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(8.2,9.9,8.0,9.8,7.5,9.3,6.8,8.3,6.5,8.0,6.6,8.5,7.6,10.4,8.0,11.1,8.1,10.9,7.7,10.0,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9.0,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9.0,7.9,9.0,7.3,9.0,6.9,9.8,6.6,10.0,6.7,9.8,6.9,9.3,7.0,9.0,7.1,9.0,7.2,9.1,7.1,9.1,6.9,9.1,7.0,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8.0,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3,8.0,8.1,8.1,8.5),dim=c(2,68),dimnames=list(c('Y','X'),1:68))
> y <- array(NA,dim=c(2,68),dimnames=list(c('Y','X'),1:68))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
1 8.2 9.9
2 8.0 9.8
3 7.5 9.3
4 6.8 8.3
5 6.5 8.0
6 6.6 8.5
7 7.6 10.4
8 8.0 11.1
9 8.1 10.9
10 7.7 10.0
11 7.5 9.2
12 7.6 9.2
13 7.8 9.5
14 7.8 9.6
15 7.8 9.5
16 7.5 9.1
17 7.5 8.9
18 7.1 9.0
19 7.5 10.1
20 7.5 10.3
21 7.6 10.2
22 7.7 9.6
23 7.7 9.2
24 7.9 9.3
25 8.1 9.4
26 8.2 9.4
27 8.2 9.2
28 8.2 9.0
29 7.9 9.0
30 7.3 9.0
31 6.9 9.8
32 6.6 10.0
33 6.7 9.8
34 6.9 9.3
35 7.0 9.0
36 7.1 9.0
37 7.2 9.1
38 7.1 9.1
39 6.9 9.1
40 7.0 9.2
41 6.8 8.8
42 6.4 8.3
43 6.7 8.4
44 6.6 8.1
45 6.4 7.7
46 6.3 7.9
47 6.2 7.9
48 6.5 8.0
49 6.8 7.9
50 6.8 7.6
51 6.4 7.1
52 6.1 6.8
53 5.8 6.5
54 6.1 6.9
55 7.2 8.2
56 7.3 8.7
57 6.9 8.3
58 6.1 7.9
59 5.8 7.5
60 6.2 7.8
61 7.1 8.3
62 7.7 8.4
63 7.9 8.2
64 7.7 7.7
65 7.4 7.2
66 7.5 7.3
67 8.0 8.1
68 8.1 8.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
3.5533 0.4152
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1049 -0.3557 -0.1000 0.3027 1.0839
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.55332 0.57192 6.213 3.95e-08 ***
X 0.41516 0.06471 6.416 1.75e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5227 on 66 degrees of freedom
Multiple R-squared: 0.3841, Adjusted R-squared: 0.3748
F-statistic: 41.16 on 1 and 66 DF, p-value: 1.747e-08
> 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,] 0.0053143608 0.0106287217 0.9946856392
[2,] 0.0146716526 0.0293433052 0.9853283474
[3,] 0.1640886973 0.3281773946 0.8359113027
[4,] 0.1552217031 0.3104434061 0.8447782969
[5,] 0.0894998359 0.1789996717 0.9105001641
[6,] 0.0475229845 0.0950459690 0.9524770155
[7,] 0.0286243900 0.0572487800 0.9713756100
[8,] 0.0198904270 0.0397808540 0.9801095730
[9,] 0.0146426031 0.0292852063 0.9853573969
[10,] 0.0091731329 0.0183462657 0.9908268671
[11,] 0.0061767195 0.0123534389 0.9938232805
[12,] 0.0032966470 0.0065932940 0.9967033530
[13,] 0.0020319756 0.0040639512 0.9979680244
[14,] 0.0011587032 0.0023174064 0.9988412968
[15,] 0.0009092398 0.0018184796 0.9990907602
[16,] 0.0008700998 0.0017401996 0.9991299002
[17,] 0.0005011396 0.0010022792 0.9994988604
[18,] 0.0002513491 0.0005026982 0.9997486509
[19,] 0.0001842540 0.0003685079 0.9998157460
[20,] 0.0002290926 0.0004581852 0.9997709074
[21,] 0.0005170978 0.0010341956 0.9994829022
[22,] 0.0015134219 0.0030268439 0.9984865781
[23,] 0.0049203559 0.0098407117 0.9950796441
[24,] 0.0165911447 0.0331822894 0.9834088553
[25,] 0.0205022801 0.0410045602 0.9794977199
[26,] 0.0142655457 0.0285310913 0.9857344543
[27,] 0.0277999507 0.0555999013 0.9722000493
[28,] 0.1012472982 0.2024945965 0.8987527018
[29,] 0.1748496310 0.3496992620 0.8251503690
[30,] 0.1757394335 0.3514788671 0.8242605665
[31,] 0.1493558807 0.2987117615 0.8506441193
[32,] 0.1181385012 0.2362770024 0.8818614988
[33,] 0.0890478277 0.1780956553 0.9109521723
