R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(6.1,0,6.2,6.3,6.3,0,6.1,6.2,6.5,0,6.3,6.1,6.6,0,6.5,6.3,6.5,0,6.6,6.5,6.2,0,6.5,6.6,6.2,0,6.2,6.5,5.9,0,6.2,6.2,6.1,0,5.9,6.2,6.1,0,6.1,5.9,6.1,0,6.1,6.1,6.1,0,6.1,6.1,6.1,0,6.1,6.1,6.4,0,6.1,6.1,6.7,0,6.4,6.1,6.9,0,6.7,6.4,7,0,6.9,6.7,7,0,7,6.9,6.8,0,7,7,6.4,0,6.8,7,5.9,0,6.4,6.8,5.5,0,5.9,6.4,5.5,0,5.5,5.9,5.6,0,5.5,5.5,5.8,0,5.6,5.5,5.9,0,5.8,5.6,6.1,0,5.9,5.8,6.1,0,6.1,5.9,6,0,6.1,6.1,6,0,6,6.1,5.9,0,6,6,5.5,0,5.9,6,5.6,0,5.5,5.9,5.4,0,5.6,5.5,5.2,0,5.4,5.6,5.2,0,5.2,5.4,5.2,0,5.2,5.2,5.5,0,5.2,5.2,5.8,1,5.5,5.2,5.8,1,5.8,5.5,5.5,1,5.8,5.8,5.3,1,5.5,5.8,5.1,1,5.3,5.5,5.2,1,5.1,5.3,5.8,1,5.2,5.1,5.8,1,5.8,5.2,5.5,1,5.8,5.8,5,1,5.5,5.8,4.9,1,5,5.5,5.3,1,4.9,5,6.1,1,5.3,4.9,6.5,1,6.1,5.3,6.8,1,6.5,6.1,6.6,1,6.8,6.5,6.4,1,6.6,6.8,6.4,1,6.4,6.6),dim=c(4,56),dimnames=list(c('y','x','y-1','y-2'),1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('y','x','y-1','y-2'),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 x y-1 y-2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.1 0 6.2 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.3 0 6.1 6.2 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 0 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.6 0 6.5 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.5 0 6.6 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 6.2 0 6.5 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 6.2 0 6.2 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 5.9 0 6.2 6.2 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 0 5.9 6.2 0 0 0 0 0 0 0 0 1 0 0 9
10 6.1 0 6.1 5.9 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 0 6.1 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 6.1 0 6.1 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 6.1 0 6.1 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 6.4 0 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 14
15 6.7 0 6.4 6.1 0 0 1 0 0 0 0 0 0 0 0 15
16 6.9 0 6.7 6.4 0 0 0 1 0 0 0 0 0 0 0 16
17 7.0 0 6.9 6.7 0 0 0 0 1 0 0 0 0 0 0 17
18 7.0 0 7.0 6.9 0 0 0 0 0 1 0 0 0 0 0 18
19 6.8 0 7.0 7.0 0 0 0 0 0 0 1 0 0 0 0 19
20 6.4 0 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 20
21 5.9 0 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 21
22 5.5 0 5.9 6.4 0 0 0 0 0 0 0 0 0 1 0 22
23 5.5 0 5.5 5.9 0 0 0 0 0 0 0 0 0 0 1 23
24 5.6 0 5.5 5.5 0 0 0 0 0 0 0 0 0 0 0 24
25 5.8 0 5.6 5.5 1 0 0 0 0 0 0 0 0 0 0 25
26 5.9 0 5.8 5.6 0 1 0 0 0 0 0 0 0 0 0 26
27 6.1 0 5.9 5.8 0 0 1 0 0 0 0 0 0 0 0 27
28 6.1 0 6.1 5.9 0 0 0 1 0 0 0 0 0 0 0 28
29 6.0 0 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 29
30 6.0 0 6.0 6.1 0 0 0 0 0 1 0 0 0 0 0 30
31 5.9 0 6.0 6.0 0 0 0 0 0 0 1 0 0 0 0 31
32 5.5 0 5.9 6.0 0 0 0 0 0 0 0 1 0 0 0 32
33 5.6 0 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 33
34 5.4 0 5.6 5.5 0 0 0 0 0 0 0 0 0 1 0 34
35 5.2 0 5.4 5.6 0 0 0 0 0 0 0 0 0 0 1 35
36 5.2 0 5.2 5.4 0 0 0 0 0 0 0 0 0 0 0 36
37 5.2 0 5.2 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.5 0 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0 38
39 5.8 1 5.5 5.2 0 0 1 0 0 0 0 0 0 0 0 39
40 5.8 1 5.8 5.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.5 1 5.8 5.8 0 0 0 0 1 0 0 0 0 0 0 41
42 5.3 1 5.5 5.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.1 1 5.3 5.5 0 0 0 0 0 0 1 0 0 0 0 43
