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(24,24,22,23,25,24,24,24,29,27,26,28,26,25,21,19,23,19,22,19,21,20,16,16,19,22,16,21,25,25,27,29,23,28,22,25,23,26,20,24,24,28,23,28,20,28,21,28,22,32,17,31,21,22,19,29,23,31,22,29,15,32,23,32,21,31,18,29,18,28,18,28,18,29,10,22,13,26,10,24,9,27,9,27,6,23,11,21,9,19,10,17,9,19,16,21,10,13,7,8,7,5,14,10,11,6,10,6,6,8,8,11,13,12,12,13,15,19,16,19,16,18),dim=c(2,61),dimnames=list(c('s','consv'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('s','consv'),1:61))
> 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
s consv M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 24 1 0 0 0 0 0 0 0 0 0 0 1
2 22 23 0 1 0 0 0 0 0 0 0 0 0 2
3 25 24 0 0 1 0 0 0 0 0 0 0 0 3
4 24 24 0 0 0 1 0 0 0 0 0 0 0 4
5 29 27 0 0 0 0 1 0 0 0 0 0 0 5
6 26 28 0 0 0 0 0 1 0 0 0 0 0 6
7 26 25 0 0 0 0 0 0 1 0 0 0 0 7
8 21 19 0 0 0 0 0 0 0 1 0 0 0 8
9 23 19 0 0 0 0 0 0 0 0 1 0 0 9
10 22 19 0 0 0 0 0 0 0 0 0 1 0 10
11 21 20 0 0 0 0 0 0 0 0 0 0 1 11
12 16 16 0 0 0 0 0 0 0 0 0 0 0 12
13 19 22 1 0 0 0 0 0 0 0 0 0 0 13
14 16 21 0 1 0 0 0 0 0 0 0 0 0 14
15 25 25 0 0 1 0 0 0 0 0 0 0 0 15
16 27 29 0 0 0 1 0 0 0 0 0 0 0 16
17 23 28 0 0 0 0 1 0 0 0 0 0 0 17
18 22 25 0 0 0 0 0 1 0 0 0 0 0 18
19 23 26 0 0 0 0 0 0 1 0 0 0 0 19
20 20 24 0 0 0 0 0 0 0 1 0 0 0 20
21 24 28 0 0 0 0 0 0 0 0 1 0 0 21
22 23 28 0 0 0 0 0 0 0 0 0 1 0 22
23 20 28 0 0 0 0 0 0 0 0 0 0 1 23
24 21 28 0 0 0 0 0 0 0 0 0 0 0 24
25 22 32 1 0 0 0 0 0 0 0 0 0 0 25
26 17 31 0 1 0 0 0 0 0 0 0 0 0 26
27 21 22 0 0 1 0 0 0 0 0 0 0 0 27
28 19 29 0 0 0 1 0 0 0 0 0 0 0 28
29 23 31 0 0 0 0 1 0 0 0 0 0 0 29
30 22 29 0 0 0 0 0 1 0 0 0 0 0 30
31 15 32 0 0 0 0 0 0 1 0 0 0 0 31
32 23 32 0 0 0 0 0 0 0 1 0 0 0 32
33 21 31 0 0 0 0 0 0 0 0 1 0 0 33
34 18 29 0 0 0 0 0 0 0 0 0 1 0 34
35 18 28 0 0 0 0 0 0 0 0 0 0 1 35
36 18 28 0 0 0 0 0 0 0 0 0 0 0 36
37 18 29 1 0 0 0 0 0 0 0 0 0 0 37
38 10 22 0 1 0 0 0 0 0 0 0 0 0 38
39 13 26 0 0 1 0 0 0 0 0 0 0 0 39
40 10 24 0 0 0 1 0 0 0 0 0 0 0 40
41 9 27 0 0 0 0 1 0 0 0 0 0 0 41
42 9 27 0 0 0 0 0 1 0 0 0 0 0 42
43 6 23 0 0 0 0 0 0 1 0 0 0 0 43
44 11 21 0 0 0 0 0 0 0 1 0 0 0 44
45 9 19 0 0 0 0 0 0 0 0 1 0 0 45
46 10 17 0 0 0 0 0 0 0 0 0 1 0 46
47 9 19 0 0 0 0 0 0 0 0 0 0 1 47
48 16 21 0 0 0 0 0 0 0 0 0 0 0 48
49 10 13 1 0 0 0 0 0 0 0 0 0 0 49
50 7 8 0 1 0 0 0 0 0 0 0 0 0 50
51 7 5 0 0 1 0 0 0 0 0 0 0 0 51
52 14 10 0 0 0 1 0 0 0 0 0 0 0 52
53 11 6 0 0 0 0 1 0 0 0 0 0 0 53
54 10 6 0 0 0 0 0 1 0 0 0 0 0 54
55 6 8 0 0 0 0 0 0 1 0 0 0 0 55
56 8 11 0 0 0 0 0 0 0 1 0 0 0 56
57 13 12 0 0 0 0 0 0 0 0 1 0 0 57
58 12 13 0 0 0 0 0 0 0 0 0 1 0 58
59 15 19 0 0 0 0 0 0 0 0 0 0 1 59
60 16 19 0 0 0 0 0 0 0 0 0 0 0 60
61 16 18 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) consv M1 M2 M3 M4
21.348582 0.212254 -0.569446 -5.120365 -0.951260 -0.703820
M5 M6 M7 M8 M9 M10
-0.389420 -1.177865 -3.493662 -1.554754 0.002096 -0.628799
M11 t
-1.126654 -0.241752
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.77819 -1.63148 0.01032 1.79818 6.14717
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.348582 2.909627 7.337 2.52e-09 ***
consv 0.212254 0.075131 2.825 0.00692 **
M1 -0.569446 2.133534 -0.267 0.79071
M2 -5.120365 2.254835 -2.271 0.02778 *
