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(0.6348,1.5291,0.634,1.5358,0.62915,1.5355,0.62168,1.5287,0.61328,1.5334,0.6089,1.5225,0.60857,1.5135,0.62672,1.5144,0.62291,1.4913,0.62393,1.4793,0.61838,1.4663,0.62012,1.4749,0.61659,1.4745,0.6116,1.4775,0.61573,1.4678,0.61407,1.4658,0.62823,1.4572,0.64405,1.4721,0.6387,1.4624,0.63633,1.4636,0.63059,1.4649,0.62994,1.465,0.63709,1.4673,0.64217,1.4679,0.65711,1.4621,0.66977,1.4674,0.68255,1.4695,0.68902,1.4964,0.71322,1.5155,0.70224,1.5411,0.70045,1.5476,0.69919,1.54,0.69693,1.5474,0.69763,1.5485,0.69278,1.559,0.70196,1.5544,0.69215,1.5657,0.6769,1.5734,0.67124,1.567,0.66532,1.5547,0.67157,1.54,0.66428,1.5192,0.66576,1.527,0.66942,1.5387,0.6813,1.5431,0.69144,1.5426,0.69862,1.5216,0.695,1.5364,0.69867,1.5469,0.68968,1.5501,0.69233,1.5494,0.68293,1.5475,0.68399,1.5448,0.66895,1.5391,0.68756,1.5578,0.68527,1.5528,0.6776,1.5496,0.68137,1.549,0.67933,1.5449,0.67922,1.5479),dim=c(2,60),dimnames=list(c('Britse_pond','Zwitserse_frank'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Britse_pond','Zwitserse_frank'),1:60))
> 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
Britse_pond Zwitserse_frank M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0.63480 1.5291 1 0 0 0 0 0 0 0 0 0 0 1
2 0.63400 1.5358 0 1 0 0 0 0 0 0 0 0 0 2
3 0.62915 1.5355 0 0 1 0 0 0 0 0 0 0 0 3
4 0.62168 1.5287 0 0 0 1 0 0 0 0 0 0 0 4
5 0.61328 1.5334 0 0 0 0 1 0 0 0 0 0 0 5
6 0.60890 1.5225 0 0 0 0 0 1 0 0 0 0 0 6
7 0.60857 1.5135 0 0 0 0 0 0 1 0 0 0 0 7
8 0.62672 1.5144 0 0 0 0 0 0 0 1 0 0 0 8
9 0.62291 1.4913 0 0 0 0 0 0 0 0 1 0 0 9
10 0.62393 1.4793 0 0 0 0 0 0 0 0 0 1 0 10
11 0.61838 1.4663 0 0 0 0 0 0 0 0 0 0 1 11
12 0.62012 1.4749 0 0 0 0 0 0 0 0 0 0 0 12
13 0.61659 1.4745 1 0 0 0 0 0 0 0 0 0 0 13
14 0.61160 1.4775 0 1 0 0 0 0 0 0 0 0 0 14
15 0.61573 1.4678 0 0 1 0 0 0 0 0 0 0 0 15
16 0.61407 1.4658 0 0 0 1 0 0 0 0 0 0 0 16
17 0.62823 1.4572 0 0 0 0 1 0 0 0 0 0 0 17
18 0.64405 1.4721 0 0 0 0 0 1 0 0 0 0 0 18
19 0.63870 1.4624 0 0 0 0 0 0 1 0 0 0 0 19
20 0.63633 1.4636 0 0 0 0 0 0 0 1 0 0 0 20
21 0.63059 1.4649 0 0 0 0 0 0 0 0 1 0 0 21
22 0.62994 1.4650 0 0 0 0 0 0 0 0 0 1 0 22
23 0.63709 1.4673 0 0 0 0 0 0 0 0 0 0 1 23
24 0.64217 1.4679 0 0 0 0 0 0 0 0 0 0 0 24
25 0.65711 1.4621 1 0 0 0 0 0 0 0 0 0 0 25
26 0.66977 1.4674 0 1 0 0 0 0 0 0 0 0 0 26
27 0.68255 1.4695 0 0 1 0 0 0 0 0 0 0 0 27
28 0.68902 1.4964 0 0 0 1 0 0 0 0 0 0 0 28
29 0.71322 1.5155 0 0 0 0 1 0 0 0 0 0 0 29
30 0.70224 1.5411 0 0 0 0 0 1 0 0 0 0 0 30
31 0.70045 1.5476 0 0 0 0 0 0 1 0 0 0 0 31
32 0.69919 1.5400 0 0 0 0 0 0 0 1 0 0 0 32
33 0.69693 1.5474 0 0 0 0 0 0 0 0 1 0 0 33
34 0.69763 1.5485 0 0 0 0 0 0 0 0 0 1 0 34
35 0.69278 1.5590 0 0 0 0 0 0 0 0 0 0 1 35
36 0.70196 1.5544 0 0 0 0 0 0 0 0 0 0 0 36
37 0.69215 1.5657 1 0 0 0 0 0 0 0 0 0 0 37
38 0.67690 1.5734 0 1 0 0 0 0 0 0 0 0 0 38
39 0.67124 1.5670 0 0 1 0 0 0 0 0 0 0 0 39
