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(2.155,22.782,2.172,19.169,2.15,13.807,2.533,29.743,2.058,25.591,2.16,29.096,2.26,26.482,2.498,22.405,2.695,27.044,2.799,17.97,2.947,18.73,2.93,19.684,2.318,19.785,2.54,18.479,2.57,10.698,2.669,31.956,2.45,29.506,2.842,34.506,3.44,27.165,2.678,26.736,2.981,23.691,2.26,18.157,2.844,17.328,2.546,18.205,2.456,20.995,2.295,17.382,2.379,9.367,2.479,31.124,2.057,26.551,2.28,30.651,2.351,25.859,2.276,25.1,2.548,25.778,2.311,20.418,2.201,18.688,2.725,20.424,2.408,24.776,2.139,19.814,1.898,12.738,2.537,31.566,2.069,30.111,2.063,30.019,2.524,31.934,2.437,25.826,2.189,26.835,2.793,20.205,2.074,17.789,2.622,20.52,2.278,22.518,2.144,15.572,2.427,11.509,2.139,25.447,1.828,24.09,2.072,27.786,1.8,26.195,1.758,20.516,2.246,22.759,1.987,19.028,1.868,16.971,2.514,20.036,2.121,22.485),dim=c(2,61),dimnames=list(c('geb','aut'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('geb','aut'),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
geb aut M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2.155 22.782 1 0 0 0 0 0 0 0 0 0 0 1
2 2.172 19.169 0 1 0 0 0 0 0 0 0 0 0 2
3 2.150 13.807 0 0 1 0 0 0 0 0 0 0 0 3
4 2.533 29.743 0 0 0 1 0 0 0 0 0 0 0 4
5 2.058 25.591 0 0 0 0 1 0 0 0 0 0 0 5
6 2.160 29.096 0 0 0 0 0 1 0 0 0 0 0 6
7 2.260 26.482 0 0 0 0 0 0 1 0 0 0 0 7
8 2.498 22.405 0 0 0 0 0 0 0 1 0 0 0 8
9 2.695 27.044 0 0 0 0 0 0 0 0 1 0 0 9
10 2.799 17.970 0 0 0 0 0 0 0 0 0 1 0 10
11 2.947 18.730 0 0 0 0 0 0 0 0 0 0 1 11
12 2.930 19.684 0 0 0 0 0 0 0 0 0 0 0 12
13 2.318 19.785 1 0 0 0 0 0 0 0 0 0 0 13
14 2.540 18.479 0 1 0 0 0 0 0 0 0 0 0 14
15 2.570 10.698 0 0 1 0 0 0 0 0 0 0 0 15
16 2.669 31.956 0 0 0 1 0 0 0 0 0 0 0 16
17 2.450 29.506 0 0 0 0 1 0 0 0 0 0 0 17
18 2.842 34.506 0 0 0 0 0 1 0 0 0 0 0 18
19 3.440 27.165 0 0 0 0 0 0 1 0 0 0 0 19
20 2.678 26.736 0 0 0 0 0 0 0 1 0 0 0 20
21 2.981 23.691 0 0 0 0 0 0 0 0 1 0 0 21
22 2.260 18.157 0 0 0 0 0 0 0 0 0 1 0 22
23 2.844 17.328 0 0 0 0 0 0 0 0 0 0 1 23
24 2.546 18.205 0 0 0 0 0 0 0 0 0 0 0 24
25 2.456 20.995 1 0 0 0 0 0 0 0 0 0 0 25
26 2.295 17.382 0 1 0 0 0 0 0 0 0 0 0 26
27 2.379 9.367 0 0 1 0 0 0 0 0 0 0 0 27
28 2.479 31.124 0 0 0 1 0 0 0 0 0 0 0 28
