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)
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> x <- array(list(14
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+ ,110.28
+ ,110.28
+ ,90120
+ ,90120
+ ,-2091.3
+ ,-2091.3
+ ,2008
+ ,1
+ ,-9
+ ,534
+ ,534
+ ,0
+ ,0
+ ,6
+ ,6
+ ,110.17
+ ,110.17
+ ,90120
+ ,90120
+ ,-1023
+ ,-1023
+ ,2008
+ ,1
+ ,-17
+ ,518
+ ,518
+ ,-5
+ ,-5
+ ,0
+ ,0
+ ,109.99
+ ,109.99
+ ,89703
+ ,89703
+ ,-765.6
+ ,-765.6
+ ,2008
+ ,1
+ ,-22
+ ,506
+ ,506
+ ,-4
+ ,-4
+ ,-3
+ ,-3
+ ,109.26
+ ,109.26
+ ,89703
+ ,89703
+ ,-1592.3
+ ,-1592.3
+ ,2008
+ ,1
+ ,-25
+ ,502
+ ,502
+ ,-5
+ ,-5
+ ,-3
+ ,-3
+ ,109.11
+ ,109.11
+ ,89703
+ ,89703
+ ,-1588.8
+ ,-1588.8
+ ,2008
+ ,1
+ ,-20
+ ,516
+ ,0
+ ,-1
+ ,0
+ ,4
+ ,0
+ ,107.06
+ ,0
+ ,87818
+ ,0
+ ,-1318
+ ,0
+ ,2009
+ ,0
+ ,-24
+ ,528
+ ,0
+ ,-2
+ ,0
+ ,1
+ ,0
+ ,109.53
+ ,0
+ ,87818
+ ,0
+ ,-402.4
+ ,0
+ ,2009
+ ,0
+ ,-24
+ ,533
+ ,0
+ ,-4
+ ,0
+ ,0
+ ,0
+ ,108.92
+ ,0
+ ,87818
+ ,0
+ ,-814.5
+ ,0
+ ,2009
+ ,0
+ ,-22
+ ,536
+ ,0
+ ,-1
+ ,0
+ ,-4
+ ,0
+ ,109.24
+ ,0
+ ,86273
+ ,0
+ ,-98.4
+ ,0
+ ,2009
+ ,0
+ ,-19
+ ,537
+ ,0
+ ,1
+ ,0
+ ,-2
+ ,0
+ ,109.12
+ ,0
+ ,86273
+ ,0
+ ,-305.9
+ ,0
+ ,2009
+ ,0
+ ,-18
+ ,524
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,109
+ ,0
+ ,86273
+ ,0
+ ,-18.4
+ ,0
+ ,2009
+ ,0
+ ,-17
+ ,536
+ ,0
+ ,-2
+ ,0
+ ,2
+ ,0
+ ,107.23
+ ,0
+ ,86316
+ ,0
+ ,610.3
+ ,0
+ ,2009
+ ,0
+ ,-11
+ ,587
+ ,0
+ ,1
+ ,0
+ ,5
+ ,0
+ ,109.49
+ ,0
+ ,86316
+ ,0
+ ,-917.3
+ ,0
+ ,2009
+ ,0
+ ,-11
+ ,597
+ ,0
+ ,1
+ ,0
+ ,6
+ ,0
+ ,109.04
+ ,0
+ ,86316
+ ,0
+ ,88.4
+ ,0
+ ,2009
+ ,0
+ ,-12
+ ,581
+ ,0
+ ,3
+ ,0
+ ,6
+ ,0
+ ,109.02
+ ,0
+ ,87234
+ ,0
+ ,-740.2
+ ,0
+ ,2009
+ ,0
+ ,-10
+ ,564
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,109.23
+ ,0
+ ,87234
+ ,0
+ ,29.3
+ ,0
+ ,2009
+ ,0
+ ,-15
+ ,558
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,109.46
+ ,0
+ ,87234
+ ,0
+ ,-893.2
+ ,0
+ ,2009
+ ,0
+ ,-15
+ ,575
+ ,0
+ ,1
+ ,0
+ ,7
+ ,0
+ ,107.9
+ ,0
+ ,87885
+ ,0
+ ,-1030.2
+ ,0
+ ,2010
+ ,0
+ ,-15
+ ,580
+ ,0
+ ,0
+ ,0
+ ,5
+ ,0
+ ,110.42
+ ,0
+ ,87885
+ ,0
+ ,-403.4
+ ,0
+ ,2010
+ ,0
+ ,-13
+ ,575
+ ,0
+ ,2
+ ,0
+ ,6
+ ,0
+ ,110.98
+ ,0
+ ,87885
+ ,0
+ ,-46.9
+ ,0
+ ,2010
+ ,0
+ ,-8
+ ,563
+ ,0
+ ,2
+ ,0
+ ,1
+ ,0
+ ,111.48
+ ,0
+ ,88003
+ ,0
+ ,-321.2
+ ,0
+ ,2010
+ ,0
+ ,-13
+ ,552
+ ,0
+ ,-1
+ ,0
+ ,3
+ ,0
+ ,111.88
+ ,0
+ ,88003
+ ,0
+ ,-239.9
+ ,0
+ ,2010
+ ,0
+ ,-9
+ ,537
+ ,0
+ ,1
+ ,0
+ ,6
+ ,0
+ ,111.89
+ ,0
+ ,88003
+ ,0
+ ,640.9
+ ,0
+ ,2010
+ ,0
+ ,-7
+ ,545
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,109.85
+ ,0
+ ,88910
+ ,0
+ ,511.6
+ ,0
+ ,2010
+ ,0
+ ,-4
+ ,601
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,112.1
+ ,0
+ ,88910
+ ,0
+ ,-665.1
+ ,0
+ ,2010
+ ,0
+ ,-4
+ ,604
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,112.24
+ ,0
+ ,88910
+ ,0
+ ,657.7
+ ,0
+ ,2010
+ ,0
+ ,-2
+ ,586
+ ,0
+ ,3
+ ,0
+ ,7
+ ,0
+ ,112.39
+ ,0
+ ,89397
+ ,0
+ ,-207.7
+ ,0
+ ,2010
+ ,0
+ ,0
+ ,564
+ ,0
+ ,2
+ ,0
+ ,6
+ ,0
+ ,112.52
+ ,0
+ ,89397
+ ,0
+ ,-885.2
+ ,0
+ ,2010
+ ,0
+ ,-2
+ ,549
+ ,0
+ ,0
+ ,0
+ ,6
+ ,0
+ ,113.16
+ ,0
+ ,89397
+ ,0
+ ,-1595.8
+ ,0
+ ,2010
+ ,0
+ ,-3
+ ,551
+ ,0
+ ,0
+ ,0
+ ,6
+ ,0
+ ,111.84
+ ,0
+ ,89813
+ ,0
+ ,-1374.9
+ ,0
+ ,2011
+ ,0
+ ,1
+ ,556
+ ,0
+ ,3
+ ,0
+ ,6
+ ,0
+ ,114.33
+ ,0
+ ,89813
+ ,0
+ ,-316.6
+ ,0
+ ,2011
+ ,0
+ ,-2
+ ,548
+ ,0
+ ,-2
+ ,0
+ ,2
+ ,0
+ ,114.82
+ ,0
+ ,89813
+ ,0
+ ,-283.4
+ ,0
+ ,2011
+ ,0
+ ,-1
+ ,540
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,115.2
+ ,0
+ ,90539
+ ,0
+ ,-175.8
+ ,0
+ ,2011
+ ,0
+ ,1
+ ,531
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,115.4
+ ,0
+ ,90539
+ ,0
+ ,-694.2
+ ,0
+ ,2011
+ ,0
+ ,-3
+ ,521
+ ,0
+ ,-1
+ ,0
+ ,3
+ ,0
+ ,115.74
+ ,0
+ ,90539
+ ,0
+ ,-249.9
+ ,0
+ ,2011
+ ,0
+ ,-4
+ ,519
+ ,0
+ ,-2
+ ,0
+ ,-1
+ ,0
+ ,114.19
+ ,0
+ ,90688
+ ,0
+ ,268.2
+ ,0
+ ,2011
+ ,0
+ ,-9
+ ,572
+ ,0
+ ,-1
+ ,0
+ ,-4
+ ,0
+ ,115.94
+ ,0
+ ,90688
+ ,0
+ ,-2105.1
+ ,0
+ ,2011
+ ,0
+ ,-9
+ ,581
+ ,0
+ ,-1
+ ,0
+ ,4
+ ,0
+ ,116.03
+ ,0
+ ,90688
+ ,0
+ ,-762.8
+ ,0
+ ,2011
+ ,0
+ ,-7
+ ,563
+ ,0
+ ,1
+ ,0
+ ,5
+ ,0
+ ,116.24
+ ,0
+ ,90691
+ ,0
+ ,-117.1
+ ,0
+ ,2011
+ ,0
+ ,-14
+ ,548
+ ,0
+ ,-2
+ ,0
+ ,3
+ ,0
+ ,116.66
+ ,0
+ ,90691
+ ,0
+ ,-1094.4
+ ,0
+ ,2011
+ ,0
+ ,-12
+ ,539
+ ,0
+ ,-5
+ ,0
+ ,-1
+ ,0
+ ,116.79
+ ,0
+ ,90691
+ ,0
+ ,-2095.2
+ ,0
+ ,2011
+ ,0
+ ,-16
+ ,541
+ ,0
+ ,-5
+ ,0
+ ,-4
+ ,0
+ ,115.48
+ ,0
+ ,90645
+ ,0
+ ,-1587.6
+ ,0
+ ,2012
+ ,0
+ ,-20
+ ,562
+ ,0
+ ,-6
+ ,0
+ ,0
+ ,0
+ ,118.16
+ ,0
+ ,90645
+ ,0
+ ,-528
+ ,0
