R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(100.5
+ ,99.5
+ ,101.5
+ ,467
+ ,9
+ ,99
+ ,93.5
+ ,99.2
+ ,460
+ ,9
+ ,104.1
+ ,104.6
+ ,107.8
+ ,448
+ ,9
+ ,98.6
+ ,95.3
+ ,92.3
+ ,443
+ ,9
+ ,101.4
+ ,102.8
+ ,99.2
+ ,436
+ ,9
+ ,102.1
+ ,103.3
+ ,101.6
+ ,431
+ ,9
+ ,93
+ ,100.2
+ ,87
+ ,484
+ ,9
+ ,96.9
+ ,107.9
+ ,71.4
+ ,510
+ ,9
+ ,91.2
+ ,107.5
+ ,104.7
+ ,513
+ ,9
+ ,96.9
+ ,119.8
+ ,115.1
+ ,503
+ ,9
+ ,94
+ ,112
+ ,102.5
+ ,471
+ ,9
+ ,90.4
+ ,102.1
+ ,75.3
+ ,471
+ ,9
+ ,105.2
+ ,105.3
+ ,96.7
+ ,476
+ ,9
+ ,103.4
+ ,101.3
+ ,94.6
+ ,475
+ ,9
+ ,111.7
+ ,108.4
+ ,98.6
+ ,470
+ ,9
+ ,114.2
+ ,107.4
+ ,99.5
+ ,461
+ ,9
+ ,111.4
+ ,109.1
+ ,92
+ ,455
+ ,9
+ ,106.3
+ ,109.5
+ ,93.6
+ ,456
+ ,9
+ ,111.8
+ ,111.4
+ ,89.3
+ ,517
+ ,9
+ ,101.5
+ ,110.1
+ ,66.9
+ ,525
+ ,9
+ ,103
+ ,117
+ ,108.8
+ ,523
+ ,9
+ ,105.2
+ ,129.6
+ ,113.2
+ ,519
+ ,9
+ ,101.1
+ ,113.5
+ ,105.5
+ ,509
+ ,9
+ ,100.7
+ ,113.3
+ ,77.8
+ ,512
+ ,9
+ ,116.7
+ ,110.1
+ ,102.1
+ ,519
+ ,9
+ ,109
+ ,107.4
+ ,97
+ ,517
+ ,9
+ ,119.5
+ ,110.1
+ ,95.5
+ ,510
+ ,9
+ ,115.1
+ ,112.5
+ ,99.3
+ ,509
+ ,9
+ ,107.1
+ ,106
+ ,86.4
+ ,501
+ ,9
+ ,109.7
+ ,117.6
+ ,92.4
+ ,507
+ ,9
+ ,110.4
+ ,117.8
+ ,85.7
+ ,569
+ ,9
+ ,105
+ ,113.5
+ ,61.9
+ ,580
+ ,9
+ ,115.8
+ ,121.2
+ ,104.9
+ ,578
+ ,9
+ ,116.4
+ ,130.4
+ ,107.9
+ ,565
+ ,9
+ ,111.1
+ ,115.2
+ ,95.6
+ ,547
+ ,9
+ ,119.5
+ ,117.9
+ ,79.8
+ ,555
+ ,9
+ ,110.9
+ ,110.7
+ ,94.8
+ ,562
+ ,9
+ ,115.1
+ ,107.6
+ ,93.7
+ ,561
+ ,9
+ ,125.2
+ ,124.3
+ ,108.1
+ ,555
+ ,9
+ ,116
+ ,115.1
+ ,96.9
+ ,544
+ ,9
+ ,112.9
+ ,112.5
+ ,88.8
+ ,537
+ ,9
+ ,121.7
+ ,127.9
+ ,106.7
+ ,543
+ ,10
+ ,123.2
+ ,117.4
+ ,86.8
+ ,594
+ ,10
+ ,116.6
+ ,119.3
+ ,69.8
+ ,611
+ ,10
+ ,136.2
+ ,130.4
+ ,110.9
+ ,613
+ ,10
+ ,120.9
+ ,126
+ ,105.4
+ ,611
+ ,10
+ ,119.6
+ ,125.4
+ ,99.2
+ ,594
+ ,10
+ ,125.9
+ ,130.5
+ ,84.4
+ ,595
+ ,10
+ ,116.1
+ ,115.9
+ ,87.2
+ ,591
+ ,10
+ ,107.5
+ ,108.7
+ ,91.9
+ ,589
+ ,10
+ ,116.7
+ ,124
+ ,97.9
+ ,584
+ ,10
+ ,112.5
+ ,119.4
+ ,94.5
+ ,573
+ ,10
+ ,113
+ ,118.6
+ ,85
+ ,567
+ ,10
+ ,126.4
+ ,131.3
+ ,100.3
+ ,569
+ ,10
+ ,114.1
+ ,111.1
+ ,78.7
+ ,621
+ ,10
+ ,112.5
+ ,124.8
+ ,65.8
+ ,629
+ ,10
+ ,112.4
+ ,132.3
+ ,104.8
+ ,628
+ ,10
+ ,113.1
+ ,126.7
+ ,96
+ ,612
+ ,10
+ ,116.3
+ ,131.7
+ ,103.3
+ ,595
+ ,10
+ ,111.7
+ ,130.9
+ ,82.9
+ ,597
+ ,10
+ ,118.8
+ ,122.1
+ ,91.4
+ ,593
+ ,10
+ ,116.5
+ ,113.2
+ ,94.5
+ ,590
+ ,10
+ ,125.1
+ ,133.6
+ ,109.3
+ ,580
+ ,10
+ ,113.1
+ ,119.2
+ ,92.1
+ ,574
+ ,10
+ ,119.6
+ ,129.4
+ ,99.3
+ ,573
+ ,10
+ ,114.4
+ ,131.4
+ ,109.6
+ ,573
+ ,10
+ ,114
+ ,117.1
+ ,87.5
+ ,620
+ ,10
+ ,117.8
+ ,130.5
+ ,73.1
+ ,626
+ ,10
+ ,117
+ ,132.3
+ ,110.7
+ ,620
+ ,10
+ ,120.9
+ ,140.8
+ ,111.6
+ ,588
+ ,10
+ ,115
+ ,137.5
+ ,110.7
+ ,566
+ ,10
+ ,117.3
+ ,128.6
+ ,84
+ ,557
+ ,10
+ ,119.4
+ ,126.7
+ ,101.6
+ ,561
+ ,10
+ ,114.9
+ ,120.8
+ ,102.1
+ ,549
+ ,10
+ ,125.8
+ ,139.3
+ ,113.9
+ ,532
+ ,10
+ ,117.6
+ ,128.6
+ ,99
+ ,526
+ ,10
+ ,117.6
+ ,131.3
+ ,100.4
+ ,511
+ ,10
+ ,114.9
+ ,136.3
+ ,109.5
+ ,499
+ ,10
+ ,121.9
+ ,128.8
+ ,93.1
+ ,555
+ ,10
+ ,117
+ ,133.2
+ ,77
+ ,565
+ ,10
+ ,106.4
+ ,136.3
+ ,108
+ ,542
+ ,10
+ ,110.5
+ ,151.1
+ ,119.9
+ ,527
+ ,10
+ ,113.6
+ ,145
+ ,105.9
+ ,510
+ ,11
+ ,114.2
+ ,134.4
+ ,78.2
+ ,514
+ ,11
+ ,125.4
+ ,135.7
+ ,100.3
+ ,517
+ ,11
+ ,124.6
