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(1.4
+ ,1.9
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,1
+ ,1
+ ,1.6
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,-0.8
+ ,0
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-2.9
+ ,-1.3
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.7
+ ,-0.4
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.3
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,1.5
+ ,1.4
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,3
+ ,2.6
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3.2
+ ,2.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.1
+ ,2.6
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.9
+ ,3.4
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,1
+ ,1.7
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1.3
+ ,1.2
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,0.8
+ ,0
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,1.2
+ ,0
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,2.9
+ ,1.6
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,3.9
+ ,2.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,4.5
+ ,3.2
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.4
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,3.3
+ ,2.3
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,2
+ ,1.9
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,1.5
+ ,1.7
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1
+ ,1.9
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,2.1
+ ,3.3
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,3
+ ,3.8
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,4
+ ,4.4
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,5.1
+ ,4.5
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,4.5
+ ,3.5
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.2
+ ,3
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,3.3
+ ,2.8
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,2.7
+ ,2.9
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,1.8
+ ,2.6
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.4
+ ,2.1
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,0.5
+ ,1.5
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,-0.4
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,0.8
+ ,1.5
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.7
+ ,1.7
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,1.9
+ ,2.3
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,2
+ ,2.3
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,1.1
+ ,1.9
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,0.9
+ ,2
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.4
+ ,1.6
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.7
+ ,1.2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,2.1
+ ,1.9
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.8
+ ,2.1
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,3.9
+ ,2.4
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.5
+ ,2.9
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2
+ ,2.5
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2.3
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,1.5
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,2.5
+ ,2.6
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,3.1
+ ,2.4
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,2.7
+ ,2.5
