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(613,0,611,611,0,613,594,0,611,595,0,594,591,0,595,589,0,591,584,0,589,573,0,584,567,0,573,569,0,567,621,0,569,629,0,621,628,0,629,612,0,628,595,0,612,597,0,595,593,0,597,590,0,593,580,0,590,574,0,580,573,0,574,573,0,573,620,0,573,626,0,620,620,0,626,588,0,620,566,0,588,557,0,566,561,0,557,549,0,561,532,0,549,526,0,532,511,0,526,499,0,511,555,0,499,565,0,555,542,0,565,527,0,542,510,0,527,514,0,510,517,0,514,508,0,517,493,0,508,490,0,493,469,0,490,478,0,469,528,0,478,534,0,528,518,1,534,506,1,518,502,1,506,516,1,502,528,1,516,533,1,528,536,1,533,537,1,536,524,1,537,536,1,524,587,1,536,597,1,587,581,1,597),dim=c(3,61),dimnames=list(c('WklBe','X','Y1'),1:61))
> y <- array(NA,dim=c(3,61),dimnames=list(c('WklBe','X','Y1'),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
WklBe X Y1 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 613 0 611 1 0 0 0 0 0 0 0 0 0 0 1
2 611 0 613 0 1 0 0 0 0 0 0 0 0 0 2
3 594 0 611 0 0 1 0 0 0 0 0 0 0 0 3
4 595 0 594 0 0 0 1 0 0 0 0 0 0 0 4
5 591 0 595 0 0 0 0 1 0 0 0 0 0 0 5
6 589 0 591 0 0 0 0 0 1 0 0 0 0 0 6
7 584 0 589 0 0 0 0 0 0 1 0 0 0 0 7
8 573 0 584 0 0 0 0 0 0 0 1 0 0 0 8
9 567 0 573 0 0 0 0 0 0 0 0 1 0 0 9
10 569 0 567 0 0 0 0 0 0 0 0 0 1 0 10
11 621 0 569 0 0 0 0 0 0 0 0 0 0 1 11
12 629 0 621 0 0 0 0 0 0 0 0 0 0 0 12
13 628 0 629 1 0 0 0 0 0 0 0 0 0 0 13
14 612 0 628 0 1 0 0 0 0 0 0 0 0 0 14
15 595 0 612 0 0 1 0 0 0 0 0 0 0 0 15
16 597 0 595 0 0 0 1 0 0 0 0 0 0 0 16
17 593 0 597 0 0 0 0 1 0 0 0 0 0 0 17
18 590 0 593 0 0 0 0 0 1 0 0 0 0 0 18
19 580 0 590 0 0 0 0 0 0 1 0 0 0 0 19
20 574 0 580 0 0 0 0 0 0 0 1 0 0 0 20
21 573 0 574 0 0 0 0 0 0 0 0 1 0 0 21
22 573 0 573 0 0 0 0 0 0 0 0 0 1 0 22
23 620 0 573 0 0 0 0 0 0 0 0 0 0 1 23
24 626 0 620 0 0 0 0 0 0 0 0 0 0 0 24
25 620 0 626 1 0 0 0 0 0 0 0 0 0 0 25
26 588 0 620 0 1 0 0 0 0 0 0 0 0 0 26
27 566 0 588 0 0 1 0 0 0 0 0 0 0 0 27
28 557 0 566 0 0 0 1 0 0 0 0 0 0 0 28
29 561 0 557 0 0 0 0 1 0 0 0 0 0 0 29
30 549 0 561 0 0 0 0 0 1 0 0 0 0 0 30
31 532 0 549 0 0 0 0 0 0 1 0 0 0 0 31
32 526 0 532 0 0 0 0 0 0 0 1 0 0 0 32
33 511 0 526 0 0 0 0 0 0 0 0 1 0 0 33
34 499 0 511 0 0 0 0 0 0 0 0 0 1 0 34
35 555 0 499 0 0 0 0 0 0 0 0 0 0 1 35
36 565 0 555 0 0 0 0 0 0 0 0 0 0 0 36
37 542 0 565 1 0 0 0 0 0 0 0 0 0 0 37
38 527 0 542 0 1 0 0 0 0 0 0 0 0 0 38
39 510 0 527 0 0 1 0 0 0 0 0 0 0 0 39
40 514 0 510 0 0 0 1 0 0 0 0 0 0 0 40
41 517 0 514 0 0 0 0 1 0 0 0 0 0 0 41
42 508 0 517 0 0 0 0 0 1 0 0 0 0 0 42
43 493 0 508 0 0 0 0 0 0 1 0 0 0 0 43
44 490 0 493 0 0 0 0 0 0 0 1 0 0 0 44
45 469 0 490 0 0 0 0 0 0 0 0 1 0 0 45
46 478 0 469 0 0 0 0 0 0 0 0 0 1 0 46
47 528 0 478 0 0 0 0 0 0 0 0 0 0 1 47
48 534 0 528 0 0 0 0 0 0 0 0 0 0 0 48
49 518 1 534 1 0 0 0 0 0 0 0 0 0 0 49
50 506 1 518 0 1 0 0 0 0 0 0 0 0 0 50
51 502 1 506 0 0 1 0 0 0 0 0 0 0 0 51
52 516 1 502 0 0 0 1 0 0 0 0 0 0 0 52
53 528 1 516 0 0 0 0 1 0 0 0 0 0 0 53
54 533 1 528 0 0 0 0 0 1 0 0 0 0 0 54
55 536 1 533 0 0 0 0 0 0 1 0 0 0 0 55
56 537 1 536 0 0 0 0 0 0 0 1 0 0 0 56
57 524 1 537 0 0 0 0 0 0 0 0 1 0 0 57
58 536 1 524 0 0 0 0 0 0 0 0 0 1 0 58
59 587 1 536 0 0 0 0 0 0 0 0 0 0 1 59
60 597 1 587 0 0 0 0 0 0 0 0 0 0 0 60
61 581 1 597 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) X Y1 M1 M2 M3
78.3799 14.6196 0.8981 -20.7234 -27.0811 -28.2617
