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
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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(-0.7
+ ,-0.4
+ ,-2.9
+ ,-0.8
+ ,1
+ ,1.4
+ ,-0.7
+ ,-0.3
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,1
+ ,1.5
+ ,1.4
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,3
+ ,2.6
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,3.2
+ ,2.8
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,3.1
+ ,2.6
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,3.9
+ ,3.4
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,1
+ ,1.7
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,1.3
+ ,1.2
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,0.8
+ ,0
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,1.2
+ ,0
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,2.9
+ ,1.6
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,2.5
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,4.5
+ ,3.2
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,4.5
+ ,3.4
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,3.3
+ ,2.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,2
+ ,1.9
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,1.5
+ ,1.7
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,1
+ ,1.9
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,2.1
+ ,3.3
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,3
+ ,3.8
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,4
+ ,4.4
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,5.1
+ ,4.5
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,4.5
+ ,3.5
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,4.2
+ ,3
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,3.3
+ ,2.8
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,2.7
+ ,2.9
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,1.8
+ ,2.6
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,1.4
+ ,2.1
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,0.5
+ ,1.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,-0.4
+ ,1.1
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,0.8
+ ,1.5
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,0.7
+ ,1.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.9
+ ,2.3
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,2
+ ,2.3
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,1.1
+ ,1.9
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,0.9
+ ,2
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.4
+ ,1.6
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,1.2
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,2.1
+ ,1.9
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2.8
+ ,2.1
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,3.9
+ ,2.4
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,3.5
+ ,2.9
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,2
+ ,2.5
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,2
+ ,2.3
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,1.5
+ ,2.5
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,2.5
+ ,2.6
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.1
+ ,2.4
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2.7
+ ,2.5
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2.8
+ ,2.1
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2.5
+ ,2.2
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,3
+ ,2.7
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,3.2
+ ,3
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,2.8
