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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+ ,4
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+ ,52
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+ ,30
+ ,15
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+ ,64
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+ ,11
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+ ,11
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+ ,41
+ ,11
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+ ,11
+ ,7
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+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),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 = '3'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> 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
Learning Connected Separate Software Happiness Depression Sport1 Sport2
1 13 41 38 12 14 12.0 53 32
2 16 39 32 11 18 11.0 83 51
3 19 30 35 15 11 14.0 66 42
4 15 31 33 6 12 12.0 67 41
5 14 34 37 13 16 21.0 76 46
6 13 35 29 10 18 12.0 78 47
7 19 39 31 12 14 22.0 53 37
8 15 34 36 14 14 11.0 80 49
9 14 36 35 12 15 10.0 74 45
10 15 37 38 9 15 13.0 76 47
11 16 38 31 10 17 10.0 79 49
12 16 36 34 12 19 8.0 54 33
13 16 38 35 12 10 15.0 67 42
14 16 39 38 11 16 14.0 54 33
15 17 33 37 15 18 10.0 87 53
16 15 32 33 12 14 14.0 58 36
17 15 36 32 10 14 14.0 75 45
18 20 38 38 12 17 11.0 88 54
19 18 39 38 11 14 10.0 64 41
20 16 32 32 12 16 13.0 57 36
21 16 32 33 11 18 9.5 66 41
22 16 31 31 12 11 14.0 68 44
23 19 39 38 13 14 12.0 54 33
24 16 37 39 11 12 14.0 56 37
25 17 39 32 12 17 11.0 86 52
26 17 41 32 13 9 9.0 80 47
27 16 36 35 10 16 11.0 76 43
28 15 33 37 14 14 15.0 69 44
29 16 33 33 12 15 14.0 78 45
30 14 34 33 10 11 13.0 67 44
31 15 31 31 12 16 9.0 80 49
32 12 27 32 8 13 15.0 54 33
33 14 37 31 10 17 10.0 71 43
34 16 34 37 12 15 11.0 84 54
35 14 34 30 12 14 13.0 74 42
36 10 32 33 7 16 8.0 71 44
37 10 29 31 9 9 20.0 63 37
38 14 36 33 12 15 12.0 71 43
39 16 29 31 10 17 10.0 76 46
40 16 35 33 10 13 10.0 69 42
41 16 37 32 10 15 9.0 74 45
42 14 34 33 12 16 14.0 75 44
43 20 38 32 15 16 8.0 54 33
44 14 35 33 10 12 14.0 52 31
45 14 38 28 10 15 11.0 69 42
46 11 37 35 12 11 13.0 68 40
47 14 38 39 13 15 9.0 65 43
48 15 33 34 11 15 11.0 75 46
49 16 36 38 11 17 15.0 74 42
50 14 38 32 12 13 11.0 75 45
51 16 32 38 14 16 10.0 72 44
52 14 32 30 10 14 14.0 67 40
53 12 32 33 12 11 18.0 63 37
54 16 34 38 13 12 14.0 62 46
55 9 32 32 5 12 11.0 63 36
56 14 37 35 6 15 14.5 76 47
57 16 39 34 12 16 13.0 74 45
58 16 29 34 12 15 9.0 67 42
59 15 37 36 11 12 10.0 73 43
60 16 35 34 10 12 15.0 70 43
61 12 30 28 7 8 20.0 53 32
62 16 38 34 12 13 12.0 77 45
63 16 34 35 14 11 12.0 80 48
64 14 31 35 11 14 14.0 52 31
65 16 34 31 12 15 13.0 54 33
66 17 35 37 13 10 11.0 80 49
67 18 36 35 14 11 17.0 66 42
68 18 30 27 11 12 12.0 73 41
69 12 39 40 12 15 13.0 63 38
70 16 35 37 12 15 14.0 69 42
71 10 38 36 8 14 13.0 67 44
72 14 31 38 11 16 15.0 54 33
73 18 34 39 14 15 13.0 81 48
74 18 38 41 14 15 10.0 69 40
75 16 34 27 12 13 11.0 84 50
76 17 39 30 9 12 19.0 80 49
77 16 37 37 13 17 13.0 70 43
78 16 34 31 11 13 17.0 69 44
79 13 28 31 12 15 13.0 77 47
80 16 37 27 12 13 9.0 54 33
81 16 33 36 12 15 11.0 79 46
82 16 35 37 12 15 9.0 71 45
83 15 37 33 12 16 12.0 73 43
84 15 32 34 11 15 12.0 72 44
85 16 33 31 10 14 13.0 77 47
86 14 38 39 9 15 13.0 75 45
87 16 33 34 12 14 12.0 69 42
88 16 29 32 12 13 15.0 54 33
89 15 33 33 12 7 22.0 70 43
90 12 31 36 9 17 13.0 73 46
91 17 36 32 15 13 15.0 54 33
92 16 35 41 12 15 13.0 77 46
93 15 32 28 12 14 15.0 82 48
94 13 29 30 12 13 12.5 80 47
95 16 39 36 10 16 11.0 80 47
96 16 37 35 13 12 16.0 69 43
97 16 35 31 9 14 11.0 78 46
98 16 37 34 12 17 11.0 81 48
99 14 32 36 10 15 10.0 76 46
100 16 38 36 14 17 10.0 76 45
101 16 37 35 11 12 16.0 73 45
102 20 36 37 15 16 12.0 85 52
103 15 32 28 11 11 11.0 66 42
104 16 33 39 11 15 16.0 79 47
105 13 40 32 12 9 19.0 68 41
106 17 38 35 12 16 11.0 76 47
107 16 41 39 12 15 16.0 71 43
108 16 36 35 11 10 15.0 54 33
109 12 43 42 7 10 24.0 46 30
110 16 30 34 12 15 14.0 85 52
111 16 31 33 14 11 15.0 74 44
112 17 32 41 11 13 11.0 88 55
113 13 32 33 11 14 15.0 38 11
114 12 37 34 10 18 12.0 76 47
115 18 37 32 13 16 10.0 86 53
116 14 33 40 13 14 14.0 54 33
117 14 34 40 8 14 13.0 67 44
118 13 33 35 11 14 9.0 69 42
119 16 38 36 12 14 15.0 90 55
120 13 33 37 11 12 15.0 54 33
121 16 31 27 13 14 14.0 76 46
122 13 38 39 12 15 11.0 89 54
123 16 37 38 14 15 8.0 76 47
124 15 36 31 13 15 11.0 73 45
125 16 31 33 15 13 11.0 79 47
126 15 39 32 10 17 8.0 90 55
127 17 44 39 11 17 10.0 74 44
128 15 33 36 9 19 11.0 81 53
129 12 35 33 11 15 13.0 72 44
130 16 32 33 10 13 11.0 71 42
131 10 28 32 11 9 20.0 66 40
132 16 40 37 8 15 10.0 77 46
133 12 27 30 11 15 15.0 65 40
134 14 37 38 12 15 12.0 74 46
135 15 32 29 12 16 14.0 85 53
136 13 28 22 9 11 23.0 54 33
137 15 34 35 11 14 14.0 63 42
138 11 30 35 10 11 16.0 54 35
139 12 35 34 8 15 11.0 64 40
140 11 31 35 9 13 12.0 69 41
141 16 32 34 8 15 10.0 54 33
142 15 30 37 9 16 14.0 84 51
143 17 30 35 15 14 12.0 86 53
144 16 31 23 11 15 12.0 77 46
145 10 40 31 8 16 11.0 89 55
146 18 32 27 13 16 12.0 76 47
147 13 36 36 12 11 13.0 60 38
148 16 32 31 12 12 11.0 75 46
149 13 35 32 9 9 19.0 73 46
150 10 38 39 7 16 12.0 85 53
151 15 42 37 13 13 17.0 79 47
152 16 34 38 9 16 9.0 71 41
153 16 35 39 6 12 12.0 72 44
154 14 38 34 8 9 19.0 69 43
155 10 33 31 8 13 18.0 78 51
156 17 36 32 15 13 15.0 54 33
157 13 32 37 6 14 14.0 69 43
158 15 33 36 9 19 11.0 81 53
159 16 34 32 11 13 9.0 84 51
160 12 32 38 8 12 18.0 84 50
161 13 34 36 8 13 16.0 69 46
162 13 27 26 10 10 24.0 66 43
163 12 31 26 8 14 14.0 81 47
164 17 38 33 14 16 20.0 82 50
165 15 34 39 10 10 18.0 72 43
166 10 24 30 8 11 23.0 54 33
167 14 30 33 11 14 12.0 78 48
168 11 26 25 12 12 14.0 74 44
169 13 34 38 12 9 16.0 82 50
170 16 27 37 12 9 18.0 73 41
171 12 37 31 5 11 20.0 55 34
172 16 36 37 12 16 12.0 72 44
173 12 41 35 10 9 12.0 78 47
174 9 29 25 7 13 17.0 59 35
175 12 36 28 12 16 13.0 72 44
176 15 32 35 11 13 9.0 78 44
177 12 37 33 8 9 16.0 68 43
178 12 30 30 9 12 18.0 69 41
179 14 31 31 10 16 10.0 67 41
180 12 38 37 9 11 14.0 74 42
181 16 36 36 12 14 11.0 54 33
182 11 35 30 6 13 9.0 67 41
183 19 31 36 15 15 11.0 70 44
184 15 38 32 12 14 10.0 80 48
185 8 22 28 12 16 11.0 89 55
186 16 32 36 12 13 19.0 76 44
187 17 36 34 11 14 14.0 74 43
188 12 39 31 7 15 12.0 87 52
189 11 28 28 7 13 14.0 54 30
190 11 32 36 5 11 21.0 61 39
191 14 32 36 12 11 13.0 38 11
