R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(109.6
+ ,22.93
+ ,2.28
+ ,109.3
+ ,15.45
+ ,2.26
+ ,108.8
+ ,12.61
+ ,2.16
+ ,108.6
+ ,12.84
+ ,2.1
+ ,108.9
+ ,15.38
+ ,1.96
+ ,109.5
+ ,13.43
+ ,1.85
+ ,109.5
+ ,11.58
+ ,1.8
+ ,109.7
+ ,15.1
+ ,1.77
+ ,110.2
+ ,14.87
+ ,1.78
+ ,110.3
+ ,14.9
+ ,1.73
+ ,110.4
+ ,15.22
+ ,1.77
+ ,110.5
+ ,16.11
+ ,1.76
+ ,111.2
+ ,18.65
+ ,1.74
+ ,111.6
+ ,17.75
+ ,1.73
+ ,112.1
+ ,18.3
+ ,1.73
+ ,112.7
+ ,18.68
+ ,1.69
+ ,113.1
+ ,19.44
+ ,1.65
+ ,113.5
+ ,20.07
+ ,1.65
+ ,113.8
+ ,21.34
+ ,1.66
+ ,114.4
+ ,20.31
+ ,1.63
+ ,115
+ ,19.53
+ ,1.56
+ ,115.3
+ ,19.86
+ ,1.57
+ ,115.4
+ ,18.85
+ ,1.64
+ ,115.4
+ ,17.27
+ ,1.7
+ ,115.7
+ ,17.13
+ ,1.96
+ ,116
+ ,16.8
+ ,1.84
+ ,116.5
+ ,16.2
+ ,1.7
+ ,117.1
+ ,17.86
+ ,1.59
+ ,117.5
+ ,17.42
+ ,1.52
+ ,118
+ ,16.53
+ ,1.53
+ ,118.5
+ ,15.5
+ ,1.56
+ ,119
+ ,15.52
+ ,1.62
+ ,119.8
+ ,14.54
+ ,1.53
+ ,120.2
+ ,13.77
+ ,1.68
+ ,120.3
+ ,14.14
+ ,1.76
+ ,120.5
+ ,16.38
+ ,1.89
+ ,121.1
+ ,18.02
+ ,1.99
+ ,121.6
+ ,17.94
+ ,1.81
+ ,122.3
+ ,19.48
+ ,1.69
+ ,123.1
+ ,21.07
+ ,1.56
+ ,123.8
+ ,20.12
+ ,1.61
+ ,124.1
+ ,20.05
+ ,1.65
+ ,124.4
+ ,19.78
+ ,1.65
+ ,124.6
+ ,18.58
+ ,1.61
+ ,125
+ ,19.59
+ ,1.55
+ ,125.6
+ ,20.1
+ ,1.58
+ ,125.9
+ ,19.86
+ ,1.66
+ ,126.1
+ ,21.1
+ ,1.92
+ ,127.4
+ ,22.86
+ ,2.23
+ ,128
+ ,22.11
+ ,1.85
+ ,128.7
+ ,20.39
+ ,1.55
+ ,128.9
+ ,18.43
+ ,1.49
+ ,129.2
+ ,18.2
+ ,1.47
+ ,129.9
+ ,16.7
+ ,1.48
+ ,130.4
+ ,18.45
+ ,1.49
+ ,131.6
+ ,27.31
+ ,1.51
+ ,132.7
+ ,33.51
+ ,1.56
+ ,133.5
+ ,36.04
+ ,1.76
+ ,133.8
+ ,32.33
+ ,1.94
+ ,133.8
+ ,27.28
+ ,2.04
+ ,134.6
+ ,25.23
+ ,1.96
+ ,134.8
+ ,20.48
+ ,1.62
+ ,135
+ ,19.9
+ ,1.49
+ ,135.2
+ ,20.83
+ ,1.5
+ ,135.6
+ ,21.23
+ ,1.48
+ ,136
+ ,20.19
+ ,1.43
+ ,136.2
+ ,21.4
+ ,1.34
+ ,136.6
+ ,21.69
+ ,1.43
+ ,137.2
+ ,21.89
+ ,1.59
+ ,137.4
+ ,23.23
+ ,1.82
+ ,137.8
+ ,22.46
+ ,1.89
+ ,137.9
+ ,19.5
+ ,2
+ ,138.1
+ ,18.79
+ ,1.74
+ ,138.6
+ ,19.01
+ ,1.26
+ ,139.3
+ ,18.92
+ ,1.35
+ ,139.5
+ ,20.23
+ ,1.42
+ ,139.7
+ ,20.98
+ ,1.51
+ ,140.2
+ ,22.38
+ ,1.62
+ ,140.5
+ ,21.78
+ ,1.55
+ ,140.9
+ ,21.34
+ ,1.84
+ ,141.3
+ ,21.88
+ ,1.92
+ ,141.8
+ ,21.69
+ ,2.38
+ ,142
+ ,20.34
+ ,2.13
+ ,141.9
+ ,19.41
+ ,2.07
+ ,142.6
+ ,19.03
+ ,2.03
+ ,143.1
+ ,20.09
+ ,1.76
+ ,143.6
+ ,20.32
+ ,2
+ ,144
+ ,20.25
+ ,2.06
+ ,144.2
+ ,19.95
+ ,2.18
+ ,144.4
+ ,19.09
+ ,1.98
+ ,144.4
+ ,17.89
+ ,1.99
+ ,144.8
+ ,18.01
+ ,2.04
+ ,145.1
+ ,17.5
+ ,2.09
+ ,145.7
+ ,18.15
+ ,2.02
+ ,145.8
+ ,16.61
+ ,2.03
+ ,145.8
+ ,14.51
+ ,2.15
+ ,146.2
+ ,15.03
+ ,1.93
+ ,146.7
+ ,14.78
+ ,1.88
+ ,147.2
+ ,14.68
+ ,1.93
+ ,147.4
+ ,16.42
+ ,1.91
+ ,147.5
+ ,17.89
+ ,2
+ ,148
+ ,19.06
+ ,1.8
+ ,148.4
+ ,19.65
+ ,1.81
+ ,149
+ ,18.38
+ ,1.83
+ ,149.4
+ ,17.45
+ ,1.78
+ ,149.5
+ ,17.72
+ ,1.7
+ ,149.7
+ ,18.07
+ ,1.75
+ ,149.7
+ ,17.16
+ ,1.88
+ ,150.3
+ ,18.04
+ ,1.62
+ ,150.9
+ ,18.57
+ ,1.48
+ ,151.4
+ ,18.54
+ ,1.47
+ ,151.9
+ ,19.9
+ ,1.52
+ ,152.2
+ ,19.74
+ ,1.55
+ ,152.5
+ ,18.45
+ ,1.58
+ ,152.5
+ ,17.33
+ ,1.43
+ ,152.9
+ ,18.02
+ ,1.43
+ ,153.2
+ ,18.23
+ ,1.52
+ ,153.7
+ ,17.43
+ ,1.54
+ ,153.6
+ ,17.99
+ ,1.61
+ ,153.5
+ ,19.03
+ ,1.84
+ ,154.4
+ ,18.85
+ ,2.05
+ ,154.9
+ ,19.09
+ ,1.89
+ ,155.7
+ ,21.33
+ ,1.95
+ ,156.3
+ ,23.5
+ ,2.08
+ ,156.6
+ ,21.17
+ ,2.01
+ ,156.7
+ ,20.42
+ ,2.08
+ ,157
+ ,21.3
+ ,2.25
+ ,157.3
+ ,21.9
+ ,2.1
+ ,157.8
+ ,23.97
+ ,1.85
+ ,158.3
+ ,24.88
+ ,1.94
+ ,158.6
+ ,23.71
+ ,2.5
+ ,158.6
+ ,25.23
+ ,3.26
+ ,159.1
+ ,25.13
+ ,3.4
+ ,159.6
+ ,22.18
+ ,2.49
+ ,160
+ ,20.97
+ ,1.79
+ ,160.2
+ ,19.7
+ ,1.81
+ ,160.1
+ ,20.82
+ ,2
+ ,160.3
+ ,19.26
+ ,2.08
+ ,160.5
+ ,19.66
+ ,2
+ ,160.8
+ ,19.95
+ ,2.08
+ ,161.2
+ ,19.8
+ ,2.33
+ ,161.6
+ ,21.33
+ ,2.68
+ ,161.5
+ ,20.19
+ ,2.92
+ ,161.3
+ ,18.33
+ ,2.28
+ ,161.6
+ ,16.72
+ ,1.96
+ ,161.9
+ ,16.06
+ ,1.96
+ ,162.2
+ ,15.12
+ ,2.06
+ ,162.5
+ ,15.35
+ ,2.16
+ ,162.8
+ ,14.91
+ ,2.04
+ ,163
+ ,13.72
+ ,1.91
+ ,163.2
+ ,14.17
+ ,2.09
+ ,163.4
+ ,13.47
+ ,1.82
+ ,163.6
+ ,15.03
+ ,1.7
+ ,164
+ ,14.46
+ ,1.86
+ ,164
+ ,13
+ ,1.94
+ ,163.9
+ ,11.35
+ ,1.95
+ ,164.3
+ ,12.51
+ ,1.85
+ ,164.5
+ ,12.01
+ ,1.77
+ ,165
+ ,14.68
+ ,1.7
+ ,166.2
+ ,17.31
+ ,1.9
+ ,166.2
+ ,17.72
+ ,2.17
+ ,166.2
+ ,17.92
+ ,2.14
+ ,166.7
+ ,20.1
+ ,2.2
+ ,167.1
+ ,21.28
+ ,2.51
+ ,167.9
+ ,23.8
+ ,2.62
+ ,168.2
+ ,22.69
+ ,2.52
+ ,168.3
+ ,25
+ ,2.68
+ ,168.3
+ ,26.1
+ ,2.24
+ ,168.8
+ ,27.26
+ ,2.6
+ ,169.8
+ ,29.37
+ ,2.73
+ ,171.2
+ ,29.84
+ ,2.66
+ ,171.3
+ ,25.72
+ ,2.86
+ ,171.5
+ ,28.79
+ ,3.04
+ ,172.4
+ ,31.82
+ ,3.77
+ ,172.8
+ ,29.7
+ ,3.84
+ ,172.8
+ ,31.26
+ ,3.73
+ ,173.7
+ ,33.88
+ ,4.26
+ ,174
+ ,33.11
+ ,4.58
+ ,174.1
+ ,34.42
+ ,4.4
+ ,174
+ ,28.44
+ ,5.77
+ ,175.1
+ ,29.59
+ ,6.82
+ ,175.8
+ ,29.61
+ ,5.08
+ ,176.2
+ ,27.24
+ ,4.37
+ ,176.9
+ ,27.49
+ ,4.52
+ ,177.7
+ ,28.63
+ ,4.36
+ ,178
+ ,27.6
+ ,3.79
+ ,177.5
+ ,26.42
+ ,3.35
+ ,177.5
+ ,27.37
+ ,3.33
+ ,178.3
+ ,26.2
+ ,2.93
+ ,177.7
+ ,22.17
+ ,2.78
+ ,177.4
+ ,19.64
+ ,3.41
+ ,176.7
+ ,19.39
+ ,3.42
+ ,177.1
+ ,19.71
+ ,2.5
+ ,177.8
+ ,20.72
+ ,2.19
+ ,178.8
+ ,24.53
+ ,2.4
+ ,179.8
+ ,26.18
+ ,2.94
+ ,179.8
+ ,27.04
+ ,2.94
+ ,179.9
+ ,25.52
+ ,2.96
+ ,180.1
+ ,26.97
+ ,2.92
+ ,180.7
+ ,28.39
+ ,2.76
+ ,181
+ ,29.66
+ ,2.97
+ ,181.3
+ ,28.84
+ ,3.24
+ ,181.3
+ ,26.35
+ ,3.59
+ ,180.9
+ ,29.46
+ ,3.96
+ ,181.7
+ ,32.95
+ ,4.43
+ ,183.1
+ ,35.83
+ ,5.05
+ ,184.2
+ ,33.51
+ ,6.96
+ ,183.8
+ ,28.17
+ ,4.47
+ ,183.5
+ ,28.11
+ ,4.77
+ ,183.7
+ ,30.66
+ ,5.41
+ ,183.9
+ ,30.75
+ ,5.08
+ ,184.6
+ ,31.57
+ ,4.46
+ ,185.2
+ ,28.31
+ ,4.59
+ ,185
+ ,30.34
+ ,4.32
+ ,184.5
+ ,31.11
+ ,4.26
+ ,184.3
+ ,32.13
+ ,4.76
+ ,185.2
+ ,34.31
+ ,5.21
+ ,186.2
+ ,34.68
+ ,5.02
+ ,187.4
+ ,36.74
+ ,5.12
+ ,188
+ ,36.75
+ ,5.03
+ ,189.1
+ ,40.28
+ ,5.4
+ ,189.7
+ ,38.03
+ ,5.82
+ ,189.4
+ ,40.78
+ ,5.62
+ ,189.5
+ ,44.9
+ ,5.52
+ ,189.9
+ ,45.94
+ ,5.06
+ ,190.9
+ ,53.28
+ ,5.43
+ ,191
+ ,48.47
+ ,6.21
+ ,190.3
+ ,43.15
+ ,6.01
+ ,190.7
+ ,46.84
+ ,5.8
+ ,191.8
+ ,48.15
+ ,5.73
+ ,193.3
+ ,54.19
+ ,5.95
+ ,194.6
+ ,52.98
+ ,6.57
+ ,194.4
+ ,49.83
+ ,6.25
+ ,194.5
+ ,56.35
+ ,6.09
+ ,195.4
+ ,59
+ ,6.71
+ ,196.4
+ ,64.99
+ ,6.48
+ ,198.8
+ ,65.59
+ ,8.95
+ ,199.2
+ ,62.26
+ ,10.33
+ ,197.6
+ ,58.32
+ ,9.89
+ ,196.8
+ ,59.41
+ ,9.08
+ ,198.3
+ ,65.49
+ ,8.01
+ ,198.7
+ ,61.63
+ ,6.85
+ ,199.8
+ ,62.69
+ ,6.43
+ ,201.5
+ ,69.44
+ ,6.37
+ ,202.5
+ ,70.84
+ ,6.23
+ ,202.9
+ ,70.95
+ ,5.77
+ ,203.5
+ ,74.41
+ ,5.91
+ ,203.9
+ ,73.04
+ ,6.55
+ ,202.9
+ ,63.8
+ ,6.06
+ ,201.8
+ ,58.89
+ ,5.09
+ ,201.5
+ ,59.08
+ ,6.71
+ ,201.8
+ ,61.96
+ ,6.76
+ ,202.416
+ ,54.51
+ ,5.7
+ ,203.499
+ ,59.28
+ ,6.8
+ ,205.352
+ ,60.44
+ ,6.65
+ ,206.686
+ ,63.98
+ ,6.26
+ ,207.949
+ ,63.45
+ ,6.75
+ ,208.352
+ ,67.49
+ ,6.62
+ ,208.299
+ ,74.12
+ ,6.21
+ ,207.917
+ ,72.36
+ ,5.76
+ ,208.49
+ ,79.91
+ ,5.3
+ ,208.936
+ ,85.8
+ ,5.78
+ ,210.177
+ ,94.77
+ ,6.46
+ ,210.036
+ ,91.69
+ ,6.87
+ ,211.08
+ ,92.97
+ ,7.16
+ ,211.693
+ ,95.39
+ ,7.71
+ ,213.528
+ ,105.45
+ ,8.44
+ ,214.823
+ ,112.58
+ ,9.04
+ ,216.632
+ ,125.4
+ ,10.15
+ ,218.815
+ ,133.88
+ ,10.79
+ ,219.964
+ ,133.37
+ ,11.32
+ ,219.086
+ ,116.67
+ ,8.34
+ ,218.783
+ ,104.11
+ ,6.72
+ ,216.573
+ ,76.61
+ ,5.5
+ ,212.425
+ ,57.31
+ ,4.75
+ ,210.228
+ ,41.12
+ ,5.52
+ ,211.143
+ ,41.71
+ ,5.15
+ ,212.193
+ ,39.09
+ ,4.19
+ ,212.709
+ ,47.94
+ ,3.72
+ ,213.24
+ ,49.65
+ ,3.43
+ ,213.856
+ ,59.03
+ ,3.45
+ ,215.693
+ ,69.64
+ ,3.45
+ ,215.351
+ ,64.15
+ ,3.43
+ ,215.834
+ ,71.05
+ ,3.14
+ ,215.969
+ ,69.41
+ ,2.92
+ ,216.177
+ ,75.72
+ ,3.6
+ ,216.33
+ ,77.99
+ ,3.64
+ ,215.949
+ ,74.47
+ ,4.44
+ ,216.687
+ ,78.33
+ ,5.14
+ ,216.741
+ ,76.39
+ ,4.89
+ ,217.631
+ ,81.2
+ ,4.36
+ ,218.009
+ ,84.29
+ ,3.92
+ ,218.178
+ ,73.74
+ ,4.04
+ ,217.965
+ ,75.34
+ ,4.25
+ ,218.011
+ ,76.32
+ ,4.36
+ ,218.312
+ ,76.6
+ ,4.22)
+ ,dim=c(3
+ ,296)
+ ,dimnames=list(c('CPI-index'
+ ,'Olieprijzen'
+ ,'Gasprijzen')
+ ,1:296))
> y <- array(NA,dim=c(3,296),dimnames=list(c('CPI-index','Olieprijzen','Gasprijzen'),1:296))
> 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
> 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
CPI-index Olieprijzen Gasprijzen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 109.600 22.93 2.28 1 0 0 0 0 0 0 0 0 0 0
2 109.300 15.45 2.26 0 1 0 0 0 0 0 0 0 0 0
3 108.800 12.61 2.16 0 0 1 0 0 0 0 0 0 0 0
4 108.600 12.84 2.10 0 0 0 1 0 0 0 0 0 0 0
5 108.900 15.38 1.96 0 0 0 0 1 0 0 0 0 0 0
6 109.500 13.43 1.85 0 0 0 0 0 1 0 0 0 0 0