+ ,2012
+ ,0
+ ,-12
+ ,559
+ ,0
+ ,-4
+ ,0
+ ,-1
+ ,0
+ ,118.38
+ ,0
+ ,90645
+ ,0
+ ,-324.2
+ ,0
+ ,2012
+ ,0
+ ,-12
+ ,546
+ ,0
+ ,-3
+ ,0
+ ,-1
+ ,0
+ ,118.51
+ ,0
+ ,90861
+ ,0
+ ,-276.1
+ ,0
+ ,2012
+ ,0
+ ,-10
+ ,536
+ ,0
+ ,-3
+ ,0
+ ,3
+ ,0
+ ,118.42
+ ,0
+ ,90861
+ ,0
+ ,-139.1
+ ,0
+ ,2012
+ ,0
+ ,-10
+ ,528
+ ,0
+ ,-1
+ ,0
+ ,2
+ ,0
+ ,118.24
+ ,0
+ ,90861
+ ,0
+ ,268
+ ,0
+ ,2012
+ ,0
+ ,-13
+ ,530
+ ,0
+ ,-2
+ ,0
+ ,-4
+ ,0
+ ,116.47
+ ,0
+ ,90401
+ ,0
+ ,570.5
+ ,0
+ ,2012
+ ,0
+ ,-16
+ ,582
+ ,0
+ ,-3
+ ,0
+ ,-3
+ ,0
+ ,118.96
+ ,0
+ ,90401
+ ,0
+ ,-316.5
+ ,0
+ ,2012
+ ,0)
+ ,dim=c(15
+ ,143)
+ ,dimnames=list(c('I'
+ ,'W'
+ ,'W_c'
+ ,'F'
+ ,'F_c'
+ ,'S'
+ ,'S_c'
+ ,'C'
+ ,'C_c'
+ ,'B'
+ ,'B_c'
+ ,'H'
+ ,'H_c'
+ ,'T'
+ ,'c')
+ ,1:143))
> y <- array(NA,dim=c(15,143),dimnames=list(c('I','W','W_c','F','F_c','S','S_c','C','C_c','B','B_c','H','H_c','T','c'),1:143))
> 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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal 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, 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
I W W_c F F_c S S_c C C_c B B_c H H_c T c
1 14 501 501 11 11 20 20 91.81 91.81 77585 77585 1303.2 1303.2 2000 1
2 14 485 485 11 11 19 19 91.98 91.98 77585 77585 -58.7 -58.7 2000 1
3 15 464 464 11 11 18 18 91.72 91.72 77585 77585 -378.9 -378.9 2000 1
4 13 460 0 11 0 13 0 90.27 0.00 78302 0 175.6 0.0 2001 0
5 8 467 0 11 0 17 0 91.89 0.00 78302 0 233.7 0.0 2001 0
6 7 460 0 9 0 17 0 92.07 0.00 78302 0 706.8 0.0 2001 0
7 3 448 0 8 0 13 0 92.92 0.00 78224 0 -23.6 0.0 2001 0
8 3 443 0 6 0 14 0 93.34 0.00 78224 0 420.9 0.0 2001 0
9 4 436 0 7 0 13 0 93.60 0.00 78224 0 722.1 0.0 2001 0
10 4 431 0 8 0 17 0 92.41 0.00 78178 0 1401.3 0.0 2001 0
11 0 484 0 6 0 17 0 93.60 0.00 78178 0 -94.9 0.0 2001 0
12 -4 510 0 5 0 15 0 93.77 0.00 78178 0 1043.6 0.0 2001 0
13 -14 513 0 2 0 9 0 93.60 0.00 77988 0 1300.1 0.0 2001 0
14 -18 503 0 3 0 10 0 93.60 0.00 77988 0 721.1 0.0 2001 0
15 -8 471 0 3 0 9 0 93.51 0.00 77988 0 -45.6 0.0 2001 0
16 -1 471 0 7 0 14 0 92.66 0.00 77876 0 787.5 0.0 2002 0
17 1 476 0 8 0 18 0 94.20 0.00 77876 0 694.3 0.0 2002 0
18 2 475 0 7 0 18 0 94.37 0.00 77876 0 1054.7 0.0 2002 0
19 0 470 0 7 0 12 0 94.45 0.00 78432 0 821.9 0.0 2002 0
20 1 461 0 6 0 16 0 94.62 0.00 78432 0 1100.7 0.0 2002 0
21 0 455 0 6 0 12 0 94.37 0.00 78432 0 862.4 0.0 2002 0
22 -1 456 0 7 0 19 0 93.43 0.00 79025 0 1656.1 0.0 2002 0
23 -3 517 0 5 0 13 0 94.79 0.00 79025 0 -174.0 0.0 2002 0
24 -3 525 0 5 0 12 0 94.88 0.00 79025 0 1337.6 0.0 2002 0
25 -3 523 0 5 0 13 0 94.79 0.00 79407 0 1394.9 0.0 2002 0
26 -4 519 0 4 0 11 0 94.62 0.00 79407 0 915.7 0.0 2002 0
27 -8 509 0 4 0 10 0 94.71 0.00 79407 0 -481.1 0.0 2002 0
28 -9 512 0 4 0 16 0 93.77 0.00 79644 0 167.9 0.0 2003 0
29 -13 519 0 1 0 12 0 95.73 0.00 79644 0 208.2 0.0 2003 0
30 -18 517 0 -1 0 6 0 95.99 0.00 79644 0 382.2 0.0 2003 0
31 -11 510 0 3 0 8 0 95.82 0.00 79381 0 1004.0 0.0 2003 0
32 -9 509 0 4 0 6 0 95.47 0.00 79381 0 864.7 0.0 2003 0
33 -10 501 0 3 0 8 0 95.82 0.00 79381 0 1052.9 0.0 2003 0
34 -13 507 0 2 0 8 0 94.71 0.00 79536 0 1417.6 0.0 2003 0
35 -11 569 0 1 0 9 0 96.33 0.00 79536 0 -197.7 0.0 2003 0
36 -5 580 0 4 0 13 0 96.50 0.00 79536 0 1262.1 0.0 2003 0
37 -15 578 0 3 0 8 0 96.16 0.00 79813 0 1147.2 0.0 2003 0
38 -6 565 0 5 0 11 0 96.33 0.00 79813 0 700.2 0.0 2003 0
39 -6 547 0 6 0 8 0 96.33 0.00 79813 0 45.3 0.0 2003 0
40 -3 555 0 6 0 10 0 95.05 0.00 80332 0 458.5 0.0 2004 0
41 -1 562 0 6 0 15 0 96.84 0.00 80332 0 610.2 0.0 2004 0
42 -3 561 0 6 0 12 0 96.92 0.00 80332 0 786.4 0.0 2004 0
43 -4 555 0 6 0 13 0 97.44 0.00 81434 0 787.2 0.0 2004 0
44 -6 544 0 5 0 12 0 97.78 0.00 81434 0 1040.0 0.0 2004 0
45 0 537 0 6 0 15 0 97.69 0.00 81434 0 324.1 0.0 2004 0
46 -4 543 0 5 0 13 0 96.67 0.00 82167 0 1343.0 0.0 2004 0
47 -2 594 0 6 0 13 0 98.29 0.00 82167 0 -501.2 0.0 2004 0
48 -2 611 0 5 0 16 0 98.20 0.00 82167 0 800.4 0.0 2004 0
49 -6 613 0 7 0 14 0 98.71 0.00 82816 0 916.7 0.0 2004 0
50 -7 611 0 4 0 12 0 98.54 0.00 82816 0 695.8 0.0 2004 0
51 -6 594 0 5 0 15 0 98.20 0.00 82816 0 28.0 0.0 2004 0
52 -6 595 595 6 6 14 14 96.92 96.92 83000 83000 495.6 495.6 2005 1
53 -3 591 591 6 6 19 19 99.06 99.06 83000 83000 366.2 366.2 2005 1
54 -2 589 589 5 5 16 16 99.65 99.65 83000 83000 633.0 633.0 2005 1
55 -5 584 584 3 3 16 16 99.82 99.82 83251 83251 848.3 848.3 2005 1
56 -11 573 573 2 2 11 11 99.99 99.99 83251 83251 472.2 472.2 2005 1
57 -11 567 567 3 3 13 13 100.33 100.33 83251 83251 357.8 357.8 2005 1
58 -11 569 569 3 3 12 12 99.31 99.31 83591 83591 824.3 824.3 2005 1
