R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1418
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+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16
+ ,64
+ ,39)
+ ,dim=c(10
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120')
+ ,1:289))
> y <- array(NA,dim=c(10,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120'),1:289))
> 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 = '10'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
feedback_messages_p120 pageviews time_in_rfc logins compendium_views_info
1 94 1418 210907 56 396
2 103 869 120982 56 297
3 93 1530 176508 54 559
4 103 2172 179321 89 967
5 51 901 123185 40 270
6 70 463 52746 25 143
7 91 3201 385534 92 1562
8 22 371 33170 18 109
9 38 1192 101645 63 371
10 93 1583 149061 44 656
11 60 1439 165446 33 511
12 123 1764 237213 84 655
13 148 1495 173326 88 465
14 90 1373 133131 55 525
15 124 2187 258873 60 885
16 70 1491 180083 66 497
17 168 4041 324799 154 1436
18 115 1706 230964 53 612
19 71 2152 236785 119 865
20 66 1036 135473 41 385
21 134 1882 202925 61 567
22 117 1929 215147 58 639
23 108 2242 344297 75 963
24 84 1220 153935 33 398
25 156 1289 132943 40 410
26 120 2515 174724 92 966
27 114 2147 174415 100 801
28 94 2352 225548 112 892
29 120 1638 223632 73 513
30 81 1222 124817 40 469
31 110 1812 221698 45 683
32 133 1677 210767 60 643
33 122 1579 170266 62 535
34 158 1731 260561 75 625
35 109 807 84853 31 264
36 124 2452 294424 77 992
37 39 829 101011 34 238
38 92 1940 215641 46 818
39 126 2662 325107 99 937
40 0 186 7176 17 70
41 70 1499 167542 66 507
42 37 865 106408 30 260
43 38 1793 96560 76 503
44 120 2527 265769 146 927
45 93 2747 269651 67 1269
46 95 1324 149112 56 537
47 77 2702 175824 107 910
48 90 1383 152871 58 532
49 80 1179 111665 34 345
50 31 2099 116408 61 918
51 110 4308 362301 119 1635
52 66 918 78800 42 330
53 138 1831 183167 66 557
54 133 3373 277965 89 1178
55 113 1713 150629 44 740
56 100 1438 168809 66 452
57 7 496 24188 24 218
58 140 2253 329267 259 764
59 61 744 65029 17 255
60 41 1161 101097 64 454
61 96 2352 218946 41 866
62 164 2144 244052 68 574
63 78 4691 341570 168 1276
64 49 1112 103597 43 379
65 102 2694 233328 132 825
66 124 1973 256462 105 798
67 99 1769 206161 71 663
68 129 3148 311473 112 1069
69 62 2474 235800 94 921
70 73 2084 177939 82 858
71 114 1954 207176 70 711
72 99 1226 196553 57 503
73 70 1389 174184 53 382
74 104 1496 143246 103 464
75 116 2269 187559 121 717
76 91 1833 187681 62 690
77 74 1268 119016 52 462
78 138 1943 182192 52 657
79 67 893 73566 32 385
80 151 1762 194979 62 577
81 72 1403 167488 45 619
82 120 1425 143756 46 479
83 115 1857 275541 63 817
84 105 1840 243199 75 752
85 104 1502 182999 88 430
86 108 1441 135649 46 451
87 98 1420 152299 53 537
88 69 1416 120221 37 519
89 111 2970 346485 90 1000
90 99 1317 145790 63 637
91 71 1644 193339 78 465
92 27 870 80953 25 437
93 69 1654 122774 45 711
94 107 1054 130585 46 299
95 73 937 112611 41 248
96 107 3004 286468 144 1162
97 93 2008 241066 82 714
98 129 2547 148446 91 905
99 69 1885 204713 71 649
100 118 1626 182079 63 512
101 73 1468 140344 53 472
102 119 2445 220516 62 905
103 104 1964 243060 63 786
104 107 1381 162765 32 489
105 99 1369 182613 39 479
106 90 1659 232138 62 617
107 197 2888 265318 117 925
108 36 1290 85574 34 351
109 85 2845 310839 92 1144
110 139 1982 225060 93 669
111 106 1904 232317 54 707
112 50 1391 144966 144 458
113 64 602 43287 14 214
114 31 1743 155754 61 599
115 63 1559 164709 109 572
116 92 2014 201940 38 897
117 106 2143 235454 73 819
118 63 2146 220801 75 720
119 69 874 99466 50 273
120 41 1590 92661 61 508
121 56 1590 133328 55 506
122 25 1210 61361 77 451
123 65 2072 125930 75 699
124 93 1281 100750 72 407
125 114 1401 224549 50 465
126 38 834 82316 32 245
127 44 1105 102010 53 370
128 87 1272 101523 42 316
129 110 1944 243511 71 603
130 0 391 22938 10 154
131 27 761 41566 35 229
132 83 1605 152474 65 577
133 30 530 61857 25 192
134 80 1988 99923 66 617
135 98 1386 132487 41 411
136 82 2395 317394 86 975
137 0 387 21054 16 146
138 60 1742 209641 42 705
139 28 620 22648 19 184
140 9 449 31414 19 200
141 33 800 46698 45 274
142 59 1684 131698 65 502
143 49 1050 91735 35 382
144 115 2699 244749 95 964
145 140 1606 184510 49 537
146 49 1502 79863 37 438
147 120 1204 128423 64 369
148 66 1138 97839 38 417
149 21 568 38214 34 276
150 124 1459 151101 32 514
151 152 2158 272458 65 822
152 139 1111 172494 52 389
153 38 1421 108043 62 466
154 144 2833 328107 65 1255
155 120 1955 250579 83 694
156 160 2922 351067 95 1024
157 114 1002 158015 29 400
158 39 1060 98866 18 397
159 78 956 85439 33 350
160 119 2186 229242 247 719
161 141 3604 351619 139 1277
162 101 1035 84207 29 356
163 56 1417 120445 118 457
164 133 3261 324598 110 1402
165 83 1587 131069 67 600
166 116 1424 204271 42 480
167 90 1701 165543 65 595
168 36 1249 141722 94 436
169 50 946 116048 64 230
170 61 1926 250047 81 651
171 97 3352 299775 95 1367
172 98 1641 195838 67 564
173 78 2035 173260 63 716
174 117 2312 254488 83 747
175 148 1369 104389 45 467
176 41 1577 136084 30 671
177 105 2201 199476 70 861
178 55 961 92499 32 319
179 132 1900 224330 83 612
180 44 1254 135781 31 433
181 21 1335 74408 67 434
182 50 1597 81240 66 503
183 0 207 14688 10 85
184 73 1645 181633 70 564
185 86 2429 271856 103 824
186 0 151 7199 5 74
187 13 474 46660 20 259
188 4 141 17547 5 69
189 57 1639 133368 36 535
190 48 872 95227 34 239
191 46 1318 152601 48 438
192 48 1018 98146 40 459
193 32 1383 79619 43 426
194 68 1314 59194 31 288
195 87 1335 139942 42 498
196 43 1403 118612 46 454
197 67 910 72880 33 376
198 46 616 65475 18 225
199 46 1407 99643 55 555
200 56 771 71965 35 252
201 48 766 77272 59 208
202 44 473 49289 19 130
203 60 1376 135131 66 481
204 65 1232 108446 60 389
205 55 1521 89746 36 565
206 38 572 44296 25 173
207 52 1059 77648 47 278
208 60 1544 181528 54 609
209 54 1230 134019 53 422
210 86 1206 124064 40 445
211 24 1205 92630 40 387
212 52 1255 121848 39 339
213 49 613 52915 14 181
214 61 721 81872 45 245
215 61 1109 58981 36 384
216 81 740 53515 28 212
217 43 1126 60812 44 399
218 40 728 56375 30 229
219 40 689 65490 22 224
220 56 592 80949 17 203
221 68 995 76302 31 333
222 79 1613 104011 55 384
223 47 2048 98104 54 636
224 57 705 67989 21 185
225 41 301 30989 14 93
226 29 1803 135458 81 581
227 3 799 73504 35 248
228 60 861 63123 43 304
229 30 1186 61254 46 344
230 79 1451 74914 30 407
231 47 628 31774 23 170
232 40 1161 81437 38 312
233 48 1463 87186 54 507
234 36 742 50090 20 224
235 42 979 65745 53 340
236 49 675 56653 45 168
237 57 1241 158399 39 443
238 12 676 46455 20 204
239 40 1049 73624 24 367
240 43 620 38395 31 210
241 33 1081 91899 35 335
242 77 1688 139526 151 364
243 43 736 52164 52 178
244 45 617 51567 30 206
245 47 812 70551 31 279
246 43 1051 84856 29 387
247 45 1656 102538 57 490
248 50 705 86678 40 238
249 35 945 85709 44 343
250 7 554 34662 25 232
251 71 1597 150580 77 530
252 67 982 99611 35 291
253 0 222 19349 11 67
254 62 1212 99373 63 397
255 54 1143 86230 44 467
256 4 435 30837 19 178
257 25 532 31706 13 175
258 40 882 89806 42 299
259 38 608 62088 38 154
260 19 459 40151 29 106
261 17 578 27634 20 189
262 67 826 76990 27 194
263 14 509 37460 20 135
264 30 717 54157 19 201
265 54 637 49862 37 207
266 35 857 84337 26 280
267 59 830 64175 42 260
268 24 652 59382 49 227
269 58 707 119308 30 239
270 42 954 76702 49 333
271 46 1461 103425 67 428
