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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+ ,4
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+ ,52
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+ ,11
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+ ,11
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+ ,41
+ ,11
+ ,40
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+ ,11
+ ,7
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+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> 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
Software month Connected Separate Learning Happiness Depression Belonging
1 12 9 41 38 13 14 12.0 53
2 11 9 39 32 16 18 11.0 83
3 15 9 30 35 19 11 14.0 66
4 6 9 31 33 15 12 12.0 67
5 13 9 34 37 14 16 21.0 76
6 10 9 35 29 13 18 12.0 78
7 12 9 39 31 19 14 22.0 53
8 14 9 34 36 15 14 11.0 80
9 12 9 36 35 14 15 10.0 74
10 9 9 37 38 15 15 13.0 76
11 10 9 38 31 16 17 10.0 79
12 12 9 36 34 16 19 8.0 54
13 12 9 38 35 16 10 15.0 67
14 11 9 39 38 16 16 14.0 54
15 15 9 33 37 17 18 10.0 87
16 12 9 32 33 15 14 14.0 58
17 10 9 36 32 15 14 14.0 75
18 12 9 38 38 20 17 11.0 88
19 11 9 39 38 18 14 10.0 64
20 12 9 32 32 16 16 13.0 57
21 11 9 32 33 16 18 9.5 66
22 12 9 31 31 16 11 14.0 68
23 13 9 39 38 19 14 12.0 54
24 11 9 37 39 16 12 14.0 56
25 12 9 39 32 17 17 11.0 86
26 13 9 41 32 17 9 9.0 80
27 10 9 36 35 16 16 11.0 76
28 14 9 33 37 15 14 15.0 69
29 12 9 33 33 16 15 14.0 78
30 10 9 34 33 14 11 13.0 67
31 12 9 31 31 15 16 9.0 80
32 8 9 27 32 12 13 15.0 54
33 10 9 37 31 14 17 10.0 71
34 12 9 34 37 16 15 11.0 84
35 12 9 34 30 14 14 13.0 74
36 7 9 32 33 10 16 8.0 71
37 9 9 29 31 10 9 20.0 63
38 12 9 36 33 14 15 12.0 71
39 10 9 29 31 16 17 10.0 76
40 10 9 35 33 16 13 10.0 69
41 10 9 37 32 16 15 9.0 74
42 12 9 34 33 14 16 14.0 75
43 15 9 38 32 20 16 8.0 54
44 10 9 35 33 14 12 14.0 52
45 10 9 38 28 14 15 11.0 69
46 12 9 37 35 11 11 13.0 68
47 13 9 38 39 14 15 9.0 65
48 11 9 33 34 15 15 11.0 75
49 11 9 36 38 16 17 15.0 74
50 12 9 38 32 14 13 11.0 75
51 14 9 32 38 16 16 10.0 72
52 10 9 32 30 14 14 14.0 67
53 12 9 32 33 12 11 18.0 63
54 13 9 34 38 16 12 14.0 62
55 5 9 32 32 9 12 11.0 63
56 6 9 37 35 14 15 14.5 76
57 12 9 39 34 16 16 13.0 74
58 12 9 29 34 16 15 9.0 67
59 11 9 37 36 15 12 10.0 73
60 10 9 35 34 16 12 15.0 70
61 7 9 30 28 12 8 20.0 53
62 12 9 38 34 16 13 12.0 77
63 14 9 34 35 16 11 12.0 80
64 11 9 31 35 14 14 14.0 52
65 12 9 34 31 16 15 13.0 54
66 13 10 35 37 17 10 11.0 80
67 14 10 36 35 18 11 17.0 66
68 11 10 30 27 18 12 12.0 73
69 12 10 39 40 12 15 13.0 63
70 12 10 35 37 16 15 14.0 69
71 8 10 38 36 10 14 13.0 67
72 11 10 31 38 14 16 15.0 54
73 14 10 34 39 18 15 13.0 81
74 14 10 38 41 18 15 10.0 69
75 12 10 34 27 16 13 11.0 84
76 9 10 39 30 17 12 19.0 80
77 13 10 37 37 16 17 13.0 70
78 11 10 34 31 16 13 17.0 69
79 12 10 28 31 13 15 13.0 77
80 12 10 37 27 16 13 9.0 54
81 12 10 33 36 16 15 11.0 79
82 12 10 35 37 16 15 9.0 71
83 12 10 37 33 15 16 12.0 73
84 11 10 32 34 15 15 12.0 72
85 10 10 33 31 16 14 13.0 77
86 9 10 38 39 14 15 13.0 75
87 12 10 33 34 16 14 12.0 69
88 12 10 29 32 16 13 15.0 54
89 12 10 33 33 15 7 22.0 70
90 9 10 31 36 12 17 13.0 73
91 15 10 36 32 17 13 15.0 54
92 12 10 35 41 16 15 13.0 77
93 12 10 32 28 15 14 15.0 82
94 12 10 29 30 13 13 12.5 80
95 10 10 39 36 16 16 11.0 80
96 13 10 37 35 16 12 16.0 69
97 9 10 35 31 16 14 11.0 78
98 12 10 37 34 16 17 11.0 81
99 10 10 32 36 14 15 10.0 76
100 14 10 38 36 16 17 10.0 76
101 11 10 37 35 16 12 16.0 73
102 15 10 36 37 20 16 12.0 85
103 11 10 32 28 15 11 11.0 66
104 11 10 33 39 16 15 16.0 79
105 12 10 40 32 13 9 19.0 68
106 12 10 38 35 17 16 11.0 76
107 12 10 41 39 16 15 16.0 71
108 11 10 36 35 16 10 15.0 54
109 7 10 43 42 12 10 24.0 46
110 12 10 30 34 16 15 14.0 85
111 14 10 31 33 16 11 15.0 74
112 11 10 32 41 17 13 11.0 88
113 11 10 32 33 13 14 15.0 38
114 10 10 37 34 12 18 12.0 76
115 13 10 37 32 18 16 10.0 86
116 13 10 33 40 14 14 14.0 54
117 8 10 34 40 14 14 13.0 67
118 11 10 33 35 13 14 9.0 69
119 12 10 38 36 16 14 15.0 90
120 11 10 33 37 13 12 15.0 54
121 13 10 31 27 16 14 14.0 76
122 12 10 38 39 13 15 11.0 89
123 14 10 37 38 16 15 8.0 76
124 13 10 36 31 15 15 11.0 73
125 15 10 31 33 16 13 11.0 79
126 10 10 39 32 15 17 8.0 90
127 11 10 44 39 17 17 10.0 74
128 9 10 33 36 15 19 11.0 81
129 11 10 35 33 12 15 13.0 72
130 10 10 32 33 16 13 11.0 71
131 11 10 28 32 10 9 20.0 66
132 8 10 40 37 16 15 10.0 77
133 11 10 27 30 12 15 15.0 65
134 12 10 37 38 14 15 12.0 74
135 12 10 32 29 15 16 14.0 85
136 9 10 28 22 13 11 23.0 54
137 11 10 34 35 15 14 14.0 63
138 10 10 30 35 11 11 16.0 54
139 8 10 35 34 12 15 11.0 64
140 9 10 31 35 11 13 12.0 69
141 8 10 32 34 16 15 10.0 54
142 9 10 30 37 15 16 14.0 84
143 15 10 30 35 17 14 12.0 86
144 11 10 31 23 16 15 12.0 77
145 8 10 40 31 10 16 11.0 89
146 13 10 32 27 18 16 12.0 76
147 12 10 36 36 13 11 13.0 60
148 12 10 32 31 16 12 11.0 75
149 9 10 35 32 13 9 19.0 73
150 7 10 38 39 10 16 12.0 85
151 13 10 42 37 15 13 17.0 79
152 9 10 34 38 16 16 9.0 71
153 6 10 35 39 16 12 12.0 72
154 8 9 38 34 14 9 19.0 69
155 8 10 33 31 10 13 18.0 78
156 15 10 36 32 17 13 15.0 54
157 6 10 32 37 13 14 14.0 69
158 9 10 33 36 15 19 11.0 81
159 11 10 34 32 16 13 9.0 84
160 8 10 32 38 12 12 18.0 84
161 8 10 34 36 13 13 16.0 69
162 10 11 27 26 13 10 24.0 66
163 8 11 31 26 12 14 14.0 81
164 14 11 38 33 17 16 20.0 82
165 10 11 34 39 15 10 18.0 72
166 8 11 24 30 10 11 23.0 54
167 11 11 30 33 14 14 12.0 78
168 12 11 26 25 11 12 14.0 74
169 12 11 34 38 13 9 16.0 82
170 12 11 27 37 16 9 18.0 73
171 5 11 37 31 12 11 20.0 55
172 12 11 36 37 16 16 12.0 72
173 10 11 41 35 12 9 12.0 78
174 7 11 29 25 9 13 17.0 59
175 12 11 36 28 12 16 13.0 72
176 11 11 32 35 15 13 9.0 78
