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
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+ ,0
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+ ,7
+ ,0
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+ ,0
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+ ,0
+ ,14
+ ,0
+ ,12
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+ ,0)
+ ,dim=c(16
+ ,264)
+ ,dimnames=list(c('Pop'
+ ,'t'
+ ,'Pop_t'
+ ,'Connected'
+ ,'Connected_p'
+ ,'Separate'
+ ,'Separate_p'
+ ,'Learning'
+ ,'Software'
+ ,'Software_p'
+ ,'Happiness'
+ ,'Happiness_p'
+ ,'Depression'
+ ,'Depression_p'
+ ,'Belonging'
+ ,'Belonging_p')
+ ,1:264))
> y <- array(NA,dim=c(16,264),dimnames=list(c('Pop','t','Pop_t','Connected','Connected_p','Separate','Separate_p','Learning','Software','Software_p','Happiness','Happiness_p','Depression','Depression_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 Depression Depression_p Belonging
1 12 14 14 12.0 12.0 53
2 11 18 18 11.0 11.0 83
3 15 11 11 14.0 14.0 66
4 6 12 12 12.0 12.0 67
5 13 16 16 21.0 21.0 76
6 10 18 18 12.0 12.0 78
7 12 14 14 22.0 22.0 53
8 14 14 14 11.0 11.0 80
9 12 15 15 10.0 10.0 74
10 9 15 15 13.0 13.0 76
11 10 17 17 10.0 10.0 79
12 12 19 19 8.0 8.0 54
13 12 10 10 15.0 15.0 67
14 11 16 16 14.0 14.0 54
15 15 18 18 10.0 10.0 87
16 12 14 14 14.0 14.0 58
17 10 14 14 14.0 14.0 75
18 12 17 17 11.0 11.0 88
19 11 14 14 10.0 10.0 64
20 12 16 16 13.0 13.0 57
21 11 18 18 9.5 9.5 66
22 12 11 11 14.0 14.0 68
23 13 14 14 12.0 12.0 54
24 11 12 12 14.0 14.0 56
25 12 17 17 11.0 11.0 86
26 13 9 9 9.0 9.0 80
27 10 16 16 11.0 11.0 76
28 14 14 14 15.0 15.0 69
29 12 15 15 14.0 14.0 78
30 10 11 11 13.0 13.0 67
31 12 16 16 9.0 9.0 80
32 8 13 13 15.0 15.0 54
33 10 17 17 10.0 10.0 71
34 12 15 15 11.0 11.0 84
35 12 14 14 13.0 13.0 74
36 7 16 16 8.0 8.0 71
37 9 9 9 20.0 20.0 63
38 12 15 15 12.0 12.0 71
39 10 17 17 10.0 10.0 76
40 10 13 13 10.0 10.0 69
41 10 15 15 9.0 9.0 74
42 12 16 16 14.0 14.0 75
43 15 16 16 8.0 8.0 54
44 10 12 12 14.0 14.0 52
45 10 15 15 11.0 11.0 69
46 12 11 11 13.0 13.0 68
47 13 15 15 9.0 9.0 65
48 11 15 15 11.0 11.0 75
49 11 17 17 15.0 15.0 74
50 12 13 13 11.0 11.0 75
51 14 16 16 10.0 10.0 72
52 10 14 14 14.0 14.0 67
53 12 11 11 18.0 18.0 63
54 13 12 12 14.0 14.0 62
55 5 12 12 11.0 11.0 63
56 6 15 15 14.5 14.5 76
57 12 16 16 13.0 13.0 74
58 12 15 15 9.0 9.0 67
59 11 12 12 10.0 10.0 73
60 10 12 12 15.0 15.0 70
61 7 8 8 20.0 20.0 53
62 12 13 13 12.0 12.0 77
63 14 11 11 12.0 12.0 80
64 11 14 14 14.0 14.0 52
65 12 15 15 13.0 13.0 54
66 13 10 10 11.0 11.0 80
67 14 11 11 17.0 17.0 66
68 11 12 12 12.0 12.0 73
69 12 15 15 13.0 13.0 63
70 12 15 15 14.0 14.0 69
71 8 14 14 13.0 13.0 67
72 11 16 16 15.0 15.0 54
73 14 15 15 13.0 13.0 81
74 14 15 15 10.0 10.0 69
75 12 13 13 11.0 11.0 84
76 9 12 12 19.0 19.0 80
77 13 17 17 13.0 13.0 70
78 11 13 13 17.0 17.0 69
79 12 15 15 13.0 13.0 77
80 12 13 13 9.0 9.0 54
81 12 15 15 11.0 11.0 79
82 12 15 15 9.0 9.0 71
83 12 16 16 12.0 12.0 73
84 11 15 15 12.0 12.0 72
85 10 14 14 13.0 13.0 77
86 9 15 15 13.0 13.0 75
87 12 14 14 12.0 12.0 69
88 12 13 13 15.0 15.0 54
89 12 7 7 22.0 22.0 70
90 9 17 17 13.0 13.0 73
91 15 13 13 15.0 15.0 54
92 12 15 15 13.0 13.0 77
93 12 14 14 15.0 15.0 82
94 12 13 13 12.5 12.5 80
95 10 16 16 11.0 11.0 80
96 13 12 12 16.0 16.0 69
97 9 14 14 11.0 11.0 78
98 12 17 17 11.0 11.0 81
99 10 15 15 10.0 10.0 76
100 14 17 17 10.0 10.0 76
101 11 12 12 16.0 16.0 73
102 15 16 16 12.0 12.0 85
103 11 11 11 11.0 11.0 66
104 11 15 15 16.0 16.0 79
105 12 9 9 19.0 19.0 68
106 12 16 16 11.0 11.0 76
107 12 15 15 16.0 16.0 71
108 11 10 10 15.0 15.0 54
109 7 10 10 24.0 24.0 46
110 12 15 15 14.0 14.0 85
111 14 11 11 15.0 15.0 74
112 11 13 13 11.0 11.0 88
113 11 14 14 15.0 15.0 38
114 10 18 18 12.0 12.0 76
115 13 16 16 10.0 10.0 86
116 13 14 14 14.0 14.0 54
117 8 14 14 13.0 13.0 67
118 11 14 14 9.0 9.0 69
119 12 14 14 15.0 15.0 90
120 11 12 12 15.0 15.0 54
121 13 14 14 14.0 14.0 76
122 12 15 15 11.0 11.0 89
123 14 15 15 8.0 8.0 76
124 13 15 15 11.0 11.0 73
125 15 13 13 11.0 11.0 79
126 10 17 17 8.0 8.0 90
127 11 17 17 10.0 10.0 74
128 9 19 19 11.0 11.0 81
129 11 15 15 13.0 13.0 72
130 10 13 13 11.0 11.0 71
131 11 9 9 20.0 20.0 66
132 8 15 15 10.0 10.0 77
133 11 15 15 15.0 15.0 65
134 12 15 15 12.0 12.0 74
135 12 16 16 14.0 14.0 85
136 9 11 11 23.0 23.0 54
137 11 14 14 14.0 14.0 63
138 10 11 11 16.0 16.0 54
139 8 15 15 11.0 11.0 64
140 9 13 13 12.0 12.0 69
141 8 15 15 10.0 10.0 54
142 9 16 16 14.0 14.0 84
143 15 14 14 12.0 12.0 86
144 11 15 15 12.0 12.0 77
145 8 16 16 11.0 11.0 89
146 13 16 16 12.0 12.0 76
147 12 11 11 13.0 13.0 60
148 12 12 12 11.0 11.0 75
149 9 9 9 19.0 19.0 73
150 7 16 16 12.0 12.0 85
151 13 13 13 17.0 17.0 79
152 9 16 16 9.0 9.0 71
153 6 12 12 12.0 12.0 72
154 8 9 9 19.0 19.0 69
155 8 13 13 18.0 18.0 78
156 15 13 13 15.0 15.0 54
157 6 14 14 14.0 14.0 69
158 9 19 19 11.0 11.0 81
159 11 13 13 9.0 9.0 84
160 8 12 12 18.0 18.0 84
161 8 13 13 16.0 16.0 69
162 0 10 0 24.0 0.0 66
163 0 14 0 14.0 0.0 81
164 0 16 0 20.0 0.0 82
165 0 10 0 18.0 0.0 72
166 0 11 0 23.0 0.0 54
167 0 14 0 12.0 0.0 78
168 0 12 0 14.0 0.0 74
169 0 9 0 16.0 0.0 82
170 0 9 0 18.0 0.0 73
171 0 11 0 20.0 0.0 55
172 0 16 0 12.0 0.0 72
173 0 9 0 12.0 0.0 78
174 0 13 0 17.0 0.0 59
175 0 16 0 13.0 0.0 72
176 0 13 0 9.0 0.0 78
177 0 9 0 16.0 0.0 68
178 0 12 0 18.0 0.0 69
179 0 16 0 10.0 0.0 67
180 0 11 0 14.0 0.0 74
181 0 14 0 11.0 0.0 54
182 0 13 0 9.0 0.0 67
183 0 15 0 11.0 0.0 70
184 0 14 0 10.0 0.0 80
185 0 16 0 11.0 0.0 89
186 0 13 0 19.0 0.0 76
187 0 14 0 14.0 0.0 74
188 0 15 0 12.0 0.0 87
189 0 13 0 14.0 0.0 54
190 0 11 0 21.0 0.0 61
191 0 11 0 13.0 0.0 38
192 0 14 0 10.0 0.0 75
193 0 15 0 15.0 0.0 69
194 0 11 0 16.0 0.0 62
195 0 15 0 14.0 0.0 72
196 0 12 0 12.0 0.0 70
197 0 14 0 19.0 0.0 79
198 0 14 0 15.0 0.0 87
199 0 8 0 19.0 0.0 62
200 0 13 0 13.0 0.0 77
201 0 9 0 17.0 0.0 69
202 0 15 0 12.0 0.0 69
203 0 17 0 11.0 0.0 75
204 0 13 0 14.0 0.0 54
205 0 15 0 11.0 0.0 72
206 0 15 0 13.0 0.0 74
207 0 14 0 12.0 0.0 85
208 0 16 0 15.0 0.0 52
209 0 13 0 14.0 0.0 70
210 0 16 0 12.0 0.0 84
211 0 9 0 17.0 0.0 64
212 0 16 0 11.0 0.0 84
213 0 11 0 18.0 0.0 87
214 0 10 0 13.0 0.0 79
215 0 11 0 17.0 0.0 67
216 0 15 0 13.0 0.0 65
217 0 17 0 11.0 0.0 85
218 0 14 0 12.0 0.0 83
219 0 8 0 22.0 0.0 61
220 0 15 0 14.0 0.0 82
