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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> x <- array(list(358.59
+ ,122.36
+ ,362.96
+ ,123.33
+ ,362.42
+ ,123.04
+ ,364.97
+ ,124.53
+ ,364.04
+ ,125.13
+ ,361.06
+ ,125.85
+ ,358.48
+ ,126.50
+ ,352.96
+ ,126.53
+ ,359.59
+ ,127.07
+ ,360.39
+ ,124.55
+ ,357.40
+ ,124.90
+ ,362.93
+ ,124.32
+ ,364.55
+ ,122.84
+ ,365.73
+ ,123.31
+ ,364.70
+ ,123.31
+ ,364.65
+ ,124.87
+ ,359.43
+ ,124.64
+ ,362.14
+ ,124.73
+ ,356.97
+ ,124.90
+ ,354.82
+ ,124.04
+ ,353.17
+ ,123.28
+ ,357.06
+ ,123.86
+ ,356.18
+ ,122.29
+ ,355.01
+ ,124.09
+ ,355.65
+ ,124.54
+ ,357.31
+ ,125.65
+ ,357.07
+ ,125.70
+ ,357.91
+ ,125.53
+ ,358.48
+ ,125.61
+ ,358.97
+ ,125.55
+ ,351.77
+ ,125.41
+ ,352.16
+ ,127.60
+ ,359.08
+ ,124.68
+ ,360.35
+ ,124.41
+ ,359.53
+ ,126.43
+ ,359.30
+ ,126.38
+ ,358.41
+ ,125.78
+ ,359.68
+ ,124.70
+ ,355.31
+ ,125.07
+ ,357.08
+ ,125.25
+ ,349.71
+ ,126.58
+ ,354.13
+ ,127.13
+ ,345.49
+ ,125.82
+ ,341.69
+ ,123.70
+ ,344.25
+ ,124.39
+ ,340.17
+ ,123.70
+ ,342.47
+ ,124.42
+ ,344.43
+ ,121.05
+ ,333.23
+ ,121.02
+ ,339.72
+ ,123.23
+ ,342.61
+ ,121.32
+ ,346.36
+ ,120.91
+ ,339.09
+ ,120.72
+ ,339.73
+ ,123.31
+ ,341.12
+ ,119.58
+ ,335.94
+ ,119.53
+ ,333.46
+ ,120.59
+ ,335.66
+ ,118.63
+ ,341.12
+ ,118.47
+ ,342.21
+ ,111.81
+ ,342.62
+ ,114.71
+ ,346.06
+ ,117.34
+ ,344.43
+ ,115.77
+ ,346.65
+ ,118.38
+ ,343.74
+ ,117.84
+ ,335.67
+ ,118.83
+ ,342.75
+ ,120.02
+ ,341.77
+ ,116.21
+ ,345.84
+ ,117.08
+ ,346.52
+ ,120.20
+ ,350.79
+ ,119.83
+ ,345.44
+ ,118.92
+ ,345.87
+ ,118.03
+ ,338.48
+ ,117.71
+ ,337.21
+ ,119.55
+ ,340.81
+ ,116.13
+ ,339.86
+ ,115.97
+ ,342.86
+ ,115.99
+ ,343.33
+ ,114.96
+ ,341.73
+ ,116.46
+ ,351.38
+ ,116.55
+ ,351.13
+ ,113.05
+ ,345.99
+ ,117.44
+ ,347.55
+ ,118.84
+ ,346.02
+ ,117.06
+ ,345.29
+ ,117.54
+ ,347.03
+ ,119.31
+ ,348.01
+ ,118.72
+ ,345.48
+ ,121.55
+ ,349.40
+ ,122.61
+ ,351.05
+ ,121.53
+ ,349.70
+ ,123.31
+ ,350.86
+ ,124.07
+ ,354.45
+ ,123.59
+ ,355.30
+ ,122.97
+ ,357.48
+ ,123.22
+ ,355.24
+ ,123.04
+ ,351.79
+ ,122.96
+ ,355.22
+ ,122.81
+ ,351.02
+ ,122.81
+ ,350.28
+ ,122.62
+ ,350.17
+ ,120.82
+ ,348.16
+ ,119.41
+ ,340.30
+ ,121.56
+ ,343.75
+ ,121.59
+ ,344.71
+ ,118.50
+ ,344.13
+ ,118.77
+ ,342.14
+ ,118.86
+ ,345.04
+ ,117.60
+ ,346.02
+ ,119.90
+ ,346.43
+ ,121.83
+ ,347.07
+ ,121.84
+ ,339.33
+ ,122.12
+ ,339.10
+ ,122.12
+ ,337.19
+ ,121.36
+ ,339.58
+ ,119.66
+ ,327.85
+ ,119.32
+ ,326.81
+ ,120.36
+ ,321.73
+ ,117.06
+ ,320.45
+ ,117.48
+ ,327.69
+ ,115.60
+ ,323.95
+ ,113.86
+ ,320.47
+ ,116.92
+ ,322.13
+ ,117.75
+ ,316.34
+ ,117.75
+ ,314.78
+ ,115.31
+ ,308.90
+ ,116.28
+ ,308.62
+ ,115.22
+ ,314.41
+ ,115.65
+ ,306.88
+ ,115.11
+ ,310.60
+ ,118.67
+ ,321.60
+ ,118.04
+ ,321.50
+ ,116.50
+ ,325.68
+ ,119.78
+ ,324.35
+ ,119.95
+ ,320.01
+ ,120.37
+ ,326.88
+ ,119.79
+ ,332.39
+ ,119.43
+ ,331.48
+ ,121.06
+ ,332.62
+ ,121.74
+ ,324.79
+ ,121.09
+ ,327.12
+ ,122.97
+ ,328.91
+ ,120.50
+ ,328.37
+ ,117.18
+ ,324.83
+ ,115.03
+ ,325.90
+ ,113.36
+ ,326.18
+ ,112.59
+ ,328.94
+ ,111.65
+ ,333.78
+ ,111.98
+ ,328.06
+ ,114.87
+ ,325.87
+ ,114.67
+ ,325.41
+ ,114.09
+ ,318.86
+ ,114.77
+ ,319.13
+ ,117.05
+ ,310.16
+ ,117.22
+ ,311.73
+ ,113.18
+ ,306.54
+ ,110.95
+ ,311.16
+ ,112.14
+ ,311.98
+ ,112.72
+ ,306.72
+ ,110.01
+ ,308.05
+ ,110.29
+ ,300.76
+ ,110.74
+ ,301.90
+ ,110.32
+ ,293.09
+ ,105.89
+ ,292.76
+ ,108.97
+ ,294.58
+ ,109.34
+ ,289.90
+ ,106.57
+ ,296.69
+ ,99.49
+ ,297.21
+ ,101.81
+ ,293.31
+ ,104.29
+ ,296.25
+ ,109.73
+ ,298.60
+ ,105.06
+ ,296.87
+ ,107.97
+ ,301.02
+ ,108.13
+ ,304.73
+ ,109.86
+ ,301.92
+ ,108.95
+ ,295.72
+ ,111.20
+ ,293.18
+ ,110.69
+ ,298.35
+ ,106.10
+ ,297.99
+ ,105.68
+ ,299.85
+ ,104.12
+ ,299.85
+ ,104.71
+ ,304.45
+ ,104.30
+ ,299.45
+ ,103.52
+ ,298.14
+ ,107.76
+ ,298.78
+ ,107.80
+ ,297.02
+ ,107.30
+ ,301.33
+ ,108.64
+ ,294.96
+ ,105.03
+ ,296.69
+ ,108.30
+ ,300.73
+ ,107.21
+ ,301.96
+ ,109.27
+ ,297.38
+ ,109.50
+ ,293.87
+ ,111.68
+ ,285.96
+ ,111.80
+ ,285.41
+ ,111.75
+ ,283.70
+ ,106.68
+ ,284.76
+ ,106.37
+ ,277.11
+ ,105.76
+ ,274.73
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.73
+ ,109.01
+ ,274.69
+ ,107.69
+ ,275.42
+ ,105.19
+ ,264.15
+ ,105.48
+ ,276.24
+ ,102.22
+ ,268.88
+ ,100.54
+ ,277.97
+ ,105.00
+ ,280.49
+ ,105.44
+ ,281.09
+ ,107.89
+ ,276.16
+ ,108.64
+ ,272.58
+ ,106.70
+ ,270.94
+ ,109.10
+ ,284.31
+ ,105.23
+ ,283.94
+ ,108.41
+ ,284.18
+ ,108.80
+ ,282.83
+ ,110.39
+ ,283.84
+ ,110.22
+ ,282.71
+ ,110.86
+ ,279.29
+ ,108.58
+ ,280.70
+ ,107.70
+ ,274.47
+ ,106.62
+ ,273.44
+ ,109.84
+ ,275.49
+ ,107.16
+ ,279.46
+ ,107.26
+ ,280.19
+ ,108.70
+ ,288.21
+ ,109.85
+ ,284.80
+ ,109.41
+ ,281.41
+ ,112.36
+ ,283.39
+ ,111.03
+ ,287.97
+ ,110.67
+ ,290.77
+ ,109.21
+ ,290.60
+ ,113.58
+ ,289.67
+ ,113.88
+ ,289.84
+ ,114.08
+ ,298.55
+ ,112.33
+ ,296.07
+ ,113.92
+ ,297.14
+ ,114.41
+ ,295.34
+ ,114.57
+ ,296.25
+ ,115.35
+ ,294.30
+ ,113.13
+ ,296.15
+ ,113.29
+ ,296.49
+ ,112.56
+ ,298.05
+ ,113.06
+ ,301.03
+ ,113.46
+ ,300.52
+ ,115.39
+ ,301.50
+ ,116.62
+ ,296.93
+ ,117.04
+ ,289.84
+ ,117.42
+ ,291.44
+ ,115.62
+ ,286.88
+ ,115.16
+ ,286.74
+ ,115.69
+ ,288.93
+ ,112.85
+ ,292.19
+ ,114.05
+ ,295.39
+ ,112.00
+ ,295.86
+ ,113.74
+ ,293.36
+ ,116.26
+ ,292.86
+ ,118.63
+ ,292.73
+ ,116.49
+ ,296.73
+ ,118.23
+ ,285.02
+ ,116.83
+ ,285.24
+ ,118.82
+ ,288.62
+ ,114.36
+ ,283.36
+ ,112.02
+ ,285.84
+ ,113.24
+ ,291.48
+ ,109.75
+ ,291.41
+ ,110.33
+ ,287.77
+ ,112.86
+ ,284.97
+ ,113.04
+ ,286.05
+ ,113.80
+ ,278.19
+ ,110.90
+ ,281.21
+ ,109.96
+ ,277.92
+ ,108.69
+ ,280.08
+ ,108.84
+ ,269.24
+ ,108.47
+ ,268.48
+ ,108.07
+ ,268.83
+ ,107.94
+ ,269.54
+ ,108.11
+ ,262.37
+ ,108.11
+ ,265.12
+ ,106.81
+ ,265.34
+ ,105.58
+ ,263.32
+ ,105.61
+ ,267.18
+ ,106.52
+ ,260.75
+ ,103.86
+ ,261.78
+ ,104.60
+ ,257.27
+ ,104.73
+ ,255.63
+ ,105.12
+ ,251.39
+ ,104.76
+ ,259.49
+ ,103.85
+ ,261.18
+ ,103.83
+ ,261.65
+ ,103.22
+ ,262.01
+ ,101.64
+ ,265.23
+ ,102.13
+ ,268.10
+ ,104.33
+ ,262.27
+ ,104.92
+ ,263.59
+ ,107.78
+ ,257.85
+ ,104.49
+ ,265.69
+ ,102.80
+ ,271.15
+ ,102.86
+ ,266.69
+ ,104.51
+ ,265.77
+ ,104.73
+ ,262.32
+ ,102.58
+ ,270.48
+ ,99.93
+ ,273.03
+ ,101.41
+ ,269.13
+ ,101.05
+ ,280.65
+ ,99.86
+ ,282.75
+ ,101.11
+ ,281.44
+ ,100.89
+ ,281.99
+ ,101.09
+ ,282.86
+ ,98.31
+ ,287.21
+ ,98.08
+ ,283.11
+ ,99.55
+ ,280.66
+ ,99.62
+ ,282.39
+ ,97.37
+ ,280.83
+ ,98.16
+ ,284.71
+ ,97.98
+ ,279.99
+ ,98.15
+ ,283.50
+ ,97.10
+ ,284.88
+ ,97.24
+ ,288.60
+ ,96.70
+ ,284.80
+ ,96.64
+ ,287.20
+ ,100.65
+ ,286.22
+ ,96.75
+ ,286.54
+ ,97.74
+ ,279.58
+ ,97.92
+ ,283.08
+ ,98.34
+ ,288.88
+ ,93.84
+ ,280.18
+ ,97.80
+ ,284.16
+ ,96.20
+ ,290.57
+ ,95.99
+ ,286.82
+ ,95.18
+ ,273.00
+ ,95.95
+ ,278.69
+ ,92.23
+ ,264.54
+ ,91.78
+ ,271.92
+ ,92.97
+ ,283.60
+ ,89.76
+ ,269.25
+ ,92.88
+ ,263.58
+ ,96.23
+ ,264.16
+ ,95.79
+ ,268.85
+ ,93.97
+ ,269.67
+ ,93.90
+ ,249.41
+ ,93.60
+ ,268.99
+ ,93.96
+ ,268.65
+ ,88.69
+ ,260.16
+ ,88.57
+ ,256.55
+ ,85.62
+ ,251.47
+ ,86.25
+ ,234.93
+ ,85.33
+ ,232.96
+ ,83.33
+ ,215.49
+ ,77.78
+ ,213.68
+ ,78.70
+ ,236.07
+ ,72.05
+ ,235.41
+ ,80.75
+ ,214.77
+ ,81.41
+ ,225.85
+ ,82.65
+ ,224.64
+ ,75.85
+ ,238.26
+ ,75.70
+ ,232.44
+ ,78.25
+ ,222.50
+ ,77.41
+ ,225.28
+ ,76.84
+ ,220.49
+ ,74.25
+ ,216.86
+ ,74.95
+ ,234.70
+ ,68.78
+ ,230.06
+ ,73.21
+ ,238.27
+ ,73.26
+ ,238.56
+ ,78.67
+ ,242.70
+ ,75.63
+ ,249.14
+ ,74.99
+ ,234.89
+ ,83.87
+ ,227.78
+ ,79.62
+ ,234.04
+ ,80.13
+ ,230.70
+ ,79.76
+ ,230.17
+ ,78.20
+ ,218.23
+ ,78.05
+ ,232.20
+ ,79.05
+ ,220.76
+ ,73.32
+ ,215.60
+ ,75.17
+ ,217.69
+ ,73.26
+ ,204.35
+ ,73.72
+ ,191.44
+ ,73.57
+ ,203.84
+ ,70.60
+ ,211.86
+ ,71.25
+ ,210.57
+ ,74.22
+ ,219.57
+ ,73.32
+ ,219.98
+ ,73.01
+ ,226.01
+ ,74.21
+ ,207.04
+ ,75.32
+ ,212.52
+ ,71.73
+ ,217.92
+ ,71.94
+ ,210.45
+ ,72.94
+ ,218.53
+ ,72.47
+ ,223.32
+ ,71.94
+ ,218.76
+ ,74.30
+ ,217.63
+ ,74.30)
+ ,dim=c(2
+ ,395)
+ ,dimnames=list(c('Amerika'
+ ,'Japan')
+ ,1:395))
> y <- array(NA,dim=c(2,395),dimnames=list(c('Amerika','Japan'),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 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Amerika Japan M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 358.59 122.36 1 0 0 0 0 0 0 0 0 0 0
2 362.96 123.33 0 1 0 0 0 0 0 0 0 0 0
3 362.42 123.04 0 0 1 0 0 0 0 0 0 0 0
4 364.97 124.53 0 0 0 1 0 0 0 0 0 0 0
5 364.04 125.13 0 0 0 0 1 0 0 0 0 0 0
