.8 0.8 2.9 1.8 2.3 0.8 2.6
2 1.7 -0.1 2.9 1.7 2.2 1.0 2.2
3 1.4 -1.5 2.9 1.6 2.1 0.6 2.3
4 1.2 -4.4 1.4 1.8 2.4 0.9 2.4
5 1.0 -4.2 1.1 1.6 2.5 0.6 2.1
6 1.7 3.5 1.9 1.5 2.4 0.6 1.9
7 2.4 10.0 2.8 1.5 2.3 0.4 2.2
8 2.0 8.6 1.4 1.3 2.1 0.3 1.9
9 2.1 9.5 0.7 1.4 2.3 0.0 2.3
10 2.0 9.9 -0.8 1.4 2.2 0.3 2.1
11 1.8 10.4 -3.1 1.3 2.1 0.1 2.2
12 2.7 16.0 0.1 1.3 2.0 0.0 2.3
13 2.3 12.7 1.0 1.2 2.1 0.0 1.9
14 1.9 10.2 1.9 1.1 2.1 0.0 1.7
15 2.0 8.9 -0.5 1.4 2.5 -0.2 2.5
16 2.3 12.6 1.5 1.2 2.2 -0.3 2.1
17 2.8 13.6 3.9 1.5 2.3 0.1 2.4
18 2.4 14.8 1.9 1.1 2.3 0.1 1.5
19 2.3 9.5 2.6 1.3 2.2 0.4 1.9
20 2.7 13.7 1.7 1.5 2.2 0.4 2.1
21 2.7 17.0 1.4 1.1 1.6 -0.5 2.2
22 2.9 14.7 2.8 1.4 1.8 0.5 2.0
23 3.0 17.4 0.5 1.3 1.7 0.4 2.0
24 2.2 9.0 1.0 1.5 1.9 0.7 2.2
25 2.3 9.1 1.5 1.6 1.8 0.8 2.3
26 2.8 12.2 1.8 1.7 1.9 0.8 2.3
27 2.8 15.9 2.7 1.1 1.5 0.0 2.0
28 2.8 12.9 3.0 1.6 1.0 1.1 2.2
29 2.2 10.9 -0.3 1.3 0.8 0.9 1.9
30 2.6 10.6 1.1 1.7 1.1 1.1 2.3
31 2.8 13.2 1.7 1.6 1.5 1.0 2.2
32 2.5 9.6 1.6 1.7 1.7 1.1 2.3
33 2.4 6.4 3.0 1.9 2.3 1.5 2.1
34 2.3 5.8 3.3 1.8 2.4 1.0 2.4
35 1.9 -1.0 6.7 1.9 3.0 1.0 2.3
36 1.7 -0.2 5.6 1.6 3.0 0.9 1.9
37 2.0 2.7 6.0 1.5 3.2 0.8 1.6
38 2.1 3.6 4.8 1.6 3.2 0.8 1.8
39 1.7 -0.9 5.9 1.6 3.2 0.8 1.8
40 1.8 0.3 4.3 1.7 3.5 0.8 2.0
41 1.8 -1.1 3.7 2.0 4.0 0.9 2.3
42 1.8 -2.5 5.6 2.0 4.3 0.8 2.2
43 1.3 -3.4 1.7 1.9 4.1 0.7 2.2
44 1.3 -3.5 3.2 1.7 4.0 0.6 2.0
45 1.3 -3.9 3.6 1.8 4.1 0.6 2.0
46 1.2 -4.6 1.7 1.9 4.2 1.0 1.9
47 1.4 -0.1 0.5 1.7 4.5 1.0 1.5
48 2.2 4.3 2.1 2.0 5.6 1.0 1.6
49 2.9 10.2 1.5 2.1 6.5 1.1 1.5
50 3.1 8.7 2.7 2.4 7.6 1.1 2.0
51 3.5 13.3 1.4 2.5 8.5 1.4 1.5
52 3.6 15.0 1.2 2.5 8.7 1.2 1.5
53 4.4 20.7 2.3 2.6 8.3 1.2 1.9
54 4.1 20.7 1.6 2.2 8.3 1.3 1.1
55 5.1 26.4 4.7 2.5 8.5 1.4 1.5
56 5.8 31.2 3.5 2.8 8.7 1.4 2.1
57 5.9 31.4 4.4 2.8 8.7 1.1 2.3
58 5.4 26.6 3.9 2.9 8.5 1.1 2.6
59 5.5 26.6 3.5 3.0 7.9 1.3 2.9
60 4.8 19.2 3.0 3.1 7.0 1.5 3.2
61 3.2 6.5 1.6 2.9 5.8 1.5 3.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ED NBL IT BL NEI
-0.09996 0.10277 0.07992 0.29950 0.07595 0.25417
D
0.25891
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.129235 -0.027321 -0.003326 0.033107 0.133829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0999629 0.0409914 -2.439 0.01806 *
ED 0.1027719 0.0009024 113.886 < 2e-16 ***
NBL 0.0799172 0.0042116 18.976 < 2e-16 ***
IT 0.2994971 0.2008637 1.491 0.14177
BL 0.0759468 0.0305164 2.489 0.01594 *
NEI 0.2541713 0.0784725 3.239 0.00205 **
D 0.2589101 0.0935186 2.769 0.00770 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05281 on 54 degrees of freedom
Multiple R-squared: 0.9982, Adjusted R-squared: 0.998
F-statistic: 4878 on 6 and 54 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,] 0.1885609 3.771218e-01 8.114391e-01
[2,] 0.1008301 2.016601e-01 8.991699e-01
[3,] 0.4147265 8.294530e-01 5.852735e-01
[4,] 0.3130561 6.261121e-01 6.869439e-01
[5,] 0.5786849 8.426303e-01 4.213151e-01
[6,] 0.6261176 7.477647e-01 3.738824e-01
[7,] 0.5368465 9.263069e-01 4.631535e-01
[8,] 0.6594874 6.810253e-01 3.405126e-01
[9,] 0.7794129 4.411742e-01 2.205871e-01
[10,] 0.8711724 2.576552e-01 1.288276e-01
[11,] 0.8852139 2.295723e-01 1.147861e-01
[12,] 0.9398180 1.203640e-01 6.018199e-02
