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.
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> x <- array(list(24,33,22,34,25,36,24,36,29,38,26,42,26,35,21,25,23,24,22,22,21,27,16,17,19,30,16,30,25,34,27,37,23,36,22,33,23,33,20,33,24,37,23,40,20,35,21,37,22,43,17,42,21,33,19,39,23,40,22,37,15,44,23,42,21,43,18,40,18,30,18,30,18,31,10,18,13,24,10,22,9,26,9,28,6,23,11,17,9,12,10,9,9,19,16,21,10,18,7,18,7,15,14,24,11,18,10,19,6,30,8,33,13,35,12,36,15,47,16,46,16,43),dim=c(2,61),dimnames=list(c('S.','E.S'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('S.','E.S'),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. E.S M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 33 1 0 0 0 0 0 0 0 0 0 0 1
2 22 34 0 1 0 0 0 0 0 0 0 0 0 2
3 25 36 0 0 1 0 0 0 0 0 0 0 0 3
4 24 36 0 0 0 1 0 0 0 0 0 0 0 4
5 29 38 0 0 0 0 1 0 0 0 0 0 0 5
6 26 42 0 0 0 0 0 1 0 0 0 0 0 6
7 26 35 0 0 0 0 0 0 1 0 0 0 0 7
8 21 25 0 0 0 0 0 0 0 1 0 0 0 8
9 23 24 0 0 0 0 0 0 0 0 1 0 0 9
10 22 22 0 0 0 0 0 0 0 0 0 1 0 10
11 21 27 0 0 0 0 0 0 0 0 0 0 1 11
12 16 17 0 0 0 0 0 0 0 0 0 0 0 12
13 19 30 1 0 0 0 0 0 0 0 0 0 0 13
14 16 30 0 1 0 0 0 0 0 0 0 0 0 14
15 25 34 0 0 1 0 0 0 0 0 0 0 0 15
16 27 37 0 0 0 1 0 0 0 0 0 0 0 16
17 23 36 0 0 0 0 1 0 0 0 0 0 0 17
18 22 33 0 0 0 0 0 1 0 0 0 0 0 18
19 23 33 0 0 0 0 0 0 1 0 0 0 0 19
20 20 33 0 0 0 0 0 0 0 1 0 0 0 20
21 24 37 0 0 0 0 0 0 0 0 1 0 0 21
22 23 40 0 0 0 0 0 0 0 0 0 1 0 22
23 20 35 0 0 0 0 0 0 0 0 0 0 1 23
24 21 37 0 0 0 0 0 0 0 0 0 0 0 24
25 22 43 1 0 0 0 0 0 0 0 0 0 0 25
26 17 42 0 1 0 0 0 0 0 0 0 0 0 26
27 21 33 0 0 1 0 0 0 0 0 0 0 0 27
28 19 39 0 0 0 1 0 0 0 0 0 0 0 28
29 23 40 0 0 0 0 1 0 0 0 0 0 0 29
30 22 37 0 0 0 0 0 1 0 0 0 0 0 30
31 15 44 0 0 0 0 0 0 1 0 0 0 0 31
32 23 42 0 0 0 0 0 0 0 1 0 0 0 32
33 21 43 0 0 0 0 0 0 0 0 1 0 0 33
34 18 40 0 0 0 0 0 0 0 0 0 1 0 34
35 18 30 0 0 0 0 0 0 0 0 0 0 1 35
36 18 30 0 0 0 0 0 0 0 0 0 0 0 36
37 18 31 1 0 0 0 0 0 0 0 0 0 0 37
38 10 18 0 1 0 0 0 0 0 0 0 0 0 38
39 13 24 0 0 1 0 0 0 0 0 0 0 0 39
40 10 22 0 0 0 1 0 0 0 0 0 0 0 40
41 9 26 0 0 0 0 1 0 0 0 0 0 0 41
42 9 28 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 17 0 0 0 0 0 0 0 1 0 0 0 44
45 9 12 0 0 0 0 0 0 0 0 1 0 0 45
46 10 9 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 18 1 0 0 0 0 0 0 0 0 0 0 49
50 7 18 0 1 0 0 0 0 0 0 0 0 0 50
51 7 15 0 0 1 0 0 0 0 0 0 0 0 51
52 14 24 0 0 0 1 0 0 0 0 0 0 0 52
53 11 18 0 0 0 0 1 0 0 0 0 0 0 53
54 10 19 0 0 0 0 0 1 0 0 0 0 0 54
55 6 30 0 0 0 0 0 0 1 0 0 0 0 55
56 8 33 0 0 0 0 0 0 0 1 0 0 0 56
57 13 35 0 0 0 0 0 0 0 0 1 0 0 57
58 12 36 0 0 0 0 0 0 0 0 0 1 0 58
59 15 47 0 0 0 0 0 0 0 0 0 0 1 59
60 16 46 0 0 0 0 0 0 0 0 0 0 0 60
61 16 43 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) E.S M1 M2 M3 M4
17.8744 0.2836 -1.2830 -5.0005 -0.9494 -1.0059
M5 M6 M7 M8 M9 M10
-0.5548 -1.5604 -4.2497 -1.7477 -0.1533 -0.6753
M11 t
-1.4482 -0.2511
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.3984 -1.3729 -0.2084 1.6176 4.7867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.87440 1.83681 9.731 7.66e-13 ***
E.S 0.28362 0.03684 7.698 7.21e-10 ***
M1 -1.28301 1.53653 -0.835 0.40794