6 361.06 125.85 0 0 0 0 0 1 0 0 0 0 0
7 358.48 126.50 0 0 0 0 0 0 1 0 0 0 0
8 352.96 126.53 0 0 0 0 0 0 0 1 0 0 0
9 359.59 127.07 0 0 0 0 0 0 0 0 1 0 0
10 360.39 124.55 0 0 0 0 0 0 0 0 0 1 0
11 357.40 124.90 0 0 0 0 0 0 0 0 0 0 1
12 362.93 124.32 0 0 0 0 0 0 0 0 0 0 0
13 364.55 122.84 1 0 0 0 0 0 0 0 0 0 0
14 365.73 123.31 0 1 0 0 0 0 0 0 0 0 0
15 364.70 123.31 0 0 1 0 0 0 0 0 0 0 0
16 364.65 124.87 0 0 0 1 0 0 0 0 0 0 0
17 359.43 124.64 0 0 0 0 1 0 0 0 0 0 0
18 362.14 124.73 0 0 0 0 0 1 0 0 0 0 0
19 356.97 124.90 0 0 0 0 0 0 1 0 0 0 0
20 354.82 124.04 0 0 0 0 0 0 0 1 0 0 0
21 353.17 123.28 0 0 0 0 0 0 0 0 1 0 0
22 357.06 123.86 0 0 0 0 0 0 0 0 0 1 0
23 356.18 122.29 0 0 0 0 0 0 0 0 0 0 1
24 355.01 124.09 0 0 0 0 0 0 0 0 0 0 0
25 355.65 124.54 1 0 0 0 0 0 0 0 0 0 0
26 357.31 125.65 0 1 0 0 0 0 0 0 0 0 0
27 357.07 125.70 0 0 1 0 0 0 0 0 0 0 0
28 357.91 125.53 0 0 0 1 0 0 0 0 0 0 0
29 358.48 125.61 0 0 0 0 1 0 0 0 0 0 0
30 358.97 125.55 0 0 0 0 0 1 0 0 0 0 0
31 351.77 125.41 0 0 0 0 0 0 1 0 0 0 0
32 352.16 127.60 0 0 0 0 0 0 0 1 0 0 0
33 359.08 124.68 0 0 0 0 0 0 0 0 1 0 0
34 360.35 124.41 0 0 0 0 0 0 0 0 0 1 0
35 359.53 126.43 0 0 0 0 0 0 0 0 0 0 1
36 359.30 126.38 0 0 0 0 0 0 0 0 0 0 0
37 358.41 125.78 1 0 0 0 0 0 0 0 0 0 0
38 359.68 124.70 0 1 0 0 0 0 0 0 0 0 0
39 355.31 125.07 0 0 1 0 0 0 0 0 0 0 0
40 357.08 125.25 0 0 0 1 0 0 0 0 0 0 0
41 349.71 126.58 0 0 0 0 1 0 0 0 0 0 0
42 354.13 127.13 0 0 0 0 0 1 0 0 0 0 0
43 345.49 125.82 0 0 0 0 0 0 1 0 0 0 0
44 341.69 123.70 0 0 0 0 0 0 0 1 0 0 0
45 344.25 124.39 0 0 0 0 0 0 0 0 1 0 0
46 340.17 123.70 0 0 0 0 0 0 0 0 0 1 0
47 342.47 124.42 0 0 0 0 0 0 0 0 0 0 1
48 344.43 121.05 0 0 0 0 0 0 0 0 0 0 0
49 333.23 121.02 1 0 0 0 0 0 0 0 0 0 0
50 339.72 123.23 0 1 0 0 0 0 0 0 0 0 0
51 342.61 121.32 0 0 1 0 0 0 0 0 0 0 0
52 346.36 120.91 0 0 0 1 0 0 0 0 0 0 0
53 339.09 120.72 0 0 0 0 1 0 0 0 0 0 0
54 339.73 123.31 0 0 0 0 0 1 0 0 0 0 0
55 341.12 119.58 0 0 0 0 0 0 1 0 0 0 0
56 335.94 119.53 0 0 0 0 0 0 0 1 0 0 0
57 333.46 120.59 0 0 0 0 0 0 0 0 1 0 0
58 335.66 118.63 0 0 0 0 0 0 0 0 0 1 0
59 341.12 118.47 0 0 0 0 0 0 0 0 0 0 1
60 342.21 111.81 0 0 0 0 0 0 0 0 0 0 0
61 342.62 114.71 1 0 0 0 0 0 0 0 0 0 0
62 346.06 117.34 0 1 0 0 0 0 0 0 0 0 0
63 344.43 115.77 0 0 1 0 0 0 0 0 0 0 0
64 346.65 118.38 0 0 0 1 0 0 0 0 0 0 0
65 343.74 117.84 0 0 0 0 1 0 0 0 0 0 0
66 335.67 118.83 0 0 0 0 0 1 0 0 0 0 0
67 342.75 120.02 0 0 0 0 0 0 1 0 0 0 0
68 341.77 116.21 0 0 0 0 0 0 0 1 0 0 0
69 345.84 117.08 0 0 0 0 0 0 0 0 1 0 0
70 346.52 120.20 0 0 0 0 0 0 0 0 0 1 0
71 350.79 119.83 0 0 0 0 0 0 0 0 0 0 1
72 345.44 118.92 0 0 0 0 0 0 0 0 0 0 0
73 345.87 118.03 1 0 0 0 0 0 0 0 0 0 0
74 338.48 117.71 0 1 0 0 0 0 0 0 0 0 0
75 337.21 119.55 0 0 1 0 0 0 0 0 0 0 0
76 340.81 116.13 0 0 0 1 0 0 0 0 0 0 0
77 339.86 115.97 0 0 0 0 1 0 0 0 0 0 0
78 342.86 115.99 0 0 0 0 0 1 0 0 0 0 0
79 343.33 114.96 0 0 0 0 0 0 1 0 0 0 0
80 341.73 116.46 0 0 0 0 0 0 0 1 0 0 0
81 351.38 116.55 0 0 0 0 0 0 0 0 1 0 0
82 351.13 113.05 0 0 0 0 0 0 0 0 0 1 0
83 345.99 117.44 0 0 0 0 0 0 0 0 0 0 1
84 347.55 118.84 0 0 0 0 0 0 0 0 0 0 0
85 346.02 117.06 1 0 0 0 0 0 0 0 0 0 0
86 345.29 117.54 0 1 0 0 0 0 0 0 0 0 0
87 347.03 119.31 0 0 1 0 0 0 0 0 0 0 0
88 348.01 118.72 0 0 0 1 0 0 0 0 0 0 0
89 345.48 121.55 0 0 0 0 1 0 0 0 0 0 0
90 349.40 122.61 0 0 0 0 0 1 0 0 0 0 0
91 351.05 121.53 0 0 0 0 0 0 1 0 0 0 0
92 349.70 123.31 0 0 0 0 0 0 0 1 0 0 0
93 350.86 124.07 0 0 0 0 0 0 0 0 1 0 0
94 354.45 123.59 0 0 0 0 0 0 0 0 0 1 0
95 355.30 122.97 0 0 0 0 0 0 0 0 0 0 1
96 357.48 123.22 0 0 0 0 0 0 0 0 0 0 0
97 355.24 123.04 1 0 0 0 0 0 0 0 0 0 0
98 351.79 122.96 0 1 0 0 0 0 0 0 0 0 0
99 355.22 122.81 0 0 1 0 0 0 0 0 0 0 0
100 351.02 122.81 0 0 0 1 0 0 0 0 0 0 0
101 350.28 122.62 0 0 0 0 1 0 0 0 0 0 0
102 350.17 120.82 0 0 0 0 0 1 0 0 0 0 0
103 348.16 119.41 0 0 0 0 0 0 1 0 0 0 0
104 340.30 121.56 0 0 0 0 0 0 0 1 0 0 0
105 343.75 121.59 0 0 0 0 0 0 0 0 1 0 0
106 344.71 118.50 0 0 0 0 0 0 0 0 0 1 0
107 344.13 118.77 0 0 0 0 0 0 0 0 0 0 1
108 342.14 118.86 0 0 0 0 0 0 0 0 0 0 0
109 345.04 117.60 1 0 0 0 0 0 0 0 0 0 0
110 346.02 119.90 0 1 0 0 0 0 0 0 0 0 0
111 346.43 121.83 0 0 1 0 0 0 0 0 0 0 0
112 347.07 121.84 0 0 0 1 0 0 0 0 0 0 0
113 339.33 122.12 0 0 0 0 1 0 0 0 0 0 0
114 339.10 122.12 0 0 0 0 0 1 0 0 0 0 0
115 337.19 121.36 0 0 0 0 0 0 1 0 0 0 0
116 339.58 119.66 0 0 0 0 0 0 0 1 0 0 0
117 327.85 119.32 0 0 0 0 0 0 0 0 1 0 0
118 326.81 120.36 0 0 0 0 0 0 0 0 0 1 0
119 321.73 117.06 0 0 0 0 0 0 0 0 0 0 1
120 320.45 117.48 0 0 0 0 0 0 0 0 0 0 0
121 327.69 115.60 1 0 0 0 0 0 0 0 0 0 0
122 323.95 113.86 0 1 0 0 0 0 0 0 0 0 0
123 320.47 116.92 0 0 1 0 0 0 0 0 0 0 0
124 322.13 117.75 0 0 0 1 0 0 0 0 0 0 0
125 316.34 117.75 0 0 0 0 1 0 0 0 0 0 0
126 314.78 115.31 0 0 0 0 0 1 0 0 0 0 0
127 308.90 116.28 0 0 0 0 0 0 1 0 0 0 0
128 308.62 115.22 0 0 0 0 0 0 0 1 0 0 0
129 314.41 115.65 0 0 0 0 0 0 0 0 1 0 0
130 306.88 115.11 0 0 0 0 0 0 0 0 0 1 0
131 310.60 118.67 0 0 0 0 0 0 0 0 0 0 1
132 321.60 118.04 0 0 0 0 0 0 0 0 0 0 0
133 321.50 116.50 1 0 0 0 0 0 0 0 0 0 0
134 325.68 119.78 0 1 0 0 0 0 0 0 0 0 0
135 324.35 119.95 0 0 1 0 0 0 0 0 0 0 0
136 320.01 120.37 0 0 0 1 0 0 0 0 0 0 0
137 326.88 119.79 0 0 0 0 1 0 0 0 0 0 0
138 332.39 119.43 0 0 0 0 0 1 0 0 0 0 0
139 331.48 121.06 0 0 0 0 0 0 1 0 0 0 0
140 332.62 121.74 0 0 0 0 0 0 0 1 0 0 0
141 324.79 121.09 0 0 0 0 0 0 0 0 1 0 0
142 327.12 122.97 0 0 0 0 0 0 0 0 0 1 0
143 328.91 120.50 0 0 0 0 0 0 0 0 0 0 1
144 328.37 117.18 0 0 0 0 0 0 0 0 0 0 0
145 324.83 115.03 1 0 0 0 0 0 0 0 0 0 0
146 325.90 113.36 0 1 0 0 0 0 0 0 0 0 0
147 326.18 112.59 0 0 1 0 0 0 0 0 0 0 0
148 328.94 111.65 0 0 0 1 0 0 0 0 0 0 0
149 333.78 111.98 0 0 0 0 1 0 0 0 0 0 0
150 328.06 114.87 0 0 0 0 0 1 0 0 0 0 0
151 325.87 114.67 0 0 0 0 0 0 1 0 0 0 0
152 325.41 114.09 0 0 0 0 0 0 0 1 0 0 0
153 318.86 114.77 0 0 0 0 0 0 0 0 1 0 0
154 319.13 117.05 0 0 0 0 0 0 0 0 0 1 0
155 310.16 117.22 0 0 0 0 0 0 0 0 0 0 1
156 311.73 113.18 0 0 0 0 0 0 0 0 0 0 0
157 306.54 110.95 1 0 0 0 0 0 0 0 0 0 0
158 311.16 112.14 0 1 0 0 0 0 0 0 0 0 0
159 311.98 112.72 0 0 1 0 0 0 0 0 0 0 0
160 306.72 110.01 0 0 0 1 0 0 0 0 0 0 0
161 308.05 110.29 0 0 0 0 1 0 0 0 0 0 0
162 300.76 110.74 0 0 0 0 0 1 0 0 0 0 0
163 301.90 110.32 0 0 0 0 0 0 1 0 0 0 0
164 293.09 105.89 0 0 0 0 0 0 0 1 0 0 0
165 292.76 108.97 0 0 0 0 0 0 0 0 1 0 0
166 294.58 109.34 0 0 0 0 0 0 0 0 0 1 0
167 289.90 106.57 0 0 0 0 0 0 0 0 0 0 1
168 296.69 99.49 0 0 0 0 0 0 0 0 0 0 0
169 297.21 101.81 1 0 0 0 0 0 0 0 0 0 0
170 293.31 104.29 0 1 0 0 0 0 0 0 0 0 0
171 296.25 109.73 0 0 1 0 0 0 0 0 0 0 0
172 298.60 105.06 0 0 0 1 0 0 0 0 0 0 0
173 296.87 107.97 0 0 0 0 1 0 0 0 0 0 0
174 301.02 108.13 0 0 0 0 0 1 0 0 0 0 0
175 304.73 109.86 0 0 0 0 0 0 1 0 0 0 0
176 301.92 108.95 0 0 0 0 0 0 0 1 0 0 0
177 295.72 111.20 0 0 0 0 0 0 0 0 1 0 0
178 293.18 110.69 0 0 0 0 0 0 0 0 0 1 0
179 298.35 106.10 0 0 0 0 0 0 0 0 0 0 1
180 297.99 105.68 0 0 0 0 0 0 0 0 0 0 0
181 299.85 104.12 1 0 0 0 0 0 0 0 0 0 0
182 299.85 104.71 0 1 0 0 0 0 0 0 0 0 0
183 304.45 104.30 0 0 1 0 0 0 0 0 0 0 0
184 299.45 103.52 0 0 0 1 0 0 0 0 0 0 0
185 298.14 107.76 0 0 0 0 1 0 0 0 0 0 0
186 298.78 107.80 0 0 0 0 0 1 0 0 0 0 0
187 297.02 107.30 0 0 0 0 0 0 1 0 0 0 0
188 301.33 108.64 0 0 0 0 0 0 0 1 0 0 0
189 294.96 105.03 0 0 0 0 0 0 0 0 1 0 0
190 296.69 108.30 0 0 0 0 0 0 0 0 0 1 0
191 300.73 107.21 0 0 0 0 0 0 0 0 0 0 1
192 301.96 109.27 0 0 0 0 0 0 0 0 0 0 0
193 297.38 109.50 1 0 0 0 0 0 0 0 0 0 0
194 293.87 111.68 0 1 0 0 0 0 0 0 0 0 0
195 285.96 111.80 0 0 1 0 0 0 0 0 0 0 0
196 285.41 111.75 0 0 0 1 0 0 0 0 0 0 0
197 283.70 106.68 0 0 0 0 1 0 0 0 0 0 0
198 284.76 106.37 0 0 0 0 0 1 0 0 0 0 0
199 277.11 105.76 0 0 0 0 0 0 1 0 0 0 0
200 274.73 109.01 0 0 0 0 0 0 0 1 0 0 0
201 274.73 109.01 0 0 0 0 0 0 0 0 1 0 0
202 274.73 109.01 0 0 0 0 0 0 0 0 0 1 0
203 274.73 109.01 0 0 0 0 0 0 0 0 0 0 1
204 274.69 107.69 0 0 0 0 0 0 0 0 0 0 0
205 275.42 105.19 1 0 0 0 0 0 0 0 0 0 0
206 264.15 105.48 0 1 0 0 0 0 0 0 0 0 0
207 276.24 102.22 0 0 1 0 0 0 0 0 0 0 0
208 268.88 100.54 0 0 0 1 0 0 0 0 0 0 0
209 277.97 105.00 0 0 0 0 1 0 0 0 0 0 0
210 280.49 105.44 0 0 0 0 0 1 0 0 0 0 0
211 281.09 107.89 0 0 0 0 0 0 1 0 0 0 0
212 276.16 108.64 0 0 0 0 0 0 0 1 0 0 0
213 272.58 106.70 0 0 0 0 0 0 0 0 1 0 0
214 270.94 109.10 0 0 0 0 0 0 0 0 0 1 0
215 284.31 105.23 0 0 0 0 0 0 0 0 0 0 1
216 283.94 108.41 0 0 0 0 0 0 0 0 0 0 0
217 284.18 108.80 1 0 0 0 0 0 0 0 0 0 0
218 282.83 110.39 0 1 0 0 0 0 0 0 0 0 0
