R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(110.92
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+ ,3566.59
+ ,38.58
+ ,111.19
+ ,88.96
+ ,3557.28
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+ ,3492.46
+ ,39.44
+ ,106.75
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+ ,3483.25
+ ,39.34
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+ ,86.69
+ ,3420.28
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+ ,89.23
+ ,3389.08
+ ,38.13
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+ ,89.91
+ ,3413.72
+ ,38.75
+ ,110.14
+ ,90.51
+ ,3365.87
+ ,38.2
+ ,112.47
+ ,92.48
+ ,3382.78
+ ,38.39
+ ,113.17
+ ,92.42
+ ,3406.53
+ ,38.29
+ ,113.7
+ ,92.92
+ ,3457.04
+ ,38.73
+ ,114.47
+ ,92.19
+ ,3401.2
+ ,38.33
+ ,113.05
+ ,91.53
+ ,3406.02
+ ,38.73
+ ,114.56
+ ,91.62
+ ,3414.23
+ ,39.06
+ ,115.72
+ ,92.47
+ ,3434.98
+ ,39.28
+ ,114.51
+ ,91.27
+ ,3354.82
+ ,38.6
+ ,114.42
+ ,91.88
+ ,3439.32
+ ,39.68
+ ,112.06
+ ,89.99
+ ,3414.84
+ ,39.61
+ ,111.59
+ ,89.68
+ ,3513.81
+ ,40.74
+ ,111.9
+ ,89.92
+ ,3497.22
+ ,40.55
+ ,112.25
+ ,91.48
+ ,3530.72
+ ,40.8
+ ,108.07
+ ,88.34
+ ,3509.92
+ ,40.81
+ ,111.3
+ ,91.66
+ ,3531.82
+ ,41.47
+ ,112.23
+ ,92.43
+ ,3512.69
+ ,41
+ ,111.88
+ ,91.5
+ ,3553.69
+ ,41.42
+ ,112.14
+ ,92.03
+ ,3581.58
+ ,41.84
+ ,112.47
+ ,92.22
+ ,3502.09
+ ,40.89
+ ,110.11
+ ,90.31
+ ,3543.79
+ ,41.05
+ ,109.96
+ ,90.86
+ ,3537.3
+ ,41.2
+ ,109.86
+ ,92.1
+ ,3506.05
+ ,40.45
+ ,111.41
+ ,92.9
+ ,3519.05
+ ,40.55
+ ,111.56
+ ,93.06
+ ,3509.88
+ ,40.5
+ ,110.56
+ ,92.82
+ ,3405.79
+ ,39.23
+ ,108.36
+ ,92.19
+ ,3399.04
+ ,39.42
+ ,111.67
+ ,95.78
+ ,3453.71
+ ,40.05
+ ,113.6
+ ,97.3
+ ,3413.07
+ ,39.7
+ ,116.85
+ ,99.3
+ ,3379.11
+ ,39.17
+ ,116.7
+ ,98.99
+ ,3413.89
+ ,39.42
+ ,115.64
+ ,98.14
+ ,3431.55
+ ,39.24
+ ,115.27
+ ,96.96
+ ,3462.83
+ ,39.6
+ ,114.41
+ ,96.88
+ ,3433.21
+ ,39.38
+ ,114.26
+ ,96.25
+ ,3432.56
+ ,39.38
+ ,113.91
+ ,96.31
+ ,3461.65
+ ,39.8
+ ,114.54
+ ,96.34
+ ,3513.28
+ ,40.45
+ ,112.6
+ ,94.83
+ ,3480.58
+ ,40.45
+ ,113.78
+ ,95.78
+ ,3488.38
+ ,40.38
+ ,114.17
+ ,95.36
+ ,3480.49
+ ,40.25
+ ,115.78
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+ ,3449.2
+ ,40.03
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+ ,3450.27
+ ,40
+ ,114.88
+ ,96.44
+ ,3426.41
+ ,39.76
+ ,112.79
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+ ,3435.62
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+ ,112.73
+ ,95.15
+ ,3456.71
+ ,40
+ ,112.38
+ ,96.17
+ ,3438.26
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+ ,95.67
+ ,3453.28
+ ,39.76
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+ ,3374.19
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+ ,3232.46
+ ,37.34
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+ ,3280.19
+ ,37.35
+ ,113.73
+ ,96.17
+ ,3207.12
+ ,36.12
+ ,114.4
+ ,95.3
+ ,3081.74
+ ,34.83
+ ,114.48
+ ,94.36
+ ,3074.68
+ ,34.83
+ ,112.08
+ ,93.14
+ ,3101.53
+ ,35.39
+ ,111.58
+ ,92.58
+ ,3193.89
+ ,36.05
+ ,111.44
+ ,93.23
+ ,3263.64
+ ,36.66
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+ ,93.35
+ ,3235.4
+ ,36.54
+ ,113.3
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+ ,3176.97
+ ,35.87
+ ,112.03
+ ,93.32
+ ,3179.9
+ ,36.01
+ ,111.69
+ ,93.29
+ ,3180.81
+ ,36.03
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+ ,91.88
+ ,3135.18
+ ,35.63
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+ ,91.25
+ ,3157.25
+ ,36.04
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+ ,91.36
+ ,3175.41
+ ,35.96
+ ,105.87
+ ,87.44
+ ,3156.8
+ ,35.94
+ ,105.81
+ ,88.73
+ ,3168.79
+ ,36.06
+ ,104.62
+ ,87.75
+ ,3229.36
+ ,36.52
+ ,106.05
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+ ,3267.75
+ ,36.91
+ ,106.44
+ ,90.02
+ ,3271.2
+ ,37.05
+ ,106.55
+ ,90.13
+ ,3240.2
+ ,35.98
+ ,105.33
+ ,89.16
+ ,3196.65
+ ,35.5
+ ,104.61
+ ,88.77
+ ,3051.68
+ ,34.04
+ ,103.05
+ ,88.21
+ ,3063.12
+ ,34.2
+ ,103.27
+ ,87.98
+ ,3012.71
+ ,33.63
+ ,106.32
+ ,91.25
+ ,3021.64
+ ,33.84
+ ,106.71
+ ,91.59
+ ,3090.9
+ ,34.41
+ ,107.55
+ ,92.44
+ ,3114.22
+ ,34.84
+ ,105.54
+ ,90.19
+ ,3126.52
+ ,35.2
+ ,103.58
+ ,89.34
+ ,3117.92
+ ,35.3
+ ,103.13
+ ,88.52
+ ,3066.19
+ ,34.8
+ ,101.66
+ ,87.5
+ ,3087.62
+ ,35.43
+ ,102.69
+ ,87.07
+ ,3032.45
+ ,34.88
+ ,100.66
+ ,85.66
+ ,3030.04
+ ,34.81
+ ,100.68
+ ,86
+ ,3046.91
+ ,34.83
+ ,97.92
+ ,84.33
+ ,3042.76
+ ,34.7
+ ,100.28
+ ,85.17
+ ,3051.69
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+ ,3071.16
+ ,34.77
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+ ,3058.44
+ ,34.8
+ ,100.03
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+ ,2986.1
+ ,34.32
+ ,99.72
+ ,86.88
+ ,2954.49
+ ,33.96
+ ,100.65
+ ,87.68
+ ,2950.47
+ ,34
+ ,97.35
+ ,83.35
+ ,3017.01
+ ,34.77
+ ,97.38
+ ,84.56
+ ,3015.58
+ ,34.53
+ ,97.62
+ ,85.02
+ ,3084.7
+ ,35.2
+ ,92
+ ,78.39
+ ,3042.97
+ ,35.06
+ ,93.78
+ ,80.41
+ ,3047.94
+ ,35.18
+ ,93.19
+ ,79.43
+ ,3038.25
+ ,35
+ ,91.36
+ ,79.3
+ ,3003.27
+ ,34.49
+ ,91.34
+ ,80.05
+ ,3084.09
+ ,35.15
+ ,91.39
+ ,80.11
+ ,3027.15
+ ,34.59
+ ,89.23
+ ,78.43
+ ,3008
+ ,34.65
+ ,92.66
+ ,80.75
+ ,3011.99
+ ,34.59
+ ,95.76
+ ,84.31
+ ,3048.67
+ ,34.92
+ ,95.73
+ ,83.45
+ ,3039.27
+ ,34.37
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+ ,3057.99
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+ ,97.53
+ ,84.05
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+ ,35.13
+ ,97.81
+ ,84.3
+ ,3130.17
+ ,35.15
+ ,96.56
+ ,82.59
+ ,3118.65
+ ,34.48
+ ,97.16
+ ,83.08
+ ,3124.8
+ ,34.91
+ ,96.25
+ ,81.5
+ ,3214.22
+ ,35.95
+ ,101.86
+ ,86.44
+ ,3161.97
+ ,35.52
+ ,99.95
+ ,84.46
+ ,3223.36
+ ,36.16
+ ,99.17
+ ,83.92
+ ,3226.33
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+ ,100.92
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+ ,36.07
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+ ,84.4
+ ,3266.27
+ ,36.5
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+ ,84.42
+ ,3229.32
+ ,36.51
+ ,97.83
+ ,82.46
+ ,3233.46
+ ,36.23
+ ,98.75
+ ,83.28
+ ,3169.32
+ ,35.91
+ ,101.63
+ ,86.57
+ ,3098.37
+ ,35.56
+ ,103.15
+ ,87.7
+ ,3188.58
+ ,36.45
+ ,106.88
+ ,90.72
+ ,3174.02
+ ,36.34
+ ,107.28
+ ,91.06
+ ,3240.29
+ ,37.32
+ ,106.98
+ ,91.32
+ ,3292.51
+ ,37.47
+ ,106.75
+ ,90.75
+ ,3205.28
+ ,36.98
+ ,106.84
+ ,90.83
+ ,3189.09
+ ,36.42
+ ,106.16
+ ,90.28
+ ,3269.79
+ ,37.04
+ ,108.22
+ ,91.52
+ ,3237.69
+ ,37.15
+ ,109.28
+ ,93.22
+ ,3217.6
+ ,37.13
+ ,106.92
+ ,91.56
+ ,3319.81
+ ,38.21
+ ,107.14
+ ,91.32
+ ,3313.47
+ ,37.91
+ ,106.86
+ ,92.61
+ ,3406.78
+ ,38.4
+ ,109.34
+ ,92.94
+ ,3462.91
+ ,39.12
+ ,111.03
+ ,93.32
+ ,3423.81
+ ,38.05
+ ,110.33
+ ,94.28
+ ,3381.12
+ ,37.83
+ ,111.43
+ ,95.5
+ ,3430.15
+ ,38.02
+ ,112.06
+ ,95.65
+ ,3469.59
+ ,38.43
+ ,112.24
+ ,96.19
+ ,3501.98
+ ,40.94
+ ,112.7
+ ,96.43
+ ,3476.18
+ ,40.75
+ ,113.23
+ ,97.11
+ ,3472.46
+ ,41.06
+ ,113.44
+ ,98.08
+ ,3530
+ ,41.69
+ ,111.25
+ ,96.27
+ ,3530.83
+ ,41.73
+ ,113.47
+ ,98.55
+ ,3577.88
+ ,42.34
+ ,115.98
+ ,102.56
+ ,3594.83
+ ,42.54
+ ,118.33
+ ,105.28
+ ,3580.21
+ ,42.5
+ ,119.72
+ ,105.84
+ ,3564.51
+ ,42.78
+ ,119.42
+ ,104.84
+ ,3550.16
+ ,42.65
+ ,119.53
+ ,104.8
+ ,3490.06
+ ,42
+ ,119.64
+ ,104.78
+ ,3487.48
+ ,42.3
+ ,119.17
+ ,104.02
+ ,3478.36
+ ,42.02
+ ,118.45
+ ,103.75
+ ,3392.33
+ ,41.52
+ ,118.71
+ ,103.1
+ ,3362
+ ,41.77
+ ,118.81
+ ,103.86
+ ,3487.54
+ ,42.43
+ ,118.85
+ ,104.08
+ ,3501.17
+ ,42.43
+ ,118.12
+ ,103
+ ,3499.73
+ ,42.48
+ ,118.14
+ ,103.22
+ ,3452.45
+ ,42.01
+ ,118.71
+ ,104.77
+ ,3453.99
+ ,41.99
+ ,118.44
+ ,103.61
+ ,3441.45
+ ,42.01
+ ,120.48
+ ,102.83
+ ,3467.03
+ ,42.05
+ ,121.39
+ ,102.86
+ ,3447.31
+ ,41.84
+ ,121.52
+ ,103.83
+ ,3447.37
+ ,41.79
+ ,119.79
+ ,102.62
+ ,3465.24
+ ,41.82
+ ,119.39
+ ,100.95
+ ,3472.54
+ ,41.87
+ ,121.97
+ ,102.34
+ ,3439.62
+ ,41.66
+ ,121.72
+ ,102.19
+ ,3393.25
+ ,41.19
+ ,123.3
+ ,103.25
+ ,3390.35
+ ,40.99
+ ,122.69
+ ,102.08
+ ,3375.64
+ ,41.15
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+ ,103.98
+ ,3384.55
+ ,40.95
+ ,125.37
+ ,104.92
+ ,3373.14
+ ,40.67
+ ,123.42
+ ,103.44
+ ,3424.71
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+ ,3410