221 0 11 0 12.0 0.0 76
222 0 16 0 12.0 0.0 58
223 0 10 0 17.0 0.0 72
224 0 15 0 9.0 0.0 72
225 0 9 0 21.0 0.0 38
226 0 16 0 10.0 0.0 78
227 0 19 0 11.0 0.0 54
228 0 12 0 12.0 0.0 63
229 0 8 0 23.0 0.0 66
230 0 11 0 13.0 0.0 70
231 0 14 0 12.0 0.0 71
232 0 9 0 16.0 0.0 67
233 0 15 0 9.0 0.0 58
234 0 13 0 17.0 0.0 72
235 0 16 0 9.0 0.0 72
236 0 11 0 14.0 0.0 70
237 0 12 0 17.0 0.0 76
238 0 13 0 13.0 0.0 50
239 0 10 0 11.0 0.0 72
240 0 11 0 12.0 0.0 72
241 0 12 0 10.0 0.0 88
242 0 8 0 19.0 0.0 53
243 0 12 0 16.0 0.0 58
244 0 12 0 16.0 0.0 66
245 0 15 0 14.0 0.0 82
246 0 11 0 20.0 0.0 69
247 0 13 0 15.0 0.0 68
248 0 14 0 23.0 0.0 44
249 0 10 0 20.0 0.0 56
250 0 12 0 16.0 0.0 53
251 0 15 0 14.0 0.0 70
252 0 13 0 17.0 0.0 78
253 0 13 0 11.0 0.0 71
254 0 13 0 13.0 0.0 72
255 0 12 0 17.0 0.0 68
256 0 12 0 15.0 0.0 67
257 0 9 0 21.0 0.0 75
258 0 9 0 18.0 0.0 62
259 0 15 0 15.0 0.0 67
260 0 10 0 8.0 0.0 83
261 0 14 0 12.0 0.0 64
262 0 15 0 12.0 0.0 68
263 0 7 0 22.0 0.0 62
264 0 14 0 12.0 0.0 72
Belonging_p
1 53
2 83
3 66
4 67
5 76
6 78
7 53
8 80
9 74
10 76
11 79
12 54
13 67
14 54
15 87
16 58
17 75
18 88
19 64
20 57
21 66
22 68
23 54
24 56
25 86
26 80
27 76
28 69
29 78
30 67
31 80
32 54
33 71
34 84
35 74
36 71
37 63
38 71
39 76
40 69
41 74
42 75
43 54
44 52
45 69
46 68
47 65
48 75
49 74
50 75
51 72
52 67
53 63
54 62
55 63
56 76
57 74
58 67
59 73
60 70
61 53
62 77
63 80
64 52
65 54
66 80
67 66
68 73
69 63
70 69
71 67
72 54
73 81
74 69
75 84
76 80
77 70
78 69
79 77
80 54
81 79
82 71
83 73
84 72
85 77
86 75
87 69
88 54
89 70
90 73
91 54
92 77
93 82
94 80
95 80
96 69
97 78
98 81
99 76
100 76
101 73
102 85
103 66
104 79
105 68
106 76
107 71
108 54
109 46
110 85
111 74
112 88
113 38
114 76
115 86
116 54
117 67
118 69
119 90
120 54
121 76
122 89
123 76
124 73
125 79
126 90
127 74
128 81
129 72
130 71
131 66
132 77
133 65
134 74
135 85
136 54
137 63
138 54
139 64
140 69
141 54
142 84
143 86
144 77
145 89
146 76
147 60
148 75
149 73
150 85
151 79
152 71
153 72
154 69
155 78
156 54
157 69
158 81
159 84
160 84
161 69
162 0
163 0
164 0
165 0
166 0
167 0
168 0
169 0
170 0
171 0
172 0
173 0
174 0
175 0
176 0
177 0
178 0
179 0
180 0
181 0
182 0
183 0
184 0
185 0
186 0
187 0
188 0
189 0
190 0
191 0
192 0
193 0
194 0
195 0
196 0
197 0
198 0
199 0
200 0
201 0
202 0
203 0
204 0
205 0
206 0
207 0
208 0
209 0
210 0
211 0
212 0
213 0
214 0
215 0
216 0
217 0
218 0
219 0
220 0
221 0
222 0
223 0
224 0
225 0
226 0
227 0
228 0
229 0
230 0
231 0
232 0
233 0
234 0
235 0
236 0
237 0
238 0
239 0
240 0
241 0
242 0
243 0
244 0
245 0
246 0
247 0
248 0
249 0
250 0
251 0
252 0
253 0
254 0
255 0
256 0
257 0
258 0
259 0
260 0
261 0
262 0
263 0
264 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop t Pop_t Connected
4.0221319 2.5122087 -0.0041633 0.0001722 -0.0555568
Connected_p Separate Separate_p Software Software_p
0.1428762 0.1570048 -0.1710505 0.5616887 -0.0423405
Happiness Happiness_p Depression Depression_p Belonging
0.1295590 -0.0990921 0.0223866 -0.1059047 -0.0094106
Belonging_p
0.0247480
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6477 -1.0599 0.2827 1.1002 4.4184
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.0221319 3.3275782 1.209 0.22792
Pop 2.5122087 4.2401553 0.592 0.55407
t -0.0041633 0.0063718 -0.653 0.51411
Pop_t 0.0001722 0.0071811 0.024 0.98089
Connected -0.0555568 0.0522639 -1.063 0.28881
Connected_p 0.1428762 0.0700299 2.040 0.04239 *
Separate 0.1570048 0.0597976 2.626 0.00919 **
Separate_p -0.1710505 0.0744738 -2.297 0.02246 *
Software 0.5616887 0.0822318 6.831 6.49e-11 ***
Software_p -0.0423405 0.1094055 -0.387 0.69908
Happiness 0.1295590 0.0893820 1.449 0.14846
Happiness_p -0.0990921 0.1178677 -0.841 0.40132
Depression 0.0223866 0.0622067 0.360 0.71925
Depression_p -0.1059047 0.0843116 -1.256 0.21026
Belonging -0.0094106 0.0190539 -0.494 0.62182
Belonging_p 0.0247480 0.0246210 1.005 0.31580
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.846 on 248 degrees of freedom
Multiple R-squared: 0.4674, Adjusted R-squared: 0.4352
F-statistic: 14.51 on 15 and 248 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,] 9.983424e-01 0.0033151781 0.001657589
[2,] 9.959061e-01 0.0081877651 0.004093883
[3,] 9.908263e-01 0.0183474359 0.009173718
[4,] 9.849251e-01 0.0301498246 0.015074912
[5,] 9.773700e-01 0.0452599793 0.022629990
[6,] 9.685848e-01 0.0628304245 0.031415212
[7,] 9.504942e-01 0.0990115571 0.049505779
[8,] 9.300801e-01 0.1398398923 0.069919946
[9,] 9.013017e-01 0.1973966873 0.098698344
[10,] 9.129317e-01 0.1741365359 0.087068268
[11,] 8.806837e-01 0.2386325970 0.119316298
[12,] 8.772129e-01 0.2455742306 0.122787115
[13,] 8.436563e-01 0.3126873020 0.156343651
[14,] 8.323305e-01 0.3353390839 0.167669542
[15,] 8.122311e-01 0.3755377613 0.187768881
[16,] 7.624886e-01 0.4750228451 0.237511423
[17,] 7.444202e-01 0.5111596196 0.255579810
[18,] 8.132881e-01 0.3734237135 0.186711857
[19,] 8.531152e-01 0.2937695119 0.146884756
[20,] 8.343443e-01 0.3313113909 0.165655695
[21,] 8.560053e-01 0.2879893325 0.143994666
[22,] 8.394663e-01 0.3210673627 0.160533681
[23,] 8.111489e-01 0.3777021897 0.188851095
[24,] 7.849083e-01 0.4301833255 0.215091663
[25,] 7.912692e-01 0.4174616986 0.208730849
[26,] 7.511931e-01 0.4976137622 0.248806881
[27,] 7.146455e-01 0.5707089376 0.285354469
[28,] 8.660350e-01 0.2679299610 0.133964981
[29,] 8.718187e-01 0.2563626896 0.128181345
[30,] 8.477742e-01 0.3044516400 0.152225820
[31,] 8.380646e-01 0.3238708426 0.161935421
[32,] 8.217975e-01 0.3564050241 0.178202512
[33,] 7.876072e-01 0.4247856014 0.212392801
[34,] 7.533089e-01 0.4933821027 0.246691051
[35,] 7.520813e-01 0.4958374560 0.247918728
[36,] 7.243457e-01 0.5513086281 0.275654314
[37,] 7.298814e-01 0.5402371542 0.270118577
[38,] 7.371446e-01 0.5257107761 0.262855388
[39,] 6.992661e-01 0.6014677379 0.300733869
[40,] 6.824616e-01 0.6350767437 0.317538372
[41,] 6.434533e-01 0.7130934045 0.356546702
[42,] 6.640826e-01 0.6718348863 0.335917443
[43,] 6.349177e-01 0.7301645461 0.365082273
[44,] 5.956362e-01 0.8087276158 0.404363808
[45,] 5.557220e-01 0.8885560130 0.444278007
[46,] 5.103929e-01 0.9792141653 0.489607083
[47,] 4.758416e-01 0.9516831231 0.524158438
[48,] 4.576166e-01 0.9152331713 0.542383414
[49,] 4.603214e-01 0.9206427125 0.539678644
[50,] 5.827819e-01 0.8344361632 0.417218082