7 109.500 11.58 1.80 0 0 0 0 0 0 1 0 0 0 0
8 109.700 15.10 1.77 0 0 0 0 0 0 0 1 0 0 0
9 110.200 14.87 1.78 0 0 0 0 0 0 0 0 1 0 0
10 110.300 14.90 1.73 0 0 0 0 0 0 0 0 0 1 0
11 110.400 15.22 1.77 0 0 0 0 0 0 0 0 0 0 1
12 110.500 16.11 1.76 0 0 0 0 0 0 0 0 0 0 0
13 111.200 18.65 1.74 1 0 0 0 0 0 0 0 0 0 0
14 111.600 17.75 1.73 0 1 0 0 0 0 0 0 0 0 0
15 112.100 18.30 1.73 0 0 1 0 0 0 0 0 0 0 0
16 112.700 18.68 1.69 0 0 0 1 0 0 0 0 0 0 0
17 113.100 19.44 1.65 0 0 0 0 1 0 0 0 0 0 0
18 113.500 20.07 1.65 0 0 0 0 0 1 0 0 0 0 0
19 113.800 21.34 1.66 0 0 0 0 0 0 1 0 0 0 0
20 114.400 20.31 1.63 0 0 0 0 0 0 0 1 0 0 0
21 115.000 19.53 1.56 0 0 0 0 0 0 0 0 1 0 0
22 115.300 19.86 1.57 0 0 0 0 0 0 0 0 0 1 0
23 115.400 18.85 1.64 0 0 0 0 0 0 0 0 0 0 1
24 115.400 17.27 1.70 0 0 0 0 0 0 0 0 0 0 0
25 115.700 17.13 1.96 1 0 0 0 0 0 0 0 0 0 0
26 116.000 16.80 1.84 0 1 0 0 0 0 0 0 0 0 0
27 116.500 16.20 1.70 0 0 1 0 0 0 0 0 0 0 0
28 117.100 17.86 1.59 0 0 0 1 0 0 0 0 0 0 0
29 117.500 17.42 1.52 0 0 0 0 1 0 0 0 0 0 0
30 118.000 16.53 1.53 0 0 0 0 0 1 0 0 0 0 0
31 118.500 15.50 1.56 0 0 0 0 0 0 1 0 0 0 0
32 119.000 15.52 1.62 0 0 0 0 0 0 0 1 0 0 0
33 119.800 14.54 1.53 0 0 0 0 0 0 0 0 1 0 0
34 120.200 13.77 1.68 0 0 0 0 0 0 0 0 0 1 0
35 120.300 14.14 1.76 0 0 0 0 0 0 0 0 0 0 1
36 120.500 16.38 1.89 0 0 0 0 0 0 0 0 0 0 0
37 121.100 18.02 1.99 1 0 0 0 0 0 0 0 0 0 0
38 121.600 17.94 1.81 0 1 0 0 0 0 0 0 0 0 0
39 122.300 19.48 1.69 0 0 1 0 0 0 0 0 0 0 0
40 123.100 21.07 1.56 0 0 0 1 0 0 0 0 0 0 0
41 123.800 20.12 1.61 0 0 0 0 1 0 0 0 0 0 0
42 124.100 20.05 1.65 0 0 0 0 0 1 0 0 0 0 0
43 124.400 19.78 1.65 0 0 0 0 0 0 1 0 0 0 0
44 124.600 18.58 1.61 0 0 0 0 0 0 0 1 0 0 0
45 125.000 19.59 1.55 0 0 0 0 0 0 0 0 1 0 0
46 125.600 20.10 1.58 0 0 0 0 0 0 0 0 0 1 0
47 125.900 19.86 1.66 0 0 0 0 0 0 0 0 0 0 1
48 126.100 21.10 1.92 0 0 0 0 0 0 0 0 0 0 0
49 127.400 22.86 2.23 1 0 0 0 0 0 0 0 0 0 0
50 128.000 22.11 1.85 0 1 0 0 0 0 0 0 0 0 0
51 128.700 20.39 1.55 0 0 1 0 0 0 0 0 0 0 0
52 128.900 18.43 1.49 0 0 0 1 0 0 0 0 0 0 0
53 129.200 18.20 1.47 0 0 0 0 1 0 0 0 0 0 0
54 129.900 16.70 1.48 0 0 0 0 0 1 0 0 0 0 0
55 130.400 18.45 1.49 0 0 0 0 0 0 1 0 0 0 0
56 131.600 27.31 1.51 0 0 0 0 0 0 0 1 0 0 0
57 132.700 33.51 1.56 0 0 0 0 0 0 0 0 1 0 0
58 133.500 36.04 1.76 0 0 0 0 0 0 0 0 0 1 0
59 133.800 32.33 1.94 0 0 0 0 0 0 0 0 0 0 1
60 133.800 27.28 2.04 0 0 0 0 0 0 0 0 0 0 0
61 134.600 25.23 1.96 1 0 0 0 0 0 0 0 0 0 0
62 134.800 20.48 1.62 0 1 0 0 0 0 0 0 0 0 0
63 135.000 19.90 1.49 0 0 1 0 0 0 0 0 0 0 0
64 135.200 20.83 1.50 0 0 0 1 0 0 0 0 0 0 0
65 135.600 21.23 1.48 0 0 0 0 1 0 0 0 0 0 0
66 136.000 20.19 1.43 0 0 0 0 0 1 0 0 0 0 0
67 136.200 21.40 1.34 0 0 0 0 0 0 1 0 0 0 0
68 136.600 21.69 1.43 0 0 0 0 0 0 0 1 0 0 0
69 137.200 21.89 1.59 0 0 0 0 0 0 0 0 1 0 0
70 137.400 23.23 1.82 0 0 0 0 0 0 0 0 0 1 0
71 137.800 22.46 1.89 0 0 0 0 0 0 0 0 0 0 1
72 137.900 19.50 2.00 0 0 0 0 0 0 0 0 0 0 0
73 138.100 18.79 1.74 1 0 0 0 0 0 0 0 0 0 0
74 138.600 19.01 1.26 0 1 0 0 0 0 0 0 0 0 0
75 139.300 18.92 1.35 0 0 1 0 0 0 0 0 0 0 0
76 139.500 20.23 1.42 0 0 0 1 0 0 0 0 0 0 0
77 139.700 20.98 1.51 0 0 0 0 1 0 0 0 0 0 0
78 140.200 22.38 1.62 0 0 0 0 0 1 0 0 0 0 0
79 140.500 21.78 1.55 0 0 0 0 0 0 1 0 0 0 0
80 140.900 21.34 1.84 0 0 0 0 0 0 0 1 0 0 0
81 141.300 21.88 1.92 0 0 0 0 0 0 0 0 1 0 0
82 141.800 21.69 2.38 0 0 0 0 0 0 0 0 0 1 0
83 142.000 20.34 2.13 0 0 0 0 0 0 0 0 0 0 1
84 141.900 19.41 2.07 0 0 0 0 0 0 0 0 0 0 0
85 142.600 19.03 2.03 1 0 0 0 0 0 0 0 0 0 0
86 143.100 20.09 1.76 0 1 0 0 0 0 0 0 0 0 0
87 143.600 20.32 2.00 0 0 1 0 0 0 0 0 0 0 0
88 144.000 20.25 2.06 0 0 0 1 0 0 0 0 0 0 0
89 144.200 19.95 2.18 0 0 0 0 1 0 0 0 0 0 0
90 144.400 19.09 1.98 0 0 0 0 0 1 0 0 0 0 0
91 144.400 17.89 1.99 0 0 0 0 0 0 1 0 0 0 0
92 144.800 18.01 2.04 0 0 0 0 0 0 0 1 0 0 0
93 145.100 17.50 2.09 0 0 0 0 0 0 0 0 1 0 0
94 145.700 18.15 2.02 0 0 0 0 0 0 0 0 0 1 0
95 145.800 16.61 2.03 0 0 0 0 0 0 0 0 0 0 1
96 145.800 14.51 2.15 0 0 0 0 0 0 0 0 0 0 0
97 146.200 15.03 1.93 1 0 0 0 0 0 0 0 0 0 0
98 146.700 14.78 1.88 0 1 0 0 0 0 0 0 0 0 0
99 147.200 14.68 1.93 0 0 1 0 0 0 0 0 0 0 0
100 147.400 16.42 1.91 0 0 0 1 0 0 0 0 0 0 0
101 147.500 17.89 2.00 0 0 0 0 1 0 0 0 0 0 0
102 148.000 19.06 1.80 0 0 0 0 0 1 0 0 0 0 0
103 148.400 19.65 1.81 0 0 0 0 0 0 1 0 0 0 0
104 149.000 18.38 1.83 0 0 0 0 0 0 0 1 0 0 0
105 149.400 17.45 1.78 0 0 0 0 0 0 0 0 1 0 0
106 149.500 17.72 1.70 0 0 0 0 0 0 0 0 0 1 0
107 149.700 18.07 1.75 0 0 0 0 0 0 0 0 0 0 1
108 149.700 17.16 1.88 0 0 0 0 0 0 0 0 0 0 0
109 150.300 18.04 1.62 1 0 0 0 0 0 0 0 0 0 0
110 150.900 18.57 1.48 0 1 0 0 0 0 0 0 0 0 0
111 151.400 18.54 1.47 0 0 1 0 0 0 0 0 0 0 0
112 151.900 19.90 1.52 0 0 0 1 0 0 0 0 0 0 0
113 152.200 19.74 1.55 0 0 0 0 1 0 0 0 0 0 0
114 152.500 18.45 1.58 0 0 0 0 0 1 0 0 0 0 0
115 152.500 17.33 1.43 0 0 0 0 0 0 1 0 0 0 0
116 152.900 18.02 1.43 0 0 0 0 0 0 0 1 0 0 0
117 153.200 18.23 1.52 0 0 0 0 0 0 0 0 1 0 0
118 153.700 17.43 1.54 0 0 0 0 0 0 0 0 0 1 0
119 153.600 17.99 1.61 0 0 0 0 0 0 0 0 0 0 1
120 153.500 19.03 1.84 0 0 0 0 0 0 0 0 0 0 0
121 154.400 18.85 2.05 1 0 0 0 0 0 0 0 0 0 0
122 154.900 19.09 1.89 0 1 0 0 0 0 0 0 0 0 0
123 155.700 21.33 1.95 0 0 1 0 0 0 0 0 0 0 0
124 156.300 23.50 2.08 0 0 0 1 0 0 0 0 0 0 0
125 156.600 21.17 2.01 0 0 0 0 1 0 0 0 0 0 0
126 156.700 20.42 2.08 0 0 0 0 0 1 0 0 0 0 0
127 157.000 21.30 2.25 0 0 0 0 0 0 1 0 0 0 0
128 157.300 21.90 2.10 0 0 0 0 0 0 0 1 0 0 0
129 157.800 23.97 1.85 0 0 0 0 0 0 0 0 1 0 0
130 158.300 24.88 1.94 0 0 0 0 0 0 0 0 0 1 0
131 158.600 23.71 2.50 0 0 0 0 0 0 0 0 0 0 1
132 158.600 25.23 3.26 0 0 0 0 0 0 0 0 0 0 0
133 159.100 25.13 3.40 1 0 0 0 0 0 0 0 0 0 0
134 159.600 22.18 2.49 0 1 0 0 0 0 0 0 0 0 0
135 160.000 20.97 1.79 0 0 1 0 0 0 0 0 0 0 0
136 160.200 19.70 1.81 0 0 0 1 0 0 0 0 0 0 0
137 160.100 20.82 2.00 0 0 0 0 1 0 0 0 0 0 0
138 160.300 19.26 2.08 0 0 0 0 0 1 0 0 0 0 0
139 160.500 19.66 2.00 0 0 0 0 0 0 1 0 0 0 0
140 160.800 19.95 2.08 0 0 0 0 0 0 0 1 0 0 0
141 161.200 19.80 2.33 0 0 0 0 0 0 0 0 1 0 0
142 161.600 21.33 2.68 0 0 0 0 0 0 0 0 0 1 0
143 161.500 20.19 2.92 0 0 0 0 0 0 0 0 0 0 1
144 161.300 18.33 2.28 0 0 0 0 0 0 0 0 0 0 0
145 161.600 16.72 1.96 1 0 0 0 0 0 0 0 0 0 0
146 161.900 16.06 1.96 0 1 0 0 0 0 0 0 0 0 0
147 162.200 15.12 2.06 0 0 1 0 0 0 0 0 0 0 0
148 162.500 15.35 2.16 0 0 0 1 0 0 0 0 0 0 0
149 162.800 14.91 2.04 0 0 0 0 1 0 0 0 0 0 0
150 163.000 13.72 1.91 0 0 0 0 0 1 0 0 0 0 0
151 163.200 14.17 2.09 0 0 0 0 0 0 1 0 0 0 0
152 163.400 13.47 1.82 0 0 0 0 0 0 0 1 0 0 0
153 163.600 15.03 1.70 0 0 0 0 0 0 0 0 1 0 0
154 164.000 14.46 1.86 0 0 0 0 0 0 0 0 0 1 0
155 164.000 13.00 1.94 0 0 0 0 0 0 0 0 0 0 1
156 163.900 11.35 1.95 0 0 0 0 0 0 0 0 0 0 0
157 164.300 12.51 1.85 1 0 0 0 0 0 0 0 0 0 0
158 164.500 12.01 1.77 0 1 0 0 0 0 0 0 0 0 0
159 165.000 14.68 1.70 0 0 1 0 0 0 0 0 0 0 0
160 166.200 17.31 1.90 0 0 0 1 0 0 0 0 0 0 0
161 166.200 17.72 2.17 0 0 0 0 1 0 0 0 0 0 0
162 166.200 17.92 2.14 0 0 0 0 0 1 0 0 0 0 0
163 166.700 20.10 2.20 0 0 0 0 0 0 1 0 0 0 0
164 167.100 21.28 2.51 0 0 0 0 0 0 0 1 0 0 0
165 167.900 23.80 2.62 0 0 0 0 0 0 0 0 1 0 0
166 168.200 22.69 2.52 0 0 0 0 0 0 0 0 0 1 0
167 168.300 25.00 2.68 0 0 0 0 0 0 0 0 0 0 1
168 168.300 26.10 2.24 0 0 0 0 0 0 0 0 0 0 0
169 168.800 27.26 2.60 1 0 0 0 0 0 0 0 0 0 0
170 169.800 29.37 2.73 0 1 0 0 0 0 0 0 0 0 0
171 171.200 29.84 2.66 0 0 1 0 0 0 0 0 0 0 0
172 171.300 25.72 2.86 0 0 0 1 0 0 0 0 0 0 0
173 171.500 28.79 3.04 0 0 0 0 1 0 0 0 0 0 0
174 172.400 31.82 3.77 0 0 0 0 0 1 0 0 0 0 0
175 172.800 29.70 3.84 0 0 0 0 0 0 1 0 0 0 0
176 172.800 31.26 3.73 0 0 0 0 0 0 0 1 0 0 0
177 173.700 33.88 4.26 0 0 0 0 0 0 0 0 1 0 0
178 174.000 33.11 4.58 0 0 0 0 0 0 0 0 0 1 0
179 174.100 34.42 4.40 0 0 0 0 0 0 0 0 0 0 1
180 174.000 28.44 5.77 0 0 0 0 0 0 0 0 0 0 0
181 175.100 29.59 6.82 1 0 0 0 0 0 0 0 0 0 0
182 175.800 29.61 5.08 0 1 0 0 0 0 0 0 0 0 0
183 176.200 27.24 4.37 0 0 1 0 0 0 0 0 0 0 0
184 176.900 27.49 4.52 0 0 0 1 0 0 0 0 0 0 0
185 177.700 28.63 4.36 0 0 0 0 1 0 0 0 0 0 0
186 178.000 27.60 3.79 0 0 0 0 0 1 0 0 0 0 0
187 177.500 26.42 3.35 0 0 0 0 0 0 1 0 0 0 0
188 177.500 27.37 3.33 0 0 0 0 0 0 0 1 0 0 0
189 178.300 26.20 2.93 0 0 0 0 0 0 0 0 1 0 0
190 177.700 22.17 2.78 0 0 0 0 0 0 0 0 0 1 0
191 177.400 19.64 3.41 0 0 0 0 0 0 0 0 0 0 1
192 176.700 19.39 3.42 0 0 0 0 0 0 0 0 0 0 0
193 177.100 19.71 2.50 1 0 0 0 0 0 0 0 0 0 0