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.8
+ ,2.1
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.5
+ ,2.2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,3
+ ,2.7
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3.2
+ ,3
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,2.8
+ ,3.2
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,2.4
+ ,3
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2)
+ ,dim=c(6
+ ,59)
+ ,dimnames=list(c('bbp'
+ ,'dnst'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4')
+ ,1:59))
> y <- array(NA,dim=c(6,59),dimnames=list(c('bbp','dnst','y1','y2','y3','y4'),1:59))
> 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
bbp dnst y1 y2 y3 y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.4 1.9 -0.7 -2.9 -0.8 1.0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.0 1.6 -0.7 -0.7 -2.9 -0.8 0 1 0 0 0 0 0 0 0 0 0 2
3 -0.8 0.0 1.5 -0.7 -0.7 -2.9 0 0 1 0 0 0 0 0 0 0 0 3
4 -2.9 -1.3 3.0 1.5 -0.7 -0.7 0 0 0 1 0 0 0 0 0 0 0 4
5 -0.7 -0.4 3.2 3.0 1.5 -0.7 0 0 0 0 1 0 0 0 0 0 0 5
6 -0.7 -0.3 3.1 3.2 3.0 1.5 0 0 0 0 0 1 0 0 0 0 0 6
7 1.5 1.4 3.9 3.1 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0 7
8 3.0 2.6 1.0 3.9 3.1 3.2 0 0 0 0 0 0 0 1 0 0 0 8
9 3.2 2.8 1.3 1.0 3.9 3.1 0 0 0 0 0 0 0 0 1 0 0 9
10 3.1 2.6 0.8 1.3 1.0 3.9 0 0 0 0 0 0 0 0 0 1 0 10
11 3.9 3.4 1.2 0.8 1.3 1.0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.0 1.7 2.9 1.2 0.8 1.3 0 0 0 0 0 0 0 0 0 0 0 12
13 1.3 1.2 3.9 2.9 1.2 0.8 1 0 0 0 0 0 0 0 0 0 0 13
14 0.8 0.0 4.5 3.9 2.9 1.2 0 1 0 0 0 0 0 0 0 0 0 14
15 1.2 0.0 4.5 4.5 3.9 2.9 0 0 1 0 0 0 0 0 0 0 0 15
16 2.9 1.6 3.3 4.5 4.5 3.9 0 0 0 1 0 0 0 0 0 0 0 16
17 3.9 2.5 2.0 3.3 4.5 4.5 0 0 0 0 1 0 0 0 0 0 0 17
18 4.5 3.2 1.5 2.0 3.3 4.5 0 0 0 0 0 1 0 0 0 0 0 18
19 4.5 3.4 1.0 1.5 2.0 3.3 0 0 0 0 0 0 1 0 0 0 0 19
20 3.3 2.3 2.1 1.0 1.5 2.0 0 0 0 0 0 0 0 1 0 0 0 20
21 2.0 1.9 3.0 2.1 1.0 1.5 0 0 0 0 0 0 0 0 1 0 0 21
22 1.5 1.7 4.0 3.0 2.1 1.0 0 0 0 0 0 0 0 0 0 1 0 22
23 1.0 1.9 5.1 4.0 3.0 2.1 0 0 0 0 0 0 0 0 0 0 1 23
24 2.1 3.3 4.5 5.1 4.0 3.0 0 0 0 0 0 0 0 0 0 0 0 24
25 3.0 3.8 4.2 4.5 5.1 4.0 1 0 0 0 0 0 0 0 0 0 0 25
26 4.0 4.4 3.3 4.2 4.5 5.1 0 1 0 0 0 0 0 0 0 0 0 26
27 5.1 4.5 2.7 3.3 4.2 4.5 0 0 1 0 0 0 0 0 0 0 0 27
28 4.5 3.5 1.8 2.7 3.3 4.2 0 0 0 1 0 0 0 0 0 0 0 28
29 4.2 3.0 1.4 1.8 2.7 3.3 0 0 0 0 1 0 0 0 0 0 0 29
30 3.3 2.8 0.5 1.4 1.8 2.7 0 0 0 0 0 1 0 0 0 0 0 30
31 2.7 2.9 -0.4 0.5 1.4 1.8 0 0 0 0 0 0 1 0 0 0 0 31
32 1.8 2.6 0.8 -0.4 0.5 1.4 0 0 0 0 0 0 0 1 0 0 0 32
33 1.4 2.1 0.7 0.8 -0.4 0.5 0 0 0 0 0 0 0 0 1 0 0 33
34 0.5 1.5 1.9 0.7 0.8 -0.4 0 0 0 0 0 0 0 0 0 1 0 34
35 -0.4 1.1 2.0 1.9 0.7 0.8 0 0 0 0 0 0 0 0 0 0 1 35
36 0.8 1.5 1.1 2.0 1.9 0.7 0 0 0 0 0 0 0 0 0 0 0 36
37 0.7 1.7 0.9 1.1 2.0 1.9 1 0 0 0 0 0 0 0 0 0 0 37
38 1.9 2.3 0.4 0.9 1.1 2.0 0 1 0 0 0 0 0 0 0 0 0 38
39 2.0 2.3 0.7 0.4 0.9 1.1 0 0 1 0 0 0 0 0 0 0 0 39
40 1.1 1.9 2.1 0.7 0.4 0.9 0 0 0 1 0 0 0 0 0 0 0 40
41 0.9 2.0 2.8 2.1 0.7 0.4 0 0 0 0 1 0 0 0 0 0 0 41
42 0.4 1.6 3.9 2.8 2.1 0.7 0 0 0 0 0 1 0 0 0 0 0 42
43 0.7 1.2 3.5 3.9 2.8 2.1 0 0 0 0 0 0 1 0 0 0 0 43
44 2.1 1.9 2.0 3.5 3.9 2.8 0 0 0 0 0 0 0 1 0 0 0 44
45 2.8 2.1 2.0 2.0 3.5 3.9 0 0 0 0 0 0 0 0 1 0 0 45
46 3.9 2.4 1.5 2.0 2.0 3.5 0 0 0 0 0 0 0 0 0 1 0 46
47 3.5 2.9 2.5 1.5 2.0 2.0 0 0 0 0 0 0 0 0 0 0 1 47
48 2.0 2.5 3.1 2.5 1.5 2.0 0 0 0 0 0 0 0 0 0 0 0 48