M2 -5.00054 1.61456 -3.097 0.00329 **
M3 -0.94944 1.61218 -0.589 0.55874
M4 -1.00593 1.60674 -0.626 0.53430
M5 -0.55482 1.60516 -0.346 0.73115
M6 -1.56044 1.60393 -0.973 0.33559
M7 -4.24968 1.60442 -2.649 0.01097 *
M8 -1.74770 1.60168 -1.091 0.28076
M9 -0.15332 1.60076 -0.096 0.92410
M10 -0.67531 1.60063 -0.422 0.67502
M11 -1.44818 1.60052 -0.905 0.37018
t -0.25111 0.01925 -13.045 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.529 on 47 degrees of freedom
Multiple R-squared: 0.8694, Adjusted R-squared: 0.8333
F-statistic: 24.07 on 13 and 47 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.28690358 0.57380715 0.7130964
[2,] 0.50394611 0.99210777 0.4960539
[3,] 0.48102203 0.96204406 0.5189780
[4,] 0.40711998 0.81423996 0.5928800
[5,] 0.29738267 0.59476535 0.7026173
[6,] 0.21144647 0.42289294 0.7885535
[7,] 0.13519123 0.27038246 0.8648088
[8,] 0.11353556 0.22707111 0.8864644
[9,] 0.07483313 0.14966626 0.9251669
[10,] 0.05356761 0.10713521 0.9464324
[11,] 0.03386293 0.06772585 0.9661371
[12,] 0.04474315 0.08948631 0.9552568
[13,] 0.03295327 0.06590653 0.9670467
[14,] 0.04266190 0.08532379 0.9573381
[15,] 0.26582105 0.53164209 0.7341790
[16,] 0.41895129 0.83790258 0.5810487
[17,] 0.38934186 0.77868371 0.6106581
[18,] 0.33067559 0.66135118 0.6693244
[19,] 0.37463173 0.74926345 0.6253683
[20,] 0.31967109 0.63934217 0.6803289
[21,] 0.35793036 0.71586071 0.6420696
[22,] 0.31059379 0.62118758 0.6894062
[23,] 0.53614330 0.92771341 0.4638567
[24,] 0.54729008 0.90541984 0.4527099
[25,] 0.59461552 0.81076897 0.4053845
[26,] 0.65119135 0.69761730 0.3488087
[27,] 0.70120034 0.59759932 0.2987997
[28,] 0.57559147 0.84881706 0.4244085
> postscript(file="/var/www/html/rcomp/tmp/1keoq1260370819.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wsam1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pqvk1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xtap1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v9431260370819.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
-1.69986888 -0.01485638 -1.38210445 -2.07450753 2.15824439 -0.71952695
7 8 9 10 11 12
4.20619016 -0.20844158 0.73190727 1.07225611 -0.32189119 -3.68272447
13 14 15 16 17 18
-2.83571588 -1.86707934 2.19842451 3.65514931 -0.26122665 0.84637027
19 20 21 22 23 24
4.78671912 -0.46415300 1.05807566 -0.01969568 -0.57760261 -1.34192434
25 26 27 28 29 30
-0.50954749 -1.25728691 1.49532943 -1.89881788 1.61755808 2.72515500
31 32 33 34 35 36
-3.31986442 2.99651155 -0.63038768 -2.00641480 1.85379846 0.65672481
37 38 39 40 41 42
1.90722185 1.56297088 -0.93877335 -3.06392835 -5.39842451 -4.70894777
43 44 45 46 47 48
-3.35047874 1.10039337 -0.82476163 1.79921125 -1.01305624 4.22262203
49 50 51 52 53 54
0.60761523 1.57625176 -1.37287613 3.38210445 1.88384868 1.85694945
55 56 57 58 59 60
-2.32256612 -3.42431035 -0.33483361 -0.84535688 0.05875158 0.14530197
61
2.53029516
> postscript(file="/var/www/html/rcomp/tmp/6tjeh1260370819.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 -1.69986888 NA
1 -0.01485638 -1.69986888
2 -1.38210445 -0.01485638
3 -2.07450753 -1.38210445
4 2.15824439 -2.07450753
5 -0.71952695 2.15824439