194 177.800 20.72 2.19 0 1 0 0 0 0 0 0 0 0 0
195 178.800 24.53 2.40 0 0 1 0 0 0 0 0 0 0 0
196 179.800 26.18 2.94 0 0 0 1 0 0 0 0 0 0 0
197 179.800 27.04 2.94 0 0 0 0 1 0 0 0 0 0 0
198 179.900 25.52 2.96 0 0 0 0 0 1 0 0 0 0 0
199 180.100 26.97 2.92 0 0 0 0 0 0 1 0 0 0 0
200 180.700 28.39 2.76 0 0 0 0 0 0 0 1 0 0 0
201 181.000 29.66 2.97 0 0 0 0 0 0 0 0 1 0 0
202 181.300 28.84 3.24 0 0 0 0 0 0 0 0 0 1 0
203 181.300 26.35 3.59 0 0 0 0 0 0 0 0 0 0 1
204 180.900 29.46 3.96 0 0 0 0 0 0 0 0 0 0 0
205 181.700 32.95 4.43 1 0 0 0 0 0 0 0 0 0 0
206 183.100 35.83 5.05 0 1 0 0 0 0 0 0 0 0 0
207 184.200 33.51 6.96 0 0 1 0 0 0 0 0 0 0 0
208 183.800 28.17 4.47 0 0 0 1 0 0 0 0 0 0 0
209 183.500 28.11 4.77 0 0 0 0 1 0 0 0 0 0 0
210 183.700 30.66 5.41 0 0 0 0 0 1 0 0 0 0 0
211 183.900 30.75 5.08 0 0 0 0 0 0 1 0 0 0 0
212 184.600 31.57 4.46 0 0 0 0 0 0 0 1 0 0 0
213 185.200 28.31 4.59 0 0 0 0 0 0 0 0 1 0 0
214 185.000 30.34 4.32 0 0 0 0 0 0 0 0 0 1 0
215 184.500 31.11 4.26 0 0 0 0 0 0 0 0 0 0 1
216 184.300 32.13 4.76 0 0 0 0 0 0 0 0 0 0 0
217 185.200 34.31 5.21 1 0 0 0 0 0 0 0 0 0 0
218 186.200 34.68 5.02 0 1 0 0 0 0 0 0 0 0 0
219 187.400 36.74 5.12 0 0 1 0 0 0 0 0 0 0 0
220 188.000 36.75 5.03 0 0 0 1 0 0 0 0 0 0 0
221 189.100 40.28 5.40 0 0 0 0 1 0 0 0 0 0 0
222 189.700 38.03 5.82 0 0 0 0 0 1 0 0 0 0 0
223 189.400 40.78 5.62 0 0 0 0 0 0 1 0 0 0 0
224 189.500 44.90 5.52 0 0 0 0 0 0 0 1 0 0 0
225 189.900 45.94 5.06 0 0 0 0 0 0 0 0 1 0 0
226 190.900 53.28 5.43 0 0 0 0 0 0 0 0 0 1 0
227 191.000 48.47 6.21 0 0 0 0 0 0 0 0 0 0 1
228 190.300 43.15 6.01 0 0 0 0 0 0 0 0 0 0 0
229 190.700 46.84 5.80 1 0 0 0 0 0 0 0 0 0 0
230 191.800 48.15 5.73 0 1 0 0 0 0 0 0 0 0 0
231 193.300 54.19 5.95 0 0 1 0 0 0 0 0 0 0 0
232 194.600 52.98 6.57 0 0 0 1 0 0 0 0 0 0 0
233 194.400 49.83 6.25 0 0 0 0 1 0 0 0 0 0 0
234 194.500 56.35 6.09 0 0 0 0 0 1 0 0 0 0 0
235 195.400 59.00 6.71 0 0 0 0 0 0 1 0 0 0 0
236 196.400 64.99 6.48 0 0 0 0 0 0 0 1 0 0 0
237 198.800 65.59 8.95 0 0 0 0 0 0 0 0 1 0 0
238 199.200 62.26 10.33 0 0 0 0 0 0 0 0 0 1 0
239 197.600 58.32 9.89 0 0 0 0 0 0 0 0 0 0 1
240 196.800 59.41 9.08 0 0 0 0 0 0 0 0 0 0 0
241 198.300 65.49 8.01 1 0 0 0 0 0 0 0 0 0 0
242 198.700 61.63 6.85 0 1 0 0 0 0 0 0 0 0 0
243 199.800 62.69 6.43 0 0 1 0 0 0 0 0 0 0 0
244 201.500 69.44 6.37 0 0 0 1 0 0 0 0 0 0 0
245 202.500 70.84 6.23 0 0 0 0 1 0 0 0 0 0 0
246 202.900 70.95 5.77 0 0 0 0 0 1 0 0 0 0 0
247 203.500 74.41 5.91 0 0 0 0 0 0 1 0 0 0 0
248 203.900 73.04 6.55 0 0 0 0 0 0 0 1 0 0 0
249 202.900 63.80 6.06 0 0 0 0 0 0 0 0 1 0 0
250 201.800 58.89 5.09 0 0 0 0 0 0 0 0 0 1 0
251 201.500 59.08 6.71 0 0 0 0 0 0 0 0 0 0 1
252 201.800 61.96 6.76 0 0 0 0 0 0 0 0 0 0 0
253 202.416 54.51 5.70 1 0 0 0 0 0 0 0 0 0 0
254 203.499 59.28 6.80 0 1 0 0 0 0 0 0 0 0 0
255 205.352 60.44 6.65 0 0 1 0 0 0 0 0 0 0 0
256 206.686 63.98 6.26 0 0 0 1 0 0 0 0 0 0 0
257 207.949 63.45 6.75 0 0 0 0 1 0 0 0 0 0 0
258 208.352 67.49 6.62 0 0 0 0 0 1 0 0 0 0 0
259 208.299 74.12 6.21 0 0 0 0 0 0 1 0 0 0 0
260 207.917 72.36 5.76 0 0 0 0 0 0 0 1 0 0 0
261 208.490 79.91 5.30 0 0 0 0 0 0 0 0 1 0 0
262 208.936 85.80 5.78 0 0 0 0 0 0 0 0 0 1 0
263 210.177 94.77 6.46 0 0 0 0 0 0 0 0 0 0 1
264 210.036 91.69 6.87 0 0 0 0 0 0 0 0 0 0 0
265 211.080 92.97 7.16 1 0 0 0 0 0 0 0 0 0 0
266 211.693 95.39 7.71 0 1 0 0 0 0 0 0 0 0 0
267 213.528 105.45 8.44 0 0 1 0 0 0 0 0 0 0 0
268 214.823 112.58 9.04 0 0 0 1 0 0 0 0 0 0 0
269 216.632 125.40 10.15 0 0 0 0 1 0 0 0 0 0 0
270 218.815 133.88 10.79 0 0 0 0 0 1 0 0 0 0 0
271 219.964 133.37 11.32 0 0 0 0 0 0 1 0 0 0 0
272 219.086 116.67 8.34 0 0 0 0 0 0 0 1 0 0 0
273 218.783 104.11 6.72 0 0 0 0 0 0 0 0 1 0 0
274 216.573 76.61 5.50 0 0 0 0 0 0 0 0 0 1 0
275 212.425 57.31 4.75 0 0 0 0 0 0 0 0 0 0 1
276 210.228 41.12 5.52 0 0 0 0 0 0 0 0 0 0 0
277 211.143 41.71 5.15 1 0 0 0 0 0 0 0 0 0 0
278 212.193 39.09 4.19 0 1 0 0 0 0 0 0 0 0 0
279 212.709 47.94 3.72 0 0 1 0 0 0 0 0 0 0 0
280 213.240 49.65 3.43 0 0 0 1 0 0 0 0 0 0 0
281 213.856 59.03 3.45 0 0 0 0 1 0 0 0 0 0 0
282 215.693 69.64 3.45 0 0 0 0 0 1 0 0 0 0 0
283 215.351 64.15 3.43 0 0 0 0 0 0 1 0 0 0 0
284 215.834 71.05 3.14 0 0 0 0 0 0 0 1 0 0 0
285 215.969 69.41 2.92 0 0 0 0 0 0 0 0 1 0 0
286 216.177 75.72 3.60 0 0 0 0 0 0 0 0 0 1 0
287 216.330 77.99 3.64 0 0 0 0 0 0 0 0 0 0 1
288 215.949 74.47 4.44 0 0 0 0 0 0 0 0 0 0 0
289 216.687 78.33 5.14 1 0 0 0 0 0 0 0 0 0 0
290 216.741 76.39 4.89 0 1 0 0 0 0 0 0 0 0 0
291 217.631 81.20 4.36 0 0 1 0 0 0 0 0 0 0 0
292 218.009 84.29 3.92 0 0 0 1 0 0 0 0 0 0 0
293 218.178 73.74 4.04 0 0 0 0 1 0 0 0 0 0 0
294 217.965 75.34 4.25 0 0 0 0 0 1 0 0 0 0 0
295 218.011 76.32 4.36 0 0 0 0 0 0 1 0 0 0 0
296 218.312 76.60 4.22 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Olieprijzen Gasprijzen M1 M2 M3
122.57576 0.61510 6.13628 -1.31770 0.83788 0.84741
M4 M5 M6 M7 M8 M9
1.17183 0.50558 0.13457 0.07764 1.08784 0.83309
M10 M11
0.81219 0.34441
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-54.188 -11.336 3.186 13.464 39.024
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122.57576 4.29608 28.532 < 2e-16 ***
Olieprijzen 0.61510 0.08470 7.262 3.73e-12 ***
Gasprijzen 6.13628 0.97423 6.299 1.15e-09 ***
M1 -1.31770 5.39215 -0.244 0.807
M2 0.83788 5.39876 0.155 0.877
M3 0.84741 5.40547 0.157 0.876
M4 1.17183 5.41662 0.216 0.829
M5 0.50558 5.41451 0.093 0.926
M6 0.13457 5.41882 0.025 0.980
M7 0.07764 5.42248 0.014 0.989
M8 1.08784 5.44654 0.200 0.842
M9 0.83309 5.48721 0.152 0.879
M10 0.81219 5.47174 0.148 0.882
M11 0.34441 5.45282 0.063 0.950
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.86 on 282 degrees of freedom
Multiple R-squared: 0.671, Adjusted R-squared: 0.6559
F-statistic: 44.25 on 13 and 282 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,] 1.213347e-04 2.426693e-04 9.998787e-01
[2,] 6.115352e-06 1.223070e-05 9.999939e-01
[3,] 2.262432e-07 4.524863e-07 9.999998e-01
[4,] 6.156999e-08 1.231400e-07 9.999999e-01
[5,] 9.010908e-09 1.802182e-08 1.000000e+00
[6,] 1.537883e-09 3.075767e-09 1.000000e+00
[7,] 4.671097e-10 9.342194e-10 1.000000e+00
[8,] 4.735746e-10 9.471493e-10 1.000000e+00
[9,] 6.043913e-09 1.208783e-08 1.000000e+00
[10,] 3.214892e-09 6.429785e-09 1.000000e+00
[11,] 1.185463e-09 2.370927e-09 1.000000e+00
[12,] 3.146681e-10 6.293363e-10 1.000000e+00
[13,] 1.044558e-10 2.089117e-10 1.000000e+00
[14,] 4.236863e-11 8.473726e-11 1.000000e+00
[15,] 2.531735e-11 5.063469e-11 1.000000e+00
[16,] 2.663009e-11 5.326019e-11 1.000000e+00
[17,] 1.639740e-11 3.279480e-11 1.000000e+00
[18,] 2.873030e-11 5.746060e-11 1.000000e+00
[19,] 4.316323e-11 8.632645e-11 1.000000e+00
[20,] 1.343421e-10 2.686842e-10 1.000000e+00
[21,] 2.302578e-10 4.605156e-10 1.000000e+00
[22,] 2.450472e-10 4.900943e-10 1.000000e+00
[23,] 2.604759e-10 5.209519e-10 1.000000e+00
[24,] 2.115191e-10 4.230382e-10 1.000000e+00
[25,] 3.041879e-10 6.083758e-10 1.000000e+00
[26,] 5.019447e-10 1.003889e-09 1.000000e+00
[27,] 6.903166e-10 1.380633e-09 1.000000e+00
[28,] 8.046614e-10 1.609323e-09 1.000000e+00
[29,] 7.234394e-10 1.446879e-09 1.000000e+00
[30,] 6.150859e-10 1.230172e-09 1.000000e+00
[31,] 5.463619e-10 1.092724e-09 1.000000e+00
[32,] 7.517325e-10 1.503465e-09 1.000000e+00
[33,] 2.314180e-09 4.628361e-09 1.000000e+00
[34,] 2.298833e-09 4.597665e-09 1.000000e+00
[35,] 2.053080e-09 4.106160e-09 1.000000e+00
[36,] 2.980585e-09 5.961170e-09 1.000000e+00
[37,] 4.618222e-09 9.236443e-09 1.000000e+00
[38,] 8.456792e-09 1.691358e-08 1.000000e+00
[39,] 1.023174e-08 2.046347e-08 1.000000e+00
[40,] 6.375631e-09 1.275126e-08 1.000000e+00
[41,] 4.202643e-09 8.405286e-09 1.000000e+00
[42,] 2.902455e-09 5.804911e-09 1.000000e+00
[43,] 2.341974e-09 4.683947e-09 1.000000e+00
[44,] 3.190603e-09 6.381205e-09 1.000000e+00
[45,] 5.725917e-09 1.145183e-08 1.000000e+00
[46,] 9.045757e-09 1.809151e-08 1.000000e+00
[47,] 1.227468e-08 2.454936e-08 1.000000e+00
[48,] 1.811465e-08 3.622929e-08 1.000000e+00
[49,] 2.672708e-08 5.345417e-08 1.000000e+00
[50,] 3.348271e-08 6.696542e-08 1.000000e+00
[51,] 2.770948e-08 5.541896e-08 1.000000e+00
[52,] 3.632071e-08 7.264141e-08 1.000000e+00
[53,] 1.127779e-07 2.255559e-07 9.999999e-01
[54,] 5.157939e-07 1.031588e-06 9.999995e-01
[55,] 1.787671e-06 3.575343e-06 9.999982e-01
[56,] 8.957700e-06 1.791540e-05 9.999910e-01
[57,] 1.973361e-05 3.946722e-05 9.999803e-01
[58,] 1.739152e-05 3.478303e-05 9.999826e-01
[59,] 1.925682e-05 3.851363e-05 9.999807e-01
