R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(97.57,0,97.74,0,97.92,0,98.19,0,98.23,0,98.41,0,98.59,0,98.71,0,99.14,0,99.62,0,100.18,1,100.66,1,101.19,1,101.75,1,102.2,1,102.87,1,98.81,0,97.6,0,96.68,0,95.96,0,98.89,0,99.05,0,99.2,0,99.11,0,99.19,0,99.77,0,100.6956867,0,100.7751938,0,100.5267342,0,101.013715,0,100.9242695,0,101.1031604,0,103.1107136,0,102.991453,0,102.3057046,0,102.6137945,0,103.6772014,0,104.7207315,0,107.6624925,0,108.8749752,0,108.1196581,0,107.6128006,0,106.4201948,0,105.6052475,0,105.7145697,0,105.4859869,0,105.5654939,0,105.177897,0,106.0922282,0,106.3406877,0,108.4675015,1,116.8654343,1,121.0793083,1,123.2657523,1,124.1800835,1,125.6012721,1,126.5652952,1,127.1814749,1,128.0361757,1,128.5529716,1,129.6660704,1),dim=c(2,61),dimnames=list(c('elektrictietsindex','dumivariable'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('elektrictietsindex','dumivariable'),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 = 'No 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
elektrictietsindex dumivariable M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 97.5700 0 1 0 0 0 0 0 0 0 0 0 0
2 97.7400 0 0 1 0 0 0 0 0 0 0 0 0
3 97.9200 0 0 0 1 0 0 0 0 0 0 0 0
4 98.1900 0 0 0 0 1 0 0 0 0 0 0 0
5 98.2300 0 0 0 0 0 1 0 0 0 0 0 0
6 98.4100 0 0 0 0 0 0 1 0 0 0 0 0
7 98.5900 0 0 0 0 0 0 0 1 0 0 0 0
8 98.7100 0 0 0 0 0 0 0 0 1 0 0 0
9 99.1400 0 0 0 0 0 0 0 0 0 1 0 0
10 99.6200 0 0 0 0 0 0 0 0 0 0 1 0
11 100.1800 1 0 0 0 0 0 0 0 0 0 0 1
12 100.6600 1 0 0 0 0 0 0 0 0 0 0 0
13 101.1900 1 1 0 0 0 0 0 0 0 0 0 0
14 101.7500 1 0 1 0 0 0 0 0 0 0 0 0
15 102.2000 1 0 0 1 0 0 0 0 0 0 0 0
16 102.8700 1 0 0 0 1 0 0 0 0 0 0 0
17 98.8100 0 0 0 0 0 1 0 0 0 0 0 0
18 97.6000 0 0 0 0 0 0 1 0 0 0 0 0
19 96.6800 0 0 0 0 0 0 0 1 0 0 0 0
20 95.9600 0 0 0 0 0 0 0 0 1 0 0 0
21 98.8900 0 0 0 0 0 0 0 0 0 1 0 0
22 99.0500 0 0 0 0 0 0 0 0 0 0 1 0
23 99.2000 0 0 0 0 0 0 0 0 0 0 0 1
24 99.1100 0 0 0 0 0 0 0 0 0 0 0 0
25 99.1900 0 1 0 0 0 0 0 0 0 0 0 0
26 99.7700 0 0 1 0 0 0 0 0 0 0 0 0
27 100.6957 0 0 0 1 0 0 0 0 0 0 0 0
28 100.7752 0 0 0 0 1 0 0 0 0 0 0 0
29 100.5267 0 0 0 0 0 1 0 0 0 0 0 0
30 101.0137 0 0 0 0 0 0 1 0 0 0 0 0
31 100.9243 0 0 0 0 0 0 0 1 0 0 0 0
32 101.1032 0 0 0 0 0 0 0 0 1 0 0 0
33 103.1107 0 0 0 0 0 0 0 0 0 1 0 0
34 102.9915 0 0 0 0 0 0 0 0 0 0 1 0
35 102.3057 0 0 0 0 0 0 0 0 0 0 0 1
36 102.6138 0 0 0 0 0 0 0 0 0 0 0 0
37 103.6772 0 1 0 0 0 0 0 0 0 0 0 0
38 104.7207 0 0 1 0 0 0 0 0 0 0 0 0
39 107.6625 0 0 0 1 0 0 0 0 0 0 0 0
40 108.8750 0 0 0 0 1 0 0 0 0 0 0 0
41 108.1197 0 0 0 0 0 1 0 0 0 0 0 0
42 107.6128 0 0 0 0 0 0 1 0 0 0 0 0
43 106.4202 0 0 0 0 0 0 0 1 0 0 0 0
44 105.6052 0 0 0 0 0 0 0 0 1 0 0 0
45 105.7146 0 0 0 0 0 0 0 0 0 1 0 0
46 105.4860 0 0 0 0 0 0 0 0 0 0 1 0
47 105.5655 0 0 0 0 0 0 0 0 0 0 0 1
48 105.1779 0 0 0 0 0 0 0 0 0 0 0 0
49 106.0922 0 1 0 0 0 0 0 0 0 0 0 0
50 106.3407 0 0 1 0 0 0 0 0 0 0 0 0
51 108.4675 1 0 0 1 0 0 0 0 0 0 0 0
52 116.8654 1 0 0 0 1 0 0 0 0 0 0 0
53 121.0793 1 0 0 0 0 1 0 0 0 0 0 0
54 123.2658 1 0 0 0 0 0 1 0 0 0 0 0
55 124.1801 1 0 0 0 0 0 0 1 0 0 0 0
56 125.6013 1 0 0 0 0 0 0 0 1 0 0 0
57 126.5653 1 0 0 0 0 0 0 0 0 1 0 0
58 127.1815 1 0 0 0 0 0 0 0 0 0 1 0
59 128.0362 1 0 0 0 0 0 0 0 0 0 0 1
60 128.5530 1 0 0 0 0 0 0 0 0 0 0 0