[51,] 6.845653e-01 0.6308694257 0.315434713
[52,] 6.531385e-01 0.6937229921 0.346861496
[53,] 7.264777e-01 0.5470445562 0.273522278
[54,] 6.903255e-01 0.6193490901 0.309674545
[55,] 6.843136e-01 0.6313727290 0.315686364
[56,] 6.632904e-01 0.6734192147 0.336709607
[57,] 6.246817e-01 0.7506365417 0.375318271
[58,] 6.814790e-01 0.6370420091 0.318521005
[59,] 6.433647e-01 0.7132706318 0.356635316
[60,] 6.218991e-01 0.7562018021 0.378100901
[61,] 6.264638e-01 0.7470724135 0.373536207
[62,] 5.865563e-01 0.8268873492 0.413443675
[63,] 5.507548e-01 0.8984904764 0.449245238
[64,] 5.131696e-01 0.9736608036 0.486830402
[65,] 4.808343e-01 0.9616686430 0.519165678
[66,] 4.420128e-01 0.8840256889 0.557987156
[67,] 4.304254e-01 0.8608507954 0.569574602
[68,] 3.914703e-01 0.7829405551 0.608529722
[69,] 3.576461e-01 0.7152922338 0.642353883
[70,] 3.398866e-01 0.6797732985 0.660113351
[71,] 3.073320e-01 0.6146640924 0.692667954
[72,] 2.945789e-01 0.5891578182 0.705421091
[73,] 2.632403e-01 0.5264805255 0.736759737
[74,] 2.359743e-01 0.4719485879 0.764025706
[75,] 2.087683e-01 0.4175365510 0.791231724
[76,] 2.181971e-01 0.4363941163 0.781802942
[77,] 1.976039e-01 0.3952078379 0.802396081
[78,] 1.719765e-01 0.3439529685 0.828023516
[79,] 1.706978e-01 0.3413956489 0.829302176
[80,] 1.468973e-01 0.2937946884 0.853102656
[81,] 1.273083e-01 0.2546166502 0.872691675
[82,] 1.149094e-01 0.2298188711 0.885090564
[83,] 1.035147e-01 0.2070294546 0.896485273
[84,] 1.221563e-01 0.2443126268 0.877843687
[85,] 1.037424e-01 0.2074848874 0.896257556
[86,] 9.814799e-02 0.1962959770 0.901852011
[87,] 1.118713e-01 0.2237426870 0.888128656
[88,] 1.002148e-01 0.2004295274 0.899785236
[89,] 8.769065e-02 0.1753813027 0.912309349
[90,] 8.438410e-02 0.1687681902 0.915615905
[91,] 7.707971e-02 0.1541594210 0.922920289
[92,] 6.831905e-02 0.1366381095 0.931680945
[93,] 5.817858e-02 0.1163571620 0.941821419
[94,] 6.678473e-02 0.1335694518 0.933215274
[95,] 5.740816e-02 0.1148163135 0.942591843
[96,] 6.916809e-02 0.1383361899 0.930831905
[97,] 6.691465e-02 0.1338293085 0.933085346
[98,] 5.911112e-02 0.1182222382 0.940888881
[99,] 5.458951e-02 0.1091790117 0.945410494
[100,] 5.340056e-02 0.1068011224 0.946599439
[101,] 5.188456e-02 0.1037691149 0.948115443
[102,] 4.433770e-02 0.0886754005 0.955662300
[103,] 3.846792e-02 0.0769358364 0.961532082
[104,] 4.628136e-02 0.0925627111 0.953718644
[105,] 3.892564e-02 0.0778512787 0.961074361
[106,] 3.348914e-02 0.0669782860 0.966510857
[107,] 2.774466e-02 0.0554893228 0.972255339
[108,] 2.229561e-02 0.0445912235 0.977704388
[109,] 2.168490e-02 0.0433698058 0.978315097
[110,] 2.015087e-02 0.0403017394 0.979849130
[111,] 2.342960e-02 0.0468591962 0.976570402
[112,] 2.510549e-02 0.0502109717 0.974894514
[113,] 3.414939e-02 0.0682987742 0.965850613
[114,] 4.552772e-02 0.0910554389 0.954472281
[115,] 4.554187e-02 0.0910837333 0.954458133
[116,] 4.011021e-02 0.0802204274 0.959889786
[117,] 3.338554e-02 0.0667710787 0.966614461
[118,] 2.911144e-02 0.0582228788 0.970888561
[119,] 2.559560e-02 0.0511911913 0.974404404
[120,] 2.783574e-02 0.0556714795 0.972164260
[121,] 2.392766e-02 0.0478553158 0.976072342
[122,] 3.184330e-02 0.0636866075 0.968156696
[123,] 4.065572e-02 0.0813114365 0.959344282
[124,] 3.883549e-02 0.0776709847 0.961164508
[125,] 3.195130e-02 0.0639026013 0.968048699
[126,] 2.826672e-02 0.0565334479 0.971733276
[127,] 4.557875e-02 0.0911574946 0.954421253
[128,] 5.508200e-02 0.1101640030 0.944917999
[129,] 6.836063e-02 0.1367212522 0.931639374
[130,] 5.871908e-02 0.1174381680 0.941280916
[131,] 4.932245e-02 0.0986449017 0.950677549
[132,] 6.595039e-02 0.1319007769 0.934049612
[133,] 5.928356e-02 0.1185671204 0.940716440
[134,] 6.073440e-02 0.1214688098 0.939265595
[135,] 7.992991e-02 0.1598598162 0.920070092
[136,] 6.972811e-02 0.1394562234 0.930271888
[137,] 6.789971e-02 0.1357994146 0.932100293
[138,] 5.698172e-02 0.1139634397 0.943018280
[139,] 4.965500e-02 0.0993099994 0.950345000
[140,] 4.218398e-02 0.0843679674 0.957816016
[141,] 3.516994e-02 0.0703398721 0.964830064
[142,] 2.858423e-02 0.0571684632 0.971415768
[143,] 2.300562e-02 0.0460112476 0.976994376
[144,] 1.833276e-02 0.0366655149 0.981667243
[145,] 1.457156e-02 0.0291431170 0.985428442
[146,] 1.194192e-02 0.0238838417 0.988058079
[147,] 9.530738e-03 0.0190614765 0.990469262
[148,] 9.651050e-03 0.0193021008 0.990348950
[149,] 7.481607e-03 0.0149632143 0.992518393
[150,] 8.028105e-03 0.0160562094 0.991971895
[151,] 7.340082e-03 0.0146801641 0.992659918
[152,] 5.956849e-03 0.0119136975 0.994043151
[153,] 6.979487e-03 0.0139589739 0.993020513
[154,] 5.408013e-03 0.0108160258 0.994591987
[155,] 4.288396e-03 0.0085767914 0.995711604
[156,] 4.236450e-03 0.0084729002 0.995763550
[157,] 4.231209e-03 0.0084624183 0.995768791
[158,] 3.502176e-03 0.0070043519 0.996497824
[159,] 2.619412e-03 0.0052388233 0.997380588
[160,] 2.105809e-03 0.0042116178 0.997894191
[161,] 1.616453e-03 0.0032329058 0.998383547
[162,] 1.391319e-03 0.0027826387 0.998608681
[163,] 1.094176e-03 0.0021883523 0.998905824
[164,] 7.935067e-04 0.0015870135 0.999206493
[165,] 7.847055e-04 0.0015694109 0.999215295
[166,] 5.798807e-04 0.0011597614 0.999420119
[167,] 1.682086e-02 0.0336417282 0.983179136
[168,] 1.437744e-02 0.0287548765 0.985622562
[169,] 1.698939e-02 0.0339787731 0.983010613
[170,] 1.337776e-02 0.0267555195 0.986622240
[171,] 1.131635e-02 0.0226327030 0.988683648
[172,] 8.964567e-03 0.0179291335 0.991035433
[173,] 7.904797e-03 0.0158095933 0.992095203
[174,] 6.102130e-03 0.0122042604 0.993897870
[175,] 5.608552e-03 0.0112171047 0.994391448
[176,] 4.873788e-03 0.0097475761 0.995126212
[177,] 3.964897e-03 0.0079297934 0.996035103
[178,] 2.897804e-03 0.0057956084 0.997102196
[179,] 3.971742e-03 0.0079434849 0.996028258
[180,] 2.898204e-03 0.0057964077 0.997101796
[181,] 2.446589e-03 0.0048931772 0.997553411
[182,] 2.053784e-03 0.0041075688 0.997946216
[183,] 1.811492e-03 0.0036229841 0.998188508
[184,] 1.389441e-03 0.0027788822 0.998610559
[185,] 1.663396e-03 0.0033267918 0.998336604
[186,] 2.645812e-03 0.0052916232 0.997354188
[187,] 2.640137e-03 0.0052802730 0.997359863
[188,] 1.880584e-03 0.0037611677 0.998119416
[189,] 1.521576e-03 0.0030431527 0.998478424
[190,] 1.078572e-03 0.0021571436 0.998921428
