R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(9
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+ ,4
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+ ,38
+ ,11
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+ ,41
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+ ,12
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+ ,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 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
Happiness month Connected Separate Learning Software Depression Belonging
1 14 9 41 38 13 12 12.0 53
2 18 9 39 32 16 11 11.0 83
3 11 9 30 35 19 15 14.0 66
4 12 9 31 33 15 6 12.0 67
5 16 9 34 37 14 13 21.0 76
6 18 9 35 29 13 10 12.0 78
7 14 9 39 31 19 12 22.0 53
8 14 9 34 36 15 14 11.0 80
9 15 9 36 35 14 12 10.0 74
10 15 9 37 38 15 9 13.0 76
11 17 9 38 31 16 10 10.0 79
12 19 9 36 34 16 12 8.0 54
13 10 9 38 35 16 12 15.0 67
14 16 9 39 38 16 11 14.0 54
15 18 9 33 37 17 15 10.0 87
16 14 9 32 33 15 12 14.0 58
17 14 9 36 32 15 10 14.0 75
18 17 9 38 38 20 12 11.0 88
19 14 9 39 38 18 11 10.0 64
20 16 9 32 32 16 12 13.0 57
21 18 9 32 33 16 11 9.5 66
22 11 9 31 31 16 12 14.0 68
23 14 9 39 38 19 13 12.0 54
24 12 9 37 39 16 11 14.0 56
25 17 9 39 32 17 12 11.0 86
26 9 9 41 32 17 13 9.0 80
27 16 9 36 35 16 10 11.0 76
28 14 9 33 37 15 14 15.0 69
29 15 9 33 33 16 12 14.0 78
30 11 9 34 33 14 10 13.0 67
31 16 9 31 31 15 12 9.0 80
32 13 9 27 32 12 8 15.0 54
33 17 9 37 31 14 10 10.0 71
34 15 9 34 37 16 12 11.0 84
35 14 9 34 30 14 12 13.0 74
36 16 9 32 33 10 7 8.0 71
37 9 9 29 31 10 9 20.0 63
38 15 9 36 33 14 12 12.0 71
39 17 9 29 31 16 10 10.0 76
40 13 9 35 33 16 10 10.0 69
41 15 9 37 32 16 10 9.0 74
42 16 9 34 33 14 12 14.0 75
43 16 9 38 32 20 15 8.0 54
44 12 9 35 33 14 10 14.0 52
45 15 9 38 28 14 10 11.0 69
46 11 9 37 35 11 12 13.0 68
47 15 9 38 39 14 13 9.0 65
48 15 9 33 34 15 11 11.0 75
49 17 9 36 38 16 11 15.0 74
50 13 9 38 32 14 12 11.0 75
51 16 9 32 38 16 14 10.0 72
52 14 9 32 30 14 10 14.0 67
53 11 9 32 33 12 12 18.0 63
54 12 9 34 38 16 13 14.0 62
55 12 9 32 32 9 5 11.0 63
56 15 9 37 35 14 6 14.5 76
57 16 9 39 34 16 12 13.0 74
58 15 9 29 34 16 12 9.0 67
59 12 9 37 36 15 11 10.0 73
60 12 9 35 34 16 10 15.0 70
61 8 9 30 28 12 7 20.0 53
62 13 9 38 34 16 12 12.0 77
63 11 9 34 35 16 14 12.0 80
64 14 9 31 35 14 11 14.0 52
65 15 9 34 31 16 12 13.0 54
66 10 10 35 37 17 13 11.0 80
67 11 10 36 35 18 14 17.0 66
68 12 10 30 27 18 11 12.0 73
69 15 10 39 40 12 12 13.0 63
70 15 10 35 37 16 12 14.0 69
71 14 10 38 36 10 8 13.0 67
72 16 10 31 38 14 11 15.0 54
73 15 10 34 39 18 14 13.0 81
74 15 10 38 41 18 14 10.0 69
75 13 10 34 27 16 12 11.0 84
76 12 10 39 30 17 9 19.0 80
77 17 10 37 37 16 13 13.0 70
78 13 10 34 31 16 11 17.0 69
79 15 10 28 31 13 12 13.0 77
80 13 10 37 27 16 12 9.0 54
81 15 10 33 36 16 12 11.0 79
82 15 10 35 37 16 12 9.0 71
83 16 10 37 33 15 12 12.0 73
84 15 10 32 34 15 11 12.0 72
85 14 10 33 31 16 10 13.0 77
86 15 10 38 39 14 9 13.0 75
87 14 10 33 34 16 12 12.0 69
88 13 10 29 32 16 12 15.0 54
89 7 10 33 33 15 12 22.0 70
90 17 10 31 36 12 9 13.0 73
91 13 10 36 32 17 15 15.0 54
92 15 10 35 41 16 12 13.0 77
93 14 10 32 28 15 12 15.0 82
94 13 10 29 30 13 12 12.5 80
95 16 10 39 36 16 10 11.0 80
96 12 10 37 35 16 13 16.0 69
97 14 10 35 31 16 9 11.0 78
98 17 10 37 34 16 12 11.0 81
99 15 10 32 36 14 10 10.0 76
100 17 10 38 36 16 14 10.0 76
101 12 10 37 35 16 11 16.0 73
102 16 10 36 37 20 15 12.0 85
103 11 10 32 28 15 11 11.0 66
104 15 10 33 39 16 11 16.0 79
105 9 10 40 32 13 12 19.0 68
106 16 10 38 35 17 12 11.0 76
107 15 10 41 39 16 12 16.0 71
108 10 10 36 35 16 11 15.0 54
109 10 10 43 42 12 7 24.0 46
110 15 10 30 34 16 12 14.0 85
111 11 10 31 33 16 14 15.0 74
112 13 10 32 41 17 11 11.0 88
113 14 10 32 33 13 11 15.0 38
114 18 10 37 34 12 10 12.0 76
115 16 10 37 32 18 13 10.0 86
116 14 10 33 40 14 13 14.0 54
117 14 10 34 40 14 8 13.0 67
118 14 10 33 35 13 11 9.0 69
119 14 10 38 36 16 12 15.0 90
120 12 10 33 37 13 11 15.0 54
121 14 10 31 27 16 13 14.0 76
122 15 10 38 39 13 12 11.0 89
123 15 10 37 38 16 14 8.0 76
124 15 10 36 31 15 13 11.0 73
125 13 10 31 33 16 15 11.0 79
126 17 10 39 32 15 10 8.0 90
127 17 10 44 39 17 11 10.0 74
128 19 10 33 36 15 9 11.0 81
129 15 10 35 33 12 11 13.0 72
130 13 10 32 33 16 10 11.0 71
131 9 10 28 32 10 11 20.0 66
132 15 10 40 37 16 8 10.0 77
133 15 10 27 30 12 11 15.0 65
134 15 10 37 38 14 12 12.0 74
135 16 10 32 29 15 12 14.0 85
136 11 10 28 22 13 9 23.0 54
137 14 10 34 35 15 11 14.0 63
138 11 10 30 35 11 10 16.0 54
139 15 10 35 34 12 8 11.0 64
140 13 10 31 35 11 9 12.0 69
141 15 10 32 34 16 8 10.0 54
142 16 10 30 37 15 9 14.0 84
143 14 10 30 35 17 15 12.0 86
144 15 10 31 23 16 11 12.0 77
145 16 10 40 31 10 8 11.0 89
146 16 10 32 27 18 13 12.0 76
147 11 10 36 36 13 12 13.0 60
148 12 10 32 31 16 12 11.0 75
149 9 10 35 32 13 9 19.0 73
150 16 10 38 39 10 7 12.0 85
151 13 10 42 37 15 13 17.0 79
152 16 10 34 38 16 9 9.0 71
153 12 10 35 39 16 6 12.0 72
154 9 9 38 34 14 8 19.0 69
155 13 10 33 31 10 8 18.0 78
156 13 10 36 32 17 15 15.0 54
157 14 10 32 37 13 6 14.0 69
158 19 10 33 36 15 9 11.0 81
159 13 10 34 32 16 11 9.0 84
160 12 10 32 38 12 8 18.0 84
161 13 10 34 36 13 8 16.0 69
162 10 11 27 26 13 10 24.0 66
163 14 11 31 26 12 8 14.0 81
164 16 11 38 33 17 14 20.0 82
165 10 11 34 39 15 10 18.0 72
166 11 11 24 30 10 8 23.0 54
167 14 11 30 33 14 11 12.0 78
168 12 11 26 25 11 12 14.0 74
169 9 11 34 38 13 12 16.0 82
170 9 11 27 37 16 12 18.0 73
171 11 11 37 31 12 5 20.0 55
172 16 11 36 37 16 12 12.0 72
173 9 11 41 35 12 10 12.0 78
174 13 11 29 25 9 7 17.0 59
175 16 11 36 28 12 12 13.0 72
176 13 11 32 35 15 11 9.0 78
177 9 11 37 33 12 8 16.0 68
178 12 11 30 30 12 9 18.0 69
179 16 11 31 31 14 10 10.0 67
180 11 11 38 37 12 9 14.0 74
181 14 11 36 36 16 12 11.0 54
182 13 11 35 30 11 6 9.0 67
183 15 11 31 36 19 15 11.0 70
184 14 11 38 32 15 12 10.0 80
185 16 11 22 28 8 12 11.0 89
186 13 11 32 36 16 12 19.0 76
187 14 11 36 34 17 11 14.0 74
