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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Type 'q()' to quit R.
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+ ,0
+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,4
+ ,3
+ ,0
+ ,10
+ ,0
+ ,83
+ ,0
+ ,0
+ ,261
+ ,0
+ ,27
+ ,0
+ ,30
+ ,0
+ ,15
+ ,11
+ ,0
+ ,14
+ ,0
+ ,64
+ ,0
+ ,0
+ ,262
+ ,0
+ ,34
+ ,0
+ ,38
+ ,0
+ ,11
+ ,12
+ ,0
+ ,15
+ ,0
+ ,68
+ ,0
+ ,0
+ ,263
+ ,0
+ ,40
+ ,0
+ ,36
+ ,0
+ ,11
+ ,7
+ ,0
+ ,7
+ ,0
+ ,62
+ ,0
+ ,0
+ ,264
+ ,0
+ ,29
+ ,0
+ ,32
+ ,0
+ ,14
+ ,9
+ ,0
+ ,14
+ ,0
+ ,72
+ ,0)
+ ,dim=c(14
+ ,264)
+ ,dimnames=list(c('Pop'
+ ,'t'
+ ,'Pop_t'
+ ,'Connected'
+ ,'Connected_p'
+ ,'Separate'
+ ,'Separate_p'
+ ,'Learning'
+ ,'Software'
+ ,'Software_p'
+ ,'Happiness'
+ ,'Happiness_p'
+ ,'Belonging'
+ ,'Belonging_p')
+ ,1:264))
> y <- array(NA,dim=c(14,264),dimnames=list(c('Pop','t','Pop_t','Connected','Connected_p','Separate','Separate_p','Learning','Software','Software_p','Happiness','Happiness_p','Belonging','Belonging_p'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '8'
> 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 Pop t Pop_t Connected Connected_p Separate Separate_p Software
1 13 1 1 1 41 41 38 38 12
2 16 1 2 2 39 39 32 32 11
3 19 1 3 3 30 30 35 35 15
4 15 1 4 4 31 31 33 33 6
5 14 1 5 5 34 34 37 37 13
6 13 1 6 6 35 35 29 29 10
7 19 1 7 7 39 39 31 31 12
8 15 1 8 8 34 34 36 36 14
9 14 1 9 9 36 36 35 35 12
10 15 1 10 10 37 37 38 38 9
11 16 1 11 11 38 38 31 31 10
12 16 1 12 12 36 36 34 34 12
13 16 1 13 13 38 38 35 35 12
14 16 1 14 14 39 39 38 38 11
15 17 1 15 15 33 33 37 37 15
16 15 1 16 16 32 32 33 33 12
17 15 1 17 17 36 36 32 32 10
18 20 1 18 18 38 38 38 38 12
19 18 1 19 19 39 39 38 38 11
20 16 1 20 20 32 32 32 32 12
21 16 1 21 21 32 32 33 33 11
22 16 1 22 22 31 31 31 31 12
23 19 1 23 23 39 39 38 38 13
24 16 1 24 24 37 37 39 39 11
25 17 1 25 25 39 39 32 32 12
26 17 1 26 26 41 41 32 32 13
27 16 1 27 27 36 36 35 35 10
28 15 1 28 28 33 33 37 37 14
29 16 1 29 29 33 33 33 33 12
30 14 1 30 30 34 34 33 33 10
31 15 1 31 31 31 31 31 31 12
32 12 1 32 32 27 27 32 32 8
33 14 1 33 33 37 37 31 31 10
34 16 1 34 34 34 34 37 37 12
35 14 1 35 35 34 34 30 30 12
36 10 1 36 36 32 32 33 33 7
37 10 1 37 37 29 29 31 31 9
38 14 1 38 38 36 36 33 33 12
39 16 1 39 39 29 29 31 31 10
40 16 1 40 40 35 35 33 33 10
41 16 1 41 41 37 37 32 32 10
42 14 1 42 42 34 34 33 33 12
43 20 1 43 43 38 38 32 32 15
44 14 1 44 44 35 35 33 33 10
45 14 1 45 45 38 38 28 28 10
46 11 1 46 46 37 37 35 35 12
47 14 1 47 47 38 38 39 39 13
48 15 1 48 48 33 33 34 34 11
49 16 1 49 49 36 36 38 38 11
50 14 1 50 50 38 38 32 32 12
51 16 1 51 51 32 32 38 38 14
52 14 1 52 52 32 32 30 30 10
53 12 1 53 53 32 32 33 33 12
54 16 1 54 54 34 34 38 38 13
55 9 1 55 55 32 32 32 32 5
56 14 1 56 56 37 37 35 35 6
57 16 1 57 57 39 39 34 34 12
58 16 1 58 58 29 29 34 34 12
59 15 1 59 59 37 37 36 36 11
60 16 1 60 60 35 35 34 34 10
61 12 1 61 61 30 30 28 28 7
62 16 1 62 62 38 38 34 34 12
63 16 1 63 63 34 34 35 35 14
64 14 1 64 64 31 31 35 35 11
65 16 1 65 65 34 34 31 31 12
66 17 1 66 66 35 35 37 37 13
67 18 1 67 67 36 36 35 35 14
68 18 1 68 68 30 30 27 27 11
69 12 1 69 69 39 39 40 40 12
70 16 1 70 70 35 35 37 37 12
71 10 1 71 71 38 38 36 36 8
72 14 1 72 72 31 31 38 38 11
73 18 1 73 73 34 34 39 39 14
74 18 1 74 74 38 38 41 41 14
75 16 1 75 75 34 34 27 27 12
76 17 1 76 76 39 39 30 30 9
77 16 1 77 77 37 37 37 37 13
78 16 1 78 78 34 34 31 31 11
79 13 1 79 79 28 28 31 31 12
80 16 1 80 80 37 37 27 27 12
81 16 1 81 81 33 33 36 36 12
82 16 1 82 82 35 35 37 37 12
83 15 1 83 83 37 37 33 33 12
84 15 1 84 84 32 32 34 34 11
85 16 1 85 85 33 33 31 31 10
86 14 1 86 86 38 38 39 39 9
87 16 1 87 87 33 33 34 34 12
88 16 1 88 88 29 29 32 32 12
89 15 1 89 89 33 33 33 33 12
90 12 1 90 90 31 31 36 36 9
91 17 1 91 91 36 36 32 32 15
92 16 1 92 92 35 35 41 41 12
93 15 1 93 93 32 32 28 28 12
94 13 1 94 94 29 29 30 30 12
95 16 1 95 95 39 39 36 36 10
96 16 1 96 96 37 37 35 35 13
97 16 1 97 97 35 35 31 31 9
98 16 1 98 98 37 37 34 34 12
99 14 1 99 99 32 32 36 36 10
100 16 1 100 100 38 38 36 36 14
101 16 1 101 101 37 37 35 35 11
102 20 1 102 102 36 36 37 37 15
103 15 1 103 103 32 32 28 28 11
104 16 1 104 104 33 33 39 39 11
105 13 1 105 105 40 40 32 32 12
106 17 1 106 106 38 38 35 35 12
107 16 1 107 107 41 41 39 39 12
108 16 1 108 108 36 36 35 35 11
109 12 1 109 109 43 43 42 42 7
110 16 1 110 110 30 30 34 34 12
111 16 1 111 111 31 31 33 33 14
112 17 1 112 112 32 32 41 41 11
113 13 1 113 113 32 32 33 33 11
114 12 1 114 114 37 37 34 34 10
115 18 1 115 115 37 37 32 32 13
116 14 1 116 116 33 33 40 40 13
117 14 1 117 117 34 34 40 40 8
118 13 1 118 118 33 33 35 35 11
119 16 1 119 119 38 38 36 36 12
120 13 1 120 120 33 33 37 37 11
121 16 1 121 121 31 31 27 27 13
122 13 1 122 122 38 38 39 39 12
123 16 1 123 123 37 37 38 38 14
124 15 1 124 124 36 36 31 31 13
125 16 1 125 125 31 31 33 33 15
126 15 1 126 126 39 39 32 32 10
127 17 1 127 127 44 44 39 39 11
128 15 1 128 128 33 33 36 36 9
129 12 1 129 129 35 35 33 33 11
130 16 1 130 130 32 32 33 33 10
131 10 1 131 131 28 28 32 32 11
132 16 1 132 132 40 40 37 37 8
133 12 1 133 133 27 27 30 30 11
134 14 1 134 134 37 37 38 38 12
135 15 1 135 135 32 32 29 29 12
136 13 1 136 136 28 28 22 22 9
137 15 1 137 137 34 34 35 35 11
138 11 1 138 138 30 30 35 35 10
139 12 1 139 139 35 35 34 34 8
140 11 1 140 140 31 31 35 35 9
141 16 1 141 141 32 32 34 34 8
142 15 1 142 142 30 30 37 37 9
143 17 1 143 143 30 30 35 35 15
144 16 1 144 144 31 31 23 23 11
145 10 1 145 145 40 40 31 31 8
146 18 1 146 146 32 32 27 27 13
147 13 1 147 147 36 36 36 36 12
148 16 1 148 148 32 32 31 31 12
149 13 1 149 149 35 35 32 32 9
150 10 1 150 150 38 38 39 39 7
151 15 1 151 151 42 42 37 37 13
152 16 1 152 152 34 34 38 38 9
153 16 1 153 153 35 35 39 39 6
154 14 1 154 154 38 38 34 34 8
155 10 1 155 155 33 33 31 31 8
156 17 1 156 156 36 36 32 32 15
157 13 1 157 157 32 32 37 37 6
158 15 1 158 158 33 33 36 36 9
159 16 1 159 159 34 34 32 32 11
160 12 1 160 160 32 32 38 38 8
161 13 1 161 161 34 34 36 36 8
162 13 0 162 0 27 0 26 0 10
163 12 0 163 0 31 0 26 0 8
164 17 0 164 0 38 0 33 0 14
165 15 0 165 0 34 0 39 0 10
166 10 0 166 0 24 0 30 0 8
167 14 0 167 0 30 0 33 0 11
168 11 0 168 0 26 0 25 0 12
169 13 0 169 0 34 0 38 0 12
170 16 0 170 0 27 0 37 0 12
171 12 0 171 0 37 0 31 0 5
172 16 0 172 0 36 0 37 0 12
173 12 0 173 0 41 0 35 0 10
174 9 0 174 0 29 0 25 0 7
175 12 0 175 0 36 0 28 0 12
176 15 0 176 0 32 0 35 0 11
177 12 0 177 0 37 0 33 0 8
178 12 0 178 0 30 0 30 0 9
179 14 0 179 0 31 0 31 0 10
180 12 0 180 0 38 0 37 0 9
181 16 0 181 0 36 0 36 0 12
182 11 0 182 0 35 0 30 0 6
183 19 0 183 0 31 0 36 0 15
