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(1
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+ ,13
+ ,14
+ ,0
+ ,69
+ ,0
+ ,5
+ ,0
+ ,9
+ ,0
+ ,15
+ ,0
+ ,13
+ ,0
+ ,14
+ ,0
+ ,13
+ ,0
+ ,3
+ ,0
+ ,0
+ ,119
+ ,0
+ ,14
+ ,11
+ ,0
+ ,71
+ ,0
+ ,9
+ ,0
+ ,5
+ ,0
+ ,13
+ ,0
+ ,12
+ ,0
+ ,15
+ ,0
+ ,14
+ ,0
+ ,6
+ ,0
+ ,1
+ ,72
+ ,72
+ ,13
+ ,13
+ ,13
+ ,77
+ ,77
+ ,9
+ ,9
+ ,9
+ ,9
+ ,14
+ ,14
+ ,12
+ ,12
+ ,13
+ ,13
+ ,13
+ ,13
+ ,5
+ ,5
+ ,1
+ ,131
+ ,131
+ ,16
+ ,15
+ ,15
+ ,74
+ ,74
+ ,14
+ ,14
+ ,12
+ ,12
+ ,11
+ ,11
+ ,12
+ ,12
+ ,14
+ ,14
+ ,16
+ ,16
+ ,8
+ ,8
+ ,1
+ ,100
+ ,100
+ ,13
+ ,14
+ ,14
+ ,82
+ ,82
+ ,5
+ ,5
+ ,6
+ ,6
+ ,15
+ ,15
+ ,12
+ ,12
+ ,11
+ ,11
+ ,13
+ ,13
+ ,6
+ ,6
+ ,1
+ ,9
+ ,9
+ ,12
+ ,14
+ ,14
+ ,54
+ ,54
+ ,12
+ ,12
+ ,4
+ ,4
+ ,16
+ ,16
+ ,12
+ ,12
+ ,14
+ ,14
+ ,14
+ ,14
+ ,4
+ ,4
+ ,1
+ ,107
+ ,107
+ ,9
+ ,14
+ ,14
+ ,54
+ ,54
+ ,6
+ ,6
+ ,6
+ ,6
+ ,14
+ ,14
+ ,10
+ ,10
+ ,11
+ ,11
+ ,13
+ ,13
+ ,3
+ ,3
+ ,1
+ ,79
+ ,79
+ ,14
+ ,10
+ ,10
+ ,80
+ ,80
+ ,6
+ ,6
+ ,7
+ ,7
+ ,13
+ ,13
+ ,12
+ ,12
+ ,8
+ ,8
+ ,14
+ ,14
+ ,4
+ ,4
+ ,0
+ ,115
+ ,0
+ ,15
+ ,8
+ ,0
+ ,76
+ ,0
+ ,8
+ ,0
+ ,9
+ ,0
+ ,15
+ ,0
+ ,12
+ ,0
+ ,12
+ ,0
+ ,16
+ ,0
+ ,7
+ ,0)
+ ,dim=c(22
+ ,156)
+ ,dimnames=list(c('Gender'
+ ,'trend'
+ ,'trend_G'
+ ,'Popularity'
+ ,'Depression'
+ ,'Depression_G'
+ ,'Belonging'
+ ,'Belonging_G'
+ ,'WeightedPopularity'
+ ,'WeightedPopularity_G'
+ ,'ParentalCriticism'
+ ,'ParentalCriticism_G'
+ ,'Happiness'
+ ,'Happiness_G'
+ ,'FindingFriends'
+ ,'FindingFriends_G'
+ ,'KnowingPeople'
+ ,'KnowingPeople_G'
+ ,'Liked'
+ ,'Liked_G'
+ ,'Celebrity'
+ ,'Celebrity_G')
+ ,1:156))
> y <- array(NA,dim=c(22,156),dimnames=list(c('Gender','trend','trend_G','Popularity','Depression','Depression_G','Belonging','Belonging_G','WeightedPopularity','WeightedPopularity_G','ParentalCriticism','ParentalCriticism_G','Happiness','Happiness_G','FindingFriends','FindingFriends_G','KnowingPeople','KnowingPeople_G','Liked','Liked_G','Celebrity','Celebrity_G'),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 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Popularity Gender trend trend_G Depression Depression_G Belonging
1 15 1 132 132 10 10 77
2 12 0 24 0 20 0 63
3 15 0 135 0 16 0 73
4 12 0 95 0 10 0 76
5 14 0 122 0 8 0 90
6 8 0 144 0 14 0 67
7 11 1 23 23 19 19 69
8 15 1 42 42 15 15 70
9 4 0 105 0 23 0 54
10 13 0 68 0 9 0 54
11 19 1 139 139 12 12 76
12 10 1 51 51 14 14 75
13 15 1 7 7 13 13 76
14 6 0 52 0 11 0 80
15 7 1 138 138 11 11 89
16 14 0 41 0 10 0 73
17 16 0 125 0 12 0 74
18 16 1 152 152 18 18 78
19 14 1 106 106 12 12 76
20 15 0 26 0 10 0 69
21 14 1 33 33 15 15 74
22 12 1 136 136 15 15 82
23 9 0 48 0 12 0 77
24 12 1 156 156 9 9 84
25 14 1 114 114 11 11 75
26 12 1 75 75 15 15 54
27 14 1 91 91 16 16 79
28 10 1 146 146 17 17 79
29 14 1 82 82 12 12 69
30 16 1 102 102 11 11 88
31 10 1 96 96 13 13 57
32 8 1 109 109 9 9 69
33 12 1 2 2 11 11 86
34 11 1 113 113 9 9 65
35 8 0 123 0 20 0 66
36 13 0 29 0 8 0 54
37 11 1 104 104 12 12 85
38 12 0 6 0 10 0 79
39 16 0 62 0 11 0 84
40 16 1 64 64 13 13 70
41 13 1 50 50 13 13 54
42 14 1 108 108 13 13 70
43 5 0 70 0 15 0 54
44 14 0 154 0 12 0 69
45 13 1 31 31 13 13 68
46 16 1 101 101 13 13 68
47 14 0 149 0 9 0 71
48 15 0 149 0 9 0 71
49 15 1 3 3 14 14 66
50 11 1 111 111 9 9 67
51 15 1 69 69 9 9 71
52 16 1 116 116 15 15 54
53 13 1 28 28 10 10 76
54 11 0 67 0 13 0 77
55 12 0 32 0 8 0 71
56 12 1 88 88 15 15 69
57 10 1 92 92 13 13 73
58 8 1 97 97 24 24 46
59 9 0 87 0 11 0 66
60 12 1 78 78 13 13 77
61 14 0 137 0 12 0 77
62 12 1 76 76 22 22 70
63 11 0 34 0 11 0 86
64 14 0 103 0 15 0 38
65 7 0 14 0 7 0 66
66 16 0 46 0 14 0 75
67 16 1 127 127 19 19 80
68 11 0 15 0 10 0 64
69 16 1 58 58 9 9 80