192 16 38 40 12 14 10.0 75 44
193 12 32 33 3 15 15.0 69 42
194 16 35 37 11 11 16.0 62 41
195 13 32 32 10 15 14.0 72 44
196 15 37 38 12 12 12.0 70 44
197 16 34 31 9 14 19.0 79 48
198 16 33 37 12 14 15.0 87 53
199 14 33 33 9 8 19.0 62 37
200 16 26 32 12 13 13.0 77 44
201 16 30 30 12 9 17.0 69 44
202 14 24 30 10 15 12.0 69 40
203 11 34 31 9 17 11.0 75 42
204 12 34 32 12 13 14.0 54 35
205 15 33 34 8 15 11.0 72 43
206 15 34 36 11 15 13.0 74 45
207 16 35 37 11 14 12.0 85 55
208 16 35 36 12 16 15.0 52 31
209 11 36 33 10 13 14.0 70 44
210 15 34 33 10 16 12.0 84 50
211 12 34 33 12 9 17.0 64 40
212 12 41 44 12 16 11.0 84 53
213 15 32 39 11 11 18.0 87 54
214 15 30 32 8 10 13.0 79 49
215 16 35 35 12 11 17.0 67 40
216 14 28 25 10 15 13.0 65 41
217 17 33 35 11 17 11.0 85 52
218 14 39 34 10 14 12.0 83 52
219 13 36 35 8 8 22.0 61 36
220 15 36 39 12 15 14.0 82 52
221 13 35 33 12 11 12.0 76 46
222 14 38 36 10 16 12.0 58 31
223 15 33 32 12 10 17.0 72 44
224 12 31 32 9 15 9.0 72 44
225 13 34 36 9 9 21.0 38 11
226 8 32 36 6 16 10.0 78 46
227 14 31 32 10 19 11.0 54 33
228 14 33 34 9 12 12.0 63 34
229 11 34 33 9 8 23.0 66 42
230 12 34 35 9 11 13.0 70 43
231 13 34 30 6 14 12.0 71 43
232 10 33 38 10 9 16.0 67 44
233 16 32 34 6 15 9.0 58 36
234 18 41 33 14 13 17.0 72 46
235 13 34 32 10 16 9.0 72 44
236 11 36 31 10 11 14.0 70 43
237 4 37 30 6 12 17.0 76 50
238 13 36 27 12 13 13.0 50 33
239 16 29 31 12 10 11.0 72 43
240 10 37 30 7 11 12.0 72 44
241 12 27 32 8 12 10.0 88 53
242 12 35 35 11 8 19.0 53 34
243 10 28 28 3 12 16.0 58 35
244 13 35 33 6 12 16.0 66 40
245 15 37 31 10 15 14.0 82 53
246 12 29 35 8 11 20.0 69 42
247 14 32 35 9 13 15.0 68 43
248 10 36 32 9 14 23.0 44 29
249 12 19 21 8 10 20.0 56 36
250 12 21 20 9 12 16.0 53 30
251 11 31 34 7 15 14.0 70 42
252 10 33 32 7 13 17.0 78 47
253 12 36 34 6 13 11.0 71 44
254 16 33 32 9 13 13.0 72 45
255 12 37 33 10 12 17.0 68 44
256 14 34 33 11 12 15.0 67 43
257 16 35 37 12 9 21.0 75 43
258 14 31 32 8 9 18.0 62 40
259 13 37 34 11 15 15.0 67 41
260 4 35 30 3 10 8.0 83 52
261 15 27 30 11 14 12.0 64 38
262 11 34 38 12 15 12.0 68 41
263 11 40 36 7 7 22.0 62 39
264 14 29 32 9 14 12.0 72 43
Month t
1 9 1
2 9 2
3 9 3
4 9 4
5 9 5
6 9 6
7 9 7
8 9 8
9 9 9
10 9 10
11 9 11
12 9 12
13 9 13
14 9 14
15 9 15
16 9 16
17 9 17
18 9 18
19 9 19
20 9 20
21 9 21
22 9 22
23 9 23
24 9 24
25 9 25
26 9 26
27 9 27
28 9 28
29 9 29
30 9 30
31 9 31
32 9 32
33 9 33
34 9 34
35 9 35
36 9 36
37 9 37
38 9 38
39 9 39
40 9 40
41 9 41
42 9 42
43 9 43
44 9 44
45 9 45
46 9 46
47 9 47
48 9 48
49 9 49
50 9 50
51 9 51
52 9 52
53 9 53
54 9 54
55 9 55
56 9 56
57 9 57
58 9 58
59 9 59
60 9 60
61 9 61
62 9 62
63 9 63
64 9 64
65 9 65
66 10 66
67 10 67
68 10 68
69 10 69
70 10 70
71 10 71
72 10 72
73 10 73
74 10 74
75 10 75
76 10 76
77 10 77
78 10 78
79 10 79
80 10 80
81 10 81
82 10 82
83 10 83
84 10 84
85 10 85
86 10 86
87 10 87
88 10 88
89 10 89
90 10 90
91 10 91
92 10 92
93 10 93
94 10 94
95 10 95
96 10 96
97 10 97
98 10 98
99 10 99
100 10 100
101 10 101
102 10 102
103 10 103
104 10 104
105 10 105
106 10 106
107 10 107
108 10 108
109 10 109
110 10 110
111 10 111
112 10 112
113 10 113
114 10 114
115 10 115
116 10 116
117 10 117
118 10 118
119 10 119
120 10 120
121 10 121
122 10 122
123 10 123
124 10 124
125 10 125
126 10 126
127 10 127
128 10 128
129 10 129
130 10 130
131 10 131
132 10 132
133 10 133
134 10 134
135 10 135
136 10 136
137 10 137
138 10 138
139 10 139
140 10 140
141 10 141
142 10 142
143 10 143
144 10 144
145 10 145
146 10 146
147 10 147
148 10 148
149 10 149
150 10 150
151 10 151
152 10 152
153 10 153
154 9 154
155 10 155
156 10 156
157 10 157
158 10 158
159 10 159
160 10 160
161 10 161
162 11 162
163 11 163
164 11 164
165 11 165
166 11 166
167 11 167
168 11 168
169 11 169
170 11 170
171 11 171
172 11 172
173 11 173
174 11 174
175 11 175
176 11 176
177 11 177
178 11 178
179 11 179
180 11 180
181 11 181
182 11 182
183 11 183
184 11 184
185 11 185
186 11 186
187 11 187
188 11 188
189 11 189
190 11 190
191 11 191
192 11 192
193 11 193
194 11 194
195 11 195
196 11 196
197 11 197
198 11 198
199 11 199
200 11 200
201 11 201
202 11 202
203 11 203
204 11 204
205 11 205
206 11 206
207 11 207
208 11 208
209 11 209
210 11 210
211 11 211
212 11 212
213 11 213
214 11 214
215 11 215
216 11 216
217 11 217
218 11 218
219 11 219
220 11 220
221 11 221
222 11 222
223 11 223
224 11 224
225 11 225
226 11 226
227 11 227
228 11 228
229 11 229
230 11 230
231 11 231
232 11 232
233 11 233
234 11 234
235 11 235
236 11 236
237 11 237
238 11 238
239 11 239
240 11 240
241 11 241
242 11 242
243 11 243
244 11 244
245 11 245
246 11 246
247 11 247
248 11 248
249 11 249
250 11 250
251 11 251
252 11 252
253 11 253
254 11 254
255 11 255
256 11 256
257 11 257
258 11 258
259 11 259
260 11 260
261 11 261
262 11 262
263 11 263
264 11 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
4.004454 0.033972 0.042665 0.553914 0.069384 -0.030972
Sport1 Sport2 Month t
0.021059 -0.021837 0.161768 -0.006517
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9144 -1.0411 0.2563 1.2191 4.6761
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.004454 3.893744 1.028 0.305
Connected 0.033972 0.034675 0.980 0.328
Separate 0.042665 0.035196 1.212 0.227
Software 0.553914 0.054661 10.134 <2e-16 ***
Happiness 0.069384 0.058102 1.194 0.234
Depression -0.030972 0.042456 -0.730 0.466
Sport1 0.021059 0.037979 0.554 0.580
Sport2 -0.021837 0.056419 -0.387 0.699
Month 0.161768 0.408738 0.396 0.693
t -0.006517 0.004332 -1.504 0.134
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.86 on 254 degrees of freedom
Multiple R-squared: 0.446, Adjusted R-squared: 0.4263
F-statistic: 22.72 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,] 0.309807656 0.619615313 0.6901923
[2,] 0.298245890 0.596491779 0.7017541
[3,] 0.180880646 0.361761291 0.8191194
[4,] 0.137959528 0.275919056 0.8620405
[5,] 0.143729596 0.287459191 0.8562704
[6,] 0.295672612 0.591345224 0.7043274
[7,] 0.245953052 0.491906104 0.7540469
[8,] 0.197531583 0.395063165 0.8024684
[9,] 0.154638598 0.309277197 0.8453614
[10,] 0.181179794 0.362359588 0.8188202
[11,] 0.292370722 0.584741444 0.7076293
[12,] 0.413563053 0.827126106 0.5864369
[13,] 0.343871514 0.687743029 0.6561285
[14,] 0.292745396 0.585490791 0.7072546
[15,] 0.280275008 0.560550017 0.7197250
[16,] 0.396379430 0.792758860 0.6036206
[17,] 0.342705495 0.685410990 0.6572945
[18,] 0.453590892 0.907181785 0.5464091
[19,] 0.409820304 0.819640608 0.5901797
[20,] 0.379700408 0.759400816 0.6202996