[60,] 2.505853e-05 5.011706e-05 9.999749e-01
[61,] 4.058368e-05 8.116735e-05 9.999594e-01
[62,] 6.622268e-05 1.324454e-04 9.999338e-01
[63,] 9.963599e-05 1.992720e-04 9.999004e-01
[64,] 4.289438e-04 8.578876e-04 9.995711e-01
[65,] 1.730941e-03 3.461881e-03 9.982691e-01
[66,] 1.017624e-02 2.035248e-02 9.898238e-01
[67,] 2.326073e-02 4.652147e-02 9.767393e-01
[68,] 4.075860e-02 8.151720e-02 9.592414e-01
[69,] 7.655678e-02 1.531136e-01 9.234432e-01
[70,] 1.076403e-01 2.152806e-01 8.923597e-01
[71,] 1.545690e-01 3.091380e-01 8.454310e-01
[72,] 2.171418e-01 4.342837e-01 7.828582e-01
[73,] 2.890750e-01 5.781499e-01 7.109250e-01
[74,] 3.534751e-01 7.069502e-01 6.465249e-01
[75,] 4.258574e-01 8.517149e-01 5.741426e-01
[76,] 5.100407e-01 9.799186e-01 4.899593e-01
[77,] 5.959672e-01 8.080656e-01 4.040328e-01
[78,] 6.820650e-01 6.358700e-01 3.179350e-01
[79,] 7.593909e-01 4.812182e-01 2.406091e-01
[80,] 8.267312e-01 3.465375e-01 1.732688e-01
[81,] 8.983074e-01 2.033851e-01 1.016926e-01
[82,] 9.348209e-01 1.303583e-01 6.517914e-02
[83,] 9.560308e-01 8.793830e-02 4.396915e-02
[84,] 9.692071e-01 6.158583e-02 3.079291e-02
[85,] 9.768223e-01 4.635544e-02 2.317772e-02
[86,] 9.825328e-01 3.493441e-02 1.746720e-02
[87,] 9.864322e-01 2.713564e-02 1.356782e-02
[88,] 9.905183e-01 1.896347e-02 9.481734e-03
[89,] 9.939501e-01 1.209974e-02 6.049868e-03
[90,] 9.967270e-01 6.545994e-03 3.272997e-03
[91,] 9.981267e-01 3.746674e-03 1.873337e-03
[92,] 9.989107e-01 2.178684e-03 1.089342e-03
[93,] 9.995436e-01 9.128159e-04 4.564079e-04
[94,] 9.997824e-01 4.351169e-04 2.175584e-04
[95,] 9.998882e-01 2.235647e-04 1.117823e-04
[96,] 9.999405e-01 1.189004e-04 5.945021e-05
[97,] 9.999684e-01 6.320403e-05 3.160202e-05
[98,] 9.999822e-01 3.561098e-05 1.780549e-05
[99,] 9.999904e-01 1.922188e-05 9.610939e-06
[100,] 9.999949e-01 1.014297e-05 5.071485e-06
[101,] 9.999972e-01 5.522621e-06 2.761311e-06
[102,] 9.999986e-01 2.825412e-06 1.412706e-06
[103,] 9.999992e-01 1.511573e-06 7.557867e-07
[104,] 9.999996e-01 7.992376e-07 3.996188e-07
[105,] 9.999998e-01 3.665057e-07 1.832528e-07
[106,] 9.999999e-01 1.727411e-07 8.637056e-08
[107,] 1.000000e+00 8.845477e-08 4.422738e-08
[108,] 1.000000e+00 4.569692e-08 2.284846e-08
[109,] 1.000000e+00 2.459512e-08 1.229756e-08
[110,] 1.000000e+00 1.525974e-08 7.629870e-09
[111,] 1.000000e+00 1.081814e-08 5.409072e-09
[112,] 1.000000e+00 7.241297e-09 3.620648e-09
[113,] 1.000000e+00 4.321545e-09 2.160773e-09
[114,] 1.000000e+00 2.373693e-09 1.186846e-09
[115,] 1.000000e+00 1.615246e-09 8.076231e-10
[116,] 1.000000e+00 1.095299e-09 5.476493e-10
[117,] 1.000000e+00 7.697532e-10 3.848766e-10
[118,] 1.000000e+00 5.022001e-10 2.511001e-10
[119,] 1.000000e+00 2.430434e-10 1.215217e-10
[120,] 1.000000e+00 1.143560e-10 5.717801e-11
[121,] 1.000000e+00 6.384974e-11 3.192487e-11
[122,] 1.000000e+00 4.370056e-11 2.185028e-11
[123,] 1.000000e+00 2.980769e-11 1.490385e-11
[124,] 1.000000e+00 2.112179e-11 1.056090e-11
[125,] 1.000000e+00 1.791251e-11 8.956255e-12
[126,] 1.000000e+00 1.598080e-11 7.990399e-12
[127,] 1.000000e+00 1.497829e-11 7.489147e-12
[128,] 1.000000e+00 7.702553e-12 3.851276e-12
[129,] 1.000000e+00 2.250009e-12 1.125004e-12
[130,] 1.000000e+00 9.951934e-13 4.975967e-13
[131,] 1.000000e+00 6.156337e-13 3.078169e-13
[132,] 1.000000e+00 4.377860e-13 2.188930e-13
[133,] 1.000000e+00 2.840206e-13 1.420103e-13
[134,] 1.000000e+00 1.882150e-13 9.410751e-14
[135,] 1.000000e+00 1.672725e-13 8.363626e-14
[136,] 1.000000e+00 1.092359e-13 5.461795e-14
[137,] 1.000000e+00 6.094695e-14 3.047347e-14
[138,] 1.000000e+00 3.553792e-14 1.776896e-14
[139,] 1.000000e+00 2.116596e-14 1.058298e-14
[140,] 1.000000e+00 1.135873e-14 5.679365e-15
[141,] 1.000000e+00 4.914582e-15 2.457291e-15
[142,] 1.000000e+00 2.812666e-15 1.406333e-15
[143,] 1.000000e+00 1.560060e-15 7.800298e-16
[144,] 1.000000e+00 1.021090e-15 5.105452e-16
[145,] 1.000000e+00 8.629150e-16 4.314575e-16
[146,] 1.000000e+00 7.930904e-16 3.965452e-16
[147,] 1.000000e+00 7.067416e-16 3.533708e-16
[148,] 1.000000e+00 7.974466e-16 3.987233e-16
[149,] 1.000000e+00 8.158135e-16 4.079067e-16
[150,] 1.000000e+00 7.125321e-16 3.562661e-16
[151,] 1.000000e+00 4.586136e-16 2.293068e-16
[152,] 1.000000e+00 1.078035e-16 5.390176e-17
[153,] 1.000000e+00 3.191993e-17 1.595997e-17
[154,] 1.000000e+00 9.521838e-18 4.760919e-18
[155,] 1.000000e+00 3.500681e-18 1.750340e-18
[156,] 1.000000e+00 2.547524e-18 1.273762e-18
[157,] 1.000000e+00 1.329055e-18 6.645276e-19
[158,] 1.000000e+00 6.701252e-19 3.350626e-19
[159,] 1.000000e+00 5.215030e-19 2.607515e-19
[160,] 1.000000e+00 4.253919e-19 2.126960e-19
[161,] 1.000000e+00 2.677151e-19 1.338575e-19
[162,] 1.000000e+00 1.694139e-19 8.470695e-20
[163,] 1.000000e+00 7.784323e-20 3.892161e-20
[164,] 1.000000e+00 4.543504e-20 2.271752e-20
[165,] 1.000000e+00 3.245672e-20 1.622836e-20
[166,] 1.000000e+00 3.984215e-20 1.992107e-20
[167,] 1.000000e+00 5.814096e-20 2.907048e-20
[168,] 1.000000e+00 1.027633e-19 5.138167e-20
[169,] 1.000000e+00 1.702187e-19 8.510934e-20
[170,] 1.000000e+00 2.813637e-19 1.406819e-19
[171,] 1.000000e+00 3.999996e-19 1.999998e-19
[172,] 1.000000e+00 5.813345e-19 2.906673e-19
[173,] 1.000000e+00 7.122098e-19 3.561049e-19
[174,] 1.000000e+00 7.849972e-19 3.924986e-19
[175,] 1.000000e+00 1.213000e-18 6.065002e-19
[176,] 1.000000e+00 1.445617e-18 7.228084e-19
[177,] 1.000000e+00 7.663587e-19 3.831794e-19
[178,] 1.000000e+00 3.099525e-19 1.549762e-19
[179,] 1.000000e+00 1.366046e-19 6.830230e-20
[180,] 1.000000e+00 1.168302e-19 5.841512e-20
[181,] 1.000000e+00 7.879238e-20 3.939619e-20
[182,] 1.000000e+00 7.405968e-20 3.702984e-20
[183,] 1.000000e+00 6.050589e-20 3.025294e-20
[184,] 1.000000e+00 4.855376e-20 2.427688e-20
[185,] 1.000000e+00 3.658592e-20 1.829296e-20
[186,] 1.000000e+00 2.966850e-20 1.483425e-20
[187,] 1.000000e+00 2.938365e-20 1.469182e-20
[188,] 1.000000e+00 1.525766e-20 7.628832e-21
[189,] 1.000000e+00 9.535836e-21 4.767918e-21
[190,] 1.000000e+00 7.772753e-21 3.886376e-21
[191,] 1.000000e+00 1.145870e-20 5.729350e-21
[192,] 1.000000e+00 2.358133e-20 1.179066e-20
[193,] 1.000000e+00 4.768388e-20 2.384194e-20
[194,] 1.000000e+00 1.113601e-19 5.568005e-20
[195,] 1.000000e+00 2.447900e-19 1.223950e-19
[196,] 1.000000e+00 5.132921e-19 2.566461e-19
[197,] 1.000000e+00 1.329909e-18 6.649544e-19
[198,] 1.000000e+00 2.215579e-18 1.107790e-18
[199,] 1.000000e+00 2.092610e-18 1.046305e-18
[200,] 1.000000e+00 1.492847e-18 7.464235e-19
[201,] 1.000000e+00 1.599758e-18 7.998789e-19
[202,] 1.000000e+00 1.578133e-18 7.890666e-19
[203,] 1.000000e+00 2.262907e-18 1.131453e-18
[204,] 1.000000e+00 3.563441e-18 1.781721e-18
[205,] 1.000000e+00 5.157249e-18 2.578624e-18
[206,] 1.000000e+00 1.276546e-17 6.382731e-18
[207,] 1.000000e+00 2.293272e-17 1.146636e-17
[208,] 1.000000e+00 3.461578e-17 1.730789e-17
[209,] 1.000000e+00 3.419240e-17 1.709620e-17
[210,] 1.000000e+00 1.511873e-17 7.559363e-18
[211,] 1.000000e+00 1.592980e-17 7.964902e-18
[212,] 1.000000e+00 1.362538e-17 6.812690e-18
[213,] 1.000000e+00 7.228783e-18 3.614392e-18
[214,] 1.000000e+00 3.399860e-18 1.699930e-18
[215,] 1.000000e+00 2.016542e-18 1.008271e-18
[216,] 1.000000e+00 3.534477e-18 1.767238e-18
[217,] 1.000000e+00 4.768186e-18 2.384093e-18
[218,] 1.000000e+00 3.150313e-18 1.575156e-18
[219,] 1.000000e+00 2.878508e-18 1.439254e-18
[220,] 1.000000e+00 1.989247e-18 9.946235e-19
[221,] 1.000000e+00 4.456481e-18 2.228241e-18
[222,] 1.000000e+00 2.875129e-18 1.437564e-18
[223,] 1.000000e+00 3.629227e-18 1.814614e-18
[224,] 1.000000e+00 1.183437e-17 5.917186e-18
[225,] 1.000000e+00 2.988602e-17 1.494301e-17
[226,] 1.000000e+00 3.834547e-17 1.917273e-17
[227,] 1.000000e+00 5.418412e-17 2.709206e-17
[228,] 1.000000e+00 7.398859e-17 3.699430e-17
[229,] 1.000000e+00 6.128495e-17 3.064248e-17
[230,] 1.000000e+00 3.298416e-17 1.649208e-17
[231,] 1.000000e+00 1.227075e-17 6.135374e-18
[232,] 1.000000e+00 2.625148e-17 1.312574e-17
[233,] 1.000000e+00 9.506658e-17 4.753329e-17
[234,] 1.000000e+00 1.289673e-16 6.448367e-17
[235,] 1.000000e+00 4.947738e-16 2.473869e-16
[236,] 1.000000e+00 8.262649e-16 4.131324e-16
[237,] 1.000000e+00 8.210906e-16 4.105453e-16
[238,] 1.000000e+00 1.703803e-15 8.519013e-16
[239,] 1.000000e+00 6.047255e-15 3.023628e-15
[240,] 1.000000e+00 1.999669e-14 9.998345e-15
[241,] 1.000000e+00 8.379012e-14 4.189506e-14