70 13 1 134 134 12 12 86
71 11 1 129 129 16 16 54
72 13 1 39 39 13 13 74
73 14 1 63 63 11 11 88
74 15 1 143 143 12 12 85
75 10 0 38 0 11 0 63
76 15 1 60 60 13 13 81
77 11 0 118 0 13 0 81
78 11 1 45 45 10 10 74
79 6 1 80 80 11 11 80
80 11 1 21 21 9 9 80
81 12 0 141 0 13 0 60
82 13 0 124 0 15 0 65
83 12 1 37 37 14 14 62
84 8 0 147 0 14 0 63
85 9 1 153 153 11 11 89
86 10 1 133 133 10 10 76
87 16 1 117 117 11 11 81
88 15 1 71 71 12 12 72
89 14 0 155 0 14 0 84
90 12 1 112 112 14 14 76
91 12 1 4 4 21 21 76
92 10 1 19 19 14 14 78
93 12 1 121 121 13 13 72
94 8 0 98 0 11 0 81
95 16 1 150 150 12 12 72
96 11 1 10 10 12 12 78
97 12 1 145 145 11 11 79
98 9 1 49 49 14 14 52
99 14 0 55 0 13 0 67
100 15 0 22 0 13 0 74
101 8 0 54 0 12 0 73
102 12 1 140 140 14 14 69
103 10 0 5 0 12 0 67
104 16 1 89 89 12 12 76
105 17 1 47 47 12 12 77
106 8 0 59 0 18 0 63
107 9 1 65 65 11 11 84
108 8 1 110 110 15 15 90
109 11 0 73 0 13 0 75
110 16 1 93 93 11 11 76
111 13 0 142 0 11 0 75
112 5 1 43 43 22 22 53
113 15 1 13 13 10 10 87
114 15 1 94 94 11 11 78
115 12 1 77 77 15 15 54
116 12 0 86 0 14 0 58
117 16 1 40 40 11 11 80
118 12 1 128 128 10 10 74
119 10 1 17 17 14 14 56
120 12 1 126 126 14 14 82
121 4 1 130 130 11 11 64
122 11 0 11 0 15 0 67
123 16 0 36 0 11 0 75
124 7 0 61 0 10 0 69
125 9 1 35 35 10 10 72
126 14 0 120 0 16 0 71
127 11 1 16 16 12 12 54
128 10 1 84 84 14 14 68
129 6 0 20 0 15 0 54
130 14 1 25 25 10 10 71
131 11 1 12 12 12 12 53
132 11 1 57 57 15 15 54
133 9 0 27 0 12 0 71
134 16 1 30 30 11 11 69
135 7 0 81 0 10 0 30
136 8 0 44 0 20 0 53
137 10 0 90 0 19 0 68
138 14 1 66 66 17 17 69
139 9 1 8 8 8 8 54
140 13 1 151 151 17 17 66
141 13 0 85 0 11 0 79
142 12 0 53 0 13 0 67
143 11 0 99 0 9 0 74
144 10 0 148 0 10 0 86
145 12 1 56 56 13 13 63
146 14 1 83 83 16 16 69
147 11 0 74 0 12 0 73
148 13 0 1 0 14 0 69
149 14 0 119 0 11 0 71
150 13 1 72 72 13 13 77
151 16 1 131 131 15 15 74
152 13 1 100 100 14 14 82
153 12 1 9 9 14 14 54
154 9 1 107 107 14 14 54
155 14 1 79 79 10 10 80
156 15 0 115 0 8 0 76
Belonging_G WeightedPopularity WeightedPopularity_G ParentalCriticism
1 77 5 5 4
2 0 6 0 4
3 0 4 0 10
4 0 6 0 6
5 0 3 0 5
6 0 10 0 8
7 69 8 8 9
8 70 3 3 6
9 0 4 0 8
10 0 3 0 11
11 76 5 5 6
12 75 5 5 8
13 76 6 6 11
14 0 5 0 5
15 89 3 3 10
16 0 4 0 7
17 0 8 0 7
18 78 8 8 13
19 76 8 8 10
20 0 5 0 8
21 74 8 8 6
22 82 2 2 8
23 0 0 0 7
24 84 5 5 5
25 75 2 2 9
26 54 7 7 9
27 79 5 5 11
28 79 2 2 11
29 69 12 12 11
30 88 7 7 9
31 57 0 0 7
32 69 2 2 6
33 86 3 3 6
34 65 0 0 6
35 0 9 0 5
36 0 2 0 4
37 85 3 3 10
38 0 1 0 8
39 0 10 0 6
40 70 1 1 5
41 54 4 4 9
42 70 6 6 10
43 0 6 0 6
44 0 4 0 9
45 68 4 4 10
46 68 7 7 6
47 0 7 0 6
48 0 7 0 6
49 66 0 0 13
50 67 3 3 8
51 71 8 8 10
52 54 8 8 5
53 76 10 10 8
54 0 11 0 6
55 0 6 0 9
56 69 2 2 9
57 73 6 6 7
58 46 1 1 20
59 0 5 0 8
60 77 4 4 8
61 0 6 0 7
62 70 6 6 7
63 0 4 0 10
64 0 1 0 5
65 0 6 0 8
66 0 7 0 9
67 80 7 7 9
68 0 2 0 20
69 80 7 7 6
70 86 8 8 10
71 54 5 5 11
72 74 4 4 7
73 88 2 2 12
74 85 0 0 12
75 0 7 0 8
76 81 0 0 6
77 0 5 0 6
78 74 3 3 9
79 80 3 3 5
80 80 3 3 11
81 0 3 0 6
82 0 7 0 6
83 62 6 6 10
84 0 3 0 8
85 89 0 0 7
86 76 2 2 8
87 81 0 0 9
88 72 9 9 8
89 0 10 0 10
90 76 3 3 13
91 76 7 7 7
92 78 3 3 7
93 72 6 6 7
94 0 5 0 8
95 72 0 0 9
96 78 0 0 9
97 79 4 4 8
98 52 0 0 7
99 0 0 0 6
100 0 7 0 8
101 0 3 0 8
102 69 9 9 4
103 0 4 0 8
104 76 4 4 10
105 77 15 15 7
106 0 7 0 8
107 84 8 8 7
108 90 2 2 10
109 0 8 0 9
110 76 7 7 8
111 0 3 0 8
112 53 3 3 5
113 87 6 6 8
114 78 8 8 9
115 54 5 5 11
116 0 6 0 7
117 80 10 10 8
118 74 0 0 4
119 56 5 5 16
120 82 0 0 9
121 64 0 0 16
122 0 5 0 12
123 0 10 0 8
124 0 0 0 4
125 72 5 5 11
126 0 6 0 11
127 54 1 1 8
128 68 5 5 8
129 0 3 0 12
130 71 3 3 8
131 53 6 6 6
132 54 2 2 8
133 0 5 0 6