44 5.2 1 5.1 5.3 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 1 5.2 5.1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.8 1 5.8 5.2 0 0 0 0 0 0 0 0 0 1 0 46
47 5.5 1 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 5.0 1 5.5 5.8 0 0 0 0 0 0 0 0 0 0 0 48
49 4.9 1 5.0 5.5 1 0 0 0 0 0 0 0 0 0 0 49
50 5.3 1 4.9 5.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.1 1 5.3 4.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.5 1 6.1 5.3 0 0 0 1 0 0 0 0 0 0 0 52
53 6.8 1 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.6 1 6.8 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.4 1 6.6 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.4 1 6.4 6.6 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 `y-1` `y-2` M1 M2
0.989098 0.004876 1.400737 -0.574367 0.085276 0.289524
M3 M4 M5 M6 M7 M8
0.286041 0.072796 0.065149 0.035260 0.081560 -0.001064
M9 M10 M11 t
0.297128 -0.134853 0.009379 -0.001684
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.40987 -0.09561 -0.00214 0.10644 0.32752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.989098 0.461344 2.144 0.038169 *
x 0.004876 0.092619 0.053 0.958276
`y-1` 1.400737 0.134845 10.388 6.37e-13 ***
`y-2` -0.574367 0.141296 -4.065 0.000219 ***
M1 0.085276 0.123746 0.689 0.494726
M2 0.289524 0.124883 2.318 0.025626 *
M3 0.286041 0.134802 2.122 0.040087 *
M4 0.072796 0.141842 0.513 0.610621
M5 0.065149 0.136653 0.477 0.636135
M6 0.035260 0.135379 0.260 0.795850
M7 0.081560 0.133362 0.612 0.544282
M8 -0.001064 0.130128 -0.008 0.993518
M9 0.297128 0.132039 2.250 0.029998 *
M10 -0.134853 0.133602 -1.009 0.318866
M11 0.009379 0.130278 0.072 0.942969
t -0.001684 0.002882 -0.584 0.562264
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1837 on 40 degrees of freedom
Multiple R-squared: 0.9148, Adjusted R-squared: 0.8828
F-statistic: 28.62 on 15 and 40 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,] 0.2019678 0.4039355 0.7980322
[2,] 0.1651741 0.3303482 0.8348259
[3,] 0.5922348 0.8155305 0.4077652
[4,] 0.4838013 0.9676026 0.5161987
[5,] 0.5121900 0.9756199 0.4878100
[6,] 0.5411769 0.9176462 0.4588231
[7,] 0.6164637 0.7670727 0.3835363
[8,] 0.7486111 0.5027778 0.2513889
[9,] 0.6522230 0.6955540 0.3477770
[10,] 0.6084036 0.7831929 0.3915964
[11,] 0.5180153 0.9639694 0.4819847
[12,] 0.6490288 0.7019425 0.3509712
[13,] 0.7081139 0.5837721 0.2918861
[14,] 0.6834008 0.6331984 0.3165992
[15,] 0.6699550 0.6600901 0.3300450
[16,] 0.6225176 0.7549647 0.3774824
[17,] 0.5620265 0.8759471 0.4379735
[18,] 0.6148444 0.7703113 0.3851556
[19,] 0.4418618 0.8837236 0.5581382
> postscript(file="/var/www/html/rcomp/tmp/13dns1259051467.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/217pj1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fs8s1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cdua1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54o1p1259051467.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
-0.038743984 0.041329708 -0.091087597 0.058568268 -0.057301035 -0.128216776
7 8 9 10 11 12
0.189951742 -0.198050207 0.125663310 0.106871249 0.079197448 0.090260439
13 14 15 16 17 18
0.006668665 0.104105402 -0.010948858 0.156070052 0.157563795 0.163937406
19 20 21 22 23 24
-0.023241678 -0.058786030 -0.409872295 0.094414752 0.224978559 0.106294600
25 26 27 28 29 30
0.082629133 -0.242644777 -0.062678177 -0.070459050 -0.046254660 0.125392861
31 32 33 34 35 36
-0.076659698 -0.252277742 0.054072730 -0.082082376 -0.087045530 0.089291371