[242,] 1.000000e+00 2.490589e-13 1.245294e-13
[243,] 1.000000e+00 2.124199e-13 1.062100e-13
[244,] 1.000000e+00 1.364465e-13 6.822325e-14
[245,] 1.000000e+00 5.758121e-14 2.879060e-14
[246,] 1.000000e+00 1.729718e-14 8.648592e-15
[247,] 1.000000e+00 2.065978e-14 1.032989e-14
[248,] 1.000000e+00 1.672817e-14 8.364085e-15
[249,] 1.000000e+00 9.251459e-15 4.625730e-15
[250,] 1.000000e+00 3.078695e-15 1.539347e-15
[251,] 1.000000e+00 5.008131e-15 2.504066e-15
[252,] 1.000000e+00 1.702412e-14 8.512059e-15
[253,] 1.000000e+00 4.351555e-14 2.175778e-14
[254,] 1.000000e+00 2.791409e-13 1.395705e-13
[255,] 1.000000e+00 3.215416e-12 1.607708e-12
[256,] 1.000000e+00 1.088834e-11 5.444168e-12
[257,] 1.000000e+00 4.021535e-11 2.010767e-11
[258,] 1.000000e+00 8.217630e-10 4.108815e-10
[259,] 1.000000e+00 9.556387e-09 4.778194e-09
[260,] 1.000000e+00 2.987341e-08 1.493671e-08
[261,] 9.999999e-01 2.866047e-07 1.433023e-07
[262,] 9.999976e-01 4.707132e-06 2.353566e-06
[263,] 9.999295e-01 1.409244e-04 7.046218e-05
> postscript(file="/var/www/rcomp/tmp/1li201290545932.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2li201290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3d9j31290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4d9j31290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5d9j31290545932.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 = 296
Frequency = 1
1 2 3 4 5 6
-39.75303702 -37.48494386 -35.63395296 -35.93167238 -35.66869822 -32.82325090
7 8 9 10 11 12
-31.32157136 -34.11283236 -33.27797878 -32.86871702 -32.74322128 -32.78488449
13 14 15 16 17 18
-32.20681391 -33.34744539 -33.19527373 -32.90798388 -32.06375896 -31.68026223
19 20 21 22 23 24
-32.16587346 -31.75842682 -29.99436530 -29.93781067 -29.17831965 -28.23022420
25 26 27 28 29 30
-28.12184321 -29.03809093 -27.31947403 -27.38997314 -25.62353904 -24.26645225
31 32 33 34 35 36
-23.26005780 -24.15073221 -21.94092492 -21.96683923 -22.11754983 -23.74867839
37 38 39 40 41 42
-23.45337120 -23.95521711 -23.47564110 -23.18035752 -21.53657605 -21.06796022
43 44 45 46 47 48
-20.54495372 -20.37157716 -19.96990851 -19.84679764 -19.42229689 -21.23604161
49 50 51 52 53 54
-21.60316585 -20.36563784 -16.77630306 -15.32695221 -14.09650336 -12.16420522
55 56 57 58 59 60
-12.74506473 -18.12777696 -20.89347142 -22.85603177 -20.91076027 -18.07371712
61 62 63 64 65 66
-14.20415791 -11.15167889 -9.80672682 -10.56455643 -9.62162095 -7.90409211
67 68 69 70 71 72
-7.83916904 -9.18000909 -9.43009115 -11.44477029 -10.53290340 -8.94278329
73 74 75 76 77 78
-5.39292799 -4.23841928 -4.04484868 -5.40459346 -5.55193428 -6.21705609
79 80 81 82 83 84
-5.06152662 -7.18059977 -7.34891342 -9.53383368 -6.50159801 -5.31696403
85 86 87 88 89 90
-2.82007410 -3.47086923 -4.59457321 -4.84411637 -4.52969006 -2.20243693
91 92 93 94 95 96
-1.46874973 -2.45957137 -1.89794093 -1.24731587 0.20635542 1.10612621
97 98 99 100 101 102
3.85395649 2.65896093 2.90413385 1.83216125 1.14194801 2.52054696
103 104 105 106 107 108
2.55320413 2.80146077 4.33506172 4.78078783 4.92646772 5.03290598
109 110 111 112 113 114
8.00475137 6.98224279 7.55253562 6.58476146 7.46533913 8.74574050
115 116 117 118 119 120
10.41202488 9.37741004 9.25071676 10.14097222 9.73475533 7.92811920
121 122 123 124 125 126
8.96791835 8.14651461 7.19098933 5.33408109 8.16305528 8.66585103
127 128 129 130 131 132
7.43832380 7.27951041 8.29506615 7.70395983 5.75508845 0.50097424
133 134 135 136 137 138
1.52110513 7.26408422 12.69423077 13.22825961 11.93970331 12.97936770
139 140 141 142 143 144
13.48115942 12.10168220 11.31461990 8.64671773 8.24300378 13.45872524
145 146 147 148 149 150
18.03034803 16.58072958 16.83577285 16.05624821 18.02949644 20.13019297
151 152 153 154 155 156
19.00579617 20.28296745 20.51450774 20.30421049 21.17913395 22.37710057
157 158 159 160 161 162
23.99491257 22.83778063 22.11547886 20.14608456 18.90334706 19.33542552
163 164 165 166 167 168
18.18325863 14.94499689 13.77469561 15.39198616 13.55707783 15.92484504
169 170 171 172 173 174
14.81996701 11.56880321 13.09972271 14.18225733 12.05561773 6.98338673
175 176 177 178 179 180
8.31478951 7.02002826 3.31097821 2.14189585 3.00842419 -1.47556808
181 182 183 184 185 186
-6.20833013 3.00091484 9.20594088 8.50730010 10.25414072 15.05638552
187 188 189 190 191 192
18.03909791 16.56728258 20.79620659 23.61640498 21.47453060 21.21135640
193 194 195 196 197 198
28.37760466 28.20301586 25.56134028 21.90840845 22.04567201 23.32890933
199 200 201 202 203 204
22.93939413 22.63756110 21.12250728 20.29099408 20.14267483 15.90370093
205 206 207 208 209 210
12.99064746 6.65907771 -2.54371183 15.29584584 13.85811713 8.93339985
211 212 213 214 215 216
11.15994341 14.14986074 16.21211517 16.44115795 16.30348669 12.75235625
217 218 219 220 221 222
10.86781020 10.65053185 9.96027338 10.78196457 8.10648500 7.88423268
223 224 225 226 227 228
7.17689195 4.34611039 7.18383908 1.41947689 0.15958944 4.30359454
229 230 231 232 233 234
5.04019316 3.60836635 0.03365365 -2.05099314 2.31643420 -0.24120627
235 236 237 238 239 240
-4.71878871 -7.00209162 -19.87302684 -25.87191132 -21.88067144 -18.03632864
241 242 243 244 245 246
-12.39261733 -4.65582605 -1.64011696 -4.04829216 -2.38410335 1.14193571
247 248 249 250 251 252
-1.18846254 -4.88319108 3.06186006 10.95509873 1.06523095 -0.36865937
253 254 255 256 257 258
12.65199997 1.89547445 3.94587716 5.17114810 4.41962297 3.50634346
259 260 261 262 263 264
1.94803183 4.39974061 3.40616453 -2.69529292 -10.67663811 -11.09459072
265 266 267 268 269 270
-11.29974103 -17.70582473 -26.54774592 -33.64460589 -45.86621902 -52.45548274
271 272 273 274 275 276
-54.18808192 -27.51797536 -9.89979089 12.31264056 25.10607298 28.48702722
277 278 279 280 281 282
32.62724286 39.02405289 36.97094259 37.90521932 33.29510028 28.97689322
283 284 285 286 287 288
32.19145040 29.19958292 31.94807334 24.12301711 23.10206672 20.32160812
289 290 291 292 293 294
15.70762243 16.33340338 17.50747638 18.36035671 24.94856398 22.83379379
295 296
21.65893346 21.63658943
> postscript(file="/var/www/rcomp/tmp/66ii61290545932.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 = 296
Frequency = 1
lag(myerror, k = 1) myerror
0 -39.75303702 NA
1 -37.48494386 -39.75303702
2 -35.63395296 -37.48494386
3 -35.93167238 -35.63395296
4 -35.66869822 -35.93167238
5 -32.82325090 -35.66869822
6 -31.32157136 -32.82325090
7 -34.11283236 -31.32157136
8 -33.27797878 -34.11283236
9 -32.86871702 -33.27797878
10 -32.74322128 -32.86871702
11 -32.78488449 -32.74322128
12 -32.20681391 -32.78488449
13 -33.34744539 -32.20681391
14 -33.19527373 -33.34744539
15 -32.90798388 -33.19527373
16 -32.06375896 -32.90798388
17 -31.68026223 -32.06375896
18 -32.16587346 -31.68026223
19 -31.75842682 -32.16587346
20 -29.99436530 -31.75842682
21 -29.93781067 -29.99436530
22 -29.17831965 -29.93781067
23 -28.23022420 -29.17831965
24 -28.12184321 -28.23022420
25 -29.03809093 -28.12184321
26 -27.31947403 -29.03809093
27 -27.38997314 -27.31947403
28 -25.62353904 -27.38997314
29 -24.26645225 -25.62353904
30 -23.26005780 -24.26645225
31 -24.15073221 -23.26005780
32 -21.94092492 -24.15073221
33 -21.96683923 -21.94092492
34 -22.11754983 -21.96683923
35 -23.74867839 -22.11754983
36 -23.45337120 -23.74867839
37 -23.95521711 -23.45337120
38 -23.47564110 -23.95521711
39 -23.18035752 -23.47564110
40 -21.53657605 -23.18035752
41 -21.06796022 -21.53657605
42 -20.54495372 -21.06796022
43 -20.37157716 -20.54495372
44 -19.96990851 -20.37157716
45 -19.84679764 -19.96990851
46 -19.42229689 -19.84679764
47 -21.23604161 -19.42229689
48 -21.60316585 -21.23604161
49 -20.36563784 -21.60316585
50 -16.77630306 -20.36563784
51 -15.32695221 -16.77630306
52 -14.09650336 -15.32695221
53 -12.16420522 -14.09650336
54 -12.74506473 -12.16420522
55 -18.12777696 -12.74506473
56 -20.89347142 -18.12777696
57 -22.85603177 -20.89347142
58 -20.91076027 -22.85603177
59 -18.07371712 -20.91076027
60 -14.20415791 -18.07371712
61 -11.15167889 -14.20415791
62 -9.80672682 -11.15167889
63 -10.56455643 -9.80672682
64 -9.62162095 -10.56455643
65 -7.90409211 -9.62162095
66 -7.83916904 -7.90409211
67 -9.18000909 -7.83916904
68 -9.43009115 -9.18000909
69 -11.44477029 -9.43009115
70 -10.53290340 -11.44477029
71 -8.94278329 -10.53290340
72 -5.39292799 -8.94278329
73 -4.23841928 -5.39292799
74 -4.04484868 -4.23841928
75 -5.40459346 -4.04484868