177 8 11 37 33 12 9 16.0 68
178 9 11 30 30 12 12 18.0 69
179 10 11 31 31 14 16 10.0 67
180 9 11 38 37 12 11 14.0 74
181 12 11 36 36 16 14 11.0 54
182 6 11 35 30 11 13 9.0 67
183 15 11 31 36 19 15 11.0 70
184 12 11 38 32 15 14 10.0 80
185 12 11 22 28 8 16 11.0 89
186 12 11 32 36 16 13 19.0 76
187 11 11 36 34 17 14 14.0 74
188 7 11 39 31 12 15 12.0 87
189 7 11 28 28 11 13 14.0 54
190 5 11 32 36 11 11 21.0 61
191 12 11 32 36 14 11 13.0 38
192 12 11 38 40 16 14 10.0 75
193 3 11 32 33 12 15 15.0 69
194 11 11 35 37 16 11 16.0 62
195 10 11 32 32 13 15 14.0 72
196 12 11 37 38 15 12 12.0 70
197 9 11 34 31 16 14 19.0 79
198 12 11 33 37 16 14 15.0 87
199 9 11 33 33 14 8 19.0 62
200 12 11 26 32 16 13 13.0 77
201 12 11 30 30 16 9 17.0 69
202 10 11 24 30 14 15 12.0 69
203 9 11 34 31 11 17 11.0 75
204 12 11 34 32 12 13 14.0 54
205 8 11 33 34 15 15 11.0 72
206 11 11 34 36 15 15 13.0 74
207 11 11 35 37 16 14 12.0 85
208 12 11 35 36 16 16 15.0 52
209 10 11 36 33 11 13 14.0 70
210 10 11 34 33 15 16 12.0 84
211 12 11 34 33 12 9 17.0 64
212 12 11 41 44 12 16 11.0 84
213 11 11 32 39 15 11 18.0 87
214 8 11 30 32 15 10 13.0 79
215 12 11 35 35 16 11 17.0 67
216 10 11 28 25 14 15 13.0 65
217 11 11 33 35 17 17 11.0 85
218 10 11 39 34 14 14 12.0 83
219 8 11 36 35 13 8 22.0 61
220 12 11 36 39 15 15 14.0 82
221 12 11 35 33 13 11 12.0 76
222 10 11 38 36 14 16 12.0 58
223 12 11 33 32 15 10 17.0 72
224 9 11 31 32 12 15 9.0 72
225 9 11 34 36 13 9 21.0 38
226 6 11 32 36 8 16 10.0 78
227 10 11 31 32 14 19 11.0 54
228 9 11 33 34 14 12 12.0 63
229 9 11 34 33 11 8 23.0 66
230 9 11 34 35 12 11 13.0 70
231 6 11 34 30 13 14 12.0 71
232 10 11 33 38 10 9 16.0 67
233 6 11 32 34 16 15 9.0 58
234 14 11 41 33 18 13 17.0 72
235 10 11 34 32 13 16 9.0 72
236 10 11 36 31 11 11 14.0 70
237 6 11 37 30 4 12 17.0 76
238 12 11 36 27 13 13 13.0 50
239 12 11 29 31 16 10 11.0 72
240 7 11 37 30 10 11 12.0 72
241 8 11 27 32 12 12 10.0 88
242 11 11 35 35 12 8 19.0 53
243 3 11 28 28 10 12 16.0 58
244 6 11 35 33 13 12 16.0 66
245 10 11 37 31 15 15 14.0 82
246 8 11 29 35 12 11 20.0 69
247 9 11 32 35 14 13 15.0 68
248 9 11 36 32 10 14 23.0 44
249 8 11 19 21 12 10 20.0 56
250 9 11 21 20 12 12 16.0 53
251 7 11 31 34 11 15 14.0 70
252 7 11 33 32 10 13 17.0 78
253 6 11 36 34 12 13 11.0 71
254 9 11 33 32 16 13 13.0 72
255 10 11 37 33 12 12 17.0 68
256 11 11 34 33 14 12 15.0 67
257 12 11 35 37 16 9 21.0 75
258 8 11 31 32 14 9 18.0 62
259 11 11 37 34 13 15 15.0 67
260 3 11 35 30 4 10 8.0 83
261 11 11 27 30 15 14 12.0 64
262 12 11 34 38 11 15 12.0 68
263 7 11 40 36 11 7 22.0 62
264 9 11 29 32 14 14 12.0 72
Belonging_Final t
1 32 1
2 51 2
3 42 3
4 41 4
5 46 5
6 47 6
7 37 7
8 49 8
9 45 9
10 47 10
11 49 11
12 33 12
13 42 13
14 33 14
15 53 15
16 36 16
17 45 17
18 54 18
19 41 19
20 36 20
21 41 21
22 44 22
23 33 23
24 37 24
25 52 25
26 47 26
27 43 27
28 44 28
29 45 29
30 44 30
31 49 31
32 33 32
33 43 33
34 54 34
35 42 35
36 44 36
37 37 37
38 43 38
39 46 39
40 42 40
41 45 41
42 44 42
43 33 43
44 31 44
45 42 45
46 40 46
47 43 47
48 46 48
49 42 49
50 45 50
51 44 51
52 40 52
53 37 53
54 46 54
55 36 55
56 47 56
57 45 57
58 42 58
59 43 59
60 43 60
61 32 61
62 45 62
63 48 63
64 31 64
65 33 65
66 49 66
67 42 67
68 41 68
69 38 69
70 42 70
71 44 71
72 33 72
73 48 73
74 40 74
75 50 75
76 49 76
77 43 77
78 44 78
79 47 79
80 33 80
81 46 81
82 45 82
83 43 83
84 44 84
85 47 85
86 45 86
87 42 87
88 33 88
89 43 89
90 46 90
91 33 91
92 46 92
93 48 93
94 47 94
95 47 95
96 43 96
97 46 97
98 48 98
99 46 99
100 45 100
101 45 101
102 52 102
103 42 103
104 47 104
105 41 105
106 47 106
107 43 107
108 33 108
109 30 109
110 52 110
111 44 111
112 55 112
113 11 113
114 47 114
115 53 115
116 33 116
117 44 117
118 42 118
119 55 119
120 33 120
121 46 121
122 54 122
123 47 123
124 45 124
125 47 125
126 55 126
127 44 127
128 53 128
129 44 129
130 42 130
131 40 131
132 46 132
133 40 133
134 46 134
135 53 135
136 33 136
137 42 137
138 35 138
139 40 139
140 41 140
141 33 141
142 51 142
143 53 143
144 46 144
145 55 145
146 47 146
147 38 147
148 46 148
149 46 149
150 53 150
151 47 151
152 41 152
153 44 153
154 43 154
155 51 155
156 33 156
157 43 157
158 53 158
159 51 159
160 50 160
161 46 161
162 43 162
163 47 163
164 50 164
165 43 165
166 33 166
167 48 167
168 44 168
169 50 169
170 41 170
171 34 171
172 44 172
173 47 173
174 35 174
175 44 175
176 44 176
177 43 177
178 41 178
179 41 179
180 42 180
181 33 181
182 41 182
183 44 183
184 48 184
185 55 185
186 44 186
187 43 187
188 52 188
189 30 189
190 39 190
191 11 191
192 44 192
193 42 193
194 41 194
195 44 195
196 44 196
197 48 197
198 53 198
199 37 199
200 44 200
201 44 201
202 40 202
203 42 203
204 35 204
205 43 205
206 45 206
207 55 207
208 31 208
209 44 209
210 50 210
211 40 211
212 53 212
213 54 213
214 49 214
215 40 215
216 41 216
217 52 217
218 52 218
219 36 219
220 52 220
221 46 221
222 31 222
223 44 223
224 44 224
225 11 225
226 46 226
227 33 227
228 34 228
229 42 229
230 43 230
231 43 231
232 44 232
233 36 233
234 46 234
235 44 235
236 43 236
237 50 237
238 33 238
239 43 239
240 44 240
241 53 241
242 34 242
243 35 243
244 40 244
245 53 245
246 42 246
247 43 247
248 29 248
249 36 249
250 30 250
251 42 251
252 47 252
253 44 253
254 45 254
255 44 255
256 43 256
257 43 257
258 40 258
259 41 259
260 52 260
261 38 261
262 41 262
263 39 263
264 43 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month Connected Separate
-3.584040 0.769535 -0.017943 0.036920
Learning Happiness Depression Belonging
0.519756 -0.033210 -0.011533 0.008105
Belonging_Final t
-0.003066 -0.011815
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2411 -0.9752 0.1003 1.1854 4.6134
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.584040 3.772925 -0.950 0.34305
month 0.769535 0.393103 1.958 0.05137 .
