R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,0
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+ ,0)
+ ,dim=c(11
+ ,269)
+ ,dimnames=list(c('hours'
+ ,'lfm'
+ ,'blogs'
+ ,'uk'
+ ,'spr'
+ ,'hours_blogs'
+ ,'hours_uk'
+ ,'hours_spr'
+ ,'blogs_uk'
+ ,'blogs_spr'
+ ,'uk_spr')
+ ,1:269))
> y <- array(NA,dim=c(11,269),dimnames=list(c('hours','lfm','blogs','uk','spr','hours_blogs','hours_uk','hours_spr','blogs_uk','blogs_spr','uk_spr'),1:269))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
lfm hours blogs uk spr hours_blogs hours_uk hours_spr blogs_uk blogs_spr
1 122 102 88 1 9 8976 102 918 88 792
2 114 99 106 1 3 10494 99 297 106 318
3 140 97 70 1 2 6790 97 194 70 140
4 143 82 70 1 21 5740 82 1722 70 1470
5 122 77 56 1 10 4312 77 770 56 560
6 127 65 50 1 2 3250 65 130 50 100
7 113 64 48 1 4 3072 64 256 48 192
8 118 62 71 1 4 4402 62 248 71 284
9 161 62 61 1 4 3782 62 248 61 244
10 134 62 66 1 5 4092 62 310 66 330
11 96 61 80 1 2 4880 61 122 80 160
12 104 59 37 1 3 2183 59 177 37 111
13 135 57 53 1 3 3021 57 171 53 159
14 110 56 39 1 6 2184 56 336 39 234
15 128 54 40 1 3 2160 54 162 40 120
16 142 54 59 1 4 3186 54 216 59 236
17 117 53 42 1 3 2226 53 159 42 126
18 94 52 33 1 13 1716 52 676 33 429
19 135 51 36 1 3 1836 51 153 36 108
20 121 51 57 1 4 2907 51 204 57 228
21 103 51 38 1 8 1938 51 408 38 304
22 118 50 98 1 8 4900 50 400 98 784
23 127 50 43 1 3 2150 50 150 43 129
24 116 50 73 1 4 3650 50 200 73 292
25 129 49 52 1 2 2548 49 98 52 104
26 115 49 53 1 5 2597 49 245 53 265
27 135 49 51 1 4 2499 49 196 51 204
28 133 48 32 1 3 1536 48 144 32 96
29 113 48 43 1 3 2064 48 144 43 129
30 111 47 53 1 2 2491 47 94 53 106
31 92 47 50 1 3 2350 47 141 50 150
32 118 46 50 1 3 2300 46 138 50 150
33 134 46 56 1 3 2576 46 138 56 168
34 106 45 53 1 5 2385 45 225 53 265
35 137 45 47 1 3 2115 45 135 47 141
36 100 45 42 1 4 1890 45 180 42 168
37 102 44 29 1 3 1276 44 132 29 87
38 134 43 54 1 4 2322 43 172 54 216
39 130 42 40 1 8 1680 42 336 40 320
40 144 42 41 1 3 1722 42 126 41 123
41 120 42 37 1 4 1554 42 168 37 148
42 91 42 25 1 2 1050 42 84 25 50
43 100 42 27 1 5 1134 42 210 27 135
44 134 42 61 1 4 2562 42 168 61 244
45 161 41 54 1 7 2214 41 287 54 378
46 128 41 35 1 3 1435 41 123 35 105
47 124 41 55 1 4 2255 41 164 55 220
48 115 41 47 1 6 1927 41 246 47 282
49 123 41 49 1 7 2009 41 287 49 343
50 117 41 38 1 20 1558 41 820 38 760
51 111 41 52 1 49 2132 41 2009 52 2548
52 146 40 35 1 3 1400 40 120 35 105
53 101 40 52 1 3 2080 40 120 52 156
54 131 40 54 1 6 2160 40 240 54 324
55 122 40 40 1 6 1600 40 240 40 240
56 78 40 52 1 4 2080 40 160 52 208
57 120 39 34 1 5 1326 39 195 34 170
58 115 39 51 1 4 1989 39 156 51 204
59 142 38 43 1 4 1634 38 152 43 172
60 94 38 40 1 31 1520 38 1178 40 1240
61 114 36 38 1 3 1368 36 108 38 114
62 108 36 33 1 3 1188 36 108 33 99
63 119 35 27 1 4 945 35 140 27 108
64 117 35 34 1 6 1190 35 210 34 204
65 86 35 44 1 5 1540 35 175 44 220
66 138 35 46 1 3 1610 35 105 46 138
67 119 34 50 1 3 1700 34 102 50 150
68 117 34 31 1 2 1054 34 68 31 62
69 117 34 33 1 3 1122 34 102 33 99
70 76 33 37 1 3 1221 33 99 37 111
71 119 33 48 1 16 1584 33 528 48 768
72 119 33 33 1 3 1089 33 99 33 99
73 124 32 40 1 3 1280 32 96 40 120
74 116 32 21 1 3 672 32 96 21 63
75 118 32 33 1 2 1056 32 64 33 66
76 102 31 41 1 5 1271 31 155 41 205
77 116 31 35 1 3 1085 31 93 35 105
78 103 30 60 1 4 1800 30 120 60 240
79 117 30 30 1 5 900 30 150 30 150
80 108 30 45 1 2 1350 30 60 45 90
81 122 30 26 1 3 780 30 90 26 78
82 90 29 41 1 3 1189 29 87 41 123
83 133 28 48 1 14 1344 28 392 48 672
84 116 28 10 1 8 280 28 224 10 80
85 110 27 35 1 4 945 27 108 35 140
86 90 27 23 1 4 621 27 108 23 92
87 74 27 29 1 3 783 27 81 29 87
88 75 26 17 1 4 442 26 104 17 68
89 107 25 35 1 3 875 25 75 35 105
90 90 25 50 1 5 1250 25 125 50 250
91 96 25 33 1 3 825 25 75 33 99
92 115 24 23 1 3 552 24 72 23 69
93 91 24 22 1 2 528 24 48 22 44
94 77 23 52 1 4 1196 23 92 52 208
95 108 23 38 1 31 874 23 713 38 1178
96 83 23 32 1 2 736 23 46 32 64
97 77 23 28 1 5 644 23 115 28 140
98 99 23 43 1 5 989 23 115 43 215
99 115 22 32 1 2 704 22 44 32 64
100 99 22 35 1 2 770 22 44 35 70
101 106 22 25 1 3 550 22 66 25 75
102 77 22 14 1 8 308 22 176 14 112
103 115 20 17 1 6 340 20 120 17 102
104 67 19 18 1 3 342 19 57 18 54
105 8 19 12 1 2 228 19 38 12 24
106 69 17 27 1 5 459 17 85 27 135
107 88 17 28 1 3 476 17 51 28 84
108 107 16 12 1 5 192 16 80 12 60
109 120 16 21 1 5 336 16 80 21 105
110 3 5 9 1 4 45 5 20 9 36
111 1 4 11 1 2 44 4 8 11 22
112 0 3 3 1 4 9 3 12 3 12
113 111 156 111 0 4 17316 0 624 0 444
114 69 109 137 0 8 14933 0 872 0 1096
115 116 104 112 0 3 11648 0 312 0 336
116 103 98 73 0 4 7154 0 392 0 292
117 139 78 99 0 3 7722 0 234 0 297
118 135 77 115 0 3 8855 0 231 0 345
119 113 73 95 0 3 6935 0 219 0 285
120 99 71 60 0 4 4260 0 284 0 240
121 76 67 94 0 4 6298 0 268 0 376
122 110 64 70 0 5 4480 0 320 0 350
123 121 62 87 0 2 5394 0 124 0 174
124 95 61 102 0 3 6222 0 183 0 306
125 66 58 69 0 4 4002 0 232 0 276
126 111 58 111 0 7 6438 0 406 0 777
127 77 56 55 0 3 3080 0 168 0 165
128 101 56 118 0 4 6608 0 224 0 472
129 108 52 90 0 3 4680 0 156 0 270
130 135 51 81 0 4 4131 0 204 0 324
131 70 51 88 0 4 4488 0 204 0 352
132 124 50 63 0 3 3150 0 150 0 189
133 92 49 84 0 6 4116 0 294 0 504
134 104 49 87 0 4 4263 0 196 0 348
135 113 48 78 0 4 3744 0 192 0 312
136 95 47 93 0 4 4371 0 188 0 372
137 89 47 69 0 4 3243 0 188 0 276
138 83 46 67 0 3 3082 0 138 0 201
139 96 45 61 0 6 2745 0 270 0 366
140 95 45 123 0 4 5535 0 180 0 492
141 110 45 91 0 6 4095 0 270 0 546
142 106 45 98 0 6 4410 0 270 0 588
143 78 44 38 0 2 1672 0 88 0 76
144 115 44 72 0 3 3168 0 132 0 216
145 74 44 59 0 3 2596 0 132 0 177
146 93 43 78 0 2 3354 0 86 0 156
147 88 43 58 0 4 2494 0 172 0 232
148 104 42 97 0 5 4074 0 210 0 485
149 86 41 69 0 3 2829 0 123 0 207
150 104 41 50 0 7 2050 0 287 0 350
151 99 40 66 0 4 2640 0 160 0 264
152 101 39 70 0 3 2730 0 117 0 210
153 53 39 65 0 4 2535 0 156 0 260
154 96 39 69 0 4 2691 0 156 0 276
155 58 39 49 0 3 1911 0 117 0 147