37 38 39 40 41 42
-0.109173879 -0.011737141 -0.131667486 -0.164648575 -0.283007449 -0.031212543
43 44 45 46 47 48
-0.167991192 0.181590981 0.230136255 -0.119203626 -0.217130477 -0.285846409
49 50 51 52 53 54
0.058620065 0.108946808 0.296382118 0.020469304 0.228999349 -0.129900949
55 56
0.077940826 0.327522999
> postscript(file="/var/www/html/rcomp/tmp/6fwhs1259051467.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 -0.038743984 NA
1 0.041329708 -0.038743984
2 -0.091087597 0.041329708
3 0.058568268 -0.091087597
4 -0.057301035 0.058568268
5 -0.128216776 -0.057301035
6 0.189951742 -0.128216776
7 -0.198050207 0.189951742
8 0.125663310 -0.198050207
9 0.106871249 0.125663310
10 0.079197448 0.106871249
11 0.090260439 0.079197448
12 0.006668665 0.090260439
13 0.104105402 0.006668665
14 -0.010948858 0.104105402
15 0.156070052 -0.010948858
16 0.157563795 0.156070052
17 0.163937406 0.157563795
18 -0.023241678 0.163937406
19 -0.058786030 -0.023241678
20 -0.409872295 -0.058786030
21 0.094414752 -0.409872295
22 0.224978559 0.094414752
23 0.106294600 0.224978559
24 0.082629133 0.106294600
25 -0.242644777 0.082629133
26 -0.062678177 -0.242644777
27 -0.070459050 -0.062678177
28 -0.046254660 -0.070459050
29 0.125392861 -0.046254660
30 -0.076659698 0.125392861
31 -0.252277742 -0.076659698
32 0.054072730 -0.252277742
33 -0.082082376 0.054072730
34 -0.087045530 -0.082082376
35 0.089291371 -0.087045530
36 -0.109173879 0.089291371
37 -0.011737141 -0.109173879
38 -0.131667486 -0.011737141
39 -0.164648575 -0.131667486
40 -0.283007449 -0.164648575
41 -0.031212543 -0.283007449
42 -0.167991192 -0.031212543
43 0.181590981 -0.167991192
44 0.230136255 0.181590981
45 -0.119203626 0.230136255
46 -0.217130477 -0.119203626
47 -0.285846409 -0.217130477
48 0.058620065 -0.285846409
49 0.108946808 0.058620065
50 0.296382118 0.108946808
51 0.020469304 0.296382118
52 0.228999349 0.020469304
53 -0.129900949 0.228999349
54 0.077940826 -0.129900949
55 0.327522999 0.077940826
56 NA 0.327522999
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.041329708 -0.038743984
[2,] -0.091087597 0.041329708
[3,] 0.058568268 -0.091087597
[4,] -0.057301035 0.058568268
[5,] -0.128216776 -0.057301035
[6,] 0.189951742 -0.128216776
[7,] -0.198050207 0.189951742
[8,] 0.125663310 -0.198050207
[9,] 0.106871249 0.125663310
[10,] 0.079197448 0.106871249
[11,] 0.090260439 0.079197448
[12,] 0.006668665 0.090260439
[13,] 0.104105402 0.006668665
[14,] -0.010948858 0.104105402
[15,] 0.156070052 -0.010948858
[16,] 0.157563795 0.156070052
[17,] 0.163937406 0.157563795
[18,] -0.023241678 0.163937406
[19,] -0.058786030 -0.023241678
[20,] -0.409872295 -0.058786030
[21,] 0.094414752 -0.409872295
[22,] 0.224978559 0.094414752
[23,] 0.106294600 0.224978559
[24,] 0.082629133 0.106294600
[25,] -0.242644777 0.082629133
[26,] -0.062678177 -0.242644777
[27,] -0.070459050 -0.062678177
[28,] -0.046254660 -0.070459050
[29,] 0.125392861 -0.046254660
[30,] -0.076659698 0.125392861
[31,] -0.252277742 -0.076659698
[32,] 0.054072730 -0.252277742
[33,] -0.082082376 0.054072730
[34,] -0.087045530 -0.082082376
[35,] 0.089291371 -0.087045530
[36,] -0.109173879 0.089291371
[37,] -0.011737141 -0.109173879
[38,] -0.131667486 -0.011737141
[39,] -0.164648575 -0.131667486
[40,] -0.283007449 -0.164648575
[41,] -0.031212543 -0.283007449
[42,] -0.167991192 -0.031212543