[34,] 0.0690501388 0.1381002775 0.9309498612
[35,] 0.0640186372 0.1280372743 0.9359813628
[36,] 0.0573791656 0.1147583311 0.9426208344
[37,] 0.0554387026 0.1108774052 0.9445612974
[38,] 0.0692011175 0.1384022350 0.9307988825
[39,] 0.0623381591 0.1246763182 0.9376618409
[40,] 0.0523975617 0.1047951235 0.9476024383
[41,] 0.0418636844 0.0837273688 0.9581363156
[42,] 0.0440950831 0.0881901663 0.9559049169
[43,] 0.0565956824 0.1131913649 0.9434043176
[44,] 0.0535691387 0.1071382775 0.9464308613
[45,] 0.0385757169 0.0771514338 0.9614242831
[46,] 0.0262839145 0.0525678291 0.9737160855
[47,] 0.0165281801 0.0330563601 0.9834718199
[48,] 0.0101321158 0.0202642316 0.9898678842
[49,] 0.0067035563 0.0134071126 0.9932964437
[50,] 0.0047619942 0.0095239883 0.9952380058
[51,] 0.0028871658 0.0057743316 0.9971128342
[52,] 0.0017171588 0.0034343176 0.9982828412
[53,] 0.0012775504 0.0025551008 0.9987224496
[54,] 0.0062464411 0.0124928821 0.9937535589
[55,] 0.0848971832 0.1697943664 0.9151028168
[56,] 0.7324164660 0.5351670680 0.2675835340
[57,] 0.9871668446 0.0256663108 0.0128331554
[58,] 0.9993259845 0.0013480310 0.0006740155
[59,] 0.9976513945 0.0046972110 0.0023486055
> postscript(file="/var/www/html/rcomp/tmp/1s6581258569525.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nbfb1258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wd1i1258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4m3jt1258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yaq51258569525.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 = 68
Frequency = 1
1 2 3 4 5 6
0.536609986 0.378125824 0.085705013 -0.199136608 -0.374589094 -0.482168284
7 8 9 10 11 12
-0.270969203 -0.161580068 0.021451607 -0.004905852 0.127220851 0.227220851
13 14 15 16 17 18
0.302673338 0.261157500 0.302673338 0.168736689 0.251768365 -0.189747473
19 20 21 22 23 24
-0.246421690 -0.329453365 -0.187937527 0.161157500 0.327220851 0.485705013
25 26 27 28 29 30
0.644189175 0.744189175 0.827220851 0.910252527 0.610252527 0.010252527
31 32 33 34 35 36
-0.721874176 -1.104905852 -0.921874176 -0.514294987 -0.289747473 -0.189747473
37 38 39 40 41 42
-0.131263311 -0.231263311 -0.431263311 -0.372779149 -0.406715797 -0.599136608
43 44 45 46 47 48
-0.340652446 -0.316104932 -0.350041581 -0.533073256 -0.633073256 -0.374589094
49 50 51 52 53 54
-0.033073256 0.091474257 -0.100946554 -0.276399040 -0.451851526 -0.317914878
55 56 57 58 59 60
0.242379230 0.134800041 -0.099136608 -0.733073256 -0.867009905 -0.591557419
61 62 63 64 65 66
0.100863392 0.659347554 0.942379230 0.949958419 0.857537609 0.916021771
67 68
1.083895068 1.017831716
> postscript(file="/var/www/html/rcomp/tmp/676sr1258569525.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 0.536609986 NA
1 0.378125824 0.536609986
2 0.085705013 0.378125824
3 -0.199136608 0.085705013
4 -0.374589094 -0.199136608
5 -0.482168284 -0.374589094
6 -0.270969203 -0.482168284
7 -0.161580068 -0.270969203
8 0.021451607 -0.161580068
9 -0.004905852 0.021451607
10 0.127220851 -0.004905852
11 0.227220851 0.127220851
12 0.302673338 0.227220851
13 0.261157500 0.302673338
14 0.302673338 0.261157500
15 0.168736689 0.302673338
16 0.251768365 0.168736689
17 -0.189747473 0.251768365
18 -0.246421690 -0.189747473
19 -0.329453365 -0.246421690
20 -0.187937527 -0.329453365
21 0.161157500 -0.187937527
22 0.327220851 0.161157500
23 0.485705013 0.327220851