[13,] 0.9359496 1.281007e-01 6.405037e-02
[14,] 0.9807584 3.848311e-02 1.924156e-02
[15,] 0.9925346 1.493079e-02 7.465397e-03
[16,] 0.9991850 1.630092e-03 8.150458e-04
[17,] 0.9986095 2.780907e-03 1.390453e-03
[18,] 0.9979618 4.076326e-03 2.038163e-03
[19,] 0.9997439 5.122863e-04 2.561431e-04
[20,] 0.9994524 1.095204e-03 5.476019e-04
[21,] 0.9996584 6.831113e-04 3.415557e-04
[22,] 0.9992708 1.458354e-03 7.291769e-04
[23,] 0.9986260 2.747935e-03 1.373967e-03
[24,] 0.9976278 4.744335e-03 2.372167e-03
[25,] 0.9990399 1.920278e-03 9.601391e-04
[26,] 0.9996894 6.212368e-04 3.106184e-04
[27,] 0.9999841 3.178065e-05 1.589033e-05
[28,] 0.9999752 4.956041e-05 2.478020e-05
[29,] 0.9999641 7.174288e-05 3.587144e-05
[30,] 0.9999041 1.917159e-04 9.585796e-05
[31,] 0.9997683 4.634101e-04 2.317050e-04
[32,] 0.9994773 1.045415e-03 5.227073e-04
[33,] 0.9989695 2.061076e-03 1.030538e-03
[34,] 0.9978777 4.244609e-03 2.122305e-03
[35,] 0.9950641 9.871844e-03 4.935922e-03
[36,] 0.9883803 2.323943e-02 1.161971e-02
[37,] 0.9769321 4.613574e-02 2.306787e-02
[38,] 0.9950311 9.937891e-03 4.968946e-03
[39,] 0.9883814 2.323718e-02 1.161859e-02
[40,] 0.9742883 5.142332e-02 2.571166e-02
[41,] 0.9329924 1.340151e-01 6.700755e-02
[42,] 0.9218786 1.562429e-01 7.812143e-02
> postscript(file="/var/www/html/rcomp/tmp/1crf71293346154.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2crf71293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3crf71293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44jxa1293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54jxa1293346154.ps",horizontal=F,onefile=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
-0.004290124 0.078478734 0.035681262 0.068769622 0.078419584 0.012468811
7 8 9 10 11 12
-0.046717939 -0.012774309 -0.021778678 -0.059886342 -0.064975108 0.010888189
13 14 15 16 17 18
0.004028989 -0.129235059 0.019647960 -0.008777721 -0.080136221 -0.090810141
19 20 21 22 23 24
0.065818569 -0.005579278 0.047478770 0.064542234 0.133829225 -0.045967820
25 26 27 28 29 30
-0.069866780 0.050020868 0.088926524 -0.069878662 0.032937593 0.054903909
31 32 33 34 35 36
-0.009374127 -0.027850868 -0.066218914 -0.056763287 -0.079259735 -0.054738016
37 38 39 40 41 42
0.033107202 0.054781406 0.029345933 0.029371422 -0.009710320 0.010851764
43 44 45 46 47 48
-0.014420320 0.020674337 -0.007728173 0.002732893 -0.023160474 -0.002505801
49 50 51 52 53 54
0.041262539 -0.003325905 -0.017282310 -0.040366145 -0.017209793 0.040242046
55 56 57 58 59 60
-0.027320586 0.014890486 0.046880014 -0.012289790 0.006788128 0.017153665
61
-0.014723935
> postscript(file="/var/www/html/rcomp/tmp/6fawd1293346154.ps",horizontal=F,onefile=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 -0.004290124 NA
1 0.078478734 -0.004290124
2 0.035681262 0.078478734
3 0.068769622 0.035681262
4 0.078419584 0.068769622
5 0.012468811 0.078419584
6 -0.046717939 0.012468811
7 -0.012774309 -0.046717939
8 -0.021778678 -0.012774309
9 -0.059886342 -0.021778678
10 -0.064975108 -0.059886342
11 0.010888189 -0.064975108
12 0.004028989 0.010888189
13 -0.129235059 0.004028989
14 0.019647960 -0.129235059
15 -0.008777721 0.019647960
16 -0.080136221 -0.008777721
17 -0.090810141 -0.080136221
18 0.065818569 -0.090810141