219 283.84 110.22 0 0 1 0 0 0 0 0 0 0 0
220 282.71 110.86 0 0 0 1 0 0 0 0 0 0 0
221 279.29 108.58 0 0 0 0 1 0 0 0 0 0 0
222 280.70 107.70 0 0 0 0 0 1 0 0 0 0 0
223 274.47 106.62 0 0 0 0 0 0 1 0 0 0 0
224 273.44 109.84 0 0 0 0 0 0 0 1 0 0 0
225 275.49 107.16 0 0 0 0 0 0 0 0 1 0 0
226 279.46 107.26 0 0 0 0 0 0 0 0 0 1 0
227 280.19 108.70 0 0 0 0 0 0 0 0 0 0 1
228 288.21 109.85 0 0 0 0 0 0 0 0 0 0 0
229 284.80 109.41 1 0 0 0 0 0 0 0 0 0 0
230 281.41 112.36 0 1 0 0 0 0 0 0 0 0 0
231 283.39 111.03 0 0 1 0 0 0 0 0 0 0 0
232 287.97 110.67 0 0 0 1 0 0 0 0 0 0 0
233 290.77 109.21 0 0 0 0 1 0 0 0 0 0 0
234 290.60 113.58 0 0 0 0 0 1 0 0 0 0 0
235 289.67 113.88 0 0 0 0 0 0 1 0 0 0 0
236 289.84 114.08 0 0 0 0 0 0 0 1 0 0 0
237 298.55 112.33 0 0 0 0 0 0 0 0 1 0 0
238 296.07 113.92 0 0 0 0 0 0 0 0 0 1 0
239 297.14 114.41 0 0 0 0 0 0 0 0 0 0 1
240 295.34 114.57 0 0 0 0 0 0 0 0 0 0 0
241 296.25 115.35 1 0 0 0 0 0 0 0 0 0 0
242 294.30 113.13 0 1 0 0 0 0 0 0 0 0 0
243 296.15 113.29 0 0 1 0 0 0 0 0 0 0 0
244 296.49 112.56 0 0 0 1 0 0 0 0 0 0 0
245 298.05 113.06 0 0 0 0 1 0 0 0 0 0 0
246 301.03 113.46 0 0 0 0 0 1 0 0 0 0 0
247 300.52 115.39 0 0 0 0 0 0 1 0 0 0 0
248 301.50 116.62 0 0 0 0 0 0 0 1 0 0 0
249 296.93 117.04 0 0 0 0 0 0 0 0 1 0 0
250 289.84 117.42 0 0 0 0 0 0 0 0 0 1 0
251 291.44 115.62 0 0 0 0 0 0 0 0 0 0 1
252 286.88 115.16 0 0 0 0 0 0 0 0 0 0 0
253 286.74 115.69 1 0 0 0 0 0 0 0 0 0 0
254 288.93 112.85 0 1 0 0 0 0 0 0 0 0 0
255 292.19 114.05 0 0 1 0 0 0 0 0 0 0 0
256 295.39 112.00 0 0 0 1 0 0 0 0 0 0 0
257 295.86 113.74 0 0 0 0 1 0 0 0 0 0 0
258 293.36 116.26 0 0 0 0 0 1 0 0 0 0 0
259 292.86 118.63 0 0 0 0 0 0 1 0 0 0 0
260 292.73 116.49 0 0 0 0 0 0 0 1 0 0 0
261 296.73 118.23 0 0 0 0 0 0 0 0 1 0 0
262 285.02 116.83 0 0 0 0 0 0 0 0 0 1 0
263 285.24 118.82 0 0 0 0 0 0 0 0 0 0 1
264 288.62 114.36 0 0 0 0 0 0 0 0 0 0 0
265 283.36 112.02 1 0 0 0 0 0 0 0 0 0 0
266 285.84 113.24 0 1 0 0 0 0 0 0 0 0 0
267 291.48 109.75 0 0 1 0 0 0 0 0 0 0 0
268 291.41 110.33 0 0 0 1 0 0 0 0 0 0 0
269 287.77 112.86 0 0 0 0 1 0 0 0 0 0 0
270 284.97 113.04 0 0 0 0 0 1 0 0 0 0 0
271 286.05 113.80 0 0 0 0 0 0 1 0 0 0 0
272 278.19 110.90 0 0 0 0 0 0 0 1 0 0 0
273 281.21 109.96 0 0 0 0 0 0 0 0 1 0 0
274 277.92 108.69 0 0 0 0 0 0 0 0 0 1 0
275 280.08 108.84 0 0 0 0 0 0 0 0 0 0 1
276 269.24 108.47 0 0 0 0 0 0 0 0 0 0 0
277 268.48 108.07 1 0 0 0 0 0 0 0 0 0 0
278 268.83 107.94 0 1 0 0 0 0 0 0 0 0 0
279 269.54 108.11 0 0 1 0 0 0 0 0 0 0 0
280 262.37 108.11 0 0 0 1 0 0 0 0 0 0 0
281 265.12 106.81 0 0 0 0 1 0 0 0 0 0 0
282 265.34 105.58 0 0 0 0 0 1 0 0 0 0 0
283 263.32 105.61 0 0 0 0 0 0 1 0 0 0 0
284 267.18 106.52 0 0 0 0 0 0 0 1 0 0 0
285 260.75 103.86 0 0 0 0 0 0 0 0 1 0 0
286 261.78 104.60 0 0 0 0 0 0 0 0 0 1 0
287 257.27 104.73 0 0 0 0 0 0 0 0 0 0 1
288 255.63 105.12 0 0 0 0 0 0 0 0 0 0 0
289 251.39 104.76 1 0 0 0 0 0 0 0 0 0 0
290 259.49 103.85 0 1 0 0 0 0 0 0 0 0 0
291 261.18 103.83 0 0 1 0 0 0 0 0 0 0 0
292 261.65 103.22 0 0 0 1 0 0 0 0 0 0 0
293 262.01 101.64 0 0 0 0 1 0 0 0 0 0 0
294 265.23 102.13 0 0 0 0 0 1 0 0 0 0 0
295 268.10 104.33 0 0 0 0 0 0 1 0 0 0 0
296 262.27 104.92 0 0 0 0 0 0 0 1 0 0 0
297 263.59 107.78 0 0 0 0 0 0 0 0 1 0 0
298 257.85 104.49 0 0 0 0 0 0 0 0 0 1 0
299 265.69 102.80 0 0 0 0 0 0 0 0 0 0 1
300 271.15 102.86 0 0 0 0 0 0 0 0 0 0 0
301 266.69 104.51 1 0 0 0 0 0 0 0 0 0 0
302 265.77 104.73 0 1 0 0 0 0 0 0 0 0 0
303 262.32 102.58 0 0 1 0 0 0 0 0 0 0 0
304 270.48 99.93 0 0 0 1 0 0 0 0 0 0 0
305 273.03 101.41 0 0 0 0 1 0 0 0 0 0 0
306 269.13 101.05 0 0 0 0 0 1 0 0 0 0 0
307 280.65 99.86 0 0 0 0 0 0 1 0 0 0 0
308 282.75 101.11 0 0 0 0 0 0 0 1 0 0 0
309 281.44 100.89 0 0 0 0 0 0 0 0 1 0 0
310 281.99 101.09 0 0 0 0 0 0 0 0 0 1 0
311 282.86 98.31 0 0 0 0 0 0 0 0 0 0 1
312 287.21 98.08 0 0 0 0 0 0 0 0 0 0 0
313 283.11 99.55 1 0 0 0 0 0 0 0 0 0 0
314 280.66 99.62 0 1 0 0 0 0 0 0 0 0 0
315 282.39 97.37 0 0 1 0 0 0 0 0 0 0 0
316 280.83 98.16 0 0 0 1 0 0 0 0 0 0 0
317 284.71 97.98 0 0 0 0 1 0 0 0 0 0 0
318 279.99 98.15 0 0 0 0 0 1 0 0 0 0 0
319 283.50 97.10 0 0 0 0 0 0 1 0 0 0 0
320 284.88 97.24 0 0 0 0 0 0 0 1 0 0 0
321 288.60 96.70 0 0 0 0 0 0 0 0 1 0 0
322 284.80 96.64 0 0 0 0 0 0 0 0 0 1 0
323 287.20 100.65 0 0 0 0 0 0 0 0 0 0 1
324 286.22 96.75 0 0 0 0 0 0 0 0 0 0 0
325 286.54 97.74 1 0 0 0 0 0 0 0 0 0 0
326 279.58 97.92 0 1 0 0 0 0 0 0 0 0 0
327 283.08 98.34 0 0 1 0 0 0 0 0 0 0 0
328 288.88 93.84 0 0 0 1 0 0 0 0 0 0 0
329 280.18 97.80 0 0 0 0 1 0 0 0 0 0 0
330 284.16 96.20 0 0 0 0 0 1 0 0 0 0 0
331 290.57 95.99 0 0 0 0 0 0 1 0 0 0 0
332 286.82 95.18 0 0 0 0 0 0 0 1 0 0 0
333 273.00 95.95 0 0 0 0 0 0 0 0 1 0 0
334 278.69 92.23 0 0 0 0 0 0 0 0 0 1 0
335 264.54 91.78 0 0 0 0 0 0 0 0 0 0 1
336 271.92 92.97 0 0 0 0 0 0 0 0 0 0 0
337 283.60 89.76 1 0 0 0 0 0 0 0 0 0 0
338 269.25 92.88 0 1 0 0 0 0 0 0 0 0 0
339 263.58 96.23 0 0 1 0 0 0 0 0 0 0 0
340 264.16 95.79 0 0 0 1 0 0 0 0 0 0 0
341 268.85 93.97 0 0 0 0 1 0 0 0 0 0 0
342 269.67 93.90 0 0 0 0 0 1 0 0 0 0 0
343 249.41 93.60 0 0 0 0 0 0 1 0 0 0 0
344 268.99 93.96 0 0 0 0 0 0 0 1 0 0 0
345 268.65 88.69 0 0 0 0 0 0 0 0 1 0 0
346 260.16 88.57 0 0 0 0 0 0 0 0 0 1 0
347 256.55 85.62 0 0 0 0 0 0 0 0 0 0 1
348 251.47 86.25 0 0 0 0 0 0 0 0 0 0 0
349 234.93 85.33 1 0 0 0 0 0 0 0 0 0 0
350 232.96 83.33 0 1 0 0 0 0 0 0 0 0 0
351 215.49 77.78 0 0 1 0 0 0 0 0 0 0 0
352 213.68 78.70 0 0 0 1 0 0 0 0 0 0 0
353 236.07 72.05 0 0 0 0 1 0 0 0 0 0 0
354 235.41 80.75 0 0 0 0 0 1 0 0 0 0 0
355 214.77 81.41 0 0 0 0 0 0 1 0 0 0 0
356 225.85 82.65 0 0 0 0 0 0 0 1 0 0 0
357 224.64 75.85 0 0 0 0 0 0 0 0 1 0 0
358 238.26 75.70 0 0 0 0 0 0 0 0 0 1 0
359 232.44 78.25 0 0 0 0 0 0 0 0 0 0 1
360 222.50 77.41 0 0 0 0 0 0 0 0 0 0 0
361 225.28 76.84 1 0 0 0 0 0 0 0 0 0 0
362 220.49 74.25 0 1 0 0 0 0 0 0 0 0 0
363 216.86 74.95 0 0 1 0 0 0 0 0 0 0 0
364 234.70 68.78 0 0 0 1 0 0 0 0 0 0 0
365 230.06 73.21 0 0 0 0 1 0 0 0 0 0 0
366 238.27 73.26 0 0 0 0 0 1 0 0 0 0 0
367 238.56 78.67 0 0 0 0 0 0 1 0 0 0 0
368 242.70 75.63 0 0 0 0 0 0 0 1 0 0 0
369 249.14 74.99 0 0 0 0 0 0 0 0 1 0 0
370 234.89 83.87 0 0 0 0 0 0 0 0 0 1 0
371 227.78 79.62 0 0 0 0 0 0 0 0 0 0 1
372 234.04 80.13 0 0 0 0 0 0 0 0 0 0 0
373 230.70 79.76 1 0 0 0 0 0 0 0 0 0 0
374 230.17 78.20 0 1 0 0 0 0 0 0 0 0 0
375 218.23 78.05 0 0 1 0 0 0 0 0 0 0 0
376 232.20 79.05 0 0 0 1 0 0 0 0 0 0 0
377 220.76 73.32 0 0 0 0 1 0 0 0 0 0 0
378 215.60 75.17 0 0 0 0 0 1 0 0 0 0 0
379 217.69 73.26 0 0 0 0 0 0 1 0 0 0 0
380 204.35 73.72 0 0 0 0 0 0 0 1 0 0 0
381 191.44 73.57 0 0 0 0 0 0 0 0 1 0 0
382 203.84 70.60 0 0 0 0 0 0 0 0 0 1 0
383 211.86 71.25 0 0 0 0 0 0 0 0 0 0 1
384 210.57 74.22 0 0 0 0 0 0 0 0 0 0 0
385 219.57 73.32 1 0 0 0 0 0 0 0 0 0 0
386 219.98 73.01 0 1 0 0 0 0 0 0 0 0 0
387 226.01 74.21 0 0 1 0 0 0 0 0 0 0 0
388 207.04 75.32 0 0 0 1 0 0 0 0 0 0 0
389 212.52 71.73 0 0 0 0 1 0 0 0 0 0 0
390 217.92 71.94 0 0 0 0 0 1 0 0 0 0 0
391 210.45 72.94 0 0 0 0 0 0 1 0 0 0 0
392 218.53 72.47 0 0 0 0 0 0 0 1 0 0 0
393 223.32 71.94 0 0 0 0 0 0 0 0 1 0 0
394 218.76 74.30 0 0 0 0 0 0 0 0 0 1 0
395 217.63 74.30 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Japan M1 M2 M3 M4
21.3307 2.5809 0.9758 -0.8738 -1.0234 1.2266
M5 M6 M7 M8 M9 M10
0.9381 -0.3271 -2.6749 -3.1686 -2.3020 -3.1665
M11
-2.2281
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-41.295 -15.062 3.183 13.331 41.191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.33066 7.22348 2.953 0.00334 **
Japan 2.58094 0.06027 42.820 < 2e-16 ***
M1 0.97579 4.37157 0.223 0.82349
M2 -0.87384 4.37161 -0.200 0.84167
M3 -1.02335 4.37160 -0.234 0.81504
M4 1.22658 4.37162 0.281 0.77919
M5 0.93810 4.37162 0.215 0.83020
M6 -0.32705 4.37160 -0.075 0.94040
M7 -2.67486 4.37166 -0.612 0.54099
M8 -3.16864 4.37164 -0.725 0.46901
M9 -2.30196 4.37157 -0.527 0.59880
M10 -3.16649 4.37157 -0.724 0.46930
M11 -2.22805 4.37162 -0.510 0.61058
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.62 on 382 degrees of freedom
Multiple R-squared: 0.8278, Adjusted R-squared: 0.8224
F-statistic: 153 on 12 and 382 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,] 1.854207e-03 3.708415e-03 0.9981457925
[2,] 1.725659e-04 3.451319e-04 0.9998274341
[3,] 1.654688e-04 3.309377e-04 0.9998345312
[4,] 2.264898e-05 4.529796e-05 0.9999773510
[5,] 5.192901e-06 1.038580e-05 0.9999948071
[6,] 1.217264e-06 2.434528e-06 0.9999987827
[7,] 2.115411e-07 4.230821e-07 0.9999997885
[8,] 2.996244e-08 5.992489e-08 0.9999999700
[9,] 4.545853e-08 9.091706e-08 0.9999999545