188 15 11 39 31 12 7 12.0 87
189 13 11 28 28 11 7 14.0 54
190 11 11 32 36 11 5 21.0 61
191 11 11 32 36 14 12 13.0 38
192 14 11 38 40 16 12 10.0 75
193 15 11 32 33 12 3 15.0 69
194 11 11 35 37 16 11 16.0 62
195 15 11 32 32 13 10 14.0 72
196 12 11 37 38 15 12 12.0 70
197 14 11 34 31 16 9 19.0 79
198 14 11 33 37 16 12 15.0 87
199 8 11 33 33 14 9 19.0 62
200 13 11 26 32 16 12 13.0 77
201 9 11 30 30 16 12 17.0 69
202 15 11 24 30 14 10 12.0 69
203 17 11 34 31 11 9 11.0 75
204 13 11 34 32 12 12 14.0 54
205 15 11 33 34 15 8 11.0 72
206 15 11 34 36 15 11 13.0 74
207 14 11 35 37 16 11 12.0 85
208 16 11 35 36 16 12 15.0 52
209 13 11 36 33 11 10 14.0 70
210 16 11 34 33 15 10 12.0 84
211 9 11 34 33 12 12 17.0 64
212 16 11 41 44 12 12 11.0 84
213 11 11 32 39 15 11 18.0 87
214 10 11 30 32 15 8 13.0 79
215 11 11 35 35 16 12 17.0 67
216 15 11 28 25 14 10 13.0 65
217 17 11 33 35 17 11 11.0 85
218 14 11 39 34 14 10 12.0 83
219 8 11 36 35 13 8 22.0 61
220 15 11 36 39 15 12 14.0 82
221 11 11 35 33 13 12 12.0 76
222 16 11 38 36 14 10 12.0 58
223 10 11 33 32 15 12 17.0 72
224 15 11 31 32 12 9 9.0 72
225 9 11 34 36 13 9 21.0 38
226 16 11 32 36 8 6 10.0 78
227 19 11 31 32 14 10 11.0 54
228 12 11 33 34 14 9 12.0 63
229 8 11 34 33 11 9 23.0 66
230 11 11 34 35 12 9 13.0 70
231 14 11 34 30 13 6 12.0 71
232 9 11 33 38 10 10 16.0 67
233 15 11 32 34 16 6 9.0 58
234 13 11 41 33 18 14 17.0 72
235 16 11 34 32 13 10 9.0 72
236 11 11 36 31 11 10 14.0 70
237 12 11 37 30 4 6 17.0 76
238 13 11 36 27 13 12 13.0 50
239 10 11 29 31 16 12 11.0 72
240 11 11 37 30 10 7 12.0 72
241 12 11 27 32 12 8 10.0 88
242 8 11 35 35 12 11 19.0 53
243 12 11 28 28 10 3 16.0 58
244 12 11 35 33 13 6 16.0 66
245 15 11 37 31 15 10 14.0 82
246 11 11 29 35 12 8 20.0 69
247 13 11 32 35 14 9 15.0 68
248 14 11 36 32 10 9 23.0 44
249 10 11 19 21 12 8 20.0 56
250 12 11 21 20 12 9 16.0 53
251 15 11 31 34 11 7 14.0 70
252 13 11 33 32 10 7 17.0 78
253 13 11 36 34 12 6 11.0 71
254 13 11 33 32 16 9 13.0 72
255 12 11 37 33 12 10 17.0 68
256 12 11 34 33 14 11 15.0 67
257 9 11 35 37 16 12 21.0 75
258 9 11 31 32 14 8 18.0 62
259 15 11 37 34 13 11 15.0 67
260 10 11 35 30 4 3 8.0 83
261 14 11 27 30 15 11 12.0 64
262 15 11 34 38 11 12 12.0 68
263 7 11 40 36 11 7 22.0 62
264 14 11 29 32 14 9 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
12.665640 0.391360 0.003321 0.011327
Learning Software Depression Belonging
0.080466 -0.041045 -0.365596 0.007602
Belonging_Final t
0.027717 -0.008161
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9291 -1.3971 0.3185 1.3248 5.4065
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.665640 4.126056 3.070 0.00238 **
month 0.391360 0.439621 0.890 0.37419
Connected 0.003321 0.037411 0.089 0.92934
Separate 0.011327 0.038005 0.298 0.76591
Learning 0.080466 0.067382 1.194 0.23352
Software -0.041045 0.069708 -0.589 0.55651
Depression -0.365596 0.039605 -9.231 < 2e-16 ***
Belonging 0.007602 0.040921 0.186 0.85278
Belonging_Final 0.027717 0.060751 0.456 0.64861
t -0.008161 0.004658 -1.752 0.08099 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.003 on 254 degrees of freedom
Multiple R-squared: 0.3792, Adjusted R-squared: 0.3572
F-statistic: 17.24 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,] 0.99209183 0.015816344 0.0079081719
[2,] 0.98197176 0.036056474 0.0180282370
[3,] 0.97758703 0.044825935 0.0224129677
[4,] 0.95842531 0.083149387 0.0415746937
[5,] 0.98087628 0.038247441 0.0191237207
[6,] 0.96706799 0.065864027 0.0329320135
[7,] 0.94716402 0.105671957 0.0528359784
[8,] 0.93836501 0.123269977 0.0616349884
[9,] 0.94348240 0.113035209 0.0565176043
[10,] 0.95233210 0.095335806 0.0476679030
[11,] 0.95467969 0.090640613 0.0453203066
[12,] 0.93550768 0.128984639 0.0644923196
[13,] 0.92030407 0.159391859 0.0796959297
[14,] 0.99937598 0.001248048 0.0006240241
[15,] 0.99904075 0.001918509 0.0009592547
[16,] 0.99848295 0.003034098 0.0015170489
[17,] 0.99761366 0.004772684 0.0023863419
[18,] 0.99748124 0.005037520 0.0025187599
[19,] 0.99647620 0.007047608 0.0035238041
[20,] 0.99459521 0.010809575 0.0054047877
[21,] 0.99461680 0.010766395 0.0053831977
[22,] 0.99254078 0.014918447 0.0074592233
[23,] 0.98963401 0.020731981 0.0103659905
[24,] 0.98582769 0.028344624 0.0141723119
[25,] 0.98796816 0.024063671 0.0120318356
[26,] 0.98422678 0.031546440 0.0157732200
[27,] 0.98360938 0.032781243 0.0163906214
[28,] 0.98112804 0.037743920 0.0188719600
[29,] 0.97442688 0.051146244 0.0255731219
[30,] 0.97562839 0.048743220 0.0243716101
[31,] 0.96919309 0.061613811 0.0308069053
[32,] 0.96098107 0.078037854 0.0390189269
[33,] 0.95009379 0.099812410 0.0499062050
[34,] 0.95116547 0.097669063 0.0488345314
[35,] 0.94231409 0.115371819 0.0576859094
[36,] 0.92869375 0.142612494 0.0713062470
[37,] 0.95067061 0.098658789 0.0493293945
[38,] 0.94353424 0.112931514 0.0564657571
[39,] 0.93324007 0.133519867 0.0667599337
[40,] 0.91827080 0.163458399 0.0817291995
[41,] 0.90209788 0.195804240 0.0979021202
[42,] 0.88463192 0.230736157 0.1153680783
[43,] 0.87479739 0.250405228 0.1252026139
[44,] 0.86405470 0.271890601 0.1359453007
[45,] 0.85993295 0.280134108 0.1400670542
[46,] 0.83403450 0.331931006 0.1659655030
[47,] 0.85864275 0.282714498 0.1413572492
[48,] 0.84267547 0.314649065 0.1573245326
[49,] 0.85158318 0.296833648 0.1484168239
[50,] 0.83623932 0.327521365 0.1637606823
[51,] 0.87100056 0.257998887 0.1289994436
[52,] 0.86485384 0.270292315 0.1351461577
[53,] 0.86550208 0.268995832 0.1344979162
[54,] 0.88357701 0.232845982 0.1164229908
[55,] 0.88626372 0.227472553 0.1137362765
[56,] 0.87663670 0.246726603 0.1233633014
[57,] 0.91489227 0.170215465 0.0851077324
[58,] 0.92211653 0.155766936 0.0778834678
[59,] 0.91543245 0.169135110 0.0845675549
[60,] 0.93999150 0.120017009 0.0600085046
[61,] 0.92854549 0.142909015 0.0714545075
[62,] 0.91396007 0.172079860 0.0860399300
[63,] 0.90556889 0.188862223 0.0944311113
[64,] 0.88860733 0.222785346 0.1113926732