272 61 672 70344 28 230
273 3 778 43410 19 292
274 52 1141 104838 49 350
275 25 680 62215 27 186
276 40 1090 69304 30 326
277 32 616 53117 22 155
278 4 285 19764 12 75
279 49 1145 86680 31 361
280 63 733 84105 20 261
281 67 888 77945 20 299
282 32 849 89113 39 300
283 23 1182 91005 29 450
284 7 528 40248 16 183
285 54 642 64187 27 238
286 37 947 50857 21 165
287 35 819 56613 19 234
288 51 757 62792 35 176
289 39 894 72535 14 329
compendium_views_pr shared_compendiums blogged_computations
1 81 3 79
2 55 4 58
3 50 12 60
4 125 2 108
5 40 1 49
6 37 3 0
7 63 0 121
8 44 0 1
9 88 0 20
10 66 5 43
11 57 0 69
12 74 0 78
13 49 7 86
14 52 7 44
15 88 3 104
16 36 9 63
17 108 0 158
18 43 4 102
19 75 3 77
20 32 0 82
21 44 7 115
22 85 0 101
23 86 1 80
24 56 5 50
25 50 7 83
26 135 0 123
27 63 0 73
28 81 5 81
29 52 0 105
30 44 0 47
31 113 0 105
32 39 3 94
33 73 4 44
34 48 1 114
35 33 4 38
36 59 2 107
37 41 0 30
38 69 0 71
39 64 0 84
40 1 0 0
41 59 2 59
42 32 1 33
43 129 0 42
44 37 2 96
45 31 10 106
46 65 6 56
47 107 0 57
48 74 5 59
49 54 4 39
50 76 1 34
51 715 2 76
52 57 2 20
53 66 0 91
54 106 8 115
55 54 3 85
56 32 0 76
57 20 0 8
58 71 8 79
59 21 5 21
60 70 3 30
61 112 1 76
62 66 5 101
63 190 1 94
64 66 1 27
65 165 5 92
66 56 0 123
67 61 12 75
68 53 8 128
69 127 8 105
70 63 8 55
71 38 8 56
72 50 2 41
73 52 0 72
74 42 5 67
75 76 8 75
76 67 2 114
77 50 5 118
78 53 12 77
79 39 6 22
80 50 7 66
81 77 2 69
82 57 0 105
83 73 4 116
84 34 3 88
85 39 6 73
86 46 2 99
87 63 0 62
88 35 1 53
89 106 0 118
90 43 5 30
91 47 2 100
92 31 0 49
93 162 0 24
94 57 5 67
95 36 0 46
96 263 1 57
97 78 0 75
98 63 1 135
99 54 1 68
100 63 2 124
101 77 6 33
102 79 1 98
103 110 4 58
104 56 2 68
105 56 3 81
106 43 0 131
107 111 10 110
108 71 0 37
109 62 9 130
110 56 7 93
111 74 0 118
112 60 0 39
113 43 4 13
114 68 4 74
115 53 0 81
116 87 0 109
117 46 0 151
118 105 1 51
119 32 0 28
120 133 1 40
121 79 0 56
122 51 0 27
123 207 4 37
124 67 0 83
125 47 4 54
126 34 4 27
127 66 3 28
128 76 0 59
129 65 0 133
130 9 0 12
131 42 5 0
132 45 0 106
133 25 4 23
134 115 0 44
135 97 0 71
136 53 1 116
137 2 0 4
138 52 5 62
139 44 0 12
140 22 0 18
141 35 0 14
142 74 0 60
143 103 0 7
144 144 2 98
145 60 7 64
146 134 1 29
147 89 8 32
148 42 2 25
149 52 0 16
150 98 2 48
151 99 0 100
152 52 0 46
153 29 1 45
154 125 3 129
155 106 0 130
156 95 3 136
157 40 0 59
158 140 0 25
159 43 0 32
160 128 4 63
161 142 4 95
162 73 11 14
163 72 0 36
164 128 0 113
165 61 4 47
166 73 0 92
167 148 1 70
168 64 0 19
169 45 0 50
170 58 0 41
171 97 9 91
172 50 1 111
173 37 3 41
174 50 10 120
175 105 5 135
176 69 0 27
177 46 2 87
178 57 0 25
179 52 1 131
180 98 2 45
181 61 4 29
182 89 0 58
183 0 0 4
184 48 2 47
185 91 1 109
186 0 0 7
187 7 0 12
188 3 0 0
189 54 1 37
190 70 0 37
191 36 2 46
192 37 0 15
193 123 3 42
194 247 6 7
195 46 0 54
196 72 2 54
197 41 0 14
198 24 2 16
199 45 1 33
200 33 1 32
201 27 2 21
202 36 1 15
203 87 0 38
204 90 1 22
205 114 3 28
206 31 0 10
207 45 0 31
208 69 0 32
209 51 0 32
210 34 1 43
211 60 4 27
212 45 0 37
213 54 0 20
214 25 0 32
215 38 7 0
216 52 2 5
217 67 0 26
218 74 7 10
219 38 3 27
220 30 0 11
221 26 0 29
222 67 6 25
223 132 2 55
224 42 0 23
225 35 0 5
226 118 3 43
227 68 0 23
228 43 1 34
229 76 1 36
230 64 0 35
231 48 1 0
232 64 0 37
233 56 0 28
234 71 0 16
235 75 0 26
236 39 0 38
237 42 0 23
238 39 0 22
239 93 0 30
240 38 0 16
241 60 0 18
242 71 0 28
243 52 0 32
244 27 2 21
245 59 0 23
246 40 1 29
247 79 1 50
248 44 0 12
249 65 0 21
250 10 0 18
251 124 0 27
252 81 0 41
253 15 0 13
254 92 1 12
255 42 0 21
256 10 0 8
257 24 0 26
258 64 0 27
259 45 1 13
260 22 0 16
261 56 0 2
262 94 0 42
263 19 0 5
264 35 0 37
265 32 0 17
266 35 0 38
267 48 0 37
268 49 0 29
269 48 0 32
270 62 0 35
271 96 1 17
272 45 0 20
273 63 0 7
274 71 1 46
275 26 0 24
276 48 6 40
277 29 3 3
278 19 1 10
279 45 2 37
280 45 0 17
281 67 0 28
282 30 0 19
283 36 3 29
284 34 1 8
285 36 0 10
286 34 0 15
287 37 1 15
288 46 0 28
289 44 0 17
compendiums_reviewed feedback_messages_p1
1 30 115
2 28 109
3 38 146
4 30 116
5 22 68
6 26 101
7 25 96
8 18 67
9 11 44
10 26 100
11 25 93
12 38 140
13 44 166
14 30 99
15 40 139
16 34 130
17 47 181
18 30 116
19 31 116
20 23 88
21 36 139
22 36 135
23 30 108
24 25 89
25 39 156
26 34 129
27 31 118
28 31 118
29 33 125
30 25 95
31 33 126
32 35 135
33 42 154
34 43 165
35 30 113
36 33 127
37 13 52
38 32 121
39 36 136
40 0 0
41 28 108
42 14 46
43 17 54
44 32 124
45 30 115
46 35 128
47 20 80
48 28 97
49 28 104
50 39 59
51 34 125
52 26 82
53 39 149
54 39 149
55 33 122
56 28 118
57 4 12
58 39 144
59 18 67
60 14 52
61 29 108
62 44 166
63 21 80
64 16 60
65 28 107
66 35 127
67 28 107
68 38 146
69 23 84
70 36 141
71 32 123
72 29 111
73 25 98
74 27 105
75 36 135
76 28 107
77 23 85
78 40 155
79 23 88
80 40 155
81 28 104
82 34 132
83 33 127
84 28 108
85 34 129
86 30 116
87 33 122
88 22 85
89 38 147
90 26 99
91 35 87
92 8 28
93 24 90
94 29 109
95 20 78
96 29 111
97 45 158
98 37 141
99 33 122
100 33 124
101 25 93
102 32 124
103 29 112
104 28 108
105 28 99
106 31 117
107 52 199
108 21 78
109 24 91
110 41 158
111 33 126
112 32 122
113 19 71
114 20 75
115 31 115
116 31 119
117 32 124
118 18 72
119 23 91
120 17 45
121 20 78
122 12 39
123 17 68
124 30 119
125 31 117
126 10 39
127 13 50
128 22 88
129 42 155
130 1 0
131 9 36
132 32 123
133 11 32
134 25 99
135 36 136
136 31 117
137 0 0
138 24 88
139 13 39
140 8 25
141 13 52
142 19 75
143 18 71
144 33 124
145 40 151
146 22 71
147 38 145
148 24 87
149 8 27
150 35 131
151 43 162
152 43 165
153 14 54
154 41 159
155 38 147
156 45 170
157 31 119
158 13 49
159 28 104
160 31 120
161 40 150
162 30 112
163 16 59
164 37 136
165 30 107
166 35 130
167 32 115
168 27 107
169 20 75
170 18 71
171 31 120
172 31 116
173 21 79
174 39 150
175 41 156
176 13 51
177 32 118
178 18 71
179 39 144
180 14 47
181 7 28
182 17 68
183 0 0
184 30 110
185 37 147
186 0 0
187 5 15
188 1 4
189 16 64
190 32 111
191 24 85
192 17 68
193 11 40
194 24 80
195 22 88
196 12 48
197 19 76
198 13 51
199 17 67
200 15 59
201 16 61
202 24 76
203 15 60
204 17 68
205 18 71
206 20 76
207 16 62
208 16 61
209 18 67
210 22 88
211 8 30
212 17 64
213 18 68
214 16 64
215 23 91
216 22 88
217 13 52
218 13 49
219 16 62
220 16 61
221 20 76
222 22 88
223 17 66
224 18 71
225 17 68
226 12 48
227 7 25
228 17 68
229 14 41
230 23 90
231 17 66
232 14 54
233 15 59
234 17 60
235 21 77
236 18 68
237 18 72
238 17 67
239 17 64
240 16 63
241 15 59
242 21 84
243 16 64
244 14 56
245 15 54
246 17 67
247 15 58
248 15 59
249 10 40
250 6 22
251 22 83
252 21 81
253 1 2
254 18 72
255 17 61
256 4 15
257 10 32
258 16 62
259 16 58
260 9 36
261 16 59
262 17 68
263 7 21
264 15 55
265 14 54
266 14 55
267 18 72
268 12 41
269 16 61
270 21 67
271 19 76
272 16 64
273 1 3
274 16 63
275 10 40
276 19 69
277 12 48
278 2 8
279 14 52
280 17 66
281 19 76
282 14 43
283 11 39
284 4 14
285 16 61
286 20 71
287 12 44
288 15 60
289 16 64
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc
-5.848e+00 3.060e-04 5.143e-05
logins compendium_views_info compendium_views_pr
-1.998e-02 -6.526e-03 1.675e-02
shared_compendiums blogged_computations compendiums_reviewed
7.343e-01 1.127e-01 -8.811e-01
feedback_messages_p1
9.890e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-46.344 -5.375 1.658 8.609 24.106
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.848e+00 2.055e+00 -2.846 0.00475 **
pageviews 3.060e-04 4.764e-03 0.064 0.94883
time_in_rfc 5.143e-05 2.725e-05 1.887 0.06014 .
