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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9.3,7.5,9.8,9.9,8.3,6.8,9.3,9.8,8,6.5,8.3,9.3,8.5,6.6,8,8.3,10.4,7.6,8.5,8,11.1,8,10.4,8.5,10.9,8.1,11.1,10.4,10,7.7,10.9,11.1,9.2,7.5,10,10.9,9.2,7.6,9.2,10,9.5,7.8,9.2,9.2,9.6,7.8,9.5,9.2,9.5,7.8,9.6,9.5,9.1,7.5,9.5,9.6,8.9,7.5,9.1,9.5,9,7.1,8.9,9.1,10.1,7.5,9,8.9,10.3,7.5,10.1,9,10.2,7.6,10.3,10.1,9.6,7.7,10.2,10.3,9.2,7.7,9.6,10.2,9.3,7.9,9.2,9.6,9.4,8.1,9.3,9.2,9.4,8.2,9.4,9.3,9.2,8.2,9.4,9.4,9,8.2,9.2,9.4,9,7.9,9,9.2,9,7.3,9,9,9.8,6.9,9,9,10,6.6,9.8,9,9.8,6.7,10,9.8,9.3,6.9,9.8,10,9,7,9.3,9.8,9,7.1,9,9.3,9.1,7.2,9,9,9.1,7.1,9.1,9,9.1,6.9,9.1,9.1,9.2,7,9.1,9.1,8.8,6.8,9.2,9.1,8.3,6.4,8.8,9.2,8.4,6.7,8.3,8.8,8.1,6.6,8.4,8.3,7.7,6.4,8.1,8.4,7.9,6.3,7.7,8.1,7.9,6.2,7.9,7.7,8,6.5,7.9,7.9,7.9,6.8,8,7.9,7.6,6.8,7.9,8,7.1,6.4,7.6,7.9,6.8,6.1,7.1,7.6,6.5,5.8,6.8,7.1,6.9,6.1,6.5,6.8,8.2,7.2,6.9,6.5,8.7,7.3,8.2,6.9,8.3,6.9,8.7,8.2,7.9,6.1,8.3,8.7,7.5,5.8,7.9,8.3,7.8,6.2,7.5,7.9),dim=c(4,58),dimnames=list(c('WLVrouw','WLMan','Yt-1','Yt-2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('WLVrouw','WLMan','Yt-1','Yt-2'),1:58))
> 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 = '2'
> #'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
WLMan WLVrouw Yt-1 Yt-2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.5 9.3 9.8 9.9 1 0 0 0 0 0 0 0 0 0 0 1
2 6.8 8.3 9.3 9.8 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 8.0 8.3 9.3 0 0 1 0 0 0 0 0 0 0 0 3
4 6.6 8.5 8.0 8.3 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 10.4 8.5 8.0 0 0 0 0 1 0 0 0 0 0 0 5
6 8.0 11.1 10.4 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.1 10.9 11.1 10.4 0 0 0 0 0 0 1 0 0 0 0 7
8 7.7 10.0 10.9 11.1 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 9.2 10.0 10.9 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 9.2 9.2 10.0 0 0 0 0 0 0 0 0 0 1 0 10
11 7.8 9.5 9.2 9.2 0 0 0 0 0 0 0 0 0 0 1 11
12 7.8 9.6 9.5 9.2 0 0 0 0 0 0 0 0 0 0 0 12
13 7.8 9.5 9.6 9.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.5 9.1 9.5 9.6 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 8.9 9.1 9.5 0 0 1 0 0 0 0 0 0 0 0 15
16 7.1 9.0 8.9 9.1 0 0 0 1 0 0 0 0 0 0 0 16
17 7.5 10.1 9.0 8.9 0 0 0 0 1 0 0 0 0 0 0 17
18 7.5 10.3 10.1 9.0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.6 10.2 10.3 10.1 0 0 0 0 0 0 1 0 0 0 0 19
20 7.7 9.6 10.2 10.3 0 0 0 0 0 0 0 1 0 0 0 20
21 7.7 9.2 9.6 10.2 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 9.3 9.2 9.6 0 0 0 0 0 0 0 0 0 1 0 22
23 8.1 9.4 9.3 9.2 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 9.4 9.4 9.3 0 0 0 0 0 0 0 0 0 0 0 24
25 8.2 9.2 9.4 9.4 1 0 0 0 0 0 0 0 0 0 0 25
26 8.2 9.0 9.2 9.4 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 9.0 9.0 9.2 0 0 1 0 0 0 0 0 0 0 0 27
28 7.3 9.0 9.0 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 6.9 9.8 9.0 9.0 0 0 0 0 1 0 0 0 0 0 0 29
30 6.6 10.0 9.8 9.0 0 0 0 0 0 1 0 0 0 0 0 30
31 6.7 9.8 10.0 9.8 0 0 0 0 0 0 1 0 0 0 0 31
32 6.9 9.3 9.8 10.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 9.0 9.3 9.8 0 0 0 0 0 0 0 0 1 0 0 33
34 7.1 9.0 9.0 9.3 0 0 0 0 0 0 0 0 0 1 0 34
35 7.2 9.1 9.0 9.0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.1 9.1 9.1 9.0 0 0 0 0 0 0 0 0 0 0 0 36
37 6.9 9.1 9.1 9.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 9.2 9.1 9.1 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 8.8 9.2 9.1 0 0 1 0 0 0 0 0 0 0 0 39
40 6.4 8.3 8.8 9.2 0 0 0 1 0 0 0 0 0 0 0 40