M3 -0.951260 2.255604 -0.422 0.67514
M4 -0.703820 2.234698 -0.315 0.75419
M5 -0.389420 2.231390 -0.175 0.86221
M6 -1.177865 2.230011 -0.528 0.59985
M7 -3.493662 2.228325 -1.568 0.12363
M8 -1.554754 2.230761 -0.697 0.48926
M9 0.002096 2.227292 0.001 0.99925
M10 -0.628799 2.227939 -0.282 0.77900
M11 -1.126654 2.224166 -0.507 0.61484
t -0.241752 0.030543 -7.915 3.41e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.516 on 47 degrees of freedom
Multiple R-squared: 0.7476, Adjusted R-squared: 0.6777
F-statistic: 10.71 on 13 and 47 DF, p-value: 4.743e-10
> 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,] 0.1313210117 0.2626420233 0.8686790
[2,] 0.0775578710 0.1551157420 0.9224421
[3,] 0.0385097278 0.0770194557 0.9614903
[4,] 0.0207005926 0.0414011851 0.9792994
[5,] 0.0108430990 0.0216861981 0.9891569
[6,] 0.0045805828 0.0091611656 0.9954194
[7,] 0.0023330140 0.0046660280 0.9976670
[8,] 0.0011018633 0.0022037266 0.9988981
[9,] 0.0003660874 0.0007321748 0.9996339
[10,] 0.0002074330 0.0004148660 0.9997926
[11,] 0.0002066985 0.0004133971 0.9997933
[12,] 0.0002050684 0.0004101367 0.9997949
[13,] 0.0001472362 0.0002944725 0.9998528
[14,] 0.0002026918 0.0004053836 0.9997973
[15,] 0.0079641114 0.0159282228 0.9920359
[16,] 0.0269567420 0.0539134839 0.9730433
[17,] 0.0464365043 0.0928730086 0.9535635
[18,] 0.0612827374 0.1225654748 0.9387173
[19,] 0.1043910440 0.2087820881 0.8956090
[20,] 0.1023179751 0.2046359502 0.8976820
[21,] 0.2542314266 0.5084628532 0.7457686
[22,] 0.2370962501 0.4741925003 0.7629037
[23,] 0.5314296450 0.9371407099 0.4685704
[24,] 0.5563222366 0.8873555267 0.4436778
[25,] 0.6471303506 0.7057392989 0.3528696
[26,] 0.6981320845 0.6037358311 0.3018679
[27,] 0.6348042893 0.7303914214 0.3651957
[28,] 0.6113662379 0.7772675242 0.3886338
> postscript(file="/var/www/html/rcomp/tmp/1uowm1258565654.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ysw01258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mygc1258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4tpy71258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t2as1258565654.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.63148253 1.37344237 0.23383578 -0.77185269 3.51873742 1.33668001
7 8 9 10 11 12
4.53099154 -0.89263951 -0.20773786 -0.33509033 -0.80773786 -5.84362303
13 14 15 16 17 18
-3.30594959 -1.30102470 2.92260635 4.06790141 0.20750800 0.87446706
19 20 21 22 23 24
4.21976212 -0.05288541 1.78299976 1.65564729 -0.60474612 -0.48964776
25 26 27 28 29 30
0.47253391 0.47745881 2.46039341 -1.03107389 2.47177034 2.92647528
31 32 33 34 35 36
-2.15273790 4.15010634 1.04726210 -0.65558213 0.29627858 -0.58862307
37 38 39 40 41 42
0.01032097 -1.71122942 -3.48759837 -6.06877860 -7.77818848 -6.74799178
43 44 45 46 47 48
-6.34142613 -2.61407366 -5.50466377 -3.20750800 -3.89240965 1.79818046
49 50 51 52 53 54
-1.69258843 1.16135294 -2.12923717 3.80380377 1.58017272 1.61036942
55 56 57 58 59 60
-0.25658964 -0.59050777 2.88213976 2.54253317 5.00861505 5.12371340
61
6.14716567
> postscript(file="/var/www/html/rcomp/tmp/68lsg1258565654.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.63148253 NA
1 1.37344237 -1.63148253
