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(22
+ ,591
+ ,19
+ ,19
+ ,18
+ ,19
+ ,23
+ ,589
+ ,22
+ ,19
+ ,19
+ ,18
+ ,20
+ ,584
+ ,23
+ ,22
+ ,19
+ ,19
+ ,14
+ ,573
+ ,20
+ ,23
+ ,22
+ ,19
+ ,14
+ ,567
+ ,14
+ ,20
+ ,23
+ ,22
+ ,14
+ ,569
+ ,14
+ ,14
+ ,20
+ ,23
+ ,15
+ ,621
+ ,14
+ ,14
+ ,14
+ ,20
+ ,11
+ ,629
+ ,15
+ ,14
+ ,14
+ ,14
+ ,17
+ ,628
+ ,11
+ ,15
+ ,14
+ ,14
+ ,16
+ ,612
+ ,17
+ ,11
+ ,15
+ ,14
+ ,20
+ ,595
+ ,16
+ ,17
+ ,11
+ ,15
+ ,24
+ ,597
+ ,20
+ ,16
+ ,17
+ ,11
+ ,23
+ ,593
+ ,24
+ ,20
+ ,16
+ ,17
+ ,20
+ ,590
+ ,23
+ ,24
+ ,20
+ ,16
+ ,21
+ ,580
+ ,20
+ ,23
+ ,24
+ ,20
+ ,19
+ ,574
+ ,21
+ ,20
+ ,23
+ ,24
+ ,23
+ ,573
+ ,19
+ ,21
+ ,20
+ ,23
+ ,23
+ ,573
+ ,23
+ ,19
+ ,21
+ ,20
+ ,23
+ ,620
+ ,23
+ ,23
+ ,19
+ ,21
+ ,23
+ ,626
+ ,23
+ ,23
+ ,23
+ ,19
+ ,27
+ ,620
+ ,23
+ ,23
+ ,23
+ ,23
+ ,26
+ ,588
+ ,27
+ ,23
+ ,23
+ ,23
+ ,17
+ ,566
+ ,26
+ ,27
+ ,23
+ ,23
+ ,24
+ ,557
+ ,17
+ ,26
+ ,27
+ ,23
+ ,26
+ ,561
+ ,24
+ ,17
+ ,26
+ ,27
+ ,24
+ ,549
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+ ,17
+ ,26
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+ ,532
+ ,24
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+ ,526
+ ,27
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+ ,511
+ ,27
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+ ,499
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+ ,27
+ ,24
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+ ,24
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+ ,22
+ ,19
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+ ,469
+ ,16
+ ,18
+ ,22
+ ,22
+ ,12
+ ,478
+ ,14
+ ,16
+ ,18
+ ,22
+ ,14
+ ,528
+ ,12
+ ,14
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+ ,18
+ ,16
+ ,534
+ ,14
+ ,12
+ ,14
+ ,16
+ ,8
+ ,518
+ ,16
+ ,14
+ ,12
+ ,14
+ ,3
+ ,506
+ ,8
+ ,16
+ ,14
+ ,12
+ ,0
+ ,502
+ ,3
+ ,8
+ ,16
+ ,14
+ ,5
+ ,516
+ ,0
+ ,3
+ ,8
+ ,16
+ ,1
+ ,528
+ ,5
+ ,0
+ ,3
+ ,8
+ ,1
+ ,533
+ ,1
+ ,5
+ ,0
+ ,3
+ ,3
+ ,536
+ ,1
+ ,1
+ ,5
+ ,0
+ ,6
+ ,537
+ ,3
+ ,1
+ ,1
+ ,5
+ ,7
+ ,524
+ ,6
+ ,3
+ ,1
+ ,1
+ ,8
+ ,536
+ ,7
+ ,6
+ ,3
+ ,1
+ ,14
+ ,587
+ ,8
+ ,7
+ ,6
+ ,3
+ ,14
+ ,597
+ ,14
+ ,8
+ ,7
+ ,6
+ ,13
+ ,581
+ ,14
+ ,14
+ ,8
+ ,7)
+ ,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 22 591 19 19 18 19 1 0 0 0 0 0 0 0 0 0 0 1
2 23 589 22 19 19 18 0 1 0 0 0 0 0 0 0 0 0 2
3 20 584 23 22 19 19 0 0 1 0 0 0 0 0 0 0 0 3
4 14 573 20 23 22 19 0 0 0 1 0 0 0 0 0 0 0 4
5 14 567 14 20 23 22 0 0 0 0 1 0 0 0 0 0 0 5
6 14 569 14 14 20 23 0 0 0 0 0 1 0 0 0 0 0 6
7 15 621 14 14 14 20 0 0 0 0 0 0 1 0 0 0 0 7
8 11 629 15 14 14 14 0 0 0 0 0 0 0 1 0 0 0 8
9 17 628 11 15 14 14 0 0 0 0 0 0 0 0 1 0 0 9
10 16 612 17 11 15 14 0 0 0 0 0 0 0 0 0 1 0 10
11 20 595 16 17 11 15 0 0 0 0 0 0 0 0 0 0 1 11
