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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(7.2
+ ,2.4
+ ,7.5
+ ,8.3
+ ,8.9
+ ,7.4
+ ,2
+ ,7.2
+ ,7.5
+ ,8.8
+ ,8.8
+ ,2.1
+ ,7.4
+ ,7.2
+ ,8.3
+ ,9.3
+ ,2
+ ,8.8
+ ,7.4
+ ,7.5
+ ,9.3
+ ,1.8
+ ,9.3
+ ,8.8
+ ,7.2
+ ,8.7
+ ,2.7
+ ,9.3
+ ,9.3
+ ,7.4
+ ,8.2
+ ,2.3
+ ,8.7
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.5
+ ,2
+ ,8.3
+ ,8.2
+ ,9.3
+ ,8.6
+ ,2.3
+ ,8.5
+ ,8.3
+ ,8.7
+ ,8.5
+ ,2.8
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.2
+ ,2.4
+ ,8.5
+ ,8.6
+ ,8.3
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8.5
+ ,8.5
+ ,7.9
+ ,2.7
+ ,8.1
+ ,8.2
+ ,8.6
+ ,8.6
+ ,2.7
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8.7
+ ,2.9
+ ,8.6
+ ,7.9
+ ,8.2
+ ,8.7
+ ,3
+ ,8.7
+ ,8.6
+ ,8.1
+ ,8.5
+ ,2.2
+ ,8.7
+ ,8.7
+ ,7.9
+ ,8.4
+ ,2.3
+ ,8.5
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.5
+ ,8.4
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.7
+ ,8.5
+ ,8.5
+ ,8.6
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.4
+ ,8.5
+ ,2.6
+ ,8.6
+ ,8.7
+ ,8.5
+ ,8.3
+ ,2.8
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,2.5
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.2
+ ,2.4
+ ,8
+ ,8.3
+ ,8.6
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8
+ ,8.5
+ ,8.1
+ ,1.9
+ ,8.1
+ ,8.2
+ ,8.3
+ ,8
+ ,1.7
+ ,8.1
+ ,8.1
+ ,8
+ ,7.9
+ ,2
+ ,8
+ ,8.1
+ ,8.2
+ ,7.9
+ ,2.1
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,1.7
+ ,7.9
+ ,7.9
+ ,8.1
+ ,8
+ ,1.8
+ ,8
+ ,7.9
+ ,8
+ ,7.9
+ ,1.8
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,1.8
+ ,7.9
+ ,8
+ ,7.9
+ ,7.7
+ ,1.3
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,1.3
+ ,7.7
+ ,8
+ ,8
+ ,7.5
+ ,1.3
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.3
+ ,1.2
+ ,7.5
+ ,7.2
+ ,8
+ ,7
+ ,1.4
+ ,7.3
+ ,7.5
+ ,7.7
+ ,7
+ ,2.2
+ ,7
+ ,7.3
+ ,7.2
+ ,7
+ ,2.9
+ ,7
+ ,7
+ ,7.5
+ ,7.2
+ ,3.1
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,3.5
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,3.6
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,4.4
+ ,7.1
+ ,7.3
+ ,7
+ ,6.4
+ ,4.1
+ ,6.8
+ ,7.1
+ ,7.2
+ ,6.1
+ ,5.1
+ ,6.4
+ ,6.8
+ ,7.3
+ ,6.5
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.1
+ ,7.7
+ ,5.9
+ ,6.5
+ ,6.1
+ ,6.8
+ ,7.9
+ ,5.4
+ ,7.7
+ ,6.5
+ ,6.4
+ ,7.5
+ ,5.5
+ ,7.9
+ ,7.7
+ ,6.1
+ ,6.9
+ ,4.8
+ ,7.5
+ ,7.9
+ ,6.5
+ ,6.6
+ ,3.2
+ ,6.9
+ ,7.5
+ ,7.7
+ ,6.9
+ ,2.7
+ ,6.6
+ ,6.9
+ ,7.9)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-4)
')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-4)
'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '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) Y(t-1) Y(t-2) Y(t-4)\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 2.4 7.5 8.3 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 2.0 7.2 7.5 8.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 2.1 7.4 7.2 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 2.0 8.8 7.4 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 1.8 9.3 8.8 7.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 2.7 9.3 9.3 7.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 2.3 8.7 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 1.9 8.2 8.7 9.3 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 2.0 8.3 8.2 9.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 2.3 8.5 8.3 8.7 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 2.8 8.6 8.5 8.2 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 2.4 8.5 8.6 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 2.3 8.2 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 2.7 8.1 8.2 8.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 2.7 7.9 8.1 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 2.9 8.6 7.9 8.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 3.0 8.7 8.6 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 2.2 8.7 8.7 7.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 2.3 8.5 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.8 8.4 8.5 8.7 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 2.8 8.5 8.4 8.