24 0.644189175 0.485705013
25 0.744189175 0.644189175
26 0.827220851 0.744189175
27 0.910252527 0.827220851
28 0.610252527 0.910252527
29 0.010252527 0.610252527
30 -0.721874176 0.010252527
31 -1.104905852 -0.721874176
32 -0.921874176 -1.104905852
33 -0.514294987 -0.921874176
34 -0.289747473 -0.514294987
35 -0.189747473 -0.289747473
36 -0.131263311 -0.189747473
37 -0.231263311 -0.131263311
38 -0.431263311 -0.231263311
39 -0.372779149 -0.431263311
40 -0.406715797 -0.372779149
41 -0.599136608 -0.406715797
42 -0.340652446 -0.599136608
43 -0.316104932 -0.340652446
44 -0.350041581 -0.316104932
45 -0.533073256 -0.350041581
46 -0.633073256 -0.533073256
47 -0.374589094 -0.633073256
48 -0.033073256 -0.374589094
49 0.091474257 -0.033073256
50 -0.100946554 0.091474257
51 -0.276399040 -0.100946554
52 -0.451851526 -0.276399040
53 -0.317914878 -0.451851526
54 0.242379230 -0.317914878
55 0.134800041 0.242379230
56 -0.099136608 0.134800041
57 -0.733073256 -0.099136608
58 -0.867009905 -0.733073256
59 -0.591557419 -0.867009905
60 0.100863392 -0.591557419
61 0.659347554 0.100863392
62 0.942379230 0.659347554
63 0.949958419 0.942379230
64 0.857537609 0.949958419
65 0.916021771 0.857537609
66 1.083895068 0.916021771
67 1.017831716 1.083895068
68 NA 1.017831716
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.378125824 0.536609986
[2,] 0.085705013 0.378125824
[3,] -0.199136608 0.085705013
[4,] -0.374589094 -0.199136608
[5,] -0.482168284 -0.374589094
[6,] -0.270969203 -0.482168284
[7,] -0.161580068 -0.270969203
[8,] 0.021451607 -0.161580068
[9,] -0.004905852 0.021451607
[10,] 0.127220851 -0.004905852
[11,] 0.227220851 0.127220851
[12,] 0.302673338 0.227220851
[13,] 0.261157500 0.302673338
[14,] 0.302673338 0.261157500
[15,] 0.168736689 0.302673338
[16,] 0.251768365 0.168736689
[17,] -0.189747473 0.251768365
[18,] -0.246421690 -0.189747473
[19,] -0.329453365 -0.246421690
[20,] -0.187937527 -0.329453365
[21,] 0.161157500 -0.187937527
[22,] 0.327220851 0.161157500
[23,] 0.485705013 0.327220851
[24,] 0.644189175 0.485705013
[25,] 0.744189175 0.644189175
[26,] 0.827220851 0.744189175
[27,] 0.910252527 0.827220851
[28,] 0.610252527 0.910252527
[29,] 0.010252527 0.610252527
[30,] -0.721874176 0.010252527
[31,] -1.104905852 -0.721874176
[32,] -0.921874176 -1.104905852
[33,] -0.514294987 -0.921874176
[34,] -0.289747473 -0.514294987
[35,] -0.189747473 -0.289747473
[36,] -0.131263311 -0.189747473
[37,] -0.231263311 -0.131263311
[38,] -0.431263311 -0.231263311
[39,] -0.372779149 -0.431263311
[40,] -0.406715797 -0.372779149
[41,] -0.599136608 -0.406715797
[42,] -0.340652446 -0.599136608
[43,] -0.316104932 -0.340652446
[44,] -0.350041581 -0.316104932
[45,] -0.533073256 -0.350041581
[46,] -0.633073256 -0.533073256
[47,] -0.374589094 -0.633073256
[48,] -0.033073256 -0.374589094
[49,] 0.091474257 -0.033073256
[50,] -0.100946554 0.091474257
[51,] -0.276399040 -0.100946554
[52,] -0.451851526 -0.276399040
[53,] -0.317914878 -0.451851526
[54,] 0.242379230 -0.317914878
[55,] 0.134800041 0.242379230
[56,] -0.099136608 0.134800041
[57,] -0.733073256 -0.099136608
[58,] -0.867009905 -0.733073256
[59,] -0.591557419 -0.867009905
[60,] 0.100863392 -0.591557419
[61,] 0.659347554 0.100863392
[62,] 0.942379230 0.659347554
[63,] 0.949958419 0.942379230
[64,] 0.857537609 0.949958419
[65,] 0.916021771 0.857537609
[66,] 1.083895068 0.916021771
