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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> x <- array(list(1418
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+ ,44
+ ,0
+ ,17
+ ,16
+ ,64
+ ,39
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(15
+ ,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'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogsd')
+ ,1:289))
> y <- array(NA,dim=c(15,289),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','totsize','totrevisions','totseconds','tothyperlinks','totblogsd'),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 = '9'
> 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
feedback_messages_p1 pageviews time_in_rfc logins compendium_views_info
1 115 1418 210907 56 396
2 109 869 120982 56 297
3 146 1530 176508 54 559
4 116 2172 179321 89 967
5 68 901 123185 40 270
6 101 463 52746 25 143
7 96 3201 385534 92 1562
8 67 371 33170 18 109
9 44 1192 101645 63 371
10 100 1583 149061 44 656
11 93 1439 165446 33 511
12 140 1764 237213 84 655
13 166 1495 173326 88 465
14 99 1373 133131 55 525
15 139 2187 258873 60 885
16 130 1491 180083 66 497
17 181 4041 324799 154 1436
18 116 1706 230964 53 612
19 116 2152 236785 119 865
20 88 1036 135473 41 385
21 139 1882 202925 61 567
22 135 1929 215147 58 639
23 108 2242 344297 75 963
24 89 1220 153935 33 398
25 156 1289 132943 40 410
26 129 2515 174724 92 966
27 118 2147 174415 100 801
28 118 2352 225548 112 892
29 125 1638 223632 73 513
30 95 1222 124817 40 469
31 126 1812 221698 45 683
32 135 1677 210767 60 643
33 154 1579 170266 62 535
34 165 1731 260561 75 625
35 113 807 84853 31 264
36 127 2452 294424 77 992
37 52 829 101011 34 238
38 121 1940 215641 46 818
39 136 2662 325107 99 937
40 0 186 7176 17 70
41 108 1499 167542 66 507
42 46 865 106408 30 260
43 54 1793 96560 76 503
44 124 2527 265769 146 927
45 115 2747 269651 67 1269
46 128 1324 149112 56 537
47 80 2702 175824 107 910
48 97 1383 152871 58 532
49 104 1179 111665 34 345
50 59 2099 116408 61 918
51 125 4308 362301 119 1635
52 82 918 78800 42 330
53 149 1831 183167 66 557
54 149 3373 277965 89 1178
55 122 1713 150629 44 740
56 118 1438 168809 66 452
57 12 496 24188 24 218
58 144 2253 329267 259 764
59 67 744 65029 17 255
60 52 1161 101097 64 454
61 108 2352 218946 41 866
62 166 2144 244052 68 574
63 80 4691 341570 168 1276
64 60 1112 103597 43 379
65 107 2694 233328 132 825
66 127 1973 256462 105 798
67 107 1769 206161 71 663
68 146 3148 311473 112 1069
69 84 2474 235800 94 921
70 141 2084 177939 82 858
71 123 1954 207176 70 711
72 111 1226 196553 57 503
73 98 1389 174184 53 382
74 105 1496 143246 103 464
75 135 2269 187559 121 717
76 107 1833 187681 62 690
77 85 1268 119016 52 462
78 155 1943 182192 52 657
79 88 893 73566 32 385
80 155 1762 194979 62 577
81 104 1403 167488 45 619
82 132 1425 143756 46 479
83 127 1857 275541 63 817
84 108 1840 243199 75 752
85 129 1502 182999 88 430
86 116 1441 135649 46 451
87 122 1420 152299 53 537
88 85 1416 120221 37 519
89 147 2970 346485 90 1000
90 99 1317 145790 63 637
91 87 1644 193339 78 465
92 28 870 80953 25 437
93 90 1654 122774 45 711
94 109 1054 130585 46 299
95 78 937 112611 41 248
96 111 3004 286468 144 1162
97 158 2008 241066 82 714
98 141 2547 148446 91 905
99 122 1885 204713 71 649
100 124 1626 182079 63 512
101 93 1468 140344 53 472
102 124 2445 220516 62 905
103 112 1964 243060 63 786
104 108 1381 162765 32 489
105 99 1369 182613 39 479
106 117 1659 232138 62 617
107 199 2888 265318 117 925
108 78 1290 85574 34 351
109 91 2845 310839 92 1144
110 158 1982 225060 93 669
111 126 1904 232317 54 707
112 122 1391 144966 144 458
113 71 602 43287 14 214
114 75 1743 155754 61 599
115 115 1559 164709 109 572
116 119 2014 201940 38 897
117 124 2143 235454 73 819
118 72 2146 220801 75 720
119 91 874 99466 50 273
120 45 1590 92661 61 508
121 78 1590 133328 55 506
122 39 1210 61361 77 451
123 68 2072 125930 75 699
124 119 1281 100750 72 407
125 117 1401 224549 50 465
126 39 834 82316 32 245
127 50 1105 102010 53 370
128 88 1272 101523 42 316
129 155 1944 243511 71 603
130 0 391 22938 10 154
131 36 761 41566 35 229
132 123 1605 152474 65 577
133 32 530 61857 25 192
134 99 1988 99923 66 617
135 136 1386 132487 41 411
136 117 2395 317394 86 975
137 0 387 21054 16 146
138 88 1742 209641 42 705
139 39 620 22648 19 184
140 25 449 31414 19 200
141 52 800 46698 45 274
142 75 1684 131698 65 502
143 71 1050 91735 35 382
144 124 2699 244749 95 964
145 151 1606 184510 49 537
146 71 1502 79863 37 438
147 145 1204 128423 64 369
148 87 1138 97839 38 417
149 27 568 38214 34 276
150 131 1459 151101 32 514
151 162 2158 272458 65 822
152 165 1111 172494 52 389
153 54 1421 108043 62 466
154 159 2833 328107 65 1255
155 147 1955 250579 83 694
156 170 2922 351067 95 1024
157 119 1002 158015 29 400
158 49 1060 98866 18 397
159 104 956 85439 33 350
160 120 2186 229242 247 719
161 150 3604 351619 139 1277
162 112 1035 84207 29 356
163 59 1417 120445 118 457