134 69 6 6 14
135 0 2 0 10
136 0 3 0 5
137 0 7 0 8
138 69 6 6 12
139 54 3 3 11
140 66 6 6 8
141 0 9 0 8
142 0 2 0 9
143 0 5 0 6
144 0 10 0 5
145 63 9 9 8
146 69 8 8 7
147 0 8 0 4
148 0 5 0 9
149 0 9 0 5
150 77 9 9 9
151 74 14 14 12
152 82 5 5 6
153 54 12 12 4
154 54 6 6 6
155 80 6 6 7
156 0 8 0 9
ParentalCriticism_G Happiness Happiness_G FindingFriends FindingFriends_G
1 4 15 15 11 11
2 0 9 0 12 0
3 0 12 0 12 0
4 0 15 0 11 0
5 0 17 0 11 0
6 0 14 0 10 0
7 9 9 9 11 11
8 6 12 12 9 9
9 0 11 0 10 0
10 0 13 0 12 0
11 6 16 16 12 12
12 8 16 16 12 12
13 11 15 15 13 13
14 0 10 0 9 0
15 10 16 16 12 12
16 0 12 0 12 0
17 0 15 0 12 0
18 13 13 13 12 12
19 10 18 18 13 13
20 0 13 0 11 0
21 6 17 17 12 12
22 8 14 14 12 12
23 0 13 0 15 0
24 5 13 13 11 11
25 9 15 15 12 12
26 9 13 13 10 10
27 11 15 15 11 11
28 11 13 13 13 13
29 11 14 14 6 6
30 9 13 13 12 12
31 7 16 16 12 12
32 6 14 14 10 10
33 6 18 18 12 12
34 6 15 15 12 12
35 0 9 0 11 0
36 0 16 0 9 0
37 10 16 16 10 10
38 0 17 0 12 0
39 0 13 0 12 0
40 5 17 17 11 11
41 9 15 15 12 12
42 10 14 14 11 11
43 0 10 0 14 0
44 0 13 0 10 0
45 10 11 11 10 10
46 6 11 11 11 11
47 0 16 0 11 0
48 0 16 0 11 0
49 13 11 11 10 10
50 8 15 15 10 10
51 10 15 15 12 12
52 5 12 12 11 11
53 8 17 17 8 8
54 0 15 0 12 0
55 0 16 0 10 0
56 9 14 14 7 7
57 7 17 17 11 11
58 20 10 10 7 7
59 0 11 0 11 0
60 8 15 15 8 8
61 0 15 0 11 0
62 7 7 7 12 12
63 0 17 0 8 0
64 0 14 0 14 0
65 0 18 0 14 0
66 0 14 0 11 0
67 9 12 12 12 12
68 0 14 0 14 0
69 6 9 9 9 9
70 10 14 14 13 13
71 11 11 11 8 8
72 7 16 16 11 11
73 12 17 17 9 9
74 12 16 16 12 12
75 0 12 0 7 0
76 6 15 15 11 11
77 0 15 0 12 0
78 9 15 15 11 11
79 5 16 16 12 12
80 11 16 16 9 9
81 0 11 0 11 0
82 0 15 0 13 0
83 10 12 12 12 12
84 0 14 0 12 0
85 7 15 15 11 11
86 8 17 17 12 12
87 9 19 19 12 12
88 8 15 15 11 11
89 0 16 0 11 0
90 13 14 14 8 8
91 7 16 16 9 9
92 7 15 15 11 11
93 7 15 15 12 12
94 0 17 0 13 0
95 9 12 12 12 12
96 9 18 18 6 6
97 8 13 13 12 12
98 7 14 14 11 11
99 0 14 0 13 0
100 0 14 0 11 0
101 0 12 0 12 0
102 4 14 14 10 10
103 0 12 0 10 0
104 10 15 15 11 11
105 7 11 11 11 11
106 0 11 0 11 0
107 7 15 15 9 9
108 10 14 14 7 7
109 0 15 0 11 0
110 8 16 16 12 12
111 0 12 0 12 0
112 5 14 14 15 15
113 8 18 18 11 11
114 9 14 14 10 10
115 11 13 13 13 13
116 0 14 0 13 0
117 8 14 14 11 11
118 4 17 17 12 12
119 16 12 12 12 12
120 9 16 16 12 12
121 16 15 15 8 8
122 0 10 0 5 0
123 0 13 0 11 0
124 0 15 0 12 0
125 11 16 16 12 12
126 0 15 0 11 0
127 8 14 14 12 12
128 8 11 11 10 10
129 0 13 0 7 0
130 8 17 17 12 12
131 6 14 14 12 12
132 8 16 16 9 9
133 0 15 0 11 0
134 14 12 12 12 12
135 0 16 0 12 0
136 0 8 0 11 0
137 0 9 0 11 0
138 12 13 13 12 12
139 11 19 19 12 12
140 8 11 11 11 11
141 0 15 0 12 0
142 0 11 0 12 0
143 0 15 0 8 0
144 0 16 0 15 0
145 8 15 15 11 11
146 7 12 12 11 11
147 0 16 0 6 0
148 0 15 0 13 0
149 0 13 0 12 0
150 9 14 14 12 12
151 12 11 11 12 12
152 6 15 15 12 12
153 4 16 16 12 12
154 6 14 14 10 10
155 7 13 13 12 12
156 0 15 0 12 0
KnowingPeople KnowingPeople_G Liked Liked_G Celebrity Celebrity_G
1 12 12 13 13 6 6
2 7 0 11 0 4 0
3 13 0 14 0 6 0
4 11 0 12 0 5 0
5 16 0 12 0 5 0
6 10 0 6 0 4 0
7 15 15 10 10 5 5
8 5 5 11 11 3 3
9 4 0 10 0 2 0
10 7 0 12 0 5 0
11 15 15 15 15 6 6
12 5 5 13 13 6 6
13 16 16 18 18 8 8
14 15 0 11 0 6 0
15 13 13 12 12 3 3
16 13 0 13 0 6 0
17 15 0 14 0 6 0
18 15 15 16 16 7 7
19 10 10 16 16 8 8
20 17 0 16 0 6 0
21 14 14 15 15 7 7
22 9 9 13 13 4 4
23 6 0 8 0 4 0
24 11 11 14 14 2 2
25 13 13 15 15 6 6
26 12 12 13 13 6 6
27 10 10 16 16 6 6
28 4 4 13 13 6 6
29 13 13 12 12 6 6
30 15 15 15 15 7 7
31 8 8 11 11 4 4
32 10 10 14 14 3 3
33 8 8 13 13 5 5
34 7 7 13 13 6 6
35 9 0 12 0 4 0
36 14 0 14 0 6 0
37 5 5 13 13 3 3
38 7 0 12 0 3 0
39 16 0 14 0 6 0
40 14 14 15 15 6 6
41 16 16 16 16 6 6