+ ,128.7
+ ,102.2
+ ,508
+ ,11
+ ,120.2
+ ,129.2
+ ,97
+ ,493
+ ,11
+ ,120.8
+ ,138.6
+ ,101.3
+ ,490
+ ,11
+ ,111.4
+ ,132.7
+ ,89.2
+ ,469
+ ,11
+ ,124.1
+ ,132.5
+ ,93.3
+ ,478
+ ,11
+ ,120.2
+ ,137.3
+ ,88.5
+ ,528
+ ,11
+ ,125.5
+ ,127.1
+ ,61.5
+ ,534
+ ,11
+ ,116
+ ,143.7
+ ,96.3
+ ,518
+ ,11
+ ,117
+ ,149.9
+ ,95.4
+ ,506
+ ,11
+ ,105.7
+ ,131.6
+ ,79.9
+ ,502
+ ,11
+ ,102
+ ,138.8
+ ,66.7
+ ,516
+ ,11
+ ,106.4
+ ,122.5
+ ,71.2
+ ,528
+ ,11
+ ,96.9
+ ,122
+ ,73.1
+ ,533
+ ,11
+ ,107.6
+ ,135.6
+ ,81
+ ,536
+ ,11
+ ,98.8
+ ,133.4
+ ,77.2
+ ,537
+ ,11
+ ,101.1
+ ,127.3
+ ,67.7
+ ,524
+ ,11
+ ,105.7
+ ,138.9
+ ,76.7
+ ,536
+ ,11
+ ,104.6
+ ,131.4
+ ,73.3
+ ,587
+ ,11
+ ,103.2
+ ,131.6
+ ,54.1
+ ,597
+ ,11
+ ,101.6
+ ,135.8
+ ,85
+ ,581
+ ,11
+ ,106.7
+ ,141.6
+ ,85.9
+ ,564
+ ,11
+ ,99.5
+ ,132.6
+ ,79.3
+ ,558
+ ,11
+ ,101
+ ,132.3
+ ,67.2
+ ,575
+ ,11
+ ,104.9
+ ,120.6
+ ,72.4
+ ,580
+ ,11
+ ,118.4
+ ,123.8
+ ,76.1
+ ,575
+ ,11
+ ,129
+ ,145.1
+ ,89.8
+ ,563
+ ,11
+ ,123.7
+ ,135
+ ,84
+ ,552
+ ,11
+ ,127.6
+ ,127.6
+ ,75.4
+ ,537
+ ,11
+ ,129.4
+ ,142
+ ,90
+ ,545
+ ,11
+ ,128.3
+ ,130.1
+ ,76.8
+ ,601
+ ,11
+ ,124.8
+ ,131
+ ,59.6
+ ,604
+ ,11
+ ,125.2
+ ,141.3
+ ,92.1
+ ,586
+ ,11
+ ,129.6
+ ,139.6
+ ,88.4
+ ,564
+ ,11
+ ,124.8
+ ,142.2
+ ,82.8
+ ,549
+ ,11
+ ,121.9
+ ,140
+ ,69.4
+ ,551
+ ,11
+ ,129.2
+ ,132
+ ,73.4
+ ,556
+ ,11)
+ ,dim=c(5
+ ,121)
+ ,dimnames=list(c('chemie'
+ ,'vm'
+ ,'textiel'
+ ,'werkloosheid'
+ ,'maand')
+ ,1:121))
> y <- array(NA,dim=c(5,121),dimnames=list(c('chemie','vm','textiel','werkloosheid','maand'),1:121))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '4'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
werkloosheid chemie vm textiel maand
1 467 100.5 99.5 101.5 9
2 460 99.0 93.5 99.2 9
3 448 104.1 104.6 107.8 9
4 443 98.6 95.3 92.3 9
5 436 101.4 102.8 99.2 9
6 431 102.1 103.3 101.6 9
7 484 93.0 100.2 87.0 9
8 510 96.9 107.9 71.4 9
9 513 91.2 107.5 104.7 9
10 503 96.9 119.8 115.1 9
11 471 94.0 112.0 102.5 9
12 471 90.4 102.1 75.3 9
13 476 105.2 105.3 96.7 9
14 475 103.4 101.3 94.6 9
15 470 111.7 108.4 98.6 9
16 461 114.2 107.4 99.5 9
17 455 111.4 109.1 92.0 9
18 456 106.3 109.5 93.6 9
19 517 111.8 111.4 89.3 9
20 525 101.5 110.1 66.9 9
21 523 103.0 117.0 108.8 9
22 519 105.2 129.6 113.2 9
23 509 101.1 113.5 105.5 9
24 512 100.7 113.3 77.8 9
25 519 116.7 110.1 102.1 9
26 517 109.0 107.4 97.0 9
27 510 119.5 110.1 95.5 9
28 509 115.1 112.5 99.3 9
29 501 107.1 106.0 86.4 9
30 507 109.7 117.6 92.4 9
31 569 110.4 117.8 85.7 9
32 580 105.0 113.5 61.9 9
33 578 115.8 121.2 104.9 9
34 565 116.4 130.4 107.9 9
35 547 111.1 115.2 95.6 9
36 555 119.5 117.9 79.8 9
37 562 110.9 110.7 94.8 9
38 561 115.1 107.6 93.7 9
39 555 125.2 124.3 108.1 9
40 544 116.0 115.1 96.9 9
41 537 112.9 112.5 88.8 9
42 543 121.7 127.9 106.7 10
43 594 123.2 117.4 86.8 10
44 611 116.6 119.3 69.8 10
45 613 136.2 130.4 110.9 10
46 611 120.9 126.0 105.4 10
47 594 119.6 125.4 99.2 10
48 595 125.9 130.5 84.4 10
49 591 116.1 115.9 87.2 10
50 589 107.5 108.7 91.9 10
51 584 116.7 124.0 97.9 10
52 573 112.5 119.4 94.5 10
53 567 113.0 118.6 85.0 10
54 569 126.4 131.3 100.3 10
55 621 114.1 111.1 78.7 10
56 629 112.5 124.8 65.8 10
57 628 112.4 132.3 104.8 10
58 612 113.1 126.7 96.0 10
59 595 116.3 131.7 103.3 10
60 597 111.7 130.9 82.9 10