49 2.0 2.3 2.7 3.1 2.5 1.5 1 0 0 0 0 0 0 0 0 0 0 49
50 1.5 2.5 2.8 2.7 3.1 2.5 0 1 0 0 0 0 0 0 0 0 0 50
51 2.5 2.6 2.5 2.8 2.7 3.1 0 0 1 0 0 0 0 0 0 0 0 51
52 3.1 2.4 3.0 2.5 2.8 2.7 0 0 0 1 0 0 0 0 0 0 0 52
53 2.7 2.5 3.2 3.0 2.5 2.8 0 0 0 0 1 0 0 0 0 0 0 53
54 2.8 2.1 2.8 3.2 3.0 2.5 0 0 0 0 0 1 0 0 0 0 0 54
55 2.5 2.2 2.4 2.8 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0 55
56 3.0 2.7 2.0 2.4 2.8 3.2 0 0 0 0 0 0 0 1 0 0 0 56
57 3.2 3.0 1.8 2.0 2.4 2.8 0 0 0 0 0 0 0 0 1 0 0 57
58 2.8 3.2 1.1 1.8 2.0 2.4 0 0 0 0 0 0 0 0 0 1 0 58
59 2.4 3.0 -1.5 1.1 1.8 2.0 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dnst y1 y2 y3 y4
-0.658401 0.862606 -0.040011 -0.093803 0.054495 0.389840
M1 M2 M3 M4 M5 M6
0.076280 0.250611 0.735839 0.576576 0.842257 0.587923
M7 M8 M9 M10 M11 t
0.501343 0.605788 0.565963 0.657248 0.424207 -0.007737
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.09407 -0.38207 -0.04168 0.33959 1.23614
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.658401 0.469228 -1.403 0.16810
dnst 0.862606 0.130378 6.616 5.76e-08 ***
y1 -0.040011 0.112815 -0.355 0.72466
y2 -0.093803 0.145014 -0.647 0.52133
y3 0.054495 0.146718 0.371 0.71223
y4 0.389840 0.133275 2.925 0.00559 **
M1 0.076280 0.450718 0.169 0.86644
M2 0.250611 0.445299 0.563 0.57664
M3 0.735839 0.451297 1.630 0.11066
M4 0.576576 0.458038 1.259 0.21523
M5 0.842257 0.445936 1.889 0.06602 .
M6 0.587923 0.457028 1.286 0.20552
M7 0.501343 0.455131 1.102 0.27709
M8 0.605788 0.456644 1.327 0.19198
M9 0.565963 0.459473 1.232 0.22506
M10 0.657248 0.450472 1.459 0.15218
M11 0.424207 0.445081 0.953 0.34612
t -0.007737 0.005566 -1.390 0.17200
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6524 on 41 degrees of freedom
Multiple R-squared: 0.8724, Adjusted R-squared: 0.8195
F-statistic: 16.49 on 17 and 41 DF, p-value: 3.466e-13
> 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,] 0.1770943 0.35418863 0.82290569
[2,] 0.3086500 0.61729991 0.69135005
[3,] 0.7472367 0.50552663 0.25276332
[4,] 0.8625327 0.27493468 0.13746734
[5,] 0.9419413 0.11611746 0.05805873
[6,] 0.9594016 0.08119685 0.04059842
[7,] 0.9367462 0.12650767 0.06325383
[8,] 0.9172282 0.16554352 0.08277176
[9,] 0.8701416 0.25971685 0.12985843
[10,] 0.8321171 0.33576584 0.16788292
[11,] 0.8468924 0.30621528 0.15310764
[12,] 0.8805939 0.23881214 0.11940607
[13,] 0.8060421 0.38791578 0.19395789
[14,] 0.7900847 0.41983055 0.20991528
[15,] 0.7149954 0.57000927 0.28500463
[16,] 0.7371556 0.52568878 0.26284439
[17,] 0.7594150 0.48116991 0.24058495
[18,] 0.8115732 0.37685361 0.18842680
> postscript(file="/var/www/html/rcomp/tmp/13qvx1258645537.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20u0s1258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35qnl1258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42wli1258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5anrd1258645537.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.29537433 0.41933125 0.30880937 -1.09406917 0.10045769 -0.64836321
7 8 9 10 11 12
-0.39350866 -0.13885245 -0.32844799 -0.48517734 0.94880681 -0.03698395
13 14 15 16 17 18
0.99837326 1.23613857 0.49770501 0.51398460 0.08121274 0.26290462
19 20 21 22 23 24
0.65644512 0.83975386 0.29371872 0.14209999 -0.62969886 -0.63157877
25 26 27 28 29 30
-0.74949495 -0.89393076 -0.21585924 0.28745345 0.44393846 0.18795000
31 32 33 34 35 36
