R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(13
+ ,14
+ ,13
+ ,3
+ ,25
+ ,55
+ ,147
+ ,12
+ ,8
+ ,13
+ ,5
+ ,158
+ ,7
+ ,71
+ ,10
+ ,12
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,9
+ ,7
+ ,12
+ ,6
+ ,143
+ ,10
+ ,0
+ ,10
+ ,10
+ ,11
+ ,5
+ ,67
+ ,74
+ ,43
+ ,12
+ ,7
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,13
+ ,16
+ ,18
+ ,8
+ ,148
+ ,138
+ ,8
+ ,12
+ ,11
+ ,11
+ ,4
+ ,28
+ ,0
+ ,0
+ ,12
+ ,14
+ ,14
+ ,4
+ ,114
+ ,113
+ ,34
+ ,6
+ ,6
+ ,9
+ ,4
+ ,0
+ ,0
+ ,0
+ ,5
+ ,16
+ ,14
+ ,6
+ ,123
+ ,115
+ ,103
+ ,12
+ ,11
+ ,12
+ ,6
+ ,145
+ ,9
+ ,0
+ ,11
+ ,16
+ ,11
+ ,5
+ ,113
+ ,114
+ ,73
+ ,14
+ ,12
+ ,12
+ ,4
+ ,152
+ ,59
+ ,159
+ ,14
+ ,7
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,13
+ ,11
+ ,4
+ ,36
+ ,114
+ ,113
+ ,12
+ ,11
+ ,12
+ ,6
+ ,0
+ ,0
+ ,0
+ ,11
+ ,15
+ ,16
+ ,6
+ ,8
+ ,102
+ ,44
+ ,11
+ ,7
+ ,9
+ ,4
+ ,108
+ ,0
+ ,0
+ ,7
+ ,9
+ ,11
+ ,4
+ ,112
+ ,86
+ ,0
+ ,9
+ ,7
+ ,13
+ ,2
+ ,51
+ ,17
+ ,41
+ ,11
+ ,14
+ ,15
+ ,7
+ ,43
+ ,45
+ ,74
+ ,11
+ ,15
+ ,10
+ ,5
+ ,120
+ ,123
+ ,0
+ ,12
+ ,7
+ ,11
+ ,4
+ ,13
+ ,24
+ ,0
+ ,12
+ ,15
+ ,13
+ ,6
+ ,55
+ ,5
+ ,0
+ ,11
+ ,17
+ ,16
+ ,6
+ ,103
+ ,123
+ ,32
+ ,11
+ ,15
+ ,15
+ ,7
+ ,127
+ ,136
+ ,126
+ ,8
+ ,14
+ ,14
+ ,5
+ ,14
+ ,4
+ ,154
+ ,9
+ ,14
+ ,14
+ ,6
+ ,135
+ ,76
+ ,129
+ ,12
+ ,8
+ ,14
+ ,4
+ ,38
+ ,99
+ ,98
+ ,10
+ ,8
+ ,8
+ ,4
+ ,11
+ ,98
+ ,82
+ ,10
+ ,14
+ ,13
+ ,7
+ ,43
+ ,67
+ ,45
+ ,12
+ ,14
+ ,15
+ ,7
+ ,141
+ ,92
+ ,8
+ ,8
+ ,8
+ ,13
+ ,4
+ ,62
+ ,13
+ ,0
+ ,12
+ ,11
+ ,11
+ ,4
+ ,62
+ ,24
+ ,129
+ ,11
+ ,16
+ ,15
+ ,6
+ ,135
+ ,129
+ ,31
+ ,12
+ ,10
+ ,15
+ ,6
+ ,117
+ ,117
+ ,117
+ ,7
+ ,8
+ ,9
+ ,5
+ ,82
+ ,11
+ ,99
+ ,11
+ ,14
+ ,13
+ ,6
+ ,145
+ ,20
+ ,55
+ ,11
+ ,16
+ ,16
+ ,7
+ ,87
+ ,91
+ ,132
+ ,12
+ ,13
+ ,13
+ ,6
+ ,76
+ ,111
+ ,58
+ ,9
+ ,5
+ ,11
+ ,3
+ ,124
+ ,0
+ ,0
+ ,15
+ ,8
+ ,12
+ ,3
+ ,151
+ ,58
+ ,0
+ ,11
+ ,10
+ ,12
+ ,4
+ ,131
+ ,0
+ ,0
+ ,11
+ ,8
+ ,12
+ ,6
+ ,127
+ ,146
+ ,101
+ ,11
+ ,13
+ ,14
+ ,7
+ ,76
+ ,129
+ ,31
+ ,11
+ ,15
+ ,14
+ ,5
+ ,25
+ ,48
+ ,147
+ ,15
+ ,6
+ ,8
+ ,4
+ ,0
+ ,0
+ ,0
+ ,11
+ ,12
+ ,13
+ ,5
+ ,58
+ ,111
+ ,132
+ ,12
+ ,16
+ ,16
+ ,6
+ ,115
+ ,32
+ ,123
+ ,12
+ ,5
+ ,13
+ ,6
+ ,130
+ ,112
+ ,39
+ ,9
+ ,15
+ ,11
+ ,6
+ ,17
+ ,51
+ ,136
+ ,12
+ ,12
+ ,14
+ ,5
+ ,102
+ ,53
+ ,141
+ ,12
+ ,8
+ ,13
+ ,4
+ ,21
+ ,131
+ ,0
+ ,13
+ ,13
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,11
+ ,14
+ ,13
+ ,5
+ ,14
+ ,76
+ ,135
+ ,9
+ ,12
+ ,12
+ ,4
+ ,110
+ ,106
+ ,118
+ ,9
+ ,16
+ ,16
+ ,6
+ ,133
+ ,26
+ ,154
+ ,11
+ ,10
+ ,15
+ ,2
+ ,83
+ ,44
+ ,11
+ ,15
+ ,15
+ ,8
+ ,56
+ ,63
+ ,116
+ ,12
+ ,8
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,12
+ ,16
+ ,14
+ ,6
+ ,44
+ ,116
+ ,88
+ ,9
+ ,19
+ ,12
+ ,6
+ ,70
+ ,119
+ ,25
+ ,11
+ ,14
+ ,15
+ ,6
+ ,36
+ ,18
+ ,113
+ ,9
+ ,6
+ ,12
+ ,5
+ ,5
+ ,134
+ ,157
+ ,12
+ ,13
+ ,13
+ ,5
+ ,118
+ ,138
+ ,26
+ ,12
+ ,15
+ ,12
+ ,6
+ ,17
+ ,41
+ ,38
+ ,12
+ ,7
+ ,12
+ ,5
+ ,79
+ ,0
+ ,0
+ ,12
+ ,13
+ ,13
+ ,6
+ ,122
+ ,57
+ ,53
+ ,14
+ ,4
+ ,5
+ ,2
+ ,119
+ ,101
+ ,0
+ ,11
+ ,14
+ ,13
+ ,5
+ ,36
+ ,114
+ ,106
+ ,12
+ ,13
+ ,13
+ ,5
+ ,36
+ ,113
+ ,106
+ ,11
+ ,11
+ ,14
+ ,5
+ ,141
+ ,122
+ ,102
+ ,6
+ ,14
+ ,17
+ ,6
+ ,0
+ ,14
+ ,138
+ ,10
+ ,12
+ ,13
+ ,6
+ ,37
+ ,10
+ ,142
+ ,12
+ ,15
+ ,13
+ ,6
+ ,110
+ ,27
+ ,73
+ ,13
+ ,14
+ ,12
+ ,5
+ ,10
+ ,39
+ ,130
+ ,8
+ ,13
+ ,13
+ ,5
+ ,14
+ ,133
+ ,86
+ ,12
+ ,8
+ ,14
+ ,4
+ ,157
+ ,42
+ ,78
+ ,12
+ ,6
+ ,11
+ ,2
+ ,59
+ ,0
+ ,0
+ ,12
+ ,7
+ ,12
+ ,4
+ ,77
+ ,58
+ ,0
+ ,6
+ ,13
+ ,12
+ ,6
+ ,129
+ ,133
+ ,4
+ ,11
+ ,13
+ ,16
+ ,6
+ ,125
+ ,151
+ ,91
+ ,10
+ ,11
+ ,12
+ ,5
+ ,87
+ ,111
+ ,132
+ ,12
+ ,5
+ ,12
+ ,3
+ ,61
+ ,139
+ ,0
+ ,13
+ ,12
+ ,12
+ ,6
+ ,146
+ ,126
+ ,0
+ ,11
+ ,8
+ ,10
+ ,4
+ ,96
+ ,139
+ ,0
+ ,7
+ ,11
+ ,15
+ ,5
+ ,133
+ ,138
+ ,14
+ ,11
+ ,14
+ ,15
+ ,8
+ ,47
+ ,52
+ ,97
+ ,11
+ ,9
+ ,12
+ ,4
+ ,74
+ ,67
+ ,45
+ ,11
+ ,10
+ ,16
+ ,6
+ ,109
+ ,97
+ ,0
+ ,11
+ ,13
+ ,15
+ ,6
+ ,30
+ ,137
+ ,149
+ ,12
+ ,16
+ ,16
+ ,7
+ ,116
+ ,56
+ ,57
+ ,10
+ ,16
+ ,13
+ ,6
+ ,149
+ ,3
+ ,105
+ ,11
+ ,11
+ ,12
+ ,5
+ ,19
+ ,78
+ ,0
+ ,12
+ ,8
+ ,11
+ ,4
+ ,96
+ ,0
+ ,0
+ ,7
+ ,4
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,13
+ ,7
+ ,10
+ ,3
+ ,21
+ ,0
+ ,0
+ ,8
+ ,14
+ ,15
+ ,5
+ ,26
+ ,118
+ ,128
+ ,12
+ ,11
+ ,13
+ ,6
+ ,156
+ ,39
+ ,29
+ ,11
+ ,17
+ ,16
+ ,7
+ ,53
+ ,63
+ ,148
+ ,12
+ ,15
+ ,15
+ ,7
+ ,72
+ ,78
+ ,93
+ ,14
+ ,17
+ ,18
+ ,6
+ ,27
+ ,26
+ ,4
+ ,10