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 2.8 8.7 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 2.2 8.7 8.7 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 2.6 8.6 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 2.8 8.5 8.6 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 2.5 8.3 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 2.4 8.0 8.3 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 2.3 8.2 8.0 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 1.9 8.1 8.2 8.3 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.7 8.1 8.1 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 2.0 8.0 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 2.1 7.9 8.0 8.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 1.7 7.9 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 1.8 8.0 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.8 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 1.8 7.9 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 1.3 8.0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 1.3 7.7 8.0 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 1.3 7.2 7.7 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 1.2 7.5 7.2 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 1.4 7.3 7.5 7.7 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 2.2 7.0 7.3 7.2 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 2.9 7.0 7.0 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 3.1 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 3.5 7.2 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 3.6 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 4.4 7.1 7.3 7.0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 4.1 6.8 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 5.1 6.4 6.8 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 5.8 6.1 6.4 7.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 5.9 6.5 6.1 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 5.4 7.7 6.5 6.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 5.5 7.9 7.7 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 4.8 7.5 7.9 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 3.2 6.9 7.5 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 2.7 6.6 6.9 7.9 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)` `Y(t-1)` `Y(t-2)` `Y(t-4)\r` M1
1.021279 0.036041 1.510845 -0.906468 0.274643 -0.139705
M2 M3 M4 M5 M6 M7
-0.114153 0.615649 -0.411606 0.060368 0.091291 0.022185
M8 M9 M10 M11 t
0.172457 0.013630 -0.083274 -0.010991 -0.006787
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.265197 -0.075476 -0.001807 0.076234 0.358442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.021279 0.659961 1.547 0.129824
`X(t)` 0.036041 0.024169 1.491 0.143947
`Y(t-1)` 1.510845 0.099900 15.124 < 2e-16 ***
`Y(t-2)` -0.906468 0.111302 -8.144 6.08e-10 ***
`Y(t-4)\r` 0.274643 0.069650 3.943 0.000324 ***
M1 -0.139705 0.102037 -1.369 0.178784
M2 -0.114153 0.104870 -1.089 0.283045
M3 0.615649 0.106117 5.802 9.77e-07 ***
M4 -0.411606 0.131738 -3.124 0.003355 **
M5 0.060368 0.104663 0.577 0.567402
M6 0.091291 0.108199 0.844 0.403965
M7 0.022185 0.099983 0.222 0.825561
M8 0.172457 0.102790 1.678 0.101392
M9 0.013630 0.111607 0.122 0.903429
M10 -0.083274 0.109081 -0.763 0.449809
M11 -0.010991 0.105006 -0.105 0.917176
t -0.006787 0.002399 -2.828 0.007348 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1477 on 39 degrees of freedom
Multiple R-squared: 0.972, Adjusted R-squared: 0.9606
F-statistic: 84.75 on 16 and 39 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,] 0.14151855 0.28303710 0.8584814
[2,] 0.18733009 0.37466018 0.8126699
[3,] 0.09139235 0.18278470 0.9086076
[4,] 0.04125363 0.08250727 0.9587464
[5,] 0.05131280 0.10262561 0.9486872
[6,] 0.02575481 0.05150961 0.9742452
[7,] 0.01429216 0.02858431 0.9857078
[8,] 0.29278439 0.58556877 0.7072156
