R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
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> x <- array(list(122.36
+ ,358.59
+ ,123.33
+ ,362.96
+ ,123.04
+ ,362.42
+ ,124.53
+ ,364.97
+ ,125.13
+ ,364.04
+ ,125.85
+ ,361.06
+ ,126.50
+ ,358.48
+ ,126.53
+ ,352.96
+ ,127.07
+ ,359.59
+ ,124.55
+ ,360.39
+ ,124.90
+ ,357.40
+ ,124.32
+ ,362.93
+ ,122.84
+ ,364.55
+ ,123.31
+ ,365.73
+ ,123.31
+ ,364.70
+ ,124.87
+ ,364.65
+ ,124.64
+ ,359.43
+ ,124.73
+ ,362.14
+ ,124.90
+ ,356.97
+ ,124.04
+ ,354.82
+ ,123.28
+ ,353.17
+ ,123.86
+ ,357.06
+ ,122.29
+ ,356.18
+ ,124.09
+ ,355.01
+ ,124.54
+ ,355.65
+ ,125.65
+ ,357.31
+ ,125.70
+ ,357.07
+ ,125.53
+ ,357.91
+ ,125.61
+ ,358.48
+ ,125.55
+ ,358.97
+ ,125.41
+ ,351.77
+ ,127.60
+ ,352.16
+ ,124.68
+ ,359.08
+ ,124.41
+ ,360.35
+ ,126.43
+ ,359.53
+ ,126.38
+ ,359.30
+ ,125.78
+ ,358.41
+ ,124.70
+ ,359.68
+ ,125.07
+ ,355.31
+ ,125.25
+ ,357.08
+ ,126.58
+ ,349.71
+ ,127.13
+ ,354.13
+ ,125.82
+ ,345.49
+ ,123.70
+ ,341.69
+ ,124.39
+ ,344.25
+ ,123.70
+ ,340.17
+ ,124.42
+ ,342.47
+ ,121.05
+ ,344.43
+ ,121.02
+ ,333.23
+ ,123.23
+ ,339.72
+ ,121.32
+ ,342.61
+ ,120.91
+ ,346.36
+ ,120.72
+ ,339.09
+ ,123.31
+ ,339.73
+ ,119.58
+ ,341.12
+ ,119.53
+ ,335.94
+ ,120.59
+ ,333.46
+ ,118.63
+ ,335.66
+ ,118.47
+ ,341.12
+ ,111.81
+ ,342.21
+ ,114.71
+ ,342.62
+ ,117.34
+ ,346.06
+ ,115.77
+ ,344.43
+ ,118.38
+ ,346.65
+ ,117.84
+ ,343.74
+ ,118.83
+ ,335.67
+ ,120.02
+ ,342.75
+ ,116.21
+ ,341.77
+ ,117.08
+ ,345.84
+ ,120.20
+ ,346.52
+ ,119.83
+ ,350.79
+ ,118.92
+ ,345.44
+ ,118.03
+ ,345.87
+ ,117.71
+ ,338.48
+ ,119.55
+ ,337.21
+ ,116.13
+ ,340.81
+ ,115.97
+ ,339.86
+ ,115.99
+ ,342.86
+ ,114.96
+ ,343.33
+ ,116.46
+ ,341.73
+ ,116.55
+ ,351.38
+ ,113.05
+ ,351.13
+ ,117.44
+ ,345.99
+ ,118.84
+ ,347.55
+ ,117.06
+ ,346.02
+ ,117.54
+ ,345.29
+ ,119.31
+ ,347.03
+ ,118.72
+ ,348.01
+ ,121.55
+ ,345.48
+ ,122.61
+ ,349.40
+ ,121.53
+ ,351.05
+ ,123.31
+ ,349.70
+ ,124.07
+ ,350.86
+ ,123.59
+ ,354.45
+ ,122.97
+ ,355.30
+ ,123.22
+ ,357.48
+ ,123.04
+ ,355.24
+ ,122.96
+ ,351.79
+ ,122.81
+ ,355.22
+ ,122.81
+ ,351.02
+ ,122.62
+ ,350.28
+ ,120.82
+ ,350.17
+ ,119.41
+ ,348.16
+ ,121.56
+ ,340.30
+ ,121.59
+ ,343.75
+ ,118.50
+ ,344.71
+ ,118.77
+ ,344.13
+ ,118.86
+ ,342.14
+ ,117.60
+ ,345.04
+ ,119.90
+ ,346.02
+ ,121.83
+ ,346.43
+ ,121.84
+ ,347.07
+ ,122.12
+ ,339.33
+ ,122.12
+ ,339.10
+ ,121.36
+ ,337.19
+ ,119.66
+ ,339.58
+ ,119.32
+ ,327.85
+ ,120.36
+ ,326.81
+ ,117.06
+ ,321.73
+ ,117.48
+ ,320.45
+ ,115.60
+ ,327.69
+ ,113.86
+ ,323.95
+ ,116.92
+ ,320.47
+ ,117.75
+ ,322.13
+ ,117.75
+ ,316.34
+ ,115.31
+ ,314.78
+ ,116.28
+ ,308.90
+ ,115.22
+ ,308.62
+ ,115.65
+ ,314.41
+ ,115.11
+ ,306.88
+ ,118.67
+ ,310.60
+ ,118.04
+ ,321.60
+ ,116.50
+ ,321.50
+ ,119.78
+ ,325.68
+ ,119.95
+ ,324.35
+ ,120.37
+ ,320.01
+ ,119.79
+ ,326.88
+ ,119.43
+ ,332.39
+ ,121.06
+ ,331.48
+ ,121.74
+ ,332.62
+ ,121.09
+ ,324.79
+ ,122.97
+ ,327.12
+ ,120.50
+ ,328.91
+ ,117.18
+ ,328.37
+ ,115.03
+ ,324.83
+ ,113.36
+ ,325.90
+ ,112.59
+ ,326.18
+ ,111.65
+ ,328.94
+ ,111.98
+ ,333.78
+ ,114.87
+ ,328.06
+ ,114.67
+ ,325.87
+ ,114.09
+ ,325.41
+ ,114.77
+ ,318.86
+ ,117.05
+ ,319.13
+ ,117.22
+ ,310.16
+ ,113.18
+ ,311.73
+ ,110.95
+ ,306.54
+ ,112.14
+ ,311.16
+ ,112.72
+ ,311.98
+ ,110.01
+ ,306.72
+ ,110.29
+ ,308.05
+ ,110.74
+ ,300.76
+ ,110.32
+ ,301.90
+ ,105.89
+ ,293.09
+ ,108.97
+ ,292.76
+ ,109.34
+ ,294.58
+ ,106.57
+ ,289.90
+ ,99.49
+ ,296.69
+ ,101.81
+ ,297.21
+ ,104.29
+ ,293.31
+ ,109.73
+ ,296.25
+ ,105.06
+ ,298.60
+ ,107.97
+ ,296.87
+ ,108.13
+ ,301.02
+ ,109.86
+ ,304.73
+ ,108.95
+ ,301.92
+ ,111.20
+ ,295.72
+ ,110.69
+ ,293.18
+ ,106.10
+ ,298.35
+ ,105.68
+ ,297.99
+ ,104.12
+ ,299.85
+ ,104.71
+ ,299.85
+ ,104.30
+ ,304.45
+ ,103.52
+ ,299.45
+ ,107.76
+ ,298.14
+ ,107.80
+ ,298.78
+ ,107.30
+ ,297.02
+ ,108.64
+ ,301.33
+ ,105.03
+ ,294.96
+ ,108.30
+ ,296.69
+ ,107.21
+ ,300.73
+ ,109.27
+ ,301.96
+ ,109.50
+ ,297.38
+ ,111.68
+ ,293.87
+ ,111.80
+ ,285.96
+ ,111.75
+ ,285.41
+ ,106.68
+ ,283.70
+ ,106.37
+ ,284.76
+ ,105.76
+ ,277.11
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.73
+ ,107.69
+ ,274.69
+ ,105.19
+ ,275.42
+ ,105.48
+ ,264.15
+ ,102.22
+ ,276.24
+ ,100.54
+ ,268.88
+ ,105.00
+ ,277.97
+ ,105.44
+ ,280.49
+ ,107.89
+ ,281.09
+ ,108.64
+ ,276.16
+ ,106.70
+ ,272.58
+ ,109.10
+ ,270.94
+ ,105.23
+ ,284.31
+ ,108.41
+ ,283.94
+ ,108.80
+ ,284.18
+ ,110.39
+ ,282.83
+ ,110.22
+ ,283.84
+ ,110.86
+ ,282.71
+ ,108.58
+ ,279.29
+ ,107.70
+ ,280.70
+ ,106.62
+ ,274.47
+ ,109.84
+ ,273.44
+ ,107.16
+ ,275.49
+ ,107.26
+ ,279.46
+ ,108.70
+ ,280.19
+ ,109.85
+ ,288.21
+ ,109.41
+ ,284.80
+ ,112.36
+ ,281.41
+ ,111.03
+ ,283.39
+ ,110.67
+ ,287.97
+ ,109.21
+ ,290.77
+ ,113.58
+ ,290.60
+ ,113.88
+ ,289.67
+ ,114.08
+ ,289.84
+ ,112.33
+ ,298.55
+ ,113.92
+ ,296.07
+ ,114.41
+ ,297.14
+ ,114.57
+ ,295.34
+ ,115.35
+ ,296.25
+ ,113.13
+ ,294.30
+ ,113.29
+ ,296.15
+ ,112.56
+ ,296.49
+ ,113.06
+ ,298.05
+ ,113.46
+ ,301.03
+ ,115.39
+ ,300.52
+ ,116.62
+ ,301.50
+ ,117.04
+ ,296.93
+ ,117.42
+ ,289.84
+ ,115.62
+ ,291.44
+ ,115.16
+ ,286.88
+ ,115.69
+ ,286.74
+ ,112.85
+ ,288.93
+ ,114.05
+ ,292.19
+ ,112.00
+ ,295.39
+ ,113.74
+ ,295.86
+ ,116.26
+ ,293.36
+ ,118.63
+ ,292.86
+ ,116.49
+ ,292.73
+ ,118.23
+ ,296.73
+ ,116.83
+ ,285.02
+ ,118.82
+ ,285.24
+ ,114.36
+ ,288.62
+ ,112.02
+ ,283.36
+ ,113.24
+ ,285.84
+ ,109.75
+ ,291.48
+ ,110.33
+ ,291.41
+ ,112.86
+ ,287.77
+ ,113.04
+ ,284.97
+ ,113.80
+ ,286.05
+ ,110.90
+ ,278.19
+ ,109.96
+ ,281.21
+ ,108.69
+ ,277.92
+ ,108.84
+ ,280.08
+ ,108.47
+ ,269.24
+ ,108.07
+ ,268.48
+ ,107.94
+ ,268.83
+ ,108.11
+ ,269.54
+ ,108.11
+ ,262.37
+ ,106.81
+ ,265.12
+ ,105.58
+ ,265.34
+ ,105.61
+ ,263.32
+ ,106.52
+ ,267.18
+ ,103.86
+ ,260.75
+ ,104.60
+ ,261.78
+ ,104.73
+ ,257.27
+ ,105.12
+ ,255.63
+ ,104.76
+ ,251.39
+ ,103.85
+ ,259.49
+ ,103.83
+ ,261.18
+ ,103.22
+ ,261.65
+ ,101.64
+ ,262.01
+ ,102.13
+ ,265.23
+ ,104.33
+ ,268.10
+ ,104.92
+ ,262.27
+ ,107.78
+ ,263.59
+ ,104.49
+ ,257.85
+ ,102.80
+ ,265.69
+ ,102.86
+ ,271.15
+ ,104.51
+ ,266.69
+ ,104.73
+ ,265.77
+ ,102.58
+ ,262.32
+ ,99.93
+ ,270.48
+ ,101.41
+ ,273.03
+ ,101.05
+ ,269.13
+ ,99.86
+ ,280.65
+ ,101.11
+ ,282.75
+ ,100.89
+ ,281.44
+ ,101.09
+ ,281.99
+ ,98.31
+ ,282.86
+ ,98.08
+ ,287.21
+ ,99.55
+ ,283.11
+ ,99.62
+ ,280.66
+ ,97.37
+ ,282.39
+ ,98.16
+ ,280.83
+ ,97.98
+ ,284.71
+ ,98.15
+ ,279.99
+ ,97.10
+ ,283.50
+ ,97.24
+ ,284.88
+ ,96.70
+ ,288.60
+ ,96.64
+ ,284.80
+ ,100.65
+ ,287.20
+ ,96.75
+ ,286.22
+ ,97.74
+ ,286.54
+ ,97.92
+ ,279.58
+ ,98.34
+ ,283.08
+ ,93.84
+ ,288.88
+ ,97.80
+ ,280.18
+ ,96.20
+ ,284.16
+ ,95.99
+ ,290.57
+ ,95.18
+ ,286.82
+ ,95.95
+ ,273.00
+ ,92.23
+ ,278.69
+ ,91.78
+ ,264.54
+ ,92.97
+ ,271.92
+ ,89.76
+ ,283.60
+ ,92.88
+ ,269.25
+ ,96.23
+ ,263.58
+ ,95.79
+ ,264.16
+ ,93.97
+ ,268.85
+ ,93.90
+ ,269.67
+ ,93.60
+ ,249.41
+ ,93.96
+ ,268.99
+ ,88.69
+ ,268.65
+ ,88.57
+ ,260.16
+ ,85.62
+ ,256.55
+ ,86.25
+ ,251.47
+ ,85.33
+ ,234.93
+ ,83.33
+ ,232.96
+ ,77.78
+ ,215.49
+ ,78.70
+ ,213.68
+ ,72.05
+ ,236.07
+ ,80.75
+ ,235.41
+ ,81.41
+ ,214.77
+ ,82.65
+ ,225.85
+ ,75.85
+ ,224.64
+ ,75.70
+ ,238.26
+ ,78.25
+ ,232.44
+ ,77.41
+ ,222.50
+ ,76.84
+ ,225.28
+ ,74.25
+ ,220.49
+ ,74.95
+ ,216.86
+ ,68.78
+ ,234.70
+ ,73.21
+ ,230.06
+ ,73.26
+ ,238.27
+ ,78.67
+ ,238.56
+ ,75.63
+ ,242.70
+ ,74.99
+ ,249.14
+ ,83.87
+ ,234.89
+ ,79.62
+ ,227.78
+ ,80.13
+ ,234.04
+ ,79.76
+ ,230.70
+ ,78.20
+ ,230.17
+ ,78.05
+ ,218.23
+ ,79.05
+ ,232.20
+ ,73.32
+ ,220.76
+ ,75.17
+ ,215.60
+ ,73.26
+ ,217.69
+ ,73.72
+ ,204.35
+ ,73.57
+ ,191.44
+ ,70.60
+ ,203.84
+ ,71.25
+ ,211.86
+ ,74.22
+ ,210.57
+ ,73.32
+ ,219.57
+ ,73.01
+ ,219.98
+ ,74.21
+ ,226.01
+ ,75.32
+ ,207.04
+ ,71.73
+ ,212.52
+ ,71.94
+ ,217.92
+ ,72.94
+ ,210.45
+ ,72.47
+ ,218.53
+ ,71.94
+ ,223.32
+ ,74.30
+ ,218.76
+ ,74.30
+ ,217.63)
+ ,dim=c(2
+ ,395)
+ ,dimnames=list(c('Japan'
+ ,'VS')
+ ,1:395))
> y <- array(NA,dim=c(2,395),dimnames=list(c('Japan','VS'),1:395))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Japan VS M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 122.36 358.59 1 0 0 0 0 0 0 0 0 0 0 1
2 123.33 362.96 0 1 0 0 0 0 0 0 0 0 0 2
3 123.04 362.42 0 0 1 0 0 0 0 0 0 0 0 3
4 124.53 364.97 0 0 0 1 0 0 0 0 0 0 0 4
5 125.13 364.04 0 0 0 0 1 0 0 0 0 0 0 5
6 125.85 361.06 0 0 0 0 0 1 0 0 0 0 0 6
7 126.50 358.48 0 0 0 0 0 0 1 0 0 0 0 7
8 126.53 352.96 0 0 0 0 0 0 0 1 0 0 0 8
9 127.07 359.59 0 0 0 0 0 0 0 0 1 0 0 9
10 124.55 360.39 0 0 0 0 0 0 0 0 0 1 0 10
11 124.90 357.40 0 0 0 0 0 0 0 0 0 0 1 11
12 124.32 362.93 0 0 0 0 0 0 0 0 0 0 0 12
13 122.84 364.55 1 0 0 0 0 0 0 0 0 0 0 13
14 123.31 365.73 0 1 0 0 0 0 0 0 0 0 0 14
15 123.31 364.70 0 0 1 0 0 0 0 0 0 0 0 15
