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(549
+ ,3
+ ,0
+ ,1
+ ,564
+ ,3.1
+ ,-2
+ ,1
+ ,586
+ ,2.9
+ ,-4
+ ,1
+ ,604
+ ,2.4
+ ,-4
+ ,1
+ ,601
+ ,2.4
+ ,-7
+ ,1
+ ,545
+ ,2.7
+ ,-9
+ ,1
+ ,537
+ ,2.5
+ ,-13
+ ,1
+ ,552
+ ,2.1
+ ,-8
+ ,1
+ ,563
+ ,1.9
+ ,-13
+ ,1
+ ,575
+ ,0.8
+ ,-15
+ ,1
+ ,580
+ ,0.8
+ ,-15
+ ,1
+ ,575
+ ,0.3
+ ,-15
+ ,1
+ ,558
+ ,0
+ ,-10
+ ,1
+ ,564
+ ,-0.9
+ ,-12
+ ,1
+ ,581
+ ,-1
+ ,-11
+ ,1
+ ,597
+ ,-0.7
+ ,-11
+ ,1
+ ,587
+ ,-1.7
+ ,-17
+ ,1
+ ,536
+ ,-1
+ ,-18
+ ,1
+ ,524
+ ,-0.2
+ ,-19
+ ,1.09
+ ,537
+ ,0.7
+ ,-22
+ ,1.31
+ ,536
+ ,0.6
+ ,-24
+ ,1.66
+ ,533
+ ,1.9
+ ,-24
+ ,2
+ ,528
+ ,2.1
+ ,-20
+ ,2.31
+ ,516
+ ,2.7
+ ,-25
+ ,2.75
+ ,502
+ ,3.2
+ ,-22
+ ,3.42
+ ,506
+ ,4.8
+ ,-17
+ ,3.97
+ ,518
+ ,5.5
+ ,-9
+ ,4.25
+ ,534
+ ,5.4
+ ,-11
+ ,4.25
+ ,528
+ ,5.9
+ ,-13
+ ,4.18
+ ,478
+ ,5.8
+ ,-11
+ ,4
+ ,469
+ ,5.1
+ ,-9
+ ,4
+ ,490
+ ,4.1
+ ,-7
+ ,4
+ ,493
+ ,4.4
+ ,-3
+ ,4
+ ,508
+ ,3.6
+ ,-3
+ ,4
+ ,517
+ ,3.5
+ ,-6
+ ,4
+ ,514
+ ,3.1
+ ,-4
+ ,4
+ ,510
+ ,2.9
+ ,-8
+ ,4
+ ,527
+ ,2.2
+ ,-1
+ ,4
+ ,542
+ ,1.4
+ ,-2
+ ,4
+ ,565
+ ,1.2
+ ,-2
+ ,4
+ ,555
+ ,1.3
+ ,-1
+ ,4
+ ,499
+ ,1.3
+ ,1
+ ,3.9
+ ,511
+ ,1.3
+ ,2
+ ,3.75
+ ,526
+ ,1.8
+ ,2
+ ,3.75
+ ,532
+ ,1.8
+ ,-1
+ ,3.65
+ ,549
+ ,1.8
+ ,1
+ ,3.5
+ ,561
+ ,1.7
+ ,-1
+ ,3.5
+ ,557
+ ,2.1
+ ,-8
+ ,3.39
+ ,566
+ ,2
+ ,1
+ ,3.25
+ ,588
+ ,1.7
+ ,2
+ ,3.17
+ ,620
+ ,1.9
+ ,-2
+ ,3
+ ,626
+ ,2.3
+ ,-2
+ ,2.93
+ ,620
+ ,2.4
+ ,-2
+ ,2.75
+ ,573
+ ,2.5
+ ,-2
+ ,2.64
+ ,573
+ ,2.8
+ ,-6
+ ,2.5
+ ,574
+ ,2.6
+ ,-4
+ ,2.5
+ ,580
+ ,2.2
+ ,-5
+ ,2.45
+ ,590
+ ,2.8
+ ,-2
+ ,2.25
+ ,593
+ ,2.8
+ ,-1
+ ,2.25
+ ,597
+ ,2.8
+ ,-5
+ ,2.21
+ ,595
+ ,2.3
+ ,-9
+ ,2
+ ,612
+ ,2.2
+ ,-8
+ ,2
+ ,628
+ ,3
+ ,-14
+ ,2
+ ,629
+ ,2.9
+ ,-10
+ ,2
+ ,621
+ ,2.7
+ ,-11
+ ,2
+ ,569
+ ,2.7
+ ,-11
+ ,2
+ ,567
+ ,2.3
+ ,-11
+ ,2
+ ,573
+ ,2.4
+ ,-5
+ ,2
+ ,584
+ ,2.8
+ ,-2
+ ,2
+ ,589
+ ,2.3
+ ,-3
+ ,2
+ ,591
+ ,2
+ ,-6
+ ,2
+ ,595
+ ,1.9
+ ,-6
+ ,2
+ ,594
+ ,2.3
+ ,-7
+ ,2
+ ,611
+ ,2.7
+ ,-6
+ ,2
+ ,613
+ ,1.8
+ ,-2
+ ,2
+ ,611
+ ,2
+ ,-2
+ ,2
+ ,594
+ ,2.1
+ ,-4
+ ,2
+ ,543
+ ,2
+ ,0
+ ,2
+ ,537
+ ,2.4
+ ,-6
+ ,2
+ ,544
+ ,1.7
+ ,-4
+ ,2
+ ,555
+ ,1
+ ,-3
+ ,2
+ ,561
+ ,1.2
+ ,-1
+ ,2
+ ,562
+ ,1.4
+ ,-3
+ ,2
+ ,555
+ ,1.7
+ ,-6
+ ,2
+ ,547
+ ,1.8
+ ,-6
+ ,2
+ ,565
+ ,1.4
+ ,-15
+ ,2
+ ,578
+ ,1.7
+ ,-5
+ ,2
+ ,580
+ ,1.6
+ ,-11
+ ,2
+ ,569
+ ,1.4
+ ,-13
+ ,2
+ ,507
+ ,1.5
+ ,-10
+ ,2.1
+ ,501
+ ,0.9
+ ,-9
+ ,2.5
+ ,509
+ ,1.5
+ ,-11
+ ,2.5
+ ,510
+ ,1.7
+ ,-18
+ ,2.55
+ ,517
+ ,1.6
+ ,-13
+ ,2.75
+ ,519
+ ,1.2
+ ,-9
+ ,2.75
+ ,512
+ ,1.3
+ ,-8
+ ,2.85
+ ,509
+ ,1.1
+ ,-4
+ ,3.25
+ ,519
+ ,1.3
+ ,-3
+ ,3.25
+ ,523
+ ,1.2
+ ,-3
+ ,3.25
+ ,525
+ ,1.3
+ ,-3
+ ,3.25
+ ,517
+ ,1.1
+ ,-1
+ ,3.25
+ ,456
+ ,0.8
+ ,0
+ ,3.25
+ ,455
+ ,1.4
+ ,1
+ ,3.25
+ ,461
+ ,1.6
+ ,0
+ ,3.25
+ ,470
+ ,2.5
+ ,2
+ ,3.25
+ ,475
+ ,2.5
+ ,1
+ ,3.25
+ ,476
+ ,2.6
+ ,-1
+ ,3.25
+ ,471
+ ,2
+ ,-8
+ ,3.25
+ ,471
+ ,1.8
+ ,-18
+ ,3.39
+ ,503
+ ,1.9
+ ,-14
+ ,3.75
+ ,513
+ ,1.9
+ ,-4
+ ,4.03
+ ,510
+ ,2.5
+ ,0
+ ,4.49
+ ,484
+ ,2.8
+ ,4
+ ,4.5
+ ,431
+ ,3
+ ,4
+ ,4.5
+ ,436
+ ,3.1
+ ,3
+ ,4.58
+ ,443
+ ,2.9
+ ,3
+ ,4.75
+ ,448
+ ,2.2
+ ,7
+ ,4.75
+ ,460
+ ,2.5
+ ,8
+ ,4.75
+ ,467
+ ,2.7
+ ,13
+ ,4.75
+ ,460
+ ,3
+ ,15
+ ,4.75
+ ,464
+ ,3.7
+ ,14
+ ,4.75
+ ,485
+ ,3.7
+ ,14
+ ,4.7
+ ,501
+ ,4
+ ,10
+ ,4.5
+ ,521
+ ,3.5
+ ,16
+ ,4.25
+ ,488
+ ,1.7
+ ,13
+ ,4.25
+ ,439
+ ,3
+ ,15
+ ,4.11
+ ,442
+ ,2.4
+ ,13
+ ,3.75
+ ,457
+ ,2.3
+ ,12
+ ,3.51
+ ,462
+ ,2.5
+ ,13
+ ,3.37
+ ,481
+ ,2.1
+ ,11
+ ,3.21
+ ,493
+ ,0.3
+ ,9
+ ,3)
+ ,dim=c(4
+ ,131)
+ ,dimnames=list(c('Werkl'
+ ,'HICP'
+ ,'cons'
+ ,'Rente')
+ ,1:131))
> y <- array(NA,dim=c(4,131),dimnames=list(c('Werkl','HICP','cons','Rente'),1:131))