156 117 39 72 0 3 2808 0 117 0 216
157 82 39 74 0 4 2886 0 156 0 296
158 57 39 82 0 5 3198 0 195 0 410
159 71 38 61 0 3 2318 0 114 0 183
160 105 38 72 0 4 2736 0 152 0 288
161 60 38 77 0 6 2926 0 228 0 462
162 77 38 64 0 5 2432 0 190 0 320
163 73 37 23 0 7 851 0 259 0 161
164 78 37 39 0 5 1443 0 185 0 195
165 81 37 87 0 5 3219 0 185 0 435
166 101 36 46 0 3 1656 0 108 0 138
167 118 36 66 0 5 2376 0 180 0 330
168 59 36 57 0 4 2052 0 144 0 228
169 101 36 48 0 9 1728 0 324 0 432
170 22 36 75 0 3 2700 0 108 0 225
171 77 36 35 0 3 1260 0 108 0 105
172 100 35 53 0 3 1855 0 105 0 159
173 39 35 60 0 3 2100 0 105 0 180
174 42 34 20 0 15 680 0 510 0 300
175 80 34 66 0 4 2244 0 136 0 264
176 48 34 34 0 6 1156 0 204 0 204
177 131 34 80 0 3 2720 0 102 0 240
178 46 34 63 0 3 2142 0 102 0 189
179 89 33 46 0 3 1518 0 99 0 138
180 51 33 20 0 5 660 0 165 0 100
181 108 33 73 0 3 2409 0 99 0 219
182 86 33 57 0 4 1881 0 132 0 228
183 105 33 65 0 7 2145 0 231 0 455
184 85 33 70 0 4 2310 0 132 0 280
185 103 32 53 0 5 1696 0 160 0 265
186 83 32 60 0 7 1920 0 224 0 420
187 77 32 34 0 14 1088 0 448 0 476
188 26 32 18 0 25 576 0 800 0 450
189 73 32 49 0 6 1568 0 192 0 294
190 42 31 27 0 4 837 0 124 0 108
191 71 31 45 0 3 1395 0 93 0 135
192 105 31 9 0 62 279 0 1922 0 558
193 73 30 23 0 5 690 0 150 0 115
194 98 30 61 0 10 1830 0 300 0 610
195 108 29 67 0 4 1943 0 116 0 268
196 57 29 72 0 5 2088 0 145 0 360
197 37 29 58 0 5 1682 0 145 0 290
198 70 28 55 0 4 1540 0 112 0 220
199 73 28 33 0 10 924 0 280 0 330
200 47 28 40 0 5 1120 0 140 0 200
201 73 28 57 0 3 1596 0 84 0 171
202 91 28 61 0 3 1708 0 84 0 183
203 110 28 87 0 17 2436 0 476 0 1479
204 78 27 65 0 4 1755 0 108 0 260
205 92 27 85 0 6 2295 0 162 0 510
206 52 27 85 0 3 2295 0 81 0 255
207 88 26 54 0 4 1404 0 104 0 216
208 100 26 24 0 8 624 0 208 0 192
209 33 26 31 0 3 806 0 78 0 93
210 42 26 64 0 4 1664 0 104 0 256
211 81 25 70 0 4 1750 0 100 0 280
212 67 25 2 0 47 50 0 1175 0 94
213 8 24 27 0 8 648 0 192 0 216
214 46 24 29 0 3 696 0 72 0 87
215 83 24 68 0 5 1632 0 120 0 340
216 87 24 42 0 3 1008 0 72 0 126
217 82 24 78 0 4 1872 0 96 0 312
218 63 24 13 0 30 312 0 720 0 390
219 27 24 52 0 4 1248 0 96 0 208
220 14 23 25 0 12 575 0 276 0 300
221 83 23 38 0 8 874 0 184 0 304
222 168 23 40 0 3 920 0 69 0 120
223 67 23 42 0 8 966 0 184 0 336
224 21 23 40 0 4 920 0 92 0 160
225 55 23 74 0 3 1702 0 69 0 222
226 54 23 73 0 4 1679 0 92 0 292
227 118 22 56 0 4 1232 0 88 0 224
228 69 22 3 0 21 66 0 462 0 63
229 77 21 9 0 12 189 0 252 0 108
230 72 21 68 0 3 1428 0 63 0 204
231 53 21 28 0 3 588 0 63 0 84
232 40 21 36 0 4 756 0 84 0 144
233 102 20 38 0 6 760 0 120 0 228
234 25 20 55 0 17 1100 0 340 0 935
235 31 20 36 0 3 720 0 60 0 108
236 77 20 17 0 18 340 0 360 0 306
237 38 20 54 0 5 1080 0 100 0 270
238 23 19 57 0 5 1083 0 95 0 285
239 91 19 30 0 16 570 0 304 0 480
240 58 19 40 0 10 760 0 190 0 400
241 42 18 37 0 5 666 0 90 0 185
242 44 18 46 0 4 828 0 72 0 184
243 58 18 32 0 3 576 0 54 0 96
244 35 18 34 0 5 612 0 90 0 170
245 88 18 22 0 4 396 0 72 0 88
246 25 17 59 0 5 1003 0 85 0 295
247 39 17 32 0 10 544 0 170 0 320
248 48 16 18 0 12 288 0 192 0 216
249 64 16 28 0 4 448 0 64 0 112
250 65 15 34 0 9 510 0 135 0 306
251 95 15 29 0 12 435 0 180 0 348
252 29 15 24 0 10 360 0 150 0 240
253 2 15 24 0 9 360 0 135 0 216
254 83 14 23 0 17 322 0 238 0 391
255 11 13 43 0 6 559 0 78 0 258
256 16 13 28 0 3 364 0 39 0 84
257 9 12 19 0 4 228 0 48 0 76
258 46 11 16 0 19 176 0 209 0 304
259 41 11 40 0 3 440 0 33 0 120
260 14 10 14 0 9 140 0 90 0 126
261 63 10 19 0 7 190 0 70 0 133
262 9 10 22 0 4 220 0 40 0 88
263 0 10 8 0 3 80 0 30 0 24
264 58 9 31 0 46 279 0 414 0 1426
265 18 8 9 0 31 72 0 248 0 279
266 42 8 18 0 21 144 0 168 0 378
267 26 7 9 0 7 63 0 49 0 63
268 38 7 5 0 29 35 0 203 0 145
269 1 4 11 0 5 44 0 20 0 55
uk_spr
1 9
2 3
3 2
4 21
5 10
6 2
7 4
8 4
9 4
10 5
11 2
12 3
13 3
14 6
15 3
16 4
17 3
18 13
19 3
20 4
21 8
22 8
23 3
24 4
25 2
26 5
27 4
28 3
29 3
30 2
31 3
32 3
33 3
34 5
35 3
36 4
37 3
38 4
39 8
40 3
41 4
42 2
43 5
44 4
45 7
46 3
47 4
48 6
49 7
50 20
51 49
52 3
53 3
54 6
55 6
56 4
57 5
58 4
59 4
60 31
61 3
62 3
63 4
64 6
65 5
66 3
67 3
68 2
69 3
70 3
71 16
72 3
73 3
74 3
75 2
76 5
77 3
78 4
79 5
80 2
81 3
82 3
83 14
84 8
85 4
86 4
87 3
88 4
89 3
90 5
91 3
92 3
93 2
94 4
95 31
96 2
97 5
98 5
99 2
100 2
101 3
102 8
103 6
104 3
105 2
106 5
107 3
108 5
109 5
110 4
111 2
112 4
113 0
114 0
115 0
116 0
117 0
118 0
119 0
120 0
121 0
122 0
123 0
124 0
125 0
126 0
127 0
128 0
129 0
130 0
131 0
132 0
133 0
134 0
135 0
136 0
137 0
138 0
139 0
140 0
141 0
142 0
143 0
144 0
145 0
146 0
147 0
148 0
149 0
150 0
151 0
152 0
153 0
154 0
155 0
156 0
157 0
158 0
159 0
160 0
161 0
162 0
163 0
164 0
165 0
166 0
167 0
168 0
169 0
170 0
171 0
172 0
173 0
174 0
175 0
176 0
177 0
178 0
179 0
180 0
181 0
182 0
183 0
184 0
185 0
186 0
187 0
188 0
189 0
190 0
191 0
192 0
193 0
194 0
195 0
196 0
197 0
198 0
199 0
200 0
201 0
202 0
203 0
204 0
205 0
206 0
207 0
208 0
209 0
210 0
211 0
212 0
213 0
214 0
215 0
216 0
217 0
218 0
219 0
220 0
221 0
222 0
223 0
224 0
225 0
226 0
227 0
228 0
229 0
230 0
231 0
232 0
233 0
234 0
235 0
236 0
237 0
238 0
239 0
240 0
241 0
242 0
243 0
244 0
245 0
246 0
247 0
248 0
249 0
250 0
251 0
252 0
253 0
254 0
255 0
256 0
257 0
258 0
259 0
260 0
261 0
262 0
263 0
264 0
265 0
266 0
267 0
268 0
269 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) hours blogs uk spr hours_blogs
-16.748155 2.127783 0.977409 48.980061 1.332002 -0.017445
hours_uk hours_spr blogs_uk blogs_spr uk_spr
-0.429690 -0.021253 0.045482 -0.006073 -0.188076
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-66.127 -14.120 1.095 14.413 110.962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -16.748155 8.366498 -2.002 0.04635 *
hours 2.127783 0.283799 7.497 1.05e-12 ***
blogs 0.977409 0.141894 6.888 4.30e-11 ***
uk 48.980061 8.493851 5.767 2.31e-08 ***
spr 1.332002 0.476534 2.795 0.00558 **
hours_blogs -0.017445 0.002454 -7.108 1.15e-11 ***
hours_uk -0.429690 0.255939 -1.679 0.09439 .