12 24 597 20 16 17 11 0 0 0 0 0 0 0 0 0 0 0 12
13 23 593 24 20 16 17 1 0 0 0 0 0 0 0 0 0 0 13
14 20 590 23 24 20 16 0 1 0 0 0 0 0 0 0 0 0 14
15 21 580 20 23 24 20 0 0 1 0 0 0 0 0 0 0 0 15
16 19 574 21 20 23 24 0 0 0 1 0 0 0 0 0 0 0 16
17 23 573 19 21 20 23 0 0 0 0 1 0 0 0 0 0 0 17
18 23 573 23 19 21 20 0 0 0 0 0 1 0 0 0 0 0 18
19 23 620 23 23 19 21 0 0 0 0 0 0 1 0 0 0 0 19
20 23 626 23 23 23 19 0 0 0 0 0 0 0 1 0 0 0 20
21 27 620 23 23 23 23 0 0 0 0 0 0 0 0 1 0 0 21
22 26 588 27 23 23 23 0 0 0 0 0 0 0 0 0 1 0 22
23 17 566 26 27 23 23 0 0 0 0 0 0 0 0 0 0 1 23
24 24 557 17 26 27 23 0 0 0 0 0 0 0 0 0 0 0 24
25 26 561 24 17 26 27 1 0 0 0 0 0 0 0 0 0 0 25
26 24 549 26 24 17 26 0 1 0 0 0 0 0 0 0 0 0 26
27 27 532 24 26 24 17 0 0 1 0 0 0 0 0 0 0 0 27
28 27 526 27 24 26 24 0 0 0 1 0 0 0 0 0 0 0 28
29 26 511 27 27 24 26 0 0 0 0 1 0 0 0 0 0 0 29
30 24 499 26 27 27 24 0 0 0 0 0 1 0 0 0 0 0 30
31 23 555 24 26 27 27 0 0 0 0 0 0 1 0 0 0 0 31
32 23 565 23 24 26 27 0 0 0 0 0 0 0 1 0 0 0 32
33 24 542 23 23 24 26 0 0 0 0 0 0 0 0 1 0 0 33
34 17 527 24 23 23 24 0 0 0 0 0 0 0 0 0 1 0 34
35 21 510 17 24 23 23 0 0 0 0 0 0 0 0 0 0 1 35
36 19 514 21 17 24 23 0 0 0 0 0 0 0 0 0 0 0 36
37 22 517 19 21 17 24 1 0 0 0 0 0 0 0 0 0 0 37
38 22 508 22 19 21 17 0 1 0 0 0 0 0 0 0 0 0 38
39 18 493 22 22 19 21 0 0 1 0 0 0 0 0 0 0 0 39
40 16 490 18 22 22 19 0 0 0 1 0 0 0 0 0 0 0 40
41 14 469 16 18 22 22 0 0 0 0 1 0 0 0 0 0 0 41
42 12 478 14 16 18 22 0 0 0 0 0 1 0 0 0 0 0 42
43 14 528 12 14 16 18 0 0 0 0 0 0 1 0 0 0 0 43
44 16 534 14 12 14 16 0 0 0 0 0 0 0 1 0 0 0 44
45 8 518 16 14 12 14 0 0 0 0 0 0 0 0 1 0 0 45
46 3 506 8 16 14 12 0 0 0 0 0 0 0 0 0 1 0 46
47 0 502 3 8 16 14 0 0 0 0 0 0 0 0 0 0 1 47
48 5 516 0 3 8 16 0 0 0 0 0 0 0 0 0 0 0 48
49 1 528 5 0 3 8 1 0 0 0 0 0 0 0 0 0 0 49
50 1 533 1 5 0 3 0 1 0 0 0 0 0 0 0 0 0 50
51 3 536 1 1 5 0 0 0 1 0 0 0 0 0 0 0 0 51
52 6 537 3 1 1 5 0 0 0 1 0 0 0 0 0 0 0 52
53 7 524 6 3 1 1 0 0 0 0 1 0 0 0 0 0 0 53
54 8 536 7 6 3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 14 587 8 7 6 3 0 0 0 0 0 0 1 0 0 0 0 55
56 14 597 14 8 7 6 0 0 0 0 0 0 0 1 0 0 0 56
57 13 581 14 14 8 7 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
-37.98113 0.06702 0.68292 0.11918 0.15051 0.08148
M1 M2 M3 M4 M5 M6
-1.58003 -2.61700 -2.23469 -3.49583 -1.40349 -2.27170
M7 M8 M9 M10 M11 t
-3.62375 -5.63694 -4.37543 -6.46479 -4.23387 0.10973
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.5516 -1.8903 0.1422 2.0135 5.3885
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -37.98113 17.51813 -2.168 0.03631 *
x 0.06702 0.02616 2.562 0.01438 *
y1 0.68292 0.15477 4.413 7.83e-05 ***
y2 0.11918 0.18824 0.633 0.53034
y3 0.15051 0.19169 0.785 0.43707
y4 0.08148 0.16312 0.499 0.62025