29 2.057 26.551 0 0 0 0 1 0 0 0 0 0 0 29
30 2.280 30.651 0 0 0 0 0 1 0 0 0 0 0 30
31 2.351 25.859 0 0 0 0 0 0 1 0 0 0 0 31
32 2.276 25.100 0 0 0 0 0 0 0 1 0 0 0 32
33 2.548 25.778 0 0 0 0 0 0 0 0 1 0 0 33
34 2.311 20.418 0 0 0 0 0 0 0 0 0 1 0 34
35 2.201 18.688 0 0 0 0 0 0 0 0 0 0 1 35
36 2.725 20.424 0 0 0 0 0 0 0 0 0 0 0 36
37 2.408 24.776 1 0 0 0 0 0 0 0 0 0 0 37
38 2.139 19.814 0 1 0 0 0 0 0 0 0 0 0 38
39 1.898 12.738 0 0 1 0 0 0 0 0 0 0 0 39
40 2.537 31.566 0 0 0 1 0 0 0 0 0 0 0 40
41 2.069 30.111 0 0 0 0 1 0 0 0 0 0 0 41
42 2.063 30.019 0 0 0 0 0 1 0 0 0 0 0 42
43 2.524 31.934 0 0 0 0 0 0 1 0 0 0 0 43
44 2.437 25.826 0 0 0 0 0 0 0 1 0 0 0 44
45 2.189 26.835 0 0 0 0 0 0 0 0 1 0 0 45
46 2.793 20.205 0 0 0 0 0 0 0 0 0 1 0 46
47 2.074 17.789 0 0 0 0 0 0 0 0 0 0 1 47
48 2.622 20.520 0 0 0 0 0 0 0 0 0 0 0 48
49 2.278 22.518 1 0 0 0 0 0 0 0 0 0 0 49
50 2.144 15.572 0 1 0 0 0 0 0 0 0 0 0 50
51 2.427 11.509 0 0 1 0 0 0 0 0 0 0 0 51
52 2.139 25.447 0 0 0 1 0 0 0 0 0 0 0 52
53 1.828 24.090 0 0 0 0 1 0 0 0 0 0 0 53
54 2.072 27.786 0 0 0 0 0 1 0 0 0 0 0 54
55 1.800 26.195 0 0 0 0 0 0 1 0 0 0 0 55
56 1.758 20.516 0 0 0 0 0 0 0 1 0 0 0 56
57 2.246 22.759 0 0 0 0 0 0 0 0 1 0 0 57
58 1.987 19.028 0 0 0 0 0 0 0 0 0 1 0 58
59 1.868 16.971 0 0 0 0 0 0 0 0 0 0 1 59
60 2.514 20.036 0 0 0 0 0 0 0 0 0 0 0 60
61 2.121 22.485 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) aut M1 M2 M3 M4
2.184673 0.039357 -0.515523 -0.424949 -0.135718 -0.662851
M5 M6 M7 M8 M9 M10
-0.923545 -0.851924 -0.538586 -0.541754 -0.374627 -0.229487
M11 t
-0.215109 -0.008209
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.42557 -0.16868 0.00812 0.13883 0.88074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.184673 0.422630 5.169 4.74e-06 ***
aut 0.039357 0.019602 2.008 0.050427 .
M1 -0.515523 0.170905 -3.016 0.004119 **
M2 -0.424949 0.176354 -2.410 0.019937 *
M3 -0.135718 0.236876 -0.573 0.569411
M4 -0.662851 0.261951 -2.530 0.014801 *
M5 -0.923545 0.223612 -4.130 0.000148 ***
M6 -0.851924 0.268830 -3.169 0.002690 **
M7 -0.538586 0.228334 -2.359 0.022545 *
M8 -0.541754 0.191054 -2.836 0.006725 **
M9 -0.374627 0.201596 -1.858 0.069395 .
