R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(591
+ ,0
+ ,595
+ ,594
+ ,611
+ ,613
+ ,589
+ ,0
+ ,591
+ ,595
+ ,594
+ ,611
+ ,584
+ ,0
+ ,589
+ ,591
+ ,595
+ ,594
+ ,573
+ ,0
+ ,584
+ ,589
+ ,591
+ ,595
+ ,567
+ ,0
+ ,573
+ ,584
+ ,589
+ ,591
+ ,569
+ ,0
+ ,567
+ ,573
+ ,584
+ ,589
+ ,621
+ ,0
+ ,569
+ ,567
+ ,573
+ ,584
+ ,629
+ ,0
+ ,621
+ ,569
+ ,567
+ ,573
+ ,628
+ ,0
+ ,629
+ ,621
+ ,569
+ ,567
+ ,612
+ ,0
+ ,628
+ ,629
+ ,621
+ ,569
+ ,595
+ ,0
+ ,612
+ ,628
+ ,629
+ ,621
+ ,597
+ ,0
+ ,595
+ ,612
+ ,628
+ ,629
+ ,593
+ ,0
+ ,597
+ ,595
+ ,612
+ ,628
+ ,590
+ ,0
+ ,593
+ ,597
+ ,595
+ ,612
+ ,580
+ ,0
+ ,590
+ ,593
+ ,597
+ ,595
+ ,574
+ ,0
+ ,580
+ ,590
+ ,593
+ ,597
+ ,573
+ ,0
+ ,574
+ ,580
+ ,590
+ ,593
+ ,573
+ ,0
+ ,573
+ ,574
+ ,580
+ ,590
+ ,620
+ ,0
+ ,573
+ ,573
+ ,574
+ ,580
+ ,626
+ ,0
+ ,620
+ ,573
+ ,573
+ ,574
+ ,620
+ ,0
+ ,626
+ ,620
+ ,573
+ ,573
+ ,588
+ ,0
+ ,620
+ ,626
+ ,620
+ ,573
+ ,566
+ ,0
+ ,588
+ ,620
+ ,626
+ ,620
+ ,557
+ ,0
+ ,566
+ ,588
+ ,620
+ ,626
+ ,561
+ ,0
+ ,557
+ ,566
+ ,588
+ ,620
+ ,549
+ ,0
+ ,561
+ ,557
+ ,566
+ ,588
+ ,532
+ ,0
+ ,549
+ ,561
+ ,557
+ ,566
+ ,526
+ ,0
+ ,532
+ ,549
+ ,561
+ ,557
+ ,511
+ ,0
+ ,526
+ ,532
+ ,549
+ ,561
+ ,499
+ ,0
+ ,511
+ ,526
+ ,532
+ ,549
+ ,555
+ ,0
+ ,499
+ ,511
+ ,526
+ ,532
+ ,565
+ ,0
+ ,555
+ ,499
+ ,511
+ ,526
+ ,542
+ ,0
+ ,565
+ ,555
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+ ,511
+ ,527
+ ,0
+ ,542
+ ,565
+ ,555
+ ,499
+ ,510
+ ,0
+ ,527
+ ,542
+ ,565
+ ,555
+ ,514
+ ,0
+ ,510
+ ,527
+ ,542
+ ,565
+ ,517
+ ,0
+ ,514
+ ,510
+ ,527
+ ,542
+ ,508
+ ,0
+ ,517
+ ,514
+ ,510
+ ,527
+ ,493
+ ,0
+ ,508
+ ,517
+ ,514
+ ,510
+ ,490
+ ,0
+ ,493
+ ,508
+ ,517
+ ,514
+ ,469
+ ,0
+ ,490
+ ,493
+ ,508
+ ,517
+ ,478
+ ,0
+ ,469
+ ,490
+ ,493
+ ,508
+ ,528
+ ,0
+ ,478
+ ,469
+ ,490
+ ,493
+ ,534
+ ,0
+ ,528
+ ,478
+ ,469
+ ,490
+ ,518
+ ,1
+ ,534
+ ,528
+ ,478
+ ,469
+ ,506
+ ,1
+ ,518
+ ,534
+ ,528
+ ,478
+ ,502
+ ,1
+ ,506
+ ,518
+ ,534
+ ,528
+ ,516
+ ,1
+ ,502
+ ,506
+ ,518
+ ,534
+ ,528
+ ,1
+ ,516
+ ,502
+ ,506
+ ,518
+ ,533
+ ,1
+ ,528
+ ,516
+ ,502
+ ,506
+ ,536
+ ,1
+ ,533
+ ,528
+ ,516
+ ,502
+ ,537
+ ,1
+ ,536
+ ,533
+ ,528
+ ,516
+ ,524
+ ,1
+ ,537
+ ,536
+ ,533
+ ,528
+ ,536
+ ,1
+ ,524
+ ,537
+ ,536
+ ,533
+ ,587
+ ,1
+ ,536
+ ,524
+ ,537
+ ,536
+ ,597
+ ,1
+ ,587
+ ,536
+ ,524
+ ,537
+ ,581
+ ,1
+ ,597
+ ,587
+ ,536
+ ,524)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:57))
> 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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 591 0 595 594 611 613 1 0 0 0 0 0 0 0 0 0 0 1
2 589 0 591 595 594 611 0 1 0 0 0 0 0 0 0 0 0 2
3 584 0 589 591 595 594 0 0 1 0 0 0 0 0 0 0 0 3
4 573 0 584 589 591 595 0 0 0 1 0 0 0 0 0 0 0 4
5 567 0 573 584 589 591 0 0 0 0 1 0 0 0 0 0 0 5
6 569 0 567 573 584 589 0 0 0 0 0 1 0 0 0 0 0 6