164 136 3261 324598 110 1402
165 107 1587 131069 67 600
166 130 1424 204271 42 480
167 115 1701 165543 65 595
168 107 1249 141722 94 436
169 75 946 116048 64 230
170 71 1926 250047 81 651
171 120 3352 299775 95 1367
172 116 1641 195838 67 564
173 79 2035 173260 63 716
174 150 2312 254488 83 747
175 156 1369 104389 45 467
176 51 1577 136084 30 671
177 118 2201 199476 70 861
178 71 961 92499 32 319
179 144 1900 224330 83 612
180 47 1254 135781 31 433
181 28 1335 74408 67 434
182 68 1597 81240 66 503
183 0 207 14688 10 85
184 110 1645 181633 70 564
185 147 2429 271856 103 824
186 0 151 7199 5 74
187 15 474 46660 20 259
188 4 141 17547 5 69
189 64 1639 133368 36 535
190 111 872 95227 34 239
191 85 1318 152601 48 438
192 68 1018 98146 40 459
193 40 1383 79619 43 426
194 80 1314 59194 31 288
195 88 1335 139942 42 498
196 48 1403 118612 46 454
197 76 910 72880 33 376
198 51 616 65475 18 225
199 67 1407 99643 55 555
200 59 771 71965 35 252
201 61 766 77272 59 208
202 76 473 49289 19 130
203 60 1376 135131 66 481
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205 71 1521 89746 36 565
206 76 572 44296 25 173
207 62 1059 77648 47 278
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209 67 1230 134019 53 422
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211 30 1205 92630 40 387
212 64 1255 121848 39 339
213 68 613 52915 14 181
214 64 721 81872 45 245
215 91 1109 58981 36 384
216 88 740 53515 28 212
217 52 1126 60812 44 399
218 49 728 56375 30 229
219 62 689 65490 22 224
220 61 592 80949 17 203
221 76 995 76302 31 333
222 88 1613 104011 55 384
223 66 2048 98104 54 636
224 71 705 67989 21 185
225 68 301 30989 14 93
226 48 1803 135458 81 581
227 25 799 73504 35 248
228 68 861 63123 43 304
229 41 1186 61254 46 344
230 90 1451 74914 30 407
231 66 628 31774 23 170
232 54 1161 81437 38 312
233 59 1463 87186 54 507
234 60 742 50090 20 224
235 77 979 65745 53 340
236 68 675 56653 45 168
237 72 1241 158399 39 443
238 67 676 46455 20 204
239 64 1049 73624 24 367
240 63 620 38395 31 210
241 59 1081 91899 35 335
242 84 1688 139526 151 364
243 64 736 52164 52 178
244 56 617 51567 30 206
245 54 812 70551 31 279
246 67 1051 84856 29 387
247 58 1656 102538 57 490
248 59 705 86678 40 238
249 40 945 85709 44 343
250 22 554 34662 25 232
251 83 1597 150580 77 530
252 81 982 99611 35 291
253 2 222 19349 11 67
254 72 1212 99373 63 397
255 61 1143 86230 44 467
256 15 435 30837 19 178
257 32 532 31706 13 175
258 62 882 89806 42 299
259 58 608 62088 38 154
260 36 459 40151 29 106
261 59 578 27634 20 189
262 68 826 76990 27 194
263 21 509 37460 20 135
264 55 717 54157 19 201
265 54 637 49862 37 207
266 55 857 84337 26 280
267 72 830 64175 42 260
268 41 652 59382 49 227
269 61 707 119308 30 239
270 67 954 76702 49 333
271 76 1461 103425 67 428
272 64 672 70344 28 230
273 3 778 43410 19 292
274 63 1141 104838 49 350
275 40 680 62215 27 186
276 69 1090 69304 30 326
277 48 616 53117 22 155
278 8 285 19764 12 75
279 52 1145 86680 31 361
280 66 733 84105 20 261
281 76 888 77945 20 299
282 43 849 89113 39 300
283 39 1182 91005 29 450
284 14 528 40248 16 183
285 61 642 64187 27 238
286 71 947 50857 21 165
287 44 819 56613 19 234
288 60 757 62792 35 176
289 64 894 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_p120 totsize totrevisions totseconds
1 30 94 112285 24188 146283
2 28 103 84786 18273 98364
3 38 93 83123 14130 86146
4 30 103 101193 32287 96933
5 22 51 38361 8654 79234
6 26 70 68504 9245 42551
7 25 91 119182 33251 195663
8 18 22 22807 1271 6853
9 11 38 17140 5279 21529
10 26 93 116174 27101 95757
11 25 60 57635 16373 85584
12 38 123 66198 19716 143983
13 44 148 71701 17753 75851
14 30 90 57793 9028 59238
15 40 124 80444 18653 93163
16 34 70 53855 8828 96037
17 47 168 97668 29498 151511
18 30 115 133824 27563 136368
19 31 71 101481 18293 112642
20 23 66 99645 22530 94728
21 36 134 114789 15977 105499
22 36 117 99052 35082 121527
23 30 108 67654 16116 127766
24 25 84 65553 15849 98958
25 39 156 97500 16026 77900
26 34 120 69112 26569 85646
27 31 114 82753 24785 98579
28 31 94 85323 17569 130767
29 33 120 72654 23825 131741
30 25 81 30727 7869 53907
31 33 110 77873 14975 178812
32 35 133 117478 37791 146761
33 42 122 74007 9605 82036
34 43 158 90183 27295 163253
35 30 109 61542 2746 27032
36 33 124 101494 34461 171975
37 13 39 27570 8098 65990
38 32 92 55813 4787 86572
39 36 126 79215 24919 159676
40 0 0 1423 603 1929
41 28 70 55461 16329 85371
42 14 37 31081 12558 58391
43 17 38 22996 7784 31580
44 32 120 83122 28522 136815
45 30 93 70106 22265 120642
46 35 95 60578 14459 69107
47 20 77 39992 14526 50495
48 28 90 79892 22240 108016
49 28 80 49810 11802 46341
50 39 31 71570 7623 78348
51 34 110 100708 11912 79336
52 26 66 33032 7935 56968
53 39 138 82875 18220 93176
54 39 133 139077 19199 161632
55 33 113 71595 19918 87850
56 28 100 72260 21884 127969
57 4 7 5950 2694 15049
58 39 140 115762 15808 155135
59 18 61 32551 3597 25109
60 14 41 31701 5296 45824
