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(8.9,6.3,8.2,6.2,7.6,6.1,7.7,6.3,8.1,6.5,8.3,6.6,8.3,6.5,7.9,6.2,7.8,6.2,8,5.9,8.5,6.1,8.6,6.1,8.5,6.1,8,6.1,7.8,6.1,8,6.4,8.2,6.7,8.3,6.9,8.2,7,8.1,7,8,6.8,7.8,6.4,7.8,5.9,7.7,5.5,7.6,5.5,7.6,5.6,7.6,5.8,7.8,5.9,8,6.1,8,6.1,7.9,6,7.7,6,7.4,5.9,6.9,5.5,6.7,5.6,6.5,5.4,6.4,5.2,6.7,5.2,6.8,5.2,6.9,5.5,6.9,5.8,6.7,5.8,6.4,5.5,6.2,5.3,5.9,5.1,6.1,5.2,6.7,5.8,6.8,5.8,6.6,5.5,6.4,5,6.4,4.9,6.7,5.3,7.1,6.1,7.1,6.5,6.9,6.8,6.4,6.6,6,6.4,6,6.4),dim=c(2,58),dimnames=list(c('X','Y'),1:58))
> y <- array(NA,dim=c(2,58),dimnames=list(c('X','Y'),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 = '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
X Y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.2 6.2 0 1 0 0 0 0 0 0 0 0 0 2
3 7.6 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 7.7 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 8.1 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 8.3 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 7.9 6.2 0 0 0 0 0 0 0 1 0 0 0 8
9 7.8 6.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.0 5.9 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 8.6 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 8.0 6.1 0 1 0 0 0 0 0 0 0 0 0 14
15 7.8 6.1 0 0 1 0 0 0 0 0 0 0 0 15
16 8.0 6.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 6.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 6.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 7.0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.1 7.0 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 6.8 0 0 0 0 0 0 0 0 1 0 0 21
22 7.8 6.4 0 0 0 0 0 0 0 0 0 1 0 22
23 7.8 5.9 0 0 0 0 0 0 0 0 0 0 1 23
24 7.7 5.5 0 0 0 0 0 0 0 0 0 0 0 24
25 7.6 5.5 1 0 0 0 0 0 0 0 0 0 0 25
26 7.6 5.6 0 1 0 0 0 0 0 0 0 0 0 26
27 7.6 5.8 0 0 1 0 0 0 0 0 0 0 0 27
28 7.8 5.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 6.1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 6.1 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 6.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.7 6.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.4 5.9 0 0 0 0 0 0 0 0 1 0 0 33
34 6.9 5.5 0 0 0 0 0 0 0 0 0 1 0 34
35 6.7 5.6 0 0 0 0 0 0 0 0 0 0 1 35
36 6.5 5.4 0 0 0 0 0 0 0 0 0 0 0 36
37 6.4 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 6.7 5.2 0 1 0 0 0 0 0 0 0 0 0 38
39 6.8 5.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.9 5.5 0 0 0 1 0 0 0 0 0 0 0 40
41 6.9 5.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.7 5.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 5.5 0 0 0 0 0 0 1 0 0 0 0 43
44 6.2 5.3 0 0 0 0 0 0 0 1 0 0 0 44
45 5.9 5.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 5.2 0 0 0 0 0 0 0 0 0 1 0 46
47 6.7 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 6.8 5.8 0 0 0 0 0 0 0 0 0 0 0 48
49 6.6 5.5 1 0 0 0 0 0 0 0 0 0 0 49
50 6.4 5.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.4 4.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.7 5.3 0 0 0 1 0 0 0 0 0 0 0 52
53 7.1 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.4 6.6 0 0 0 0 0 0 0 1 0 0 0 56
57 6.0 6.4 0 0 0 0 0 0 0 0 1 0 0 57
58 6.0 6.4 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) Y M1 M2 M3 M4
5.84831 0.44828 0.02379 -0.11794 -0.22449 -0.12759
M5 M6 M7 M8 M9 M10
-0.01552 -0.02483 -0.12241 -0.30620 -0.45000 -0.38689
M11 t
-0.07569 -0.03345
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.71107 -0.21101 -0.02140 0.23515 0.53862
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.848310 0.774291 7.553 1.78e-09 ***
Y 0.448277 0.122045 3.673 0.000646 ***
M1 0.023787 0.236036 0.101 0.920187
M2 -0.117936 0.236405 -0.499 0.620353
M3 -0.224487 0.236168 -0.951 0.347030
M4 -0.127589 0.236315 -0.540 0.591981
M5 -0.015519 0.244118 -0.064 0.949599
M6 -0.024828 0.249726 -0.099 0.921255
M7 -0.122413 0.249527 -0.491 0.626162
M8 -0.306205 0.244954 -1.250 0.217890
M9 -0.449996 0.241298 -1.865 0.068873 .