7 621 0 569 567 573 584 0 0 0 0 0 0 1 0 0 0 0 7
8 629 0 621 569 567 573 0 0 0 0 0 0 0 1 0 0 0 8
9 628 0 629 621 569 567 0 0 0 0 0 0 0 0 1 0 0 9
10 612 0 628 629 621 569 0 0 0 0 0 0 0 0 0 1 0 10
11 595 0 612 628 629 621 0 0 0 0 0 0 0 0 0 0 1 11
12 597 0 595 612 628 629 0 0 0 0 0 0 0 0 0 0 0 12
13 593 0 597 595 612 628 1 0 0 0 0 0 0 0 0 0 0 13
14 590 0 593 597 595 612 0 1 0 0 0 0 0 0 0 0 0 14
15 580 0 590 593 597 595 0 0 1 0 0 0 0 0 0 0 0 15
16 574 0 580 590 593 597 0 0 0 1 0 0 0 0 0 0 0 16
17 573 0 574 580 590 593 0 0 0 0 1 0 0 0 0 0 0 17
18 573 0 573 574 580 590 0 0 0 0 0 1 0 0 0 0 0 18
19 620 0 573 573 574 580 0 0 0 0 0 0 1 0 0 0 0 19
20 626 0 620 573 573 574 0 0 0 0 0 0 0 1 0 0 0 20
21 620 0 626 620 573 573 0 0 0 0 0 0 0 0 1 0 0 21
22 588 0 620 626 620 573 0 0 0 0 0 0 0 0 0 1 0 22
23 566 0 588 620 626 620 0 0 0 0 0 0 0 0 0 0 1 23
24 557 0 566 588 620 626 0 0 0 0 0 0 0 0 0 0 0 24
25 561 0 557 566 588 620 1 0 0 0 0 0 0 0 0 0 0 25
26 549 0 561 557 566 588 0 1 0 0 0 0 0 0 0 0 0 26
27 532 0 549 561 557 566 0 0 1 0 0 0 0 0 0 0 0 27
28 526 0 532 549 561 557 0 0 0 1 0 0 0 0 0 0 0 28
29 511 0 526 532 549 561 0 0 0 0 1 0 0 0 0 0 0 29
30 499 0 511 526 532 549 0 0 0 0 0 1 0 0 0 0 0 30
31 555 0 499 511 526 532 0 0 0 0 0 0 1 0 0 0 0 31
32 565 0 555 499 511 526 0 0 0 0 0 0 0 1 0 0 0 32
33 542 0 565 555 499 511 0 0 0 0 0 0 0 0 1 0 0 33
34 527 0 542 565 555 499 0 0 0 0 0 0 0 0 0 1 0 34
35 510 0 527 542 565 555 0 0 0 0 0 0 0 0 0 0 1 35
36 514 0 510 527 542 565 0 0 0 0 0 0 0 0 0 0 0 36
37 517 0 514 510 527 542 1 0 0 0 0 0 0 0 0 0 0 37
38 508 0 517 514 510 527 0 1 0 0 0 0 0 0 0 0 0 38
39 493 0 508 517 514 510 0 0 1 0 0 0 0 0 0 0 0 39
40 490 0 493 508 517 514 0 0 0 1 0 0 0 0 0 0 0 40
41 469 0 490 493 508 517 0 0 0 0 1 0 0 0 0 0 0 41
42 478 0 469 490 493 508 0 0 0 0 0 1 0 0 0 0 0 42
43 528 0 478 469 490 493 0 0 0 0 0 0 1 0 0 0 0 43
44 534 0 528 478 469 490 0 0 0 0 0 0 0 1 0 0 0 44
45 518 1 534 528 478 469 0 0 0 0 0 0 0 0 1 0 0 45
46 506 1 518 534 528 478 0 0 0 0 0 0 0 0 0 1 0 46
47 502 1 506 518 534 528 0 0 0 0 0 0 0 0 0 0 1 47
48 516 1 502 506 518 534 0 0 0 0 0 0 0 0 0 0 0 48
49 528 1 516 502 506 518 1 0 0 0 0 0 0 0 0 0 0 49
50 533 1 528 516 502 506 0 1 0 0 0 0 0 0 0 0 0 50
51 536 1 533 528 516 502 0 0 1 0 0 0 0 0 0 0 0 51
52 537 1 536 533 528 516 0 0 0 1 0 0 0 0 0 0 0 52
53 524 1 537 536 533 528 0 0 0 0 1 0 0 0 0 0 0 53
54 536 1 524 537 536 533 0 0 0 0 0 1 0 0 0 0 0 54
55 587 1 536 524 537 536 0 0 0 0 0 0 1 0 0 0 0 55
56 597 1 587 536 524 537 0 0 0 0 0 0 0 1 0 0 0 56
57 581 1 597 587 536 524 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
62.06124 11.23139 0.90384 0.13850 -0.02684 -0.10638
M1 M2 M3 M4 M5 M6
-0.30442 -8.57352 -15.15253 -10.99930 -16.03665 -3.40079
M7 M8 M9 M10 M11 t
46.59494 7.48451 -22.31251 -29.19336 -19.69694 -0.30339
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.5860 -3.0507 0.4882 3.5694 12.0523