16 124.87 364.65 0 0 0 1 0 0 0 0 0 0 0 16
17 124.64 359.43 0 0 0 0 1 0 0 0 0 0 0 17
18 124.73 362.14 0 0 0 0 0 1 0 0 0 0 0 18
19 124.90 356.97 0 0 0 0 0 0 1 0 0 0 0 19
20 124.04 354.82 0 0 0 0 0 0 0 1 0 0 0 20
21 123.28 353.17 0 0 0 0 0 0 0 0 1 0 0 21
22 123.86 357.06 0 0 0 0 0 0 0 0 0 1 0 22
23 122.29 356.18 0 0 0 0 0 0 0 0 0 0 1 23
24 124.09 355.01 0 0 0 0 0 0 0 0 0 0 0 24
25 124.54 355.65 1 0 0 0 0 0 0 0 0 0 0 25
26 125.65 357.31 0 1 0 0 0 0 0 0 0 0 0 26
27 125.70 357.07 0 0 1 0 0 0 0 0 0 0 0 27
28 125.53 357.91 0 0 0 1 0 0 0 0 0 0 0 28
29 125.61 358.48 0 0 0 0 1 0 0 0 0 0 0 29
30 125.55 358.97 0 0 0 0 0 1 0 0 0 0 0 30
31 125.41 351.77 0 0 0 0 0 0 1 0 0 0 0 31
32 127.60 352.16 0 0 0 0 0 0 0 1 0 0 0 32
33 124.68 359.08 0 0 0 0 0 0 0 0 1 0 0 33
34 124.41 360.35 0 0 0 0 0 0 0 0 0 1 0 34
35 126.43 359.53 0 0 0 0 0 0 0 0 0 0 1 35
36 126.38 359.30 0 0 0 0 0 0 0 0 0 0 0 36
37 125.78 358.41 1 0 0 0 0 0 0 0 0 0 0 37
38 124.70 359.68 0 1 0 0 0 0 0 0 0 0 0 38
39 125.07 355.31 0 0 1 0 0 0 0 0 0 0 0 39
40 125.25 357.08 0 0 0 1 0 0 0 0 0 0 0 40
41 126.58 349.71 0 0 0 0 1 0 0 0 0 0 0 41
42 127.13 354.13 0 0 0 0 0 1 0 0 0 0 0 42
43 125.82 345.49 0 0 0 0 0 0 1 0 0 0 0 43
44 123.70 341.69 0 0 0 0 0 0 0 1 0 0 0 44
45 124.39 344.25 0 0 0 0 0 0 0 0 1 0 0 45
46 123.70 340.17 0 0 0 0 0 0 0 0 0 1 0 46
47 124.42 342.47 0 0 0 0 0 0 0 0 0 0 1 47
48 121.05 344.43 0 0 0 0 0 0 0 0 0 0 0 48
49 121.02 333.23 1 0 0 0 0 0 0 0 0 0 0 49
50 123.23 339.72 0 1 0 0 0 0 0 0 0 0 0 50
51 121.32 342.61 0 0 1 0 0 0 0 0 0 0 0 51
52 120.91 346.36 0 0 0 1 0 0 0 0 0 0 0 52
53 120.72 339.09 0 0 0 0 1 0 0 0 0 0 0 53
54 123.31 339.73 0 0 0 0 0 1 0 0 0 0 0 54
55 119.58 341.12 0 0 0 0 0 0 1 0 0 0 0 55
56 119.53 335.94 0 0 0 0 0 0 0 1 0 0 0 56
57 120.59 333.46 0 0 0 0 0 0 0 0 1 0 0 57
58 118.63 335.66 0 0 0 0 0 0 0 0 0 1 0 58
59 118.47 341.12 0 0 0 0 0 0 0 0 0 0 1 59
60 111.81 342.21 0 0 0 0 0 0 0 0 0 0 0 60
61 114.71 342.62 1 0 0 0 0 0 0 0 0 0 0 61
62 117.34 346.06 0 1 0 0 0 0 0 0 0 0 0 62
63 115.77 344.43 0 0 1 0 0 0 0 0 0 0 0 63
64 118.38 346.65 0 0 0 1 0 0 0 0 0 0 0 64
65 117.84 343.74 0 0 0 0 1 0 0 0 0 0 0 65
66 118.83 335.67 0 0 0 0 0 1 0 0 0 0 0 66
67 120.02 342.75 0 0 0 0 0 0 1 0 0 0 0 67
68 116.21 341.77 0 0 0 0 0 0 0 1 0 0 0 68
69 117.08 345.84 0 0 0 0 0 0 0 0 1 0 0 69
70 120.20 346.52 0 0 0 0 0 0 0 0 0 1 0 70
71 119.83 350.79 0 0 0 0 0 0 0 0 0 0 1 71
72 118.92 345.44 0 0 0 0 0 0 0 0 0 0 0 72
73 118.03 345.87 1 0 0 0 0 0 0 0 0 0 0 73
74 117.71 338.48 0 1 0 0 0 0 0 0 0 0 0 74
75 119.55 337.21 0 0 1 0 0 0 0 0 0 0 0 75
76 116.13 340.81 0 0 0 1 0 0 0 0 0 0 0 76
77 115.97 339.86 0 0 0 0 1 0 0 0 0 0 0 77
78 115.99 342.86 0 0 0 0 0 1 0 0 0 0 0 78
79 114.96 343.33 0 0 0 0 0 0 1 0 0 0 0 79
80 116.46 341.73 0 0 0 0 0 0 0 1 0 0 0 80
81 116.55 351.38 0 0 0 0 0 0 0 0 1 0 0 81
82 113.05 351.13 0 0 0 0 0 0 0 0 0 1 0 82
83 117.44 345.99 0 0 0 0 0 0 0 0 0 0 1 83
84 118.84 347.55 0 0 0 0 0 0 0 0 0 0 0 84
85 117.06 346.02 1 0 0 0 0 0 0 0 0 0 0 85
86 117.54 345.29 0 1 0 0 0 0 0 0 0 0 0 86
87 119.31 347.03 0 0 1 0 0 0 0 0 0 0 0 87
88 118.72 348.01 0 0 0 1 0 0 0 0 0 0 0 88
89 121.55 345.48 0 0 0 0 1 0 0 0 0 0 0 89
90 122.61 349.40 0 0 0 0 0 1 0 0 0 0 0 90
91 121.53 351.05 0 0 0 0 0 0 1 0 0 0 0 91
92 123.31 349.70 0 0 0 0 0 0 0 1 0 0 0 92
93 124.07 350.86 0 0 0 0 0 0 0 0 1 0 0 93
94 123.59 354.45 0 0 0 0 0 0 0 0 0 1 0 94
95 122.97 355.30 0 0 0 0 0 0 0 0 0 0 1 95
96 123.22 357.48 0 0 0 0 0 0 0 0 0 0 0 96
97 123.04 355.24 1 0 0 0 0 0 0 0 0 0 0 97
98 122.96 351.79 0 1 0 0 0 0 0 0 0 0 0 98
99 122.81 355.22 0 0 1 0 0 0 0 0 0 0 0 99
100 122.81 351.02 0 0 0 1 0 0 0 0 0 0 0 100
101 122.62 350.28 0 0 0 0 1 0 0 0 0 0 0 101
102 120.82 350.17 0 0 0 0 0 1 0 0 0 0 0 102
103 119.41 348.16 0 0 0 0 0 0 1 0 0 0 0 103
104 121.56 340.30 0 0 0 0 0 0 0 1 0 0 0 104
105 121.59 343.75 0 0 0 0 0 0 0 0 1 0 0 105
106 118.50 344.71 0 0 0 0 0 0 0 0 0 1 0 106
107 118.77 344.13 0 0 0 0 0 0 0 0 0 0 1 107
108 118.86 342.14 0 0 0 0 0 0 0 0 0 0 0 108
109 117.60 345.04 1 0 0 0 0 0 0 0 0 0 0 109
110 119.90 346.02 0 1 0 0 0 0 0 0 0 0 0 110
111 121.83 346.43 0 0 1 0 0 0 0 0 0 0 0 111
112 121.84 347.07 0 0 0 1 0 0 0 0 0 0 0 112
113 122.12 339.33 0 0 0 0 1 0 0 0 0 0 0 113
114 122.12 339.10 0 0 0 0 0 1 0 0 0 0 0 114
115 121.36 337.19 0 0 0 0 0 0 1 0 0 0 0 115
116 119.66 339.58 0 0 0 0 0 0 0 1 0 0 0 116
117 119.32 327.85 0 0 0 0 0 0 0 0 1 0 0 117
118 120.36 326.81 0 0 0 0 0 0 0 0 0 1 0 118
119 117.06 321.73 0 0 0 0 0 0 0 0 0 0 1 119
120 117.48 320.45 0 0 0 0 0 0 0 0 0 0 0 120
121 115.60 327.69 1 0 0 0 0 0 0 0 0 0 0 121
122 113.86 323.95 0 1 0 0 0 0 0 0 0 0 0 122
123 116.92 320.47 0 0 1 0 0 0 0 0 0 0 0 123
124 117.75 322.13 0 0 0 1 0 0 0 0 0 0 0 124
125 117.75 316.34 0 0 0 0 1 0 0 0 0 0 0 125
126 115.31 314.78 0 0 0 0 0 1 0 0 0 0 0 126
127 116.28 308.90 0 0 0 0 0 0 1 0 0 0 0 127
128 115.22 308.62 0 0 0 0 0 0 0 1 0 0 0 128
129 115.65 314.41 0 0 0 0 0 0 0 0 1 0 0 129
130 115.11 306.88 0 0 0 0 0 0 0 0 0 1 0 130
131 118.67 310.60 0 0 0 0 0 0 0 0 0 0 1 131
132 118.04 321.60 0 0 0 0 0 0 0 0 0 0 0 132
133 116.50 321.50 1 0 0 0 0 0 0 0 0 0 0 133
134 119.78 325.68 0 1 0 0 0 0 0 0 0 0 0 134
135 119.95 324.35 0 0 1 0 0 0 0 0 0 0 0 135
136 120.37 320.01 0 0 0 1 0 0 0 0 0 0 0 136
137 119.79 326.88 0 0 0 0 1 0 0 0 0 0 0 137
138 119.43 332.39 0 0 0 0 0 1 0 0 0 0 0 138
139 121.06 331.48 0 0 0 0 0 0 1 0 0 0 0 139
140 121.74 332.62 0 0 0 0 0 0 0 1 0 0 0 140
141 121.09 324.79 0 0 0 0 0 0 0 0 1 0 0 141
142 122.97 327.12 0 0 0 0 0 0 0 0 0 1 0 142
143 120.50 328.91 0 0 0 0 0 0 0 0 0 0 1 143
144 117.18 328.37 0 0 0 0 0 0 0 0 0 0 0 144
145 115.03 324.83 1 0 0 0 0 0 0 0 0 0 0 145
146 113.36 325.90 0 1 0 0 0 0 0 0 0 0 0 146
147 112.59 326.18 0 0 1 0 0 0 0 0 0 0 0 147
148 111.65 328.94 0 0 0 1 0 0 0 0 0 0 0 148
149 111.98 333.78 0 0 0 0 1 0 0 0 0 0 0 149
150 114.87 328.06 0 0 0 0 0 1 0 0 0 0 0 150
151 114.67 325.87 0 0 0 0 0 0 1 0 0 0 0 151
152 114.09 325.41 0 0 0 0 0 0 0 1 0 0 0 152
153 114.77 318.86 0 0 0 0 0 0 0 0 1 0 0 153
154 117.05 319.13 0 0 0 0 0 0 0 0 0 1 0 154
155 117.22 310.16 0 0 0 0 0 0 0 0 0 0 1 155
156 113.18 311.73 0 0 0 0 0 0 0 0 0 0 0 156
157 110.95 306.54 1 0 0 0 0 0 0 0 0 0 0 157
158 112.14 311.16 0 1 0 0 0 0 0 0 0 0 0 158
159 112.72 311.98 0 0 1 0 0 0 0 0 0 0 0 159
160 110.01 306.72 0 0 0 1 0 0 0 0 0 0 0 160
161 110.29 308.05 0 0 0 0 1 0 0 0 0 0 0 161
162 110.74 300.76 0 0 0 0 0 1 0 0 0 0 0 162
163 110.32 301.90 0 0 0 0 0 0 1 0 0 0 0 163
164 105.89 293.09 0 0 0 0 0 0 0 1 0 0 0 164
165 108.97 292.76 0 0 0 0 0 0 0 0 1 0 0 165
166 109.34 294.58 0 0 0 0 0 0 0 0 0 1 0 166
167 106.57 289.90 0 0 0 0 0 0 0 0 0 0 1 167
168 99.49 296.69 0 0 0 0 0 0 0 0 0 0 0 168
169 101.81 297.21 1 0 0 0 0 0 0 0 0 0 0 169
170 104.29 293.31 0 1 0 0 0 0 0 0 0 0 0 170
171 109.73 296.25 0 0 1 0 0 0 0 0 0 0 0 171
172 105.06 298.60 0 0 0 1 0 0 0 0 0 0 0 172
173 107.97 296.87 0 0 0 0 1 0 0 0 0 0 0 173
174 108.13 301.02 0 0 0 0 0 1 0 0 0 0 0 174
175 109.86 304.73 0 0 0 0 0 0 1 0 0 0 0 175
176 108.95 301.92 0 0 0 0 0 0 0 1 0 0 0 176
177 111.20 295.72 0 0 0 0 0 0 0 0 1 0 0 177
178 110.69 293.18 0 0 0 0 0 0 0 0 0 1 0 178
179 106.10 298.35 0 0 0 0 0 0 0 0 0 0 1 179
180 105.68 297.99 0 0 0 0 0 0 0 0 0 0 0 180
181 104.12 299.85 1 0 0 0 0 0 0 0 0 0 0 181
182 104.71 299.85 0 1 0 0 0 0 0 0 0 0 0 182
183 104.30 304.45 0 0 1 0 0 0 0 0 0 0 0 183
184 103.52 299.45 0 0 0 1 0 0 0 0 0 0 0 184
185 107.76 298.14 0 0 0 0 1 0 0 0 0 0 0 185
186 107.80 298.78 0 0 0 0 0 1 0 0 0 0 0 186
187 107.30 297.02 0 0 0 0 0 0 1 0 0 0 0 187
188 108.64 301.33 0 0 0 0 0 0 0 1 0 0 0 188
189 105.03 294.96 0 0 0 0 0 0 0 0 1 0 0 189
190 108.30 296.69 0 0 0 0 0 0 0 0 0 1 0 190
191 107.21 300.73 0 0 0 0 0 0 0 0 0 0 1 191
192 109.27 301.96 0 0 0 0 0 0 0 0 0 0 0 192
193 109.50 297.38 1 0 0 0 0 0 0 0 0 0 0 193
194 111.68 293.87 0 1 0 0 0 0 0 0 0 0 0 194
195 111.80 285.96 0 0 1 0 0 0 0 0 0 0 0 195
196 111.75 285.41 0 0 0 1 0 0 0 0 0 0 0 196
197 106.68 283.70 0 0 0 0 1 0 0 0 0 0 0 197
198 106.37 284.76 0 0 0 0 0 1 0 0 0 0 0 198
199 105.76 277.11 0 0 0 0 0 0 1 0 0 0 0 199
200 109.01 274.73 0 0 0 0 0 0 0 1 0 0 0 200
201 109.01 274.73 0 0 0 0 0 0 0 0 1 0 0 201
202 109.01 274.73 0 0 0 0 0 0 0 0 0 1 0 202
203 109.01 274.73 0 0 0 0 0 0 0 0 0 0 1 203
204 107.69 274.69 0 0 0 0 0 0 0 0 0 0 0 204
205 105.19 275.42 1 0 0 0 0 0 0 0 0 0 0 205
206 105.48 264.15 0 1 0 0 0 0 0 0 0 0 0 206
207 102.22 276.24 0 0 1 0 0 0 0 0 0 0 0 207
208 100.54 268.88 0 0 0 1 0 0 0 0 0 0 0 208
209 105.00 277.97 0 0 0 0 1 0 0 0 0 0 0 209
210 105.44 280.49 0 0 0 0 0 1 0 0 0 0 0 210
211 107.89 281.09 0 0 0 0 0 0 1 0 0 0 0 211
212 108.64 276.16 0 0 0 0 0 0 0 1 0 0 0 212
213 106.70 272.58 0 0 0 0 0 0 0 0 1 0 0 213
214 109.10 270.94 0 0 0 0 0 0 0 0 0 1 0 214
215 105.23 284.31 0 0 0 0 0 0 0 0 0 0 1 215
216 108.41 283.94 0 0 0 0 0 0 0 0 0 0 0 216
217 108.80 284.18 1 0 0 0 0 0 0 0 0 0 0 217
218 110.39 282.83 0 1 0 0 0 0 0 0 0 0 0 218