76 -5.55193428 -5.40459346
77 -6.21705609 -5.55193428
78 -5.06152662 -6.21705609
79 -7.18059977 -5.06152662
80 -7.34891342 -7.18059977
81 -9.53383368 -7.34891342
82 -6.50159801 -9.53383368
83 -5.31696403 -6.50159801
84 -2.82007410 -5.31696403
85 -3.47086923 -2.82007410
86 -4.59457321 -3.47086923
87 -4.84411637 -4.59457321
88 -4.52969006 -4.84411637
89 -2.20243693 -4.52969006
90 -1.46874973 -2.20243693
91 -2.45957137 -1.46874973
92 -1.89794093 -2.45957137
93 -1.24731587 -1.89794093
94 0.20635542 -1.24731587
95 1.10612621 0.20635542
96 3.85395649 1.10612621
97 2.65896093 3.85395649
98 2.90413385 2.65896093
99 1.83216125 2.90413385
100 1.14194801 1.83216125
101 2.52054696 1.14194801
102 2.55320413 2.52054696
103 2.80146077 2.55320413
104 4.33506172 2.80146077
105 4.78078783 4.33506172
106 4.92646772 4.78078783
107 5.03290598 4.92646772
108 8.00475137 5.03290598
109 6.98224279 8.00475137
110 7.55253562 6.98224279
111 6.58476146 7.55253562
112 7.46533913 6.58476146
113 8.74574050 7.46533913
114 10.41202488 8.74574050
115 9.37741004 10.41202488
116 9.25071676 9.37741004
117 10.14097222 9.25071676
118 9.73475533 10.14097222
119 7.92811920 9.73475533
120 8.96791835 7.92811920
121 8.14651461 8.96791835
122 7.19098933 8.14651461
123 5.33408109 7.19098933
124 8.16305528 5.33408109
125 8.66585103 8.16305528
126 7.43832380 8.66585103
127 7.27951041 7.43832380
128 8.29506615 7.27951041
129 7.70395983 8.29506615
130 5.75508845 7.70395983
131 0.50097424 5.75508845
132 1.52110513 0.50097424
133 7.26408422 1.52110513
134 12.69423077 7.26408422
135 13.22825961 12.69423077
136 11.93970331 13.22825961
137 12.97936770 11.93970331
138 13.48115942 12.97936770
139 12.10168220 13.48115942
140 11.31461990 12.10168220
141 8.64671773 11.31461990
142 8.24300378 8.64671773
143 13.45872524 8.24300378
144 18.03034803 13.45872524
145 16.58072958 18.03034803
146 16.83577285 16.58072958
147 16.05624821 16.83577285
148 18.02949644 16.05624821
149 20.13019297 18.02949644
150 19.00579617 20.13019297
151 20.28296745 19.00579617
152 20.51450774 20.28296745
153 20.30421049 20.51450774
154 21.17913395 20.30421049
155 22.37710057 21.17913395
156 23.99491257 22.37710057
157 22.83778063 23.99491257
158 22.11547886 22.83778063
159 20.14608456 22.11547886
160 18.90334706 20.14608456
161 19.33542552 18.90334706
162 18.18325863 19.33542552
163 14.94499689 18.18325863
164 13.77469561 14.94499689
165 15.39198616 13.77469561
166 13.55707783 15.39198616
167 15.92484504 13.55707783
168 14.81996701 15.92484504
169 11.56880321 14.81996701
170 13.09972271 11.56880321
171 14.18225733 13.09972271
172 12.05561773 14.18225733
173 6.98338673 12.05561773
174 8.31478951 6.98338673
175 7.02002826 8.31478951
176 3.31097821 7.02002826
177 2.14189585 3.31097821
178 3.00842419 2.14189585
179 -1.47556808 3.00842419
180 -6.20833013 -1.47556808
181 3.00091484 -6.20833013
182 9.20594088 3.00091484
183 8.50730010 9.20594088
184 10.25414072 8.50730010
185 15.05638552 10.25414072
186 18.03909791 15.05638552
187 16.56728258 18.03909791
188 20.79620659 16.56728258
189 23.61640498 20.79620659
190 21.47453060 23.61640498
191 21.21135640 21.47453060
192 28.37760466 21.21135640
193 28.20301586 28.37760466
194 25.56134028 28.20301586
195 21.90840845 25.56134028
196 22.04567201 21.90840845
197 23.32890933 22.04567201
198 22.93939413 23.32890933
199 22.63756110 22.93939413
200 21.12250728 22.63756110
201 20.29099408 21.12250728
202 20.14267483 20.29099408
203 15.90370093 20.14267483
204 12.99064746 15.90370093
205 6.65907771 12.99064746
206 -2.54371183 6.65907771
207 15.29584584 -2.54371183
208 13.85811713 15.29584584
209 8.93339985 13.85811713
210 11.15994341 8.93339985
211 14.14986074 11.15994341
212 16.21211517 14.14986074
213 16.44115795 16.21211517
214 16.30348669 16.44115795
215 12.75235625 16.30348669
216 10.86781020 12.75235625
217 10.65053185 10.86781020
218 9.96027338 10.65053185
219 10.78196457 9.96027338
220 8.10648500 10.78196457
221 7.88423268 8.10648500
222 7.17689195 7.88423268
223 4.34611039 7.17689195
224 7.18383908 4.34611039
225 1.41947689 7.18383908
226 0.15958944 1.41947689
227 4.30359454 0.15958944
228 5.04019316 4.30359454
229 3.60836635 5.04019316
230 0.03365365 3.60836635
231 -2.05099314 0.03365365
232 2.31643420 -2.05099314
233 -0.24120627 2.31643420
234 -4.71878871 -0.24120627
235 -7.00209162 -4.71878871
236 -19.87302684 -7.00209162
237 -25.87191132 -19.87302684
238 -21.88067144 -25.87191132
239 -18.03632864 -21.88067144
240 -12.39261733 -18.03632864
241 -4.65582605 -12.39261733
242 -1.64011696 -4.65582605
243 -4.04829216 -1.64011696
244 -2.38410335 -4.04829216
245 1.14193571 -2.38410335
246 -1.18846254 1.14193571
247 -4.88319108 -1.18846254
248 3.06186006 -4.88319108
249 10.95509873 3.06186006
250 1.06523095 10.95509873
251 -0.36865937 1.06523095
252 12.65199997 -0.36865937
253 1.89547445 12.65199997
254 3.94587716 1.89547445
255 5.17114810 3.94587716
256 4.41962297 5.17114810
257 3.50634346 4.41962297
258 1.94803183 3.50634346
259 4.39974061 1.94803183
260 3.40616453 4.39974061
261 -2.69529292 3.40616453
262 -10.67663811 -2.69529292
263 -11.09459072 -10.67663811
264 -11.29974103 -11.09459072
265 -17.70582473 -11.29974103
266 -26.54774592 -17.70582473
267 -33.64460589 -26.54774592
268 -45.86621902 -33.64460589
269 -52.45548274 -45.86621902
270 -54.18808192 -52.45548274
271 -27.51797536 -54.18808192
272 -9.89979089 -27.51797536
273 12.31264056 -9.89979089
274 25.10607298 12.31264056
275 28.48702722 25.10607298
276 32.62724286 28.48702722
277 39.02405289 32.62724286
278 36.97094259 39.02405289
279 37.90521932 36.97094259
280 33.29510028 37.90521932
281 28.97689322 33.29510028
282 32.19145040 28.97689322
283 29.19958292 32.19145040
284 31.94807334 29.19958292
285 24.12301711 31.94807334
286 23.10206672 24.12301711
287 20.32160812 23.10206672
288 15.70762243 20.32160812
289 16.33340338 15.70762243
290 17.50747638 16.33340338
291 18.36035671 17.50747638
292 24.94856398 18.36035671
293 22.83379379 24.94856398
294 21.65893346 22.83379379
295 21.63658943 21.65893346
296 NA 21.63658943
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -37.48494386 -39.75303702
[2,] -35.63395296 -37.48494386
[3,] -35.93167238 -35.63395296
[4,] -35.66869822 -35.93167238
[5,] -32.82325090 -35.66869822
[6,] -31.32157136 -32.82325090
[7,] -34.11283236 -31.32157136
[8,] -33.27797878 -34.11283236
[9,] -32.86871702 -33.27797878
[10,] -32.74322128 -32.86871702
[11,] -32.78488449 -32.74322128
[12,] -32.20681391 -32.78488449
[13,] -33.34744539 -32.20681391
[14,] -33.19527373 -33.34744539
[15,] -32.90798388 -33.19527373
[16,] -32.06375896 -32.90798388
[17,] -31.68026223 -32.06375896
[18,] -32.16587346 -31.68026223
[19,] -31.75842682 -32.16587346
[20,] -29.99436530 -31.75842682
[21,] -29.93781067 -29.99436530
[22,] -29.17831965 -29.93781067
[23,] -28.23022420 -29.17831965
[24,] -28.12184321 -28.23022420
[25,] -29.03809093 -28.12184321
[26,] -27.31947403 -29.03809093
[27,] -27.38997314 -27.31947403
[28,] -25.62353904 -27.38997314
[29,] -24.26645225 -25.62353904
[30,] -23.26005780 -24.26645225
[31,] -24.15073221 -23.26005780
[32,] -21.94092492 -24.15073221
[33,] -21.96683923 -21.94092492
[34,] -22.11754983 -21.96683923
[35,] -23.74867839 -22.11754983
[36,] -23.45337120 -23.74867839
[37,] -23.95521711 -23.45337120
[38,] -23.47564110 -23.95521711
[39,] -23.18035752 -23.47564110
[40,] -21.53657605 -23.18035752
[41,] -21.06796022 -21.53657605
[42,] -20.54495372 -21.06796022
[43,] -20.37157716 -20.54495372
[44,] -19.96990851 -20.37157716
[45,] -19.84679764 -19.96990851
[46,] -19.42229689 -19.84679764
[47,] -21.23604161 -19.42229689
[48,] -21.60316585 -21.23604161
[49,] -20.36563784 -21.60316585
[50,] -16.77630306 -20.36563784
[51,] -15.32695221 -16.77630306
[52,] -14.09650336 -15.32695221
[53,] -12.16420522 -14.09650336
[54,] -12.74506473 -12.16420522
[55,] -18.12777696 -12.74506473
[56,] -20.89347142 -18.12777696
[57,] -22.85603177 -20.89347142
[58,] -20.91076027 -22.85603177
[59,] -18.07371712 -20.91076027
[60,] -14.20415791 -18.07371712
[61,] -11.15167889 -14.20415791
[62,] -9.80672682 -11.15167889
[63,] -10.56455643 -9.80672682
[64,] -9.62162095 -10.56455643
[65,] -7.90409211 -9.62162095
[66,] -7.83916904 -7.90409211
[67,] -9.18000909 -7.83916904
[68,] -9.43009115 -9.18000909
[69,] -11.44477029 -9.43009115
[70,] -10.53290340 -11.44477029
[71,] -8.94278329 -10.53290340