M10 -0.229487 0.171965 -1.334 0.188471
M11 -0.215109 0.175407 -1.226 0.226180
t -0.008209 0.002023 -4.058 0.000186 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2711 on 47 degrees of freedom
Multiple R-squared: 0.4639, Adjusted R-squared: 0.3157
F-statistic: 3.129 on 13 and 47 DF, p-value: 0.002041
> 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,] 0.09156801 0.18313602 0.90843199
[2,] 0.10675358 0.21350716 0.89324642
[3,] 0.78483770 0.43032460 0.21516230
[4,] 0.76101703 0.47796594 0.23898297
[5,] 0.73233656 0.53532688 0.26766344
[6,] 0.95115056 0.09769888 0.04884944
[7,] 0.97948197 0.04103605 0.02051803
[8,] 0.98594197 0.02811607 0.01405803
[9,] 0.97427624 0.05144752 0.02572376
[10,] 0.96297887 0.07404227 0.03702113
[11,] 0.94373676 0.11252647 0.05626324
[12,] 0.93218859 0.13562282 0.06781141
[13,] 0.91173883 0.17652234 0.08826117
[14,] 0.88328106 0.23343787 0.11671894
[15,] 0.89980738 0.20038524 0.10019262
[16,] 0.87491955 0.25016090 0.12508045
[17,] 0.84976873 0.30046255 0.15023127
[18,] 0.82155777 0.35688446 0.17844223
[19,] 0.82825439 0.34349122 0.17174561
[20,] 0.75152810 0.49694380 0.24847190
[21,] 0.65656687 0.68686626 0.34343313
[22,] 0.60143958 0.79712083 0.39856042
[23,] 0.80641772 0.38716456 0.19358228
[24,] 0.70920674 0.58158652 0.29079326
[25,] 0.63193089 0.73613822 0.36806911
[26,] 0.59336639 0.81326723 0.40663361
[27,] 0.48175271 0.96350543 0.51824729
[28,] 0.47889557 0.95779114 0.52110443
> postscript(file="/var/www/html/rcomp/tmp/1cs1q1258722186.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23wdk1258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38xsb1258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/472e71258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jwpg1258722186.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
-0.402573359 -0.325741154 -0.417731545 -0.126583462 -0.169270447 -0.268629214
7 8 9 10 11 12
-0.370879222 0.038956165 -0.105539404 0.218654374 0.330573599 0.069126955
13 14 15 16 17 18
-0.023116726 0.167918840 0.223133074 0.020823112 0.167150484 0.298952977
19 20 21 22 23 24
0.880743588 0.147004577 0.410928326 -0.229201735 0.381255790 -0.158160364
25 26 27 28 29 30
0.165764936 0.064597140 0.183020916 -0.037928197 -0.011045878 -0.012822070
31 32 33 34 35 36
-0.058352496 -0.092103691 -0.005706116 -0.168684298 -0.216766101 0.032010068
37 38 39 40 41 42
0.067459707 -0.088615473 -0.332147936 0.101179654 -0.040653206 -0.106444783
43 44 45 46 47 48
-0.025942721 0.138826768 -0.307802830 0.420202399 -0.209880490 0.023735446
49 50 51 52 53 54
0.124831504 0.181840646 0.343725491 0.042508893 0.053819046 0.088943089
55 56 57 58 59 60
-0.425569148 -0.232683819 0.008120024 -0.240970740 -0.285182798 0.033287895
61
0.067633938
> postscript(file="/var/www/html/rcomp/tmp/6s7yl1258722186.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 -0.402573359 NA
1 -0.325741154 -0.402573359
2 -0.417731545 -0.325741154
3 -0.126583462 -0.417731545
4 -0.169270447 -0.126583462
5 -0.268629214 -0.169270447
6 -0.370879222 -0.268629214
7 0.038956165 -0.370879222
8 -0.105539404 0.038956165
9 0.218654374 -0.105539404
10 0.330573599 0.218654374
11 0.069126955 0.330573599
12 -0.023116726 0.069126955