[43,] 0.181590981 -0.167991192
[44,] 0.230136255 0.181590981
[45,] -0.119203626 0.230136255
[46,] -0.217130477 -0.119203626
[47,] -0.285846409 -0.217130477
[48,] 0.058620065 -0.285846409
[49,] 0.108946808 0.058620065
[50,] 0.296382118 0.108946808
[51,] 0.020469304 0.296382118
[52,] 0.228999349 0.020469304
[53,] -0.129900949 0.228999349
[54,] 0.077940826 -0.129900949
[55,] 0.327522999 0.077940826
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.041329708 -0.038743984
2 -0.091087597 0.041329708
3 0.058568268 -0.091087597
4 -0.057301035 0.058568268
5 -0.128216776 -0.057301035
6 0.189951742 -0.128216776
7 -0.198050207 0.189951742
8 0.125663310 -0.198050207
9 0.106871249 0.125663310
10 0.079197448 0.106871249
11 0.090260439 0.079197448
12 0.006668665 0.090260439
13 0.104105402 0.006668665
14 -0.010948858 0.104105402
15 0.156070052 -0.010948858
16 0.157563795 0.156070052
17 0.163937406 0.157563795
18 -0.023241678 0.163937406
19 -0.058786030 -0.023241678
20 -0.409872295 -0.058786030
21 0.094414752 -0.409872295
22 0.224978559 0.094414752
23 0.106294600 0.224978559
24 0.082629133 0.106294600
25 -0.242644777 0.082629133
26 -0.062678177 -0.242644777
27 -0.070459050 -0.062678177
28 -0.046254660 -0.070459050
29 0.125392861 -0.046254660
30 -0.076659698 0.125392861
31 -0.252277742 -0.076659698
32 0.054072730 -0.252277742
33 -0.082082376 0.054072730
34 -0.087045530 -0.082082376
35 0.089291371 -0.087045530
36 -0.109173879 0.089291371
37 -0.011737141 -0.109173879
38 -0.131667486 -0.011737141
39 -0.164648575 -0.131667486
40 -0.283007449 -0.164648575
41 -0.031212543 -0.283007449
42 -0.167991192 -0.031212543
43 0.181590981 -0.167991192
44 0.230136255 0.181590981
45 -0.119203626 0.230136255
46 -0.217130477 -0.119203626
47 -0.285846409 -0.217130477
48 0.058620065 -0.285846409
49 0.108946808 0.058620065
50 0.296382118 0.108946808
51 0.020469304 0.296382118
52 0.228999349 0.020469304
53 -0.129900949 0.228999349
54 0.077940826 -0.129900949
55 0.327522999 0.077940826
> 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/7ox1j1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gt5w1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vw9e1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108g8n1259051467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vd381259051467.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/12a7ba1259051467.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/13is1s1259051467.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/144be01259051468.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/1552s81259051468.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/16q0j41259051468.tab")
+ }
>
> system("convert tmp/13dns1259051467.ps tmp/13dns1259051467.png")
> system("convert tmp/217pj1259051467.ps tmp/217pj1259051467.png")
> system("convert tmp/3fs8s1259051467.ps tmp/3fs8s1259051467.png")
> system("convert tmp/4cdua1259051467.ps tmp/4cdua1259051467.png")
> system("convert tmp/54o1p1259051467.ps tmp/54o1p1259051467.png")
> system("convert tmp/6fwhs1259051467.ps tmp/6fwhs1259051467.png")
> system("convert tmp/7ox1j1259051467.ps tmp/7ox1j1259051467.png")
> system("convert tmp/8gt5w1259051467.ps tmp/8gt5w1259051467.png")
> system("convert tmp/9vw9e1259051467.ps tmp/9vw9e1259051467.png")
> system("convert tmp/108g8n1259051467.ps tmp/108g8n1259051467.png")
>
>
> proc.time()
user system elapsed
2.343 1.588 14.086