61 29 96 80670 25239 102996
62 44 164 143558 29801 160604
63 21 78 117105 18450 158051
64 16 49 23789 7132 44547
65 28 102 120733 34861 162647
66 35 124 105195 35940 174141
67 28 99 73107 16688 60622
68 38 129 132068 24683 179566
69 23 62 149193 46230 184301
70 36 73 46821 10387 75661
71 32 114 87011 21436 96144
72 29 99 95260 30546 129847
73 25 70 55183 19746 117286
74 27 104 106671 15977 71180
75 36 116 73511 22583 109377
76 28 91 92945 17274 85298
77 23 74 78664 16469 73631
78 40 138 70054 14251 86767
79 23 67 22618 3007 23824
80 40 151 74011 16851 93487
81 28 72 83737 21113 82981
82 34 120 69094 17401 73815
83 33 115 93133 23958 94552
84 28 105 95536 23567 132190
85 34 104 225920 13065 128754
86 30 108 62133 15358 66363
87 33 98 61370 14587 67808
88 22 69 43836 12770 61724
89 38 111 106117 24021 131722
90 26 99 38692 9648 68580
91 35 71 84651 20537 106175
92 8 27 56622 7905 55792
93 24 69 15986 4527 25157
94 29 107 95364 30495 76669
95 20 73 26706 7117 57283
96 29 107 89691 17719 105805
97 45 93 67267 27056 129484
98 37 129 126846 33473 72413
99 33 69 41140 9758 87831
100 33 118 102860 21115 96971
101 25 73 51715 7236 71299
102 32 119 55801 13790 77494
103 29 104 111813 32902 120336
104 28 107 120293 25131 93913
105 28 99 138599 30910 136048
106 31 90 161647 35947 181248
107 52 197 115929 29848 146123
108 21 36 24266 6943 32036
109 24 85 162901 42705 186646
110 41 139 109825 31808 102255
111 33 106 129838 26675 168237
112 32 50 37510 8435 64219
113 19 64 43750 7409 19630
114 20 31 40652 14993 76825
115 31 63 87771 36867 115338
116 31 92 85872 33835 109427
117 32 106 89275 24164 118168
118 18 63 44418 12607 84845
119 23 69 192565 22609 153197
120 17 41 35232 5892 29877
121 20 56 40909 17014 63506
122 12 25 13294 5394 22445
123 17 65 32387 9178 47695
124 30 93 140867 6440 68370
125 31 114 120662 21916 146304
126 10 38 21233 4011 38233
127 13 44 44332 5818 42071
128 22 87 61056 18647 50517
129 42 110 101338 20556 103950
130 1 0 1168 238 5841
131 9 27 13497 70 2341
132 32 83 65567 22392 84396
133 11 30 25162 3913 24610
134 25 80 32334 12237 35753
135 36 98 40735 8388 55515
136 31 82 91413 22120 209056
137 0 0 855 338 6622
138 24 60 97068 11727 115814
139 13 28 44339 3704 11609
140 8 9 14116 3988 13155
141 13 33 10288 3030 18274
142 19 59 65622 13520 72875
143 18 49 16563 1421 10112
144 33 115 76643 20923 142775
145 40 140 110681 20237 68847
146 22 49 29011 3219 17659
147 38 120 92696 3769 20112
148 24 66 94785 12252 61023
149 8 21 8773 1888 13983
150 35 124 83209 14497 65176
151 43 152 93815 28864 132432
152 43 139 86687 21721 112494
153 14 38 34553 4821 45109
154 41 144 105547 33644 170875
155 38 120 103487 15923 180759
156 45 160 213688 42935 214921
157 31 114 71220 18864 100226
158 13 39 23517 4977 32043
159 28 78 56926 7785 54454
160 31 119 91721 17939 78876
161 40 141 115168 23436 170745
162 30 101 111194 325 6940
163 16 56 51009 13539 49025
164 37 133 135777 34538 122037
165 30 83 51513 12198 53782
166 35 116 74163 26924 127748
167 32 90 51633 12716 86839
168 27 36 75345 8172 44830
169 20 50 33416 10855 77395
170 18 61 83305 11932 89324
171 31 97 98952 14300 103300
172 31 98 102372 25515 112283
173 21 78 37238 2805 10901
174 39 117 103772 29402 120691
175 41 148 123969 16440 58106
176 13 41 27142 11221 57140
177 32 105 135400 28732 122422
178 18 55 21399 5250 25899
179 39 132 130115 28608 139296
180 14 44 24874 8092 52678
181 7 21 34988 4473 23853
182 17 50 45549 1572 17306
183 0 0 6023 2065 7953
184 30 73 64466 14817 89455
185 37 86 54990 16714 147866
186 0 0 1644 556 4245
187 5 13 6179 2089 21509
188 1 4 3926 2658 7670
189 16 57 32755 10695 66675
190 32 48 34777 1669 14336
191 24 46 73224 16267 53608
192 17 48 27114 7768 30059
193 11 32 20760 7252 29668
194 24 68 37636 6387 22097
195 22 87 65461 18715 96841
196 12 43 30080 7936 41907
197 19 67 24094 8643 27080
198 13 46 69008 7294 35885
199 17 46 54968 4570 41247
200 15 56 46090 7185 28313
201 16 48 27507 10058 36845
202 24 44 10672 2342 16548
203 15 60 34029 8509 36134
204 17 65 46300 13275 55764
205 18 55 24760 6816 28910
206 20 38 18779 1930 13339
207 16 52 21280 8086 25319
208 16 60 40662 10737 66956
209 18 54 28987 8033 47487
210 22 86 22827 7058 52785
211 8 24 18513 6782 44683
212 17 52 30594 5401 35619
213 18 49 24006 6521 21920
214 16 61 27913 10856 45608
215 23 61 42744 2154 7721
216 22 81 12934 6117 20634
217 13 43 22574 5238 29788
218 13 40 41385 4820 31931
219 16 40 18653 5615 37754
220 16 56 18472 4272 32505
221 20 68 30976 8702 40557
222 22 79 63339 15340 94238
223 17 47 25568 8030 44197
224 18 57 33747 9526 43228
225 17 41 4154 1278 4103
226 12 29 19474 4236 44144
227 7 3 35130 3023 32868
228 17 60 39067 7196 27640
229 14 30 13310 3394 14063
230 23 79 65892 6371 28990
231 17 47 4143 1574 4694
232 14 40 28579 9620 42648
233 15 48 51776 6978 64329
234 17 36 21152 4911 21928
235 21 42 38084 8645 25836
236 18 49 27717 8987 22779
237 18 57 32928 5544 40820