[65,] 0.91238259 0.175234811 0.0876174053
[66,] 0.89841165 0.203176710 0.1015883548
[67,] 0.88946921 0.221061589 0.1105307944
[68,] 0.88302772 0.233944556 0.1169722782
[69,] 0.86275735 0.274485305 0.1372426526
[70,] 0.84192610 0.316147799 0.1580738993
[71,] 0.83708758 0.325824848 0.1629124241
[72,] 0.81634606 0.367307882 0.1836539408
[73,] 0.79075656 0.418486881 0.2092434403
[74,] 0.76498612 0.470027761 0.2350138806
[75,] 0.73531850 0.529362995 0.2646814973
[76,] 0.70388750 0.592224994 0.2961124969
[77,] 0.77468080 0.450638408 0.2253192039
[78,] 0.81219448 0.375611037 0.1878055187
[79,] 0.78875152 0.422496959 0.2112484796
[80,] 0.76260103 0.474797939 0.2373989694
[81,] 0.74034583 0.519308331 0.2596541655
[82,] 0.71582047 0.568359064 0.2841795320
[83,] 0.69080516 0.618389674 0.3091948372
[84,] 0.66190333 0.676193343 0.3380966714
[85,] 0.63389058 0.732218831 0.3661094156
[86,] 0.64046797 0.719064051 0.3595320257
[87,] 0.60497022 0.790059551 0.3950297753
[88,] 0.60113420 0.797731591 0.3988657957
[89,] 0.57299876 0.854002475 0.4270012373
[90,] 0.54900004 0.901999921 0.4509999603
[91,] 0.60077194 0.798456122 0.3992280612
[92,] 0.58875116 0.822497679 0.4112488396
[93,] 0.60186809 0.796263827 0.3981319133
[94,] 0.58144214 0.837115727 0.4185578633
[95,] 0.57655641 0.846887174 0.4234435872
[96,] 0.61373659 0.772526820 0.3862634100
[97,] 0.57906519 0.841869625 0.4209348124
[98,] 0.55654406 0.886911888 0.4434559438
[99,] 0.55779148 0.884417041 0.4422085205
[100,] 0.57257839 0.854843212 0.4274216060
[101,] 0.55949279 0.881014421 0.4405072107
[102,] 0.66560200 0.668796003 0.3343980015
[103,] 0.64141822 0.717163552 0.3585817761
[104,] 0.61475161 0.770496778 0.3852483891
[105,] 0.57956598 0.840868048 0.4204340241
[106,] 0.55333216 0.893335679 0.4466678397
[107,] 0.51823889 0.963522225 0.4817611127
[108,] 0.48453513 0.969070259 0.5154648707
[109,] 0.46396811 0.927936218 0.5360318910
[110,] 0.42872874 0.857457472 0.5712712642
[111,] 0.39886178 0.797723556 0.6011382221
[112,] 0.37308550 0.746171000 0.6269144999
[113,] 0.35949863 0.718997253 0.6405013736
[114,] 0.33557153 0.671143058 0.6644284711
[115,] 0.32312962 0.646259243 0.6768703783
[116,] 0.43621483 0.872429663 0.5637851683
[117,] 0.41935495 0.838709903 0.5806450485
[118,] 0.40619474 0.812389470 0.5938052649
[119,] 0.39165074 0.783301481 0.6083492593
[120,] 0.35921730 0.718434602 0.6407826989
[121,] 0.38405340 0.768106799 0.6159466003
[122,] 0.35500865 0.710017296 0.6449913521
[123,] 0.37191137 0.743822741 0.6280886297
[124,] 0.36473043 0.729460862 0.6352695691
[125,] 0.33531363 0.670627266 0.6646863669
[126,] 0.31125654 0.622513083 0.6887434586
[127,] 0.28373125 0.567462499 0.7162687503
[128,] 0.25911084 0.518221679 0.7408891606
[129,] 0.23192913 0.463858252 0.7680708738
[130,] 0.23722096 0.474441928 0.7627790361
[131,] 0.21105803 0.422116069 0.7889419657
[132,] 0.19189056 0.383781115 0.8081094424
[133,] 0.17905397 0.358107935 0.8209460324
[134,] 0.17369391 0.347387825 0.8263060877
[135,] 0.18111580 0.362231594 0.8188842032
[136,] 0.19633521 0.392670415 0.8036647924
[137,] 0.21315456 0.426309116 0.7868454422
[138,] 0.20848249 0.416964979 0.7915175104
[139,] 0.18667592 0.373351832 0.8133240842
[140,] 0.16661060 0.333221192 0.8333894041
[141,] 0.17669491 0.353389826 0.8233050871
[142,] 0.18871940 0.377438801 0.8112805995
[143,] 0.17032741 0.340654818 0.8296725910
[144,] 0.15417663 0.308353257 0.8458233717
[145,] 0.13418218 0.268364351 0.8658178244
[146,] 0.20651947 0.413038930 0.7934805349
[147,] 0.22351473 0.447029456 0.7764852718
[148,] 0.19790707 0.395814146 0.8020929270
[149,] 0.17341669 0.346833381 0.8265833096
[150,] 0.15101247 0.302024932 0.8489875342
[151,] 0.13122856 0.262457119 0.8687714405
[152,] 0.21949485 0.438989693 0.7805051534
[153,] 0.22169136 0.443382724 0.7783086379
[154,] 0.21195747 0.423914934 0.7880425328
[155,] 0.18695992 0.373919830 0.8130400849
[156,] 0.16892823 0.337856463 0.8310717687
[157,] 0.22624517 0.452490338 0.7737548311
[158,] 0.25335473 0.506709462 0.7466452690
[159,] 0.22498316 0.449966327 0.7750168364
[160,] 0.21767521 0.435350421 0.7823247893
[161,] 0.38293531 0.765870617 0.6170646914
[162,] 0.36255574 0.725111473 0.6374442635
[163,] 0.37940777 0.758815534 0.6205922332
[164,] 0.38900136 0.778002711 0.6109986444
[165,] 0.45821403 0.916428053 0.5417859734
[166,] 0.42118952 0.842379048 0.5788104761
[167,] 0.39633357 0.792667139 0.6036664304
[168,] 0.40058675 0.801173496 0.5994132520
[169,] 0.36608348 0.732166955 0.6339165224
[170,] 0.37249647 0.744992949 0.6275035257
[171,] 0.33676136 0.673522718 0.6632386410
[172,] 0.31446133 0.628922651 0.6855386746
[173,] 0.31299668 0.625993351 0.6870033246
[174,] 0.30078326 0.601566521 0.6992167397
[175,] 0.26794578 0.535891553 0.7320542237
[176,] 0.23895104 0.477902074 0.7610489632
[177,] 0.20977675 0.419553496 0.7902232518
[178,] 0.18488325 0.369766500 0.8151167501
[179,] 0.19155688 0.383113753 0.8084431236
[180,] 0.17621950 0.352438991 0.8237805046
[181,] 0.18186478 0.363729559 0.8181352205
[182,] 0.17242475 0.344849505 0.8275752476
[183,] 0.16847627 0.336952532 0.8315237341
[184,] 0.17979038 0.359580755 0.8202096223
[185,] 0.21446746 0.428934928 0.7855325358
[186,] 0.19705965 0.394119305 0.8029403474
[187,] 0.22877917 0.457558345 0.7712208273
[188,] 0.19903942 0.398078841 0.8009605793
[189,] 0.23812738 0.476254751 0.7618726245
[190,] 0.21497666 0.429953319 0.7850233406
[191,] 0.26184698 0.523693952 0.7381530238
[192,] 0.23332158 0.466643163 0.7666784183
[193,] 0.20223929 0.404478590 0.7977607050
[194,] 0.18281926 0.365638518 0.8171807411
[195,] 0.15532322 0.310646433 0.8446767835
[196,] 0.17441034 0.348820687 0.8255896563
[197,] 0.14685418 0.293708359 0.8531458203
[198,] 0.16112669 0.322253370 0.8388733148
[199,] 0.18204884 0.364097671 0.8179511645
[200,] 0.16836601 0.336732027 0.8316339863
[201,] 0.14385356 0.287707126 0.8561464371
[202,] 0.18468084 0.369361689 0.8153191554
