R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '3'
> par3 <- 'Linear Trend'
> par2 <- 'Include Monthly 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, 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
Learning Connected Separate Software Happiness Depression Belonging
1 13 41 38 12 14 12 53
2 16 39 32 11 18 11 86
3 19 30 35 15 11 14 66
4 15 31 33 6 12 12 67
5 14 34 37 13 16 21 76
6 13 35 29 10 18 12 78
7 19 39 31 12 14 22 53
8 15 34 36 14 14 11 80
9 14 36 35 12 15 10 74
10 15 37 38 6 15 13 76
11 16 38 31 10 17 10 79
12 16 36 34 12 19 8 54
13 16 38 35 12 10 15 67
14 16 39 38 11 16 14 54
15 17 33 37 15 18 10 87
16 15 32 33 12 14 14 58
17 15 36 32 10 14 14 75
18 20 38 38 12 17 11 88
19 18 39 38 11 14 10 64
20 16 32 32 12 16 13 57
21 16 32 33 11 18 7 66
22 16 31 31 12 11 14 68
23 19 39 38 13 14 12 54
24 16 37 39 11 12 14 56
25 17 39 32 9 17 11 86
26 17 41 32 13 9 9 80
27 16 36 35 10 16 11 76
28 15 33 37 14 14 15 69
29 16 33 33 12 15 14 78
30 14 34 33 10 11 13 67
31 15 31 28 12 16 9 80
32 12 27 32 8 13 15 54
33 14 37 31 10 17 10 71
34 16 34 37 12 15 11 84
35 14 34 30 12 14 13 74
36 7 32 33 7 16 8 71
37 10 29 31 6 9 20 63
38 14 36 33 12 15 12 71
39 16 29 31 10 17 10 76
40 16 35 33 10 13 10 69
41 16 37 32 10 15 9 74
42 14 34 33 12 16 14 75
43 20 38 32 15 16 8 54
44 14 35 33 10 12 14 52
45 14 38 28 10 12 11 69
46 11 37 35 12 11 13 68
47 14 38 39 13 15 9 65
48 15 33 34 11 15 11 75
49 16 36 38 11 17 15 74
50 14 38 32 12 13 11 75
51 16 32 38 14 16 10 72
52 14 32 30 10 14 14 67
53 12 32 33 12 11 18 63
54 16 34 38 13 12 14 62
55 9 32 32 5 12 11 63
56 14 37 32 6 15 12 76
57 16 39 34 12 16 13 74
58 16 29 34 12 15 9 67
59 15 37 36 11 12 10 73
60 16 35 34 10 12 15 70
61 12 30 28 7 8 20 53
62 16 38 34 12 13 12 77
63 16 34 35 14 11 12 77
64 14 31 35 11 14 14 52
65 16 34 31 12 15 13 54
66 17 35 37 13 10 11 80
67 18 36 35 14 11 17 66
68 18 30 27 11 12 12 73
69 12 39 40 12 15 13 63
70 16 35 37 12 15 14 69
71 10 38 36 8 14 13 67
72 14 31 38 11 16 15 54
73 18 34 39 14 15 13 81
74 18 38 41 14 15 10 69
75 16 34 27 12 13 11 84
76 17 39 30 9 12 19 80
77 16 37 37 13 17 13 70
78 16 34 31 11 13 17 69
79 13 28 31 12 15 13 77
80 16 37 27 12 13 9 54
81 16 33 36 12 15 11 79
82 20 37 38 12 16 10 30
83 16 35 37 12 15 9 71
84 15 37 33 12 16 12 73
85 15 32 34 11 15 12 72
86 16 33 31 10 14 13 77
87 14 38 39 9 15 13 75
88 16 33 34 12 14 12 69
89 16 29 32 12 13 15 54
90 15 33 33 12 7 22 70
91 12 31 36 9 17 13 73
92 17 36 32 15 13 15 54
93 16 35 41 12 15 13 77
94 15 32 28 12 14 15 82
95 13 29 30 12 13 10 80
96 16 39 36 10 16 11 80
97 16 37 35 13 12 16 69
98 16 35 31 9 14 11 78
99 16 37 34 12 17 11 81
100 14 32 36 10 15 10 76
101 16 38 36 14 17 10 76
102 16 37 35 11 12 16 73
103 20 36 37 15 16 12 85
104 15 32 28 11 11 11 66
105 16 33 39 11 15 16 79
106 13 40 32 12 9 19 68
107 17 38 35 12 16 11 76
108 16 41 39 12 15 16 71
109 16 36 35 11 10 15 54
110 12 43 42 7 10 24 46
111 16 30 34 12 15 14 82
112 16 31 33 14 11 15 74
113 17 32 41 11 13 11 88
114 13 32 33 11 14 15 38
115 12 37 34 10 18 12 76
116 18 37 32 13 16 10 86
117 14 33 40 13 14 14 54
118 14 34 40 8 14 13 70
119 13 33 35 11 14 9 69
120 16 38 36 12 14 15 90
121 13 33 37 11 12 15 54
122 16 31 27 13 14 14 76
123 13 38 39 12 15 11 89
124 16 37 38 14 15 8 76
125 15 33 31 13 15 11 73
126 16 31 33 15 13 11 79
127 15 39 32 10 17 8 90
128 17 44 39 11 17 10 74
129 15 33 36 9 19 11 81
130 12 35 33 11 15 13 72
131 16 32 33 10 13 11 71
132 10 28 32 11 9 20 66
133 16 40 37 8 15 10 77
134 12 27 30 11 15 15 65
135 14 37 38 12 15 12 74
136 15 32 29 12 16 14 82
137 13 28 22 9 11 23 54
138 15 34 35 11 14 14 63
139 11 30 35 10 11 16 54
140 12 35 34 8 15 11 64
141 8 31 35 9 13 12 69
142 16 32 34 8 15 10 54
143 15 30 34 9 16 14 84
144 17 30 35 15 14 12 86
145 16 31 23 11 15 12 77
146 10 40 31 8 16 11 89
147 18 32 27 13 16 12 76
148 13 36 36 12 11 13 60
149 16 32 31 12 12 11 75
150 13 35 32 9 9 19 73
151 10 38 39 7 16 12 85
152 15 42 37 13 13 17 79
153 16 34 38 9 16 9 71
154 16 35 39 6 12 12 72
155 14 35 34 8 9 19 69
156 10 33 31 8 13 18 78
157 17 36 32 15 13 15 54
158 13 32 37 6 14 14 69
159 15 33 36 9 19 11 81
160 16 34 32 11 13 9 84