[191,] 1.378861e-03 0.0027577229 0.998621139
[192,] 1.036224e-03 0.0020724486 0.998963776
[193,] 1.323843e-03 0.0026476857 0.998676157
[194,] 4.032498e-03 0.0080649950 0.995967502
[195,] 2.803907e-03 0.0056078137 0.997196093
[196,] 3.704304e-03 0.0074086085 0.996295696
[197,] 2.829671e-03 0.0056593411 0.997170329
[198,] 2.030202e-03 0.0040604040 0.997969798
[199,] 2.165603e-03 0.0043312066 0.997834397
[200,] 1.679637e-03 0.0033592741 0.998320363
[201,] 1.463459e-03 0.0029269184 0.998536541
[202,] 1.007588e-03 0.0020151762 0.998992412
[203,] 7.379952e-04 0.0014759903 0.999262005
[204,] 4.748724e-04 0.0009497448 0.999525128
[205,] 3.234869e-04 0.0006469738 0.999676513
[206,] 2.187256e-04 0.0004374512 0.999781274
[207,] 1.329043e-04 0.0002658087 0.999867096
[208,] 3.450013e-04 0.0006900026 0.999654999
[209,] 2.401043e-04 0.0004802087 0.999759896
[210,] 1.499246e-04 0.0002998492 0.999850075
[211,] 9.767003e-05 0.0001953401 0.999902330
[212,] 5.844210e-05 0.0001168842 0.999941558
[213,] 6.141390e-05 0.0001228278 0.999938586
[214,] 2.817367e-04 0.0005634735 0.999718263
[215,] 1.400861e-03 0.0028017224 0.998599139
[216,] 2.289741e-03 0.0045794819 0.997710259
[217,] 1.337848e-03 0.0026756953 0.998662152
[218,] 8.735973e-04 0.0017471947 0.999126403
[219,] 1.918091e-02 0.0383618280 0.980819086
[220,] 1.165371e-02 0.0233074224 0.988346289
[221,] 7.944134e-03 0.0158882683 0.992055866
[222,] 4.536145e-03 0.0090722896 0.995463855
[223,] 3.125833e-03 0.0062516668 0.996874167
[224,] 5.117903e-03 0.0102358054 0.994882097
[225,] 2.930621e-03 0.0058612429 0.997069379
[226,] 2.124768e-03 0.0042495361 0.997875232
[227,] 1.113815e-03 0.0022276296 0.998886185
> postscript(file="/var/wessaorg/rcomp/tmp/1cyj81352154925.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/2zjm91352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32tix1352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4avm71352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5b1ho1352154925.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.046083853 -0.097885472 2.381281244 2.731155412 -1.614307618 -2.095227848
7 8 9 10 11 12
3.889361672 -1.971324220 -2.139282147 0.597450973 0.538954384 -0.123511784
13 14 15 16 17 18
0.379329329 0.790552310 -0.674116582 -0.180222203 0.238407564 3.571997802
19 20 21 22 23 24
2.383996153 0.692581919 0.738684169 0.840979686 2.681673323 1.110340647
25 26 27 28 29 30
0.459016248 -0.062256701 0.993630452 -1.287354597 0.447129194 -0.390442523
31 32 33 34 35 36
-0.877073040 -0.441086324 -1.163223672 0.093370145 -1.650082732 -3.265086391
37 38 39 40 41 42
-2.727741818 -1.838583996 1.482591400 1.219986431 0.814154553 -1.572760963
43 44 45 46 47 48
2.330837638 -0.138775670 -1.069660746 -4.614485627 -2.570906971 -0.148188267
49 50 51 52 53 54
0.938503281 -2.063309004 -0.618730355 -0.178019628 -2.683765704 0.347265324
55 56 57 58 59 60
-2.669484673 1.422225656 -0.003626575 0.677314469 -0.386915103 1.746573703
61 62 63 64 65 66
0.461124336 0.065518931 -0.590941349 -0.261866865 0.759975261 0.828101104
67 68 69 70 71 72
1.882696936 3.300864888 -3.672281623 0.630343924 -3.586652412 -0.195891331
73 74 75 76 77 78
1.451465642 1.067764404 0.177481194 3.105017756 -0.195494635 1.496153743
79 80 81 82 83 84
-2.012991571 0.228562094 0.430911605 0.229972159 -0.807445923 0.212340342
85 86 87 88 89 90
1.643521380 -0.157162591 0.694124829 1.530382878 0.721172556 -1.602358824
91 92 93 94 95 96
0.373075636 0.568114210 -0.227714771 -2.081327575 0.955762767 0.270470473
97 98 99 100 101 102
1.853750511 0.029782633 -0.408738965 -1.066991022 1.267773121 2.669794486
103 104 105 106 107 108
0.334270075 1.501782303 -2.121064767 1.095591200 0.418550755 1.651855036
109 110 111 112 113 114
0.094684451 0.939050063 0.177075686 2.154429571 -0.883473804 -2.737925680
115 116 117 118 119 120
1.420453657 -1.228111477 1.002398217 -1.899311535 0.341804495 -1.071136018
121 122 123 124 125 126
0.446467035 -2.953286739 -0.965887394 -1.156982748 -0.758089866 -0.411090666
127 128 129 130 131 132
1.147708624 1.023995324 -2.800546536 1.893986019 -3.335922783 2.061816729
133 134 135 136 137 138
-1.853766898 -1.518542121 -0.236507084 0.955939989 0.598813891 -2.132096531
139 140 141 142 143 144
-1.232882644 -2.317151286 3.106912918 1.651817307 0.374850487 1.307935338
145 146 147 148 149 150
-4.101570283 2.230955010 -1.987321910 0.868155244 -0.027501747 -3.130394560
151 152 153 154 155 156
-1.018847217 2.138291381 3.984137915 1.339284535 -2.605686135 0.632496425
157 158 159 160 161 162
1.386083240 1.143727995 0.935273542 -0.461648177 0.372169536 0.241572893
163 164 165 166 167 168
0.438130834 0.977998836 0.792681890 -2.633186375 -0.368331427 -2.715343737
169 170 171 172 173 174
-1.888599055 0.754202065 1.714500846 0.480536123 -0.836752333 -2.053128439
175 176 177 178 179 180
-2.116317591 0.662960929 0.211407502 -0.688041152 0.295021024 -1.068134703
181 182 183 184 185 186
0.787123339 0.344561643 1.853610710 0.805809359 -6.647726530 0.743212520
187 188 189 190 191 192
2.808854280 0.735010043 -0.497144058 -0.235128060 -1.200137894 0.536023918
193 194 195 196 197 198
2.063122417 1.542401986 -0.652748953 -0.021578718 3.268885220 0.755228294
199 200 201 202 203 204
1.525018472 1.239906700 2.133711339 0.262490053 -1.953361859 -2.537816964
205 206 207 208 209 210
2.320968306 0.355660628 1.513838673 0.476488803 -2.288943941 1.391950854
211 212 213 214 215 216
-2.120496256 -4.038868305 0.331316763 3.174673096 1.406817956 1.267997862
217 218 219 220 221 222
2.392076776 0.795743770 0.946062926 -0.454744585 -1.057563970 -0.051553752
223 224 225 226 227 228
0.976636997 -0.913949293 -0.182381939 -3.888500612 -0.195549171 1.136629250
229 230 231 232 233 234
-1.346430686 -0.783445263 2.333928414 -3.699652462 4.418384561 2.797828284
235 236 237 238 239 240
-0.392730636 -1.603408206 -6.280183329 -0.515384322 2.112346369 -0.625532740
241 242 243 244 245 246
0.013148320 -1.706930392 1.096855527 2.095111636 2.084309672 -0.599045499
247 248 249 250 251 252
0.853503967 -2.983598495 1.063163587 0.575953092 -1.122927656 -1.426397800
253 254 255 256 257 258
1.060560372 3.491633848 -0.998299111 0.312867479 1.512522153 2.271058486
259 260 261 262 263 264
-1.053654052 -4.084003559 1.195610175 -4.320972188 -0.104872489 1.203866722