13 0.167918840 -0.023116726
14 0.223133074 0.167918840
15 0.020823112 0.223133074
16 0.167150484 0.020823112
17 0.298952977 0.167150484
18 0.880743588 0.298952977
19 0.147004577 0.880743588
20 0.410928326 0.147004577
21 -0.229201735 0.410928326
22 0.381255790 -0.229201735
23 -0.158160364 0.381255790
24 0.165764936 -0.158160364
25 0.064597140 0.165764936
26 0.183020916 0.064597140
27 -0.037928197 0.183020916
28 -0.011045878 -0.037928197
29 -0.012822070 -0.011045878
30 -0.058352496 -0.012822070
31 -0.092103691 -0.058352496
32 -0.005706116 -0.092103691
33 -0.168684298 -0.005706116
34 -0.216766101 -0.168684298
35 0.032010068 -0.216766101
36 0.067459707 0.032010068
37 -0.088615473 0.067459707
38 -0.332147936 -0.088615473
39 0.101179654 -0.332147936
40 -0.040653206 0.101179654
41 -0.106444783 -0.040653206
42 -0.025942721 -0.106444783
43 0.138826768 -0.025942721
44 -0.307802830 0.138826768
45 0.420202399 -0.307802830
46 -0.209880490 0.420202399
47 0.023735446 -0.209880490
48 0.124831504 0.023735446
49 0.181840646 0.124831504
50 0.343725491 0.181840646
51 0.042508893 0.343725491
52 0.053819046 0.042508893
53 0.088943089 0.053819046
54 -0.425569148 0.088943089
55 -0.232683819 -0.425569148
56 0.008120024 -0.232683819
57 -0.240970740 0.008120024
58 -0.285182798 -0.240970740
59 0.033287895 -0.285182798
60 0.067633938 0.033287895
61 NA 0.067633938
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.325741154 -0.402573359
[2,] -0.417731545 -0.325741154
[3,] -0.126583462 -0.417731545
[4,] -0.169270447 -0.126583462
[5,] -0.268629214 -0.169270447
[6,] -0.370879222 -0.268629214
[7,] 0.038956165 -0.370879222
[8,] -0.105539404 0.038956165
[9,] 0.218654374 -0.105539404
[10,] 0.330573599 0.218654374
[11,] 0.069126955 0.330573599
[12,] -0.023116726 0.069126955
[13,] 0.167918840 -0.023116726
[14,] 0.223133074 0.167918840
[15,] 0.020823112 0.223133074
[16,] 0.167150484 0.020823112
[17,] 0.298952977 0.167150484
[18,] 0.880743588 0.298952977
[19,] 0.147004577 0.880743588
[20,] 0.410928326 0.147004577
[21,] -0.229201735 0.410928326
[22,] 0.381255790 -0.229201735
[23,] -0.158160364 0.381255790
[24,] 0.165764936 -0.158160364
[25,] 0.064597140 0.165764936
[26,] 0.183020916 0.064597140
[27,] -0.037928197 0.183020916
[28,] -0.011045878 -0.037928197
[29,] -0.012822070 -0.011045878
[30,] -0.058352496 -0.012822070
[31,] -0.092103691 -0.058352496
[32,] -0.005706116 -0.092103691
[33,] -0.168684298 -0.005706116
[34,] -0.216766101 -0.168684298
[35,] 0.032010068 -0.216766101
[36,] 0.067459707 0.032010068
[37,] -0.088615473 0.067459707
[38,] -0.332147936 -0.088615473
[39,] 0.101179654 -0.332147936
[40,] -0.040653206 0.101179654
[41,] -0.106444783 -0.040653206
[42,] -0.025942721 -0.106444783
[43,] 0.138826768 -0.025942721
[44,] -0.307802830 0.138826768
[45,] 0.420202399 -0.307802830
[46,] -0.209880490 0.420202399
[47,] 0.023735446 -0.209880490
[48,] 0.124831504 0.023735446
[49,] 0.181840646 0.124831504
[50,] 0.343725491 0.181840646
[51,] 0.042508893 0.343725491
[52,] 0.053819046 0.042508893
[53,] 0.088943089 0.053819046
[54,] -0.425569148 0.088943089
[55,] -0.232683819 -0.425569148
[56,] 0.008120024 -0.232683819
[57,] -0.240970740 0.008120024
[58,] -0.285182798 -0.240970740