61 593 118.8 122.1 91.4 10
62 590 116.5 113.2 94.5 10
63 580 125.1 133.6 109.3 10
64 574 113.1 119.2 92.1 10
65 573 119.6 129.4 99.3 10
66 573 114.4 131.4 109.6 10
67 620 114.0 117.1 87.5 10
68 626 117.8 130.5 73.1 10
69 620 117.0 132.3 110.7 10
70 588 120.9 140.8 111.6 10
71 566 115.0 137.5 110.7 10
72 557 117.3 128.6 84.0 10
73 561 119.4 126.7 101.6 10
74 549 114.9 120.8 102.1 10
75 532 125.8 139.3 113.9 10
76 526 117.6 128.6 99.0 10
77 511 117.6 131.3 100.4 10
78 499 114.9 136.3 109.5 10
79 555 121.9 128.8 93.1 10
80 565 117.0 133.2 77.0 10
81 542 106.4 136.3 108.0 10
82 527 110.5 151.1 119.9 10
83 510 113.6 145.0 105.9 11
84 514 114.2 134.4 78.2 11
85 517 125.4 135.7 100.3 11
86 508 124.6 128.7 102.2 11
87 493 120.2 129.2 97.0 11
88 490 120.8 138.6 101.3 11
89 469 111.4 132.7 89.2 11
90 478 124.1 132.5 93.3 11
91 528 120.2 137.3 88.5 11
92 534 125.5 127.1 61.5 11
93 518 116.0 143.7 96.3 11
94 506 117.0 149.9 95.4 11
95 502 105.7 131.6 79.9 11
96 516 102.0 138.8 66.7 11
97 528 106.4 122.5 71.2 11
98 533 96.9 122.0 73.1 11
99 536 107.6 135.6 81.0 11
100 537 98.8 133.4 77.2 11
101 524 101.1 127.3 67.7 11
102 536 105.7 138.9 76.7 11
103 587 104.6 131.4 73.3 11
104 597 103.2 131.6 54.1 11
105 581 101.6 135.8 85.0 11
106 564 106.7 141.6 85.9 11
107 558 99.5 132.6 79.3 11
108 575 101.0 132.3 67.2 11
109 580 104.9 120.6 72.4 11
110 575 118.4 123.8 76.1 11
111 563 129.0 145.1 89.8 11
112 552 123.7 135.0 84.0 11
113 537 127.6 127.6 75.4 11
114 545 129.4 142.0 90.0 11
115 601 128.3 130.1 76.8 11
116 604 124.8 131.0 59.6 11
117 586 125.2 141.3 92.1 11
118 564 129.6 139.6 88.4 11
119 549 124.8 142.2 82.8 11
120 551 121.9 140.0 69.4 11
121 556 129.2 132.0 73.4 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) chemie vm textiel maand
419.778 1.761 1.604 -1.210 -16.572
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-77.675 -31.795 -5.462 28.753 90.594
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 419.7780 70.3597 5.966 2.7e-08 ***
chemie 1.7613 0.4474 3.937 0.000141 ***
vm 1.6038 0.5329 3.010 0.003209 **
textiel -1.2099 0.3273 -3.696 0.000335 ***
maand -16.5724 8.6662 -1.912 0.058304 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 39.98 on 116 degrees of freedom
Multiple R-squared: 0.3036, Adjusted R-squared: 0.2796
F-statistic: 12.64 on 4 and 116 DF, p-value: 1.427e-08
> 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,] 0.187784374 0.375568747 0.812215626
[2,] 0.092818449 0.185636899 0.907181551
[3,] 0.038487093 0.076974185 0.961512907
[4,] 0.048522708 0.097045416 0.951477292
[5,] 0.053146348 0.106292696 0.946853652
[6,] 0.043677840 0.087355679 0.956322160
[7,] 0.031807672 0.063615343 0.968192328
[8,] 0.019864437 0.039728873 0.980135563
[9,] 0.012104781 0.024209561 0.987895219
[10,] 0.009263754 0.018527508 0.990736246
[11,] 0.007975508 0.015951016 0.992024492
[12,] 0.019433069 0.038866138 0.980566931
[13,] 0.014167404 0.028334809 0.985832596
[14,] 0.015049009 0.030098018 0.984950991
[15,] 0.009120934 0.018241868 0.990879066
[16,] 0.007000925 0.014001850 0.992999075
[17,] 0.004559857 0.009119713 0.995440143
[18,] 0.012468714 0.024937427 0.987531286
[19,] 0.018818075 0.037636150 0.981181925
[20,] 0.018794542 0.037589085 0.981205458
[21,] 0.016602228 0.033204455 0.983397772
[22,] 0.017594564 0.035189127 0.982405436