42 15 15 15 15 8 8
43 4 0 5 0 2 0
44 12 0 15 0 6 0
45 8 8 8 8 4 4
46 17 17 16 16 7 7
47 15 0 16 0 6 0
48 16 0 14 0 6 0
49 12 12 16 16 6 6
50 12 12 14 14 5 5
51 13 13 13 13 6 6
52 14 14 14 14 6 6
53 14 14 14 14 5 5
54 15 0 12 0 6 0
55 14 0 13 0 7 0
56 11 11 15 15 5 5
57 13 13 15 15 6 6
58 4 4 13 13 6 6
59 8 0 10 0 4 0
60 13 13 13 13 5 5
61 15 0 14 0 6 0
62 15 15 13 13 6 6
63 8 0 13 0 4 0
64 17 0 18 0 6 0
65 12 0 12 0 4 0
66 13 0 14 0 7 0
67 14 14 16 16 8 8
68 7 0 13 0 6 0
69 16 16 16 16 6 6
70 11 11 15 15 6 6
71 10 10 14 14 5 5
72 14 14 13 13 6 6
73 19 19 12 12 6 6
74 14 14 16 16 4 4
75 8 0 9 0 5 0
76 15 15 15 15 8 8
77 8 0 16 0 6 0
78 8 8 12 12 6 6
79 6 6 11 11 2 2
80 7 7 13 13 2 2
81 16 0 13 0 4 0
82 15 0 14 0 6 0
83 10 10 15 15 6 6
84 8 0 14 0 5 0
85 9 9 12 12 4 4
86 8 8 16 16 4 4
87 14 14 14 14 6 6
88 14 14 13 13 5 5
89 14 0 12 0 6 0
90 15 15 13 13 7 7
91 7 7 12 12 6 6
92 7 7 9 9 4 4
93 12 12 13 13 4 4
94 7 0 10 0 3 0
95 12 12 15 15 8 8
96 6 6 9 9 4 4
97 10 10 13 13 4 4
98 12 12 13 13 5 5
99 13 0 13 0 5 0
100 14 0 15 0 7 0
101 8 0 13 0 4 0
102 14 14 14 14 5 5
103 10 0 11 0 5 0
104 14 14 15 15 8 8
105 15 15 14 14 5 5
106 10 0 15 0 2 0
107 6 6 12 12 5 5
108 9 9 15 15 4 4
109 11 0 14 0 5 0
110 16 16 16 16 7 7
111 14 0 14 0 6 0
112 8 8 12 12 3 3
113 16 16 11 11 5 5
114 16 16 13 13 6 6
115 14 14 12 12 5 5
116 12 0 12 0 6 0
117 16 16 16 16 7 7
118 15 15 13 13 6 6
119 11 11 12 12 6 6
120 6 6 14 14 5 5
121 6 6 4 4 4 4
122 16 0 14 0 6 0
123 16 0 15 0 6 0
124 8 0 12 0 3 0
125 11 11 11 11 4 4
126 12 0 12 0 4 0
127 13 13 11 11 4 4
128 11 11 12 12 5 5
129 9 0 11 0 4 0
130 15 15 13 13 6 6
131 11 11 12 12 6 6
132 12 12 12 12 4 4
133 15 0 15 0 7 0
134 8 8 14 14 4 4
135 7 0 12 0 4 0
136 10 0 12 0 4 0
137 9 0 12 0 4 0
138 13 13 13 13 5 5
139 11 11 11 11 4 4
140 12 12 13 13 7 7
141 5 0 12 0 3 0
142 12 0 14 0 5 0
143 14 0 15 0 5 0
144 15 0 15 0 6 0
145 14 14 13 13 5 5
146 13 13 16 16 6 6
147 14 0 17 0 6 0
148 14 0 13 0 3 0
149 15 0 14 0 6 0
150 13 13 13 13 5 5
151 14 14 16 16 8 8
152 11 11 13 13 6 6
153 14 14 14 14 4 4
154 11 11 13 13 3 3
155 8 8 14 14 4 4
156 12 0 16 0 7 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender trend
-5.634184 9.184925 0.002303
trend_G Depression Depression_G
-0.009272 -0.021567 -0.121607
Belonging Belonging_G WeightedPopularity
0.048530 0.005099 0.033130
WeightedPopularity_G ParentalCriticism ParentalCriticism_G
0.044696 0.171942 -0.217764
Happiness Happiness_G FindingFriends
-0.007898 -0.189090 0.259336
FindingFriends_G KnowingPeople KnowingPeople_G
-0.264147 0.264170 -0.060189
Liked Liked_G Celebrity
0.290831 0.056980 0.527765
Celebrity_G
0.102235
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.73943 -1.18111 -0.02254 1.29856 5.84829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.634184 4.276467 -1.317 0.1899
Gender 9.184925 5.486919 1.674 0.0965 .
trend 0.002303 0.006202 0.371 0.7110
trend_G -0.009272 0.008071 -1.149 0.2527
Depression -0.021567 0.118609 -0.182 0.8560
Depression_G -0.121607 0.145592 -0.835 0.4051
Belonging 0.048530 0.028317 1.714 0.0889 .
Belonging_G 0.005099 0.036379 0.140 0.8887
WeightedPopularity 0.033130 0.114090 0.290 0.7720
WeightedPopularity_G 0.044696 0.135552 0.330 0.7421
ParentalCriticism 0.171942 0.113319 1.517 0.1315
ParentalCriticism_G -0.217764 0.140323 -1.552 0.1230
Happiness -0.007898 0.158552 -0.050 0.9603
Happiness_G -0.189090 0.195098 -0.969 0.3342
FindingFriends 0.259336 0.140909 1.840 0.0679 .
FindingFriends_G -0.264147 0.194901 -1.355 0.1776
KnowingPeople 0.264170 0.116816 2.261 0.0253 *
KnowingPeople_G -0.060189 0.141009 -0.427 0.6702
Liked 0.290831 0.154827 1.878 0.0625 .
Liked_G 0.056980 0.201886 0.282 0.7782
Celebrity 0.527765 0.315830 1.671 0.0970 .