41 6.7 8.4 8.3 8.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.6 8.1 8.4 8.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 7.7 8.1 8.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.3 7.9 7.7 8.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.2 7.9 7.9 7.7 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 7.9 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.8 7.6 7.9 8.0 0 0 0 0 0 0 0 0 0 0 0 48
49 6.4 7.1 7.6 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 6.8 7.1 7.6 0 1 0 0 0 0 0 0 0 0 0 50
51 5.8 6.5 6.8 7.1 0 0 1 0 0 0 0 0 0 0 0 51
52 6.1 6.9 6.5 6.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.2 8.2 6.9 6.5 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 8.7 8.2 6.9 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 8.3 8.7 8.2 0 0 0 0 0 0 1 0 0 0 0 55
56 6.1 7.9 8.3 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 5.8 7.5 7.9 8.3 0 0 0 0 0 0 0 0 1 0 0 57
58 6.2 7.8 7.5 7.9 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WLVrouw `Yt-1` `Yt-2` M1 M2
3.657189 0.395498 0.191003 -0.138060 -0.099407 -0.148951
M3 M4 M5 M6 M7 M8
-0.234467 -0.471179 -0.448032 -0.708999 -0.565549 -0.499275
M9 M10 M11 t
-0.394141 -0.195156 -0.014277 -0.006699
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.73148 -0.23564 -0.03241 0.23113 0.84700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.657189 1.264152 2.893 0.00602 **
WLVrouw 0.395498 0.254641 1.553 0.12789
`Yt-1` 0.191003 0.406998 0.469 0.64128
`Yt-2` -0.138060 0.255103 -0.541 0.59123
M1 -0.099407 0.278415 -0.357 0.72284
M2 -0.148951 0.285278 -0.522 0.60433
M3 -0.234467 0.290255 -0.808 0.42376
M4 -0.471179 0.292672 -1.610 0.11491
M5 -0.448032 0.366738 -1.222 0.22865
M6 -0.708999 0.327840 -2.163 0.03631 *
M7 -0.565549 0.285832 -1.979 0.05444 .
M8 -0.499275 0.290653 -1.718 0.09320 .
M9 -0.394141 0.294210 -1.340 0.18756
M10 -0.195156 0.306503 -0.637 0.52776
M11 -0.014277 0.292262 -0.049 0.96127
t -0.006699 0.006096 -1.099 0.27805
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4093 on 42 degrees of freedom
Multiple R-squared: 0.713, Adjusted R-squared: 0.6105
F-statistic: 6.956 on 15 and 42 DF, p-value: 3.106e-07
> 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,] 2.037128e-03 4.074255e-03 0.997962872
[2,] 1.932692e-04 3.865384e-04 0.999806731
[3,] 3.236868e-05 6.473736e-05 0.999967631
[4,] 6.403595e-06 1.280719e-05 0.999993596
[5,] 2.854927e-06 5.709853e-06 0.999997145
[6,] 1.415148e-04 2.830296e-04 0.999858485
[7,] 4.765340e-04 9.530681e-04 0.999523466
[8,] 3.799990e-03 7.599981e-03 0.996200010
[9,] 9.320783e-03 1.864157e-02 0.990679217
[10,] 7.513431e-03 1.502686e-02 0.992486569
[11,] 3.674584e-02 7.349168e-02 0.963254159
[12,] 4.343975e-01 8.687951e-01 0.565602463
[13,] 8.040408e-01 3.919184e-01 0.195959186
[14,] 7.823672e-01 4.352655e-01 0.217632754
[15,] 9.765011e-01 4.699770e-02 0.023498851
[16,] 9.974604e-01 5.079193e-03 0.002539596
[17,] 9.954000e-01 9.200034e-03 0.004600017
[18,] 9.951667e-01 9.666580e-03 0.004833290
[19,] 9.985213e-01 2.957377e-03 0.001478689
[20,] 9.989267e-01 2.146632e-03 0.001073316
[21,] 9.946113e-01 1.077733e-02 0.005388663
> postscript(file="/var/www/html/rcomp/tmp/18qun1258737633.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28c4b1258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vzld1258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dlr11258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qajo1258737633.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 = 58
Frequency = 1
1 2 3 4 5 6
-0.234246616 -0.400809892 -0.367972505 -0.303069644 -0.207882282 -0.110939797
7 8 9 10 11 12
0.060020529 0.091235544 0.253489458 0.189752920 -0.013524694 -0.117952908
13 14 15 16 17 18
0.050020522 -0.002630475 0.231279323 0.018116809 -0.080090647 -0.087820975