61 129.6661 1 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dumivariable M1 M2 M3
101.35324 14.67422 -0.01373 -2.22380 -3.83380
M4 M5 M6 M7 M8
-1.70781 1.06505 1.29237 1.07082 1.10785
M9 M10 M11
2.39603 2.57770 -0.16546
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.6820 -3.8341 -0.6386 4.3777 13.6523
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.35324 3.43357 29.518 < 2e-16 ***
dumivariable 14.67422 2.16869 6.766 1.68e-08 ***
M1 -0.01373 4.50058 -0.003 0.998
M2 -2.22380 4.71826 -0.471 0.640
M3 -3.83380 4.69828 -0.816 0.419
M4 -1.70781 4.69828 -0.363 0.718
M5 1.06505 4.71826 0.226 0.822
M6 1.29237 4.71826 0.274 0.785
M7 1.07082 4.71826 0.227 0.821
M8 1.10785 4.71826 0.235 0.815
M9 2.39603 4.71826 0.508 0.614
M10 2.57770 4.71826 0.546 0.587
M11 -0.16546 4.69828 -0.035 0.972
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.429 on 48 degrees of freedom
Multiple R-squared: 0.5005, Adjusted R-squared: 0.3757
F-statistic: 4.008 on 12 and 48 DF, p-value: 0.0002676
> 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,] 4.167718e-04 8.335436e-04 0.999583228
[2,] 5.557406e-05 1.111481e-04 0.999944426
[3,] 1.367923e-05 2.735846e-05 0.999986321
[4,] 3.952914e-05 7.905828e-05 0.999960471
[5,] 1.311599e-04 2.623198e-04 0.999868840
[6,] 2.928665e-05 5.857330e-05 0.999970713
[7,] 7.052487e-06 1.410497e-05 0.999992948
[8,] 1.476369e-05 2.952739e-05 0.999985236
[9,] 8.643601e-06 1.728720e-05 0.999991356
[10,] 5.242306e-06 1.048461e-05 0.999994758
[11,] 2.978748e-06 5.957497e-06 0.999997021
[12,] 2.081044e-06 4.162089e-06 0.999997919
[13,] 9.249156e-07 1.849831e-06 0.999999075
[14,] 6.862943e-07 1.372589e-06 0.999999314
[15,] 1.369866e-06 2.739731e-06 0.999998630
[16,] 2.915352e-06 5.830704e-06 0.999997085
[17,] 7.896333e-06 1.579267e-05 0.999992104
[18,] 1.770834e-05 3.541668e-05 0.999982292
[19,] 2.510893e-05 5.021785e-05 0.999974891
[20,] 4.350898e-05 8.701796e-05 0.999956491
[21,] 6.786670e-05 1.357334e-04 0.999932133
[22,] 2.098141e-04 4.196283e-04 0.999790186
[23,] 3.672627e-04 7.345255e-04 0.999632737
[24,] 8.258984e-02 1.651797e-01 0.917410159
[25,] 5.986954e-01 8.026092e-01 0.401304604
[26,] 9.040785e-01 1.918431e-01 0.095921539
[27,] 9.796963e-01 4.060740e-02 0.020303699
[28,] 9.950581e-01 9.883762e-03 0.004941881
[29,] 9.953704e-01 9.259169e-03 0.004629585
[30,] 9.937851e-01 1.242975e-02 0.006214873
> postscript(file="/var/www/html/rcomp/tmp/1rf451229947901.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21vx81229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pmij1229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zmgl1229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a9bq1229947901.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
-3.7695088 -1.3894391 0.4005533 -1.4554312 -4.1882954 -4.2356089
7 8 9 10 11 12
-3.8340649 -3.7510913 -4.6092710 -4.3109383 -15.6820090 -15.3674667
13 14 15 16 17 18
-14.8237324 -12.0536627 -9.9936703 -11.4496548 -3.6082954 -5.0456089
19 20 21 22 23 24
-5.7440649 -6.5010913 -4.8592710 -4.8809383 -1.9877854 -2.2432432
25 26 27 28 29 30
-2.1495088 0.6405609 3.1762400 1.1297626 -1.8915612 -1.6318939
31 32 33 34 35 36
-1.4997954 -1.3579309 -0.6385574 -0.9394853 1.1179192 1.2605513
37 38 39 40 41 42