161 12 32 35 8 12 18 84
162 13 34 36 8 13 16 69
Belonging_Final M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 32 1 0 0 0 0 0 0 0 0 0 0 1
2 51 0 1 0 0 0 0 0 0 0 0 0 2
3 42 0 0 1 0 0 0 0 0 0 0 0 3
4 41 0 0 0 1 0 0 0 0 0 0 0 4
5 46 0 0 0 0 1 0 0 0 0 0 0 5
6 47 0 0 0 0 0 1 0 0 0 0 0 6
7 37 0 0 0 0 0 0 1 0 0 0 0 7
8 49 0 0 0 0 0 0 0 1 0 0 0 8
9 45 0 0 0 0 0 0 0 0 1 0 0 9
10 47 0 0 0 0 0 0 0 0 0 1 0 10
11 49 0 0 0 0 0 0 0 0 0 0 1 11
12 33 0 0 0 0 0 0 0 0 0 0 0 12
13 42 1 0 0 0 0 0 0 0 0 0 0 13
14 33 0 1 0 0 0 0 0 0 0 0 0 14
15 53 0 0 1 0 0 0 0 0 0 0 0 15
16 36 0 0 0 1 0 0 0 0 0 0 0 16
17 45 0 0 0 0 1 0 0 0 0 0 0 17
18 54 0 0 0 0 0 1 0 0 0 0 0 18
19 41 0 0 0 0 0 0 1 0 0 0 0 19
20 36 0 0 0 0 0 0 0 1 0 0 0 20
21 41 0 0 0 0 0 0 0 0 1 0 0 21
22 44 0 0 0 0 0 0 0 0 0 1 0 22
23 33 0 0 0 0 0 0 0 0 0 0 1 23
24 37 0 0 0 0 0 0 0 0 0 0 0 24
25 52 1 0 0 0 0 0 0 0 0 0 0 25
26 47 0 1 0 0 0 0 0 0 0 0 0 26
27 43 0 0 1 0 0 0 0 0 0 0 0 27
28 44 0 0 0 1 0 0 0 0 0 0 0 28
29 45 0 0 0 0 1 0 0 0 0 0 0 29
30 44 0 0 0 0 0 1 0 0 0 0 0 30
31 49 0 0 0 0 0 0 1 0 0 0 0 31
32 33 0 0 0 0 0 0 0 1 0 0 0 32
33 43 0 0 0 0 0 0 0 0 1 0 0 33
34 54 0 0 0 0 0 0 0 0 0 1 0 34
35 42 0 0 0 0 0 0 0 0 0 0 1 35
36 44 0 0 0 0 0 0 0 0 0 0 0 36
37 37 1 0 0 0 0 0 0 0 0 0 0 37
38 43 0 1 0 0 0 0 0 0 0 0 0 38
39 46 0 0 1 0 0 0 0 0 0 0 0 39
40 42 0 0 0 1 0 0 0 0 0 0 0 40
41 45 0 0 0 0 1 0 0 0 0 0 0 41
42 44 0 0 0 0 0 1 0 0 0 0 0 42
43 33 0 0 0 0 0 0 1 0 0 0 0 43
44 31 0 0 0 0 0 0 0 1 0 0 0 44
45 42 0 0 0 0 0 0 0 0 1 0 0 45
46 40 0 0 0 0 0 0 0 0 0 1 0 46
47 43 0 0 0 0 0 0 0 0 0 0 1 47
48 46 0 0 0 0 0 0 0 0 0 0 0 48
49 42 1 0 0 0 0 0 0 0 0 0 0 49
50 45 0 1 0 0 0 0 0 0 0 0 0 50
51 44 0 0 1 0 0 0 0 0 0 0 0 51
52 40 0 0 0 1 0 0 0 0 0 0 0 52
53 37 0 0 0 0 1 0 0 0 0 0 0 53
54 46 0 0 0 0 0 1 0 0 0 0 0 54
55 36 0 0 0 0 0 0 1 0 0 0 0 55
56 47 0 0 0 0 0 0 0 1 0 0 0 56
57 45 0 0 0 0 0 0 0 0 1 0 0 57
58 42 0 0 0 0 0 0 0 0 0 1 0 58
59 43 0 0 0 0 0 0 0 0 0 0 1 59
60 43 0 0 0 0 0 0 0 0 0 0 0 60
61 32 1 0 0 0 0 0 0 0 0 0 0 61
62 45 0 1 0 0 0 0 0 0 0 0 0 62
63 45 0 0 1 0 0 0 0 0 0 0 0 63
64 31 0 0 0 1 0 0 0 0 0 0 0 64
65 33 0 0 0 0 1 0 0 0 0 0 0 65
66 49 0 0 0 0 0 1 0 0 0 0 0 66
67 42 0 0 0 0 0 0 1 0 0 0 0 67
68 41 0 0 0 0 0 0 0 1 0 0 0 68
69 38 0 0 0 0 0 0 0 0 1 0 0 69
70 42 0 0 0 0 0 0 0 0 0 1 0 70
71 44 0 0 0 0 0 0 0 0 0 0 1 71
72 33 0 0 0 0 0 0 0 0 0 0 0 72
73 48 1 0 0 0 0 0 0 0 0 0 0 73
74 40 0 1 0 0 0 0 0 0 0 0 0 74
75 50 0 0 1 0 0 0 0 0 0 0 0 75
76 49 0 0 0 1 0 0 0 0 0 0 0 76
77 43 0 0 0 0 1 0 0 0 0 0 0 77
78 44 0 0 0 0 0 1 0 0 0 0 0 78
79 47 0 0 0 0 0 0 1 0 0 0 0 79
80 33 0 0 0 0 0 0 0 1 0 0 0 80
81 46 0 0 0 0 0 0 0 0 1 0 0 81
82 0 0 0 0 0 0 0 0 0 0 1 0 82
83 45 0 0 0 0 0 0 0 0 0 0 1 83
84 43 0 0 0 0 0 0 0 0 0 0 0 84
85 44 1 0 0 0 0 0 0 0 0 0 0 85
86 47 0 1 0 0 0 0 0 0 0 0 0 86
87 45 0 0 1 0 0 0 0 0 0 0 0 87
88 42 0 0 0 1 0 0 0 0 0 0 0 88
89 33 0 0 0 0 1 0 0 0 0 0 0 89
90 43 0 0 0 0 0 1 0 0 0 0 0 90
91 46 0 0 0 0 0 0 1 0 0 0 0 91
92 33 0 0 0 0 0 0 0 1 0 0 0 92
93 46 0 0 0 0 0 0 0 0 1 0 0 93
94 48 0 0 0 0 0 0 0 0 0 1 0 94
95 47 0 0 0 0 0 0 0 0 0 0 1 95
96 47 0 0 0 0 0 0 0 0 0 0 0 96
97 43 1 0 0 0 0 0 0 0 0 0 0 97
98 46 0 1 0 0 0 0 0 0 0 0 0 98
99 48 0 0 1 0 0 0 0 0 0 0 0 99
100 46 0 0 0 1 0 0 0 0 0 0 0 100
101 45 0 0 0 0 1 0 0 0 0 0 0 101
102 45 0 0 0 0 0 1 0 0 0 0 0 102
103 52 0 0 0 0 0 0 1 0 0 0 0 103
104 42 0 0 0 0 0 0 0 1 0 0 0 104
105 47 0 0 0 0 0 0 0 0 1 0 0 105
106 41 0 0 0 0 0 0 0 0 0 1 0 106
107 47 0 0 0 0 0 0 0 0 0 0 1 107
108 43 0 0 0 0 0 0 0 0 0 0 0 108
109 33 1 0 0 0 0 0 0 0 0 0 0 109
110 30 0 1 0 0 0 0 0 0 0 0 0 110
111 49 0 0 1 0 0 0 0 0 0 0 0 111
112 44 0 0 0 1 0 0 0 0 0 0 0 112
113 55 0 0 0 0 1 0 0 0 0 0 0 113
114 11 0 0 0 0 0 1 0 0 0 0 0 114
115 47 0 0 0 0 0 0 1 0 0 0 0 115
116 53 0 0 0 0 0 0 0 1 0 0 0 116
117 33 0 0 0 0 0 0 0 0 1 0 0 117
118 44 0 0 0 0 0 0 0 0 0 1 0 118
119 42 0 0 0 0 0 0 0 0 0 0 1 119
120 55 0 0 0 0 0 0 0 0 0 0 0 120
121 33 1 0 0 0 0 0 0 0 0 0 0 121
122 46 0 1 0 0 0 0 0 0 0 0 0 122
123 54 0 0 1 0 0 0 0 0 0 0 0 123
124 47 0 0 0 1 0 0 0 0 0 0 0 124
125 45 0 0 0 0 1 0 0 0 0 0 0 125
126 47 0 0 0 0 0 1 0 0 0 0 0 126
127 55 0 0 0 0 0 0 1 0 0 0 0 127
128 44 0 0 0 0 0 0 0 1 0 0 0 128
129 53 0 0 0 0 0 0 0 0 1 0 0 129