Celebrity_G 0.102235 0.369213 0.277 0.7823
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.028 on 134 degrees of freedom
Multiple R-squared: 0.5878, Adjusted R-squared: 0.5232
F-statistic: 9.1 on 21 and 134 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,] 0.6213375 0.75732509 0.378662546
[2,] 0.8270475 0.34590508 0.172952538
[3,] 0.7976679 0.40466424 0.202332121
[4,] 0.7425207 0.51495851 0.257479256
[5,] 0.6474002 0.70519960 0.352599802
[6,] 0.5549262 0.89014769 0.445073847
[7,] 0.5086389 0.98272217 0.491361083
[8,] 0.8321709 0.33565828 0.167829140
[9,] 0.7816417 0.43671667 0.218358336
[10,] 0.7253755 0.54924906 0.274624528
[11,] 0.8321294 0.33574113 0.167870564
[12,] 0.8411049 0.31779027 0.158895134
[13,] 0.8212607 0.35747864 0.178739322
[14,] 0.7992706 0.40145888 0.200729439
[15,] 0.7546185 0.49076292 0.245381461
[16,] 0.7457054 0.50858910 0.254294552
[17,] 0.6964131 0.60717382 0.303586911
[18,] 0.6736987 0.65260266 0.326301328
[19,] 0.6103490 0.77930191 0.389650954
[20,] 0.5700899 0.85982018 0.429910089
[21,] 0.8303470 0.33930609 0.169653043
[22,] 0.7976900 0.40462006 0.202310031
[23,] 0.8074940 0.38501204 0.192506021
[24,] 0.7735312 0.45293759 0.226468795
[25,] 0.7307089 0.53858210 0.269291051
[26,] 0.7053799 0.58924012 0.294620062
[27,] 0.7945080 0.41098403 0.205492013
[28,] 0.8178710 0.36425794 0.182128969
[29,] 0.7869537 0.42609253 0.213046264
[30,] 0.8842151 0.23156987 0.115784935
[31,] 0.8690610 0.26187797 0.130938986
[32,] 0.8608852 0.27822964 0.139114821
[33,] 0.9169188 0.16616230 0.083081151
[34,] 0.8982418 0.20351637 0.101758185
[35,] 0.8716524 0.25669525 0.128347625
[36,] 0.8459541 0.30809175 0.154045874
[37,] 0.8122209 0.37555813 0.187779065
[38,] 0.8361623 0.32767550 0.163837748
[39,] 0.8186271 0.36274587 0.181372937
[40,] 0.8390479 0.32190412 0.160952060
[41,] 0.9430808 0.11383846 0.056919228
[42,] 0.9454605 0.10907904 0.054539518
[43,] 0.9293842 0.14123155 0.070615776
[44,] 0.9237468 0.15250633 0.076253164
[45,] 0.9082550 0.18348998 0.091744988
[46,] 0.8923172 0.21536563 0.107682816
[47,] 0.8714119 0.25717615 0.128588076
[48,] 0.8426132 0.31477361 0.157386807
[49,] 0.8231062 0.35378761 0.176893807
[50,] 0.8411035 0.31779292 0.158896459
[51,] 0.8272168 0.34556632 0.172783162
[52,] 0.7994064 0.40118723 0.200593614
[53,] 0.8690055 0.26198903 0.130994513
[54,] 0.8468581 0.30628373 0.153141865
[55,] 0.8718764 0.25624723 0.128123615
[56,] 0.8497539 0.30049213 0.150246065
[57,] 0.8198463 0.36030733 0.180153664
[58,] 0.7847397 0.43052055 0.215260273
[59,] 0.7536701 0.49265980 0.246329900
[60,] 0.7695119 0.46097628 0.230488142
[61,] 0.7661535 0.46769298 0.233846490
[62,] 0.7469072 0.50618562 0.253092811
[63,] 0.7734587 0.45308253 0.226541264
[64,] 0.7669596 0.46608085 0.233040426
[65,] 0.7261016 0.54779688 0.273898441
[66,] 0.7069091 0.58618188 0.293090942
[67,] 0.6845336 0.63093290 0.315466449
[68,] 0.6399408 0.72011835 0.360059174
[69,] 0.5917436 0.81651280 0.408256399
[70,] 0.5843830 0.83123403 0.415617017
[71,] 0.5575092 0.88498162 0.442490811
[72,] 0.6727847 0.65443059 0.327215293
[73,] 0.6258641 0.74827173 0.374135865
[74,] 0.6084241 0.78315175 0.391575874
[75,] 0.7166190 0.56676191 0.283380953
[76,] 0.7364828 0.52703439 0.263517193
[77,] 0.7337693 0.53246135 0.266230677
[78,] 0.6902883 0.61942348 0.309711740
[79,] 0.6384511 0.72309778 0.361548891
[80,] 0.5964722 0.80705551 0.403527756
[81,] 0.5639283 0.87214335 0.436071674
[82,] 0.5937397 0.81252050 0.406260250
[83,] 0.5707166 0.85856681 0.429283404
[84,] 0.7864741 0.42705178 0.213525892
[85,] 0.7566370 0.48672593 0.243362963
[86,] 0.7035755 0.59284890 0.296424452
[87,] 0.6603687 0.67926268 0.339631340
[88,] 0.7063898 0.58722040 0.293610200
[89,] 0.6803383 0.63932338 0.319661690
[90,] 0.6445936 0.71081279 0.355406396
[91,] 0.5800631 0.83987375 0.419936873
[92,] 0.6824735 0.63505308 0.317526542
[93,] 0.6165276 0.76694481 0.383472407
[94,] 0.5483202 0.90335965 0.451679826
[95,] 0.6224108 0.75517846 0.377589231
[96,] 0.5885216 0.82295676 0.411478378
[97,] 0.5561395 0.88772094 0.443860472
[98,] 0.5154940 0.96901210 0.484506048
[99,] 0.4560981 0.91219613 0.543901936
[100,] 0.3869656 0.77393129 0.613034357
[101,] 0.3695361 0.73907213 0.630463933
[102,] 0.9764545 0.04709093 0.023545466
[103,] 0.9536947 0.09261060 0.046305302
[104,] 0.9738941 0.05221185 0.026105925
[105,] 0.9485500 0.10290001 0.051450003
[106,] 0.9837064 0.03258722 0.016293610
[107,] 0.9919807 0.01603855 0.008019274
> postscript(file="/var/www/rcomp/tmp/1ip6q1290266691.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4by5b1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59sc31290266691.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 = 156
Frequency = 1
1 2 3 4 5 6
1.724004160 3.865973772 1.584002053 0.943446042 1.124982351 -0.334418588
7 8 9 10 11 12
-1.442799048 5.848291323 -1.767286366 3.072951697 5.098636320 -1.347554967
13 14 15 16 17 18
-2.226374172 -5.739427090 -4.068347585 1.477761343 2.350894997 1.459515437
19 20 21 22 23 24
-0.370639018 0.839415761 -0.565779574 1.296134066 0.031942223 0.400691284
25 26 27 28 29 30
0.416791120 -0.049207024 0.875350932 -0.481746874 0.789707831 -0.185078507
31 32 33 34 35 36
1.475172519 -3.076946132 -0.232425625 -0.402536491 -1.338805147 2.221017932
37 38 39 40 41 42
1.326823736 2.693282677 1.814849625 2.702567930 -0.732059847 -1.205770744
43 44 45 46 47 48
-0.172673423 1.319942676 3.307321304 -0.125146206 0.267148653 1.584640817
49 50 51 52 53 54
0.952978354 -1.412906754 1.305856413 3.027446421 -0.899189815 -1.985324274
55 56 57 58 59 60
-1.099398634 -0.053006078 -3.356697856 -0.180846598 0.028329905 -0.550087808
61 62 63 64 65 66