+ ,5
+ ,13
+ ,3
+ ,66
+ ,50
+ ,0
+ ,10
+ ,4
+ ,10
+ ,2
+ ,71
+ ,104
+ ,158
+ ,13
+ ,10
+ ,16
+ ,8
+ ,66
+ ,54
+ ,144
+ ,10
+ ,11
+ ,13
+ ,3
+ ,40
+ ,104
+ ,0
+ ,11
+ ,15
+ ,15
+ ,8
+ ,57
+ ,148
+ ,122
+ ,10
+ ,10
+ ,14
+ ,3
+ ,3
+ ,30
+ ,149
+ ,7
+ ,9
+ ,15
+ ,4
+ ,12
+ ,38
+ ,17
+ ,10
+ ,12
+ ,14
+ ,5
+ ,107
+ ,132
+ ,91
+ ,8
+ ,15
+ ,13
+ ,7
+ ,80
+ ,132
+ ,111
+ ,12
+ ,7
+ ,13
+ ,6
+ ,98
+ ,84
+ ,99
+ ,12
+ ,13
+ ,15
+ ,6
+ ,155
+ ,71
+ ,40
+ ,12
+ ,12
+ ,16
+ ,7
+ ,111
+ ,125
+ ,132
+ ,11
+ ,14
+ ,14
+ ,6
+ ,81
+ ,25
+ ,123
+ ,12
+ ,14
+ ,14
+ ,6
+ ,50
+ ,66
+ ,54
+ ,12
+ ,8
+ ,16
+ ,6
+ ,49
+ ,86
+ ,90
+ ,12
+ ,15
+ ,14
+ ,6
+ ,96
+ ,61
+ ,86
+ ,11
+ ,12
+ ,12
+ ,4
+ ,2
+ ,60
+ ,152
+ ,12
+ ,12
+ ,13
+ ,4
+ ,1
+ ,144
+ ,152
+ ,11
+ ,16
+ ,12
+ ,5
+ ,22
+ ,120
+ ,123
+ ,11
+ ,9
+ ,12
+ ,4
+ ,64
+ ,139
+ ,100
+ ,13
+ ,15
+ ,14
+ ,6
+ ,56
+ ,131
+ ,116
+ ,12
+ ,15
+ ,14
+ ,6
+ ,144
+ ,159
+ ,59
+ ,12
+ ,6
+ ,14
+ ,5
+ ,0
+ ,0
+ ,0
+ ,12
+ ,14
+ ,16
+ ,8
+ ,94
+ ,18
+ ,5
+ ,12
+ ,15
+ ,13
+ ,6
+ ,25
+ ,123
+ ,147
+ ,8
+ ,10
+ ,14
+ ,5
+ ,93
+ ,18
+ ,139
+ ,8
+ ,6
+ ,4
+ ,4
+ ,0
+ ,0
+ ,0
+ ,12
+ ,14
+ ,16
+ ,8
+ ,48
+ ,123
+ ,81
+ ,11
+ ,12
+ ,13
+ ,6
+ ,30
+ ,105
+ ,3
+ ,12
+ ,8
+ ,16
+ ,4
+ ,19
+ ,0
+ ,0
+ ,13
+ ,11
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,13
+ ,14
+ ,6
+ ,10
+ ,68
+ ,37
+ ,12
+ ,9
+ ,13
+ ,4
+ ,78
+ ,157
+ ,5
+ ,11
+ ,15
+ ,14
+ ,6
+ ,93
+ ,94
+ ,69
+ ,12
+ ,13
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,12
+ ,15
+ ,15
+ ,6
+ ,95
+ ,87
+ ,0
+ ,10
+ ,14
+ ,14
+ ,5
+ ,50
+ ,156
+ ,142
+ ,11
+ ,16
+ ,13
+ ,4
+ ,86
+ ,139
+ ,17
+ ,12
+ ,14
+ ,14
+ ,6
+ ,33
+ ,145
+ ,100
+ ,12
+ ,14
+ ,16
+ ,4
+ ,152
+ ,55
+ ,70
+ ,10
+ ,10
+ ,6
+ ,4
+ ,51
+ ,41
+ ,0
+ ,12
+ ,10
+ ,13
+ ,4
+ ,48
+ ,25
+ ,123
+ ,13
+ ,4
+ ,13
+ ,6
+ ,97
+ ,47
+ ,109
+ ,12
+ ,8
+ ,14
+ ,5
+ ,77
+ ,0
+ ,0
+ ,15
+ ,15
+ ,15
+ ,6
+ ,130
+ ,143
+ ,37
+ ,11
+ ,16
+ ,14
+ ,6
+ ,8
+ ,102
+ ,44
+ ,12
+ ,12
+ ,15
+ ,8
+ ,84
+ ,148
+ ,98
+ ,11
+ ,12
+ ,13
+ ,7
+ ,51
+ ,153
+ ,11
+ ,12
+ ,15
+ ,16
+ ,7
+ ,33
+ ,32
+ ,9
+ ,11
+ ,9
+ ,12
+ ,4
+ ,6
+ ,106
+ ,0
+ ,10
+ ,12
+ ,15
+ ,6
+ ,116
+ ,63
+ ,57
+ ,11
+ ,14
+ ,12
+ ,6
+ ,88
+ ,56
+ ,63
+ ,11
+ ,11
+ ,14
+ ,2
+ ,142
+ ,39
+ ,66)
+ ,dim=c(7
+ ,156)
+ ,dimnames=list(c('FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'firstbestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(7,156),dimnames=list(c('FindingFriends','KnowingPeople','Liked','Celebrity','firstbestfriend','secondbestfriend','thirdbestfriend'),1:156))
> 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 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Liked FindingFriends KnowingPeople Celebrity firstbestfriend
1 13 13 14 3 25
2 13 12 8 5 158
3 16 10 12 6 0
4 12 9 7 6 143
5 11 10 10 5 67
6 12 12 7 3 0
7 18 13 16 8 148
8 11 12 11 4 28
9 14 12 14 4 114
10 9 6 6 4 0
11 14 5 16 6 123
12 12 12 11 6 145
13 11 11 16 5 113
14 12 14 12 4 152
15 13 14 7 6 0
16 11 12 13 4 36
17 12 12 11 6 0
18 16 11 15 6 8
19 9 11 7 4 108
20 11 7 9 4 112
21 13 9 7 2 51
22 15 11 14 7 43
23 10 11 15 5 120
24 11 12 7 4 13
25 13 12 15 6 55
26 16 11 17 6 103
27 15 11 15 7 127
28 14 8 14 5 14
29 14 9 14 6 135
30 14 12 8 4 38
31 8 10 8 4 11
32 13 10 14 7 43
33 15 12 14 7 141
34 13 8 8 4 62
35 11 12 11 4 62
36 15 11 16 6 135
37 15 12 10 6 117
38 9 7 8 5 82
39 13 11 14 6 145
40 16 11 16 7 87
41 13 12 13 6 76
42 11 9 5 3 124
43 12 15 8 3 151
44 12 11 10 4 131
45 12 11 8 6 127
46 14 11 13 7 76
47 14 11 15 5 25
48 8 15 6 4 0
49 13 11 12 5 58
50 16 12 16 6 115
51 13 12 5 6 130
52 11 9 15 6 17
53 14 12 12 5 102
54 13 12 8 4 21
55 13 13 13 5 0
56 13 11 14 5 14
57 12 9 12 4 110
58 16 9 16 6 133
59 15 11 10 2 83
60 8 15 15 56 63
61 3 8 12 0 0
62 6 16 14 44 116
63 6 19 12 70 119
64 6 14 15 36 18
65 5 6 12 5 134
66 5 13 13 118 138
67 6 15 12 17 41
68 5 7 12 79 0
69 6 13 13 122 57
70 2 4 5 119 101
71 5 14 13 36 114
72 5 13 13 36 113
73 5 11 14 141 122
74 6 14 17 0 14
75 6 12 13 37 10
76 6 15 13 110 27
77 5 14 12 10 39
78 5 13 13 14 133
79 4 8 14 157 42
80 2 6 11 59 0
81 4 7 12 77 58
82 6 13 12 129 133
83 6 13 16 125 151
84 5 11 12 87 111
85 3 5 12 61 139
86 6 12 12 146 126
87 4 8 10 96 139
88 5 11 15 133 138
89 8 14 15 47 52
90 4 9 12 74 67
91 6 10 16 109 97
92 6 13 15 30 137
93 7 16 16 116 56
94 6 16 13 149 3
95 5 11 12 19 78
96 4 8 11 96 0
97 6 4 13 0 0
98 3 7 10 21 0
99 5 14 15 26 118
100 6 11 13 156 39
101 7 17 16 53 63
102 7 15 15 72 78