logins -1.998e-02 3.624e-02 -0.551 0.58193
compendium_views_info -6.526e-03 1.074e-02 -0.608 0.54388
compendium_views_pr 1.675e-02 1.921e-02 0.872 0.38415
shared_compendiums 7.343e-01 3.085e-01 2.380 0.01799 *
blogged_computations 1.127e-01 4.242e-02 2.656 0.00836 **
compendiums_reviewed -8.811e-01 4.040e-01 -2.181 0.03003 *
feedback_messages_p1 9.890e-01 1.090e-01 9.071 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.81 on 279 degrees of freedom
Multiple R-squared: 0.8954, Adjusted R-squared: 0.892
F-statistic: 265.3 on 9 and 279 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,] 0.9472778 1.054444e-01 5.272221e-02
[2,] 0.9252575 1.494851e-01 7.474254e-02
[3,] 0.8831699 2.336602e-01 1.168301e-01
[4,] 0.9672373 6.552533e-02 3.276266e-02
[5,] 0.9475101 1.049797e-01 5.248986e-02
[6,] 0.9817434 3.651325e-02 1.825662e-02
[7,] 0.9950038 9.992372e-03 4.996186e-03
[8,] 0.9927065 1.458692e-02 7.293458e-03
[9,] 0.9916353 1.672931e-02 8.364653e-03
[10,] 0.9869920 2.601608e-02 1.300804e-02
[11,] 0.9858652 2.826968e-02 1.413484e-02
[12,] 0.9830055 3.398890e-02 1.699445e-02
[13,] 0.9925397 1.492053e-02 7.460267e-03
[14,] 0.9894577 2.108454e-02 1.054227e-02
[15,] 0.9931950 1.361008e-02 6.805041e-03
[16,] 0.9896868 2.062647e-02 1.031323e-02
[17,] 0.9859942 2.801157e-02 1.400579e-02
[18,] 0.9803352 3.932968e-02 1.966484e-02
[19,] 0.9764017 4.719665e-02 2.359833e-02
[20,] 0.9760230 4.795399e-02 2.397699e-02
[21,] 0.9666071 6.678573e-02 3.339287e-02
[22,] 0.9587380 8.252404e-02 4.126202e-02
[23,] 0.9672415 6.551699e-02 3.275849e-02
[24,] 0.9596712 8.065758e-02 4.032879e-02
[25,] 0.9464828 1.070345e-01 5.351723e-02
[26,] 0.9470908 1.058184e-01 5.290922e-02
[27,] 0.9366310 1.267380e-01 6.336898e-02
[28,] 0.9276815 1.446370e-01 7.231851e-02
[29,] 0.9440974 1.118053e-01 5.590263e-02
[30,] 0.9282018 1.435963e-01 7.179815e-02
[31,] 0.9093455 1.813090e-01 9.065450e-02
[32,] 0.9015546 1.968907e-01 9.844536e-02
[33,] 0.8888814 2.222372e-01 1.111186e-01
[34,] 0.8711766 2.576468e-01 1.288234e-01
[35,] 0.8672919 2.654162e-01 1.327081e-01
[36,] 0.8659812 2.680376e-01 1.340188e-01
[37,] 0.8394180 3.211640e-01 1.605820e-01
[38,] 0.8165668 3.668664e-01 1.834332e-01
[39,] 0.8055550 3.888900e-01 1.944450e-01
[40,] 0.7856208 4.287583e-01 2.143792e-01
[41,] 0.7644195 4.711609e-01 2.355805e-01
[42,] 0.7273444 5.453112e-01 2.726556e-01
[43,] 0.7078181 5.843637e-01 2.921819e-01
[44,] 0.6752142 6.495717e-01 3.247858e-01
[45,] 0.6405704 7.188593e-01 3.594296e-01
[46,] 0.6496325 7.007349e-01 3.503675e-01
[47,] 0.6539648 6.920704e-01 3.460352e-01
[48,] 0.6125438 7.749123e-01 3.874562e-01
[49,] 0.5740151 8.519697e-01 4.259849e-01
[50,] 0.6062700 7.874600e-01 3.937300e-01
[51,] 0.5705529 8.588942e-01 4.294471e-01
[52,] 0.5302971 9.394057e-01 4.697029e-01
[53,] 0.4897271 9.794542e-01 5.102729e-01
[54,] 0.4673849 9.347698e-01 5.326151e-01
[55,] 0.4386274 8.772548e-01 5.613726e-01
[56,] 0.4079264 8.158529e-01 5.920736e-01
[57,] 0.4524602 9.049205e-01 5.475398e-01
[58,] 0.7776677 4.446645e-01 2.223323e-01
[59,] 0.7897376 4.205249e-01 2.102624e-01
[60,] 0.7714198 4.571603e-01 2.285802e-01
[61,] 0.8067260 3.865480e-01 1.932740e-01
[62,] 0.8108075 3.783851e-01 1.891925e-01
[63,] 0.7831671 4.336659e-01 2.168329e-01
[64,] 0.7681704 4.636591e-01 2.318296e-01
[65,] 0.7469200 5.061601e-01 2.530800e-01
[66,] 0.7321930 5.356141e-01 2.678070e-01
[67,] 0.7041354 5.917293e-01 2.958646e-01
[68,] 0.7507213 4.985573e-01 2.492787e-01
[69,] 0.7665066 4.669869e-01 2.334934e-01
[70,] 0.7434273 5.131454e-01 2.565727e-01
[71,] 0.7124179 5.751641e-01 2.875821e-01
[72,] 0.6960967 6.078065e-01 3.039033e-01
[73,] 0.6776468 6.447064e-01 3.223532e-01
[74,] 0.6523755 6.952490e-01 3.476245e-01
[75,] 0.6215575 7.568849e-01 3.784425e-01
[76,] 0.5852386 8.295229e-01 4.147614e-01
[77,] 0.6770558 6.458883e-01 3.229442e-01
[78,] 0.7375817 5.248366e-01 2.624183e-01
[79,] 0.7361128 5.277744e-01 2.638872e-01
[80,] 0.7111077 5.777845e-01 2.888923e-01
[81,] 0.6796662 6.406677e-01 3.203338e-01
[82,] 0.6852900 6.294200e-01 3.147100e-01
[83,] 0.6677450 6.645101e-01 3.322550e-01
[84,] 0.6539582 6.920836e-01 3.460418e-01
[85,] 0.8513928 2.972144e-01 1.486072e-01
[86,] 0.8479362 3.041276e-01 1.520638e-01
[87,] 0.9305043 1.389915e-01 6.949574e-02
[88,] 0.9242691 1.514618e-01 7.573091e-02
[89,] 0.9118945 1.762111e-01 8.810553e-02
[90,] 0.9118032 1.763936e-01 8.819681e-02
[91,] 0.9028550 1.942899e-01 9.714496e-02
[92,] 0.9094775 1.810450e-01 9.052251e-02
[93,] 0.9117891 1.764218e-01 8.821091e-02
[94,] 0.9234290 1.531420e-01 7.657100e-02
[95,] 0.9543703 9.125931e-02 4.562966e-02
[96,] 0.9717295 5.654107e-02 2.827053e-02
[97,] 0.9665697 6.686066e-02 3.343033e-02
[98,] 0.9605742 7.885154e-02 3.942577e-02
[99,] 0.9545256 9.094871e-02 4.547435e-02
[100,] 0.9961622 7.675656e-03 3.837828e-03
[101,] 0.9958731 8.253851e-03 4.126925e-03
[102,] 0.9992551 1.489855e-03 7.449274e-04
[103,] 0.9998132 3.735200e-04 1.867600e-04
[104,] 0.9997967 4.066003e-04 2.033002e-04
[105,] 0.9997413 5.174602e-04 2.587301e-04
[106,] 0.9996458 7.083905e-04 3.541953e-04
[107,] 0.9995157 9.686532e-04 4.843266e-04
[108,] 0.9994711 1.057881e-03 5.289406e-04
[109,] 0.9993542 1.291556e-03 6.457778e-04
[110,] 0.9991325 1.734916e-03 8.674580e-04
[111,] 0.9989096 2.180798e-03 1.090399e-03
[112,] 0.9985949 2.810219e-03 1.405109e-03
[113,] 0.9986727 2.654550e-03 1.327275e-03
[114,] 0.9983431 3.313771e-03 1.656885e-03
[115,] 0.9978586 4.282771e-03 2.141385e-03
[116,] 0.9981111 3.777869e-03 1.888934e-03
[117,] 0.9989238 2.152382e-03 1.076191e-03
[118,] 0.9986394 2.721104e-03 1.360552e-03
[119,] 0.9981992 3.601601e-03 1.800800e-03
[120,] 0.9987590 2.482091e-03 1.241046e-03
[121,] 0.9985033 2.993356e-03 1.496678e-03
[122,] 0.9980455 3.909089e-03 1.954545e-03
[123,] 0.9979690 4.061982e-03 2.030991e-03
[124,] 0.9990420 1.916079e-03 9.580397e-04
[125,] 0.9987829 2.434107e-03 1.217054e-03
[126,] 0.9990886 1.822882e-03 9.114409e-04
[127,] 0.9988789 2.242139e-03 1.121070e-03
[128,] 0.9985795 2.841028e-03 1.420514e-03
[129,] 0.9981566 3.686749e-03 1.843375e-03
[130,] 0.9976079 4.784249e-03 2.392125e-03
[131,] 0.9970442 5.911544e-03 2.955772e-03
[132,] 0.9964274 7.145200e-03 3.572600e-03
[133,] 0.9965903 6.819352e-03 3.409676e-03
[134,] 0.9956716 8.656718e-03 4.328359e-03
[135,] 0.9944771 1.104582e-02 5.522912e-03
[136,] 0.9929629 1.407418e-02 7.037091e-03
[137,] 0.9912761 1.744780e-02 8.723898e-03
[138,] 0.9934776 1.304486e-02 6.522428e-03
[139,] 0.9939730 1.205400e-02 6.026999e-03
[140,] 0.9932550 1.348995e-02 6.744976e-03
[141,] 0.9917122 1.657558e-02 8.287791e-03
[142,] 0.9896835 2.063292e-02 1.031646e-02
[143,] 0.9891533 2.169336e-02 1.084668e-02
[144,] 0.9879866 2.402671e-02 1.201336e-02
[145,] 0.9902704 1.945929e-02 9.729645e-03
[146,] 0.9878907 2.421870e-02 1.210935e-02
[147,] 0.9850200 2.996006e-02 1.498003e-02
[148,] 0.9879557 2.408863e-02 1.204432e-02
[149,] 0.9881907 2.361861e-02 1.180931e-02
[150,] 0.9897216 2.055681e-02 1.027840e-02
[151,] 0.9899564 2.008717e-02 1.004359e-02
[152,] 0.9917983 1.640337e-02 8.201685e-03
[153,] 0.9903467 1.930652e-02 9.653262e-03
[154,] 0.9890103 2.197939e-02 1.098970e-02
[155,] 0.9864311 2.713773e-02 1.356886e-02