0.503263013 -1.696154966 0.821955132 0.570898556 -4.283999332 2.468716696
67 68 69 70 71 72
0.592744686 -2.112426114 -1.210661749 -1.095926523 0.004128384 -0.329365033
73 74 75 76 77 78
-0.299236984 2.424079402 2.028828547 -0.649534887 -1.412260900 -1.172891222
79 80 81 82 83 84
-2.813301652 0.850089073 0.662554202 -0.199596072 -1.237604650 -2.760045956
85 86 87 88 89 90
-1.883676620 -1.367356647 3.203356556 1.947423788 0.370615302 -1.674362136
91 92 93 94 95 96
0.997443958 0.419871904 0.669463781 -1.404302268 1.480397420 2.166818687
97 98 99 100 101 102
0.390423971 -1.981375297 2.390082993 1.167897443 -2.271476637 -0.857395747
103 104 105 106 107 108
0.143661042 0.753496139 1.659507186 -1.603847385 -2.879064376 -3.942118602
109 110 111 112 113 114
-1.056399832 0.389099526 -0.524172821 -2.813568776 1.564554899 0.526643823
115 116 117 118 119 120
0.796306111 0.433532798 -0.827024207 -0.967012827 -1.862994730 1.312855427
121 122 123 124 125 126
-1.910829133 -1.230750729 1.936123328 -1.507166195 -2.001663764 2.661801319
127 128 129 130 131 132
0.489498593 -1.982821625 -2.272094223 0.425903094 -1.165367934 1.362626283
133 134 135 136 137 138
-4.565724894 4.107751094 -1.014176902 -0.599249458 0.169009091 2.020917203
139 140 141 142 143 144
-0.764229837 0.136056537 3.780398060 0.021584711 -0.844140830 -5.110890237
145 146 147 148 149 150
-0.531272544 -0.263782042 -1.011690712 1.556746657 0.783694642 -0.112952636
151 152 153 154 155 156
-0.234603933 0.105989925 -0.165747567 -0.738052711 1.592361478 0.529798964
> postscript(file="/var/www/rcomp/tmp/69sc31290266691.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.724004160 NA
1 3.865973772 1.724004160
2 1.584002053 3.865973772
3 0.943446042 1.584002053
4 1.124982351 0.943446042
5 -0.334418588 1.124982351
6 -1.442799048 -0.334418588
7 5.848291323 -1.442799048
8 -1.767286366 5.848291323
9 3.072951697 -1.767286366
10 5.098636320 3.072951697
11 -1.347554967 5.098636320
12 -2.226374172 -1.347554967
13 -5.739427090 -2.226374172
14 -4.068347585 -5.739427090
15 1.477761343 -4.068347585
16 2.350894997 1.477761343
17 1.459515437 2.350894997
18 -0.370639018 1.459515437
19 0.839415761 -0.370639018
20 -0.565779574 0.839415761
21 1.296134066 -0.565779574
22 0.031942223 1.296134066
23 0.400691284 0.031942223
24 0.416791120 0.400691284
25 -0.049207024 0.416791120
26 0.875350932 -0.049207024
27 -0.481746874 0.875350932
28 0.789707831 -0.481746874
29 -0.185078507 0.789707831
30 1.475172519 -0.185078507
31 -3.076946132 1.475172519
32 -0.232425625 -3.076946132
33 -0.402536491 -0.232425625
34 -1.338805147 -0.402536491
35 2.221017932 -1.338805147
36 1.326823736 2.221017932
37 2.693282677 1.326823736
38 1.814849625 2.693282677
39 2.702567930 1.814849625
40 -0.732059847 2.702567930
41 -1.205770744 -0.732059847
42 -0.172673423 -1.205770744
43 1.319942676 -0.172673423
44 3.307321304 1.319942676
45 -0.125146206 3.307321304
46 0.267148653 -0.125146206
47 1.584640817 0.267148653
48 0.952978354 1.584640817
49 -1.412906754 0.952978354
50 1.305856413 -1.412906754
51 3.027446421 1.305856413
52 -0.899189815 3.027446421
53 -1.985324274 -0.899189815
54 -1.099398634 -1.985324274
55 -0.053006078 -1.099398634
56 -3.356697856 -0.053006078
57 -0.180846598 -3.356697856
58 0.028329905 -0.180846598
59 -0.550087808 0.028329905
60 0.503263013 -0.550087808
61 -1.696154966 0.503263013
62 0.821955132 -1.696154966
63 0.570898556 0.821955132
64 -4.283999332 0.570898556
65 2.468716696 -4.283999332
66 0.592744686 2.468716696
67 -2.112426114 0.592744686
68 -1.210661749 -2.112426114
69 -1.095926523 -1.210661749
70 0.004128384 -1.095926523
71 -0.329365033 0.004128384
72 -0.299236984 -0.329365033
73 2.424079402 -0.299236984
74 2.028828547 2.424079402
75 -0.649534887 2.028828547
76 -1.412260900 -0.649534887
77 -1.172891222 -1.412260900
78 -2.813301652 -1.172891222
79 0.850089073 -2.813301652
80 0.662554202 0.850089073
81 -0.199596072 0.662554202
82 -1.237604650 -0.199596072
83 -2.760045956 -1.237604650
84 -1.883676620 -2.760045956
85 -1.367356647 -1.883676620
86 3.203356556 -1.367356647
87 1.947423788 3.203356556
88 0.370615302 1.947423788
89 -1.674362136 0.370615302
90 0.997443958 -1.674362136
91 0.419871904 0.997443958
92 0.669463781 0.419871904
93 -1.404302268 0.669463781
94 1.480397420 -1.404302268
95 2.166818687 1.480397420
96 0.390423971 2.166818687
97 -1.981375297 0.390423971
98 2.390082993 -1.981375297
99 1.167897443 2.390082993
100 -2.271476637 1.167897443
101 -0.857395747 -2.271476637
102 0.143661042 -0.857395747
103 0.753496139 0.143661042
104 1.659507186 0.753496139
105 -1.603847385 1.659507186
106 -2.879064376 -1.603847385
107 -3.942118602 -2.879064376
108 -1.056399832 -3.942118602
109 0.389099526 -1.056399832
110 -0.524172821 0.389099526
111 -2.813568776 -0.524172821
112 1.564554899 -2.813568776
113 0.526643823 1.564554899
114 0.796306111 0.526643823
115 0.433532798 0.796306111
116 -0.827024207 0.433532798
117 -0.967012827 -0.827024207
118 -1.862994730 -0.967012827
119 1.312855427 -1.862994730
120 -1.910829133 1.312855427
121 -1.230750729 -1.910829133
122 1.936123328 -1.230750729
123 -1.507166195 1.936123328
124 -2.001663764 -1.507166195
125 2.661801319 -2.001663764
126 0.489498593 2.661801319
127 -1.982821625 0.489498593
128 -2.272094223 -1.982821625
129 0.425903094 -2.272094223
130 -1.165367934 0.425903094
131 1.362626283 -1.165367934
132 -4.565724894 1.362626283
133 4.107751094 -4.565724894
134 -1.014176902 4.107751094
135 -0.599249458 -1.014176902
136 0.169009091 -0.599249458
137 2.020917203 0.169009091
138 -0.764229837 2.020917203
139 0.136056537 -0.764229837
140 3.780398060 0.136056537
141 0.021584711 3.780398060
142 -0.844140830 0.021584711
143 -5.110890237 -0.844140830