103 6 17 18 27 26
104 3 5 13 66 50
105 2 4 10 71 104
106 8 10 16 66 54
107 3 11 13 40 104
108 8 15 15 57 148
109 3 10 14 3 30
110 4 9 15 12 38
111 5 12 14 107 132
112 7 15 13 80 132
113 6 7 13 98 84
114 6 13 15 155 71
115 7 12 16 111 125
116 6 14 14 81 25
117 6 14 14 50 66
118 6 8 16 49 86
119 6 15 14 96 61
120 4 12 12 2 60
121 4 12 13 1 144
122 5 16 12 22 120
123 4 9 12 64 139
124 6 15 14 56 131
125 6 15 14 144 159
126 5 6 14 0 0
127 8 14 16 94 18
128 6 15 13 25 123
129 5 10 14 93 18
130 4 6 4 0 0
131 8 14 16 48 123
132 6 12 13 30 105
133 4 8 16 19 0
134 6 11 15 0 0
135 6 13 14 10 68
136 4 9 13 78 157
137 6 15 14 93 94
138 3 13 12 0 0
139 6 15 15 95 87
140 5 14 14 50 156
141 4 16 13 86 139
142 6 14 14 33 145
143 4 14 16 152 55
144 4 10 6 51 41
145 4 10 13 48 25
146 6 4 13 97 47
147 5 8 14 77 0
148 6 15 15 130 143
149 6 16 14 8 102
150 8 12 15 84 148
151 7 12 13 51 153
152 7 15 16 33 32
153 4 9 12 6 106
154 6 12 15 116 63
155 6 14 12 88 56
156 2 11 14 142 39
secondbestfriend thirdbestfriend
1 55 147
2 7 71
3 0 0
4 10 0
5 74 43
6 0 0
7 138 8
8 0 0
9 113 34
10 0 0
11 115 103
12 9 0
13 114 73
14 59 159
15 0 0
16 114 113
17 0 0
18 102 44
19 0 0
20 86 0
21 17 41
22 45 74
23 123 0
24 24 0
25 5 0
26 123 32
27 136 126
28 4 154
29 76 129
30 99 98
31 98 82
32 67 45
33 92 8
34 13 0
35 24 129
36 129 31
37 117 117
38 11 99
39 20 55
40 91 132
41 111 58
42 0 0
43 58 0
44 0 0
45 146 101
46 129 31
47 48 147
48 0 0
49 111 132
50 32 123
51 112 39
52 51 136
53 53 141
54 131 0
55 0 0
56 76 135
57 106 118
58 26 154
59 44 11
60 116 12
61 0 12
62 88 9
63 25 11
64 113 9
65 157 12
66 26 12
67 38 12
68 0 12
69 53 14
70 0 11
71 106 12
72 106 11
73 102 6
74 138 10
75 142 12
76 73 13
77 130 8
78 86 12
79 78 12
80 0 12
81 0 6
82 4 11
83 91 10
84 132 12
85 0 13
86 0 11
87 0 7
88 14 11
89 97 11
90 45 11
91 0 11
92 149 12
93 57 10
94 105 11
95 0 12
96 0 7
97 0 13
98 0 8
99 128 12
100 29 11
101 148 12
102 93 14
103 4 10
104 0 10
105 158 13
106 144 10
107 0 11
108 122 10
109 149 7
110 17 10
111 91 8
112 111 12
113 99 12
114 40 12
115 132 11
116 123 12
117 54 12
118 90 12
119 86 11
120 152 12
121 152 11
122 123 11
123 100 13
124 116 12
125 59 12
126 0 12
127 5 12
128 147 8
129 139 8
130 0 12
131 81 11
132 3 12
133 0 13
134 0 12
135 37 12
136 5 11
137 69 12
138 0 12
139 0 10
140 142 11
141 17 12
142 100 12
143 70 10
144 0 12
145 123 13
146 109 12
147 0 15
148 37 11
149 44 12
150 98 11
151 11 12
152 9 11
153 0 10
154 57 11
155 63 11
156 66 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Celebrity
6.433550 0.193381 -0.046435 -0.036421
firstbestfriend secondbestfriend thirdbestfriend
0.007353 -0.001584 0.041025
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8826 -2.4255 -0.1227 1.9069 8.8890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.433550 1.290503 4.985 1.70e-06 ***
FindingFriends 0.193381 0.085540 2.261 0.0252 *
KnowingPeople -0.046435 0.094326 -0.492 0.6232
Celebrity -0.036421 0.005816 -6.263 3.84e-09 ***
firstbestfriend 0.007353 0.004876 1.508 0.1337
secondbestfriend -0.001584 0.004979 -0.318 0.7509
thirdbestfriend 0.041025 0.006274 6.539 9.31e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.987 on 149 degrees of freedom
Multiple R-squared: 0.4792, Adjusted R-squared: 0.4582
F-statistic: 22.85 on 6 and 149 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.26358231 5.271646e-01 7.364177e-01
[2,] 0.14471415 2.894283e-01 8.552858e-01
[3,] 0.13210524 2.642105e-01 8.678948e-01
[4,] 0.18541073 3.708215e-01 8.145893e-01
[5,] 0.11900496 2.380099e-01 8.809950e-01
[6,] 0.09544001 1.908800e-01 9.045600e-01
[7,] 0.07037319 1.407464e-01 9.296268e-01
[8,] 0.07065705 1.413141e-01 9.293429e-01
[9,] 0.06176517 1.235303e-01 9.382348e-01
[10,] 0.04463203 8.926406e-02 9.553680e-01
[11,] 0.03074496 6.148991e-02 9.692550e-01
[12,] 0.08585334 1.717067e-01 9.141467e-01
[13,] 0.06259783 1.251957e-01 9.374022e-01
[14,] 0.09053794 1.810759e-01 9.094621e-01
[15,] 0.06873345 1.374669e-01 9.312666e-01
[16,] 0.06077547 1.215509e-01 9.392245e-01
[17,] 0.07475268 1.495054e-01 9.252473e-01
[18,] 0.05216339 1.043268e-01 9.478366e-01
[19,] 0.03556641 7.113282e-02 9.644336e-01
[20,] 0.02351925 4.703849e-02 9.764808e-01
[21,] 0.01970578 3.941157e-02 9.802942e-01
[22,] 0.04725262 9.450524e-02 9.527474e-01
[23,] 0.04664213 9.328426e-02 9.533579e-01
[24,] 0.05510011 1.102002e-01 9.448999e-01
[25,] 0.08465452 1.693090e-01 9.153455e-01
[26,] 0.07565827 1.513165e-01 9.243417e-01