hours_spr -0.021253 0.016642 -1.277 0.20270
blogs_uk 0.045482 0.224230 0.203 0.83942
blogs_spr -0.006073 0.013208 -0.460 0.64605
uk_spr -0.188076 0.626052 -0.300 0.76410
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23.38 on 258 degrees of freedom
Multiple R-squared: 0.5905, Adjusted R-squared: 0.5747
F-statistic: 37.21 on 10 and 258 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,] 7.246770e-01 5.506460e-01 0.2753230
[2,] 6.128136e-01 7.743728e-01 0.3871864
[3,] 5.176818e-01 9.646364e-01 0.4823182
[4,] 3.885052e-01 7.770104e-01 0.6114948
[5,] 2.848685e-01 5.697369e-01 0.7151315
[6,] 2.431155e-01 4.862309e-01 0.7568845
[7,] 1.750721e-01 3.501441e-01 0.8249279
[8,] 1.472073e-01 2.944146e-01 0.8527927
[9,] 2.309852e-01 4.619705e-01 0.7690148
[10,] 1.690364e-01 3.380728e-01 0.8309636
[11,] 1.199591e-01 2.399181e-01 0.8800409
[12,] 8.421342e-02 1.684268e-01 0.9157866
[13,] 5.683435e-02 1.136687e-01 0.9431656
[14,] 4.612577e-02 9.225153e-02 0.9538742
[15,] 3.133226e-02 6.266451e-02 0.9686677
[16,] 2.316174e-02 4.632349e-02 0.9768383
[17,] 1.727416e-02 3.454831e-02 0.9827258
[18,] 2.789925e-02 5.579850e-02 0.9721007
[19,] 1.823294e-02 3.646587e-02 0.9817671
[20,] 1.574530e-02 3.149061e-02 0.9842547
[21,] 1.148406e-02 2.296812e-02 0.9885159
[22,] 1.049901e-02 2.099802e-02 0.9895010
[23,] 9.908395e-03 1.981679e-02 0.9900916
[24,] 8.557890e-03 1.711578e-02 0.9914421
[25,] 8.001881e-03 1.600376e-02 0.9919981
[26,] 9.448937e-03 1.889787e-02 0.9905511
[27,] 1.171264e-02 2.342528e-02 0.9882874
[28,] 7.799353e-03 1.559871e-02 0.9922006
[29,] 1.278884e-02 2.557768e-02 0.9872112
[30,] 1.004515e-02 2.009030e-02 0.9899549
[31,] 8.060650e-03 1.612130e-02 0.9919393
[32,] 2.586230e-02 5.172460e-02 0.9741377
[33,] 2.035098e-02 4.070196e-02 0.9796490
[34,] 1.451218e-02 2.902437e-02 0.9854878
[35,] 1.038131e-02 2.076262e-02 0.9896187
[36,] 7.247447e-03 1.449489e-02 0.9927526
[37,] 5.195428e-03 1.039086e-02 0.9948046
[38,] 3.958719e-03 7.917438e-03 0.9960413
[39,] 5.363164e-03 1.072633e-02 0.9946368
[40,] 5.536260e-03 1.107252e-02 0.9944637
[41,] 4.085284e-03 8.170567e-03 0.9959147
[42,] 2.824495e-03 5.648990e-03 0.9971755
[43,] 1.002440e-02 2.004880e-02 0.9899756
[44,] 7.289782e-03 1.457956e-02 0.9927102
[45,] 5.319797e-03 1.063959e-02 0.9946802
[46,] 5.733478e-03 1.146696e-02 0.9942665
[47,] 4.738599e-03 9.477199e-03 0.9952614
[48,] 3.385730e-03 6.771460e-03 0.9966143
[49,] 2.564116e-03 5.128232e-03 0.9974359
[50,] 1.792355e-03 3.584710e-03 0.9982076
[51,] 1.233719e-03 2.467437e-03 0.9987663
[52,] 2.121891e-03 4.243782e-03 0.9978781
[53,] 2.052065e-03 4.104130e-03 0.9979479
[54,] 1.414343e-03 2.828685e-03 0.9985857
[55,] 9.650232e-04 1.930046e-03 0.9990350
[56,] 6.525996e-04 1.305199e-03 0.9993474
[57,] 1.872479e-03 3.744958e-03 0.9981275
[58,] 1.350724e-03 2.701448e-03 0.9986493
[59,] 9.519004e-04 1.903801e-03 0.9990481
[60,] 6.951059e-04 1.390212e-03 0.9993049
[61,] 4.774664e-04 9.549329e-04 0.9995225
[62,] 3.220087e-04 6.440173e-04 0.9996780
[63,] 2.503476e-04 5.006953e-04 0.9997497
[64,] 1.660458e-04 3.320916e-04 0.9998340
[65,] 1.361246e-04 2.722492e-04 0.9998639
[66,] 9.252260e-05 1.850452e-04 0.9999075
[67,] 6.299844e-05 1.259969e-04 0.9999370
[68,] 4.468362e-05 8.936723e-05 0.9999553
[69,] 5.181344e-05 1.036269e-04 0.9999482
[70,] 4.801846e-05 9.603692e-05 0.9999520
[71,] 4.010632e-05 8.021265e-05 0.9999599
[72,] 2.590877e-05 5.181754e-05 0.9999741
[73,] 2.440052e-05 4.880104e-05 0.9999756
[74,] 5.724098e-05 1.144820e-04 0.9999428
[75,] 8.248719e-05 1.649744e-04 0.9999175
[76,] 5.447386e-05 1.089477e-04 0.9999455
[77,] 5.331390e-05 1.066278e-04 0.9999467
[78,] 3.792397e-05 7.584795e-05 0.9999621
[79,] 2.963767e-05 5.927534e-05 0.9999704
[80,] 2.182629e-05 4.365258e-05 0.9999782
[81,] 3.758122e-05 7.516244e-05 0.9999624
[82,] 3.220629e-05 6.441259e-05 0.9999678
[83,] 2.875911e-05 5.751822e-05 0.9999712
[84,] 3.272304e-05 6.544608e-05 0.9999673
[85,] 3.137086e-05 6.274173e-05 0.9999686
[86,] 2.595336e-05 5.190672e-05 0.9999740
[87,] 1.714100e-05 3.428200e-05 0.9999829
[88,] 1.244336e-05 2.488672e-05 0.9999876
[89,] 1.583601e-05 3.167202e-05 0.9999842
[90,] 1.627138e-05 3.254276e-05 0.9999837
[91,] 1.896711e-05 3.793422e-05 0.9999810
[92,] 1.212746e-03 2.425492e-03 0.9987873
[93,] 1.181946e-03 2.363892e-03 0.9988181
[94,] 9.060774e-04 1.812155e-03 0.9990939
[95,] 1.064614e-03 2.129227e-03 0.9989354
[96,] 1.394081e-03 2.788162e-03 0.9986059
[97,] 4.471774e-03 8.943549e-03 0.9955282
[98,] 9.039415e-03 1.807883e-02 0.9909606
[99,] 1.083914e-02 2.167827e-02 0.9891609
[100,] 8.538390e-03 1.707678e-02 0.9914616
[101,] 7.548660e-03 1.509732e-02 0.9924513
[102,] 5.894949e-03 1.178990e-02 0.9941051
[103,] 5.473948e-03 1.094790e-02 0.9945261
[104,] 5.174541e-03 1.034908e-02 0.9948255
[105,] 4.318083e-03 8.636166e-03 0.9956819
[106,] 3.333254e-03 6.666509e-03 0.9966667
[107,] 2.881332e-03 5.762665e-03 0.9971187
[108,] 3.821805e-03 7.643611e-03 0.9961782
[109,] 3.936773e-03 7.873546e-03 0.9960632
[110,] 3.093504e-03 6.187007e-03 0.9969065
[111,] 2.891246e-03 5.782493e-03 0.9971088
[112,] 3.321978e-03 6.643957e-03 0.9966780
[113,] 2.674841e-03 5.349681e-03 0.9973252
[114,] 2.347304e-03 4.694608e-03 0.9976527
[115,] 2.040533e-03 4.081067e-03 0.9979595
[116,] 1.557721e-03 3.115443e-03 0.9984423
[117,] 2.886763e-03 5.773525e-03 0.9971132
[118,] 3.489233e-03 6.978465e-03 0.9965108
[119,] 5.013125e-03 1.002625e-02 0.9949869
[120,] 3.981916e-03 7.963833e-03 0.9960181
[121,] 3.061915e-03 6.123830e-03 0.9969381
[122,] 2.747515e-03 5.495029e-03 0.9972525
[123,] 2.136534e-03 4.273069e-03 0.9978635
[124,] 1.624516e-03 3.249031e-03 0.9983755
[125,] 1.256750e-03 2.513499e-03 0.9987433
[126,] 1.130221e-03 2.260442e-03 0.9988698
[127,] 1.073019e-03 2.146039e-03 0.9989270
[128,] 8.607121e-04 1.721424e-03 0.9991393
[129,] 6.366466e-04 1.273293e-03 0.9993634
[130,] 4.701000e-04 9.402000e-04 0.9995299
[131,] 4.574808e-04 9.149616e-04 0.9995425
[132,] 3.693114e-04 7.386229e-04 0.9996307
[133,] 2.660768e-04 5.321536e-04 0.9997339
[134,] 1.930814e-04 3.861628e-04 0.9998069
[135,] 1.376436e-04 2.752873e-04 0.9998624
[136,] 9.689807e-05 1.937961e-04 0.9999031
[137,] 1.046943e-04 2.093886e-04 0.9998953
[138,] 8.063415e-05 1.612683e-04 0.9999194
[139,] 6.344571e-05 1.268914e-04 0.9999366
[140,] 8.797618e-05 1.759524e-04 0.9999120
[141,] 6.456055e-05 1.291211e-04 0.9999354
[142,] 5.847950e-05 1.169590e-04 0.9999415
[143,] 7.150454e-05 1.430091e-04 0.9999285
[144,] 5.095270e-05 1.019054e-04 0.9999490
[145,] 8.110815e-05 1.622163e-04 0.9999189
[146,] 6.141066e-05 1.228213e-04 0.9999386
[147,] 5.194004e-05 1.038801e-04 0.9999481
[148,] 6.463255e-05 1.292651e-04 0.9999354
[149,] 4.674739e-05 9.349477e-05 0.9999533
[150,] 3.665708e-05 7.331416e-05 0.9999633
