R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
1 109.600 22.93 2.28
2 109.300 15.45 2.26
3 108.800 12.61 2.16
4 108.600 12.84 2.10
5 108.900 15.38 1.96
6 109.500 13.43 1.85
7 109.500 11.58 1.80
8 109.700 15.10 1.77
9 110.200 14.87 1.78
10 110.300 14.90 1.73
11 110.400 15.22 1.77
12 110.500 16.11 1.76
13 111.200 18.65 1.74
14 111.600 17.75 1.73
15 112.100 18.30 1.73
16 112.700 18.68 1.69
17 113.100 19.44 1.65
18 113.500 20.07 1.65
19 113.800 21.34 1.66
20 114.400 20.31 1.63
21 115.000 19.53 1.56
22 115.300 19.86 1.57
23 115.400 18.85 1.64
24 115.400 17.27 1.70
25 115.700 17.13 1.96
26 116.000 16.80 1.84
27 116.500 16.20 1.70
28 117.100 17.86 1.59
29 117.500 17.42 1.52
30 118.000 16.53 1.53
31 118.500 15.50 1.56
32 119.000 15.52 1.62
33 119.800 14.54 1.53
34 120.200 13.77 1.68
35 120.300 14.14 1.76
36 120.500 16.38 1.89
37 121.100 18.02 1.99
38 121.600 17.94 1.81
39 122.300 19.48 1.69
40 123.100 21.07 1.56
41 123.800 20.12 1.61
42 124.100 20.05 1.65
43 124.400 19.78 1.65
44 124.600 18.58 1.61
45 125.000 19.59 1.55
46 125.600 20.10 1.58
47 125.900 19.86 1.66
48 126.100 21.10 1.92
49 127.400 22.86 2.23
50 128.000 22.11 1.85
51 128.700 20.39 1.55
52 128.900 18.43 1.49
53 129.200 18.20 1.47
54 129.900 16.70 1.48
55 130.400 18.45 1.49
56 131.600 27.31 1.51
57 132.700 33.51 1.56
58 133.500 36.04 1.76
59 133.800 32.33 1.94
60 133.800 27.28 2.04
61 134.600 25.23 1.96
62 134.800 20.48 1.62
63 135.000 19.90 1.49
64 135.200 20.83 1.50
65 135.600 21.23 1.48
66 136.000 20.19 1.43
67 136.200 21.40 1.34
68 136.600 21.69 1.43
69 137.200 21.89 1.59
70 137.400 23.23 1.82
71 137.800 22.46 1.89
72 137.900 19.50 2.00
73 138.100 18.79 1.74
74 138.600 19.01 1.26
75 139.300 18.92 1.35
76 139.500 20.23 1.42
77 139.700 20.98 1.51
78 140.200 22.38 1.62
79 140.500 21.78 1.55
80 140.900 21.34 1.84
81 141.300 21.88 1.92
82 141.800 21.69 2.38
83 142.000 20.34 2.13
84 141.900 19.41 2.07
85 142.600 19.03 2.03
86 143.100 20.09 1.76
87 143.600 20.32 2.00
88 144.000 20.25 2.06
89 144.200 19.95 2.18
90 144.400 19.09 1.98
91 144.400 17.89 1.99
92 144.800 18.01 2.04
93 145.100 17.50 2.09
94 145.700 18.15 2.02
95 145.800 16.61 2.03
96 145.800 14.51 2.15
97 146.200 15.03 1.93
98 146.700 14.78 1.88
99 147.200 14.68 1.93
100 147.400 16.42 1.91
101 147.500 17.89 2.00
102 148.000 19.06 1.80
103 148.400 19.65 1.81
104 149.000 18.38 1.83
105 149.400 17.45 1.78
106 149.500 17.72 1.70
107 149.700 18.07 1.75
108 149.700 17.16 1.88
109 150.300 18.04 1.62
110 150.900 18.57 1.48
111 151.400 18.54 1.47
112 151.900 19.90 1.52
113 152.200 19.74 1.55
114 152.500 18.45 1.58
115 152.500 17.33 1.43
116 152.900 18.02 1.43
117 153.200 18.23 1.52
118 153.700 17.43 1.54
119 153.600 17.99 1.61
120 153.500 19.03 1.84
121 154.400 18.85 2.05
122 154.900 19.09 1.89
123 155.700 21.33 1.95
124 156.300 23.50 2.08
125 156.600 21.17 2.01
126 156.700 20.42 2.08
127 157.000 21.30 2.25
128 157.300 21.90 2.10
129 157.800 23.97 1.85
130 158.300 24.88 1.94
131 158.600 23.71 2.50
132 158.600 25.23 3.26
133 159.100 25.13 3.40
134 159.600 22.18 2.49
135 160.000 20.97 1.79
136 160.200 19.70 1.81
137 160.100 20.82 2.00
138 160.300 19.26 2.08
139 160.500 19.66 2.00
140 160.800 19.95 2.08
141 161.200 19.80 2.33
142 161.600 21.33 2.68
143 161.500 20.19 2.92
144 161.300 18.33 2.28
145 161.600 16.72 1.96
146 161.900 16.06 1.96
147 162.200 15.12 2.06
148 162.500 15.35 2.16
149 162.800 14.91 2.04
150 163.000 13.72 1.91
151 163.200 14.17 2.09
152 163.400 13.47 1.82
153 163.600 15.03 1.70
154 164.000 14.46 1.86
155 164.000 13.00 1.94
156 163.900 11.35 1.95
157 164.300 12.51 1.85
158 164.500 12.01 1.77
159 165.000 14.68 1.70
160 166.200 17.31 1.90
161 166.200 17.72 2.17
162 166.200 17.92 2.14
163 166.700 20.10 2.20
164 167.100 21.28 2.51
165 167.900 23.80 2.62
166 168.200 22.69 2.52
167 168.300 25.00 2.68
168 168.300 26.10 2.24
169 168.800 27.26 2.60
170 169.800 29.37 2.73
171 171.200 29.84 2.66
172 171.300 25.72 2.86
173 171.500 28.79 3.04
174 172.400 31.82 3.77
175 172.800 29.70 3.84
176 172.800 31.26 3.73
177 173.700 33.88 4.26
178 174.000 33.11 4.58
179 174.100 34.42 4.40
180 174.000 28.44 5.77
181 175.100 29.59 6.82
182 175.800 29.61 5.08
183 176.200 27.24 4.37
184 176.900 27.49 4.52
185 177.700 28.63 4.36
186 178.000 27.60 3.79
187 177.500 26.42 3.35
188 177.500 27.37 3.33
189 178.300 26.20 2.93
190 177.700 22.17 2.78
191 177.400 19.64 3.41
192 176.700 19.39 3.42
193 177.100 19.71 2.50
194 177.800 20.72 2.19
195 178.800 24.53 2.40
196 179.800 26.18 2.94
197 179.800 27.04 2.94
198 179.900 25.52 2.96
199 180.100 26.97 2.92
200 180.700 28.39 2.76
201 181.000 29.66 2.97
202 181.300 28.84 3.24
203 181.300 26.35 3.59
204 180.900 29.46 3.96
205 181.700 32.95 4.43
206 183.100 35.83 5.05
207 184.200 33.51 6.96
208 183.800 28.17 4.47
209 183.500 28.11 4.77
210 183.700 30.66 5.41
211 183.900 30.75 5.08
212 184.600 31.57 4.46
213 185.200 28.31 4.59
214 185.000 30.34 4.32
215 184.500 31.11 4.26
216 184.300 32.13 4.76
217 185.200 34.31 5.21
218 186.200 34.68 5.02
219 187.400 36.74 5.12
220 188.000 36.75 5.03
221 189.100 40.28 5.40
222 189.700 38.03 5.82
223 189.400 40.78 5.62
224 189.500 44.90 5.52
225 189.900 45.94 5.06
226 190.900 53.28 5.43
227 191.000 48.47 6.21
228 190.300 43.15 6.01
229 190.700 46.84 5.80
230 191.800 48.15 5.73
231 193.300 54.19 5.95
232 194.600 52.98 6.57
233 194.400 49.83 6.25
234 194.500 56.35 6.09
235 195.400 59.00 6.71
236 196.400 64.99 6.48
237 198.800 65.59 8.95
238 199.200 62.26 10.33
239 197.600 58.32 9.89
240 196.800 59.41 9.08
241 198.300 65.49 8.01
242 198.700 61.63 6.85
243 199.800 62.69 6.43
244 201.500 69.44 6.37
245 202.500 70.84 6.23
246 202.900 70.95 5.77
247 203.500 74.41 5.91
248 203.900 73.04 6.55
249 202.900 63.80 6.06
250 201.800 58.89 5.09
251 201.500 59.08 6.71
252 201.800 61.96 6.76
253 202.416 54.51 5.70
254 203.499 59.28 6.80
255 205.352 60.44 6.65
256 206.686 63.98 6.26
257 207.949 63.45 6.75
258 208.352 67.49 6.62
259 208.299 74.12 6.21
260 207.917 72.36 5.76
261 208.490 79.91 5.30
262 208.936 85.80 5.78
263 210.177 94.77 6.46
264 210.036 91.69 6.87
265 211.080 92.97 7.16
266 211.693 95.39 7.71
267 213.528 105.45 8.44
268 214.823 112.58 9.04
269 216.632 125.40 10.15
270 218.815 133.88 10.79
271 219.964 133.37 11.32
272 219.086 116.67 8.34
273 218.783 104.11 6.72
274 216.573 76.61 5.50
275 212.425 57.31 4.75
276 210.228 41.12 5.52
277 211.143 41.71 5.15
278 212.193 39.09 4.19
279 212.709 47.94 3.72
280 213.240 49.65 3.43
281 213.856 59.03 3.45
282 215.693 69.64 3.45
283 215.351 64.15 3.43
284 215.834 71.05 3.14
285 215.969 69.41 2.92
286 216.177 75.72 3.60
287 216.330 77.99 3.64