Connected -0.017943 0.033633 -0.533 0.59416
Separate 0.036920 0.034113 1.082 0.28016
Learning 0.519756 0.051290 10.134 < 2e-16 ***
Happiness -0.033210 0.056401 -0.589 0.55651
Depression -0.011533 0.041162 -0.280 0.77957
Belonging 0.008105 0.036808 0.220 0.82590
Belonging_Final -0.003066 0.054668 -0.056 0.95532
t -0.011815 0.004150 -2.847 0.00477 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.802 on 254 degrees of freedom
Multiple R-squared: 0.4174, Adjusted R-squared: 0.3968
F-statistic: 20.22 on 9 and 254 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,] 0.996070086 0.007859828 0.003929914
[2,] 0.994410440 0.011179121 0.005589560
[3,] 0.990357101 0.019285798 0.009642899
[4,] 0.985904924 0.028190152 0.014095076
[5,] 0.974704347 0.050591305 0.025295653
[6,] 0.972064845 0.055870310 0.027935155
[7,] 0.962069876 0.075860249 0.037930124
[8,] 0.941283315 0.117433371 0.058716685
[9,] 0.918028270 0.163943461 0.081971730
[10,] 0.891081780 0.217836441 0.108918220
[11,] 0.861945474 0.276109051 0.138054526
[12,] 0.829339395 0.341321209 0.170660605
[13,] 0.793946078 0.412107843 0.206053922
[14,] 0.790648948 0.418702103 0.209351052
[15,] 0.802159470 0.395681060 0.197840530
[16,] 0.803763511 0.392472979 0.196236489
[17,] 0.754001111 0.491997779 0.245998889
[18,] 0.717802053 0.564395894 0.282197947
[19,] 0.669155772 0.661688455 0.330844228
[20,] 0.686294827 0.627410346 0.313705173
[21,] 0.629774820 0.740450359 0.370225180
[22,] 0.572970924 0.854058151 0.427029076
[23,] 0.554508210 0.890983581 0.445491790
[24,] 0.541379294 0.917241411 0.458620706
[25,] 0.483971618 0.967943236 0.516028382
[26,] 0.465457456 0.930914913 0.534542544
[27,] 0.443503933 0.887007866 0.556496067
[28,] 0.412731704 0.825463407 0.587268296
[29,] 0.374617132 0.749234264 0.625382868
[30,] 0.343910733 0.687821465 0.656089267
[31,] 0.392552919 0.785105838 0.607447081
[32,] 0.346645719 0.693291438 0.653354281
[33,] 0.300287793 0.600575587 0.699712207
[34,] 0.336356213 0.672712426 0.663643787
[35,] 0.338441894 0.676883788 0.661558106
[36,] 0.296978979 0.593957957 0.703021021
[37,] 0.297017734 0.594035467 0.702982266
[38,] 0.272246999 0.544493998 0.727753001
[39,] 0.269452834 0.538905669 0.730547166
[40,] 0.234319873 0.468639745 0.765680127
[41,] 0.231109020 0.462218040 0.768890980
[42,] 0.199381697 0.398763395 0.800618303
[43,] 0.301057738 0.602115476 0.698942262
[44,] 0.619997247 0.760005506 0.380002753
[45,] 0.577618892 0.844762216 0.422381108
[46,] 0.533706364 0.932587271 0.466293636
[47,] 0.491198656 0.982397312 0.508801344
[48,] 0.487218461 0.974436921 0.512781539
[49,] 0.495898535 0.991797070 0.504101465
[50,] 0.454151569 0.908303138 0.545848431
[51,] 0.465507781 0.931015562 0.534492219
[52,] 0.423959828 0.847919657 0.576040172
[53,] 0.393913531 0.787827062 0.606086469
[54,] 0.352532328 0.705064656 0.647467672
[55,] 0.319173381 0.638346762 0.680826619
[56,] 0.305833401 0.611666802 0.694166599
[57,] 0.286172957 0.572345914 0.713827043
[58,] 0.254943759 0.509887519 0.745056241
[59,] 0.235799170 0.471598339 0.764200830
[60,] 0.205623684 0.411247368 0.794376316
[61,] 0.177933745 0.355867490 0.822066255
[62,] 0.152954325 0.305908650 0.847045675
[63,] 0.136917378 0.273834755 0.863082622
[64,] 0.180048014 0.360096029 0.819951986
[65,] 0.160718142 0.321436283 0.839281858
[66,] 0.139357405 0.278714809 0.860642595
[67,] 0.139495917 0.278991835 0.860504083
[68,] 0.128947727 0.257895454 0.871052273
[69,] 0.110239902 0.220479804 0.889760098
[70,] 0.093476065 0.186952129 0.906523935
[71,] 0.079874037 0.159748074 0.920125963
[72,] 0.067385240 0.134770481 0.932614760
[73,] 0.066619143 0.133238287 0.933380857
[74,] 0.079053285 0.158106569 0.920946715
[75,] 0.065420290 0.130840581 0.934579710
[76,] 0.054021546 0.108043092 0.945978454
[77,] 0.045739857 0.091479713 0.954260143
[78,] 0.039812521 0.079625042 0.960187479
[79,] 0.056214878 0.112429756 0.943785122
[80,] 0.047645074 0.095290148 0.952354926
[81,] 0.042536087 0.085072175 0.957463913
[82,] 0.042173432 0.084346864 0.957826568
[83,] 0.044833191 0.089666381 0.955166809
[84,] 0.039235092 0.078470184 0.960764908
[85,] 0.051804492 0.103608985 0.948195508
[86,] 0.042635030 0.085270060 0.957364970
[87,] 0.037578114 0.075156228 0.962421886
[88,] 0.040796256 0.081592512 0.959203744
[89,] 0.035149702 0.070299404 0.964850298
[90,] 0.029744256 0.059488512 0.970255744
[91,] 0.024093703 0.048187406 0.975906297
[92,] 0.021916526 0.043833051 0.978083474
[93,] 0.022442701 0.044885401 0.977557299
[94,] 0.018010719 0.036021437 0.981989281
[95,] 0.014298818 0.028597636 0.985701182
[96,] 0.012225494 0.024450988 0.987774506
[97,] 0.018350678 0.036701357 0.981649322
[98,] 0.014529249 0.029058497 0.985470751
[99,] 0.015616658 0.031233315 0.984383342
[100,] 0.016603467 0.033206933 0.983396533
[101,] 0.013667132 0.027334263 0.986332868
[102,] 0.011037424 0.022074847 0.988962576
[103,] 0.008708058 0.017416116 0.991291942
[104,] 0.009331829 0.018663658 0.990668171
[105,] 0.014152530 0.028305061 0.985847470
[106,] 0.011605861 0.023211723 0.988394139
[107,] 0.009146855 0.018293710 0.990853145
[108,] 0.007444903 0.014889807 0.992555097
[109,] 0.006996318 0.013992635 0.993003682
[110,] 0.006792383 0.013584765 0.993207617
[111,] 0.007457950 0.014915901 0.992542050
[112,] 0.007881190 0.015762381 0.992118810
[113,] 0.012984537 0.025969074 0.987015463
[114,] 0.011271971 0.022543941 0.988728029
[115,] 0.010048926 0.020097851 0.989951074
[116,] 0.010958016 0.021916031 0.989041984
[117,] 0.010499512 0.020999024 0.989500488
[118,] 0.010737117 0.021474233 0.989262883
[119,] 0.013332402 0.026664804 0.986667598
[120,] 0.026286245 0.052572490 0.973713755
[121,] 0.025787998 0.051575995 0.974212002
[122,] 0.024290552 0.048581103 0.975709448
[123,] 0.021554065 0.043108130 0.978445935
[124,] 0.017815176 0.035630352 0.982184824
[125,] 0.014350881 0.028701762 0.985649119
[126,] 0.013145901 0.026291802 0.986854099
[127,] 0.011827652 0.023655303 0.988172348
[128,] 0.009964917 0.019929834 0.990035083
[129,] 0.017221632 0.034443264 0.982778368
[130,] 0.018515716 0.037031432 0.981484284
[131,] 0.027472198 0.054944397 0.972527802
[132,] 0.022265191 0.044530383 0.977734809
[133,] 0.017901190 0.035802380 0.982098810
[134,] 0.015403444 0.030806888 0.984596556
[135,] 0.018725219 0.037450439 0.981274781
[136,] 0.016486824 0.032973648 0.983513176
[137,] 0.013796577 0.027593153 0.986203423
[138,] 0.012015125 0.024030249 0.987984875
[139,] 0.015214137 0.030428275 0.984785863
[140,] 0.017143491 0.034286981 0.982856509
[141,] 0.070019201 0.140038402 0.929980799
[142,] 0.061795221 0.123590443 0.938204779
[143,] 0.052570835 0.105141671 0.947429165
[144,] 0.108845162 0.217690323 0.891154838
[145,] 0.143779167 0.287558333 0.856220833
[146,] 0.130696238 0.261392476 0.869303762