238 17 12 11342 3083 27530
239 17 40 19499 6909 32378
240 16 43 16380 3189 10824
241 15 33 36874 6745 39613
242 21 77 48259 16724 60865
243 16 43 16734 4850 19787
244 14 45 28207 7025 20107
245 15 47 30143 6047 36605
246 17 43 41369 7377 40961
247 15 45 45833 9078 48231
248 15 50 29156 4605 39725
249 10 35 35944 3238 21455
250 6 7 36278 8100 23430
251 22 71 45588 9653 62991
252 21 67 45097 8914 49363
253 1 0 3895 786 9604
254 18 62 28394 6700 24552
255 17 54 18632 5788 31493
256 4 4 2325 593 3439
257 10 25 25139 4506 19555
258 16 40 27975 6382 21228
259 16 38 14483 5621 23177
260 9 19 13127 3997 22094
261 16 17 5839 520 2342
262 17 67 24069 8891 38798
263 7 14 3738 999 3255
264 15 30 18625 7067 24261
265 14 54 36341 4639 18511
266 14 35 24548 5654 40798
267 18 59 21792 6928 28893
268 12 24 26263 1514 21425
269 16 58 23686 9238 50276
270 21 42 49303 8204 37643
271 19 46 25659 5926 30377
272 16 61 28904 5785 27126
273 1 3 2781 4 13
274 16 52 29236 5930 42097
275 10 25 19546 3710 24451
276 19 40 22818 705 14335
277 12 32 32689 443 5084
278 2 4 5752 2416 9927
279 14 49 22197 7747 43527
280 17 63 20055 5432 27184
281 19 67 25272 4913 21610
282 14 32 82206 2650 20484
283 11 23 32073 2370 20156
284 4 7 5444 775 6012
285 16 54 20154 5576 18475
286 20 37 36944 1352 12645
287 12 35 8019 3080 11017
288 15 51 30884 10205 37623
289 16 39 19540 6095 35873
tothyperlinks totblogsd
1 144 145
2 103 101
3 98 98
4 135 132
5 61 60
6 39 38
7 150 144
8 5 5
9 28 28
10 84 84
11 80 79
12 130 127
13 82 78
14 60 60
15 131 131
16 84 84
17 140 133
18 151 150
19 91 91
20 138 132
21 150 136
22 124 124
23 119 118
24 73 70
25 110 107
26 123 119
27 90 89
28 116 112
29 113 108
30 56 52
31 115 112
32 119 116
33 129 123
34 127 125
35 27 27
36 175 162
37 35 32
38 64 64
39 96 92
40 0 0
41 84 83
42 41 41
43 47 47
44 126 120
45 105 105
46 80 79
47 70 65
48 73 70
49 57 55
50 40 39
51 68 67
52 21 21
53 127 127
54 154 152
55 116 113
56 102 99
57 7 7
58 148 141
59 21 21
60 35 35
61 112 109
62 137 133
63 135 123
64 26 26
65 230 230
66 181 166
67 71 68
68 147 147
69 190 179
70 64 61
71 105 101
72 107 108
73 94 90
74 116 114
75 106 103
76 143 142
77 81 79
78 89 88
79 26 25
80 84 83
81 113 113
82 120 118
83 110 110
84 134 129
85 54 51
86 96 93
87 78 76
88 51 49
89 121 118
90 38 38
91 145 141
92 59 58
93 27 27
94 91 91
95 48 48
96 68 63
97 58 56
98 150 144
99 74 73
100 181 168
101 65 64
102 97 97
103 121 117
104 99 100
105 152 149
106 188 187
107 138 127
108 40 37
109 254 245
110 87 87
111 178 177
112 51 49
113 49 49
114 73 73
115 176 177
116 94 94
117 120 117
118 66 60
119 56 55
120 39 39
121 66 64
122 27 26
123 65 64
124 58 58
125 98 95
126 25 25
127 26 26
128 77 76
129 130 129
130 11 11
131 2 2
132 101 101
133 31 28
134 36 36
135 120 89
136 195 193
137 4 4
138 89 84
139 24 23
140 39 39
141 14 14
142 78 78
143 15 14
144 106 101
145 83 82
146 24 24
147 37 36
148 77 75
149 16 16
150 56 55
151 132 131
152 144 131
153 40 39
154 153 144
155 143 139
156 220 211
157 79 78
158 50 50
159 39 39
160 95 90
161 169 166
162 12 12
163 63 57
164 134 133
165 69 69
166 119 119
167 119 119
168 75 65
169 63 61
170 55 49
171 103 101
172 197 196
173 16 15
174 140 136
175 89 89
176 40 40
177 125 123
178 21 21
179 167 163
180 32 29
181 36 35
182 13 13
183 5 5
184 96 96
185 151 151
186 6 6
187 13 13
188 3 3
189 57 56
190 23 23
191 61 57
192 21 14
193 43 43
194 20 20
195 82 72
196 90 87
197 25 21
198 60 56
199 61 59
200 85 82
201 43 43
202 25 25
203 41 38
204 26 25
205 38 38
206 12 12
207 29 29
208 49 47
209 46 45
210 41 40
211 31 30
212 41 41
213 26 25
214 23 23
215 14 14
216 16 16
217 25 26
218 21 21
219 32 27
220 9 9
221 35 33
222 42 42
223 68 68
224 32 32
225 6 6
226 68 67
227 33 33
228 84 77
229 46 46
230 30 30
231 0 0
232 36 36
233 47 46
234 20 18
235 50 48
236 30 29
237 30 28
238 34 34
239 33 33
240 34 34
241 37 33
242 83 80
243 32 32
244 30 30
245 43 41
246 41 41
247 51 51
248 19 18
249 37 34
250 33 31
251 41 39
252 54 54
253 14 14
254 25 24
255 25 24
256 8 8
257 26 26
258 20 19
259 11 11
260 14 14
261 3 1
262 40 39
263 5 5
264 38 37
265 32 32
266 41 38
267 46 47
268 47 47
269 37 37
270 51 51
271 49 45
272 21 21
273 1 1
274 44 42
275 26 26
276 21 21
277 4 4
278 10 10
279 43 43
280 34 34
281 32 31
282 20 19
283 34 34
284 6 6
285 12 11
286 24 24
287 16 16
288 72 72
289 27 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc
2.410e+00 1.764e-03 3.140e-05
logins compendium_views_info compendium_views_pr
6.438e-03 -1.063e-02 -6.654e-03
shared_compendiums blogged_computations compendiums_reviewed
5.039e-02 1.574e-02 2.932e+00
feedback_messages_p120 totsize totrevisions
2.324e-01 -1.902e-05 1.718e-05
totseconds tothyperlinks totblogsd
-2.051e-05 3.971e-02 -4.155e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-60.152 -2.139 0.257 2.773 16.821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.410e+00 1.003e+00 2.404 0.0169 *
pageviews 1.764e-03 2.325e-03 0.759 0.4486
time_in_rfc 3.140e-05 1.728e-05 1.817 0.0703 .