130 44 0 0 0 0 0 0 0 0 0 1 0 130
131 42 0 0 0 0 0 0 0 0 0 0 1 131
132 40 0 0 0 0 0 0 0 0 0 0 0 132
133 46 1 0 0 0 0 0 0 0 0 0 0 133
134 40 0 1 0 0 0 0 0 0 0 0 0 134
135 46 0 0 1 0 0 0 0 0 0 0 0 135
136 53 0 0 0 1 0 0 0 0 0 0 0 136
137 33 0 0 0 0 1 0 0 0 0 0 0 137
138 42 0 0 0 0 0 1 0 0 0 0 0 138
139 35 0 0 0 0 0 0 1 0 0 0 0 139
140 40 0 0 0 0 0 0 0 1 0 0 0 140
141 41 0 0 0 0 0 0 0 0 1 0 0 141
142 33 0 0 0 0 0 0 0 0 0 1 0 142
143 51 0 0 0 0 0 0 0 0 0 0 1 143
144 53 0 0 0 0 0 0 0 0 0 0 0 144
145 46 1 0 0 0 0 0 0 0 0 0 0 145
146 55 0 1 0 0 0 0 0 0 0 0 0 146
147 47 0 0 1 0 0 0 0 0 0 0 0 147
148 38 0 0 0 1 0 0 0 0 0 0 0 148
149 46 0 0 0 0 1 0 0 0 0 0 0 149
150 46 0 0 0 0 0 1 0 0 0 0 0 150
151 53 0 0 0 0 0 0 1 0 0 0 0 151
152 47 0 0 0 0 0 0 0 1 0 0 0 152
153 41 0 0 0 0 0 0 0 0 1 0 0 153
154 44 0 0 0 0 0 0 0 0 0 1 0 154
155 43 0 0 0 0 0 0 0 0 0 0 1 155
156 51 0 0 0 0 0 0 0 0 0 0 0 156
157 33 1 0 0 0 0 0 0 0 0 0 0 157
158 43 0 1 0 0 0 0 0 0 0 0 0 158
159 53 0 0 1 0 0 0 0 0 0 0 0 159
160 51 0 0 0 1 0 0 0 0 0 0 0 160
161 50 0 0 0 0 1 0 0 0 0 0 0 161
162 46 0 0 0 0 0 1 0 0 0 0 0 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software
4.275662 0.108355 -0.006354 0.522519
Happiness Depression Belonging Belonging_Final
0.086678 -0.063024 0.036434 -0.045841
M1 M2 M3 M4
1.080967 0.439346 0.893787 0.994745
M5 M6 M7 M8
0.722759 0.987732 0.485424 0.964874
M9 M10 M11 t
-0.041603 1.201586 0.498842 -0.004088
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.4963 -1.1819 0.1867 1.1023 3.9066
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.275662 2.805251 1.524 0.1297
Connected 0.108355 0.050373 2.151 0.0332 *
Separate -0.006354 0.047723 -0.133 0.8943
Software 0.522519 0.072168 7.240 2.59e-11 ***
Happiness 0.086678 0.080633 1.075 0.2842
Depression -0.063024 0.060277 -1.046 0.2975
Belonging 0.036434 0.046771 0.779 0.4373
Belonging_Final -0.045841 0.066661 -0.688 0.4928
M1 1.080967 0.733800 1.473 0.1429
M2 0.439346 0.736031 0.597 0.5515
M3 0.893787 0.739425 1.209 0.2288
M4 0.994745 0.731433 1.360 0.1760
M5 0.722759 0.731102 0.989 0.3245
M6 0.987732 0.731018 1.351 0.1788
M7 0.485424 0.740870 0.655 0.5134
M8 0.964874 0.757147 1.274 0.2046
M9 -0.041603 0.743176 -0.056 0.9554
M10 1.201586 0.747865 1.607 0.1103
M11 0.498842 0.747253 0.668 0.5055
t -0.004088 0.003308 -1.236 0.2186
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.873 on 142 degrees of freedom
Multiple R-squared: 0.3921, Adjusted R-squared: 0.3107
F-statistic: 4.82 on 19 and 142 DF, p-value: 1.507e-08
> 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,] 0.69730269 0.6053946 0.30269731
[2,] 0.86358914 0.2728217 0.13641086
[3,] 0.82465880 0.3506824 0.17534120
[4,] 0.73532832 0.5293434 0.26467168
[5,] 0.63640575 0.7271885 0.36359425
[6,] 0.64674389 0.7065122 0.35325611
[7,] 0.55840837 0.8831833 0.44159163
[8,] 0.72022265 0.5595547 0.27977735
[9,] 0.77136116 0.4572777 0.22863884
[10,] 0.71999868 0.5600026 0.28000132
[11,] 0.66229181 0.6754164 0.33770819
[12,] 0.60525205 0.7894959 0.39474795
[13,] 0.54248916 0.9150217 0.45751084
[14,] 0.90849051 0.1830190 0.09150949
[15,] 0.88314227 0.2337155 0.11685773
[16,] 0.85945097 0.2810981 0.14054903
[17,] 0.82765158 0.3446968 0.17234842
[18,] 0.78572039 0.4285592 0.21427961
[19,] 0.73499126 0.5300175 0.26500874
[20,] 0.69829260 0.6034148 0.30170740
[21,] 0.70240222 0.5951956 0.29759778
[22,] 0.65070537 0.6985893 0.34929463
[23,] 0.59562851 0.8087430 0.40437149
[24,] 0.79319772 0.4136046 0.20680228
[25,] 0.86821082 0.2635784 0.13178918
[26,] 0.88717448 0.2256510 0.11282552
[27,] 0.89609109 0.2078178 0.10390891
[28,] 0.88180213 0.2363957 0.11819787
[29,] 0.85817954 0.2836409 0.14182046
[30,] 0.82444716 0.3511057 0.17555284
[31,] 0.83180315 0.3363937 0.16819685
[32,] 0.79512536 0.4097493 0.20487464
[33,] 0.82790529 0.3441894 0.17209471
[34,] 0.79136871 0.4172626 0.20863129
[35,] 0.75571962 0.4885608 0.24428038
[36,] 0.76046483 0.4790703 0.23953517
[37,] 0.72736775 0.5452645 0.27263225
[38,] 0.77851770 0.4429646 0.22148230
[39,] 0.74931164 0.5013767 0.25068836
[40,] 0.71860531 0.5627894 0.28139469
[41,] 0.67707980 0.6458404 0.32292020