[147,] 0.116362543 0.232725086 0.883637457
[148,] 0.104041713 0.208083425 0.895958287
[149,] 0.091841554 0.183683109 0.908158446
[150,] 0.078081213 0.156162426 0.921918787
[151,] 0.073794828 0.147589657 0.926205172
[152,] 0.074846842 0.149693684 0.925153158
[153,] 0.071041741 0.142083482 0.928958259
[154,] 0.060496994 0.120993988 0.939503006
[155,] 0.050195103 0.100390206 0.949804897
[156,] 0.073638867 0.147277733 0.926361133
[157,] 0.070093941 0.140187882 0.929906059
[158,] 0.059532542 0.119065085 0.940467458
[159,] 0.125236877 0.250473753 0.874763123
[160,] 0.107010969 0.214021938 0.892989031
[161,] 0.091337325 0.182674650 0.908662675
[162,] 0.078092185 0.156184370 0.921907815
[163,] 0.101404188 0.202808376 0.898595812
[164,] 0.086483320 0.172966640 0.913516680
[165,] 0.080250928 0.160501856 0.919749072
[166,] 0.067464776 0.134929553 0.932535224
[167,] 0.056223588 0.112447177 0.943776412
[168,] 0.046827330 0.093654661 0.953172670
[169,] 0.038364397 0.076728793 0.961635603
[170,] 0.049166651 0.098333301 0.950833349
[171,] 0.047766970 0.095533940 0.952233030
[172,] 0.042443732 0.084887464 0.957556268
[173,] 0.167157484 0.334314969 0.832842516
[174,] 0.151701611 0.303403222 0.848298389
[175,] 0.133138458 0.266276916 0.866861542
[176,] 0.133855446 0.267710891 0.866144554
[177,] 0.123290483 0.246580966 0.876709517
[178,] 0.205282329 0.410564658 0.794717671
[179,] 0.204812768 0.409625537 0.795187232
[180,] 0.177904219 0.355808437 0.822095781
[181,] 0.582449294 0.835101412 0.417550706
[182,] 0.566107787 0.867784426 0.433892213
[183,] 0.524501265 0.950997470 0.475498735
[184,] 0.487836271 0.975672543 0.512163729
[185,] 0.520449315 0.959101370 0.479550685
[186,] 0.479366636 0.958733271 0.520633364
[187,] 0.474925829 0.949851659 0.525074171
[188,] 0.456382317 0.912764633 0.543617683
[189,] 0.416956304 0.833912608 0.583043696
[190,] 0.381077367 0.762154733 0.618922633
[191,] 0.348064398 0.696128795 0.651935602
[192,] 0.377697364 0.755394729 0.622302636
[193,] 0.422626237 0.845252473 0.577373763
[194,] 0.379125058 0.758250116 0.620874942
[195,] 0.344795814 0.689591628 0.655204186
[196,] 0.307171398 0.614342795 0.692828602
[197,] 0.280607710 0.561215419 0.719392290
[198,] 0.245561105 0.491122211 0.754438895
[199,] 0.285626931 0.571253863 0.714373069
[200,] 0.297247964 0.594495928 0.702752036
[201,] 0.257616253 0.515232506 0.742383747
[202,] 0.282513781 0.565027561 0.717486219
[203,] 0.247344219 0.494688438 0.752655781
[204,] 0.211720765 0.423441529 0.788279235
[205,] 0.181216168 0.362432337 0.818783832
[206,] 0.152237204 0.304474408 0.847762796
[207,] 0.161042594 0.322085188 0.838957406
[208,] 0.136710695 0.273421390 0.863289305
[209,] 0.160934238 0.321868475 0.839065762
[210,] 0.131412001 0.262824002 0.868587999
[211,] 0.122291482 0.244582964 0.877708518
[212,] 0.102359746 0.204719492 0.897640254
[213,] 0.081069220 0.162138441 0.918930780
[214,] 0.063556262 0.127112524 0.936443738
[215,] 0.049325391 0.098650783 0.950674609
[216,] 0.037552901 0.075105802 0.962447099
[217,] 0.027736436 0.055472871 0.972263564
[218,] 0.020416552 0.040833105 0.979583448
[219,] 0.029494055 0.058988110 0.970505945
[220,] 0.037887820 0.075775639 0.962112180
[221,] 0.170004873 0.340009745 0.829995127
[222,] 0.146751309 0.293502618 0.853248691
[223,] 0.113363647 0.226727294 0.886636353
[224,] 0.109105846 0.218211692 0.890894154
[225,] 0.137211516 0.274423032 0.862788484
[226,] 0.162032097 0.324064193 0.837967903
[227,] 0.181429763 0.362859526 0.818570237
[228,] 0.168107884 0.336215767 0.831892116
[229,] 0.170488037 0.340976075 0.829511963
[230,] 0.609955315 0.780089371 0.390044685
[231,] 0.667243182 0.665513636 0.332756818
[232,] 0.632452026 0.735095949 0.367547974
[233,] 0.560362908 0.879274183 0.439637092
[234,] 0.458000591 0.916001183 0.541999409
[235,] 0.362291158 0.724582315 0.637708842
[236,] 0.275878798 0.551757597 0.724121202
[237,] 0.188346782 0.376693564 0.811653218
[238,] 0.234009101 0.468018201 0.765990899
[239,] 0.168658093 0.337316185 0.831341907
> postscript(file="/var/wessaorg/rcomp/tmp/1rm8x1353260106.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/232vb1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3dede1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/48tka1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/50jzl1353260106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.517814577 -0.907584206 1.185028394 -5.633375170 1.983367835 -0.222276570
7 8 9 10 11 12
-1.176625117 2.331008790 0.993426942 -2.582810296 -1.800729966 0.261355599
13 14 15 16 17 18
-0.023793772 -0.839300087 2.396177101 0.673443342 -1.316235890 -2.101568709
19 20 21 22 23 24
-1.988807739 0.300858698 -0.755801889 0.124323573 -0.381720930 -0.930743250
25 26 27 28 29 30
-0.210057498 0.582194117 -1.811017225 2.632394649 0.223933605 -0.765084007
31 32 33 34 35 36
0.776875286 -1.629502228 -0.472792763 0.097640214 1.441515026 -1.575086539
37 38 39 40 41 42
0.406034064 1.451143821 -1.616284618 -1.659024166 -1.550841317 1.489440073
43 44 45 46 47 48
1.558685125 -0.455278918 -0.244057107 2.942842304 2.385875010 -0.075965688
49 50 51 52 53 54
-0.569366144 1.561487651 2.313960293 -0.331385176 2.578903304 1.385759230
55 56 57 58 59 60
-2.851862608 -4.391513515 0.679582928 0.480163034 -0.052222477 -1.440233058
61 62 63 64 65 66
-2.188713793 0.585236893 2.406824115 0.701827175 0.887237480 0.055403853
67 68 69 70 71 72
0.833654000 -2.051082240 1.943811347 -0.109242441 -0.910543045 -0.016072803
73 74 75 76 77 78
0.704514573 0.752391550 0.103026078 -3.337556819 1.059196345 -0.836838669
79 80 81 82 83 84
1.591238207 0.383881645 -0.081625831 -0.032138652 0.728463674 -0.408395102
85 86 87 88 89 90
-1.840637315 -1.951666056 0.110207808 0.219454144 0.568329375 -0.794039294
91 92 93 94 95 96
2.860742581 -0.061101012 0.852061732 1.726822541 -1.780389279 1.234169151
97 98 99 100 101 102
-2.697209493 0.321180521 -0.834607706 2.308706295 -0.733043351 1.118880598
103 104 105 106 107 108
-0.064272136 -0.859914802 2.001299036 -0.118786685 0.371646112 -0.629038850
109 110 111 112 113 114
-2.511604257 0.285381188 2.295373624 -1.549431983 1.186434735 0.671443098
115 116 117 118 119 120
0.486423706 2.387873178 -2.665536137 0.964218868 0.408415346 1.010760180
121 122 123 124 125 126
1.713061194 1.884487789 2.405311616 2.230157813 3.449746009 -0.804600283
127 128 129 130 131 132
-0.882008016 -1.868478500 1.784822189 -1.423729643 2.677114822 -3.385712033
133 134 135 136 137 138
1.866831560 1.634061030 1.357269062 -0.277070090 0.273422319 1.267405047
139 140 141 142 143 144
-1.104441062 0.226093491 -3.165613047 -1.889298244 3.057280692 0.134521683
145 146 147 148 149 150
0.083017719 1.033283731 2.330935803 0.809412106 -0.593755845 -1.151332560
151 152 153 154 155 156