[9,] 0.20371367 0.40742735 0.7962863
[10,] 0.16332756 0.32665513 0.8366724
[11,] 0.18158795 0.36317589 0.8184121
[12,] 0.14031543 0.28063087 0.8596846
[13,] 0.12445766 0.24891532 0.8755423
[14,] 0.08956881 0.17913761 0.9104312
[15,] 0.05982342 0.11964683 0.9401766
[16,] 0.03230956 0.06461912 0.9676904
[17,] 0.19927988 0.39855976 0.8007201
> postscript(file="/var/www/html/rcomp/tmp/1qcwb1258566964.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ay951258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39fuo1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4juhr1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jj211258566964.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
-0.013267998 -0.062072625 0.174519392 -0.002009903 0.136036530 -0.122232133
7 8 9 10 11 12
-0.009915538 0.035236260 -0.207072605 -0.060930966 -0.076917714 -0.152438036
13 14 15 16 17 18
0.205336219 -0.176165424 0.039805140 0.010146460 0.052262559 0.002534579
19 20 21 22 23 24
0.084742499 -0.034436828 0.089445525 0.036542370 0.101428538 0.106428264
25 26 27 28 29 30
0.051221024 -0.045209212 -0.265196743 0.125804262 0.062340623 -0.062841471
31 32 33 34 35 36
-0.001604824 -0.060792173 0.128591298 0.105057443 0.057671955 0.304552583
37 38 39 40 41 42
-0.100130362 -0.074994982 0.012935623 -0.083370052 -0.199262977 0.157048877
43 44 45 46 47 48
-0.146620206 -0.042385150 -0.010964218 -0.080668847 -0.082182778 -0.258542810
49 50 51 52 53 54
-0.143158883 0.358442242 0.037936589 -0.050570766 -0.051376736 0.025490148
55 56
0.073398068 0.102377891
> postscript(file="/var/www/html/rcomp/tmp/67e3n1258566964.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 -0.013267998 NA
1 -0.062072625 -0.013267998
2 0.174519392 -0.062072625
3 -0.002009903 0.174519392
4 0.136036530 -0.002009903
5 -0.122232133 0.136036530
6 -0.009915538 -0.122232133
7 0.035236260 -0.009915538
8 -0.207072605 0.035236260
9 -0.060930966 -0.207072605
10 -0.076917714 -0.060930966
11 -0.152438036 -0.076917714
12 0.205336219 -0.152438036
13 -0.176165424 0.205336219
14 0.039805140 -0.176165424
15 0.010146460 0.039805140
16 0.052262559 0.010146460
17 0.002534579 0.052262559
18 0.084742499 0.002534579
19 -0.034436828 0.084742499
20 0.089445525 -0.034436828
21 0.036542370 0.089445525
22 0.101428538 0.036542370
23 0.106428264 0.101428538
24 0.051221024 0.106428264
25 -0.045209212 0.051221024
26 -0.265196743 -0.045209212
27 0.125804262 -0.265196743
28 0.062340623 0.125804262
29 -0.062841471 0.062340623
30 -0.001604824 -0.062841471
31 -0.060792173 -0.001604824
32 0.128591298 -0.060792173
33 0.105057443 0.128591298
34 0.057671955 0.105057443
35 0.304552583 0.057671955
36 -0.100130362 0.304552583
37 -0.074994982 -0.100130362
38 0.012935623 -0.074994982
39 -0.083370052 0.012935623
40 -0.199262977 -0.083370052
41 0.157048877 -0.199262977
42 -0.146620206 0.157048877
43 -0.042385150 -0.146620206
44 -0.010964218 -0.042385150
45 -0.080668847 -0.010964218
46 -0.082182778 -0.080668847
47 -0.258542810 -0.082182778
48 -0.143158883 -0.258542810
49 0.358442242 -0.143158883
50 0.037936589 0.358442242
51 -0.050570766 0.037936589
52 -0.051376736 -0.050570766
53 0.025490148 -0.051376736
54 0.073398068 0.025490148
55 0.102377891 0.073398068
56 NA 0.102377891
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.062072625 -0.013267998
[2,] 0.174519392 -0.062072625
[3,] -0.002009903 0.174519392
[4,] 0.136036530 -0.002009903
[5,] -0.122232133 0.136036530
[6,] -0.009915538 -0.122232133
[7,] 0.035236260 -0.009915538
[8,] -0.207072605 0.035236260
[9,] -0.060930966 -0.207072605
[10,] -0.076917714 -0.060930966
[11,] -0.152438036 -0.076917714
[12,] 0.205336219 -0.152438036
[13,] -0.176165424 0.205336219
[14,] 0.039805140 -0.176165424
[15,] 0.010146460 0.039805140
[16,] 0.052262559 0.010146460
[17,] 0.002534579 0.052262559
[18,] 0.084742499 0.002534579
[19,] -0.034436828 0.084742499
[20,] 0.089445525 -0.034436828
[21,] 0.036542370 0.089445525
[22,] 0.101428538 0.036542370
[23,] 0.106428264 0.101428538
[24,] 0.051221024 0.106428264