144 -0.531272544 -5.110890237
145 -0.263782042 -0.531272544
146 -1.011690712 -0.263782042
147 1.556746657 -1.011690712
148 0.783694642 1.556746657
149 -0.112952636 0.783694642
150 -0.234603933 -0.112952636
151 0.105989925 -0.234603933
152 -0.165747567 0.105989925
153 -0.738052711 -0.165747567
154 1.592361478 -0.738052711
155 0.529798964 1.592361478
156 NA 0.529798964
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.865973772 1.724004160
[2,] 1.584002053 3.865973772
[3,] 0.943446042 1.584002053
[4,] 1.124982351 0.943446042
[5,] -0.334418588 1.124982351
[6,] -1.442799048 -0.334418588
[7,] 5.848291323 -1.442799048
[8,] -1.767286366 5.848291323
[9,] 3.072951697 -1.767286366
[10,] 5.098636320 3.072951697
[11,] -1.347554967 5.098636320
[12,] -2.226374172 -1.347554967
[13,] -5.739427090 -2.226374172
[14,] -4.068347585 -5.739427090
[15,] 1.477761343 -4.068347585
[16,] 2.350894997 1.477761343
[17,] 1.459515437 2.350894997
[18,] -0.370639018 1.459515437
[19,] 0.839415761 -0.370639018
[20,] -0.565779574 0.839415761
[21,] 1.296134066 -0.565779574
[22,] 0.031942223 1.296134066
[23,] 0.400691284 0.031942223
[24,] 0.416791120 0.400691284
[25,] -0.049207024 0.416791120
[26,] 0.875350932 -0.049207024
[27,] -0.481746874 0.875350932
[28,] 0.789707831 -0.481746874
[29,] -0.185078507 0.789707831
[30,] 1.475172519 -0.185078507
[31,] -3.076946132 1.475172519
[32,] -0.232425625 -3.076946132
[33,] -0.402536491 -0.232425625
[34,] -1.338805147 -0.402536491
[35,] 2.221017932 -1.338805147
[36,] 1.326823736 2.221017932
[37,] 2.693282677 1.326823736
[38,] 1.814849625 2.693282677
[39,] 2.702567930 1.814849625
[40,] -0.732059847 2.702567930
[41,] -1.205770744 -0.732059847
[42,] -0.172673423 -1.205770744
[43,] 1.319942676 -0.172673423
[44,] 3.307321304 1.319942676
[45,] -0.125146206 3.307321304
[46,] 0.267148653 -0.125146206
[47,] 1.584640817 0.267148653
[48,] 0.952978354 1.584640817
[49,] -1.412906754 0.952978354
[50,] 1.305856413 -1.412906754
[51,] 3.027446421 1.305856413
[52,] -0.899189815 3.027446421
[53,] -1.985324274 -0.899189815
[54,] -1.099398634 -1.985324274
[55,] -0.053006078 -1.099398634
[56,] -3.356697856 -0.053006078
[57,] -0.180846598 -3.356697856
[58,] 0.028329905 -0.180846598
[59,] -0.550087808 0.028329905
[60,] 0.503263013 -0.550087808
[61,] -1.696154966 0.503263013
[62,] 0.821955132 -1.696154966
[63,] 0.570898556 0.821955132
[64,] -4.283999332 0.570898556
[65,] 2.468716696 -4.283999332
[66,] 0.592744686 2.468716696
[67,] -2.112426114 0.592744686
[68,] -1.210661749 -2.112426114
[69,] -1.095926523 -1.210661749
[70,] 0.004128384 -1.095926523
[71,] -0.329365033 0.004128384
[72,] -0.299236984 -0.329365033
[73,] 2.424079402 -0.299236984
[74,] 2.028828547 2.424079402
[75,] -0.649534887 2.028828547
[76,] -1.412260900 -0.649534887
[77,] -1.172891222 -1.412260900
[78,] -2.813301652 -1.172891222
[79,] 0.850089073 -2.813301652
[80,] 0.662554202 0.850089073
[81,] -0.199596072 0.662554202
[82,] -1.237604650 -0.199596072
[83,] -2.760045956 -1.237604650
[84,] -1.883676620 -2.760045956
[85,] -1.367356647 -1.883676620
[86,] 3.203356556 -1.367356647
[87,] 1.947423788 3.203356556
[88,] 0.370615302 1.947423788
[89,] -1.674362136 0.370615302
[90,] 0.997443958 -1.674362136
[91,] 0.419871904 0.997443958
[92,] 0.669463781 0.419871904
[93,] -1.404302268 0.669463781
[94,] 1.480397420 -1.404302268
[95,] 2.166818687 1.480397420
[96,] 0.390423971 2.166818687
[97,] -1.981375297 0.390423971
[98,] 2.390082993 -1.981375297
[99,] 1.167897443 2.390082993
[100,] -2.271476637 1.167897443
[101,] -0.857395747 -2.271476637
[102,] 0.143661042 -0.857395747
[103,] 0.753496139 0.143661042
[104,] 1.659507186 0.753496139
[105,] -1.603847385 1.659507186
[106,] -2.879064376 -1.603847385
[107,] -3.942118602 -2.879064376
[108,] -1.056399832 -3.942118602
[109,] 0.389099526 -1.056399832
[110,] -0.524172821 0.389099526
[111,] -2.813568776 -0.524172821
[112,] 1.564554899 -2.813568776
[113,] 0.526643823 1.564554899
[114,] 0.796306111 0.526643823
[115,] 0.433532798 0.796306111
[116,] -0.827024207 0.433532798
[117,] -0.967012827 -0.827024207
[118,] -1.862994730 -0.967012827
[119,] 1.312855427 -1.862994730
[120,] -1.910829133 1.312855427
[121,] -1.230750729 -1.910829133
[122,] 1.936123328 -1.230750729
[123,] -1.507166195 1.936123328
[124,] -2.001663764 -1.507166195
[125,] 2.661801319 -2.001663764
[126,] 0.489498593 2.661801319
[127,] -1.982821625 0.489498593
[128,] -2.272094223 -1.982821625
[129,] 0.425903094 -2.272094223
[130,] -1.165367934 0.425903094
[131,] 1.362626283 -1.165367934
[132,] -4.565724894 1.362626283
[133,] 4.107751094 -4.565724894
[134,] -1.014176902 4.107751094
[135,] -0.599249458 -1.014176902
[136,] 0.169009091 -0.599249458
[137,] 2.020917203 0.169009091
[138,] -0.764229837 2.020917203
[139,] 0.136056537 -0.764229837
[140,] 3.780398060 0.136056537
[141,] 0.021584711 3.780398060
[142,] -0.844140830 0.021584711
[143,] -5.110890237 -0.844140830
[144,] -0.531272544 -5.110890237
[145,] -0.263782042 -0.531272544
[146,] -1.011690712 -0.263782042
[147,] 1.556746657 -1.011690712
[148,] 0.783694642 1.556746657
[149,] -0.112952636 0.783694642
[150,] -0.234603933 -0.112952636
[151,] 0.105989925 -0.234603933
[152,] -0.165747567 0.105989925
[153,] -0.738052711 -0.165747567
[154,] 1.592361478 -0.738052711
[155,] 0.529798964 1.592361478
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.865973772 1.724004160
2 1.584002053 3.865973772
3 0.943446042 1.584002053
4 1.124982351 0.943446042
5 -0.334418588 1.124982351
6 -1.442799048 -0.334418588
7 5.848291323 -1.442799048
8 -1.767286366 5.848291323
9 3.072951697 -1.767286366
10 5.098636320 3.072951697
11 -1.347554967 5.098636320
12 -2.226374172 -1.347554967