2.3376926 5.5912924 10.1430458 9.2295440 5.7013627 4.9671917
43 44 45 46 47 48
3.9961299 3.1441562 1.9652987 1.5550486 4.3777085 3.8246538
49 50 51 52 53 54
4.7527194 7.2112486 -3.7261688 2.5457795 3.9867893 5.9459199
55 56 57 58 59 60
7.0817951 8.4659573 8.1418007 8.5763131 12.1741667 12.5255049
61
13.6523380
> postscript(file="/var/www/html/rcomp/tmp/6d5yo1229947901.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 -3.7695088 NA
1 -1.3894391 -3.7695088
2 0.4005533 -1.3894391
3 -1.4554312 0.4005533
4 -4.1882954 -1.4554312
5 -4.2356089 -4.1882954
6 -3.8340649 -4.2356089
7 -3.7510913 -3.8340649
8 -4.6092710 -3.7510913
9 -4.3109383 -4.6092710
10 -15.6820090 -4.3109383
11 -15.3674667 -15.6820090
12 -14.8237324 -15.3674667
13 -12.0536627 -14.8237324
14 -9.9936703 -12.0536627
15 -11.4496548 -9.9936703
16 -3.6082954 -11.4496548
17 -5.0456089 -3.6082954
18 -5.7440649 -5.0456089
19 -6.5010913 -5.7440649
20 -4.8592710 -6.5010913
21 -4.8809383 -4.8592710
22 -1.9877854 -4.8809383
23 -2.2432432 -1.9877854
24 -2.1495088 -2.2432432
25 0.6405609 -2.1495088
26 3.1762400 0.6405609
27 1.1297626 3.1762400
28 -1.8915612 1.1297626
29 -1.6318939 -1.8915612
30 -1.4997954 -1.6318939
31 -1.3579309 -1.4997954
32 -0.6385574 -1.3579309
33 -0.9394853 -0.6385574
34 1.1179192 -0.9394853
35 1.2605513 1.1179192
36 2.3376926 1.2605513
37 5.5912924 2.3376926
38 10.1430458 5.5912924
39 9.2295440 10.1430458
40 5.7013627 9.2295440
41 4.9671917 5.7013627
42 3.9961299 4.9671917
43 3.1441562 3.9961299
44 1.9652987 3.1441562
45 1.5550486 1.9652987
46 4.3777085 1.5550486
47 3.8246538 4.3777085
48 4.7527194 3.8246538
49 7.2112486 4.7527194
50 -3.7261688 7.2112486
51 2.5457795 -3.7261688
52 3.9867893 2.5457795
53 5.9459199 3.9867893
54 7.0817951 5.9459199
55 8.4659573 7.0817951
56 8.1418007 8.4659573
57 8.5763131 8.1418007
58 12.1741667 8.5763131
59 12.5255049 12.1741667
60 13.6523380 12.5255049
61 NA 13.6523380
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.3894391 -3.7695088
[2,] 0.4005533 -1.3894391
[3,] -1.4554312 0.4005533
[4,] -4.1882954 -1.4554312
[5,] -4.2356089 -4.1882954
[6,] -3.8340649 -4.2356089
[7,] -3.7510913 -3.8340649
[8,] -4.6092710 -3.7510913
[9,] -4.3109383 -4.6092710
[10,] -15.6820090 -4.3109383
[11,] -15.3674667 -15.6820090
[12,] -14.8237324 -15.3674667
[13,] -12.0536627 -14.8237324
[14,] -9.9936703 -12.0536627
[15,] -11.4496548 -9.9936703
[16,] -3.6082954 -11.4496548
[17,] -5.0456089 -3.6082954
[18,] -5.7440649 -5.0456089
[19,] -6.5010913 -5.7440649
[20,] -4.8592710 -6.5010913
[21,] -4.8809383 -4.8592710
[22,] -1.9877854 -4.8809383
[23,] -2.2432432 -1.9877854
[24,] -2.1495088 -2.2432432
[25,] 0.6405609 -2.1495088
[26,] 3.1762400 0.6405609
[27,] 1.1297626 3.1762400
[28,] -1.8915612 1.1297626
[29,] -1.6318939 -1.8915612
[30,] -1.4997954 -1.6318939
[31,] -1.3579309 -1.4997954
[32,] -0.6385574 -1.3579309
[33,] -0.9394853 -0.6385574
[34,] 1.1179192 -0.9394853
[35,] 1.2605513 1.1179192
[36,] 2.3376926 1.2605513
[37,] 5.5912924 2.3376926
[38,] 10.1430458 5.5912924
[39,] 9.2295440 10.1430458
[40,] 5.7013627 9.2295440
[41,] 4.9671917 5.7013627
[42,] 3.9961299 4.9671917
[43,] 3.1441562 3.9961299
[44,] 1.9652987 3.1441562
[45,] 1.5550486 1.9652987
[46,] 4.3777085 1.5550486
[47,] 3.8246538 4.3777085
[48,] 4.7527194 3.8246538