+ ,3.2
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.4
+ ,3
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2
+ ,2.7
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,1.8
+ ,2.5
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,1.1
+ ,1.6
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,-1.5
+ ,0.1
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,-3.7
+ ,-1.9
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2)
+ ,dim=c(6
+ ,60)
+ ,dimnames=list(c('bbp'
+ ,'dnst'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4')
+ ,1:60))
> y <- array(NA,dim=c(6,60),dimnames=list(c('bbp','dnst','y1','y2','y3','y4'),1:60))
> 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 -0.7 -0.4 -2.9 -0.8 1.0 1.4 1 0 0 0 0 0 0 0 0 0 0 1
2 -0.7 -0.3 -0.7 -2.9 -0.8 1.0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.5 1.4 -0.7 -0.7 -2.9 -0.8 0 0 1 0 0 0 0 0 0 0 0 3
4 3.0 2.6 1.5 -0.7 -0.7 -2.9 0 0 0 1 0 0 0 0 0 0 0 4
5 3.2 2.8 3.0 1.5 -0.7 -0.7 0 0 0 0 1 0 0 0 0 0 0 5
6 3.1 2.6 3.2 3.0 1.5 -0.7 0 0 0 0 0 1 0 0 0 0 0 6
7 3.9 3.4 3.1 3.2 3.0 1.5 0 0 0 0 0 0 1 0 0 0 0 7
8 1.0 1.7 3.9 3.1 3.2 3.0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.3 1.2 1.0 3.9 3.1 3.2 0 0 0 0 0 0 0 0 1 0 0 9
10 0.8 0.0 1.3 1.0 3.9 3.1 0 0 0 0 0 0 0 0 0 1 0 10
11 1.2 0.0 0.8 1.3 1.0 3.9 0 0 0 0 0 0 0 0 0 0 1 11
12 2.9 1.6 1.2 0.8 1.3 1.0 0 0 0 0 0 0 0 0 0 0 0 12
13 3.9 2.5 2.9 1.2 0.8 1.3 1 0 0 0 0 0 0 0 0 0 0 13
14 4.5 3.2 3.9 2.9 1.2 0.8 0 1 0 0 0 0 0 0 0 0 0 14
15 4.5 3.4 4.5 3.9 2.9 1.2 0 0 1 0 0 0 0 0 0 0 0 15
16 3.3 2.3 4.5 4.5 3.9 2.9 0 0 0 1 0 0 0 0 0 0 0 16
17 2.0 1.9 3.3 4.5 4.5 3.9 0 0 0 0 1 0 0 0 0 0 0 17
18 1.5 1.7 2.0 3.3 4.5 4.5 0 0 0 0 0 1 0 0 0 0 0 18
19 1.0 1.9 1.5 2.0 3.3 4.5 0 0 0 0 0 0 1 0 0 0 0 19
20 2.1 3.3 1.0 1.5 2.0 3.3 0 0 0 0 0 0 0 1 0 0 0 20
21 3.0 3.8 2.1 1.0 1.5 2.0 0 0 0 0 0 0 0 0 1 0 0 21
22 4.0 4.4 3.0 2.1 1.0 1.5 0 0 0 0 0 0 0 0 0 1 0 22
23 5.1 4.5 4.0 3.0 2.1 1.0 0 0 0 0 0 0 0 0 0 0 1 23
24 4.5 3.5 5.1 4.0 3.0 2.1 0 0 0 0 0 0 0 0 0 0 0 24
25 4.2 3.0 4.5 5.1 4.0 3.0 1 0 0 0 0 0 0 0 0 0 0 25
26 3.3 2.8 4.2 4.5 5.1 4.0 0 1 0 0 0 0 0 0 0 0 0 26
27 2.7 2.9 3.3 4.2 4.5 5.1 0 0 1 0 0 0 0 0 0 0 0 27
28 1.8 2.6 2.7 3.3 4.2 4.5 0 0 0 1 0 0 0 0 0 0 0 28
29 1.4 2.1 1.8 2.7 3.3 4.2 0 0 0 0 1 0 0 0 0 0 0 29
30 0.5 1.5 1.4 1.8 2.7 3.3 0 0 0 0 0 1 0 0 0 0 0 30
31 -0.4 1.1 0.5 1.4 1.8 2.7 0 0 0 0 0 0 1 0 0 0 0 31
32 0.8 1.5 -0.4 0.5 1.4 1.8 0 0 0 0 0 0 0 1 0 0 0 32
33 0.7 1.7 0.8 -0.4 0.5 1.4 0 0 0 0 0 0 0 0 1 0 0 33
34 1.9 2.3 0.7 0.8 -0.4 0.5 0 0 0 0 0 0 0 0 0 1 0 34
35 2.0 2.3 1.9 0.7 0.8 -0.4 0 0 0 0 0 0 0 0 0 0 1 35
36 1.1 1.9 2.0 1.9 0.7 0.8 0 0 0 0 0 0 0 0 0 0 0 36
37 0.9 2.0 1.1 2.0 1.9 0.7 1 0 0 0 0 0 0 0 0 0 0 37
38 0.4 1.6 0.9 1.1 2.0 1.9 0 1 0 0 0 0 0 0 0 0 0 38
39 0.7 1.2 0.4 0.9 1.1 2.0 0 0 1 0 0 0 0 0 0 0 0 39
40 2.1 1.9 0.7 0.4 0.9 1.1 0 0 0 1 0 0 0 0 0 0 0 40
41 2.8 2.1 2.1 0.7 0.4 0.9 0 0 0 0 1 0 0 0 0 0 0 41
42 3.9 2.4 2.8 2.1 0.7 0.4 0 0 0 0 0 1 0 0 0 0 0 42
43 3.5 2.9 3.9 2.8 2.1 0.7 0 0 0 0 0 0 1 0 0 0 0 43
44 2.0 2.5 3.5 3.9 2.8 2.1 0 0 0 0 0 0 0 1 0 0 0 44
45 2.0 2.3 2.0 3.5 3.9 2.8 0 0 0 0 0 0 0 0 1 0 0 45
46 1.5 2.5 2.0 2.0 3.5 3.9 0 0 0 0 0 0 0 0 0 1 0 46
47 2.5 2.6 1.5 2.0 2.0 3.5 0 0 0 0 0 0 0 0 0 0 1 47
48 3.1 2.4 2.5 1.5 2.0 2.0 0 0 0 0 0 0 0 0 0 0 0 48
49 2.7 2.5 3.1 2.5 1.5 2.0 1 0 0 0 0 0 0 0 0 0 0 49
50 2.8 2.1 2.7 3.1 2.5 1.5 0 1 0 0 0 0 0 0 0 0 0 50
51 2.5 2.2 2.8 2.7 3.1 2.5 0 0 1 0 0 0 0 0 0 0 0 51
52 3.0 2.7 2.5 2.8 2.7 3.1 0 0 0 1 0 0 0 0 0 0 0 52