[10,] 6.119855e-08 1.223971e-07 0.9999999388
[11,] 6.110715e-08 1.222143e-07 0.9999999389
[12,] 2.886069e-08 5.772139e-08 0.9999999711
[13,] 1.627823e-08 3.255645e-08 0.9999999837
[14,] 4.034506e-09 8.069011e-09 0.9999999960
[15,] 9.519998e-10 1.904000e-09 0.9999999990
[16,] 4.928459e-10 9.856917e-10 0.9999999995
[17,] 9.892974e-11 1.978595e-10 0.9999999999
[18,] 2.333475e-11 4.666950e-11 1.0000000000
[19,] 5.169340e-12 1.033868e-11 1.0000000000
[20,] 1.684876e-12 3.369752e-12 1.0000000000
[21,] 3.403699e-13 6.807398e-13 1.0000000000
[22,] 6.502740e-14 1.300548e-13 1.0000000000
[23,] 1.457195e-14 2.914390e-14 1.0000000000
[24,] 8.074579e-15 1.614916e-14 1.0000000000
[25,] 3.893586e-15 7.787172e-15 1.0000000000
[26,] 1.415737e-14 2.831475e-14 1.0000000000
[27,] 5.948516e-15 1.189703e-14 1.0000000000
[28,] 1.482557e-14 2.965114e-14 1.0000000000
[29,] 1.205265e-13 2.410529e-13 1.0000000000
[30,] 5.680408e-13 1.136082e-12 1.0000000000
[31,] 1.877913e-11 3.755826e-11 1.0000000000
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[333,] 9.951138e-01 9.772316e-03 0.0048861579
[334,] 9.927141e-01 1.457179e-02 0.0072858941
[335,] 9.893714e-01 2.125724e-02 0.0106286212
[336,] 9.856710e-01 2.865791e-02 0.0143289535
[337,] 9.853982e-01 2.920355e-02 0.0146017760
[338,] 9.867068e-01 2.658646e-02 0.0132932302
[339,] 9.810184e-01 3.796312e-02 0.0189815616
[340,] 9.811348e-01 3.773047e-02 0.0188652363
[341,] 9.768473e-01 4.630535e-02 0.0231526759
[342,] 9.677028e-01 6.459435e-02 0.0322971737
[343,] 9.734911e-01 5.301781e-02 0.0265089038
[344,] 9.646277e-01 7.074469e-02 0.0353723449
[345,] 9.495056e-01 1.009887e-01 0.0504943742
[346,] 9.295461e-01 1.409077e-01 0.0704538694
[347,] 9.044052e-01 1.911896e-01 0.0955947986
[348,] 8.720573e-01 2.558853e-01 0.1279426579
[349,] 9.415725e-01 1.168551e-01 0.0584275286
[350,] 9.282709e-01 1.434582e-01 0.0717291187
[351,] 9.413661e-01 1.172678e-01 0.0586339068
[352,] 9.238116e-01 1.523768e-01 0.0761883765
[353,] 9.485751e-01 1.028498e-01 0.0514249182
[354,] 9.937435e-01 1.251292e-02 0.0062564596
[355,] 9.877977e-01 2.440450e-02 0.0122022516
[356,] 9.770143e-01 4.597136e-02 0.0229856805
[357,] 9.682399e-01 6.352019e-02 0.0317600962
[358,] 9.430803e-01 1.138394e-01 0.0569197171
[359,] 9.036727e-01 1.926546e-01 0.0963273014
[360,] 8.694278e-01 2.611443e-01 0.1305721719
[361,] 8.692147e-01 2.615706e-01 0.1307852785
[362,] 7.925725e-01 4.148551e-01 0.2074275448
[363,] 6.651887e-01 6.696226e-01 0.3348113209
[364,] 5.114721e-01 9.770557e-01 0.4885278520
> postscript(file="/var/www/html/rcomp/tmp/1aoue1228923439.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rbmi1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a3qt1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wesc1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52l4a1228923439.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
20.4799942 24.1961126 24.5540973 21.0085697 18.8184858 15.2453620
7 8 9 10 11 12
13.3355620 8.2319123 12.6015232 20.7700164 15.9382548 20.7371462
13 14 15 16 17 18
25.2011440 27.0177314 26.1372440 19.8110508 15.4731454 19.2160124
19 20 21 22 23 24
15.9550625 16.5184475 15.9632776 19.2208636 21.4545025 13.4107619
25 26 27 28 29 30
11.9135497 12.5583368 12.3388026 11.3676318 12.0196357 13.9296434
31 32 33 34 35 36
9.4387842 4.6703088 18.2599647 21.0913477 14.1194199 11.7904143
37 38 39 40 41 42
11.4731868 17.3802278 12.2047934 11.2602944 0.7461260 5.0117616
43 44 45 46 47 48
2.1005997 4.2659664 4.1784366 2.7438136 2.2471049 10.6768129
49 50 51 52 53 54
-1.4215491 1.2142064 9.1833103 11.7415646 5.2504217 0.4709441
55 56 57 58 59 60
13.8356518 9.2784772 3.1960004 11.3191684 16.2536850 32.3046785
61 62 63 64 65 66
24.2541686 22.7559302 25.3275153 18.5613373 17.3335227 7.9735456
67 68 69 70 71 72
14.3300391 23.6771908 24.6350922 18.1270960 22.4136096 17.1842105
73 74 75 76 77 78
18.9354550 14.2209832 8.3515703 18.5284475 18.2798764 22.4934091
79 80 81 82 83 84
27.9695846 22.9919563 31.5429893 41.1908016 23.7820510 19.5006856
85 86 87 88 89 90
21.5889647 21.4697427 18.7909954 19.0438185 9.4982433 11.9476006
91 92 93 94 95 96
18.7328230 13.2825322 11.6143368 17.3077168 18.8194648 18.1261778
97 98 99 100 101 102
15.3749564 13.9810596 17.9477130 11.4977827 11.5366398 17.3374793
103 104 105 106 107 108
21.3144112 8.3991734 10.9050626 20.7046904 18.4894037 14.0390668
109 110 111 112 113 114
19.2152583 16.1087294 11.6870320 10.0512924 1.8771087 2.9122602
115 116 117 118 119 120
5.3115824 12.5829553 0.8637915 -1.9958540 0.5028074 -4.0892390
121 122 123 124 125 126
7.0271339 9.6275939 -1.6005632 -4.3326718 -9.8341929 -3.8315532
127 128 129 130 131 132
-9.8672533 -6.9176807 -3.1041667 -8.3759304 -14.7825025 -4.3845642
133 134 135 136 137 138
-1.4857101 -3.9215581 -5.5408048 -13.2147290 -4.5593061 3.1449829
139 140 141 142 143 144
0.3758638 0.2546046 -6.7644685 -8.4221018 -1.1956188 4.6050424
145 146 147 148 149 150
5.6382685 12.8680628 15.2848976 18.2210490 22.4978184 10.5840595
151 152 153 154 155 156
11.2580566 12.7887790 3.6170586 -1.1329498 -11.4801427 -1.7112063
157 158 159 160 161 162
-2.1215051 1.2768070 0.7493757 0.2337870 1.1296033 -6.0566673
163 164 165 166 167 168
-1.4848638 1.6324693 -7.5135019 -5.7839191 -4.2531547 18.5818327
169 170 171 172 173 174
12.1382667 3.6871690 -7.2636201 4.8894293 -4.0626209 0.9395805
175 176 177 178 179 180
2.5323676 2.5647995 -10.3089933 -10.6681851 5.4098861 3.9058275
181 182 183 184 185 186
8.8163003 9.1431751 14.9508723 9.7140736 -2.2506239 -0.4487100
187 188 189 190 191 192
1.4295684 2.7748902 4.8553932 -0.9897437 4.9250451 -1.3897393
193 194 195 196 197 198
-7.5391453 -14.8259616 -22.8961615 -25.5670448 -13.9032111 -10.7779689
199 200 201 202 203 204
-14.5057873 -24.7800568 -25.6467394 -24.7822096 -25.7206430 -24.5818576
205 206 207 208 209 210
-18.3753032 -28.5441470 -7.8907770 -13.1647317 -15.2972355 -12.6476967
211 212 213 214 215 216
-16.0231849 -22.3951098 -21.8347730 -28.8044940 -6.3846980 -17.1901328
217 218 219 220 221 222
-18.9324888 -22.5365518 -20.9382797 -25.9700101 -23.2169930 -18.2706162
223 224 225 226 227 228
-19.3653938 -28.2122352 -20.1120044 -15.5355684 -19.4605523 -16.6366833
229 230 231 232 233 234
-19.8868609 -29.0409993 -23.4788393 -20.2196319 -13.3629838 -23.5465307
235 236 237 238 239 240
-22.9030025 -22.7554116 -10.3954530 -16.1146144 -17.2477073 -21.6887099
241 242 243 244 245 246
-23.7676316 -18.1383215 -16.5517588 -16.5776044 -16.0195945 -12.8068182
247 248 249 250 251 252
-15.9502187 -17.6509937 -24.1716703 -31.3778968 -26.0706421 -31.6714632
253 254 255 256 257 258
-34.1551505 -22.7856589 -22.4732716 -16.2322793 -19.9646322 -27.7034441
259 260 261 262 263 264
-31.9724573 -26.0854718 -27.4429863 -34.6751435 -40.5296432 -27.8667129
265 266 267 268 269 270
-28.0631086 -26.8822246 -12.0852389 -15.9021131 -25.7834069 -27.7828243
271 272 273 274 275 276
-26.3165275 -26.1980293 -21.6186304 -20.7663095 -19.9318836 -32.0449891
277 278 279 280 281 282
-32.7484042 -30.2132541 -29.7925008 -39.2124311 -32.8187330 -28.1590280
283 284 285 286 287 288
-27.9086466 -25.9035216 -26.3349096 -26.3502737 -32.1342291 -37.0088473
289 290 291 292 293 294
-41.2954999 -28.9972183 -27.1060869 -27.3116451 -22.5852844 -19.3647925
295 296 297 298 299 300
-19.8250462 -26.6840210 -33.6121859 -29.9963706 -18.7330191 -15.6559278
301 302 303 304 305 306
-25.3502655 -24.9884436 -22.7399146 -9.9903596 -10.9716687 -12.6773796
307 308 309 310 311 312
4.2617459 3.6293521 2.0204758 2.9188181 10.0253918 12.7409550
313 314 315 316 317 318
3.8711862 3.0901487 10.7767715 4.9279004 9.5609481 5.6673401
319 320 321 322 323 324
14.2351344 15.7475816 19.9946053 17.2139915 8.3259973 15.1836024
325 326 327 328 329 330
11.9726837 6.3977430 8.9632618 24.1275519 5.4955169 14.8701689
331 332 333 334 335 336
24.1699754 23.0043135 6.3303087 22.4859273 8.5589159 10.6395474
337 338 339 340 341 342
29.6285676 9.0756697 -5.0909593 -5.6252769 4.0505088 6.3163259
343 344 345 346 347 348
-10.8215832 8.3230577 20.7179174 13.4021598 16.4674930 7.5334496
349 350 351 352 353 354
-7.6078778 -2.5663739 -5.5626563 -11.9970493 27.8446662 5.9956585
355 356 357 358 359 360
-13.9999510 -5.6265354 9.8471592 24.7188298 11.3790048 1.3789401
361 362 363 364 365 366
4.6542845 8.3985416 3.1113978 34.6258540 18.8407783 28.1868828
367 368 369 370 371 372
16.8618187 29.3416482 36.5667658 0.2625676 3.1831200 5.8987892
373 374 375 376 377 378
2.5379460 7.8838372 -3.5195095 5.6196225 9.2568751 0.5872916