[42,] 0.63142578 0.7371484 0.36857422
[43,] 0.59229857 0.8154029 0.40770143
[44,] 0.57835547 0.8432891 0.42164453
[45,] 0.60641146 0.7871771 0.39358854
[46,] 0.72590394 0.5481921 0.27409606
[47,] 0.77132513 0.4573497 0.22867487
[48,] 0.73581793 0.5283641 0.26418207
[49,] 0.83830616 0.3233877 0.16169384
[50,] 0.81331639 0.3733672 0.18668361
[51,] 0.79841398 0.4031720 0.20158602
[52,] 0.78649991 0.4270002 0.21350009
[53,] 0.74964751 0.5007050 0.25035249
[54,] 0.76382884 0.4723423 0.23617116
[55,] 0.73130782 0.5373844 0.26869218
[56,] 0.70057424 0.5988515 0.29942576
[57,] 0.68659920 0.6268016 0.31340080
[58,] 0.64545107 0.7090979 0.35454893
[59,] 0.63109288 0.7378142 0.36890712
[60,] 0.71459181 0.5708164 0.28540819
[61,] 0.68475218 0.6304956 0.31524782
[62,] 0.64283244 0.7143351 0.35716756
[63,] 0.61202574 0.7759485 0.38797426
[64,] 0.61470408 0.7705918 0.38529592
[65,] 0.57500945 0.8499811 0.42499055
[66,] 0.52764060 0.9447188 0.47235940
[67,] 0.51053666 0.9789267 0.48946334
[68,] 0.46543123 0.9308625 0.53456877
[69,] 0.43862481 0.8772496 0.56137519
[70,] 0.39750442 0.7950088 0.60249558
[71,] 0.36635494 0.7327099 0.63364506
[72,] 0.32675757 0.6535151 0.67324243
[73,] 0.35335617 0.7067123 0.64664383
[74,] 0.32530680 0.6506136 0.67469320
[75,] 0.28456019 0.5691204 0.71543981
[76,] 0.29972394 0.5994479 0.70027606
[77,] 0.25965270 0.5193054 0.74034730
[78,] 0.22427006 0.4485401 0.77572994
[79,] 0.20913200 0.4182640 0.79086800
[80,] 0.18072323 0.3614465 0.81927677
[81,] 0.27991015 0.5598203 0.72008985
[82,] 0.23979660 0.4795932 0.76020340
[83,] 0.25114492 0.5022898 0.74885508
[84,] 0.26716848 0.5343370 0.73283152
[85,] 0.23217425 0.4643485 0.76782575
[86,] 0.21204046 0.4240809 0.78795954
[87,] 0.19049929 0.3809986 0.80950071
[88,] 0.22069639 0.4413928 0.77930361
[89,] 0.19450527 0.3890105 0.80549473
[90,] 0.17821644 0.3564329 0.82178356
[91,] 0.19796133 0.3959227 0.80203867
[92,] 0.17620150 0.3524030 0.82379850
[93,] 0.17538665 0.3507733 0.82461335
[94,] 0.17375971 0.3475194 0.82624029
[95,] 0.14517748 0.2903550 0.85482252
[96,] 0.11970852 0.2394170 0.88029148
[97,] 0.14248230 0.2849646 0.85751770
[98,] 0.16729035 0.3345807 0.83270965
[99,] 0.13476728 0.2695346 0.86523272
[100,] 0.18464495 0.3692899 0.81535505
[101,] 0.17859335 0.3571867 0.82140665
[102,] 0.14137248 0.2827450 0.85862752
[103,] 0.11532528 0.2306506 0.88467472
[104,] 0.08828815 0.1765763 0.91171185
[105,] 0.08525385 0.1705077 0.91474615
[106,] 0.08382646 0.1676529 0.91617354
[107,] 0.20160585 0.4032117 0.79839415
[108,] 0.29793077 0.5958615 0.70206923
[109,] 0.24329162 0.4865832 0.75670838
[110,] 0.22611093 0.4522219 0.77388907
[111,] 0.31570879 0.6314176 0.68429121
[112,] 0.30358884 0.6071777 0.69641116
[113,] 0.22548927 0.4509785 0.77451073
[114,] 0.32361695 0.6472339 0.67638305
[115,] 0.43935833 0.8787167 0.56064167
[116,] 0.48436476 0.9687295 0.51563524
[117,] 0.45746607 0.9149321 0.54253393
> postscript(file="/var/wessaorg/rcomp/tmp/1r4q71352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2dsam1352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3mgqz1352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/46ph61352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5med11352118659.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 = 162
Frequency = 1
1 2 3 4 5 6
-3.74518964 -0.13947442 2.42629202 2.61603031 -1.94337682 -2.56349568
7 8 9 10 11 12
3.90655848 -2.16720701 -1.44912616 1.56548140 0.64935091 0.22102027
13 14 15 16 17 18
0.09397350 0.65089995 -0.95673018 -0.52698893 0.14752978 3.15283305
19 20 21 22 23 24
2.54889896 0.31293325 1.20216314 0.64875714 2.63045998 1.81139084
25 26 27 28 29 30
1.49047680 0.38617374 0.54589579 -1.57694324 0.28698106 -0.39859622
31 32 33 34 35 36
-0.57387479 -0.64829975 -0.59548950 -0.24945960 -1.56012505 -5.49631887
37 38 39 40 41 42
-1.40469467 -1.68057140 1.31583634 0.99993282 0.77191243 -2.05642271
43 44 45 46 47 48
2.32536970 -0.49993250 -0.15041457 -5.12424099 -2.37485041 0.58234917
49 50 51 52 53 54
0.13763520 -1.79830962 -0.86504152 -0.49840532 -2.72796149 0.41392016
55 56 57 58 59 60
-2.40485952 0.88907485 0.51805630 0.31460452 -0.15988590 2.49399063