> postscript(file="/var/wessaorg/rcomp/tmp/6sdrn1352154925.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 -3.046083853 NA
1 -0.097885472 -3.046083853
2 2.381281244 -0.097885472
3 2.731155412 2.381281244
4 -1.614307618 2.731155412
5 -2.095227848 -1.614307618
6 3.889361672 -2.095227848
7 -1.971324220 3.889361672
8 -2.139282147 -1.971324220
9 0.597450973 -2.139282147
10 0.538954384 0.597450973
11 -0.123511784 0.538954384
12 0.379329329 -0.123511784
13 0.790552310 0.379329329
14 -0.674116582 0.790552310
15 -0.180222203 -0.674116582
16 0.238407564 -0.180222203
17 3.571997802 0.238407564
18 2.383996153 3.571997802
19 0.692581919 2.383996153
20 0.738684169 0.692581919
21 0.840979686 0.738684169
22 2.681673323 0.840979686
23 1.110340647 2.681673323
24 0.459016248 1.110340647
25 -0.062256701 0.459016248
26 0.993630452 -0.062256701
27 -1.287354597 0.993630452
28 0.447129194 -1.287354597
29 -0.390442523 0.447129194
30 -0.877073040 -0.390442523
31 -0.441086324 -0.877073040
32 -1.163223672 -0.441086324
33 0.093370145 -1.163223672
34 -1.650082732 0.093370145
35 -3.265086391 -1.650082732
36 -2.727741818 -3.265086391
37 -1.838583996 -2.727741818
38 1.482591400 -1.838583996
39 1.219986431 1.482591400
40 0.814154553 1.219986431
41 -1.572760963 0.814154553
42 2.330837638 -1.572760963
43 -0.138775670 2.330837638
44 -1.069660746 -0.138775670
45 -4.614485627 -1.069660746
46 -2.570906971 -4.614485627
47 -0.148188267 -2.570906971
48 0.938503281 -0.148188267
49 -2.063309004 0.938503281
50 -0.618730355 -2.063309004
51 -0.178019628 -0.618730355
52 -2.683765704 -0.178019628
53 0.347265324 -2.683765704
54 -2.669484673 0.347265324
55 1.422225656 -2.669484673
56 -0.003626575 1.422225656
57 0.677314469 -0.003626575
58 -0.386915103 0.677314469
59 1.746573703 -0.386915103
60 0.461124336 1.746573703
61 0.065518931 0.461124336
62 -0.590941349 0.065518931
63 -0.261866865 -0.590941349
64 0.759975261 -0.261866865
65 0.828101104 0.759975261
66 1.882696936 0.828101104
67 3.300864888 1.882696936
68 -3.672281623 3.300864888
69 0.630343924 -3.672281623
70 -3.586652412 0.630343924
71 -0.195891331 -3.586652412
72 1.451465642 -0.195891331
73 1.067764404 1.451465642
74 0.177481194 1.067764404
75 3.105017756 0.177481194
76 -0.195494635 3.105017756
77 1.496153743 -0.195494635
78 -2.012991571 1.496153743
79 0.228562094 -2.012991571
80 0.430911605 0.228562094
81 0.229972159 0.430911605
82 -0.807445923 0.229972159
83 0.212340342 -0.807445923
84 1.643521380 0.212340342
85 -0.157162591 1.643521380
86 0.694124829 -0.157162591
87 1.530382878 0.694124829
88 0.721172556 1.530382878
89 -1.602358824 0.721172556
90 0.373075636 -1.602358824
91 0.568114210 0.373075636
92 -0.227714771 0.568114210
93 -2.081327575 -0.227714771
94 0.955762767 -2.081327575
95 0.270470473 0.955762767
96 1.853750511 0.270470473
97 0.029782633 1.853750511
98 -0.408738965 0.029782633
99 -1.066991022 -0.408738965
100 1.267773121 -1.066991022
101 2.669794486 1.267773121
102 0.334270075 2.669794486
103 1.501782303 0.334270075
104 -2.121064767 1.501782303
105 1.095591200 -2.121064767
106 0.418550755 1.095591200
107 1.651855036 0.418550755
108 0.094684451 1.651855036
109 0.939050063 0.094684451
110 0.177075686 0.939050063
111 2.154429571 0.177075686
112 -0.883473804 2.154429571
113 -2.737925680 -0.883473804
114 1.420453657 -2.737925680
115 -1.228111477 1.420453657
116 1.002398217 -1.228111477
117 -1.899311535 1.002398217
118 0.341804495 -1.899311535
119 -1.071136018 0.341804495
120 0.446467035 -1.071136018
121 -2.953286739 0.446467035
122 -0.965887394 -2.953286739
123 -1.156982748 -0.965887394
124 -0.758089866 -1.156982748
125 -0.411090666 -0.758089866
126 1.147708624 -0.411090666
127 1.023995324 1.147708624
128 -2.800546536 1.023995324
129 1.893986019 -2.800546536
130 -3.335922783 1.893986019
131 2.061816729 -3.335922783
132 -1.853766898 2.061816729
133 -1.518542121 -1.853766898
134 -0.236507084 -1.518542121
135 0.955939989 -0.236507084
136 0.598813891 0.955939989
137 -2.132096531 0.598813891
138 -1.232882644 -2.132096531
139 -2.317151286 -1.232882644
140 3.106912918 -2.317151286
141 1.651817307 3.106912918
142 0.374850487 1.651817307
143 1.307935338 0.374850487
144 -4.101570283 1.307935338
145 2.230955010 -4.101570283
146 -1.987321910 2.230955010
147 0.868155244 -1.987321910
148 -0.027501747 0.868155244
149 -3.130394560 -0.027501747
150 -1.018847217 -3.130394560
151 2.138291381 -1.018847217
152 3.984137915 2.138291381
153 1.339284535 3.984137915
154 -2.605686135 1.339284535
155 0.632496425 -2.605686135
156 1.386083240 0.632496425
157 1.143727995 1.386083240
158 0.935273542 1.143727995
159 -0.461648177 0.935273542
160 0.372169536 -0.461648177
161 0.241572893 0.372169536
162 0.438130834 0.241572893
163 0.977998836 0.438130834
164 0.792681890 0.977998836
165 -2.633186375 0.792681890
166 -0.368331427 -2.633186375
167 -2.715343737 -0.368331427
168 -1.888599055 -2.715343737
169 0.754202065 -1.888599055
170 1.714500846 0.754202065
171 0.480536123 1.714500846
172 -0.836752333 0.480536123
173 -2.053128439 -0.836752333
174 -2.116317591 -2.053128439
175 0.662960929 -2.116317591
176 0.211407502 0.662960929
177 -0.688041152 0.211407502
178 0.295021024 -0.688041152
179 -1.068134703 0.295021024
180 0.787123339 -1.068134703
181 0.344561643 0.787123339
182 1.853610710 0.344561643
183 0.805809359 1.853610710
184 -6.647726530 0.805809359
185 0.743212520 -6.647726530
186 2.808854280 0.743212520
187 0.735010043 2.808854280
188 -0.497144058 0.735010043
189 -0.235128060 -0.497144058
190 -1.200137894 -0.235128060
191 0.536023918 -1.200137894
192 2.063122417 0.536023918
193 1.542401986 2.063122417
194 -0.652748953 1.542401986
195 -0.021578718 -0.652748953
196 3.268885220 -0.021578718
197 0.755228294 3.268885220
198 1.525018472 0.755228294
199 1.239906700 1.525018472
200 2.133711339 1.239906700
201 0.262490053 2.133711339
202 -1.953361859 0.262490053
203 -2.537816964 -1.953361859
204 2.320968306 -2.537816964
205 0.355660628 2.320968306
206 1.513838673 0.355660628
207 0.476488803 1.513838673
208 -2.288943941 0.476488803
209 1.391950854 -2.288943941
210 -2.120496256 1.391950854
211 -4.038868305 -2.120496256
212 0.331316763 -4.038868305
213 3.174673096 0.331316763
214 1.406817956 3.174673096
215 1.267997862 1.406817956
216 2.392076776 1.267997862
217 0.795743770 2.392076776
218 0.946062926 0.795743770