[27,] 0.09678236 1.935647e-01 9.032176e-01
[28,] 0.08635363 1.727073e-01 9.136464e-01
[29,] 0.10153461 2.030692e-01 8.984654e-01
[30,] 0.09092145 1.818429e-01 9.090785e-01
[31,] 0.07755396 1.551079e-01 9.224460e-01
[32,] 0.07213461 1.442692e-01 9.278654e-01
[33,] 0.08123357 1.624671e-01 9.187664e-01
[34,] 0.08496999 1.699400e-01 9.150300e-01
[35,] 0.11599941 2.319988e-01 8.840006e-01
[36,] 0.09431405 1.886281e-01 9.056859e-01
[37,] 0.14155117 2.831023e-01 8.584488e-01
[38,] 0.11448491 2.289698e-01 8.855151e-01
[39,] 0.16026235 3.205247e-01 8.397376e-01
[40,] 0.13098392 2.619678e-01 8.690161e-01
[41,] 0.13368488 2.673698e-01 8.663151e-01
[42,] 0.17523458 3.504692e-01 8.247654e-01
[43,] 0.21649728 4.329946e-01 7.835027e-01
[44,] 0.18698446 3.739689e-01 8.130155e-01
[45,] 0.45522877 9.104575e-01 5.447712e-01
[46,] 0.77907201 4.418560e-01 2.209280e-01
[47,] 0.74615594 5.076881e-01 2.538441e-01
[48,] 0.70743070 5.851386e-01 2.925693e-01
[49,] 0.69771821 6.045636e-01 3.022818e-01
[50,] 0.99989111 2.177765e-04 1.088883e-04
[51,] 0.99998291 3.418923e-05 1.709462e-05
[52,] 0.99999999 1.122675e-08 5.613377e-09
[53,] 1.00000000 5.662754e-09 2.831377e-09
[54,] 0.99999999 1.107640e-08 5.538200e-09
[55,] 1.00000000 8.269109e-09 4.134555e-09
[56,] 1.00000000 3.322442e-10 1.661221e-10
[57,] 1.00000000 1.030915e-10 5.154575e-11
[58,] 1.00000000 2.799099e-11 1.399549e-11
[59,] 1.00000000 4.853936e-11 2.426968e-11
[60,] 1.00000000 4.274604e-11 2.137302e-11
[61,] 1.00000000 9.080779e-11 4.540390e-11
[62,] 1.00000000 3.401163e-11 1.700581e-11
[63,] 1.00000000 1.860370e-11 9.301848e-12
[64,] 1.00000000 2.573584e-11 1.286792e-11
[65,] 1.00000000 7.555687e-12 3.777844e-12
[66,] 1.00000000 1.235302e-11 6.176512e-12
[67,] 1.00000000 2.260188e-11 1.130094e-11
[68,] 1.00000000 1.509378e-11 7.546892e-12
[69,] 1.00000000 5.363162e-12 2.681581e-12
[70,] 1.00000000 7.473249e-12 3.736625e-12
[71,] 1.00000000 2.512417e-12 1.256208e-12
[72,] 1.00000000 4.816034e-12 2.408017e-12
[73,] 1.00000000 9.258093e-12 4.629046e-12
[74,] 1.00000000 2.141079e-11 1.070540e-11
[75,] 1.00000000 4.982677e-11 2.491338e-11
[76,] 1.00000000 3.129271e-11 1.564635e-11
[77,] 1.00000000 4.819192e-11 2.409596e-11
[78,] 1.00000000 1.000415e-10 5.002074e-11
[79,] 1.00000000 2.083989e-10 1.041995e-10
[80,] 1.00000000 1.783772e-10 8.918862e-11
[81,] 1.00000000 3.190989e-10 1.595495e-10
[82,] 1.00000000 7.347160e-10 3.673580e-10
[83,] 1.00000000 1.019228e-09 5.096140e-10
[84,] 1.00000000 1.938604e-09 9.693020e-10
[85,] 1.00000000 2.846992e-09 1.423496e-09
[86,] 1.00000000 2.920351e-09 1.460175e-09
[87,] 1.00000000 6.424015e-09 3.212007e-09
[88,] 1.00000000 4.752592e-09 2.376296e-09
[89,] 1.00000000 5.740598e-09 2.870299e-09
[90,] 1.00000000 5.049273e-09 2.524636e-09
[91,] 1.00000000 6.822004e-09 3.411002e-09
[92,] 0.99999999 1.440341e-08 7.201705e-09
[93,] 0.99999999 2.996367e-08 1.498183e-08
[94,] 0.99999998 4.062058e-08 2.031029e-08
[95,] 0.99999997 6.045278e-08 3.022639e-08
[96,] 0.99999997 5.027684e-08 2.513842e-08
[97,] 0.99999998 3.030991e-08 1.515496e-08
[98,] 0.99999999 1.141137e-08 5.705684e-09
[99,] 0.99999999 1.118587e-08 5.592937e-09
[100,] 1.00000000 7.147938e-09 3.573969e-09
[101,] 1.00000000 7.125022e-09 3.562511e-09
[102,] 0.99999999 1.470272e-08 7.351359e-09
[103,] 0.99999999 2.094622e-08 1.047311e-08
[104,] 0.99999998 3.602549e-08 1.801275e-08
[105,] 0.99999996 7.781751e-08 3.890875e-08
[106,] 0.99999993 1.346043e-07 6.730215e-08
[107,] 0.99999987 2.683782e-07 1.341891e-07
[108,] 0.99999972 5.684971e-07 2.842486e-07
[109,] 0.99999936 1.271410e-06 6.357049e-07
[110,] 0.99999862 2.760276e-06 1.380138e-06
[111,] 0.99999797 4.068454e-06 2.034227e-06
[112,] 0.99999822 3.567904e-06 1.783952e-06
[113,] 0.99999702 5.966217e-06 2.983108e-06
[114,] 0.99999493 1.013775e-05 5.068876e-06
[115,] 0.99998868 2.263274e-05 1.131637e-05
[116,] 0.99997505 4.989011e-05 2.494505e-05
[117,] 0.99994919 1.016110e-04 5.080550e-05
[118,] 0.99997716 4.568813e-05 2.284406e-05
[119,] 0.99995284 9.431645e-05 4.715823e-05
[120,] 0.99989589 2.082160e-04 1.041080e-04
[121,] 0.99983326 3.334876e-04 1.667438e-04
[122,] 0.99975951 4.809736e-04 2.404868e-04
[123,] 0.99953109 9.378258e-04 4.689129e-04
[124,] 0.99933068 1.338641e-03 6.693203e-04
[125,] 0.99866073 2.678541e-03 1.339271e-03
[126,] 0.99737547 5.249055e-03 2.624527e-03
[127,] 0.99717527 5.649465e-03 2.824733e-03