184 15 0 184 0 38 0 32 0 12
185 8 0 185 0 22 0 28 0 12
186 16 0 186 0 32 0 36 0 12
187 17 0 187 0 36 0 34 0 11
188 12 0 188 0 39 0 31 0 7
189 11 0 189 0 28 0 28 0 7
190 11 0 190 0 32 0 36 0 5
191 14 0 191 0 32 0 36 0 12
192 16 0 192 0 38 0 40 0 12
193 12 0 193 0 32 0 33 0 3
194 16 0 194 0 35 0 37 0 11
195 13 0 195 0 32 0 32 0 10
196 15 0 196 0 37 0 38 0 12
197 16 0 197 0 34 0 31 0 9
198 16 0 198 0 33 0 37 0 12
199 14 0 199 0 33 0 33 0 9
200 16 0 200 0 26 0 32 0 12
201 16 0 201 0 30 0 30 0 12
202 14 0 202 0 24 0 30 0 10
203 11 0 203 0 34 0 31 0 9
204 12 0 204 0 34 0 32 0 12
205 15 0 205 0 33 0 34 0 8
206 15 0 206 0 34 0 36 0 11
207 16 0 207 0 35 0 37 0 11
208 16 0 208 0 35 0 36 0 12
209 11 0 209 0 36 0 33 0 10
210 15 0 210 0 34 0 33 0 10
211 12 0 211 0 34 0 33 0 12
212 12 0 212 0 41 0 44 0 12
213 15 0 213 0 32 0 39 0 11
214 15 0 214 0 30 0 32 0 8
215 16 0 215 0 35 0 35 0 12
216 14 0 216 0 28 0 25 0 10
217 17 0 217 0 33 0 35 0 11
218 14 0 218 0 39 0 34 0 10
219 13 0 219 0 36 0 35 0 8
220 15 0 220 0 36 0 39 0 12
221 13 0 221 0 35 0 33 0 12
222 14 0 222 0 38 0 36 0 10
223 15 0 223 0 33 0 32 0 12
224 12 0 224 0 31 0 32 0 9
225 13 0 225 0 34 0 36 0 9
226 8 0 226 0 32 0 36 0 6
227 14 0 227 0 31 0 32 0 10
228 14 0 228 0 33 0 34 0 9
229 11 0 229 0 34 0 33 0 9
230 12 0 230 0 34 0 35 0 9
231 13 0 231 0 34 0 30 0 6
232 10 0 232 0 33 0 38 0 10
233 16 0 233 0 32 0 34 0 6
234 18 0 234 0 41 0 33 0 14
235 13 0 235 0 34 0 32 0 10
236 11 0 236 0 36 0 31 0 10
237 4 0 237 0 37 0 30 0 6
238 13 0 238 0 36 0 27 0 12
239 16 0 239 0 29 0 31 0 12
240 10 0 240 0 37 0 30 0 7
241 12 0 241 0 27 0 32 0 8
242 12 0 242 0 35 0 35 0 11
243 10 0 243 0 28 0 28 0 3
244 13 0 244 0 35 0 33 0 6
245 15 0 245 0 37 0 31 0 10
246 12 0 246 0 29 0 35 0 8
247 14 0 247 0 32 0 35 0 9
248 10 0 248 0 36 0 32 0 9
249 12 0 249 0 19 0 21 0 8
250 12 0 250 0 21 0 20 0 9
251 11 0 251 0 31 0 34 0 7
252 10 0 252 0 33 0 32 0 7
253 12 0 253 0 36 0 34 0 6
254 16 0 254 0 33 0 32 0 9
255 12 0 255 0 37 0 33 0 10
256 14 0 256 0 34 0 33 0 11
257 16 0 257 0 35 0 37 0 12
258 14 0 258 0 31 0 32 0 8
259 13 0 259 0 37 0 34 0 11
260 4 0 260 0 35 0 30 0 3
261 15 0 261 0 27 0 30 0 11
262 11 0 262 0 34 0 38 0 12
263 11 0 263 0 40 0 36 0 7
264 14 0 264 0 29 0 32 0 9
Software_p Happiness Happiness_p Belonging Belonging_p
1 12 14 14 53 53
2 11 18 18 83 83
3 15 11 11 66 66
4 6 12 12 67 67
5 13 16 16 76 76
6 10 18 18 78 78
7 12 14 14 53 53
8 14 14 14 80 80
9 12 15 15 74 74
10 9 15 15 76 76
11 10 17 17 79 79
12 12 19 19 54 54
13 12 10 10 67 67
14 11 16 16 54 54
15 15 18 18 87 87
16 12 14 14 58 58
17 10 14 14 75 75
18 12 17 17 88 88
19 11 14 14 64 64
20 12 16 16 57 57
21 11 18 18 66 66
22 12 11 11 68 68
23 13 14 14 54 54
24 11 12 12 56 56
25 12 17 17 86 86
26 13 9 9 80 80
27 10 16 16 76 76
28 14 14 14 69 69
29 12 15 15 78 78
30 10 11 11 67 67
31 12 16 16 80 80
32 8 13 13 54 54
33 10 17 17 71 71
34 12 15 15 84 84
35 12 14 14 74 74
36 7 16 16 71 71
37 9 9 9 63 63
38 12 15 15 71 71
39 10 17 17 76 76
40 10 13 13 69 69
41 10 15 15 74 74
42 12 16 16 75 75
43 15 16 16 54 54
44 10 12 12 52 52
45 10 15 15 69 69
46 12 11 11 68 68
47 13 15 15 65 65
48 11 15 15 75 75
49 11 17 17 74 74
50 12 13 13 75 75
51 14 16 16 72 72
52 10 14 14 67 67
53 12 11 11 63 63
54 13 12 12 62 62
55 5 12 12 63 63
56 6 15 15 76 76
57 12 16 16 74 74
58 12 15 15 67 67
59 11 12 12 73 73
60 10 12 12 70 70
61 7 8 8 53 53
62 12 13 13 77 77
63 14 11 11 80 80
64 11 14 14 52 52
65 12 15 15 54 54
66 13 10 10 80 80
67 14 11 11 66 66
68 11 12 12 73 73
69 12 15 15 63 63
70 12 15 15 69 69
71 8 14 14 67 67
72 11 16 16 54 54
73 14 15 15 81 81
74 14 15 15 69 69
75 12 13 13 84 84
76 9 12 12 80 80
77 13 17 17 70 70
78 11 13 13 69 69
79 12 15 15 77 77
80 12 13 13 54 54
81 12 15 15 79 79
82 12 15 15 71 71
83 12 16 16 73 73
84 11 15 15 72 72
85 10 14 14 77 77
86 9 15 15 75 75
87 12 14 14 69 69
88 12 13 13 54 54
89 12 7 7 70 70
90 9 17 17 73 73
91 15 13 13 54 54
92 12 15 15 77 77
93 12 14 14 82 82
94 12 13 13 80 80
95 10 16 16 80 80
96 13 12 12 69 69
97 9 14 14 78 78
98 12 17 17 81 81
99 10 15 15 76 76
100 14 17 17 76 76
101 11 12 12 73 73
102 15 16 16 85 85
103 11 11 11 66 66
104 11 15 15 79 79
105 12 9 9 68 68
106 12 16 16 76 76
107 12 15 15 71 71
108 11 10 10 54 54
109 7 10 10 46 46
110 12 15 15 85 85
111 14 11 11 74 74
112 11 13 13 88 88
113 11 14 14 38 38
114 10 18 18 76 76
115 13 16 16 86 86
116 13 14 14 54 54
117 8 14 14 67 67
118 11 14 14 69 69
119 12 14 14 90 90
120 11 12 12 54 54
121 13 14 14 76 76
122 12 15 15 89 89
123 14 15 15 76 76
124 13 15 15 73 73
125 15 13 13 79 79
126 10 17 17 90 90
127 11 17 17 74 74
128 9 19 19 81 81
129 11 15 15 72 72
130 10 13 13 71 71
131 11 9 9 66 66
132 8 15 15 77 77
133 11 15 15 65 65
134 12 15 15 74 74
135 12 16 16 85 85
136 9 11 11 54 54
137 11 14 14 63 63
138 10 11 11 54 54
139 8 15 15 64 64
140 9 13 13 69 69
141 8 15 15 54 54
142 9 16 16 84 84
143 15 14 14 86 86
144 11 15 15 77 77
145 8 16 16 89 89
146 13 16 16 76 76
147 12 11 11 60 60
148 12 12 12 75 75
149 9 9 9 73 73
150 7 16 16 85 85
151 13 13 13 79 79
152 9 16 16 71 71
153 6 12 12 72 72
154 8 9 9 69 69
155 8 13 13 78 78
156 15 13 13 54 54
157 6 14 14 69 69
158 9 19 19 81 81
159 11 13 13 84 84
160 8 12 12 84 84
161 8 13 13 69 69
162 0 10 0 66 0
163 0 14 0 81 0
164 0 16 0 82 0
165 0 10 0 72 0
166 0 11 0 54 0
167 0 14 0 78 0
168 0 12 0 74 0
169 0 9 0 82 0
170 0 9 0 73 0
171 0 11 0 55 0
172 0 16 0 72 0
173 0 9 0 78 0
174 0 13 0 59 0
175 0 16 0 72 0
176 0 13 0 78 0
177 0 9 0 68 0
178 0 12 0 69 0
179 0 16 0 67 0
180 0 11 0 74 0
181 0 14 0 54 0
182 0 13 0 67 0
183 0 15 0 70 0
184 0 14 0 80 0
185 0 16 0 89 0
186 0 13 0 76 0
187 0 14 0 74 0
188 0 15 0 87 0
189 0 13 0 54 0
190 0 11 0 61 0
191 0 11 0 38 0
192 0 14 0 75 0
193 0 15 0 69 0
194 0 11 0 62 0
195 0 15 0 72 0
196 0 12 0 70 0
197 0 14 0 79 0
198 0 14 0 87 0
199 0 8 0 62 0
200 0 13 0 77 0
201 0 9 0 69 0
202 0 15 0 69 0
203 0 17 0 75 0
204 0 13 0 54 0
205 0 15 0 72 0
206 0 15 0 74 0
207 0 14 0 85 0
208 0 16 0 52 0
209 0 13 0 70 0
210 0 16 0 84 0
211 0 9 0 64 0
212 0 16 0 84 0
213 0 11 0 87 0
214 0 10 0 79 0
215 0 11 0 67 0
216 0 15 0 65 0
217 0 17 0 85 0
218 0 14 0 83 0
219 0 8 0 61 0
220 0 15 0 82 0
221 0 11 0 76 0
222 0 16 0 58 0
223 0 10 0 72 0
224 0 15 0 72 0
225 0 9 0 38 0
226 0 16 0 78 0
227 0 19 0 54 0
228 0 12 0 63 0
229 0 8 0 66 0
230 0 11 0 70 0
231 0 14 0 71 0
232 0 9 0 67 0
233 0 15 0 58 0
234 0 13 0 72 0
235 0 16 0 72 0
236 0 11 0 70 0
237 0 12 0 76 0
238 0 13 0 50 0
239 0 10 0 72 0
240 0 11 0 72 0
241 0 12 0 88 0
242 0 8 0 53 0
243 0 12 0 58 0
244 0 12 0 66 0
245 0 15 0 82 0
246 0 11 0 69 0
247 0 13 0 68 0
248 0 14 0 44 0
249 0 10 0 56 0
250 0 12 0 53 0
251 0 15 0 70 0