[23,] 0.017542236 0.035084471 0.982457764
[24,] 0.028917193 0.057834386 0.971082807
[25,] 0.035874934 0.071749867 0.964125066
[26,] 0.062994050 0.125988100 0.937005950
[27,] 0.046099161 0.092198321 0.953900839
[28,] 0.048158975 0.096317949 0.951841025
[29,] 0.045027539 0.090055078 0.954972461
[30,] 0.090982939 0.181965879 0.909017061
[31,] 0.169561572 0.339123143 0.830438428
[32,] 0.154540112 0.309080224 0.845459888
[33,] 0.180669292 0.361338584 0.819330708
[34,] 0.281430863 0.562861725 0.718569137
[35,] 0.244699423 0.489398846 0.755300577
[36,] 0.267707472 0.535414943 0.732292528
[37,] 0.237029452 0.474058904 0.762970548
[38,] 0.229432629 0.458865258 0.770567371
[39,] 0.252418712 0.504837425 0.747581288
[40,] 0.216765470 0.433530940 0.783234530
[41,] 0.218199495 0.436398990 0.781800505
[42,] 0.196924869 0.393849737 0.803075131
[43,] 0.247655809 0.495311619 0.752344191
[44,] 0.208404989 0.416809978 0.791595011
[45,] 0.171232716 0.342465433 0.828767284
[46,] 0.152665660 0.305331319 0.847334340
[47,] 0.168176479 0.336352959 0.831823521
[48,] 0.204828570 0.409657140 0.795171430
[49,] 0.170641789 0.341283579 0.829358211
[50,] 0.238457481 0.476914963 0.761542519
[51,] 0.238907702 0.477815404 0.761092298
[52,] 0.225169626 0.450339251 0.774830374
[53,] 0.204289274 0.408578548 0.795710726
[54,] 0.172709141 0.345418283 0.827290859
[55,] 0.163147408 0.326294815 0.836852592
[56,] 0.159707631 0.319415261 0.840292369
[57,] 0.130140531 0.260281063 0.869859469
[58,] 0.118920490 0.237840979 0.881079510
[59,] 0.108498195 0.216996391 0.891501805
[60,] 0.153865569 0.307731139 0.846134431
[61,] 0.143177273 0.286354545 0.856822727
[62,] 0.311941717 0.623883434 0.688058283
[63,] 0.386945692 0.773891385 0.613054308
[64,] 0.430958144 0.861916289 0.569041856
[65,] 0.471189486 0.942378971 0.528810514
[66,] 0.469516528 0.939033056 0.530483472
[67,] 0.459454949 0.918909898 0.540545051
[68,] 0.543918954 0.912162092 0.456081046
[69,] 0.582042379 0.835915241 0.417957621
[70,] 0.677155568 0.645688863 0.322844432
[71,] 0.762194557 0.475610886 0.237805443
[72,] 0.740861306 0.518277388 0.259138694
[73,] 0.753624985 0.492750031 0.246375015
[74,] 0.712511655 0.574976691 0.287488345
[75,] 0.686237875 0.627524251 0.313762125
[76,] 0.766129843 0.467740314 0.233870157
[77,] 0.860387178 0.279225645 0.139612822
[78,] 0.879254885 0.241490230 0.120745115
[79,] 0.880023378 0.239953243 0.119976622
[80,] 0.888401279 0.223197442 0.111598721
[81,] 0.903857871 0.192284258 0.096142129
[82,] 0.958006390 0.083987220 0.041993610
[83,] 0.987155679 0.025688642 0.012844321
[84,] 0.984174838 0.031650324 0.015825162
[85,] 0.990018116 0.019963767 0.009981884
[86,] 0.986738442 0.026523115 0.013261558
[87,] 0.988650683 0.022698633 0.011349317
[88,] 0.993245037 0.013509926 0.006754963
[89,] 0.995746743 0.008506513 0.004253257
[90,] 0.995658564 0.008682872 0.004341436
[91,] 0.995111377 0.009777246 0.004888623
[92,] 0.993395359 0.013209281 0.006604641
[93,] 0.991773662 0.016452675 0.008226338
[94,] 0.997534275 0.004931450 0.002465725
[95,] 0.998180998 0.003638005 0.001819002
[96,] 0.996774610 0.006450779 0.003225390
[97,] 0.994282960 0.011434080 0.005717040
[98,] 0.991156237 0.017687527 0.008843763
[99,] 0.982383354 0.035233293 0.017616646
[100,] 0.968042109 0.063915782 0.031957891
[101,] 0.940370009 0.119259982 0.059629991