> 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
Werkl HICP cons Rente M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 549 3.0 0 1.00 1 0 0 0 0 0 0 0 0 0 0
2 564 3.1 -2 1.00 0 1 0 0 0 0 0 0 0 0 0
3 586 2.9 -4 1.00 0 0 1 0 0 0 0 0 0 0 0
4 604 2.4 -4 1.00 0 0 0 1 0 0 0 0 0 0 0
5 601 2.4 -7 1.00 0 0 0 0 1 0 0 0 0 0 0
6 545 2.7 -9 1.00 0 0 0 0 0 1 0 0 0 0 0
7 537 2.5 -13 1.00 0 0 0 0 0 0 1 0 0 0 0
8 552 2.1 -8 1.00 0 0 0 0 0 0 0 1 0 0 0
9 563 1.9 -13 1.00 0 0 0 0 0 0 0 0 1 0 0
10 575 0.8 -15 1.00 0 0 0 0 0 0 0 0 0 1 0
11 580 0.8 -15 1.00 0 0 0 0 0 0 0 0 0 0 1
12 575 0.3 -15 1.00 0 0 0 0 0 0 0 0 0 0 0
13 558 0.0 -10 1.00 1 0 0 0 0 0 0 0 0 0 0
14 564 -0.9 -12 1.00 0 1 0 0 0 0 0 0 0 0 0
15 581 -1.0 -11 1.00 0 0 1 0 0 0 0 0 0 0 0
16 597 -0.7 -11 1.00 0 0 0 1 0 0 0 0 0 0 0
17 587 -1.7 -17 1.00 0 0 0 0 1 0 0 0 0 0 0
18 536 -1.0 -18 1.00 0 0 0 0 0 1 0 0 0 0 0
19 524 -0.2 -19 1.09 0 0 0 0 0 0 1 0 0 0 0
20 537 0.7 -22 1.31 0 0 0 0 0 0 0 1 0 0 0
21 536 0.6 -24 1.66 0 0 0 0 0 0 0 0 1 0 0
22 533 1.9 -24 2.00 0 0 0 0 0 0 0 0 0 1 0
23 528 2.1 -20 2.31 0 0 0 0 0 0 0 0 0 0 1
24 516 2.7 -25 2.75 0 0 0 0 0 0 0 0 0 0 0
25 502 3.2 -22 3.42 1 0 0 0 0 0 0 0 0 0 0
26 506 4.8 -17 3.97 0 1 0 0 0 0 0 0 0 0 0
27 518 5.5 -9 4.25 0 0 1 0 0 0 0 0 0 0 0
28 534 5.4 -11 4.25 0 0 0 1 0 0 0 0 0 0 0
29 528 5.9 -13 4.18 0 0 0 0 1 0 0 0 0 0 0
30 478 5.8 -11 4.00 0 0 0 0 0 1 0 0 0 0 0
31 469 5.1 -9 4.00 0 0 0 0 0 0 1 0 0 0 0
32 490 4.1 -7 4.00 0 0 0 0 0 0 0 1 0 0 0
33 493 4.4 -3 4.00 0 0 0 0 0 0 0 0 1 0 0
34 508 3.6 -3 4.00 0 0 0 0 0 0 0 0 0 1 0
35 517 3.5 -6 4.00 0 0 0 0 0 0 0 0 0 0 1
36 514 3.1 -4 4.00 0 0 0 0 0 0 0 0 0 0 0
37 510 2.9 -8 4.00 1 0 0 0 0 0 0 0 0 0 0
38 527 2.2 -1 4.00 0 1 0 0 0 0 0 0 0 0 0
39 542 1.4 -2 4.00 0 0 1 0 0 0 0 0 0 0 0
40 565 1.2 -2 4.00 0 0 0 1 0 0 0 0 0 0 0
41 555 1.3 -1 4.00 0 0 0 0 1 0 0 0 0 0 0
42 499 1.3 1 3.90 0 0 0 0 0 1 0 0 0 0 0
43 511 1.3 2 3.75 0 0 0 0 0 0 1 0 0 0 0
44 526 1.8 2 3.75 0 0 0 0 0 0 0 1 0 0 0
45 532 1.8 -1 3.65 0 0 0 0 0 0 0 0 1 0 0
46 549 1.8 1 3.50 0 0 0 0 0 0 0 0 0 1 0
47 561 1.7 -1 3.50 0 0 0 0 0 0 0 0 0 0 1
48 557 2.1 -8 3.39 0 0 0 0 0 0 0 0 0 0 0
49 566 2.0 1 3.25 1 0 0 0 0 0 0 0 0 0 0
50 588 1.7 2 3.17 0 1 0 0 0 0 0 0 0 0 0
51 620 1.9 -2 3.00 0 0 1 0 0 0 0 0 0 0 0
52 626 2.3 -2 2.93 0 0 0 1 0 0 0 0 0 0 0
53 620 2.4 -2 2.75 0 0 0 0 1 0 0 0 0 0 0
54 573 2.5 -2 2.64 0 0 0 0 0 1 0 0 0 0 0
55 573 2.8 -6 2.50 0 0 0 0 0 0 1 0 0 0 0
56 574 2.6 -4 2.50 0 0 0 0 0 0 0 1 0 0 0
57 580 2.2 -5 2.45 0 0 0 0 0 0 0 0 1 0 0
58 590 2.8 -2 2.25 0 0 0 0 0 0 0 0 0 1 0
59 593 2.8 -1 2.25 0 0 0 0 0 0 0 0 0 0 1
60 597 2.8 -5 2.21 0 0 0 0 0 0 0 0 0 0 0
61 595 2.3 -9 2.00 1 0 0 0 0 0 0 0 0 0 0
62 612 2.2 -8 2.00 0 1 0 0 0 0 0 0 0 0 0
63 628 3.0 -14 2.00 0 0 1 0 0 0 0 0 0 0 0
64 629 2.9 -10 2.00 0 0 0 1 0 0 0 0 0 0 0
65 621 2.7 -11 2.00 0 0 0 0 1 0 0 0 0 0 0
66 569 2.7 -11 2.00 0 0 0 0 0 1 0 0 0 0 0
67 567 2.3 -11 2.00 0 0 0 0 0 0 1 0 0 0 0
68 573 2.4 -5 2.00 0 0 0 0 0 0 0 1 0 0 0
69 584 2.8 -2 2.00 0 0 0 0 0 0 0 0 1 0 0
70 589 2.3 -3 2.00 0 0 0 0 0 0 0 0 0 1 0
71 591 2.0 -6 2.00 0 0 0 0 0 0 0 0 0 0 1
72 595 1.9 -6 2.00 0 0 0 0 0 0 0 0 0 0 0
73 594 2.3 -7 2.00 1 0 0 0 0 0 0 0 0 0 0
74 611 2.7 -6 2.00 0 1 0 0 0 0 0 0 0 0 0
75 613 1.8 -2 2.00 0 0 1 0 0 0 0 0 0 0 0
76 611 2.0 -2 2.00 0 0 0 1 0 0 0 0 0 0 0
77 594 2.1 -4 2.00 0 0 0 0 1 0 0 0 0 0 0
78 543 2.0 0 2.00 0 0 0 0 0 1 0 0 0 0 0
79 537 2.4 -6 2.00 0 0 0 0 0 0 1 0 0 0 0
80 544 1.7 -4 2.00 0 0 0 0 0 0 0 1 0 0 0
81 555 1.0 -3 2.00 0 0 0 0 0 0 0 0 1 0 0
82 561 1.2 -1 2.00 0 0 0 0 0 0 0 0 0 1 0
83 562 1.4 -3 2.00 0 0 0 0 0 0 0 0 0 0 1
84 555 1.7 -6 2.00 0 0 0 0 0 0 0 0 0 0 0
85 547 1.8 -6 2.00 1 0 0 0 0 0 0 0 0 0 0