288 215.949 74.47 4.44
289 216.687 78.33 5.14
290 216.741 76.39 4.89
291 217.631 81.20 4.36
292 218.009 84.29 3.92
293 218.178 73.74 4.04
294 217.965 75.34 4.25
295 218.011 76.32 4.36
296 218.312 76.60 4.22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Olieprijzen Gasprijzen
123.0577 0.6205 6.0697
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-54.561 -11.838 3.401 13.193 39.447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 123.05775 2.03065 60.600 < 2e-16 ***
Olieprijzen 0.62052 0.08164 7.601 4.00e-13 ***
Gasprijzen 6.06971 0.93975 6.459 4.38e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.52 on 293 degrees of freedom
Multiple R-squared: 0.6706, Adjusted R-squared: 0.6684
F-statistic: 298.3 on 2 and 293 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,] 1.234573e-05 2.469147e-05 9.999877e-01
[2,] 3.620092e-07 7.240184e-07 9.999996e-01
[3,] 6.826111e-09 1.365222e-08 1.000000e+00
[4,] 3.760760e-10 7.521521e-10 1.000000e+00
[5,] 1.260677e-11 2.521354e-11 1.000000e+00
[6,] 5.047650e-13 1.009530e-12 1.000000e+00
[7,] 1.536985e-14 3.073970e-14 1.000000e+00
[8,] 8.112083e-16 1.622417e-15 1.000000e+00
[9,] 1.482041e-16 2.964083e-16 1.000000e+00
[10,] 4.313574e-17 8.627147e-17 1.000000e+00
[11,] 1.359390e-17 2.718781e-17 1.000000e+00
[12,] 1.915990e-18 3.831980e-18 1.000000e+00
[13,] 2.621225e-19 5.242449e-19 1.000000e+00
[14,] 2.279347e-20 4.558694e-20 1.000000e+00
[15,] 6.980640e-21 1.396128e-20 1.000000e+00
[16,] 3.395169e-21 6.790338e-21 1.000000e+00
[17,] 1.311326e-21 2.622652e-21 1.000000e+00
[18,] 1.926078e-21 3.852155e-21 1.000000e+00
[19,] 9.109245e-21 1.821849e-20 1.000000e+00
[20,] 5.966275e-19 1.193255e-18 1.000000e+00
[21,] 2.319773e-18 4.639546e-18 1.000000e+00
[22,] 3.676453e-18 7.352905e-18 1.000000e+00
[23,] 2.004499e-18 4.008998e-18 1.000000e+00
[24,] 8.954654e-19 1.790931e-18 1.000000e+00
[25,] 5.765504e-19 1.153101e-18 1.000000e+00
[26,] 5.965717e-19 1.193143e-18 1.000000e+00
[27,] 9.651683e-19 1.930337e-18 1.000000e+00
[28,] 9.725888e-19 1.945178e-18 1.000000e+00
[29,] 3.202454e-18 6.404908e-18 1.000000e+00
[30,] 1.226521e-17 2.453043e-17 1.000000e+00
[31,] 1.062601e-16 2.125201e-16 1.000000e+00
[32,] 1.338268e-15 2.676536e-15 1.000000e+00
[33,] 3.547573e-15 7.095147e-15 1.000000e+00
[34,] 5.469477e-15 1.093895e-14 1.000000e+00
[35,] 5.107247e-15 1.021449e-14 1.000000e+00
[36,] 6.458013e-15 1.291603e-14 1.000000e+00
[37,] 8.965942e-15 1.793188e-14 1.000000e+00
[38,] 1.215384e-14 2.430768e-14 1.000000e+00
[39,] 1.568033e-14 3.136066e-14 1.000000e+00
[40,] 1.443283e-14 2.886565e-14 1.000000e+00
[41,] 1.486721e-14 2.973443e-14 1.000000e+00
[42,] 2.163238e-14 4.326475e-14 1.000000e+00
[43,] 6.763720e-14 1.352744e-13 1.000000e+00
[44,] 3.172922e-13 6.345843e-13 1.000000e+00
[45,] 4.272604e-13 8.545208e-13 1.000000e+00
[46,] 5.245536e-13 1.049107e-12 1.000000e+00
[47,] 9.158930e-13 1.831786e-12 1.000000e+00
[48,] 1.498999e-12 2.997999e-12 1.000000e+00
[49,] 3.986928e-12 7.973856e-12 1.000000e+00
[50,] 6.276166e-12 1.255233e-11 1.000000e+00
[51,] 3.807321e-12 7.614643e-12 1.000000e+00
[52,] 3.156676e-12 6.313352e-12 1.000000e+00
[53,] 2.472466e-12 4.944932e-12 1.000000e+00
[54,] 2.298878e-12 4.597757e-12 1.000000e+00
[55,] 5.429110e-12 1.085822e-11 1.000000e+00
[56,] 1.588178e-11 3.176356e-11 1.000000e+00
[57,] 6.163893e-11 1.232779e-10 1.000000e+00
[58,] 1.679928e-10 3.359856e-10 1.000000e+00
[59,] 3.367445e-10 6.734891e-10 1.000000e+00
[60,] 5.724031e-10 1.144806e-09 1.000000e+00
[61,] 1.029136e-09 2.058272e-09 1.000000e+00
[62,] 1.144469e-09 2.288938e-09 1.000000e+00
[63,] 1.496389e-09 2.992778e-09 1.000000e+00
[64,] 3.082040e-09 6.164080e-09 1.000000e+00
[65,] 9.728500e-09 1.945700e-08 1.000000e+00
[66,] 4.332100e-08 8.664200e-08 1.000000e+00
[67,] 4.933108e-07 9.866217e-07 9.999995e-01
[68,] 2.139194e-06 4.278388e-06 9.999979e-01
[69,] 3.035254e-06 6.070507e-06 9.999970e-01
[70,] 5.000486e-06 1.000097e-05 9.999950e-01
[71,] 7.567274e-06 1.513455e-05 9.999924e-01
[72,] 1.211923e-05 2.423845e-05 9.999879e-01
[73,] 2.043308e-05 4.086616e-05 9.999796e-01
[74,] 3.223471e-05 6.446942e-05 9.999678e-01
[75,] 9.963859e-05 1.992772e-04 9.999004e-01
[76,] 3.006003e-04 6.012007e-04 9.996994e-01
[77,] 1.766031e-03 3.532063e-03 9.982340e-01
[78,] 5.277269e-03 1.055454e-02 9.947227e-01
[79,] 1.299169e-02 2.598338e-02 9.870083e-01
[80,] 2.803905e-02 5.607810e-02 9.719610e-01
[81,] 4.484276e-02 8.968552e-02 9.551572e-01
[82,] 7.331643e-02 1.466329e-01 9.266836e-01
[83,] 1.136049e-01 2.272099e-01 8.863951e-01
[84,] 1.678592e-01 3.357184e-01 8.321408e-01
[85,] 2.348014e-01 4.696028e-01 7.651986e-01
[86,] 3.219247e-01 6.438494e-01 6.780753e-01
[87,] 4.137694e-01 8.275389e-01 5.862306e-01
[88,] 5.095076e-01 9.809848e-01 4.904924e-01
[89,] 5.952383e-01 8.095234e-01 4.047617e-01
[90,] 6.848870e-01 6.302259e-01 3.151130e-01
[91,] 7.692824e-01 4.614351e-01 2.307176e-01
[92,] 8.377372e-01 3.245256e-01 1.622628e-01
[93,] 8.910636e-01 2.178727e-01 1.089364e-01
[94,] 9.277636e-01 1.444727e-01 7.223637e-02
[95,] 9.503783e-01 9.924343e-02 4.962172e-02
[96,] 9.641088e-01 7.178240e-02 3.589120e-02
[97,] 9.753919e-01 4.921619e-02 2.460810e-02
[98,] 9.832215e-01 3.355707e-02 1.677854e-02
[99,] 9.891103e-01 2.177949e-02 1.088975e-02
[100,] 9.934009e-01 1.319817e-02 6.599084e-03
[101,] 9.961641e-01 7.671745e-03 3.835872e-03
[102,] 9.977359e-01 4.528152e-03 2.264076e-03
[103,] 9.986354e-01 2.729263e-03 1.364632e-03
[104,] 9.992684e-01 1.463212e-03 7.316060e-04
[105,] 9.996325e-01 7.349663e-04 3.674831e-04
[106,] 9.998168e-01 3.664915e-04 1.832458e-04
[107,] 9.999042e-01 1.916831e-04 9.584154e-05
[108,] 9.999500e-01 1.000996e-04 5.004982e-05
[109,] 9.999746e-01 5.078740e-05 2.539370e-05
[110,] 9.999878e-01 2.445183e-05 1.222591e-05
[111,] 9.999939e-01 1.215994e-05 6.079972e-06
[112,] 9.999969e-01 6.166712e-06 3.083356e-06
[113,] 9.999985e-01 3.094224e-06 1.547112e-06
[114,] 9.999992e-01 1.569691e-06 7.848456e-07
[115,] 9.999996e-01 7.879226e-07 3.939613e-07
[116,] 9.999998e-01 3.940702e-07 1.970351e-07
[117,] 9.999999e-01 1.968064e-07 9.840321e-08
[118,] 1.000000e+00 9.947366e-08 4.973683e-08
[119,] 1.000000e+00 5.117014e-08 2.558507e-08
[120,] 1.000000e+00 2.635090e-08 1.317545e-08
[121,] 1.000000e+00 1.382766e-08 6.913831e-09
[122,] 1.000000e+00 7.767514e-09 3.883757e-09
[123,] 1.000000e+00 4.217377e-09 2.108688e-09
[124,] 1.000000e+00 2.067800e-09 1.033900e-09
[125,] 1.000000e+00 1.039627e-09 5.198134e-10
[126,] 1.000000e+00 6.589934e-10 3.294967e-10
[127,] 1.000000e+00 4.703607e-10 2.351804e-10
[128,] 1.000000e+00 3.487373e-10 1.743687e-10