379 380 381 382 383 384
9.9546924 -4.0787605 -17.4683025 3.4616127 8.8655697 -2.3178682
385 386 387 388 389 390
8.0291857 11.0889045 14.1712918 -9.9134794 5.1205663 11.2437208
391 392 393 394 395
3.5405925 13.3274118 18.6186262 8.8321427 6.7637093
> postscript(file="/var/www/html/rcomp/tmp/6l0mn1228923439.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 20.4799942 NA
1 24.1961126 20.4799942
2 24.5540973 24.1961126
3 21.0085697 24.5540973
4 18.8184858 21.0085697
5 15.2453620 18.8184858
6 13.3355620 15.2453620
7 8.2319123 13.3355620
8 12.6015232 8.2319123
9 20.7700164 12.6015232
10 15.9382548 20.7700164
11 20.7371462 15.9382548
12 25.2011440 20.7371462
13 27.0177314 25.2011440
14 26.1372440 27.0177314
15 19.8110508 26.1372440
16 15.4731454 19.8110508
17 19.2160124 15.4731454
18 15.9550625 19.2160124
19 16.5184475 15.9550625
20 15.9632776 16.5184475
21 19.2208636 15.9632776
22 21.4545025 19.2208636
23 13.4107619 21.4545025
24 11.9135497 13.4107619
25 12.5583368 11.9135497
26 12.3388026 12.5583368
27 11.3676318 12.3388026
28 12.0196357 11.3676318
29 13.9296434 12.0196357
30 9.4387842 13.9296434
31 4.6703088 9.4387842
32 18.2599647 4.6703088
33 21.0913477 18.2599647
34 14.1194199 21.0913477
35 11.7904143 14.1194199
36 11.4731868 11.7904143
37 17.3802278 11.4731868
38 12.2047934 17.3802278
39 11.2602944 12.2047934
40 0.7461260 11.2602944
41 5.0117616 0.7461260
42 2.1005997 5.0117616
43 4.2659664 2.1005997
44 4.1784366 4.2659664
45 2.7438136 4.1784366
46 2.2471049 2.7438136
47 10.6768129 2.2471049
48 -1.4215491 10.6768129
49 1.2142064 -1.4215491
50 9.1833103 1.2142064
51 11.7415646 9.1833103
52 5.2504217 11.7415646
53 0.4709441 5.2504217
54 13.8356518 0.4709441
55 9.2784772 13.8356518
56 3.1960004 9.2784772
57 11.3191684 3.1960004
58 16.2536850 11.3191684
59 32.3046785 16.2536850
60 24.2541686 32.3046785
61 22.7559302 24.2541686
62 25.3275153 22.7559302
63 18.5613373 25.3275153
64 17.3335227 18.5613373
65 7.9735456 17.3335227
66 14.3300391 7.9735456
67 23.6771908 14.3300391
68 24.6350922 23.6771908
69 18.1270960 24.6350922
70 22.4136096 18.1270960
71 17.1842105 22.4136096
72 18.9354550 17.1842105
73 14.2209832 18.9354550
74 8.3515703 14.2209832
75 18.5284475 8.3515703
76 18.2798764 18.5284475
77 22.4934091 18.2798764
78 27.9695846 22.4934091
79 22.9919563 27.9695846
80 31.5429893 22.9919563
81 41.1908016 31.5429893
82 23.7820510 41.1908016
83 19.5006856 23.7820510
84 21.5889647 19.5006856
85 21.4697427 21.5889647
86 18.7909954 21.4697427
87 19.0438185 18.7909954
88 9.4982433 19.0438185
89 11.9476006 9.4982433
90 18.7328230 11.9476006
91 13.2825322 18.7328230
92 11.6143368 13.2825322
93 17.3077168 11.6143368
94 18.8194648 17.3077168
95 18.1261778 18.8194648
96 15.3749564 18.1261778
97 13.9810596 15.3749564
98 17.9477130 13.9810596
99 11.4977827 17.9477130
100 11.5366398 11.4977827
101 17.3374793 11.5366398
102 21.3144112 17.3374793
103 8.3991734 21.3144112
104 10.9050626 8.3991734
105 20.7046904 10.9050626
106 18.4894037 20.7046904
107 14.0390668 18.4894037
108 19.2152583 14.0390668
109 16.1087294 19.2152583
110 11.6870320 16.1087294
111 10.0512924 11.6870320
112 1.8771087 10.0512924
113 2.9122602 1.8771087
114 5.3115824 2.9122602
115 12.5829553 5.3115824
116 0.8637915 12.5829553
117 -1.9958540 0.8637915
118 0.5028074 -1.9958540
119 -4.0892390 0.5028074
120 7.0271339 -4.0892390
121 9.6275939 7.0271339
122 -1.6005632 9.6275939
123 -4.3326718 -1.6005632
124 -9.8341929 -4.3326718
125 -3.8315532 -9.8341929
126 -9.8672533 -3.8315532
127 -6.9176807 -9.8672533
128 -3.1041667 -6.9176807
129 -8.3759304 -3.1041667
130 -14.7825025 -8.3759304
131 -4.3845642 -14.7825025
132 -1.4857101 -4.3845642
133 -3.9215581 -1.4857101
134 -5.5408048 -3.9215581
135 -13.2147290 -5.5408048
136 -4.5593061 -13.2147290
137 3.1449829 -4.5593061
138 0.3758638 3.1449829
139 0.2546046 0.3758638
140 -6.7644685 0.2546046
141 -8.4221018 -6.7644685
142 -1.1956188 -8.4221018
143 4.6050424 -1.1956188
144 5.6382685 4.6050424
145 12.8680628 5.6382685
146 15.2848976 12.8680628
147 18.2210490 15.2848976
148 22.4978184 18.2210490
149 10.5840595 22.4978184
150 11.2580566 10.5840595
151 12.7887790 11.2580566
152 3.6170586 12.7887790
153 -1.1329498 3.6170586
154 -11.4801427 -1.1329498
155 -1.7112063 -11.4801427
156 -2.1215051 -1.7112063
157 1.2768070 -2.1215051
158 0.7493757 1.2768070
159 0.2337870 0.7493757
160 1.1296033 0.2337870
161 -6.0566673 1.1296033
162 -1.4848638 -6.0566673
163 1.6324693 -1.4848638
164 -7.5135019 1.6324693
165 -5.7839191 -7.5135019
166 -4.2531547 -5.7839191
167 18.5818327 -4.2531547
168 12.1382667 18.5818327
169 3.6871690 12.1382667
170 -7.2636201 3.6871690
171 4.8894293 -7.2636201
172 -4.0626209 4.8894293
173 0.9395805 -4.0626209
174 2.5323676 0.9395805
175 2.5647995 2.5323676
176 -10.3089933 2.5647995
177 -10.6681851 -10.3089933
178 5.4098861 -10.6681851
179 3.9058275 5.4098861
180 8.8163003 3.9058275
181 9.1431751 8.8163003
182 14.9508723 9.1431751
183 9.7140736 14.9508723
184 -2.2506239 9.7140736
185 -0.4487100 -2.2506239
186 1.4295684 -0.4487100
187 2.7748902 1.4295684
188 4.8553932 2.7748902
189 -0.9897437 4.8553932
190 4.9250451 -0.9897437
191 -1.3897393 4.9250451
192 -7.5391453 -1.3897393
193 -14.8259616 -7.5391453
194 -22.8961615 -14.8259616
195 -25.5670448 -22.8961615
196 -13.9032111 -25.5670448
197 -10.7779689 -13.9032111
198 -14.5057873 -10.7779689
199 -24.7800568 -14.5057873
200 -25.6467394 -24.7800568
201 -24.7822096 -25.6467394
202 -25.7206430 -24.7822096
203 -24.5818576 -25.7206430
204 -18.3753032 -24.5818576
205 -28.5441470 -18.3753032
206 -7.8907770 -28.5441470
207 -13.1647317 -7.8907770
208 -15.2972355 -13.1647317
209 -12.6476967 -15.2972355
210 -16.0231849 -12.6476967
211 -22.3951098 -16.0231849
212 -21.8347730 -22.3951098
213 -28.8044940 -21.8347730
214 -6.3846980 -28.8044940
215 -17.1901328 -6.3846980
216 -18.9324888 -17.1901328
217 -22.5365518 -18.9324888
218 -20.9382797 -22.5365518
219 -25.9700101 -20.9382797
220 -23.2169930 -25.9700101
221 -18.2706162 -23.2169930
222 -19.3653938 -18.2706162
223 -28.2122352 -19.3653938
224 -20.1120044 -28.2122352
225 -15.5355684 -20.1120044
226 -19.4605523 -15.5355684
227 -16.6366833 -19.4605523
228 -19.8868609 -16.6366833
229 -29.0409993 -19.8868609
230 -23.4788393 -29.0409993
231 -20.2196319 -23.4788393
232 -13.3629838 -20.2196319
233 -23.5465307 -13.3629838
234 -22.9030025 -23.5465307
235 -22.7554116 -22.9030025
236 -10.3954530 -22.7554116
237 -16.1146144 -10.3954530
238 -17.2477073 -16.1146144
239 -21.6887099 -17.2477073
240 -23.7676316 -21.6887099
241 -18.1383215 -23.7676316
242 -16.5517588 -18.1383215
243 -16.5776044 -16.5517588
244 -16.0195945 -16.5776044
245 -12.8068182 -16.0195945
246 -15.9502187 -12.8068182
247 -17.6509937 -15.9502187
248 -24.1716703 -17.6509937
249 -31.3778968 -24.1716703
250 -26.0706421 -31.3778968
251 -31.6714632 -26.0706421
252 -34.1551505 -31.6714632
253 -22.7856589 -34.1551505
254 -22.4732716 -22.7856589
255 -16.2322793 -22.4732716
256 -19.9646322 -16.2322793
257 -27.7034441 -19.9646322
258 -31.9724573 -27.7034441
259 -26.0854718 -31.9724573
260 -27.4429863 -26.0854718
261 -34.6751435 -27.4429863
262 -40.5296432 -34.6751435
263 -27.8667129 -40.5296432
264 -28.0631086 -27.8667129
265 -26.8822246 -28.0631086
266 -12.0852389 -26.8822246
267 -15.9021131 -12.0852389
268 -25.7834069 -15.9021131
269 -27.7828243 -25.7834069
270 -26.3165275 -27.7828243
271 -26.1980293 -26.3165275
272 -21.6186304 -26.1980293
273 -20.7663095 -21.6186304
274 -19.9318836 -20.7663095
275 -32.0449891 -19.9318836
276 -32.7484042 -32.0449891
277 -30.2132541 -32.7484042
278 -29.7925008 -30.2132541
279 -39.2124311 -29.7925008
280 -32.8187330 -39.2124311
281 -28.1590280 -32.8187330
282 -27.9086466 -28.1590280
283 -25.9035216 -27.9086466
284 -26.3349096 -25.9035216
285 -26.3502737 -26.3349096
286 -32.1342291 -26.3502737
287 -37.0088473 -32.1342291
288 -41.2954999 -37.0088473
289 -28.9972183 -41.2954999
290 -27.1060869 -28.9972183
291 -27.3116451 -27.1060869
292 -22.5852844 -27.3116451
293 -19.3647925 -22.5852844
294 -19.8250462 -19.3647925
295 -26.6840210 -19.8250462
296 -33.6121859 -26.6840210
297 -29.9963706 -33.6121859
298 -18.7330191 -29.9963706
299 -15.6559278 -18.7330191
300 -25.3502655 -15.6559278
301 -24.9884436 -25.3502655
302 -22.7399146 -24.9884436
303 -9.9903596 -22.7399146
304 -10.9716687 -9.9903596
305 -12.6773796 -10.9716687
306 4.2617459 -12.6773796
307 3.6293521 4.2617459
308 2.0204758 3.6293521