+ ,40.88
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+ ,103.3
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+ ,105.56
+ ,3405.27
+ ,40.89
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+ ,106.89
+ ,3427.92
+ ,40.97
+ ,125.73
+ ,107.05
+ ,3376.66
+ ,40.87
+ ,124.94
+ ,106.74
+ ,3367.46
+ ,40.56
+ ,125.11
+ ,106.8
+ ,3298.55
+ ,40.47
+ ,123.32
+ ,105.66
+ ,3265.64
+ ,40.01
+ ,124.07
+ ,106.93
+ ,3318.76
+ ,40.23
+ ,124.05
+ ,106.39
+ ,3360.7
+ ,40.45
+ ,125.28
+ ,108.19
+ ,3312.48
+ ,40.3
+ ,125.95
+ ,107.86
+ ,3322.65
+ ,40.38
+ ,126.06
+ ,107.22
+ ,3338.42
+ ,39.95
+ ,122.73
+ ,105.5
+ ,3321.5
+ ,39.74
+ ,124.47
+ ,105.66
+ ,3328.94
+ ,40.37
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+ ,106.85
+ ,3264.93
+ ,40.05
+ ,125.05
+ ,106.62
+ ,3269.99
+ ,40.14
+ ,125.05
+ ,107.13
+ ,3225
+ ,39.42
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+ ,107.42
+ ,3196.49
+ ,38.97
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+ ,106.89
+ ,3199.98
+ ,39.06
+ ,124.25
+ ,106.14
+ ,3204.83
+ ,39.86
+ ,122.19
+ ,104.94
+ ,3210.79
+ ,40.49
+ ,124.2
+ ,107.17
+ ,3144.64
+ ,39.99
+ ,123.59
+ ,106.85
+ ,3137.36
+ ,39.94
+ ,123.63
+ ,106.58
+ ,3144.91
+ ,39.97
+ ,125.96
+ ,108.94
+ ,3193.65
+ ,40.21
+ ,122.95
+ ,107.03
+ ,3245.4
+ ,40.39
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+ ,3222.3
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+ ,107.97
+ ,3159.81
+ ,39.46
+ ,125.28
+ ,109.59
+ ,3127.56
+ ,39.07
+ ,125.31
+ ,109.7
+ ,3071.08
+ ,38.64
+ ,124.12
+ ,108.58
+ ,3103.11
+ ,38.96
+ ,122.64
+ ,105.96
+ ,3102.09
+ ,38.57
+ ,121.5
+ ,106
+ ,3069.3
+ ,37.84
+ ,120.03
+ ,105.03
+ ,3030.47
+ ,37.36
+ ,120.73
+ ,105.14
+ ,3055.39
+ ,37.08
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+ ,104.28
+ ,2974.2
+ ,36.45
+ ,120.06
+ ,102.58
+ ,2972.3
+ ,37.02
+ ,119.04
+ ,102.2
+ ,2998.73
+ ,37.68
+ ,117.49
+ ,101.25
+ ,2976.17
+ ,37.67
+ ,117.23
+ ,100.92
+ ,3078.72
+ ,38.55
+ ,117.41
+ ,99.64
+ ,3089.59
+ ,38.16
+ ,117.66
+ ,99.02
+ ,3172.35
+ ,38.96
+ ,118.66
+ ,99.72
+ ,3095.49
+ ,38.05
+ ,117.83
+ ,98.97
+ ,3175.98
+ ,38.77
+ ,116.05
+ ,98.72
+ ,3179.63
+ ,38.76
+ ,116.46
+ ,97.19
+ ,3201.28
+ ,38.86
+ ,114.94
+ ,97.69
+ ,3164.95
+ ,38.49
+ ,114.66
+ ,97.83
+ ,3129.95
+ ,38.29)
+ ,dim=c(4
+ ,259)
+ ,dimnames=list(c('Brent'
+ ,'WTI'
+ ,'Cac40'
+ ,'Total_SA')
+ ,1:259))
> y <- array(NA,dim=c(4,259),dimnames=list(c('Brent','WTI','Cac40','Total_SA'),1:259))
> 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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Total_SA Brent WTI Cac40 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 38.58 110.92 89.06 3566.59 1 0 0 0 0 0 0 0 0 0 0
2 38.48 111.19 88.96 3557.28 0 1 0 0 0 0 0 0 0 0 0
3 38.56 111.19 88.95 3568.88 0 0 1 0 0 0 0 0 0 0 0
4 38.25 110.42 87.66 3515.19 0 0 0 1 0 0 0 0 0 0 0
5 37.97 109.69 86.62 3502.13 0 0 0 0 1 0 0 0 0 0 0
6 38.42 110.03 87.03 3500.94 0 0 0 0 0 1 0 0 0 0 0
7 38.60 110.77 87.83 3528.80 0 0 0 0 0 0 1 0 0 0 0
8 38.30 111.34 88.14 3498.22 0 0 0 0 0 0 0 1 0 0 0
9 38.22 111.34 88.28 3477.36 0 0 0 0 0 0 0 0 1 0 0
10 38.22 110.41 87.06 3462.06 0 0 0 0 0 0 0 0 0 1 0
11 38.22 111.05 87.69 3439.58 0 0 0 0 0 0 0 0 0 0 1
12 36.98 110.44 87.34 3341.52 0 0 0 0 0 0 0 0 0 0 0
13 37.43 111.56 88.94 3382.40 1 0 0 0 0 0 0 0 0 0 0
14 37.52 109.48 87.59 3400.02 0 1 0 0 0 0 0 0 0 0 0
15 37.98 109.18 87.12 3430.60 0 0 1 0 0 0 0 0 0 0 0
16 37.83 107.69 85.78 3411.65 0 0 0 1 0 0 0 0 0 0 0
17 38.00 108.70 86.70 3423.57 0 0 0 0 1 0 0 0 0 0 0
18 37.70 107.19 85.69 3407.68 0 0 0 0 0 1 0 0 0 0 0
19 37.88 107.84 85.92 3409.59 0 0 0 0 0 0 1 0 0 0 0
20 39.02 108.33 86.59 3478.66 0 0 0 0 0 0 0 1 0 0 0
21 38.84 109.40 86.06 3448.50 0 0 0 0 0 0 0 0 1 0 0
22 39.44 107.08 85.03 3492.46 0 0 0 0 0 0 0 0 0 1 0
23 39.12 106.75 84.69 3475.40 0 0 0 0 0 0 0 0 0 0 1
24 38.82 111.02 88.25 3429.27 0 0 0 0 0 0 0 0 0 0 0
25 38.99 107.81 85.79 3459.44 1 0 0 0 0 0 0 0 0 0 0
26 38.39 105.44 84.78 3408.89 0 1 0 0 0 0 0 0 0 0 0
27 38.71 105.75 84.83 3435.09 0 0 1 0 0 0 0 0 0 0 0
28 38.43 108.15 86.89 3411.53 0 0 0 1 0 0 0 0 0 0 0
29 38.67 108.47 86.12 3426.49 0 0 0 0 1 0 0 0 0 0 0
30 38.45 108.96 85.71 3406.50 0 0 0 0 0 1 0 0 0 0 0
31 39.34 109.29 85.28 3483.25 0 0 0 0 0 0 1 0 0 0 0
32 39.60 109.23 85.95 3504.56 0 0 0 0 0 0 0 1 0 0 0
33 39.84 109.61 86.17 3535.18 0 0 0 0 0 0 0 0 1 0 0
34 40.00 108.56 86.19 3527.50 0 0 0 0 0 0 0 0 0 1 0
35 39.55 107.77 85.77 3500.94 0 0 0 0 0 0 0 0 0 0 1
36 38.50 108.25 86.69 3420.28 0 0 0 0 0 0 0 0 0 0 0
37 38.13 109.72 89.23 3389.08 1 0 0 0 0 0 0 0 0 0 0
38 38.75 109.57 89.91 3413.72 0 1 0 0 0 0 0 0 0 0 0
39 38.20 110.14 90.51 3365.87 0 0 1 0 0 0 0 0 0 0 0
40 38.39 112.47 92.48 3382.78 0 0 0 1 0 0 0 0 0 0 0
41 38.29 113.17 92.42 3406.53 0 0 0 0 1 0 0 0 0 0 0
42 38.73 113.70 92.92 3457.04 0 0 0 0 0 1 0 0 0 0 0
43 38.33 114.47 92.19 3401.20 0 0 0 0 0 0 1 0 0 0 0
44 38.73 113.05 91.53 3406.02 0 0 0 0 0 0 0 1 0 0 0
45 39.06 114.56 91.62 3414.23 0 0 0 0 0 0 0 0 1 0 0
46 39.28 115.72 92.47 3434.98 0 0 0 0 0 0 0 0 0 1 0
47 38.60 114.51 91.27 3354.82 0 0 0 0 0 0 0 0 0 0 1
48 39.68 114.42 91.88 3439.32 0 0 0 0 0 0 0 0 0 0 0
49 39.61 112.06 89.99 3414.84 1 0 0 0 0 0 0 0 0 0 0
50 40.74 111.59 89.68 3513.81 0 1 0 0 0 0 0 0 0 0 0
51 40.55 111.90 89.92 3497.22 0 0 1 0 0 0 0 0 0 0 0
52 40.80 112.25 91.48 3530.72 0 0 0 1 0 0 0 0 0 0 0
53 40.81 108.07 88.34 3509.92 0 0 0 0 1 0 0 0 0 0 0
54 41.47 111.30 91.66 3531.82 0 0 0 0 0 1 0 0 0 0 0
55 41.00 112.23 92.43 3512.69 0 0 0 0 0 0 1 0 0 0 0
56 41.42 111.88 91.50 3553.69 0 0 0 0 0 0 0 1 0 0 0
57 41.84 112.14 92.03 3581.58 0 0 0 0 0 0 0 0 1 0 0
58 40.89 112.47 92.22 3502.09 0 0 0 0 0 0 0 0 0 1 0
59 41.05 110.11 90.31 3543.79 0 0 0 0 0 0 0 0 0 0 1
60 41.20 109.96 90.86 3537.30 0 0 0 0 0 0 0 0 0 0 0
61 40.45 109.86 92.10 3506.05 1 0 0 0 0 0 0 0 0 0 0
62 40.55 111.41 92.90 3519.05 0 1 0 0 0 0 0 0 0 0 0
63 40.50 111.56 93.06 3509.88 0 0 1 0 0 0 0 0 0 0 0
64 39.23 110.56 92.82 3405.79 0 0 0 1 0 0 0 0 0 0 0
65 39.42 108.36 92.19 3399.04 0 0 0 0 1 0 0 0 0 0 0
66 40.05 111.67 95.78 3453.71 0 0 0 0 0 1 0 0 0 0 0
67 39.70 113.60 97.30 3413.07 0 0 0 0 0 0 1 0 0 0 0
68 39.17 116.85 99.30 3379.11 0 0 0 0 0 0 0 1 0 0 0
69 39.42 116.70 98.99 3413.89 0 0 0 0 0 0 0 0 1 0 0
70 39.24 115.64 98.14 3431.55 0 0 0 0 0 0 0 0 0 1 0
71 39.60 115.27 96.96 3462.83 0 0 0 0 0 0 0 0 0 0 1
72 39.38 114.41 96.88 3433.21 0 0 0 0 0 0 0 0 0 0 0
73 39.38 114.26 96.25 3432.56 1 0 0 0 0 0 0 0 0 0 0
74 39.80 113.91 96.31 3461.65 0 1 0 0 0 0 0 0 0 0 0
75 40.45 114.54 96.34 3513.28 0 0 1 0 0 0 0 0 0 0 0
76 40.45 112.60 94.83 3480.58 0 0 0 1 0 0 0 0 0 0 0
77 40.38 113.78 95.78 3488.38 0 0 0 0 1 0 0 0 0 0 0
78 40.25 114.17 95.36 3480.49 0 0 0 0 0 1 0 0 0 0 0
79 40.03 115.78 96.86 3449.20 0 0 0 0 0 0 1 0 0 0 0
80 40.00 114.59 96.19 3450.27 0 0 0 0 0 0 0 1 0 0 0
81 39.76 114.88 96.44 3426.41 0 0 0 0 0 0 0 0 1 0 0
82 39.96 112.79 94.57 3435.62 0 0 0 0 0 0 0 0 0 1 0
83 40.00 112.73 95.15 3456.71 0 0 0 0 0 0 0 0 0 0 1
84 39.90 112.38 96.17 3438.26 0 0 0 0 0 0 0 0 0 0 0
85 39.76 112.59 95.67 3453.28 1 0 0 0 0 0 0 0 0 0 0
86 38.90 114.21 96.76 3401.56 0 1 0 0 0 0 0 0 0 0 0
87 38.92 113.47 96.09 3374.19 0 0 1 0 0 0 0 0 0 0 0
88 37.34 114.44 95.92 3232.46 0 0 0 1 0 0 0 0 0 0 0
89 37.99 115.03 97.36 3321.56 0 0 0 0 1 0 0 0 0 0 0
90 37.60 114.86 96.84 3291.66 0 0 0 0 0 1 0 0 0 0 0
91 37.45 113.61 96.17 3320.71 0 0 0 0 0 0 1 0 0 0 0
92 37.35 114.08 96.19 3280.19 0 0 0 0 0 0 0 1 0 0 0
93 36.12 113.73 96.17 3207.12 0 0 0 0 0 0 0 0 1 0 0
94 34.83 114.40 95.30 3081.74 0 0 0 0 0 0 0 0 0 1 0
95 34.83 114.48 94.36 3074.68 0 0 0 0 0 0 0 0 0 0 1
96 35.39 112.08 93.14 3101.53 0 0 0 0 0 0 0 0 0 0 0
97 36.05 111.58 92.58 3193.89 1 0 0 0 0 0 0 0 0 0 0
98 36.66 111.44 93.23 3263.64 0 1 0 0 0 0 0 0 0 0 0
99 36.54 113.38 93.35 3235.40 0 0 1 0 0 0 0 0 0 0 0
100 35.87 113.30 93.53 3176.97 0 0 0 1 0 0 0 0 0 0 0
101 36.01 112.03 93.32 3179.90 0 0 0 0 1 0 0 0 0 0 0
102 36.03 111.69 93.29 3180.81 0 0 0 0 0 1 0 0 0 0 0
103 35.63 109.56 91.88 3135.18 0 0 0 0 0 0 1 0 0 0 0
104 36.04 108.50 91.25 3157.25 0 0 0 0 0 0 0 1 0 0 0