logins 6.438e-03 1.777e-02 0.362 0.7174
compendium_views_info -1.063e-02 5.304e-03 -2.004 0.0460 *
compendium_views_pr -6.654e-03 9.518e-03 -0.699 0.4851
shared_compendiums 5.039e-02 1.580e-01 0.319 0.7500
blogged_computations 1.574e-02 2.634e-02 0.598 0.5505
compendiums_reviewed 2.932e+00 9.089e-02 32.254 < 2e-16 ***
feedback_messages_p120 2.324e-01 2.659e-02 8.739 2.39e-16 ***
totsize -1.902e-05 1.865e-05 -1.020 0.3086
totrevisions 1.718e-05 9.275e-05 0.185 0.8532
totseconds -2.051e-05 2.522e-05 -0.813 0.4167
tothyperlinks 3.971e-02 1.257e-01 0.316 0.7522
totblogsd -4.155e-02 1.302e-01 -0.319 0.7500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.197 on 274 degrees of freedom
Multiple R-squared: 0.9774, Adjusted R-squared: 0.9763
F-statistic: 846.6 on 14 and 274 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,] 0.61526325 7.694735e-01 3.847367e-01
[2,] 0.45793923 9.158785e-01 5.420608e-01
[3,] 0.77640361 4.471928e-01 2.235964e-01
[4,] 0.71696421 5.660716e-01 2.830358e-01
[5,] 0.68858627 6.228275e-01 3.114137e-01
[6,] 0.59800867 8.039827e-01 4.019913e-01
[7,] 0.53691713 9.261657e-01 4.630829e-01
[8,] 0.76357688 4.728462e-01 2.364231e-01
[9,] 0.69393659 6.121268e-01 3.060634e-01
[10,] 0.62536981 7.492604e-01 3.746302e-01
[11,] 0.54977066 9.004587e-01 4.502293e-01
[12,] 0.48290305 9.658061e-01 5.170969e-01
[13,] 0.44668322 8.933664e-01 5.533168e-01
[14,] 0.40260766 8.052153e-01 5.973923e-01
[15,] 0.33224291 6.644858e-01 6.677571e-01
[16,] 0.28011835 5.602367e-01 7.198817e-01
[17,] 0.23976344 4.795269e-01 7.602366e-01
[18,] 0.18966123 3.793225e-01 8.103388e-01
[19,] 0.14719481 2.943896e-01 8.528052e-01
[20,] 0.12376035 2.475207e-01 8.762397e-01
[21,] 0.11283847 2.256769e-01 8.871615e-01
[22,] 0.08566066 1.713213e-01 9.143393e-01
[23,] 0.06372965 1.274593e-01 9.362703e-01
[24,] 0.05977903 1.195581e-01 9.402210e-01
[25,] 0.05686294 1.137259e-01 9.431371e-01
[26,] 0.06388400 1.277680e-01 9.361160e-01
[27,] 0.04723613 9.447227e-02 9.527639e-01
[28,] 0.03830250 7.660501e-02 9.616975e-01
[29,] 0.02819251 5.638501e-02 9.718075e-01
[30,] 0.02702948 5.405896e-02 9.729705e-01
[31,] 0.04318678 8.637357e-02 9.568132e-01
[32,] 0.03200775 6.401549e-02 9.679923e-01
[33,] 1.00000000 7.320943e-11 3.660471e-11
[34,] 1.00000000 1.345914e-10 6.729572e-11
[35,] 1.00000000 4.189045e-11 2.094523e-11
[36,] 1.00000000 8.608088e-11 4.304044e-11
[37,] 1.00000000 1.595409e-10 7.977046e-11
[38,] 1.00000000 2.689390e-10 1.344695e-10
[39,] 1.00000000 9.457272e-11 4.728636e-11
[40,] 1.00000000 1.547203e-10 7.736013e-11
[41,] 1.00000000 2.045964e-11 1.022982e-11
[42,] 1.00000000 3.247011e-11 1.623505e-11
[43,] 1.00000000 6.337611e-11 3.168805e-11
[44,] 1.00000000 1.200138e-10 6.000690e-11
[45,] 1.00000000 1.527379e-10 7.636897e-11
[46,] 1.00000000 2.629168e-10 1.314584e-10
[47,] 1.00000000 4.969370e-10 2.484685e-10
[48,] 1.00000000 3.541606e-10 1.770803e-10
[49,] 1.00000000 3.187572e-10 1.593786e-10
[50,] 1.00000000 2.165049e-10 1.082524e-10
[51,] 1.00000000 4.078184e-10 2.039092e-10
[52,] 1.00000000 7.012054e-10 3.506027e-10
[53,] 1.00000000 2.170918e-12 1.085459e-12
[54,] 1.00000000 4.147158e-12 2.073579e-12
[55,] 1.00000000 7.580032e-12 3.790016e-12
[56,] 1.00000000 9.798173e-12 4.899086e-12
[57,] 1.00000000 1.868592e-11 9.342959e-12
[58,] 1.00000000 3.269651e-11 1.634825e-11
[59,] 1.00000000 5.794418e-11 2.897209e-11
[60,] 1.00000000 8.564718e-11 4.282359e-11
[61,] 1.00000000 1.392030e-10 6.960151e-11
[62,] 1.00000000 2.361832e-10 1.180916e-10
[63,] 1.00000000 3.879062e-10 1.939531e-10
[64,] 1.00000000 5.051225e-10 2.525612e-10
[65,] 1.00000000 8.695120e-10 4.347560e-10
[66,] 1.00000000 1.195217e-09 5.976086e-10
[67,] 1.00000000 2.029090e-09 1.014545e-09
[68,] 1.00000000 8.617315e-10 4.308658e-10
[69,] 1.00000000 1.475674e-09 7.378369e-10
[70,] 1.00000000 2.165378e-09 1.082689e-09
[71,] 1.00000000 3.367457e-09 1.683729e-09
[72,] 1.00000000 3.362854e-09 1.681427e-09
[73,] 1.00000000 4.836218e-09 2.418109e-09
[74,] 1.00000000 4.125794e-21 2.062897e-21
[75,] 1.00000000 7.962987e-21 3.981494e-21