86 565 1.4 -15 2.00 0 1 0 0 0 0 0 0 0 0 0
87 578 1.7 -5 2.00 0 0 1 0 0 0 0 0 0 0 0
88 580 1.6 -11 2.00 0 0 0 1 0 0 0 0 0 0 0
89 569 1.4 -13 2.00 0 0 0 0 1 0 0 0 0 0 0
90 507 1.5 -10 2.10 0 0 0 0 0 1 0 0 0 0 0
91 501 0.9 -9 2.50 0 0 0 0 0 0 1 0 0 0 0
92 509 1.5 -11 2.50 0 0 0 0 0 0 0 1 0 0 0
93 510 1.7 -18 2.55 0 0 0 0 0 0 0 0 1 0 0
94 517 1.6 -13 2.75 0 0 0 0 0 0 0 0 0 1 0
95 519 1.2 -9 2.75 0 0 0 0 0 0 0 0 0 0 1
96 512 1.3 -8 2.85 0 0 0 0 0 0 0 0 0 0 0
97 509 1.1 -4 3.25 1 0 0 0 0 0 0 0 0 0 0
98 519 1.3 -3 3.25 0 1 0 0 0 0 0 0 0 0 0
99 523 1.2 -3 3.25 0 0 1 0 0 0 0 0 0 0 0
100 525 1.3 -3 3.25 0 0 0 1 0 0 0 0 0 0 0
101 517 1.1 -1 3.25 0 0 0 0 1 0 0 0 0 0 0
102 456 0.8 0 3.25 0 0 0 0 0 1 0 0 0 0 0
103 455 1.4 1 3.25 0 0 0 0 0 0 1 0 0 0 0
104 461 1.6 0 3.25 0 0 0 0 0 0 0 1 0 0 0
105 470 2.5 2 3.25 0 0 0 0 0 0 0 0 1 0 0
106 475 2.5 1 3.25 0 0 0 0 0 0 0 0 0 1 0
107 476 2.6 -1 3.25 0 0 0 0 0 0 0 0 0 0 1
108 471 2.0 -8 3.25 0 0 0 0 0 0 0 0 0 0 0
109 471 1.8 -18 3.39 1 0 0 0 0 0 0 0 0 0 0
110 503 1.9 -14 3.75 0 1 0 0 0 0 0 0 0 0 0
111 513 1.9 -4 4.03 0 0 1 0 0 0 0 0 0 0 0
112 510 2.5 0 4.49 0 0 0 1 0 0 0 0 0 0 0
113 484 2.8 4 4.50 0 0 0 0 1 0 0 0 0 0 0
114 431 3.0 4 4.50 0 0 0 0 0 1 0 0 0 0 0
115 436 3.1 3 4.58 0 0 0 0 0 0 1 0 0 0 0
116 443 2.9 3 4.75 0 0 0 0 0 0 0 1 0 0 0
117 448 2.2 7 4.75 0 0 0 0 0 0 0 0 1 0 0
118 460 2.5 8 4.75 0 0 0 0 0 0 0 0 0 1 0
119 467 2.7 13 4.75 0 0 0 0 0 0 0 0 0 0 1
120 460 3.0 15 4.75 0 0 0 0 0 0 0 0 0 0 0
121 464 3.7 14 4.75 1 0 0 0 0 0 0 0 0 0 0
122 485 3.7 14 4.70 0 1 0 0 0 0 0 0 0 0 0
123 501 4.0 10 4.50 0 0 1 0 0 0 0 0 0 0 0
124 521 3.5 16 4.25 0 0 0 1 0 0 0 0 0 0 0
125 488 1.7 13 4.25 0 0 0 0 1 0 0 0 0 0 0
126 439 3.0 15 4.11 0 0 0 0 0 1 0 0 0 0 0
127 442 2.4 13 3.75 0 0 0 0 0 0 1 0 0 0 0
128 457 2.3 12 3.51 0 0 0 0 0 0 0 1 0 0 0
129 462 2.5 13 3.37 0 0 0 0 0 0 0 0 1 0 0
130 481 2.1 11 3.21 0 0 0 0 0 0 0 0 0 1 0
131 493 0.3 9 3.00 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) HICP cons Rente M1 M2
615.25740 6.80817 0.04323 -33.32762 -5.64522 12.96323
M3 M4 M5 M6 M7 M8
27.80677 37.14547 25.34355 -30.81866 -33.00610 -22.04162
M9 M10 M11
-15.54375 -5.62545 0.36993
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-51.912 -20.965 -9.265 28.909 64.070
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 615.25740 13.57542 45.321 < 2e-16 ***
HICP 6.80817 2.48255 2.742 0.00707 **
cons 0.04323 0.36976 0.117 0.90713
Rente -33.32762 3.16788 -10.520 < 2e-16 ***
M1 -5.64522 13.30223 -0.424 0.67207
M2 12.96323 13.30279 0.974 0.33185
M3 27.80677 13.33445 2.085 0.03923 *
M4 37.14547 13.35045 2.782 0.00630 **
M5 25.34355 13.31599 1.903 0.05949 .
M6 -30.81866 13.35873 -2.307 0.02283 *
M7 -33.00610 13.32187 -2.478 0.01467 *
M8 -22.04162 13.34388 -1.652 0.10128
M9 -15.54375 13.33304 -1.166 0.24608
M10 -5.62545 13.35149 -0.421 0.67429
M11 0.36993 13.35183 0.028 0.97794
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30.41 on 116 degrees of freedom
Multiple R-squared: 0.667, Adjusted R-squared: 0.6268
F-statistic: 16.6 on 14 and 116 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,] 9.947051e-03 1.989410e-02 9.900529e-01
[2,] 1.602877e-03 3.205753e-03 9.983971e-01
[3,] 2.586737e-04 5.173474e-04 9.997413e-01
[4,] 1.116281e-04 2.232561e-04 9.998884e-01
[5,] 1.754239e-05 3.508477e-05 9.999825e-01
[6,] 3.343978e-06 6.687955e-06 9.999967e-01
[7,] 1.009694e-06 2.019389e-06 9.999990e-01
[8,] 6.568729e-05 1.313746e-04 9.999343e-01
[9,] 5.389852e-05 1.077970e-04 9.999461e-01
[10,] 1.542999e-05 3.085998e-05 9.999846e-01
[11,] 4.174101e-06 8.348202e-06 9.999958e-01
[12,] 1.110018e-06 2.220035e-06 9.999989e-01
[13,] 3.158795e-07 6.317591e-07 9.999997e-01
[14,] 9.955668e-08 1.991134e-07 9.999999e-01