-0.15177168 -0.72112645 -0.13379728 -0.27568731 -0.93566162 0.29820041
37 38 39 40 41 42
-0.60854677 -0.12140923 -0.17204377 -0.37062996 -0.57693245 -0.55338983
43 44 45 46 47 48
-0.31077478 -0.04167762 -0.01436721 0.96097503 0.94831949 0.37036232
49 50 51 52 53 54
0.65504279 -0.64012983 -0.41861138 0.66326108 -0.04867644 0.75089841
55 56 57 58 59
0.19960999 0.06190267 0.18289376 -0.34221038 -0.33176582
> postscript(file="/var/www/html/rcomp/tmp/6ce831258645537.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.29537433 NA
1 0.41933125 -0.29537433
2 0.30880937 0.41933125
3 -1.09406917 0.30880937
4 0.10045769 -1.09406917
5 -0.64836321 0.10045769
6 -0.39350866 -0.64836321
7 -0.13885245 -0.39350866
8 -0.32844799 -0.13885245
9 -0.48517734 -0.32844799
10 0.94880681 -0.48517734
11 -0.03698395 0.94880681
12 0.99837326 -0.03698395
13 1.23613857 0.99837326
14 0.49770501 1.23613857
15 0.51398460 0.49770501
16 0.08121274 0.51398460
17 0.26290462 0.08121274
18 0.65644512 0.26290462
19 0.83975386 0.65644512
20 0.29371872 0.83975386
21 0.14209999 0.29371872
22 -0.62969886 0.14209999
23 -0.63157877 -0.62969886
24 -0.74949495 -0.63157877
25 -0.89393076 -0.74949495
26 -0.21585924 -0.89393076
27 0.28745345 -0.21585924
28 0.44393846 0.28745345
29 0.18795000 0.44393846
30 -0.15177168 0.18795000
31 -0.72112645 -0.15177168
32 -0.13379728 -0.72112645
33 -0.27568731 -0.13379728
34 -0.93566162 -0.27568731
35 0.29820041 -0.93566162
36 -0.60854677 0.29820041
37 -0.12140923 -0.60854677
38 -0.17204377 -0.12140923
39 -0.37062996 -0.17204377
40 -0.57693245 -0.37062996
41 -0.55338983 -0.57693245
42 -0.31077478 -0.55338983
43 -0.04167762 -0.31077478
44 -0.01436721 -0.04167762
45 0.96097503 -0.01436721
46 0.94831949 0.96097503
47 0.37036232 0.94831949
48 0.65504279 0.37036232
49 -0.64012983 0.65504279
50 -0.41861138 -0.64012983
51 0.66326108 -0.41861138
52 -0.04867644 0.66326108
53 0.75089841 -0.04867644
54 0.19960999 0.75089841
55 0.06190267 0.19960999
56 0.18289376 0.06190267
57 -0.34221038 0.18289376
58 -0.33176582 -0.34221038
59 NA -0.33176582
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.41933125 -0.29537433
[2,] 0.30880937 0.41933125
[3,] -1.09406917 0.30880937
[4,] 0.10045769 -1.09406917
[5,] -0.64836321 0.10045769
[6,] -0.39350866 -0.64836321
[7,] -0.13885245 -0.39350866
[8,] -0.32844799 -0.13885245
[9,] -0.48517734 -0.32844799
[10,] 0.94880681 -0.48517734
[11,] -0.03698395 0.94880681
[12,] 0.99837326 -0.03698395
[13,] 1.23613857 0.99837326
[14,] 0.49770501 1.23613857
[15,] 0.51398460 0.49770501
[16,] 0.08121274 0.51398460
[17,] 0.26290462 0.08121274
[18,] 0.65644512 0.26290462
[19,] 0.83975386 0.65644512
[20,] 0.29371872 0.83975386
[21,] 0.14209999 0.29371872
[22,] -0.62969886 0.14209999
[23,] -0.63157877 -0.62969886
[24,] -0.74949495 -0.63157877
[25,] -0.89393076 -0.74949495
[26,] -0.21585924 -0.89393076
[27,] 0.28745345 -0.21585924
[28,] 0.44393846 0.28745345
[29,] 0.18795000 0.44393846
[30,] -0.15177168 0.18795000
[31,] -0.72112645 -0.15177168
[32,] -0.13379728 -0.72112645
[33,] -0.27568731 -0.13379728
[34,] -0.93566162 -0.27568731
[35,] 0.29820041 -0.93566162
[36,] -0.60854677 0.29820041
[37,] -0.12140923 -0.60854677
[38,] -0.17204377 -0.12140923
[39,] -0.37062996 -0.17204377
[40,] -0.57693245 -0.37062996
[41,] -0.55338983 -0.57693245
[42,] -0.31077478 -0.55338983
[43,] -0.04167762 -0.31077478
[44,] -0.01436721 -0.04167762
[45,] 0.96097503 -0.01436721