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.06124 29.09016 2.133 0.039238 *
X 11.23139 4.11880 2.727 0.009532 **
Y1 0.90384 0.15386 5.874 7.74e-07 ***
Y2 0.13850 0.21134 0.655 0.516105
Y3 -0.02684 0.20430 -0.131 0.896142
Y4 -0.10638 0.15215 -0.699 0.488588
M1 -0.30442 5.15124 -0.059 0.953177
M2 -8.57352 6.08372 -1.409 0.166684
M3 -15.15253 6.20918 -2.440 0.019319 *
M4 -10.99930 5.56770 -1.976 0.055312 .
M5 -16.03665 5.11864 -3.133 0.003278 **
M6 -3.40079 5.06193 -0.672 0.505648
M7 46.59494 5.51456 8.449 2.40e-10 ***
M8 7.48451 11.60072 0.645 0.522591
M9 -22.31251 12.10119 -1.844 0.072816 .
M10 -29.19336 11.41218 -2.558 0.014524 *
M11 -19.69694 5.51984 -3.568 0.000971 ***
t -0.30339 0.13237 -2.292 0.027384 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.432 on 39 degrees of freedom
Multiple R-squared: 0.9834, Adjusted R-squared: 0.9761
F-statistic: 135.6 on 17 and 39 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,] 0.2725494 0.5450989 0.72745057
[2,] 0.4711810 0.9423620 0.52881900
[3,] 0.4745966 0.9491932 0.52540339
[4,] 0.4516105 0.9032210 0.54838948
[5,] 0.5896890 0.8206219 0.41031096
[6,] 0.4992397 0.9984794 0.50076029
[7,] 0.4192932 0.8385863 0.58070684
[8,] 0.5057533 0.9884934 0.49424669
[9,] 0.5317710 0.9364579 0.46822897
[10,] 0.8531044 0.2937911 0.14689556
[11,] 0.9205608 0.1588783 0.07943917
[12,] 0.9380809 0.1238382 0.06191911
[13,] 0.9270515 0.1458970 0.07294849
[14,] 0.9221909 0.1556183 0.07780915
[15,] 0.8383455 0.3233089 0.16165446
[16,] 0.7986238 0.4027524 0.20137622
> postscript(file="/var/www/html/rcomp/tmp/16rz01259252654.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a1rv1259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ygze1259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/489471259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t9f71259252654.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 = 57
Frequency = 1
1 2 3 4 5 6
-8.8920259 0.4882232 2.9506632 -7.1039823 2.3922660 -1.3406790
7 8 9 10 11 12
-0.8369077 -2.0309762 12.0523144 4.6410581 -1.2055045 -0.1936415
13 14 15 16 17 18
-3.5749473 3.1774783 1.5706015 1.2800305 11.9227188 0.7374959
19 20 21 22 23 24
-3.0412195 -0.7729767 11.2886897 -7.6733557 -3.9515938 -7.5515719
25 26 27 28 29 30
6.7404377 -3.0507005 -5.4581744 0.8691617 -0.9092248 -12.5860243
31 32 33 34 35 36
4.6756194 4.0954503 -7.5161591 4.2981096 1.0738351 3.5694024
37 38 39 40 41 42
3.0668898 -2.6781736 -4.7777997 3.6824714 -7.1102759 7.5933020
43 44 45 46 47 48
0.9985848 -0.9088893 -12.3801177 -1.2658120 4.0832632 4.1758110
49 50 51 52 53 54
2.6596457 2.0631726 5.7147095 1.2723187 -6.2954840 5.5959053
55 56 57
-1.7960770 -0.3826081 -3.4447273
> postscript(file="/var/www/html/rcomp/tmp/6m5vo1259252654.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.8920259 NA
1 0.4882232 -8.8920259
2 2.9506632 0.4882232