309 2.9188181 2.0204758
310 10.0253918 2.9188181
311 12.7409550 10.0253918
312 3.8711862 12.7409550
313 3.0901487 3.8711862
314 10.7767715 3.0901487
315 4.9279004 10.7767715
316 9.5609481 4.9279004
317 5.6673401 9.5609481
318 14.2351344 5.6673401
319 15.7475816 14.2351344
320 19.9946053 15.7475816
321 17.2139915 19.9946053
322 8.3259973 17.2139915
323 15.1836024 8.3259973
324 11.9726837 15.1836024
325 6.3977430 11.9726837
326 8.9632618 6.3977430
327 24.1275519 8.9632618
328 5.4955169 24.1275519
329 14.8701689 5.4955169
330 24.1699754 14.8701689
331 23.0043135 24.1699754
332 6.3303087 23.0043135
333 22.4859273 6.3303087
334 8.5589159 22.4859273
335 10.6395474 8.5589159
336 29.6285676 10.6395474
337 9.0756697 29.6285676
338 -5.0909593 9.0756697
339 -5.6252769 -5.0909593
340 4.0505088 -5.6252769
341 6.3163259 4.0505088
342 -10.8215832 6.3163259
343 8.3230577 -10.8215832
344 20.7179174 8.3230577
345 13.4021598 20.7179174
346 16.4674930 13.4021598
347 7.5334496 16.4674930
348 -7.6078778 7.5334496
349 -2.5663739 -7.6078778
350 -5.5626563 -2.5663739
351 -11.9970493 -5.5626563
352 27.8446662 -11.9970493
353 5.9956585 27.8446662
354 -13.9999510 5.9956585
355 -5.6265354 -13.9999510
356 9.8471592 -5.6265354
357 24.7188298 9.8471592
358 11.3790048 24.7188298
359 1.3789401 11.3790048
360 4.6542845 1.3789401
361 8.3985416 4.6542845
362 3.1113978 8.3985416
363 34.6258540 3.1113978
364 18.8407783 34.6258540
365 28.1868828 18.8407783
366 16.8618187 28.1868828
367 29.3416482 16.8618187
368 36.5667658 29.3416482
369 0.2625676 36.5667658
370 3.1831200 0.2625676
371 5.8987892 3.1831200
372 2.5379460 5.8987892
373 7.8838372 2.5379460
374 -3.5195095 7.8838372
375 5.6196225 -3.5195095
376 9.2568751 5.6196225
377 0.5872916 9.2568751
378 9.9546924 0.5872916
379 -4.0787605 9.9546924
380 -17.4683025 -4.0787605
381 3.4616127 -17.4683025
382 8.8655697 3.4616127
383 -2.3178682 8.8655697
384 8.0291857 -2.3178682
385 11.0889045 8.0291857
386 14.1712918 11.0889045
387 -9.9134794 14.1712918
388 5.1205663 -9.9134794
389 11.2437208 5.1205663
390 3.5405925 11.2437208
391 13.3274118 3.5405925
392 18.6186262 13.3274118
393 8.8321427 18.6186262
394 6.7637093 8.8321427
395 NA 6.7637093
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 24.1961126 20.4799942
[2,] 24.5540973 24.1961126
[3,] 21.0085697 24.5540973
[4,] 18.8184858 21.0085697
[5,] 15.2453620 18.8184858
[6,] 13.3355620 15.2453620
[7,] 8.2319123 13.3355620
[8,] 12.6015232 8.2319123
[9,] 20.7700164 12.6015232
[10,] 15.9382548 20.7700164
[11,] 20.7371462 15.9382548
[12,] 25.2011440 20.7371462
[13,] 27.0177314 25.2011440
[14,] 26.1372440 27.0177314
[15,] 19.8110508 26.1372440
[16,] 15.4731454 19.8110508
[17,] 19.2160124 15.4731454
[18,] 15.9550625 19.2160124
[19,] 16.5184475 15.9550625
[20,] 15.9632776 16.5184475
[21,] 19.2208636 15.9632776
[22,] 21.4545025 19.2208636
[23,] 13.4107619 21.4545025
[24,] 11.9135497 13.4107619
[25,] 12.5583368 11.9135497
[26,] 12.3388026 12.5583368
[27,] 11.3676318 12.3388026
[28,] 12.0196357 11.3676318
[29,] 13.9296434 12.0196357
[30,] 9.4387842 13.9296434
[31,] 4.6703088 9.4387842
[32,] 18.2599647 4.6703088
[33,] 21.0913477 18.2599647
[34,] 14.1194199 21.0913477
[35,] 11.7904143 14.1194199
[36,] 11.4731868 11.7904143
[37,] 17.3802278 11.4731868
[38,] 12.2047934 17.3802278
[39,] 11.2602944 12.2047934
[40,] 0.7461260 11.2602944
[41,] 5.0117616 0.7461260
[42,] 2.1005997 5.0117616
[43,] 4.2659664 2.1005997
[44,] 4.1784366 4.2659664
[45,] 2.7438136 4.1784366
[46,] 2.2471049 2.7438136
[47,] 10.6768129 2.2471049
[48,] -1.4215491 10.6768129
[49,] 1.2142064 -1.4215491
[50,] 9.1833103 1.2142064
[51,] 11.7415646 9.1833103
[52,] 5.2504217 11.7415646
[53,] 0.4709441 5.2504217
[54,] 13.8356518 0.4709441
[55,] 9.2784772 13.8356518
[56,] 3.1960004 9.2784772
[57,] 11.3191684 3.1960004
[58,] 16.2536850 11.3191684
[59,] 32.3046785 16.2536850
[60,] 24.2541686 32.3046785
[61,] 22.7559302 24.2541686
[62,] 25.3275153 22.7559302
[63,] 18.5613373 25.3275153
[64,] 17.3335227 18.5613373
[65,] 7.9735456 17.3335227
[66,] 14.3300391 7.9735456
[67,] 23.6771908 14.3300391
[68,] 24.6350922 23.6771908
[69,] 18.1270960 24.6350922
[70,] 22.4136096 18.1270960
[71,] 17.1842105 22.4136096
[72,] 18.9354550 17.1842105
[73,] 14.2209832 18.9354550
[74,] 8.3515703 14.2209832
[75,] 18.5284475 8.3515703
[76,] 18.2798764 18.5284475
[77,] 22.4934091 18.2798764
[78,] 27.9695846 22.4934091
[79,] 22.9919563 27.9695846
[80,] 31.5429893 22.9919563
[81,] 41.1908016 31.5429893
[82,] 23.7820510 41.1908016
[83,] 19.5006856 23.7820510
[84,] 21.5889647 19.5006856
[85,] 21.4697427 21.5889647
[86,] 18.7909954 21.4697427
[87,] 19.0438185 18.7909954
[88,] 9.4982433 19.0438185
[89,] 11.9476006 9.4982433
[90,] 18.7328230 11.9476006
[91,] 13.2825322 18.7328230
[92,] 11.6143368 13.2825322
[93,] 17.3077168 11.6143368
[94,] 18.8194648 17.3077168
[95,] 18.1261778 18.8194648
[96,] 15.3749564 18.1261778
[97,] 13.9810596 15.3749564
[98,] 17.9477130 13.9810596
[99,] 11.4977827 17.9477130
[100,] 11.5366398 11.4977827
[101,] 17.3374793 11.5366398
[102,] 21.3144112 17.3374793
[103,] 8.3991734 21.3144112
[104,] 10.9050626 8.3991734
[105,] 20.7046904 10.9050626
[106,] 18.4894037 20.7046904
[107,] 14.0390668 18.4894037
[108,] 19.2152583 14.0390668
[109,] 16.1087294 19.2152583
[110,] 11.6870320 16.1087294
[111,] 10.0512924 11.6870320
[112,] 1.8771087 10.0512924
[113,] 2.9122602 1.8771087
[114,] 5.3115824 2.9122602
[115,] 12.5829553 5.3115824
[116,] 0.8637915 12.5829553
[117,] -1.9958540 0.8637915
[118,] 0.5028074 -1.9958540
[119,] -4.0892390 0.5028074
[120,] 7.0271339 -4.0892390
[121,] 9.6275939 7.0271339
[122,] -1.6005632 9.6275939
[123,] -4.3326718 -1.6005632
[124,] -9.8341929 -4.3326718
[125,] -3.8315532 -9.8341929
[126,] -9.8672533 -3.8315532
[127,] -6.9176807 -9.8672533
[128,] -3.1041667 -6.9176807
[129,] -8.3759304 -3.1041667
[130,] -14.7825025 -8.3759304
[131,] -4.3845642 -14.7825025
[132,] -1.4857101 -4.3845642
[133,] -3.9215581 -1.4857101
[134,] -5.5408048 -3.9215581
[135,] -13.2147290 -5.5408048
[136,] -4.5593061 -13.2147290
[137,] 3.1449829 -4.5593061
[138,] 0.3758638 3.1449829
[139,] 0.2546046 0.3758638
[140,] -6.7644685 0.2546046
[141,] -8.4221018 -6.7644685
[142,] -1.1956188 -8.4221018
[143,] 4.6050424 -1.1956188
[144,] 5.6382685 4.6050424
[145,] 12.8680628 5.6382685
[146,] 15.2848976 12.8680628
[147,] 18.2210490 15.2848976
[148,] 22.4978184 18.2210490
[149,] 10.5840595 22.4978184
[150,] 11.2580566 10.5840595
[151,] 12.7887790 11.2580566
[152,] 3.6170586 12.7887790
[153,] -1.1329498 3.6170586
[154,] -11.4801427 -1.1329498
[155,] -1.7112063 -11.4801427
[156,] -2.1215051 -1.7112063
[157,] 1.2768070 -2.1215051
[158,] 0.7493757 1.2768070
[159,] 0.2337870 0.7493757
[160,] 1.1296033 0.2337870
[161,] -6.0566673 1.1296033
[162,] -1.4848638 -6.0566673
[163,] 1.6324693 -1.4848638
[164,] -7.5135019 1.6324693
[165,] -5.7839191 -7.5135019
[166,] -4.2531547 -5.7839191
[167,] 18.5818327 -4.2531547
[168,] 12.1382667 18.5818327
[169,] 3.6871690 12.1382667
[170,] -7.2636201 3.6871690
[171,] 4.8894293 -7.2636201
[172,] -4.0626209 4.8894293
[173,] 0.9395805 -4.0626209
[174,] 2.5323676 0.9395805
[175,] 2.5647995 2.5323676
[176,] -10.3089933 2.5647995
[177,] -10.6681851 -10.3089933
[178,] 5.4098861 -10.6681851
[179,] 3.9058275 5.4098861
[180,] 8.8163003 3.9058275
[181,] 9.1431751 8.8163003
[182,] 14.9508723 9.1431751
[183,] 9.7140736 14.9508723
[184,] -2.2506239 9.7140736
[185,] -0.4487100 -2.2506239
[186,] 1.4295684 -0.4487100
[187,] 2.7748902 1.4295684
[188,] 4.8553932 2.7748902
[189,] -0.9897437 4.8553932
[190,] 4.9250451 -0.9897437
[191,] -1.3897393 4.9250451
[192,] -7.5391453 -1.3897393
[193,] -14.8259616 -7.5391453
[194,] -22.8961615 -14.8259616
[195,] -25.5670448 -22.8961615
[196,] -13.9032111 -25.5670448
[197,] -10.7779689 -13.9032111
[198,] -14.5057873 -10.7779689
[199,] -24.7800568 -14.5057873
[200,] -25.6467394 -24.7800568
[201,] -24.7822096 -25.6467394
[202,] -25.7206430 -24.7822096
[203,] -24.5818576 -25.7206430
[204,] -18.3753032 -24.5818576
[205,] -28.5441470 -18.3753032
[206,] -7.8907770 -28.5441470
[207,] -13.1647317 -7.8907770
[208,] -15.2972355 -13.1647317
[209,] -12.6476967 -15.2972355
[210,] -16.0231849 -12.6476967
[211,] -22.3951098 -16.0231849
[212,] -21.8347730 -22.3951098
[213,] -28.8044940 -21.8347730
[214,] -6.3846980 -28.8044940
[215,] -17.1901328 -6.3846980
[216,] -18.9324888 -17.1901328
[217,] -22.5365518 -18.9324888
[218,] -20.9382797 -22.5365518
[219,] -25.9700101 -20.9382797