[203,] 0.16241749 0.324834971 0.8375825144
[204,] 0.14886162 0.297723250 0.8511383752
[205,] 0.17472521 0.349450423 0.8252747885
[206,] 0.14873260 0.297465205 0.8512673975
[207,] 0.13554605 0.271092099 0.8644539507
[208,] 0.14281103 0.285622059 0.8571889706
[209,] 0.14401958 0.288039156 0.8559804219
[210,] 0.14785717 0.295714339 0.8521428307
[211,] 0.13213685 0.264273706 0.8678631472
[212,] 0.10719372 0.214387432 0.8928062841
[213,] 0.09519162 0.190383232 0.9048083842
[214,] 0.11798668 0.235973357 0.8820133217
[215,] 0.30974325 0.619486500 0.6902567500
[216,] 0.27201141 0.544022815 0.7279885927
[217,] 0.23630216 0.472604319 0.7636978406
[218,] 0.21342632 0.426852643 0.7865736787
[219,] 0.18017494 0.360349889 0.8198250557
[220,] 0.20288537 0.405770744 0.7971146280
[221,] 0.16574721 0.331494419 0.8342527903
[222,] 0.13444890 0.268897803 0.8655510984
[223,] 0.13920766 0.278415317 0.8607923417
[224,] 0.11483420 0.229668409 0.8851657957
[225,] 0.09518069 0.190361387 0.9048193066
[226,] 0.06970211 0.139404222 0.9302978888
[227,] 0.14484569 0.289691374 0.8551543128
[228,] 0.16083286 0.321665714 0.8391671428
[229,] 0.16070255 0.321405104 0.8392974482
[230,] 0.76363081 0.472738376 0.2363691879
[231,] 0.68954324 0.620913527 0.3104567634
[232,] 0.63743438 0.725131249 0.3625656246
[233,] 0.58876410 0.822471806 0.4112359031
[234,] 0.50321074 0.993578529 0.4967892646
[235,] 0.47588071 0.951761420 0.5241192901
[236,] 0.45828296 0.916565924 0.5417170379
[237,] 0.33884519 0.677690377 0.6611548117
[238,] 0.35927509 0.718550184 0.6407249080
[239,] 0.24407618 0.488152366 0.7559238171
> postscript(file="/var/wessaorg/rcomp/tmp/1vid81384457442.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/2cwk21384457442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3igot1384457442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/41xmz1384457442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5c5tu1384457442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.20248767 2.47756427 -3.12011640 -2.85124406 4.55279887 3.27229796
7 8 9 10 11 12
2.96699365 -1.22033404 -0.41823015 0.37517833 1.24486027 3.21009300
13 14 15 16 17 18
-3.58881394 2.32367803 2.17922393 0.42783381 -0.02673256 1.14152049
19 20 21 22 23 24
-1.55658778 2.03334437 2.50254492 -2.87544399 -0.49337250 -1.72546932
25 26 27 28 29 30
1.57532604 -6.92910625 0.89147358 0.61946965 1.04865472 -3.12192556
31 32 33 34 35 36
0.22068411 0.14271118 1.81577163 -0.35102288 0.03717542 0.27402060
37 38 39 40 41 42
-1.96112056 0.65052692 1.60921113 -2.26112591 -0.73503469 2.36288173
43 44 45 46 47 48
0.28037142 -1.17104800 0.35288220 -2.59720901 -0.36076253 0.13010618
49 50 51 52 53 54
3.58338685 -1.69829153 0.86791400 0.57470552 -0.63214843 -1.67232183
55 56 57 58 59 60
-2.18187764 1.29029281 1.91072346 -0.37393316 -3.08330079 -1.31656834
61 62 63 64 65 66
-2.76301534 -1.43355197 -3.44730120 1.02384635 1.51123410 -5.35497852
67 68 69 70 71 72
-1.87288278 -2.73079337 1.14883388 1.09151329 0.01382629 2.95875172
73 74 75 76 77 78
0.39470300 -0.41690579 -2.18363927 -0.44676958 2.78212974 0.22839533
79 80 81 82 83 84
0.93257300 -2.18474929 -0.08442079 -0.73689118 1.52742419 0.47969986
85 86 87 88 89 90
-0.35855235 0.73291208 -0.46031899 0.04404654 -3.73155750 2.97119992
91 92 93 94 95 96
0.08795610 0.68846973 0.57206305 -1.14260758 0.89250090 -0.93576473
97 98 99 100 101 102
-1.01938373 1.99305180 -0.19815192 1.82105273 -1.06289104 1.02062888
103 104 105 106 107 108
-3.56182943 1.82852164 -2.47811744 1.02895158 2.03916706 -2.89100348
109 110 111 112 113 114
0.60663778 1.06974291 -2.16104724 -2.32412381 2.15853929 3.79472326
115 116 117 118 119 120
0.49236742 1.00503691 0.03534424 -1.11509056 0.33841047 -0.56436565
121 122 123 124 125 126
0.51124718 0.14324267 -0.79720421 0.50801705 -1.58929244 0.87672578
127 128 129 130 131 132
1.82681411 4.04725509 1.45530720 -1.55763648 -1.61721392 -0.21735337
133 134 135 136 137 138
2.44377037 0.87671474 2.37651303 1.59540340 0.74930897 -0.95480186
139 140 141 142 143 144
0.84294253 -0.72555856 0.45180196 2.28956333 -0.39611090 0.92337481
145 146 147 148 149 150
1.46442249 1.79211086 -2.21699455 -2.44726716 -2.40215833 1.83164285
151 152 153 154 155 156
0.73301385 0.81410817 -2.30947849 -2.01056241 1.32293859 0.61842458
157 158 159 160 161 162
0.77889471 4.29208669 -2.35470247 0.10894953 0.54635744 0.41249337
163 164 165 166 167 168
0.52488978 4.37728504 -2.13364179 1.57189158 -0.29233694 -1.02536450
169 170 171 172 173 174
-3.84787649 -2.99748046 0.14202571 1.71982771 -5.15494766 1.42961257
175 176 177 178 179 180
2.53371624 -2.31456988 -3.51918712 0.36627073 1.33032873 -2.25137335
181 182 183 184 185 186
-0.11927472 -1.93552353 0.36887448 -0.95465306 1.81836988 1.38746103
187 188 189 190 191 192
0.49842070 0.68928170 0.44023958 0.51891961 -1.40085411 -0.91157179
193 194 195 196 197 198
2.07728439 -1.51680063 1.86795032 -2.00328564 2.27041783 0.67528800
199 200 201 202 203 204
-3.13754621 -0.63423248 -3.09350054 1.29631112 2.99365138 0.48359860
205 206 207 208 209 210
0.61149067 1.37736856 -0.43596970 3.63741776 0.13373071 1.82275079
211 212 213 214 215 216
-2.58841346 1.56597037 -1.11313722 -3.77076479 -0.92641294 1.82220541
217 218 219 220 221 222
2.31202540 -0.10725758 -1.83541254 1.60280963 -2.67609398 2.67815281
223 224 225 226 227 228
-1.88891232 0.31937950 -0.24791787 1.75035233 5.39688651 -1.39582846
229 230 231 232 233 234
-1.36124342 -2.17029124 0.31770551 -2.89077572 0.25001325 0.94822617
235 236 237 238 239 240
1.35976886 -1.59554925 0.67685832 0.45084760 -3.98005375 -2.37168209
241 242 243 244 245 246
-2.57512915 -2.42132784 0.35942148 -0.02995763 1.78432511 0.53033721
247 248 249 250 251 252
0.56055203 5.40652670 0.01173077 0.79234509 2.41406853 1.41609983
253 254 255 256 257 258
-0.86754901 -0.32962511 0.53734529 -0.26029269 -1.28788137 -2.12786910
259 260 261 262 263 264