19 -0.005579278 0.065818569
20 0.047478770 -0.005579278
21 0.064542234 0.047478770
22 0.133829225 0.064542234
23 -0.045967820 0.133829225
24 -0.069866780 -0.045967820
25 0.050020868 -0.069866780
26 0.088926524 0.050020868
27 -0.069878662 0.088926524
28 0.032937593 -0.069878662
29 0.054903909 0.032937593
30 -0.009374127 0.054903909
31 -0.027850868 -0.009374127
32 -0.066218914 -0.027850868
33 -0.056763287 -0.066218914
34 -0.079259735 -0.056763287
35 -0.054738016 -0.079259735
36 0.033107202 -0.054738016
37 0.054781406 0.033107202
38 0.029345933 0.054781406
39 0.029371422 0.029345933
40 -0.009710320 0.029371422
41 0.010851764 -0.009710320
42 -0.014420320 0.010851764
43 0.020674337 -0.014420320
44 -0.007728173 0.020674337
45 0.002732893 -0.007728173
46 -0.023160474 0.002732893
47 -0.002505801 -0.023160474
48 0.041262539 -0.002505801
49 -0.003325905 0.041262539
50 -0.017282310 -0.003325905
51 -0.040366145 -0.017282310
52 -0.017209793 -0.040366145
53 0.040242046 -0.017209793
54 -0.027320586 0.040242046
55 0.014890486 -0.027320586
56 0.046880014 0.014890486
57 -0.012289790 0.046880014
58 0.006788128 -0.012289790
59 0.017153665 0.006788128
60 -0.014723935 0.017153665
61 NA -0.014723935
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.078478734 -0.004290124
[2,] 0.035681262 0.078478734
[3,] 0.068769622 0.035681262
[4,] 0.078419584 0.068769622
[5,] 0.012468811 0.078419584
[6,] -0.046717939 0.012468811
[7,] -0.012774309 -0.046717939
[8,] -0.021778678 -0.012774309
[9,] -0.059886342 -0.021778678
[10,] -0.064975108 -0.059886342
[11,] 0.010888189 -0.064975108
[12,] 0.004028989 0.010888189
[13,] -0.129235059 0.004028989
[14,] 0.019647960 -0.129235059
[15,] -0.008777721 0.019647960
[16,] -0.080136221 -0.008777721
[17,] -0.090810141 -0.080136221
[18,] 0.065818569 -0.090810141
[19,] -0.005579278 0.065818569
[20,] 0.047478770 -0.005579278
[21,] 0.064542234 0.047478770
[22,] 0.133829225 0.064542234
[23,] -0.045967820 0.133829225
[24,] -0.069866780 -0.045967820
[25,] 0.050020868 -0.069866780
[26,] 0.088926524 0.050020868
[27,] -0.069878662 0.088926524
[28,] 0.032937593 -0.069878662
[29,] 0.054903909 0.032937593
[30,] -0.009374127 0.054903909
[31,] -0.027850868 -0.009374127
[32,] -0.066218914 -0.027850868
[33,] -0.056763287 -0.066218914
[34,] -0.079259735 -0.056763287
[35,] -0.054738016 -0.079259735
[36,] 0.033107202 -0.054738016
[37,] 0.054781406 0.033107202
[38,] 0.029345933 0.054781406
[39,] 0.029371422 0.029345933
[40,] -0.009710320 0.029371422
[41,] 0.010851764 -0.009710320
[42,] -0.014420320 0.010851764
[43,] 0.020674337 -0.014420320
[44,] -0.007728173 0.020674337
[45,] 0.002732893 -0.007728173
[46,] -0.023160474 0.002732893
[47,] -0.002505801 -0.023160474
[48,] 0.041262539 -0.002505801
[49,] -0.003325905 0.041262539
[50,] -0.017282310 -0.003325905
[51,] -0.040366145 -0.017282310
[52,] -0.017209793 -0.040366145
[53,] 0.040242046 -0.017209793
[54,] -0.027320586 0.040242046
[55,] 0.014890486 -0.027320586
[56,] 0.046880014 0.014890486
[57,] -0.012289790 0.046880014
[58,] 0.006788128 -0.012289790
[59,] 0.017153665 0.006788128
[60,] -0.014723935 0.017153665
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.078478734 -0.004290124
2 0.035681262 0.078478734