[21,] 0.362199175 0.724398350 0.6378008
[22,] 0.323061419 0.646122839 0.6769386
[23,] 0.277419371 0.554838742 0.7225806
[24,] 0.412599374 0.825198748 0.5874006
[25,] 0.440486741 0.880973482 0.5595133
[26,] 0.414751241 0.829502482 0.5852488
[27,] 0.458996582 0.917993164 0.5410034
[28,] 0.438077281 0.876154562 0.5619227
[29,] 0.397606632 0.795213265 0.6023934
[30,] 0.357011021 0.714022041 0.6429890
[31,] 0.375803324 0.751606648 0.6241967
[32,] 0.326219196 0.652438392 0.6737808
[33,] 0.297228598 0.594457195 0.7027714
[34,] 0.477767577 0.955535153 0.5222324
[35,] 0.566983009 0.866033981 0.4330170
[36,] 0.523694346 0.952611309 0.4763057
[37,] 0.547090628 0.905818743 0.4529094
[38,] 0.519795954 0.960408092 0.4802040
[39,] 0.474361893 0.948723787 0.5256381
[40,] 0.432325414 0.864650829 0.5676746
[41,] 0.432585257 0.865170513 0.5674147
[42,] 0.389368675 0.778737351 0.6106313
[43,] 0.377808514 0.755617028 0.6221915
[44,] 0.373594208 0.747188416 0.6264058
[45,] 0.332852275 0.665704551 0.6671477
[46,] 0.312980768 0.625961536 0.6870192
[47,] 0.281726108 0.563452217 0.7182739
[48,] 0.301554968 0.603109936 0.6984450
[49,] 0.276786835 0.553573670 0.7232132
[50,] 0.250508190 0.501016379 0.7494918
[51,] 0.220683000 0.441366000 0.7793170
[52,] 0.191708281 0.383416563 0.8082917
[53,] 0.169368196 0.338736391 0.8306318
[54,] 0.143783932 0.287567864 0.8562161
[55,] 0.124528037 0.249056075 0.8754720
[56,] 0.150714127 0.301428254 0.8492859
[57,] 0.343888669 0.687777338 0.6561113
[58,] 0.305643257 0.611286514 0.6943567
[59,] 0.456877586 0.913755171 0.5431224
[60,] 0.419928698 0.839857396 0.5800713
[61,] 0.410250268 0.820500535 0.5897497
[62,] 0.389475788 0.778951576 0.6105242
[63,] 0.353067153 0.706134307 0.6469328
[64,] 0.402478662 0.804957323 0.5975213
[65,] 0.367580301 0.735160603 0.6324197
[66,] 0.338946912 0.677893824 0.6610531
[67,] 0.366387167 0.732774334 0.6336128
[68,] 0.334440607 0.668881215 0.6655594
[69,] 0.302660280 0.605320560 0.6973397
[70,] 0.268712181 0.537424363 0.7312878
[71,] 0.243465165 0.486930330 0.7565348
[72,] 0.213395170 0.426790340 0.7866048
[73,] 0.204473723 0.408947447 0.7955263
[74,] 0.177589056 0.355178111 0.8224109
[75,] 0.155174073 0.310348147 0.8448259
[76,] 0.141134380 0.282268761 0.8588656
[77,] 0.120988634 0.241977269 0.8790114
[78,] 0.120255159 0.240510318 0.8797448
[79,] 0.102479154 0.204958308 0.8975208
[80,] 0.089146102 0.178292204 0.9108539
[81,] 0.075510787 0.151021574 0.9244892
[82,] 0.079532704 0.159065408 0.9204673
[83,] 0.071832778 0.143665556 0.9281672
[84,] 0.059771881 0.119543762 0.9402281
[85,] 0.063651308 0.127302617 0.9363487
[86,] 0.052572923 0.105145845 0.9474271
[87,] 0.043348528 0.086697056 0.9566515
[88,] 0.037539241 0.075078482 0.9624608
[89,] 0.032990411 0.065980822 0.9670096
[90,] 0.039839380 0.079678759 0.9601606
[91,] 0.033549079 0.067098158 0.9664509
[92,] 0.030520175 0.061040349 0.9694798
[93,] 0.035705145 0.071410289 0.9642949
[94,] 0.031265039 0.062530078 0.9687350
[95,] 0.025309677 0.050619354 0.9746903
[96,] 0.024604968 0.049209935 0.9753950
[97,] 0.019840648 0.039681296 0.9801594
[98,] 0.016240071 0.032480143 0.9837599
[99,] 0.012811324 0.025622647 0.9871887
[100,] 0.013225903 0.026451806 0.9867741
[101,] 0.012122277 0.024244555 0.9878777
[102,] 0.016980419 0.033960838 0.9830196
[103,] 0.016192752 0.032385504 0.9838072
[104,] 0.015486735 0.030973470 0.9845133
[105,] 0.012793425 0.025586850 0.9872066
[106,] 0.012693487 0.025386975 0.9873065
[107,] 0.010127424 0.020254849 0.9898726
[108,] 0.008949416 0.017898831 0.9910506
[109,] 0.007135535 0.014271070 0.9928645
[110,] 0.010300917 0.020601834 0.9896991
[111,] 0.008418226 0.016836451 0.9915818
[112,] 0.007014959 0.014029917 0.9929850
[113,] 0.005612308 0.011224616 0.9943877
[114,] 0.004373092 0.008746184 0.9956269
[115,] 0.004114882 0.008229765 0.9958851
[116,] 0.003371336 0.006742672 0.9966287
[117,] 0.004595192 0.009190385 0.9954048
[118,] 0.005208333 0.010416667 0.9947917
[119,] 0.010657165 0.021314331 0.9893428
[120,] 0.013163784 0.026327567 0.9868362
[121,] 0.014059861 0.028119723 0.9859401
[122,] 0.013026635 0.026053270 0.9869734
[123,] 0.010268300 0.020536601 0.9897317
[124,] 0.008693013 0.017386027 0.9913070
[125,] 0.006919306 0.013838613 0.9930807
[126,] 0.008731244 0.017462488 0.9912688
[127,] 0.007445631 0.014891263 0.9925544
[128,] 0.009115022 0.018230043 0.9908850
[129,] 0.014924498 0.029848996 0.9850755
[130,] 0.014288027 0.028576054 0.9857120
[131,] 0.011787519 0.023575038 0.9882125
[132,] 0.011742474 0.023484947 0.9882575
[133,] 0.020854508 0.041709016 0.9791455
[134,] 0.024896668 0.049793336 0.9751033
[135,] 0.026423582 0.052847165 0.9735764
[136,] 0.023088558 0.046177116 0.9769114
[137,] 0.018575377 0.037150755 0.9814246
[138,] 0.026582641 0.053165283 0.9734174
[139,] 0.022685178 0.045370356 0.9773148
[140,] 0.025275197 0.050550393 0.9747248
[141,] 0.051014385 0.102028770 0.9489856
[142,] 0.049997334 0.099994669 0.9500027
[143,] 0.059056258 0.118112516 0.9409437
[144,] 0.050052945 0.100105889 0.9499471
[145,] 0.044237858 0.088475716 0.9557621
[146,] 0.037977723 0.075955445 0.9620223
[147,] 0.036754538 0.073509076 0.9632455
[148,] 0.031059385 0.062118770 0.9689406
[149,] 0.025175038 0.050350076 0.9748250
[150,] 0.020331532 0.040663065 0.9796685
[151,] 0.016886847 0.033773695 0.9831132
[152,] 0.014085069 0.028170138 0.9859149
[153,] 0.012037770 0.024075539 0.9879622
[154,] 0.012719040 0.025438080 0.9872810
[155,] 0.010156626 0.020313252 0.9898434
[156,] 0.015725394 0.031450788 0.9842746
[157,] 0.016088701 0.032177402 0.9839113
[158,] 0.015000827 0.030001654 0.9849992
[159,] 0.013589503 0.027179005 0.9864105
[160,] 0.010839421 0.021678842 0.9891606
[161,] 0.010844163 0.021688325 0.9891558
[162,] 0.012596460 0.025192919 0.9874035
[163,] 0.016631120 0.033262241 0.9833689
[164,] 0.013248075 0.026496149 0.9867519
[165,] 0.010452921 0.020905842 0.9895471
[166,] 0.008768598 0.017537197 0.9912314
[167,] 0.006752181 0.013504362 0.9932478
[168,] 0.005952276 0.011904552 0.9940477
[169,] 0.004820213 0.009640425 0.9951798
[170,] 0.003708346 0.007416693 0.9962917
[171,] 0.003986924 0.007973848 0.9960131
[172,] 0.003009610 0.006019220 0.9969904
[173,] 0.085341477 0.170682954 0.9146585
[174,] 0.076021534 0.152043067 0.9239785
[175,] 0.083741392 0.167482785 0.9162586
[176,] 0.070040319 0.140080639 0.9299597
[177,] 0.062287597 0.124575194 0.9377124
[178,] 0.051744810 0.103489620 0.9482552