M10 -0.386892 0.237591 -1.628 0.110582
M11 -0.075691 0.248838 -0.304 0.762426
t -0.033450 0.003317 -10.085 5.14e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3511 on 44 degrees of freedom
Multiple R-squared: 0.8468, Adjusted R-squared: 0.8016
F-statistic: 18.71 on 13 and 44 DF, p-value: 8.82e-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,] 0.12643116 0.25286232 0.87356884
[2,] 0.14117443 0.28234886 0.85882557
[3,] 0.15180744 0.30361489 0.84819256
[4,] 0.08610391 0.17220783 0.91389609
[5,] 0.04338148 0.08676297 0.95661852
[6,] 0.04059239 0.08118478 0.95940761
[7,] 0.08778237 0.17556473 0.91221763
[8,] 0.08698802 0.17397605 0.91301198
[9,] 0.06410468 0.12820935 0.93589532
[10,] 0.05441858 0.10883716 0.94558142
[11,] 0.07816874 0.15633748 0.92183126
[12,] 0.11100974 0.22201948 0.88899026
[13,] 0.09658433 0.19316867 0.90341567
[14,] 0.07495003 0.14990007 0.92504997
[15,] 0.07828510 0.15657020 0.92171490
[16,] 0.11382358 0.22764716 0.88617642
[17,] 0.38300723 0.76601445 0.61699277
[18,] 0.87869252 0.24261496 0.12130748
[19,] 0.94766769 0.10466462 0.05233231
[20,] 0.97980664 0.04038673 0.02019336
[21,] 0.98426820 0.03146360 0.01573180
[22,] 0.96530462 0.06939076 0.03469538
[23,] 0.94850703 0.10298594 0.05149297
[24,] 0.93110377 0.13779247 0.06889623
[25,] 0.85158476 0.29683048 0.14841524
> postscript(file="/var/www/html/rcomp/tmp/1ivby1258661337.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kos71258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3h2e81258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rfvy1258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v4lp1258661337.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
0.237209859 -0.242790141 -0.657962479 -0.711065882 -0.479341623 -0.281410558
7 8 9 10 11 12
-0.105548428 -0.153824169 -0.076582896 0.228244766 0.360838569 0.418597076
13 14 15 16 17 18
0.328260034 0.003432372 -0.056567628 -0.054498693 -0.067602096 -0.014498693
19 20 21 22 23 24
-0.028291887 0.088949386 0.255845983 0.205501307 0.151888744 0.188957899
25 26 27 28 29 30
0.098620856 0.228965532 0.279310208 0.371034468 0.402758727 0.445517453
31 32 33 34 35 36
0.521379583 0.538620856 0.460689792 0.110345115 -0.412233420 -0.564819589
37 38 39 40 41 42
-0.565501307 -0.090328969 0.149671031 0.051739966 -0.161363437 -0.318604710
43 44 45 46 47 48
-0.353087256 -0.246190659 -0.279294062 -0.153777048 -0.100493893 -0.042735386
49 50 51 52 53 54
-0.098589442 0.100721206 0.285548868 0.342790141 0.305548428 0.168996507
55 56 57 58
-0.034452011 -0.227555414 -0.360658817 -0.390314141
> postscript(file="/var/www/html/rcomp/tmp/6y2ue1258661337.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 0.237209859 NA
1 -0.242790141 0.237209859
2 -0.657962479 -0.242790141
3 -0.711065882 -0.657962479
4 -0.479341623 -0.711065882
5 -0.281410558 -0.479341623
6 -0.105548428 -0.281410558
7 -0.153824169 -0.105548428
8 -0.076582896 -0.153824169
9 0.228244766 -0.076582896
10 0.360838569 0.228244766
11 0.418597076 0.360838569
12 0.328260034 0.418597076
13 0.003432372 0.328260034