[128,] 0.99481452 1.037097e-02 5.185485e-03
[129,] 0.99386710 1.226579e-02 6.132896e-03
[130,] 0.98711395 2.577209e-02 1.288605e-02
[131,] 0.97949507 4.100985e-02 2.050493e-02
[132,] 0.97135433 5.729134e-02 2.864567e-02
[133,] 0.94851399 1.029720e-01 5.148601e-02
[134,] 0.91652313 1.669537e-01 8.347687e-02
[135,] 0.87584862 2.483028e-01 1.241514e-01
[136,] 0.81204410 3.759118e-01 1.879559e-01
[137,] 0.68833461 6.233308e-01 3.116654e-01
> postscript(file="/var/www/html/rcomp/tmp/1lf841291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2w6771291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3w6771291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w6771291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5og7a1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
-1.31550433 0.73606938 8.40838031 3.33399225 1.13958563 3.68018295
7 8 9 10 11 12
8.88896026 2.69646718 3.98754766 1.83045506 2.61321500 2.92329860
13 14 15 16 17 18
-0.28080791 -3.98508480 4.40268380 -1.72474502 3.97518386 6.65192194
19 20 21 22 23 24
0.11589613 3.08906739 3.19382751 4.06355931 1.63034331 2.65902537
25 26 27 28 29 30
4.76444296 6.57183878 1.50319264 0.43718559 0.53018802 1.61999333
31 32 33 34 35 36
-3.13991120 3.48149524 5.93167462 5.10128321 -2.80770203 5.34064495
37 38 39 40 41 42
1.45387871 -2.88057954 2.01704494 2.52632426 2.30559687 2.25572527
43 44 45 46 47 48
2.12806943 3.08608826 -0.81681694 4.67156997 0.17944879 -0.90997353
49 50 51 52 53 54
-0.48735633 2.36643769 2.31812885 -1.88252466 -0.46532552 4.81608306
55 56 57 58 59 60
4.83825106 -0.24946948 -0.95292670 1.53296559 5.98458131 0.63000726
61 62 63 64 65 66
-4.91567953 -2.35781748 -2.28775937 -1.45583656 -3.08344701 -0.51197695
67 68 69 70 71 72
-2.89147364 0.15495391 1.18998091 -1.83470360 -3.38872148 -3.14696317
73 74 75 76 77 78
1.24304462 -2.64614467 -1.14385127 0.65944474 -3.62854439 -4.16797338
79 80 81 82 83 84
1.70998164 -3.42651803 -1.09819485 0.88516759 0.97167131 -0.93431490
85 86 87 88 89 90
-3.17690695 1.74283848 -1.32903866 0.53598936 0.58741577 -1.79425528
91 92 93 94 95 96
1.18099212 -2.42201390 1.70840195 2.19566810 -3.37733346 -0.26058255
97 98 99 100 101 102
-1.13674586 -3.88622964 -3.65463321 3.03246993 -0.76890786 -0.01602019
103 104 105 106 107 108
-2.49692187 -2.17090586 -3.20463452 2.20011980 -4.71620447 0.13300190
109 110 111 112 113 114
-4.87981089 -3.70313697 -0.36164317 -0.10401221 1.43253712 2.44326549
115 116 117 118 119 120
1.87023158 -0.02203665 -1.56181266 -0.43512287 0.04863164 -4.81681564
121 122 123 124 125 126
-5.38340188 -4.30798103 -2.68280864 -1.91640905 0.99249200 -2.43604855
127 128 129 130 131 132
2.40890900 -2.81988049 0.42944314 -3.90039393 0.12289393 -2.31742118
133 134 135 136 137 138
-3.07896863 -2.35651878 -2.86689485 -2.32722141 -0.37121510 -5.88258430
139 140 141 142 143 144
-0.22768862 -3.04317230 -3.27919419 -2.68898412 0.43425628 -3.02504147
145 146 147 148 149 150
-2.53786234 2.26414391 -0.14147387 0.65286208 -3.75878528 1.61747860
151 152 153 154 155 156
-0.89284147 -1.06172629 -4.58786896 1.34299783 -0.14188311 -1.45444478
> postscript(file="/var/www/html/rcomp/tmp/6og7a1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.31550433 NA
1 0.73606938 -1.31550433
2 8.40838031 0.73606938
3 3.33399225 8.40838031
4 1.13958563 3.33399225
5 3.68018295 1.13958563
6 8.88896026 3.68018295
7 2.69646718 8.88896026
8 3.98754766 2.69646718
9 1.83045506 3.98754766
10 2.61321500 1.83045506
11 2.92329860 2.61321500
12 -0.28080791 2.92329860
13 -3.98508480 -0.28080791
14 4.40268380 -3.98508480
15 -1.72474502 4.40268380
16 3.97518386 -1.72474502
17 6.65192194 3.97518386
18 0.11589613 6.65192194
19 3.08906739 0.11589613
20 3.19382751 3.08906739
21 4.06355931 3.19382751
22 1.63034331 4.06355931
23 2.65902537 1.63034331
24 4.76444296 2.65902537
25 6.57183878 4.76444296
26 1.50319264 6.57183878
27 0.43718559 1.50319264
28 0.53018802 0.43718559
29 1.61999333 0.53018802
30 -3.13991120 1.61999333
31 3.48149524 -3.13991120
32 5.93167462 3.48149524
33 5.10128321 5.93167462
34 -2.80770203 5.10128321
35 5.34064495 -2.80770203
36 1.45387871 5.34064495
37 -2.88057954 1.45387871
38 2.01704494 -2.88057954
39 2.52632426 2.01704494
40 2.30559687 2.52632426
41 2.25572527 2.30559687
42 2.12806943 2.25572527
43 3.08608826 2.12806943
44 -0.81681694 3.08608826
45 4.67156997 -0.81681694
46 0.17944879 4.67156997
47 -0.90997353 0.17944879
48 -0.48735633 -0.90997353