[129,] 1.000000e+00 2.312985e-10 1.156492e-10
[130,] 1.000000e+00 1.016383e-10 5.081916e-11
[131,] 1.000000e+00 4.395887e-11 2.197943e-11
[132,] 1.000000e+00 2.183268e-11 1.091634e-11
[133,] 1.000000e+00 1.129313e-11 5.646563e-12
[134,] 1.000000e+00 5.524837e-12 2.762419e-12
[135,] 1.000000e+00 2.880189e-12 1.440095e-12
[136,] 1.000000e+00 1.822373e-12 9.111863e-13
[137,] 1.000000e+00 1.409696e-12 7.048480e-13
[138,] 1.000000e+00 1.247078e-12 6.235390e-13
[139,] 1.000000e+00 7.635195e-13 3.817598e-13
[140,] 1.000000e+00 3.448059e-13 1.724030e-13
[141,] 1.000000e+00 1.577521e-13 7.887603e-14
[142,] 1.000000e+00 8.258678e-14 4.129339e-14
[143,] 1.000000e+00 5.017529e-14 2.508765e-14
[144,] 1.000000e+00 2.768049e-14 1.384024e-14
[145,] 1.000000e+00 1.357752e-14 6.788762e-15
[146,] 1.000000e+00 8.666839e-15 4.333420e-15
[147,] 1.000000e+00 4.177252e-15 2.088626e-15
[148,] 1.000000e+00 1.751916e-15 8.759579e-16
[149,] 1.000000e+00 9.530367e-16 4.765184e-16
[150,] 1.000000e+00 6.134732e-16 3.067366e-16
[151,] 1.000000e+00 4.382224e-16 2.191112e-16
[152,] 1.000000e+00 2.832261e-16 1.416131e-16
[153,] 1.000000e+00 1.780573e-16 8.902866e-17
[154,] 1.000000e+00 9.377972e-17 4.688986e-17
[155,] 1.000000e+00 5.642968e-17 2.821484e-17
[156,] 1.000000e+00 4.311829e-17 2.155915e-17
[157,] 1.000000e+00 3.098184e-17 1.549092e-17
[158,] 1.000000e+00 2.026337e-17 1.013169e-17
[159,] 1.000000e+00 1.586292e-17 7.931462e-18
[160,] 1.000000e+00 1.143922e-17 5.719610e-18
[161,] 1.000000e+00 8.380679e-18 4.190340e-18
[162,] 1.000000e+00 5.374869e-18 2.687435e-18
[163,] 1.000000e+00 1.886551e-18 9.432753e-19
[164,] 1.000000e+00 8.069038e-19 4.034519e-19
[165,] 1.000000e+00 3.166289e-19 1.583145e-19
[166,] 1.000000e+00 1.282323e-19 6.411613e-20
[167,] 1.000000e+00 9.108258e-20 4.554129e-20
[168,] 1.000000e+00 4.776347e-20 2.388173e-20
[169,] 1.000000e+00 2.160162e-20 1.080081e-20
[170,] 1.000000e+00 1.370718e-20 6.853592e-21
[171,] 1.000000e+00 6.861445e-21 3.430722e-21
[172,] 1.000000e+00 2.866745e-21 1.433372e-21
[173,] 1.000000e+00 1.409177e-21 7.045885e-22
[174,] 1.000000e+00 6.240914e-22 3.120457e-22
[175,] 1.000000e+00 3.909565e-22 1.954783e-22
[176,] 1.000000e+00 2.534014e-22 1.267007e-22
[177,] 1.000000e+00 3.706297e-22 1.853148e-22
[178,] 1.000000e+00 5.743507e-22 2.871754e-22
[179,] 1.000000e+00 9.794161e-22 4.897081e-22
[180,] 1.000000e+00 1.583324e-21 7.916620e-22
[181,] 1.000000e+00 2.190517e-21 1.095258e-21
[182,] 1.000000e+00 2.342074e-21 1.171037e-21
[183,] 1.000000e+00 2.210972e-21 1.105486e-21
[184,] 1.000000e+00 1.837408e-21 9.187040e-22
[185,] 1.000000e+00 1.650226e-21 8.251128e-22
[186,] 1.000000e+00 2.597688e-21 1.298844e-21
[187,] 1.000000e+00 3.875413e-21 1.937706e-21
[188,] 1.000000e+00 2.824461e-21 1.412230e-21
[189,] 1.000000e+00 1.432112e-21 7.160561e-22
[190,] 1.000000e+00 6.901295e-22 3.450647e-22
[191,] 1.000000e+00 5.365013e-22 2.682507e-22
[192,] 1.000000e+00 3.557706e-22 1.778853e-22
[193,] 1.000000e+00 2.636815e-22 1.318407e-22
[194,] 1.000000e+00 1.517976e-22 7.589880e-23
[195,] 1.000000e+00 6.114115e-23 3.057057e-23
[196,] 1.000000e+00 2.417650e-23 1.208825e-23
[197,] 1.000000e+00 1.334771e-23 6.673854e-24
[198,] 1.000000e+00 1.267035e-23 6.335176e-24
[199,] 1.000000e+00 9.469129e-24 4.734565e-24
[200,] 1.000000e+00 7.231672e-24 3.615836e-24
[201,] 1.000000e+00 7.543350e-24 3.771675e-24
[202,] 1.000000e+00 1.163417e-23 5.817083e-24
[203,] 1.000000e+00 2.149281e-23 1.074641e-23
[204,] 1.000000e+00 4.296295e-23 2.148147e-23
[205,] 1.000000e+00 9.046119e-23 4.523059e-23
[206,] 1.000000e+00 1.689331e-22 8.446657e-23
[207,] 1.000000e+00 2.221672e-22 1.110836e-22
[208,] 1.000000e+00 4.401373e-22 2.200687e-22
[209,] 1.000000e+00 5.636510e-22 2.818255e-22
[210,] 1.000000e+00 5.081488e-22 2.540744e-22
[211,] 1.000000e+00 5.209517e-22 2.604758e-22
[212,] 1.000000e+00 6.476148e-22 3.238074e-22
[213,] 1.000000e+00 7.955444e-22 3.977722e-22
[214,] 1.000000e+00 1.028368e-21 5.141838e-22
[215,] 1.000000e+00 1.325842e-21 6.629212e-22
[216,] 1.000000e+00 1.823629e-21 9.118146e-22
[217,] 1.000000e+00 3.900794e-21 1.950397e-21
[218,] 1.000000e+00 5.596239e-21 2.798120e-21
[219,] 1.000000e+00 4.565806e-21 2.282903e-21
[220,] 1.000000e+00 2.083486e-21 1.041743e-21
[221,] 1.000000e+00 5.372108e-22 2.686054e-22
[222,] 1.000000e+00 5.011810e-22 2.505905e-22
[223,] 1.000000e+00 5.417833e-22 2.708917e-22
[224,] 1.000000e+00 3.024723e-22 1.512361e-22
[225,] 1.000000e+00 1.631902e-22 8.159509e-23
[226,] 1.000000e+00 7.098235e-23 3.549117e-23
[227,] 1.000000e+00 9.017689e-23 4.508844e-23
[228,] 1.000000e+00 1.074696e-22 5.373481e-23
[229,] 1.000000e+00 4.107954e-23 2.053977e-23
[230,] 1.000000e+00 2.619639e-23 1.309820e-23
[231,] 1.000000e+00 6.848565e-24 3.424283e-24
[232,] 1.000000e+00 1.514234e-23 7.571172e-24
[233,] 1.000000e+00 1.455994e-23 7.279968e-24
[234,] 1.000000e+00 2.474831e-23 1.237415e-23
[235,] 1.000000e+00 7.679150e-23 3.839575e-23
[236,] 1.000000e+00 2.094395e-22 1.047197e-22
[237,] 1.000000e+00 3.757267e-22 1.878633e-22
[238,] 1.000000e+00 5.339321e-22 2.669660e-22
[239,] 1.000000e+00 5.760477e-22 2.880239e-22
[240,] 1.000000e+00 6.087432e-22 3.043716e-22
[241,] 1.000000e+00 3.785262e-22 1.892631e-22
[242,] 1.000000e+00 1.927947e-22 9.639734e-23
[243,] 1.000000e+00 2.710788e-22 1.355394e-22
[244,] 1.000000e+00 3.779664e-22 1.889832e-22
[245,] 1.000000e+00 1.204077e-22 6.020384e-23
[246,] 1.000000e+00 1.973117e-22 9.865585e-23
[247,] 1.000000e+00 2.105239e-22 1.052619e-22
[248,] 1.000000e+00 1.675869e-22 8.379345e-23
[249,] 1.000000e+00 3.246133e-22 1.623066e-22
[250,] 1.000000e+00 9.317238e-22 4.658619e-22
[251,] 1.000000e+00 2.334535e-21 1.167267e-21
[252,] 1.000000e+00 1.079204e-20 5.396022e-21
[253,] 1.000000e+00 4.093271e-20 2.046636e-20
[254,] 1.000000e+00 6.878574e-20 3.439287e-20
[255,] 1.000000e+00 6.038336e-20 3.019168e-20
[256,] 1.000000e+00 1.490908e-20 7.454539e-21
[257,] 1.000000e+00 1.933507e-21 9.667534e-22
[258,] 1.000000e+00 1.986584e-22 9.932920e-23
[259,] 1.000000e+00 1.535236e-23 7.676182e-24
[260,] 1.000000e+00 1.971012e-24 9.855058e-25
[261,] 1.000000e+00 2.672672e-25 1.336336e-25
[262,] 1.000000e+00 8.244797e-26 4.122398e-26
[263,] 1.000000e+00 3.764936e-26 1.882468e-26
[264,] 1.000000e+00 2.820360e-26 1.410180e-26
[265,] 1.000000e+00 1.204165e-25 6.020824e-26
[266,] 1.000000e+00 1.832006e-24 9.160032e-25
[267,] 1.000000e+00 1.435002e-23 7.175008e-24
[268,] 1.000000e+00 7.989603e-23 3.994801e-23
[269,] 1.000000e+00 1.456232e-21 7.281161e-22
[270,] 1.000000e+00 6.354232e-21 3.177116e-21
[271,] 1.000000e+00 3.693746e-20 1.846873e-20