3 -7.1039823 2.9506632
4 2.3922660 -7.1039823
5 -1.3406790 2.3922660
6 -0.8369077 -1.3406790
7 -2.0309762 -0.8369077
8 12.0523144 -2.0309762
9 4.6410581 12.0523144
10 -1.2055045 4.6410581
11 -0.1936415 -1.2055045
12 -3.5749473 -0.1936415
13 3.1774783 -3.5749473
14 1.5706015 3.1774783
15 1.2800305 1.5706015
16 11.9227188 1.2800305
17 0.7374959 11.9227188
18 -3.0412195 0.7374959
19 -0.7729767 -3.0412195
20 11.2886897 -0.7729767
21 -7.6733557 11.2886897
22 -3.9515938 -7.6733557
23 -7.5515719 -3.9515938
24 6.7404377 -7.5515719
25 -3.0507005 6.7404377
26 -5.4581744 -3.0507005
27 0.8691617 -5.4581744
28 -0.9092248 0.8691617
29 -12.5860243 -0.9092248
30 4.6756194 -12.5860243
31 4.0954503 4.6756194
32 -7.5161591 4.0954503
33 4.2981096 -7.5161591
34 1.0738351 4.2981096
35 3.5694024 1.0738351
36 3.0668898 3.5694024
37 -2.6781736 3.0668898
38 -4.7777997 -2.6781736
39 3.6824714 -4.7777997
40 -7.1102759 3.6824714
41 7.5933020 -7.1102759
42 0.9985848 7.5933020
43 -0.9088893 0.9985848
44 -12.3801177 -0.9088893
45 -1.2658120 -12.3801177
46 4.0832632 -1.2658120
47 4.1758110 4.0832632
48 2.6596457 4.1758110
49 2.0631726 2.6596457
50 5.7147095 2.0631726
51 1.2723187 5.7147095
52 -6.2954840 1.2723187
53 5.5959053 -6.2954840
54 -1.7960770 5.5959053
55 -0.3826081 -1.7960770
56 -3.4447273 -0.3826081
57 NA -3.4447273
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4882232 -8.8920259
[2,] 2.9506632 0.4882232
[3,] -7.1039823 2.9506632
[4,] 2.3922660 -7.1039823
[5,] -1.3406790 2.3922660
[6,] -0.8369077 -1.3406790
[7,] -2.0309762 -0.8369077
[8,] 12.0523144 -2.0309762
[9,] 4.6410581 12.0523144
[10,] -1.2055045 4.6410581
[11,] -0.1936415 -1.2055045
[12,] -3.5749473 -0.1936415
[13,] 3.1774783 -3.5749473
[14,] 1.5706015 3.1774783
[15,] 1.2800305 1.5706015
[16,] 11.9227188 1.2800305
[17,] 0.7374959 11.9227188
[18,] -3.0412195 0.7374959
[19,] -0.7729767 -3.0412195
[20,] 11.2886897 -0.7729767
[21,] -7.6733557 11.2886897
[22,] -3.9515938 -7.6733557
[23,] -7.5515719 -3.9515938
[24,] 6.7404377 -7.5515719
[25,] -3.0507005 6.7404377
[26,] -5.4581744 -3.0507005
[27,] 0.8691617 -5.4581744
[28,] -0.9092248 0.8691617
[29,] -12.5860243 -0.9092248
[30,] 4.6756194 -12.5860243
[31,] 4.0954503 4.6756194
[32,] -7.5161591 4.0954503
[33,] 4.2981096 -7.5161591
[34,] 1.0738351 4.2981096
[35,] 3.5694024 1.0738351
[36,] 3.0668898 3.5694024
[37,] -2.6781736 3.0668898
[38,] -4.7777997 -2.6781736
[39,] 3.6824714 -4.7777997
[40,] -7.1102759 3.6824714
[41,] 7.5933020 -7.1102759
[42,] 0.9985848 7.5933020
[43,] -0.9088893 0.9985848
[44,] -12.3801177 -0.9088893
[45,] -1.2658120 -12.3801177
[46,] 4.0832632 -1.2658120
[47,] 4.1758110 4.0832632
[48,] 2.6596457 4.1758110
[49,] 2.0631726 2.6596457
[50,] 5.7147095 2.0631726
[51,] 1.2723187 5.7147095
[52,] -6.2954840 1.2723187
[53,] 5.5959053 -6.2954840
[54,] -1.7960770 5.5959053
[55,] -0.3826081 -1.7960770