13 -5.739427090 -2.226374172
14 -4.068347585 -5.739427090
15 1.477761343 -4.068347585
16 2.350894997 1.477761343
17 1.459515437 2.350894997
18 -0.370639018 1.459515437
19 0.839415761 -0.370639018
20 -0.565779574 0.839415761
21 1.296134066 -0.565779574
22 0.031942223 1.296134066
23 0.400691284 0.031942223
24 0.416791120 0.400691284
25 -0.049207024 0.416791120
26 0.875350932 -0.049207024
27 -0.481746874 0.875350932
28 0.789707831 -0.481746874
29 -0.185078507 0.789707831
30 1.475172519 -0.185078507
31 -3.076946132 1.475172519
32 -0.232425625 -3.076946132
33 -0.402536491 -0.232425625
34 -1.338805147 -0.402536491
35 2.221017932 -1.338805147
36 1.326823736 2.221017932
37 2.693282677 1.326823736
38 1.814849625 2.693282677
39 2.702567930 1.814849625
40 -0.732059847 2.702567930
41 -1.205770744 -0.732059847
42 -0.172673423 -1.205770744
43 1.319942676 -0.172673423
44 3.307321304 1.319942676
45 -0.125146206 3.307321304
46 0.267148653 -0.125146206
47 1.584640817 0.267148653
48 0.952978354 1.584640817
49 -1.412906754 0.952978354
50 1.305856413 -1.412906754
51 3.027446421 1.305856413
52 -0.899189815 3.027446421
53 -1.985324274 -0.899189815
54 -1.099398634 -1.985324274
55 -0.053006078 -1.099398634
56 -3.356697856 -0.053006078
57 -0.180846598 -3.356697856
58 0.028329905 -0.180846598
59 -0.550087808 0.028329905
60 0.503263013 -0.550087808
61 -1.696154966 0.503263013
62 0.821955132 -1.696154966
63 0.570898556 0.821955132
64 -4.283999332 0.570898556
65 2.468716696 -4.283999332
66 0.592744686 2.468716696
67 -2.112426114 0.592744686
68 -1.210661749 -2.112426114
69 -1.095926523 -1.210661749
70 0.004128384 -1.095926523
71 -0.329365033 0.004128384
72 -0.299236984 -0.329365033
73 2.424079402 -0.299236984
74 2.028828547 2.424079402
75 -0.649534887 2.028828547
76 -1.412260900 -0.649534887
77 -1.172891222 -1.412260900
78 -2.813301652 -1.172891222
79 0.850089073 -2.813301652
80 0.662554202 0.850089073
81 -0.199596072 0.662554202
82 -1.237604650 -0.199596072
83 -2.760045956 -1.237604650
84 -1.883676620 -2.760045956
85 -1.367356647 -1.883676620
86 3.203356556 -1.367356647
87 1.947423788 3.203356556
88 0.370615302 1.947423788
89 -1.674362136 0.370615302
90 0.997443958 -1.674362136
91 0.419871904 0.997443958
92 0.669463781 0.419871904
93 -1.404302268 0.669463781
94 1.480397420 -1.404302268
95 2.166818687 1.480397420
96 0.390423971 2.166818687
97 -1.981375297 0.390423971
98 2.390082993 -1.981375297
99 1.167897443 2.390082993
100 -2.271476637 1.167897443
101 -0.857395747 -2.271476637
102 0.143661042 -0.857395747
103 0.753496139 0.143661042
104 1.659507186 0.753496139
105 -1.603847385 1.659507186
106 -2.879064376 -1.603847385
107 -3.942118602 -2.879064376
108 -1.056399832 -3.942118602
109 0.389099526 -1.056399832
110 -0.524172821 0.389099526
111 -2.813568776 -0.524172821
112 1.564554899 -2.813568776
113 0.526643823 1.564554899
114 0.796306111 0.526643823
115 0.433532798 0.796306111
116 -0.827024207 0.433532798
117 -0.967012827 -0.827024207
118 -1.862994730 -0.967012827
119 1.312855427 -1.862994730
120 -1.910829133 1.312855427
121 -1.230750729 -1.910829133
122 1.936123328 -1.230750729
123 -1.507166195 1.936123328
124 -2.001663764 -1.507166195
125 2.661801319 -2.001663764
126 0.489498593 2.661801319
127 -1.982821625 0.489498593
128 -2.272094223 -1.982821625
129 0.425903094 -2.272094223
130 -1.165367934 0.425903094
131 1.362626283 -1.165367934
132 -4.565724894 1.362626283
133 4.107751094 -4.565724894
134 -1.014176902 4.107751094
135 -0.599249458 -1.014176902
136 0.169009091 -0.599249458
137 2.020917203 0.169009091
138 -0.764229837 2.020917203
139 0.136056537 -0.764229837
140 3.780398060 0.136056537
141 0.021584711 3.780398060
142 -0.844140830 0.021584711
143 -5.110890237 -0.844140830
144 -0.531272544 -5.110890237
145 -0.263782042 -0.531272544
146 -1.011690712 -0.263782042
147 1.556746657 -1.011690712
148 0.783694642 1.556746657
149 -0.112952636 0.783694642
150 -0.234603933 -0.112952636
151 0.105989925 -0.234603933
152 -0.165747567 0.105989925
153 -0.738052711 -0.165747567
154 1.592361478 -0.738052711
155 0.529798964 1.592361478
> 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/7wzmh1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8wzmh1290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9pq321290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10pq321290266691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sqj81290266691.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/12w90e1290266691.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/13ksx71290266691.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/14d1ws1290266691.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/15hkdy1290266691.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/16dba71290266691.tab")
+ }
> try(system("convert tmp/1ip6q1290266691.ps tmp/1ip6q1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/2by5b1290266691.ps tmp/2by5b1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/3by5b1290266691.ps tmp/3by5b1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/4by5b1290266691.ps tmp/4by5b1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/59sc31290266691.ps tmp/59sc31290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/69sc31290266691.ps tmp/69sc31290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wzmh1290266691.ps tmp/7wzmh1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wzmh1290266691.ps tmp/8wzmh1290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pq321290266691.ps tmp/9pq321290266691.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pq321290266691.ps tmp/10pq321290266691.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.280 2.310 10.693