252 0 13 0 78 0
253 0 13 0 71 0
254 0 13 0 72 0
255 0 12 0 68 0
256 0 12 0 67 0
257 0 9 0 75 0
258 0 9 0 62 0
259 0 15 0 67 0
260 0 10 0 83 0
261 0 14 0 64 0
262 0 15 0 68 0
263 0 7 0 62 0
264 0 14 0 72 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop t Pop_t Connected Connected_p
4.763e+00 -4.856e-01 -4.379e-03 -5.105e-05 -5.587e-02 1.452e-01
Separate Separate_p Software Software_p Happiness Happiness_p
1.566e-01 -1.709e-01 5.624e-01 -3.352e-02 1.121e-01 -2.796e-02
Belonging Belonging_p
-1.126e-02 3.066e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6472 -1.0256 0.2525 1.1537 4.3193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.763e+00 2.615e+00 1.822 0.0697 .
Pop -4.856e-01 3.374e+00 -0.144 0.8857
t -4.379e-03 6.347e-03 -0.690 0.4909
Pop_t -5.105e-05 7.154e-03 -0.007 0.9943
Connected -5.587e-02 5.229e-02 -1.068 0.2863
Connected_p 1.452e-01 7.005e-02 2.073 0.0392 *
Separate 1.566e-01 5.982e-02 2.617 0.0094 **
Separate_p -1.709e-01 7.451e-02 -2.294 0.0226 *
Software 5.624e-01 8.226e-02 6.837 6.18e-11 ***
Software_p -3.352e-02 1.093e-01 -0.307 0.7593
Happiness 1.121e-01 7.506e-02 1.493 0.1367
Happiness_p -2.796e-02 1.010e-01 -0.277 0.7822
Belonging -1.126e-02 1.836e-02 -0.614 0.5401
Belonging_p 3.066e-02 2.393e-02 1.281 0.2014
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.847 on 250 degrees of freedom
Multiple R-squared: 0.4625, Adjusted R-squared: 0.4346
F-statistic: 16.55 on 13 and 250 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,] 0.9779550876 0.0440898249 0.022044912
[2,] 0.9950186868 0.0099626264 0.004981313
[3,] 0.9910434663 0.0179130674 0.008956534
[4,] 0.9830046814 0.0339906372 0.016995319
[5,] 0.9692392514 0.0615214972 0.030760749
[6,] 0.9564157543 0.0871684914 0.043584246
[7,] 0.9414194236 0.1171611528 0.058580576
[8,] 0.9250375681 0.1499248638 0.074962432
[9,] 0.8932777971 0.2134444058 0.106722203
[10,] 0.8662789262 0.2674421476 0.133721074
[11,] 0.8239714168 0.3520571665 0.176028583
[12,] 0.8405239322 0.3189521355 0.159476068
[13,] 0.7947577696 0.4104844608 0.205242230
[14,] 0.7933903426 0.4132193149 0.206609657
[15,] 0.7504450742 0.4991098516 0.249554926
[16,] 0.7383233146 0.5233533708 0.261676685
[17,] 0.7154342195 0.5691315610 0.284565780
[18,] 0.6567400579 0.6865198842 0.343259942
[19,] 0.6390602109 0.7218795781 0.360939789
[20,] 0.7235918239 0.5528163523 0.276408176
[21,] 0.7838673048 0.4322653905 0.216132695
[22,] 0.7630969177 0.4738061646 0.236903082
[23,] 0.7943783150 0.4112433701 0.205621685
[24,] 0.7799661069 0.4400677863 0.220033893
[25,] 0.7498971713 0.5002056575 0.250102829
[26,] 0.7296126912 0.5407746175 0.270387309
[27,] 0.7554862619 0.4890274763 0.244513738
[28,] 0.7150456589 0.5699086823 0.284954341
[29,] 0.6790422819 0.6419154361 0.320957718
[30,] 0.8463515402 0.3072969197 0.153648460
[31,] 0.8477800979 0.3044398042 0.152219902
[32,] 0.8207314833 0.3585370334 0.179268517
[33,] 0.7969672131 0.4060655737 0.203032787
[34,] 0.7773837440 0.4452325121 0.222616256
[35,] 0.7397057963 0.5205884074 0.260294204
[36,] 0.7005411210 0.5989177579 0.299458879
[37,] 0.7168838912 0.5662322175 0.283116109
[38,] 0.6864670880 0.6270658240 0.313532912
[39,] 0.6809407427 0.6381185145 0.319059257
[40,] 0.6753136629 0.6493726743 0.324686337
[41,] 0.6331751838 0.7336496323 0.366824816
[42,] 0.6239232076 0.7521535849 0.376076792
[43,] 0.5831467885 0.8337064230 0.416853211
[44,] 0.5971911952 0.8056176097 0.402808805
[45,] 0.5625028810 0.8749942380 0.437497119
[46,] 0.5218046152 0.9563907697 0.478195385
[47,] 0.4800252620 0.9600505239 0.519974738
[48,] 0.4354061079 0.8708122158 0.564593892
[49,] 0.4016044313 0.8032088626 0.598395569
[50,] 0.3886563087 0.7773126173 0.611343691
[51,] 0.3842509068 0.7685018136 0.615749093
[52,] 0.5177200561 0.9645598878 0.482279944
[53,] 0.6306170265 0.7387659470 0.369382974
[54,] 0.5954160079 0.8091679842 0.404583992
[55,] 0.6723711585 0.6552576831 0.327628842
[56,] 0.6348543777 0.7302912445 0.365145622
[57,] 0.6246708959 0.7506582083 0.375329104
[58,] 0.6066483836 0.7867032329 0.393351616
[59,] 0.5664999471 0.8670001057 0.433500053
[60,] 0.6075920350 0.7848159301 0.392407965
[61,] 0.5683178933 0.8633642135 0.431682107
[62,] 0.5415581342 0.9168837316 0.458441866
[63,] 0.5492965079 0.9014069842 0.450703492
[64,] 0.5105676574 0.9788646851 0.489432343
[65,] 0.4749516850 0.9499033700 0.525048315
[66,] 0.4383894058 0.8767788117 0.561610594
[67,] 0.4074304583 0.8148609166 0.592569542
[68,] 0.3702547028 0.7405094057 0.629745297
[69,] 0.3605181471 0.7210362942 0.639481853
[70,] 0.3240333843 0.6480667686 0.675966616
[71,] 0.2938873789 0.5877747577 0.706112621
[72,] 0.2777302406 0.5554604813 0.722269759
[73,] 0.2464300516 0.4928601032 0.753569948
[74,] 0.2387403191 0.4774806382 0.761259681
[75,] 0.2108160589 0.4216321177 0.789183941
[76,] 0.1867657320 0.3735314641 0.813234268
[77,] 0.1640247807 0.3280495614 0.835975219
[78,] 0.1703222637 0.3406445273 0.829677736
[79,] 0.1533052194 0.3066104388 0.846694781
[80,] 0.1315470358 0.2630940716 0.868452964
[81,] 0.1341529592 0.2683059184 0.865847041
[82,] 0.1141902996 0.2283805992 0.885809700
[83,] 0.0969282591 0.1938565182 0.903071741
[84,] 0.0866880831 0.1733761662 0.913311917
[85,] 0.0769268647 0.1538537294 0.923073135
[86,] 0.0892578246 0.1785156492 0.910742175
[87,] 0.0767647712 0.1535295425 0.923235229
[88,] 0.0693115660 0.1386231321 0.930688434
[89,] 0.0813770686 0.1627541371 0.918622931
[90,] 0.0727359957 0.1454719915 0.927264004
[91,] 0.0609281946 0.1218563892 0.939071805
[92,] 0.0615427562 0.1230855123 0.938457244
[93,] 0.0519477423 0.1038954845 0.948052258
[94,] 0.0441347828 0.0882695657 0.955865217
[95,] 0.0369647950 0.0739295899 0.963035205
[96,] 0.0454516806 0.0909033612 0.954548319
[97,] 0.0388671560 0.0777343119 0.961132844
[98,] 0.0509529949 0.1019059897 0.949047005
[99,] 0.0498435718 0.0996871436 0.950156428
[100,] 0.0442789110 0.0885578219 0.955721089
[101,] 0.0403689939 0.0807379879 0.959631006
[102,] 0.0369512287 0.0739024574 0.963048771
[103,] 0.0325674223 0.0651348445 0.967432578
[104,] 0.0275450005 0.0550900011 0.972454999
[105,] 0.0232870248 0.0465740495 0.976712975
[106,] 0.0288827609 0.0577655218 0.971117239
[107,] 0.0237392429 0.0474784858 0.976260757
[108,] 0.0200542516 0.0401085031 0.979945748
[109,] 0.0166138209 0.0332276418 0.983386179
[110,] 0.0133266653 0.0266533307 0.986673335
[111,] 0.0124835919 0.0249671838 0.987516408
[112,] 0.0107994529 0.0215989058 0.989200547
[113,] 0.0132460083 0.0264920166 0.986753992
[114,] 0.0166703452 0.0333406903 0.983329655
[115,] 0.0249947969 0.0499895937 0.975005203
[116,] 0.0375281033 0.0750562065 0.962471897