[49,] 7.2112486 4.7527194
[50,] -3.7261688 7.2112486
[51,] 2.5457795 -3.7261688
[52,] 3.9867893 2.5457795
[53,] 5.9459199 3.9867893
[54,] 7.0817951 5.9459199
[55,] 8.4659573 7.0817951
[56,] 8.1418007 8.4659573
[57,] 8.5763131 8.1418007
[58,] 12.1741667 8.5763131
[59,] 12.5255049 12.1741667
[60,] 13.6523380 12.5255049
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.3894391 -3.7695088
2 0.4005533 -1.3894391
3 -1.4554312 0.4005533
4 -4.1882954 -1.4554312
5 -4.2356089 -4.1882954
6 -3.8340649 -4.2356089
7 -3.7510913 -3.8340649
8 -4.6092710 -3.7510913
9 -4.3109383 -4.6092710
10 -15.6820090 -4.3109383
11 -15.3674667 -15.6820090
12 -14.8237324 -15.3674667
13 -12.0536627 -14.8237324
14 -9.9936703 -12.0536627
15 -11.4496548 -9.9936703
16 -3.6082954 -11.4496548
17 -5.0456089 -3.6082954
18 -5.7440649 -5.0456089
19 -6.5010913 -5.7440649
20 -4.8592710 -6.5010913
21 -4.8809383 -4.8592710
22 -1.9877854 -4.8809383
23 -2.2432432 -1.9877854
24 -2.1495088 -2.2432432
25 0.6405609 -2.1495088
26 3.1762400 0.6405609
27 1.1297626 3.1762400
28 -1.8915612 1.1297626
29 -1.6318939 -1.8915612
30 -1.4997954 -1.6318939
31 -1.3579309 -1.4997954
32 -0.6385574 -1.3579309
33 -0.9394853 -0.6385574
34 1.1179192 -0.9394853
35 1.2605513 1.1179192
36 2.3376926 1.2605513
37 5.5912924 2.3376926
38 10.1430458 5.5912924
39 9.2295440 10.1430458
40 5.7013627 9.2295440
41 4.9671917 5.7013627
42 3.9961299 4.9671917
43 3.1441562 3.9961299
44 1.9652987 3.1441562
45 1.5550486 1.9652987
46 4.3777085 1.5550486
47 3.8246538 4.3777085
48 4.7527194 3.8246538
49 7.2112486 4.7527194
50 -3.7261688 7.2112486
51 2.5457795 -3.7261688
52 3.9867893 2.5457795
53 5.9459199 3.9867893
54 7.0817951 5.9459199
55 8.4659573 7.0817951
56 8.1418007 8.4659573
57 8.5763131 8.1418007
58 12.1741667 8.5763131
59 12.5255049 12.1741667
60 13.6523380 12.5255049
> 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/77z7f1229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80hsd1229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9igvy1229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ek381229947901.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ionv1229947901.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/121kdm1229947901.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/13h6591229947901.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/14en9e1229947901.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/15nqys1229947902.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/162y9a1229947902.tab")
+ }
>
> system("convert tmp/1rf451229947901.ps tmp/1rf451229947901.png")
> system("convert tmp/21vx81229947901.ps tmp/21vx81229947901.png")
> system("convert tmp/3pmij1229947901.ps tmp/3pmij1229947901.png")
> system("convert tmp/4zmgl1229947901.ps tmp/4zmgl1229947901.png")
> system("convert tmp/5a9bq1229947901.ps tmp/5a9bq1229947901.png")
> system("convert tmp/6d5yo1229947901.ps tmp/6d5yo1229947901.png")
> system("convert tmp/77z7f1229947901.ps tmp/77z7f1229947901.png")
> system("convert tmp/80hsd1229947901.ps tmp/80hsd1229947901.png")
> system("convert tmp/9igvy1229947901.ps tmp/9igvy1229947901.png")
> system("convert tmp/10ek381229947901.ps tmp/10ek381229947901.png")
>
>
> proc.time()
user system elapsed
2.489 1.585 3.240