219 -0.454744585 0.946062926
220 -1.057563970 -0.454744585
221 -0.051553752 -1.057563970
222 0.976636997 -0.051553752
223 -0.913949293 0.976636997
224 -0.182381939 -0.913949293
225 -3.888500612 -0.182381939
226 -0.195549171 -3.888500612
227 1.136629250 -0.195549171
228 -1.346430686 1.136629250
229 -0.783445263 -1.346430686
230 2.333928414 -0.783445263
231 -3.699652462 2.333928414
232 4.418384561 -3.699652462
233 2.797828284 4.418384561
234 -0.392730636 2.797828284
235 -1.603408206 -0.392730636
236 -6.280183329 -1.603408206
237 -0.515384322 -6.280183329
238 2.112346369 -0.515384322
239 -0.625532740 2.112346369
240 0.013148320 -0.625532740
241 -1.706930392 0.013148320
242 1.096855527 -1.706930392
243 2.095111636 1.096855527
244 2.084309672 2.095111636
245 -0.599045499 2.084309672
246 0.853503967 -0.599045499
247 -2.983598495 0.853503967
248 1.063163587 -2.983598495
249 0.575953092 1.063163587
250 -1.122927656 0.575953092
251 -1.426397800 -1.122927656
252 1.060560372 -1.426397800
253 3.491633848 1.060560372
254 -0.998299111 3.491633848
255 0.312867479 -0.998299111
256 1.512522153 0.312867479
257 2.271058486 1.512522153
258 -1.053654052 2.271058486
259 -4.084003559 -1.053654052
260 1.195610175 -4.084003559
261 -4.320972188 1.195610175
262 -0.104872489 -4.320972188
263 1.203866722 -0.104872489
264 NA 1.203866722
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.097885472 -3.046083853
[2,] 2.381281244 -0.097885472
[3,] 2.731155412 2.381281244
[4,] -1.614307618 2.731155412
[5,] -2.095227848 -1.614307618
[6,] 3.889361672 -2.095227848
[7,] -1.971324220 3.889361672
[8,] -2.139282147 -1.971324220
[9,] 0.597450973 -2.139282147
[10,] 0.538954384 0.597450973
[11,] -0.123511784 0.538954384
[12,] 0.379329329 -0.123511784
[13,] 0.790552310 0.379329329
[14,] -0.674116582 0.790552310
[15,] -0.180222203 -0.674116582
[16,] 0.238407564 -0.180222203
[17,] 3.571997802 0.238407564
[18,] 2.383996153 3.571997802
[19,] 0.692581919 2.383996153
[20,] 0.738684169 0.692581919
[21,] 0.840979686 0.738684169
[22,] 2.681673323 0.840979686
[23,] 1.110340647 2.681673323
[24,] 0.459016248 1.110340647
[25,] -0.062256701 0.459016248
[26,] 0.993630452 -0.062256701
[27,] -1.287354597 0.993630452
[28,] 0.447129194 -1.287354597
[29,] -0.390442523 0.447129194
[30,] -0.877073040 -0.390442523
[31,] -0.441086324 -0.877073040
[32,] -1.163223672 -0.441086324
[33,] 0.093370145 -1.163223672
[34,] -1.650082732 0.093370145
[35,] -3.265086391 -1.650082732
[36,] -2.727741818 -3.265086391
[37,] -1.838583996 -2.727741818
[38,] 1.482591400 -1.838583996
[39,] 1.219986431 1.482591400
[40,] 0.814154553 1.219986431
[41,] -1.572760963 0.814154553
[42,] 2.330837638 -1.572760963
[43,] -0.138775670 2.330837638
[44,] -1.069660746 -0.138775670
[45,] -4.614485627 -1.069660746
[46,] -2.570906971 -4.614485627
[47,] -0.148188267 -2.570906971
[48,] 0.938503281 -0.148188267
[49,] -2.063309004 0.938503281
[50,] -0.618730355 -2.063309004
[51,] -0.178019628 -0.618730355
[52,] -2.683765704 -0.178019628
[53,] 0.347265324 -2.683765704
[54,] -2.669484673 0.347265324
[55,] 1.422225656 -2.669484673
[56,] -0.003626575 1.422225656
[57,] 0.677314469 -0.003626575
[58,] -0.386915103 0.677314469
[59,] 1.746573703 -0.386915103
[60,] 0.461124336 1.746573703
[61,] 0.065518931 0.461124336
[62,] -0.590941349 0.065518931
[63,] -0.261866865 -0.590941349
[64,] 0.759975261 -0.261866865
[65,] 0.828101104 0.759975261
[66,] 1.882696936 0.828101104
[67,] 3.300864888 1.882696936
[68,] -3.672281623 3.300864888
[69,] 0.630343924 -3.672281623
[70,] -3.586652412 0.630343924
[71,] -0.195891331 -3.586652412
[72,] 1.451465642 -0.195891331
[73,] 1.067764404 1.451465642
[74,] 0.177481194 1.067764404
[75,] 3.105017756 0.177481194
[76,] -0.195494635 3.105017756
[77,] 1.496153743 -0.195494635
[78,] -2.012991571 1.496153743
[79,] 0.228562094 -2.012991571
[80,] 0.430911605 0.228562094
[81,] 0.229972159 0.430911605
[82,] -0.807445923 0.229972159
[83,] 0.212340342 -0.807445923
[84,] 1.643521380 0.212340342
[85,] -0.157162591 1.643521380
[86,] 0.694124829 -0.157162591
[87,] 1.530382878 0.694124829
[88,] 0.721172556 1.530382878
[89,] -1.602358824 0.721172556
[90,] 0.373075636 -1.602358824
[91,] 0.568114210 0.373075636
[92,] -0.227714771 0.568114210
[93,] -2.081327575 -0.227714771
[94,] 0.955762767 -2.081327575
[95,] 0.270470473 0.955762767
[96,] 1.853750511 0.270470473
[97,] 0.029782633 1.853750511
[98,] -0.408738965 0.029782633
[99,] -1.066991022 -0.408738965
[100,] 1.267773121 -1.066991022
[101,] 2.669794486 1.267773121
[102,] 0.334270075 2.669794486
[103,] 1.501782303 0.334270075
[104,] -2.121064767 1.501782303
[105,] 1.095591200 -2.121064767
[106,] 0.418550755 1.095591200
[107,] 1.651855036 0.418550755
[108,] 0.094684451 1.651855036
[109,] 0.939050063 0.094684451
[110,] 0.177075686 0.939050063
[111,] 2.154429571 0.177075686
[112,] -0.883473804 2.154429571
[113,] -2.737925680 -0.883473804
[114,] 1.420453657 -2.737925680
[115,] -1.228111477 1.420453657
[116,] 1.002398217 -1.228111477
[117,] -1.899311535 1.002398217
[118,] 0.341804495 -1.899311535
[119,] -1.071136018 0.341804495
[120,] 0.446467035 -1.071136018
[121,] -2.953286739 0.446467035
[122,] -0.965887394 -2.953286739
[123,] -1.156982748 -0.965887394
[124,] -0.758089866 -1.156982748
[125,] -0.411090666 -0.758089866
[126,] 1.147708624 -0.411090666
[127,] 1.023995324 1.147708624
[128,] -2.800546536 1.023995324
[129,] 1.893986019 -2.800546536
[130,] -3.335922783 1.893986019
[131,] 2.061816729 -3.335922783
[132,] -1.853766898 2.061816729
[133,] -1.518542121 -1.853766898
[134,] -0.236507084 -1.518542121
[135,] 0.955939989 -0.236507084
[136,] 0.598813891 0.955939989
[137,] -2.132096531 0.598813891
[138,] -1.232882644 -2.132096531
[139,] -2.317151286 -1.232882644
[140,] 3.106912918 -2.317151286
[141,] 1.651817307 3.106912918
[142,] 0.374850487 1.651817307
[143,] 1.307935338 0.374850487
[144,] -4.101570283 1.307935338
[145,] 2.230955010 -4.101570283
[146,] -1.987321910 2.230955010
[147,] 0.868155244 -1.987321910
[148,] -0.027501747 0.868155244
[149,] -3.130394560 -0.027501747
[150,] -1.018847217 -3.130394560
[151,] 2.138291381 -1.018847217
[152,] 3.984137915 2.138291381
[153,] 1.339284535 3.984137915
[154,] -2.605686135 1.339284535
[155,] 0.632496425 -2.605686135
[156,] 1.386083240 0.632496425
[157,] 1.143727995 1.386083240