[220,] -23.2169930 -25.9700101
[221,] -18.2706162 -23.2169930
[222,] -19.3653938 -18.2706162
[223,] -28.2122352 -19.3653938
[224,] -20.1120044 -28.2122352
[225,] -15.5355684 -20.1120044
[226,] -19.4605523 -15.5355684
[227,] -16.6366833 -19.4605523
[228,] -19.8868609 -16.6366833
[229,] -29.0409993 -19.8868609
[230,] -23.4788393 -29.0409993
[231,] -20.2196319 -23.4788393
[232,] -13.3629838 -20.2196319
[233,] -23.5465307 -13.3629838
[234,] -22.9030025 -23.5465307
[235,] -22.7554116 -22.9030025
[236,] -10.3954530 -22.7554116
[237,] -16.1146144 -10.3954530
[238,] -17.2477073 -16.1146144
[239,] -21.6887099 -17.2477073
[240,] -23.7676316 -21.6887099
[241,] -18.1383215 -23.7676316
[242,] -16.5517588 -18.1383215
[243,] -16.5776044 -16.5517588
[244,] -16.0195945 -16.5776044
[245,] -12.8068182 -16.0195945
[246,] -15.9502187 -12.8068182
[247,] -17.6509937 -15.9502187
[248,] -24.1716703 -17.6509937
[249,] -31.3778968 -24.1716703
[250,] -26.0706421 -31.3778968
[251,] -31.6714632 -26.0706421
[252,] -34.1551505 -31.6714632
[253,] -22.7856589 -34.1551505
[254,] -22.4732716 -22.7856589
[255,] -16.2322793 -22.4732716
[256,] -19.9646322 -16.2322793
[257,] -27.7034441 -19.9646322
[258,] -31.9724573 -27.7034441
[259,] -26.0854718 -31.9724573
[260,] -27.4429863 -26.0854718
[261,] -34.6751435 -27.4429863
[262,] -40.5296432 -34.6751435
[263,] -27.8667129 -40.5296432
[264,] -28.0631086 -27.8667129
[265,] -26.8822246 -28.0631086
[266,] -12.0852389 -26.8822246
[267,] -15.9021131 -12.0852389
[268,] -25.7834069 -15.9021131
[269,] -27.7828243 -25.7834069
[270,] -26.3165275 -27.7828243
[271,] -26.1980293 -26.3165275
[272,] -21.6186304 -26.1980293
[273,] -20.7663095 -21.6186304
[274,] -19.9318836 -20.7663095
[275,] -32.0449891 -19.9318836
[276,] -32.7484042 -32.0449891
[277,] -30.2132541 -32.7484042
[278,] -29.7925008 -30.2132541
[279,] -39.2124311 -29.7925008
[280,] -32.8187330 -39.2124311
[281,] -28.1590280 -32.8187330
[282,] -27.9086466 -28.1590280
[283,] -25.9035216 -27.9086466
[284,] -26.3349096 -25.9035216
[285,] -26.3502737 -26.3349096
[286,] -32.1342291 -26.3502737
[287,] -37.0088473 -32.1342291
[288,] -41.2954999 -37.0088473
[289,] -28.9972183 -41.2954999
[290,] -27.1060869 -28.9972183
[291,] -27.3116451 -27.1060869
[292,] -22.5852844 -27.3116451
[293,] -19.3647925 -22.5852844
[294,] -19.8250462 -19.3647925
[295,] -26.6840210 -19.8250462
[296,] -33.6121859 -26.6840210
[297,] -29.9963706 -33.6121859
[298,] -18.7330191 -29.9963706
[299,] -15.6559278 -18.7330191
[300,] -25.3502655 -15.6559278
[301,] -24.9884436 -25.3502655
[302,] -22.7399146 -24.9884436
[303,] -9.9903596 -22.7399146
[304,] -10.9716687 -9.9903596
[305,] -12.6773796 -10.9716687
[306,] 4.2617459 -12.6773796
[307,] 3.6293521 4.2617459
[308,] 2.0204758 3.6293521
[309,] 2.9188181 2.0204758
[310,] 10.0253918 2.9188181
[311,] 12.7409550 10.0253918
[312,] 3.8711862 12.7409550
[313,] 3.0901487 3.8711862
[314,] 10.7767715 3.0901487
[315,] 4.9279004 10.7767715
[316,] 9.5609481 4.9279004
[317,] 5.6673401 9.5609481
[318,] 14.2351344 5.6673401
[319,] 15.7475816 14.2351344
[320,] 19.9946053 15.7475816
[321,] 17.2139915 19.9946053
[322,] 8.3259973 17.2139915
[323,] 15.1836024 8.3259973
[324,] 11.9726837 15.1836024
[325,] 6.3977430 11.9726837
[326,] 8.9632618 6.3977430
[327,] 24.1275519 8.9632618
[328,] 5.4955169 24.1275519
[329,] 14.8701689 5.4955169
[330,] 24.1699754 14.8701689
[331,] 23.0043135 24.1699754
[332,] 6.3303087 23.0043135
[333,] 22.4859273 6.3303087
[334,] 8.5589159 22.4859273
[335,] 10.6395474 8.5589159
[336,] 29.6285676 10.6395474
[337,] 9.0756697 29.6285676
[338,] -5.0909593 9.0756697
[339,] -5.6252769 -5.0909593
[340,] 4.0505088 -5.6252769
[341,] 6.3163259 4.0505088
[342,] -10.8215832 6.3163259
[343,] 8.3230577 -10.8215832
[344,] 20.7179174 8.3230577
[345,] 13.4021598 20.7179174
[346,] 16.4674930 13.4021598
[347,] 7.5334496 16.4674930
[348,] -7.6078778 7.5334496
[349,] -2.5663739 -7.6078778
[350,] -5.5626563 -2.5663739
[351,] -11.9970493 -5.5626563
[352,] 27.8446662 -11.9970493
[353,] 5.9956585 27.8446662
[354,] -13.9999510 5.9956585
[355,] -5.6265354 -13.9999510
[356,] 9.8471592 -5.6265354
[357,] 24.7188298 9.8471592
[358,] 11.3790048 24.7188298
[359,] 1.3789401 11.3790048
[360,] 4.6542845 1.3789401
[361,] 8.3985416 4.6542845
[362,] 3.1113978 8.3985416
[363,] 34.6258540 3.1113978
[364,] 18.8407783 34.6258540
[365,] 28.1868828 18.8407783
[366,] 16.8618187 28.1868828
[367,] 29.3416482 16.8618187
[368,] 36.5667658 29.3416482
[369,] 0.2625676 36.5667658
[370,] 3.1831200 0.2625676
[371,] 5.8987892 3.1831200
[372,] 2.5379460 5.8987892
[373,] 7.8838372 2.5379460
[374,] -3.5195095 7.8838372
[375,] 5.6196225 -3.5195095
[376,] 9.2568751 5.6196225
[377,] 0.5872916 9.2568751
[378,] 9.9546924 0.5872916
[379,] -4.0787605 9.9546924
[380,] -17.4683025 -4.0787605
[381,] 3.4616127 -17.4683025
[382,] 8.8655697 3.4616127
[383,] -2.3178682 8.8655697
[384,] 8.0291857 -2.3178682
[385,] 11.0889045 8.0291857
[386,] 14.1712918 11.0889045
[387,] -9.9134794 14.1712918
[388,] 5.1205663 -9.9134794
[389,] 11.2437208 5.1205663
[390,] 3.5405925 11.2437208
[391,] 13.3274118 3.5405925
[392,] 18.6186262 13.3274118
[393,] 8.8321427 18.6186262
[394,] 6.7637093 8.8321427
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 24.1961126 20.4799942
2 24.5540973 24.1961126
3 21.0085697 24.5540973
4 18.8184858 21.0085697
5 15.2453620 18.8184858
6 13.3355620 15.2453620
7 8.2319123 13.3355620
8 12.6015232 8.2319123
9 20.7700164 12.6015232
10 15.9382548 20.7700164
11 20.7371462 15.9382548
12 25.2011440 20.7371462
13 27.0177314 25.2011440
14 26.1372440 27.0177314
15 19.8110508 26.1372440
16 15.4731454 19.8110508
17 19.2160124 15.4731454
18 15.9550625 19.2160124
19 16.5184475 15.9550625
20 15.9632776 16.5184475
21 19.2208636 15.9632776
22 21.4545025 19.2208636
23 13.4107619 21.4545025
24 11.9135497 13.4107619
25 12.5583368 11.9135497
26 12.3388026 12.5583368
27 11.3676318 12.3388026
28 12.0196357 11.3676318
29 13.9296434 12.0196357
30 9.4387842 13.9296434
31 4.6703088 9.4387842
32 18.2599647 4.6703088
33 21.0913477 18.2599647
34 14.1194199 21.0913477
35 11.7904143 14.1194199
36 11.4731868 11.7904143
37 17.3802278 11.4731868
38 12.2047934 17.3802278
39 11.2602944 12.2047934
40 0.7461260 11.2602944
41 5.0117616 0.7461260
42 2.1005997 5.0117616
43 4.2659664 2.1005997
44 4.1784366 4.2659664
45 2.7438136 4.1784366
46 2.2471049 2.7438136
47 10.6768129 2.2471049
48 -1.4215491 10.6768129
49 1.2142064 -1.4215491
50 9.1833103 1.2142064
51 11.7415646 9.1833103
52 5.2504217 11.7415646
53 0.4709441 5.2504217
54 13.8356518 0.4709441
55 9.2784772 13.8356518
56 3.1960004 9.2784772
57 11.3191684 3.1960004
58 16.2536850 11.3191684
59 32.3046785 16.2536850
60 24.2541686 32.3046785
61 22.7559302 24.2541686
62 25.3275153 22.7559302
63 18.5613373 25.3275153
64 17.3335227 18.5613373
65 7.9735456 17.3335227
66 14.3300391 7.9735456
67 23.6771908 14.3300391
68 24.6350922 23.6771908
69 18.1270960 24.6350922
70 22.4136096 18.1270960
71 17.1842105 22.4136096
72 18.9354550 17.1842105
73 14.2209832 18.9354550
74 8.3515703 14.2209832
75 18.5284475 8.3515703
76 18.2798764 18.5284475
77 22.4934091 18.2798764
78 27.9695846 22.4934091
79 22.9919563 27.9695846
80 31.5429893 22.9919563
81 41.1908016 31.5429893
82 23.7820510 41.1908016
83 19.5006856 23.7820510
84 21.5889647 19.5006856
85 21.4697427 21.5889647
86 18.7909954 21.4697427
87 19.0438185 18.7909954
88 9.4982433 19.0438185
89 11.9476006 9.4982433
90 18.7328230 11.9476006
91 13.2825322 18.7328230
92 11.6143368 13.2825322
93 17.3077168 11.6143368
94 18.8194648 17.3077168
95 18.1261778 18.8194648
96 15.3749564 18.1261778
97 13.9810596 15.3749564
98 17.9477130 13.9810596
99 11.4977827 17.9477130
100 11.5366398 11.4977827
101 17.3374793 11.5366398
102 21.3144112 17.3374793
103 8.3991734 21.3144112
104 10.9050626 8.3991734
105 20.7046904 10.9050626
106 18.4894037 20.7046904
107 14.0390668 18.4894037
108 19.2152583 14.0390668
109 16.1087294 19.2152583
110 11.6870320 16.1087294
111 10.0512924 11.6870320
112 1.8771087 10.0512924
113 2.9122602 1.8771087
114 5.3115824 2.9122602
115 12.5829553 5.3115824
116 0.8637915 12.5829553
117 -1.9958540 0.8637915
118 0.5028074 -1.9958540
119 -4.0892390 0.5028074
120 7.0271339 -4.0892390
121 9.6275939 7.0271339
122 -1.6005632 9.6275939
123 -4.3326718 -1.6005632
124 -9.8341929 -4.3326718
125 -3.8315532 -9.8341929
126 -9.8672533 -3.8315532
127 -6.9176807 -9.8672533
128 -3.1041667 -6.9176807
129 -8.3759304 -3.1041667