219 110.22 283.84 0 0 1 0 0 0 0 0 0 0 0 219
220 110.86 282.71 0 0 0 1 0 0 0 0 0 0 0 220
221 108.58 279.29 0 0 0 0 1 0 0 0 0 0 0 221
222 107.70 280.70 0 0 0 0 0 1 0 0 0 0 0 222
223 106.62 274.47 0 0 0 0 0 0 1 0 0 0 0 223
224 109.84 273.44 0 0 0 0 0 0 0 1 0 0 0 224
225 107.16 275.49 0 0 0 0 0 0 0 0 1 0 0 225
226 107.26 279.46 0 0 0 0 0 0 0 0 0 1 0 226
227 108.70 280.19 0 0 0 0 0 0 0 0 0 0 1 227
228 109.85 288.21 0 0 0 0 0 0 0 0 0 0 0 228
229 109.41 284.80 1 0 0 0 0 0 0 0 0 0 0 229
230 112.36 281.41 0 1 0 0 0 0 0 0 0 0 0 230
231 111.03 283.39 0 0 1 0 0 0 0 0 0 0 0 231
232 110.67 287.97 0 0 0 1 0 0 0 0 0 0 0 232
233 109.21 290.77 0 0 0 0 1 0 0 0 0 0 0 233
234 113.58 290.60 0 0 0 0 0 1 0 0 0 0 0 234
235 113.88 289.67 0 0 0 0 0 0 1 0 0 0 0 235
236 114.08 289.84 0 0 0 0 0 0 0 1 0 0 0 236
237 112.33 298.55 0 0 0 0 0 0 0 0 1 0 0 237
238 113.92 296.07 0 0 0 0 0 0 0 0 0 1 0 238
239 114.41 297.14 0 0 0 0 0 0 0 0 0 0 1 239
240 114.57 295.34 0 0 0 0 0 0 0 0 0 0 0 240
241 115.35 296.25 1 0 0 0 0 0 0 0 0 0 0 241
242 113.13 294.30 0 1 0 0 0 0 0 0 0 0 0 242
243 113.29 296.15 0 0 1 0 0 0 0 0 0 0 0 243
244 112.56 296.49 0 0 0 1 0 0 0 0 0 0 0 244
245 113.06 298.05 0 0 0 0 1 0 0 0 0 0 0 245
246 113.46 301.03 0 0 0 0 0 1 0 0 0 0 0 246
247 115.39 300.52 0 0 0 0 0 0 1 0 0 0 0 247
248 116.62 301.50 0 0 0 0 0 0 0 1 0 0 0 248
249 117.04 296.93 0 0 0 0 0 0 0 0 1 0 0 249
250 117.42 289.84 0 0 0 0 0 0 0 0 0 1 0 250
251 115.62 291.44 0 0 0 0 0 0 0 0 0 0 1 251
252 115.16 286.88 0 0 0 0 0 0 0 0 0 0 0 252
253 115.69 286.74 1 0 0 0 0 0 0 0 0 0 0 253
254 112.85 288.93 0 1 0 0 0 0 0 0 0 0 0 254
255 114.05 292.19 0 0 1 0 0 0 0 0 0 0 0 255
256 112.00 295.39 0 0 0 1 0 0 0 0 0 0 0 256
257 113.74 295.86 0 0 0 0 1 0 0 0 0 0 0 257
258 116.26 293.36 0 0 0 0 0 1 0 0 0 0 0 258
259 118.63 292.86 0 0 0 0 0 0 1 0 0 0 0 259
260 116.49 292.73 0 0 0 0 0 0 0 1 0 0 0 260
261 118.23 296.73 0 0 0 0 0 0 0 0 1 0 0 261
262 116.83 285.02 0 0 0 0 0 0 0 0 0 1 0 262
263 118.82 285.24 0 0 0 0 0 0 0 0 0 0 1 263
264 114.36 288.62 0 0 0 0 0 0 0 0 0 0 0 264
265 112.02 283.36 1 0 0 0 0 0 0 0 0 0 0 265
266 113.24 285.84 0 1 0 0 0 0 0 0 0 0 0 266
267 109.75 291.48 0 0 1 0 0 0 0 0 0 0 0 267
268 110.33 291.41 0 0 0 1 0 0 0 0 0 0 0 268
269 112.86 287.77 0 0 0 0 1 0 0 0 0 0 0 269
270 113.04 284.97 0 0 0 0 0 1 0 0 0 0 0 270
271 113.80 286.05 0 0 0 0 0 0 1 0 0 0 0 271
272 110.90 278.19 0 0 0 0 0 0 0 1 0 0 0 272
273 109.96 281.21 0 0 0 0 0 0 0 0 1 0 0 273
274 108.69 277.92 0 0 0 0 0 0 0 0 0 1 0 274
275 108.84 280.08 0 0 0 0 0 0 0 0 0 0 1 275
276 108.47 269.24 0 0 0 0 0 0 0 0 0 0 0 276
277 108.07 268.48 1 0 0 0 0 0 0 0 0 0 0 277
278 107.94 268.83 0 1 0 0 0 0 0 0 0 0 0 278
279 108.11 269.54 0 0 1 0 0 0 0 0 0 0 0 279
280 108.11 262.37 0 0 0 1 0 0 0 0 0 0 0 280
281 106.81 265.12 0 0 0 0 1 0 0 0 0 0 0 281
282 105.58 265.34 0 0 0 0 0 1 0 0 0 0 0 282
283 105.61 263.32 0 0 0 0 0 0 1 0 0 0 0 283
284 106.52 267.18 0 0 0 0 0 0 0 1 0 0 0 284
285 103.86 260.75 0 0 0 0 0 0 0 0 1 0 0 285
286 104.60 261.78 0 0 0 0 0 0 0 0 0 1 0 286
287 104.73 257.27 0 0 0 0 0 0 0 0 0 0 1 287
288 105.12 255.63 0 0 0 0 0 0 0 0 0 0 0 288
289 104.76 251.39 1 0 0 0 0 0 0 0 0 0 0 289
290 103.85 259.49 0 1 0 0 0 0 0 0 0 0 0 290
291 103.83 261.18 0 0 1 0 0 0 0 0 0 0 0 291
292 103.22 261.65 0 0 0 1 0 0 0 0 0 0 0 292
293 101.64 262.01 0 0 0 0 1 0 0 0 0 0 0 293
294 102.13 265.23 0 0 0 0 0 1 0 0 0 0 0 294
295 104.33 268.10 0 0 0 0 0 0 1 0 0 0 0 295
296 104.92 262.27 0 0 0 0 0 0 0 1 0 0 0 296
297 107.78 263.59 0 0 0 0 0 0 0 0 1 0 0 297
298 104.49 257.85 0 0 0 0 0 0 0 0 0 1 0 298
299 102.80 265.69 0 0 0 0 0 0 0 0 0 0 1 299
300 102.86 271.15 0 0 0 0 0 0 0 0 0 0 0 300
301 104.51 266.69 1 0 0 0 0 0 0 0 0 0 0 301
302 104.73 265.77 0 1 0 0 0 0 0 0 0 0 0 302
303 102.58 262.32 0 0 1 0 0 0 0 0 0 0 0 303
304 99.93 270.48 0 0 0 1 0 0 0 0 0 0 0 304
305 101.41 273.03 0 0 0 0 1 0 0 0 0 0 0 305
306 101.05 269.13 0 0 0 0 0 1 0 0 0 0 0 306
307 99.86 280.65 0 0 0 0 0 0 1 0 0 0 0 307
308 101.11 282.75 0 0 0 0 0 0 0 1 0 0 0 308
309 100.89 281.44 0 0 0 0 0 0 0 0 1 0 0 309
310 101.09 281.99 0 0 0 0 0 0 0 0 0 1 0 310
311 98.31 282.86 0 0 0 0 0 0 0 0 0 0 1 311
312 98.08 287.21 0 0 0 0 0 0 0 0 0 0 0 312
313 99.55 283.11 1 0 0 0 0 0 0 0 0 0 0 313
314 99.62 280.66 0 1 0 0 0 0 0 0 0 0 0 314
315 97.37 282.39 0 0 1 0 0 0 0 0 0 0 0 315
316 98.16 280.83 0 0 0 1 0 0 0 0 0 0 0 316
317 97.98 284.71 0 0 0 0 1 0 0 0 0 0 0 317
318 98.15 279.99 0 0 0 0 0 1 0 0 0 0 0 318
319 97.10 283.50 0 0 0 0 0 0 1 0 0 0 0 319
320 97.24 284.88 0 0 0 0 0 0 0 1 0 0 0 320
321 96.70 288.60 0 0 0 0 0 0 0 0 1 0 0 321
322 96.64 284.80 0 0 0 0 0 0 0 0 0 1 0 322
323 100.65 287.20 0 0 0 0 0 0 0 0 0 0 1 323
324 96.75 286.22 0 0 0 0 0 0 0 0 0 0 0 324
325 97.74 286.54 1 0 0 0 0 0 0 0 0 0 0 325
326 97.92 279.58 0 1 0 0 0 0 0 0 0 0 0 326
327 98.34 283.08 0 0 1 0 0 0 0 0 0 0 0 327
328 93.84 288.88 0 0 0 1 0 0 0 0 0 0 0 328
329 97.80 280.18 0 0 0 0 1 0 0 0 0 0 0 329
330 96.20 284.16 0 0 0 0 0 1 0 0 0 0 0 330
331 95.99 290.57 0 0 0 0 0 0 1 0 0 0 0 331
332 95.18 286.82 0 0 0 0 0 0 0 1 0 0 0 332
333 95.95 273.00 0 0 0 0 0 0 0 0 1 0 0 333
334 92.23 278.69 0 0 0 0 0 0 0 0 0 1 0 334
335 91.78 264.54 0 0 0 0 0 0 0 0 0 0 1 335
336 92.97 271.92 0 0 0 0 0 0 0 0 0 0 0 336
337 89.76 283.60 1 0 0 0 0 0 0 0 0 0 0 337
338 92.88 269.25 0 1 0 0 0 0 0 0 0 0 0 338
339 96.23 263.58 0 0 1 0 0 0 0 0 0 0 0 339
340 95.79 264.16 0 0 0 1 0 0 0 0 0 0 0 340
341 93.97 268.85 0 0 0 0 1 0 0 0 0 0 0 341
342 93.90 269.67 0 0 0 0 0 1 0 0 0 0 0 342
343 93.60 249.41 0 0 0 0 0 0 1 0 0 0 0 343
344 93.96 268.99 0 0 0 0 0 0 0 1 0 0 0 344
345 88.69 268.65 0 0 0 0 0 0 0 0 1 0 0 345
346 88.57 260.16 0 0 0 0 0 0 0 0 0 1 0 346
347 85.62 256.55 0 0 0 0 0 0 0 0 0 0 1 347
348 86.25 251.47 0 0 0 0 0 0 0 0 0 0 0 348
349 85.33 234.93 1 0 0 0 0 0 0 0 0 0 0 349
350 83.33 232.96 0 1 0 0 0 0 0 0 0 0 0 350
351 77.78 215.49 0 0 1 0 0 0 0 0 0 0 0 351
352 78.70 213.68 0 0 0 1 0 0 0 0 0 0 0 352
353 72.05 236.07 0 0 0 0 1 0 0 0 0 0 0 353
354 80.75 235.41 0 0 0 0 0 1 0 0 0 0 0 354
355 81.41 214.77 0 0 0 0 0 0 1 0 0 0 0 355
356 82.65 225.85 0 0 0 0 0 0 0 1 0 0 0 356
357 75.85 224.64 0 0 0 0 0 0 0 0 1 0 0 357
358 75.70 238.26 0 0 0 0 0 0 0 0 0 1 0 358
359 78.25 232.44 0 0 0 0 0 0 0 0 0 0 1 359
360 77.41 222.50 0 0 0 0 0 0 0 0 0 0 0 360
361 76.84 225.28 1 0 0 0 0 0 0 0 0 0 0 361
362 74.25 220.49 0 1 0 0 0 0 0 0 0 0 0 362
363 74.95 216.86 0 0 1 0 0 0 0 0 0 0 0 363
364 68.78 234.70 0 0 0 1 0 0 0 0 0 0 0 364
365 73.21 230.06 0 0 0 0 1 0 0 0 0 0 0 365
366 73.26 238.27 0 0 0 0 0 1 0 0 0 0 0 366
367 78.67 238.56 0 0 0 0 0 0 1 0 0 0 0 367
368 75.63 242.70 0 0 0 0 0 0 0 1 0 0 0 368
369 74.99 249.14 0 0 0 0 0 0 0 0 1 0 0 369
370 83.87 234.89 0 0 0 0 0 0 0 0 0 1 0 370
371 79.62 227.78 0 0 0 0 0 0 0 0 0 0 1 371
372 80.13 234.04 0 0 0 0 0 0 0 0 0 0 0 372
373 79.76 230.70 1 0 0 0 0 0 0 0 0 0 0 373
374 78.20 230.17 0 1 0 0 0 0 0 0 0 0 0 374
375 78.05 218.23 0 0 1 0 0 0 0 0 0 0 0 375
376 79.05 232.20 0 0 0 1 0 0 0 0 0 0 0 376
377 73.32 220.76 0 0 0 0 1 0 0 0 0 0 0 377
378 75.17 215.60 0 0 0 0 0 1 0 0 0 0 0 378
379 73.26 217.69 0 0 0 0 0 0 1 0 0 0 0 379
380 73.72 204.35 0 0 0 0 0 0 0 1 0 0 0 380
381 73.57 191.44 0 0 0 0 0 0 0 0 1 0 0 381
382 70.60 203.84 0 0 0 0 0 0 0 0 0 1 0 382
383 71.25 211.86 0 0 0 0 0 0 0 0 0 0 1 383
384 74.22 210.57 0 0 0 0 0 0 0 0 0 0 0 384
385 73.32 219.57 1 0 0 0 0 0 0 0 0 0 0 385
386 73.01 219.98 0 1 0 0 0 0 0 0 0 0 0 386
387 74.21 226.01 0 0 1 0 0 0 0 0 0 0 0 387
388 75.32 207.04 0 0 0 1 0 0 0 0 0 0 0 388
389 71.73 212.52 0 0 0 0 1 0 0 0 0 0 0 389
390 71.94 217.92 0 0 0 0 0 1 0 0 0 0 0 390
391 72.94 210.45 0 0 0 0 0 0 1 0 0 0 0 391
392 72.47 218.53 0 0 0 0 0 0 0 1 0 0 0 392
393 71.94 223.32 0 0 0 0 0 0 0 0 1 0 0 393
394 74.30 218.76 0 0 0 0 0 0 0 0 0 1 0 394
395 74.30 217.63 0 0 0 0 0 0 0 0 0 0 1 395
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) VS M1 M2 M3 M4
31.67130 0.26799 -0.37909 0.24989 0.30162 -0.47515
M5 M6 M7 M8 M9 M10
-0.37339 0.17138 0.88192 1.01964 0.65710 0.93399
M11 t
0.59231 -0.02044
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.8729 -4.0362 -0.8256 4.9883 15.4913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.671302 8.356780 3.790 0.000175 ***
VS 0.267986 0.022419 11.954 < 2e-16 ***
M1 -0.379093 1.530671 -0.248 0.804527
M2 0.249893 1.530862 0.163 0.870418
M3 0.301616 1.530790 0.197 0.843907
M4 -0.475153 1.530512 -0.310 0.756386
M5 -0.373389 1.530496 -0.244 0.807389
M6 0.171380 1.530506 0.112 0.910901
M7 0.881920 1.530691 0.576 0.564849
M8 1.019641 1.530811 0.666 0.505764
M9 0.657105 1.530846 0.429 0.667989
M10 0.933986 1.531027 0.610 0.542200
M11 0.592307 1.530864 0.387 0.699038
t -0.020438 0.008208 -2.490 0.013193 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.169 on 381 degrees of freedom
Multiple R-squared: 0.8304, Adjusted R-squared: 0.8246
