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(7.2
+ ,2.4
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,2
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,2.1
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,2
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,1.8
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,2.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,2.3
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,2
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,2.3
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,2.8
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,2.4
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,2.7
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,2.7
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,2.9
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,3
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,2.3
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,2.6
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,2.8
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,2.5
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,2.4
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,1.9
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,1.7
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,2
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,2.1
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,1.7
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,1.8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,1.8
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,1.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,1.3
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,1.3
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,1.3
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,1.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,1.4
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,2.2
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,2.9
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,3.1
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,3.5
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,3.6
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,4.4
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,4.1
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,5.9
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,5.4
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,5.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,4.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,3.2
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,2.7
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(t-4)
')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-3)','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-3) Y(t-4)\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 7.2 2.4 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0
2 7.4 2.0 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0
3 8.8 2.1 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0
4 9.3 2.0 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0
5 9.3 1.8 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0
6 8.7 2.7 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0
7 8.2 2.3 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0
8 8.3 1.9 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0
9 8.5 2.0 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0
10 8.6 2.3 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0
11 8.5 2.8 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1
12 8.2 2.4 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0
13 8.1 2.3 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0
14 7.9 2.7 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0
15 8.6 2.7 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0
16 8.7 2.9 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0
17 8.7 3.0 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0
18 8.5 2.2 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0
19 8.4 2.3 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0
20 8.5 2.8 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0
21 8.7 2.8 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0
22 8.7 2.8 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0
23 8.6 2.2 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1
24 8.5 2.6 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0
25 8.3 2.8 8.5 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0
26 8.0 2.5 8.3 8.5 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0
27 8.2 2.4 8.0 8.3 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0
28 8.1 2.3 8.2 8.0 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0
29 8.1 1.9 8.1 8.2 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0
30 8.0 1.7 8.1 8.1 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0
31 7.9 2.0 8.0 8.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0
32 7.9 2.1 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0
33 8.0 1.7 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0
34 8.0 1.8 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0
35 7.9 1.8 8.0 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1
36 8.0 1.8 7.9 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0
37 7.7 1.3 8.0 7.9 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0