M4 M5 M6 M7 M8 M9
-11.6422 -11.2092 -16.9966 -21.6360 -18.3441 -24.6651
M10 M11 t
-12.0177 37.5949 -0.3885
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.8328 -3.6633 0.5994 3.6862 11.9262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.37991 26.47955 2.960 0.004850 **
X 14.61959 3.28885 4.445 5.50e-05 ***
Y1 0.89811 0.03991 22.501 < 2e-16 ***
M1 -20.72344 3.95396 -5.241 3.90e-06 ***
M2 -27.08114 4.20458 -6.441 6.31e-08 ***
M3 -28.26169 4.32882 -6.529 4.65e-08 ***
M4 -11.64225 4.51167 -2.580 0.013123 *
M5 -11.20923 4.42865 -2.531 0.014855 *
M6 -16.99659 4.35600 -3.902 0.000309 ***
M7 -21.63602 4.37708 -4.943 1.06e-05 ***
M8 -18.34413 4.47327 -4.101 0.000166 ***
M9 -24.66507 4.51531 -5.463 1.83e-06 ***
M10 -12.01771 4.67794 -2.569 0.013508 *
M11 37.59493 4.59018 8.190 1.55e-10 ***
t -0.38849 0.11461 -3.390 0.001445 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.424 on 46 degrees of freedom
Multiple R-squared: 0.9821, Adjusted R-squared: 0.9767
F-statistic: 180.4 on 14 and 46 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,] 0.184311704 0.36862341 0.81568830
[2,] 0.112462404 0.22492481 0.88753760
[3,] 0.059028232 0.11805646 0.94097177
[4,] 0.066557432 0.13311486 0.93344257
[5,] 0.030795166 0.06159033 0.96920483
[6,] 0.017357242 0.03471448 0.98264276
[7,] 0.008904131 0.01780826 0.99109587
[8,] 0.039742885 0.07948577 0.96025711
[9,] 0.663342194 0.67331561 0.33665781
[10,] 0.567097598 0.86580480 0.43290240
[11,] 0.566784308 0.86643138 0.43321569
[12,] 0.627546104 0.74490779 0.37245390
[13,] 0.587205140 0.82558972 0.41279486
[14,] 0.550303074 0.89939385 0.44969693
[15,] 0.471488502 0.94297700 0.52851150
[16,] 0.498841453 0.99768291 0.50115855
[17,] 0.930639544 0.13872091 0.06936046
[18,] 0.944090046 0.11181991 0.05590995
[19,] 0.939862461 0.12027508 0.06013754
[20,] 0.953888265 0.09222347 0.04611174
[21,] 0.982939449 0.03412110 0.01706055
[22,] 0.966371435 0.06725713 0.03362856
[23,] 0.959030410 0.08193918 0.04096959
[24,] 0.980540750 0.03891850 0.01945925
[25,] 0.980583892 0.03883222 0.01941611
[26,] 0.971032312 0.05793538 0.02896769
> postscript(file="/var/www/html/rcomp/tmp/1umsw1258727879.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/274go1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a2o21258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4raop1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x77e1258727879.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
6.98450127 9.93446272 -3.70026194 -3.66327989 -8.60592060 -0.83761516
7 8 9 10 11 12
0.98653452 -8.42629784 2.16238484 -2.70780682 -1.72818406 -2.44667509
13 14 15 16 17 18
10.48034216 2.12464495 1.06351322 2.10049527 -3.74025922 3.02804623
19 20 21 22 23 24
0.75030969 0.82804623 11.92616001 0.56539944 -1.65875024 0.11332764
25 26 27 28 29 30
9.83657245 -10.02855585 -1.71986709 -7.19231614 4.84618096 -4.57042384
31 32 33 34 35 36
-5.76513635 0.59939665 -2.30248957 -13.08965720 4.46355849 2.15261234
37 38 39 40 41 42
-8.71659798 3.68620800 1.72696248 4.76394453 4.12696248 -1.39152854
43 44 45 46 47 48
-3.28058239 4.28772305 -7.30850451 8.29301054 0.98583683 0.06357336
49 50 51 52 53 54
-14.83276932 -5.71675982 2.62965333 3.99115623 3.37303637 3.77152132
55 56 57 58 59 60