F-statistic: 143.5 on 13 and 381 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,] 3.815131e-05 7.630263e-05 0.9999618487
[2,] 4.432551e-05 8.865101e-05 0.9999556745
[3,] 1.263333e-05 2.526666e-05 0.9999873667
[4,] 1.740242e-05 3.480483e-05 0.9999825976
[5,] 7.115912e-06 1.423182e-05 0.9999928841
[6,] 1.251118e-06 2.502235e-06 0.9999987489
[7,] 3.193930e-07 6.387859e-07 0.9999996806
[8,] 1.478256e-07 2.956511e-07 0.9999998522
[9,] 5.712755e-07 1.142551e-06 0.9999994287
[10,] 5.281805e-07 1.056361e-06 0.9999994718
[11,] 2.915794e-07 5.831587e-07 0.9999997084
[12,] 6.306377e-08 1.261275e-07 0.9999999369
[13,] 1.363716e-08 2.727431e-08 0.9999999864
[14,] 2.719760e-09 5.439520e-09 0.9999999973
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[324,] 7.513045e-01 4.973911e-01 0.2486955481
[325,] 8.488159e-01 3.023682e-01 0.1511841129
[326,] 8.946282e-01 2.107436e-01 0.1053717915
[327,] 9.426998e-01 1.146003e-01 0.0573001581
[328,] 9.705352e-01 5.892953e-02 0.0294647668
[329,] 9.807478e-01 3.850436e-02 0.0192521787
[330,] 9.864049e-01 2.719016e-02 0.0135950776
[331,] 9.877557e-01 2.448861e-02 0.0122443057
[332,] 9.892223e-01 2.155549e-02 0.0107777470
[333,] 9.915914e-01 1.681728e-02 0.0084086381
[334,] 9.936269e-01 1.274616e-02 0.0063730800
[335,] 9.929250e-01 1.415007e-02 0.0070750334
[336,] 9.919953e-01 1.600947e-02 0.0080047374
[337,] 9.950896e-01 9.820855e-03 0.0049104276
[338,] 9.955154e-01 8.969183e-03 0.0044845917
[339,] 9.959934e-01 8.013101e-03 0.0040065506
[340,] 9.981373e-01 3.725345e-03 0.0018626724
[341,] 9.976142e-01 4.771530e-03 0.0023857648
[342,] 9.977994e-01 4.401101e-03 0.0022005503
[343,] 9.968385e-01 6.323099e-03 0.0031615495
[344,] 9.952765e-01 9.446950e-03 0.0047234750
[345,] 9.930105e-01 1.397895e-02 0.0069894745
[346,] 9.909298e-01 1.814042e-02 0.0090702122
[347,] 9.878990e-01 2.420201e-02 0.0121010063
[348,] 9.995827e-01 8.346891e-04 0.0004173446
[349,] 9.994756e-01 1.048778e-03 0.0005243890
[350,] 9.996644e-01 6.711582e-04 0.0003355791
[351,] 9.992859e-01 1.428182e-03 0.0007140910
[352,] 9.991285e-01 1.743062e-03 0.0008715308
[353,] 9.997634e-01 4.732335e-04 0.0002366167
[354,] 9.998191e-01 3.618990e-04 0.0001809495
[355,] 9.996024e-01 7.952764e-04 0.0003976382
[356,] 9.989717e-01 2.056674e-03 0.0010283369
[357,] 9.985495e-01 2.900980e-03 0.0014504900
[358,] 9.974840e-01 5.032063e-03 0.0025160313
[359,] 9.956989e-01 8.602176e-03 0.0043010878
[360,] 9.891541e-01 2.169181e-02 0.0108459056
[361,] 9.695207e-01 6.095860e-02 0.0304792991
[362,] 9.544229e-01 9.115430e-02 0.0455771491
> postscript(file="/var/www/html/rcomp/tmp/1r2741229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lt0e1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3t2p11229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49d7l1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5iva01229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 395
Frequency = 1
1 2 3 4 5 6
-5.00889089 -5.81853734 -5.99510903 -4.39126642 -3.62336553 -2.62909712
7 8 9 10 11 12
-1.97779474 -0.58579398 -1.43956755 -4.43039935 -2.91700364 -4.36622097
13 14 15 16 17 18
-5.88082736 -6.33559829 -6.09085682 -3.72025046 -2.63268938 -3.79326164
19 20 21 22 23 24
-2.92787538 -3.32898762 -3.26383664 -3.98274536 -4.95480022 -2.22851098
25 26 27 28 29 30
-1.55049104 -1.49389527 -1.41086278 -1.00876401 -1.16284221 -1.87848542
31 32 33 34 35 36
-0.77908746 1.18911571 -3.20237381 -4.06915906 -1.46729309 -0.84291074
37 38 39 40 41 42
-0.80487213 -2.83376181 -1.32394690 -0.82107516 2.40265593 1.24382752
43 44 45 46 47 48
1.55912540 0.34019015 0.72711983 0.87405999 1.33980946 -1.94269765
49 50 51 52 53 54
1.42827720 1.29050031 -1.42526356 -2.04300421 -0.36607172 1.52808715
55 56 57 58 59 60
-3.26451511 -2.04362961 0.06394981 -2.74206248 -4.00314894 -10.34250818
61 62 63 64 65 66
-7.15285145 -6.05327087 -7.21773776 -4.40545975 -4.24694647 -1.61862903
67 68 69 70 71 72
-3.01607196 -6.68072789 -6.51845715 -3.83713063 -4.98931369 -3.85284272
73 74 75 76 77 78
-4.45854571 -3.40667614 -1.25761801 -4.84516075 -4.83190014 -6.14018836
79 80 81 82 83 84
-7.98624345 -6.17474803 -8.28783949 -11.97728593 -5.84772020 -4.25303288
85 86 87 88 89 90
-5.22348320 -5.15640076 -3.88398066 -3.93939994 -0.51272136 -1.02755675
91 92 93 94 95 96
-3.23983539 -1.21533648 -0.38322631 -2.08173922 -2.56740996 -2.28887400
97 98 99 100 101 102
-1.46905422 -1.23304971 -2.33352604 -0.41077755 -0.48379401 -2.77864559
103 104 105 106 107 108
-4.34009526 -0.20100712 -0.71258503 -4.31629460 -3.52874529 -2.29270747
109 110 111 112 113 114
-3.93033602 -2.50150974 -0.71266818 -0.07697220 2.19591373 1.73322048
115 116 117 118 119 120
0.79497220 -1.66279674 1.52365369 2.58591624 1.00940281 2.38517052
121 122 123 124 125 126
-1.03551751 -2.38179704 1.57951027 2.76186047 4.23217360 1.68590180
127 128 129 130 131 132
3.54155817 2.43931200 1.70064672 2.92213877 5.84734804 2.88224698
133 134 135 136 137 138
1.76857660 3.31984750 3.81498479 6.19525134 3.69286098 1.33192777
139 140 141 142 143 144
2.51569343 2.77290706 4.60421187 5.60336140 3.01578376 0.45324180
145 146 147 148 149 150
-0.34855655 -2.91384901 -3.79016927 -4.67260373 -5.72098239 -1.82243219
151 152 153 154 155 156
-2.12564437 -2.69965305 0.11862961 2.06983042 5.00578275 1.15779020
157 158 159 160 161 162
0.71816885 0.06152589 0.39049316 -0.11269313 -0.27044072 1.60884758
163 164 165 166 167 168
0.19324183 -1.99308328 1.55832611 1.18414853 0.03044068 -8.25643908
169 170 171 172 173 174
-5.67626082 -2.75966258 1.86117425 -2.64138592 0.65090381 -0.82556837
175 176 177 178 179 180
-0.77989829 -1.05413974 3.24034781 3.15458943 -2.45878108 -2.16956053
181 182 183 184 185 186
-3.82848358 -3.84703097 -5.52105099 -4.16391365 0.34582194 -0.31001918
187 188 189 190 191 192
-1.02846537 -0.96076754 -2.48072237 0.06921880 -1.74132747 0.60179524
193 194 195 196 197 198
2.45870240 4.97078607 7.17927162 8.07387101 3.38080102 2.26240576
199 200 201 202 203 204
3.01239744 6.78292199 7.16589598 6.90945302 7.27157043 6.57503543
205 206 207 208 209 210
4.27893663 6.98059210 0.44935651 1.53894095 3.48162155 2.72196664
211 212 213 214 215 216
4.32107336 6.27496235 5.67732643 8.26038060 1.16952443 5.06142483
217 218 219 220 221 222
5.78663919 7.12987298 6.65792290 8.39795421 6.95314038 5.17094999
223 224 225 226 227 228
5.07040148 8.44914485 5.60274742 4.38239982 5.98888740 5.60238479
229 230 231 232 233 234
6.47574826 9.72567361 7.83377705 7.04360797 4.75192087 8.64314845
235 236 237 238 239 240
8.50227384 8.53943394 4.83824938 6.83641184 7.40178416 8.65690463
241 242 243 244 245 246
9.59256833 7.28659376 6.91953539 6.89562719 6.89624280 5.97331430
247 248 249 250 251 252
7.34988555 8.19997694 10.22764721 12.25122540 10.38456511 11.75932709
253 254 255 256 257 258
12.72637615 8.69093931 8.98602060 6.87567227 8.40839268 11.07402778
259 260 261 262 263 264
12.88791916 10.66547508 11.71650484 13.19817861 15.49133908 10.73829177
265 266 267 268 269 270
10.20742945 10.15427664 5.12155112 6.51751720 9.94166030 10.34769122
271 272 273 274 275 276
10.12816463 9.21725277 7.85090887 7.20614004 7.13940756 10.28712197
277 278 279 280 281 282
10.49032239 9.65797989 9.60642563 12.32509271 10.20680491 8.39351793
283 284 285 286 287 288
8.27474812 8.03303967 7.47916401 7.68669541 9.38742993 10.82967263
289 290 291 292 293 294
12.00546452 8.31623008 7.81204948 7.87330309 6.11550197 5.21825682
295 296 297 298 299 300
5.95903519 7.99411163 10.88334403 8.87514104 5.44624776 4.65578945
301 302 303 304 305 306
7.90053828 7.75853807 6.50180580 2.46224664 3.17755605 3.33837162
307 308 309 310 311 312
-1.62892940 -1.05898238 -0.54494666 -0.74878195 -3.39981241 -4.18280620
313 314 315 316 317 318
-1.21453235 -1.09651389 -3.84141394 -1.83614863 -3.13726067 -2.22669653
319 320 321 322 323 324
-4.90742924 -5.25453226 -6.40846640 -5.70656234 -1.97761148 -5.00223958
325 326 327 328 329 330
-3.69846410 -2.26182853 -2.81106389 -8.06817596 -1.85802341 -5.04893796
331 332 333 334 335 336
-7.66683023 -7.58916479 -2.73262350 -8.23390712 -4.52978701 -4.70477855
337 338 339 340 341 342
-10.64532467 -4.28827214 0.54992464 0.75169978 -2.40648097 -3.22055958
343 344 345 346 347 348
1.21873628 -3.78571298 -8.58162373 -6.68286506 -8.30331800 -5.69920327
349 350 351 352 353 354
-1.78718287 -3.86779772 -4.76736548 -2.56510366 -15.29663761 -6.94409686
355 356 357 358 359 360
-1.44296629 -3.28953407 -9.38229695 -13.43871000 -8.96691375 -6.53038678
361 362 363 364 365 366
-7.44585700 -9.36075117 -7.71924595 -17.87291015 -12.28078099 -14.95527656
367 368 369 370 371 372
-10.31309416 -14.57983870 -16.56269492 -4.12033657 -6.10283830 -6.65768545
373 374 375 376 377 378
-5.73308101 -7.75959578 -4.74112643 -6.68768458 -9.43325024 -6.72477223
379 380 381 382 383 384
-9.88496473 -5.96731301 -2.27463902 -8.82410908 -9.96123986 -6.03279228