14 -0.056567628 0.003432372
15 -0.054498693 -0.056567628
16 -0.067602096 -0.054498693
17 -0.014498693 -0.067602096
18 -0.028291887 -0.014498693
19 0.088949386 -0.028291887
20 0.255845983 0.088949386
21 0.205501307 0.255845983
22 0.151888744 0.205501307
23 0.188957899 0.151888744
24 0.098620856 0.188957899
25 0.228965532 0.098620856
26 0.279310208 0.228965532
27 0.371034468 0.279310208
28 0.402758727 0.371034468
29 0.445517453 0.402758727
30 0.521379583 0.445517453
31 0.538620856 0.521379583
32 0.460689792 0.538620856
33 0.110345115 0.460689792
34 -0.412233420 0.110345115
35 -0.564819589 -0.412233420
36 -0.565501307 -0.564819589
37 -0.090328969 -0.565501307
38 0.149671031 -0.090328969
39 0.051739966 0.149671031
40 -0.161363437 0.051739966
41 -0.318604710 -0.161363437
42 -0.353087256 -0.318604710
43 -0.246190659 -0.353087256
44 -0.279294062 -0.246190659
45 -0.153777048 -0.279294062
46 -0.100493893 -0.153777048
47 -0.042735386 -0.100493893
48 -0.098589442 -0.042735386
49 0.100721206 -0.098589442
50 0.285548868 0.100721206
51 0.342790141 0.285548868
52 0.305548428 0.342790141
53 0.168996507 0.305548428
54 -0.034452011 0.168996507
55 -0.227555414 -0.034452011
56 -0.360658817 -0.227555414
57 -0.390314141 -0.360658817
58 NA -0.390314141
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.242790141 0.237209859
[2,] -0.657962479 -0.242790141
[3,] -0.711065882 -0.657962479
[4,] -0.479341623 -0.711065882
[5,] -0.281410558 -0.479341623
[6,] -0.105548428 -0.281410558
[7,] -0.153824169 -0.105548428
[8,] -0.076582896 -0.153824169
[9,] 0.228244766 -0.076582896
[10,] 0.360838569 0.228244766
[11,] 0.418597076 0.360838569
[12,] 0.328260034 0.418597076
[13,] 0.003432372 0.328260034
[14,] -0.056567628 0.003432372
[15,] -0.054498693 -0.056567628
[16,] -0.067602096 -0.054498693
[17,] -0.014498693 -0.067602096
[18,] -0.028291887 -0.014498693
[19,] 0.088949386 -0.028291887
[20,] 0.255845983 0.088949386
[21,] 0.205501307 0.255845983
[22,] 0.151888744 0.205501307
[23,] 0.188957899 0.151888744
[24,] 0.098620856 0.188957899
[25,] 0.228965532 0.098620856
[26,] 0.279310208 0.228965532
[27,] 0.371034468 0.279310208
[28,] 0.402758727 0.371034468
[29,] 0.445517453 0.402758727
[30,] 0.521379583 0.445517453
[31,] 0.538620856 0.521379583
[32,] 0.460689792 0.538620856
[33,] 0.110345115 0.460689792
[34,] -0.412233420 0.110345115
[35,] -0.564819589 -0.412233420
[36,] -0.565501307 -0.564819589
[37,] -0.090328969 -0.565501307
[38,] 0.149671031 -0.090328969
[39,] 0.051739966 0.149671031
[40,] -0.161363437 0.051739966
[41,] -0.318604710 -0.161363437
[42,] -0.353087256 -0.318604710
[43,] -0.246190659 -0.353087256
[44,] -0.279294062 -0.246190659
[45,] -0.153777048 -0.279294062
[46,] -0.100493893 -0.153777048
[47,] -0.042735386 -0.100493893
[48,] -0.098589442 -0.042735386
[49,] 0.100721206 -0.098589442
[50,] 0.285548868 0.100721206
[51,] 0.342790141 0.285548868
[52,] 0.305548428 0.342790141
[53,] 0.168996507 0.305548428
[54,] -0.034452011 0.168996507
[55,] -0.227555414 -0.034452011
[56,] -0.360658817 -0.227555414
[57,] -0.390314141 -0.360658817