[59,] 0.033287895 -0.285182798
[60,] 0.067633938 0.033287895
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.325741154 -0.402573359
2 -0.417731545 -0.325741154
3 -0.126583462 -0.417731545
4 -0.169270447 -0.126583462
5 -0.268629214 -0.169270447
6 -0.370879222 -0.268629214
7 0.038956165 -0.370879222
8 -0.105539404 0.038956165
9 0.218654374 -0.105539404
10 0.330573599 0.218654374
11 0.069126955 0.330573599
12 -0.023116726 0.069126955
13 0.167918840 -0.023116726
14 0.223133074 0.167918840
15 0.020823112 0.223133074
16 0.167150484 0.020823112
17 0.298952977 0.167150484
18 0.880743588 0.298952977
19 0.147004577 0.880743588
20 0.410928326 0.147004577
21 -0.229201735 0.410928326
22 0.381255790 -0.229201735
23 -0.158160364 0.381255790
24 0.165764936 -0.158160364
25 0.064597140 0.165764936
26 0.183020916 0.064597140
27 -0.037928197 0.183020916
28 -0.011045878 -0.037928197
29 -0.012822070 -0.011045878
30 -0.058352496 -0.012822070
31 -0.092103691 -0.058352496
32 -0.005706116 -0.092103691
33 -0.168684298 -0.005706116
34 -0.216766101 -0.168684298
35 0.032010068 -0.216766101
36 0.067459707 0.032010068
37 -0.088615473 0.067459707
38 -0.332147936 -0.088615473
39 0.101179654 -0.332147936
40 -0.040653206 0.101179654
41 -0.106444783 -0.040653206
42 -0.025942721 -0.106444783
43 0.138826768 -0.025942721
44 -0.307802830 0.138826768
45 0.420202399 -0.307802830
46 -0.209880490 0.420202399
47 0.023735446 -0.209880490
48 0.124831504 0.023735446
49 0.181840646 0.124831504
50 0.343725491 0.181840646
51 0.042508893 0.343725491
52 0.053819046 0.042508893
53 0.088943089 0.053819046
54 -0.425569148 0.088943089
55 -0.232683819 -0.425569148
56 0.008120024 -0.232683819
57 -0.240970740 0.008120024
58 -0.285182798 -0.240970740
59 0.033287895 -0.285182798
60 0.067633938 0.033287895
> 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/7gjqm1258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ljvg1258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sv3g1258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10try11258722186.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11s43i1258722186.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/129irk1258722186.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/13uwkq1258722186.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/149b2i1258722186.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/151laa1258722186.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/16ngur1258722186.tab")
+ }
>
> system("convert tmp/1cs1q1258722186.ps tmp/1cs1q1258722186.png")
> system("convert tmp/23wdk1258722186.ps tmp/23wdk1258722186.png")
> system("convert tmp/38xsb1258722186.ps tmp/38xsb1258722186.png")
> system("convert tmp/472e71258722186.ps tmp/472e71258722186.png")
> system("convert tmp/5jwpg1258722186.ps tmp/5jwpg1258722186.png")
> system("convert tmp/6s7yl1258722186.ps tmp/6s7yl1258722186.png")
> system("convert tmp/7gjqm1258722186.ps tmp/7gjqm1258722186.png")
> system("convert tmp/8ljvg1258722186.ps tmp/8ljvg1258722186.png")
> system("convert tmp/9sv3g1258722186.ps tmp/9sv3g1258722186.png")
> system("convert tmp/10try11258722186.ps tmp/10try11258722186.png")
>
>
> proc.time()
user system elapsed
2.412 1.575 2.813