385 386 387 388 389 390
-8.94513577 -9.97355744 -10.42079753 -3.42989497 -8.56978471 -10.33123946
391 392 393 394 395
-8.01948526 -10.77209487 -12.20277409 -8.87720063 -8.21225898
> postscript(file="/var/www/html/rcomp/tmp/6neoq1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 395
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.00889089 NA
1 -5.81853734 -5.00889089
2 -5.99510903 -5.81853734
3 -4.39126642 -5.99510903
4 -3.62336553 -4.39126642
5 -2.62909712 -3.62336553
6 -1.97779474 -2.62909712
7 -0.58579398 -1.97779474
8 -1.43956755 -0.58579398
9 -4.43039935 -1.43956755
10 -2.91700364 -4.43039935
11 -4.36622097 -2.91700364
12 -5.88082736 -4.36622097
13 -6.33559829 -5.88082736
14 -6.09085682 -6.33559829
15 -3.72025046 -6.09085682
16 -2.63268938 -3.72025046
17 -3.79326164 -2.63268938
18 -2.92787538 -3.79326164
19 -3.32898762 -2.92787538
20 -3.26383664 -3.32898762
21 -3.98274536 -3.26383664
22 -4.95480022 -3.98274536
23 -2.22851098 -4.95480022
24 -1.55049104 -2.22851098
25 -1.49389527 -1.55049104
26 -1.41086278 -1.49389527
27 -1.00876401 -1.41086278
28 -1.16284221 -1.00876401
29 -1.87848542 -1.16284221
30 -0.77908746 -1.87848542
31 1.18911571 -0.77908746
32 -3.20237381 1.18911571
33 -4.06915906 -3.20237381
34 -1.46729309 -4.06915906
35 -0.84291074 -1.46729309
36 -0.80487213 -0.84291074
37 -2.83376181 -0.80487213
38 -1.32394690 -2.83376181
39 -0.82107516 -1.32394690
40 2.40265593 -0.82107516
41 1.24382752 2.40265593
42 1.55912540 1.24382752
43 0.34019015 1.55912540
44 0.72711983 0.34019015
45 0.87405999 0.72711983
46 1.33980946 0.87405999
47 -1.94269765 1.33980946
48 1.42827720 -1.94269765
49 1.29050031 1.42827720
50 -1.42526356 1.29050031
51 -2.04300421 -1.42526356
52 -0.36607172 -2.04300421
53 1.52808715 -0.36607172
54 -3.26451511 1.52808715
55 -2.04362961 -3.26451511
56 0.06394981 -2.04362961
57 -2.74206248 0.06394981
58 -4.00314894 -2.74206248
59 -10.34250818 -4.00314894
60 -7.15285145 -10.34250818
61 -6.05327087 -7.15285145
62 -7.21773776 -6.05327087
63 -4.40545975 -7.21773776
64 -4.24694647 -4.40545975
65 -1.61862903 -4.24694647
66 -3.01607196 -1.61862903
67 -6.68072789 -3.01607196
68 -6.51845715 -6.68072789
69 -3.83713063 -6.51845715
70 -4.98931369 -3.83713063
71 -3.85284272 -4.98931369
72 -4.45854571 -3.85284272
73 -3.40667614 -4.45854571
74 -1.25761801 -3.40667614
75 -4.84516075 -1.25761801
76 -4.83190014 -4.84516075
77 -6.14018836 -4.83190014
78 -7.98624345 -6.14018836
79 -6.17474803 -7.98624345
80 -8.28783949 -6.17474803
81 -11.97728593 -8.28783949
82 -5.84772020 -11.97728593
83 -4.25303288 -5.84772020
84 -5.22348320 -4.25303288
85 -5.15640076 -5.22348320
86 -3.88398066 -5.15640076
87 -3.93939994 -3.88398066
88 -0.51272136 -3.93939994
89 -1.02755675 -0.51272136
90 -3.23983539 -1.02755675
91 -1.21533648 -3.23983539
92 -0.38322631 -1.21533648
93 -2.08173922 -0.38322631
94 -2.56740996 -2.08173922
95 -2.28887400 -2.56740996
96 -1.46905422 -2.28887400
97 -1.23304971 -1.46905422
98 -2.33352604 -1.23304971
99 -0.41077755 -2.33352604
100 -0.48379401 -0.41077755
101 -2.77864559 -0.48379401
102 -4.34009526 -2.77864559
103 -0.20100712 -4.34009526
104 -0.71258503 -0.20100712
105 -4.31629460 -0.71258503
106 -3.52874529 -4.31629460
107 -2.29270747 -3.52874529
108 -3.93033602 -2.29270747
109 -2.50150974 -3.93033602
110 -0.71266818 -2.50150974
111 -0.07697220 -0.71266818
112 2.19591373 -0.07697220
113 1.73322048 2.19591373
114 0.79497220 1.73322048
115 -1.66279674 0.79497220
116 1.52365369 -1.66279674
117 2.58591624 1.52365369
118 1.00940281 2.58591624
119 2.38517052 1.00940281
120 -1.03551751 2.38517052
121 -2.38179704 -1.03551751
122 1.57951027 -2.38179704
123 2.76186047 1.57951027
124 4.23217360 2.76186047
125 1.68590180 4.23217360
126 3.54155817 1.68590180
127 2.43931200 3.54155817
128 1.70064672 2.43931200
129 2.92213877 1.70064672
130 5.84734804 2.92213877
131 2.88224698 5.84734804
132 1.76857660 2.88224698
133 3.31984750 1.76857660
134 3.81498479 3.31984750
135 6.19525134 3.81498479
136 3.69286098 6.19525134
137 1.33192777 3.69286098
138 2.51569343 1.33192777
139 2.77290706 2.51569343
140 4.60421187 2.77290706
141 5.60336140 4.60421187
142 3.01578376 5.60336140
143 0.45324180 3.01578376
144 -0.34855655 0.45324180
145 -2.91384901 -0.34855655
146 -3.79016927 -2.91384901
147 -4.67260373 -3.79016927
148 -5.72098239 -4.67260373
149 -1.82243219 -5.72098239
150 -2.12564437 -1.82243219
151 -2.69965305 -2.12564437
152 0.11862961 -2.69965305
153 2.06983042 0.11862961
154 5.00578275 2.06983042
155 1.15779020 5.00578275
156 0.71816885 1.15779020
157 0.06152589 0.71816885
158 0.39049316 0.06152589
159 -0.11269313 0.39049316
160 -0.27044072 -0.11269313
161 1.60884758 -0.27044072
162 0.19324183 1.60884758
163 -1.99308328 0.19324183
164 1.55832611 -1.99308328
165 1.18414853 1.55832611
166 0.03044068 1.18414853
167 -8.25643908 0.03044068
168 -5.67626082 -8.25643908
169 -2.75966258 -5.67626082
170 1.86117425 -2.75966258
171 -2.64138592 1.86117425
172 0.65090381 -2.64138592
173 -0.82556837 0.65090381
174 -0.77989829 -0.82556837
175 -1.05413974 -0.77989829
176 3.24034781 -1.05413974
177 3.15458943 3.24034781
178 -2.45878108 3.15458943
179 -2.16956053 -2.45878108
180 -3.82848358 -2.16956053
181 -3.84703097 -3.82848358
182 -5.52105099 -3.84703097
183 -4.16391365 -5.52105099
184 0.34582194 -4.16391365
185 -0.31001918 0.34582194
186 -1.02846537 -0.31001918
187 -0.96076754 -1.02846537
188 -2.48072237 -0.96076754
189 0.06921880 -2.48072237
190 -1.74132747 0.06921880
191 0.60179524 -1.74132747
192 2.45870240 0.60179524
193 4.97078607 2.45870240
194 7.17927162 4.97078607
195 8.07387101 7.17927162
196 3.38080102 8.07387101
197 2.26240576 3.38080102
198 3.01239744 2.26240576
199 6.78292199 3.01239744
200 7.16589598 6.78292199
201 6.90945302 7.16589598
202 7.27157043 6.90945302
203 6.57503543 7.27157043
204 4.27893663 6.57503543
205 6.98059210 4.27893663
206 0.44935651 6.98059210
207 1.53894095 0.44935651
208 3.48162155 1.53894095
209 2.72196664 3.48162155
210 4.32107336 2.72196664
211 6.27496235 4.32107336
212 5.67732643 6.27496235
213 8.26038060 5.67732643
214 1.16952443 8.26038060
215 5.06142483 1.16952443
216 5.78663919 5.06142483
217 7.12987298 5.78663919
218 6.65792290 7.12987298
219 8.39795421 6.65792290
220 6.95314038 8.39795421
221 5.17094999 6.95314038
222 5.07040148 5.17094999
223 8.44914485 5.07040148
224 5.60274742 8.44914485
225 4.38239982 5.60274742
226 5.98888740 4.38239982
227 5.60238479 5.98888740
228 6.47574826 5.60238479
229 9.72567361 6.47574826
230 7.83377705 9.72567361
231 7.04360797 7.83377705
232 4.75192087 7.04360797
233 8.64314845 4.75192087
234 8.50227384 8.64314845
235 8.53943394 8.50227384
236 4.83824938 8.53943394
237 6.83641184 4.83824938
238 7.40178416 6.83641184
239 8.65690463 7.40178416
240 9.59256833 8.65690463
241 7.28659376 9.59256833
242 6.91953539 7.28659376
243 6.89562719 6.91953539
244 6.89624280 6.89562719
245 5.97331430 6.89624280
246 7.34988555 5.97331430
247 8.19997694 7.34988555
248 10.22764721 8.19997694
249 12.25122540 10.22764721
250 10.38456511 12.25122540
251 11.75932709 10.38456511
252 12.72637615 11.75932709
253 8.69093931 12.72637615
254 8.98602060 8.69093931
255 6.87567227 8.98602060
256 8.40839268 6.87567227
257 11.07402778 8.40839268
258 12.88791916 11.07402778
259 10.66547508 12.88791916
260 11.71650484 10.66547508
261 13.19817861 11.71650484
262 15.49133908 13.19817861
263 10.73829177 15.49133908
264 10.20742945 10.73829177
265 10.15427664 10.20742945
266 5.12155112 10.15427664
267 6.51751720 5.12155112
268 9.94166030 6.51751720
269 10.34769122 9.94166030
270 10.12816463 10.34769122
271 9.21725277 10.12816463
272 7.85090887 9.21725277
273 7.20614004 7.85090887
274 7.13940756 7.20614004
275 10.28712197 7.13940756
276 10.49032239 10.28712197
277 9.65797989 10.49032239
278 9.60642563 9.65797989
279 12.32509271 9.60642563
280 10.20680491 12.32509271
281 8.39351793 10.20680491
282 8.27474812 8.39351793
283 8.03303967 8.27474812
284 7.47916401 8.03303967
285 7.68669541 7.47916401
286 9.38742993 7.68669541
287 10.82967263 9.38742993
288 12.00546452 10.82967263
289 8.31623008 12.00546452
290 7.81204948 8.31623008
291 7.87330309 7.81204948
292 6.11550197 7.87330309
293 5.21825682 6.11550197
294 5.95903519 5.21825682
295 7.99411163 5.95903519
296 10.88334403 7.99411163
297 8.87514104 10.88334403
298 5.44624776 8.87514104
299 4.65578945 5.44624776
300 7.90053828 4.65578945
301 7.75853807 7.90053828
302 6.50180580 7.75853807
303 2.46224664 6.50180580
304 3.17755605 2.46224664
305 3.33837162 3.17755605
306 -1.62892940 3.33837162
307 -1.05898238 -1.62892940
308 -0.54494666 -1.05898238
309 -0.74878195 -0.54494666
310 -3.39981241 -0.74878195
311 -4.18280620 -3.39981241
312 -1.21453235 -4.18280620
313 -1.09651389 -1.21453235
314 -3.84141394 -1.09651389