105 35.96 109.01 91.36 3175.41 0 0 0 0 0 0 0 0 1 0 0
106 35.94 105.87 87.44 3156.80 0 0 0 0 0 0 0 0 0 1 0
107 36.06 105.81 88.73 3168.79 0 0 0 0 0 0 0 0 0 0 1
108 36.52 104.62 87.75 3229.36 0 0 0 0 0 0 0 0 0 0 0
109 36.91 106.05 89.61 3267.75 1 0 0 0 0 0 0 0 0 0 0
110 37.05 106.44 90.02 3271.20 0 1 0 0 0 0 0 0 0 0 0
111 35.98 106.55 90.13 3240.20 0 0 1 0 0 0 0 0 0 0 0
112 35.50 105.33 89.16 3196.65 0 0 0 1 0 0 0 0 0 0 0
113 34.04 104.61 88.77 3051.68 0 0 0 0 1 0 0 0 0 0 0
114 34.20 103.05 88.21 3063.12 0 0 0 0 0 1 0 0 0 0 0
115 33.63 103.27 87.98 3012.71 0 0 0 0 0 0 1 0 0 0 0
116 33.84 106.32 91.25 3021.64 0 0 0 0 0 0 0 1 0 0 0
117 34.41 106.71 91.59 3090.90 0 0 0 0 0 0 0 0 1 0 0
118 34.84 107.55 92.44 3114.22 0 0 0 0 0 0 0 0 0 1 0
119 35.20 105.54 90.19 3126.52 0 0 0 0 0 0 0 0 0 0 1
120 35.30 103.58 89.34 3117.92 0 0 0 0 0 0 0 0 0 0 0
121 34.80 103.13 88.52 3066.19 1 0 0 0 0 0 0 0 0 0 0
122 35.43 101.66 87.50 3087.62 0 1 0 0 0 0 0 0 0 0 0
123 34.88 102.69 87.07 3032.45 0 0 1 0 0 0 0 0 0 0 0
124 34.81 100.66 85.66 3030.04 0 0 0 1 0 0 0 0 0 0 0
125 34.83 100.68 86.00 3046.91 0 0 0 0 1 0 0 0 0 0 0
126 34.70 97.92 84.33 3042.76 0 0 0 0 0 1 0 0 0 0 0
127 34.59 100.28 85.17 3051.69 0 0 0 0 0 0 1 0 0 0 0
128 34.77 98.30 84.36 3071.16 0 0 0 0 0 0 0 1 0 0 0
129 34.80 97.83 84.06 3058.44 0 0 0 0 0 0 0 0 1 0 0
130 34.32 100.03 86.92 2986.10 0 0 0 0 0 0 0 0 0 1 0
131 33.96 99.72 86.88 2954.49 0 0 0 0 0 0 0 0 0 0 1
132 34.00 100.65 87.68 2950.47 0 0 0 0 0 0 0 0 0 0 0
133 34.77 97.35 83.35 3017.01 1 0 0 0 0 0 0 0 0 0 0
134 34.53 97.38 84.56 3015.58 0 1 0 0 0 0 0 0 0 0 0
135 35.20 97.62 85.02 3084.70 0 0 1 0 0 0 0 0 0 0 0
136 35.06 92.00 78.39 3042.97 0 0 0 1 0 0 0 0 0 0 0
137 35.18 93.78 80.41 3047.94 0 0 0 0 1 0 0 0 0 0 0
138 35.00 93.19 79.43 3038.25 0 0 0 0 0 1 0 0 0 0 0
139 34.49 91.36 79.30 3003.27 0 0 0 0 0 0 1 0 0 0 0
140 35.15 91.34 80.05 3084.09 0 0 0 0 0 0 0 1 0 0 0
141 34.59 91.39 80.11 3027.15 0 0 0 0 0 0 0 0 1 0 0
142 34.65 89.23 78.43 3008.00 0 0 0 0 0 0 0 0 0 1 0
143 34.59 92.66 80.75 3011.99 0 0 0 0 0 0 0 0 0 0 1
144 34.92 95.76 84.31 3048.67 0 0 0 0 0 0 0 0 0 0 0
145 34.37 95.73 83.45 3039.27 1 0 0 0 0 0 0 0 0 0 0
146 34.47 98.66 85.11 3057.99 0 1 0 0 0 0 0 0 0 0 0
147 35.13 97.53 84.05 3129.77 0 0 1 0 0 0 0 0 0 0 0
148 35.15 97.81 84.30 3130.17 0 0 0 1 0 0 0 0 0 0 0
149 34.48 96.56 82.59 3118.65 0 0 0 0 1 0 0 0 0 0 0
150 34.91 97.16 83.08 3124.80 0 0 0 0 0 1 0 0 0 0 0
151 35.95 96.25 81.50 3214.22 0 0 0 0 0 0 1 0 0 0 0
152 35.52 101.86 86.44 3161.97 0 0 0 0 0 0 0 1 0 0 0
153 36.16 99.95 84.46 3223.36 0 0 0 0 0 0 0 0 1 0 0
154 36.15 99.17 83.92 3226.33 0 0 0 0 0 0 0 0 0 1 0
155 36.07 100.92 85.39 3212.80 0 0 0 0 0 0 0 0 0 0 1
156 36.50 98.65 84.40 3266.27 0 0 0 0 0 0 0 0 0 0 0
157 36.51 98.97 84.42 3229.32 1 0 0 0 0 0 0 0 0 0 0
158 36.23 97.83 82.46 3233.46 0 1 0 0 0 0 0 0 0 0 0
159 35.91 98.75 83.28 3169.32 0 0 1 0 0 0 0 0 0 0 0
160 35.56 101.63 86.57 3098.37 0 0 0 1 0 0 0 0 0 0 0
161 36.45 103.15 87.70 3188.58 0 0 0 0 1 0 0 0 0 0 0
162 36.34 106.88 90.72 3174.02 0 0 0 0 0 1 0 0 0 0 0
163 37.32 107.28 91.06 3240.29 0 0 0 0 0 0 1 0 0 0 0
164 37.47 106.98 91.32 3292.51 0 0 0 0 0 0 0 1 0 0 0
165 36.98 106.75 90.75 3205.28 0 0 0 0 0 0 0 0 1 0 0
166 36.42 106.84 90.83 3189.09 0 0 0 0 0 0 0 0 0 1 0
167 37.04 106.16 90.28 3269.79 0 0 0 0 0 0 0 0 0 0 1
168 37.15 108.22 91.52 3237.69 0 0 0 0 0 0 0 0 0 0 0
169 37.13 109.28 93.22 3217.60 1 0 0 0 0 0 0 0 0 0 0
170 38.21 106.92 91.56 3319.81 0 1 0 0 0 0 0 0 0 0 0
171 37.91 107.14 91.32 3313.47 0 0 1 0 0 0 0 0 0 0 0
172 38.40 106.86 92.61 3406.78 0 0 0 1 0 0 0 0 0 0 0
173 39.12 109.34 92.94 3462.91 0 0 0 0 1 0 0 0 0 0 0
174 38.05 111.03 93.32 3423.81 0 0 0 0 0 1 0 0 0 0 0
175 37.83 110.33 94.28 3381.12 0 0 0 0 0 0 1 0 0 0 0
176 38.02 111.43 95.50 3430.15 0 0 0 0 0 0 0 1 0 0 0
177 38.43 112.06 95.65 3469.59 0 0 0 0 0 0 0 0 1 0 0
178 40.94 112.24 96.19 3501.98 0 0 0 0 0 0 0 0 0 1 0
179 40.75 112.70 96.43 3476.18 0 0 0 0 0 0 0 0 0 0 1
180 41.06 113.23 97.11 3472.46 0 0 0 0 0 0 0 0 0 0 0
181 41.69 113.44 98.08 3530.00 1 0 0 0 0 0 0 0 0 0 0
182 41.73 111.25 96.27 3530.83 0 1 0 0 0 0 0 0 0 0 0
183 42.34 113.47 98.55 3577.88 0 0 1 0 0 0 0 0 0 0 0
184 42.54 115.98 102.56 3594.83 0 0 0 1 0 0 0 0 0 0 0
185 42.50 118.33 105.28 3580.21 0 0 0 0 1 0 0 0 0 0 0
186 42.78 119.72 105.84 3564.51 0 0 0 0 0 1 0 0 0 0 0
187 42.65 119.42 104.84 3550.16 0 0 0 0 0 0 1 0 0 0 0
188 42.00 119.53 104.80 3490.06 0 0 0 0 0 0 0 1 0 0 0
189 42.30 119.64 104.78 3487.48 0 0 0 0 0 0 0 0 1 0 0
190 42.02 119.17 104.02 3478.36 0 0 0 0 0 0 0 0 0 1 0
191 41.52 118.45 103.75 3392.33 0 0 0 0 0 0 0 0 0 0 1
192 41.77 118.71 103.10 3362.00 0 0 0 0 0 0 0 0 0 0 0
193 42.43 118.81 103.86 3487.54 1 0 0 0 0 0 0 0 0 0 0
194 42.43 118.85 104.08 3501.17 0 1 0 0 0 0 0 0 0 0 0
195 42.48 118.12 103.00 3499.73 0 0 1 0 0 0 0 0 0 0 0
196 42.01 118.14 103.22 3452.45 0 0 0 1 0 0 0 0 0 0 0
197 41.99 118.71 104.77 3453.99 0 0 0 0 1 0 0 0 0 0 0
198 42.01 118.44 103.61 3441.45 0 0 0 0 0 1 0 0 0 0 0
199 42.05 120.48 102.83 3467.03 0 0 0 0 0 0 1 0 0 0 0
200 41.84 121.39 102.86 3447.31 0 0 0 0 0 0 0 1 0 0 0
201 41.79 121.52 103.83 3447.37 0 0 0 0 0 0 0 0 1 0 0
202 41.82 119.79 102.62 3465.24 0 0 0 0 0 0 0 0 0 1 0
203 41.87 119.39 100.95 3472.54 0 0 0 0 0 0 0 0 0 0 1
204 41.66 121.97 102.34 3439.62 0 0 0 0 0 0 0 0 0 0 0
205 41.19 121.72 102.19 3393.25 1 0 0 0 0 0 0 0 0 0 0
206 40.99 123.30 103.25 3390.35 0 1 0 0 0 0 0 0 0 0 0
207 41.15 122.69 102.08 3375.64 0 0 1 0 0 0 0 0 0 0 0
208 40.95 124.98 103.98 3384.55 0 0 0 1 0 0 0 0 0 0 0
209 40.67 125.37 104.92 3373.14 0 0 0 0 1 0 0 0 0 0 0
210 41.10 123.42 103.44 3424.71 0 0 0 0 0 1 0 0 0 0 0
211 40.88 122.92 102.97 3410.00 0 0 0 0 0 0 1 0 0 0 0
212 40.93 122.56 103.30 3411.54 0 0 0 0 0 0 0 1 0 0 0
213 40.89 124.31 105.56 3405.27 0 0 0 0 0 0 0 0 1 0 0
214 40.97 125.20 106.89 3427.92 0 0 0 0 0 0 0 0 0 1 0
215 40.87 125.73 107.05 3376.66 0 0 0 0 0 0 0 0 0 0 1
216 40.56 124.94 106.74 3367.46 0 0 0 0 0 0 0 0 0 0 0
217 40.47 125.11 106.80 3298.55 1 0 0 0 0 0 0 0 0 0 0
218 40.01 123.32 105.66 3265.64 0 1 0 0 0 0 0 0 0 0 0
219 40.23 124.07 106.93 3318.76 0 0 1 0 0 0 0 0 0 0 0
220 40.45 124.05 106.39 3360.70 0 0 0 1 0 0 0 0 0 0 0
221 40.30 125.28 108.19 3312.48 0 0 0 0 1 0 0 0 0 0 0
222 40.38 125.95 107.86 3322.65 0 0 0 0 0 1 0 0 0 0 0
223 39.95 126.06 107.22 3338.42 0 0 0 0 0 0 1 0 0 0 0
224 39.74 122.73 105.50 3321.50 0 0 0 0 0 0 0 1 0 0 0
225 40.37 124.47 105.66 3328.94 0 0 0 0 0 0 0 0 1 0 0
226 40.05 125.61 106.85 3264.93 0 0 0 0 0 0 0 0 0 1 0
227 40.14 125.05 106.62 3269.99 0 0 0 0 0 0 0 0 0 0 1
228 39.42 125.05 107.13 3225.00 0 0 0 0 0 0 0 0 0 0 0
229 38.97 125.94 107.42 3196.49 1 0 0 0 0 0 0 0 0 0 0
230 39.06 125.57 106.89 3199.98 0 1 0 0 0 0 0 0 0 0 0
231 39.86 124.25 106.14 3204.83 0 0 1 0 0 0 0 0 0 0 0
232 40.49 122.19 104.94 3210.79 0 0 0 1 0 0 0 0 0 0 0
233 39.99 124.20 107.17 3144.64 0 0 0 0 1 0 0 0 0 0 0
234 39.94 123.59 106.85 3137.36 0 0 0 0 0 1 0 0 0 0 0
235 39.97 123.63 106.58 3144.91 0 0 0 0 0 0 1 0 0 0 0
236 40.21 125.96 108.94 3193.65 0 0 0 0 0 0 0 1 0 0 0
237 40.39 122.95 107.03 3245.40 0 0 0 0 0 0 0 0 1 0 0
238 39.95 121.77 106.61 3222.30 0 0 0 0 0 0 0 0 0 1 0
239 39.46 123.38 107.97 3159.81 0 0 0 0 0 0 0 0 0 0 1
240 39.07 125.28 109.59 3127.56 0 0 0 0 0 0 0 0 0 0 0
241 38.64 125.31 109.70 3071.08 1 0 0 0 0 0 0 0 0 0 0
242 38.96 124.12 108.58 3103.11 0 1 0 0 0 0 0 0 0 0 0
243 38.57 122.64 105.96 3102.09 0 0 1 0 0 0 0 0 0 0 0
244 37.84 121.50 106.00 3069.30 0 0 0 1 0 0 0 0 0 0 0
245 37.36 120.03 105.03 3030.47 0 0 0 0 1 0 0 0 0 0 0
246 37.08 120.73 105.14 3055.39 0 0 0 0 0 1 0 0 0 0 0
247 36.45 119.76 104.28 2974.20 0 0 0 0 0 0 1 0 0 0 0
248 37.02 120.06 102.58 2972.30 0 0 0 0 0 0 0 1 0 0 0
249 37.68 119.04 102.20 2998.73 0 0 0 0 0 0 0 0 1 0 0
250 37.67 117.49 101.25 2976.17 0 0 0 0 0 0 0 0 0 1 0
251 38.55 117.23 100.92 3078.72 0 0 0 0 0 0 0 0 0 0 1
252 38.16 117.41 99.64 3089.59 0 0 0 0 0 0 0 0 0 0 0
253 38.96 117.66 99.02 3172.35 1 0 0 0 0 0 0 0 0 0 0
254 38.05 118.66 99.72 3095.49 0 1 0 0 0 0 0 0 0 0 0
255 38.77 117.83 98.97 3175.98 0 0 1 0 0 0 0 0 0 0 0