59 -10 621 621 2 2 11 11 101.10 101.10 83591 83591 -880.1 -880.1 2005 1
60 -14 629 629 0 0 6 6 101.10 101.10 83591 83591 1066.8 1066.8 2005 1
61 -8 628 628 4 4 9 9 100.93 100.93 83910 83910 1052.8 1052.8 2005 1
62 -9 612 612 4 4 6 6 100.85 100.85 83910 83910 -32.1 -32.1 2005 1
63 -5 595 595 5 5 15 15 100.93 100.93 83910 83910 -1331.4 -1331.4 2005 1
64 -1 597 597 6 6 17 17 99.60 99.60 84599 84599 -767.1 -767.1 2006 1
65 -2 593 593 6 6 13 13 101.88 101.88 84599 84599 -236.7 -236.7 2006 1
66 -5 590 590 5 5 12 12 101.81 101.81 84599 84599 -184.9 -184.9 2006 1
67 -4 580 580 5 5 13 13 102.38 102.38 85275 85275 -143.4 -143.4 2006 1
68 -6 574 574 3 3 10 10 102.74 102.74 85275 85275 493.9 493.9 2006 1
69 -2 573 573 5 5 14 14 102.82 102.82 85275 85275 549.7 549.7 2006 1
70 -2 573 573 5 5 13 13 101.72 101.72 85608 85608 982.7 982.7 2006 1
71 -2 620 620 5 5 10 10 103.47 103.47 85608 85608 -856.3 -856.3 2006 1
72 -2 626 626 3 3 11 11 102.98 102.98 85608 85608 967.0 967.0 2006 1
73 2 620 620 6 6 12 12 102.68 102.68 86303 86303 659.4 659.4 2006 1
74 1 588 588 6 6 7 7 102.90 102.90 86303 86303 577.2 577.2 2006 1
75 -8 566 566 4 4 11 11 103.03 103.03 86303 86303 -213.1 -213.1 2006 1
76 -1 557 557 6 6 9 9 101.29 101.29 87115 87115 17.7 17.7 2007 1
77 1 561 561 5 5 13 13 103.69 103.69 87115 87115 390.1 390.1 2007 1
78 -1 549 549 4 4 12 12 103.68 103.68 87115 87115 509.3 509.3 2007 1
79 2 532 532 5 5 5 5 104.20 104.20 87931 87931 410.0 410.0 2007 1
80 2 526 526 5 5 13 13 104.08 104.08 87931 87931 212.5 212.5 2007 1
81 1 511 511 4 4 11 11 104.16 104.16 87931 87931 818.0 818.0 2007 1
82 -1 499 499 3 3 8 8 103.05 103.05 88164 88164 422.7 422.7 2007 1
83 -2 555 555 2 2 8 8 104.66 104.66 88164 88164 -158.0 -158.0 2007 1
84 -2 565 565 3 3 8 8 104.46 104.46 88164 88164 427.2 427.2 2007 1
85 -1 542 542 2 2 8 8 104.95 104.95 88792 88792 243.4 243.4 2007 1
86 -8 527 527 -1 -1 0 0 105.85 105.85 88792 88792 -419.3 -419.3 2007 1
87 -4 510 510 0 0 3 3 106.23 106.23 88792 88792 -1459.8 -1459.8 2007 1
88 -6 514 514 -2 -2 0 0 104.86 104.86 89263 89263 -1389.8 -1389.8 2008 1
89 -3 517 517 1 1 -1 -1 107.44 107.44 89263 89263 -2.1 -2.1 2008 1
90 -3 508 508 -2 -2 -1 -1 108.23 108.23 89263 89263 -938.6 -938.6 2008 1
91 -7 493 493 -2 -2 -4 -4 108.45 108.45 89881 89881 -839.9 -839.9 2008 1
92 -9 490 490 -2 -2 1 1 109.39 109.39 89881 89881 -297.6 -297.6 2008 1
93 -11 469 469 -6 -6 -1 -1 110.15 110.15 89881 89881 -376.3 -376.3 2008 1
94 -13 478 478 -4 -4 0 0 109.13 109.13 90120 90120 -79.4 -79.4 2008 1
95 -11 528 528 -2 -2 -1 -1 110.28 110.28 90120 90120 -2091.3 -2091.3 2008 1
96 -9 534 534 0 0 6 6 110.17 110.17 90120 90120 -1023.0 -1023.0 2008 1
97 -17 518 518 -5 -5 0 0 109.99 109.99 89703 89703 -765.6 -765.6 2008 1
98 -22 506 506 -4 -4 -3 -3 109.26 109.26 89703 89703 -1592.3 -1592.3 2008 1
99 -25 502 502 -5 -5 -3 -3 109.11 109.11 89703 89703 -1588.8 -1588.8 2008 1
100 -20 516 0 -1 0 4 0 107.06 0.00 87818 0 -1318.0 0.0 2009 0
101 -24 528 0 -2 0 1 0 109.53 0.00 87818 0 -402.4 0.0 2009 0
102 -24 533 0 -4 0 0 0 108.92 0.00 87818 0 -814.5 0.0 2009 0
103 -22 536 0 -1 0 -4 0 109.24 0.00 86273 0 -98.4 0.0 2009 0
104 -19 537 0 1 0 -2 0 109.12 0.00 86273 0 -305.9 0.0 2009 0
105 -18 524 0 1 0 3 0 109.00 0.00 86273 0 -18.4 0.0 2009 0
106 -17 536 0 -2 0 2 0 107.23 0.00 86316 0 610.3 0.0 2009 0
107 -11 587 0 1 0 5 0 109.49 0.00 86316 0 -917.3 0.0 2009 0
108 -11 597 0 1 0 6 0 109.04 0.00 86316 0 88.4 0.0 2009 0
109 -12 581 0 3 0 6 0 109.02 0.00 87234 0 -740.2 0.0 2009 0
110 -10 564 0 3 0 3 0 109.23 0.00 87234 0 29.3 0.0 2009 0
111 -15 558 0 1 0 4 0 109.46 0.00 87234 0 -893.2 0.0 2009 0
112 -15 575 0 1 0 7 0 107.90 0.00 87885 0 -1030.2 0.0 2010 0
113 -15 580 0 0 0 5 0 110.42 0.00 87885 0 -403.4 0.0 2010 0
114 -13 575 0 2 0 6 0 110.98 0.00 87885 0 -46.9 0.0 2010 0
115 -8 563 0 2 0 1 0 111.48 0.00 88003 0 -321.2 0.0 2010 0
116 -13 552 0 -1 0 3 0 111.88 0.00 88003 0 -239.9 0.0 2010 0
117 -9 537 0 1 0 6 0 111.89 0.00 88003 0 640.9 0.0 2010 0
118 -7 545 0 0 0 0 0 109.85 0.00 88910 0 511.6 0.0 2010 0
119 -4 601 0 1 0 3 0 112.10 0.00 88910 0 -665.1 0.0 2010 0
120 -4 604 0 1 0 4 0 112.24 0.00 88910 0 657.7 0.0 2010 0
121 -2 586 0 3 0 7 0 112.39 0.00 89397 0 -207.7 0.0 2010 0
122 0 564 0 2 0 6 0 112.52 0.00 89397 0 -885.2 0.0 2010 0
123 -2 549 0 0 0 6 0 113.16 0.00 89397 0 -1595.8 0.0 2010 0
124 -3 551 0 0 0 6 0 111.84 0.00 89813 0 -1374.9 0.0 2011 0
125 1 556 0 3 0 6 0 114.33 0.00 89813 0 -316.6 0.0 2011 0
126 -2 548 0 -2 0 2 0 114.82 0.00 89813 0 -283.4 0.0 2011 0
127 -1 540 0 0 0 2 0 115.20 0.00 90539 0 -175.8 0.0 2011 0