38 7.2 1.3 7.7 8.0 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0
39 7.5 1.3 7.2 7.7 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0
40 7.3 1.2 7.5 7.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0
41 7.0 1.4 7.3 7.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0
42 7.0 2.2 7.0 7.3 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0
43 7.0 2.9 7.0 7.0 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0
44 7.2 3.1 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0
45 7.3 3.5 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0
46 7.1 3.6 7.3 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0
47 6.8 4.4 7.1 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1
48 6.4 4.1 6.8 7.1 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0
49 6.1 5.1 6.4 6.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0
50 6.5 5.8 6.1 6.4 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0
51 7.7 5.9 6.5 6.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0
52 7.9 5.4 7.7 6.5 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0
53 7.5 5.5 7.9 7.7 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0
54 6.9 4.8 7.5 7.9 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0
55 6.6 3.2 6.9 7.5 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0
56 6.9 2.7 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 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-3)` `Y(t-4)\r`
0.998292 0.037663 1.469575 -0.801807 -0.115732 0.329691
M1 M2 M3 M4 M5 M6
-0.144354 -0.120706 0.608264 -0.390328 0.010241 0.117273
M7 M8 M9 M10 M11 t
0.020458 0.172192 0.013405 -0.095853 -0.019385 -0.006779
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.2671342 -0.0770240 0.0001446 0.0801458 0.3562438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.998292 0.668951 1.492 0.14387
`X(t)` 0.037663 0.024701 1.525 0.13560
`Y(t-1)` 1.469575 0.137941 10.654 5.71e-13 ***
`Y(t-2)` -0.801807 0.263589 -3.042 0.00425 **
`Y(t-3)` -0.115732 0.263607 -0.439 0.66312
`Y(t-4)\r` 0.329691 0.143787 2.293 0.02748 *
M1 -0.144354 0.103653 -1.393 0.17181
M2 -0.120706 0.107018 -1.128 0.26643
M3 0.608264 0.108544 5.604 1.99e-06 ***
M4 -0.390328 0.141671 -2.755 0.00896 **
M5 0.010241 0.155634 0.066 0.94788
M6 0.117273 0.124325 0.943 0.35150
M7 0.020458 0.101110 0.202 0.84073
M8 0.172192 0.103872 1.658 0.10561
M9 0.013405 0.112782 0.119 0.90602
M10 -0.095853 0.113890 -0.842 0.40526
M11 -0.019385 0.107819 -0.180 0.85827
t -0.006779 0.002425 -2.796 0.00808 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1492 on 38 degrees of freedom
Multiple R-squared: 0.9722, Adjusted R-squared: 0.9597
F-statistic: 78.12 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,] 0.09964283 0.19928565 0.9003572
[2,] 0.12468572 0.24937145 0.8753143
[3,] 0.05447878 0.10895755 0.9455212
[4,] 0.05186546 0.10373092 0.9481345
[5,] 0.02517449 0.05034898 0.9748255
[6,] 0.01416094 0.02832189 0.9858391
[7,] 0.31368510 0.62737020 0.6863149
[8,] 0.21363946 0.42727891 0.7863605
[9,] 0.14054950 0.28109900 0.8594505
[10,] 0.16545145 0.33090290 0.8345485
[11,] 0.12162259 0.24324519 0.8783774
[12,] 0.10576646 0.21153293 0.8942335
[13,] 0.07117735 0.14235470 0.9288226
[14,] 0.04373571 0.08747142 0.9562643
[15,] 0.02044093 0.04088186 0.9795591
> postscript(file="/var/www/html/rcomp/tmp/1k43a1258655247.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xefa1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3k67b1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cyu41258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bjvg1258655247.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
-0.020165150 -0.047439503 0.164405355 0.005531744 0.129070933 -0.108088127
7 8 9 10 11 12
-0.011386055 0.047582429 -0.207918446 -0.076967204 -0.075664654 -0.155890419
13 14 15 16 17 18
0.205335808 -0.164725616 0.025066659 0.021174686 0.047748844 0.004757770
19 20 21 22 23 24
0.079289076 -0.030869922 0.084411239 0.041078053 0.098891065 0.108354195
25 26 27 28 29 30
0.052793354 -0.050616042 -0.267134202 0.117367926 0.077180907 -0.073666506
31 32 33 34 35 36
-0.011926354 -0.060901296 0.127975778 0.114683980 0.058145245 0.304069418
37 38 39 40 41 42
-0.086073143 -0.093462614 0.023133644 -0.077194466 -0.203018566 0.146674089
43 44 45 46 47 48
-0.138692813 -0.059961486 -0.004468572 -0.078794829 -0.081371655 -0.256533195
49 50 51 52 53 54
-0.151890869 0.356243775 0.054528543 -0.066879890 -0.050982117 0.030322775
55 56
0.082716147 0.104150273
> postscript(file="/var/www/html/rcomp/tmp/6bz1j1258655247.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 -0.020165150 NA
1 -0.047439503 -0.020165150
2 0.164405355 -0.047439503
3 0.005531744 0.164405355
4 0.129070933 0.005531744
5 -0.108088127 0.129070933
6 -0.011386055 -0.108088127
7 0.047582429 -0.011386055
8 -0.207918446 0.047582429
9 -0.076967204 -0.207918446
10 -0.075664654 -0.076967204
11 -0.155890419 -0.075664654
12 0.205335808 -0.155890419
13 -0.164725616 0.205335808
14 0.025066659 -0.164725616
15 0.021174686 0.025066659
16 0.047748844 0.021174686
17 0.004757770 0.047748844
18 0.079289076 0.004757770
19 -0.030869922 0.079289076
20 0.084411239 -0.030869922
21 0.041078053 0.084411239
22 0.098891065 0.041078053
23 0.108354195 0.098891065
24 0.052793354 0.108354195
25 -0.050616042 0.052793354
26 -0.267134202 -0.050616042
27 0.117367926 -0.267134202
28 0.077180907 0.117367926
29 -0.073666506 0.077180907
30 -0.011926354 -0.073666506
31 -0.060901296 -0.011926354