[102,] 0.896772606 0.206454788 0.103227394
[103,] 0.826031300 0.347937399 0.173968700
[104,] 0.725484150 0.549031701 0.274515850
[105,] 0.605137640 0.789724719 0.394862360
[106,] 0.870029229 0.259941541 0.129970771
> postscript(file="/var/fisher/rcomp/tmp/1gvm31353450850.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/fisher/rcomp/tmp/2v0ee1353450850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/38qre1353450850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4p4au1353450850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/58kql1353450850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 121
Frequency = 1
1 2 3 4 5 6 7
-17.407708 -14.925910 -43.305266 -42.456424 -58.068056 -62.199106 -5.864072
8 9 10 11 12 13 14
-17.956430 36.013821 8.830972 -20.796516 -31.487596 -31.795117 -25.750489
15 16 17 18 19 20 21
-51.916408 -62.626969 -75.495939 -64.219027 -21.155829 -20.031176 14.955323
22 23 24 25 26 27 28
-7.803479 5.922327 -23.566387 -10.214658 -0.492975 -32.131560 -24.633329
29 30 31 32 33 34 35
-23.726089 -33.649814 18.690257 17.302032 35.956374 10.774605 11.605071
36 37 38 39 40 41 42
-18.636242 33.206373 28.449745 -4.699808 1.707972 -5.462341 -1.430341
43 44 45 46 47 48 49
39.690452 44.699676 44.103037 69.452995 48.203611 12.021861 52.085240
50 51 52 53 54 55 56
82.465976 43.983777 43.644910 26.553313 3.095469 83.021838 56.260687
57 58 59 60 61 62 63
90.594309 71.695464 49.872691 36.575874 44.467929 63.543102 23.585503
64 65 66 67 68 69 70
41.005149 20.909528 39.322592 83.222400 43.616562 81.630720 30.218551
71 72 73 74 75 76 77
22.813711 -8.267819 16.374710 22.367705 -29.223382 -21.647840 -39.284166
78 79 80 81 82 83 84
-43.537507 -7.680510 -15.586002 12.618645 -18.940720 -31.983862 -45.554680
85 86 87 88 89 90 91
-37.627463 -31.693259 -46.036889 -59.966553 -69.587852 -77.674962 -34.311485
92 93 94 95 96 97 98
-53.954962 -37.741021 -62.534578 -36.036326 -43.037236 -7.200993 17.631965
99 100 101 102 103 104 105
-10.466974 4.963106 -13.798848 -17.615485 43.236569 32.151715 49.619593
106 107 108 109 110 111 112
15.424047 28.553987 28.753495 51.939975 22.507062 -25.747394 -18.231853
113 114 115 116 117 118 119
-38.638074 -39.238252 21.813448 8.724454 12.822587 -18.677293 -36.168275
120 121
-41.744777 -31.932505
> postscript(file="/var/fisher/rcomp/tmp/6yvv51353450850.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 -17.407708 NA
1 -14.925910 -17.407708
2 -43.305266 -14.925910
3 -42.456424 -43.305266
4 -58.068056 -42.456424
5 -62.199106 -58.068056
6 -5.864072 -62.199106
7 -17.956430 -5.864072
8 36.013821 -17.956430
9 8.830972 36.013821
10 -20.796516 8.830972
11 -31.487596 -20.796516
12 -31.795117 -31.487596
13 -25.750489 -31.795117
14 -51.916408 -25.750489
15 -62.626969 -51.916408
16 -75.495939 -62.626969
17 -64.219027 -75.495939
18 -21.155829 -64.219027
19 -20.031176 -21.155829
20 14.955323 -20.031176
21 -7.803479 14.955323
22 5.922327 -7.803479
23 -23.566387 5.922327
24 -10.214658 -23.566387
25 -0.492975 -10.214658
26 -32.131560 -0.492975
27 -24.633329 -32.131560
28 -23.726089 -24.633329
29 -33.649814 -23.726089
30 18.690257 -33.649814
31 17.302032 18.690257
32 35.956374 17.302032
33 10.774605 35.956374
34 11.605071 10.774605
35 -18.636242 11.605071
36 33.206373 -18.636242
37 28.449745 33.206373