[56,] -3.4447273 -0.3826081
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4882232 -8.8920259
2 2.9506632 0.4882232
3 -7.1039823 2.9506632
4 2.3922660 -7.1039823
5 -1.3406790 2.3922660
6 -0.8369077 -1.3406790
7 -2.0309762 -0.8369077
8 12.0523144 -2.0309762
9 4.6410581 12.0523144
10 -1.2055045 4.6410581
11 -0.1936415 -1.2055045
12 -3.5749473 -0.1936415
13 3.1774783 -3.5749473
14 1.5706015 3.1774783
15 1.2800305 1.5706015
16 11.9227188 1.2800305
17 0.7374959 11.9227188
18 -3.0412195 0.7374959
19 -0.7729767 -3.0412195
20 11.2886897 -0.7729767
21 -7.6733557 11.2886897
22 -3.9515938 -7.6733557
23 -7.5515719 -3.9515938
24 6.7404377 -7.5515719
25 -3.0507005 6.7404377
26 -5.4581744 -3.0507005
27 0.8691617 -5.4581744
28 -0.9092248 0.8691617
29 -12.5860243 -0.9092248
30 4.6756194 -12.5860243
31 4.0954503 4.6756194
32 -7.5161591 4.0954503
33 4.2981096 -7.5161591
34 1.0738351 4.2981096
35 3.5694024 1.0738351
36 3.0668898 3.5694024
37 -2.6781736 3.0668898
38 -4.7777997 -2.6781736
39 3.6824714 -4.7777997
40 -7.1102759 3.6824714
41 7.5933020 -7.1102759
42 0.9985848 7.5933020
43 -0.9088893 0.9985848
44 -12.3801177 -0.9088893
45 -1.2658120 -12.3801177
46 4.0832632 -1.2658120
47 4.1758110 4.0832632
48 2.6596457 4.1758110
49 2.0631726 2.6596457
50 5.7147095 2.0631726
51 1.2723187 5.7147095
52 -6.2954840 1.2723187
53 5.5959053 -6.2954840
54 -1.7960770 5.5959053
55 -0.3826081 -1.7960770
56 -3.4447273 -0.3826081
> 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/7a2hl1259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89a7k1259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9san11259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bh751259252654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bm571259252654.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/12cvvi1259252654.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/138vm41259252654.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/14p6yd1259252654.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/153njg1259252654.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/16f9t41259252654.tab")
+ }
>
> system("convert tmp/16rz01259252654.ps tmp/16rz01259252654.png")
> system("convert tmp/2a1rv1259252654.ps tmp/2a1rv1259252654.png")
> system("convert tmp/3ygze1259252654.ps tmp/3ygze1259252654.png")
> system("convert tmp/489471259252654.ps tmp/489471259252654.png")
> system("convert tmp/5t9f71259252654.ps tmp/5t9f71259252654.png")
> system("convert tmp/6m5vo1259252654.ps tmp/6m5vo1259252654.png")
> system("convert tmp/7a2hl1259252654.ps tmp/7a2hl1259252654.png")
> system("convert tmp/89a7k1259252654.ps tmp/89a7k1259252654.png")
> system("convert tmp/9san11259252654.ps tmp/9san11259252654.png")
> system("convert tmp/10bh751259252654.ps tmp/10bh751259252654.png")
>
>
> proc.time()
user system elapsed
2.297 1.529 3.384