[15,] 2.352915e-08 4.705830e-08 1.000000e+00
[16,] 8.238000e-09 1.647600e-08 1.000000e+00
[17,] 1.864339e-09 3.728678e-09 1.000000e+00
[18,] 6.039395e-10 1.207879e-09 1.000000e+00
[19,] 1.574691e-10 3.149382e-10 1.000000e+00
[20,] 3.146380e-10 6.292759e-10 1.000000e+00
[21,] 3.405146e-10 6.810293e-10 1.000000e+00
[22,] 1.802505e-10 3.605010e-10 1.000000e+00
[23,] 2.018289e-10 4.036578e-10 1.000000e+00
[24,] 1.026759e-10 2.053518e-10 1.000000e+00
[25,] 4.580780e-11 9.161561e-11 1.000000e+00
[26,] 7.737902e-11 1.547580e-10 1.000000e+00
[27,] 1.100765e-10 2.201530e-10 1.000000e+00
[28,] 1.309880e-10 2.619759e-10 1.000000e+00
[29,] 2.161332e-10 4.322663e-10 1.000000e+00
[30,] 1.669798e-09 3.339596e-09 1.000000e+00
[31,] 2.094034e-08 4.188067e-08 1.000000e+00
[32,] 9.341174e-07 1.868235e-06 9.999991e-01
[33,] 4.972111e-05 9.944222e-05 9.999503e-01
[34,] 8.729786e-03 1.745957e-02 9.912702e-01
[35,] 5.844489e-02 1.168898e-01 9.415551e-01
[36,] 1.576868e-01 3.153735e-01 8.423132e-01
[37,] 3.781633e-01 7.563266e-01 6.218367e-01
[38,] 6.353567e-01 7.292866e-01 3.646433e-01
[39,] 7.393366e-01 5.213269e-01 2.606634e-01
[40,] 8.377697e-01 3.244605e-01 1.622303e-01
[41,] 8.458864e-01 3.082272e-01 1.541136e-01
[42,] 8.343518e-01 3.312964e-01 1.656482e-01
[43,] 8.491490e-01 3.017020e-01 1.508510e-01
[44,] 9.030053e-01 1.939895e-01 9.699473e-02
[45,] 9.416034e-01 1.167932e-01 5.839659e-02
[46,] 9.710171e-01 5.796574e-02 2.898287e-02
[47,] 9.724138e-01 5.517232e-02 2.758616e-02
[48,] 9.738272e-01 5.234560e-02 2.617280e-02
[49,] 9.787494e-01 4.250120e-02 2.125060e-02
[50,] 9.847148e-01 3.057040e-02 1.528520e-02
[51,] 9.850983e-01 2.980341e-02 1.490170e-02
[52,] 9.853008e-01 2.939848e-02 1.469924e-02
[53,] 9.868900e-01 2.621995e-02 1.310997e-02
[54,] 9.891393e-01 2.172131e-02 1.086066e-02
[55,] 9.943299e-01 1.134029e-02 5.670147e-03
[56,] 9.970774e-01 5.845173e-03 2.922587e-03
[57,] 9.980316e-01 3.936779e-03 1.968390e-03
[58,] 9.982864e-01 3.427169e-03 1.713585e-03
[59,] 9.979038e-01 4.192330e-03 2.096165e-03
[60,] 9.975163e-01 4.967342e-03 2.483671e-03
[61,] 9.980892e-01 3.821685e-03 1.910843e-03
[62,] 9.979121e-01 4.175813e-03 2.087906e-03
[63,] 9.981453e-01 3.709350e-03 1.854675e-03
[64,] 9.992356e-01 1.528771e-03 7.643854e-04
[65,] 9.996733e-01 6.534740e-04 3.267370e-04
[66,] 9.998266e-01 3.467483e-04 1.733741e-04
[67,] 9.998983e-01 2.033060e-04 1.016530e-04
[68,] 9.998779e-01 2.441444e-04 1.220722e-04
[69,] 9.997989e-01 4.022914e-04 2.011457e-04
[70,] 9.997560e-01 4.880230e-04 2.440115e-04
[71,] 9.996015e-01 7.969738e-04 3.984869e-04
[72,] 9.996221e-01 7.557213e-04 3.778606e-04
[73,] 9.996874e-01 6.251285e-04 3.125643e-04
[74,] 9.998116e-01 3.768384e-04 1.884192e-04
[75,] 9.999112e-01 1.775627e-04 8.878136e-05
[76,] 9.999527e-01 9.461181e-05 4.730591e-05
[77,] 9.999887e-01 2.260862e-05 1.130431e-05
[78,] 9.999987e-01 2.674902e-06 1.337451e-06
[79,] 1.000000e+00 7.580024e-08 3.790012e-08
[80,] 1.000000e+00 1.137506e-09 5.687528e-10
[81,] 1.000000e+00 1.651883e-09 8.259413e-10
[82,] 1.000000e+00 6.097820e-09 3.048910e-09
[83,] 1.000000e+00 1.087751e-08 5.438753e-09
[84,] 1.000000e+00 5.794003e-10 2.897001e-10
[85,] 1.000000e+00 1.773460e-09 8.867302e-10
[86,] 1.000000e+00 6.206314e-09 3.103157e-09
[87,] 1.000000e+00 2.977527e-08 1.488763e-08
[88,] 1.000000e+00 2.552917e-08 1.276458e-08
[89,] 9.999999e-01 1.429299e-07 7.146494e-08
[90,] 9.999997e-01 5.845907e-07 2.922953e-07
[91,] 9.999985e-01 3.096526e-06 1.548263e-06
[92,] 9.999980e-01 4.060778e-06 2.030389e-06
[93,] 9.999863e-01 2.744960e-05 1.372480e-05
[94,] 9.999352e-01 1.296010e-04 6.480051e-05
[95,] 9.999138e-01 1.724038e-04 8.620191e-05
[96,] 9.989151e-01 2.169840e-03 1.084920e-03