[151,] 2.607968e-05 5.215935e-05 0.9999739
[152,] 2.063328e-05 4.126656e-05 0.9999794
[153,] 2.266283e-05 4.532567e-05 0.9999773
[154,] 3.266665e-05 6.533330e-05 0.9999673
[155,] 3.048735e-05 6.097469e-05 0.9999695
[156,] 2.865096e-05 5.730191e-05 0.9999713
[157,] 3.613754e-04 7.227509e-04 0.9996386
[158,] 2.680935e-04 5.361871e-04 0.9997319
[159,] 2.538591e-04 5.077181e-04 0.9997461
[160,] 5.523006e-04 1.104601e-03 0.9994477
[161,] 5.824709e-04 1.164942e-03 0.9994175
[162,] 4.373033e-04 8.746067e-04 0.9995627
[163,] 4.217410e-04 8.434820e-04 0.9995783
[164,] 6.999717e-04 1.399943e-03 0.9993000
[165,] 1.154432e-03 2.308865e-03 0.9988456
[166,] 9.637814e-04 1.927563e-03 0.9990362
[167,] 7.790030e-04 1.558006e-03 0.9992210
[168,] 7.033454e-04 1.406691e-03 0.9992967
[169,] 5.206407e-04 1.041281e-03 0.9994794
[170,] 4.677971e-04 9.355942e-04 0.9995322
[171,] 3.338471e-04 6.676942e-04 0.9996662
[172,] 3.491006e-04 6.982013e-04 0.9996509
[173,] 2.469203e-04 4.938406e-04 0.9997531
[174,] 1.793404e-04 3.586808e-04 0.9998207
[175,] 4.068523e-04 8.137046e-04 0.9995931
[176,] 2.946734e-04 5.893467e-04 0.9997053
[177,] 3.198911e-04 6.397822e-04 0.9996801
[178,] 2.297771e-04 4.595542e-04 0.9997702
[179,] 2.378530e-04 4.757060e-04 0.9997621
[180,] 1.878082e-04 3.756164e-04 0.9998122
[181,] 1.488632e-04 2.977264e-04 0.9998511
[182,] 1.773823e-04 3.547646e-04 0.9998226
[183,] 1.708369e-04 3.416737e-04 0.9998292
[184,] 3.125313e-04 6.250625e-04 0.9996875
[185,] 2.198570e-04 4.397140e-04 0.9997801
[186,] 1.587890e-04 3.175779e-04 0.9998412
[187,] 1.613552e-04 3.227104e-04 0.9998386
[188,] 1.110951e-04 2.221902e-04 0.9998889
[189,] 8.467071e-05 1.693414e-04 0.9999153
[190,] 6.918568e-05 1.383714e-04 0.9999308
[191,] 4.581115e-05 9.162229e-05 0.9999542
[192,] 3.683236e-05 7.366471e-05 0.9999632
[193,] 3.919458e-05 7.838917e-05 0.9999608
[194,] 3.205335e-05 6.410671e-05 0.9999679
[195,] 5.506244e-05 1.101249e-04 0.9999449
[196,] 7.293625e-05 1.458725e-04 0.9999271
[197,] 9.172701e-05 1.834540e-04 0.9999083
[198,] 6.199658e-05 1.239932e-04 0.9999380
[199,] 4.017820e-05 8.035640e-05 0.9999598
[200,] 2.097253e-04 4.194506e-04 0.9997903
[201,] 2.139351e-04 4.278701e-04 0.9997861
[202,] 1.590767e-04 3.181534e-04 0.9998409
[203,] 1.375760e-04 2.751520e-04 0.9998624
[204,] 1.117031e-04 2.234061e-04 0.9998883
[205,] 7.194706e-05 1.438941e-04 0.9999281
[206,] 1.452801e-04 2.905602e-04 0.9998547
[207,] 7.196899e-04 1.439380e-03 0.9992803
[208,] 5.651294e-04 1.130259e-03 0.9994349
[209,] 8.338580e-02 1.667716e-01 0.9166142
[210,] 6.613626e-02 1.322725e-01 0.9338637
[211,] 1.081428e-01 2.162856e-01 0.8918572
[212,] 8.988136e-02 1.797627e-01 0.9101186
[213,] 7.520049e-02 1.504010e-01 0.9247995
[214,] 2.130571e-01 4.261142e-01 0.7869429
[215,] 2.193976e-01 4.387953e-01 0.7806024
[216,] 1.956326e-01 3.912651e-01 0.8043674
[217,] 2.727459e-01 5.454917e-01 0.7272541
[218,] 2.323552e-01 4.647104e-01 0.7676448
[219,] 2.061341e-01 4.122683e-01 0.7938659
[220,] 3.792357e-01 7.584713e-01 0.6207643
[221,] 4.098505e-01 8.197010e-01 0.5901495
[222,] 3.761462e-01 7.522923e-01 0.6238538
[223,] 3.579265e-01 7.158530e-01 0.6420735
[224,] 3.072252e-01 6.144505e-01 0.6927748
[225,] 2.774024e-01 5.548048e-01 0.7225976
[226,] 2.450677e-01 4.901354e-01 0.7549323
[227,] 1.954907e-01 3.909813e-01 0.8045093
[228,] 1.534378e-01 3.068756e-01 0.8465622
[229,] 1.173929e-01 2.347859e-01 0.8826071
[230,] 9.471733e-02 1.894347e-01 0.9052827
[231,] 7.202108e-02 1.440422e-01 0.9279789
[232,] 1.540168e-01 3.080335e-01 0.8459832
[233,] 1.327378e-01 2.654756e-01 0.8672622
[234,] 1.256064e-01 2.512129e-01 0.8743936
[235,] 8.926057e-02 1.785211e-01 0.9107394
[236,] 1.394672e-01 2.789344e-01 0.8605328
[237,] 1.255396e-01 2.510792e-01 0.8744604
[238,] 3.583402e-01 7.166805e-01 0.6416598
[239,] 2.601661e-01 5.203323e-01 0.7398339
[240,] 2.556602e-01 5.113204e-01 0.7443398
[241,] 2.623791e-01 5.247582e-01 0.7376209
[242,] 1.652871e-01 3.305742e-01 0.8347129
> postscript(file="/var/wessaorg/rcomp/tmp/1m2gt1354444553.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2b7pf1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p1721354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4z22a1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5dfvo1354444553.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 = 269
Frequency = 1
1 2 3 4 5 6
-2.83834553 -6.88764768 -7.41066980 21.56090868 -14.71639726 -8.97310659
7 8 9 10 11 12
-21.38569114 -12.92515467 29.24479353 3.23442040 -35.23738385 -27.17923792
13 14 15 16 17 18
5.63377935 -17.41880467 1.57717113 14.74927313 -8.64645449 -28.25054588
19 20 21 22 23 24
11.84660344 -4.28150865 -19.52947281 -9.78706741 3.92603538 -10.68420632
25 26 27 28 29 30
5.24823787 -8.24961650 11.81859080 13.53472103 -8.30558822 -11.44571182
31 32 33 34 35 36
-29.71462244 -2.95255078 11.83430154 -14.58069998 17.46843038 -18.36584515
37 38 39 40 41 42
-12.44969133 14.41346683 14.77363635 28.54348531 5.60479090 -20.00550178
43 44 45 46 47 48
-11.82352936 13.22320964 42.92175206 15.19906823 5.47219708 -1.23526155
49 50 51 52 53 54
6.24738014 2.62083586 -4.73117619 34.22281820 -15.99384601 13.49487902
55 56 57 58 59 60
8.53589190 -38.97184182 9.35375162 -0.94767915 29.46113465 -20.05303027
61 62 63 64 65 66
5.18788760 1.07110500 15.25819109 10.15492892 -24.47092333 27.00659230
67 68 69 70 71 72
7.19230916 13.24450183 12.18838581 -29.46889035 6.84853797 15.24702621
73 74 75 76 77 78
18.18069609 18.66275893 15.56907579 -3.81880780 13.43656785 -10.71449884
79 80 81 82 83 84
17.21862206 1.88021057 24.79215101 -13.50849235 23.96653271 26.97244589
85 86 87 88 89 90
11.17403722 -2.49503255 -21.26652954 -13.01305695 10.57906762 -15.56693235
91 92 93 94 95 96
0.71614942 26.63465102 3.72087617 -27.97106947 -0.06483776 -9.10237539
97 98 99 100 101 102
-14.11952270 -0.98880457 23.99496367 6.11411517 18.85908311 -6.26792186
103 104 105 106 107 108
33.65477100 -11.83382487 -66.12731593 -14.80340816 4.72578593 35.01842666
109 110 111 112 113 114
41.59780868 -50.07535791 -50.49270060 -44.48563663 0.03364393 -5.04275549
115 116 117 118 119 120
9.86647928 -30.54564057 30.51059531 32.99301380 4.93807848 -17.48682619
121 122 123 124 125 126
-29.16836817 2.57265880 15.91870273 -7.44642428 -37.00974948 12.18027553
127 128 129 130 131 132
-24.85726928 0.83530156 8.73952627 37.10266304 -28.34121201 28.07451120
133 134 135 136 137 138
-4.49374184 6.77226827 17.33902048 -1.97681178 -4.78021335 -9.69243387
139 140 141 142 143 144
5.23224408 -6.17854609 14.55415931 9.46260620 -7.17956351 23.13995035
145 146 147 148 149 150
-15.36924702 0.63805940 -0.19132111 8.39305210 -2.70434251 19.30258172
151 152 153 154 155 156
11.85905224 13.73743554 -32.97681000 8.93217656 -23.40721212 29.17978293