[272,] 1.000000e+00 3.147311e-19 1.573655e-19
[273,] 1.000000e+00 5.356081e-18 2.678040e-18
[274,] 1.000000e+00 9.699806e-17 4.849903e-17
[275,] 1.000000e+00 1.727090e-15 8.635448e-16
[276,] 1.000000e+00 2.245750e-14 1.122875e-14
[277,] 1.000000e+00 3.765657e-13 1.882828e-13
[278,] 1.000000e+00 6.766402e-12 3.383201e-12
[279,] 1.000000e+00 1.046686e-10 5.233429e-11
[280,] 1.000000e+00 1.600398e-09 8.001989e-10
[281,] 1.000000e+00 1.300698e-08 6.503492e-09
[282,] 1.000000e+00 2.703231e-08 1.351615e-08
[283,] 1.000000e+00 2.296631e-09 1.148316e-09
[284,] 9.999999e-01 1.525608e-07 7.628041e-08
[285,] 9.999983e-01 3.432700e-06 1.716350e-06
> postscript(file="/var/www/rcomp/tmp/16ii61290544448.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/26ii61290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36ii61290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4zs0r1290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5zs0r1290544448.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
-41.52515090 -37.06228647 -35.19304596 -35.17158232 -35.59793704 -33.12025992
7 8 9 10 11 12
-31.66881718 -33.47094713 -32.88892523 -32.50405522 -32.84540922 -33.23697261
13 14 15 16 17 18
-33.99169261 -32.97252983 -32.81381441 -32.20682260 -32.03562740 -32.02655336
19 20 21 22 23 24
-32.57530758 -31.15408333 -29.64520000 -29.61066785 -29.30882502 -28.69259016
25 26 27 28 29 30
-29.88384251 -28.65070648 -26.92863653 -26.69102725 -25.59311984 -24.60155645
31 32 33 34 35 36
-23.64451484 -23.52110784 -21.56672681 -21.59938501 -22.21455331 -24.19357470
37 38 39 40 41 42
-25.21819432 -23.57600500 -23.10323652 -22.50079681 -21.51479081 -21.41414302
43 44 45 46 47 48
-20.94660332 -19.75919400 -19.62173394 -19.52028914 -19.55694182 -21.70450819
49 50 51 52 53 54
-23.37822915 -20.00635102 -16.41814786 -14.63775110 -14.07363788 -12.50355888
55 56 57 58 59 60
-13.15016145 -17.56933990 -20.62003337 -22.60388455 -21.09431290 -18.56767106
61 62 63 64 65 66
-16.01003351 -10.79887418 -9.44991169 -9.88768999 -9.61450274 -8.26567910
67 68 69 70 71 72
-8.27023120 -8.59645521 -9.09171240 -11.11923920 -10.66632054 -9.39725719
73 74 75 76 77 78
-7.17856505 -3.90161773 -3.69204513 -4.72980269 -5.54146471 -6.57785726
79 80 81 82 83 84
-5.48066706 -6.56785551 -6.98851177 -9.16268039 -6.60755421 -5.76629038
85 86 87 88 89 90
-4.58770533 -3.10663189 -4.20608147 -4.12682789 -4.46903795 -2.52145084
91 92 93 94 95 96
-1.83752706 -1.81547468 -1.50249634 -0.88095291 0.11394680 0.68866807
97 98 99 100 101 102
2.10133537 3.05995026 3.31851647 2.56021039 1.20177584 2.18971261
103 104 105 106 107 108
2.16291023 3.42957313 4.71013985 5.12817701 4.80751038 4.58311883
109 110 111 112 113 114
6.21518830 7.33607357 7.91538620 7.26799699 7.48518845 8.40356459
115 116 117 118 119 120
10.00900069 9.98084368 9.60426106 10.47928077 9.60691127 7.46553970
121 122 123 124 125 126
7.20259359 8.52482312 7.57068149 6.03509632 8.20578163 8.34628994
127 128 129 130 131 132
7.06838379 7.90652996 8.63948660 8.02854179 5.65550916 0.09934256
133 134 135 136 137 138
-0.18836520 7.66559790 13.06522147 13.93188436 11.98365983 12.66609013
139 140 141 142 143 144
13.10346002 12.73793312 11.71358305 9.03979266 8.19045194 13.02922918
145 146 147 148 149 150
16.27056963 16.98011112 17.25642641 16.80673634 18.10812928 19.83560739
151 152 153 154 155 156
18.66382663 20.93701071 20.89736884 20.67991005 21.10028860 21.96344522
157 158 159 160 161 162
22.25061610 23.24645166 22.51454993 20.86864700 18.97541297 19.03340081
163 164 165 166 167 168
17.81649022 15.60266936 14.17129731 15.76704270 13.46249378 15.45059734
169 170 171 172 173 174
13.04570129 11.94734717 13.48058373 14.92317331 12.12563694 6.71458037
175 176 177 178 179 180
8.00519753 7.70485855 3.76215626 2.59764724 2.97731736 -1.72749222
181 182 183 184 185 186
-7.71428349 3.53460281 9.71472367 9.34913771 10.41290158 14.81176962
187 188 189 190 191 192
17.71465288 17.24655555 21.20044521 24.01158697 21.45757826 20.85201051
193 194 195 196 197 198
26.63757879 28.59246653 25.95365596 22.65215845 22.11851348 23.04030572
199 200 201 202 203 204
22.58334391 23.27336291 21.51066655 20.68066893 20.10135852 15.52575642
205 206 207 208 209 210
11.30738663 7.15707585 -1.89647123 16.13067141 14.04698924 8.78005499
211 212 213 214 215 216
10.92721296 14.88160933 16.71543369 16.89460524 16.28098948 12.41320637
217 218 219 220 221 222
9.22910860 11.15276219 10.46752526 11.60759405 8.27137463 7.71826030
223 224 225 226 227 228
6.92577957 5.07621892 7.62294775 1.82255700 0.17287138 3.98796613
229 230 231 232 233 234
3.37289615 4.08489809 0.50163659 -1.21075799 2.48617928 -0.48844051
235 236 237 238 239 240
-4.99603229 -6.31689810 -19.28139401 -25.19127183 -21.67576052 -18.23565882
241 242 243 244 245 246
-14.01381420 -4.17775257 -1.18622252 -3.31053238 -2.32949726 0.79431276
247 248 249 250 251 252
-1.60243697 -4.23694298 3.47079612 11.30515599 1.05432632 -0.73624935
253 254 255 256 257 258
10.93649870 2.38294888 4.42660529 5.93116084 4.54887682 3.23404888
259 260 261 262 263 264
1.55559986 4.99708032 3.67724082 -2.44506786 -10.89751230 -11.61590007
265 266 267 268 269 270
-13.12637846 -17.35337149 -26.19166544 -32.96278098 -45.84619305 -52.80979553
271 272 273 274 275 276
-54.56127833 -26.98889968 -9.66526968 12.59400612 24.97427514 28.14977472
277 278 279 280 281 282
30.94446242 39.44714031 37.32432529 38.55445663 33.22860912 28.48191942
283 284 285 286 287 288
31.66795421 29.62960020 32.11758511 24.28271697 22.78435403 19.73180673
289 290 291 292 293 294
13.82581204 16.60104349 17.72330143 18.85457536 24.84166873 22.36120162
295 296
21.13142638 22.10844101
> postscript(file="/var/www/rcomp/tmp/6sjzu1290544448.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 -41.52515090 NA
1 -37.06228647 -41.52515090
2 -35.19304596 -37.06228647
3 -35.17158232 -35.19304596
4 -35.59793704 -35.17158232
5 -33.12025992 -35.59793704
6 -31.66881718 -33.12025992
7 -33.47094713 -31.66881718
8 -32.88892523 -33.47094713
9 -32.50405522 -32.88892523
10 -32.84540922 -32.50405522
11 -33.23697261 -32.84540922
12 -33.99169261 -33.23697261
13 -32.97252983 -33.99169261
14 -32.81381441 -32.97252983
15 -32.20682260 -32.81381441
16 -32.03562740 -32.20682260
17 -32.02655336 -32.03562740
18 -32.57530758 -32.02655336
19 -31.15408333 -32.57530758
20 -29.64520000 -31.15408333
21 -29.61066785 -29.64520000
22 -29.30882502 -29.61066785
23 -28.69259016 -29.30882502
24 -29.88384251 -28.69259016
25 -28.65070648 -29.88384251
26 -26.92863653 -28.65070648
27 -26.69102725 -26.92863653
28 -25.59311984 -26.69102725