61 62 63 64 65 66
0.26529020 0.25360570 -0.62865695 -0.69779708 0.57438607 0.81424651
67 68 69 70 71 72
2.15772582 3.14653667 -3.22819581 -0.02515868 -3.37146611 0.25719306
73 74 75 76 77 78
0.95855429 1.06496543 0.15238282 2.79112769 -0.48392136 1.26825588
79 80 81 82 83 84
-1.67714377 -0.03563866 1.10329527 2.97039463 0.48025513 -0.32109639
85 86 87 88 89 90
-0.15837411 1.98748851 -0.53677842 0.41353586 1.52002276 0.66876434
91 92 93 94 95 96
-1.42728711 -0.03587267 1.16632512 -0.70807908 -1.86489078 1.44064329
97 98 99 100 101 102
-0.11418902 2.13402516 -0.35918906 -0.65571548 -1.33904535 0.99046642
103 104 105 106 107 108
2.81271165 0.40787041 2.10394292 -2.62576828 1.18946514 0.79335551
109 110 111 112 113 114
1.28669655 0.02964933 0.82018280 0.03557178 2.39039396 -1.95117357
115 116 117 118 119 120
-2.72770669 1.17467082 -0.65597003 0.46744229 -1.62404474 1.02989715
121 122 123 124 125 126
-1.49983375 0.81199701 -3.18074635 -1.25696475 -0.86273524 -0.89281062
127 128 129 130 131 132
-0.21685017 0.49272367 1.76836486 -3.36344459 2.18303049 -2.40507152
133 134 135 136 137 138
1.54107210 -1.53934881 -1.78687846 -0.33037994 1.00615125 0.39010442
139 140 141 142 143 144
-1.75445350 -1.52988066 -4.50201256 2.54708301 1.84562457 1.28591181
145 146 147 148 149 150
1.03485235 -3.85059248 2.09782781 -2.18595883 1.09924695 -0.07569416
151 152 153 154 155 156
-2.96908905 -1.50697844 2.71906103 3.58238823 1.97707676 -1.69326495
157 158 159 160 161 162
0.11373020 1.68949189 0.95560335 0.87295512 -0.43958399 0.23960219
> postscript(file="/var/wessaorg/rcomp/tmp/6tm8l1352118659.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.74518964 NA
1 -0.13947442 -3.74518964
2 2.42629202 -0.13947442
3 2.61603031 2.42629202
4 -1.94337682 2.61603031
5 -2.56349568 -1.94337682
6 3.90655848 -2.56349568
7 -2.16720701 3.90655848
8 -1.44912616 -2.16720701
9 1.56548140 -1.44912616
10 0.64935091 1.56548140
11 0.22102027 0.64935091
12 0.09397350 0.22102027
13 0.65089995 0.09397350
14 -0.95673018 0.65089995
15 -0.52698893 -0.95673018
16 0.14752978 -0.52698893
17 3.15283305 0.14752978
18 2.54889896 3.15283305
19 0.31293325 2.54889896
20 1.20216314 0.31293325
21 0.64875714 1.20216314
22 2.63045998 0.64875714
23 1.81139084 2.63045998
24 1.49047680 1.81139084
25 0.38617374 1.49047680
26 0.54589579 0.38617374
27 -1.57694324 0.54589579
28 0.28698106 -1.57694324
29 -0.39859622 0.28698106
30 -0.57387479 -0.39859622
31 -0.64829975 -0.57387479
32 -0.59548950 -0.64829975
33 -0.24945960 -0.59548950
34 -1.56012505 -0.24945960
35 -5.49631887 -1.56012505
36 -1.40469467 -5.49631887
37 -1.68057140 -1.40469467
38 1.31583634 -1.68057140
39 0.99993282 1.31583634
40 0.77191243 0.99993282
41 -2.05642271 0.77191243
42 2.32536970 -2.05642271
43 -0.49993250 2.32536970
44 -0.15041457 -0.49993250
45 -5.12424099 -0.15041457
46 -2.37485041 -5.12424099
47 0.58234917 -2.37485041
48 0.13763520 0.58234917
49 -1.79830962 0.13763520
50 -0.86504152 -1.79830962
51 -0.49840532 -0.86504152
52 -2.72796149 -0.49840532
53 0.41392016 -2.72796149
54 -2.40485952 0.41392016
55 0.88907485 -2.40485952
56 0.51805630 0.88907485
57 0.31460452 0.51805630
58 -0.15988590 0.31460452
59 2.49399063 -0.15988590
60 0.26529020 2.49399063
61 0.25360570 0.26529020
62 -0.62865695 0.25360570
63 -0.69779708 -0.62865695
64 0.57438607 -0.69779708
65 0.81424651 0.57438607
66 2.15772582 0.81424651
67 3.14653667 2.15772582
68 -3.22819581 3.14653667
69 -0.02515868 -3.22819581
70 -3.37146611 -0.02515868
71 0.25719306 -3.37146611
72 0.95855429 0.25719306
73 1.06496543 0.95855429
74 0.15238282 1.06496543
75 2.79112769 0.15238282
76 -0.48392136 2.79112769
77 1.26825588 -0.48392136
78 -1.67714377 1.26825588
79 -0.03563866 -1.67714377
80 1.10329527 -0.03563866
81 2.97039463 1.10329527
82 0.48025513 2.97039463
83 -0.32109639 0.48025513
84 -0.15837411 -0.32109639
85 1.98748851 -0.15837411
86 -0.53677842 1.98748851
87 0.41353586 -0.53677842
88 1.52002276 0.41353586
89 0.66876434 1.52002276
90 -1.42728711 0.66876434
91 -0.03587267 -1.42728711
92 1.16632512 -0.03587267
93 -0.70807908 1.16632512
94 -1.86489078 -0.70807908
95 1.44064329 -1.86489078
96 -0.11418902 1.44064329