157 158 159 160 161 162
-6.43159025 -33.61875418 -12.75326381 17.90062955 -28.66387372 -7.91348522
163 164 165 166 167 168
0.54404655 1.53080768 -9.94459359 24.21389349 34.25852543 -21.64960898
169 170 171 172 173 174
21.89930546 -64.38980341 3.85667507 22.03518545 -41.40506482 -28.60077847
175 176 177 178 179 180
-1.79264136 -23.07910942 44.29119629 -34.48591287 15.99852019 -13.04890166
181 182 183 184 185 186
24.64408440 8.49556516 23.76847473 2.58904431 27.79340975 4.49679942
187 188 189 190 191 192
5.17184154 -46.45028323 -1.00572057 -21.03819846 0.93999272 13.51109810
193 194 195 196 197 198
12.69786938 19.97799951 30.21707507 -23.29735356 -37.12149688 -1.33317638
199 200 201 202 203 204
8.67014703 -17.85740305 1.12827043 17.24537554 21.08671678 2.92914543
205 206 207 208 209 210
6.80330649 -32.47081512 19.33292847 43.78451588 -23.58647174 -31.66248373
211 212 213 214 215 216
5.16187246 -7.58942858 -46.66776402 -6.45902147 8.64337953 27.51441871
217 218 219 220 221 222
2.70802270 -0.87117907 -38.39675413 -40.89125824 24.01553393 110.96164147
223 224 225 226 227 228
5.90519346 -36.63861408 -21.00865246 -21.85055114 52.59719243 19.38573097
229 230 231 232 233 234
33.59290428 1.09454126 5.80815172 -12.60162456 48.25236786 -45.11490458
235 236 237 238 239 240
-19.49857594 26.04144557 -24.64170399 -40.40898484 36.00581945 1.62961058
241 242 243 244 245 246
-7.72124611 -10.74843137 12.95411662 -12.82215599 48.59004016 -37.65565948
247 248 249 250 251 252
-9.97462599 7.54288747 23.86402779 18.23609523 49.03020871 -12.02061552
253 254 255 256 257 258
-38.15316542 37.88495833 -36.95714234 -18.58739872 -18.22479248 7.75446512
259 260 261 262 263 264
0.35620413 -11.08108279 36.18558102 -17.13816722 -14.16597651 -13.64750857
265 266 267 268 269
-24.14159522 4.53875092 12.25604507 2.14417485 -6.64781223
> postscript(file="/var/wessaorg/rcomp/tmp/6o2af1354444553.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 = 269
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.83834553 NA
1 -6.88764768 -2.83834553
2 -7.41066980 -6.88764768
3 21.56090868 -7.41066980
4 -14.71639726 21.56090868
5 -8.97310659 -14.71639726
6 -21.38569114 -8.97310659
7 -12.92515467 -21.38569114
8 29.24479353 -12.92515467
9 3.23442040 29.24479353
10 -35.23738385 3.23442040
11 -27.17923792 -35.23738385
12 5.63377935 -27.17923792
13 -17.41880467 5.63377935
14 1.57717113 -17.41880467
15 14.74927313 1.57717113
16 -8.64645449 14.74927313
17 -28.25054588 -8.64645449
18 11.84660344 -28.25054588
19 -4.28150865 11.84660344
20 -19.52947281 -4.28150865
21 -9.78706741 -19.52947281
22 3.92603538 -9.78706741
23 -10.68420632 3.92603538
24 5.24823787 -10.68420632
25 -8.24961650 5.24823787
26 11.81859080 -8.24961650
27 13.53472103 11.81859080
28 -8.30558822 13.53472103
29 -11.44571182 -8.30558822
30 -29.71462244 -11.44571182
31 -2.95255078 -29.71462244
32 11.83430154 -2.95255078
33 -14.58069998 11.83430154
34 17.46843038 -14.58069998
35 -18.36584515 17.46843038
36 -12.44969133 -18.36584515
37 14.41346683 -12.44969133
38 14.77363635 14.41346683
39 28.54348531 14.77363635
40 5.60479090 28.54348531
41 -20.00550178 5.60479090
42 -11.82352936 -20.00550178
43 13.22320964 -11.82352936
44 42.92175206 13.22320964
45 15.19906823 42.92175206
46 5.47219708 15.19906823
47 -1.23526155 5.47219708
48 6.24738014 -1.23526155
49 2.62083586 6.24738014
50 -4.73117619 2.62083586
51 34.22281820 -4.73117619
52 -15.99384601 34.22281820
53 13.49487902 -15.99384601
54 8.53589190 13.49487902
55 -38.97184182 8.53589190
56 9.35375162 -38.97184182
57 -0.94767915 9.35375162
58 29.46113465 -0.94767915
59 -20.05303027 29.46113465
60 5.18788760 -20.05303027
61 1.07110500 5.18788760
62 15.25819109 1.07110500
63 10.15492892 15.25819109
64 -24.47092333 10.15492892
65 27.00659230 -24.47092333
66 7.19230916 27.00659230
67 13.24450183 7.19230916
68 12.18838581 13.24450183
69 -29.46889035 12.18838581
70 6.84853797 -29.46889035
71 15.24702621 6.84853797
72 18.18069609 15.24702621
73 18.66275893 18.18069609
74 15.56907579 18.66275893
75 -3.81880780 15.56907579
76 13.43656785 -3.81880780
77 -10.71449884 13.43656785
78 17.21862206 -10.71449884
79 1.88021057 17.21862206
80 24.79215101 1.88021057
81 -13.50849235 24.79215101
82 23.96653271 -13.50849235
83 26.97244589 23.96653271
84 11.17403722 26.97244589
85 -2.49503255 11.17403722
86 -21.26652954 -2.49503255
87 -13.01305695 -21.26652954
88 10.57906762 -13.01305695
89 -15.56693235 10.57906762
90 0.71614942 -15.56693235
91 26.63465102 0.71614942
92 3.72087617 26.63465102
93 -27.97106947 3.72087617
94 -0.06483776 -27.97106947
95 -9.10237539 -0.06483776
96 -14.11952270 -9.10237539
97 -0.98880457 -14.11952270
98 23.99496367 -0.98880457
99 6.11411517 23.99496367
100 18.85908311 6.11411517
101 -6.26792186 18.85908311
102 33.65477100 -6.26792186
103 -11.83382487 33.65477100
104 -66.12731593 -11.83382487
105 -14.80340816 -66.12731593
106 4.72578593 -14.80340816
107 35.01842666 4.72578593
108 41.59780868 35.01842666
109 -50.07535791 41.59780868
110 -50.49270060 -50.07535791
111 -44.48563663 -50.49270060
112 0.03364393 -44.48563663
113 -5.04275549 0.03364393
114 9.86647928 -5.04275549
115 -30.54564057 9.86647928
116 30.51059531 -30.54564057
117 32.99301380 30.51059531
118 4.93807848 32.99301380
119 -17.48682619 4.93807848
120 -29.16836817 -17.48682619
121 2.57265880 -29.16836817
122 15.91870273 2.57265880
123 -7.44642428 15.91870273
124 -37.00974948 -7.44642428
125 12.18027553 -37.00974948
126 -24.85726928 12.18027553
127 0.83530156 -24.85726928
128 8.73952627 0.83530156
129 37.10266304 8.73952627
130 -28.34121201 37.10266304
131 28.07451120 -28.34121201
132 -4.49374184 28.07451120
133 6.77226827 -4.49374184
134 17.33902048 6.77226827
135 -1.97681178 17.33902048
136 -4.78021335 -1.97681178
137 -9.69243387 -4.78021335
138 5.23224408 -9.69243387
139 -6.17854609 5.23224408
140 14.55415931 -6.17854609
141 9.46260620 14.55415931
142 -7.17956351 9.46260620
143 23.13995035 -7.17956351
144 -15.36924702 23.13995035
145 0.63805940 -15.36924702
146 -0.19132111 0.63805940
147 8.39305210 -0.19132111
148 -2.70434251 8.39305210
149 19.30258172 -2.70434251
150 11.85905224 19.30258172
151 13.73743554 11.85905224
152 -32.97681000 13.73743554
153 8.93217656 -32.97681000
154 -23.40721212 8.93217656
155 29.17978293 -23.40721212
156 -6.43159025 29.17978293
157 -33.61875418 -6.43159025
158 -12.75326381 -33.61875418
159 17.90062955 -12.75326381
160 -28.66387372 17.90062955
161 -7.91348522 -28.66387372
162 0.54404655 -7.91348522
163 1.53080768 0.54404655
164 -9.94459359 1.53080768
165 24.21389349 -9.94459359
166 34.25852543 24.21389349
167 -21.64960898 34.25852543
168 21.89930546 -21.64960898
169 -64.38980341 21.89930546
170 3.85667507 -64.38980341