38 -4.699808 28.449745
39 1.707972 -4.699808
40 -5.462341 1.707972
41 -1.430341 -5.462341
42 39.690452 -1.430341
43 44.699676 39.690452
44 44.103037 44.699676
45 69.452995 44.103037
46 48.203611 69.452995
47 12.021861 48.203611
48 52.085240 12.021861
49 82.465976 52.085240
50 43.983777 82.465976
51 43.644910 43.983777
52 26.553313 43.644910
53 3.095469 26.553313
54 83.021838 3.095469
55 56.260687 83.021838
56 90.594309 56.260687
57 71.695464 90.594309
58 49.872691 71.695464
59 36.575874 49.872691
60 44.467929 36.575874
61 63.543102 44.467929
62 23.585503 63.543102
63 41.005149 23.585503
64 20.909528 41.005149
65 39.322592 20.909528
66 83.222400 39.322592
67 43.616562 83.222400
68 81.630720 43.616562
69 30.218551 81.630720
70 22.813711 30.218551
71 -8.267819 22.813711
72 16.374710 -8.267819
73 22.367705 16.374710
74 -29.223382 22.367705
75 -21.647840 -29.223382
76 -39.284166 -21.647840
77 -43.537507 -39.284166
78 -7.680510 -43.537507
79 -15.586002 -7.680510
80 12.618645 -15.586002
81 -18.940720 12.618645
82 -31.983862 -18.940720
83 -45.554680 -31.983862
84 -37.627463 -45.554680
85 -31.693259 -37.627463
86 -46.036889 -31.693259
87 -59.966553 -46.036889
88 -69.587852 -59.966553
89 -77.674962 -69.587852
90 -34.311485 -77.674962
91 -53.954962 -34.311485
92 -37.741021 -53.954962
93 -62.534578 -37.741021
94 -36.036326 -62.534578
95 -43.037236 -36.036326
96 -7.200993 -43.037236
97 17.631965 -7.200993
98 -10.466974 17.631965
99 4.963106 -10.466974
100 -13.798848 4.963106
101 -17.615485 -13.798848
102 43.236569 -17.615485
103 32.151715 43.236569
104 49.619593 32.151715
105 15.424047 49.619593
106 28.553987 15.424047
107 28.753495 28.553987
108 51.939975 28.753495
109 22.507062 51.939975
110 -25.747394 22.507062
111 -18.231853 -25.747394
112 -38.638074 -18.231853
113 -39.238252 -38.638074
114 21.813448 -39.238252
115 8.724454 21.813448
116 12.822587 8.724454
117 -18.677293 12.822587
118 -36.168275 -18.677293
119 -41.744777 -36.168275
120 -31.932505 -41.744777
121 NA -31.932505
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -14.925910 -17.407708
[2,] -43.305266 -14.925910
[3,] -42.456424 -43.305266
[4,] -58.068056 -42.456424
[5,] -62.199106 -58.068056
[6,] -5.864072 -62.199106
[7,] -17.956430 -5.864072
[8,] 36.013821 -17.956430
[9,] 8.830972 36.013821
[10,] -20.796516 8.830972
[11,] -31.487596 -20.796516
[12,] -31.795117 -31.487596
[13,] -25.750489 -31.795117
[14,] -51.916408 -25.750489
[15,] -62.626969 -51.916408
[16,] -75.495939 -62.626969
[17,] -64.219027 -75.495939
[18,] -21.155829 -64.219027
[19,] -20.031176 -21.155829
[20,] 14.955323 -20.031176
[21,] -7.803479 14.955323
[22,] 5.922327 -7.803479
[23,] -23.566387 5.922327
[24,] -10.214658 -23.566387
[25,] -0.492975 -10.214658
[26,] -32.131560 -0.492975
[27,] -24.633329 -32.131560
[28,] -23.726089 -24.633329
[29,] -33.649814 -23.726089
[30,] 18.690257 -33.649814
[31,] 17.302032 18.690257
[32,] 35.956374 17.302032
[33,] 10.774605 35.956374
[34,] 11.605071 10.774605
[35,] -18.636242 11.605071
[36,] 33.206373 -18.636242
[37,] 28.449745 33.206373
[38,] -4.699808 28.449745
[39,] 1.707972 -4.699808
[40,] -5.462341 1.707972
[41,] -1.430341 -5.462341
[42,] 39.690452 -1.430341
[43,] 44.699676 39.690452
[44,] 44.103037 44.699676
[45,] 69.452995 44.103037
[46,] 48.203611 69.452995
[47,] 12.021861 48.203611