[158,] 0.935273542 1.143727995
[159,] -0.461648177 0.935273542
[160,] 0.372169536 -0.461648177
[161,] 0.241572893 0.372169536
[162,] 0.438130834 0.241572893
[163,] 0.977998836 0.438130834
[164,] 0.792681890 0.977998836
[165,] -2.633186375 0.792681890
[166,] -0.368331427 -2.633186375
[167,] -2.715343737 -0.368331427
[168,] -1.888599055 -2.715343737
[169,] 0.754202065 -1.888599055
[170,] 1.714500846 0.754202065
[171,] 0.480536123 1.714500846
[172,] -0.836752333 0.480536123
[173,] -2.053128439 -0.836752333
[174,] -2.116317591 -2.053128439
[175,] 0.662960929 -2.116317591
[176,] 0.211407502 0.662960929
[177,] -0.688041152 0.211407502
[178,] 0.295021024 -0.688041152
[179,] -1.068134703 0.295021024
[180,] 0.787123339 -1.068134703
[181,] 0.344561643 0.787123339
[182,] 1.853610710 0.344561643
[183,] 0.805809359 1.853610710
[184,] -6.647726530 0.805809359
[185,] 0.743212520 -6.647726530
[186,] 2.808854280 0.743212520
[187,] 0.735010043 2.808854280
[188,] -0.497144058 0.735010043
[189,] -0.235128060 -0.497144058
[190,] -1.200137894 -0.235128060
[191,] 0.536023918 -1.200137894
[192,] 2.063122417 0.536023918
[193,] 1.542401986 2.063122417
[194,] -0.652748953 1.542401986
[195,] -0.021578718 -0.652748953
[196,] 3.268885220 -0.021578718
[197,] 0.755228294 3.268885220
[198,] 1.525018472 0.755228294
[199,] 1.239906700 1.525018472
[200,] 2.133711339 1.239906700
[201,] 0.262490053 2.133711339
[202,] -1.953361859 0.262490053
[203,] -2.537816964 -1.953361859
[204,] 2.320968306 -2.537816964
[205,] 0.355660628 2.320968306
[206,] 1.513838673 0.355660628
[207,] 0.476488803 1.513838673
[208,] -2.288943941 0.476488803
[209,] 1.391950854 -2.288943941
[210,] -2.120496256 1.391950854
[211,] -4.038868305 -2.120496256
[212,] 0.331316763 -4.038868305
[213,] 3.174673096 0.331316763
[214,] 1.406817956 3.174673096
[215,] 1.267997862 1.406817956
[216,] 2.392076776 1.267997862
[217,] 0.795743770 2.392076776
[218,] 0.946062926 0.795743770
[219,] -0.454744585 0.946062926
[220,] -1.057563970 -0.454744585
[221,] -0.051553752 -1.057563970
[222,] 0.976636997 -0.051553752
[223,] -0.913949293 0.976636997
[224,] -0.182381939 -0.913949293
[225,] -3.888500612 -0.182381939
[226,] -0.195549171 -3.888500612
[227,] 1.136629250 -0.195549171
[228,] -1.346430686 1.136629250
[229,] -0.783445263 -1.346430686
[230,] 2.333928414 -0.783445263
[231,] -3.699652462 2.333928414
[232,] 4.418384561 -3.699652462
[233,] 2.797828284 4.418384561
[234,] -0.392730636 2.797828284
[235,] -1.603408206 -0.392730636
[236,] -6.280183329 -1.603408206
[237,] -0.515384322 -6.280183329
[238,] 2.112346369 -0.515384322
[239,] -0.625532740 2.112346369
[240,] 0.013148320 -0.625532740
[241,] -1.706930392 0.013148320
[242,] 1.096855527 -1.706930392
[243,] 2.095111636 1.096855527
[244,] 2.084309672 2.095111636
[245,] -0.599045499 2.084309672
[246,] 0.853503967 -0.599045499
[247,] -2.983598495 0.853503967
[248,] 1.063163587 -2.983598495
[249,] 0.575953092 1.063163587
[250,] -1.122927656 0.575953092
[251,] -1.426397800 -1.122927656
[252,] 1.060560372 -1.426397800
[253,] 3.491633848 1.060560372
[254,] -0.998299111 3.491633848
[255,] 0.312867479 -0.998299111
[256,] 1.512522153 0.312867479
[257,] 2.271058486 1.512522153
[258,] -1.053654052 2.271058486
[259,] -4.084003559 -1.053654052
[260,] 1.195610175 -4.084003559
[261,] -4.320972188 1.195610175
[262,] -0.104872489 -4.320972188
[263,] 1.203866722 -0.104872489
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.097885472 -3.046083853
2 2.381281244 -0.097885472
3 2.731155412 2.381281244
4 -1.614307618 2.731155412
5 -2.095227848 -1.614307618
6 3.889361672 -2.095227848
7 -1.971324220 3.889361672
8 -2.139282147 -1.971324220
9 0.597450973 -2.139282147
10 0.538954384 0.597450973
11 -0.123511784 0.538954384
12 0.379329329 -0.123511784
13 0.790552310 0.379329329
14 -0.674116582 0.790552310
15 -0.180222203 -0.674116582
16 0.238407564 -0.180222203
17 3.571997802 0.238407564
18 2.383996153 3.571997802
19 0.692581919 2.383996153
20 0.738684169 0.692581919
21 0.840979686 0.738684169
22 2.681673323 0.840979686
23 1.110340647 2.681673323
24 0.459016248 1.110340647
25 -0.062256701 0.459016248
26 0.993630452 -0.062256701
27 -1.287354597 0.993630452
28 0.447129194 -1.287354597
29 -0.390442523 0.447129194
30 -0.877073040 -0.390442523
31 -0.441086324 -0.877073040
32 -1.163223672 -0.441086324
33 0.093370145 -1.163223672
34 -1.650082732 0.093370145
35 -3.265086391 -1.650082732
36 -2.727741818 -3.265086391
37 -1.838583996 -2.727741818
38 1.482591400 -1.838583996
39 1.219986431 1.482591400
40 0.814154553 1.219986431
41 -1.572760963 0.814154553
42 2.330837638 -1.572760963
43 -0.138775670 2.330837638
44 -1.069660746 -0.138775670
45 -4.614485627 -1.069660746
46 -2.570906971 -4.614485627
47 -0.148188267 -2.570906971
48 0.938503281 -0.148188267
49 -2.063309004 0.938503281
50 -0.618730355 -2.063309004
51 -0.178019628 -0.618730355
52 -2.683765704 -0.178019628
53 0.347265324 -2.683765704
54 -2.669484673 0.347265324
55 1.422225656 -2.669484673
56 -0.003626575 1.422225656
57 0.677314469 -0.003626575
58 -0.386915103 0.677314469
59 1.746573703 -0.386915103
60 0.461124336 1.746573703
61 0.065518931 0.461124336
62 -0.590941349 0.065518931
63 -0.261866865 -0.590941349
64 0.759975261 -0.261866865
65 0.828101104 0.759975261
66 1.882696936 0.828101104
67 3.300864888 1.882696936
68 -3.672281623 3.300864888
69 0.630343924 -3.672281623
70 -3.586652412 0.630343924
71 -0.195891331 -3.586652412
72 1.451465642 -0.195891331
73 1.067764404 1.451465642
74 0.177481194 1.067764404
75 3.105017756 0.177481194
76 -0.195494635 3.105017756
77 1.496153743 -0.195494635
78 -2.012991571 1.496153743
79 0.228562094 -2.012991571
80 0.430911605 0.228562094
81 0.229972159 0.430911605
82 -0.807445923 0.229972159
83 0.212340342 -0.807445923
84 1.643521380 0.212340342
85 -0.157162591 1.643521380
86 0.694124829 -0.157162591
87 1.530382878 0.694124829
88 0.721172556 1.530382878
89 -1.602358824 0.721172556
90 0.373075636 -1.602358824
91 0.568114210 0.373075636
92 -0.227714771 0.568114210
93 -2.081327575 -0.227714771
94 0.955762767 -2.081327575
95 0.270470473 0.955762767
96 1.853750511 0.270470473
97 0.029782633 1.853750511
98 -0.408738965 0.029782633
99 -1.066991022 -0.408738965
100 1.267773121 -1.066991022
101 2.669794486 1.267773121
102 0.334270075 2.669794486