40 0.66532 1.5547 0 0 0 1 0 0 0 0 0 0 0 40
41 0.67157 1.5400 0 0 0 0 1 0 0 0 0 0 0 41
42 0.66428 1.5192 0 0 0 0 0 1 0 0 0 0 0 42
43 0.66576 1.5270 0 0 0 0 0 0 1 0 0 0 0 43
44 0.66942 1.5387 0 0 0 0 0 0 0 1 0 0 0 44
45 0.68130 1.5431 0 0 0 0 0 0 0 0 1 0 0 45
46 0.69144 1.5426 0 0 0 0 0 0 0 0 0 1 0 46
47 0.69862 1.5216 0 0 0 0 0 0 0 0 0 0 1 47
48 0.69500 1.5364 0 0 0 0 0 0 0 0 0 0 0 48
49 0.69867 1.5469 1 0 0 0 0 0 0 0 0 0 0 49
50 0.68968 1.5501 0 1 0 0 0 0 0 0 0 0 0 50
51 0.69233 1.5494 0 0 1 0 0 0 0 0 0 0 0 51
52 0.68293 1.5475 0 0 0 1 0 0 0 0 0 0 0 52
53 0.68399 1.5448 0 0 0 0 1 0 0 0 0 0 0 53
54 0.66895 1.5391 0 0 0 0 0 1 0 0 0 0 0 54
55 0.68756 1.5578 0 0 0 0 0 0 1 0 0 0 0 55
56 0.68527 1.5528 0 0 0 0 0 0 0 1 0 0 0 56
57 0.67760 1.5496 0 0 0 0 0 0 0 0 1 0 0 57
58 0.68137 1.5490 0 0 0 0 0 0 0 0 0 1 0 58
59 0.67933 1.5449 0 0 0 0 0 0 0 0 0 0 1 59
60 0.67922 1.5479 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Zwitserse_frank M1 M2
0.2611413 0.2410007 0.0048897 -0.0009750
M3 M4 M5 M6
0.0004157 -0.0045106 0.0019071 -0.0037586
M7 M8 M9 M10
-0.0030662 -0.0010884 -0.0031144 -0.0006872
M11 t
-0.0002320 0.0011423
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.025031 -0.011767 -0.005113 0.007192 0.051808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2611413 0.1380323 1.892 0.0648 .
Zwitserse_frank 0.2410007 0.0935795 2.575 0.0133 *
M1 0.0048897 0.0132631 0.369 0.7141
M2 -0.0009750 0.0132830 -0.073 0.9418
M3 0.0004157 0.0132225 0.031 0.9751
M4 -0.0045106 0.0132023 -0.342 0.7342
M5 0.0019071 0.0131750 0.145 0.8855
M6 -0.0037586 0.0131582 -0.286 0.7764
M7 -0.0030662 0.0131594 -0.233 0.8168
M8 -0.0010884 0.0131432 -0.083 0.9344
M9 -0.0031144 0.0131138 -0.237 0.8133
M10 -0.0006872 0.0130970 -0.052 0.9584
M11 -0.0002320 0.0130943 -0.018 0.9859
t 0.0011423 0.0001975 5.785 6.07e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0207 on 46 degrees of freedom
Multiple R-squared: 0.6698, Adjusted R-squared: 0.5765
F-statistic: 7.178 on 13 and 46 DF, p-value: 2.326e-07
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.04137849 0.08275699 0.95862151
[2,] 0.57010417 0.85979166 0.42989583
[3,] 0.55087020 0.89825960 0.44912980
[4,] 0.42783544 0.85567089 0.57216456
[5,] 0.35934017 0.71868034 0.64065983
[6,] 0.33916971 0.67833942 0.66083029
[7,] 0.34471139 0.68942278 0.65528861
[8,] 0.42856460 0.85712920 0.57143540
[9,] 0.50794172 0.98411655 0.49205828
[10,] 0.59066324 0.81867352 0.40933676
[11,] 0.67516301 0.64967398 0.32483699
[12,] 0.62644885 0.74710230 0.37355115
[13,] 0.67289167 0.65421667 0.32710833
[14,] 0.76473398 0.47053204 0.23526602
[15,] 0.78789057 0.42421886 0.21210943
[16,] 0.80634728 0.38730544 0.19365272
[17,] 0.82202324 0.35595352 0.17797676
[18,] 0.80709768 0.38580464 0.19290232
[19,] 0.79383590 0.41232819 0.20616410
[20,] 0.86163782 0.27672437 0.13836218
[21,] 0.87057656 0.25884687 0.12942344
[22,] 0.88943069 0.22113861 0.11056931