M1 -1.58003 2.33808 -0.676 0.50317
M2 -2.61700 2.40766 -1.087 0.28373
M3 -2.23469 2.33362 -0.958 0.34416
M4 -3.49583 2.23898 -1.561 0.12652
M5 -1.40349 2.28555 -0.614 0.54273
M6 -2.27170 2.26303 -1.004 0.32165
M7 -3.62375 2.39263 -1.515 0.13795
M8 -5.63694 2.42232 -2.327 0.02525 *
M9 -4.37543 2.39563 -1.826 0.07545 .
M10 -6.46479 2.33944 -2.763 0.00869 **
M11 -4.23387 2.37534 -1.782 0.08247 .
t 0.10973 0.06531 1.680 0.10090
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.219 on 39 degrees of freedom
Multiple R-squared: 0.869, Adjusted R-squared: 0.8119
F-statistic: 15.22 on 17 and 39 DF, p-value: 3.252e-12
> 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,] 0.1526818 0.3053637 0.8473182
[2,] 0.3890235 0.7780471 0.6109765
[3,] 0.5754356 0.8491289 0.4245644
[4,] 0.4700331 0.9400663 0.5299669
[5,] 0.5409254 0.9181491 0.4590746
[6,] 0.4392568 0.8785135 0.5607432
[7,] 0.6565211 0.6869577 0.3434789
[8,] 0.5939398 0.8121203 0.4060602
[9,] 0.4869008 0.9738016 0.5130992
[10,] 0.3639621 0.7279242 0.6360379
[11,] 0.4059547 0.8119094 0.5940453
[12,] 0.5883530 0.8232941 0.4116470
[13,] 0.4742351 0.9484703 0.5257649
[14,] 0.5339884 0.9320232 0.4660116
[15,] 0.4082206 0.8164413 0.5917794
[16,] 0.7199303 0.5601395 0.2800697
> postscript(file="/var/www/html/rcomp/tmp/1iu711258659112.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ejq11258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35j2k1258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zylj1258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hadr1258659112.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 7
2.3455001 2.2889736 -1.9899127 -4.6232395 -2.3630677 -0.6534843 -0.7486830
8 9 10 11 12 13 14
-3.5754439 3.7328374 2.0134574 5.3005620 1.6332341 -1.1751303 -3.3612128
15 16 17 18 19 20 21
-0.9430796 -1.8903180 1.7543100 0.1133730 -2.0514059 -0.9891661 1.7158050
22 23 24 25 26 27 28
2.1083804 -7.5516383 1.3713580 0.6903584 -0.3421627 4.1122895 2.9840578
29 30 31 32 33 34 35
0.5678191 0.5248637 -1.7453320 0.5597303 2.2316250 -2.1529057 5.3885208
36 37 38 39 40 41 42
-3.2710992 2.8593920 2.5476747 -1.3214903 0.4740771 -0.7224460 -0.3608870
43 44 45 46 47 48 49
1.7615746 4.5994172 -4.8397333 -1.9689321 -3.1374445 0.2665070 -4.7201203
50 51 52 53 54 55 56
-1.1332728 0.1421930 3.0554226 0.7633847 0.3761346 2.7838463 -0.5945375
57
-2.8405340
> postscript(file="/var/www/html/rcomp/tmp/63ti71258659112.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 2.3455001 NA
1 2.2889736 2.3455001
2 -1.9899127 2.2889736
3 -4.6232395 -1.9899127
4 -2.3630677 -4.6232395
5 -0.6534843 -2.3630677
6 -0.7486830 -0.6534843
7 -3.5754439 -0.7486830
8 3.7328374 -3.5754439
9 2.0134574 3.7328374
10 5.3005620 2.0134574
11 1.6332341 5.3005620