2.87880183 -4.65094656 0.82187332 1.96552414 -2.47180223 0.61603746
> postscript(file="/var/wessaorg/rcomp/tmp/64ppo1384457442.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 -0.20248767 NA
1 2.47756427 -0.20248767
2 -3.12011640 2.47756427
3 -2.85124406 -3.12011640
4 4.55279887 -2.85124406
5 3.27229796 4.55279887
6 2.96699365 3.27229796
7 -1.22033404 2.96699365
8 -0.41823015 -1.22033404
9 0.37517833 -0.41823015
10 1.24486027 0.37517833
11 3.21009300 1.24486027
12 -3.58881394 3.21009300
13 2.32367803 -3.58881394
14 2.17922393 2.32367803
15 0.42783381 2.17922393
16 -0.02673256 0.42783381
17 1.14152049 -0.02673256
18 -1.55658778 1.14152049
19 2.03334437 -1.55658778
20 2.50254492 2.03334437
21 -2.87544399 2.50254492
22 -0.49337250 -2.87544399
23 -1.72546932 -0.49337250
24 1.57532604 -1.72546932
25 -6.92910625 1.57532604
26 0.89147358 -6.92910625
27 0.61946965 0.89147358
28 1.04865472 0.61946965
29 -3.12192556 1.04865472
30 0.22068411 -3.12192556
31 0.14271118 0.22068411
32 1.81577163 0.14271118
33 -0.35102288 1.81577163
34 0.03717542 -0.35102288
35 0.27402060 0.03717542
36 -1.96112056 0.27402060
37 0.65052692 -1.96112056
38 1.60921113 0.65052692
39 -2.26112591 1.60921113
40 -0.73503469 -2.26112591
41 2.36288173 -0.73503469
42 0.28037142 2.36288173
43 -1.17104800 0.28037142
44 0.35288220 -1.17104800
45 -2.59720901 0.35288220
46 -0.36076253 -2.59720901
47 0.13010618 -0.36076253
48 3.58338685 0.13010618
49 -1.69829153 3.58338685
50 0.86791400 -1.69829153
51 0.57470552 0.86791400
52 -0.63214843 0.57470552
53 -1.67232183 -0.63214843
54 -2.18187764 -1.67232183
55 1.29029281 -2.18187764
56 1.91072346 1.29029281
57 -0.37393316 1.91072346
58 -3.08330079 -0.37393316
59 -1.31656834 -3.08330079
60 -2.76301534 -1.31656834
61 -1.43355197 -2.76301534
62 -3.44730120 -1.43355197
63 1.02384635 -3.44730120
64 1.51123410 1.02384635
65 -5.35497852 1.51123410
66 -1.87288278 -5.35497852
67 -2.73079337 -1.87288278
68 1.14883388 -2.73079337
69 1.09151329 1.14883388
70 0.01382629 1.09151329
71 2.95875172 0.01382629
72 0.39470300 2.95875172
73 -0.41690579 0.39470300
74 -2.18363927 -0.41690579
75 -0.44676958 -2.18363927
76 2.78212974 -0.44676958
77 0.22839533 2.78212974
78 0.93257300 0.22839533
79 -2.18474929 0.93257300
80 -0.08442079 -2.18474929
81 -0.73689118 -0.08442079
82 1.52742419 -0.73689118
83 0.47969986 1.52742419
84 -0.35855235 0.47969986
85 0.73291208 -0.35855235
86 -0.46031899 0.73291208
87 0.04404654 -0.46031899
88 -3.73155750 0.04404654
89 2.97119992 -3.73155750
90 0.08795610 2.97119992
91 0.68846973 0.08795610
92 0.57206305 0.68846973
93 -1.14260758 0.57206305
94 0.89250090 -1.14260758
95 -0.93576473 0.89250090
96 -1.01938373 -0.93576473
97 1.99305180 -1.01938373
98 -0.19815192 1.99305180
99 1.82105273 -0.19815192
100 -1.06289104 1.82105273
101 1.02062888 -1.06289104
102 -3.56182943 1.02062888
103 1.82852164 -3.56182943
104 -2.47811744 1.82852164
105 1.02895158 -2.47811744
106 2.03916706 1.02895158
107 -2.89100348 2.03916706
108 0.60663778 -2.89100348
109 1.06974291 0.60663778
110 -2.16104724 1.06974291
111 -2.32412381 -2.16104724
112 2.15853929 -2.32412381
113 3.79472326 2.15853929
114 0.49236742 3.79472326
115 1.00503691 0.49236742
116 0.03534424 1.00503691
117 -1.11509056 0.03534424
118 0.33841047 -1.11509056
119 -0.56436565 0.33841047
120 0.51124718 -0.56436565
121 0.14324267 0.51124718
122 -0.79720421 0.14324267
123 0.50801705 -0.79720421
124 -1.58929244 0.50801705
125 0.87672578 -1.58929244
126 1.82681411 0.87672578
127 4.04725509 1.82681411
128 1.45530720 4.04725509
129 -1.55763648 1.45530720
130 -1.61721392 -1.55763648
131 -0.21735337 -1.61721392
132 2.44377037 -0.21735337
133 0.87671474 2.44377037
134 2.37651303 0.87671474
135 1.59540340 2.37651303
136 0.74930897 1.59540340
137 -0.95480186 0.74930897
138 0.84294253 -0.95480186
139 -0.72555856 0.84294253
140 0.45180196 -0.72555856
141 2.28956333 0.45180196
142 -0.39611090 2.28956333
143 0.92337481 -0.39611090
144 1.46442249 0.92337481
145 1.79211086 1.46442249
146 -2.21699455 1.79211086
147 -2.44726716 -2.21699455
148 -2.40215833 -2.44726716
149 1.83164285 -2.40215833
150 0.73301385 1.83164285
151 0.81410817 0.73301385
152 -2.30947849 0.81410817
153 -2.01056241 -2.30947849
154 1.32293859 -2.01056241
155 0.61842458 1.32293859
156 0.77889471 0.61842458
157 4.29208669 0.77889471
158 -2.35470247 4.29208669
159 0.10894953 -2.35470247
160 0.54635744 0.10894953
161 0.41249337 0.54635744
162 0.52488978 0.41249337
163 4.37728504 0.52488978
164 -2.13364179 4.37728504
165 1.57189158 -2.13364179
166 -0.29233694 1.57189158
167 -1.02536450 -0.29233694
168 -3.84787649 -1.02536450
169 -2.99748046 -3.84787649
170 0.14202571 -2.99748046
171 1.71982771 0.14202571
172 -5.15494766 1.71982771
173 1.42961257 -5.15494766
174 2.53371624 1.42961257
175 -2.31456988 2.53371624
176 -3.51918712 -2.31456988
177 0.36627073 -3.51918712
178 1.33032873 0.36627073
179 -2.25137335 1.33032873
180 -0.11927472 -2.25137335
181 -1.93552353 -0.11927472
182 0.36887448 -1.93552353
183 -0.95465306 0.36887448
184 1.81836988 -0.95465306
185 1.38746103 1.81836988
186 0.49842070 1.38746103
187 0.68928170 0.49842070
188 0.44023958 0.68928170
189 0.51891961 0.44023958
190 -1.40085411 0.51891961
191 -0.91157179 -1.40085411
192 2.07728439 -0.91157179
193 -1.51680063 2.07728439
194 1.86795032 -1.51680063
195 -2.00328564 1.86795032
196 2.27041783 -2.00328564
197 0.67528800 2.27041783
198 -3.13754621 0.67528800
199 -0.63423248 -3.13754621
200 -3.09350054 -0.63423248
201 1.29631112 -3.09350054
202 2.99365138 1.29631112
203 0.48359860 2.99365138
204 0.61149067 0.48359860
205 1.37736856 0.61149067
206 -0.43596970 1.37736856
207 3.63741776 -0.43596970
208 0.13373071 3.63741776
209 1.82275079 0.13373071
210 -2.58841346 1.82275079
211 1.56597037 -2.58841346
212 -1.11313722 1.56597037
213 -3.77076479 -1.11313722
214 -0.92641294 -3.77076479
215 1.82220541 -0.92641294
216 2.31202540 1.82220541
217 -0.10725758 2.31202540