2.395576290 -2.239016875 -5.343328459 -1.281692983 0.133550917 3.628706839
157 158 159 160 161 162
-3.606055885 -1.514033458 -0.109125677 -1.208172958 -1.486937866 0.006685188
163 164 165 166 167 168
-1.481763618 1.935141460 -1.469563110 -0.500020970 0.253917081 3.025386425
169 170 171 172 173 174
1.538268479 -0.029452893 -4.323758003 0.336241453 0.318733687 -0.648599471
175 176 177 178 179 180
2.794521246 -0.277531626 -1.517025859 -0.411592949 -0.401478042 -0.619656487
181 182 183 184 185 186
0.513699612 -2.809236760 1.825607820 1.076201233 4.613375789 0.415476143
187 188 189 190 191 192
-0.958163706 -2.250601307 -1.648996165 -3.875598333 1.585245370 0.383864958
193 194 195 196 197 198
-6.241145016 -0.469846902 0.269933576 1.003945425 -2.212915832 0.463799948
199 200 201 202 203 204
-1.336764082 0.543605209 0.679158219 -0.247837737 0.478146655 2.983778718
205 206 207 208 209 210
-2.744991038 0.223915112 -0.406237064 0.937385371 1.459494512 -0.662105392
211 212 213 214 215 216
2.865603092 2.637941561 0.007029574 -2.799969062 0.820047170 0.220957660
217 218 219 220 221 222
-0.690991384 -0.047219407 -1.561085543 1.282606754 2.411835463 0.112908199
223 224 225 226 227 228
1.447726098 0.056714137 -0.431567848 -0.968120577 0.320710117 -0.996243867
229 230 231 232 233 234
0.623934482 -0.002894539 -3.246243891 1.927103039 -4.882936631 2.230806608
235 236 237 238 239 240
0.753959932 1.782848063 1.528461472 3.100965421 1.009893757 -0.631482267
241 242 243 244 245 246
-1.004383873 2.236576682 -4.518472351 -3.174432093 -0.105655472 -0.817804746
247 248 249 250 251 252
-0.771745583 1.778683952 -0.391158112 0.719669988 -1.110628286 -0.550661858
253 254 255 256 257 258
-2.620031253 -1.649203988 1.518760539 1.419207674 1.166501314 -1.607779068
259 260 261 262 263 264
2.084815836 -1.456497698 0.984491079 3.915562696 -0.998985274 -0.547769191
> postscript(file="/var/wessaorg/rcomp/tmp/66xmb1353260106.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 1.517814577 NA
1 -0.907584206 1.517814577
2 1.185028394 -0.907584206
3 -5.633375170 1.185028394
4 1.983367835 -5.633375170
5 -0.222276570 1.983367835
6 -1.176625117 -0.222276570
7 2.331008790 -1.176625117
8 0.993426942 2.331008790
9 -2.582810296 0.993426942
10 -1.800729966 -2.582810296
11 0.261355599 -1.800729966
12 -0.023793772 0.261355599
13 -0.839300087 -0.023793772
14 2.396177101 -0.839300087
15 0.673443342 2.396177101
16 -1.316235890 0.673443342
17 -2.101568709 -1.316235890
18 -1.988807739 -2.101568709
19 0.300858698 -1.988807739
20 -0.755801889 0.300858698
21 0.124323573 -0.755801889
22 -0.381720930 0.124323573
23 -0.930743250 -0.381720930
24 -0.210057498 -0.930743250
25 0.582194117 -0.210057498
26 -1.811017225 0.582194117
27 2.632394649 -1.811017225
28 0.223933605 2.632394649
29 -0.765084007 0.223933605
30 0.776875286 -0.765084007
31 -1.629502228 0.776875286
32 -0.472792763 -1.629502228
33 0.097640214 -0.472792763
34 1.441515026 0.097640214
35 -1.575086539 1.441515026
36 0.406034064 -1.575086539
37 1.451143821 0.406034064
38 -1.616284618 1.451143821
39 -1.659024166 -1.616284618
40 -1.550841317 -1.659024166
41 1.489440073 -1.550841317
42 1.558685125 1.489440073
43 -0.455278918 1.558685125
44 -0.244057107 -0.455278918
45 2.942842304 -0.244057107
46 2.385875010 2.942842304
47 -0.075965688 2.385875010
48 -0.569366144 -0.075965688
49 1.561487651 -0.569366144
50 2.313960293 1.561487651
51 -0.331385176 2.313960293
52 2.578903304 -0.331385176
53 1.385759230 2.578903304
54 -2.851862608 1.385759230
55 -4.391513515 -2.851862608
56 0.679582928 -4.391513515
57 0.480163034 0.679582928
58 -0.052222477 0.480163034
59 -1.440233058 -0.052222477
60 -2.188713793 -1.440233058
61 0.585236893 -2.188713793
62 2.406824115 0.585236893
63 0.701827175 2.406824115
64 0.887237480 0.701827175
65 0.055403853 0.887237480
66 0.833654000 0.055403853
67 -2.051082240 0.833654000
68 1.943811347 -2.051082240
69 -0.109242441 1.943811347
70 -0.910543045 -0.109242441
71 -0.016072803 -0.910543045
72 0.704514573 -0.016072803
73 0.752391550 0.704514573
74 0.103026078 0.752391550
75 -3.337556819 0.103026078
76 1.059196345 -3.337556819
77 -0.836838669 1.059196345
78 1.591238207 -0.836838669
79 0.383881645 1.591238207
80 -0.081625831 0.383881645
81 -0.032138652 -0.081625831
82 0.728463674 -0.032138652
83 -0.408395102 0.728463674
84 -1.840637315 -0.408395102
85 -1.951666056 -1.840637315
86 0.110207808 -1.951666056
87 0.219454144 0.110207808
88 0.568329375 0.219454144
89 -0.794039294 0.568329375
90 2.860742581 -0.794039294
91 -0.061101012 2.860742581
92 0.852061732 -0.061101012
93 1.726822541 0.852061732
94 -1.780389279 1.726822541
95 1.234169151 -1.780389279
96 -2.697209493 1.234169151
97 0.321180521 -2.697209493
98 -0.834607706 0.321180521
99 2.308706295 -0.834607706
100 -0.733043351 2.308706295
101 1.118880598 -0.733043351
102 -0.064272136 1.118880598
103 -0.859914802 -0.064272136
104 2.001299036 -0.859914802
105 -0.118786685 2.001299036
106 0.371646112 -0.118786685
107 -0.629038850 0.371646112
108 -2.511604257 -0.629038850
109 0.285381188 -2.511604257
110 2.295373624 0.285381188
111 -1.549431983 2.295373624
112 1.186434735 -1.549431983
113 0.671443098 1.186434735
114 0.486423706 0.671443098
115 2.387873178 0.486423706
116 -2.665536137 2.387873178
117 0.964218868 -2.665536137
118 0.408415346 0.964218868
119 1.010760180 0.408415346
120 1.713061194 1.010760180
121 1.884487789 1.713061194
122 2.405311616 1.884487789
123 2.230157813 2.405311616
124 3.449746009 2.230157813
125 -0.804600283 3.449746009
126 -0.882008016 -0.804600283
127 -1.868478500 -0.882008016
128 1.784822189 -1.868478500
129 -1.423729643 1.784822189
130 2.677114822 -1.423729643
131 -3.385712033 2.677114822
132 1.866831560 -3.385712033
133 1.634061030 1.866831560
134 1.357269062 1.634061030
135 -0.277070090 1.357269062
136 0.273422319 -0.277070090
137 1.267405047 0.273422319
138 -1.104441062 1.267405047
139 0.226093491 -1.104441062
140 -3.165613047 0.226093491
141 -1.889298244 -3.165613047
142 3.057280692 -1.889298244
143 0.134521683 3.057280692
144 0.083017719 0.134521683
145 1.033283731 0.083017719
146 2.330935803 1.033283731
147 0.809412106 2.330935803
148 -0.593755845 0.809412106
149 -1.151332560 -0.593755845
150 2.395576290 -1.151332560
151 -2.239016875 2.395576290
152 -5.343328459 -2.239016875
153 -1.281692983 -5.343328459
154 0.133550917 -1.281692983
155 3.628706839 0.133550917
156 -3.606055885 3.628706839
157 -1.514033458 -3.606055885
158 -0.109125677 -1.514033458
159 -1.208172958 -0.109125677
160 -1.486937866 -1.208172958
161 0.006685188 -1.486937866
162 -1.481763618 0.006685188
163 1.935141460 -1.481763618
164 -1.469563110 1.935141460
165 -0.500020970 -1.469563110
166 0.253917081 -0.500020970
167 3.025386425 0.253917081
168 1.538268479 3.025386425
169 -0.029452893 1.538268479