171 22.03518545 3.85667507
172 -41.40506482 22.03518545
173 -28.60077847 -41.40506482
174 -1.79264136 -28.60077847
175 -23.07910942 -1.79264136
176 44.29119629 -23.07910942
177 -34.48591287 44.29119629
178 15.99852019 -34.48591287
179 -13.04890166 15.99852019
180 24.64408440 -13.04890166
181 8.49556516 24.64408440
182 23.76847473 8.49556516
183 2.58904431 23.76847473
184 27.79340975 2.58904431
185 4.49679942 27.79340975
186 5.17184154 4.49679942
187 -46.45028323 5.17184154
188 -1.00572057 -46.45028323
189 -21.03819846 -1.00572057
190 0.93999272 -21.03819846
191 13.51109810 0.93999272
192 12.69786938 13.51109810
193 19.97799951 12.69786938
194 30.21707507 19.97799951
195 -23.29735356 30.21707507
196 -37.12149688 -23.29735356
197 -1.33317638 -37.12149688
198 8.67014703 -1.33317638
199 -17.85740305 8.67014703
200 1.12827043 -17.85740305
201 17.24537554 1.12827043
202 21.08671678 17.24537554
203 2.92914543 21.08671678
204 6.80330649 2.92914543
205 -32.47081512 6.80330649
206 19.33292847 -32.47081512
207 43.78451588 19.33292847
208 -23.58647174 43.78451588
209 -31.66248373 -23.58647174
210 5.16187246 -31.66248373
211 -7.58942858 5.16187246
212 -46.66776402 -7.58942858
213 -6.45902147 -46.66776402
214 8.64337953 -6.45902147
215 27.51441871 8.64337953
216 2.70802270 27.51441871
217 -0.87117907 2.70802270
218 -38.39675413 -0.87117907
219 -40.89125824 -38.39675413
220 24.01553393 -40.89125824
221 110.96164147 24.01553393
222 5.90519346 110.96164147
223 -36.63861408 5.90519346
224 -21.00865246 -36.63861408
225 -21.85055114 -21.00865246
226 52.59719243 -21.85055114
227 19.38573097 52.59719243
228 33.59290428 19.38573097
229 1.09454126 33.59290428
230 5.80815172 1.09454126
231 -12.60162456 5.80815172
232 48.25236786 -12.60162456
233 -45.11490458 48.25236786
234 -19.49857594 -45.11490458
235 26.04144557 -19.49857594
236 -24.64170399 26.04144557
237 -40.40898484 -24.64170399
238 36.00581945 -40.40898484
239 1.62961058 36.00581945
240 -7.72124611 1.62961058
241 -10.74843137 -7.72124611
242 12.95411662 -10.74843137
243 -12.82215599 12.95411662
244 48.59004016 -12.82215599
245 -37.65565948 48.59004016
246 -9.97462599 -37.65565948
247 7.54288747 -9.97462599
248 23.86402779 7.54288747
249 18.23609523 23.86402779
250 49.03020871 18.23609523
251 -12.02061552 49.03020871
252 -38.15316542 -12.02061552
253 37.88495833 -38.15316542
254 -36.95714234 37.88495833
255 -18.58739872 -36.95714234
256 -18.22479248 -18.58739872
257 7.75446512 -18.22479248
258 0.35620413 7.75446512
259 -11.08108279 0.35620413
260 36.18558102 -11.08108279
261 -17.13816722 36.18558102
262 -14.16597651 -17.13816722
263 -13.64750857 -14.16597651
264 -24.14159522 -13.64750857
265 4.53875092 -24.14159522
266 12.25604507 4.53875092
267 2.14417485 12.25604507
268 -6.64781223 2.14417485
269 NA -6.64781223
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.88764768 -2.83834553
[2,] -7.41066980 -6.88764768
[3,] 21.56090868 -7.41066980
[4,] -14.71639726 21.56090868
[5,] -8.97310659 -14.71639726
[6,] -21.38569114 -8.97310659
[7,] -12.92515467 -21.38569114
[8,] 29.24479353 -12.92515467
[9,] 3.23442040 29.24479353
[10,] -35.23738385 3.23442040
[11,] -27.17923792 -35.23738385
[12,] 5.63377935 -27.17923792
[13,] -17.41880467 5.63377935
[14,] 1.57717113 -17.41880467
[15,] 14.74927313 1.57717113
[16,] -8.64645449 14.74927313
[17,] -28.25054588 -8.64645449
[18,] 11.84660344 -28.25054588
[19,] -4.28150865 11.84660344
[20,] -19.52947281 -4.28150865
[21,] -9.78706741 -19.52947281
[22,] 3.92603538 -9.78706741
[23,] -10.68420632 3.92603538
[24,] 5.24823787 -10.68420632
[25,] -8.24961650 5.24823787
[26,] 11.81859080 -8.24961650
[27,] 13.53472103 11.81859080
[28,] -8.30558822 13.53472103
[29,] -11.44571182 -8.30558822
[30,] -29.71462244 -11.44571182
[31,] -2.95255078 -29.71462244
[32,] 11.83430154 -2.95255078
[33,] -14.58069998 11.83430154
[34,] 17.46843038 -14.58069998
[35,] -18.36584515 17.46843038
[36,] -12.44969133 -18.36584515
[37,] 14.41346683 -12.44969133
[38,] 14.77363635 14.41346683
[39,] 28.54348531 14.77363635
[40,] 5.60479090 28.54348531
[41,] -20.00550178 5.60479090
[42,] -11.82352936 -20.00550178
[43,] 13.22320964 -11.82352936
[44,] 42.92175206 13.22320964
[45,] 15.19906823 42.92175206
[46,] 5.47219708 15.19906823
[47,] -1.23526155 5.47219708
[48,] 6.24738014 -1.23526155
[49,] 2.62083586 6.24738014
[50,] -4.73117619 2.62083586
[51,] 34.22281820 -4.73117619
[52,] -15.99384601 34.22281820
[53,] 13.49487902 -15.99384601
[54,] 8.53589190 13.49487902
[55,] -38.97184182 8.53589190
[56,] 9.35375162 -38.97184182
[57,] -0.94767915 9.35375162
[58,] 29.46113465 -0.94767915
[59,] -20.05303027 29.46113465
[60,] 5.18788760 -20.05303027
[61,] 1.07110500 5.18788760
[62,] 15.25819109 1.07110500
[63,] 10.15492892 15.25819109
[64,] -24.47092333 10.15492892
[65,] 27.00659230 -24.47092333
[66,] 7.19230916 27.00659230
[67,] 13.24450183 7.19230916
[68,] 12.18838581 13.24450183
[69,] -29.46889035 12.18838581
[70,] 6.84853797 -29.46889035
[71,] 15.24702621 6.84853797
[72,] 18.18069609 15.24702621
[73,] 18.66275893 18.18069609
[74,] 15.56907579 18.66275893
[75,] -3.81880780 15.56907579
[76,] 13.43656785 -3.81880780
[77,] -10.71449884 13.43656785
[78,] 17.21862206 -10.71449884
[79,] 1.88021057 17.21862206
[80,] 24.79215101 1.88021057
[81,] -13.50849235 24.79215101
[82,] 23.96653271 -13.50849235
[83,] 26.97244589 23.96653271
[84,] 11.17403722 26.97244589
[85,] -2.49503255 11.17403722
[86,] -21.26652954 -2.49503255
[87,] -13.01305695 -21.26652954
[88,] 10.57906762 -13.01305695
[89,] -15.56693235 10.57906762
[90,] 0.71614942 -15.56693235
[91,] 26.63465102 0.71614942
[92,] 3.72087617 26.63465102
[93,] -27.97106947 3.72087617
[94,] -0.06483776 -27.97106947
[95,] -9.10237539 -0.06483776
[96,] -14.11952270 -9.10237539
[97,] -0.98880457 -14.11952270
[98,] 23.99496367 -0.98880457
[99,] 6.11411517 23.99496367
[100,] 18.85908311 6.11411517
[101,] -6.26792186 18.85908311
[102,] 33.65477100 -6.26792186
[103,] -11.83382487 33.65477100
[104,] -66.12731593 -11.83382487
[105,] -14.80340816 -66.12731593
[106,] 4.72578593 -14.80340816
[107,] 35.01842666 4.72578593
[108,] 41.59780868 35.01842666
[109,] -50.07535791 41.59780868
[110,] -50.49270060 -50.07535791
[111,] -44.48563663 -50.49270060
[112,] 0.03364393 -44.48563663
[113,] -5.04275549 0.03364393
[114,] 9.86647928 -5.04275549
[115,] -30.54564057 9.86647928
[116,] 30.51059531 -30.54564057
[117,] 32.99301380 30.51059531
[118,] 4.93807848 32.99301380
[119,] -17.48682619 4.93807848
[120,] -29.16836817 -17.48682619
[121,] 2.57265880 -29.16836817
[122,] 15.91870273 2.57265880
[123,] -7.44642428 15.91870273
[124,] -37.00974948 -7.44642428
[125,] 12.18027553 -37.00974948
[126,] -24.85726928 12.18027553
[127,] 0.83530156 -24.85726928
[128,] 8.73952627 0.83530156