[67,] 1.017831716 1.083895068
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.378125824 0.536609986
2 0.085705013 0.378125824
3 -0.199136608 0.085705013
4 -0.374589094 -0.199136608
5 -0.482168284 -0.374589094
6 -0.270969203 -0.482168284
7 -0.161580068 -0.270969203
8 0.021451607 -0.161580068
9 -0.004905852 0.021451607
10 0.127220851 -0.004905852
11 0.227220851 0.127220851
12 0.302673338 0.227220851
13 0.261157500 0.302673338
14 0.302673338 0.261157500
15 0.168736689 0.302673338
16 0.251768365 0.168736689
17 -0.189747473 0.251768365
18 -0.246421690 -0.189747473
19 -0.329453365 -0.246421690
20 -0.187937527 -0.329453365
21 0.161157500 -0.187937527
22 0.327220851 0.161157500
23 0.485705013 0.327220851
24 0.644189175 0.485705013
25 0.744189175 0.644189175
26 0.827220851 0.744189175
27 0.910252527 0.827220851
28 0.610252527 0.910252527
29 0.010252527 0.610252527
30 -0.721874176 0.010252527
31 -1.104905852 -0.721874176
32 -0.921874176 -1.104905852
33 -0.514294987 -0.921874176
34 -0.289747473 -0.514294987
35 -0.189747473 -0.289747473
36 -0.131263311 -0.189747473
37 -0.231263311 -0.131263311
38 -0.431263311 -0.231263311
39 -0.372779149 -0.431263311
40 -0.406715797 -0.372779149
41 -0.599136608 -0.406715797
42 -0.340652446 -0.599136608
43 -0.316104932 -0.340652446
44 -0.350041581 -0.316104932
45 -0.533073256 -0.350041581
46 -0.633073256 -0.533073256
47 -0.374589094 -0.633073256
48 -0.033073256 -0.374589094
49 0.091474257 -0.033073256
50 -0.100946554 0.091474257
51 -0.276399040 -0.100946554
52 -0.451851526 -0.276399040
53 -0.317914878 -0.451851526
54 0.242379230 -0.317914878
55 0.134800041 0.242379230
56 -0.099136608 0.134800041
57 -0.733073256 -0.099136608
58 -0.867009905 -0.733073256
59 -0.591557419 -0.867009905
60 0.100863392 -0.591557419
61 0.659347554 0.100863392
62 0.942379230 0.659347554
63 0.949958419 0.942379230
64 0.857537609 0.949958419
65 0.916021771 0.857537609
66 1.083895068 0.916021771
67 1.017831716 1.083895068
> 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/77l3f1258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8455l1258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sb441258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10idq41258569525.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bsr71258569525.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/12ict71258569525.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/13afju1258569525.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/14sldw1258569525.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/1501d11258569525.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/16c5bp1258569525.tab")
+ }
>
> system("convert tmp/1s6581258569525.ps tmp/1s6581258569525.png")
> system("convert tmp/2nbfb1258569525.ps tmp/2nbfb1258569525.png")
> system("convert tmp/3wd1i1258569525.ps tmp/3wd1i1258569525.png")
> system("convert tmp/4m3jt1258569525.ps tmp/4m3jt1258569525.png")
> system("convert tmp/5yaq51258569525.ps tmp/5yaq51258569525.png")
> system("convert tmp/676sr1258569525.ps tmp/676sr1258569525.png")
> system("convert tmp/77l3f1258569525.ps tmp/77l3f1258569525.png")
> system("convert tmp/8455l1258569525.ps tmp/8455l1258569525.png")
> system("convert tmp/9sb441258569525.ps tmp/9sb441258569525.png")
> system("convert tmp/10idq41258569525.ps tmp/10idq41258569525.png")
>
>
> proc.time()
user system elapsed
2.476 1.552 2.932