[179,] 0.047731499 0.095462999 0.9522685
[180,] 0.039225752 0.078451504 0.9607742
[181,] 0.040024352 0.080048704 0.9599756
[182,] 0.040304708 0.080609416 0.9596953
[183,] 0.034700608 0.069401216 0.9652994
[184,] 0.027766152 0.055532304 0.9722338
[185,] 0.035105201 0.070210403 0.9648948
[186,] 0.028501418 0.057002837 0.9714986
[187,] 0.025531444 0.051062889 0.9744686
[188,] 0.021819610 0.043639220 0.9781804
[189,] 0.020326946 0.040653891 0.9796731
[190,] 0.017089958 0.034179916 0.9829100
[191,] 0.022464385 0.044928770 0.9775356
[192,] 0.026154888 0.052309776 0.9738451
[193,] 0.027321996 0.054643991 0.9726780
[194,] 0.021257012 0.042514024 0.9787430
[195,] 0.020265311 0.040530621 0.9797347
[196,] 0.016468826 0.032937652 0.9835312
[197,] 0.018327292 0.036654583 0.9816727
[198,] 0.014501004 0.029002008 0.9854990
[199,] 0.016618287 0.033236573 0.9833817
[200,] 0.027090095 0.054180191 0.9729099
[201,] 0.020913116 0.041826232 0.9790869
[202,] 0.027113933 0.054227867 0.9728861
[203,] 0.022872549 0.045745099 0.9771275
[204,] 0.017531978 0.035063957 0.9824680
[205,] 0.019255959 0.038511919 0.9807440
[206,] 0.016098071 0.032196143 0.9839019
[207,] 0.014873793 0.029747587 0.9851262
[208,] 0.011245228 0.022490457 0.9887548
[209,] 0.009261827 0.018523655 0.9907382
[210,] 0.006565628 0.013131255 0.9934344
[211,] 0.004982036 0.009964073 0.9950180
[212,] 0.003705866 0.007411732 0.9962941
[213,] 0.003129106 0.006258211 0.9968709
[214,] 0.007308024 0.014616048 0.9926920
[215,] 0.005656587 0.011313174 0.9943434
[216,] 0.004130036 0.008260072 0.9958700
[217,] 0.002900956 0.005801913 0.9970990
[218,] 0.002100065 0.004200131 0.9978999
[219,] 0.001874350 0.003748700 0.9981256
[220,] 0.004452684 0.008905369 0.9955473
[221,] 0.018651361 0.037302721 0.9813486
[222,] 0.028705624 0.057411247 0.9712944
[223,] 0.020240816 0.040481631 0.9797592
[224,] 0.016638518 0.033277036 0.9833615
[225,] 0.158811527 0.317623054 0.8411885
[226,] 0.122053660 0.244107319 0.8779463
[227,] 0.099604870 0.199209739 0.9003951
[228,] 0.075085772 0.150171543 0.9249142
[229,] 0.064642642 0.129285284 0.9353574
[230,] 0.102490656 0.204981311 0.8975093
[231,] 0.081491721 0.162983442 0.9185083
[232,] 0.084268263 0.168536525 0.9157317
[233,] 0.066587119 0.133174239 0.9334129
[234,] 0.054435684 0.108871368 0.9455643
[235,] 0.032990754 0.065981509 0.9670092
[236,] 0.026639164 0.053278328 0.9733608
[237,] 0.022491358 0.044982715 0.9775086
[238,] 0.066447339 0.132894678 0.9335527
[239,] 0.048697248 0.097394497 0.9513028
> postscript(file="/var/fisher/rcomp/tmp/171jo1383489833.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2sjg21383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ggto1383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4wdcc1383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/532n21383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
-3.131999295 0.226986679 1.935679119 2.804558516 -2.418032225 -1.880226893
7 8 9 10 11 12
3.692611150 -2.099410970 -2.071694532 0.529072337 0.995167912 -0.189823032
13 14 15 16 17 18
0.470117024 0.498538318 -0.984972301 -0.471169368 0.388482697 3.584932386
19 20 21 22 23 24
2.510108792 0.448882448 0.639130463 0.859494434 2.526189315 0.911757507
25 26 27 28 29 30
0.851015089 0.745973842 1.029242723 -1.731403298 0.285551942 -0.177697671
31 32 33 34 35 36
-0.727157091 -0.819627423 -0.790028236 0.090766983 -1.524184275 -2.994926432
37 38 39 40 41 42
-3.036017733 -1.715914366 1.481067760 1.536025997 1.307738561 -1.691739352
43 44 45 46 47 48
2.576009592 -0.126833311 -0.427775531 -4.476901087 -2.501670057 -0.087273356
49 50 51 52 53 54
0.565501318 -1.595751557 -0.947005562 -0.102905664 -2.981443297 0.214206701
55 56 57 58 59 60
-2.356371538 1.665065486 0.205418568 0.579053002 -0.083012473 1.848732075
61 62 63 64 65 66
0.493038981 0.385981197 -0.481004602 -0.638611608 0.783933502 0.871640504
67 68 69 70 71 72
1.634018971 3.453934790 -3.985955793 0.276410320 -3.436462873 -0.982476413
73 74 75 76 77 78
0.984116988 0.754511906 0.674279402 3.424247981 -0.468790299 1.447784167
79 80 81 82 83 84
-2.261400258 0.803547438 0.242550333 0.223148307 -0.729877341 0.070029770
85 86 87 88 89 90
1.785056577 -0.236630957 0.590582925 1.099972796 0.442475612 -1.919576969
91 92 93 94 95 96
0.219977319 0.137035181 -0.130179843 -2.094841811 1.169182749 0.201266765
97 98 99 100 101 102
2.244394121 0.165573237 -0.466132834 -1.039710817 1.301119982 2.539345072
103 104 105 106 107 108
0.779093109 0.995067536 -1.881628561 1.293917079 0.270053027 1.626587838
109 110 111 112 113 114
-0.305982558 0.716382937 -0.010771667 1.964918736 -1.540611240 -2.577275020
115 116 117 118 119 120
1.850084192 -1.849020562 0.828562586 -1.789048013 0.478505290 -1.417386693
121 122 123 124 125 126
0.626735160 -2.823989421 -0.820672763 -0.815194361 -0.775886466 0.343682304
127 128 129 130 131 132
1.486454155 1.043809413 -2.664969621 2.051587473 -3.699345786 2.521267351
133 134 135 136 137 138
-2.117119104 -1.496982583 -0.022851451 0.921716463 0.582024794 -2.414884325
139 140 141 142 143 144
-0.961538813 -2.329429108 3.180171327 1.388523482 0.235263838 1.902733519
145 146 147 148 149 150
-3.232606357 2.576819178 -1.864314906 1.218895990 0.240626106 -2.847945621
151 152 153 154 155 156
-0.857124586 2.175679631 4.182209460 1.820374733 -2.360367445 0.643603147
157 158 159 160 161 162
1.360040724 1.239329026 1.522216200 -0.671268683 0.449841607 0.304814334
163 164 165 166 167 168
-0.532526582 0.705563450 1.219730503 -1.696048432 -0.409813871 -3.282401493
169 170 171 172 173 174
-1.869656451 1.472274377 1.421840303 0.594604288 -1.950732603 -2.432755979
175 176 177 178 179 180
-2.970887501 0.384688185 -0.348484733 -0.741023545 0.151743067 -1.436382572
181 182 183 184 185 186
0.942577449 -0.529090078 2.297606534 -0.186108035 -6.609847763 1.205124572
187 188 189 190 191 192
2.511032191 -0.449278889 -0.525825323 0.516008059 -0.729729103 0.542659139
193 194 195 196 197 198
2.205050020 1.941762393 -0.667126774 -0.005961548 3.038724351 0.978304049
199 200 201 202 203 204
1.534504831 1.463965120 1.989822678 0.649485355 -2.424889464 -2.462945348
205 206 207 208 209 210
2.271819267 0.560792297 1.515804166 1.136082517 -2.573550732 1.067008366
211 212 213 214 215 216
-2.190939360 -3.700365776 0.901527852 2.910196045 1.513882632 0.955208844
217 218 219 220 221 222
2.429615296 0.110123039 1.123659284 -0.082459821 -1.575055682 0.013966772
223 224 225 226 227 228
0.813396739 -1.045100363 0.472207287 -3.696045992 0.343800671 1.099928300