12 -1.1751303 1.6332341
13 -3.3612128 -1.1751303
14 -0.9430796 -3.3612128
15 -1.8903180 -0.9430796
16 1.7543100 -1.8903180
17 0.1133730 1.7543100
18 -2.0514059 0.1133730
19 -0.9891661 -2.0514059
20 1.7158050 -0.9891661
21 2.1083804 1.7158050
22 -7.5516383 2.1083804
23 1.3713580 -7.5516383
24 0.6903584 1.3713580
25 -0.3421627 0.6903584
26 4.1122895 -0.3421627
27 2.9840578 4.1122895
28 0.5678191 2.9840578
29 0.5248637 0.5678191
30 -1.7453320 0.5248637
31 0.5597303 -1.7453320
32 2.2316250 0.5597303
33 -2.1529057 2.2316250
34 5.3885208 -2.1529057
35 -3.2710992 5.3885208
36 2.8593920 -3.2710992
37 2.5476747 2.8593920
38 -1.3214903 2.5476747
39 0.4740771 -1.3214903
40 -0.7224460 0.4740771
41 -0.3608870 -0.7224460
42 1.7615746 -0.3608870
43 4.5994172 1.7615746
44 -4.8397333 4.5994172
45 -1.9689321 -4.8397333
46 -3.1374445 -1.9689321
47 0.2665070 -3.1374445
48 -4.7201203 0.2665070
49 -1.1332728 -4.7201203
50 0.1421930 -1.1332728
51 3.0554226 0.1421930
52 0.7633847 3.0554226
53 0.3761346 0.7633847
54 2.7838463 0.3761346
55 -0.5945375 2.7838463
56 -2.8405340 -0.5945375
57 NA -2.8405340
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.2889736 2.3455001
[2,] -1.9899127 2.2889736
[3,] -4.6232395 -1.9899127
[4,] -2.3630677 -4.6232395
[5,] -0.6534843 -2.3630677
[6,] -0.7486830 -0.6534843
[7,] -3.5754439 -0.7486830
[8,] 3.7328374 -3.5754439
[9,] 2.0134574 3.7328374
[10,] 5.3005620 2.0134574
[11,] 1.6332341 5.3005620
[12,] -1.1751303 1.6332341
[13,] -3.3612128 -1.1751303
[14,] -0.9430796 -3.3612128
[15,] -1.8903180 -0.9430796
[16,] 1.7543100 -1.8903180
[17,] 0.1133730 1.7543100
[18,] -2.0514059 0.1133730
[19,] -0.9891661 -2.0514059
[20,] 1.7158050 -0.9891661
[21,] 2.1083804 1.7158050
[22,] -7.5516383 2.1083804
[23,] 1.3713580 -7.5516383
[24,] 0.6903584 1.3713580
[25,] -0.3421627 0.6903584
[26,] 4.1122895 -0.3421627
[27,] 2.9840578 4.1122895
[28,] 0.5678191 2.9840578
[29,] 0.5248637 0.5678191
[30,] -1.7453320 0.5248637
[31,] 0.5597303 -1.7453320
[32,] 2.2316250 0.5597303
[33,] -2.1529057 2.2316250
[34,] 5.3885208 -2.1529057
[35,] -3.2710992 5.3885208
[36,] 2.8593920 -3.2710992
[37,] 2.5476747 2.8593920
[38,] -1.3214903 2.5476747
[39,] 0.4740771 -1.3214903
[40,] -0.7224460 0.4740771
[41,] -0.3608870 -0.7224460
[42,] 1.7615746 -0.3608870
[43,] 4.5994172 1.7615746
[44,] -4.8397333 4.5994172
[45,] -1.9689321 -4.8397333
[46,] -3.1374445 -1.9689321
[47,] 0.2665070 -3.1374445
[48,] -4.7201203 0.2665070
[49,] -1.1332728 -4.7201203
[50,] 0.1421930 -1.1332728
[51,] 3.0554226 0.1421930
[52,] 0.7633847 3.0554226
[53,] 0.3761346 0.7633847
[54,] 2.7838463 0.3761346
[55,] -0.5945375 2.7838463
[56,] -2.8405340 -0.5945375
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.2889736 2.3455001