29 -24.60155645 -25.59311984
30 -23.64451484 -24.60155645
31 -23.52110784 -23.64451484
32 -21.56672681 -23.52110784
33 -21.59938501 -21.56672681
34 -22.21455331 -21.59938501
35 -24.19357470 -22.21455331
36 -25.21819432 -24.19357470
37 -23.57600500 -25.21819432
38 -23.10323652 -23.57600500
39 -22.50079681 -23.10323652
40 -21.51479081 -22.50079681
41 -21.41414302 -21.51479081
42 -20.94660332 -21.41414302
43 -19.75919400 -20.94660332
44 -19.62173394 -19.75919400
45 -19.52028914 -19.62173394
46 -19.55694182 -19.52028914
47 -21.70450819 -19.55694182
48 -23.37822915 -21.70450819
49 -20.00635102 -23.37822915
50 -16.41814786 -20.00635102
51 -14.63775110 -16.41814786
52 -14.07363788 -14.63775110
53 -12.50355888 -14.07363788
54 -13.15016145 -12.50355888
55 -17.56933990 -13.15016145
56 -20.62003337 -17.56933990
57 -22.60388455 -20.62003337
58 -21.09431290 -22.60388455
59 -18.56767106 -21.09431290
60 -16.01003351 -18.56767106
61 -10.79887418 -16.01003351
62 -9.44991169 -10.79887418
63 -9.88768999 -9.44991169
64 -9.61450274 -9.88768999
65 -8.26567910 -9.61450274
66 -8.27023120 -8.26567910
67 -8.59645521 -8.27023120
68 -9.09171240 -8.59645521
69 -11.11923920 -9.09171240
70 -10.66632054 -11.11923920
71 -9.39725719 -10.66632054
72 -7.17856505 -9.39725719
73 -3.90161773 -7.17856505
74 -3.69204513 -3.90161773
75 -4.72980269 -3.69204513
76 -5.54146471 -4.72980269
77 -6.57785726 -5.54146471
78 -5.48066706 -6.57785726
79 -6.56785551 -5.48066706
80 -6.98851177 -6.56785551
81 -9.16268039 -6.98851177
82 -6.60755421 -9.16268039
83 -5.76629038 -6.60755421
84 -4.58770533 -5.76629038
85 -3.10663189 -4.58770533
86 -4.20608147 -3.10663189
87 -4.12682789 -4.20608147
88 -4.46903795 -4.12682789
89 -2.52145084 -4.46903795
90 -1.83752706 -2.52145084
91 -1.81547468 -1.83752706
92 -1.50249634 -1.81547468
93 -0.88095291 -1.50249634
94 0.11394680 -0.88095291
95 0.68866807 0.11394680
96 2.10133537 0.68866807
97 3.05995026 2.10133537
98 3.31851647 3.05995026
99 2.56021039 3.31851647
100 1.20177584 2.56021039
101 2.18971261 1.20177584
102 2.16291023 2.18971261
103 3.42957313 2.16291023
104 4.71013985 3.42957313
105 5.12817701 4.71013985
106 4.80751038 5.12817701
107 4.58311883 4.80751038
108 6.21518830 4.58311883
109 7.33607357 6.21518830
110 7.91538620 7.33607357
111 7.26799699 7.91538620
112 7.48518845 7.26799699
113 8.40356459 7.48518845
114 10.00900069 8.40356459
115 9.98084368 10.00900069
116 9.60426106 9.98084368
117 10.47928077 9.60426106
118 9.60691127 10.47928077
119 7.46553970 9.60691127
120 7.20259359 7.46553970
121 8.52482312 7.20259359
122 7.57068149 8.52482312
123 6.03509632 7.57068149
124 8.20578163 6.03509632
125 8.34628994 8.20578163
126 7.06838379 8.34628994
127 7.90652996 7.06838379
128 8.63948660 7.90652996
129 8.02854179 8.63948660
130 5.65550916 8.02854179
131 0.09934256 5.65550916
132 -0.18836520 0.09934256
133 7.66559790 -0.18836520
134 13.06522147 7.66559790
135 13.93188436 13.06522147
136 11.98365983 13.93188436
137 12.66609013 11.98365983
138 13.10346002 12.66609013
139 12.73793312 13.10346002
140 11.71358305 12.73793312
141 9.03979266 11.71358305
142 8.19045194 9.03979266
143 13.02922918 8.19045194
144 16.27056963 13.02922918
145 16.98011112 16.27056963
146 17.25642641 16.98011112
147 16.80673634 17.25642641
148 18.10812928 16.80673634
149 19.83560739 18.10812928
150 18.66382663 19.83560739
151 20.93701071 18.66382663
152 20.89736884 20.93701071
153 20.67991005 20.89736884
154 21.10028860 20.67991005
155 21.96344522 21.10028860
156 22.25061610 21.96344522
157 23.24645166 22.25061610
158 22.51454993 23.24645166
159 20.86864700 22.51454993
160 18.97541297 20.86864700
161 19.03340081 18.97541297
162 17.81649022 19.03340081
163 15.60266936 17.81649022
164 14.17129731 15.60266936
165 15.76704270 14.17129731
166 13.46249378 15.76704270
167 15.45059734 13.46249378
168 13.04570129 15.45059734
169 11.94734717 13.04570129
170 13.48058373 11.94734717
171 14.92317331 13.48058373
172 12.12563694 14.92317331
173 6.71458037 12.12563694
174 8.00519753 6.71458037
175 7.70485855 8.00519753
176 3.76215626 7.70485855
177 2.59764724 3.76215626
178 2.97731736 2.59764724
179 -1.72749222 2.97731736
180 -7.71428349 -1.72749222
181 3.53460281 -7.71428349
182 9.71472367 3.53460281
183 9.34913771 9.71472367
184 10.41290158 9.34913771
185 14.81176962 10.41290158
186 17.71465288 14.81176962
187 17.24655555 17.71465288
188 21.20044521 17.24655555
189 24.01158697 21.20044521
190 21.45757826 24.01158697
191 20.85201051 21.45757826
192 26.63757879 20.85201051
193 28.59246653 26.63757879
194 25.95365596 28.59246653
195 22.65215845 25.95365596
196 22.11851348 22.65215845
197 23.04030572 22.11851348
198 22.58334391 23.04030572
199 23.27336291 22.58334391
200 21.51066655 23.27336291
201 20.68066893 21.51066655
202 20.10135852 20.68066893
203 15.52575642 20.10135852
204 11.30738663 15.52575642
205 7.15707585 11.30738663
206 -1.89647123 7.15707585
207 16.13067141 -1.89647123
208 14.04698924 16.13067141
209 8.78005499 14.04698924
210 10.92721296 8.78005499
211 14.88160933 10.92721296
212 16.71543369 14.88160933
213 16.89460524 16.71543369
214 16.28098948 16.89460524
215 12.41320637 16.28098948
216 9.22910860 12.41320637
217 11.15276219 9.22910860
218 10.46752526 11.15276219
219 11.60759405 10.46752526
220 8.27137463 11.60759405
221 7.71826030 8.27137463
222 6.92577957 7.71826030
223 5.07621892 6.92577957
224 7.62294775 5.07621892
225 1.82255700 7.62294775
226 0.17287138 1.82255700
227 3.98796613 0.17287138
228 3.37289615 3.98796613
229 4.08489809 3.37289615
230 0.50163659 4.08489809
231 -1.21075799 0.50163659
232 2.48617928 -1.21075799
233 -0.48844051 2.48617928
234 -4.99603229 -0.48844051
235 -6.31689810 -4.99603229
236 -19.28139401 -6.31689810
237 -25.19127183 -19.28139401
238 -21.67576052 -25.19127183
239 -18.23565882 -21.67576052
240 -14.01381420 -18.23565882
241 -4.17775257 -14.01381420
242 -1.18622252 -4.17775257
243 -3.31053238 -1.18622252
244 -2.32949726 -3.31053238
245 0.79431276 -2.32949726
246 -1.60243697 0.79431276
247 -4.23694298 -1.60243697
248 3.47079612 -4.23694298
249 11.30515599 3.47079612
250 1.05432632 11.30515599
251 -0.73624935 1.05432632
252 10.93649870 -0.73624935
253 2.38294888 10.93649870
254 4.42660529 2.38294888
255 5.93116084 4.42660529
256 4.54887682 5.93116084
257 3.23404888 4.54887682
258 1.55559986 3.23404888
259 4.99708032 1.55559986
260 3.67724082 4.99708032
261 -2.44506786 3.67724082
262 -10.89751230 -2.44506786
263 -11.61590007 -10.89751230
264 -13.12637846 -11.61590007
265 -17.35337149 -13.12637846
266 -26.19166544 -17.35337149
267 -32.96278098 -26.19166544