[156,] 0.9994781 1.043837e-03 5.219186e-04
[157,] 0.9994040 1.192014e-03 5.960070e-04
[158,] 0.9991863 1.627303e-03 8.136515e-04
[159,] 0.9990811 1.837896e-03 9.189479e-04
[160,] 0.9987679 2.464202e-03 1.232101e-03
[161,] 0.9990873 1.825354e-03 9.126768e-04
[162,] 0.9992111 1.577836e-03 7.889182e-04
[163,] 0.9995081 9.837308e-04 4.918654e-04
[164,] 0.9993311 1.337716e-03 6.688582e-04
[165,] 0.9994329 1.134171e-03 5.670856e-04
[166,] 0.9992201 1.559884e-03 7.799422e-04
[167,] 0.9997452 5.096017e-04 2.548008e-04
[168,] 0.9996836 6.328742e-04 3.164371e-04
[169,] 0.9995637 8.726176e-04 4.363088e-04
[170,] 0.9994012 1.197545e-03 5.987723e-04
[171,] 0.9991963 1.607494e-03 8.037469e-04
[172,] 0.9990899 1.820192e-03 9.100960e-04
[173,] 0.9999691 6.175842e-05 3.087921e-05
[174,] 0.9999563 8.739099e-05 4.369549e-05
[175,] 0.9999438 1.123645e-04 5.618223e-05
[176,] 0.9999223 1.554923e-04 7.774616e-05
[177,] 0.9998892 2.215787e-04 1.107894e-04
[178,] 0.9999903 1.939669e-05 9.698344e-06
[179,] 0.9999980 4.032838e-06 2.016419e-06
[180,] 0.9999972 5.691445e-06 2.845723e-06
[181,] 0.9999955 8.924668e-06 4.462334e-06
[182,] 0.9999976 4.723839e-06 2.361919e-06
[183,] 0.9999971 5.755894e-06 2.877947e-06
[184,] 0.9999954 9.167793e-06 4.583896e-06
[185,] 0.9999946 1.070618e-05 5.353090e-06
[186,] 0.9999927 1.456768e-05 7.283842e-06
[187,] 0.9999908 1.833750e-05 9.168751e-06
[188,] 0.9999891 2.186923e-05 1.093461e-05
[189,] 0.9999829 3.415076e-05 1.707538e-05
[190,] 0.9999747 5.058955e-05 2.529478e-05
[191,] 0.9999661 6.781914e-05 3.390957e-05
[192,] 0.9999620 7.604211e-05 3.802105e-05
[193,] 0.9999443 1.114786e-04 5.573932e-05
[194,] 0.9999635 7.294105e-05 3.647053e-05
[195,] 0.9999467 1.065112e-04 5.325558e-05
[196,] 0.9999230 1.540025e-04 7.700123e-05
[197,] 0.9998890 2.220550e-04 1.110275e-04
[198,] 0.9998792 2.416468e-04 1.208234e-04
[199,] 0.9998190 3.620060e-04 1.810030e-04
[200,] 0.9997331 5.338392e-04 2.669196e-04
[201,] 0.9996053 7.893293e-04 3.946647e-04
[202,] 0.9995251 9.497952e-04 4.748976e-04
[203,] 0.9993901 1.219813e-03 6.099067e-04
[204,] 0.9995751 8.497256e-04 4.248628e-04
[205,] 0.9994621 1.075844e-03 5.379219e-04
[206,] 0.9993608 1.278376e-03 6.391879e-04
[207,] 0.9991716 1.656754e-03 8.283768e-04
[208,] 0.9990273 1.945345e-03 9.726724e-04
[209,] 0.9989687 2.062547e-03 1.031273e-03
[210,] 0.9990790 1.841935e-03 9.209676e-04
[211,] 0.9987048 2.590318e-03 1.295159e-03
[212,] 0.9981890 3.622056e-03 1.811028e-03
[213,] 0.9976202 4.759507e-03 2.379754e-03
[214,] 0.9977619 4.476246e-03 2.238123e-03
[215,] 0.9986721 2.655840e-03 1.327920e-03
[216,] 0.9985325 2.934962e-03 1.467481e-03
[217,] 0.9981061 3.787756e-03 1.893878e-03
[218,] 0.9991941 1.611824e-03 8.059122e-04
[219,] 0.9990888 1.822485e-03 9.112425e-04
[220,] 0.9986270 2.746074e-03 1.373037e-03
[221,] 0.9984273 3.145345e-03 1.572672e-03
[222,] 0.9976895 4.620916e-03 2.310458e-03
[223,] 0.9975919 4.816159e-03 2.408079e-03
[224,] 0.9964604 7.079225e-03 3.539612e-03
[225,] 0.9963646 7.270876e-03 3.635438e-03
[226,] 0.9999233 1.533715e-04 7.668573e-05
[227,] 0.9998876 2.247110e-04 1.123555e-04
[228,] 0.9998085 3.829129e-04 1.914564e-04
[229,] 0.9998655 2.689059e-04 1.344529e-04
[230,] 0.9998441 3.118646e-04 1.559323e-04
[231,] 0.9997443 5.114136e-04 2.557068e-04
[232,] 0.9996205 7.590101e-04 3.795051e-04
[233,] 0.9994747 1.050685e-03 5.253425e-04
[234,] 0.9993932 1.213605e-03 6.068023e-04
[235,] 0.9990468 1.906332e-03 9.531660e-04
[236,] 0.9984402 3.119696e-03 1.559848e-03
[237,] 0.9974759 5.048287e-03 2.524143e-03
[238,] 0.9965601 6.879775e-03 3.439888e-03
[239,] 0.9945790 1.084194e-02 5.420970e-03
[240,] 0.9915647 1.687053e-02 8.435263e-03
[241,] 0.9870884 2.582316e-02 1.291158e-02
[242,] 0.9841147 3.177064e-02 1.588532e-02
[243,] 0.9858252 2.834965e-02 1.417482e-02
[244,] 0.9797748 4.045047e-02 2.022524e-02
[245,] 0.9735875 5.282501e-02 2.641251e-02
[246,] 0.9751927 4.961456e-02 2.480728e-02
[247,] 0.9641293 7.174150e-02 3.587075e-02
[248,] 0.9579110 8.417790e-02 4.208895e-02
[249,] 0.9906817 1.863664e-02 9.318321e-03
[250,] 0.9891610 2.167804e-02 1.083902e-02
[251,] 0.9825888 3.482243e-02 1.741122e-02
[252,] 0.9809077 3.818468e-02 1.909234e-02
[253,] 0.9875482 2.490356e-02 1.245178e-02
[254,] 0.9912280 1.754398e-02 8.771992e-03
[255,] 0.9841946 3.161074e-02 1.580537e-02
[256,] 0.9769456 4.610870e-02 2.305435e-02
[257,] 0.9764929 4.701421e-02 2.350710e-02
[258,] 0.9630377 7.392468e-02 3.696234e-02
[259,] 0.9407330 1.185341e-01 5.926704e-02
[260,] 0.9090548 1.818904e-01 9.094518e-02
[261,] 0.8424740 3.150519e-01 1.575260e-01
[262,] 0.8395947 3.208107e-01 1.604053e-01
[263,] 0.8025509 3.948982e-01 1.974491e-01
[264,] 0.7181363 5.637275e-01 2.818637e-01
> postscript(file="/var/www/rcomp/tmp/1jt3m1323872053.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/www/rcomp/tmp/2jbg91323872053.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/388961323872053.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/45hos1323872053.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5k3101323872053.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 289
Frequency = 1
1 2 3 4 5 6
-7.49071482 11.89489619 -33.29054088 3.02918562 -1.99392547 -5.37516443
7 8 9 10 11 12
0.46857182 -24.15218621 4.38532046 10.24590874 -17.78443576 7.05680250
13 14 15 16 17 18
8.21475781 10.66079457 5.21920587 -42.22866042 11.14843717 10.06079925
19 20 21 22 23 24
-27.50903056 -8.64557125 9.17045069 1.92422044 11.67210969 8.57939765
25 26 27 28 29 30
22.84248799 10.49242279 18.77924878 -7.95368977 11.40282767 5.95579406
31 32 33 34 35 36
-0.01139572 16.76780832 -1.07704833 15.81600861 19.48528600 10.92985834
37 38 39 40 41 42
-2.40775618 -8.20634175 9.08704819 6.20214545 -19.84424108 1.26075028
43 44 45 46 47 48
-2.18486593 13.02853571 -13.34650928 -10.15857836 11.34380614 9.37393553
49 50 51 52 53 54
-3.74448118 7.60289447 -6.72662508 7.64220817 14.46591130 -0.61731114
55 56 57 58 59 60
12.02045687 -0.13441089 3.77495785 14.36901148 8.48830259 -0.31221984
61 62 63 64 65 66
3.90684537 20.18441954 1.40629625 2.38907149 5.09338270 13.80008999
67 68 69 70 71 72
0.01496961 -5.01494001 -19.78907468 -44.55752342 8.36308403 7.63452061
73 74 75 76 77 78
-13.86003812 15.13099754 1.21275209 -4.21015027 -4.21025233 2.78584513
79 80 81 82 83 84
-2.35999956 19.82238775 -14.97298344 8.68893245 -1.05578029 9.35593009
85 86 87 88 89 90
-6.36496400 8.60944519 0.52299712 0.38614566 -20.52637564 19.59006076
91 92 93 94 95 96
2.26777547 5.08689227 0.28730182 14.25692922 9.90250423 11.86955336
97 98 99 100 101 102
-34.23408018 10.31063021 -31.48390987 8.53247167 -4.04142031 12.36478067
103 104 105 106 107 108
6.60586801 15.67696644 13.43629125 -15.21131398 24.10563540 -23.97366968
109 110 111 112 113 114
-7.85090236 2.19907389 -5.06098341 -44.02786242 10.51480858 -35.53370166
115 116 117 118 119 120
-30.62332359 -10.65501449 -6.33763276 -0.55321453 -1.17765550 9.13572912
121 122 123 124 125 126
-8.24622419 -0.08992391 6.95279549 -4.36181045 13.69852430 5.28964077
127 128 129 130 131 132
3.27928088 14.57158039 -24.27834675 5.13212576 0.62312120 -20.56839502
133 134 135 136 137 138
6.35450913 2.67999547 -12.29576078 -24.21677779 5.43546700 -17.43913741