[46,] 0.94831949 0.96097503
[47,] 0.37036232 0.94831949
[48,] 0.65504279 0.37036232
[49,] -0.64012983 0.65504279
[50,] -0.41861138 -0.64012983
[51,] 0.66326108 -0.41861138
[52,] -0.04867644 0.66326108
[53,] 0.75089841 -0.04867644
[54,] 0.19960999 0.75089841
[55,] 0.06190267 0.19960999
[56,] 0.18289376 0.06190267
[57,] -0.34221038 0.18289376
[58,] -0.33176582 -0.34221038
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.41933125 -0.29537433
2 0.30880937 0.41933125
3 -1.09406917 0.30880937
4 0.10045769 -1.09406917
5 -0.64836321 0.10045769
6 -0.39350866 -0.64836321
7 -0.13885245 -0.39350866
8 -0.32844799 -0.13885245
9 -0.48517734 -0.32844799
10 0.94880681 -0.48517734
11 -0.03698395 0.94880681
12 0.99837326 -0.03698395
13 1.23613857 0.99837326
14 0.49770501 1.23613857
15 0.51398460 0.49770501
16 0.08121274 0.51398460
17 0.26290462 0.08121274
18 0.65644512 0.26290462
19 0.83975386 0.65644512
20 0.29371872 0.83975386
21 0.14209999 0.29371872
22 -0.62969886 0.14209999
23 -0.63157877 -0.62969886
24 -0.74949495 -0.63157877
25 -0.89393076 -0.74949495
26 -0.21585924 -0.89393076
27 0.28745345 -0.21585924
28 0.44393846 0.28745345
29 0.18795000 0.44393846
30 -0.15177168 0.18795000
31 -0.72112645 -0.15177168
32 -0.13379728 -0.72112645
33 -0.27568731 -0.13379728
34 -0.93566162 -0.27568731
35 0.29820041 -0.93566162
36 -0.60854677 0.29820041
37 -0.12140923 -0.60854677
38 -0.17204377 -0.12140923
39 -0.37062996 -0.17204377
40 -0.57693245 -0.37062996
41 -0.55338983 -0.57693245
42 -0.31077478 -0.55338983
43 -0.04167762 -0.31077478
44 -0.01436721 -0.04167762
45 0.96097503 -0.01436721
46 0.94831949 0.96097503
47 0.37036232 0.94831949
48 0.65504279 0.37036232
49 -0.64012983 0.65504279
50 -0.41861138 -0.64012983
51 0.66326108 -0.41861138
52 -0.04867644 0.66326108
53 0.75089841 -0.04867644
54 0.19960999 0.75089841
55 0.06190267 0.19960999
56 0.18289376 0.06190267
57 -0.34221038 0.18289376
58 -0.33176582 -0.34221038
> 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/78utm1258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8c8wm1258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9d9l51258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10er971258645537.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mbba1258645537.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/12jnf31258645537.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/138e5s1258645537.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/14lvwe1258645537.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/15bvmx1258645537.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/166iw41258645537.tab")
+ }
>
> system("convert tmp/13qvx1258645537.ps tmp/13qvx1258645537.png")
> system("convert tmp/20u0s1258645537.ps tmp/20u0s1258645537.png")
> system("convert tmp/35qnl1258645537.ps tmp/35qnl1258645537.png")
> system("convert tmp/42wli1258645537.ps tmp/42wli1258645537.png")
> system("convert tmp/5anrd1258645537.ps tmp/5anrd1258645537.png")
> system("convert tmp/6ce831258645537.ps tmp/6ce831258645537.png")
> system("convert tmp/78utm1258645537.ps tmp/78utm1258645537.png")
> system("convert tmp/8c8wm1258645537.ps tmp/8c8wm1258645537.png")
> system("convert tmp/9d9l51258645537.ps tmp/9d9l51258645537.png")
> system("convert tmp/10er971258645537.ps tmp/10er971258645537.png")
>
>
> proc.time()
user system elapsed
2.374 1.557 2.783