315 -1.83614863 -3.84141394
316 -3.13726067 -1.83614863
317 -2.22669653 -3.13726067
318 -4.90742924 -2.22669653
319 -5.25453226 -4.90742924
320 -6.40846640 -5.25453226
321 -5.70656234 -6.40846640
322 -1.97761148 -5.70656234
323 -5.00223958 -1.97761148
324 -3.69846410 -5.00223958
325 -2.26182853 -3.69846410
326 -2.81106389 -2.26182853
327 -8.06817596 -2.81106389
328 -1.85802341 -8.06817596
329 -5.04893796 -1.85802341
330 -7.66683023 -5.04893796
331 -7.58916479 -7.66683023
332 -2.73262350 -7.58916479
333 -8.23390712 -2.73262350
334 -4.52978701 -8.23390712
335 -4.70477855 -4.52978701
336 -10.64532467 -4.70477855
337 -4.28827214 -10.64532467
338 0.54992464 -4.28827214
339 0.75169978 0.54992464
340 -2.40648097 0.75169978
341 -3.22055958 -2.40648097
342 1.21873628 -3.22055958
343 -3.78571298 1.21873628
344 -8.58162373 -3.78571298
345 -6.68286506 -8.58162373
346 -8.30331800 -6.68286506
347 -5.69920327 -8.30331800
348 -1.78718287 -5.69920327
349 -3.86779772 -1.78718287
350 -4.76736548 -3.86779772
351 -2.56510366 -4.76736548
352 -15.29663761 -2.56510366
353 -6.94409686 -15.29663761
354 -1.44296629 -6.94409686
355 -3.28953407 -1.44296629
356 -9.38229695 -3.28953407
357 -13.43871000 -9.38229695
358 -8.96691375 -13.43871000
359 -6.53038678 -8.96691375
360 -7.44585700 -6.53038678
361 -9.36075117 -7.44585700
362 -7.71924595 -9.36075117
363 -17.87291015 -7.71924595
364 -12.28078099 -17.87291015
365 -14.95527656 -12.28078099
366 -10.31309416 -14.95527656
367 -14.57983870 -10.31309416
368 -16.56269492 -14.57983870
369 -4.12033657 -16.56269492
370 -6.10283830 -4.12033657
371 -6.65768545 -6.10283830
372 -5.73308101 -6.65768545
373 -7.75959578 -5.73308101
374 -4.74112643 -7.75959578
375 -6.68768458 -4.74112643
376 -9.43325024 -6.68768458
377 -6.72477223 -9.43325024
378 -9.88496473 -6.72477223
379 -5.96731301 -9.88496473
380 -2.27463902 -5.96731301
381 -8.82410908 -2.27463902
382 -9.96123986 -8.82410908
383 -6.03279228 -9.96123986
384 -8.94513577 -6.03279228
385 -9.97355744 -8.94513577
386 -10.42079753 -9.97355744
387 -3.42989497 -10.42079753
388 -8.56978471 -3.42989497
389 -10.33123946 -8.56978471
390 -8.01948526 -10.33123946
391 -10.77209487 -8.01948526
392 -12.20277409 -10.77209487
393 -8.87720063 -12.20277409
394 -8.21225898 -8.87720063
395 NA -8.21225898
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.81853734 -5.00889089
[2,] -5.99510903 -5.81853734
[3,] -4.39126642 -5.99510903
[4,] -3.62336553 -4.39126642
[5,] -2.62909712 -3.62336553
[6,] -1.97779474 -2.62909712
[7,] -0.58579398 -1.97779474
[8,] -1.43956755 -0.58579398
[9,] -4.43039935 -1.43956755
[10,] -2.91700364 -4.43039935
[11,] -4.36622097 -2.91700364
[12,] -5.88082736 -4.36622097
[13,] -6.33559829 -5.88082736
[14,] -6.09085682 -6.33559829
[15,] -3.72025046 -6.09085682
[16,] -2.63268938 -3.72025046
[17,] -3.79326164 -2.63268938
[18,] -2.92787538 -3.79326164
[19,] -3.32898762 -2.92787538
[20,] -3.26383664 -3.32898762
[21,] -3.98274536 -3.26383664
[22,] -4.95480022 -3.98274536
[23,] -2.22851098 -4.95480022
[24,] -1.55049104 -2.22851098
[25,] -1.49389527 -1.55049104
[26,] -1.41086278 -1.49389527
[27,] -1.00876401 -1.41086278
[28,] -1.16284221 -1.00876401
[29,] -1.87848542 -1.16284221
[30,] -0.77908746 -1.87848542
[31,] 1.18911571 -0.77908746
[32,] -3.20237381 1.18911571
[33,] -4.06915906 -3.20237381
[34,] -1.46729309 -4.06915906
[35,] -0.84291074 -1.46729309
[36,] -0.80487213 -0.84291074
[37,] -2.83376181 -0.80487213
[38,] -1.32394690 -2.83376181
[39,] -0.82107516 -1.32394690
[40,] 2.40265593 -0.82107516
[41,] 1.24382752 2.40265593
[42,] 1.55912540 1.24382752
[43,] 0.34019015 1.55912540
[44,] 0.72711983 0.34019015
[45,] 0.87405999 0.72711983
[46,] 1.33980946 0.87405999
[47,] -1.94269765 1.33980946
[48,] 1.42827720 -1.94269765
[49,] 1.29050031 1.42827720
[50,] -1.42526356 1.29050031
[51,] -2.04300421 -1.42526356
[52,] -0.36607172 -2.04300421
[53,] 1.52808715 -0.36607172
[54,] -3.26451511 1.52808715
[55,] -2.04362961 -3.26451511
[56,] 0.06394981 -2.04362961
[57,] -2.74206248 0.06394981
[58,] -4.00314894 -2.74206248
[59,] -10.34250818 -4.00314894
[60,] -7.15285145 -10.34250818
[61,] -6.05327087 -7.15285145
[62,] -7.21773776 -6.05327087
[63,] -4.40545975 -7.21773776
[64,] -4.24694647 -4.40545975
[65,] -1.61862903 -4.24694647
[66,] -3.01607196 -1.61862903
[67,] -6.68072789 -3.01607196
[68,] -6.51845715 -6.68072789
[69,] -3.83713063 -6.51845715
[70,] -4.98931369 -3.83713063
[71,] -3.85284272 -4.98931369
[72,] -4.45854571 -3.85284272
[73,] -3.40667614 -4.45854571
[74,] -1.25761801 -3.40667614
[75,] -4.84516075 -1.25761801
[76,] -4.83190014 -4.84516075
[77,] -6.14018836 -4.83190014
[78,] -7.98624345 -6.14018836
[79,] -6.17474803 -7.98624345
[80,] -8.28783949 -6.17474803
[81,] -11.97728593 -8.28783949
[82,] -5.84772020 -11.97728593
[83,] -4.25303288 -5.84772020
[84,] -5.22348320 -4.25303288
[85,] -5.15640076 -5.22348320
[86,] -3.88398066 -5.15640076
[87,] -3.93939994 -3.88398066
[88,] -0.51272136 -3.93939994
[89,] -1.02755675 -0.51272136
[90,] -3.23983539 -1.02755675
[91,] -1.21533648 -3.23983539
[92,] -0.38322631 -1.21533648
[93,] -2.08173922 -0.38322631
[94,] -2.56740996 -2.08173922
[95,] -2.28887400 -2.56740996
[96,] -1.46905422 -2.28887400
[97,] -1.23304971 -1.46905422
[98,] -2.33352604 -1.23304971
[99,] -0.41077755 -2.33352604
[100,] -0.48379401 -0.41077755
[101,] -2.77864559 -0.48379401
[102,] -4.34009526 -2.77864559
[103,] -0.20100712 -4.34009526
[104,] -0.71258503 -0.20100712
[105,] -4.31629460 -0.71258503
[106,] -3.52874529 -4.31629460
[107,] -2.29270747 -3.52874529
[108,] -3.93033602 -2.29270747
[109,] -2.50150974 -3.93033602
[110,] -0.71266818 -2.50150974
[111,] -0.07697220 -0.71266818
[112,] 2.19591373 -0.07697220
[113,] 1.73322048 2.19591373
[114,] 0.79497220 1.73322048
[115,] -1.66279674 0.79497220
[116,] 1.52365369 -1.66279674
[117,] 2.58591624 1.52365369
[118,] 1.00940281 2.58591624
[119,] 2.38517052 1.00940281
[120,] -1.03551751 2.38517052
[121,] -2.38179704 -1.03551751
[122,] 1.57951027 -2.38179704
[123,] 2.76186047 1.57951027
[124,] 4.23217360 2.76186047
[125,] 1.68590180 4.23217360
[126,] 3.54155817 1.68590180
[127,] 2.43931200 3.54155817
[128,] 1.70064672 2.43931200
[129,] 2.92213877 1.70064672
[130,] 5.84734804 2.92213877
[131,] 2.88224698 5.84734804
[132,] 1.76857660 2.88224698
[133,] 3.31984750 1.76857660
[134,] 3.81498479 3.31984750
[135,] 6.19525134 3.81498479
[136,] 3.69286098 6.19525134
[137,] 1.33192777 3.69286098
[138,] 2.51569343 1.33192777
[139,] 2.77290706 2.51569343
[140,] 4.60421187 2.77290706
[141,] 5.60336140 4.60421187
[142,] 3.01578376 5.60336140
[143,] 0.45324180 3.01578376
[144,] -0.34855655 0.45324180
[145,] -2.91384901 -0.34855655
[146,] -3.79016927 -2.91384901
[147,] -4.67260373 -3.79016927
[148,] -5.72098239 -4.67260373
[149,] -1.82243219 -5.72098239
[150,] -2.12564437 -1.82243219
[151,] -2.69965305 -2.12564437
[152,] 0.11862961 -2.69965305
[153,] 2.06983042 0.11862961
[154,] 5.00578275 2.06983042
[155,] 1.15779020 5.00578275
[156,] 0.71816885 1.15779020
[157,] 0.06152589 0.71816885
[158,] 0.39049316 0.06152589
[159,] -0.11269313 0.39049316
[160,] -0.27044072 -0.11269313
[161,] 1.60884758 -0.27044072
[162,] 0.19324183 1.60884758
[163,] -1.99308328 0.19324183
[164,] 1.55832611 -1.99308328
[165,] 1.18414853 1.55832611
[166,] 0.03044068 1.18414853
[167,] -8.25643908 0.03044068
[168,] -5.67626082 -8.25643908
[169,] -2.75966258 -5.67626082
[170,] 1.86117425 -2.75966258
[171,] -2.64138592 1.86117425
[172,] 0.65090381 -2.64138592
[173,] -0.82556837 0.65090381
[174,] -0.77989829 -0.82556837
[175,] -1.05413974 -0.77989829
[176,] 3.24034781 -1.05413974
[177,] 3.15458943 3.24034781
[178,] -2.45878108 3.15458943
[179,] -2.16956053 -2.45878108
[180,] -3.82848358 -2.16956053
[181,] -3.84703097 -3.82848358
[182,] -5.52105099 -3.84703097
[183,] -4.16391365 -5.52105099
[184,] 0.34582194 -4.16391365
[185,] -0.31001918 0.34582194
[186,] -1.02846537 -0.31001918
[187,] -0.96076754 -1.02846537
[188,] -2.48072237 -0.96076754
[189,] 0.06921880 -2.48072237
[190,] -1.74132747 0.06921880
[191,] 0.60179524 -1.74132747
[192,] 2.45870240 0.60179524
[193,] 4.97078607 2.45870240
[194,] 7.17927162 4.97078607
[195,] 8.07387101 7.17927162
[196,] 3.38080102 8.07387101
[197,] 2.26240576 3.38080102
[198,] 3.01239744 2.26240576
[199,] 6.78292199 3.01239744
[200,] 7.16589598 6.78292199
[201,] 6.90945302 7.16589598
[202,] 7.27157043 6.90945302
[203,] 6.57503543 7.27157043
[204,] 4.27893663 6.57503543
[205,] 6.98059210 4.27893663
[206,] 0.44935651 6.98059210
[207,] 1.53894095 0.44935651
[208,] 3.48162155 1.53894095
[209,] 2.72196664 3.48162155
[210,] 4.32107336 2.72196664
[211,] 6.27496235 4.32107336
[212,] 5.67732643 6.27496235
[213,] 8.26038060 5.67732643
[214,] 1.16952443 8.26038060
[215,] 5.06142483 1.16952443
[216,] 5.78663919 5.06142483