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.242790141 0.237209859
2 -0.657962479 -0.242790141
3 -0.711065882 -0.657962479
4 -0.479341623 -0.711065882
5 -0.281410558 -0.479341623
6 -0.105548428 -0.281410558
7 -0.153824169 -0.105548428
8 -0.076582896 -0.153824169
9 0.228244766 -0.076582896
10 0.360838569 0.228244766
11 0.418597076 0.360838569
12 0.328260034 0.418597076
13 0.003432372 0.328260034
14 -0.056567628 0.003432372
15 -0.054498693 -0.056567628
16 -0.067602096 -0.054498693
17 -0.014498693 -0.067602096
18 -0.028291887 -0.014498693
19 0.088949386 -0.028291887
20 0.255845983 0.088949386
21 0.205501307 0.255845983
22 0.151888744 0.205501307
23 0.188957899 0.151888744
24 0.098620856 0.188957899
25 0.228965532 0.098620856
26 0.279310208 0.228965532
27 0.371034468 0.279310208
28 0.402758727 0.371034468
29 0.445517453 0.402758727
30 0.521379583 0.445517453
31 0.538620856 0.521379583
32 0.460689792 0.538620856
33 0.110345115 0.460689792
34 -0.412233420 0.110345115
35 -0.564819589 -0.412233420
36 -0.565501307 -0.564819589
37 -0.090328969 -0.565501307
38 0.149671031 -0.090328969
39 0.051739966 0.149671031
40 -0.161363437 0.051739966
41 -0.318604710 -0.161363437
42 -0.353087256 -0.318604710
43 -0.246190659 -0.353087256
44 -0.279294062 -0.246190659
45 -0.153777048 -0.279294062
46 -0.100493893 -0.153777048
47 -0.042735386 -0.100493893
48 -0.098589442 -0.042735386
49 0.100721206 -0.098589442
50 0.285548868 0.100721206
51 0.342790141 0.285548868
52 0.305548428 0.342790141
53 0.168996507 0.305548428
54 -0.034452011 0.168996507
55 -0.227555414 -0.034452011
56 -0.360658817 -0.227555414
57 -0.390314141 -0.360658817
> 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/7q0ji1258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wtkm1258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zw701258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rnh21258661337.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kavo1258661337.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/12pu7c1258661337.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/13iki51258661337.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/14u9981258661337.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/1561md1258661337.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/161jej1258661337.tab")
+ }
>
> system("convert tmp/1ivby1258661337.ps tmp/1ivby1258661337.png")
> system("convert tmp/2kos71258661337.ps tmp/2kos71258661337.png")
> system("convert tmp/3h2e81258661337.ps tmp/3h2e81258661337.png")
> system("convert tmp/4rfvy1258661337.ps tmp/4rfvy1258661337.png")
> system("convert tmp/5v4lp1258661337.ps tmp/5v4lp1258661337.png")
> system("convert tmp/6y2ue1258661337.ps tmp/6y2ue1258661337.png")
> system("convert tmp/7q0ji1258661337.ps tmp/7q0ji1258661337.png")
> system("convert tmp/8wtkm1258661337.ps tmp/8wtkm1258661337.png")
> system("convert tmp/9zw701258661337.ps tmp/9zw701258661337.png")
> system("convert tmp/10rnh21258661337.ps tmp/10rnh21258661337.png")
>
>
> proc.time()
user system elapsed
2.382 1.555 2.790