3 0.068769622 0.035681262
4 0.078419584 0.068769622
5 0.012468811 0.078419584
6 -0.046717939 0.012468811
7 -0.012774309 -0.046717939
8 -0.021778678 -0.012774309
9 -0.059886342 -0.021778678
10 -0.064975108 -0.059886342
11 0.010888189 -0.064975108
12 0.004028989 0.010888189
13 -0.129235059 0.004028989
14 0.019647960 -0.129235059
15 -0.008777721 0.019647960
16 -0.080136221 -0.008777721
17 -0.090810141 -0.080136221
18 0.065818569 -0.090810141
19 -0.005579278 0.065818569
20 0.047478770 -0.005579278
21 0.064542234 0.047478770
22 0.133829225 0.064542234
23 -0.045967820 0.133829225
24 -0.069866780 -0.045967820
25 0.050020868 -0.069866780
26 0.088926524 0.050020868
27 -0.069878662 0.088926524
28 0.032937593 -0.069878662
29 0.054903909 0.032937593
30 -0.009374127 0.054903909
31 -0.027850868 -0.009374127
32 -0.066218914 -0.027850868
33 -0.056763287 -0.066218914
34 -0.079259735 -0.056763287
35 -0.054738016 -0.079259735
36 0.033107202 -0.054738016
37 0.054781406 0.033107202
38 0.029345933 0.054781406
39 0.029371422 0.029345933
40 -0.009710320 0.029371422
41 0.010851764 -0.009710320
42 -0.014420320 0.010851764
43 0.020674337 -0.014420320
44 -0.007728173 0.020674337
45 0.002732893 -0.007728173
46 -0.023160474 0.002732893
47 -0.002505801 -0.023160474
48 0.041262539 -0.002505801
49 -0.003325905 0.041262539
50 -0.017282310 -0.003325905
51 -0.040366145 -0.017282310
52 -0.017209793 -0.040366145
53 0.040242046 -0.017209793
54 -0.027320586 0.040242046
55 0.014890486 -0.027320586
56 0.046880014 0.014890486
57 -0.012289790 0.046880014
58 0.006788128 -0.012289790
59 0.017153665 0.006788128
60 -0.014723935 0.017153665
> 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/7fawd1293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8q1vg1293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9q1vg1293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105vkq1293346154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114tb71293346154.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/12ptrv1293346154.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/133l7m1293346154.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/1474oa1293346154.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/15a44x1293346154.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/16wn3l1293346154.tab")
+ }
>
> try(system("convert tmp/1crf71293346154.ps tmp/1crf71293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/2crf71293346154.ps tmp/2crf71293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/3crf71293346154.ps tmp/3crf71293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/44jxa1293346154.ps tmp/44jxa1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/54jxa1293346154.ps tmp/54jxa1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fawd1293346154.ps tmp/6fawd1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fawd1293346154.ps tmp/7fawd1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/8q1vg1293346154.ps tmp/8q1vg1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q1vg1293346154.ps tmp/9q1vg1293346154.png",intern=TRUE))
character(0)
> try(system("convert tmp/105vkq1293346154.ps tmp/105vkq1293346154.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.537 1.619 5.962