2 -1.9899127 2.2889736
3 -4.6232395 -1.9899127
4 -2.3630677 -4.6232395
5 -0.6534843 -2.3630677
6 -0.7486830 -0.6534843
7 -3.5754439 -0.7486830
8 3.7328374 -3.5754439
9 2.0134574 3.7328374
10 5.3005620 2.0134574
11 1.6332341 5.3005620
12 -1.1751303 1.6332341
13 -3.3612128 -1.1751303
14 -0.9430796 -3.3612128
15 -1.8903180 -0.9430796
16 1.7543100 -1.8903180
17 0.1133730 1.7543100
18 -2.0514059 0.1133730
19 -0.9891661 -2.0514059
20 1.7158050 -0.9891661
21 2.1083804 1.7158050
22 -7.5516383 2.1083804
23 1.3713580 -7.5516383
24 0.6903584 1.3713580
25 -0.3421627 0.6903584
26 4.1122895 -0.3421627
27 2.9840578 4.1122895
28 0.5678191 2.9840578
29 0.5248637 0.5678191
30 -1.7453320 0.5248637
31 0.5597303 -1.7453320
32 2.2316250 0.5597303
33 -2.1529057 2.2316250
34 5.3885208 -2.1529057
35 -3.2710992 5.3885208
36 2.8593920 -3.2710992
37 2.5476747 2.8593920
38 -1.3214903 2.5476747
39 0.4740771 -1.3214903
40 -0.7224460 0.4740771
41 -0.3608870 -0.7224460
42 1.7615746 -0.3608870
43 4.5994172 1.7615746
44 -4.8397333 4.5994172
45 -1.9689321 -4.8397333
46 -3.1374445 -1.9689321
47 0.2665070 -3.1374445
48 -4.7201203 0.2665070
49 -1.1332728 -4.7201203
50 0.1421930 -1.1332728
51 3.0554226 0.1421930
52 0.7633847 3.0554226
53 0.3761346 0.7633847
54 2.7838463 0.3761346
55 -0.5945375 2.7838463
56 -2.8405340 -0.5945375
> 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/7nh8t1258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87mf11258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ulnb1258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10q0371258659112.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mmzw1258659112.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/12rsl21258659112.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/13o3031258659112.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/14v3im1258659112.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/15j9j11258659112.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/16qz1n1258659112.tab")
+ }
>
> system("convert tmp/1iu711258659112.ps tmp/1iu711258659112.png")
> system("convert tmp/2ejq11258659112.ps tmp/2ejq11258659112.png")
> system("convert tmp/35j2k1258659112.ps tmp/35j2k1258659112.png")
> system("convert tmp/4zylj1258659112.ps tmp/4zylj1258659112.png")
> system("convert tmp/5hadr1258659112.ps tmp/5hadr1258659112.png")
> system("convert tmp/63ti71258659112.ps tmp/63ti71258659112.png")
> system("convert tmp/7nh8t1258659112.ps tmp/7nh8t1258659112.png")
> system("convert tmp/87mf11258659112.ps tmp/87mf11258659112.png")
> system("convert tmp/9ulnb1258659112.ps tmp/9ulnb1258659112.png")
> system("convert tmp/10q0371258659112.ps tmp/10q0371258659112.png")
>
>
> proc.time()
user system elapsed
2.335 1.525 2.748