6 4.20619016 -0.71952695
7 -0.20844158 4.20619016
8 0.73190727 -0.20844158
9 1.07225611 0.73190727
10 -0.32189119 1.07225611
11 -3.68272447 -0.32189119
12 -2.83571588 -3.68272447
13 -1.86707934 -2.83571588
14 2.19842451 -1.86707934
15 3.65514931 2.19842451
16 -0.26122665 3.65514931
17 0.84637027 -0.26122665
18 4.78671912 0.84637027
19 -0.46415300 4.78671912
20 1.05807566 -0.46415300
21 -0.01969568 1.05807566
22 -0.57760261 -0.01969568
23 -1.34192434 -0.57760261
24 -0.50954749 -1.34192434
25 -1.25728691 -0.50954749
26 1.49532943 -1.25728691
27 -1.89881788 1.49532943
28 1.61755808 -1.89881788
29 2.72515500 1.61755808
30 -3.31986442 2.72515500
31 2.99651155 -3.31986442
32 -0.63038768 2.99651155
33 -2.00641480 -0.63038768
34 1.85379846 -2.00641480
35 0.65672481 1.85379846
36 1.90722185 0.65672481
37 1.56297088 1.90722185
38 -0.93877335 1.56297088
39 -3.06392835 -0.93877335
40 -5.39842451 -3.06392835
41 -4.70894777 -5.39842451
42 -3.35047874 -4.70894777
43 1.10039337 -3.35047874
44 -0.82476163 1.10039337
45 1.79921125 -0.82476163
46 -1.01305624 1.79921125
47 4.22262203 -1.01305624
48 0.60761523 4.22262203
49 1.57625176 0.60761523
50 -1.37287613 1.57625176
51 3.38210445 -1.37287613
52 1.88384868 3.38210445
53 1.85694945 1.88384868
54 -2.32256612 1.85694945
55 -3.42431035 -2.32256612
56 -0.33483361 -3.42431035
57 -0.84535688 -0.33483361
58 0.05875158 -0.84535688
59 0.14530197 0.05875158
60 2.53029516 0.14530197
61 NA 2.53029516
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.01485638 -1.69986888
[2,] -1.38210445 -0.01485638
[3,] -2.07450753 -1.38210445
[4,] 2.15824439 -2.07450753
[5,] -0.71952695 2.15824439
[6,] 4.20619016 -0.71952695
[7,] -0.20844158 4.20619016
[8,] 0.73190727 -0.20844158
[9,] 1.07225611 0.73190727
[10,] -0.32189119 1.07225611
[11,] -3.68272447 -0.32189119
[12,] -2.83571588 -3.68272447
[13,] -1.86707934 -2.83571588
[14,] 2.19842451 -1.86707934
[15,] 3.65514931 2.19842451
[16,] -0.26122665 3.65514931
[17,] 0.84637027 -0.26122665
[18,] 4.78671912 0.84637027
[19,] -0.46415300 4.78671912
[20,] 1.05807566 -0.46415300
[21,] -0.01969568 1.05807566
[22,] -0.57760261 -0.01969568
[23,] -1.34192434 -0.57760261
[24,] -0.50954749 -1.34192434
[25,] -1.25728691 -0.50954749
[26,] 1.49532943 -1.25728691
[27,] -1.89881788 1.49532943
[28,] 1.61755808 -1.89881788
[29,] 2.72515500 1.61755808
[30,] -3.31986442 2.72515500
[31,] 2.99651155 -3.31986442
[32,] -0.63038768 2.99651155
[33,] -2.00641480 -0.63038768
[34,] 1.85379846 -2.00641480
[35,] 0.65672481 1.85379846
[36,] 1.90722185 0.65672481
[37,] 1.56297088 1.90722185
[38,] -0.93877335 1.56297088
[39,] -3.06392835 -0.93877335
[40,] -5.39842451 -3.06392835
[41,] -4.70894777 -5.39842451
[42,] -3.35047874 -4.70894777
[43,] 1.10039337 -3.35047874
[44,] -0.82476163 1.10039337
[45,] 1.79921125 -0.82476163
[46,] -1.01305624 1.79921125
[47,] 4.22262203 -1.01305624
[48,] 0.60761523 4.22262203
[49,] 1.57625176 0.60761523
[50,] -1.37287613 1.57625176
[51,] 3.38210445 -1.37287613
[52,] 1.88384868 3.38210445
[53,] 1.85694945 1.88384868
[54,] -2.32256612 1.85694945
[55,] -3.42431035 -2.32256612
[56,] -0.33483361 -3.42431035