7.30887453 2.71113192 -4.47755076 6.93905404 -2.06246101 0.11716175
61
-3.75204857
> postscript(file="/var/www/html/rcomp/tmp/6i6pp1258727879.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 6.98450127 NA
1 9.93446272 6.98450127
2 -3.70026194 9.93446272
3 -3.66327989 -3.70026194
4 -8.60592060 -3.66327989
5 -0.83761516 -8.60592060
6 0.98653452 -0.83761516
7 -8.42629784 0.98653452
8 2.16238484 -8.42629784
9 -2.70780682 2.16238484
10 -1.72818406 -2.70780682
11 -2.44667509 -1.72818406
12 10.48034216 -2.44667509
13 2.12464495 10.48034216
14 1.06351322 2.12464495
15 2.10049527 1.06351322
16 -3.74025922 2.10049527
17 3.02804623 -3.74025922
18 0.75030969 3.02804623
19 0.82804623 0.75030969
20 11.92616001 0.82804623
21 0.56539944 11.92616001
22 -1.65875024 0.56539944
23 0.11332764 -1.65875024
24 9.83657245 0.11332764
25 -10.02855585 9.83657245
26 -1.71986709 -10.02855585
27 -7.19231614 -1.71986709
28 4.84618096 -7.19231614
29 -4.57042384 4.84618096
30 -5.76513635 -4.57042384
31 0.59939665 -5.76513635
32 -2.30248957 0.59939665
33 -13.08965720 -2.30248957
34 4.46355849 -13.08965720
35 2.15261234 4.46355849
36 -8.71659798 2.15261234
37 3.68620800 -8.71659798
38 1.72696248 3.68620800
39 4.76394453 1.72696248
40 4.12696248 4.76394453
41 -1.39152854 4.12696248
42 -3.28058239 -1.39152854
43 4.28772305 -3.28058239
44 -7.30850451 4.28772305
45 8.29301054 -7.30850451
46 0.98583683 8.29301054
47 0.06357336 0.98583683
48 -14.83276932 0.06357336
49 -5.71675982 -14.83276932
50 2.62965333 -5.71675982
51 3.99115623 2.62965333
52 3.37303637 3.99115623
53 3.77152132 3.37303637
54 7.30887453 3.77152132
55 2.71113192 7.30887453
56 -4.47755076 2.71113192
57 6.93905404 -4.47755076
58 -2.06246101 6.93905404
59 0.11716175 -2.06246101
60 -3.75204857 0.11716175
61 NA -3.75204857
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.93446272 6.98450127
[2,] -3.70026194 9.93446272
[3,] -3.66327989 -3.70026194
[4,] -8.60592060 -3.66327989
[5,] -0.83761516 -8.60592060
[6,] 0.98653452 -0.83761516
[7,] -8.42629784 0.98653452
[8,] 2.16238484 -8.42629784
[9,] -2.70780682 2.16238484
[10,] -1.72818406 -2.70780682
[11,] -2.44667509 -1.72818406
[12,] 10.48034216 -2.44667509
[13,] 2.12464495 10.48034216
[14,] 1.06351322 2.12464495
[15,] 2.10049527 1.06351322
[16,] -3.74025922 2.10049527
[17,] 3.02804623 -3.74025922
[18,] 0.75030969 3.02804623
[19,] 0.82804623 0.75030969
[20,] 11.92616001 0.82804623
[21,] 0.56539944 11.92616001
[22,] -1.65875024 0.56539944
[23,] 0.11332764 -1.65875024
[24,] 9.83657245 0.11332764
[25,] -10.02855585 9.83657245
[26,] -1.71986709 -10.02855585
[27,] -7.19231614 -1.71986709
[28,] 4.84618096 -7.19231614
[29,] -4.57042384 4.84618096
[30,] -5.76513635 -4.57042384
[31,] 0.59939665 -5.76513635
[32,] -2.30248957 0.59939665
[33,] -13.08965720 -2.30248957
[34,] 4.46355849 -13.08965720
[35,] 2.15261234 4.46355849
[36,] -8.71659798 2.15261234
[37,] 3.68620800 -8.71659798
[38,] 1.72696248 3.68620800
[39,] 4.76394453 1.72696248
[40,] 4.12696248 4.76394453
[41,] -1.39152854 4.12696248
[42,] -3.28058239 -1.39152854
[43,] 4.28772305 -3.28058239
[44,] -7.30850451 4.28772305
[45,] 8.29301054 -7.30850451
[46,] 0.98583683 8.29301054
[47,] 0.06357336 0.98583683
[48,] -14.83276932 0.06357336
[49,] -5.71675982 -14.83276932
[50,] 2.62965333 -5.71675982
[51,] 3.99115623 2.62965333