170 -4.323758003 -0.029452893
171 0.336241453 -4.323758003
172 0.318733687 0.336241453
173 -0.648599471 0.318733687
174 2.794521246 -0.648599471
175 -0.277531626 2.794521246
176 -1.517025859 -0.277531626
177 -0.411592949 -1.517025859
178 -0.401478042 -0.411592949
179 -0.619656487 -0.401478042
180 0.513699612 -0.619656487
181 -2.809236760 0.513699612
182 1.825607820 -2.809236760
183 1.076201233 1.825607820
184 4.613375789 1.076201233
185 0.415476143 4.613375789
186 -0.958163706 0.415476143
187 -2.250601307 -0.958163706
188 -1.648996165 -2.250601307
189 -3.875598333 -1.648996165
190 1.585245370 -3.875598333
191 0.383864958 1.585245370
192 -6.241145016 0.383864958
193 -0.469846902 -6.241145016
194 0.269933576 -0.469846902
195 1.003945425 0.269933576
196 -2.212915832 1.003945425
197 0.463799948 -2.212915832
198 -1.336764082 0.463799948
199 0.543605209 -1.336764082
200 0.679158219 0.543605209
201 -0.247837737 0.679158219
202 0.478146655 -0.247837737
203 2.983778718 0.478146655
204 -2.744991038 2.983778718
205 0.223915112 -2.744991038
206 -0.406237064 0.223915112
207 0.937385371 -0.406237064
208 1.459494512 0.937385371
209 -0.662105392 1.459494512
210 2.865603092 -0.662105392
211 2.637941561 2.865603092
212 0.007029574 2.637941561
213 -2.799969062 0.007029574
214 0.820047170 -2.799969062
215 0.220957660 0.820047170
216 -0.690991384 0.220957660
217 -0.047219407 -0.690991384
218 -1.561085543 -0.047219407
219 1.282606754 -1.561085543
220 2.411835463 1.282606754
221 0.112908199 2.411835463
222 1.447726098 0.112908199
223 0.056714137 1.447726098
224 -0.431567848 0.056714137
225 -0.968120577 -0.431567848
226 0.320710117 -0.968120577
227 -0.996243867 0.320710117
228 0.623934482 -0.996243867
229 -0.002894539 0.623934482
230 -3.246243891 -0.002894539
231 1.927103039 -3.246243891
232 -4.882936631 1.927103039
233 2.230806608 -4.882936631
234 0.753959932 2.230806608
235 1.782848063 0.753959932
236 1.528461472 1.782848063
237 3.100965421 1.528461472
238 1.009893757 3.100965421
239 -0.631482267 1.009893757
240 -1.004383873 -0.631482267
241 2.236576682 -1.004383873
242 -4.518472351 2.236576682
243 -3.174432093 -4.518472351
244 -0.105655472 -3.174432093
245 -0.817804746 -0.105655472
246 -0.771745583 -0.817804746
247 1.778683952 -0.771745583
248 -0.391158112 1.778683952
249 0.719669988 -0.391158112
250 -1.110628286 0.719669988
251 -0.550661858 -1.110628286
252 -2.620031253 -0.550661858
253 -1.649203988 -2.620031253
254 1.518760539 -1.649203988
255 1.419207674 1.518760539
256 1.166501314 1.419207674
257 -1.607779068 1.166501314
258 2.084815836 -1.607779068
259 -1.456497698 2.084815836
260 0.984491079 -1.456497698
261 3.915562696 0.984491079
262 -0.998985274 3.915562696
263 -0.547769191 -0.998985274
264 NA -0.547769191
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.907584206 1.517814577
[2,] 1.185028394 -0.907584206
[3,] -5.633375170 1.185028394
[4,] 1.983367835 -5.633375170
[5,] -0.222276570 1.983367835
[6,] -1.176625117 -0.222276570
[7,] 2.331008790 -1.176625117
[8,] 0.993426942 2.331008790
[9,] -2.582810296 0.993426942
[10,] -1.800729966 -2.582810296
[11,] 0.261355599 -1.800729966
[12,] -0.023793772 0.261355599
[13,] -0.839300087 -0.023793772
[14,] 2.396177101 -0.839300087
[15,] 0.673443342 2.396177101
[16,] -1.316235890 0.673443342
[17,] -2.101568709 -1.316235890
[18,] -1.988807739 -2.101568709
[19,] 0.300858698 -1.988807739
[20,] -0.755801889 0.300858698
[21,] 0.124323573 -0.755801889
[22,] -0.381720930 0.124323573
[23,] -0.930743250 -0.381720930
[24,] -0.210057498 -0.930743250
[25,] 0.582194117 -0.210057498
[26,] -1.811017225 0.582194117
[27,] 2.632394649 -1.811017225
[28,] 0.223933605 2.632394649
[29,] -0.765084007 0.223933605
[30,] 0.776875286 -0.765084007
[31,] -1.629502228 0.776875286
[32,] -0.472792763 -1.629502228
[33,] 0.097640214 -0.472792763
[34,] 1.441515026 0.097640214
[35,] -1.575086539 1.441515026
[36,] 0.406034064 -1.575086539
[37,] 1.451143821 0.406034064
[38,] -1.616284618 1.451143821
[39,] -1.659024166 -1.616284618
[40,] -1.550841317 -1.659024166
[41,] 1.489440073 -1.550841317
[42,] 1.558685125 1.489440073
[43,] -0.455278918 1.558685125
[44,] -0.244057107 -0.455278918
[45,] 2.942842304 -0.244057107
[46,] 2.385875010 2.942842304
[47,] -0.075965688 2.385875010
[48,] -0.569366144 -0.075965688
[49,] 1.561487651 -0.569366144
[50,] 2.313960293 1.561487651
[51,] -0.331385176 2.313960293
[52,] 2.578903304 -0.331385176
[53,] 1.385759230 2.578903304
[54,] -2.851862608 1.385759230
[55,] -4.391513515 -2.851862608
[56,] 0.679582928 -4.391513515
[57,] 0.480163034 0.679582928
[58,] -0.052222477 0.480163034
[59,] -1.440233058 -0.052222477
[60,] -2.188713793 -1.440233058
[61,] 0.585236893 -2.188713793
[62,] 2.406824115 0.585236893
[63,] 0.701827175 2.406824115
[64,] 0.887237480 0.701827175
[65,] 0.055403853 0.887237480
[66,] 0.833654000 0.055403853
[67,] -2.051082240 0.833654000
[68,] 1.943811347 -2.051082240
[69,] -0.109242441 1.943811347
[70,] -0.910543045 -0.109242441
[71,] -0.016072803 -0.910543045
[72,] 0.704514573 -0.016072803
[73,] 0.752391550 0.704514573
[74,] 0.103026078 0.752391550
[75,] -3.337556819 0.103026078
[76,] 1.059196345 -3.337556819
[77,] -0.836838669 1.059196345
[78,] 1.591238207 -0.836838669
[79,] 0.383881645 1.591238207
[80,] -0.081625831 0.383881645
[81,] -0.032138652 -0.081625831
[82,] 0.728463674 -0.032138652
[83,] -0.408395102 0.728463674
[84,] -1.840637315 -0.408395102
[85,] -1.951666056 -1.840637315
[86,] 0.110207808 -1.951666056
[87,] 0.219454144 0.110207808
[88,] 0.568329375 0.219454144
[89,] -0.794039294 0.568329375
[90,] 2.860742581 -0.794039294
[91,] -0.061101012 2.860742581
[92,] 0.852061732 -0.061101012
[93,] 1.726822541 0.852061732
[94,] -1.780389279 1.726822541
[95,] 1.234169151 -1.780389279
[96,] -2.697209493 1.234169151
[97,] 0.321180521 -2.697209493
[98,] -0.834607706 0.321180521
[99,] 2.308706295 -0.834607706
[100,] -0.733043351 2.308706295
[101,] 1.118880598 -0.733043351
[102,] -0.064272136 1.118880598
[103,] -0.859914802 -0.064272136
[104,] 2.001299036 -0.859914802
[105,] -0.118786685 2.001299036
[106,] 0.371646112 -0.118786685
[107,] -0.629038850 0.371646112
[108,] -2.511604257 -0.629038850
[109,] 0.285381188 -2.511604257
[110,] 2.295373624 0.285381188
[111,] -1.549431983 2.295373624
[112,] 1.186434735 -1.549431983
[113,] 0.671443098 1.186434735
[114,] 0.486423706 0.671443098
[115,] 2.387873178 0.486423706
[116,] -2.665536137 2.387873178
[117,] 0.964218868 -2.665536137
[118,] 0.408415346 0.964218868
[119,] 1.010760180 0.408415346
[120,] 1.713061194 1.010760180
[121,] 1.884487789 1.713061194
[122,] 2.405311616 1.884487789
[123,] 2.230157813 2.405311616
[124,] 3.449746009 2.230157813
[125,] -0.804600283 3.449746009
[126,] -0.882008016 -0.804600283
[127,] -1.868478500 -0.882008016
[128,] 1.784822189 -1.868478500