256 38.76 116.05 98.72 3179.63 0 0 0 1 0 0 0 0 0 0 0
257 38.86 116.46 97.19 3201.28 0 0 0 0 1 0 0 0 0 0 0
258 38.49 114.94 97.69 3164.95 0 0 0 0 0 1 0 0 0 0 0
259 38.29 114.66 97.83 3129.95 0 0 0 0 0 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Brent WTI Cac40 M1 M2
-8.35563 -0.05700 0.21361 0.01002 -0.09337 -0.10228
M3 M4 M5 M6 M7 M8
-0.04607 -0.08091 -0.14676 -0.16190 -0.15579 -0.24662
M9 M10 M11
-0.18256 -0.01422 0.03916
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.83860 -0.46613 0.03375 0.52965 1.50763
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.3556333 1.0076026 -8.293 7.5e-15 ***
Brent -0.0569987 0.0190012 -3.000 0.00298 **
WTI 0.2136096 0.0191123 11.177 < 2e-16 ***
Cac40 0.0100191 0.0003297 30.391 < 2e-16 ***
M1 -0.0933746 0.2253856 -0.414 0.67903
M2 -0.1022809 0.2255670 -0.453 0.65064
M3 -0.0460718 0.2255882 -0.204 0.83834
M4 -0.0809117 0.2254306 -0.359 0.71996
M5 -0.1467633 0.2254120 -0.651 0.51560
M6 -0.1618962 0.2253767 -0.718 0.47324
M7 -0.1557903 0.2253354 -0.691 0.48999
M8 -0.2466179 0.2280179 -1.082 0.28051
M9 -0.1825585 0.2280384 -0.801 0.42417
M10 -0.0142232 0.2279613 -0.062 0.95030
M11 0.0391640 0.2279541 0.172 0.86373
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7385 on 244 degrees of freedom
Multiple R-squared: 0.9047, Adjusted R-squared: 0.8993
F-statistic: 165.5 on 14 and 244 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,] 0.035872656 0.071745313 0.964127344
[2,] 0.009318809 0.018637618 0.990681191
[3,] 0.014924888 0.029849777 0.985075112
[4,] 0.012204051 0.024408102 0.987795949
[5,] 0.006719071 0.013438142 0.993280929
[6,] 0.002821307 0.005642614 0.997178693
[7,] 0.023685479 0.047370958 0.976314521
[8,] 0.015246680 0.030493360 0.984753320
[9,] 0.007418344 0.014836688 0.992581656
[10,] 0.003479515 0.006959029 0.996520485
[11,] 0.003720403 0.007440806 0.996279597
[12,] 0.005877635 0.011755270 0.994122365
[13,] 0.007963033 0.015926065 0.992036967
[14,] 0.007519952 0.015039903 0.992480048
[15,] 0.004999396 0.009998792 0.995000604
[16,] 0.003337080 0.006674161 0.996662920
[17,] 0.004200907 0.008401815 0.995799093
[18,] 0.002772435 0.005544871 0.997227565
[19,] 0.001528005 0.003056010 0.998471995
[20,] 0.002192673 0.004385345 0.997807327
[21,] 0.008689376 0.017378752 0.991310624
[22,] 0.008687183 0.017374365 0.991312817
[23,] 0.010122860 0.020245719 0.989877140
[24,] 0.007765442 0.015530885 0.992234558
[25,] 0.005824961 0.011649921 0.994175039
[26,] 0.004385997 0.008771994 0.995614003
[27,] 0.002879021 0.005758042 0.997120979
[28,] 0.002935790 0.005871580 0.997064210
[29,] 0.003439259 0.006878518 0.996560741
[30,] 0.002953802 0.005907605 0.997046198
[31,] 0.008335172 0.016670343 0.991664828
[32,] 0.029639501 0.059279003 0.970360499
[33,] 0.154875679 0.309751358 0.845124321
[34,] 0.320294367 0.640588735 0.679705633
[35,] 0.419109441 0.838218882 0.580890559
[36,] 0.531204552 0.937590896 0.468795448
[37,] 0.611122122 0.777755756 0.388877878
[38,] 0.598857291 0.802285418 0.401142709
[39,] 0.615290829 0.769418342 0.384709171
[40,] 0.616572332 0.766855335 0.383427668
[41,] 0.602849804 0.794300392 0.397150196
[42,] 0.594250516 0.811498969 0.405749484
[43,] 0.590354083 0.819291835 0.409645917
[44,] 0.569053486 0.861893029 0.430946514
[45,] 0.533959124 0.932081752 0.466040876
[46,] 0.496742773 0.993485546 0.503257227
[47,] 0.472029574 0.944059148 0.527970426
[48,] 0.467881011 0.935762022 0.532118989
[49,] 0.485696613 0.971393226 0.514303387
[50,] 0.489079701 0.978159401 0.510920299
[51,] 0.486717080 0.973434161 0.513282920
[52,] 0.503392287 0.993215426 0.496607713
[53,] 0.565354981 0.869290037 0.434645019
[54,] 0.575383624 0.849232751 0.424616376
[55,] 0.580778705 0.838442590 0.419221295
[56,] 0.543167463 0.913665074 0.456832537
[57,] 0.503144881 0.993710237 0.496855119
[58,] 0.460982086 0.921964173 0.539017914
[59,] 0.428815867 0.857631733 0.571184133
[60,] 0.390896721 0.781793441 0.609103279
[61,] 0.358346291 0.716692582 0.641653709
[62,] 0.323946490 0.647892979 0.676053510
[63,] 0.290759781 0.581519561 0.709240219
[64,] 0.258387945 0.516775890 0.741612055
[65,] 0.231389047 0.462778094 0.768610953
[66,] 0.205921122 0.411842245 0.794078878
[67,] 0.187632290 0.375264581 0.812367710
[68,] 0.163576383 0.327152765 0.836423617
[69,] 0.149744456 0.299488912 0.850255544
[70,] 0.129491664 0.258983328 0.870508336
[71,] 0.113575340 0.227150680 0.886424660
[72,] 0.105332491 0.210664983 0.894667509
[73,] 0.096754517 0.193509035 0.903245483
[74,] 0.121069135 0.242138270 0.878930865
[75,] 0.114204714 0.228409428 0.885795286
[76,] 0.140422405 0.280844810 0.859577595
[77,] 0.144311904 0.288623809 0.855688096
[78,] 0.141056955 0.282113910 0.858943045
[79,] 0.128402834 0.256805668 0.871597166
[80,] 0.118654973 0.237309946 0.881345027
[81,] 0.118742185 0.237484370 0.881257815
[82,] 0.111413540 0.222827080 0.888586460
[83,] 0.107926025 0.215852050 0.892073975
[84,] 0.100651555 0.201303110 0.899348445
[85,] 0.094091242 0.188182483 0.905908758
[86,] 0.085096844 0.170193688 0.914903156
[87,] 0.071612105 0.143224210 0.928387895
[88,] 0.064057100 0.128114199 0.935942900
[89,] 0.053276788 0.106553576 0.946723212
[90,] 0.044557866 0.089115732 0.955442134
[91,] 0.038921582 0.077843163 0.961078418
[92,] 0.034080464 0.068160928 0.965919536
[93,] 0.028933542 0.057867084 0.971066458
[94,] 0.041624851 0.083249703 0.958375149
[95,] 0.054807756 0.109615512 0.945192244
[96,] 0.059694269 0.119388537 0.940305731
[97,] 0.063194985 0.126389970 0.936805015
[98,] 0.068229060 0.136458119 0.931770940
[99,] 0.084724463 0.169448926 0.915275537
[100,] 0.132402041 0.264804082 0.867597959
[101,] 0.213154872 0.426309744 0.786845128
[102,] 0.238947537 0.477895075 0.761052463
[103,] 0.239054387 0.478108775 0.760945613
[104,] 0.246013163 0.492026326 0.753986837
[105,] 0.251525175 0.503050350 0.748474825
[106,] 0.282448314 0.564896628 0.717551686
[107,] 0.288938640 0.577877280 0.711061360
[108,] 0.275297665 0.550595329 0.724702335
[109,] 0.252684138 0.505368275 0.747315862
[110,] 0.231244227 0.462488453 0.768755773
[111,] 0.203747942 0.407495885 0.796252058
[112,] 0.179735793 0.359471586 0.820264207
[113,] 0.166428366 0.332856732 0.833571634
[114,] 0.157445158 0.314890316 0.842554842
[115,] 0.149977930 0.299955859 0.850022070
[116,] 0.152333994 0.304667987 0.847666006
[117,] 0.137271943 0.274543886 0.862728057
[118,] 0.120482478 0.240964955 0.879517522
[119,] 0.147416456 0.294832913 0.852583544
[120,] 0.169429887 0.338859774 0.830570113
[121,] 0.208094739 0.416189477 0.791905261
[122,] 0.215558228 0.431116455 0.784441772
[123,] 0.231479576 0.462959152 0.768520424
[124,] 0.231933130 0.463866260 0.768066870
[125,] 0.267697654 0.535395308 0.732302346
[126,] 0.266012420 0.532024840 0.733987580
[127,] 0.244143830 0.488287659 0.755856170
[128,] 0.218800327 0.437600655 0.781199673
[129,] 0.199343018 0.398686035 0.800656982
[130,] 0.186818223 0.373636445 0.813181777
[131,] 0.176680576 0.353361151 0.823319424
[132,] 0.183833325 0.367666651 0.816166675
[133,] 0.170365697 0.340731394 0.829634303
[134,] 0.164997308 0.329994615 0.835002692
[135,] 0.147347444 0.294694889 0.852652556
[136,] 0.133705450 0.267410900 0.866294550
[137,] 0.125640618 0.251281237 0.874359382
[138,] 0.117570695 0.235141389 0.882429305
[139,] 0.120766546 0.241533091 0.879233454
[140,] 0.104716756 0.209433513 0.895283244
[141,] 0.090633726 0.181267452 0.909366274
[142,] 0.078029191 0.156058382 0.921970809
[143,] 0.068000135 0.136000271 0.931999865
[144,] 0.057606588 0.115213177 0.942393412
[145,] 0.048172350 0.096344700 0.951827650
[146,] 0.040169284 0.080338568 0.959830716
[147,] 0.033431174 0.066862347 0.966568826
[148,] 0.027944660 0.055889320 0.972055340
[149,] 0.024462725 0.048925450 0.975537275
[150,] 0.024671721 0.049343442 0.975328279
[151,] 0.021360106 0.042720212 0.978639894
[152,] 0.020086100 0.040172200 0.979913900
[153,] 0.016254206 0.032508413 0.983745794
[154,] 0.014689573 0.029379145 0.985310427
[155,] 0.024784685 0.049569371 0.975215315
[156,] 0.028782235 0.057564469 0.971217765