229 230 231 232 233 234
-1.155109630 -0.814198702 1.807201525 -3.132401339 4.676127579 2.298337625
235 236 237 238 239 240
-0.698624739 -2.195321322 -6.914416877 -1.086386543 1.888555398 -1.581042084
241 242 243 244 245 246
-0.145789506 -1.322333028 1.198044918 2.032402960 1.517485572 0.229882402
247 248 249 250 251 252
1.329833795 -2.293447889 1.398592634 0.495413938 -0.693327081 -1.497025470
253 254 255 256 257 258
0.772227707 3.366970537 -1.103306445 0.388489873 1.861977961 2.548703480
259 260 261 262 263 264
-0.988365848 -5.278553518 1.509182186 -3.705447878 -0.100381546 1.424001562
> postscript(file="/var/fisher/rcomp/tmp/65wbg1383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.131999295 NA
1 0.226986679 -3.131999295
2 1.935679119 0.226986679
3 2.804558516 1.935679119
4 -2.418032225 2.804558516
5 -1.880226893 -2.418032225
6 3.692611150 -1.880226893
7 -2.099410970 3.692611150
8 -2.071694532 -2.099410970
9 0.529072337 -2.071694532
10 0.995167912 0.529072337
11 -0.189823032 0.995167912
12 0.470117024 -0.189823032
13 0.498538318 0.470117024
14 -0.984972301 0.498538318
15 -0.471169368 -0.984972301
16 0.388482697 -0.471169368
17 3.584932386 0.388482697
18 2.510108792 3.584932386
19 0.448882448 2.510108792
20 0.639130463 0.448882448
21 0.859494434 0.639130463
22 2.526189315 0.859494434
23 0.911757507 2.526189315
24 0.851015089 0.911757507
25 0.745973842 0.851015089
26 1.029242723 0.745973842
27 -1.731403298 1.029242723
28 0.285551942 -1.731403298
29 -0.177697671 0.285551942
30 -0.727157091 -0.177697671
31 -0.819627423 -0.727157091
32 -0.790028236 -0.819627423
33 0.090766983 -0.790028236
34 -1.524184275 0.090766983
35 -2.994926432 -1.524184275
36 -3.036017733 -2.994926432
37 -1.715914366 -3.036017733
38 1.481067760 -1.715914366
39 1.536025997 1.481067760
40 1.307738561 1.536025997
41 -1.691739352 1.307738561
42 2.576009592 -1.691739352
43 -0.126833311 2.576009592
44 -0.427775531 -0.126833311
45 -4.476901087 -0.427775531
46 -2.501670057 -4.476901087
47 -0.087273356 -2.501670057
48 0.565501318 -0.087273356
49 -1.595751557 0.565501318
50 -0.947005562 -1.595751557
51 -0.102905664 -0.947005562
52 -2.981443297 -0.102905664
53 0.214206701 -2.981443297
54 -2.356371538 0.214206701
55 1.665065486 -2.356371538
56 0.205418568 1.665065486
57 0.579053002 0.205418568
58 -0.083012473 0.579053002
59 1.848732075 -0.083012473
60 0.493038981 1.848732075
61 0.385981197 0.493038981
62 -0.481004602 0.385981197
63 -0.638611608 -0.481004602
64 0.783933502 -0.638611608
65 0.871640504 0.783933502
66 1.634018971 0.871640504
67 3.453934790 1.634018971
68 -3.985955793 3.453934790
69 0.276410320 -3.985955793
70 -3.436462873 0.276410320
71 -0.982476413 -3.436462873
72 0.984116988 -0.982476413
73 0.754511906 0.984116988
74 0.674279402 0.754511906
75 3.424247981 0.674279402
76 -0.468790299 3.424247981
77 1.447784167 -0.468790299
78 -2.261400258 1.447784167
79 0.803547438 -2.261400258
80 0.242550333 0.803547438
81 0.223148307 0.242550333
82 -0.729877341 0.223148307
83 0.070029770 -0.729877341
84 1.785056577 0.070029770
85 -0.236630957 1.785056577
86 0.590582925 -0.236630957
87 1.099972796 0.590582925
88 0.442475612 1.099972796
89 -1.919576969 0.442475612
90 0.219977319 -1.919576969
91 0.137035181 0.219977319
92 -0.130179843 0.137035181
93 -2.094841811 -0.130179843
94 1.169182749 -2.094841811
95 0.201266765 1.169182749
96 2.244394121 0.201266765
97 0.165573237 2.244394121
98 -0.466132834 0.165573237
99 -1.039710817 -0.466132834
100 1.301119982 -1.039710817
101 2.539345072 1.301119982
102 0.779093109 2.539345072
103 0.995067536 0.779093109
104 -1.881628561 0.995067536
105 1.293917079 -1.881628561
106 0.270053027 1.293917079
107 1.626587838 0.270053027
108 -0.305982558 1.626587838
109 0.716382937 -0.305982558
110 -0.010771667 0.716382937
111 1.964918736 -0.010771667
112 -1.540611240 1.964918736
113 -2.577275020 -1.540611240
114 1.850084192 -2.577275020
115 -1.849020562 1.850084192
116 0.828562586 -1.849020562
117 -1.789048013 0.828562586
118 0.478505290 -1.789048013
119 -1.417386693 0.478505290
120 0.626735160 -1.417386693
121 -2.823989421 0.626735160
122 -0.820672763 -2.823989421
123 -0.815194361 -0.820672763
124 -0.775886466 -0.815194361
125 0.343682304 -0.775886466
126 1.486454155 0.343682304
127 1.043809413 1.486454155
128 -2.664969621 1.043809413
129 2.051587473 -2.664969621
130 -3.699345786 2.051587473
131 2.521267351 -3.699345786
132 -2.117119104 2.521267351
133 -1.496982583 -2.117119104
134 -0.022851451 -1.496982583
135 0.921716463 -0.022851451
136 0.582024794 0.921716463
137 -2.414884325 0.582024794
138 -0.961538813 -2.414884325
139 -2.329429108 -0.961538813
140 3.180171327 -2.329429108
141 1.388523482 3.180171327
142 0.235263838 1.388523482
143 1.902733519 0.235263838
144 -3.232606357 1.902733519
145 2.576819178 -3.232606357
146 -1.864314906 2.576819178
147 1.218895990 -1.864314906
148 0.240626106 1.218895990
149 -2.847945621 0.240626106
150 -0.857124586 -2.847945621
151 2.175679631 -0.857124586
152 4.182209460 2.175679631
153 1.820374733 4.182209460
154 -2.360367445 1.820374733
155 0.643603147 -2.360367445
156 1.360040724 0.643603147
157 1.239329026 1.360040724
158 1.522216200 1.239329026
159 -0.671268683 1.522216200
160 0.449841607 -0.671268683
161 0.304814334 0.449841607
162 -0.532526582 0.304814334
163 0.705563450 -0.532526582
164 1.219730503 0.705563450
165 -1.696048432 1.219730503
166 -0.409813871 -1.696048432
167 -3.282401493 -0.409813871
168 -1.869656451 -3.282401493
169 1.472274377 -1.869656451
170 1.421840303 1.472274377
171 0.594604288 1.421840303
172 -1.950732603 0.594604288
173 -2.432755979 -1.950732603
174 -2.970887501 -2.432755979
175 0.384688185 -2.970887501
176 -0.348484733 0.384688185
177 -0.741023545 -0.348484733
178 0.151743067 -0.741023545
179 -1.436382572 0.151743067
180 0.942577449 -1.436382572
181 -0.529090078 0.942577449
182 2.297606534 -0.529090078
183 -0.186108035 2.297606534
184 -6.609847763 -0.186108035
185 1.205124572 -6.609847763
186 2.511032191 1.205124572
187 -0.449278889 2.511032191
188 -0.525825323 -0.449278889
189 0.516008059 -0.525825323
190 -0.729729103 0.516008059
191 0.542659139 -0.729729103
192 2.205050020 0.542659139
193 1.941762393 2.205050020
194 -0.667126774 1.941762393
195 -0.005961548 -0.667126774
196 3.038724351 -0.005961548
197 0.978304049 3.038724351
198 1.534504831 0.978304049
199 1.463965120 1.534504831
200 1.989822678 1.463965120
201 0.649485355 1.989822678