> postscript(file="/var/www/html/rcomp/tmp/1witq1293397020.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2witq1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3witq1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4p9at1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5p9at1293397020.ps",horizontal=F,onefile=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 = 131
Frequency = 1
1 2 3 4 5 6
-47.7090774 -51.9118866 -43.3073369 -31.2419496 -22.3103465 -24.1041256
7 8 9 10 11 12
-28.3821422 -21.8394992 -15.7595898 -6.1024464 -7.0978194 -8.3238080
13 14 15 16 17 18
-17.8522805 -24.2469192 -21.4528722 -16.8340213 -7.9645620 -7.5248379
19 20 21 22 23 24
-19.7412251 -16.3713000 -11.4372275 -21.8747609 -24.0731211 -24.9078035
25 26 27 28 29 30
-14.4668525 -21.8543282 -20.4776848 -13.0491087 -12.8977524 -12.1401485
31 32 33 34 35 36
-14.2734510 2.4837799 -3.2294527 7.2987824 11.1139121 11.1206493
37 38 39 40 41 42
14.3004166 17.1550870 22.8013105 37.8242466 38.9021186 35.6451148
43 44 45 46 47 48
44.7901790 45.4216112 41.7206678 43.7167671 50.4886682 40.7718881
49 50 51 52 53 54
51.0430010 53.7675652 64.0696090 55.6747096 54.7968392 59.6121993
55 56 57 58 59 60
55.2642312 46.5749257 47.1771743 36.3787619 33.3401604 36.5498960
61 62 63 64 65 66
36.7733150 35.8024543 31.7717476 23.9409525 29.1477327 33.3099477
67 68 69 70 71 72
36.2206511 30.3159802 31.9651591 30.4941716 28.6709354 33.7216786
73 74 75 76 77 78
35.6868579 31.3119120 24.4228098 11.7224777 5.9300353 11.6001531
79 80 81 82 83 84
5.3236913 6.0384710 15.2630946 9.8967021 3.6261521 -4.9166873
85 86 87 88 89 90
-7.9522854 -5.4484095 -9.7666875 -16.1651973 -13.9151885 -17.2307146
91 92 93 94 95 96
-3.6705592 -10.6334870 -15.5240078 -11.3121115 -12.7571304 -16.7784883
97 98 99 100 101 102
0.3864972 -9.6268146 -19.7895391 -27.8090541 -22.7319595 -25.5705220
103 104 105 106 107 108
-28.5112176 -34.7941058 -38.5057836 -43.3808563 -48.9705893 -49.2131611
109 110 111 112 113 114
-37.1081564 -12.5723949 -8.5164892 -9.7823002 -25.8624718 -24.0618910
115 116 117 118 119 120
-14.8458350 -11.7829887 -8.6880508 -8.6920318 -9.2651815 -18.0241636
121 122 123 124 125 126
-13.1014355 -12.3762654 -19.7548672 -14.2807552 -23.0944451 -29.5351752
127 128 129 130 131
-32.1743225 -35.4133873 -42.9819836 -36.4229782 -25.0759865
> postscript(file="/var/www/html/rcomp/tmp/6p9at1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 -47.7090774 NA
1 -51.9118866 -47.7090774
2 -43.3073369 -51.9118866
3 -31.2419496 -43.3073369
4 -22.3103465 -31.2419496
5 -24.1041256 -22.3103465
6 -28.3821422 -24.1041256
7 -21.8394992 -28.3821422
8 -15.7595898 -21.8394992
9 -6.1024464 -15.7595898
10 -7.0978194 -6.1024464
11 -8.3238080 -7.0978194
12 -17.8522805 -8.3238080
13 -24.2469192 -17.8522805
14 -21.4528722 -24.2469192
15 -16.8340213 -21.4528722
16 -7.9645620 -16.8340213
17 -7.5248379 -7.9645620
18 -19.7412251 -7.5248379
19 -16.3713000 -19.7412251
20 -11.4372275 -16.3713000
21 -21.8747609 -11.4372275
22 -24.0731211 -21.8747609
23 -24.9078035 -24.0731211
24 -14.4668525 -24.9078035
25 -21.8543282 -14.4668525
26 -20.4776848 -21.8543282
27 -13.0491087 -20.4776848
28 -12.8977524 -13.0491087
29 -12.1401485 -12.8977524
30 -14.2734510 -12.1401485
31 2.4837799 -14.2734510
32 -3.2294527 2.4837799
33 7.2987824 -3.2294527
34 11.1139121 7.2987824
35 11.1206493 11.1139121
36 14.3004166 11.1206493
37 17.1550870 14.3004166
38 22.8013105 17.1550870
39 37.8242466 22.8013105
40 38.9021186 37.8242466
41 35.6451148 38.9021186
42 44.7901790 35.6451148
43 45.4216112 44.7901790
44 41.7206678 45.4216112
45 43.7167671 41.7206678
46 50.4886682 43.7167671
47 40.7718881 50.4886682