[157,] 0.080513358 0.161026715 0.919486642
[158,] 0.210214772 0.420429543 0.789785228
[159,] 0.650370185 0.699259630 0.349629815
[160,] 0.985826752 0.028346496 0.014173248
[161,] 0.986819213 0.026361575 0.013180787
[162,] 0.990005198 0.019989605 0.009994802
[163,] 0.990281714 0.019436573 0.009718286
[164,] 0.990474895 0.019050210 0.009525105
[165,] 0.990033341 0.019933317 0.009966659
[166,] 0.989740740 0.020518519 0.010259260
[167,] 0.990226636 0.019546729 0.009773364
[168,] 0.991383543 0.017232913 0.008616457
[169,] 0.989701579 0.020596842 0.010298421
[170,] 0.987671060 0.024657880 0.012328940
[171,] 0.986737956 0.026524089 0.013262044
[172,] 0.985884261 0.028231477 0.014115739
[173,] 0.983484825 0.033030350 0.016515175
[174,] 0.983170892 0.033658215 0.016829108
[175,] 0.992637211 0.014725578 0.007362789
[176,] 0.991671358 0.016657284 0.008328642
[177,] 0.989937468 0.020125063 0.010062532
[178,] 0.988307353 0.023385294 0.011692647
[179,] 0.986616536 0.026766928 0.013383464
[180,] 0.983305208 0.033389585 0.016694792
[181,] 0.983569529 0.032860941 0.016430471
[182,] 0.986087347 0.027825307 0.013912653
[183,] 0.988383797 0.023232405 0.011616203
[184,] 0.987075348 0.025849304 0.012924652
[185,] 0.984171725 0.031656550 0.015828275
[186,] 0.981661043 0.036677913 0.018338957
[187,] 0.983180419 0.033639163 0.016819581
[188,] 0.984265587 0.031468826 0.015734413
[189,] 0.982659605 0.034680790 0.017340395
[190,] 0.985017123 0.029965754 0.014982877
[191,] 0.981597351 0.036805298 0.018402649
[192,] 0.976466710 0.047066580 0.023533290
[193,] 0.969524718 0.060950564 0.030475282
[194,] 0.961094965 0.077810070 0.038905035
[195,] 0.950246891 0.099506218 0.049753109
[196,] 0.936837547 0.126324905 0.063162453
[197,] 0.927903485 0.144193030 0.072096515
[198,] 0.910336798 0.179326404 0.089663202
[199,] 0.885280140 0.229439719 0.114719860
[200,] 0.863701029 0.272597941 0.136298971
[201,] 0.837719846 0.324560308 0.162280154
[202,] 0.802892511 0.394214978 0.197107489
[203,] 0.776341166 0.447317669 0.223658834
[204,] 0.737207928 0.525584144 0.262792072
[205,] 0.686662655 0.626674690 0.313337345
[206,] 0.703358793 0.593282413 0.296641207
[207,] 0.740050166 0.519899668 0.259949834
[208,] 0.742661446 0.514677108 0.257338554
[209,] 0.744531858 0.510936285 0.255468142
[210,] 0.759261817 0.481476365 0.240738183
[211,] 0.769487991 0.461024018 0.230512009
[212,] 0.871812099 0.256375802 0.128187901
[213,] 0.962430824 0.075138353 0.037569176
[214,] 0.947584537 0.104830927 0.052415463
[215,] 0.940840026 0.118319948 0.059159974
[216,] 0.940172442 0.119655115 0.059827558
[217,] 0.974262848 0.051474304 0.025737152
[218,] 0.997135759 0.005728483 0.002864241
[219,] 0.998204849 0.003590302 0.001795151
[220,] 0.994780584 0.010438831 0.005219416
[221,] 0.998065083 0.003869834 0.001934917
[222,] 0.996725610 0.006548779 0.003274390
[223,] 0.991483623 0.017032753 0.008516377
[224,] 0.976584635 0.046830731 0.023415365
> postscript(file="/var/wessaorg/rcomp/tmp/1rlsr1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jqlh1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3y82o1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bg0n1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mtl51356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 259
Frequency = 1
1 2 3 4 5 6
-1.406928863 -1.367993810 -1.458288774 -0.963853874 -0.866607411 -0.457752062
7 8 9 10 11 12
-0.691699889 -0.628216670 -0.593182151 -0.400629708 -0.326881541 -0.505246496
13 14 15 16 17 18
-0.649391104 -0.557206518 -0.376503958 -0.100492599 -0.123021296 -0.119006605
19 20 21 22 23 24
0.023669850 0.447286399 0.679606025 0.758610144 0.609967122 0.294248480
25 26 27 28 29 30
0.597859360 0.593891816 0.602170441 0.289822440 0.628506684 0.739431568
31 32 33 34 35 36
0.965018333 0.955799701 0.799619644 0.804110459 0.611518614 0.239665035
37 38 39 40 41 42
-0.183143442 0.045086827 -0.177382846 -0.409970491 -0.629357796 -0.756887093
43 44 45 46 47 48
-0.403700213 0.098879278 0.349405983 0.077723887 0.334833982 0.471948929
49 50 51 52 53 54
1.009797262 1.196538778 1.082950578 0.718867828 1.435596970 1.366232749
55 56 57 58 59 60
0.970322363 1.249072623 1.227185985 0.883495881 0.845787842 0.973940996
61 62 63 64 65 66
0.359837954 0.255955726 0.215994477 0.017994221 0.350651810 -0.130154330
67 68 69 70 71 72
-0.293761484 -0.634657195 -0.739513110 -1.143636920 -0.919453039 -0.835452224
73 74 75 76 77 78
-0.609540895 -0.504857582 -0.398853898 0.175584837 -0.042383505 0.033746005
79 80 81 82 83 84
-0.107507515 0.017889559 -0.083985924 0.135725106 -0.216279300 -0.330093503
85 86 87 88 89 90
-0.408431801 -0.881832163 -0.542877960 -0.576422930 -1.027245333 -1.001152885
91 92 93 94 95 96
-1.376444848 -0.957124461 -1.534762661 -1.512868656 -1.290167819 -0.836210941
97 98 99 100 101 102
-0.917082063 -1.143836926 -0.952161035 -1.044912421 -0.895947316 -0.882903075
103 104 105 106 107 108
-0.652053434 -0.298192850 -0.618627527 0.037867022 -0.294626041 -0.260812393
109 110 111 112 113 114
-0.477878233 -0.428888465 -1.261731356 -1.132895051 -1.032300090 -0.941082764
115 116 117 118 119 120
-0.950453936 -1.263754533 -1.502137329 -1.607808213 -1.058376701 -0.763197421
121 122 123 124 125 126
-0.502022213 0.056267509 0.153375276 0.327843409 0.173184791 0.299308697
127 128 129 130 131 132
0.048816774 0.184738009 0.315415551 -0.093661442 -0.199468888 -0.197906771
133 134 135 136 137 138
0.735628092 0.262104016 0.098791293 1.507628744 1.313651520 1.421578101
139 140 141 142 143 144
1.179403207 0.959136730 0.895600529 1.214878631 0.761446399 0.179354237
145 146 147 148 149 150
-0.001096896 -0.267334611 -0.220699903 -0.207310423 -0.402014336 -0.088968572
151 152 153 154 155 156
0.334648310 -0.216492579 0.058452466 -0.078749511 -0.290836123 -0.275309129
157 158 159 160 161 162
0.212240139 0.253363424 0.397060987 0.254139596 0.151423764 -0.230060352
163 164 165 166 167 168
0.030037505 -0.324972500 0.103585464 -0.474498846 -0.637704540 -0.314384621
169 170 171 172 173 174
-0.342443147 -0.057518284 -0.286399938 -0.987962066 -0.693619112 -1.341581569
175 176 177 178 179 180
-1.384934798 -1.793250740 -1.838597253 0.073458074 0.063517764 0.334907796
181 182 183 184 185 186
0.286549518 0.588946030 0.310843731 -0.337648534 -0.612388255 -0.200347945
187 188 189 190 191 192
0.003830754 0.061622932 0.333954973 0.112548127 0.437743010 1.184453495
193 194 195 196 197 198
0.523381852 0.351013041 0.548320875 0.541011567 0.272828033 0.665998493
199 200 201 202 203 204
0.726495878 0.850361504 0.535909463 0.378391938 0.635793550 0.644927068
205 206 207 208 209 210
0.750680987 0.452274547 0.918601084 0.388839284 0.110445709 0.243886287
211 212 213 214 215 216
0.237059028 0.271446442 -0.152802887 -0.701443628 -0.345217950 -0.502687849
217 218 219 220 221 222
0.187978939 0.208102288 -0.388858629 -0.440012249 -0.355426607 -0.253507983
223 224 225 226 227 228
-0.704635761 -0.476681570 0.079716911 0.043489880 0.046616710 -0.292399048
229 230 231 232 233 234
-0.374596634 -0.218533634 0.561633373 1.305673321 1.172509029 1.244167156
235 236 237 238 239 240
1.252371237 0.723554334 0.557432595 0.202996946 0.086964665 -0.178504001
241 242 243 244 245 246
0.028964571 0.208372020 0.247681574 -0.192473887 -0.094165929 -0.592307893
247 248 249 250 251 252
-0.286544379 0.773555583 1.127723251 1.290000831 1.144822283 0.968758360
253 254 255 256 257 258
1.179636620 0.956085969 0.926334608 0.866549278 1.165678675 0.981364072
259
1.080063018
> postscript(file="/var/wessaorg/rcomp/tmp/6knks1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 259
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.406928863 NA
1 -1.367993810 -1.406928863
2 -1.458288774 -1.367993810
3 -0.963853874 -1.458288774
4 -0.866607411 -0.963853874
5 -0.457752062 -0.866607411
6 -0.691699889 -0.457752062
7 -0.628216670 -0.691699889
8 -0.593182151 -0.628216670
9 -0.400629708 -0.593182151
10 -0.326881541 -0.400629708
11 -0.505246496 -0.326881541
12 -0.649391104 -0.505246496
13 -0.557206518 -0.649391104
14 -0.376503958 -0.557206518
15 -0.100492599 -0.376503958