[117,] 0.0398526373 0.0797052746 0.960147363
[118,] 0.0349080697 0.0698161394 0.965091930
[119,] 0.0285660994 0.0571321988 0.971433901
[120,] 0.0237453746 0.0474907493 0.976254625
[121,] 0.0200038681 0.0400077362 0.979996132
[122,] 0.0241396729 0.0482793459 0.975860327
[123,] 0.0205801450 0.0411602901 0.979419855
[124,] 0.0265135963 0.0530271926 0.973486404
[125,] 0.0359695406 0.0719390811 0.964030459
[126,] 0.0322941685 0.0645883369 0.967705832
[127,] 0.0267285224 0.0534570448 0.973271478
[128,] 0.0234775669 0.0469551338 0.976522433
[129,] 0.0351128413 0.0702256826 0.964887159
[130,] 0.0381457465 0.0762914931 0.961854253
[131,] 0.0453973783 0.0907947566 0.954602622
[132,] 0.0393477893 0.0786955787 0.960652211
[133,] 0.0321126205 0.0642252410 0.967887379
[134,] 0.0426887771 0.0853775541 0.957311223
[135,] 0.0502760726 0.1005521452 0.949723927
[136,] 0.0518655507 0.1037311014 0.948134449
[137,] 0.0760346521 0.1520693042 0.923965348
[138,] 0.0804755301 0.1609510601 0.919524470
[139,] 0.0814825954 0.1629651908 0.918517405
[140,] 0.0689051158 0.1378102315 0.931094884
[141,] 0.0604351108 0.1208702217 0.939564889
[142,] 0.0515058456 0.1030116912 0.948494154
[143,] 0.0441864062 0.0883728125 0.955813594
[144,] 0.0366373858 0.0732747716 0.963362614
[145,] 0.0297494802 0.0594989604 0.970250520
[146,] 0.0240461981 0.0480923963 0.975953802
[147,] 0.0193476025 0.0386952050 0.980652397
[148,] 0.0161820082 0.0323640165 0.983817992
[149,] 0.0130707974 0.0261415947 0.986929203
[150,] 0.0130553702 0.0261107404 0.986944630
[151,] 0.0102469789 0.0204939579 0.989753021
[152,] 0.0110148111 0.0220296222 0.988985189
[153,] 0.0102006347 0.0204012695 0.989799365
[154,] 0.0084874753 0.0169749507 0.991512525
[155,] 0.0100754549 0.0201509098 0.989924545
[156,] 0.0078614686 0.0157229372 0.992138531
[157,] 0.0064944868 0.0129889736 0.993505513
[158,] 0.0062439150 0.0124878301 0.993756085
[159,] 0.0061809060 0.0123618119 0.993819094
[160,] 0.0052354214 0.0104708428 0.994764579
[161,] 0.0040625883 0.0081251765 0.995937412
[162,] 0.0033102813 0.0066205626 0.996689719
[163,] 0.0025394159 0.0050788317 0.997460584
[164,] 0.0021410090 0.0042820179 0.997858991
[165,] 0.0016491752 0.0032983504 0.998350825
[166,] 0.0012161704 0.0024323408 0.998783830
[167,] 0.0012056890 0.0024113780 0.998794311
[168,] 0.0009084384 0.0018168768 0.999091562
[169,] 0.0223564822 0.0447129643 0.977643518
[170,] 0.0191915934 0.0383831869 0.980808407
[171,] 0.0234753547 0.0469507095 0.976524645
[172,] 0.0190876552 0.0381753104 0.980912345
[173,] 0.0164597524 0.0329195049 0.983540248
[174,] 0.0128207743 0.0256415486 0.987179226
[175,] 0.0121100815 0.0242201629 0.987889919
[176,] 0.0093923088 0.0187846176 0.990607691
[177,] 0.0090127555 0.0180255111 0.990987244
[178,] 0.0080417586 0.0160835173 0.991958241
[179,] 0.0065425618 0.0130851237 0.993457438
[180,] 0.0048679181 0.0097358362 0.995132082
[181,] 0.0082731572 0.0165463144 0.991726843
[182,] 0.0062987443 0.0125974887 0.993701256
[183,] 0.0055315068 0.0110630136 0.994468493
[184,] 0.0045778442 0.0091556884 0.995422156
[185,] 0.0041319659 0.0082639318 0.995868034
[186,] 0.0032049491 0.0064098983 0.996795051
[187,] 0.0037386982 0.0074773964 0.996261302
[188,] 0.0056263061 0.0112526123 0.994373694
[189,] 0.0055560435 0.0111120870 0.994443957
[190,] 0.0040429329 0.0080858657 0.995957067
[191,] 0.0033417275 0.0066834549 0.996658273
[192,] 0.0024156836 0.0048313672 0.997584316
[193,] 0.0029859659 0.0059719319 0.997014034
[194,] 0.0023098582 0.0046197164 0.997690142
[195,] 0.0028635714 0.0057271429 0.997136429
[196,] 0.0076971964 0.0153943928 0.992302804
[197,] 0.0055198344 0.0110396687 0.994480166
[198,] 0.0069946811 0.0139893623 0.993005319
[199,] 0.0055164660 0.0110329320 0.994483534
[200,] 0.0040649859 0.0081299717 0.995935014
[201,] 0.0042841674 0.0085683348 0.995715833
[202,] 0.0033806263 0.0067612527 0.996619374
[203,] 0.0028611428 0.0057222856 0.997138857
[204,] 0.0019860363 0.0039720726 0.998013964
[205,] 0.0014877383 0.0029754766 0.998512262
[206,] 0.0009940238 0.0019880476 0.999005976
[207,] 0.0006876275 0.0013752550 0.999312372
[208,] 0.0004816784 0.0009633568 0.999518322
[209,] 0.0003022357 0.0006044714 0.999697764
[210,] 0.0008276246 0.0016552492 0.999172375
[211,] 0.0006039376 0.0012078752 0.999396062
[212,] 0.0003994450 0.0007988900 0.999600555
[213,] 0.0002674079 0.0005348157 0.999732592
[214,] 0.0001681696 0.0003363392 0.999831830
[215,] 0.0001772598 0.0003545195 0.999822740
[216,] 0.0007347750 0.0014695501 0.999265225
[217,] 0.0035808283 0.0071616566 0.996419172
[218,] 0.0059240613 0.0118481227 0.994075939
[219,] 0.0037440294 0.0074880588 0.996255971
[220,] 0.0026153482 0.0052306963 0.997384652
[221,] 0.0378183871 0.0756367742 0.962181613
[222,] 0.0259026736 0.0518053472 0.974097326
[223,] 0.0175292997 0.0350585995 0.982470700
[224,] 0.0119394593 0.0238789186 0.988060541
[225,] 0.0097694534 0.0195389069 0.990230547
[226,] 0.0140459992 0.0280919984 0.985954001
[227,] 0.0094755602 0.0189511203 0.990524440
[228,] 0.0091940105 0.0183880210 0.990805990
[229,] 0.0059719212 0.0119438423 0.994028079
[230,] 0.0038279235 0.0076558470 0.996172076
[231,] 0.0014509314 0.0029018627 0.998549069
> postscript(file="/var/wessaorg/rcomp/tmp/1if281355998349.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/2vkga1355998350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3sg9c1355998350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4m44i1355998350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ss1m1355998350.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 264
Frequency = 1
1 2 3 4 5 6
-2.94405652 -0.23634364 2.41840913 2.96091553 -2.45830173 -2.27837547
7 8 9 10 11 12
3.16090401 -1.89754530 -1.99621882 0.50960237 0.56908728 0.05418189
13 14 15 16 17 18
0.39910239 0.63348287 -0.76389065 -0.24202184 0.11862945 3.46819745
19 20 21 22 23 24
2.62991296 0.61253817 0.81737642 0.90363961 2.78388768 1.16849128
25 26 27 28 29 30
0.36263748 0.44876089 1.01831856 -1.49190812 0.25421678 -0.22323832
31 32 33 34 35 36
-0.70977433 -0.46161131 -1.08893182 0.12798020 -1.68987213 -2.92953706
37 38 39 40 41 42
-2.99945973 -1.83822283 1.55554125 1.52469686 1.07088387 -1.80347413
43 44 45 46 47 48
2.64993706 -0.04376642 -0.96111628 -4.46884293 -2.30354075 -0.06024397
49 50 51 52 53 54
0.58462095 -1.88746353 -0.51272293 -0.24238915 -2.92273713 0.38108528
55 56 57 58 59 60
-2.31040470 1.25693329 -0.15007950 0.96780079 -0.04915181 1.69235475
61 62 63 64 65 66
0.31025467 0.15557997 -0.41587602 -0.26612703 0.76114926 1.14968126
67 68 69 70 71 72
1.69465094 3.48722119 -3.71346260 0.48903118 -3.55066527 -0.39469303
73 74 75 76 77 78