139 140 141 142 143 144
4.86891143 -5.29241453 -3.24772976 -3.29860299 -3.87060593 7.14345403
145 146 147 148 149 150
12.90162257 -3.20097895 1.66991917 0.06430801 4.86210551 18.38721779
151 152 153 154 155 156
14.58406070 7.86462178 -5.22368876 1.64027267 -9.77258237 7.88321310
157 158 159 160 161 162
16.91144458 0.22238241 -0.40485570 18.47254654 9.64905242 9.89276366
163 164 165 166 167 168
11.04527105 15.72584918 -1.76733000 5.55973680 -4.64310962 -46.34399731
169 170 171 172 173 174
-10.56946187 -0.68324310 -12.62652442 -3.19391573 13.17709047 -20.10097448
175 176 177 178 179 180
13.20865332 1.16360433 6.38587366 0.38834914 6.96438803 3.59949293
181 182 183 184 185 186
-1.96627403 -4.51567650 5.33347924 -15.84114180 -42.76181777 5.22603998
187 188 189 190 191 192
6.49477776 6.32796019 4.69319130 -36.00102086 -22.75799689 -2.29847774
193 194 195 196 197 198
-1.89292042 5.59737558 14.83001354 0.54366395 11.24839025 7.46308561
199 200 201 202 203 204
-5.47583125 10.22865611 1.65836670 -8.64753279 11.07376566 11.63853863
205 206 207 208 209 210
-1.45150627 -16.16351306 4.81779000 10.10149614 1.53015429 15.00636861
211 212 213 214 215 216
-1.56447369 0.95005244 -1.14905430 11.69293949 -8.80818829 15.26293801
217 218 219 220 221 222
4.83442107 0.30805829 -8.93043665 11.19593815 11.16791020 6.61895945
223 224 225 226 227 228
-7.76530738 3.10948180 -7.37300318 -13.18217805 -15.14595921 7.62345183
229 230 231 232 233 234
1.22253506 10.04770547 0.75826365 -2.21069997 4.07763538 -6.44591479
235 236 237 238 239 240
-14.38951299 -2.60284974 -0.64973794 -37.43242755 -8.63888988 -1.97420163
241 242 243 244 245 246
-11.49004588 11.63232710 -5.53339386 2.61626479 7.64304831 -8.68775226
247 248 249 250 251 252
-2.43130571 6.30438989 5.06548327 -5.75714055 5.79191393 2.44198985
253 254 255 256 257 258
2.62950929 7.24182463 10.57129418 -2.70873579 4.28848910 -7.58358794
259 260 261 262 263 264
-3.98309723 -5.93086484 -22.53269936 11.86235819 3.56403814 -11.39813901
265 266 267 268 269 270
15.65769082 -8.33141161 3.50935584 -5.00839616 9.01501966 -5.97748132
271 272 273 274 275 276
-12.46578460 12.88052076 4.73343212 0.05539106 -4.69433714 -16.53858390
277 278 279 280 281 282
-3.54556608 1.14502024 7.53210050 13.43780422 8.21955653 2.90812740
283 284 285 286 287 288
-7.62932629 -0.39564524 10.48303066 -13.41647873 3.60650520 5.18701708
289
-8.57892159
> postscript(file="/var/www/rcomp/tmp/6l2nk1323872053.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 -7.49071482 NA
1 11.89489619 -7.49071482
2 -33.29054088 11.89489619
3 3.02918562 -33.29054088
4 -1.99392547 3.02918562
5 -5.37516443 -1.99392547
6 0.46857182 -5.37516443
7 -24.15218621 0.46857182
8 4.38532046 -24.15218621
9 10.24590874 4.38532046
10 -17.78443576 10.24590874
11 7.05680250 -17.78443576
12 8.21475781 7.05680250
13 10.66079457 8.21475781
14 5.21920587 10.66079457
15 -42.22866042 5.21920587
16 11.14843717 -42.22866042
17 10.06079925 11.14843717
18 -27.50903056 10.06079925
19 -8.64557125 -27.50903056
20 9.17045069 -8.64557125
21 1.92422044 9.17045069
22 11.67210969 1.92422044
23 8.57939765 11.67210969
24 22.84248799 8.57939765
25 10.49242279 22.84248799
26 18.77924878 10.49242279
27 -7.95368977 18.77924878
28 11.40282767 -7.95368977
29 5.95579406 11.40282767
30 -0.01139572 5.95579406
31 16.76780832 -0.01139572
32 -1.07704833 16.76780832
33 15.81600861 -1.07704833
34 19.48528600 15.81600861
35 10.92985834 19.48528600
36 -2.40775618 10.92985834
37 -8.20634175 -2.40775618
38 9.08704819 -8.20634175
39 6.20214545 9.08704819
40 -19.84424108 6.20214545
41 1.26075028 -19.84424108
42 -2.18486593 1.26075028
43 13.02853571 -2.18486593
44 -13.34650928 13.02853571
45 -10.15857836 -13.34650928
46 11.34380614 -10.15857836
47 9.37393553 11.34380614
48 -3.74448118 9.37393553
49 7.60289447 -3.74448118
50 -6.72662508 7.60289447
51 7.64220817 -6.72662508
52 14.46591130 7.64220817
53 -0.61731114 14.46591130
54 12.02045687 -0.61731114
55 -0.13441089 12.02045687
56 3.77495785 -0.13441089
57 14.36901148 3.77495785
58 8.48830259 14.36901148
59 -0.31221984 8.48830259
60 3.90684537 -0.31221984
61 20.18441954 3.90684537
62 1.40629625 20.18441954
63 2.38907149 1.40629625
64 5.09338270 2.38907149
65 13.80008999 5.09338270
66 0.01496961 13.80008999
67 -5.01494001 0.01496961
68 -19.78907468 -5.01494001
69 -44.55752342 -19.78907468
70 8.36308403 -44.55752342
71 7.63452061 8.36308403
72 -13.86003812 7.63452061
73 15.13099754 -13.86003812
74 1.21275209 15.13099754
75 -4.21015027 1.21275209
76 -4.21025233 -4.21015027
77 2.78584513 -4.21025233
78 -2.35999956 2.78584513
79 19.82238775 -2.35999956
80 -14.97298344 19.82238775
81 8.68893245 -14.97298344
82 -1.05578029 8.68893245
83 9.35593009 -1.05578029
84 -6.36496400 9.35593009
85 8.60944519 -6.36496400
86 0.52299712 8.60944519
87 0.38614566 0.52299712
88 -20.52637564 0.38614566
89 19.59006076 -20.52637564
90 2.26777547 19.59006076
91 5.08689227 2.26777547
92 0.28730182 5.08689227
93 14.25692922 0.28730182
94 9.90250423 14.25692922
95 11.86955336 9.90250423
96 -34.23408018 11.86955336
97 10.31063021 -34.23408018
98 -31.48390987 10.31063021
99 8.53247167 -31.48390987
100 -4.04142031 8.53247167
101 12.36478067 -4.04142031
102 6.60586801 12.36478067
103 15.67696644 6.60586801
104 13.43629125 15.67696644
105 -15.21131398 13.43629125
106 24.10563540 -15.21131398
107 -23.97366968 24.10563540
108 -7.85090236 -23.97366968
109 2.19907389 -7.85090236
110 -5.06098341 2.19907389
111 -44.02786242 -5.06098341
112 10.51480858 -44.02786242
113 -35.53370166 10.51480858
114 -30.62332359 -35.53370166
115 -10.65501449 -30.62332359
116 -6.33763276 -10.65501449
117 -0.55321453 -6.33763276
118 -1.17765550 -0.55321453
119 9.13572912 -1.17765550
120 -8.24622419 9.13572912
121 -0.08992391 -8.24622419
122 6.95279549 -0.08992391
123 -4.36181045 6.95279549
124 13.69852430 -4.36181045
125 5.28964077 13.69852430
126 3.27928088 5.28964077
127 14.57158039 3.27928088
128 -24.27834675 14.57158039
129 5.13212576 -24.27834675
130 0.62312120 5.13212576
131 -20.56839502 0.62312120
132 6.35450913 -20.56839502
133 2.67999547 6.35450913
134 -12.29576078 2.67999547
135 -24.21677779 -12.29576078
136 5.43546700 -24.21677779
137 -17.43913741 5.43546700
138 4.86891143 -17.43913741
139 -5.29241453 4.86891143
140 -3.24772976 -5.29241453
141 -3.29860299 -3.24772976
142 -3.87060593 -3.29860299
143 7.14345403 -3.87060593
144 12.90162257 7.14345403
145 -3.20097895 12.90162257
146 1.66991917 -3.20097895
147 0.06430801 1.66991917
148 4.86210551 0.06430801
149 18.38721779 4.86210551
150 14.58406070 18.38721779
151 7.86462178 14.58406070
152 -5.22368876 7.86462178
153 1.64027267 -5.22368876
154 -9.77258237 1.64027267
155 7.88321310 -9.77258237
156 16.91144458 7.88321310
157 0.22238241 16.91144458
158 -0.40485570 0.22238241
159 18.47254654 -0.40485570
160 9.64905242 18.47254654
161 9.89276366 9.64905242
162 11.04527105 9.89276366
163 15.72584918 11.04527105
164 -1.76733000 15.72584918
165 5.55973680 -1.76733000
166 -4.64310962 5.55973680
167 -46.34399731 -4.64310962
168 -10.56946187 -46.34399731
169 -0.68324310 -10.56946187
170 -12.62652442 -0.68324310
171 -3.19391573 -12.62652442
172 13.17709047 -3.19391573
173 -20.10097448 13.17709047