16 -0.123021296 -0.100492599
17 -0.119006605 -0.123021296
18 0.023669850 -0.119006605
19 0.447286399 0.023669850
20 0.679606025 0.447286399
21 0.758610144 0.679606025
22 0.609967122 0.758610144
23 0.294248480 0.609967122
24 0.597859360 0.294248480
25 0.593891816 0.597859360
26 0.602170441 0.593891816
27 0.289822440 0.602170441
28 0.628506684 0.289822440
29 0.739431568 0.628506684
30 0.965018333 0.739431568
31 0.955799701 0.965018333
32 0.799619644 0.955799701
33 0.804110459 0.799619644
34 0.611518614 0.804110459
35 0.239665035 0.611518614
36 -0.183143442 0.239665035
37 0.045086827 -0.183143442
38 -0.177382846 0.045086827
39 -0.409970491 -0.177382846
40 -0.629357796 -0.409970491
41 -0.756887093 -0.629357796
42 -0.403700213 -0.756887093
43 0.098879278 -0.403700213
44 0.349405983 0.098879278
45 0.077723887 0.349405983
46 0.334833982 0.077723887
47 0.471948929 0.334833982
48 1.009797262 0.471948929
49 1.196538778 1.009797262
50 1.082950578 1.196538778
51 0.718867828 1.082950578
52 1.435596970 0.718867828
53 1.366232749 1.435596970
54 0.970322363 1.366232749
55 1.249072623 0.970322363
56 1.227185985 1.249072623
57 0.883495881 1.227185985
58 0.845787842 0.883495881
59 0.973940996 0.845787842
60 0.359837954 0.973940996
61 0.255955726 0.359837954
62 0.215994477 0.255955726
63 0.017994221 0.215994477
64 0.350651810 0.017994221
65 -0.130154330 0.350651810
66 -0.293761484 -0.130154330
67 -0.634657195 -0.293761484
68 -0.739513110 -0.634657195
69 -1.143636920 -0.739513110
70 -0.919453039 -1.143636920
71 -0.835452224 -0.919453039
72 -0.609540895 -0.835452224
73 -0.504857582 -0.609540895
74 -0.398853898 -0.504857582
75 0.175584837 -0.398853898
76 -0.042383505 0.175584837
77 0.033746005 -0.042383505
78 -0.107507515 0.033746005
79 0.017889559 -0.107507515
80 -0.083985924 0.017889559
81 0.135725106 -0.083985924
82 -0.216279300 0.135725106
83 -0.330093503 -0.216279300
84 -0.408431801 -0.330093503
85 -0.881832163 -0.408431801
86 -0.542877960 -0.881832163
87 -0.576422930 -0.542877960
88 -1.027245333 -0.576422930
89 -1.001152885 -1.027245333
90 -1.376444848 -1.001152885
91 -0.957124461 -1.376444848
92 -1.534762661 -0.957124461
93 -1.512868656 -1.534762661
94 -1.290167819 -1.512868656
95 -0.836210941 -1.290167819
96 -0.917082063 -0.836210941
97 -1.143836926 -0.917082063
98 -0.952161035 -1.143836926
99 -1.044912421 -0.952161035
100 -0.895947316 -1.044912421
101 -0.882903075 -0.895947316
102 -0.652053434 -0.882903075
103 -0.298192850 -0.652053434
104 -0.618627527 -0.298192850
105 0.037867022 -0.618627527
106 -0.294626041 0.037867022
107 -0.260812393 -0.294626041
108 -0.477878233 -0.260812393
109 -0.428888465 -0.477878233
110 -1.261731356 -0.428888465
111 -1.132895051 -1.261731356
112 -1.032300090 -1.132895051
113 -0.941082764 -1.032300090
114 -0.950453936 -0.941082764
115 -1.263754533 -0.950453936
116 -1.502137329 -1.263754533
117 -1.607808213 -1.502137329
118 -1.058376701 -1.607808213
119 -0.763197421 -1.058376701
120 -0.502022213 -0.763197421
121 0.056267509 -0.502022213
122 0.153375276 0.056267509
123 0.327843409 0.153375276
124 0.173184791 0.327843409
125 0.299308697 0.173184791
126 0.048816774 0.299308697
127 0.184738009 0.048816774
128 0.315415551 0.184738009
129 -0.093661442 0.315415551
130 -0.199468888 -0.093661442
131 -0.197906771 -0.199468888
132 0.735628092 -0.197906771
133 0.262104016 0.735628092
134 0.098791293 0.262104016
135 1.507628744 0.098791293
136 1.313651520 1.507628744
137 1.421578101 1.313651520
138 1.179403207 1.421578101
139 0.959136730 1.179403207
140 0.895600529 0.959136730
141 1.214878631 0.895600529
142 0.761446399 1.214878631
143 0.179354237 0.761446399
144 -0.001096896 0.179354237
145 -0.267334611 -0.001096896
146 -0.220699903 -0.267334611
147 -0.207310423 -0.220699903
148 -0.402014336 -0.207310423
149 -0.088968572 -0.402014336
150 0.334648310 -0.088968572
151 -0.216492579 0.334648310
152 0.058452466 -0.216492579
153 -0.078749511 0.058452466
154 -0.290836123 -0.078749511
155 -0.275309129 -0.290836123
156 0.212240139 -0.275309129
157 0.253363424 0.212240139
158 0.397060987 0.253363424
159 0.254139596 0.397060987
160 0.151423764 0.254139596
161 -0.230060352 0.151423764
162 0.030037505 -0.230060352
163 -0.324972500 0.030037505
164 0.103585464 -0.324972500
165 -0.474498846 0.103585464
166 -0.637704540 -0.474498846
167 -0.314384621 -0.637704540
168 -0.342443147 -0.314384621
169 -0.057518284 -0.342443147
170 -0.286399938 -0.057518284
171 -0.987962066 -0.286399938
172 -0.693619112 -0.987962066
173 -1.341581569 -0.693619112
174 -1.384934798 -1.341581569
175 -1.793250740 -1.384934798
176 -1.838597253 -1.793250740
177 0.073458074 -1.838597253
178 0.063517764 0.073458074
179 0.334907796 0.063517764
180 0.286549518 0.334907796
181 0.588946030 0.286549518
182 0.310843731 0.588946030
183 -0.337648534 0.310843731
184 -0.612388255 -0.337648534
185 -0.200347945 -0.612388255
186 0.003830754 -0.200347945
187 0.061622932 0.003830754
188 0.333954973 0.061622932
189 0.112548127 0.333954973
190 0.437743010 0.112548127
191 1.184453495 0.437743010
192 0.523381852 1.184453495
193 0.351013041 0.523381852
194 0.548320875 0.351013041
195 0.541011567 0.548320875
196 0.272828033 0.541011567
197 0.665998493 0.272828033
198 0.726495878 0.665998493
199 0.850361504 0.726495878
200 0.535909463 0.850361504
201 0.378391938 0.535909463
202 0.635793550 0.378391938
203 0.644927068 0.635793550
204 0.750680987 0.644927068
205 0.452274547 0.750680987
206 0.918601084 0.452274547
207 0.388839284 0.918601084
208 0.110445709 0.388839284
209 0.243886287 0.110445709
210 0.237059028 0.243886287
211 0.271446442 0.237059028
212 -0.152802887 0.271446442
213 -0.701443628 -0.152802887
214 -0.345217950 -0.701443628
215 -0.502687849 -0.345217950
216 0.187978939 -0.502687849
217 0.208102288 0.187978939
218 -0.388858629 0.208102288
219 -0.440012249 -0.388858629
220 -0.355426607 -0.440012249
221 -0.253507983 -0.355426607
222 -0.704635761 -0.253507983
223 -0.476681570 -0.704635761
224 0.079716911 -0.476681570
225 0.043489880 0.079716911
226 0.046616710 0.043489880
227 -0.292399048 0.046616710
228 -0.374596634 -0.292399048
229 -0.218533634 -0.374596634
230 0.561633373 -0.218533634
231 1.305673321 0.561633373
232 1.172509029 1.305673321
233 1.244167156 1.172509029
234 1.252371237 1.244167156
235 0.723554334 1.252371237
236 0.557432595 0.723554334
237 0.202996946 0.557432595
238 0.086964665 0.202996946
239 -0.178504001 0.086964665
240 0.028964571 -0.178504001
241 0.208372020 0.028964571
242 0.247681574 0.208372020
243 -0.192473887 0.247681574
244 -0.094165929 -0.192473887
245 -0.592307893 -0.094165929
246 -0.286544379 -0.592307893
247 0.773555583 -0.286544379
248 1.127723251 0.773555583
249 1.290000831 1.127723251
250 1.144822283 1.290000831
251 0.968758360 1.144822283
252 1.179636620 0.968758360
253 0.956085969 1.179636620
254 0.926334608 0.956085969
255 0.866549278 0.926334608
256 1.165678675 0.866549278
257 0.981364072 1.165678675
258 1.080063018 0.981364072
259 NA 1.080063018
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.367993810 -1.406928863
[2,] -1.458288774 -1.367993810
[3,] -0.963853874 -1.458288774
[4,] -0.866607411 -0.963853874
[5,] -0.457752062 -0.866607411
[6,] -0.691699889 -0.457752062
[7,] -0.628216670 -0.691699889
[8,] -0.593182151 -0.628216670
[9,] -0.400629708 -0.593182151
[10,] -0.326881541 -0.400629708
[11,] -0.505246496 -0.326881541
[12,] -0.649391104 -0.505246496
[13,] -0.557206518 -0.649391104
[14,] -0.376503958 -0.557206518
[15,] -0.100492599 -0.376503958
[16,] -0.123021296 -0.100492599
[17,] -0.119006605 -0.123021296
[18,] 0.023669850 -0.119006605
[19,] 0.447286399 0.023669850
[20,] 0.679606025 0.447286399
[21,] 0.758610144 0.679606025
[22,] 0.609967122 0.758610144
[23,] 0.294248480 0.609967122
[24,] 0.597859360 0.294248480
[25,] 0.593891816 0.597859360
[26,] 0.602170441 0.593891816
[27,] 0.289822440 0.602170441
[28,] 0.628506684 0.289822440
[29,] 0.739431568 0.628506684
[30,] 0.965018333 0.739431568
[31,] 0.955799701 0.965018333
[32,] 0.799619644 0.955799701
[33,] 0.804110459 0.799619644
[34,] 0.611518614 0.804110459