[25,] -0.045209212 0.051221024
[26,] -0.265196743 -0.045209212
[27,] 0.125804262 -0.265196743
[28,] 0.062340623 0.125804262
[29,] -0.062841471 0.062340623
[30,] -0.001604824 -0.062841471
[31,] -0.060792173 -0.001604824
[32,] 0.128591298 -0.060792173
[33,] 0.105057443 0.128591298
[34,] 0.057671955 0.105057443
[35,] 0.304552583 0.057671955
[36,] -0.100130362 0.304552583
[37,] -0.074994982 -0.100130362
[38,] 0.012935623 -0.074994982
[39,] -0.083370052 0.012935623
[40,] -0.199262977 -0.083370052
[41,] 0.157048877 -0.199262977
[42,] -0.146620206 0.157048877
[43,] -0.042385150 -0.146620206
[44,] -0.010964218 -0.042385150
[45,] -0.080668847 -0.010964218
[46,] -0.082182778 -0.080668847
[47,] -0.258542810 -0.082182778
[48,] -0.143158883 -0.258542810
[49,] 0.358442242 -0.143158883
[50,] 0.037936589 0.358442242
[51,] -0.050570766 0.037936589
[52,] -0.051376736 -0.050570766
[53,] 0.025490148 -0.051376736
[54,] 0.073398068 0.025490148
[55,] 0.102377891 0.073398068
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.062072625 -0.013267998
2 0.174519392 -0.062072625
3 -0.002009903 0.174519392
4 0.136036530 -0.002009903
5 -0.122232133 0.136036530
6 -0.009915538 -0.122232133
7 0.035236260 -0.009915538
8 -0.207072605 0.035236260
9 -0.060930966 -0.207072605
10 -0.076917714 -0.060930966
11 -0.152438036 -0.076917714
12 0.205336219 -0.152438036
13 -0.176165424 0.205336219
14 0.039805140 -0.176165424
15 0.010146460 0.039805140
16 0.052262559 0.010146460
17 0.002534579 0.052262559
18 0.084742499 0.002534579
19 -0.034436828 0.084742499
20 0.089445525 -0.034436828
21 0.036542370 0.089445525
22 0.101428538 0.036542370
23 0.106428264 0.101428538
24 0.051221024 0.106428264
25 -0.045209212 0.051221024
26 -0.265196743 -0.045209212
27 0.125804262 -0.265196743
28 0.062340623 0.125804262
29 -0.062841471 0.062340623
30 -0.001604824 -0.062841471
31 -0.060792173 -0.001604824
32 0.128591298 -0.060792173
33 0.105057443 0.128591298
34 0.057671955 0.105057443
35 0.304552583 0.057671955
36 -0.100130362 0.304552583
37 -0.074994982 -0.100130362
38 0.012935623 -0.074994982
39 -0.083370052 0.012935623
40 -0.199262977 -0.083370052
41 0.157048877 -0.199262977
42 -0.146620206 0.157048877
43 -0.042385150 -0.146620206
44 -0.010964218 -0.042385150
45 -0.080668847 -0.010964218
46 -0.082182778 -0.080668847
47 -0.258542810 -0.082182778
48 -0.143158883 -0.258542810
49 0.358442242 -0.143158883
50 0.037936589 0.358442242
51 -0.050570766 0.037936589
52 -0.051376736 -0.050570766
53 0.025490148 -0.051376736
54 0.073398068 0.025490148
55 0.102377891 0.073398068
> 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/7r5it1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hogv1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oi7d1258566964.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bxzm1258566965.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jp2u1258566965.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/12glza1258566965.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/13w14q1258566965.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/14w01z1258566965.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/15jaqh1258566965.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/16d9l51258566965.tab")
+ }
>
> system("convert tmp/1qcwb1258566964.ps tmp/1qcwb1258566964.png")
> system("convert tmp/2ay951258566964.ps tmp/2ay951258566964.png")
> system("convert tmp/39fuo1258566964.ps tmp/39fuo1258566964.png")
> system("convert tmp/4juhr1258566964.ps tmp/4juhr1258566964.png")
> system("convert tmp/5jj211258566964.ps tmp/5jj211258566964.png")
> system("convert tmp/67e3n1258566964.ps tmp/67e3n1258566964.png")
> system("convert tmp/7r5it1258566964.ps tmp/7r5it1258566964.png")
> system("convert tmp/8hogv1258566964.ps tmp/8hogv1258566964.png")
> system("convert tmp/9oi7d1258566964.ps tmp/9oi7d1258566964.png")
> system("convert tmp/10bxzm1258566965.ps tmp/10bxzm1258566965.png")
>
>
> proc.time()
user system elapsed
2.365 1.562 3.169