48 51.0430010 40.7718881
49 53.7675652 51.0430010
50 64.0696090 53.7675652
51 55.6747096 64.0696090
52 54.7968392 55.6747096
53 59.6121993 54.7968392
54 55.2642312 59.6121993
55 46.5749257 55.2642312
56 47.1771743 46.5749257
57 36.3787619 47.1771743
58 33.3401604 36.3787619
59 36.5498960 33.3401604
60 36.7733150 36.5498960
61 35.8024543 36.7733150
62 31.7717476 35.8024543
63 23.9409525 31.7717476
64 29.1477327 23.9409525
65 33.3099477 29.1477327
66 36.2206511 33.3099477
67 30.3159802 36.2206511
68 31.9651591 30.3159802
69 30.4941716 31.9651591
70 28.6709354 30.4941716
71 33.7216786 28.6709354
72 35.6868579 33.7216786
73 31.3119120 35.6868579
74 24.4228098 31.3119120
75 11.7224777 24.4228098
76 5.9300353 11.7224777
77 11.6001531 5.9300353
78 5.3236913 11.6001531
79 6.0384710 5.3236913
80 15.2630946 6.0384710
81 9.8967021 15.2630946
82 3.6261521 9.8967021
83 -4.9166873 3.6261521
84 -7.9522854 -4.9166873
85 -5.4484095 -7.9522854
86 -9.7666875 -5.4484095
87 -16.1651973 -9.7666875
88 -13.9151885 -16.1651973
89 -17.2307146 -13.9151885
90 -3.6705592 -17.2307146
91 -10.6334870 -3.6705592
92 -15.5240078 -10.6334870
93 -11.3121115 -15.5240078
94 -12.7571304 -11.3121115
95 -16.7784883 -12.7571304
96 0.3864972 -16.7784883
97 -9.6268146 0.3864972
98 -19.7895391 -9.6268146
99 -27.8090541 -19.7895391
100 -22.7319595 -27.8090541
101 -25.5705220 -22.7319595
102 -28.5112176 -25.5705220
103 -34.7941058 -28.5112176
104 -38.5057836 -34.7941058
105 -43.3808563 -38.5057836
106 -48.9705893 -43.3808563
107 -49.2131611 -48.9705893
108 -37.1081564 -49.2131611
109 -12.5723949 -37.1081564
110 -8.5164892 -12.5723949
111 -9.7823002 -8.5164892
112 -25.8624718 -9.7823002
113 -24.0618910 -25.8624718
114 -14.8458350 -24.0618910
115 -11.7829887 -14.8458350
116 -8.6880508 -11.7829887
117 -8.6920318 -8.6880508
118 -9.2651815 -8.6920318
119 -18.0241636 -9.2651815
120 -13.1014355 -18.0241636
121 -12.3762654 -13.1014355
122 -19.7548672 -12.3762654
123 -14.2807552 -19.7548672
124 -23.0944451 -14.2807552
125 -29.5351752 -23.0944451
126 -32.1743225 -29.5351752
127 -35.4133873 -32.1743225
128 -42.9819836 -35.4133873
129 -36.4229782 -42.9819836
130 -25.0759865 -36.4229782
131 NA -25.0759865
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -51.9118866 -47.7090774
[2,] -43.3073369 -51.9118866
[3,] -31.2419496 -43.3073369
[4,] -22.3103465 -31.2419496
[5,] -24.1041256 -22.3103465
[6,] -28.3821422 -24.1041256
[7,] -21.8394992 -28.3821422
[8,] -15.7595898 -21.8394992
[9,] -6.1024464 -15.7595898
[10,] -7.0978194 -6.1024464
[11,] -8.3238080 -7.0978194
[12,] -17.8522805 -8.3238080
[13,] -24.2469192 -17.8522805
[14,] -21.4528722 -24.2469192
[15,] -16.8340213 -21.4528722
[16,] -7.9645620 -16.8340213
[17,] -7.5248379 -7.9645620
[18,] -19.7412251 -7.5248379
[19,] -16.3713000 -19.7412251
[20,] -11.4372275 -16.3713000
[21,] -21.8747609 -11.4372275
[22,] -24.0731211 -21.8747609
[23,] -24.9078035 -24.0731211
[24,] -14.4668525 -24.9078035
[25,] -21.8543282 -14.4668525
[26,] -20.4776848 -21.8543282
[27,] -13.0491087 -20.4776848
[28,] -12.8977524 -13.0491087
[29,] -12.1401485 -12.8977524
[30,] -14.2734510 -12.1401485
[31,] 2.4837799 -14.2734510
[32,] -3.2294527 2.4837799
[33,] 7.2987824 -3.2294527
[34,] 11.1139121 7.2987824
[35,] 11.1206493 11.1139121
[36,] 14.3004166 11.1206493
[37,] 17.1550870 14.3004166
[38,] 22.8013105 17.1550870
[39,] 37.8242466 22.8013105
[40,] 38.9021186 37.8242466
[41,] 35.6451148 38.9021186
[42,] 44.7901790 35.6451148
[43,] 45.4216112 44.7901790
[44,] 41.7206678 45.4216112
[45,] 43.7167671 41.7206678
[46,] 50.4886682 43.7167671
[47,] 40.7718881 50.4886682
[48,] 51.0430010 40.7718881
[49,] 53.7675652 51.0430010
[50,] 64.0696090 53.7675652