97 2.13402516 -0.11418902
98 -0.35918906 2.13402516
99 -0.65571548 -0.35918906
100 -1.33904535 -0.65571548
101 0.99046642 -1.33904535
102 2.81271165 0.99046642
103 0.40787041 2.81271165
104 2.10394292 0.40787041
105 -2.62576828 2.10394292
106 1.18946514 -2.62576828
107 0.79335551 1.18946514
108 1.28669655 0.79335551
109 0.02964933 1.28669655
110 0.82018280 0.02964933
111 0.03557178 0.82018280
112 2.39039396 0.03557178
113 -1.95117357 2.39039396
114 -2.72770669 -1.95117357
115 1.17467082 -2.72770669
116 -0.65597003 1.17467082
117 0.46744229 -0.65597003
118 -1.62404474 0.46744229
119 1.02989715 -1.62404474
120 -1.49983375 1.02989715
121 0.81199701 -1.49983375
122 -3.18074635 0.81199701
123 -1.25696475 -3.18074635
124 -0.86273524 -1.25696475
125 -0.89281062 -0.86273524
126 -0.21685017 -0.89281062
127 0.49272367 -0.21685017
128 1.76836486 0.49272367
129 -3.36344459 1.76836486
130 2.18303049 -3.36344459
131 -2.40507152 2.18303049
132 1.54107210 -2.40507152
133 -1.53934881 1.54107210
134 -1.78687846 -1.53934881
135 -0.33037994 -1.78687846
136 1.00615125 -0.33037994
137 0.39010442 1.00615125
138 -1.75445350 0.39010442
139 -1.52988066 -1.75445350
140 -4.50201256 -1.52988066
141 2.54708301 -4.50201256
142 1.84562457 2.54708301
143 1.28591181 1.84562457
144 1.03485235 1.28591181
145 -3.85059248 1.03485235
146 2.09782781 -3.85059248
147 -2.18595883 2.09782781
148 1.09924695 -2.18595883
149 -0.07569416 1.09924695
150 -2.96908905 -0.07569416
151 -1.50697844 -2.96908905
152 2.71906103 -1.50697844
153 3.58238823 2.71906103
154 1.97707676 3.58238823
155 -1.69326495 1.97707676
156 0.11373020 -1.69326495
157 1.68949189 0.11373020
158 0.95560335 1.68949189
159 0.87295512 0.95560335
160 -0.43958399 0.87295512
161 0.23960219 -0.43958399
162 NA 0.23960219
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.13947442 -3.74518964
[2,] 2.42629202 -0.13947442
[3,] 2.61603031 2.42629202
[4,] -1.94337682 2.61603031
[5,] -2.56349568 -1.94337682
[6,] 3.90655848 -2.56349568
[7,] -2.16720701 3.90655848
[8,] -1.44912616 -2.16720701
[9,] 1.56548140 -1.44912616
[10,] 0.64935091 1.56548140
[11,] 0.22102027 0.64935091
[12,] 0.09397350 0.22102027
[13,] 0.65089995 0.09397350
[14,] -0.95673018 0.65089995
[15,] -0.52698893 -0.95673018
[16,] 0.14752978 -0.52698893
[17,] 3.15283305 0.14752978
[18,] 2.54889896 3.15283305
[19,] 0.31293325 2.54889896
[20,] 1.20216314 0.31293325
[21,] 0.64875714 1.20216314
[22,] 2.63045998 0.64875714
[23,] 1.81139084 2.63045998
[24,] 1.49047680 1.81139084
[25,] 0.38617374 1.49047680
[26,] 0.54589579 0.38617374
[27,] -1.57694324 0.54589579
[28,] 0.28698106 -1.57694324
[29,] -0.39859622 0.28698106
[30,] -0.57387479 -0.39859622
[31,] -0.64829975 -0.57387479
[32,] -0.59548950 -0.64829975
[33,] -0.24945960 -0.59548950
[34,] -1.56012505 -0.24945960
[35,] -5.49631887 -1.56012505
[36,] -1.40469467 -5.49631887
[37,] -1.68057140 -1.40469467
[38,] 1.31583634 -1.68057140
[39,] 0.99993282 1.31583634
[40,] 0.77191243 0.99993282
[41,] -2.05642271 0.77191243
[42,] 2.32536970 -2.05642271
[43,] -0.49993250 2.32536970
[44,] -0.15041457 -0.49993250
[45,] -5.12424099 -0.15041457
[46,] -2.37485041 -5.12424099
[47,] 0.58234917 -2.37485041
[48,] 0.13763520 0.58234917
[49,] -1.79830962 0.13763520
[50,] -0.86504152 -1.79830962
[51,] -0.49840532 -0.86504152
[52,] -2.72796149 -0.49840532
[53,] 0.41392016 -2.72796149
[54,] -2.40485952 0.41392016
[55,] 0.88907485 -2.40485952
[56,] 0.51805630 0.88907485
[57,] 0.31460452 0.51805630
[58,] -0.15988590 0.31460452
[59,] 2.49399063 -0.15988590
[60,] 0.26529020 2.49399063
[61,] 0.25360570 0.26529020
[62,] -0.62865695 0.25360570
[63,] -0.69779708 -0.62865695
[64,] 0.57438607 -0.69779708
[65,] 0.81424651 0.57438607
[66,] 2.15772582 0.81424651
[67,] 3.14653667 2.15772582
[68,] -3.22819581 3.14653667
[69,] -0.02515868 -3.22819581
[70,] -3.37146611 -0.02515868
[71,] 0.25719306 -3.37146611
[72,] 0.95855429 0.25719306
[73,] 1.06496543 0.95855429
[74,] 0.15238282 1.06496543
[75,] 2.79112769 0.15238282
[76,] -0.48392136 2.79112769
[77,] 1.26825588 -0.48392136
[78,] -1.67714377 1.26825588
[79,] -0.03563866 -1.67714377
[80,] 1.10329527 -0.03563866