[72,] -5.39292799 -8.94278329
[73,] -4.23841928 -5.39292799
[74,] -4.04484868 -4.23841928
[75,] -5.40459346 -4.04484868
[76,] -5.55193428 -5.40459346
[77,] -6.21705609 -5.55193428
[78,] -5.06152662 -6.21705609
[79,] -7.18059977 -5.06152662
[80,] -7.34891342 -7.18059977
[81,] -9.53383368 -7.34891342
[82,] -6.50159801 -9.53383368
[83,] -5.31696403 -6.50159801
[84,] -2.82007410 -5.31696403
[85,] -3.47086923 -2.82007410
[86,] -4.59457321 -3.47086923
[87,] -4.84411637 -4.59457321
[88,] -4.52969006 -4.84411637
[89,] -2.20243693 -4.52969006
[90,] -1.46874973 -2.20243693
[91,] -2.45957137 -1.46874973
[92,] -1.89794093 -2.45957137
[93,] -1.24731587 -1.89794093
[94,] 0.20635542 -1.24731587
[95,] 1.10612621 0.20635542
[96,] 3.85395649 1.10612621
[97,] 2.65896093 3.85395649
[98,] 2.90413385 2.65896093
[99,] 1.83216125 2.90413385
[100,] 1.14194801 1.83216125
[101,] 2.52054696 1.14194801
[102,] 2.55320413 2.52054696
[103,] 2.80146077 2.55320413
[104,] 4.33506172 2.80146077
[105,] 4.78078783 4.33506172
[106,] 4.92646772 4.78078783
[107,] 5.03290598 4.92646772
[108,] 8.00475137 5.03290598
[109,] 6.98224279 8.00475137
[110,] 7.55253562 6.98224279
[111,] 6.58476146 7.55253562
[112,] 7.46533913 6.58476146
[113,] 8.74574050 7.46533913
[114,] 10.41202488 8.74574050
[115,] 9.37741004 10.41202488
[116,] 9.25071676 9.37741004
[117,] 10.14097222 9.25071676
[118,] 9.73475533 10.14097222
[119,] 7.92811920 9.73475533
[120,] 8.96791835 7.92811920
[121,] 8.14651461 8.96791835
[122,] 7.19098933 8.14651461
[123,] 5.33408109 7.19098933
[124,] 8.16305528 5.33408109
[125,] 8.66585103 8.16305528
[126,] 7.43832380 8.66585103
[127,] 7.27951041 7.43832380
[128,] 8.29506615 7.27951041
[129,] 7.70395983 8.29506615
[130,] 5.75508845 7.70395983
[131,] 0.50097424 5.75508845
[132,] 1.52110513 0.50097424
[133,] 7.26408422 1.52110513
[134,] 12.69423077 7.26408422
[135,] 13.22825961 12.69423077
[136,] 11.93970331 13.22825961
[137,] 12.97936770 11.93970331
[138,] 13.48115942 12.97936770
[139,] 12.10168220 13.48115942
[140,] 11.31461990 12.10168220
[141,] 8.64671773 11.31461990
[142,] 8.24300378 8.64671773
[143,] 13.45872524 8.24300378
[144,] 18.03034803 13.45872524
[145,] 16.58072958 18.03034803
[146,] 16.83577285 16.58072958
[147,] 16.05624821 16.83577285
[148,] 18.02949644 16.05624821
[149,] 20.13019297 18.02949644
[150,] 19.00579617 20.13019297
[151,] 20.28296745 19.00579617
[152,] 20.51450774 20.28296745
[153,] 20.30421049 20.51450774
[154,] 21.17913395 20.30421049
[155,] 22.37710057 21.17913395
[156,] 23.99491257 22.37710057
[157,] 22.83778063 23.99491257
[158,] 22.11547886 22.83778063
[159,] 20.14608456 22.11547886
[160,] 18.90334706 20.14608456
[161,] 19.33542552 18.90334706
[162,] 18.18325863 19.33542552
[163,] 14.94499689 18.18325863
[164,] 13.77469561 14.94499689
[165,] 15.39198616 13.77469561
[166,] 13.55707783 15.39198616
[167,] 15.92484504 13.55707783
[168,] 14.81996701 15.92484504
[169,] 11.56880321 14.81996701
[170,] 13.09972271 11.56880321
[171,] 14.18225733 13.09972271
[172,] 12.05561773 14.18225733
[173,] 6.98338673 12.05561773
[174,] 8.31478951 6.98338673
[175,] 7.02002826 8.31478951
[176,] 3.31097821 7.02002826
[177,] 2.14189585 3.31097821
[178,] 3.00842419 2.14189585
[179,] -1.47556808 3.00842419
[180,] -6.20833013 -1.47556808
[181,] 3.00091484 -6.20833013
[182,] 9.20594088 3.00091484
[183,] 8.50730010 9.20594088
[184,] 10.25414072 8.50730010
[185,] 15.05638552 10.25414072
[186,] 18.03909791 15.05638552
[187,] 16.56728258 18.03909791
[188,] 20.79620659 16.56728258
[189,] 23.61640498 20.79620659
[190,] 21.47453060 23.61640498
[191,] 21.21135640 21.47453060
[192,] 28.37760466 21.21135640
[193,] 28.20301586 28.37760466
[194,] 25.56134028 28.20301586
[195,] 21.90840845 25.56134028
[196,] 22.04567201 21.90840845
[197,] 23.32890933 22.04567201
[198,] 22.93939413 23.32890933
[199,] 22.63756110 22.93939413
[200,] 21.12250728 22.63756110
[201,] 20.29099408 21.12250728
[202,] 20.14267483 20.29099408
[203,] 15.90370093 20.14267483
[204,] 12.99064746 15.90370093
[205,] 6.65907771 12.99064746
[206,] -2.54371183 6.65907771
[207,] 15.29584584 -2.54371183
[208,] 13.85811713 15.29584584
[209,] 8.93339985 13.85811713
[210,] 11.15994341 8.93339985
[211,] 14.14986074 11.15994341
[212,] 16.21211517 14.14986074
[213,] 16.44115795 16.21211517
[214,] 16.30348669 16.44115795
[215,] 12.75235625 16.30348669
[216,] 10.86781020 12.75235625
[217,] 10.65053185 10.86781020
[218,] 9.96027338 10.65053185
[219,] 10.78196457 9.96027338
[220,] 8.10648500 10.78196457
[221,] 7.88423268 8.10648500
[222,] 7.17689195 7.88423268
[223,] 4.34611039 7.17689195
[224,] 7.18383908 4.34611039
[225,] 1.41947689 7.18383908
[226,] 0.15958944 1.41947689
[227,] 4.30359454 0.15958944
[228,] 5.04019316 4.30359454
[229,] 3.60836635 5.04019316
[230,] 0.03365365 3.60836635
[231,] -2.05099314 0.03365365
[232,] 2.31643420 -2.05099314
[233,] -0.24120627 2.31643420
[234,] -4.71878871 -0.24120627
[235,] -7.00209162 -4.71878871
[236,] -19.87302684 -7.00209162
[237,] -25.87191132 -19.87302684
[238,] -21.88067144 -25.87191132
[239,] -18.03632864 -21.88067144
[240,] -12.39261733 -18.03632864
[241,] -4.65582605 -12.39261733
[242,] -1.64011696 -4.65582605
[243,] -4.04829216 -1.64011696
[244,] -2.38410335 -4.04829216
[245,] 1.14193571 -2.38410335
[246,] -1.18846254 1.14193571
[247,] -4.88319108 -1.18846254
[248,] 3.06186006 -4.88319108
[249,] 10.95509873 3.06186006
[250,] 1.06523095 10.95509873
[251,] -0.36865937 1.06523095
[252,] 12.65199997 -0.36865937
[253,] 1.89547445 12.65199997
[254,] 3.94587716 1.89547445
[255,] 5.17114810 3.94587716
[256,] 4.41962297 5.17114810
[257,] 3.50634346 4.41962297
[258,] 1.94803183 3.50634346
[259,] 4.39974061 1.94803183
[260,] 3.40616453 4.39974061
[261,] -2.69529292 3.40616453
[262,] -10.67663811 -2.69529292
[263,] -11.09459072 -10.67663811
[264,] -11.29974103 -11.09459072
[265,] -17.70582473 -11.29974103
[266,] -26.54774592 -17.70582473
[267,] -33.64460589 -26.54774592
[268,] -45.86621902 -33.64460589
[269,] -52.45548274 -45.86621902
[270,] -54.18808192 -52.45548274
[271,] -27.51797536 -54.18808192
[272,] -9.89979089 -27.51797536
[273,] 12.31264056 -9.89979089
[274,] 25.10607298 12.31264056
[275,] 28.48702722 25.10607298
[276,] 32.62724286 28.48702722
[277,] 39.02405289 32.62724286
[278,] 36.97094259 39.02405289
[279,] 37.90521932 36.97094259
[280,] 33.29510028 37.90521932
[281,] 28.97689322 33.29510028
[282,] 32.19145040 28.97689322
[283,] 29.19958292 32.19145040
[284,] 31.94807334 29.19958292
[285,] 24.12301711 31.94807334
[286,] 23.10206672 24.12301711
[287,] 20.32160812 23.10206672
[288,] 15.70762243 20.32160812
[289,] 16.33340338 15.70762243
[290,] 17.50747638 16.33340338
[291,] 18.36035671 17.50747638
[292,] 24.94856398 18.36035671
[293,] 22.83379379 24.94856398
[294,] 21.65893346 22.83379379
[295,] 21.63658943 21.65893346
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -37.48494386 -39.75303702
2 -35.63395296 -37.48494386
3 -35.93167238 -35.63395296
4 -35.66869822 -35.93167238
5 -32.82325090 -35.66869822
6 -31.32157136 -32.82325090
7 -34.11283236 -31.32157136
8 -33.27797878 -34.11283236
9 -32.86871702 -33.27797878
10 -32.74322128 -32.86871702
11 -32.78488449 -32.74322128
12 -32.20681391 -32.78488449
13 -33.34744539 -32.20681391
14 -33.19527373 -33.34744539
15 -32.90798388 -33.19527373
16 -32.06375896 -32.90798388
17 -31.68026223 -32.06375896
18 -32.16587346 -31.68026223
19 -31.75842682 -32.16587346
20 -29.99436530 -31.75842682
21 -29.93781067 -29.99436530
22 -29.17831965 -29.93781067
23 -28.23022420 -29.17831965
24 -28.12184321 -28.23022420
25 -29.03809093 -28.12184321
26 -27.31947403 -29.03809093
27 -27.38997314 -27.31947403
28 -25.62353904 -27.38997314
29 -24.26645225 -25.62353904
30 -23.26005780 -24.26645225
31 -24.15073221 -23.26005780
32 -21.94092492 -24.15073221
33 -21.96683923 -21.94092492
34 -22.11754983 -21.96683923
35 -23.74867839 -22.11754983
36 -23.45337120 -23.74867839
37 -23.95521711 -23.45337120
38 -23.47564110 -23.95521711
39 -23.18035752 -23.47564110
40 -21.53657605 -23.18035752
41 -21.06796022 -21.53657605
42 -20.54495372 -21.06796022
43 -20.37157716 -20.54495372
44 -19.96990851 -20.37157716
45 -19.84679764 -19.96990851
46 -19.42229689 -19.84679764
47 -21.23604161 -19.42229689
48 -21.60316585 -21.23604161
49 -20.36563784 -21.60316585
50 -16.77630306 -20.36563784
51 -15.32695221 -16.77630306