[51,] 55.6747096 64.0696090
[52,] 54.7968392 55.6747096
[53,] 59.6121993 54.7968392
[54,] 55.2642312 59.6121993
[55,] 46.5749257 55.2642312
[56,] 47.1771743 46.5749257
[57,] 36.3787619 47.1771743
[58,] 33.3401604 36.3787619
[59,] 36.5498960 33.3401604
[60,] 36.7733150 36.5498960
[61,] 35.8024543 36.7733150
[62,] 31.7717476 35.8024543
[63,] 23.9409525 31.7717476
[64,] 29.1477327 23.9409525
[65,] 33.3099477 29.1477327
[66,] 36.2206511 33.3099477
[67,] 30.3159802 36.2206511
[68,] 31.9651591 30.3159802
[69,] 30.4941716 31.9651591
[70,] 28.6709354 30.4941716
[71,] 33.7216786 28.6709354
[72,] 35.6868579 33.7216786
[73,] 31.3119120 35.6868579
[74,] 24.4228098 31.3119120
[75,] 11.7224777 24.4228098
[76,] 5.9300353 11.7224777
[77,] 11.6001531 5.9300353
[78,] 5.3236913 11.6001531
[79,] 6.0384710 5.3236913
[80,] 15.2630946 6.0384710
[81,] 9.8967021 15.2630946
[82,] 3.6261521 9.8967021
[83,] -4.9166873 3.6261521
[84,] -7.9522854 -4.9166873
[85,] -5.4484095 -7.9522854
[86,] -9.7666875 -5.4484095
[87,] -16.1651973 -9.7666875
[88,] -13.9151885 -16.1651973
[89,] -17.2307146 -13.9151885
[90,] -3.6705592 -17.2307146
[91,] -10.6334870 -3.6705592
[92,] -15.5240078 -10.6334870
[93,] -11.3121115 -15.5240078
[94,] -12.7571304 -11.3121115
[95,] -16.7784883 -12.7571304
[96,] 0.3864972 -16.7784883
[97,] -9.6268146 0.3864972
[98,] -19.7895391 -9.6268146
[99,] -27.8090541 -19.7895391
[100,] -22.7319595 -27.8090541
[101,] -25.5705220 -22.7319595
[102,] -28.5112176 -25.5705220
[103,] -34.7941058 -28.5112176
[104,] -38.5057836 -34.7941058
[105,] -43.3808563 -38.5057836
[106,] -48.9705893 -43.3808563
[107,] -49.2131611 -48.9705893
[108,] -37.1081564 -49.2131611
[109,] -12.5723949 -37.1081564
[110,] -8.5164892 -12.5723949
[111,] -9.7823002 -8.5164892
[112,] -25.8624718 -9.7823002
[113,] -24.0618910 -25.8624718
[114,] -14.8458350 -24.0618910
[115,] -11.7829887 -14.8458350
[116,] -8.6880508 -11.7829887
[117,] -8.6920318 -8.6880508
[118,] -9.2651815 -8.6920318
[119,] -18.0241636 -9.2651815
[120,] -13.1014355 -18.0241636
[121,] -12.3762654 -13.1014355
[122,] -19.7548672 -12.3762654
[123,] -14.2807552 -19.7548672
[124,] -23.0944451 -14.2807552
[125,] -29.5351752 -23.0944451
[126,] -32.1743225 -29.5351752
[127,] -35.4133873 -32.1743225
[128,] -42.9819836 -35.4133873
[129,] -36.4229782 -42.9819836
[130,] -25.0759865 -36.4229782
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -51.9118866 -47.7090774
2 -43.3073369 -51.9118866
3 -31.2419496 -43.3073369
4 -22.3103465 -31.2419496
5 -24.1041256 -22.3103465
6 -28.3821422 -24.1041256
7 -21.8394992 -28.3821422
8 -15.7595898 -21.8394992
9 -6.1024464 -15.7595898
10 -7.0978194 -6.1024464
11 -8.3238080 -7.0978194
12 -17.8522805 -8.3238080
13 -24.2469192 -17.8522805
14 -21.4528722 -24.2469192
15 -16.8340213 -21.4528722
16 -7.9645620 -16.8340213
17 -7.5248379 -7.9645620
18 -19.7412251 -7.5248379
19 -16.3713000 -19.7412251
20 -11.4372275 -16.3713000
21 -21.8747609 -11.4372275
22 -24.0731211 -21.8747609
23 -24.9078035 -24.0731211
24 -14.4668525 -24.9078035
25 -21.8543282 -14.4668525
26 -20.4776848 -21.8543282
27 -13.0491087 -20.4776848
28 -12.8977524 -13.0491087
29 -12.1401485 -12.8977524
30 -14.2734510 -12.1401485
31 2.4837799 -14.2734510
32 -3.2294527 2.4837799
33 7.2987824 -3.2294527
34 11.1139121 7.2987824
35 11.1206493 11.1139121
36 14.3004166 11.1206493
37 17.1550870 14.3004166
38 22.8013105 17.1550870
39 37.8242466 22.8013105
40 38.9021186 37.8242466
41 35.6451148 38.9021186
42 44.7901790 35.6451148
43 45.4216112 44.7901790
44 41.7206678 45.4216112
45 43.7167671 41.7206678
46 50.4886682 43.7167671
47 40.7718881 50.4886682
48 51.0430010 40.7718881
49 53.7675652 51.0430010
50 64.0696090 53.7675652