[129,] 37.10266304 8.73952627
[130,] -28.34121201 37.10266304
[131,] 28.07451120 -28.34121201
[132,] -4.49374184 28.07451120
[133,] 6.77226827 -4.49374184
[134,] 17.33902048 6.77226827
[135,] -1.97681178 17.33902048
[136,] -4.78021335 -1.97681178
[137,] -9.69243387 -4.78021335
[138,] 5.23224408 -9.69243387
[139,] -6.17854609 5.23224408
[140,] 14.55415931 -6.17854609
[141,] 9.46260620 14.55415931
[142,] -7.17956351 9.46260620
[143,] 23.13995035 -7.17956351
[144,] -15.36924702 23.13995035
[145,] 0.63805940 -15.36924702
[146,] -0.19132111 0.63805940
[147,] 8.39305210 -0.19132111
[148,] -2.70434251 8.39305210
[149,] 19.30258172 -2.70434251
[150,] 11.85905224 19.30258172
[151,] 13.73743554 11.85905224
[152,] -32.97681000 13.73743554
[153,] 8.93217656 -32.97681000
[154,] -23.40721212 8.93217656
[155,] 29.17978293 -23.40721212
[156,] -6.43159025 29.17978293
[157,] -33.61875418 -6.43159025
[158,] -12.75326381 -33.61875418
[159,] 17.90062955 -12.75326381
[160,] -28.66387372 17.90062955
[161,] -7.91348522 -28.66387372
[162,] 0.54404655 -7.91348522
[163,] 1.53080768 0.54404655
[164,] -9.94459359 1.53080768
[165,] 24.21389349 -9.94459359
[166,] 34.25852543 24.21389349
[167,] -21.64960898 34.25852543
[168,] 21.89930546 -21.64960898
[169,] -64.38980341 21.89930546
[170,] 3.85667507 -64.38980341
[171,] 22.03518545 3.85667507
[172,] -41.40506482 22.03518545
[173,] -28.60077847 -41.40506482
[174,] -1.79264136 -28.60077847
[175,] -23.07910942 -1.79264136
[176,] 44.29119629 -23.07910942
[177,] -34.48591287 44.29119629
[178,] 15.99852019 -34.48591287
[179,] -13.04890166 15.99852019
[180,] 24.64408440 -13.04890166
[181,] 8.49556516 24.64408440
[182,] 23.76847473 8.49556516
[183,] 2.58904431 23.76847473
[184,] 27.79340975 2.58904431
[185,] 4.49679942 27.79340975
[186,] 5.17184154 4.49679942
[187,] -46.45028323 5.17184154
[188,] -1.00572057 -46.45028323
[189,] -21.03819846 -1.00572057
[190,] 0.93999272 -21.03819846
[191,] 13.51109810 0.93999272
[192,] 12.69786938 13.51109810
[193,] 19.97799951 12.69786938
[194,] 30.21707507 19.97799951
[195,] -23.29735356 30.21707507
[196,] -37.12149688 -23.29735356
[197,] -1.33317638 -37.12149688
[198,] 8.67014703 -1.33317638
[199,] -17.85740305 8.67014703
[200,] 1.12827043 -17.85740305
[201,] 17.24537554 1.12827043
[202,] 21.08671678 17.24537554
[203,] 2.92914543 21.08671678
[204,] 6.80330649 2.92914543
[205,] -32.47081512 6.80330649
[206,] 19.33292847 -32.47081512
[207,] 43.78451588 19.33292847
[208,] -23.58647174 43.78451588
[209,] -31.66248373 -23.58647174
[210,] 5.16187246 -31.66248373
[211,] -7.58942858 5.16187246
[212,] -46.66776402 -7.58942858
[213,] -6.45902147 -46.66776402
[214,] 8.64337953 -6.45902147
[215,] 27.51441871 8.64337953
[216,] 2.70802270 27.51441871
[217,] -0.87117907 2.70802270
[218,] -38.39675413 -0.87117907
[219,] -40.89125824 -38.39675413
[220,] 24.01553393 -40.89125824
[221,] 110.96164147 24.01553393
[222,] 5.90519346 110.96164147
[223,] -36.63861408 5.90519346
[224,] -21.00865246 -36.63861408
[225,] -21.85055114 -21.00865246
[226,] 52.59719243 -21.85055114
[227,] 19.38573097 52.59719243
[228,] 33.59290428 19.38573097
[229,] 1.09454126 33.59290428
[230,] 5.80815172 1.09454126
[231,] -12.60162456 5.80815172
[232,] 48.25236786 -12.60162456
[233,] -45.11490458 48.25236786
[234,] -19.49857594 -45.11490458
[235,] 26.04144557 -19.49857594
[236,] -24.64170399 26.04144557
[237,] -40.40898484 -24.64170399
[238,] 36.00581945 -40.40898484
[239,] 1.62961058 36.00581945
[240,] -7.72124611 1.62961058
[241,] -10.74843137 -7.72124611
[242,] 12.95411662 -10.74843137
[243,] -12.82215599 12.95411662
[244,] 48.59004016 -12.82215599
[245,] -37.65565948 48.59004016
[246,] -9.97462599 -37.65565948
[247,] 7.54288747 -9.97462599
[248,] 23.86402779 7.54288747
[249,] 18.23609523 23.86402779
[250,] 49.03020871 18.23609523
[251,] -12.02061552 49.03020871
[252,] -38.15316542 -12.02061552
[253,] 37.88495833 -38.15316542
[254,] -36.95714234 37.88495833
[255,] -18.58739872 -36.95714234
[256,] -18.22479248 -18.58739872
[257,] 7.75446512 -18.22479248
[258,] 0.35620413 7.75446512
[259,] -11.08108279 0.35620413
[260,] 36.18558102 -11.08108279
[261,] -17.13816722 36.18558102
[262,] -14.16597651 -17.13816722
[263,] -13.64750857 -14.16597651
[264,] -24.14159522 -13.64750857
[265,] 4.53875092 -24.14159522
[266,] 12.25604507 4.53875092
[267,] 2.14417485 12.25604507
[268,] -6.64781223 2.14417485
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.88764768 -2.83834553
2 -7.41066980 -6.88764768
3 21.56090868 -7.41066980
4 -14.71639726 21.56090868
5 -8.97310659 -14.71639726
6 -21.38569114 -8.97310659
7 -12.92515467 -21.38569114
8 29.24479353 -12.92515467
9 3.23442040 29.24479353
10 -35.23738385 3.23442040
11 -27.17923792 -35.23738385
12 5.63377935 -27.17923792
13 -17.41880467 5.63377935
14 1.57717113 -17.41880467
15 14.74927313 1.57717113
16 -8.64645449 14.74927313
17 -28.25054588 -8.64645449
18 11.84660344 -28.25054588
19 -4.28150865 11.84660344
20 -19.52947281 -4.28150865
21 -9.78706741 -19.52947281
22 3.92603538 -9.78706741
23 -10.68420632 3.92603538
24 5.24823787 -10.68420632
25 -8.24961650 5.24823787
26 11.81859080 -8.24961650
27 13.53472103 11.81859080
28 -8.30558822 13.53472103
29 -11.44571182 -8.30558822
30 -29.71462244 -11.44571182
31 -2.95255078 -29.71462244
32 11.83430154 -2.95255078
33 -14.58069998 11.83430154
34 17.46843038 -14.58069998
35 -18.36584515 17.46843038
36 -12.44969133 -18.36584515
37 14.41346683 -12.44969133
38 14.77363635 14.41346683
39 28.54348531 14.77363635
40 5.60479090 28.54348531
41 -20.00550178 5.60479090
42 -11.82352936 -20.00550178
43 13.22320964 -11.82352936
44 42.92175206 13.22320964
45 15.19906823 42.92175206
46 5.47219708 15.19906823
47 -1.23526155 5.47219708
48 6.24738014 -1.23526155
49 2.62083586 6.24738014
50 -4.73117619 2.62083586
51 34.22281820 -4.73117619
52 -15.99384601 34.22281820
53 13.49487902 -15.99384601
54 8.53589190 13.49487902
55 -38.97184182 8.53589190
56 9.35375162 -38.97184182
57 -0.94767915 9.35375162
58 29.46113465 -0.94767915
59 -20.05303027 29.46113465
60 5.18788760 -20.05303027
61 1.07110500 5.18788760
62 15.25819109 1.07110500
63 10.15492892 15.25819109
64 -24.47092333 10.15492892
65 27.00659230 -24.47092333
66 7.19230916 27.00659230
67 13.24450183 7.19230916
68 12.18838581 13.24450183
69 -29.46889035 12.18838581
70 6.84853797 -29.46889035
71 15.24702621 6.84853797
72 18.18069609 15.24702621
73 18.66275893 18.18069609
74 15.56907579 18.66275893
75 -3.81880780 15.56907579
76 13.43656785 -3.81880780
77 -10.71449884 13.43656785
78 17.21862206 -10.71449884
79 1.88021057 17.21862206
80 24.79215101 1.88021057
81 -13.50849235 24.79215101
82 23.96653271 -13.50849235
83 26.97244589 23.96653271
84 11.17403722 26.97244589
85 -2.49503255 11.17403722