[52,] 3.37303637 3.99115623
[53,] 3.77152132 3.37303637
[54,] 7.30887453 3.77152132
[55,] 2.71113192 7.30887453
[56,] -4.47755076 2.71113192
[57,] 6.93905404 -4.47755076
[58,] -2.06246101 6.93905404
[59,] 0.11716175 -2.06246101
[60,] -3.75204857 0.11716175
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.93446272 6.98450127
2 -3.70026194 9.93446272
3 -3.66327989 -3.70026194
4 -8.60592060 -3.66327989
5 -0.83761516 -8.60592060
6 0.98653452 -0.83761516
7 -8.42629784 0.98653452
8 2.16238484 -8.42629784
9 -2.70780682 2.16238484
10 -1.72818406 -2.70780682
11 -2.44667509 -1.72818406
12 10.48034216 -2.44667509
13 2.12464495 10.48034216
14 1.06351322 2.12464495
15 2.10049527 1.06351322
16 -3.74025922 2.10049527
17 3.02804623 -3.74025922
18 0.75030969 3.02804623
19 0.82804623 0.75030969
20 11.92616001 0.82804623
21 0.56539944 11.92616001
22 -1.65875024 0.56539944
23 0.11332764 -1.65875024
24 9.83657245 0.11332764
25 -10.02855585 9.83657245
26 -1.71986709 -10.02855585
27 -7.19231614 -1.71986709
28 4.84618096 -7.19231614
29 -4.57042384 4.84618096
30 -5.76513635 -4.57042384
31 0.59939665 -5.76513635
32 -2.30248957 0.59939665
33 -13.08965720 -2.30248957
34 4.46355849 -13.08965720
35 2.15261234 4.46355849
36 -8.71659798 2.15261234
37 3.68620800 -8.71659798
38 1.72696248 3.68620800
39 4.76394453 1.72696248
40 4.12696248 4.76394453
41 -1.39152854 4.12696248
42 -3.28058239 -1.39152854
43 4.28772305 -3.28058239
44 -7.30850451 4.28772305
45 8.29301054 -7.30850451
46 0.98583683 8.29301054
47 0.06357336 0.98583683
48 -14.83276932 0.06357336
49 -5.71675982 -14.83276932
50 2.62965333 -5.71675982
51 3.99115623 2.62965333
52 3.37303637 3.99115623
53 3.77152132 3.37303637
54 7.30887453 3.77152132
55 2.71113192 7.30887453
56 -4.47755076 2.71113192
57 6.93905404 -4.47755076
58 -2.06246101 6.93905404
59 0.11716175 -2.06246101
60 -3.75204857 0.11716175
> 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/71sgd1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qpia1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90fjy1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10h9pf1258727879.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n59q1258727879.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/12ibu91258727879.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/13bpst1258727879.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/14hrhr1258727880.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/15oany1258727880.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/169mfm1258727880.tab")
+ }
>
> system("convert tmp/1umsw1258727879.ps tmp/1umsw1258727879.png")
> system("convert tmp/274go1258727879.ps tmp/274go1258727879.png")
> system("convert tmp/3a2o21258727879.ps tmp/3a2o21258727879.png")
> system("convert tmp/4raop1258727879.ps tmp/4raop1258727879.png")
> system("convert tmp/5x77e1258727879.ps tmp/5x77e1258727879.png")
> system("convert tmp/6i6pp1258727879.ps tmp/6i6pp1258727879.png")
> system("convert tmp/71sgd1258727879.ps tmp/71sgd1258727879.png")
> system("convert tmp/8qpia1258727879.ps tmp/8qpia1258727879.png")
> system("convert tmp/90fjy1258727879.ps tmp/90fjy1258727879.png")
> system("convert tmp/10h9pf1258727879.ps tmp/10h9pf1258727879.png")
>
>
> proc.time()
user system elapsed
2.486 1.626 4.770