128 1 531 0 1 0 2 0 115.40 0.00 90539 0 -694.2 0.0 2011 0
129 -3 521 0 -1 0 3 0 115.74 0.00 90539 0 -249.9 0.0 2011 0
130 -4 519 0 -2 0 -1 0 114.19 0.00 90688 0 268.2 0.0 2011 0
131 -9 572 0 -1 0 -4 0 115.94 0.00 90688 0 -2105.1 0.0 2011 0
132 -9 581 0 -1 0 4 0 116.03 0.00 90688 0 -762.8 0.0 2011 0
133 -7 563 0 1 0 5 0 116.24 0.00 90691 0 -117.1 0.0 2011 0
134 -14 548 0 -2 0 3 0 116.66 0.00 90691 0 -1094.4 0.0 2011 0
135 -12 539 0 -5 0 -1 0 116.79 0.00 90691 0 -2095.2 0.0 2011 0
136 -16 541 0 -5 0 -4 0 115.48 0.00 90645 0 -1587.6 0.0 2012 0
137 -20 562 0 -6 0 0 0 118.16 0.00 90645 0 -528.0 0.0 2012 0
138 -12 559 0 -4 0 -1 0 118.38 0.00 90645 0 -324.2 0.0 2012 0
139 -12 546 0 -3 0 -1 0 118.51 0.00 90861 0 -276.1 0.0 2012 0
140 -10 536 0 -3 0 3 0 118.42 0.00 90861 0 -139.1 0.0 2012 0
141 -10 528 0 -1 0 2 0 118.24 0.00 90861 0 268.0 0.0 2012 0
142 -13 530 0 -2 0 -4 0 116.47 0.00 90401 0 570.5 0.0 2012 0
143 -16 582 0 -3 0 -3 0 118.96 0.00 90401 0 -316.5 0.0 2012 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) W W_c F F_c S
5.170e+03 -4.186e-02 -1.175e-03 1.972e+00 3.556e-02 4.104e-01
S_c C C_c B B_c H
-3.815e-01 8.852e-01 -8.873e-01 1.773e-03 4.108e-04 1.482e-04
H_c T c
6.274e-04 -2.694e+00 5.890e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.9520 -1.6001 0.3105 1.9765 9.6244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.170e+03 1.426e+03 3.625 0.000415 ***
W -4.186e-02 1.088e-02 -3.849 0.000187 ***
W_c -1.175e-03 1.722e-02 -0.068 0.945691
F 1.972e+00 2.304e-01 8.557 3.07e-14 ***
F_c 3.556e-02 4.120e-01 0.086 0.931350
S 4.104e-01 1.527e-01 2.688 0.008145 **
S_c -3.815e-01 2.539e-01 -1.503 0.135395
C 8.852e-01 3.235e-01 2.737 0.007092 **
C_c -8.873e-01 6.465e-01 -1.372 0.172339
B 1.773e-03 6.361e-04 2.787 0.006122 **
B_c 4.108e-04 8.400e-04 0.489 0.625623
H 1.482e-04 6.407e-04 0.231 0.817425
H_c 6.274e-04 1.012e-03 0.620 0.536579
T -2.694e+00 7.314e-01 -3.683 0.000339 ***
c 5.890e+01 3.231e+01 1.823 0.070642 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.615 on 128 degrees of freedom
Multiple R-squared: 0.7904, Adjusted R-squared: 0.7675
F-statistic: 34.47 on 14 and 128 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,] 7.035762e-01 0.5928475618 0.2964238
[2,] 7.501409e-01 0.4997182957 0.2498591
[3,] 6.366954e-01 0.7266091625 0.3633046
[4,] 5.127086e-01 0.9745827373 0.4872914
[5,] 5.814987e-01 0.8370025485 0.4185013
[6,] 4.981402e-01 0.9962803494 0.5018598
[7,] 4.608990e-01 0.9217979450 0.5391010
[8,] 3.683512e-01 0.7367024798 0.6316488
[9,] 2.984009e-01 0.5968017093 0.7015991
[10,] 2.909525e-01 0.5819050938 0.7090475
[11,] 2.237742e-01 0.4475483247 0.7762258
[12,] 1.715273e-01 0.3430546117 0.8284727
[13,] 1.248172e-01 0.2496343772 0.8751828
[14,] 1.010673e-01 0.2021346839 0.8989327
[15,] 7.772943e-02 0.1554588582 0.9222706
[16,] 5.349353e-02 0.1069870596 0.9465065
[17,] 3.532125e-02 0.0706424919 0.9646788
[18,] 5.075043e-02 0.1015008576 0.9492496
[19,] 4.280590e-02 0.0856117952 0.9571941
[20,] 5.649787e-02 0.1129957326 0.9435021
[21,] 4.319284e-02 0.0863856798 0.9568072
[22,] 4.639670e-02 0.0927934032 0.9536033
[23,] 4.268595e-02 0.0853719027 0.9573140
[24,] 3.610625e-02 0.0722125043 0.9638937
[25,] 3.247693e-02 0.0649538639 0.9675231
[26,] 3.280377e-02 0.0656075482 0.9671962
[27,] 2.681844e-02 0.0536368791 0.9731816
[28,] 2.585803e-02 0.0517160567 0.9741420
[29,] 1.976357e-02 0.0395271481 0.9802364
[30,] 1.863323e-02 0.0372664532 0.9813668
[31,] 2.068765e-02 0.0413752953 0.9793124
[32,] 3.846452e-02 0.0769290345 0.9615355
[33,] 3.104342e-02 0.0620868471 0.9689566
[34,] 3.188667e-02 0.0637733410 0.9681133
[35,] 2.404497e-02 0.0480899337 0.9759550
[36,] 1.681078e-02 0.0336215644 0.9831892
[37,] 1.284594e-02 0.0256918873 0.9871541
[38,] 9.818753e-03 0.0196375058 0.9901812
[39,] 8.742486e-03 0.0174849718 0.9912575
[40,] 8.401554e-03 0.0168031079 0.9915984
[41,] 6.367262e-03 0.0127345245 0.9936327
[42,] 4.448924e-03 0.0088978471 0.9955511
[43,] 3.035143e-03 0.0060702853 0.9969649
[44,] 2.235381e-03 0.0044707613 0.9977646
[45,] 1.594026e-03 0.0031880523 0.9984060
[46,] 1.045158e-03 0.0020903170 0.9989548
[47,] 1.451244e-03 0.0029024885 0.9985488
[48,] 1.051181e-03 0.0021023626 0.9989488
[49,] 6.600521e-04 0.0013201042 0.9993399
[50,] 4.505316e-04 0.0009010632 0.9995495
[51,] 4.177903e-04 0.0008355806 0.9995822
[52,] 2.655356e-04 0.0005310713 0.9997345
[53,] 2.046452e-04 0.0004092904 0.9997954
[54,] 1.913470e-04 0.0003826940 0.9998087
[55,] 6.029315e-04 0.0012058631 0.9993971
[56,] 5.235683e-04 0.0010471367 0.9994764