19 20 21 22 23 24
0.028643195 0.353078727 0.513638732 0.475369213 0.387314750 0.474442868
25 26 27 28 29 30
0.673454373 0.846998186 0.649801900 0.265600515 -0.467245418 -0.731480840
31 32 33 34 35 36
-0.616884880 -0.212898800 -0.024794830 -0.128808909 -0.283957088 -0.410634957
37 38 39 40 41 42
-0.490722953 -0.374028987 -0.342715190 -0.211348672 0.072931191 0.271116672
43 44 45 46 47 48
0.163671714 -0.040019798 -0.331879137 -0.236101953 -0.089832968 0.054144997
49 50 51 52 53 54
0.001494673 -0.069528831 -0.170393527 0.230700992 0.682287155 0.659124940
55 56 57 58
0.364549442 -0.191395673 -0.410454224 -0.300211271
> postscript(file="/var/www/html/rcomp/tmp/6ybmo1258737633.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.234246616 NA
1 -0.400809892 -0.234246616
2 -0.367972505 -0.400809892
3 -0.303069644 -0.367972505
4 -0.207882282 -0.303069644
5 -0.110939797 -0.207882282
6 0.060020529 -0.110939797
7 0.091235544 0.060020529
8 0.253489458 0.091235544
9 0.189752920 0.253489458
10 -0.013524694 0.189752920
11 -0.117952908 -0.013524694
12 0.050020522 -0.117952908
13 -0.002630475 0.050020522
14 0.231279323 -0.002630475
15 0.018116809 0.231279323
16 -0.080090647 0.018116809
17 -0.087820975 -0.080090647
18 0.028643195 -0.087820975
19 0.353078727 0.028643195
20 0.513638732 0.353078727
21 0.475369213 0.513638732
22 0.387314750 0.475369213
23 0.474442868 0.387314750
24 0.673454373 0.474442868
25 0.846998186 0.673454373
26 0.649801900 0.846998186
27 0.265600515 0.649801900
28 -0.467245418 0.265600515
29 -0.731480840 -0.467245418
30 -0.616884880 -0.731480840
31 -0.212898800 -0.616884880
32 -0.024794830 -0.212898800
33 -0.128808909 -0.024794830
34 -0.283957088 -0.128808909
35 -0.410634957 -0.283957088
36 -0.490722953 -0.410634957
37 -0.374028987 -0.490722953
38 -0.342715190 -0.374028987
39 -0.211348672 -0.342715190
40 0.072931191 -0.211348672
41 0.271116672 0.072931191
42 0.163671714 0.271116672
43 -0.040019798 0.163671714
44 -0.331879137 -0.040019798
45 -0.236101953 -0.331879137
46 -0.089832968 -0.236101953
47 0.054144997 -0.089832968
48 0.001494673 0.054144997
49 -0.069528831 0.001494673
50 -0.170393527 -0.069528831
51 0.230700992 -0.170393527
52 0.682287155 0.230700992
53 0.659124940 0.682287155
54 0.364549442 0.659124940
55 -0.191395673 0.364549442
56 -0.410454224 -0.191395673
57 -0.300211271 -0.410454224
58 NA -0.300211271
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.400809892 -0.234246616
[2,] -0.367972505 -0.400809892
[3,] -0.303069644 -0.367972505
[4,] -0.207882282 -0.303069644
[5,] -0.110939797 -0.207882282
[6,] 0.060020529 -0.110939797
[7,] 0.091235544 0.060020529
[8,] 0.253489458 0.091235544
[9,] 0.189752920 0.253489458
[10,] -0.013524694 0.189752920
[11,] -0.117952908 -0.013524694
[12,] 0.050020522 -0.117952908
[13,] -0.002630475 0.050020522
[14,] 0.231279323 -0.002630475
[15,] 0.018116809 0.231279323
[16,] -0.080090647 0.018116809
[17,] -0.087820975 -0.080090647
[18,] 0.028643195 -0.087820975
[19,] 0.353078727 0.028643195
[20,] 0.513638732 0.353078727
[21,] 0.475369213 0.513638732
[22,] 0.387314750 0.475369213
[23,] 0.474442868 0.387314750
[24,] 0.673454373 0.474442868
[25,] 0.846998186 0.673454373
[26,] 0.649801900 0.846998186
[27,] 0.265600515 0.649801900
[28,] -0.467245418 0.265600515
[29,] -0.731480840 -0.467245418
[30,] -0.616884880 -0.731480840
[31,] -0.212898800 -0.616884880
[32,] -0.024794830 -0.212898800
[33,] -0.128808909 -0.024794830
[34,] -0.283957088 -0.128808909
[35,] -0.410634957 -0.283957088
[36,] -0.490722953 -0.410634957
[37,] -0.374028987 -0.490722953
[38,] -0.342715190 -0.374028987
[39,] -0.211348672 -0.342715190