1.32992507 1.23832433 0.33450817 2.68337520 -0.37513344 1.22489568
79 80 81 82 83 84
-2.08674904 0.67041758 0.50820332 0.50340687 -0.85111377 0.24679025
85 86 87 88 89 90
1.63484540 -0.20931915 0.78417237 1.49239277 0.34808160 -1.73852773
91 92 93 94 95 96
0.29364334 0.48867998 -0.43801783 -2.01394642 0.98826657 0.12032496
97 98 99 100 101 102
2.01874501 -0.00959025 -0.20681865 -1.02213814 1.12258978 2.56048861
103 104 105 106 107 108
0.69777389 1.18194875 -2.35030447 1.13191158 0.10668689 1.77967514
109 110 111 112 113 114
-0.47052614 0.75972350 0.15255360 2.32908997 -0.89554344 -2.86815302
115 116 117 118 119 120
1.49535210 -1.23927317 1.06789869 -1.53530706 0.10054106 -1.03864103
121 122 123 124 125 126
0.34857742 -2.90788271 -0.63399206 -1.05351289 -0.57946024 -0.20978229
127 128 129 130 131 132
1.22966094 0.92773373 -2.83625057 2.15271484 -3.59518686 2.27715081
133 134 135 136 137 138
-2.01090753 -1.48877882 -0.46401347 0.40582684 0.57587833 -2.10653251
139 140 141 142 143 144
-1.03592234 -2.11732185 3.43495560 1.46631611 0.39844050 1.34732538
145 146 147 148 149 150
-4.06810124 2.20175687 -1.76252897 1.15263671 -0.21901522 -3.14613975
151 152 153 154 155 156
-1.33218078 2.41964915 4.25264076 1.17015363 -2.93262173 0.58161732
157 158 159 160 161 162
1.39972911 1.06064479 1.30717393 -0.75302993 0.25082152 0.38262346
163 164 165 166 167 168
0.45583908 1.16826797 0.81903862 -2.51622846 -0.39934988 -2.74917001
169 170 171 172 173 174
-1.90690143 0.76161218 1.77372016 0.47739879 -0.94894180 -2.02449726
175 176 177 178 179 180
-2.10036261 0.55073794 0.17033804 -0.63397067 0.23652924 -1.10585660
181 182 183 184 185 186
0.69480001 0.21536788 1.80526654 0.73876546 -6.64724493 0.85307783
187 188 189 190 191 192
2.82182193 0.74728837 -0.54068753 -0.13766648 -1.32885605 0.46495334
193 194 195 196 197 198
2.11170184 1.52797911 -0.62571527 -0.09242916 3.40461687 0.81674478
199 200 201 202 203 204
1.52535131 1.21671539 2.11588919 0.23734925 -1.95037732 -2.57786955
205 206 207 208 209 210
2.28553441 0.36808808 1.50772912 0.51050285 -2.29588180 1.41821926
211 212 213 214 215 216
-2.14286234 -4.02890965 0.41201535 3.10968490 1.42702960 1.25991144
217 218 219 220 221 222
2.41670176 0.78890644 1.01850430 -0.40082856 -1.13219803 -0.06828727
223 224 225 226 227 228
0.98841629 -0.99222065 -0.15699415 -3.91127551 -0.19246329 1.05875418
229 230 231 232 233 234
-1.24235186 -0.84227568 2.30706435 -3.73108684 4.31933540 2.86601581
235 236 237 238 239 240
-0.45088169 -1.64036013 -6.21859918 -0.57945512 1.99158386 -0.70079960
241 242 243 244 245 246
-0.06245829 -1.71382323 1.10236498 2.11799616 2.14177827 -0.50044302
247 248 249 250 251 252
0.87376691 -2.81105989 1.11161869 0.56400318 -1.08491473 -1.34142078
253 254 255 256 257 258
1.00094983 3.47503963 -0.94901789 0.31413622 1.61207453 2.27885186
259 260 261 262 263 264
-0.99791124 -4.23954017 1.15672931 -4.32974033 -0.03620686 1.18329126
> postscript(file="/var/wessaorg/rcomp/tmp/6ohz51355998350.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.94405652 NA
1 -0.23634364 -2.94405652
2 2.41840913 -0.23634364
3 2.96091553 2.41840913
4 -2.45830173 2.96091553
5 -2.27837547 -2.45830173
6 3.16090401 -2.27837547
7 -1.89754530 3.16090401
8 -1.99621882 -1.89754530
9 0.50960237 -1.99621882
10 0.56908728 0.50960237
11 0.05418189 0.56908728
12 0.39910239 0.05418189
13 0.63348287 0.39910239
14 -0.76389065 0.63348287
15 -0.24202184 -0.76389065
16 0.11862945 -0.24202184
17 3.46819745 0.11862945
18 2.62991296 3.46819745
19 0.61253817 2.62991296
20 0.81737642 0.61253817
21 0.90363961 0.81737642
22 2.78388768 0.90363961
23 1.16849128 2.78388768
24 0.36263748 1.16849128
25 0.44876089 0.36263748
26 1.01831856 0.44876089
27 -1.49190812 1.01831856
28 0.25421678 -1.49190812
29 -0.22323832 0.25421678
30 -0.70977433 -0.22323832
31 -0.46161131 -0.70977433
32 -1.08893182 -0.46161131
33 0.12798020 -1.08893182
34 -1.68987213 0.12798020
35 -2.92953706 -1.68987213
36 -2.99945973 -2.92953706
37 -1.83822283 -2.99945973
38 1.55554125 -1.83822283
39 1.52469686 1.55554125
40 1.07088387 1.52469686
41 -1.80347413 1.07088387
42 2.64993706 -1.80347413
43 -0.04376642 2.64993706
44 -0.96111628 -0.04376642
45 -4.46884293 -0.96111628
46 -2.30354075 -4.46884293
47 -0.06024397 -2.30354075
48 0.58462095 -0.06024397
49 -1.88746353 0.58462095
50 -0.51272293 -1.88746353
51 -0.24238915 -0.51272293
52 -2.92273713 -0.24238915
53 0.38108528 -2.92273713
54 -2.31040470 0.38108528
55 1.25693329 -2.31040470
56 -0.15007950 1.25693329
57 0.96780079 -0.15007950
58 -0.04915181 0.96780079
59 1.69235475 -0.04915181
60 0.31025467 1.69235475
61 0.15557997 0.31025467
62 -0.41587602 0.15557997
63 -0.26612703 -0.41587602
64 0.76114926 -0.26612703
65 1.14968126 0.76114926
66 1.69465094 1.14968126
67 3.48722119 1.69465094
68 -3.71346260 3.48722119
69 0.48903118 -3.71346260
70 -3.55066527 0.48903118
71 -0.39469303 -3.55066527
72 1.32992507 -0.39469303
73 1.23832433 1.32992507
74 0.33450817 1.23832433
75 2.68337520 0.33450817
76 -0.37513344 2.68337520
77 1.22489568 -0.37513344
78 -2.08674904 1.22489568
79 0.67041758 -2.08674904
80 0.50820332 0.67041758
81 0.50340687 0.50820332
82 -0.85111377 0.50340687
83 0.24679025 -0.85111377
84 1.63484540 0.24679025
85 -0.20931915 1.63484540
86 0.78417237 -0.20931915
87 1.49239277 0.78417237
88 0.34808160 1.49239277
89 -1.73852773 0.34808160
90 0.29364334 -1.73852773
91 0.48867998 0.29364334
92 -0.43801783 0.48867998
93 -2.01394642 -0.43801783
94 0.98826657 -2.01394642
95 0.12032496 0.98826657
96 2.01874501 0.12032496
97 -0.00959025 2.01874501
98 -0.20681865 -0.00959025
99 -1.02213814 -0.20681865
100 1.12258978 -1.02213814
101 2.56048861 1.12258978
102 0.69777389 2.56048861
103 1.18194875 0.69777389
104 -2.35030447 1.18194875
105 1.13191158 -2.35030447
106 0.10668689 1.13191158
107 1.77967514 0.10668689
108 -0.47052614 1.77967514
109 0.75972350 -0.47052614
110 0.15255360 0.75972350
111 2.32908997 0.15255360
112 -0.89554344 2.32908997
113 -2.86815302 -0.89554344
114 1.49535210 -2.86815302
115 -1.23927317 1.49535210
116 1.06789869 -1.23927317
117 -1.53530706 1.06789869
118 0.10054106 -1.53530706
119 -1.03864103 0.10054106
120 0.34857742 -1.03864103
121 -2.90788271 0.34857742
122 -0.63399206 -2.90788271
123 -1.05351289 -0.63399206
124 -0.57946024 -1.05351289
125 -0.20978229 -0.57946024
126 1.22966094 -0.20978229
127 0.92773373 1.22966094
128 -2.83625057 0.92773373
129 2.15271484 -2.83625057
130 -3.59518686 2.15271484
131 2.27715081 -3.59518686
132 -2.01090753 2.27715081
133 -1.48877882 -2.01090753
134 -0.46401347 -1.48877882
135 0.40582684 -0.46401347
136 0.57587833 0.40582684
137 -2.10653251 0.57587833