174 13.20865332 -20.10097448
175 1.16360433 13.20865332
176 6.38587366 1.16360433
177 0.38834914 6.38587366
178 6.96438803 0.38834914
179 3.59949293 6.96438803
180 -1.96627403 3.59949293
181 -4.51567650 -1.96627403
182 5.33347924 -4.51567650
183 -15.84114180 5.33347924
184 -42.76181777 -15.84114180
185 5.22603998 -42.76181777
186 6.49477776 5.22603998
187 6.32796019 6.49477776
188 4.69319130 6.32796019
189 -36.00102086 4.69319130
190 -22.75799689 -36.00102086
191 -2.29847774 -22.75799689
192 -1.89292042 -2.29847774
193 5.59737558 -1.89292042
194 14.83001354 5.59737558
195 0.54366395 14.83001354
196 11.24839025 0.54366395
197 7.46308561 11.24839025
198 -5.47583125 7.46308561
199 10.22865611 -5.47583125
200 1.65836670 10.22865611
201 -8.64753279 1.65836670
202 11.07376566 -8.64753279
203 11.63853863 11.07376566
204 -1.45150627 11.63853863
205 -16.16351306 -1.45150627
206 4.81779000 -16.16351306
207 10.10149614 4.81779000
208 1.53015429 10.10149614
209 15.00636861 1.53015429
210 -1.56447369 15.00636861
211 0.95005244 -1.56447369
212 -1.14905430 0.95005244
213 11.69293949 -1.14905430
214 -8.80818829 11.69293949
215 15.26293801 -8.80818829
216 4.83442107 15.26293801
217 0.30805829 4.83442107
218 -8.93043665 0.30805829
219 11.19593815 -8.93043665
220 11.16791020 11.19593815
221 6.61895945 11.16791020
222 -7.76530738 6.61895945
223 3.10948180 -7.76530738
224 -7.37300318 3.10948180
225 -13.18217805 -7.37300318
226 -15.14595921 -13.18217805
227 7.62345183 -15.14595921
228 1.22253506 7.62345183
229 10.04770547 1.22253506
230 0.75826365 10.04770547
231 -2.21069997 0.75826365
232 4.07763538 -2.21069997
233 -6.44591479 4.07763538
234 -14.38951299 -6.44591479
235 -2.60284974 -14.38951299
236 -0.64973794 -2.60284974
237 -37.43242755 -0.64973794
238 -8.63888988 -37.43242755
239 -1.97420163 -8.63888988
240 -11.49004588 -1.97420163
241 11.63232710 -11.49004588
242 -5.53339386 11.63232710
243 2.61626479 -5.53339386
244 7.64304831 2.61626479
245 -8.68775226 7.64304831
246 -2.43130571 -8.68775226
247 6.30438989 -2.43130571
248 5.06548327 6.30438989
249 -5.75714055 5.06548327
250 5.79191393 -5.75714055
251 2.44198985 5.79191393
252 2.62950929 2.44198985
253 7.24182463 2.62950929
254 10.57129418 7.24182463
255 -2.70873579 10.57129418
256 4.28848910 -2.70873579
257 -7.58358794 4.28848910
258 -3.98309723 -7.58358794
259 -5.93086484 -3.98309723
260 -22.53269936 -5.93086484
261 11.86235819 -22.53269936
262 3.56403814 11.86235819
263 -11.39813901 3.56403814
264 15.65769082 -11.39813901
265 -8.33141161 15.65769082
266 3.50935584 -8.33141161
267 -5.00839616 3.50935584
268 9.01501966 -5.00839616
269 -5.97748132 9.01501966
270 -12.46578460 -5.97748132
271 12.88052076 -12.46578460
272 4.73343212 12.88052076
273 0.05539106 4.73343212
274 -4.69433714 0.05539106
275 -16.53858390 -4.69433714
276 -3.54556608 -16.53858390
277 1.14502024 -3.54556608
278 7.53210050 1.14502024
279 13.43780422 7.53210050
280 8.21955653 13.43780422
281 2.90812740 8.21955653
282 -7.62932629 2.90812740
283 -0.39564524 -7.62932629
284 10.48303066 -0.39564524
285 -13.41647873 10.48303066
286 3.60650520 -13.41647873
287 5.18701708 3.60650520
288 -8.57892159 5.18701708
289 NA -8.57892159
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 11.89489619 -7.49071482
[2,] -33.29054088 11.89489619
[3,] 3.02918562 -33.29054088
[4,] -1.99392547 3.02918562
[5,] -5.37516443 -1.99392547
[6,] 0.46857182 -5.37516443
[7,] -24.15218621 0.46857182
[8,] 4.38532046 -24.15218621
[9,] 10.24590874 4.38532046
[10,] -17.78443576 10.24590874
[11,] 7.05680250 -17.78443576
[12,] 8.21475781 7.05680250
[13,] 10.66079457 8.21475781
[14,] 5.21920587 10.66079457
[15,] -42.22866042 5.21920587
[16,] 11.14843717 -42.22866042
[17,] 10.06079925 11.14843717
[18,] -27.50903056 10.06079925
[19,] -8.64557125 -27.50903056
[20,] 9.17045069 -8.64557125
[21,] 1.92422044 9.17045069
[22,] 11.67210969 1.92422044
[23,] 8.57939765 11.67210969
[24,] 22.84248799 8.57939765
[25,] 10.49242279 22.84248799
[26,] 18.77924878 10.49242279
[27,] -7.95368977 18.77924878
[28,] 11.40282767 -7.95368977
[29,] 5.95579406 11.40282767
[30,] -0.01139572 5.95579406
[31,] 16.76780832 -0.01139572
[32,] -1.07704833 16.76780832
[33,] 15.81600861 -1.07704833
[34,] 19.48528600 15.81600861
[35,] 10.92985834 19.48528600
[36,] -2.40775618 10.92985834
[37,] -8.20634175 -2.40775618
[38,] 9.08704819 -8.20634175
[39,] 6.20214545 9.08704819
[40,] -19.84424108 6.20214545
[41,] 1.26075028 -19.84424108
[42,] -2.18486593 1.26075028
[43,] 13.02853571 -2.18486593
[44,] -13.34650928 13.02853571
[45,] -10.15857836 -13.34650928
[46,] 11.34380614 -10.15857836
[47,] 9.37393553 11.34380614
[48,] -3.74448118 9.37393553
[49,] 7.60289447 -3.74448118
[50,] -6.72662508 7.60289447
[51,] 7.64220817 -6.72662508
[52,] 14.46591130 7.64220817
[53,] -0.61731114 14.46591130
[54,] 12.02045687 -0.61731114
[55,] -0.13441089 12.02045687
[56,] 3.77495785 -0.13441089
[57,] 14.36901148 3.77495785
[58,] 8.48830259 14.36901148
[59,] -0.31221984 8.48830259
[60,] 3.90684537 -0.31221984
[61,] 20.18441954 3.90684537
[62,] 1.40629625 20.18441954
[63,] 2.38907149 1.40629625
[64,] 5.09338270 2.38907149
[65,] 13.80008999 5.09338270
[66,] 0.01496961 13.80008999
[67,] -5.01494001 0.01496961
[68,] -19.78907468 -5.01494001
[69,] -44.55752342 -19.78907468
[70,] 8.36308403 -44.55752342
[71,] 7.63452061 8.36308403
[72,] -13.86003812 7.63452061
[73,] 15.13099754 -13.86003812
[74,] 1.21275209 15.13099754
[75,] -4.21015027 1.21275209
[76,] -4.21025233 -4.21015027
[77,] 2.78584513 -4.21025233
[78,] -2.35999956 2.78584513
[79,] 19.82238775 -2.35999956
[80,] -14.97298344 19.82238775
[81,] 8.68893245 -14.97298344
[82,] -1.05578029 8.68893245
[83,] 9.35593009 -1.05578029
[84,] -6.36496400 9.35593009
[85,] 8.60944519 -6.36496400
[86,] 0.52299712 8.60944519
[87,] 0.38614566 0.52299712
[88,] -20.52637564 0.38614566
[89,] 19.59006076 -20.52637564
[90,] 2.26777547 19.59006076
[91,] 5.08689227 2.26777547
[92,] 0.28730182 5.08689227
[93,] 14.25692922 0.28730182
[94,] 9.90250423 14.25692922
[95,] 11.86955336 9.90250423
[96,] -34.23408018 11.86955336
[97,] 10.31063021 -34.23408018
[98,] -31.48390987 10.31063021
[99,] 8.53247167 -31.48390987
[100,] -4.04142031 8.53247167
[101,] 12.36478067 -4.04142031
[102,] 6.60586801 12.36478067
[103,] 15.67696644 6.60586801
[104,] 13.43629125 15.67696644
[105,] -15.21131398 13.43629125
[106,] 24.10563540 -15.21131398
[107,] -23.97366968 24.10563540
[108,] -7.85090236 -23.97366968
[109,] 2.19907389 -7.85090236
[110,] -5.06098341 2.19907389
[111,] -44.02786242 -5.06098341
[112,] 10.51480858 -44.02786242
[113,] -35.53370166 10.51480858
[114,] -30.62332359 -35.53370166
[115,] -10.65501449 -30.62332359
[116,] -6.33763276 -10.65501449
[117,] -0.55321453 -6.33763276
[118,] -1.17765550 -0.55321453
[119,] 9.13572912 -1.17765550
[120,] -8.24622419 9.13572912
[121,] -0.08992391 -8.24622419
[122,] 6.95279549 -0.08992391
[123,] -4.36181045 6.95279549
[124,] 13.69852430 -4.36181045
[125,] 5.28964077 13.69852430
[126,] 3.27928088 5.28964077
[127,] 14.57158039 3.27928088
[128,] -24.27834675 14.57158039
[129,] 5.13212576 -24.27834675
[130,] 0.62312120 5.13212576
[131,] -20.56839502 0.62312120
[132,] 6.35450913 -20.56839502