103 1.501782303 0.334270075
104 -2.121064767 1.501782303
105 1.095591200 -2.121064767
106 0.418550755 1.095591200
107 1.651855036 0.418550755
108 0.094684451 1.651855036
109 0.939050063 0.094684451
110 0.177075686 0.939050063
111 2.154429571 0.177075686
112 -0.883473804 2.154429571
113 -2.737925680 -0.883473804
114 1.420453657 -2.737925680
115 -1.228111477 1.420453657
116 1.002398217 -1.228111477
117 -1.899311535 1.002398217
118 0.341804495 -1.899311535
119 -1.071136018 0.341804495
120 0.446467035 -1.071136018
121 -2.953286739 0.446467035
122 -0.965887394 -2.953286739
123 -1.156982748 -0.965887394
124 -0.758089866 -1.156982748
125 -0.411090666 -0.758089866
126 1.147708624 -0.411090666
127 1.023995324 1.147708624
128 -2.800546536 1.023995324
129 1.893986019 -2.800546536
130 -3.335922783 1.893986019
131 2.061816729 -3.335922783
132 -1.853766898 2.061816729
133 -1.518542121 -1.853766898
134 -0.236507084 -1.518542121
135 0.955939989 -0.236507084
136 0.598813891 0.955939989
137 -2.132096531 0.598813891
138 -1.232882644 -2.132096531
139 -2.317151286 -1.232882644
140 3.106912918 -2.317151286
141 1.651817307 3.106912918
142 0.374850487 1.651817307
143 1.307935338 0.374850487
144 -4.101570283 1.307935338
145 2.230955010 -4.101570283
146 -1.987321910 2.230955010
147 0.868155244 -1.987321910
148 -0.027501747 0.868155244
149 -3.130394560 -0.027501747
150 -1.018847217 -3.130394560
151 2.138291381 -1.018847217
152 3.984137915 2.138291381
153 1.339284535 3.984137915
154 -2.605686135 1.339284535
155 0.632496425 -2.605686135
156 1.386083240 0.632496425
157 1.143727995 1.386083240
158 0.935273542 1.143727995
159 -0.461648177 0.935273542
160 0.372169536 -0.461648177
161 0.241572893 0.372169536
162 0.438130834 0.241572893
163 0.977998836 0.438130834
164 0.792681890 0.977998836
165 -2.633186375 0.792681890
166 -0.368331427 -2.633186375
167 -2.715343737 -0.368331427
168 -1.888599055 -2.715343737
169 0.754202065 -1.888599055
170 1.714500846 0.754202065
171 0.480536123 1.714500846
172 -0.836752333 0.480536123
173 -2.053128439 -0.836752333
174 -2.116317591 -2.053128439
175 0.662960929 -2.116317591
176 0.211407502 0.662960929
177 -0.688041152 0.211407502
178 0.295021024 -0.688041152
179 -1.068134703 0.295021024
180 0.787123339 -1.068134703
181 0.344561643 0.787123339
182 1.853610710 0.344561643
183 0.805809359 1.853610710
184 -6.647726530 0.805809359
185 0.743212520 -6.647726530
186 2.808854280 0.743212520
187 0.735010043 2.808854280
188 -0.497144058 0.735010043
189 -0.235128060 -0.497144058
190 -1.200137894 -0.235128060
191 0.536023918 -1.200137894
192 2.063122417 0.536023918
193 1.542401986 2.063122417
194 -0.652748953 1.542401986
195 -0.021578718 -0.652748953
196 3.268885220 -0.021578718
197 0.755228294 3.268885220
198 1.525018472 0.755228294
199 1.239906700 1.525018472
200 2.133711339 1.239906700
201 0.262490053 2.133711339
202 -1.953361859 0.262490053
203 -2.537816964 -1.953361859
204 2.320968306 -2.537816964
205 0.355660628 2.320968306
206 1.513838673 0.355660628
207 0.476488803 1.513838673
208 -2.288943941 0.476488803
209 1.391950854 -2.288943941
210 -2.120496256 1.391950854
211 -4.038868305 -2.120496256
212 0.331316763 -4.038868305
213 3.174673096 0.331316763
214 1.406817956 3.174673096
215 1.267997862 1.406817956
216 2.392076776 1.267997862
217 0.795743770 2.392076776
218 0.946062926 0.795743770
219 -0.454744585 0.946062926
220 -1.057563970 -0.454744585
221 -0.051553752 -1.057563970
222 0.976636997 -0.051553752
223 -0.913949293 0.976636997
224 -0.182381939 -0.913949293
225 -3.888500612 -0.182381939
226 -0.195549171 -3.888500612
227 1.136629250 -0.195549171
228 -1.346430686 1.136629250
229 -0.783445263 -1.346430686
230 2.333928414 -0.783445263
231 -3.699652462 2.333928414
232 4.418384561 -3.699652462
233 2.797828284 4.418384561
234 -0.392730636 2.797828284
235 -1.603408206 -0.392730636
236 -6.280183329 -1.603408206
237 -0.515384322 -6.280183329
238 2.112346369 -0.515384322
239 -0.625532740 2.112346369
240 0.013148320 -0.625532740
241 -1.706930392 0.013148320
242 1.096855527 -1.706930392
243 2.095111636 1.096855527
244 2.084309672 2.095111636
245 -0.599045499 2.084309672
246 0.853503967 -0.599045499
247 -2.983598495 0.853503967
248 1.063163587 -2.983598495
249 0.575953092 1.063163587
250 -1.122927656 0.575953092
251 -1.426397800 -1.122927656
252 1.060560372 -1.426397800
253 3.491633848 1.060560372
254 -0.998299111 3.491633848
255 0.312867479 -0.998299111
256 1.512522153 0.312867479
257 2.271058486 1.512522153
258 -1.053654052 2.271058486
259 -4.084003559 -1.053654052
260 1.195610175 -4.084003559
261 -4.320972188 1.195610175
262 -0.104872489 -4.320972188
263 1.203866722 -0.104872489
> 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/7urlz1352154925.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/8j4si1352154925.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/9we7r1352154925.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/100y6f1352154925.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1180d31352154925.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12x2mf1352154925.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13rza01352154925.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/143hm11352154925.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/15lw851352154925.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/16ggqp1352154925.tab")
+ }
>
> try(system("convert tmp/1cyj81352154925.ps tmp/1cyj81352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zjm91352154925.ps tmp/2zjm91352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/32tix1352154925.ps tmp/32tix1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/4avm71352154925.ps tmp/4avm71352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b1ho1352154925.ps tmp/5b1ho1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sdrn1352154925.ps tmp/6sdrn1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/7urlz1352154925.ps tmp/7urlz1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j4si1352154925.ps tmp/8j4si1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/9we7r1352154925.ps tmp/9we7r1352154925.png",intern=TRUE))
character(0)
> try(system("convert tmp/100y6f1352154925.ps tmp/100y6f1352154925.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.235 1.190 17.428