[217,] 7.12987298 5.78663919
[218,] 6.65792290 7.12987298
[219,] 8.39795421 6.65792290
[220,] 6.95314038 8.39795421
[221,] 5.17094999 6.95314038
[222,] 5.07040148 5.17094999
[223,] 8.44914485 5.07040148
[224,] 5.60274742 8.44914485
[225,] 4.38239982 5.60274742
[226,] 5.98888740 4.38239982
[227,] 5.60238479 5.98888740
[228,] 6.47574826 5.60238479
[229,] 9.72567361 6.47574826
[230,] 7.83377705 9.72567361
[231,] 7.04360797 7.83377705
[232,] 4.75192087 7.04360797
[233,] 8.64314845 4.75192087
[234,] 8.50227384 8.64314845
[235,] 8.53943394 8.50227384
[236,] 4.83824938 8.53943394
[237,] 6.83641184 4.83824938
[238,] 7.40178416 6.83641184
[239,] 8.65690463 7.40178416
[240,] 9.59256833 8.65690463
[241,] 7.28659376 9.59256833
[242,] 6.91953539 7.28659376
[243,] 6.89562719 6.91953539
[244,] 6.89624280 6.89562719
[245,] 5.97331430 6.89624280
[246,] 7.34988555 5.97331430
[247,] 8.19997694 7.34988555
[248,] 10.22764721 8.19997694
[249,] 12.25122540 10.22764721
[250,] 10.38456511 12.25122540
[251,] 11.75932709 10.38456511
[252,] 12.72637615 11.75932709
[253,] 8.69093931 12.72637615
[254,] 8.98602060 8.69093931
[255,] 6.87567227 8.98602060
[256,] 8.40839268 6.87567227
[257,] 11.07402778 8.40839268
[258,] 12.88791916 11.07402778
[259,] 10.66547508 12.88791916
[260,] 11.71650484 10.66547508
[261,] 13.19817861 11.71650484
[262,] 15.49133908 13.19817861
[263,] 10.73829177 15.49133908
[264,] 10.20742945 10.73829177
[265,] 10.15427664 10.20742945
[266,] 5.12155112 10.15427664
[267,] 6.51751720 5.12155112
[268,] 9.94166030 6.51751720
[269,] 10.34769122 9.94166030
[270,] 10.12816463 10.34769122
[271,] 9.21725277 10.12816463
[272,] 7.85090887 9.21725277
[273,] 7.20614004 7.85090887
[274,] 7.13940756 7.20614004
[275,] 10.28712197 7.13940756
[276,] 10.49032239 10.28712197
[277,] 9.65797989 10.49032239
[278,] 9.60642563 9.65797989
[279,] 12.32509271 9.60642563
[280,] 10.20680491 12.32509271
[281,] 8.39351793 10.20680491
[282,] 8.27474812 8.39351793
[283,] 8.03303967 8.27474812
[284,] 7.47916401 8.03303967
[285,] 7.68669541 7.47916401
[286,] 9.38742993 7.68669541
[287,] 10.82967263 9.38742993
[288,] 12.00546452 10.82967263
[289,] 8.31623008 12.00546452
[290,] 7.81204948 8.31623008
[291,] 7.87330309 7.81204948
[292,] 6.11550197 7.87330309
[293,] 5.21825682 6.11550197
[294,] 5.95903519 5.21825682
[295,] 7.99411163 5.95903519
[296,] 10.88334403 7.99411163
[297,] 8.87514104 10.88334403
[298,] 5.44624776 8.87514104
[299,] 4.65578945 5.44624776
[300,] 7.90053828 4.65578945
[301,] 7.75853807 7.90053828
[302,] 6.50180580 7.75853807
[303,] 2.46224664 6.50180580
[304,] 3.17755605 2.46224664
[305,] 3.33837162 3.17755605
[306,] -1.62892940 3.33837162
[307,] -1.05898238 -1.62892940
[308,] -0.54494666 -1.05898238
[309,] -0.74878195 -0.54494666
[310,] -3.39981241 -0.74878195
[311,] -4.18280620 -3.39981241
[312,] -1.21453235 -4.18280620
[313,] -1.09651389 -1.21453235
[314,] -3.84141394 -1.09651389
[315,] -1.83614863 -3.84141394
[316,] -3.13726067 -1.83614863
[317,] -2.22669653 -3.13726067
[318,] -4.90742924 -2.22669653
[319,] -5.25453226 -4.90742924
[320,] -6.40846640 -5.25453226
[321,] -5.70656234 -6.40846640
[322,] -1.97761148 -5.70656234
[323,] -5.00223958 -1.97761148
[324,] -3.69846410 -5.00223958
[325,] -2.26182853 -3.69846410
[326,] -2.81106389 -2.26182853
[327,] -8.06817596 -2.81106389
[328,] -1.85802341 -8.06817596
[329,] -5.04893796 -1.85802341
[330,] -7.66683023 -5.04893796
[331,] -7.58916479 -7.66683023
[332,] -2.73262350 -7.58916479
[333,] -8.23390712 -2.73262350
[334,] -4.52978701 -8.23390712
[335,] -4.70477855 -4.52978701
[336,] -10.64532467 -4.70477855
[337,] -4.28827214 -10.64532467
[338,] 0.54992464 -4.28827214
[339,] 0.75169978 0.54992464
[340,] -2.40648097 0.75169978
[341,] -3.22055958 -2.40648097
[342,] 1.21873628 -3.22055958
[343,] -3.78571298 1.21873628
[344,] -8.58162373 -3.78571298
[345,] -6.68286506 -8.58162373
[346,] -8.30331800 -6.68286506
[347,] -5.69920327 -8.30331800
[348,] -1.78718287 -5.69920327
[349,] -3.86779772 -1.78718287
[350,] -4.76736548 -3.86779772
[351,] -2.56510366 -4.76736548
[352,] -15.29663761 -2.56510366
[353,] -6.94409686 -15.29663761
[354,] -1.44296629 -6.94409686
[355,] -3.28953407 -1.44296629
[356,] -9.38229695 -3.28953407
[357,] -13.43871000 -9.38229695
[358,] -8.96691375 -13.43871000
[359,] -6.53038678 -8.96691375
[360,] -7.44585700 -6.53038678
[361,] -9.36075117 -7.44585700
[362,] -7.71924595 -9.36075117
[363,] -17.87291015 -7.71924595
[364,] -12.28078099 -17.87291015
[365,] -14.95527656 -12.28078099
[366,] -10.31309416 -14.95527656
[367,] -14.57983870 -10.31309416
[368,] -16.56269492 -14.57983870
[369,] -4.12033657 -16.56269492
[370,] -6.10283830 -4.12033657
[371,] -6.65768545 -6.10283830
[372,] -5.73308101 -6.65768545
[373,] -7.75959578 -5.73308101
[374,] -4.74112643 -7.75959578
[375,] -6.68768458 -4.74112643
[376,] -9.43325024 -6.68768458
[377,] -6.72477223 -9.43325024
[378,] -9.88496473 -6.72477223
[379,] -5.96731301 -9.88496473
[380,] -2.27463902 -5.96731301
[381,] -8.82410908 -2.27463902
[382,] -9.96123986 -8.82410908
[383,] -6.03279228 -9.96123986
[384,] -8.94513577 -6.03279228
[385,] -9.97355744 -8.94513577
[386,] -10.42079753 -9.97355744
[387,] -3.42989497 -10.42079753
[388,] -8.56978471 -3.42989497
[389,] -10.33123946 -8.56978471
[390,] -8.01948526 -10.33123946
[391,] -10.77209487 -8.01948526
[392,] -12.20277409 -10.77209487
[393,] -8.87720063 -12.20277409
[394,] -8.21225898 -8.87720063
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.81853734 -5.00889089
2 -5.99510903 -5.81853734
3 -4.39126642 -5.99510903
4 -3.62336553 -4.39126642
5 -2.62909712 -3.62336553
6 -1.97779474 -2.62909712
7 -0.58579398 -1.97779474
8 -1.43956755 -0.58579398
9 -4.43039935 -1.43956755
10 -2.91700364 -4.43039935
11 -4.36622097 -2.91700364
12 -5.88082736 -4.36622097
13 -6.33559829 -5.88082736
14 -6.09085682 -6.33559829
15 -3.72025046 -6.09085682
16 -2.63268938 -3.72025046
17 -3.79326164 -2.63268938
18 -2.92787538 -3.79326164
19 -3.32898762 -2.92787538
20 -3.26383664 -3.32898762
21 -3.98274536 -3.26383664
22 -4.95480022 -3.98274536
23 -2.22851098 -4.95480022
24 -1.55049104 -2.22851098
25 -1.49389527 -1.55049104
26 -1.41086278 -1.49389527
27 -1.00876401 -1.41086278
28 -1.16284221 -1.00876401
29 -1.87848542 -1.16284221
30 -0.77908746 -1.87848542
31 1.18911571 -0.77908746
32 -3.20237381 1.18911571
33 -4.06915906 -3.20237381
34 -1.46729309 -4.06915906
35 -0.84291074 -1.46729309
36 -0.80487213 -0.84291074
37 -2.83376181 -0.80487213
38 -1.32394690 -2.83376181
39 -0.82107516 -1.32394690
40 2.40265593 -0.82107516
41 1.24382752 2.40265593
42 1.55912540 1.24382752
43 0.34019015 1.55912540
44 0.72711983 0.34019015
45 0.87405999 0.72711983
46 1.33980946 0.87405999
47 -1.94269765 1.33980946
48 1.42827720 -1.94269765
49 1.29050031 1.42827720
50 -1.42526356 1.29050031
51 -2.04300421 -1.42526356
52 -0.36607172 -2.04300421
53 1.52808715 -0.36607172
54 -3.26451511 1.52808715
55 -2.04362961 -3.26451511
56 0.06394981 -2.04362961
57 -2.74206248 0.06394981
58 -4.00314894 -2.74206248
59 -10.34250818 -4.00314894
60 -7.15285145 -10.34250818
61 -6.05327087 -7.15285145
62 -7.21773776 -6.05327087
63 -4.40545975 -7.21773776
64 -4.24694647 -4.40545975
65 -1.61862903 -4.24694647
66 -3.01607196 -1.61862903
67 -6.68072789 -3.01607196
68 -6.51845715 -6.68072789
69 -3.83713063 -6.51845715
70 -4.98931369 -3.83713063
71 -3.85284272 -4.98931369
72 -4.45854571 -3.85284272
73 -3.40667614 -4.45854571
74 -1.25761801 -3.40667614
75 -4.84516075 -1.25761801
76 -4.83190014 -4.84516075
77 -6.14018836 -4.83190014
78 -7.98624345 -6.14018836
79 -6.17474803 -7.98624345
80 -8.28783949 -6.17474803
81 -11.97728593 -8.28783949
82 -5.84772020 -11.97728593
83 -4.25303288 -5.84772020
84 -5.22348320 -4.25303288
85 -5.15640076 -5.22348320
86 -3.88398066 -5.15640076
87 -3.93939994 -3.88398066
88 -0.51272136 -3.93939994
89 -1.02755675 -0.51272136
90 -3.23983539 -1.02755675
91 -1.21533648 -3.23983539
92 -0.38322631 -1.21533648
93 -2.08173922 -0.38322631
94 -2.56740996 -2.08173922
95 -2.28887400 -2.56740996
96 -1.46905422 -2.28887400
97 -1.23304971 -1.46905422
98 -2.33352604 -1.23304971
99 -0.41077755 -2.33352604
100 -0.48379401 -0.41077755
101 -2.77864559 -0.48379401
102 -4.34009526 -2.77864559
103 -0.20100712 -4.34009526
104 -0.71258503 -0.20100712
105 -4.31629460 -0.71258503
106 -3.52874529 -4.31629460
107 -2.29270747 -3.52874529
108 -3.93033602 -2.29270747
109 -2.50150974 -3.93033602
110 -0.71266818 -2.50150974
111 -0.07697220 -0.71266818
112 2.19591373 -0.07697220
113 1.73322048 2.19591373
114 0.79497220 1.73322048
115 -1.66279674 0.79497220
116 1.52365369 -1.66279674
117 2.58591624 1.52365369
118 1.00940281 2.58591624
119 2.38517052 1.00940281
120 -1.03551751 2.38517052
121 -2.38179704 -1.03551751
122 1.57951027 -2.38179704
123 2.76186047 1.57951027