218 -1.83541254 -0.10725758
219 1.60280963 -1.83541254
220 -2.67609398 1.60280963
221 2.67815281 -2.67609398
222 -1.88891232 2.67815281
223 0.31937950 -1.88891232
224 -0.24791787 0.31937950
225 1.75035233 -0.24791787
226 5.39688651 1.75035233
227 -1.39582846 5.39688651
228 -1.36124342 -1.39582846
229 -2.17029124 -1.36124342
230 0.31770551 -2.17029124
231 -2.89077572 0.31770551
232 0.25001325 -2.89077572
233 0.94822617 0.25001325
234 1.35976886 0.94822617
235 -1.59554925 1.35976886
236 0.67685832 -1.59554925
237 0.45084760 0.67685832
238 -3.98005375 0.45084760
239 -2.37168209 -3.98005375
240 -2.57512915 -2.37168209
241 -2.42132784 -2.57512915
242 0.35942148 -2.42132784
243 -0.02995763 0.35942148
244 1.78432511 -0.02995763
245 0.53033721 1.78432511
246 0.56055203 0.53033721
247 5.40652670 0.56055203
248 0.01173077 5.40652670
249 0.79234509 0.01173077
250 2.41406853 0.79234509
251 1.41609983 2.41406853
252 -0.86754901 1.41609983
253 -0.32962511 -0.86754901
254 0.53734529 -0.32962511
255 -0.26029269 0.53734529
256 -1.28788137 -0.26029269
257 -2.12786910 -1.28788137
258 2.87880183 -2.12786910
259 -4.65094656 2.87880183
260 0.82187332 -4.65094656
261 1.96552414 0.82187332
262 -2.47180223 1.96552414
263 0.61603746 -2.47180223
264 NA 0.61603746
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.47756427 -0.20248767
[2,] -3.12011640 2.47756427
[3,] -2.85124406 -3.12011640
[4,] 4.55279887 -2.85124406
[5,] 3.27229796 4.55279887
[6,] 2.96699365 3.27229796
[7,] -1.22033404 2.96699365
[8,] -0.41823015 -1.22033404
[9,] 0.37517833 -0.41823015
[10,] 1.24486027 0.37517833
[11,] 3.21009300 1.24486027
[12,] -3.58881394 3.21009300
[13,] 2.32367803 -3.58881394
[14,] 2.17922393 2.32367803
[15,] 0.42783381 2.17922393
[16,] -0.02673256 0.42783381
[17,] 1.14152049 -0.02673256
[18,] -1.55658778 1.14152049
[19,] 2.03334437 -1.55658778
[20,] 2.50254492 2.03334437
[21,] -2.87544399 2.50254492
[22,] -0.49337250 -2.87544399
[23,] -1.72546932 -0.49337250
[24,] 1.57532604 -1.72546932
[25,] -6.92910625 1.57532604
[26,] 0.89147358 -6.92910625
[27,] 0.61946965 0.89147358
[28,] 1.04865472 0.61946965
[29,] -3.12192556 1.04865472
[30,] 0.22068411 -3.12192556
[31,] 0.14271118 0.22068411
[32,] 1.81577163 0.14271118
[33,] -0.35102288 1.81577163
[34,] 0.03717542 -0.35102288
[35,] 0.27402060 0.03717542
[36,] -1.96112056 0.27402060
[37,] 0.65052692 -1.96112056
[38,] 1.60921113 0.65052692
[39,] -2.26112591 1.60921113
[40,] -0.73503469 -2.26112591
[41,] 2.36288173 -0.73503469
[42,] 0.28037142 2.36288173
[43,] -1.17104800 0.28037142
[44,] 0.35288220 -1.17104800
[45,] -2.59720901 0.35288220
[46,] -0.36076253 -2.59720901
[47,] 0.13010618 -0.36076253
[48,] 3.58338685 0.13010618
[49,] -1.69829153 3.58338685
[50,] 0.86791400 -1.69829153
[51,] 0.57470552 0.86791400
[52,] -0.63214843 0.57470552
[53,] -1.67232183 -0.63214843
[54,] -2.18187764 -1.67232183
[55,] 1.29029281 -2.18187764
[56,] 1.91072346 1.29029281
[57,] -0.37393316 1.91072346
[58,] -3.08330079 -0.37393316
[59,] -1.31656834 -3.08330079
[60,] -2.76301534 -1.31656834
[61,] -1.43355197 -2.76301534
[62,] -3.44730120 -1.43355197
[63,] 1.02384635 -3.44730120
[64,] 1.51123410 1.02384635
[65,] -5.35497852 1.51123410
[66,] -1.87288278 -5.35497852
[67,] -2.73079337 -1.87288278
[68,] 1.14883388 -2.73079337
[69,] 1.09151329 1.14883388
[70,] 0.01382629 1.09151329
[71,] 2.95875172 0.01382629
[72,] 0.39470300 2.95875172
[73,] -0.41690579 0.39470300
[74,] -2.18363927 -0.41690579
[75,] -0.44676958 -2.18363927
[76,] 2.78212974 -0.44676958
[77,] 0.22839533 2.78212974
[78,] 0.93257300 0.22839533
[79,] -2.18474929 0.93257300
[80,] -0.08442079 -2.18474929
[81,] -0.73689118 -0.08442079
[82,] 1.52742419 -0.73689118
[83,] 0.47969986 1.52742419
[84,] -0.35855235 0.47969986
[85,] 0.73291208 -0.35855235
[86,] -0.46031899 0.73291208
[87,] 0.04404654 -0.46031899
[88,] -3.73155750 0.04404654
[89,] 2.97119992 -3.73155750
[90,] 0.08795610 2.97119992
[91,] 0.68846973 0.08795610
[92,] 0.57206305 0.68846973
[93,] -1.14260758 0.57206305
[94,] 0.89250090 -1.14260758
[95,] -0.93576473 0.89250090
[96,] -1.01938373 -0.93576473
[97,] 1.99305180 -1.01938373
[98,] -0.19815192 1.99305180
[99,] 1.82105273 -0.19815192
[100,] -1.06289104 1.82105273
[101,] 1.02062888 -1.06289104
[102,] -3.56182943 1.02062888
[103,] 1.82852164 -3.56182943
[104,] -2.47811744 1.82852164
[105,] 1.02895158 -2.47811744
[106,] 2.03916706 1.02895158
[107,] -2.89100348 2.03916706
[108,] 0.60663778 -2.89100348
[109,] 1.06974291 0.60663778
[110,] -2.16104724 1.06974291
[111,] -2.32412381 -2.16104724
[112,] 2.15853929 -2.32412381
[113,] 3.79472326 2.15853929
[114,] 0.49236742 3.79472326
[115,] 1.00503691 0.49236742
[116,] 0.03534424 1.00503691
[117,] -1.11509056 0.03534424
[118,] 0.33841047 -1.11509056
[119,] -0.56436565 0.33841047
[120,] 0.51124718 -0.56436565
[121,] 0.14324267 0.51124718
[122,] -0.79720421 0.14324267
[123,] 0.50801705 -0.79720421
[124,] -1.58929244 0.50801705
[125,] 0.87672578 -1.58929244
[126,] 1.82681411 0.87672578
[127,] 4.04725509 1.82681411
[128,] 1.45530720 4.04725509
[129,] -1.55763648 1.45530720
[130,] -1.61721392 -1.55763648
[131,] -0.21735337 -1.61721392
[132,] 2.44377037 -0.21735337
[133,] 0.87671474 2.44377037
[134,] 2.37651303 0.87671474
[135,] 1.59540340 2.37651303
[136,] 0.74930897 1.59540340
[137,] -0.95480186 0.74930897
[138,] 0.84294253 -0.95480186
[139,] -0.72555856 0.84294253
[140,] 0.45180196 -0.72555856
[141,] 2.28956333 0.45180196
[142,] -0.39611090 2.28956333
[143,] 0.92337481 -0.39611090
[144,] 1.46442249 0.92337481
[145,] 1.79211086 1.46442249
[146,] -2.21699455 1.79211086
[147,] -2.44726716 -2.21699455
[148,] -2.40215833 -2.44726716
[149,] 1.83164285 -2.40215833
[150,] 0.73301385 1.83164285
[151,] 0.81410817 0.73301385
[152,] -2.30947849 0.81410817
[153,] -2.01056241 -2.30947849
[154,] 1.32293859 -2.01056241
[155,] 0.61842458 1.32293859
[156,] 0.77889471 0.61842458
[157,] 4.29208669 0.77889471