[76,] 1.00000000 1.315275e-20 6.576377e-21
[77,] 1.00000000 1.919391e-20 9.596955e-21
[78,] 1.00000000 4.226677e-20 2.113339e-20
[79,] 1.00000000 9.416292e-20 4.708146e-20
[80,] 1.00000000 9.846593e-21 4.923296e-21
[81,] 1.00000000 1.800360e-20 9.001798e-21
[82,] 1.00000000 1.152152e-20 5.760760e-21
[83,] 1.00000000 2.440218e-20 1.220109e-20
[84,] 1.00000000 5.427056e-20 2.713528e-20
[85,] 1.00000000 1.202529e-19 6.012647e-20
[86,] 1.00000000 2.172358e-19 1.086179e-19
[87,] 1.00000000 4.196696e-19 2.098348e-19
[88,] 1.00000000 2.812156e-19 1.406078e-19
[89,] 1.00000000 3.959089e-19 1.979545e-19
[90,] 1.00000000 4.264179e-19 2.132089e-19
[91,] 1.00000000 4.175182e-19 2.087591e-19
[92,] 1.00000000 9.012661e-19 4.506331e-19
[93,] 1.00000000 1.943228e-18 9.716139e-19
[94,] 1.00000000 3.249823e-18 1.624911e-18
[95,] 1.00000000 2.327530e-19 1.163765e-19
[96,] 1.00000000 5.225855e-19 2.612928e-19
[97,] 1.00000000 7.547760e-19 3.773880e-19
[98,] 1.00000000 6.718274e-19 3.359137e-19
[99,] 1.00000000 6.516083e-19 3.258042e-19
[100,] 1.00000000 1.337931e-18 6.689655e-19
[101,] 1.00000000 2.790148e-18 1.395074e-18
[102,] 1.00000000 1.243571e-18 6.217855e-19
[103,] 1.00000000 6.365676e-22 3.182838e-22
[104,] 1.00000000 1.081834e-21 5.409171e-22
[105,] 1.00000000 6.719055e-22 3.359527e-22
[106,] 1.00000000 1.055217e-21 5.276083e-22
[107,] 1.00000000 3.425220e-22 1.712610e-22
[108,] 1.00000000 6.915993e-22 3.457997e-22
[109,] 1.00000000 1.446972e-21 7.234861e-22
[110,] 1.00000000 3.302441e-21 1.651220e-21
[111,] 1.00000000 5.464745e-21 2.732372e-21
[112,] 1.00000000 1.245537e-20 6.227684e-21
[113,] 1.00000000 1.605442e-20 8.027210e-21
[114,] 1.00000000 3.609454e-20 1.804727e-20
[115,] 1.00000000 3.455132e-20 1.727566e-20
[116,] 1.00000000 3.379314e-21 1.689657e-21
[117,] 1.00000000 4.251801e-21 2.125900e-21
[118,] 1.00000000 5.592571e-22 2.796286e-22
[119,] 1.00000000 9.728091e-22 4.864046e-22
[120,] 1.00000000 2.191009e-21 1.095504e-21
[121,] 1.00000000 4.239323e-21 2.119661e-21
[122,] 1.00000000 1.406191e-21 7.030953e-22
[123,] 1.00000000 2.506564e-21 1.253282e-21
[124,] 1.00000000 4.370828e-21 2.185414e-21
[125,] 1.00000000 6.176248e-21 3.088124e-21
[126,] 1.00000000 6.477749e-21 3.238874e-21
[127,] 1.00000000 7.889484e-21 3.944742e-21
[128,] 1.00000000 1.837787e-20 9.188936e-21
[129,] 1.00000000 2.712410e-21 1.356205e-21
[130,] 1.00000000 4.685168e-21 2.342584e-21
[131,] 1.00000000 1.015318e-20 5.076592e-21
[132,] 1.00000000 1.814964e-20 9.074818e-21
[133,] 1.00000000 4.133082e-20 2.066541e-20
[134,] 1.00000000 9.471805e-20 4.735903e-20
[135,] 1.00000000 1.097935e-19 5.489677e-20
[136,] 1.00000000 2.460204e-19 1.230102e-19
[137,] 1.00000000 5.231831e-19 2.615915e-19
[138,] 1.00000000 1.091489e-18 5.457445e-19
[139,] 1.00000000 2.492910e-18 1.246455e-18
[140,] 1.00000000 5.680116e-18 2.840058e-18
[141,] 1.00000000 9.709382e-18 4.854691e-18
[142,] 1.00000000 1.928878e-17 9.644388e-18
[143,] 1.00000000 3.613766e-17 1.806883e-17
[144,] 1.00000000 2.117503e-17 1.058751e-17
[145,] 1.00000000 4.792808e-17 2.396404e-17
[146,] 1.00000000 3.261557e-17 1.630779e-17
[147,] 1.00000000 6.953795e-17 3.476898e-17
[148,] 1.00000000 4.108988e-17 2.054494e-17
[149,] 1.00000000 8.520340e-17 4.260170e-17
[150,] 1.00000000 9.100082e-17 4.550041e-17
[151,] 1.00000000 3.903680e-18 1.951840e-18
[152,] 1.00000000 7.866633e-18 3.933316e-18
[153,] 1.00000000 1.651274e-17 8.256370e-18
[154,] 1.00000000 3.221310e-17 1.610655e-17
[155,] 1.00000000 7.485005e-17 3.742503e-17
[156,] 1.00000000 7.133343e-17 3.566671e-17
[157,] 1.00000000 1.216584e-16 6.082920e-17
[158,] 1.00000000 1.634633e-16 8.173164e-17
[159,] 1.00000000 3.475413e-16 1.737706e-16
[160,] 1.00000000 7.648314e-16 3.824157e-16
[161,] 1.00000000 1.342900e-15 6.714501e-16
[162,] 1.00000000 1.366486e-15 6.832429e-16
[163,] 1.00000000 6.635969e-16 3.317984e-16
[164,] 1.00000000 1.516878e-15 7.584388e-16
[165,] 1.00000000 1.925851e-15 9.629257e-16
[166,] 1.00000000 4.348241e-15 2.174121e-15
[167,] 1.00000000 6.177118e-15 3.088559e-15
[168,] 1.00000000 1.382512e-15 6.912562e-16