[81,] 2.97039463 1.10329527
[82,] 0.48025513 2.97039463
[83,] -0.32109639 0.48025513
[84,] -0.15837411 -0.32109639
[85,] 1.98748851 -0.15837411
[86,] -0.53677842 1.98748851
[87,] 0.41353586 -0.53677842
[88,] 1.52002276 0.41353586
[89,] 0.66876434 1.52002276
[90,] -1.42728711 0.66876434
[91,] -0.03587267 -1.42728711
[92,] 1.16632512 -0.03587267
[93,] -0.70807908 1.16632512
[94,] -1.86489078 -0.70807908
[95,] 1.44064329 -1.86489078
[96,] -0.11418902 1.44064329
[97,] 2.13402516 -0.11418902
[98,] -0.35918906 2.13402516
[99,] -0.65571548 -0.35918906
[100,] -1.33904535 -0.65571548
[101,] 0.99046642 -1.33904535
[102,] 2.81271165 0.99046642
[103,] 0.40787041 2.81271165
[104,] 2.10394292 0.40787041
[105,] -2.62576828 2.10394292
[106,] 1.18946514 -2.62576828
[107,] 0.79335551 1.18946514
[108,] 1.28669655 0.79335551
[109,] 0.02964933 1.28669655
[110,] 0.82018280 0.02964933
[111,] 0.03557178 0.82018280
[112,] 2.39039396 0.03557178
[113,] -1.95117357 2.39039396
[114,] -2.72770669 -1.95117357
[115,] 1.17467082 -2.72770669
[116,] -0.65597003 1.17467082
[117,] 0.46744229 -0.65597003
[118,] -1.62404474 0.46744229
[119,] 1.02989715 -1.62404474
[120,] -1.49983375 1.02989715
[121,] 0.81199701 -1.49983375
[122,] -3.18074635 0.81199701
[123,] -1.25696475 -3.18074635
[124,] -0.86273524 -1.25696475
[125,] -0.89281062 -0.86273524
[126,] -0.21685017 -0.89281062
[127,] 0.49272367 -0.21685017
[128,] 1.76836486 0.49272367
[129,] -3.36344459 1.76836486
[130,] 2.18303049 -3.36344459
[131,] -2.40507152 2.18303049
[132,] 1.54107210 -2.40507152
[133,] -1.53934881 1.54107210
[134,] -1.78687846 -1.53934881
[135,] -0.33037994 -1.78687846
[136,] 1.00615125 -0.33037994
[137,] 0.39010442 1.00615125
[138,] -1.75445350 0.39010442
[139,] -1.52988066 -1.75445350
[140,] -4.50201256 -1.52988066
[141,] 2.54708301 -4.50201256
[142,] 1.84562457 2.54708301
[143,] 1.28591181 1.84562457
[144,] 1.03485235 1.28591181
[145,] -3.85059248 1.03485235
[146,] 2.09782781 -3.85059248
[147,] -2.18595883 2.09782781
[148,] 1.09924695 -2.18595883
[149,] -0.07569416 1.09924695
[150,] -2.96908905 -0.07569416
[151,] -1.50697844 -2.96908905
[152,] 2.71906103 -1.50697844
[153,] 3.58238823 2.71906103
[154,] 1.97707676 3.58238823
[155,] -1.69326495 1.97707676
[156,] 0.11373020 -1.69326495
[157,] 1.68949189 0.11373020
[158,] 0.95560335 1.68949189
[159,] 0.87295512 0.95560335
[160,] -0.43958399 0.87295512
[161,] 0.23960219 -0.43958399
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.13947442 -3.74518964
2 2.42629202 -0.13947442
3 2.61603031 2.42629202
4 -1.94337682 2.61603031
5 -2.56349568 -1.94337682
6 3.90655848 -2.56349568
7 -2.16720701 3.90655848
8 -1.44912616 -2.16720701
9 1.56548140 -1.44912616
10 0.64935091 1.56548140
11 0.22102027 0.64935091
12 0.09397350 0.22102027
13 0.65089995 0.09397350
14 -0.95673018 0.65089995
15 -0.52698893 -0.95673018
16 0.14752978 -0.52698893
17 3.15283305 0.14752978
18 2.54889896 3.15283305
19 0.31293325 2.54889896
20 1.20216314 0.31293325
21 0.64875714 1.20216314
22 2.63045998 0.64875714
23 1.81139084 2.63045998
24 1.49047680 1.81139084
25 0.38617374 1.49047680
26 0.54589579 0.38617374
27 -1.57694324 0.54589579
28 0.28698106 -1.57694324
29 -0.39859622 0.28698106
30 -0.57387479 -0.39859622
31 -0.64829975 -0.57387479
32 -0.59548950 -0.64829975
33 -0.24945960 -0.59548950
34 -1.56012505 -0.24945960
35 -5.49631887 -1.56012505
36 -1.40469467 -5.49631887
37 -1.68057140 -1.40469467
38 1.31583634 -1.68057140
39 0.99993282 1.31583634
40 0.77191243 0.99993282
41 -2.05642271 0.77191243
42 2.32536970 -2.05642271
43 -0.49993250 2.32536970
44 -0.15041457 -0.49993250
45 -5.12424099 -0.15041457
46 -2.37485041 -5.12424099
47 0.58234917 -2.37485041
48 0.13763520 0.58234917
49 -1.79830962 0.13763520
50 -0.86504152 -1.79830962
51 -0.49840532 -0.86504152
52 -2.72796149 -0.49840532
53 0.41392016 -2.72796149
54 -2.40485952 0.41392016
55 0.88907485 -2.40485952
56 0.51805630 0.88907485
57 0.31460452 0.51805630
58 -0.15988590 0.31460452
59 2.49399063 -0.15988590
60 0.26529020 2.49399063
61 0.25360570 0.26529020
62 -0.62865695 0.25360570
63 -0.69779708 -0.62865695
64 0.57438607 -0.69779708
65 0.81424651 0.57438607