268 -45.84619305 -32.96278098
269 -52.80979553 -45.84619305
270 -54.56127833 -52.80979553
271 -26.98889968 -54.56127833
272 -9.66526968 -26.98889968
273 12.59400612 -9.66526968
274 24.97427514 12.59400612
275 28.14977472 24.97427514
276 30.94446242 28.14977472
277 39.44714031 30.94446242
278 37.32432529 39.44714031
279 38.55445663 37.32432529
280 33.22860912 38.55445663
281 28.48191942 33.22860912
282 31.66795421 28.48191942
283 29.62960020 31.66795421
284 32.11758511 29.62960020
285 24.28271697 32.11758511
286 22.78435403 24.28271697
287 19.73180673 22.78435403
288 13.82581204 19.73180673
289 16.60104349 13.82581204
290 17.72330143 16.60104349
291 18.85457536 17.72330143
292 24.84166873 18.85457536
293 22.36120162 24.84166873
294 21.13142638 22.36120162
295 22.10844101 21.13142638
296 NA 22.10844101
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -37.06228647 -41.52515090
[2,] -35.19304596 -37.06228647
[3,] -35.17158232 -35.19304596
[4,] -35.59793704 -35.17158232
[5,] -33.12025992 -35.59793704
[6,] -31.66881718 -33.12025992
[7,] -33.47094713 -31.66881718
[8,] -32.88892523 -33.47094713
[9,] -32.50405522 -32.88892523
[10,] -32.84540922 -32.50405522
[11,] -33.23697261 -32.84540922
[12,] -33.99169261 -33.23697261
[13,] -32.97252983 -33.99169261
[14,] -32.81381441 -32.97252983
[15,] -32.20682260 -32.81381441
[16,] -32.03562740 -32.20682260
[17,] -32.02655336 -32.03562740
[18,] -32.57530758 -32.02655336
[19,] -31.15408333 -32.57530758
[20,] -29.64520000 -31.15408333
[21,] -29.61066785 -29.64520000
[22,] -29.30882502 -29.61066785
[23,] -28.69259016 -29.30882502
[24,] -29.88384251 -28.69259016
[25,] -28.65070648 -29.88384251
[26,] -26.92863653 -28.65070648
[27,] -26.69102725 -26.92863653
[28,] -25.59311984 -26.69102725
[29,] -24.60155645 -25.59311984
[30,] -23.64451484 -24.60155645
[31,] -23.52110784 -23.64451484
[32,] -21.56672681 -23.52110784
[33,] -21.59938501 -21.56672681
[34,] -22.21455331 -21.59938501
[35,] -24.19357470 -22.21455331
[36,] -25.21819432 -24.19357470
[37,] -23.57600500 -25.21819432
[38,] -23.10323652 -23.57600500
[39,] -22.50079681 -23.10323652
[40,] -21.51479081 -22.50079681
[41,] -21.41414302 -21.51479081
[42,] -20.94660332 -21.41414302
[43,] -19.75919400 -20.94660332
[44,] -19.62173394 -19.75919400
[45,] -19.52028914 -19.62173394
[46,] -19.55694182 -19.52028914
[47,] -21.70450819 -19.55694182
[48,] -23.37822915 -21.70450819
[49,] -20.00635102 -23.37822915
[50,] -16.41814786 -20.00635102
[51,] -14.63775110 -16.41814786
[52,] -14.07363788 -14.63775110
[53,] -12.50355888 -14.07363788
[54,] -13.15016145 -12.50355888
[55,] -17.56933990 -13.15016145
[56,] -20.62003337 -17.56933990
[57,] -22.60388455 -20.62003337
[58,] -21.09431290 -22.60388455
[59,] -18.56767106 -21.09431290
[60,] -16.01003351 -18.56767106
[61,] -10.79887418 -16.01003351
[62,] -9.44991169 -10.79887418
[63,] -9.88768999 -9.44991169
[64,] -9.61450274 -9.88768999
[65,] -8.26567910 -9.61450274
[66,] -8.27023120 -8.26567910
[67,] -8.59645521 -8.27023120
[68,] -9.09171240 -8.59645521
[69,] -11.11923920 -9.09171240
[70,] -10.66632054 -11.11923920
[71,] -9.39725719 -10.66632054
[72,] -7.17856505 -9.39725719
[73,] -3.90161773 -7.17856505
[74,] -3.69204513 -3.90161773
[75,] -4.72980269 -3.69204513
[76,] -5.54146471 -4.72980269
[77,] -6.57785726 -5.54146471
[78,] -5.48066706 -6.57785726
[79,] -6.56785551 -5.48066706
[80,] -6.98851177 -6.56785551
[81,] -9.16268039 -6.98851177
[82,] -6.60755421 -9.16268039
[83,] -5.76629038 -6.60755421
[84,] -4.58770533 -5.76629038
[85,] -3.10663189 -4.58770533
[86,] -4.20608147 -3.10663189
[87,] -4.12682789 -4.20608147
[88,] -4.46903795 -4.12682789
[89,] -2.52145084 -4.46903795
[90,] -1.83752706 -2.52145084
[91,] -1.81547468 -1.83752706
[92,] -1.50249634 -1.81547468
[93,] -0.88095291 -1.50249634
[94,] 0.11394680 -0.88095291
[95,] 0.68866807 0.11394680
[96,] 2.10133537 0.68866807
[97,] 3.05995026 2.10133537
[98,] 3.31851647 3.05995026
[99,] 2.56021039 3.31851647
[100,] 1.20177584 2.56021039
[101,] 2.18971261 1.20177584
[102,] 2.16291023 2.18971261
[103,] 3.42957313 2.16291023
[104,] 4.71013985 3.42957313
[105,] 5.12817701 4.71013985
[106,] 4.80751038 5.12817701
[107,] 4.58311883 4.80751038
[108,] 6.21518830 4.58311883
[109,] 7.33607357 6.21518830
[110,] 7.91538620 7.33607357
[111,] 7.26799699 7.91538620
[112,] 7.48518845 7.26799699
[113,] 8.40356459 7.48518845
[114,] 10.00900069 8.40356459
[115,] 9.98084368 10.00900069
[116,] 9.60426106 9.98084368
[117,] 10.47928077 9.60426106
[118,] 9.60691127 10.47928077
[119,] 7.46553970 9.60691127
[120,] 7.20259359 7.46553970
[121,] 8.52482312 7.20259359
[122,] 7.57068149 8.52482312
[123,] 6.03509632 7.57068149
[124,] 8.20578163 6.03509632
[125,] 8.34628994 8.20578163
[126,] 7.06838379 8.34628994
[127,] 7.90652996 7.06838379
[128,] 8.63948660 7.90652996
[129,] 8.02854179 8.63948660
[130,] 5.65550916 8.02854179
[131,] 0.09934256 5.65550916
[132,] -0.18836520 0.09934256
[133,] 7.66559790 -0.18836520
[134,] 13.06522147 7.66559790
[135,] 13.93188436 13.06522147
[136,] 11.98365983 13.93188436
[137,] 12.66609013 11.98365983
[138,] 13.10346002 12.66609013
[139,] 12.73793312 13.10346002
[140,] 11.71358305 12.73793312
[141,] 9.03979266 11.71358305
[142,] 8.19045194 9.03979266
[143,] 13.02922918 8.19045194
[144,] 16.27056963 13.02922918
[145,] 16.98011112 16.27056963
[146,] 17.25642641 16.98011112
[147,] 16.80673634 17.25642641
[148,] 18.10812928 16.80673634
[149,] 19.83560739 18.10812928
[150,] 18.66382663 19.83560739
[151,] 20.93701071 18.66382663
[152,] 20.89736884 20.93701071
[153,] 20.67991005 20.89736884
[154,] 21.10028860 20.67991005
[155,] 21.96344522 21.10028860
[156,] 22.25061610 21.96344522
[157,] 23.24645166 22.25061610
[158,] 22.51454993 23.24645166
[159,] 20.86864700 22.51454993
[160,] 18.97541297 20.86864700
[161,] 19.03340081 18.97541297
[162,] 17.81649022 19.03340081
[163,] 15.60266936 17.81649022
[164,] 14.17129731 15.60266936
[165,] 15.76704270 14.17129731
[166,] 13.46249378 15.76704270
[167,] 15.45059734 13.46249378
[168,] 13.04570129 15.45059734
[169,] 11.94734717 13.04570129
[170,] 13.48058373 11.94734717
[171,] 14.92317331 13.48058373
[172,] 12.12563694 14.92317331
[173,] 6.71458037 12.12563694
[174,] 8.00519753 6.71458037
[175,] 7.70485855 8.00519753
[176,] 3.76215626 7.70485855
[177,] 2.59764724 3.76215626
[178,] 2.97731736 2.59764724
[179,] -1.72749222 2.97731736
[180,] -7.71428349 -1.72749222
[181,] 3.53460281 -7.71428349
[182,] 9.71472367 3.53460281
[183,] 9.34913771 9.71472367
[184,] 10.41290158 9.34913771
[185,] 14.81176962 10.41290158
[186,] 17.71465288 14.81176962
[187,] 17.24655555 17.71465288