86 -21.26652954 -2.49503255
87 -13.01305695 -21.26652954
88 10.57906762 -13.01305695
89 -15.56693235 10.57906762
90 0.71614942 -15.56693235
91 26.63465102 0.71614942
92 3.72087617 26.63465102
93 -27.97106947 3.72087617
94 -0.06483776 -27.97106947
95 -9.10237539 -0.06483776
96 -14.11952270 -9.10237539
97 -0.98880457 -14.11952270
98 23.99496367 -0.98880457
99 6.11411517 23.99496367
100 18.85908311 6.11411517
101 -6.26792186 18.85908311
102 33.65477100 -6.26792186
103 -11.83382487 33.65477100
104 -66.12731593 -11.83382487
105 -14.80340816 -66.12731593
106 4.72578593 -14.80340816
107 35.01842666 4.72578593
108 41.59780868 35.01842666
109 -50.07535791 41.59780868
110 -50.49270060 -50.07535791
111 -44.48563663 -50.49270060
112 0.03364393 -44.48563663
113 -5.04275549 0.03364393
114 9.86647928 -5.04275549
115 -30.54564057 9.86647928
116 30.51059531 -30.54564057
117 32.99301380 30.51059531
118 4.93807848 32.99301380
119 -17.48682619 4.93807848
120 -29.16836817 -17.48682619
121 2.57265880 -29.16836817
122 15.91870273 2.57265880
123 -7.44642428 15.91870273
124 -37.00974948 -7.44642428
125 12.18027553 -37.00974948
126 -24.85726928 12.18027553
127 0.83530156 -24.85726928
128 8.73952627 0.83530156
129 37.10266304 8.73952627
130 -28.34121201 37.10266304
131 28.07451120 -28.34121201
132 -4.49374184 28.07451120
133 6.77226827 -4.49374184
134 17.33902048 6.77226827
135 -1.97681178 17.33902048
136 -4.78021335 -1.97681178
137 -9.69243387 -4.78021335
138 5.23224408 -9.69243387
139 -6.17854609 5.23224408
140 14.55415931 -6.17854609
141 9.46260620 14.55415931
142 -7.17956351 9.46260620
143 23.13995035 -7.17956351
144 -15.36924702 23.13995035
145 0.63805940 -15.36924702
146 -0.19132111 0.63805940
147 8.39305210 -0.19132111
148 -2.70434251 8.39305210
149 19.30258172 -2.70434251
150 11.85905224 19.30258172
151 13.73743554 11.85905224
152 -32.97681000 13.73743554
153 8.93217656 -32.97681000
154 -23.40721212 8.93217656
155 29.17978293 -23.40721212
156 -6.43159025 29.17978293
157 -33.61875418 -6.43159025
158 -12.75326381 -33.61875418
159 17.90062955 -12.75326381
160 -28.66387372 17.90062955
161 -7.91348522 -28.66387372
162 0.54404655 -7.91348522
163 1.53080768 0.54404655
164 -9.94459359 1.53080768
165 24.21389349 -9.94459359
166 34.25852543 24.21389349
167 -21.64960898 34.25852543
168 21.89930546 -21.64960898
169 -64.38980341 21.89930546
170 3.85667507 -64.38980341
171 22.03518545 3.85667507
172 -41.40506482 22.03518545
173 -28.60077847 -41.40506482
174 -1.79264136 -28.60077847
175 -23.07910942 -1.79264136
176 44.29119629 -23.07910942
177 -34.48591287 44.29119629
178 15.99852019 -34.48591287
179 -13.04890166 15.99852019
180 24.64408440 -13.04890166
181 8.49556516 24.64408440
182 23.76847473 8.49556516
183 2.58904431 23.76847473
184 27.79340975 2.58904431
185 4.49679942 27.79340975
186 5.17184154 4.49679942
187 -46.45028323 5.17184154
188 -1.00572057 -46.45028323
189 -21.03819846 -1.00572057
190 0.93999272 -21.03819846
191 13.51109810 0.93999272
192 12.69786938 13.51109810
193 19.97799951 12.69786938
194 30.21707507 19.97799951
195 -23.29735356 30.21707507
196 -37.12149688 -23.29735356
197 -1.33317638 -37.12149688
198 8.67014703 -1.33317638
199 -17.85740305 8.67014703
200 1.12827043 -17.85740305
201 17.24537554 1.12827043
202 21.08671678 17.24537554
203 2.92914543 21.08671678
204 6.80330649 2.92914543
205 -32.47081512 6.80330649
206 19.33292847 -32.47081512
207 43.78451588 19.33292847
208 -23.58647174 43.78451588
209 -31.66248373 -23.58647174
210 5.16187246 -31.66248373
211 -7.58942858 5.16187246
212 -46.66776402 -7.58942858
213 -6.45902147 -46.66776402
214 8.64337953 -6.45902147
215 27.51441871 8.64337953
216 2.70802270 27.51441871
217 -0.87117907 2.70802270
218 -38.39675413 -0.87117907
219 -40.89125824 -38.39675413
220 24.01553393 -40.89125824
221 110.96164147 24.01553393
222 5.90519346 110.96164147
223 -36.63861408 5.90519346
224 -21.00865246 -36.63861408
225 -21.85055114 -21.00865246
226 52.59719243 -21.85055114
227 19.38573097 52.59719243
228 33.59290428 19.38573097
229 1.09454126 33.59290428
230 5.80815172 1.09454126
231 -12.60162456 5.80815172
232 48.25236786 -12.60162456
233 -45.11490458 48.25236786
234 -19.49857594 -45.11490458
235 26.04144557 -19.49857594
236 -24.64170399 26.04144557
237 -40.40898484 -24.64170399
238 36.00581945 -40.40898484
239 1.62961058 36.00581945
240 -7.72124611 1.62961058
241 -10.74843137 -7.72124611
242 12.95411662 -10.74843137
243 -12.82215599 12.95411662
244 48.59004016 -12.82215599
245 -37.65565948 48.59004016
246 -9.97462599 -37.65565948
247 7.54288747 -9.97462599
248 23.86402779 7.54288747
249 18.23609523 23.86402779
250 49.03020871 18.23609523
251 -12.02061552 49.03020871
252 -38.15316542 -12.02061552
253 37.88495833 -38.15316542
254 -36.95714234 37.88495833
255 -18.58739872 -36.95714234
256 -18.22479248 -18.58739872
257 7.75446512 -18.22479248
258 0.35620413 7.75446512
259 -11.08108279 0.35620413
260 36.18558102 -11.08108279
261 -17.13816722 36.18558102
262 -14.16597651 -17.13816722
263 -13.64750857 -14.16597651
264 -24.14159522 -13.64750857
265 4.53875092 -24.14159522
266 12.25604507 4.53875092
267 2.14417485 12.25604507
268 -6.64781223 2.14417485
> 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/77jic1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8gybd1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/987va1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/109jkl1354444553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113mpg1354444553.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/1264lt1354444553.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/13gj951354444554.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/14u6y21354444554.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/15yd4j1354444554.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/16zxsw1354444554.tab")
+ }
>
> try(system("convert tmp/1m2gt1354444553.ps tmp/1m2gt1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b7pf1354444553.ps tmp/2b7pf1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p1721354444553.ps tmp/3p1721354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z22a1354444553.ps tmp/4z22a1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dfvo1354444553.ps tmp/5dfvo1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/6o2af1354444553.ps tmp/6o2af1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/77jic1354444553.ps tmp/77jic1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gybd1354444553.ps tmp/8gybd1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/987va1354444553.ps tmp/987va1354444553.png",intern=TRUE))
character(0)
> try(system("convert tmp/109jkl1354444553.ps tmp/109jkl1354444553.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.837 1.202 16.028