66 2.15772582 0.81424651
67 3.14653667 2.15772582
68 -3.22819581 3.14653667
69 -0.02515868 -3.22819581
70 -3.37146611 -0.02515868
71 0.25719306 -3.37146611
72 0.95855429 0.25719306
73 1.06496543 0.95855429
74 0.15238282 1.06496543
75 2.79112769 0.15238282
76 -0.48392136 2.79112769
77 1.26825588 -0.48392136
78 -1.67714377 1.26825588
79 -0.03563866 -1.67714377
80 1.10329527 -0.03563866
81 2.97039463 1.10329527
82 0.48025513 2.97039463
83 -0.32109639 0.48025513
84 -0.15837411 -0.32109639
85 1.98748851 -0.15837411
86 -0.53677842 1.98748851
87 0.41353586 -0.53677842
88 1.52002276 0.41353586
89 0.66876434 1.52002276
90 -1.42728711 0.66876434
91 -0.03587267 -1.42728711
92 1.16632512 -0.03587267
93 -0.70807908 1.16632512
94 -1.86489078 -0.70807908
95 1.44064329 -1.86489078
96 -0.11418902 1.44064329
97 2.13402516 -0.11418902
98 -0.35918906 2.13402516
99 -0.65571548 -0.35918906
100 -1.33904535 -0.65571548
101 0.99046642 -1.33904535
102 2.81271165 0.99046642
103 0.40787041 2.81271165
104 2.10394292 0.40787041
105 -2.62576828 2.10394292
106 1.18946514 -2.62576828
107 0.79335551 1.18946514
108 1.28669655 0.79335551
109 0.02964933 1.28669655
110 0.82018280 0.02964933
111 0.03557178 0.82018280
112 2.39039396 0.03557178
113 -1.95117357 2.39039396
114 -2.72770669 -1.95117357
115 1.17467082 -2.72770669
116 -0.65597003 1.17467082
117 0.46744229 -0.65597003
118 -1.62404474 0.46744229
119 1.02989715 -1.62404474
120 -1.49983375 1.02989715
121 0.81199701 -1.49983375
122 -3.18074635 0.81199701
123 -1.25696475 -3.18074635
124 -0.86273524 -1.25696475
125 -0.89281062 -0.86273524
126 -0.21685017 -0.89281062
127 0.49272367 -0.21685017
128 1.76836486 0.49272367
129 -3.36344459 1.76836486
130 2.18303049 -3.36344459
131 -2.40507152 2.18303049
132 1.54107210 -2.40507152
133 -1.53934881 1.54107210
134 -1.78687846 -1.53934881
135 -0.33037994 -1.78687846
136 1.00615125 -0.33037994
137 0.39010442 1.00615125
138 -1.75445350 0.39010442
139 -1.52988066 -1.75445350
140 -4.50201256 -1.52988066
141 2.54708301 -4.50201256
142 1.84562457 2.54708301
143 1.28591181 1.84562457
144 1.03485235 1.28591181
145 -3.85059248 1.03485235
146 2.09782781 -3.85059248
147 -2.18595883 2.09782781
148 1.09924695 -2.18595883
149 -0.07569416 1.09924695
150 -2.96908905 -0.07569416
151 -1.50697844 -2.96908905
152 2.71906103 -1.50697844
153 3.58238823 2.71906103
154 1.97707676 3.58238823
155 -1.69326495 1.97707676
156 0.11373020 -1.69326495
157 1.68949189 0.11373020
158 0.95560335 1.68949189
159 0.87295512 0.95560335
160 -0.43958399 0.87295512
161 0.23960219 -0.43958399
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7p3x21352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ipld1352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/997qc1352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10hwj41352118659.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/113dq81352118660.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12azig1352118660.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1376az1352118660.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14udt71352118660.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/155tgf1352118660.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16539f1352118660.tab")
+ }
>
> try(system("convert tmp/1r4q71352118659.ps tmp/1r4q71352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dsam1352118659.ps tmp/2dsam1352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mgqz1352118659.ps tmp/3mgqz1352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/46ph61352118659.ps tmp/46ph61352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/5med11352118659.ps tmp/5med11352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tm8l1352118659.ps tmp/6tm8l1352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p3x21352118659.ps tmp/7p3x21352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ipld1352118659.ps tmp/8ipld1352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/997qc1352118659.ps tmp/997qc1352118659.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hwj41352118659.ps tmp/10hwj41352118659.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.185 0.932 10.114