49 2.36643769 -0.48735633
50 2.31812885 2.36643769
51 -1.88252466 2.31812885
52 -0.46532552 -1.88252466
53 4.81608306 -0.46532552
54 4.83825106 4.81608306
55 -0.24946948 4.83825106
56 -0.95292670 -0.24946948
57 1.53296559 -0.95292670
58 5.98458131 1.53296559
59 0.63000726 5.98458131
60 -4.91567953 0.63000726
61 -2.35781748 -4.91567953
62 -2.28775937 -2.35781748
63 -1.45583656 -2.28775937
64 -3.08344701 -1.45583656
65 -0.51197695 -3.08344701
66 -2.89147364 -0.51197695
67 0.15495391 -2.89147364
68 1.18998091 0.15495391
69 -1.83470360 1.18998091
70 -3.38872148 -1.83470360
71 -3.14696317 -3.38872148
72 1.24304462 -3.14696317
73 -2.64614467 1.24304462
74 -1.14385127 -2.64614467
75 0.65944474 -1.14385127
76 -3.62854439 0.65944474
77 -4.16797338 -3.62854439
78 1.70998164 -4.16797338
79 -3.42651803 1.70998164
80 -1.09819485 -3.42651803
81 0.88516759 -1.09819485
82 0.97167131 0.88516759
83 -0.93431490 0.97167131
84 -3.17690695 -0.93431490
85 1.74283848 -3.17690695
86 -1.32903866 1.74283848
87 0.53598936 -1.32903866
88 0.58741577 0.53598936
89 -1.79425528 0.58741577
90 1.18099212 -1.79425528
91 -2.42201390 1.18099212
92 1.70840195 -2.42201390
93 2.19566810 1.70840195
94 -3.37733346 2.19566810
95 -0.26058255 -3.37733346
96 -1.13674586 -0.26058255
97 -3.88622964 -1.13674586
98 -3.65463321 -3.88622964
99 3.03246993 -3.65463321
100 -0.76890786 3.03246993
101 -0.01602019 -0.76890786
102 -2.49692187 -0.01602019
103 -2.17090586 -2.49692187
104 -3.20463452 -2.17090586
105 2.20011980 -3.20463452
106 -4.71620447 2.20011980
107 0.13300190 -4.71620447
108 -4.87981089 0.13300190
109 -3.70313697 -4.87981089
110 -0.36164317 -3.70313697
111 -0.10401221 -0.36164317
112 1.43253712 -0.10401221
113 2.44326549 1.43253712
114 1.87023158 2.44326549
115 -0.02203665 1.87023158
116 -1.56181266 -0.02203665
117 -0.43512287 -1.56181266
118 0.04863164 -0.43512287
119 -4.81681564 0.04863164
120 -5.38340188 -4.81681564
121 -4.30798103 -5.38340188
122 -2.68280864 -4.30798103
123 -1.91640905 -2.68280864
124 0.99249200 -1.91640905
125 -2.43604855 0.99249200
126 2.40890900 -2.43604855
127 -2.81988049 2.40890900
128 0.42944314 -2.81988049
129 -3.90039393 0.42944314
130 0.12289393 -3.90039393
131 -2.31742118 0.12289393
132 -3.07896863 -2.31742118
133 -2.35651878 -3.07896863
134 -2.86689485 -2.35651878
135 -2.32722141 -2.86689485
136 -0.37121510 -2.32722141
137 -5.88258430 -0.37121510
138 -0.22768862 -5.88258430
139 -3.04317230 -0.22768862
140 -3.27919419 -3.04317230
141 -2.68898412 -3.27919419
142 0.43425628 -2.68898412
143 -3.02504147 0.43425628
144 -2.53786234 -3.02504147
145 2.26414391 -2.53786234
146 -0.14147387 2.26414391
147 0.65286208 -0.14147387
148 -3.75878528 0.65286208
149 1.61747860 -3.75878528
150 -0.89284147 1.61747860
151 -1.06172629 -0.89284147
152 -4.58786896 -1.06172629
153 1.34299783 -4.58786896
154 -0.14188311 1.34299783
155 -1.45444478 -0.14188311
156 NA -1.45444478
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.73606938 -1.31550433
[2,] 8.40838031 0.73606938
[3,] 3.33399225 8.40838031
[4,] 1.13958563 3.33399225
[5,] 3.68018295 1.13958563
[6,] 8.88896026 3.68018295
[7,] 2.69646718 8.88896026
[8,] 3.98754766 2.69646718
[9,] 1.83045506 3.98754766
[10,] 2.61321500 1.83045506
[11,] 2.92329860 2.61321500
[12,] -0.28080791 2.92329860
[13,] -3.98508480 -0.28080791
[14,] 4.40268380 -3.98508480
[15,] -1.72474502 4.40268380
[16,] 3.97518386 -1.72474502
[17,] 6.65192194 3.97518386
[18,] 0.11589613 6.65192194
[19,] 3.08906739 0.11589613
[20,] 3.19382751 3.08906739
[21,] 4.06355931 3.19382751
[22,] 1.63034331 4.06355931
[23,] 2.65902537 1.63034331
[24,] 4.76444296 2.65902537
[25,] 6.57183878 4.76444296
[26,] 1.50319264 6.57183878
[27,] 0.43718559 1.50319264
[28,] 0.53018802 0.43718559
[29,] 1.61999333 0.53018802
[30,] -3.13991120 1.61999333
[31,] 3.48149524 -3.13991120
[32,] 5.93167462 3.48149524
[33,] 5.10128321 5.93167462
[34,] -2.80770203 5.10128321
[35,] 5.34064495 -2.80770203
[36,] 1.45387871 5.34064495
[37,] -2.88057954 1.45387871
[38,] 2.01704494 -2.88057954
[39,] 2.52632426 2.01704494
[40,] 2.30559687 2.52632426
[41,] 2.25572527 2.30559687
[42,] 2.12806943 2.25572527
[43,] 3.08608826 2.12806943
[44,] -0.81681694 3.08608826
[45,] 4.67156997 -0.81681694
[46,] 0.17944879 4.67156997
[47,] -0.90997353 0.17944879
[48,] -0.48735633 -0.90997353
[49,] 2.36643769 -0.48735633
[50,] 2.31812885 2.36643769
[51,] -1.88252466 2.31812885
[52,] -0.46532552 -1.88252466