202 -2.424889464 0.649485355
203 -2.462945348 -2.424889464
204 2.271819267 -2.462945348
205 0.560792297 2.271819267
206 1.515804166 0.560792297
207 1.136082517 1.515804166
208 -2.573550732 1.136082517
209 1.067008366 -2.573550732
210 -2.190939360 1.067008366
211 -3.700365776 -2.190939360
212 0.901527852 -3.700365776
213 2.910196045 0.901527852
214 1.513882632 2.910196045
215 0.955208844 1.513882632
216 2.429615296 0.955208844
217 0.110123039 2.429615296
218 1.123659284 0.110123039
219 -0.082459821 1.123659284
220 -1.575055682 -0.082459821
221 0.013966772 -1.575055682
222 0.813396739 0.013966772
223 -1.045100363 0.813396739
224 0.472207287 -1.045100363
225 -3.696045992 0.472207287
226 0.343800671 -3.696045992
227 1.099928300 0.343800671
228 -1.155109630 1.099928300
229 -0.814198702 -1.155109630
230 1.807201525 -0.814198702
231 -3.132401339 1.807201525
232 4.676127579 -3.132401339
233 2.298337625 4.676127579
234 -0.698624739 2.298337625
235 -2.195321322 -0.698624739
236 -6.914416877 -2.195321322
237 -1.086386543 -6.914416877
238 1.888555398 -1.086386543
239 -1.581042084 1.888555398
240 -0.145789506 -1.581042084
241 -1.322333028 -0.145789506
242 1.198044918 -1.322333028
243 2.032402960 1.198044918
244 1.517485572 2.032402960
245 0.229882402 1.517485572
246 1.329833795 0.229882402
247 -2.293447889 1.329833795
248 1.398592634 -2.293447889
249 0.495413938 1.398592634
250 -0.693327081 0.495413938
251 -1.497025470 -0.693327081
252 0.772227707 -1.497025470
253 3.366970537 0.772227707
254 -1.103306445 3.366970537
255 0.388489873 -1.103306445
256 1.861977961 0.388489873
257 2.548703480 1.861977961
258 -0.988365848 2.548703480
259 -5.278553518 -0.988365848
260 1.509182186 -5.278553518
261 -3.705447878 1.509182186
262 -0.100381546 -3.705447878
263 1.424001562 -0.100381546
264 NA 1.424001562
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.226986679 -3.131999295
[2,] 1.935679119 0.226986679
[3,] 2.804558516 1.935679119
[4,] -2.418032225 2.804558516
[5,] -1.880226893 -2.418032225
[6,] 3.692611150 -1.880226893
[7,] -2.099410970 3.692611150
[8,] -2.071694532 -2.099410970
[9,] 0.529072337 -2.071694532
[10,] 0.995167912 0.529072337
[11,] -0.189823032 0.995167912
[12,] 0.470117024 -0.189823032
[13,] 0.498538318 0.470117024
[14,] -0.984972301 0.498538318
[15,] -0.471169368 -0.984972301
[16,] 0.388482697 -0.471169368
[17,] 3.584932386 0.388482697
[18,] 2.510108792 3.584932386
[19,] 0.448882448 2.510108792
[20,] 0.639130463 0.448882448
[21,] 0.859494434 0.639130463
[22,] 2.526189315 0.859494434
[23,] 0.911757507 2.526189315
[24,] 0.851015089 0.911757507
[25,] 0.745973842 0.851015089
[26,] 1.029242723 0.745973842
[27,] -1.731403298 1.029242723
[28,] 0.285551942 -1.731403298
[29,] -0.177697671 0.285551942
[30,] -0.727157091 -0.177697671
[31,] -0.819627423 -0.727157091
[32,] -0.790028236 -0.819627423
[33,] 0.090766983 -0.790028236
[34,] -1.524184275 0.090766983
[35,] -2.994926432 -1.524184275
[36,] -3.036017733 -2.994926432
[37,] -1.715914366 -3.036017733
[38,] 1.481067760 -1.715914366
[39,] 1.536025997 1.481067760
[40,] 1.307738561 1.536025997
[41,] -1.691739352 1.307738561
[42,] 2.576009592 -1.691739352
[43,] -0.126833311 2.576009592
[44,] -0.427775531 -0.126833311
[45,] -4.476901087 -0.427775531
[46,] -2.501670057 -4.476901087
[47,] -0.087273356 -2.501670057
[48,] 0.565501318 -0.087273356
[49,] -1.595751557 0.565501318
[50,] -0.947005562 -1.595751557
[51,] -0.102905664 -0.947005562
[52,] -2.981443297 -0.102905664
[53,] 0.214206701 -2.981443297
[54,] -2.356371538 0.214206701
[55,] 1.665065486 -2.356371538
[56,] 0.205418568 1.665065486
[57,] 0.579053002 0.205418568
[58,] -0.083012473 0.579053002
[59,] 1.848732075 -0.083012473
[60,] 0.493038981 1.848732075
[61,] 0.385981197 0.493038981
[62,] -0.481004602 0.385981197
[63,] -0.638611608 -0.481004602
[64,] 0.783933502 -0.638611608
[65,] 0.871640504 0.783933502
[66,] 1.634018971 0.871640504
[67,] 3.453934790 1.634018971
[68,] -3.985955793 3.453934790
[69,] 0.276410320 -3.985955793
[70,] -3.436462873 0.276410320
[71,] -0.982476413 -3.436462873
[72,] 0.984116988 -0.982476413
[73,] 0.754511906 0.984116988
[74,] 0.674279402 0.754511906
[75,] 3.424247981 0.674279402
[76,] -0.468790299 3.424247981
[77,] 1.447784167 -0.468790299
[78,] -2.261400258 1.447784167
[79,] 0.803547438 -2.261400258
[80,] 0.242550333 0.803547438
[81,] 0.223148307 0.242550333
[82,] -0.729877341 0.223148307
[83,] 0.070029770 -0.729877341
[84,] 1.785056577 0.070029770
[85,] -0.236630957 1.785056577
[86,] 0.590582925 -0.236630957
[87,] 1.099972796 0.590582925
[88,] 0.442475612 1.099972796
[89,] -1.919576969 0.442475612
[90,] 0.219977319 -1.919576969
[91,] 0.137035181 0.219977319
[92,] -0.130179843 0.137035181
[93,] -2.094841811 -0.130179843
[94,] 1.169182749 -2.094841811
[95,] 0.201266765 1.169182749
[96,] 2.244394121 0.201266765
[97,] 0.165573237 2.244394121
[98,] -0.466132834 0.165573237
[99,] -1.039710817 -0.466132834
[100,] 1.301119982 -1.039710817
[101,] 2.539345072 1.301119982
[102,] 0.779093109 2.539345072
[103,] 0.995067536 0.779093109
[104,] -1.881628561 0.995067536
[105,] 1.293917079 -1.881628561
[106,] 0.270053027 1.293917079
[107,] 1.626587838 0.270053027
[108,] -0.305982558 1.626587838
[109,] 0.716382937 -0.305982558
[110,] -0.010771667 0.716382937
[111,] 1.964918736 -0.010771667
[112,] -1.540611240 1.964918736
[113,] -2.577275020 -1.540611240
[114,] 1.850084192 -2.577275020
[115,] -1.849020562 1.850084192
[116,] 0.828562586 -1.849020562
[117,] -1.789048013 0.828562586
[118,] 0.478505290 -1.789048013
[119,] -1.417386693 0.478505290
[120,] 0.626735160 -1.417386693
[121,] -2.823989421 0.626735160
[122,] -0.820672763 -2.823989421
[123,] -0.815194361 -0.820672763
[124,] -0.775886466 -0.815194361
[125,] 0.343682304 -0.775886466
[126,] 1.486454155 0.343682304
[127,] 1.043809413 1.486454155
[128,] -2.664969621 1.043809413
[129,] 2.051587473 -2.664969621
[130,] -3.699345786 2.051587473
[131,] 2.521267351 -3.699345786
[132,] -2.117119104 2.521267351
[133,] -1.496982583 -2.117119104
[134,] -0.022851451 -1.496982583
[135,] 0.921716463 -0.022851451
[136,] 0.582024794 0.921716463
[137,] -2.414884325 0.582024794
[138,] -0.961538813 -2.414884325
[139,] -2.329429108 -0.961538813
[140,] 3.180171327 -2.329429108
[141,] 1.388523482 3.180171327
[142,] 0.235263838 1.388523482
[143,] 1.902733519 0.235263838
[144,] -3.232606357 1.902733519
[145,] 2.576819178 -3.232606357
[146,] -1.864314906 2.576819178
[147,] 1.218895990 -1.864314906