53 3.2 3.0 3.0 2.5 2.8 2.7 0 0 0 0 1 0 0 0 0 0 0 53
54 2.8 3.2 3.2 3.0 2.5 2.8 0 0 0 0 0 1 0 0 0 0 0 54
55 2.4 3.0 2.8 3.2 3.0 2.5 0 0 0 0 0 0 1 0 0 0 0 55
56 2.0 2.7 2.4 2.8 3.2 3.0 0 0 0 0 0 0 0 1 0 0 0 56
57 1.8 2.5 2.0 2.4 2.8 3.2 0 0 0 0 0 0 0 0 1 0 0 57
58 1.1 1.6 1.8 2.0 2.4 2.8 0 0 0 0 0 0 0 0 0 1 0 58
59 -1.5 0.1 1.1 1.8 2.0 2.4 0 0 0 0 0 0 0 0 0 0 1 59
60 -3.7 -1.9 -1.5 1.1 1.8 2.0 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dnst y1 y2 y3 y4
0.2835835 0.8715753 0.3880415 0.0001697 -0.0877779 -0.0561761
M1 M2 M3 M4 M5 M6
0.1634669 -0.0834876 -0.0058048 -0.0048925 -0.1638177 -0.1563103
M7 M8 M9 M10 M11 t
-0.4766834 -0.7457384 -0.3439880 -0.2116913 -0.0699299 -0.0136048
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1149021 -0.3650099 -0.0001292 0.4269611 1.2493890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2835835 0.3711194 0.764 0.44906
dnst 0.8715753 0.1295720 6.727 3.58e-08 ***
y1 0.3880415 0.1355886 2.862 0.00654 **
y2 0.0001697 0.1446072 0.001 0.99907
y3 -0.0877779 0.1483477 -0.592 0.55722
y4 -0.0561761 0.1084665 -0.518 0.60724
M1 0.1634669 0.4308141 0.379 0.70627
M2 -0.0834876 0.4287717 -0.195 0.84656
M3 -0.0058048 0.4354808 -0.013 0.98943
M4 -0.0048925 0.4348769 -0.011 0.99108
M5 -0.1638177 0.4328029 -0.379 0.70696
M6 -0.1563103 0.4291491 -0.364 0.71751
M7 -0.4766834 0.4384549 -1.087 0.28315
M8 -0.7457384 0.4404221 -1.693 0.09782 .
M9 -0.3439880 0.4489332 -0.766 0.44782
M10 -0.2116913 0.4392684 -0.482 0.63237
M11 -0.0699299 0.4285180 -0.163 0.87115
t -0.0136048 0.0052322 -2.600 0.01281 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6634 on 42 degrees of freedom
Multiple R-squared: 0.8762, Adjusted R-squared: 0.8261
F-statistic: 17.49 on 17 and 42 DF, p-value: 7.833e-14
> 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,] 0.14910220 0.2982044 0.8508978
[2,] 0.59739330 0.8052134 0.4026067
[3,] 0.55325714 0.8934857 0.4467429
[4,] 0.52068366 0.9586327 0.4793163
[5,] 0.48256222 0.9651244 0.5174378
[6,] 0.40312785 0.8062557 0.5968722
[7,] 0.31777369 0.6355474 0.6822263
[8,] 0.25682246 0.5136449 0.7431775
[9,] 0.18312628 0.3662526 0.8168737
[10,] 0.11415255 0.2283051 0.8858474
[11,] 0.06913931 0.1382786 0.9308607
[12,] 0.18812585 0.3762517 0.8118741
[13,] 0.12488745 0.2497749 0.8751125
[14,] 0.07377017 0.1475403 0.9262298
[15,] 0.07368597 0.1473719 0.9263140
[16,] 0.55109260 0.8978148 0.4489074
[17,] 0.79930322 0.4013936 0.2006968
[18,] 0.67262291 0.6547542 0.3273771
[19,] 0.54528375 0.9094325 0.4547163
> postscript(file="/var/www/html/rcomp/tmp/1rdmn1259259683.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/224ey1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3iusc1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/458jx1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u19m1259259683.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 = 60
Frequency = 1
1 2 3 4 5 6
0.507065031 -0.353338826 0.015081069 -0.296666482 -0.557299631 -0.361638628
7 8 9 10 11 12
0.369103636 -0.975154993 0.500128978 0.876011824 1.132209966 1.089655178
13 14 15 16 17 18
0.468600709 0.337750436 0.038055660 -0.007343420 -0.211690669 0.007084956
19 20 21 22 23 24
-0.244344347 -0.069307122 -0.536919160 -0.599957295 -0.034997987 -0.105969223
25 26 27 28 29 30
0.250930946 0.055051415 -0.337768951 -0.790665273 -0.328861478 -0.647674914
31 32 33 34 35 36
-0.628467454 0.769282534 -0.460145832 0.007258921 -0.431755505 -1.019824707
37 38 39 40 41 42
-1.007908145 -0.744768551 -0.039544325 0.578603777 0.678385508 1.249388955
43 44 45 46 47 48