[23,] 0.90219293 0.19561414 0.09780707
[24,] 0.91559633 0.16880734 0.08440367
[25,] 0.91623685 0.16752630 0.08376315
[26,] 0.84924793 0.30150414 0.15075207
[27,] 0.84637669 0.30724661 0.15362331
> postscript(file="/var/www/html/rcomp/tmp/1wlth1258707809.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21ozf1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ujjf1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yg921258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52qxl1258707809.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 = 60
Frequency = 1
1 2 3 4 5
-8.875317e-04 1.420147e-03 -5.890555e-03 -7.937769e-03 -2.503051e-02
6 7 8 9 10
-2.226018e-02 -2.225592e-02 -7.442977e-03 -4.802102e-03 -4.459675e-03
11 12 13 14 15
-8.474129e-03 -1.018105e-02 -1.964665e-02 -2.063727e-02 -1.670256e-02
16 17 18 19 20
-1.409658e-02 -5.424017e-03 1.132849e-02 6.481462e-03 7.021010e-04
21 22 23 24 25
-4.467442e-03 -8.711124e-03 -3.712889e-03 -1.518062e-04 1.015400e-02
26 27 28 29 30
2.625908e-02 3.599997e-02 3.977104e-02 5.180788e-02 3.918168e-02
31 32 33 34 35
3.399044e-02 3.144189e-02 2.828224e-02 2.514756e-02 1.616959e-02
36 37 38 39 40
2.508387e-02 6.518565e-03 -5.864757e-03 -1.251535e-02 -1.168706e-02
41 42 43 44 45
-9.454395e-03 -7.208159e-03 -9.442703e-03 -1.172257e-02 -1.921616e-05
46 47 48 49 50
6.671702e-03 1.731525e-02 8.754127e-03 3.861620e-03 -1.177199e-03
51 52 53 54 55
-8.915007e-04 -6.049619e-03 -1.189896e-02 -2.104183e-02 -8.773284e-03
56 57 58 59 60
-1.297844e-02 -1.899348e-02 -1.864846e-02 -2.129782e-02 -2.350514e-02
> postscript(file="/var/www/html/rcomp/tmp/69bxd1258707809.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.875317e-04 NA
1 1.420147e-03 -8.875317e-04
2 -5.890555e-03 1.420147e-03
3 -7.937769e-03 -5.890555e-03
4 -2.503051e-02 -7.937769e-03
5 -2.226018e-02 -2.503051e-02
6 -2.225592e-02 -2.226018e-02
7 -7.442977e-03 -2.225592e-02
8 -4.802102e-03 -7.442977e-03
9 -4.459675e-03 -4.802102e-03
10 -8.474129e-03 -4.459675e-03
11 -1.018105e-02 -8.474129e-03
12 -1.964665e-02 -1.018105e-02
13 -2.063727e-02 -1.964665e-02
14 -1.670256e-02 -2.063727e-02
15 -1.409658e-02 -1.670256e-02
16 -5.424017e-03 -1.409658e-02
17 1.132849e-02 -5.424017e-03
18 6.481462e-03 1.132849e-02
19 7.021010e-04 6.481462e-03
20 -4.467442e-03 7.021010e-04
21 -8.711124e-03 -4.467442e-03
22 -3.712889e-03 -8.711124e-03
23 -1.518062e-04 -3.712889e-03
24 1.015400e-02 -1.518062e-04
25 2.625908e-02 1.015400e-02
26 3.599997e-02 2.625908e-02
27 3.977104e-02 3.599997e-02
28 5.180788e-02 3.977104e-02
29 3.918168e-02 5.180788e-02
30 3.399044e-02 3.918168e-02
31 3.144189e-02 3.399044e-02
32 2.828224e-02 3.144189e-02
33 2.514756e-02 2.828224e-02
34 1.616959e-02 2.514756e-02
35 2.508387e-02 1.616959e-02
36 6.518565e-03 2.508387e-02
37 -5.864757e-03 6.518565e-03
38 -1.251535e-02 -5.864757e-03
39 -1.168706e-02 -1.251535e-02
40 -9.454395e-03 -1.168706e-02
41 -7.208159e-03 -9.454395e-03