[129,] -1.423729643 1.784822189
[130,] 2.677114822 -1.423729643
[131,] -3.385712033 2.677114822
[132,] 1.866831560 -3.385712033
[133,] 1.634061030 1.866831560
[134,] 1.357269062 1.634061030
[135,] -0.277070090 1.357269062
[136,] 0.273422319 -0.277070090
[137,] 1.267405047 0.273422319
[138,] -1.104441062 1.267405047
[139,] 0.226093491 -1.104441062
[140,] -3.165613047 0.226093491
[141,] -1.889298244 -3.165613047
[142,] 3.057280692 -1.889298244
[143,] 0.134521683 3.057280692
[144,] 0.083017719 0.134521683
[145,] 1.033283731 0.083017719
[146,] 2.330935803 1.033283731
[147,] 0.809412106 2.330935803
[148,] -0.593755845 0.809412106
[149,] -1.151332560 -0.593755845
[150,] 2.395576290 -1.151332560
[151,] -2.239016875 2.395576290
[152,] -5.343328459 -2.239016875
[153,] -1.281692983 -5.343328459
[154,] 0.133550917 -1.281692983
[155,] 3.628706839 0.133550917
[156,] -3.606055885 3.628706839
[157,] -1.514033458 -3.606055885
[158,] -0.109125677 -1.514033458
[159,] -1.208172958 -0.109125677
[160,] -1.486937866 -1.208172958
[161,] 0.006685188 -1.486937866
[162,] -1.481763618 0.006685188
[163,] 1.935141460 -1.481763618
[164,] -1.469563110 1.935141460
[165,] -0.500020970 -1.469563110
[166,] 0.253917081 -0.500020970
[167,] 3.025386425 0.253917081
[168,] 1.538268479 3.025386425
[169,] -0.029452893 1.538268479
[170,] -4.323758003 -0.029452893
[171,] 0.336241453 -4.323758003
[172,] 0.318733687 0.336241453
[173,] -0.648599471 0.318733687
[174,] 2.794521246 -0.648599471
[175,] -0.277531626 2.794521246
[176,] -1.517025859 -0.277531626
[177,] -0.411592949 -1.517025859
[178,] -0.401478042 -0.411592949
[179,] -0.619656487 -0.401478042
[180,] 0.513699612 -0.619656487
[181,] -2.809236760 0.513699612
[182,] 1.825607820 -2.809236760
[183,] 1.076201233 1.825607820
[184,] 4.613375789 1.076201233
[185,] 0.415476143 4.613375789
[186,] -0.958163706 0.415476143
[187,] -2.250601307 -0.958163706
[188,] -1.648996165 -2.250601307
[189,] -3.875598333 -1.648996165
[190,] 1.585245370 -3.875598333
[191,] 0.383864958 1.585245370
[192,] -6.241145016 0.383864958
[193,] -0.469846902 -6.241145016
[194,] 0.269933576 -0.469846902
[195,] 1.003945425 0.269933576
[196,] -2.212915832 1.003945425
[197,] 0.463799948 -2.212915832
[198,] -1.336764082 0.463799948
[199,] 0.543605209 -1.336764082
[200,] 0.679158219 0.543605209
[201,] -0.247837737 0.679158219
[202,] 0.478146655 -0.247837737
[203,] 2.983778718 0.478146655
[204,] -2.744991038 2.983778718
[205,] 0.223915112 -2.744991038
[206,] -0.406237064 0.223915112
[207,] 0.937385371 -0.406237064
[208,] 1.459494512 0.937385371
[209,] -0.662105392 1.459494512
[210,] 2.865603092 -0.662105392
[211,] 2.637941561 2.865603092
[212,] 0.007029574 2.637941561
[213,] -2.799969062 0.007029574
[214,] 0.820047170 -2.799969062
[215,] 0.220957660 0.820047170
[216,] -0.690991384 0.220957660
[217,] -0.047219407 -0.690991384
[218,] -1.561085543 -0.047219407
[219,] 1.282606754 -1.561085543
[220,] 2.411835463 1.282606754
[221,] 0.112908199 2.411835463
[222,] 1.447726098 0.112908199
[223,] 0.056714137 1.447726098
[224,] -0.431567848 0.056714137
[225,] -0.968120577 -0.431567848
[226,] 0.320710117 -0.968120577
[227,] -0.996243867 0.320710117
[228,] 0.623934482 -0.996243867
[229,] -0.002894539 0.623934482
[230,] -3.246243891 -0.002894539
[231,] 1.927103039 -3.246243891
[232,] -4.882936631 1.927103039
[233,] 2.230806608 -4.882936631
[234,] 0.753959932 2.230806608
[235,] 1.782848063 0.753959932
[236,] 1.528461472 1.782848063
[237,] 3.100965421 1.528461472
[238,] 1.009893757 3.100965421
[239,] -0.631482267 1.009893757
[240,] -1.004383873 -0.631482267
[241,] 2.236576682 -1.004383873
[242,] -4.518472351 2.236576682
[243,] -3.174432093 -4.518472351
[244,] -0.105655472 -3.174432093
[245,] -0.817804746 -0.105655472
[246,] -0.771745583 -0.817804746
[247,] 1.778683952 -0.771745583
[248,] -0.391158112 1.778683952
[249,] 0.719669988 -0.391158112
[250,] -1.110628286 0.719669988
[251,] -0.550661858 -1.110628286
[252,] -2.620031253 -0.550661858
[253,] -1.649203988 -2.620031253
[254,] 1.518760539 -1.649203988
[255,] 1.419207674 1.518760539
[256,] 1.166501314 1.419207674
[257,] -1.607779068 1.166501314
[258,] 2.084815836 -1.607779068
[259,] -1.456497698 2.084815836
[260,] 0.984491079 -1.456497698
[261,] 3.915562696 0.984491079
[262,] -0.998985274 3.915562696
[263,] -0.547769191 -0.998985274
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.907584206 1.517814577
2 1.185028394 -0.907584206
3 -5.633375170 1.185028394
4 1.983367835 -5.633375170
5 -0.222276570 1.983367835
6 -1.176625117 -0.222276570
7 2.331008790 -1.176625117
8 0.993426942 2.331008790
9 -2.582810296 0.993426942
10 -1.800729966 -2.582810296
11 0.261355599 -1.800729966
12 -0.023793772 0.261355599
13 -0.839300087 -0.023793772
14 2.396177101 -0.839300087
15 0.673443342 2.396177101
16 -1.316235890 0.673443342
17 -2.101568709 -1.316235890
18 -1.988807739 -2.101568709
19 0.300858698 -1.988807739
20 -0.755801889 0.300858698
21 0.124323573 -0.755801889
22 -0.381720930 0.124323573
23 -0.930743250 -0.381720930
24 -0.210057498 -0.930743250
25 0.582194117 -0.210057498
26 -1.811017225 0.582194117
27 2.632394649 -1.811017225
28 0.223933605 2.632394649
29 -0.765084007 0.223933605
30 0.776875286 -0.765084007
31 -1.629502228 0.776875286
32 -0.472792763 -1.629502228
33 0.097640214 -0.472792763
34 1.441515026 0.097640214
35 -1.575086539 1.441515026
36 0.406034064 -1.575086539
37 1.451143821 0.406034064
38 -1.616284618 1.451143821
39 -1.659024166 -1.616284618
40 -1.550841317 -1.659024166
41 1.489440073 -1.550841317
42 1.558685125 1.489440073
43 -0.455278918 1.558685125
44 -0.244057107 -0.455278918
45 2.942842304 -0.244057107
46 2.385875010 2.942842304
47 -0.075965688 2.385875010
48 -0.569366144 -0.075965688
49 1.561487651 -0.569366144
50 2.313960293 1.561487651
51 -0.331385176 2.313960293
52 2.578903304 -0.331385176
53 1.385759230 2.578903304
54 -2.851862608 1.385759230
55 -4.391513515 -2.851862608
56 0.679582928 -4.391513515
57 0.480163034 0.679582928
58 -0.052222477 0.480163034
59 -1.440233058 -0.052222477
60 -2.188713793 -1.440233058
61 0.585236893 -2.188713793
62 2.406824115 0.585236893
63 0.701827175 2.406824115
64 0.887237480 0.701827175
65 0.055403853 0.887237480
66 0.833654000 0.055403853
67 -2.051082240 0.833654000
68 1.943811347 -2.051082240
69 -0.109242441 1.943811347
70 -0.910543045 -0.109242441
71 -0.016072803 -0.910543045
72 0.704514573 -0.016072803
73 0.752391550 0.704514573
74 0.103026078 0.752391550
75 -3.337556819 0.103026078
76 1.059196345 -3.337556819
77 -0.836838669 1.059196345
78 1.591238207 -0.836838669
79 0.383881645 1.591238207
80 -0.081625831 0.383881645
81 -0.032138652 -0.081625831
82 0.728463674 -0.032138652
83 -0.408395102 0.728463674
84 -1.840637315 -0.408395102
85 -1.951666056 -1.840637315