[57,] 3.546867e-04 0.0007093734 0.9996453
[58,] 2.936689e-04 0.0005873378 0.9997063
[59,] 2.891696e-04 0.0005783391 0.9997108
[60,] 2.307814e-04 0.0004615629 0.9997692
[61,] 1.735487e-04 0.0003470975 0.9998265
[62,] 1.317465e-04 0.0002634931 0.9998683
[63,] 8.185287e-05 0.0001637057 0.9999181
[64,] 5.573714e-05 0.0001114743 0.9999443
[65,] 1.019653e-04 0.0002039305 0.9998980
[66,] 1.330326e-04 0.0002660653 0.9998670
[67,] 9.507960e-05 0.0001901592 0.9999049
[68,] 8.591625e-05 0.0001718325 0.9999141
[69,] 6.362587e-05 0.0001272517 0.9999364
[70,] 5.708662e-05 0.0001141732 0.9999429
[71,] 1.881111e-04 0.0003762221 0.9998119
[72,] 2.178008e-04 0.0004356015 0.9997822
[73,] 1.189742e-03 0.0023794840 0.9988103
[74,] 1.056175e-03 0.0021123491 0.9989438
[75,] 8.808442e-04 0.0017616885 0.9991192
[76,] 5.609500e-04 0.0011219001 0.9994390
[77,] 4.829284e-04 0.0009658568 0.9995171
[78,] 3.550789e-04 0.0007101577 0.9996449
[79,] 2.763095e-04 0.0005526191 0.9997237
[80,] 1.811964e-04 0.0003623928 0.9998188
[81,] 6.653274e-04 0.0013306548 0.9993347
[82,] 1.702583e-03 0.0034051651 0.9982974
[83,] 2.367887e-03 0.0047357730 0.9976321
[84,] 7.012234e-03 0.0140244684 0.9929878
[85,] 1.436448e-02 0.0287289620 0.9856355
[86,] 1.026184e-02 0.0205236886 0.9897382
[87,] 7.927309e-03 0.0158546190 0.9920727
[88,] 7.809773e-03 0.0156195453 0.9921902
[89,] 8.184210e-03 0.0163684207 0.9918158
[90,] 1.251205e-02 0.0250240934 0.9874880
[91,] 2.163130e-02 0.0432626068 0.9783687
[92,] 1.888032e-02 0.0377606325 0.9811197
[93,] 1.446819e-02 0.0289363757 0.9855318
[94,] 1.698315e-02 0.0339662983 0.9830169
[95,] 2.500747e-02 0.0500149465 0.9749925
[96,] 2.433848e-02 0.0486769523 0.9756615
[97,] 4.338880e-02 0.0867775991 0.9566112
[98,] 3.624719e-02 0.0724943824 0.9637528
[99,] 3.531316e-02 0.0706263290 0.9646868
[100,] 8.192683e-02 0.1638536500 0.9180732
[101,] 2.122874e-01 0.4245747974 0.7877126
[102,] 1.935116e-01 0.3870231158 0.8064884
[103,] 1.553538e-01 0.3107076876 0.8446462
[104,] 1.287845e-01 0.2575690583 0.8712155
[105,] 1.047560e-01 0.2095119786 0.8952440
[106,] 1.071874e-01 0.2143748699 0.8928126
[107,] 7.238062e-02 0.1447612404 0.9276194
[108,] 3.968352e-02 0.0793670390 0.9603165
> postscript(file="/var/wessaorg/rcomp/tmp/1ooud1353097932.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xo451353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3vn6w1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ux441353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hpeb1353097932.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 = 143
Frequency = 1
1 2 3 4 5 6
0.52803230 0.92503331 1.29798817 5.93344556 -1.85787535 0.56267931
7 8 9 10 11 12
-0.83217344 2.05345964 0.92457371 -1.86379588 -0.53369742 -0.97213638
13 14 15 16 17 18
-2.01997826 -8.73472794 0.52947378 1.11226688 -1.64105427 1.08470517
19 20 21 22 23 24
0.31588958 1.07707848 1.72430234 -4.41553963 1.61112839 2.05274083
25 26 27 28 29 30
0.95240203 2.79887056 -1.08191952 -1.40979582 0.69867566 1.76472314
31 32 33 34 35 36
0.28938802 1.42735201 0.90539132 0.78170537 5.74362560 4.28091158
37 38 39 40 41 42
-1.95224810 1.24497039 -0.15163869 5.20744465 3.84132843 2.93387120
43 44 45 46 47 48
-1.14215214 -1.55908585 1.13081449 -0.37349586 0.62929217 1.96784818
49 50 51 52 53 54
-6.69001986 -0.85508098 -3.36962994 -2.93849794 -0.15033041 2.65182743
55 56 57 58 59 60
2.73600844 -1.29357026 -3.52727608 -4.51884465 2.08084812 1.07397038
61 62 63 64 65 66
-1.77039812 -2.53090795 -0.52211419 2.24728862 0.78443047 -0.34898958
67 68 69 70 71 72
-1.31562294 0.03361700 -0.18242494 -1.21896862 2.32063796 5.14888684
73 74 75 76 77 78
1.56053859 -0.60772348 -6.04286425 -2.64906196 1.13078085 0.55788846
79 80 81 82 83 84
-0.68227015 -1.01904182 -1.06908561 -1.69631319 2.17466742 0.14363978
85 86 87 88 89 90
0.93295017 0.05605408 2.03837792 5.91916968 1.98524991 8.34721746
91 92 93 94 95 96
2.36269127 -0.32966303 4.91544987 -1.49477212 0.23471580 -2.55257349
97 98 99 100 101 102
-0.32144216 -7.11855052 -8.28665281 -7.33512754 -9.95200211 -4.78818800
103 104 105 106 107 108
-4.58525582 -6.17034301 -7.70315319 1.52147251 0.73646279 0.99388054
109 110 111 112 113 114
-6.10615994 -3.88640435 -5.67182983 -3.25110092 -2.57289752 -5.68421242
115 116 117 118 119 120
0.25452522 -0.47839643 -2.42008617 4.56562825 4.88979780 4.28494321
121 122 123 124 125 126
-0.51094724 2.93542476 3.78937134 5.96472231 1.89856342 9.62439161
127 128 129 130 131 132
4.70685270 4.25839428 3.00556497 6.56614078 1.84733884 -1.33809398
133 134 135 136 137 138