[148,] 0.240626106 1.218895990
[149,] -2.847945621 0.240626106
[150,] -0.857124586 -2.847945621
[151,] 2.175679631 -0.857124586
[152,] 4.182209460 2.175679631
[153,] 1.820374733 4.182209460
[154,] -2.360367445 1.820374733
[155,] 0.643603147 -2.360367445
[156,] 1.360040724 0.643603147
[157,] 1.239329026 1.360040724
[158,] 1.522216200 1.239329026
[159,] -0.671268683 1.522216200
[160,] 0.449841607 -0.671268683
[161,] 0.304814334 0.449841607
[162,] -0.532526582 0.304814334
[163,] 0.705563450 -0.532526582
[164,] 1.219730503 0.705563450
[165,] -1.696048432 1.219730503
[166,] -0.409813871 -1.696048432
[167,] -3.282401493 -0.409813871
[168,] -1.869656451 -3.282401493
[169,] 1.472274377 -1.869656451
[170,] 1.421840303 1.472274377
[171,] 0.594604288 1.421840303
[172,] -1.950732603 0.594604288
[173,] -2.432755979 -1.950732603
[174,] -2.970887501 -2.432755979
[175,] 0.384688185 -2.970887501
[176,] -0.348484733 0.384688185
[177,] -0.741023545 -0.348484733
[178,] 0.151743067 -0.741023545
[179,] -1.436382572 0.151743067
[180,] 0.942577449 -1.436382572
[181,] -0.529090078 0.942577449
[182,] 2.297606534 -0.529090078
[183,] -0.186108035 2.297606534
[184,] -6.609847763 -0.186108035
[185,] 1.205124572 -6.609847763
[186,] 2.511032191 1.205124572
[187,] -0.449278889 2.511032191
[188,] -0.525825323 -0.449278889
[189,] 0.516008059 -0.525825323
[190,] -0.729729103 0.516008059
[191,] 0.542659139 -0.729729103
[192,] 2.205050020 0.542659139
[193,] 1.941762393 2.205050020
[194,] -0.667126774 1.941762393
[195,] -0.005961548 -0.667126774
[196,] 3.038724351 -0.005961548
[197,] 0.978304049 3.038724351
[198,] 1.534504831 0.978304049
[199,] 1.463965120 1.534504831
[200,] 1.989822678 1.463965120
[201,] 0.649485355 1.989822678
[202,] -2.424889464 0.649485355
[203,] -2.462945348 -2.424889464
[204,] 2.271819267 -2.462945348
[205,] 0.560792297 2.271819267
[206,] 1.515804166 0.560792297
[207,] 1.136082517 1.515804166
[208,] -2.573550732 1.136082517
[209,] 1.067008366 -2.573550732
[210,] -2.190939360 1.067008366
[211,] -3.700365776 -2.190939360
[212,] 0.901527852 -3.700365776
[213,] 2.910196045 0.901527852
[214,] 1.513882632 2.910196045
[215,] 0.955208844 1.513882632
[216,] 2.429615296 0.955208844
[217,] 0.110123039 2.429615296
[218,] 1.123659284 0.110123039
[219,] -0.082459821 1.123659284
[220,] -1.575055682 -0.082459821
[221,] 0.013966772 -1.575055682
[222,] 0.813396739 0.013966772
[223,] -1.045100363 0.813396739
[224,] 0.472207287 -1.045100363
[225,] -3.696045992 0.472207287
[226,] 0.343800671 -3.696045992
[227,] 1.099928300 0.343800671
[228,] -1.155109630 1.099928300
[229,] -0.814198702 -1.155109630
[230,] 1.807201525 -0.814198702
[231,] -3.132401339 1.807201525
[232,] 4.676127579 -3.132401339
[233,] 2.298337625 4.676127579
[234,] -0.698624739 2.298337625
[235,] -2.195321322 -0.698624739
[236,] -6.914416877 -2.195321322
[237,] -1.086386543 -6.914416877
[238,] 1.888555398 -1.086386543
[239,] -1.581042084 1.888555398
[240,] -0.145789506 -1.581042084
[241,] -1.322333028 -0.145789506
[242,] 1.198044918 -1.322333028
[243,] 2.032402960 1.198044918
[244,] 1.517485572 2.032402960
[245,] 0.229882402 1.517485572
[246,] 1.329833795 0.229882402
[247,] -2.293447889 1.329833795
[248,] 1.398592634 -2.293447889
[249,] 0.495413938 1.398592634
[250,] -0.693327081 0.495413938
[251,] -1.497025470 -0.693327081
[252,] 0.772227707 -1.497025470
[253,] 3.366970537 0.772227707
[254,] -1.103306445 3.366970537
[255,] 0.388489873 -1.103306445
[256,] 1.861977961 0.388489873
[257,] 2.548703480 1.861977961
[258,] -0.988365848 2.548703480
[259,] -5.278553518 -0.988365848
[260,] 1.509182186 -5.278553518
[261,] -3.705447878 1.509182186
[262,] -0.100381546 -3.705447878
[263,] 1.424001562 -0.100381546
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.226986679 -3.131999295
2 1.935679119 0.226986679
3 2.804558516 1.935679119
4 -2.418032225 2.804558516
5 -1.880226893 -2.418032225
6 3.692611150 -1.880226893
7 -2.099410970 3.692611150
8 -2.071694532 -2.099410970
9 0.529072337 -2.071694532
10 0.995167912 0.529072337
11 -0.189823032 0.995167912
12 0.470117024 -0.189823032
13 0.498538318 0.470117024
14 -0.984972301 0.498538318
15 -0.471169368 -0.984972301
16 0.388482697 -0.471169368
17 3.584932386 0.388482697
18 2.510108792 3.584932386
19 0.448882448 2.510108792
20 0.639130463 0.448882448
21 0.859494434 0.639130463
22 2.526189315 0.859494434
23 0.911757507 2.526189315
24 0.851015089 0.911757507
25 0.745973842 0.851015089
26 1.029242723 0.745973842
27 -1.731403298 1.029242723
28 0.285551942 -1.731403298
29 -0.177697671 0.285551942
30 -0.727157091 -0.177697671
31 -0.819627423 -0.727157091
32 -0.790028236 -0.819627423
33 0.090766983 -0.790028236
34 -1.524184275 0.090766983
35 -2.994926432 -1.524184275
36 -3.036017733 -2.994926432
37 -1.715914366 -3.036017733
38 1.481067760 -1.715914366
39 1.536025997 1.481067760
40 1.307738561 1.536025997
41 -1.691739352 1.307738561
42 2.576009592 -1.691739352
43 -0.126833311 2.576009592
44 -0.427775531 -0.126833311
45 -4.476901087 -0.427775531
46 -2.501670057 -4.476901087
47 -0.087273356 -2.501670057
48 0.565501318 -0.087273356
49 -1.595751557 0.565501318
50 -0.947005562 -1.595751557
51 -0.102905664 -0.947005562
52 -2.981443297 -0.102905664
53 0.214206701 -2.981443297
54 -2.356371538 0.214206701
55 1.665065486 -2.356371538
56 0.205418568 1.665065486
57 0.579053002 0.205418568
58 -0.083012473 0.579053002
59 1.848732075 -0.083012473
60 0.493038981 1.848732075
61 0.385981197 0.493038981
62 -0.481004602 0.385981197
63 -0.638611608 -0.481004602
64 0.783933502 -0.638611608
65 0.871640504 0.783933502
66 1.634018971 0.871640504
67 3.453934790 1.634018971
68 -3.985955793 3.453934790
69 0.276410320 -3.985955793
70 -3.436462873 0.276410320
71 -0.982476413 -3.436462873
72 0.984116988 -0.982476413
73 0.754511906 0.984116988
74 0.674279402 0.754511906
75 3.424247981 0.674279402
76 -0.468790299 3.424247981
77 1.447784167 -0.468790299
78 -2.261400258 1.447784167
79 0.803547438 -2.261400258
80 0.242550333 0.803547438
81 0.223148307 0.242550333
82 -0.729877341 0.223148307
83 0.070029770 -0.729877341
84 1.785056577 0.070029770
85 -0.236630957 1.785056577
86 0.590582925 -0.236630957
87 1.099972796 0.590582925
88 0.442475612 1.099972796
89 -1.919576969 0.442475612
90 0.219977319 -1.919576969
91 0.137035181 0.219977319
92 -0.130179843 0.137035181
93 -2.094841811 -0.130179843
94 1.169182749 -2.094841811
95 0.201266765 1.169182749
96 2.244394121 0.201266765
97 0.165573237 2.244394121