86 0.110207808 -1.951666056
87 0.219454144 0.110207808
88 0.568329375 0.219454144
89 -0.794039294 0.568329375
90 2.860742581 -0.794039294
91 -0.061101012 2.860742581
92 0.852061732 -0.061101012
93 1.726822541 0.852061732
94 -1.780389279 1.726822541
95 1.234169151 -1.780389279
96 -2.697209493 1.234169151
97 0.321180521 -2.697209493
98 -0.834607706 0.321180521
99 2.308706295 -0.834607706
100 -0.733043351 2.308706295
101 1.118880598 -0.733043351
102 -0.064272136 1.118880598
103 -0.859914802 -0.064272136
104 2.001299036 -0.859914802
105 -0.118786685 2.001299036
106 0.371646112 -0.118786685
107 -0.629038850 0.371646112
108 -2.511604257 -0.629038850
109 0.285381188 -2.511604257
110 2.295373624 0.285381188
111 -1.549431983 2.295373624
112 1.186434735 -1.549431983
113 0.671443098 1.186434735
114 0.486423706 0.671443098
115 2.387873178 0.486423706
116 -2.665536137 2.387873178
117 0.964218868 -2.665536137
118 0.408415346 0.964218868
119 1.010760180 0.408415346
120 1.713061194 1.010760180
121 1.884487789 1.713061194
122 2.405311616 1.884487789
123 2.230157813 2.405311616
124 3.449746009 2.230157813
125 -0.804600283 3.449746009
126 -0.882008016 -0.804600283
127 -1.868478500 -0.882008016
128 1.784822189 -1.868478500
129 -1.423729643 1.784822189
130 2.677114822 -1.423729643
131 -3.385712033 2.677114822
132 1.866831560 -3.385712033
133 1.634061030 1.866831560
134 1.357269062 1.634061030
135 -0.277070090 1.357269062
136 0.273422319 -0.277070090
137 1.267405047 0.273422319
138 -1.104441062 1.267405047
139 0.226093491 -1.104441062
140 -3.165613047 0.226093491
141 -1.889298244 -3.165613047
142 3.057280692 -1.889298244
143 0.134521683 3.057280692
144 0.083017719 0.134521683
145 1.033283731 0.083017719
146 2.330935803 1.033283731
147 0.809412106 2.330935803
148 -0.593755845 0.809412106
149 -1.151332560 -0.593755845
150 2.395576290 -1.151332560
151 -2.239016875 2.395576290
152 -5.343328459 -2.239016875
153 -1.281692983 -5.343328459
154 0.133550917 -1.281692983
155 3.628706839 0.133550917
156 -3.606055885 3.628706839
157 -1.514033458 -3.606055885
158 -0.109125677 -1.514033458
159 -1.208172958 -0.109125677
160 -1.486937866 -1.208172958
161 0.006685188 -1.486937866
162 -1.481763618 0.006685188
163 1.935141460 -1.481763618
164 -1.469563110 1.935141460
165 -0.500020970 -1.469563110
166 0.253917081 -0.500020970
167 3.025386425 0.253917081
168 1.538268479 3.025386425
169 -0.029452893 1.538268479
170 -4.323758003 -0.029452893
171 0.336241453 -4.323758003
172 0.318733687 0.336241453
173 -0.648599471 0.318733687
174 2.794521246 -0.648599471
175 -0.277531626 2.794521246
176 -1.517025859 -0.277531626
177 -0.411592949 -1.517025859
178 -0.401478042 -0.411592949
179 -0.619656487 -0.401478042
180 0.513699612 -0.619656487
181 -2.809236760 0.513699612
182 1.825607820 -2.809236760
183 1.076201233 1.825607820
184 4.613375789 1.076201233
185 0.415476143 4.613375789
186 -0.958163706 0.415476143
187 -2.250601307 -0.958163706
188 -1.648996165 -2.250601307
189 -3.875598333 -1.648996165
190 1.585245370 -3.875598333
191 0.383864958 1.585245370
192 -6.241145016 0.383864958
193 -0.469846902 -6.241145016
194 0.269933576 -0.469846902
195 1.003945425 0.269933576
196 -2.212915832 1.003945425
197 0.463799948 -2.212915832
198 -1.336764082 0.463799948
199 0.543605209 -1.336764082
200 0.679158219 0.543605209
201 -0.247837737 0.679158219
202 0.478146655 -0.247837737
203 2.983778718 0.478146655
204 -2.744991038 2.983778718
205 0.223915112 -2.744991038
206 -0.406237064 0.223915112
207 0.937385371 -0.406237064
208 1.459494512 0.937385371
209 -0.662105392 1.459494512
210 2.865603092 -0.662105392
211 2.637941561 2.865603092
212 0.007029574 2.637941561
213 -2.799969062 0.007029574
214 0.820047170 -2.799969062
215 0.220957660 0.820047170
216 -0.690991384 0.220957660
217 -0.047219407 -0.690991384
218 -1.561085543 -0.047219407
219 1.282606754 -1.561085543
220 2.411835463 1.282606754
221 0.112908199 2.411835463
222 1.447726098 0.112908199
223 0.056714137 1.447726098
224 -0.431567848 0.056714137
225 -0.968120577 -0.431567848
226 0.320710117 -0.968120577
227 -0.996243867 0.320710117
228 0.623934482 -0.996243867
229 -0.002894539 0.623934482
230 -3.246243891 -0.002894539
231 1.927103039 -3.246243891
232 -4.882936631 1.927103039
233 2.230806608 -4.882936631
234 0.753959932 2.230806608
235 1.782848063 0.753959932
236 1.528461472 1.782848063
237 3.100965421 1.528461472
238 1.009893757 3.100965421
239 -0.631482267 1.009893757
240 -1.004383873 -0.631482267
241 2.236576682 -1.004383873
242 -4.518472351 2.236576682
243 -3.174432093 -4.518472351
244 -0.105655472 -3.174432093
245 -0.817804746 -0.105655472
246 -0.771745583 -0.817804746
247 1.778683952 -0.771745583
248 -0.391158112 1.778683952
249 0.719669988 -0.391158112
250 -1.110628286 0.719669988
251 -0.550661858 -1.110628286
252 -2.620031253 -0.550661858
253 -1.649203988 -2.620031253
254 1.518760539 -1.649203988
255 1.419207674 1.518760539
256 1.166501314 1.419207674
257 -1.607779068 1.166501314
258 2.084815836 -1.607779068
259 -1.456497698 2.084815836
260 0.984491079 -1.456497698
261 3.915562696 0.984491079
262 -0.998985274 3.915562696
263 -0.547769191 -0.998985274
> 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/7x9k11353260106.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/8icmq1353260106.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/9w9x01353260106.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/10brzd1353260106.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11s4ix1353260106.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12bedh1353260106.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/138tqs1353260106.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14hgkh1353260106.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/15i0mw1353260106.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/162i751353260106.tab")
+ }
>
> try(system("convert tmp/1rm8x1353260106.ps tmp/1rm8x1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/232vb1353260106.ps tmp/232vb1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dede1353260106.ps tmp/3dede1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/48tka1353260106.ps tmp/48tka1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/50jzl1353260106.ps tmp/50jzl1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/66xmb1353260106.ps tmp/66xmb1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/7x9k11353260106.ps tmp/7x9k11353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/8icmq1353260106.ps tmp/8icmq1353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w9x01353260106.ps tmp/9w9x01353260106.png",intern=TRUE))
character(0)
> try(system("convert tmp/10brzd1353260106.ps tmp/10brzd1353260106.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.310 1.288 14.583