[169,] 1.00000000 3.143155e-15 1.571578e-15
[170,] 1.00000000 2.822505e-15 1.411253e-15
[171,] 1.00000000 6.344028e-15 3.172014e-15
[172,] 1.00000000 1.404805e-14 7.024024e-15
[173,] 1.00000000 3.091468e-14 1.545734e-14
[174,] 1.00000000 6.884025e-14 3.442012e-14
[175,] 1.00000000 1.339206e-13 6.696029e-14
[176,] 1.00000000 2.962321e-13 1.481161e-13
[177,] 1.00000000 4.219043e-14 2.109521e-14
[178,] 1.00000000 7.569210e-14 3.784605e-14
[179,] 1.00000000 1.725568e-13 8.627838e-14
[180,] 1.00000000 3.685646e-13 1.842823e-13
[181,] 1.00000000 8.252514e-13 4.126257e-13
[182,] 1.00000000 1.008971e-12 5.044855e-13
[183,] 1.00000000 2.246350e-12 1.123175e-12
[184,] 1.00000000 4.969898e-12 2.484949e-12
[185,] 1.00000000 2.623096e-14 1.311548e-14
[186,] 1.00000000 5.888330e-14 2.944165e-14
[187,] 1.00000000 1.345970e-13 6.729851e-14
[188,] 1.00000000 2.560750e-13 1.280375e-13
[189,] 1.00000000 4.491926e-13 2.245963e-13
[190,] 1.00000000 1.056863e-12 5.284315e-13
[191,] 1.00000000 2.226688e-12 1.113344e-12
[192,] 1.00000000 4.242469e-12 2.121235e-12
[193,] 1.00000000 9.764773e-12 4.882386e-12
[194,] 1.00000000 1.844596e-11 9.222981e-12
[195,] 1.00000000 3.959462e-11 1.979731e-11
[196,] 1.00000000 8.681054e-11 4.340527e-11
[197,] 1.00000000 1.749617e-10 8.748083e-11
[198,] 1.00000000 1.639782e-10 8.198911e-11
[199,] 1.00000000 3.571611e-10 1.785806e-10
[200,] 1.00000000 5.543340e-10 2.771670e-10
[201,] 1.00000000 1.201628e-09 6.008139e-10
[202,] 1.00000000 2.337290e-09 1.168645e-09
[203,] 1.00000000 4.105489e-09 2.052745e-09
[204,] 1.00000000 6.832479e-09 3.416240e-09
[205,] 0.99999999 1.147875e-08 5.739377e-09
[206,] 0.99999999 2.128447e-08 1.064223e-08
[207,] 0.99999998 4.384750e-08 2.192375e-08
[208,] 0.99999997 5.352157e-08 2.676079e-08
[209,] 0.99999996 8.330529e-08 4.165265e-08
[210,] 0.99999993 1.325881e-07 6.629403e-08
[211,] 0.99999987 2.633845e-07 1.316923e-07
[212,] 1.00000000 7.563537e-09 3.781768e-09
[213,] 0.99999999 1.063311e-08 5.316553e-09
[214,] 0.99999999 2.370623e-08 1.185312e-08
[215,] 0.99999997 5.208312e-08 2.604156e-08
[216,] 0.99999996 7.351686e-08 3.675843e-08
[217,] 0.99999996 8.237534e-08 4.118767e-08
[218,] 0.99999991 1.761077e-07 8.805386e-08
[219,] 0.99999981 3.893560e-07 1.946780e-07
[220,] 0.99999968 6.478854e-07 3.239427e-07
[221,] 0.99999994 1.191843e-07 5.959216e-08
[222,] 0.99999987 2.554788e-07 1.277394e-07
[223,] 0.99999981 3.885460e-07 1.942730e-07
[224,] 0.99999964 7.277027e-07 3.638514e-07
[225,] 0.99999960 7.974445e-07 3.987222e-07
[226,] 0.99999937 1.256666e-06 6.283331e-07
[227,] 0.99999871 2.576583e-06 1.288292e-06
[228,] 0.99999867 2.663530e-06 1.331765e-06
[229,] 0.99999978 4.304771e-07 2.152385e-07
[230,] 0.99999976 4.705267e-07 2.352634e-07
[231,] 0.99999952 9.666185e-07 4.833092e-07
[232,] 0.99999887 2.269162e-06 1.134581e-06
[233,] 0.99999841 3.188910e-06 1.594455e-06
[234,] 0.99999598 8.040228e-06 4.020114e-06
[235,] 0.99999512 9.768183e-06 4.884092e-06
[236,] 0.99998982 2.036781e-05 1.018390e-05
[237,] 0.99997714 4.571837e-05 2.285919e-05
[238,] 0.99995205 9.590258e-05 4.795129e-05
[239,] 0.99989113 2.177368e-04 1.088684e-04
[240,] 0.99981598 3.680338e-04 1.840169e-04
[241,] 0.99980800 3.840001e-04 1.920000e-04
[242,] 0.99956199 8.760261e-04 4.380131e-04
[243,] 0.99946264 1.074722e-03 5.373612e-04
[244,] 0.99937772 1.244554e-03 6.222770e-04
[245,] 0.99889073 2.218544e-03 1.109272e-03
[246,] 0.99951008 9.798390e-04 4.899195e-04
[247,] 0.99865223 2.695534e-03 1.347767e-03
[248,] 0.99730694 5.386123e-03 2.693062e-03
[249,] 0.99331053 1.337894e-02 6.689470e-03
[250,] 0.99361138 1.277723e-02 6.388617e-03
[251,] 0.98902787 2.194426e-02 1.097213e-02
[252,] 0.98183927 3.632147e-02 1.816073e-02
[253,] 0.96754367 6.491266e-02 3.245633e-02
[254,] 0.90757220 1.848556e-01 9.242780e-02
> postscript(file="/var/fisher/rcomp/tmp/10qzz1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2gxnj1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/35wff1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4z4sc1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5jicn1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.68990576 0.70454994 9.99524882 4.45935721 -11.88316006 7.12688175