[53,] 4.81608306 -0.46532552
[54,] 4.83825106 4.81608306
[55,] -0.24946948 4.83825106
[56,] -0.95292670 -0.24946948
[57,] 1.53296559 -0.95292670
[58,] 5.98458131 1.53296559
[59,] 0.63000726 5.98458131
[60,] -4.91567953 0.63000726
[61,] -2.35781748 -4.91567953
[62,] -2.28775937 -2.35781748
[63,] -1.45583656 -2.28775937
[64,] -3.08344701 -1.45583656
[65,] -0.51197695 -3.08344701
[66,] -2.89147364 -0.51197695
[67,] 0.15495391 -2.89147364
[68,] 1.18998091 0.15495391
[69,] -1.83470360 1.18998091
[70,] -3.38872148 -1.83470360
[71,] -3.14696317 -3.38872148
[72,] 1.24304462 -3.14696317
[73,] -2.64614467 1.24304462
[74,] -1.14385127 -2.64614467
[75,] 0.65944474 -1.14385127
[76,] -3.62854439 0.65944474
[77,] -4.16797338 -3.62854439
[78,] 1.70998164 -4.16797338
[79,] -3.42651803 1.70998164
[80,] -1.09819485 -3.42651803
[81,] 0.88516759 -1.09819485
[82,] 0.97167131 0.88516759
[83,] -0.93431490 0.97167131
[84,] -3.17690695 -0.93431490
[85,] 1.74283848 -3.17690695
[86,] -1.32903866 1.74283848
[87,] 0.53598936 -1.32903866
[88,] 0.58741577 0.53598936
[89,] -1.79425528 0.58741577
[90,] 1.18099212 -1.79425528
[91,] -2.42201390 1.18099212
[92,] 1.70840195 -2.42201390
[93,] 2.19566810 1.70840195
[94,] -3.37733346 2.19566810
[95,] -0.26058255 -3.37733346
[96,] -1.13674586 -0.26058255
[97,] -3.88622964 -1.13674586
[98,] -3.65463321 -3.88622964
[99,] 3.03246993 -3.65463321
[100,] -0.76890786 3.03246993
[101,] -0.01602019 -0.76890786
[102,] -2.49692187 -0.01602019
[103,] -2.17090586 -2.49692187
[104,] -3.20463452 -2.17090586
[105,] 2.20011980 -3.20463452
[106,] -4.71620447 2.20011980
[107,] 0.13300190 -4.71620447
[108,] -4.87981089 0.13300190
[109,] -3.70313697 -4.87981089
[110,] -0.36164317 -3.70313697
[111,] -0.10401221 -0.36164317
[112,] 1.43253712 -0.10401221
[113,] 2.44326549 1.43253712
[114,] 1.87023158 2.44326549
[115,] -0.02203665 1.87023158
[116,] -1.56181266 -0.02203665
[117,] -0.43512287 -1.56181266
[118,] 0.04863164 -0.43512287
[119,] -4.81681564 0.04863164
[120,] -5.38340188 -4.81681564
[121,] -4.30798103 -5.38340188
[122,] -2.68280864 -4.30798103
[123,] -1.91640905 -2.68280864
[124,] 0.99249200 -1.91640905
[125,] -2.43604855 0.99249200
[126,] 2.40890900 -2.43604855
[127,] -2.81988049 2.40890900
[128,] 0.42944314 -2.81988049
[129,] -3.90039393 0.42944314
[130,] 0.12289393 -3.90039393
[131,] -2.31742118 0.12289393
[132,] -3.07896863 -2.31742118
[133,] -2.35651878 -3.07896863
[134,] -2.86689485 -2.35651878
[135,] -2.32722141 -2.86689485
[136,] -0.37121510 -2.32722141
[137,] -5.88258430 -0.37121510
[138,] -0.22768862 -5.88258430
[139,] -3.04317230 -0.22768862
[140,] -3.27919419 -3.04317230
[141,] -2.68898412 -3.27919419
[142,] 0.43425628 -2.68898412
[143,] -3.02504147 0.43425628
[144,] -2.53786234 -3.02504147
[145,] 2.26414391 -2.53786234
[146,] -0.14147387 2.26414391
[147,] 0.65286208 -0.14147387
[148,] -3.75878528 0.65286208
[149,] 1.61747860 -3.75878528
[150,] -0.89284147 1.61747860
[151,] -1.06172629 -0.89284147
[152,] -4.58786896 -1.06172629
[153,] 1.34299783 -4.58786896
[154,] -0.14188311 1.34299783
[155,] -1.45444478 -0.14188311
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.73606938 -1.31550433
2 8.40838031 0.73606938
3 3.33399225 8.40838031
4 1.13958563 3.33399225
5 3.68018295 1.13958563
6 8.88896026 3.68018295
7 2.69646718 8.88896026
8 3.98754766 2.69646718
9 1.83045506 3.98754766
10 2.61321500 1.83045506
11 2.92329860 2.61321500
12 -0.28080791 2.92329860
13 -3.98508480 -0.28080791
14 4.40268380 -3.98508480
15 -1.72474502 4.40268380
16 3.97518386 -1.72474502
17 6.65192194 3.97518386
18 0.11589613 6.65192194
19 3.08906739 0.11589613
20 3.19382751 3.08906739
21 4.06355931 3.19382751
22 1.63034331 4.06355931
23 2.65902537 1.63034331
24 4.76444296 2.65902537
25 6.57183878 4.76444296
26 1.50319264 6.57183878
27 0.43718559 1.50319264
28 0.53018802 0.43718559
29 1.61999333 0.53018802
30 -3.13991120 1.61999333
31 3.48149524 -3.13991120
32 5.93167462 3.48149524
33 5.10128321 5.93167462
34 -2.80770203 5.10128321
35 5.34064495 -2.80770203
36 1.45387871 5.34064495
37 -2.88057954 1.45387871
38 2.01704494 -2.88057954
39 2.52632426 2.01704494
40 2.30559687 2.52632426
41 2.25572527 2.30559687
42 2.12806943 2.25572527
43 3.08608826 2.12806943
44 -0.81681694 3.08608826
45 4.67156997 -0.81681694
46 0.17944879 4.67156997
47 -0.90997353 0.17944879
48 -0.48735633 -0.90997353
49 2.36643769 -0.48735633
50 2.31812885 2.36643769