124 4.23217360 2.76186047
125 1.68590180 4.23217360
126 3.54155817 1.68590180
127 2.43931200 3.54155817
128 1.70064672 2.43931200
129 2.92213877 1.70064672
130 5.84734804 2.92213877
131 2.88224698 5.84734804
132 1.76857660 2.88224698
133 3.31984750 1.76857660
134 3.81498479 3.31984750
135 6.19525134 3.81498479
136 3.69286098 6.19525134
137 1.33192777 3.69286098
138 2.51569343 1.33192777
139 2.77290706 2.51569343
140 4.60421187 2.77290706
141 5.60336140 4.60421187
142 3.01578376 5.60336140
143 0.45324180 3.01578376
144 -0.34855655 0.45324180
145 -2.91384901 -0.34855655
146 -3.79016927 -2.91384901
147 -4.67260373 -3.79016927
148 -5.72098239 -4.67260373
149 -1.82243219 -5.72098239
150 -2.12564437 -1.82243219
151 -2.69965305 -2.12564437
152 0.11862961 -2.69965305
153 2.06983042 0.11862961
154 5.00578275 2.06983042
155 1.15779020 5.00578275
156 0.71816885 1.15779020
157 0.06152589 0.71816885
158 0.39049316 0.06152589
159 -0.11269313 0.39049316
160 -0.27044072 -0.11269313
161 1.60884758 -0.27044072
162 0.19324183 1.60884758
163 -1.99308328 0.19324183
164 1.55832611 -1.99308328
165 1.18414853 1.55832611
166 0.03044068 1.18414853
167 -8.25643908 0.03044068
168 -5.67626082 -8.25643908
169 -2.75966258 -5.67626082
170 1.86117425 -2.75966258
171 -2.64138592 1.86117425
172 0.65090381 -2.64138592
173 -0.82556837 0.65090381
174 -0.77989829 -0.82556837
175 -1.05413974 -0.77989829
176 3.24034781 -1.05413974
177 3.15458943 3.24034781
178 -2.45878108 3.15458943
179 -2.16956053 -2.45878108
180 -3.82848358 -2.16956053
181 -3.84703097 -3.82848358
182 -5.52105099 -3.84703097
183 -4.16391365 -5.52105099
184 0.34582194 -4.16391365
185 -0.31001918 0.34582194
186 -1.02846537 -0.31001918
187 -0.96076754 -1.02846537
188 -2.48072237 -0.96076754
189 0.06921880 -2.48072237
190 -1.74132747 0.06921880
191 0.60179524 -1.74132747
192 2.45870240 0.60179524
193 4.97078607 2.45870240
194 7.17927162 4.97078607
195 8.07387101 7.17927162
196 3.38080102 8.07387101
197 2.26240576 3.38080102
198 3.01239744 2.26240576
199 6.78292199 3.01239744
200 7.16589598 6.78292199
201 6.90945302 7.16589598
202 7.27157043 6.90945302
203 6.57503543 7.27157043
204 4.27893663 6.57503543
205 6.98059210 4.27893663
206 0.44935651 6.98059210
207 1.53894095 0.44935651
208 3.48162155 1.53894095
209 2.72196664 3.48162155
210 4.32107336 2.72196664
211 6.27496235 4.32107336
212 5.67732643 6.27496235
213 8.26038060 5.67732643
214 1.16952443 8.26038060
215 5.06142483 1.16952443
216 5.78663919 5.06142483
217 7.12987298 5.78663919
218 6.65792290 7.12987298
219 8.39795421 6.65792290
220 6.95314038 8.39795421
221 5.17094999 6.95314038
222 5.07040148 5.17094999
223 8.44914485 5.07040148
224 5.60274742 8.44914485
225 4.38239982 5.60274742
226 5.98888740 4.38239982
227 5.60238479 5.98888740
228 6.47574826 5.60238479
229 9.72567361 6.47574826
230 7.83377705 9.72567361
231 7.04360797 7.83377705
232 4.75192087 7.04360797
233 8.64314845 4.75192087
234 8.50227384 8.64314845
235 8.53943394 8.50227384
236 4.83824938 8.53943394
237 6.83641184 4.83824938
238 7.40178416 6.83641184
239 8.65690463 7.40178416
240 9.59256833 8.65690463
241 7.28659376 9.59256833
242 6.91953539 7.28659376
243 6.89562719 6.91953539
244 6.89624280 6.89562719
245 5.97331430 6.89624280
246 7.34988555 5.97331430
247 8.19997694 7.34988555
248 10.22764721 8.19997694
249 12.25122540 10.22764721
250 10.38456511 12.25122540
251 11.75932709 10.38456511
252 12.72637615 11.75932709
253 8.69093931 12.72637615
254 8.98602060 8.69093931
255 6.87567227 8.98602060
256 8.40839268 6.87567227
257 11.07402778 8.40839268
258 12.88791916 11.07402778
259 10.66547508 12.88791916
260 11.71650484 10.66547508
261 13.19817861 11.71650484
262 15.49133908 13.19817861
263 10.73829177 15.49133908
264 10.20742945 10.73829177
265 10.15427664 10.20742945
266 5.12155112 10.15427664
267 6.51751720 5.12155112
268 9.94166030 6.51751720
269 10.34769122 9.94166030
270 10.12816463 10.34769122
271 9.21725277 10.12816463
272 7.85090887 9.21725277
273 7.20614004 7.85090887
274 7.13940756 7.20614004
275 10.28712197 7.13940756
276 10.49032239 10.28712197
277 9.65797989 10.49032239
278 9.60642563 9.65797989
279 12.32509271 9.60642563
280 10.20680491 12.32509271
281 8.39351793 10.20680491
282 8.27474812 8.39351793
283 8.03303967 8.27474812
284 7.47916401 8.03303967
285 7.68669541 7.47916401
286 9.38742993 7.68669541
287 10.82967263 9.38742993
288 12.00546452 10.82967263
289 8.31623008 12.00546452
290 7.81204948 8.31623008
291 7.87330309 7.81204948
292 6.11550197 7.87330309
293 5.21825682 6.11550197
294 5.95903519 5.21825682
295 7.99411163 5.95903519
296 10.88334403 7.99411163
297 8.87514104 10.88334403
298 5.44624776 8.87514104
299 4.65578945 5.44624776
300 7.90053828 4.65578945
301 7.75853807 7.90053828
302 6.50180580 7.75853807
303 2.46224664 6.50180580
304 3.17755605 2.46224664
305 3.33837162 3.17755605
306 -1.62892940 3.33837162
307 -1.05898238 -1.62892940
308 -0.54494666 -1.05898238
309 -0.74878195 -0.54494666
310 -3.39981241 -0.74878195
311 -4.18280620 -3.39981241
312 -1.21453235 -4.18280620
313 -1.09651389 -1.21453235
314 -3.84141394 -1.09651389
315 -1.83614863 -3.84141394
316 -3.13726067 -1.83614863
317 -2.22669653 -3.13726067
318 -4.90742924 -2.22669653
319 -5.25453226 -4.90742924
320 -6.40846640 -5.25453226
321 -5.70656234 -6.40846640
322 -1.97761148 -5.70656234
323 -5.00223958 -1.97761148
324 -3.69846410 -5.00223958
325 -2.26182853 -3.69846410
326 -2.81106389 -2.26182853
327 -8.06817596 -2.81106389
328 -1.85802341 -8.06817596
329 -5.04893796 -1.85802341
330 -7.66683023 -5.04893796
331 -7.58916479 -7.66683023
332 -2.73262350 -7.58916479
333 -8.23390712 -2.73262350
334 -4.52978701 -8.23390712
335 -4.70477855 -4.52978701
336 -10.64532467 -4.70477855
337 -4.28827214 -10.64532467
338 0.54992464 -4.28827214
339 0.75169978 0.54992464
340 -2.40648097 0.75169978
341 -3.22055958 -2.40648097
342 1.21873628 -3.22055958
343 -3.78571298 1.21873628
344 -8.58162373 -3.78571298
345 -6.68286506 -8.58162373
346 -8.30331800 -6.68286506
347 -5.69920327 -8.30331800
348 -1.78718287 -5.69920327
349 -3.86779772 -1.78718287
350 -4.76736548 -3.86779772
351 -2.56510366 -4.76736548
352 -15.29663761 -2.56510366
353 -6.94409686 -15.29663761
354 -1.44296629 -6.94409686
355 -3.28953407 -1.44296629
356 -9.38229695 -3.28953407
357 -13.43871000 -9.38229695
358 -8.96691375 -13.43871000
359 -6.53038678 -8.96691375
360 -7.44585700 -6.53038678
361 -9.36075117 -7.44585700
362 -7.71924595 -9.36075117
363 -17.87291015 -7.71924595
364 -12.28078099 -17.87291015
365 -14.95527656 -12.28078099
366 -10.31309416 -14.95527656
367 -14.57983870 -10.31309416
368 -16.56269492 -14.57983870
369 -4.12033657 -16.56269492
370 -6.10283830 -4.12033657
371 -6.65768545 -6.10283830
372 -5.73308101 -6.65768545
373 -7.75959578 -5.73308101
374 -4.74112643 -7.75959578
375 -6.68768458 -4.74112643
376 -9.43325024 -6.68768458
377 -6.72477223 -9.43325024
378 -9.88496473 -6.72477223
379 -5.96731301 -9.88496473
380 -2.27463902 -5.96731301
381 -8.82410908 -2.27463902
382 -9.96123986 -8.82410908
383 -6.03279228 -9.96123986
384 -8.94513577 -6.03279228
385 -9.97355744 -8.94513577
386 -10.42079753 -9.97355744
387 -3.42989497 -10.42079753
388 -8.56978471 -3.42989497
389 -10.33123946 -8.56978471
390 -8.01948526 -10.33123946
391 -10.77209487 -8.01948526
392 -12.20277409 -10.77209487
393 -8.87720063 -12.20277409
394 -8.21225898 -8.87720063
> 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/7dmx01229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vfvj1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oxbl1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tz2r1229196322.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11s5p71229196322.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/12m0vz1229196322.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/13rna71229196322.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/1485vd1229196322.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/15raeo1229196322.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/16wuin1229196322.tab")
+ }
>
> system("convert tmp/1r2741229196322.ps tmp/1r2741229196322.png")
> system("convert tmp/2lt0e1229196322.ps tmp/2lt0e1229196322.png")
> system("convert tmp/3t2p11229196322.ps tmp/3t2p11229196322.png")
> system("convert tmp/49d7l1229196322.ps tmp/49d7l1229196322.png")
> system("convert tmp/5iva01229196322.ps tmp/5iva01229196322.png")
> system("convert tmp/6neoq1229196322.ps tmp/6neoq1229196322.png")
> system("convert tmp/7dmx01229196322.ps tmp/7dmx01229196322.png")
> system("convert tmp/8vfvj1229196322.ps tmp/8vfvj1229196322.png")
> system("convert tmp/9oxbl1229196322.ps tmp/9oxbl1229196322.png")
> system("convert tmp/10tz2r1229196322.ps tmp/10tz2r1229196322.png")
>
>
> proc.time()
user system elapsed
12.878 2.279 14.344