[57,] -0.84535688 -0.33483361
[58,] 0.05875158 -0.84535688
[59,] 0.14530197 0.05875158
[60,] 2.53029516 0.14530197
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.01485638 -1.69986888
2 -1.38210445 -0.01485638
3 -2.07450753 -1.38210445
4 2.15824439 -2.07450753
5 -0.71952695 2.15824439
6 4.20619016 -0.71952695
7 -0.20844158 4.20619016
8 0.73190727 -0.20844158
9 1.07225611 0.73190727
10 -0.32189119 1.07225611
11 -3.68272447 -0.32189119
12 -2.83571588 -3.68272447
13 -1.86707934 -2.83571588
14 2.19842451 -1.86707934
15 3.65514931 2.19842451
16 -0.26122665 3.65514931
17 0.84637027 -0.26122665
18 4.78671912 0.84637027
19 -0.46415300 4.78671912
20 1.05807566 -0.46415300
21 -0.01969568 1.05807566
22 -0.57760261 -0.01969568
23 -1.34192434 -0.57760261
24 -0.50954749 -1.34192434
25 -1.25728691 -0.50954749
26 1.49532943 -1.25728691
27 -1.89881788 1.49532943
28 1.61755808 -1.89881788
29 2.72515500 1.61755808
30 -3.31986442 2.72515500
31 2.99651155 -3.31986442
32 -0.63038768 2.99651155
33 -2.00641480 -0.63038768
34 1.85379846 -2.00641480
35 0.65672481 1.85379846
36 1.90722185 0.65672481
37 1.56297088 1.90722185
38 -0.93877335 1.56297088
39 -3.06392835 -0.93877335
40 -5.39842451 -3.06392835
41 -4.70894777 -5.39842451
42 -3.35047874 -4.70894777
43 1.10039337 -3.35047874
44 -0.82476163 1.10039337
45 1.79921125 -0.82476163
46 -1.01305624 1.79921125
47 4.22262203 -1.01305624
48 0.60761523 4.22262203
49 1.57625176 0.60761523
50 -1.37287613 1.57625176
51 3.38210445 -1.37287613
52 1.88384868 3.38210445
53 1.85694945 1.88384868
54 -2.32256612 1.85694945
55 -3.42431035 -2.32256612
56 -0.33483361 -3.42431035
57 -0.84535688 -0.33483361
58 0.05875158 -0.84535688
59 0.14530197 0.05875158
60 2.53029516 0.14530197
> 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/7kl8x1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8n2o71260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99tjm1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10glhh1260370819.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ozio1260370820.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/12i4bg1260370820.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/13brjy1260370820.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/14ax591260370820.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/154uti1260370820.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/1651sb1260370820.tab")
+ }
>
> system("convert tmp/1keoq1260370819.ps tmp/1keoq1260370819.png")
> system("convert tmp/2wsam1260370819.ps tmp/2wsam1260370819.png")
> system("convert tmp/3pqvk1260370819.ps tmp/3pqvk1260370819.png")
> system("convert tmp/4xtap1260370819.ps tmp/4xtap1260370819.png")
> system("convert tmp/5v9431260370819.ps tmp/5v9431260370819.png")
> system("convert tmp/6tjeh1260370819.ps tmp/6tjeh1260370819.png")
> system("convert tmp/7kl8x1260370819.ps tmp/7kl8x1260370819.png")
> system("convert tmp/8n2o71260370819.ps tmp/8n2o71260370819.png")
> system("convert tmp/99tjm1260370819.ps tmp/99tjm1260370819.png")
> system("convert tmp/10glhh1260370819.ps tmp/10glhh1260370819.png")
>
>
> proc.time()
user system elapsed
2.438 1.580 3.321