[188,] 21.20044521 17.24655555
[189,] 24.01158697 21.20044521
[190,] 21.45757826 24.01158697
[191,] 20.85201051 21.45757826
[192,] 26.63757879 20.85201051
[193,] 28.59246653 26.63757879
[194,] 25.95365596 28.59246653
[195,] 22.65215845 25.95365596
[196,] 22.11851348 22.65215845
[197,] 23.04030572 22.11851348
[198,] 22.58334391 23.04030572
[199,] 23.27336291 22.58334391
[200,] 21.51066655 23.27336291
[201,] 20.68066893 21.51066655
[202,] 20.10135852 20.68066893
[203,] 15.52575642 20.10135852
[204,] 11.30738663 15.52575642
[205,] 7.15707585 11.30738663
[206,] -1.89647123 7.15707585
[207,] 16.13067141 -1.89647123
[208,] 14.04698924 16.13067141
[209,] 8.78005499 14.04698924
[210,] 10.92721296 8.78005499
[211,] 14.88160933 10.92721296
[212,] 16.71543369 14.88160933
[213,] 16.89460524 16.71543369
[214,] 16.28098948 16.89460524
[215,] 12.41320637 16.28098948
[216,] 9.22910860 12.41320637
[217,] 11.15276219 9.22910860
[218,] 10.46752526 11.15276219
[219,] 11.60759405 10.46752526
[220,] 8.27137463 11.60759405
[221,] 7.71826030 8.27137463
[222,] 6.92577957 7.71826030
[223,] 5.07621892 6.92577957
[224,] 7.62294775 5.07621892
[225,] 1.82255700 7.62294775
[226,] 0.17287138 1.82255700
[227,] 3.98796613 0.17287138
[228,] 3.37289615 3.98796613
[229,] 4.08489809 3.37289615
[230,] 0.50163659 4.08489809
[231,] -1.21075799 0.50163659
[232,] 2.48617928 -1.21075799
[233,] -0.48844051 2.48617928
[234,] -4.99603229 -0.48844051
[235,] -6.31689810 -4.99603229
[236,] -19.28139401 -6.31689810
[237,] -25.19127183 -19.28139401
[238,] -21.67576052 -25.19127183
[239,] -18.23565882 -21.67576052
[240,] -14.01381420 -18.23565882
[241,] -4.17775257 -14.01381420
[242,] -1.18622252 -4.17775257
[243,] -3.31053238 -1.18622252
[244,] -2.32949726 -3.31053238
[245,] 0.79431276 -2.32949726
[246,] -1.60243697 0.79431276
[247,] -4.23694298 -1.60243697
[248,] 3.47079612 -4.23694298
[249,] 11.30515599 3.47079612
[250,] 1.05432632 11.30515599
[251,] -0.73624935 1.05432632
[252,] 10.93649870 -0.73624935
[253,] 2.38294888 10.93649870
[254,] 4.42660529 2.38294888
[255,] 5.93116084 4.42660529
[256,] 4.54887682 5.93116084
[257,] 3.23404888 4.54887682
[258,] 1.55559986 3.23404888
[259,] 4.99708032 1.55559986
[260,] 3.67724082 4.99708032
[261,] -2.44506786 3.67724082
[262,] -10.89751230 -2.44506786
[263,] -11.61590007 -10.89751230
[264,] -13.12637846 -11.61590007
[265,] -17.35337149 -13.12637846
[266,] -26.19166544 -17.35337149
[267,] -32.96278098 -26.19166544
[268,] -45.84619305 -32.96278098
[269,] -52.80979553 -45.84619305
[270,] -54.56127833 -52.80979553
[271,] -26.98889968 -54.56127833
[272,] -9.66526968 -26.98889968
[273,] 12.59400612 -9.66526968
[274,] 24.97427514 12.59400612
[275,] 28.14977472 24.97427514
[276,] 30.94446242 28.14977472
[277,] 39.44714031 30.94446242
[278,] 37.32432529 39.44714031
[279,] 38.55445663 37.32432529
[280,] 33.22860912 38.55445663
[281,] 28.48191942 33.22860912
[282,] 31.66795421 28.48191942
[283,] 29.62960020 31.66795421
[284,] 32.11758511 29.62960020
[285,] 24.28271697 32.11758511
[286,] 22.78435403 24.28271697
[287,] 19.73180673 22.78435403
[288,] 13.82581204 19.73180673
[289,] 16.60104349 13.82581204
[290,] 17.72330143 16.60104349
[291,] 18.85457536 17.72330143
[292,] 24.84166873 18.85457536
[293,] 22.36120162 24.84166873
[294,] 21.13142638 22.36120162
[295,] 22.10844101 21.13142638
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -37.06228647 -41.52515090
2 -35.19304596 -37.06228647
3 -35.17158232 -35.19304596
4 -35.59793704 -35.17158232
5 -33.12025992 -35.59793704
6 -31.66881718 -33.12025992
7 -33.47094713 -31.66881718
8 -32.88892523 -33.47094713
9 -32.50405522 -32.88892523
10 -32.84540922 -32.50405522
11 -33.23697261 -32.84540922
12 -33.99169261 -33.23697261
13 -32.97252983 -33.99169261
14 -32.81381441 -32.97252983
15 -32.20682260 -32.81381441
16 -32.03562740 -32.20682260
17 -32.02655336 -32.03562740
18 -32.57530758 -32.02655336
19 -31.15408333 -32.57530758
20 -29.64520000 -31.15408333
21 -29.61066785 -29.64520000
22 -29.30882502 -29.61066785
23 -28.69259016 -29.30882502
24 -29.88384251 -28.69259016
25 -28.65070648 -29.88384251
26 -26.92863653 -28.65070648
27 -26.69102725 -26.92863653
28 -25.59311984 -26.69102725
29 -24.60155645 -25.59311984
30 -23.64451484 -24.60155645
31 -23.52110784 -23.64451484
32 -21.56672681 -23.52110784
33 -21.59938501 -21.56672681
34 -22.21455331 -21.59938501
35 -24.19357470 -22.21455331
36 -25.21819432 -24.19357470
37 -23.57600500 -25.21819432
38 -23.10323652 -23.57600500
39 -22.50079681 -23.10323652
40 -21.51479081 -22.50079681
41 -21.41414302 -21.51479081
42 -20.94660332 -21.41414302
43 -19.75919400 -20.94660332
44 -19.62173394 -19.75919400
45 -19.52028914 -19.62173394
46 -19.55694182 -19.52028914
47 -21.70450819 -19.55694182
48 -23.37822915 -21.70450819
49 -20.00635102 -23.37822915
50 -16.41814786 -20.00635102
51 -14.63775110 -16.41814786
52 -14.07363788 -14.63775110
53 -12.50355888 -14.07363788
54 -13.15016145 -12.50355888
55 -17.56933990 -13.15016145
56 -20.62003337 -17.56933990
57 -22.60388455 -20.62003337
58 -21.09431290 -22.60388455
59 -18.56767106 -21.09431290
60 -16.01003351 -18.56767106
61 -10.79887418 -16.01003351
62 -9.44991169 -10.79887418
63 -9.88768999 -9.44991169
64 -9.61450274 -9.88768999
65 -8.26567910 -9.61450274
66 -8.27023120 -8.26567910
67 -8.59645521 -8.27023120
68 -9.09171240 -8.59645521
69 -11.11923920 -9.09171240
70 -10.66632054 -11.11923920
71 -9.39725719 -10.66632054
72 -7.17856505 -9.39725719
73 -3.90161773 -7.17856505
74 -3.69204513 -3.90161773
75 -4.72980269 -3.69204513
76 -5.54146471 -4.72980269
77 -6.57785726 -5.54146471
78 -5.48066706 -6.57785726
79 -6.56785551 -5.48066706
80 -6.98851177 -6.56785551
81 -9.16268039 -6.98851177
82 -6.60755421 -9.16268039
83 -5.76629038 -6.60755421
84 -4.58770533 -5.76629038
85 -3.10663189 -4.58770533
86 -4.20608147 -3.10663189
87 -4.12682789 -4.20608147
88 -4.46903795 -4.12682789
89 -2.52145084 -4.46903795
90 -1.83752706 -2.52145084
91 -1.81547468 -1.83752706
92 -1.50249634 -1.81547468
93 -0.88095291 -1.50249634
94 0.11394680 -0.88095291
95 0.68866807 0.11394680
96 2.10133537 0.68866807
97 3.05995026 2.10133537
98 3.31851647 3.05995026
99 2.56021039 3.31851647
100 1.20177584 2.56021039
101 2.18971261 1.20177584
102 2.16291023 2.18971261
103 3.42957313 2.16291023
104 4.71013985 3.42957313
105 5.12817701 4.71013985
106 4.80751038 5.12817701
107 4.58311883 4.80751038
108 6.21518830 4.58311883
109 7.33607357 6.21518830
110 7.91538620 7.33607357
111 7.26799699 7.91538620
112 7.48518845 7.26799699
113 8.40356459 7.48518845
114 10.00900069 8.40356459