-4.73198138 -5.85136210 3.36149128 4.53602387 -0.78444132 3.33244258
139 140 141 142 143
0.31153211 0.31050223 -3.45798892 0.39746042 -0.93726879
> postscript(file="/var/wessaorg/rcomp/tmp/695aw1353097932.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 0.52803230 NA
1 0.92503331 0.52803230
2 1.29798817 0.92503331
3 5.93344556 1.29798817
4 -1.85787535 5.93344556
5 0.56267931 -1.85787535
6 -0.83217344 0.56267931
7 2.05345964 -0.83217344
8 0.92457371 2.05345964
9 -1.86379588 0.92457371
10 -0.53369742 -1.86379588
11 -0.97213638 -0.53369742
12 -2.01997826 -0.97213638
13 -8.73472794 -2.01997826
14 0.52947378 -8.73472794
15 1.11226688 0.52947378
16 -1.64105427 1.11226688
17 1.08470517 -1.64105427
18 0.31588958 1.08470517
19 1.07707848 0.31588958
20 1.72430234 1.07707848
21 -4.41553963 1.72430234
22 1.61112839 -4.41553963
23 2.05274083 1.61112839
24 0.95240203 2.05274083
25 2.79887056 0.95240203
26 -1.08191952 2.79887056
27 -1.40979582 -1.08191952
28 0.69867566 -1.40979582
29 1.76472314 0.69867566
30 0.28938802 1.76472314
31 1.42735201 0.28938802
32 0.90539132 1.42735201
33 0.78170537 0.90539132
34 5.74362560 0.78170537
35 4.28091158 5.74362560
36 -1.95224810 4.28091158
37 1.24497039 -1.95224810
38 -0.15163869 1.24497039
39 5.20744465 -0.15163869
40 3.84132843 5.20744465
41 2.93387120 3.84132843
42 -1.14215214 2.93387120
43 -1.55908585 -1.14215214
44 1.13081449 -1.55908585
45 -0.37349586 1.13081449
46 0.62929217 -0.37349586
47 1.96784818 0.62929217
48 -6.69001986 1.96784818
49 -0.85508098 -6.69001986
50 -3.36962994 -0.85508098
51 -2.93849794 -3.36962994
52 -0.15033041 -2.93849794
53 2.65182743 -0.15033041
54 2.73600844 2.65182743
55 -1.29357026 2.73600844
56 -3.52727608 -1.29357026
57 -4.51884465 -3.52727608
58 2.08084812 -4.51884465
59 1.07397038 2.08084812
60 -1.77039812 1.07397038
61 -2.53090795 -1.77039812
62 -0.52211419 -2.53090795
63 2.24728862 -0.52211419
64 0.78443047 2.24728862
65 -0.34898958 0.78443047
66 -1.31562294 -0.34898958
67 0.03361700 -1.31562294
68 -0.18242494 0.03361700
69 -1.21896862 -0.18242494
70 2.32063796 -1.21896862
71 5.14888684 2.32063796
72 1.56053859 5.14888684
73 -0.60772348 1.56053859
74 -6.04286425 -0.60772348
75 -2.64906196 -6.04286425
76 1.13078085 -2.64906196
77 0.55788846 1.13078085
78 -0.68227015 0.55788846
79 -1.01904182 -0.68227015
80 -1.06908561 -1.01904182
81 -1.69631319 -1.06908561
82 2.17466742 -1.69631319
83 0.14363978 2.17466742
84 0.93295017 0.14363978
85 0.05605408 0.93295017
86 2.03837792 0.05605408
87 5.91916968 2.03837792
88 1.98524991 5.91916968
89 8.34721746 1.98524991
90 2.36269127 8.34721746
91 -0.32966303 2.36269127
92 4.91544987 -0.32966303
93 -1.49477212 4.91544987
94 0.23471580 -1.49477212
95 -2.55257349 0.23471580
96 -0.32144216 -2.55257349
97 -7.11855052 -0.32144216
98 -8.28665281 -7.11855052
99 -7.33512754 -8.28665281
100 -9.95200211 -7.33512754
101 -4.78818800 -9.95200211
102 -4.58525582 -4.78818800
103 -6.17034301 -4.58525582
104 -7.70315319 -6.17034301
105 1.52147251 -7.70315319
106 0.73646279 1.52147251
107 0.99388054 0.73646279
108 -6.10615994 0.99388054
109 -3.88640435 -6.10615994
110 -5.67182983 -3.88640435
111 -3.25110092 -5.67182983
112 -2.57289752 -3.25110092
113 -5.68421242 -2.57289752
114 0.25452522 -5.68421242
115 -0.47839643 0.25452522
116 -2.42008617 -0.47839643
117 4.56562825 -2.42008617
118 4.88979780 4.56562825
119 4.28494321 4.88979780
120 -0.51094724 4.28494321
121 2.93542476 -0.51094724
122 3.78937134 2.93542476
123 5.96472231 3.78937134
124 1.89856342 5.96472231
125 9.62439161 1.89856342
126 4.70685270 9.62439161
127 4.25839428 4.70685270
128 3.00556497 4.25839428
129 6.56614078 3.00556497
130 1.84733884 6.56614078
131 -1.33809398 1.84733884
132 -4.73198138 -1.33809398
133 -5.85136210 -4.73198138
134 3.36149128 -5.85136210
135 4.53602387 3.36149128
136 -0.78444132 4.53602387
137 3.33244258 -0.78444132
138 0.31153211 3.33244258
139 0.31050223 0.31153211
140 -3.45798892 0.31050223
141 0.39746042 -3.45798892
142 -0.93726879 0.39746042
143 NA -0.93726879
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.92503331 0.52803230
[2,] 1.29798817 0.92503331
[3,] 5.93344556 1.29798817
[4,] -1.85787535 5.93344556
[5,] 0.56267931 -1.85787535
[6,] -0.83217344 0.56267931
[7,] 2.05345964 -0.83217344
[8,] 0.92457371 2.05345964
[9,] -1.86379588 0.92457371
[10,] -0.53369742 -1.86379588
[11,] -0.97213638 -0.53369742
[12,] -2.01997826 -0.97213638
[13,] -8.73472794 -2.01997826
[14,] 0.52947378 -8.73472794
[15,] 1.11226688 0.52947378
[16,] -1.64105427 1.11226688
[17,] 1.08470517 -1.64105427
[18,] 0.31588958 1.08470517