[158,] -2.35470247 4.29208669
[159,] 0.10894953 -2.35470247
[160,] 0.54635744 0.10894953
[161,] 0.41249337 0.54635744
[162,] 0.52488978 0.41249337
[163,] 4.37728504 0.52488978
[164,] -2.13364179 4.37728504
[165,] 1.57189158 -2.13364179
[166,] -0.29233694 1.57189158
[167,] -1.02536450 -0.29233694
[168,] -3.84787649 -1.02536450
[169,] -2.99748046 -3.84787649
[170,] 0.14202571 -2.99748046
[171,] 1.71982771 0.14202571
[172,] -5.15494766 1.71982771
[173,] 1.42961257 -5.15494766
[174,] 2.53371624 1.42961257
[175,] -2.31456988 2.53371624
[176,] -3.51918712 -2.31456988
[177,] 0.36627073 -3.51918712
[178,] 1.33032873 0.36627073
[179,] -2.25137335 1.33032873
[180,] -0.11927472 -2.25137335
[181,] -1.93552353 -0.11927472
[182,] 0.36887448 -1.93552353
[183,] -0.95465306 0.36887448
[184,] 1.81836988 -0.95465306
[185,] 1.38746103 1.81836988
[186,] 0.49842070 1.38746103
[187,] 0.68928170 0.49842070
[188,] 0.44023958 0.68928170
[189,] 0.51891961 0.44023958
[190,] -1.40085411 0.51891961
[191,] -0.91157179 -1.40085411
[192,] 2.07728439 -0.91157179
[193,] -1.51680063 2.07728439
[194,] 1.86795032 -1.51680063
[195,] -2.00328564 1.86795032
[196,] 2.27041783 -2.00328564
[197,] 0.67528800 2.27041783
[198,] -3.13754621 0.67528800
[199,] -0.63423248 -3.13754621
[200,] -3.09350054 -0.63423248
[201,] 1.29631112 -3.09350054
[202,] 2.99365138 1.29631112
[203,] 0.48359860 2.99365138
[204,] 0.61149067 0.48359860
[205,] 1.37736856 0.61149067
[206,] -0.43596970 1.37736856
[207,] 3.63741776 -0.43596970
[208,] 0.13373071 3.63741776
[209,] 1.82275079 0.13373071
[210,] -2.58841346 1.82275079
[211,] 1.56597037 -2.58841346
[212,] -1.11313722 1.56597037
[213,] -3.77076479 -1.11313722
[214,] -0.92641294 -3.77076479
[215,] 1.82220541 -0.92641294
[216,] 2.31202540 1.82220541
[217,] -0.10725758 2.31202540
[218,] -1.83541254 -0.10725758
[219,] 1.60280963 -1.83541254
[220,] -2.67609398 1.60280963
[221,] 2.67815281 -2.67609398
[222,] -1.88891232 2.67815281
[223,] 0.31937950 -1.88891232
[224,] -0.24791787 0.31937950
[225,] 1.75035233 -0.24791787
[226,] 5.39688651 1.75035233
[227,] -1.39582846 5.39688651
[228,] -1.36124342 -1.39582846
[229,] -2.17029124 -1.36124342
[230,] 0.31770551 -2.17029124
[231,] -2.89077572 0.31770551
[232,] 0.25001325 -2.89077572
[233,] 0.94822617 0.25001325
[234,] 1.35976886 0.94822617
[235,] -1.59554925 1.35976886
[236,] 0.67685832 -1.59554925
[237,] 0.45084760 0.67685832
[238,] -3.98005375 0.45084760
[239,] -2.37168209 -3.98005375
[240,] -2.57512915 -2.37168209
[241,] -2.42132784 -2.57512915
[242,] 0.35942148 -2.42132784
[243,] -0.02995763 0.35942148
[244,] 1.78432511 -0.02995763
[245,] 0.53033721 1.78432511
[246,] 0.56055203 0.53033721
[247,] 5.40652670 0.56055203
[248,] 0.01173077 5.40652670
[249,] 0.79234509 0.01173077
[250,] 2.41406853 0.79234509
[251,] 1.41609983 2.41406853
[252,] -0.86754901 1.41609983
[253,] -0.32962511 -0.86754901
[254,] 0.53734529 -0.32962511
[255,] -0.26029269 0.53734529
[256,] -1.28788137 -0.26029269
[257,] -2.12786910 -1.28788137
[258,] 2.87880183 -2.12786910
[259,] -4.65094656 2.87880183
[260,] 0.82187332 -4.65094656
[261,] 1.96552414 0.82187332
[262,] -2.47180223 1.96552414
[263,] 0.61603746 -2.47180223
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.47756427 -0.20248767
2 -3.12011640 2.47756427
3 -2.85124406 -3.12011640
4 4.55279887 -2.85124406
5 3.27229796 4.55279887
6 2.96699365 3.27229796
7 -1.22033404 2.96699365
8 -0.41823015 -1.22033404
9 0.37517833 -0.41823015
10 1.24486027 0.37517833
11 3.21009300 1.24486027
12 -3.58881394 3.21009300
13 2.32367803 -3.58881394
14 2.17922393 2.32367803
15 0.42783381 2.17922393
16 -0.02673256 0.42783381
17 1.14152049 -0.02673256
18 -1.55658778 1.14152049
19 2.03334437 -1.55658778
20 2.50254492 2.03334437
21 -2.87544399 2.50254492
22 -0.49337250 -2.87544399
23 -1.72546932 -0.49337250
24 1.57532604 -1.72546932
25 -6.92910625 1.57532604
26 0.89147358 -6.92910625
27 0.61946965 0.89147358
28 1.04865472 0.61946965
29 -3.12192556 1.04865472
30 0.22068411 -3.12192556
31 0.14271118 0.22068411
32 1.81577163 0.14271118
33 -0.35102288 1.81577163
34 0.03717542 -0.35102288
35 0.27402060 0.03717542
36 -1.96112056 0.27402060
37 0.65052692 -1.96112056
38 1.60921113 0.65052692
39 -2.26112591 1.60921113
40 -0.73503469 -2.26112591
41 2.36288173 -0.73503469
42 0.28037142 2.36288173
43 -1.17104800 0.28037142
44 0.35288220 -1.17104800
45 -2.59720901 0.35288220
46 -0.36076253 -2.59720901
47 0.13010618 -0.36076253
48 3.58338685 0.13010618
49 -1.69829153 3.58338685
50 0.86791400 -1.69829153
51 0.57470552 0.86791400
52 -0.63214843 0.57470552
53 -1.67232183 -0.63214843
54 -2.18187764 -1.67232183
55 1.29029281 -2.18187764
56 1.91072346 1.29029281
57 -0.37393316 1.91072346
58 -3.08330079 -0.37393316
59 -1.31656834 -3.08330079
60 -2.76301534 -1.31656834
61 -1.43355197 -2.76301534
62 -3.44730120 -1.43355197
63 1.02384635 -3.44730120
64 1.51123410 1.02384635
65 -5.35497852 1.51123410
66 -1.87288278 -5.35497852
67 -2.73079337 -1.87288278
68 1.14883388 -2.73079337
69 1.09151329 1.14883388
70 0.01382629 1.09151329
71 2.95875172 0.01382629
72 0.39470300 2.95875172
73 -0.41690579 0.39470300
74 -2.18363927 -0.41690579
75 -0.44676958 -2.18363927
76 2.78212974 -0.44676958
77 0.22839533 2.78212974
78 0.93257300 0.22839533
79 -2.18474929 0.93257300
80 -0.08442079 -2.18474929
81 -0.73689118 -0.08442079
82 1.52742419 -0.73689118
83 0.47969986 1.52742419
84 -0.35855235 0.47969986
85 0.73291208 -0.35855235
86 -0.46031899 0.73291208
87 0.04404654 -0.46031899
88 -3.73155750 0.04404654
89 2.97119992 -3.73155750
90 0.08795610 2.97119992
91 0.68846973 0.08795610
92 0.57206305 0.68846973
93 -1.14260758 0.57206305
94 0.89250090 -1.14260758
95 -0.93576473 0.89250090
96 -1.01938373 -0.93576473
97 1.99305180 -1.01938373
98 -0.19815192 1.99305180
99 1.82105273 -0.19815192
100 -1.06289104 1.82105273
101 1.02062888 -1.06289104
102 -3.56182943 1.02062888
103 1.82852164 -3.56182943
104 -2.47811744 1.82852164