42 -9.442703e-03 -7.208159e-03
43 -1.172257e-02 -9.442703e-03
44 -1.921616e-05 -1.172257e-02
45 6.671702e-03 -1.921616e-05
46 1.731525e-02 6.671702e-03
47 8.754127e-03 1.731525e-02
48 3.861620e-03 8.754127e-03
49 -1.177199e-03 3.861620e-03
50 -8.915007e-04 -1.177199e-03
51 -6.049619e-03 -8.915007e-04
52 -1.189896e-02 -6.049619e-03
53 -2.104183e-02 -1.189896e-02
54 -8.773284e-03 -2.104183e-02
55 -1.297844e-02 -8.773284e-03
56 -1.899348e-02 -1.297844e-02
57 -1.864846e-02 -1.899348e-02
58 -2.129782e-02 -1.864846e-02
59 -2.350514e-02 -2.129782e-02
60 NA -2.350514e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.420147e-03 -8.875317e-04
[2,] -5.890555e-03 1.420147e-03
[3,] -7.937769e-03 -5.890555e-03
[4,] -2.503051e-02 -7.937769e-03
[5,] -2.226018e-02 -2.503051e-02
[6,] -2.225592e-02 -2.226018e-02
[7,] -7.442977e-03 -2.225592e-02
[8,] -4.802102e-03 -7.442977e-03
[9,] -4.459675e-03 -4.802102e-03
[10,] -8.474129e-03 -4.459675e-03
[11,] -1.018105e-02 -8.474129e-03
[12,] -1.964665e-02 -1.018105e-02
[13,] -2.063727e-02 -1.964665e-02
[14,] -1.670256e-02 -2.063727e-02
[15,] -1.409658e-02 -1.670256e-02
[16,] -5.424017e-03 -1.409658e-02
[17,] 1.132849e-02 -5.424017e-03
[18,] 6.481462e-03 1.132849e-02
[19,] 7.021010e-04 6.481462e-03
[20,] -4.467442e-03 7.021010e-04
[21,] -8.711124e-03 -4.467442e-03
[22,] -3.712889e-03 -8.711124e-03
[23,] -1.518062e-04 -3.712889e-03
[24,] 1.015400e-02 -1.518062e-04
[25,] 2.625908e-02 1.015400e-02
[26,] 3.599997e-02 2.625908e-02
[27,] 3.977104e-02 3.599997e-02
[28,] 5.180788e-02 3.977104e-02
[29,] 3.918168e-02 5.180788e-02
[30,] 3.399044e-02 3.918168e-02
[31,] 3.144189e-02 3.399044e-02
[32,] 2.828224e-02 3.144189e-02
[33,] 2.514756e-02 2.828224e-02
[34,] 1.616959e-02 2.514756e-02
[35,] 2.508387e-02 1.616959e-02
[36,] 6.518565e-03 2.508387e-02
[37,] -5.864757e-03 6.518565e-03
[38,] -1.251535e-02 -5.864757e-03
[39,] -1.168706e-02 -1.251535e-02
[40,] -9.454395e-03 -1.168706e-02
[41,] -7.208159e-03 -9.454395e-03
[42,] -9.442703e-03 -7.208159e-03
[43,] -1.172257e-02 -9.442703e-03
[44,] -1.921616e-05 -1.172257e-02
[45,] 6.671702e-03 -1.921616e-05
[46,] 1.731525e-02 6.671702e-03
[47,] 8.754127e-03 1.731525e-02
[48,] 3.861620e-03 8.754127e-03
[49,] -1.177199e-03 3.861620e-03
[50,] -8.915007e-04 -1.177199e-03
[51,] -6.049619e-03 -8.915007e-04
[52,] -1.189896e-02 -6.049619e-03
[53,] -2.104183e-02 -1.189896e-02
[54,] -8.773284e-03 -2.104183e-02
[55,] -1.297844e-02 -8.773284e-03
[56,] -1.899348e-02 -1.297844e-02
[57,] -1.864846e-02 -1.899348e-02
[58,] -2.129782e-02 -1.864846e-02
[59,] -2.350514e-02 -2.129782e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.420147e-03 -8.875317e-04
2 -5.890555e-03 1.420147e-03
3 -7.937769e-03 -5.890555e-03
4 -2.503051e-02 -7.937769e-03
5 -2.226018e-02 -2.503051e-02
6 -2.225592e-02 -2.226018e-02
7 -7.442977e-03 -2.225592e-02
8 -4.802102e-03 -7.442977e-03
9 -4.459675e-03 -4.802102e-03
10 -8.474129e-03 -4.459675e-03