51 55.6747096 64.0696090
52 54.7968392 55.6747096
53 59.6121993 54.7968392
54 55.2642312 59.6121993
55 46.5749257 55.2642312
56 47.1771743 46.5749257
57 36.3787619 47.1771743
58 33.3401604 36.3787619
59 36.5498960 33.3401604
60 36.7733150 36.5498960
61 35.8024543 36.7733150
62 31.7717476 35.8024543
63 23.9409525 31.7717476
64 29.1477327 23.9409525
65 33.3099477 29.1477327
66 36.2206511 33.3099477
67 30.3159802 36.2206511
68 31.9651591 30.3159802
69 30.4941716 31.9651591
70 28.6709354 30.4941716
71 33.7216786 28.6709354
72 35.6868579 33.7216786
73 31.3119120 35.6868579
74 24.4228098 31.3119120
75 11.7224777 24.4228098
76 5.9300353 11.7224777
77 11.6001531 5.9300353
78 5.3236913 11.6001531
79 6.0384710 5.3236913
80 15.2630946 6.0384710
81 9.8967021 15.2630946
82 3.6261521 9.8967021
83 -4.9166873 3.6261521
84 -7.9522854 -4.9166873
85 -5.4484095 -7.9522854
86 -9.7666875 -5.4484095
87 -16.1651973 -9.7666875
88 -13.9151885 -16.1651973
89 -17.2307146 -13.9151885
90 -3.6705592 -17.2307146
91 -10.6334870 -3.6705592
92 -15.5240078 -10.6334870
93 -11.3121115 -15.5240078
94 -12.7571304 -11.3121115
95 -16.7784883 -12.7571304
96 0.3864972 -16.7784883
97 -9.6268146 0.3864972
98 -19.7895391 -9.6268146
99 -27.8090541 -19.7895391
100 -22.7319595 -27.8090541
101 -25.5705220 -22.7319595
102 -28.5112176 -25.5705220
103 -34.7941058 -28.5112176
104 -38.5057836 -34.7941058
105 -43.3808563 -38.5057836
106 -48.9705893 -43.3808563
107 -49.2131611 -48.9705893
108 -37.1081564 -49.2131611
109 -12.5723949 -37.1081564
110 -8.5164892 -12.5723949
111 -9.7823002 -8.5164892
112 -25.8624718 -9.7823002
113 -24.0618910 -25.8624718
114 -14.8458350 -24.0618910
115 -11.7829887 -14.8458350
116 -8.6880508 -11.7829887
117 -8.6920318 -8.6880508
118 -9.2651815 -8.6920318
119 -18.0241636 -9.2651815
120 -13.1014355 -18.0241636
121 -12.3762654 -13.1014355
122 -19.7548672 -12.3762654
123 -14.2807552 -19.7548672
124 -23.0944451 -14.2807552
125 -29.5351752 -23.0944451
126 -32.1743225 -29.5351752
127 -35.4133873 -32.1743225
128 -42.9819836 -35.4133873
129 -36.4229782 -42.9819836
130 -25.0759865 -36.4229782
> 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/70i9v1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8barh1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9barh1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10barh1293397020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ea7m1293397020.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/12hs6s1293397020.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/13ou341293397020.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/14z32p1293397020.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/15kljd1293397020.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/16ydhm1293397020.tab")
+ }
> try(system("convert tmp/1witq1293397020.ps tmp/1witq1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/2witq1293397020.ps tmp/2witq1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/3witq1293397020.ps tmp/3witq1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/4p9at1293397020.ps tmp/4p9at1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/5p9at1293397020.ps tmp/5p9at1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p9at1293397020.ps tmp/6p9at1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/70i9v1293397020.ps tmp/70i9v1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/8barh1293397020.ps tmp/8barh1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/9barh1293397020.ps tmp/9barh1293397020.png",intern=TRUE))
character(0)
> try(system("convert tmp/10barh1293397020.ps tmp/10barh1293397020.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.533 1.723 7.822