[133,] 2.67999547 6.35450913
[134,] -12.29576078 2.67999547
[135,] -24.21677779 -12.29576078
[136,] 5.43546700 -24.21677779
[137,] -17.43913741 5.43546700
[138,] 4.86891143 -17.43913741
[139,] -5.29241453 4.86891143
[140,] -3.24772976 -5.29241453
[141,] -3.29860299 -3.24772976
[142,] -3.87060593 -3.29860299
[143,] 7.14345403 -3.87060593
[144,] 12.90162257 7.14345403
[145,] -3.20097895 12.90162257
[146,] 1.66991917 -3.20097895
[147,] 0.06430801 1.66991917
[148,] 4.86210551 0.06430801
[149,] 18.38721779 4.86210551
[150,] 14.58406070 18.38721779
[151,] 7.86462178 14.58406070
[152,] -5.22368876 7.86462178
[153,] 1.64027267 -5.22368876
[154,] -9.77258237 1.64027267
[155,] 7.88321310 -9.77258237
[156,] 16.91144458 7.88321310
[157,] 0.22238241 16.91144458
[158,] -0.40485570 0.22238241
[159,] 18.47254654 -0.40485570
[160,] 9.64905242 18.47254654
[161,] 9.89276366 9.64905242
[162,] 11.04527105 9.89276366
[163,] 15.72584918 11.04527105
[164,] -1.76733000 15.72584918
[165,] 5.55973680 -1.76733000
[166,] -4.64310962 5.55973680
[167,] -46.34399731 -4.64310962
[168,] -10.56946187 -46.34399731
[169,] -0.68324310 -10.56946187
[170,] -12.62652442 -0.68324310
[171,] -3.19391573 -12.62652442
[172,] 13.17709047 -3.19391573
[173,] -20.10097448 13.17709047
[174,] 13.20865332 -20.10097448
[175,] 1.16360433 13.20865332
[176,] 6.38587366 1.16360433
[177,] 0.38834914 6.38587366
[178,] 6.96438803 0.38834914
[179,] 3.59949293 6.96438803
[180,] -1.96627403 3.59949293
[181,] -4.51567650 -1.96627403
[182,] 5.33347924 -4.51567650
[183,] -15.84114180 5.33347924
[184,] -42.76181777 -15.84114180
[185,] 5.22603998 -42.76181777
[186,] 6.49477776 5.22603998
[187,] 6.32796019 6.49477776
[188,] 4.69319130 6.32796019
[189,] -36.00102086 4.69319130
[190,] -22.75799689 -36.00102086
[191,] -2.29847774 -22.75799689
[192,] -1.89292042 -2.29847774
[193,] 5.59737558 -1.89292042
[194,] 14.83001354 5.59737558
[195,] 0.54366395 14.83001354
[196,] 11.24839025 0.54366395
[197,] 7.46308561 11.24839025
[198,] -5.47583125 7.46308561
[199,] 10.22865611 -5.47583125
[200,] 1.65836670 10.22865611
[201,] -8.64753279 1.65836670
[202,] 11.07376566 -8.64753279
[203,] 11.63853863 11.07376566
[204,] -1.45150627 11.63853863
[205,] -16.16351306 -1.45150627
[206,] 4.81779000 -16.16351306
[207,] 10.10149614 4.81779000
[208,] 1.53015429 10.10149614
[209,] 15.00636861 1.53015429
[210,] -1.56447369 15.00636861
[211,] 0.95005244 -1.56447369
[212,] -1.14905430 0.95005244
[213,] 11.69293949 -1.14905430
[214,] -8.80818829 11.69293949
[215,] 15.26293801 -8.80818829
[216,] 4.83442107 15.26293801
[217,] 0.30805829 4.83442107
[218,] -8.93043665 0.30805829
[219,] 11.19593815 -8.93043665
[220,] 11.16791020 11.19593815
[221,] 6.61895945 11.16791020
[222,] -7.76530738 6.61895945
[223,] 3.10948180 -7.76530738
[224,] -7.37300318 3.10948180
[225,] -13.18217805 -7.37300318
[226,] -15.14595921 -13.18217805
[227,] 7.62345183 -15.14595921
[228,] 1.22253506 7.62345183
[229,] 10.04770547 1.22253506
[230,] 0.75826365 10.04770547
[231,] -2.21069997 0.75826365
[232,] 4.07763538 -2.21069997
[233,] -6.44591479 4.07763538
[234,] -14.38951299 -6.44591479
[235,] -2.60284974 -14.38951299
[236,] -0.64973794 -2.60284974
[237,] -37.43242755 -0.64973794
[238,] -8.63888988 -37.43242755
[239,] -1.97420163 -8.63888988
[240,] -11.49004588 -1.97420163
[241,] 11.63232710 -11.49004588
[242,] -5.53339386 11.63232710
[243,] 2.61626479 -5.53339386
[244,] 7.64304831 2.61626479
[245,] -8.68775226 7.64304831
[246,] -2.43130571 -8.68775226
[247,] 6.30438989 -2.43130571
[248,] 5.06548327 6.30438989
[249,] -5.75714055 5.06548327
[250,] 5.79191393 -5.75714055
[251,] 2.44198985 5.79191393
[252,] 2.62950929 2.44198985
[253,] 7.24182463 2.62950929
[254,] 10.57129418 7.24182463
[255,] -2.70873579 10.57129418
[256,] 4.28848910 -2.70873579
[257,] -7.58358794 4.28848910
[258,] -3.98309723 -7.58358794
[259,] -5.93086484 -3.98309723
[260,] -22.53269936 -5.93086484
[261,] 11.86235819 -22.53269936
[262,] 3.56403814 11.86235819
[263,] -11.39813901 3.56403814
[264,] 15.65769082 -11.39813901
[265,] -8.33141161 15.65769082
[266,] 3.50935584 -8.33141161
[267,] -5.00839616 3.50935584
[268,] 9.01501966 -5.00839616
[269,] -5.97748132 9.01501966
[270,] -12.46578460 -5.97748132
[271,] 12.88052076 -12.46578460
[272,] 4.73343212 12.88052076
[273,] 0.05539106 4.73343212
[274,] -4.69433714 0.05539106
[275,] -16.53858390 -4.69433714
[276,] -3.54556608 -16.53858390
[277,] 1.14502024 -3.54556608
[278,] 7.53210050 1.14502024
[279,] 13.43780422 7.53210050
[280,] 8.21955653 13.43780422
[281,] 2.90812740 8.21955653
[282,] -7.62932629 2.90812740
[283,] -0.39564524 -7.62932629
[284,] 10.48303066 -0.39564524
[285,] -13.41647873 10.48303066
[286,] 3.60650520 -13.41647873
[287,] 5.18701708 3.60650520
[288,] -8.57892159 5.18701708
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 11.89489619 -7.49071482
2 -33.29054088 11.89489619
3 3.02918562 -33.29054088
4 -1.99392547 3.02918562
5 -5.37516443 -1.99392547
6 0.46857182 -5.37516443
7 -24.15218621 0.46857182
8 4.38532046 -24.15218621
9 10.24590874 4.38532046
10 -17.78443576 10.24590874
11 7.05680250 -17.78443576
12 8.21475781 7.05680250
13 10.66079457 8.21475781
14 5.21920587 10.66079457
15 -42.22866042 5.21920587
16 11.14843717 -42.22866042
17 10.06079925 11.14843717
18 -27.50903056 10.06079925
19 -8.64557125 -27.50903056
20 9.17045069 -8.64557125
21 1.92422044 9.17045069
22 11.67210969 1.92422044
23 8.57939765 11.67210969
24 22.84248799 8.57939765
25 10.49242279 22.84248799
26 18.77924878 10.49242279
27 -7.95368977 18.77924878
28 11.40282767 -7.95368977
29 5.95579406 11.40282767
30 -0.01139572 5.95579406
31 16.76780832 -0.01139572
32 -1.07704833 16.76780832
33 15.81600861 -1.07704833
34 19.48528600 15.81600861
35 10.92985834 19.48528600
36 -2.40775618 10.92985834
37 -8.20634175 -2.40775618
38 9.08704819 -8.20634175
39 6.20214545 9.08704819
40 -19.84424108 6.20214545
41 1.26075028 -19.84424108
42 -2.18486593 1.26075028
43 13.02853571 -2.18486593
44 -13.34650928 13.02853571
45 -10.15857836 -13.34650928
46 11.34380614 -10.15857836
47 9.37393553 11.34380614
48 -3.74448118 9.37393553
49 7.60289447 -3.74448118
50 -6.72662508 7.60289447
51 7.64220817 -6.72662508
52 14.46591130 7.64220817
53 -0.61731114 14.46591130
54 12.02045687 -0.61731114
55 -0.13441089 12.02045687
56 3.77495785 -0.13441089
57 14.36901148 3.77495785
58 8.48830259 14.36901148
59 -0.31221984 8.48830259
60 3.90684537 -0.31221984
61 20.18441954 3.90684537
62 1.40629625 20.18441954
63 2.38907149 1.40629625
64 5.09338270 2.38907149
65 13.80008999 5.09338270
66 0.01496961 13.80008999
67 -5.01494001 0.01496961
68 -19.78907468 -5.01494001
69 -44.55752342 -19.78907468
70 8.36308403 -44.55752342
71 7.63452061 8.36308403
72 -13.86003812 7.63452061
73 15.13099754 -13.86003812
74 1.21275209 15.13099754
75 -4.21015027 1.21275209
76 -4.21025233 -4.21015027
77 2.78584513 -4.21025233
78 -2.35999956 2.78584513
79 19.82238775 -2.35999956
80 -14.97298344 19.82238775
81 8.68893245 -14.97298344
82 -1.05578029 8.68893245
83 9.35593009 -1.05578029
84 -6.36496400 9.35593009
85 8.60944519 -6.36496400
86 0.52299712 8.60944519
87 0.38614566 0.52299712
88 -20.52637564 0.38614566
89 19.59006076 -20.52637564