0.460356755 -0.113232297 0.390946209 -0.375123671 0.449445583 0.695214696
49 50 51 52 53 54
-0.218688540 0.705305525 0.324176546 0.516071398 0.419466270 -0.247160368
55 56 57 58 59 60
0.043351410 0.388411878 0.105989805 0.091810221 -1.114902057 -0.659075943
> postscript(file="/var/www/html/rcomp/tmp/6opwt1259259683.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.507065031 NA
1 -0.353338826 0.507065031
2 0.015081069 -0.353338826
3 -0.296666482 0.015081069
4 -0.557299631 -0.296666482
5 -0.361638628 -0.557299631
6 0.369103636 -0.361638628
7 -0.975154993 0.369103636
8 0.500128978 -0.975154993
9 0.876011824 0.500128978
10 1.132209966 0.876011824
11 1.089655178 1.132209966
12 0.468600709 1.089655178
13 0.337750436 0.468600709
14 0.038055660 0.337750436
15 -0.007343420 0.038055660
16 -0.211690669 -0.007343420
17 0.007084956 -0.211690669
18 -0.244344347 0.007084956
19 -0.069307122 -0.244344347
20 -0.536919160 -0.069307122
21 -0.599957295 -0.536919160
22 -0.034997987 -0.599957295
23 -0.105969223 -0.034997987
24 0.250930946 -0.105969223
25 0.055051415 0.250930946
26 -0.337768951 0.055051415
27 -0.790665273 -0.337768951
28 -0.328861478 -0.790665273
29 -0.647674914 -0.328861478
30 -0.628467454 -0.647674914
31 0.769282534 -0.628467454
32 -0.460145832 0.769282534
33 0.007258921 -0.460145832
34 -0.431755505 0.007258921
35 -1.019824707 -0.431755505
36 -1.007908145 -1.019824707
37 -0.744768551 -1.007908145
38 -0.039544325 -0.744768551
39 0.578603777 -0.039544325
40 0.678385508 0.578603777
41 1.249388955 0.678385508
42 0.460356755 1.249388955
43 -0.113232297 0.460356755
44 0.390946209 -0.113232297
45 -0.375123671 0.390946209
46 0.449445583 -0.375123671
47 0.695214696 0.449445583
48 -0.218688540 0.695214696
49 0.705305525 -0.218688540
50 0.324176546 0.705305525
51 0.516071398 0.324176546
52 0.419466270 0.516071398
53 -0.247160368 0.419466270
54 0.043351410 -0.247160368
55 0.388411878 0.043351410
56 0.105989805 0.388411878
57 0.091810221 0.105989805
58 -1.114902057 0.091810221
59 -0.659075943 -1.114902057
60 NA -0.659075943
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.353338826 0.507065031
[2,] 0.015081069 -0.353338826
[3,] -0.296666482 0.015081069
[4,] -0.557299631 -0.296666482
[5,] -0.361638628 -0.557299631
[6,] 0.369103636 -0.361638628
[7,] -0.975154993 0.369103636
[8,] 0.500128978 -0.975154993
[9,] 0.876011824 0.500128978
[10,] 1.132209966 0.876011824
[11,] 1.089655178 1.132209966
[12,] 0.468600709 1.089655178
[13,] 0.337750436 0.468600709
[14,] 0.038055660 0.337750436
[15,] -0.007343420 0.038055660
[16,] -0.211690669 -0.007343420
[17,] 0.007084956 -0.211690669
[18,] -0.244344347 0.007084956
[19,] -0.069307122 -0.244344347
[20,] -0.536919160 -0.069307122
[21,] -0.599957295 -0.536919160
[22,] -0.034997987 -0.599957295
[23,] -0.105969223 -0.034997987
[24,] 0.250930946 -0.105969223
[25,] 0.055051415 0.250930946
[26,] -0.337768951 0.055051415
[27,] -0.790665273 -0.337768951
[28,] -0.328861478 -0.790665273
[29,] -0.647674914 -0.328861478
[30,] -0.628467454 -0.647674914
[31,] 0.769282534 -0.628467454
[32,] -0.460145832 0.769282534
[33,] 0.007258921 -0.460145832
[34,] -0.431755505 0.007258921
[35,] -1.019824707 -0.431755505
[36,] -1.007908145 -1.019824707
[37,] -0.744768551 -1.007908145
[38,] -0.039544325 -0.744768551
[39,] 0.578603777 -0.039544325
[40,] 0.678385508 0.578603777
[41,] 1.249388955 0.678385508
[42,] 0.460356755 1.249388955
[43,] -0.113232297 0.460356755
[44,] 0.390946209 -0.113232297
[45,] -0.375123671 0.390946209
[46,] 0.449445583 -0.375123671