138 -1.03592234 -2.10653251
139 -2.11732185 -1.03592234
140 3.43495560 -2.11732185
141 1.46631611 3.43495560
142 0.39844050 1.46631611
143 1.34732538 0.39844050
144 -4.06810124 1.34732538
145 2.20175687 -4.06810124
146 -1.76252897 2.20175687
147 1.15263671 -1.76252897
148 -0.21901522 1.15263671
149 -3.14613975 -0.21901522
150 -1.33218078 -3.14613975
151 2.41964915 -1.33218078
152 4.25264076 2.41964915
153 1.17015363 4.25264076
154 -2.93262173 1.17015363
155 0.58161732 -2.93262173
156 1.39972911 0.58161732
157 1.06064479 1.39972911
158 1.30717393 1.06064479
159 -0.75302993 1.30717393
160 0.25082152 -0.75302993
161 0.38262346 0.25082152
162 0.45583908 0.38262346
163 1.16826797 0.45583908
164 0.81903862 1.16826797
165 -2.51622846 0.81903862
166 -0.39934988 -2.51622846
167 -2.74917001 -0.39934988
168 -1.90690143 -2.74917001
169 0.76161218 -1.90690143
170 1.77372016 0.76161218
171 0.47739879 1.77372016
172 -0.94894180 0.47739879
173 -2.02449726 -0.94894180
174 -2.10036261 -2.02449726
175 0.55073794 -2.10036261
176 0.17033804 0.55073794
177 -0.63397067 0.17033804
178 0.23652924 -0.63397067
179 -1.10585660 0.23652924
180 0.69480001 -1.10585660
181 0.21536788 0.69480001
182 1.80526654 0.21536788
183 0.73876546 1.80526654
184 -6.64724493 0.73876546
185 0.85307783 -6.64724493
186 2.82182193 0.85307783
187 0.74728837 2.82182193
188 -0.54068753 0.74728837
189 -0.13766648 -0.54068753
190 -1.32885605 -0.13766648
191 0.46495334 -1.32885605
192 2.11170184 0.46495334
193 1.52797911 2.11170184
194 -0.62571527 1.52797911
195 -0.09242916 -0.62571527
196 3.40461687 -0.09242916
197 0.81674478 3.40461687
198 1.52535131 0.81674478
199 1.21671539 1.52535131
200 2.11588919 1.21671539
201 0.23734925 2.11588919
202 -1.95037732 0.23734925
203 -2.57786955 -1.95037732
204 2.28553441 -2.57786955
205 0.36808808 2.28553441
206 1.50772912 0.36808808
207 0.51050285 1.50772912
208 -2.29588180 0.51050285
209 1.41821926 -2.29588180
210 -2.14286234 1.41821926
211 -4.02890965 -2.14286234
212 0.41201535 -4.02890965
213 3.10968490 0.41201535
214 1.42702960 3.10968490
215 1.25991144 1.42702960
216 2.41670176 1.25991144
217 0.78890644 2.41670176
218 1.01850430 0.78890644
219 -0.40082856 1.01850430
220 -1.13219803 -0.40082856
221 -0.06828727 -1.13219803
222 0.98841629 -0.06828727
223 -0.99222065 0.98841629
224 -0.15699415 -0.99222065
225 -3.91127551 -0.15699415
226 -0.19246329 -3.91127551
227 1.05875418 -0.19246329
228 -1.24235186 1.05875418
229 -0.84227568 -1.24235186
230 2.30706435 -0.84227568
231 -3.73108684 2.30706435
232 4.31933540 -3.73108684
233 2.86601581 4.31933540
234 -0.45088169 2.86601581
235 -1.64036013 -0.45088169
236 -6.21859918 -1.64036013
237 -0.57945512 -6.21859918
238 1.99158386 -0.57945512
239 -0.70079960 1.99158386
240 -0.06245829 -0.70079960
241 -1.71382323 -0.06245829
242 1.10236498 -1.71382323
243 2.11799616 1.10236498
244 2.14177827 2.11799616
245 -0.50044302 2.14177827
246 0.87376691 -0.50044302
247 -2.81105989 0.87376691
248 1.11161869 -2.81105989
249 0.56400318 1.11161869
250 -1.08491473 0.56400318
251 -1.34142078 -1.08491473
252 1.00094983 -1.34142078
253 3.47503963 1.00094983
254 -0.94901789 3.47503963
255 0.31413622 -0.94901789
256 1.61207453 0.31413622
257 2.27885186 1.61207453
258 -0.99791124 2.27885186
259 -4.23954017 -0.99791124
260 1.15672931 -4.23954017
261 -4.32974033 1.15672931
262 -0.03620686 -4.32974033
263 1.18329126 -0.03620686
264 NA 1.18329126
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.23634364 -2.94405652
[2,] 2.41840913 -0.23634364
[3,] 2.96091553 2.41840913
[4,] -2.45830173 2.96091553
[5,] -2.27837547 -2.45830173
[6,] 3.16090401 -2.27837547
[7,] -1.89754530 3.16090401
[8,] -1.99621882 -1.89754530
[9,] 0.50960237 -1.99621882
[10,] 0.56908728 0.50960237
[11,] 0.05418189 0.56908728
[12,] 0.39910239 0.05418189
[13,] 0.63348287 0.39910239
[14,] -0.76389065 0.63348287
[15,] -0.24202184 -0.76389065
[16,] 0.11862945 -0.24202184
[17,] 3.46819745 0.11862945
[18,] 2.62991296 3.46819745
[19,] 0.61253817 2.62991296
[20,] 0.81737642 0.61253817
[21,] 0.90363961 0.81737642
[22,] 2.78388768 0.90363961
[23,] 1.16849128 2.78388768
[24,] 0.36263748 1.16849128
[25,] 0.44876089 0.36263748
[26,] 1.01831856 0.44876089
[27,] -1.49190812 1.01831856
[28,] 0.25421678 -1.49190812
[29,] -0.22323832 0.25421678
[30,] -0.70977433 -0.22323832
[31,] -0.46161131 -0.70977433
[32,] -1.08893182 -0.46161131
[33,] 0.12798020 -1.08893182
[34,] -1.68987213 0.12798020
[35,] -2.92953706 -1.68987213
[36,] -2.99945973 -2.92953706
[37,] -1.83822283 -2.99945973
[38,] 1.55554125 -1.83822283
[39,] 1.52469686 1.55554125
[40,] 1.07088387 1.52469686
[41,] -1.80347413 1.07088387
[42,] 2.64993706 -1.80347413
[43,] -0.04376642 2.64993706
[44,] -0.96111628 -0.04376642
[45,] -4.46884293 -0.96111628
[46,] -2.30354075 -4.46884293
[47,] -0.06024397 -2.30354075
[48,] 0.58462095 -0.06024397
[49,] -1.88746353 0.58462095
[50,] -0.51272293 -1.88746353
[51,] -0.24238915 -0.51272293
[52,] -2.92273713 -0.24238915
[53,] 0.38108528 -2.92273713
[54,] -2.31040470 0.38108528
[55,] 1.25693329 -2.31040470
[56,] -0.15007950 1.25693329
[57,] 0.96780079 -0.15007950
[58,] -0.04915181 0.96780079
[59,] 1.69235475 -0.04915181
[60,] 0.31025467 1.69235475
[61,] 0.15557997 0.31025467
[62,] -0.41587602 0.15557997
[63,] -0.26612703 -0.41587602
[64,] 0.76114926 -0.26612703
[65,] 1.14968126 0.76114926
[66,] 1.69465094 1.14968126
[67,] 3.48722119 1.69465094
[68,] -3.71346260 3.48722119
[69,] 0.48903118 -3.71346260
[70,] -3.55066527 0.48903118
[71,] -0.39469303 -3.55066527
[72,] 1.32992507 -0.39469303
[73,] 1.23832433 1.32992507
[74,] 0.33450817 1.23832433
[75,] 2.68337520 0.33450817
[76,] -0.37513344 2.68337520
[77,] 1.22489568 -0.37513344
[78,] -2.08674904 1.22489568
[79,] 0.67041758 -2.08674904
[80,] 0.50820332 0.67041758
[81,] 0.50340687 0.50820332
[82,] -0.85111377 0.50340687
[83,] 0.24679025 -0.85111377
[84,] 1.63484540 0.24679025
[85,] -0.20931915 1.63484540
[86,] 0.78417237 -0.20931915
[87,] 1.49239277 0.78417237
[88,] 0.34808160 1.49239277
[89,] -1.73852773 0.34808160
[90,] 0.29364334 -1.73852773
[91,] 0.48867998 0.29364334
[92,] -0.43801783 0.48867998
[93,] -2.01394642 -0.43801783
[94,] 0.98826657 -2.01394642
[95,] 0.12032496 0.98826657
[96,] 2.01874501 0.12032496
[97,] -0.00959025 2.01874501
[98,] -0.20681865 -0.00959025
[99,] -1.02213814 -0.20681865
[100,] 1.12258978 -1.02213814
[101,] 2.56048861 1.12258978
[102,] 0.69777389 2.56048861
[103,] 1.18194875 0.69777389
[104,] -2.35030447 1.18194875
[105,] 1.13191158 -2.35030447
[106,] 0.10668689 1.13191158
[107,] 1.77967514 0.10668689
[108,] -0.47052614 1.77967514
[109,] 0.75972350 -0.47052614