105 1.02895158 -2.47811744
106 2.03916706 1.02895158
107 -2.89100348 2.03916706
108 0.60663778 -2.89100348
109 1.06974291 0.60663778
110 -2.16104724 1.06974291
111 -2.32412381 -2.16104724
112 2.15853929 -2.32412381
113 3.79472326 2.15853929
114 0.49236742 3.79472326
115 1.00503691 0.49236742
116 0.03534424 1.00503691
117 -1.11509056 0.03534424
118 0.33841047 -1.11509056
119 -0.56436565 0.33841047
120 0.51124718 -0.56436565
121 0.14324267 0.51124718
122 -0.79720421 0.14324267
123 0.50801705 -0.79720421
124 -1.58929244 0.50801705
125 0.87672578 -1.58929244
126 1.82681411 0.87672578
127 4.04725509 1.82681411
128 1.45530720 4.04725509
129 -1.55763648 1.45530720
130 -1.61721392 -1.55763648
131 -0.21735337 -1.61721392
132 2.44377037 -0.21735337
133 0.87671474 2.44377037
134 2.37651303 0.87671474
135 1.59540340 2.37651303
136 0.74930897 1.59540340
137 -0.95480186 0.74930897
138 0.84294253 -0.95480186
139 -0.72555856 0.84294253
140 0.45180196 -0.72555856
141 2.28956333 0.45180196
142 -0.39611090 2.28956333
143 0.92337481 -0.39611090
144 1.46442249 0.92337481
145 1.79211086 1.46442249
146 -2.21699455 1.79211086
147 -2.44726716 -2.21699455
148 -2.40215833 -2.44726716
149 1.83164285 -2.40215833
150 0.73301385 1.83164285
151 0.81410817 0.73301385
152 -2.30947849 0.81410817
153 -2.01056241 -2.30947849
154 1.32293859 -2.01056241
155 0.61842458 1.32293859
156 0.77889471 0.61842458
157 4.29208669 0.77889471
158 -2.35470247 4.29208669
159 0.10894953 -2.35470247
160 0.54635744 0.10894953
161 0.41249337 0.54635744
162 0.52488978 0.41249337
163 4.37728504 0.52488978
164 -2.13364179 4.37728504
165 1.57189158 -2.13364179
166 -0.29233694 1.57189158
167 -1.02536450 -0.29233694
168 -3.84787649 -1.02536450
169 -2.99748046 -3.84787649
170 0.14202571 -2.99748046
171 1.71982771 0.14202571
172 -5.15494766 1.71982771
173 1.42961257 -5.15494766
174 2.53371624 1.42961257
175 -2.31456988 2.53371624
176 -3.51918712 -2.31456988
177 0.36627073 -3.51918712
178 1.33032873 0.36627073
179 -2.25137335 1.33032873
180 -0.11927472 -2.25137335
181 -1.93552353 -0.11927472
182 0.36887448 -1.93552353
183 -0.95465306 0.36887448
184 1.81836988 -0.95465306
185 1.38746103 1.81836988
186 0.49842070 1.38746103
187 0.68928170 0.49842070
188 0.44023958 0.68928170
189 0.51891961 0.44023958
190 -1.40085411 0.51891961
191 -0.91157179 -1.40085411
192 2.07728439 -0.91157179
193 -1.51680063 2.07728439
194 1.86795032 -1.51680063
195 -2.00328564 1.86795032
196 2.27041783 -2.00328564
197 0.67528800 2.27041783
198 -3.13754621 0.67528800
199 -0.63423248 -3.13754621
200 -3.09350054 -0.63423248
201 1.29631112 -3.09350054
202 2.99365138 1.29631112
203 0.48359860 2.99365138
204 0.61149067 0.48359860
205 1.37736856 0.61149067
206 -0.43596970 1.37736856
207 3.63741776 -0.43596970
208 0.13373071 3.63741776
209 1.82275079 0.13373071
210 -2.58841346 1.82275079
211 1.56597037 -2.58841346
212 -1.11313722 1.56597037
213 -3.77076479 -1.11313722
214 -0.92641294 -3.77076479
215 1.82220541 -0.92641294
216 2.31202540 1.82220541
217 -0.10725758 2.31202540
218 -1.83541254 -0.10725758
219 1.60280963 -1.83541254
220 -2.67609398 1.60280963
221 2.67815281 -2.67609398
222 -1.88891232 2.67815281
223 0.31937950 -1.88891232
224 -0.24791787 0.31937950
225 1.75035233 -0.24791787
226 5.39688651 1.75035233
227 -1.39582846 5.39688651
228 -1.36124342 -1.39582846
229 -2.17029124 -1.36124342
230 0.31770551 -2.17029124
231 -2.89077572 0.31770551
232 0.25001325 -2.89077572
233 0.94822617 0.25001325
234 1.35976886 0.94822617
235 -1.59554925 1.35976886
236 0.67685832 -1.59554925
237 0.45084760 0.67685832
238 -3.98005375 0.45084760
239 -2.37168209 -3.98005375
240 -2.57512915 -2.37168209
241 -2.42132784 -2.57512915
242 0.35942148 -2.42132784
243 -0.02995763 0.35942148
244 1.78432511 -0.02995763
245 0.53033721 1.78432511
246 0.56055203 0.53033721
247 5.40652670 0.56055203
248 0.01173077 5.40652670
249 0.79234509 0.01173077
250 2.41406853 0.79234509
251 1.41609983 2.41406853
252 -0.86754901 1.41609983
253 -0.32962511 -0.86754901
254 0.53734529 -0.32962511
255 -0.26029269 0.53734529
256 -1.28788137 -0.26029269
257 -2.12786910 -1.28788137
258 2.87880183 -2.12786910
259 -4.65094656 2.87880183
260 0.82187332 -4.65094656
261 1.96552414 0.82187332
262 -2.47180223 1.96552414
263 0.61603746 -2.47180223
> 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/7fyqm1384457442.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/8k5411384457442.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/9kbro1384457442.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/10410h1384457442.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11ab4k1384457442.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12q4cd1384457442.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lhbj1384457442.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/147jba1384457442.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/15pm4s1384457442.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/163rpb1384457442.tab")
+ }
>
> try(system("convert tmp/1vid81384457442.ps tmp/1vid81384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cwk21384457442.ps tmp/2cwk21384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/3igot1384457442.ps tmp/3igot1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/41xmz1384457442.ps tmp/41xmz1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/5c5tu1384457442.ps tmp/5c5tu1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/64ppo1384457442.ps tmp/64ppo1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fyqm1384457442.ps tmp/7fyqm1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k5411384457442.ps tmp/8k5411384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kbro1384457442.ps tmp/9kbro1384457442.png",intern=TRUE))
character(0)
> try(system("convert tmp/10410h1384457442.ps tmp/10410h1384457442.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
18.484 3.306 21.786