[48,] 52.085240 12.021861
[49,] 82.465976 52.085240
[50,] 43.983777 82.465976
[51,] 43.644910 43.983777
[52,] 26.553313 43.644910
[53,] 3.095469 26.553313
[54,] 83.021838 3.095469
[55,] 56.260687 83.021838
[56,] 90.594309 56.260687
[57,] 71.695464 90.594309
[58,] 49.872691 71.695464
[59,] 36.575874 49.872691
[60,] 44.467929 36.575874
[61,] 63.543102 44.467929
[62,] 23.585503 63.543102
[63,] 41.005149 23.585503
[64,] 20.909528 41.005149
[65,] 39.322592 20.909528
[66,] 83.222400 39.322592
[67,] 43.616562 83.222400
[68,] 81.630720 43.616562
[69,] 30.218551 81.630720
[70,] 22.813711 30.218551
[71,] -8.267819 22.813711
[72,] 16.374710 -8.267819
[73,] 22.367705 16.374710
[74,] -29.223382 22.367705
[75,] -21.647840 -29.223382
[76,] -39.284166 -21.647840
[77,] -43.537507 -39.284166
[78,] -7.680510 -43.537507
[79,] -15.586002 -7.680510
[80,] 12.618645 -15.586002
[81,] -18.940720 12.618645
[82,] -31.983862 -18.940720
[83,] -45.554680 -31.983862
[84,] -37.627463 -45.554680
[85,] -31.693259 -37.627463
[86,] -46.036889 -31.693259
[87,] -59.966553 -46.036889
[88,] -69.587852 -59.966553
[89,] -77.674962 -69.587852
[90,] -34.311485 -77.674962
[91,] -53.954962 -34.311485
[92,] -37.741021 -53.954962
[93,] -62.534578 -37.741021
[94,] -36.036326 -62.534578
[95,] -43.037236 -36.036326
[96,] -7.200993 -43.037236
[97,] 17.631965 -7.200993
[98,] -10.466974 17.631965
[99,] 4.963106 -10.466974
[100,] -13.798848 4.963106
[101,] -17.615485 -13.798848
[102,] 43.236569 -17.615485
[103,] 32.151715 43.236569
[104,] 49.619593 32.151715
[105,] 15.424047 49.619593
[106,] 28.553987 15.424047
[107,] 28.753495 28.553987
[108,] 51.939975 28.753495
[109,] 22.507062 51.939975
[110,] -25.747394 22.507062
[111,] -18.231853 -25.747394
[112,] -38.638074 -18.231853
[113,] -39.238252 -38.638074
[114,] 21.813448 -39.238252
[115,] 8.724454 21.813448
[116,] 12.822587 8.724454
[117,] -18.677293 12.822587
[118,] -36.168275 -18.677293
[119,] -41.744777 -36.168275
[120,] -31.932505 -41.744777
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -14.925910 -17.407708
2 -43.305266 -14.925910
3 -42.456424 -43.305266
4 -58.068056 -42.456424
5 -62.199106 -58.068056
6 -5.864072 -62.199106
7 -17.956430 -5.864072
8 36.013821 -17.956430
9 8.830972 36.013821
10 -20.796516 8.830972
11 -31.487596 -20.796516
12 -31.795117 -31.487596
13 -25.750489 -31.795117
14 -51.916408 -25.750489
15 -62.626969 -51.916408
16 -75.495939 -62.626969
17 -64.219027 -75.495939
18 -21.155829 -64.219027
19 -20.031176 -21.155829
20 14.955323 -20.031176
21 -7.803479 14.955323
22 5.922327 -7.803479
23 -23.566387 5.922327
24 -10.214658 -23.566387
25 -0.492975 -10.214658
26 -32.131560 -0.492975
27 -24.633329 -32.131560
28 -23.726089 -24.633329
29 -33.649814 -23.726089
30 18.690257 -33.649814
31 17.302032 18.690257
32 35.956374 17.302032
33 10.774605 35.956374
34 11.605071 10.774605
35 -18.636242 11.605071
36 33.206373 -18.636242
37 28.449745 33.206373
38 -4.699808 28.449745
39 1.707972 -4.699808
40 -5.462341 1.707972
41 -1.430341 -5.462341
42 39.690452 -1.430341
43 44.699676 39.690452
44 44.103037 44.699676
45 69.452995 44.103037
46 48.203611 69.452995
47 12.021861 48.203611
48 52.085240 12.021861
49 82.465976 52.085240
50 43.983777 82.465976
51 43.644910 43.983777
52 26.553313 43.644910
53 3.095469 26.553313
54 83.021838 3.095469