[110,] 0.15255360 0.75972350
[111,] 2.32908997 0.15255360
[112,] -0.89554344 2.32908997
[113,] -2.86815302 -0.89554344
[114,] 1.49535210 -2.86815302
[115,] -1.23927317 1.49535210
[116,] 1.06789869 -1.23927317
[117,] -1.53530706 1.06789869
[118,] 0.10054106 -1.53530706
[119,] -1.03864103 0.10054106
[120,] 0.34857742 -1.03864103
[121,] -2.90788271 0.34857742
[122,] -0.63399206 -2.90788271
[123,] -1.05351289 -0.63399206
[124,] -0.57946024 -1.05351289
[125,] -0.20978229 -0.57946024
[126,] 1.22966094 -0.20978229
[127,] 0.92773373 1.22966094
[128,] -2.83625057 0.92773373
[129,] 2.15271484 -2.83625057
[130,] -3.59518686 2.15271484
[131,] 2.27715081 -3.59518686
[132,] -2.01090753 2.27715081
[133,] -1.48877882 -2.01090753
[134,] -0.46401347 -1.48877882
[135,] 0.40582684 -0.46401347
[136,] 0.57587833 0.40582684
[137,] -2.10653251 0.57587833
[138,] -1.03592234 -2.10653251
[139,] -2.11732185 -1.03592234
[140,] 3.43495560 -2.11732185
[141,] 1.46631611 3.43495560
[142,] 0.39844050 1.46631611
[143,] 1.34732538 0.39844050
[144,] -4.06810124 1.34732538
[145,] 2.20175687 -4.06810124
[146,] -1.76252897 2.20175687
[147,] 1.15263671 -1.76252897
[148,] -0.21901522 1.15263671
[149,] -3.14613975 -0.21901522
[150,] -1.33218078 -3.14613975
[151,] 2.41964915 -1.33218078
[152,] 4.25264076 2.41964915
[153,] 1.17015363 4.25264076
[154,] -2.93262173 1.17015363
[155,] 0.58161732 -2.93262173
[156,] 1.39972911 0.58161732
[157,] 1.06064479 1.39972911
[158,] 1.30717393 1.06064479
[159,] -0.75302993 1.30717393
[160,] 0.25082152 -0.75302993
[161,] 0.38262346 0.25082152
[162,] 0.45583908 0.38262346
[163,] 1.16826797 0.45583908
[164,] 0.81903862 1.16826797
[165,] -2.51622846 0.81903862
[166,] -0.39934988 -2.51622846
[167,] -2.74917001 -0.39934988
[168,] -1.90690143 -2.74917001
[169,] 0.76161218 -1.90690143
[170,] 1.77372016 0.76161218
[171,] 0.47739879 1.77372016
[172,] -0.94894180 0.47739879
[173,] -2.02449726 -0.94894180
[174,] -2.10036261 -2.02449726
[175,] 0.55073794 -2.10036261
[176,] 0.17033804 0.55073794
[177,] -0.63397067 0.17033804
[178,] 0.23652924 -0.63397067
[179,] -1.10585660 0.23652924
[180,] 0.69480001 -1.10585660
[181,] 0.21536788 0.69480001
[182,] 1.80526654 0.21536788
[183,] 0.73876546 1.80526654
[184,] -6.64724493 0.73876546
[185,] 0.85307783 -6.64724493
[186,] 2.82182193 0.85307783
[187,] 0.74728837 2.82182193
[188,] -0.54068753 0.74728837
[189,] -0.13766648 -0.54068753
[190,] -1.32885605 -0.13766648
[191,] 0.46495334 -1.32885605
[192,] 2.11170184 0.46495334
[193,] 1.52797911 2.11170184
[194,] -0.62571527 1.52797911
[195,] -0.09242916 -0.62571527
[196,] 3.40461687 -0.09242916
[197,] 0.81674478 3.40461687
[198,] 1.52535131 0.81674478
[199,] 1.21671539 1.52535131
[200,] 2.11588919 1.21671539
[201,] 0.23734925 2.11588919
[202,] -1.95037732 0.23734925
[203,] -2.57786955 -1.95037732
[204,] 2.28553441 -2.57786955
[205,] 0.36808808 2.28553441
[206,] 1.50772912 0.36808808
[207,] 0.51050285 1.50772912
[208,] -2.29588180 0.51050285
[209,] 1.41821926 -2.29588180
[210,] -2.14286234 1.41821926
[211,] -4.02890965 -2.14286234
[212,] 0.41201535 -4.02890965
[213,] 3.10968490 0.41201535
[214,] 1.42702960 3.10968490
[215,] 1.25991144 1.42702960
[216,] 2.41670176 1.25991144
[217,] 0.78890644 2.41670176
[218,] 1.01850430 0.78890644
[219,] -0.40082856 1.01850430
[220,] -1.13219803 -0.40082856
[221,] -0.06828727 -1.13219803
[222,] 0.98841629 -0.06828727
[223,] -0.99222065 0.98841629
[224,] -0.15699415 -0.99222065
[225,] -3.91127551 -0.15699415
[226,] -0.19246329 -3.91127551
[227,] 1.05875418 -0.19246329
[228,] -1.24235186 1.05875418
[229,] -0.84227568 -1.24235186
[230,] 2.30706435 -0.84227568
[231,] -3.73108684 2.30706435
[232,] 4.31933540 -3.73108684
[233,] 2.86601581 4.31933540
[234,] -0.45088169 2.86601581
[235,] -1.64036013 -0.45088169
[236,] -6.21859918 -1.64036013
[237,] -0.57945512 -6.21859918
[238,] 1.99158386 -0.57945512
[239,] -0.70079960 1.99158386
[240,] -0.06245829 -0.70079960
[241,] -1.71382323 -0.06245829
[242,] 1.10236498 -1.71382323
[243,] 2.11799616 1.10236498
[244,] 2.14177827 2.11799616
[245,] -0.50044302 2.14177827
[246,] 0.87376691 -0.50044302
[247,] -2.81105989 0.87376691
[248,] 1.11161869 -2.81105989
[249,] 0.56400318 1.11161869
[250,] -1.08491473 0.56400318
[251,] -1.34142078 -1.08491473
[252,] 1.00094983 -1.34142078
[253,] 3.47503963 1.00094983
[254,] -0.94901789 3.47503963
[255,] 0.31413622 -0.94901789
[256,] 1.61207453 0.31413622
[257,] 2.27885186 1.61207453
[258,] -0.99791124 2.27885186
[259,] -4.23954017 -0.99791124
[260,] 1.15672931 -4.23954017
[261,] -4.32974033 1.15672931
[262,] -0.03620686 -4.32974033
[263,] 1.18329126 -0.03620686
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.23634364 -2.94405652
2 2.41840913 -0.23634364
3 2.96091553 2.41840913
4 -2.45830173 2.96091553
5 -2.27837547 -2.45830173
6 3.16090401 -2.27837547
7 -1.89754530 3.16090401
8 -1.99621882 -1.89754530
9 0.50960237 -1.99621882
10 0.56908728 0.50960237
11 0.05418189 0.56908728
12 0.39910239 0.05418189
13 0.63348287 0.39910239
14 -0.76389065 0.63348287
15 -0.24202184 -0.76389065
16 0.11862945 -0.24202184
17 3.46819745 0.11862945
18 2.62991296 3.46819745
19 0.61253817 2.62991296
20 0.81737642 0.61253817
21 0.90363961 0.81737642
22 2.78388768 0.90363961
23 1.16849128 2.78388768
24 0.36263748 1.16849128
25 0.44876089 0.36263748
26 1.01831856 0.44876089
27 -1.49190812 1.01831856
28 0.25421678 -1.49190812
29 -0.22323832 0.25421678
30 -0.70977433 -0.22323832
31 -0.46161131 -0.70977433
32 -1.08893182 -0.46161131
33 0.12798020 -1.08893182
34 -1.68987213 0.12798020
35 -2.92953706 -1.68987213
36 -2.99945973 -2.92953706
37 -1.83822283 -2.99945973
38 1.55554125 -1.83822283
39 1.52469686 1.55554125
40 1.07088387 1.52469686
41 -1.80347413 1.07088387
42 2.64993706 -1.80347413
43 -0.04376642 2.64993706
44 -0.96111628 -0.04376642
45 -4.46884293 -0.96111628
46 -2.30354075 -4.46884293
47 -0.06024397 -2.30354075
48 0.58462095 -0.06024397
49 -1.88746353 0.58462095
50 -0.51272293 -1.88746353
51 -0.24238915 -0.51272293
52 -2.92273713 -0.24238915
53 0.38108528 -2.92273713
54 -2.31040470 0.38108528
55 1.25693329 -2.31040470
56 -0.15007950 1.25693329
57 0.96780079 -0.15007950
58 -0.04915181 0.96780079
59 1.69235475 -0.04915181
60 0.31025467 1.69235475
61 0.15557997 0.31025467
62 -0.41587602 0.15557997
63 -0.26612703 -0.41587602
64 0.76114926 -0.26612703
65 1.14968126 0.76114926
66 1.69465094 1.14968126
67 3.48722119 1.69465094
68 -3.71346260 3.48722119
69 0.48903118 -3.71346260
70 -3.55066527 0.48903118
71 -0.39469303 -3.55066527
72 1.32992507 -0.39469303
73 1.23832433 1.32992507
74 0.33450817 1.23832433
75 2.68337520 0.33450817
76 -0.37513344 2.68337520