98 -0.466132834 0.165573237
99 -1.039710817 -0.466132834
100 1.301119982 -1.039710817
101 2.539345072 1.301119982
102 0.779093109 2.539345072
103 0.995067536 0.779093109
104 -1.881628561 0.995067536
105 1.293917079 -1.881628561
106 0.270053027 1.293917079
107 1.626587838 0.270053027
108 -0.305982558 1.626587838
109 0.716382937 -0.305982558
110 -0.010771667 0.716382937
111 1.964918736 -0.010771667
112 -1.540611240 1.964918736
113 -2.577275020 -1.540611240
114 1.850084192 -2.577275020
115 -1.849020562 1.850084192
116 0.828562586 -1.849020562
117 -1.789048013 0.828562586
118 0.478505290 -1.789048013
119 -1.417386693 0.478505290
120 0.626735160 -1.417386693
121 -2.823989421 0.626735160
122 -0.820672763 -2.823989421
123 -0.815194361 -0.820672763
124 -0.775886466 -0.815194361
125 0.343682304 -0.775886466
126 1.486454155 0.343682304
127 1.043809413 1.486454155
128 -2.664969621 1.043809413
129 2.051587473 -2.664969621
130 -3.699345786 2.051587473
131 2.521267351 -3.699345786
132 -2.117119104 2.521267351
133 -1.496982583 -2.117119104
134 -0.022851451 -1.496982583
135 0.921716463 -0.022851451
136 0.582024794 0.921716463
137 -2.414884325 0.582024794
138 -0.961538813 -2.414884325
139 -2.329429108 -0.961538813
140 3.180171327 -2.329429108
141 1.388523482 3.180171327
142 0.235263838 1.388523482
143 1.902733519 0.235263838
144 -3.232606357 1.902733519
145 2.576819178 -3.232606357
146 -1.864314906 2.576819178
147 1.218895990 -1.864314906
148 0.240626106 1.218895990
149 -2.847945621 0.240626106
150 -0.857124586 -2.847945621
151 2.175679631 -0.857124586
152 4.182209460 2.175679631
153 1.820374733 4.182209460
154 -2.360367445 1.820374733
155 0.643603147 -2.360367445
156 1.360040724 0.643603147
157 1.239329026 1.360040724
158 1.522216200 1.239329026
159 -0.671268683 1.522216200
160 0.449841607 -0.671268683
161 0.304814334 0.449841607
162 -0.532526582 0.304814334
163 0.705563450 -0.532526582
164 1.219730503 0.705563450
165 -1.696048432 1.219730503
166 -0.409813871 -1.696048432
167 -3.282401493 -0.409813871
168 -1.869656451 -3.282401493
169 1.472274377 -1.869656451
170 1.421840303 1.472274377
171 0.594604288 1.421840303
172 -1.950732603 0.594604288
173 -2.432755979 -1.950732603
174 -2.970887501 -2.432755979
175 0.384688185 -2.970887501
176 -0.348484733 0.384688185
177 -0.741023545 -0.348484733
178 0.151743067 -0.741023545
179 -1.436382572 0.151743067
180 0.942577449 -1.436382572
181 -0.529090078 0.942577449
182 2.297606534 -0.529090078
183 -0.186108035 2.297606534
184 -6.609847763 -0.186108035
185 1.205124572 -6.609847763
186 2.511032191 1.205124572
187 -0.449278889 2.511032191
188 -0.525825323 -0.449278889
189 0.516008059 -0.525825323
190 -0.729729103 0.516008059
191 0.542659139 -0.729729103
192 2.205050020 0.542659139
193 1.941762393 2.205050020
194 -0.667126774 1.941762393
195 -0.005961548 -0.667126774
196 3.038724351 -0.005961548
197 0.978304049 3.038724351
198 1.534504831 0.978304049
199 1.463965120 1.534504831
200 1.989822678 1.463965120
201 0.649485355 1.989822678
202 -2.424889464 0.649485355
203 -2.462945348 -2.424889464
204 2.271819267 -2.462945348
205 0.560792297 2.271819267
206 1.515804166 0.560792297
207 1.136082517 1.515804166
208 -2.573550732 1.136082517
209 1.067008366 -2.573550732
210 -2.190939360 1.067008366
211 -3.700365776 -2.190939360
212 0.901527852 -3.700365776
213 2.910196045 0.901527852
214 1.513882632 2.910196045
215 0.955208844 1.513882632
216 2.429615296 0.955208844
217 0.110123039 2.429615296
218 1.123659284 0.110123039
219 -0.082459821 1.123659284
220 -1.575055682 -0.082459821
221 0.013966772 -1.575055682
222 0.813396739 0.013966772
223 -1.045100363 0.813396739
224 0.472207287 -1.045100363
225 -3.696045992 0.472207287
226 0.343800671 -3.696045992
227 1.099928300 0.343800671
228 -1.155109630 1.099928300
229 -0.814198702 -1.155109630
230 1.807201525 -0.814198702
231 -3.132401339 1.807201525
232 4.676127579 -3.132401339
233 2.298337625 4.676127579
234 -0.698624739 2.298337625
235 -2.195321322 -0.698624739
236 -6.914416877 -2.195321322
237 -1.086386543 -6.914416877
238 1.888555398 -1.086386543
239 -1.581042084 1.888555398
240 -0.145789506 -1.581042084
241 -1.322333028 -0.145789506
242 1.198044918 -1.322333028
243 2.032402960 1.198044918
244 1.517485572 2.032402960
245 0.229882402 1.517485572
246 1.329833795 0.229882402
247 -2.293447889 1.329833795
248 1.398592634 -2.293447889
249 0.495413938 1.398592634
250 -0.693327081 0.495413938
251 -1.497025470 -0.693327081
252 0.772227707 -1.497025470
253 3.366970537 0.772227707
254 -1.103306445 3.366970537
255 0.388489873 -1.103306445
256 1.861977961 0.388489873
257 2.548703480 1.861977961
258 -0.988365848 2.548703480
259 -5.278553518 -0.988365848
260 1.509182186 -5.278553518
261 -3.705447878 1.509182186
262 -0.100381546 -3.705447878
263 1.424001562 -0.100381546
> 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/fisher/rcomp/tmp/71k8e1383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8l8wq1383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/95b751383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10sqc41383489833.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/fisher/rcomp/tmp/11vy531383489833.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12iaz81383489834.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13e3x21383489834.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14lk511383489834.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15x63o1383489834.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/fisher/rcomp/tmp/16j14m1383489834.tab")
+ }
>
> try(system("convert tmp/171jo1383489833.ps tmp/171jo1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sjg21383489833.ps tmp/2sjg21383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ggto1383489833.ps tmp/3ggto1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wdcc1383489833.ps tmp/4wdcc1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/532n21383489833.ps tmp/532n21383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/65wbg1383489833.ps tmp/65wbg1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/71k8e1383489833.ps tmp/71k8e1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l8wq1383489833.ps tmp/8l8wq1383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/95b751383489833.ps tmp/95b751383489833.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sqc41383489833.ps tmp/10sqc41383489833.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.704 2.152 14.851