90 2.26777547 19.59006076
91 5.08689227 2.26777547
92 0.28730182 5.08689227
93 14.25692922 0.28730182
94 9.90250423 14.25692922
95 11.86955336 9.90250423
96 -34.23408018 11.86955336
97 10.31063021 -34.23408018
98 -31.48390987 10.31063021
99 8.53247167 -31.48390987
100 -4.04142031 8.53247167
101 12.36478067 -4.04142031
102 6.60586801 12.36478067
103 15.67696644 6.60586801
104 13.43629125 15.67696644
105 -15.21131398 13.43629125
106 24.10563540 -15.21131398
107 -23.97366968 24.10563540
108 -7.85090236 -23.97366968
109 2.19907389 -7.85090236
110 -5.06098341 2.19907389
111 -44.02786242 -5.06098341
112 10.51480858 -44.02786242
113 -35.53370166 10.51480858
114 -30.62332359 -35.53370166
115 -10.65501449 -30.62332359
116 -6.33763276 -10.65501449
117 -0.55321453 -6.33763276
118 -1.17765550 -0.55321453
119 9.13572912 -1.17765550
120 -8.24622419 9.13572912
121 -0.08992391 -8.24622419
122 6.95279549 -0.08992391
123 -4.36181045 6.95279549
124 13.69852430 -4.36181045
125 5.28964077 13.69852430
126 3.27928088 5.28964077
127 14.57158039 3.27928088
128 -24.27834675 14.57158039
129 5.13212576 -24.27834675
130 0.62312120 5.13212576
131 -20.56839502 0.62312120
132 6.35450913 -20.56839502
133 2.67999547 6.35450913
134 -12.29576078 2.67999547
135 -24.21677779 -12.29576078
136 5.43546700 -24.21677779
137 -17.43913741 5.43546700
138 4.86891143 -17.43913741
139 -5.29241453 4.86891143
140 -3.24772976 -5.29241453
141 -3.29860299 -3.24772976
142 -3.87060593 -3.29860299
143 7.14345403 -3.87060593
144 12.90162257 7.14345403
145 -3.20097895 12.90162257
146 1.66991917 -3.20097895
147 0.06430801 1.66991917
148 4.86210551 0.06430801
149 18.38721779 4.86210551
150 14.58406070 18.38721779
151 7.86462178 14.58406070
152 -5.22368876 7.86462178
153 1.64027267 -5.22368876
154 -9.77258237 1.64027267
155 7.88321310 -9.77258237
156 16.91144458 7.88321310
157 0.22238241 16.91144458
158 -0.40485570 0.22238241
159 18.47254654 -0.40485570
160 9.64905242 18.47254654
161 9.89276366 9.64905242
162 11.04527105 9.89276366
163 15.72584918 11.04527105
164 -1.76733000 15.72584918
165 5.55973680 -1.76733000
166 -4.64310962 5.55973680
167 -46.34399731 -4.64310962
168 -10.56946187 -46.34399731
169 -0.68324310 -10.56946187
170 -12.62652442 -0.68324310
171 -3.19391573 -12.62652442
172 13.17709047 -3.19391573
173 -20.10097448 13.17709047
174 13.20865332 -20.10097448
175 1.16360433 13.20865332
176 6.38587366 1.16360433
177 0.38834914 6.38587366
178 6.96438803 0.38834914
179 3.59949293 6.96438803
180 -1.96627403 3.59949293
181 -4.51567650 -1.96627403
182 5.33347924 -4.51567650
183 -15.84114180 5.33347924
184 -42.76181777 -15.84114180
185 5.22603998 -42.76181777
186 6.49477776 5.22603998
187 6.32796019 6.49477776
188 4.69319130 6.32796019
189 -36.00102086 4.69319130
190 -22.75799689 -36.00102086
191 -2.29847774 -22.75799689
192 -1.89292042 -2.29847774
193 5.59737558 -1.89292042
194 14.83001354 5.59737558
195 0.54366395 14.83001354
196 11.24839025 0.54366395
197 7.46308561 11.24839025
198 -5.47583125 7.46308561
199 10.22865611 -5.47583125
200 1.65836670 10.22865611
201 -8.64753279 1.65836670
202 11.07376566 -8.64753279
203 11.63853863 11.07376566
204 -1.45150627 11.63853863
205 -16.16351306 -1.45150627
206 4.81779000 -16.16351306
207 10.10149614 4.81779000
208 1.53015429 10.10149614
209 15.00636861 1.53015429
210 -1.56447369 15.00636861
211 0.95005244 -1.56447369
212 -1.14905430 0.95005244
213 11.69293949 -1.14905430
214 -8.80818829 11.69293949
215 15.26293801 -8.80818829
216 4.83442107 15.26293801
217 0.30805829 4.83442107
218 -8.93043665 0.30805829
219 11.19593815 -8.93043665
220 11.16791020 11.19593815
221 6.61895945 11.16791020
222 -7.76530738 6.61895945
223 3.10948180 -7.76530738
224 -7.37300318 3.10948180
225 -13.18217805 -7.37300318
226 -15.14595921 -13.18217805
227 7.62345183 -15.14595921
228 1.22253506 7.62345183
229 10.04770547 1.22253506
230 0.75826365 10.04770547
231 -2.21069997 0.75826365
232 4.07763538 -2.21069997
233 -6.44591479 4.07763538
234 -14.38951299 -6.44591479
235 -2.60284974 -14.38951299
236 -0.64973794 -2.60284974
237 -37.43242755 -0.64973794
238 -8.63888988 -37.43242755
239 -1.97420163 -8.63888988
240 -11.49004588 -1.97420163
241 11.63232710 -11.49004588
242 -5.53339386 11.63232710
243 2.61626479 -5.53339386
244 7.64304831 2.61626479
245 -8.68775226 7.64304831
246 -2.43130571 -8.68775226
247 6.30438989 -2.43130571
248 5.06548327 6.30438989
249 -5.75714055 5.06548327
250 5.79191393 -5.75714055
251 2.44198985 5.79191393
252 2.62950929 2.44198985
253 7.24182463 2.62950929
254 10.57129418 7.24182463
255 -2.70873579 10.57129418
256 4.28848910 -2.70873579
257 -7.58358794 4.28848910
258 -3.98309723 -7.58358794
259 -5.93086484 -3.98309723
260 -22.53269936 -5.93086484
261 11.86235819 -22.53269936
262 3.56403814 11.86235819
263 -11.39813901 3.56403814
264 15.65769082 -11.39813901
265 -8.33141161 15.65769082
266 3.50935584 -8.33141161
267 -5.00839616 3.50935584
268 9.01501966 -5.00839616
269 -5.97748132 9.01501966
270 -12.46578460 -5.97748132
271 12.88052076 -12.46578460
272 4.73343212 12.88052076
273 0.05539106 4.73343212
274 -4.69433714 0.05539106
275 -16.53858390 -4.69433714
276 -3.54556608 -16.53858390
277 1.14502024 -3.54556608
278 7.53210050 1.14502024
279 13.43780422 7.53210050
280 8.21955653 13.43780422
281 2.90812740 8.21955653
282 -7.62932629 2.90812740
283 -0.39564524 -7.62932629
284 10.48303066 -0.39564524
285 -13.41647873 10.48303066
286 3.60650520 -13.41647873
287 5.18701708 3.60650520
288 -8.57892159 5.18701708
> 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/www/rcomp/tmp/7skyj1323872053.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/www/rcomp/tmp/8j1531323872053.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/www/rcomp/tmp/9u8pe1323872053.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/www/rcomp/tmp/10j5ns1323872053.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11ewyj1323872053.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12ency1323872053.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13qdyb1323872053.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14gmv91323872053.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/www/rcomp/tmp/156usp1323872053.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/www/rcomp/tmp/16vxsq1323872053.tab")
+ }
>
> try(system("convert tmp/1jt3m1323872053.ps tmp/1jt3m1323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jbg91323872053.ps tmp/2jbg91323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/388961323872053.ps tmp/388961323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/45hos1323872053.ps tmp/45hos1323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k3101323872053.ps tmp/5k3101323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l2nk1323872053.ps tmp/6l2nk1323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/7skyj1323872053.ps tmp/7skyj1323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j1531323872053.ps tmp/8j1531323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u8pe1323872053.ps tmp/9u8pe1323872053.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j5ns1323872053.ps tmp/10j5ns1323872053.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.360 0.390 8.748