77 1.22489568 -0.37513344
78 -2.08674904 1.22489568
79 0.67041758 -2.08674904
80 0.50820332 0.67041758
81 0.50340687 0.50820332
82 -0.85111377 0.50340687
83 0.24679025 -0.85111377
84 1.63484540 0.24679025
85 -0.20931915 1.63484540
86 0.78417237 -0.20931915
87 1.49239277 0.78417237
88 0.34808160 1.49239277
89 -1.73852773 0.34808160
90 0.29364334 -1.73852773
91 0.48867998 0.29364334
92 -0.43801783 0.48867998
93 -2.01394642 -0.43801783
94 0.98826657 -2.01394642
95 0.12032496 0.98826657
96 2.01874501 0.12032496
97 -0.00959025 2.01874501
98 -0.20681865 -0.00959025
99 -1.02213814 -0.20681865
100 1.12258978 -1.02213814
101 2.56048861 1.12258978
102 0.69777389 2.56048861
103 1.18194875 0.69777389
104 -2.35030447 1.18194875
105 1.13191158 -2.35030447
106 0.10668689 1.13191158
107 1.77967514 0.10668689
108 -0.47052614 1.77967514
109 0.75972350 -0.47052614
110 0.15255360 0.75972350
111 2.32908997 0.15255360
112 -0.89554344 2.32908997
113 -2.86815302 -0.89554344
114 1.49535210 -2.86815302
115 -1.23927317 1.49535210
116 1.06789869 -1.23927317
117 -1.53530706 1.06789869
118 0.10054106 -1.53530706
119 -1.03864103 0.10054106
120 0.34857742 -1.03864103
121 -2.90788271 0.34857742
122 -0.63399206 -2.90788271
123 -1.05351289 -0.63399206
124 -0.57946024 -1.05351289
125 -0.20978229 -0.57946024
126 1.22966094 -0.20978229
127 0.92773373 1.22966094
128 -2.83625057 0.92773373
129 2.15271484 -2.83625057
130 -3.59518686 2.15271484
131 2.27715081 -3.59518686
132 -2.01090753 2.27715081
133 -1.48877882 -2.01090753
134 -0.46401347 -1.48877882
135 0.40582684 -0.46401347
136 0.57587833 0.40582684
137 -2.10653251 0.57587833
138 -1.03592234 -2.10653251
139 -2.11732185 -1.03592234
140 3.43495560 -2.11732185
141 1.46631611 3.43495560
142 0.39844050 1.46631611
143 1.34732538 0.39844050
144 -4.06810124 1.34732538
145 2.20175687 -4.06810124
146 -1.76252897 2.20175687
147 1.15263671 -1.76252897
148 -0.21901522 1.15263671
149 -3.14613975 -0.21901522
150 -1.33218078 -3.14613975
151 2.41964915 -1.33218078
152 4.25264076 2.41964915
153 1.17015363 4.25264076
154 -2.93262173 1.17015363
155 0.58161732 -2.93262173
156 1.39972911 0.58161732
157 1.06064479 1.39972911
158 1.30717393 1.06064479
159 -0.75302993 1.30717393
160 0.25082152 -0.75302993
161 0.38262346 0.25082152
162 0.45583908 0.38262346
163 1.16826797 0.45583908
164 0.81903862 1.16826797
165 -2.51622846 0.81903862
166 -0.39934988 -2.51622846
167 -2.74917001 -0.39934988
168 -1.90690143 -2.74917001
169 0.76161218 -1.90690143
170 1.77372016 0.76161218
171 0.47739879 1.77372016
172 -0.94894180 0.47739879
173 -2.02449726 -0.94894180
174 -2.10036261 -2.02449726
175 0.55073794 -2.10036261
176 0.17033804 0.55073794
177 -0.63397067 0.17033804
178 0.23652924 -0.63397067
179 -1.10585660 0.23652924
180 0.69480001 -1.10585660
181 0.21536788 0.69480001
182 1.80526654 0.21536788
183 0.73876546 1.80526654
184 -6.64724493 0.73876546
185 0.85307783 -6.64724493
186 2.82182193 0.85307783
187 0.74728837 2.82182193
188 -0.54068753 0.74728837
189 -0.13766648 -0.54068753
190 -1.32885605 -0.13766648
191 0.46495334 -1.32885605
192 2.11170184 0.46495334
193 1.52797911 2.11170184
194 -0.62571527 1.52797911
195 -0.09242916 -0.62571527
196 3.40461687 -0.09242916
197 0.81674478 3.40461687
198 1.52535131 0.81674478
199 1.21671539 1.52535131
200 2.11588919 1.21671539
201 0.23734925 2.11588919
202 -1.95037732 0.23734925
203 -2.57786955 -1.95037732
204 2.28553441 -2.57786955
205 0.36808808 2.28553441
206 1.50772912 0.36808808
207 0.51050285 1.50772912
208 -2.29588180 0.51050285
209 1.41821926 -2.29588180
210 -2.14286234 1.41821926
211 -4.02890965 -2.14286234
212 0.41201535 -4.02890965
213 3.10968490 0.41201535
214 1.42702960 3.10968490
215 1.25991144 1.42702960
216 2.41670176 1.25991144
217 0.78890644 2.41670176
218 1.01850430 0.78890644
219 -0.40082856 1.01850430
220 -1.13219803 -0.40082856
221 -0.06828727 -1.13219803
222 0.98841629 -0.06828727
223 -0.99222065 0.98841629
224 -0.15699415 -0.99222065
225 -3.91127551 -0.15699415
226 -0.19246329 -3.91127551
227 1.05875418 -0.19246329
228 -1.24235186 1.05875418
229 -0.84227568 -1.24235186
230 2.30706435 -0.84227568
231 -3.73108684 2.30706435
232 4.31933540 -3.73108684
233 2.86601581 4.31933540
234 -0.45088169 2.86601581
235 -1.64036013 -0.45088169
236 -6.21859918 -1.64036013
237 -0.57945512 -6.21859918
238 1.99158386 -0.57945512
239 -0.70079960 1.99158386
240 -0.06245829 -0.70079960
241 -1.71382323 -0.06245829
242 1.10236498 -1.71382323
243 2.11799616 1.10236498
244 2.14177827 2.11799616
245 -0.50044302 2.14177827
246 0.87376691 -0.50044302
247 -2.81105989 0.87376691
248 1.11161869 -2.81105989
249 0.56400318 1.11161869
250 -1.08491473 0.56400318
251 -1.34142078 -1.08491473
252 1.00094983 -1.34142078
253 3.47503963 1.00094983
254 -0.94901789 3.47503963
255 0.31413622 -0.94901789
256 1.61207453 0.31413622
257 2.27885186 1.61207453
258 -0.99791124 2.27885186
259 -4.23954017 -0.99791124
260 1.15672931 -4.23954017
261 -4.32974033 1.15672931
262 -0.03620686 -4.32974033
263 1.18329126 -0.03620686
> 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/74lnv1355998350.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/8pjv61355998350.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/95sob1355998350.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/10ema31355998350.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11jqvc1355998350.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ms811355998350.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13cdbk1355998350.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14whoo1355998350.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/1582ge1355998350.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/16jlwx1355998350.tab")
+ }
>
> try(system("convert tmp/1if281355998349.ps tmp/1if281355998349.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vkga1355998350.ps tmp/2vkga1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sg9c1355998350.ps tmp/3sg9c1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m44i1355998350.ps tmp/4m44i1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ss1m1355998350.ps tmp/5ss1m1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ohz51355998350.ps tmp/6ohz51355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/74lnv1355998350.ps tmp/74lnv1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pjv61355998350.ps tmp/8pjv61355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/95sob1355998350.ps tmp/95sob1355998350.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ema31355998350.ps tmp/10ema31355998350.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.604 1.397 16.100