32 0.127975778 -0.060901296
33 0.114683980 0.127975778
34 0.058145245 0.114683980
35 0.304069418 0.058145245
36 -0.086073143 0.304069418
37 -0.093462614 -0.086073143
38 0.023133644 -0.093462614
39 -0.077194466 0.023133644
40 -0.203018566 -0.077194466
41 0.146674089 -0.203018566
42 -0.138692813 0.146674089
43 -0.059961486 -0.138692813
44 -0.004468572 -0.059961486
45 -0.078794829 -0.004468572
46 -0.081371655 -0.078794829
47 -0.256533195 -0.081371655
48 -0.151890869 -0.256533195
49 0.356243775 -0.151890869
50 0.054528543 0.356243775
51 -0.066879890 0.054528543
52 -0.050982117 -0.066879890
53 0.030322775 -0.050982117
54 0.082716147 0.030322775
55 0.104150273 0.082716147
56 NA 0.104150273
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.047439503 -0.020165150
[2,] 0.164405355 -0.047439503
[3,] 0.005531744 0.164405355
[4,] 0.129070933 0.005531744
[5,] -0.108088127 0.129070933
[6,] -0.011386055 -0.108088127
[7,] 0.047582429 -0.011386055
[8,] -0.207918446 0.047582429
[9,] -0.076967204 -0.207918446
[10,] -0.075664654 -0.076967204
[11,] -0.155890419 -0.075664654
[12,] 0.205335808 -0.155890419
[13,] -0.164725616 0.205335808
[14,] 0.025066659 -0.164725616
[15,] 0.021174686 0.025066659
[16,] 0.047748844 0.021174686
[17,] 0.004757770 0.047748844
[18,] 0.079289076 0.004757770
[19,] -0.030869922 0.079289076
[20,] 0.084411239 -0.030869922
[21,] 0.041078053 0.084411239
[22,] 0.098891065 0.041078053
[23,] 0.108354195 0.098891065
[24,] 0.052793354 0.108354195
[25,] -0.050616042 0.052793354
[26,] -0.267134202 -0.050616042
[27,] 0.117367926 -0.267134202
[28,] 0.077180907 0.117367926
[29,] -0.073666506 0.077180907
[30,] -0.011926354 -0.073666506
[31,] -0.060901296 -0.011926354
[32,] 0.127975778 -0.060901296
[33,] 0.114683980 0.127975778
[34,] 0.058145245 0.114683980
[35,] 0.304069418 0.058145245
[36,] -0.086073143 0.304069418
[37,] -0.093462614 -0.086073143
[38,] 0.023133644 -0.093462614
[39,] -0.077194466 0.023133644
[40,] -0.203018566 -0.077194466
[41,] 0.146674089 -0.203018566
[42,] -0.138692813 0.146674089
[43,] -0.059961486 -0.138692813
[44,] -0.004468572 -0.059961486
[45,] -0.078794829 -0.004468572
[46,] -0.081371655 -0.078794829
[47,] -0.256533195 -0.081371655
[48,] -0.151890869 -0.256533195
[49,] 0.356243775 -0.151890869
[50,] 0.054528543 0.356243775
[51,] -0.066879890 0.054528543
[52,] -0.050982117 -0.066879890
[53,] 0.030322775 -0.050982117
[54,] 0.082716147 0.030322775
[55,] 0.104150273 0.082716147
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.047439503 -0.020165150
2 0.164405355 -0.047439503
3 0.005531744 0.164405355
4 0.129070933 0.005531744
5 -0.108088127 0.129070933
6 -0.011386055 -0.108088127
7 0.047582429 -0.011386055
8 -0.207918446 0.047582429
9 -0.076967204 -0.207918446
10 -0.075664654 -0.076967204
11 -0.155890419 -0.075664654
12 0.205335808 -0.155890419
13 -0.164725616 0.205335808
14 0.025066659 -0.164725616
15 0.021174686 0.025066659
16 0.047748844 0.021174686
17 0.004757770 0.047748844
18 0.079289076 0.004757770
19 -0.030869922 0.079289076
20 0.084411239 -0.030869922
21 0.041078053 0.084411239
22 0.098891065 0.041078053
23 0.108354195 0.098891065
24 0.052793354 0.108354195
25 -0.050616042 0.052793354
26 -0.267134202 -0.050616042
27 0.117367926 -0.267134202
28 0.077180907 0.117367926
29 -0.073666506 0.077180907
30 -0.011926354 -0.073666506
31 -0.060901296 -0.011926354
32 0.127975778 -0.060901296
33 0.114683980 0.127975778
34 0.058145245 0.114683980
35 0.304069418 0.058145245
36 -0.086073143 0.304069418
37 -0.093462614 -0.086073143
38 0.023133644 -0.093462614
39 -0.077194466 0.023133644
40 -0.203018566 -0.077194466
41 0.146674089 -0.203018566
42 -0.138692813 0.146674089
43 -0.059961486 -0.138692813
44 -0.004468572 -0.059961486
45 -0.078794829 -0.004468572
46 -0.081371655 -0.078794829
47 -0.256533195 -0.081371655
48 -0.151890869 -0.256533195
49 0.356243775 -0.151890869
50 0.054528543 0.356243775
51 -0.066879890 0.054528543
52 -0.050982117 -0.066879890
53 0.030322775 -0.050982117
54 0.082716147 0.030322775
55 0.104150273 0.082716147
> 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/72cqy1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8px8k1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/984zy1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w05p1258655247.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pr1g1258655247.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/12zosq1258655247.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/13ylpu1258655247.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/14351d1258655247.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/15cvcu1258655247.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/16tclf1258655247.tab")
+ }
>
> system("convert tmp/1k43a1258655247.ps tmp/1k43a1258655247.png")
> system("convert tmp/2xefa1258655247.ps tmp/2xefa1258655247.png")
> system("convert tmp/3k67b1258655247.ps tmp/3k67b1258655247.png")
> system("convert tmp/4cyu41258655247.ps tmp/4cyu41258655247.png")
> system("convert tmp/5bjvg1258655247.ps tmp/5bjvg1258655247.png")
> system("convert tmp/6bz1j1258655247.ps tmp/6bz1j1258655247.png")
> system("convert tmp/72cqy1258655247.ps tmp/72cqy1258655247.png")
> system("convert tmp/8px8k1258655247.ps tmp/8px8k1258655247.png")
> system("convert tmp/984zy1258655247.ps tmp/984zy1258655247.png")
> system("convert tmp/10w05p1258655247.ps tmp/10w05p1258655247.png")
>
>
> proc.time()
user system elapsed
2.384 1.614 5.711