51 -1.88252466 2.31812885
52 -0.46532552 -1.88252466
53 4.81608306 -0.46532552
54 4.83825106 4.81608306
55 -0.24946948 4.83825106
56 -0.95292670 -0.24946948
57 1.53296559 -0.95292670
58 5.98458131 1.53296559
59 0.63000726 5.98458131
60 -4.91567953 0.63000726
61 -2.35781748 -4.91567953
62 -2.28775937 -2.35781748
63 -1.45583656 -2.28775937
64 -3.08344701 -1.45583656
65 -0.51197695 -3.08344701
66 -2.89147364 -0.51197695
67 0.15495391 -2.89147364
68 1.18998091 0.15495391
69 -1.83470360 1.18998091
70 -3.38872148 -1.83470360
71 -3.14696317 -3.38872148
72 1.24304462 -3.14696317
73 -2.64614467 1.24304462
74 -1.14385127 -2.64614467
75 0.65944474 -1.14385127
76 -3.62854439 0.65944474
77 -4.16797338 -3.62854439
78 1.70998164 -4.16797338
79 -3.42651803 1.70998164
80 -1.09819485 -3.42651803
81 0.88516759 -1.09819485
82 0.97167131 0.88516759
83 -0.93431490 0.97167131
84 -3.17690695 -0.93431490
85 1.74283848 -3.17690695
86 -1.32903866 1.74283848
87 0.53598936 -1.32903866
88 0.58741577 0.53598936
89 -1.79425528 0.58741577
90 1.18099212 -1.79425528
91 -2.42201390 1.18099212
92 1.70840195 -2.42201390
93 2.19566810 1.70840195
94 -3.37733346 2.19566810
95 -0.26058255 -3.37733346
96 -1.13674586 -0.26058255
97 -3.88622964 -1.13674586
98 -3.65463321 -3.88622964
99 3.03246993 -3.65463321
100 -0.76890786 3.03246993
101 -0.01602019 -0.76890786
102 -2.49692187 -0.01602019
103 -2.17090586 -2.49692187
104 -3.20463452 -2.17090586
105 2.20011980 -3.20463452
106 -4.71620447 2.20011980
107 0.13300190 -4.71620447
108 -4.87981089 0.13300190
109 -3.70313697 -4.87981089
110 -0.36164317 -3.70313697
111 -0.10401221 -0.36164317
112 1.43253712 -0.10401221
113 2.44326549 1.43253712
114 1.87023158 2.44326549
115 -0.02203665 1.87023158
116 -1.56181266 -0.02203665
117 -0.43512287 -1.56181266
118 0.04863164 -0.43512287
119 -4.81681564 0.04863164
120 -5.38340188 -4.81681564
121 -4.30798103 -5.38340188
122 -2.68280864 -4.30798103
123 -1.91640905 -2.68280864
124 0.99249200 -1.91640905
125 -2.43604855 0.99249200
126 2.40890900 -2.43604855
127 -2.81988049 2.40890900
128 0.42944314 -2.81988049
129 -3.90039393 0.42944314
130 0.12289393 -3.90039393
131 -2.31742118 0.12289393
132 -3.07896863 -2.31742118
133 -2.35651878 -3.07896863
134 -2.86689485 -2.35651878
135 -2.32722141 -2.86689485
136 -0.37121510 -2.32722141
137 -5.88258430 -0.37121510
138 -0.22768862 -5.88258430
139 -3.04317230 -0.22768862
140 -3.27919419 -3.04317230
141 -2.68898412 -3.27919419
142 0.43425628 -2.68898412
143 -3.02504147 0.43425628
144 -2.53786234 -3.02504147
145 2.26414391 -2.53786234
146 -0.14147387 2.26414391
147 0.65286208 -0.14147387
148 -3.75878528 0.65286208
149 1.61747860 -3.75878528
150 -0.89284147 1.61747860
151 -1.06172629 -0.89284147
152 -4.58786896 -1.06172629
153 1.34299783 -4.58786896
154 -0.14188311 1.34299783
155 -1.45444478 -0.14188311
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7z7od1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z7od1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9agng1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10agng1291122827.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11dhml1291122827.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12hz291291122827.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13n0zl1291122827.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14yrgo1291122827.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15jsfc1291122827.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16g2dl1291122827.tab")
+ }
>
> try(system("convert tmp/1lf841291122827.ps tmp/1lf841291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w6771291122827.ps tmp/2w6771291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w6771291122827.ps tmp/3w6771291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w6771291122827.ps tmp/4w6771291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/5og7a1291122827.ps tmp/5og7a1291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/6og7a1291122827.ps tmp/6og7a1291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z7od1291122827.ps tmp/7z7od1291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z7od1291122827.ps tmp/8z7od1291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/9agng1291122827.ps tmp/9agng1291122827.png",intern=TRUE))
character(0)
> try(system("convert tmp/10agng1291122827.ps tmp/10agng1291122827.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.028 1.765 9.260