[35,] 0.239665035 0.611518614
[36,] -0.183143442 0.239665035
[37,] 0.045086827 -0.183143442
[38,] -0.177382846 0.045086827
[39,] -0.409970491 -0.177382846
[40,] -0.629357796 -0.409970491
[41,] -0.756887093 -0.629357796
[42,] -0.403700213 -0.756887093
[43,] 0.098879278 -0.403700213
[44,] 0.349405983 0.098879278
[45,] 0.077723887 0.349405983
[46,] 0.334833982 0.077723887
[47,] 0.471948929 0.334833982
[48,] 1.009797262 0.471948929
[49,] 1.196538778 1.009797262
[50,] 1.082950578 1.196538778
[51,] 0.718867828 1.082950578
[52,] 1.435596970 0.718867828
[53,] 1.366232749 1.435596970
[54,] 0.970322363 1.366232749
[55,] 1.249072623 0.970322363
[56,] 1.227185985 1.249072623
[57,] 0.883495881 1.227185985
[58,] 0.845787842 0.883495881
[59,] 0.973940996 0.845787842
[60,] 0.359837954 0.973940996
[61,] 0.255955726 0.359837954
[62,] 0.215994477 0.255955726
[63,] 0.017994221 0.215994477
[64,] 0.350651810 0.017994221
[65,] -0.130154330 0.350651810
[66,] -0.293761484 -0.130154330
[67,] -0.634657195 -0.293761484
[68,] -0.739513110 -0.634657195
[69,] -1.143636920 -0.739513110
[70,] -0.919453039 -1.143636920
[71,] -0.835452224 -0.919453039
[72,] -0.609540895 -0.835452224
[73,] -0.504857582 -0.609540895
[74,] -0.398853898 -0.504857582
[75,] 0.175584837 -0.398853898
[76,] -0.042383505 0.175584837
[77,] 0.033746005 -0.042383505
[78,] -0.107507515 0.033746005
[79,] 0.017889559 -0.107507515
[80,] -0.083985924 0.017889559
[81,] 0.135725106 -0.083985924
[82,] -0.216279300 0.135725106
[83,] -0.330093503 -0.216279300
[84,] -0.408431801 -0.330093503
[85,] -0.881832163 -0.408431801
[86,] -0.542877960 -0.881832163
[87,] -0.576422930 -0.542877960
[88,] -1.027245333 -0.576422930
[89,] -1.001152885 -1.027245333
[90,] -1.376444848 -1.001152885
[91,] -0.957124461 -1.376444848
[92,] -1.534762661 -0.957124461
[93,] -1.512868656 -1.534762661
[94,] -1.290167819 -1.512868656
[95,] -0.836210941 -1.290167819
[96,] -0.917082063 -0.836210941
[97,] -1.143836926 -0.917082063
[98,] -0.952161035 -1.143836926
[99,] -1.044912421 -0.952161035
[100,] -0.895947316 -1.044912421
[101,] -0.882903075 -0.895947316
[102,] -0.652053434 -0.882903075
[103,] -0.298192850 -0.652053434
[104,] -0.618627527 -0.298192850
[105,] 0.037867022 -0.618627527
[106,] -0.294626041 0.037867022
[107,] -0.260812393 -0.294626041
[108,] -0.477878233 -0.260812393
[109,] -0.428888465 -0.477878233
[110,] -1.261731356 -0.428888465
[111,] -1.132895051 -1.261731356
[112,] -1.032300090 -1.132895051
[113,] -0.941082764 -1.032300090
[114,] -0.950453936 -0.941082764
[115,] -1.263754533 -0.950453936
[116,] -1.502137329 -1.263754533
[117,] -1.607808213 -1.502137329
[118,] -1.058376701 -1.607808213
[119,] -0.763197421 -1.058376701
[120,] -0.502022213 -0.763197421
[121,] 0.056267509 -0.502022213
[122,] 0.153375276 0.056267509
[123,] 0.327843409 0.153375276
[124,] 0.173184791 0.327843409
[125,] 0.299308697 0.173184791
[126,] 0.048816774 0.299308697
[127,] 0.184738009 0.048816774
[128,] 0.315415551 0.184738009
[129,] -0.093661442 0.315415551
[130,] -0.199468888 -0.093661442
[131,] -0.197906771 -0.199468888
[132,] 0.735628092 -0.197906771
[133,] 0.262104016 0.735628092
[134,] 0.098791293 0.262104016
[135,] 1.507628744 0.098791293
[136,] 1.313651520 1.507628744
[137,] 1.421578101 1.313651520
[138,] 1.179403207 1.421578101
[139,] 0.959136730 1.179403207
[140,] 0.895600529 0.959136730
[141,] 1.214878631 0.895600529
[142,] 0.761446399 1.214878631
[143,] 0.179354237 0.761446399
[144,] -0.001096896 0.179354237
[145,] -0.267334611 -0.001096896
[146,] -0.220699903 -0.267334611
[147,] -0.207310423 -0.220699903
[148,] -0.402014336 -0.207310423
[149,] -0.088968572 -0.402014336
[150,] 0.334648310 -0.088968572
[151,] -0.216492579 0.334648310
[152,] 0.058452466 -0.216492579
[153,] -0.078749511 0.058452466
[154,] -0.290836123 -0.078749511
[155,] -0.275309129 -0.290836123
[156,] 0.212240139 -0.275309129
[157,] 0.253363424 0.212240139
[158,] 0.397060987 0.253363424
[159,] 0.254139596 0.397060987
[160,] 0.151423764 0.254139596
[161,] -0.230060352 0.151423764
[162,] 0.030037505 -0.230060352
[163,] -0.324972500 0.030037505
[164,] 0.103585464 -0.324972500
[165,] -0.474498846 0.103585464
[166,] -0.637704540 -0.474498846
[167,] -0.314384621 -0.637704540
[168,] -0.342443147 -0.314384621
[169,] -0.057518284 -0.342443147
[170,] -0.286399938 -0.057518284
[171,] -0.987962066 -0.286399938
[172,] -0.693619112 -0.987962066
[173,] -1.341581569 -0.693619112
[174,] -1.384934798 -1.341581569
[175,] -1.793250740 -1.384934798
[176,] -1.838597253 -1.793250740
[177,] 0.073458074 -1.838597253
[178,] 0.063517764 0.073458074
[179,] 0.334907796 0.063517764
[180,] 0.286549518 0.334907796
[181,] 0.588946030 0.286549518
[182,] 0.310843731 0.588946030
[183,] -0.337648534 0.310843731
[184,] -0.612388255 -0.337648534
[185,] -0.200347945 -0.612388255
[186,] 0.003830754 -0.200347945
[187,] 0.061622932 0.003830754
[188,] 0.333954973 0.061622932
[189,] 0.112548127 0.333954973
[190,] 0.437743010 0.112548127
[191,] 1.184453495 0.437743010
[192,] 0.523381852 1.184453495
[193,] 0.351013041 0.523381852
[194,] 0.548320875 0.351013041
[195,] 0.541011567 0.548320875
[196,] 0.272828033 0.541011567
[197,] 0.665998493 0.272828033
[198,] 0.726495878 0.665998493
[199,] 0.850361504 0.726495878
[200,] 0.535909463 0.850361504
[201,] 0.378391938 0.535909463
[202,] 0.635793550 0.378391938
[203,] 0.644927068 0.635793550
[204,] 0.750680987 0.644927068
[205,] 0.452274547 0.750680987
[206,] 0.918601084 0.452274547
[207,] 0.388839284 0.918601084
[208,] 0.110445709 0.388839284
[209,] 0.243886287 0.110445709
[210,] 0.237059028 0.243886287
[211,] 0.271446442 0.237059028
[212,] -0.152802887 0.271446442
[213,] -0.701443628 -0.152802887
[214,] -0.345217950 -0.701443628
[215,] -0.502687849 -0.345217950
[216,] 0.187978939 -0.502687849
[217,] 0.208102288 0.187978939
[218,] -0.388858629 0.208102288
[219,] -0.440012249 -0.388858629
[220,] -0.355426607 -0.440012249
[221,] -0.253507983 -0.355426607
[222,] -0.704635761 -0.253507983
[223,] -0.476681570 -0.704635761
[224,] 0.079716911 -0.476681570
[225,] 0.043489880 0.079716911
[226,] 0.046616710 0.043489880
[227,] -0.292399048 0.046616710
[228,] -0.374596634 -0.292399048
[229,] -0.218533634 -0.374596634
[230,] 0.561633373 -0.218533634
[231,] 1.305673321 0.561633373
[232,] 1.172509029 1.305673321
[233,] 1.244167156 1.172509029
[234,] 1.252371237 1.244167156
[235,] 0.723554334 1.252371237
[236,] 0.557432595 0.723554334
[237,] 0.202996946 0.557432595
[238,] 0.086964665 0.202996946
[239,] -0.178504001 0.086964665
[240,] 0.028964571 -0.178504001
[241,] 0.208372020 0.028964571
[242,] 0.247681574 0.208372020
[243,] -0.192473887 0.247681574
[244,] -0.094165929 -0.192473887
[245,] -0.592307893 -0.094165929
[246,] -0.286544379 -0.592307893
[247,] 0.773555583 -0.286544379
[248,] 1.127723251 0.773555583
[249,] 1.290000831 1.127723251
[250,] 1.144822283 1.290000831
[251,] 0.968758360 1.144822283
[252,] 1.179636620 0.968758360
[253,] 0.956085969 1.179636620
[254,] 0.926334608 0.956085969
[255,] 0.866549278 0.926334608
[256,] 1.165678675 0.866549278
[257,] 0.981364072 1.165678675
[258,] 1.080063018 0.981364072
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.367993810 -1.406928863
2 -1.458288774 -1.367993810
3 -0.963853874 -1.458288774
4 -0.866607411 -0.963853874
5 -0.457752062 -0.866607411
6 -0.691699889 -0.457752062
7 -0.628216670 -0.691699889
8 -0.593182151 -0.628216670
9 -0.400629708 -0.593182151
10 -0.326881541 -0.400629708
11 -0.505246496 -0.326881541
12 -0.649391104 -0.505246496
13 -0.557206518 -0.649391104
14 -0.376503958 -0.557206518
15 -0.100492599 -0.376503958
16 -0.123021296 -0.100492599
17 -0.119006605 -0.123021296
18 0.023669850 -0.119006605
19 0.447286399 0.023669850
20 0.679606025 0.447286399
21 0.758610144 0.679606025
22 0.609967122 0.758610144
23 0.294248480 0.609967122
24 0.597859360 0.294248480
25 0.593891816 0.597859360
26 0.602170441 0.593891816
27 0.289822440 0.602170441
28 0.628506684 0.289822440
29 0.739431568 0.628506684
30 0.965018333 0.739431568
31 0.955799701 0.965018333
32 0.799619644 0.955799701