130 -14.7825025 -8.3759304
131 -4.3845642 -14.7825025
132 -1.4857101 -4.3845642
133 -3.9215581 -1.4857101
134 -5.5408048 -3.9215581
135 -13.2147290 -5.5408048
136 -4.5593061 -13.2147290
137 3.1449829 -4.5593061
138 0.3758638 3.1449829
139 0.2546046 0.3758638
140 -6.7644685 0.2546046
141 -8.4221018 -6.7644685
142 -1.1956188 -8.4221018
143 4.6050424 -1.1956188
144 5.6382685 4.6050424
145 12.8680628 5.6382685
146 15.2848976 12.8680628
147 18.2210490 15.2848976
148 22.4978184 18.2210490
149 10.5840595 22.4978184
150 11.2580566 10.5840595
151 12.7887790 11.2580566
152 3.6170586 12.7887790
153 -1.1329498 3.6170586
154 -11.4801427 -1.1329498
155 -1.7112063 -11.4801427
156 -2.1215051 -1.7112063
157 1.2768070 -2.1215051
158 0.7493757 1.2768070
159 0.2337870 0.7493757
160 1.1296033 0.2337870
161 -6.0566673 1.1296033
162 -1.4848638 -6.0566673
163 1.6324693 -1.4848638
164 -7.5135019 1.6324693
165 -5.7839191 -7.5135019
166 -4.2531547 -5.7839191
167 18.5818327 -4.2531547
168 12.1382667 18.5818327
169 3.6871690 12.1382667
170 -7.2636201 3.6871690
171 4.8894293 -7.2636201
172 -4.0626209 4.8894293
173 0.9395805 -4.0626209
174 2.5323676 0.9395805
175 2.5647995 2.5323676
176 -10.3089933 2.5647995
177 -10.6681851 -10.3089933
178 5.4098861 -10.6681851
179 3.9058275 5.4098861
180 8.8163003 3.9058275
181 9.1431751 8.8163003
182 14.9508723 9.1431751
183 9.7140736 14.9508723
184 -2.2506239 9.7140736
185 -0.4487100 -2.2506239
186 1.4295684 -0.4487100
187 2.7748902 1.4295684
188 4.8553932 2.7748902
189 -0.9897437 4.8553932
190 4.9250451 -0.9897437
191 -1.3897393 4.9250451
192 -7.5391453 -1.3897393
193 -14.8259616 -7.5391453
194 -22.8961615 -14.8259616
195 -25.5670448 -22.8961615
196 -13.9032111 -25.5670448
197 -10.7779689 -13.9032111
198 -14.5057873 -10.7779689
199 -24.7800568 -14.5057873
200 -25.6467394 -24.7800568
201 -24.7822096 -25.6467394
202 -25.7206430 -24.7822096
203 -24.5818576 -25.7206430
204 -18.3753032 -24.5818576
205 -28.5441470 -18.3753032
206 -7.8907770 -28.5441470
207 -13.1647317 -7.8907770
208 -15.2972355 -13.1647317
209 -12.6476967 -15.2972355
210 -16.0231849 -12.6476967
211 -22.3951098 -16.0231849
212 -21.8347730 -22.3951098
213 -28.8044940 -21.8347730
214 -6.3846980 -28.8044940
215 -17.1901328 -6.3846980
216 -18.9324888 -17.1901328
217 -22.5365518 -18.9324888
218 -20.9382797 -22.5365518
219 -25.9700101 -20.9382797
220 -23.2169930 -25.9700101
221 -18.2706162 -23.2169930
222 -19.3653938 -18.2706162
223 -28.2122352 -19.3653938
224 -20.1120044 -28.2122352
225 -15.5355684 -20.1120044
226 -19.4605523 -15.5355684
227 -16.6366833 -19.4605523
228 -19.8868609 -16.6366833
229 -29.0409993 -19.8868609
230 -23.4788393 -29.0409993
231 -20.2196319 -23.4788393
232 -13.3629838 -20.2196319
233 -23.5465307 -13.3629838
234 -22.9030025 -23.5465307
235 -22.7554116 -22.9030025
236 -10.3954530 -22.7554116
237 -16.1146144 -10.3954530
238 -17.2477073 -16.1146144
239 -21.6887099 -17.2477073
240 -23.7676316 -21.6887099
241 -18.1383215 -23.7676316
242 -16.5517588 -18.1383215
243 -16.5776044 -16.5517588
244 -16.0195945 -16.5776044
245 -12.8068182 -16.0195945
246 -15.9502187 -12.8068182
247 -17.6509937 -15.9502187
248 -24.1716703 -17.6509937
249 -31.3778968 -24.1716703
250 -26.0706421 -31.3778968
251 -31.6714632 -26.0706421
252 -34.1551505 -31.6714632
253 -22.7856589 -34.1551505
254 -22.4732716 -22.7856589
255 -16.2322793 -22.4732716
256 -19.9646322 -16.2322793
257 -27.7034441 -19.9646322
258 -31.9724573 -27.7034441
259 -26.0854718 -31.9724573
260 -27.4429863 -26.0854718
261 -34.6751435 -27.4429863
262 -40.5296432 -34.6751435
263 -27.8667129 -40.5296432
264 -28.0631086 -27.8667129
265 -26.8822246 -28.0631086
266 -12.0852389 -26.8822246
267 -15.9021131 -12.0852389
268 -25.7834069 -15.9021131
269 -27.7828243 -25.7834069
270 -26.3165275 -27.7828243
271 -26.1980293 -26.3165275
272 -21.6186304 -26.1980293
273 -20.7663095 -21.6186304
274 -19.9318836 -20.7663095
275 -32.0449891 -19.9318836
276 -32.7484042 -32.0449891
277 -30.2132541 -32.7484042
278 -29.7925008 -30.2132541
279 -39.2124311 -29.7925008
280 -32.8187330 -39.2124311
281 -28.1590280 -32.8187330
282 -27.9086466 -28.1590280
283 -25.9035216 -27.9086466
284 -26.3349096 -25.9035216
285 -26.3502737 -26.3349096
286 -32.1342291 -26.3502737
287 -37.0088473 -32.1342291
288 -41.2954999 -37.0088473
289 -28.9972183 -41.2954999
290 -27.1060869 -28.9972183
291 -27.3116451 -27.1060869
292 -22.5852844 -27.3116451
293 -19.3647925 -22.5852844
294 -19.8250462 -19.3647925
295 -26.6840210 -19.8250462
296 -33.6121859 -26.6840210
297 -29.9963706 -33.6121859
298 -18.7330191 -29.9963706
299 -15.6559278 -18.7330191
300 -25.3502655 -15.6559278
301 -24.9884436 -25.3502655
302 -22.7399146 -24.9884436
303 -9.9903596 -22.7399146
304 -10.9716687 -9.9903596
305 -12.6773796 -10.9716687
306 4.2617459 -12.6773796
307 3.6293521 4.2617459
308 2.0204758 3.6293521
309 2.9188181 2.0204758
310 10.0253918 2.9188181
311 12.7409550 10.0253918
312 3.8711862 12.7409550
313 3.0901487 3.8711862
314 10.7767715 3.0901487
315 4.9279004 10.7767715
316 9.5609481 4.9279004
317 5.6673401 9.5609481
318 14.2351344 5.6673401
319 15.7475816 14.2351344
320 19.9946053 15.7475816
321 17.2139915 19.9946053
322 8.3259973 17.2139915
323 15.1836024 8.3259973
324 11.9726837 15.1836024
325 6.3977430 11.9726837
326 8.9632618 6.3977430
327 24.1275519 8.9632618
328 5.4955169 24.1275519
329 14.8701689 5.4955169
330 24.1699754 14.8701689
331 23.0043135 24.1699754
332 6.3303087 23.0043135
333 22.4859273 6.3303087
334 8.5589159 22.4859273
335 10.6395474 8.5589159
336 29.6285676 10.6395474
337 9.0756697 29.6285676
338 -5.0909593 9.0756697
339 -5.6252769 -5.0909593
340 4.0505088 -5.6252769
341 6.3163259 4.0505088
342 -10.8215832 6.3163259
343 8.3230577 -10.8215832
344 20.7179174 8.3230577
345 13.4021598 20.7179174
346 16.4674930 13.4021598
347 7.5334496 16.4674930
348 -7.6078778 7.5334496
349 -2.5663739 -7.6078778
350 -5.5626563 -2.5663739
351 -11.9970493 -5.5626563
352 27.8446662 -11.9970493
353 5.9956585 27.8446662
354 -13.9999510 5.9956585
355 -5.6265354 -13.9999510
356 9.8471592 -5.6265354
357 24.7188298 9.8471592
358 11.3790048 24.7188298
359 1.3789401 11.3790048
360 4.6542845 1.3789401
361 8.3985416 4.6542845
362 3.1113978 8.3985416
363 34.6258540 3.1113978
364 18.8407783 34.6258540
365 28.1868828 18.8407783
366 16.8618187 28.1868828
367 29.3416482 16.8618187
368 36.5667658 29.3416482
369 0.2625676 36.5667658
370 3.1831200 0.2625676
371 5.8987892 3.1831200
372 2.5379460 5.8987892
373 7.8838372 2.5379460
374 -3.5195095 7.8838372
375 5.6196225 -3.5195095
376 9.2568751 5.6196225
377 0.5872916 9.2568751
378 9.9546924 0.5872916
379 -4.0787605 9.9546924
380 -17.4683025 -4.0787605
381 3.4616127 -17.4683025
382 8.8655697 3.4616127
383 -2.3178682 8.8655697
384 8.0291857 -2.3178682
385 11.0889045 8.0291857
386 14.1712918 11.0889045
387 -9.9134794 14.1712918
388 5.1205663 -9.9134794
389 11.2437208 5.1205663
390 3.5405925 11.2437208
391 13.3274118 3.5405925
392 18.6186262 13.3274118
393 8.8321427 18.6186262
394 6.7637093 8.8321427
> 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/7vrjc1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x6771228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9256s1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xyuc1228923439.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yvb41228923439.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/12ql9j1228923439.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/13gdw61228923439.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/14dabk1228923439.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/15c8c91228923439.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/16kkdk1228923439.tab")
+ }
>
> system("convert tmp/1aoue1228923439.ps tmp/1aoue1228923439.png")
> system("convert tmp/2rbmi1228923439.ps tmp/2rbmi1228923439.png")
> system("convert tmp/3a3qt1228923439.ps tmp/3a3qt1228923439.png")
> system("convert tmp/4wesc1228923439.ps tmp/4wesc1228923439.png")
> system("convert tmp/52l4a1228923439.ps tmp/52l4a1228923439.png")
> system("convert tmp/6l0mn1228923439.ps tmp/6l0mn1228923439.png")
> system("convert tmp/7vrjc1228923439.ps tmp/7vrjc1228923439.png")
> system("convert tmp/8x6771228923439.ps tmp/8x6771228923439.png")
> system("convert tmp/9256s1228923439.ps tmp/9256s1228923439.png")
> system("convert tmp/10xyuc1228923439.ps tmp/10xyuc1228923439.png")
>
>
> proc.time()
user system elapsed
11.031 2.065 11.730