115 9.98084368 10.00900069
116 9.60426106 9.98084368
117 10.47928077 9.60426106
118 9.60691127 10.47928077
119 7.46553970 9.60691127
120 7.20259359 7.46553970
121 8.52482312 7.20259359
122 7.57068149 8.52482312
123 6.03509632 7.57068149
124 8.20578163 6.03509632
125 8.34628994 8.20578163
126 7.06838379 8.34628994
127 7.90652996 7.06838379
128 8.63948660 7.90652996
129 8.02854179 8.63948660
130 5.65550916 8.02854179
131 0.09934256 5.65550916
132 -0.18836520 0.09934256
133 7.66559790 -0.18836520
134 13.06522147 7.66559790
135 13.93188436 13.06522147
136 11.98365983 13.93188436
137 12.66609013 11.98365983
138 13.10346002 12.66609013
139 12.73793312 13.10346002
140 11.71358305 12.73793312
141 9.03979266 11.71358305
142 8.19045194 9.03979266
143 13.02922918 8.19045194
144 16.27056963 13.02922918
145 16.98011112 16.27056963
146 17.25642641 16.98011112
147 16.80673634 17.25642641
148 18.10812928 16.80673634
149 19.83560739 18.10812928
150 18.66382663 19.83560739
151 20.93701071 18.66382663
152 20.89736884 20.93701071
153 20.67991005 20.89736884
154 21.10028860 20.67991005
155 21.96344522 21.10028860
156 22.25061610 21.96344522
157 23.24645166 22.25061610
158 22.51454993 23.24645166
159 20.86864700 22.51454993
160 18.97541297 20.86864700
161 19.03340081 18.97541297
162 17.81649022 19.03340081
163 15.60266936 17.81649022
164 14.17129731 15.60266936
165 15.76704270 14.17129731
166 13.46249378 15.76704270
167 15.45059734 13.46249378
168 13.04570129 15.45059734
169 11.94734717 13.04570129
170 13.48058373 11.94734717
171 14.92317331 13.48058373
172 12.12563694 14.92317331
173 6.71458037 12.12563694
174 8.00519753 6.71458037
175 7.70485855 8.00519753
176 3.76215626 7.70485855
177 2.59764724 3.76215626
178 2.97731736 2.59764724
179 -1.72749222 2.97731736
180 -7.71428349 -1.72749222
181 3.53460281 -7.71428349
182 9.71472367 3.53460281
183 9.34913771 9.71472367
184 10.41290158 9.34913771
185 14.81176962 10.41290158
186 17.71465288 14.81176962
187 17.24655555 17.71465288
188 21.20044521 17.24655555
189 24.01158697 21.20044521
190 21.45757826 24.01158697
191 20.85201051 21.45757826
192 26.63757879 20.85201051
193 28.59246653 26.63757879
194 25.95365596 28.59246653
195 22.65215845 25.95365596
196 22.11851348 22.65215845
197 23.04030572 22.11851348
198 22.58334391 23.04030572
199 23.27336291 22.58334391
200 21.51066655 23.27336291
201 20.68066893 21.51066655
202 20.10135852 20.68066893
203 15.52575642 20.10135852
204 11.30738663 15.52575642
205 7.15707585 11.30738663
206 -1.89647123 7.15707585
207 16.13067141 -1.89647123
208 14.04698924 16.13067141
209 8.78005499 14.04698924
210 10.92721296 8.78005499
211 14.88160933 10.92721296
212 16.71543369 14.88160933
213 16.89460524 16.71543369
214 16.28098948 16.89460524
215 12.41320637 16.28098948
216 9.22910860 12.41320637
217 11.15276219 9.22910860
218 10.46752526 11.15276219
219 11.60759405 10.46752526
220 8.27137463 11.60759405
221 7.71826030 8.27137463
222 6.92577957 7.71826030
223 5.07621892 6.92577957
224 7.62294775 5.07621892
225 1.82255700 7.62294775
226 0.17287138 1.82255700
227 3.98796613 0.17287138
228 3.37289615 3.98796613
229 4.08489809 3.37289615
230 0.50163659 4.08489809
231 -1.21075799 0.50163659
232 2.48617928 -1.21075799
233 -0.48844051 2.48617928
234 -4.99603229 -0.48844051
235 -6.31689810 -4.99603229
236 -19.28139401 -6.31689810
237 -25.19127183 -19.28139401
238 -21.67576052 -25.19127183
239 -18.23565882 -21.67576052
240 -14.01381420 -18.23565882
241 -4.17775257 -14.01381420
242 -1.18622252 -4.17775257
243 -3.31053238 -1.18622252
244 -2.32949726 -3.31053238
245 0.79431276 -2.32949726
246 -1.60243697 0.79431276
247 -4.23694298 -1.60243697
248 3.47079612 -4.23694298
249 11.30515599 3.47079612
250 1.05432632 11.30515599
251 -0.73624935 1.05432632
252 10.93649870 -0.73624935
253 2.38294888 10.93649870
254 4.42660529 2.38294888
255 5.93116084 4.42660529
256 4.54887682 5.93116084
257 3.23404888 4.54887682
258 1.55559986 3.23404888
259 4.99708032 1.55559986
260 3.67724082 4.99708032
261 -2.44506786 3.67724082
262 -10.89751230 -2.44506786
263 -11.61590007 -10.89751230
264 -13.12637846 -11.61590007
265 -17.35337149 -13.12637846
266 -26.19166544 -17.35337149
267 -32.96278098 -26.19166544
268 -45.84619305 -32.96278098
269 -52.80979553 -45.84619305
270 -54.56127833 -52.80979553
271 -26.98889968 -54.56127833
272 -9.66526968 -26.98889968
273 12.59400612 -9.66526968
274 24.97427514 12.59400612
275 28.14977472 24.97427514
276 30.94446242 28.14977472
277 39.44714031 30.94446242
278 37.32432529 39.44714031
279 38.55445663 37.32432529
280 33.22860912 38.55445663
281 28.48191942 33.22860912
282 31.66795421 28.48191942
283 29.62960020 31.66795421
284 32.11758511 29.62960020
285 24.28271697 32.11758511
286 22.78435403 24.28271697
287 19.73180673 22.78435403
288 13.82581204 19.73180673
289 16.60104349 13.82581204
290 17.72330143 16.60104349
291 18.85457536 17.72330143
292 24.84166873 18.85457536
293 22.36120162 24.84166873
294 21.13142638 22.36120162
295 22.10844101 21.13142638
> 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/72sgx1290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/82sgx1290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9d2yi1290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10d2yi1290544448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g2w51290544448.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/1222cb1290544448.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/1383951290544448.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/14umqb1290544448.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/15mvpe1290544448.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/1615nn1290544448.tab")
+ }
>
> try(system("convert tmp/16ii61290544448.ps tmp/16ii61290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/26ii61290544448.ps tmp/26ii61290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/36ii61290544448.ps tmp/36ii61290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zs0r1290544448.ps tmp/4zs0r1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zs0r1290544448.ps tmp/5zs0r1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sjzu1290544448.ps tmp/6sjzu1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/72sgx1290544448.ps tmp/72sgx1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/82sgx1290544448.ps tmp/82sgx1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d2yi1290544448.ps tmp/9d2yi1290544448.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d2yi1290544448.ps tmp/10d2yi1290544448.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.840 2.230 11.071