33 0.804110459 0.799619644
34 0.611518614 0.804110459
35 0.239665035 0.611518614
36 -0.183143442 0.239665035
37 0.045086827 -0.183143442
38 -0.177382846 0.045086827
39 -0.409970491 -0.177382846
40 -0.629357796 -0.409970491
41 -0.756887093 -0.629357796
42 -0.403700213 -0.756887093
43 0.098879278 -0.403700213
44 0.349405983 0.098879278
45 0.077723887 0.349405983
46 0.334833982 0.077723887
47 0.471948929 0.334833982
48 1.009797262 0.471948929
49 1.196538778 1.009797262
50 1.082950578 1.196538778
51 0.718867828 1.082950578
52 1.435596970 0.718867828
53 1.366232749 1.435596970
54 0.970322363 1.366232749
55 1.249072623 0.970322363
56 1.227185985 1.249072623
57 0.883495881 1.227185985
58 0.845787842 0.883495881
59 0.973940996 0.845787842
60 0.359837954 0.973940996
61 0.255955726 0.359837954
62 0.215994477 0.255955726
63 0.017994221 0.215994477
64 0.350651810 0.017994221
65 -0.130154330 0.350651810
66 -0.293761484 -0.130154330
67 -0.634657195 -0.293761484
68 -0.739513110 -0.634657195
69 -1.143636920 -0.739513110
70 -0.919453039 -1.143636920
71 -0.835452224 -0.919453039
72 -0.609540895 -0.835452224
73 -0.504857582 -0.609540895
74 -0.398853898 -0.504857582
75 0.175584837 -0.398853898
76 -0.042383505 0.175584837
77 0.033746005 -0.042383505
78 -0.107507515 0.033746005
79 0.017889559 -0.107507515
80 -0.083985924 0.017889559
81 0.135725106 -0.083985924
82 -0.216279300 0.135725106
83 -0.330093503 -0.216279300
84 -0.408431801 -0.330093503
85 -0.881832163 -0.408431801
86 -0.542877960 -0.881832163
87 -0.576422930 -0.542877960
88 -1.027245333 -0.576422930
89 -1.001152885 -1.027245333
90 -1.376444848 -1.001152885
91 -0.957124461 -1.376444848
92 -1.534762661 -0.957124461
93 -1.512868656 -1.534762661
94 -1.290167819 -1.512868656
95 -0.836210941 -1.290167819
96 -0.917082063 -0.836210941
97 -1.143836926 -0.917082063
98 -0.952161035 -1.143836926
99 -1.044912421 -0.952161035
100 -0.895947316 -1.044912421
101 -0.882903075 -0.895947316
102 -0.652053434 -0.882903075
103 -0.298192850 -0.652053434
104 -0.618627527 -0.298192850
105 0.037867022 -0.618627527
106 -0.294626041 0.037867022
107 -0.260812393 -0.294626041
108 -0.477878233 -0.260812393
109 -0.428888465 -0.477878233
110 -1.261731356 -0.428888465
111 -1.132895051 -1.261731356
112 -1.032300090 -1.132895051
113 -0.941082764 -1.032300090
114 -0.950453936 -0.941082764
115 -1.263754533 -0.950453936
116 -1.502137329 -1.263754533
117 -1.607808213 -1.502137329
118 -1.058376701 -1.607808213
119 -0.763197421 -1.058376701
120 -0.502022213 -0.763197421
121 0.056267509 -0.502022213
122 0.153375276 0.056267509
123 0.327843409 0.153375276
124 0.173184791 0.327843409
125 0.299308697 0.173184791
126 0.048816774 0.299308697
127 0.184738009 0.048816774
128 0.315415551 0.184738009
129 -0.093661442 0.315415551
130 -0.199468888 -0.093661442
131 -0.197906771 -0.199468888
132 0.735628092 -0.197906771
133 0.262104016 0.735628092
134 0.098791293 0.262104016
135 1.507628744 0.098791293
136 1.313651520 1.507628744
137 1.421578101 1.313651520
138 1.179403207 1.421578101
139 0.959136730 1.179403207
140 0.895600529 0.959136730
141 1.214878631 0.895600529
142 0.761446399 1.214878631
143 0.179354237 0.761446399
144 -0.001096896 0.179354237
145 -0.267334611 -0.001096896
146 -0.220699903 -0.267334611
147 -0.207310423 -0.220699903
148 -0.402014336 -0.207310423
149 -0.088968572 -0.402014336
150 0.334648310 -0.088968572
151 -0.216492579 0.334648310
152 0.058452466 -0.216492579
153 -0.078749511 0.058452466
154 -0.290836123 -0.078749511
155 -0.275309129 -0.290836123
156 0.212240139 -0.275309129
157 0.253363424 0.212240139
158 0.397060987 0.253363424
159 0.254139596 0.397060987
160 0.151423764 0.254139596
161 -0.230060352 0.151423764
162 0.030037505 -0.230060352
163 -0.324972500 0.030037505
164 0.103585464 -0.324972500
165 -0.474498846 0.103585464
166 -0.637704540 -0.474498846
167 -0.314384621 -0.637704540
168 -0.342443147 -0.314384621
169 -0.057518284 -0.342443147
170 -0.286399938 -0.057518284
171 -0.987962066 -0.286399938
172 -0.693619112 -0.987962066
173 -1.341581569 -0.693619112
174 -1.384934798 -1.341581569
175 -1.793250740 -1.384934798
176 -1.838597253 -1.793250740
177 0.073458074 -1.838597253
178 0.063517764 0.073458074
179 0.334907796 0.063517764
180 0.286549518 0.334907796
181 0.588946030 0.286549518
182 0.310843731 0.588946030
183 -0.337648534 0.310843731
184 -0.612388255 -0.337648534
185 -0.200347945 -0.612388255
186 0.003830754 -0.200347945
187 0.061622932 0.003830754
188 0.333954973 0.061622932
189 0.112548127 0.333954973
190 0.437743010 0.112548127
191 1.184453495 0.437743010
192 0.523381852 1.184453495
193 0.351013041 0.523381852
194 0.548320875 0.351013041
195 0.541011567 0.548320875
196 0.272828033 0.541011567
197 0.665998493 0.272828033
198 0.726495878 0.665998493
199 0.850361504 0.726495878
200 0.535909463 0.850361504
201 0.378391938 0.535909463
202 0.635793550 0.378391938
203 0.644927068 0.635793550
204 0.750680987 0.644927068
205 0.452274547 0.750680987
206 0.918601084 0.452274547
207 0.388839284 0.918601084
208 0.110445709 0.388839284
209 0.243886287 0.110445709
210 0.237059028 0.243886287
211 0.271446442 0.237059028
212 -0.152802887 0.271446442
213 -0.701443628 -0.152802887
214 -0.345217950 -0.701443628
215 -0.502687849 -0.345217950
216 0.187978939 -0.502687849
217 0.208102288 0.187978939
218 -0.388858629 0.208102288
219 -0.440012249 -0.388858629
220 -0.355426607 -0.440012249
221 -0.253507983 -0.355426607
222 -0.704635761 -0.253507983
223 -0.476681570 -0.704635761
224 0.079716911 -0.476681570
225 0.043489880 0.079716911
226 0.046616710 0.043489880
227 -0.292399048 0.046616710
228 -0.374596634 -0.292399048
229 -0.218533634 -0.374596634
230 0.561633373 -0.218533634
231 1.305673321 0.561633373
232 1.172509029 1.305673321
233 1.244167156 1.172509029
234 1.252371237 1.244167156
235 0.723554334 1.252371237
236 0.557432595 0.723554334
237 0.202996946 0.557432595
238 0.086964665 0.202996946
239 -0.178504001 0.086964665
240 0.028964571 -0.178504001
241 0.208372020 0.028964571
242 0.247681574 0.208372020
243 -0.192473887 0.247681574
244 -0.094165929 -0.192473887
245 -0.592307893 -0.094165929
246 -0.286544379 -0.592307893
247 0.773555583 -0.286544379
248 1.127723251 0.773555583
249 1.290000831 1.127723251
250 1.144822283 1.290000831
251 0.968758360 1.144822283
252 1.179636620 0.968758360
253 0.956085969 1.179636620
254 0.926334608 0.956085969
255 0.866549278 0.926334608
256 1.165678675 0.866549278
257 0.981364072 1.165678675
258 1.080063018 0.981364072
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7zv8b1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8r6ts1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9nlca1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10sf5y1356041673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1173hg1356041673.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12gzvp1356041673.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13qx4j1356041673.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14x2hw1356041673.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15vpsh1356041673.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16hjo41356041673.tab")
+ }
>
> try(system("convert tmp/1rlsr1356041673.ps tmp/1rlsr1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jqlh1356041673.ps tmp/2jqlh1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y82o1356041673.ps tmp/3y82o1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bg0n1356041673.ps tmp/4bg0n1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mtl51356041673.ps tmp/5mtl51356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/6knks1356041673.ps tmp/6knks1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zv8b1356041673.ps tmp/7zv8b1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r6ts1356041673.ps tmp/8r6ts1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nlca1356041673.ps tmp/9nlca1356041673.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sf5y1356041673.ps tmp/10sf5y1356041673.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.784 0.975 11.763