55 56.260687 83.021838
56 90.594309 56.260687
57 71.695464 90.594309
58 49.872691 71.695464
59 36.575874 49.872691
60 44.467929 36.575874
61 63.543102 44.467929
62 23.585503 63.543102
63 41.005149 23.585503
64 20.909528 41.005149
65 39.322592 20.909528
66 83.222400 39.322592
67 43.616562 83.222400
68 81.630720 43.616562
69 30.218551 81.630720
70 22.813711 30.218551
71 -8.267819 22.813711
72 16.374710 -8.267819
73 22.367705 16.374710
74 -29.223382 22.367705
75 -21.647840 -29.223382
76 -39.284166 -21.647840
77 -43.537507 -39.284166
78 -7.680510 -43.537507
79 -15.586002 -7.680510
80 12.618645 -15.586002
81 -18.940720 12.618645
82 -31.983862 -18.940720
83 -45.554680 -31.983862
84 -37.627463 -45.554680
85 -31.693259 -37.627463
86 -46.036889 -31.693259
87 -59.966553 -46.036889
88 -69.587852 -59.966553
89 -77.674962 -69.587852
90 -34.311485 -77.674962
91 -53.954962 -34.311485
92 -37.741021 -53.954962
93 -62.534578 -37.741021
94 -36.036326 -62.534578
95 -43.037236 -36.036326
96 -7.200993 -43.037236
97 17.631965 -7.200993
98 -10.466974 17.631965
99 4.963106 -10.466974
100 -13.798848 4.963106
101 -17.615485 -13.798848
102 43.236569 -17.615485
103 32.151715 43.236569
104 49.619593 32.151715
105 15.424047 49.619593
106 28.553987 15.424047
107 28.753495 28.553987
108 51.939975 28.753495
109 22.507062 51.939975
110 -25.747394 22.507062
111 -18.231853 -25.747394
112 -38.638074 -18.231853
113 -39.238252 -38.638074
114 21.813448 -39.238252
115 8.724454 21.813448
116 12.822587 8.724454
117 -18.677293 12.822587
118 -36.168275 -18.677293
119 -41.744777 -36.168275
120 -31.932505 -41.744777
> 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/fisher/rcomp/tmp/79pap1353450850.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/fisher/rcomp/tmp/82bfv1353450850.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/fisher/rcomp/tmp/9ce9n1353450850.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/fisher/rcomp/tmp/10vi171353450850.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/110nbq1353450850.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12kd0z1353450850.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13r5zl1353450850.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14zzmk1353450851.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/fisher/rcomp/tmp/15068o1353450851.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/fisher/rcomp/tmp/16cnrj1353450851.tab")
+ }
>
> try(system("convert tmp/1gvm31353450850.ps tmp/1gvm31353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v0ee1353450850.ps tmp/2v0ee1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/38qre1353450850.ps tmp/38qre1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/4p4au1353450850.ps tmp/4p4au1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/58kql1353450850.ps tmp/58kql1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yvv51353450850.ps tmp/6yvv51353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/79pap1353450850.ps tmp/79pap1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/82bfv1353450850.ps tmp/82bfv1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ce9n1353450850.ps tmp/9ce9n1353450850.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vi171353450850.ps tmp/10vi171353450850.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.801 1.315 8.126