2 0.23383578 1.37344237
3 -0.77185269 0.23383578
4 3.51873742 -0.77185269
5 1.33668001 3.51873742
6 4.53099154 1.33668001
7 -0.89263951 4.53099154
8 -0.20773786 -0.89263951
9 -0.33509033 -0.20773786
10 -0.80773786 -0.33509033
11 -5.84362303 -0.80773786
12 -3.30594959 -5.84362303
13 -1.30102470 -3.30594959
14 2.92260635 -1.30102470
15 4.06790141 2.92260635
16 0.20750800 4.06790141
17 0.87446706 0.20750800
18 4.21976212 0.87446706
19 -0.05288541 4.21976212
20 1.78299976 -0.05288541
21 1.65564729 1.78299976
22 -0.60474612 1.65564729
23 -0.48964776 -0.60474612
24 0.47253391 -0.48964776
25 0.47745881 0.47253391
26 2.46039341 0.47745881
27 -1.03107389 2.46039341
28 2.47177034 -1.03107389
29 2.92647528 2.47177034
30 -2.15273790 2.92647528
31 4.15010634 -2.15273790
32 1.04726210 4.15010634
33 -0.65558213 1.04726210
34 0.29627858 -0.65558213
35 -0.58862307 0.29627858
36 0.01032097 -0.58862307
37 -1.71122942 0.01032097
38 -3.48759837 -1.71122942
39 -6.06877860 -3.48759837
40 -7.77818848 -6.06877860
41 -6.74799178 -7.77818848
42 -6.34142613 -6.74799178
43 -2.61407366 -6.34142613
44 -5.50466377 -2.61407366
45 -3.20750800 -5.50466377
46 -3.89240965 -3.20750800
47 1.79818046 -3.89240965
48 -1.69258843 1.79818046
49 1.16135294 -1.69258843
50 -2.12923717 1.16135294
51 3.80380377 -2.12923717
52 1.58017272 3.80380377
53 1.61036942 1.58017272
54 -0.25658964 1.61036942
55 -0.59050777 -0.25658964
56 2.88213976 -0.59050777
57 2.54253317 2.88213976
58 5.00861505 2.54253317
59 5.12371340 5.00861505
60 6.14716567 5.12371340
61 NA 6.14716567
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.37344237 -1.63148253
[2,] 0.23383578 1.37344237
[3,] -0.77185269 0.23383578
[4,] 3.51873742 -0.77185269
[5,] 1.33668001 3.51873742
[6,] 4.53099154 1.33668001
[7,] -0.89263951 4.53099154
[8,] -0.20773786 -0.89263951
[9,] -0.33509033 -0.20773786
[10,] -0.80773786 -0.33509033
[11,] -5.84362303 -0.80773786
[12,] -3.30594959 -5.84362303
[13,] -1.30102470 -3.30594959
[14,] 2.92260635 -1.30102470
[15,] 4.06790141 2.92260635
[16,] 0.20750800 4.06790141
[17,] 0.87446706 0.20750800
[18,] 4.21976212 0.87446706
[19,] -0.05288541 4.21976212
[20,] 1.78299976 -0.05288541
[21,] 1.65564729 1.78299976
[22,] -0.60474612 1.65564729
[23,] -0.48964776 -0.60474612
[24,] 0.47253391 -0.48964776
[25,] 0.47745881 0.47253391
[26,] 2.46039341 0.47745881
[27,] -1.03107389 2.46039341
[28,] 2.47177034 -1.03107389
[29,] 2.92647528 2.47177034
[30,] -2.15273790 2.92647528
[31,] 4.15010634 -2.15273790
[32,] 1.04726210 4.15010634
[33,] -0.65558213 1.04726210
[34,] 0.29627858 -0.65558213
[35,] -0.58862307 0.29627858
[36,] 0.01032097 -0.58862307
[37,] -1.71122942 0.01032097
[38,] -3.48759837 -1.71122942
[39,] -6.06877860 -3.48759837
[40,] -7.77818848 -6.06877860
[41,] -6.74799178 -7.77818848
[42,] -6.34142613 -6.74799178
[43,] -2.61407366 -6.34142613
[44,] -5.50466377 -2.61407366
[45,] -3.20750800 -5.50466377
[46,] -3.89240965 -3.20750800
[47,] 1.79818046 -3.89240965
[48,] -1.69258843 1.79818046
[49,] 1.16135294 -1.69258843
[50,] -2.12923717 1.16135294
[51,] 3.80380377 -2.12923717
[52,] 1.58017272 3.80380377
[53,] 1.61036942 1.58017272
[54,] -0.25658964 1.61036942
[55,] -0.59050777 -0.25658964