52 -14.09650336 -15.32695221
53 -12.16420522 -14.09650336
54 -12.74506473 -12.16420522
55 -18.12777696 -12.74506473
56 -20.89347142 -18.12777696
57 -22.85603177 -20.89347142
58 -20.91076027 -22.85603177
59 -18.07371712 -20.91076027
60 -14.20415791 -18.07371712
61 -11.15167889 -14.20415791
62 -9.80672682 -11.15167889
63 -10.56455643 -9.80672682
64 -9.62162095 -10.56455643
65 -7.90409211 -9.62162095
66 -7.83916904 -7.90409211
67 -9.18000909 -7.83916904
68 -9.43009115 -9.18000909
69 -11.44477029 -9.43009115
70 -10.53290340 -11.44477029
71 -8.94278329 -10.53290340
72 -5.39292799 -8.94278329
73 -4.23841928 -5.39292799
74 -4.04484868 -4.23841928
75 -5.40459346 -4.04484868
76 -5.55193428 -5.40459346
77 -6.21705609 -5.55193428
78 -5.06152662 -6.21705609
79 -7.18059977 -5.06152662
80 -7.34891342 -7.18059977
81 -9.53383368 -7.34891342
82 -6.50159801 -9.53383368
83 -5.31696403 -6.50159801
84 -2.82007410 -5.31696403
85 -3.47086923 -2.82007410
86 -4.59457321 -3.47086923
87 -4.84411637 -4.59457321
88 -4.52969006 -4.84411637
89 -2.20243693 -4.52969006
90 -1.46874973 -2.20243693
91 -2.45957137 -1.46874973
92 -1.89794093 -2.45957137
93 -1.24731587 -1.89794093
94 0.20635542 -1.24731587
95 1.10612621 0.20635542
96 3.85395649 1.10612621
97 2.65896093 3.85395649
98 2.90413385 2.65896093
99 1.83216125 2.90413385
100 1.14194801 1.83216125
101 2.52054696 1.14194801
102 2.55320413 2.52054696
103 2.80146077 2.55320413
104 4.33506172 2.80146077
105 4.78078783 4.33506172
106 4.92646772 4.78078783
107 5.03290598 4.92646772
108 8.00475137 5.03290598
109 6.98224279 8.00475137
110 7.55253562 6.98224279
111 6.58476146 7.55253562
112 7.46533913 6.58476146
113 8.74574050 7.46533913
114 10.41202488 8.74574050
115 9.37741004 10.41202488
116 9.25071676 9.37741004
117 10.14097222 9.25071676
118 9.73475533 10.14097222
119 7.92811920 9.73475533
120 8.96791835 7.92811920
121 8.14651461 8.96791835
122 7.19098933 8.14651461
123 5.33408109 7.19098933
124 8.16305528 5.33408109
125 8.66585103 8.16305528
126 7.43832380 8.66585103
127 7.27951041 7.43832380
128 8.29506615 7.27951041
129 7.70395983 8.29506615
130 5.75508845 7.70395983
131 0.50097424 5.75508845
132 1.52110513 0.50097424
133 7.26408422 1.52110513
134 12.69423077 7.26408422
135 13.22825961 12.69423077
136 11.93970331 13.22825961
137 12.97936770 11.93970331
138 13.48115942 12.97936770
139 12.10168220 13.48115942
140 11.31461990 12.10168220
141 8.64671773 11.31461990
142 8.24300378 8.64671773
143 13.45872524 8.24300378
144 18.03034803 13.45872524
145 16.58072958 18.03034803
146 16.83577285 16.58072958
147 16.05624821 16.83577285
148 18.02949644 16.05624821
149 20.13019297 18.02949644
150 19.00579617 20.13019297
151 20.28296745 19.00579617
152 20.51450774 20.28296745
153 20.30421049 20.51450774
154 21.17913395 20.30421049
155 22.37710057 21.17913395
156 23.99491257 22.37710057
157 22.83778063 23.99491257
158 22.11547886 22.83778063
159 20.14608456 22.11547886
160 18.90334706 20.14608456
161 19.33542552 18.90334706
162 18.18325863 19.33542552
163 14.94499689 18.18325863
164 13.77469561 14.94499689
165 15.39198616 13.77469561
166 13.55707783 15.39198616
167 15.92484504 13.55707783
168 14.81996701 15.92484504
169 11.56880321 14.81996701
170 13.09972271 11.56880321
171 14.18225733 13.09972271
172 12.05561773 14.18225733
173 6.98338673 12.05561773
174 8.31478951 6.98338673
175 7.02002826 8.31478951
176 3.31097821 7.02002826
177 2.14189585 3.31097821
178 3.00842419 2.14189585
179 -1.47556808 3.00842419
180 -6.20833013 -1.47556808
181 3.00091484 -6.20833013
182 9.20594088 3.00091484
183 8.50730010 9.20594088
184 10.25414072 8.50730010
185 15.05638552 10.25414072
186 18.03909791 15.05638552
187 16.56728258 18.03909791
188 20.79620659 16.56728258
189 23.61640498 20.79620659
190 21.47453060 23.61640498
191 21.21135640 21.47453060
192 28.37760466 21.21135640
193 28.20301586 28.37760466
194 25.56134028 28.20301586
195 21.90840845 25.56134028
196 22.04567201 21.90840845
197 23.32890933 22.04567201
198 22.93939413 23.32890933
199 22.63756110 22.93939413
200 21.12250728 22.63756110
201 20.29099408 21.12250728
202 20.14267483 20.29099408
203 15.90370093 20.14267483
204 12.99064746 15.90370093
205 6.65907771 12.99064746
206 -2.54371183 6.65907771
207 15.29584584 -2.54371183
208 13.85811713 15.29584584
209 8.93339985 13.85811713
210 11.15994341 8.93339985
211 14.14986074 11.15994341
212 16.21211517 14.14986074
213 16.44115795 16.21211517
214 16.30348669 16.44115795
215 12.75235625 16.30348669
216 10.86781020 12.75235625
217 10.65053185 10.86781020
218 9.96027338 10.65053185
219 10.78196457 9.96027338
220 8.10648500 10.78196457
221 7.88423268 8.10648500
222 7.17689195 7.88423268
223 4.34611039 7.17689195
224 7.18383908 4.34611039
225 1.41947689 7.18383908
226 0.15958944 1.41947689
227 4.30359454 0.15958944
228 5.04019316 4.30359454
229 3.60836635 5.04019316
230 0.03365365 3.60836635
231 -2.05099314 0.03365365
232 2.31643420 -2.05099314
233 -0.24120627 2.31643420
234 -4.71878871 -0.24120627
235 -7.00209162 -4.71878871
236 -19.87302684 -7.00209162
237 -25.87191132 -19.87302684
238 -21.88067144 -25.87191132
239 -18.03632864 -21.88067144
240 -12.39261733 -18.03632864
241 -4.65582605 -12.39261733
242 -1.64011696 -4.65582605
243 -4.04829216 -1.64011696
244 -2.38410335 -4.04829216
245 1.14193571 -2.38410335
246 -1.18846254 1.14193571
247 -4.88319108 -1.18846254
248 3.06186006 -4.88319108
249 10.95509873 3.06186006
250 1.06523095 10.95509873
251 -0.36865937 1.06523095
252 12.65199997 -0.36865937
253 1.89547445 12.65199997
254 3.94587716 1.89547445
255 5.17114810 3.94587716
256 4.41962297 5.17114810
257 3.50634346 4.41962297
258 1.94803183 3.50634346
259 4.39974061 1.94803183
260 3.40616453 4.39974061
261 -2.69529292 3.40616453
262 -10.67663811 -2.69529292
263 -11.09459072 -10.67663811
264 -11.29974103 -11.09459072
265 -17.70582473 -11.29974103
266 -26.54774592 -17.70582473
267 -33.64460589 -26.54774592
268 -45.86621902 -33.64460589
269 -52.45548274 -45.86621902
270 -54.18808192 -52.45548274
271 -27.51797536 -54.18808192
272 -9.89979089 -27.51797536
273 12.31264056 -9.89979089
274 25.10607298 12.31264056
275 28.48702722 25.10607298
276 32.62724286 28.48702722
277 39.02405289 32.62724286
278 36.97094259 39.02405289
279 37.90521932 36.97094259
280 33.29510028 37.90521932
281 28.97689322 33.29510028
282 32.19145040 28.97689322
283 29.19958292 32.19145040
284 31.94807334 29.19958292
285 24.12301711 31.94807334
286 23.10206672 24.12301711
287 20.32160812 23.10206672
288 15.70762243 20.32160812
289 16.33340338 15.70762243
290 17.50747638 16.33340338
291 18.36035671 17.50747638
292 24.94856398 18.36035671
293 22.83379379 24.94856398
294 21.65893346 22.83379379
295 21.63658943 21.65893346
> 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/rcomp/tmp/7zs0r1290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8zs0r1290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9zs0r1290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10sjzu1290545932.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11d2yi1290545932.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/rcomp/tmp/12g2w51290545932.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/rcomp/tmp/1353th1290545932.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/rcomp/tmp/14yuak1290545932.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/rcomp/tmp/156gge1290545932.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/rcomp/tmp/16fmoz1290545932.tab")
+ }
>
> try(system("convert tmp/1li201290545932.ps tmp/1li201290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/2li201290545932.ps tmp/2li201290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d9j31290545932.ps tmp/3d9j31290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d9j31290545932.ps tmp/4d9j31290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/5d9j31290545932.ps tmp/5d9j31290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ii61290545932.ps tmp/66ii61290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zs0r1290545932.ps tmp/7zs0r1290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zs0r1290545932.ps tmp/8zs0r1290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zs0r1290545932.ps tmp/9zs0r1290545932.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sjzu1290545932.ps tmp/10sjzu1290545932.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.90 2.39 12.28