[40,] 0.072931191 -0.211348672
[41,] 0.271116672 0.072931191
[42,] 0.163671714 0.271116672
[43,] -0.040019798 0.163671714
[44,] -0.331879137 -0.040019798
[45,] -0.236101953 -0.331879137
[46,] -0.089832968 -0.236101953
[47,] 0.054144997 -0.089832968
[48,] 0.001494673 0.054144997
[49,] -0.069528831 0.001494673
[50,] -0.170393527 -0.069528831
[51,] 0.230700992 -0.170393527
[52,] 0.682287155 0.230700992
[53,] 0.659124940 0.682287155
[54,] 0.364549442 0.659124940
[55,] -0.191395673 0.364549442
[56,] -0.410454224 -0.191395673
[57,] -0.300211271 -0.410454224
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.400809892 -0.234246616
2 -0.367972505 -0.400809892
3 -0.303069644 -0.367972505
4 -0.207882282 -0.303069644
5 -0.110939797 -0.207882282
6 0.060020529 -0.110939797
7 0.091235544 0.060020529
8 0.253489458 0.091235544
9 0.189752920 0.253489458
10 -0.013524694 0.189752920
11 -0.117952908 -0.013524694
12 0.050020522 -0.117952908
13 -0.002630475 0.050020522
14 0.231279323 -0.002630475
15 0.018116809 0.231279323
16 -0.080090647 0.018116809
17 -0.087820975 -0.080090647
18 0.028643195 -0.087820975
19 0.353078727 0.028643195
20 0.513638732 0.353078727
21 0.475369213 0.513638732
22 0.387314750 0.475369213
23 0.474442868 0.387314750
24 0.673454373 0.474442868
25 0.846998186 0.673454373
26 0.649801900 0.846998186
27 0.265600515 0.649801900
28 -0.467245418 0.265600515
29 -0.731480840 -0.467245418
30 -0.616884880 -0.731480840
31 -0.212898800 -0.616884880
32 -0.024794830 -0.212898800
33 -0.128808909 -0.024794830
34 -0.283957088 -0.128808909
35 -0.410634957 -0.283957088
36 -0.490722953 -0.410634957
37 -0.374028987 -0.490722953
38 -0.342715190 -0.374028987
39 -0.211348672 -0.342715190
40 0.072931191 -0.211348672
41 0.271116672 0.072931191
42 0.163671714 0.271116672
43 -0.040019798 0.163671714
44 -0.331879137 -0.040019798
45 -0.236101953 -0.331879137
46 -0.089832968 -0.236101953
47 0.054144997 -0.089832968
48 0.001494673 0.054144997
49 -0.069528831 0.001494673
50 -0.170393527 -0.069528831
51 0.230700992 -0.170393527
52 0.682287155 0.230700992
53 0.659124940 0.682287155
54 0.364549442 0.659124940
55 -0.191395673 0.364549442
56 -0.410454224 -0.191395673
57 -0.300211271 -0.410454224
> 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/7dtzu1258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85c9i1258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/975601258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108u411258737633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l00w1258737633.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/12oftv1258737633.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/1382z31258737633.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/14iv4h1258737633.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/15c1zo1258737633.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/16tasx1258737633.tab")
+ }
>
> system("convert tmp/18qun1258737633.ps tmp/18qun1258737633.png")
> system("convert tmp/28c4b1258737633.ps tmp/28c4b1258737633.png")
> system("convert tmp/3vzld1258737633.ps tmp/3vzld1258737633.png")
> system("convert tmp/4dlr11258737633.ps tmp/4dlr11258737633.png")
> system("convert tmp/5qajo1258737633.ps tmp/5qajo1258737633.png")
> system("convert tmp/6ybmo1258737633.ps tmp/6ybmo1258737633.png")
> system("convert tmp/7dtzu1258737633.ps tmp/7dtzu1258737633.png")
> system("convert tmp/85c9i1258737633.ps tmp/85c9i1258737633.png")
> system("convert tmp/975601258737633.ps tmp/975601258737633.png")
> system("convert tmp/108u411258737633.ps tmp/108u411258737633.png")
>
>
> proc.time()
user system elapsed
2.361 1.570 2.758