[56,] 2.88213976 -0.59050777
[57,] 2.54253317 2.88213976
[58,] 5.00861505 2.54253317
[59,] 5.12371340 5.00861505
[60,] 6.14716567 5.12371340
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.37344237 -1.63148253
2 0.23383578 1.37344237
3 -0.77185269 0.23383578
4 3.51873742 -0.77185269
5 1.33668001 3.51873742
6 4.53099154 1.33668001
7 -0.89263951 4.53099154
8 -0.20773786 -0.89263951
9 -0.33509033 -0.20773786
10 -0.80773786 -0.33509033
11 -5.84362303 -0.80773786
12 -3.30594959 -5.84362303
13 -1.30102470 -3.30594959
14 2.92260635 -1.30102470
15 4.06790141 2.92260635
16 0.20750800 4.06790141
17 0.87446706 0.20750800
18 4.21976212 0.87446706
19 -0.05288541 4.21976212
20 1.78299976 -0.05288541
21 1.65564729 1.78299976
22 -0.60474612 1.65564729
23 -0.48964776 -0.60474612
24 0.47253391 -0.48964776
25 0.47745881 0.47253391
26 2.46039341 0.47745881
27 -1.03107389 2.46039341
28 2.47177034 -1.03107389
29 2.92647528 2.47177034
30 -2.15273790 2.92647528
31 4.15010634 -2.15273790
32 1.04726210 4.15010634
33 -0.65558213 1.04726210
34 0.29627858 -0.65558213
35 -0.58862307 0.29627858
36 0.01032097 -0.58862307
37 -1.71122942 0.01032097
38 -3.48759837 -1.71122942
39 -6.06877860 -3.48759837
40 -7.77818848 -6.06877860
41 -6.74799178 -7.77818848
42 -6.34142613 -6.74799178
43 -2.61407366 -6.34142613
44 -5.50466377 -2.61407366
45 -3.20750800 -5.50466377
46 -3.89240965 -3.20750800
47 1.79818046 -3.89240965
48 -1.69258843 1.79818046
49 1.16135294 -1.69258843
50 -2.12923717 1.16135294
51 3.80380377 -2.12923717
52 1.58017272 3.80380377
53 1.61036942 1.58017272
54 -0.25658964 1.61036942
55 -0.59050777 -0.25658964
56 2.88213976 -0.59050777
57 2.54253317 2.88213976
58 5.00861505 2.54253317
59 5.12371340 5.00861505
60 6.14716567 5.12371340
> 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/7ijv21258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gc8h1258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9eq3z1258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10n9u01258565654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l1p91258565654.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/12vq8s1258565654.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/1328z21258565654.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/14bzbc1258565654.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/15u89m1258565654.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/1697bc1258565654.tab")
+ }
>
> system("convert tmp/1uowm1258565654.ps tmp/1uowm1258565654.png")
> system("convert tmp/2ysw01258565654.ps tmp/2ysw01258565654.png")
> system("convert tmp/3mygc1258565654.ps tmp/3mygc1258565654.png")
> system("convert tmp/4tpy71258565654.ps tmp/4tpy71258565654.png")
> system("convert tmp/5t2as1258565654.ps tmp/5t2as1258565654.png")
> system("convert tmp/68lsg1258565654.ps tmp/68lsg1258565654.png")
> system("convert tmp/7ijv21258565654.ps tmp/7ijv21258565654.png")
> system("convert tmp/8gc8h1258565654.ps tmp/8gc8h1258565654.png")
> system("convert tmp/9eq3z1258565654.ps tmp/9eq3z1258565654.png")
> system("convert tmp/10n9u01258565654.ps tmp/10n9u01258565654.png")
>
>
> proc.time()
user system elapsed
2.405 1.583 2.851