[47,] 0.695214696 0.449445583
[48,] -0.218688540 0.695214696
[49,] 0.705305525 -0.218688540
[50,] 0.324176546 0.705305525
[51,] 0.516071398 0.324176546
[52,] 0.419466270 0.516071398
[53,] -0.247160368 0.419466270
[54,] 0.043351410 -0.247160368
[55,] 0.388411878 0.043351410
[56,] 0.105989805 0.388411878
[57,] 0.091810221 0.105989805
[58,] -1.114902057 0.091810221
[59,] -0.659075943 -1.114902057
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.353338826 0.507065031
2 0.015081069 -0.353338826
3 -0.296666482 0.015081069
4 -0.557299631 -0.296666482
5 -0.361638628 -0.557299631
6 0.369103636 -0.361638628
7 -0.975154993 0.369103636
8 0.500128978 -0.975154993
9 0.876011824 0.500128978
10 1.132209966 0.876011824
11 1.089655178 1.132209966
12 0.468600709 1.089655178
13 0.337750436 0.468600709
14 0.038055660 0.337750436
15 -0.007343420 0.038055660
16 -0.211690669 -0.007343420
17 0.007084956 -0.211690669
18 -0.244344347 0.007084956
19 -0.069307122 -0.244344347
20 -0.536919160 -0.069307122
21 -0.599957295 -0.536919160
22 -0.034997987 -0.599957295
23 -0.105969223 -0.034997987
24 0.250930946 -0.105969223
25 0.055051415 0.250930946
26 -0.337768951 0.055051415
27 -0.790665273 -0.337768951
28 -0.328861478 -0.790665273
29 -0.647674914 -0.328861478
30 -0.628467454 -0.647674914
31 0.769282534 -0.628467454
32 -0.460145832 0.769282534
33 0.007258921 -0.460145832
34 -0.431755505 0.007258921
35 -1.019824707 -0.431755505
36 -1.007908145 -1.019824707
37 -0.744768551 -1.007908145
38 -0.039544325 -0.744768551
39 0.578603777 -0.039544325
40 0.678385508 0.578603777
41 1.249388955 0.678385508
42 0.460356755 1.249388955
43 -0.113232297 0.460356755
44 0.390946209 -0.113232297
45 -0.375123671 0.390946209
46 0.449445583 -0.375123671
47 0.695214696 0.449445583
48 -0.218688540 0.695214696
49 0.705305525 -0.218688540
50 0.324176546 0.705305525
51 0.516071398 0.324176546
52 0.419466270 0.516071398
53 -0.247160368 0.419466270
54 0.043351410 -0.247160368
55 0.388411878 0.043351410
56 0.105989805 0.388411878
57 0.091810221 0.105989805
58 -1.114902057 0.091810221
59 -0.659075943 -1.114902057
> 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/778tj1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81hk11259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bwfg1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108rhj1259259683.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mpfl1259259683.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/12ndwn1259259683.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/13xv0k1259259683.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/14tqlk1259259683.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/15kc571259259683.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/16xpdk1259259683.tab")
+ }
>
> system("convert tmp/1rdmn1259259683.ps tmp/1rdmn1259259683.png")
> system("convert tmp/224ey1259259683.ps tmp/224ey1259259683.png")
> system("convert tmp/3iusc1259259683.ps tmp/3iusc1259259683.png")
> system("convert tmp/458jx1259259683.ps tmp/458jx1259259683.png")
> system("convert tmp/5u19m1259259683.ps tmp/5u19m1259259683.png")
> system("convert tmp/6opwt1259259683.ps tmp/6opwt1259259683.png")
> system("convert tmp/778tj1259259683.ps tmp/778tj1259259683.png")
> system("convert tmp/81hk11259259683.ps tmp/81hk11259259683.png")
> system("convert tmp/9bwfg1259259683.ps tmp/9bwfg1259259683.png")
> system("convert tmp/108rhj1259259683.ps tmp/108rhj1259259683.png")
>
>
> proc.time()
user system elapsed
2.418 1.568 3.018