11 -1.018105e-02 -8.474129e-03
12 -1.964665e-02 -1.018105e-02
13 -2.063727e-02 -1.964665e-02
14 -1.670256e-02 -2.063727e-02
15 -1.409658e-02 -1.670256e-02
16 -5.424017e-03 -1.409658e-02
17 1.132849e-02 -5.424017e-03
18 6.481462e-03 1.132849e-02
19 7.021010e-04 6.481462e-03
20 -4.467442e-03 7.021010e-04
21 -8.711124e-03 -4.467442e-03
22 -3.712889e-03 -8.711124e-03
23 -1.518062e-04 -3.712889e-03
24 1.015400e-02 -1.518062e-04
25 2.625908e-02 1.015400e-02
26 3.599997e-02 2.625908e-02
27 3.977104e-02 3.599997e-02
28 5.180788e-02 3.977104e-02
29 3.918168e-02 5.180788e-02
30 3.399044e-02 3.918168e-02
31 3.144189e-02 3.399044e-02
32 2.828224e-02 3.144189e-02
33 2.514756e-02 2.828224e-02
34 1.616959e-02 2.514756e-02
35 2.508387e-02 1.616959e-02
36 6.518565e-03 2.508387e-02
37 -5.864757e-03 6.518565e-03
38 -1.251535e-02 -5.864757e-03
39 -1.168706e-02 -1.251535e-02
40 -9.454395e-03 -1.168706e-02
41 -7.208159e-03 -9.454395e-03
42 -9.442703e-03 -7.208159e-03
43 -1.172257e-02 -9.442703e-03
44 -1.921616e-05 -1.172257e-02
45 6.671702e-03 -1.921616e-05
46 1.731525e-02 6.671702e-03
47 8.754127e-03 1.731525e-02
48 3.861620e-03 8.754127e-03
49 -1.177199e-03 3.861620e-03
50 -8.915007e-04 -1.177199e-03
51 -6.049619e-03 -8.915007e-04
52 -1.189896e-02 -6.049619e-03
53 -2.104183e-02 -1.189896e-02
54 -8.773284e-03 -2.104183e-02
55 -1.297844e-02 -8.773284e-03
56 -1.899348e-02 -1.297844e-02
57 -1.864846e-02 -1.899348e-02
58 -2.129782e-02 -1.864846e-02
59 -2.350514e-02 -2.129782e-02
> 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/7o1qm1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8r2fj1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/925bc1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i2lt1258707809.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113y6w1258707809.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/12yhml1258707809.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/13zg8b1258707809.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/14nbfq1258707809.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/15moyb1258707809.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/16x4z91258707809.tab")
+ }
>
> system("convert tmp/1wlth1258707809.ps tmp/1wlth1258707809.png")
> system("convert tmp/21ozf1258707809.ps tmp/21ozf1258707809.png")
> system("convert tmp/3ujjf1258707809.ps tmp/3ujjf1258707809.png")
> system("convert tmp/4yg921258707809.ps tmp/4yg921258707809.png")
> system("convert tmp/52qxl1258707809.ps tmp/52qxl1258707809.png")
> system("convert tmp/69bxd1258707809.ps tmp/69bxd1258707809.png")
> system("convert tmp/7o1qm1258707809.ps tmp/7o1qm1258707809.png")
> system("convert tmp/8r2fj1258707809.ps tmp/8r2fj1258707809.png")
> system("convert tmp/925bc1258707809.ps tmp/925bc1258707809.png")
> system("convert tmp/10i2lt1258707809.ps tmp/10i2lt1258707809.png")
>
>
> proc.time()
user system elapsed
2.336 1.520 3.441