[19,] 1.07707848 0.31588958
[20,] 1.72430234 1.07707848
[21,] -4.41553963 1.72430234
[22,] 1.61112839 -4.41553963
[23,] 2.05274083 1.61112839
[24,] 0.95240203 2.05274083
[25,] 2.79887056 0.95240203
[26,] -1.08191952 2.79887056
[27,] -1.40979582 -1.08191952
[28,] 0.69867566 -1.40979582
[29,] 1.76472314 0.69867566
[30,] 0.28938802 1.76472314
[31,] 1.42735201 0.28938802
[32,] 0.90539132 1.42735201
[33,] 0.78170537 0.90539132
[34,] 5.74362560 0.78170537
[35,] 4.28091158 5.74362560
[36,] -1.95224810 4.28091158
[37,] 1.24497039 -1.95224810
[38,] -0.15163869 1.24497039
[39,] 5.20744465 -0.15163869
[40,] 3.84132843 5.20744465
[41,] 2.93387120 3.84132843
[42,] -1.14215214 2.93387120
[43,] -1.55908585 -1.14215214
[44,] 1.13081449 -1.55908585
[45,] -0.37349586 1.13081449
[46,] 0.62929217 -0.37349586
[47,] 1.96784818 0.62929217
[48,] -6.69001986 1.96784818
[49,] -0.85508098 -6.69001986
[50,] -3.36962994 -0.85508098
[51,] -2.93849794 -3.36962994
[52,] -0.15033041 -2.93849794
[53,] 2.65182743 -0.15033041
[54,] 2.73600844 2.65182743
[55,] -1.29357026 2.73600844
[56,] -3.52727608 -1.29357026
[57,] -4.51884465 -3.52727608
[58,] 2.08084812 -4.51884465
[59,] 1.07397038 2.08084812
[60,] -1.77039812 1.07397038
[61,] -2.53090795 -1.77039812
[62,] -0.52211419 -2.53090795
[63,] 2.24728862 -0.52211419
[64,] 0.78443047 2.24728862
[65,] -0.34898958 0.78443047
[66,] -1.31562294 -0.34898958
[67,] 0.03361700 -1.31562294
[68,] -0.18242494 0.03361700
[69,] -1.21896862 -0.18242494
[70,] 2.32063796 -1.21896862
[71,] 5.14888684 2.32063796
[72,] 1.56053859 5.14888684
[73,] -0.60772348 1.56053859
[74,] -6.04286425 -0.60772348
[75,] -2.64906196 -6.04286425
[76,] 1.13078085 -2.64906196
[77,] 0.55788846 1.13078085
[78,] -0.68227015 0.55788846
[79,] -1.01904182 -0.68227015
[80,] -1.06908561 -1.01904182
[81,] -1.69631319 -1.06908561
[82,] 2.17466742 -1.69631319
[83,] 0.14363978 2.17466742
[84,] 0.93295017 0.14363978
[85,] 0.05605408 0.93295017
[86,] 2.03837792 0.05605408
[87,] 5.91916968 2.03837792
[88,] 1.98524991 5.91916968
[89,] 8.34721746 1.98524991
[90,] 2.36269127 8.34721746
[91,] -0.32966303 2.36269127
[92,] 4.91544987 -0.32966303
[93,] -1.49477212 4.91544987
[94,] 0.23471580 -1.49477212
[95,] -2.55257349 0.23471580
[96,] -0.32144216 -2.55257349
[97,] -7.11855052 -0.32144216
[98,] -8.28665281 -7.11855052
[99,] -7.33512754 -8.28665281
[100,] -9.95200211 -7.33512754
[101,] -4.78818800 -9.95200211
[102,] -4.58525582 -4.78818800
[103,] -6.17034301 -4.58525582
[104,] -7.70315319 -6.17034301
[105,] 1.52147251 -7.70315319
[106,] 0.73646279 1.52147251
[107,] 0.99388054 0.73646279
[108,] -6.10615994 0.99388054
[109,] -3.88640435 -6.10615994
[110,] -5.67182983 -3.88640435
[111,] -3.25110092 -5.67182983
[112,] -2.57289752 -3.25110092
[113,] -5.68421242 -2.57289752
[114,] 0.25452522 -5.68421242
[115,] -0.47839643 0.25452522
[116,] -2.42008617 -0.47839643
[117,] 4.56562825 -2.42008617
[118,] 4.88979780 4.56562825
[119,] 4.28494321 4.88979780
[120,] -0.51094724 4.28494321
[121,] 2.93542476 -0.51094724
[122,] 3.78937134 2.93542476
[123,] 5.96472231 3.78937134
[124,] 1.89856342 5.96472231
[125,] 9.62439161 1.89856342
[126,] 4.70685270 9.62439161
[127,] 4.25839428 4.70685270
[128,] 3.00556497 4.25839428
[129,] 6.56614078 3.00556497
[130,] 1.84733884 6.56614078
[131,] -1.33809398 1.84733884
[132,] -4.73198138 -1.33809398
[133,] -5.85136210 -4.73198138
[134,] 3.36149128 -5.85136210
[135,] 4.53602387 3.36149128
[136,] -0.78444132 4.53602387
[137,] 3.33244258 -0.78444132
[138,] 0.31153211 3.33244258
[139,] 0.31050223 0.31153211
[140,] -3.45798892 0.31050223
[141,] 0.39746042 -3.45798892
[142,] -0.93726879 0.39746042
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.92503331 0.52803230
2 1.29798817 0.92503331
3 5.93344556 1.29798817
4 -1.85787535 5.93344556
5 0.56267931 -1.85787535
6 -0.83217344 0.56267931
7 2.05345964 -0.83217344
8 0.92457371 2.05345964
9 -1.86379588 0.92457371
10 -0.53369742 -1.86379588
11 -0.97213638 -0.53369742
12 -2.01997826 -0.97213638
13 -8.73472794 -2.01997826
14 0.52947378 -8.73472794
15 1.11226688 0.52947378
16 -1.64105427 1.11226688
17 1.08470517 -1.64105427
18 0.31588958 1.08470517
19 1.07707848 0.31588958
20 1.72430234 1.07707848
21 -4.41553963 1.72430234
22 1.61112839 -4.41553963
23 2.05274083 1.61112839
24 0.95240203 2.05274083
25 2.79887056 0.95240203
26 -1.08191952 2.79887056
27 -1.40979582 -1.08191952
28 0.69867566 -1.40979582
29 1.76472314 0.69867566
30 0.28938802 1.76472314
31 1.42735201 0.28938802
32 0.90539132 1.42735201
33 0.78170537 0.90539132
34 5.74362560 0.78170537
35 4.28091158 5.74362560
36 -1.95224810 4.28091158
37 1.24497039 -1.95224810
38 -0.15163869 1.24497039
39 5.20744465 -0.15163869
40 3.84132843 5.20744465
41 2.93387120 3.84132843
42 -1.14215214 2.93387120
43 -1.55908585 -1.14215214
44 1.13081449 -1.55908585
45 -0.37349586 1.13081449
46 0.62929217 -0.37349586
47 1.96784818 0.62929217
48 -6.69001986 1.96784818
49 -0.85508098 -6.69001986
50 -3.36962994 -0.85508098
51 -2.93849794 -3.36962994
52 -0.15033041 -2.93849794
53 2.65182743 -0.15033041
54 2.73600844 2.65182743
55 -1.29357026 2.73600844
56 -3.52727608 -1.29357026
57 -4.51884465 -3.52727608
58 2.08084812 -4.51884465
59 1.07397038 2.08084812
60 -1.77039812 1.07397038
61 -2.53090795 -1.77039812
62 -0.52211419 -2.53090795
63 2.24728862 -0.52211419
64 0.78443047 2.24728862
65 -0.34898958 0.78443047
66 -1.31562294 -0.34898958
67 0.03361700 -1.31562294
68 -0.18242494 0.03361700
69 -1.21896862 -0.18242494
70 2.32063796 -1.21896862
71 5.14888684 2.32063796
72 1.56053859 5.14888684
73 -0.60772348 1.56053859
74 -6.04286425 -0.60772348
75 -2.64906196 -6.04286425
76 1.13078085 -2.64906196
77 0.55788846 1.13078085
78 -0.68227015 0.55788846
79 -1.01904182 -0.68227015
80 -1.06908561 -1.01904182
81 -1.69631319 -1.06908561
82 2.17466742 -1.69631319
83 0.14363978 2.17466742
84 0.93295017 0.14363978
85 0.05605408 0.93295017
86 2.03837792 0.05605408
87 5.91916968 2.03837792
88 1.98524991 5.91916968
89 8.34721746 1.98524991
90 2.36269127 8.34721746
91 -0.32966303 2.36269127
92 4.91544987 -0.32966303
93 -1.49477212 4.91544987
94 0.23471580 -1.49477212
95 -2.55257349 0.23471580
96 -0.32144216 -2.55257349
97 -7.11855052 -0.32144216
98 -8.28665281 -7.11855052
99 -7.33512754 -8.28665281
100 -9.95200211 -7.33512754
101 -4.78818800 -9.95200211
102 -4.58525582 -4.78818800
103 -6.17034301 -4.58525582
104 -7.70315319 -6.17034301
105 1.52147251 -7.70315319
106 0.73646279 1.52147251
107 0.99388054 0.73646279
108 -6.10615994 0.99388054
109 -3.88640435 -6.10615994
110 -5.67182983 -3.88640435
111 -3.25110092 -5.67182983
112 -2.57289752 -3.25110092
113 -5.68421242 -2.57289752
114 0.25452522 -5.68421242
115 -0.47839643 0.25452522
116 -2.42008617 -0.47839643
117 4.56562825 -2.42008617
118 4.88979780 4.56562825
119 4.28494321 4.88979780
120 -0.51094724 4.28494321
121 2.93542476 -0.51094724
122 3.78937134 2.93542476
123 5.96472231 3.78937134
124 1.89856342 5.96472231
125 9.62439161 1.89856342
126 4.70685270 9.62439161
127 4.25839428 4.70685270
128 3.00556497 4.25839428
129 6.56614078 3.00556497
130 1.84733884 6.56614078
131 -1.33809398 1.84733884
132 -4.73198138 -1.33809398
133 -5.85136210 -4.73198138
134 3.36149128 -5.85136210
135 4.53602387 3.36149128
136 -0.78444132 4.53602387
137 3.33244258 -0.78444132
138 0.31153211 3.33244258
139 0.31050223 0.31153211
140 -3.45798892 0.31050223
141 0.39746042 -3.45798892
142 -0.93726879 0.39746042
> 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/wessaorg/rcomp/tmp/7m60w1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8u2d31353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9axni1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1070ow1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11nl4g1353097932.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/wessaorg/rcomp/tmp/12qrxa1353097932.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/wessaorg/rcomp/tmp/13rg511353097932.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/wessaorg/rcomp/tmp/14x1go1353097932.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/wessaorg/rcomp/tmp/15uruo1353097932.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/wessaorg/rcomp/tmp/16f9y91353097932.tab")
+ }
>
> try(system("convert tmp/1ooud1353097932.ps tmp/1ooud1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xo451353097932.ps tmp/2xo451353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vn6w1353097932.ps tmp/3vn6w1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ux441353097932.ps tmp/4ux441353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hpeb1353097932.ps tmp/5hpeb1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/695aw1353097932.ps tmp/695aw1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m60w1353097932.ps tmp/7m60w1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u2d31353097932.ps tmp/8u2d31353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/9axni1353097932.ps tmp/9axni1353097932.png",intern=TRUE))
character(0)
> try(system("convert tmp/1070ow1353097932.ps tmp/1070ow1353097932.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.126 1.178 10.591