7 8 9 10 11 12
1.65892811 6.89262213 -0.24696136 2.34340118 2.80896871 -3.28487429
13 14 15 16 17 18
-2.28342423 -12.08170006 -10.30133718 10.00951167 1.36071703 -1.59993310
19 20 21 22 23 24
6.63469950 2.94774447 -2.25280177 -0.90291023 -9.40136603 -6.84356398
25 26 27 28 29 30
2.58084831 0.41521288 -0.61447365 3.45429090 -4.60399248 0.17137792
31 32 33 34 35 36
2.17066385 -0.73714313 -0.23234016 -2.04586852 -3.02552646 -1.23931850
37 38 39 40 41 42
1.57036316 3.81569642 -3.34650542 -2.26602344 6.29566144 -7.05739709
43 44 45 46 47 48
-7.18220062 -1.48673090 4.42413890 0.90896982 1.04119622 -7.69665985
49 50 51 52 53 54
0.03411801 -60.15217063 1.86944878 -11.16533549 -0.88744342 2.93900965
55 56 57 58 59 60
-1.80482643 9.50811806 -2.83966310 -9.22179538 -2.44150558 -0.43184374
61 62 63 64 65 66
-1.02827479 -5.10320705 -3.98513514 -0.91190499 -0.50709189 -7.46191063
67 68 69 70 71 72
-2.53423840 1.49140611 1.67403575 16.82103364 -0.30840181 0.98443991
73 74 75 76 77 78
4.16482027 -1.28522421 -0.71218995 1.15582689 -2.42131497 2.27787452
79 80 81 82 83 84
3.07637549 -1.17590107 3.79162135 1.18603410 -0.57040719 -1.41838956
85 86 87 88 89 90
3.84859974 -0.78513331 -0.03942466 2.30275282 4.57458620 -1.56761353
91 92 93 94 95 96
-36.87637093 -2.14319237 3.18185304 -4.27041592 -1.66122063 0.12683783
97 98 99 100 101 102
0.83416526 1.61163114 5.24690041 -4.38573079 0.10279057 -0.37067145
103 104 105 106 107 108
0.95807947 -0.71106120 -7.81615748 3.43829810 -2.99669621 5.04610377
109 110 111 112 113 114
-0.53655666 1.39305963 3.10690764 12.77383429 -1.14437673 6.07268314
115 116 117 118 119 120
7.36763461 6.05653430 1.93595655 0.71639527 9.29130822 -16.02570733
121 122 123 124 125 126
3.44601791 -3.62001062 2.09152325 8.68331559 -3.45074613 -2.45099865
127 128 129 130 131 132
-0.80673769 0.08524510 1.01545912 -5.15009359 0.82611903 6.99310990
133 134 135 136 137 138
-10.17659049 5.47234034 3.00398573 4.66860596 -2.20101537 1.94803734
139 140 141 142 143 144
-6.87595265 -2.32540682 4.11103028 3.19010173 4.59412835 -1.36329897
145 146 147 148 149 150
-2.12685719 -6.74646819 2.47544172 0.68756651 -2.68596604 -2.36892780
151 152 153 154 155 156
-2.54885539 3.57804453 1.33617412 4.03952142 4.98204479 -1.31830853
157 158 159 160 161 162
-0.92203515 0.19337371 2.43491008 -3.26960220 -2.44038811 -0.71963010
163 164 165 166 167 168
-3.99263659 -3.77072092 -2.15312600 -3.24674569 -1.83608331 16.50590135
169 170 171 172 173 174
0.63797287 -0.40304841 5.37993724 -0.88021813 -4.60578639 3.37565584
175 176 177 178 179 180
-0.26940474 2.43758629 -0.49465481 2.49544432 -4.63202380 -7.55997019
181 182 183 184 185 186
0.52870948 4.58282599 -2.20893461 2.07563420 14.31459426 -2.13854849
187 188 189 190 191 192
-4.36213848 -2.15804976 1.40904935 2.26700987 0.34302076 5.09717900
193 194 195 196 197 198
-1.81967714 -7.56108800 1.24965406 -0.38876200 3.00565390 0.59554089
199 200 201 202 203 204
5.56753601 -0.53504200 -0.48507610 -7.61716461 -1.13788162 0.82056921
205 206 207 208 209 210
4.49420743 6.07198460 -0.40554359 -2.63384521 -1.69721352 0.48490208
211 212 213 214 215 216
-1.67901219 -2.00882552 1.36812880 -0.03647451 7.88734992 2.11643758
217 218 219 220 221 222
2.62168301 -0.25094520 2.91518357 -1.88119556 -0.68828858 3.05119635
223 224 225 226 227 228
3.83992548 2.36693883 5.92397801 3.13417406 1.50573430 2.01164277
229 230 231 232 233 234
-9.62416501 2.69184794 2.79929529 0.87974818 3.34405889 -0.30235144
235 236 237 238 239 240
3.94996987 0.47358506 2.12589530 12.05425704 3.16339433 3.96563153
241 242 243 244 245 246
4.90442564 -0.39332436 3.85283104 2.00920584 -2.94279762 5.50182972
247 248 249 250 251 252
1.34449164 0.68942991 0.25710498 1.40479263 -0.42885677 1.17178361
253 254 255 256 257 258
-3.52124186 2.27308666 -2.98677937 0.02762243 -5.09430395 2.77324715
259 260 261 262 263 264
-1.05890479 2.21322444 6.13910667 -0.70850926 -5.77572289 1.10086359
265 266 267 268 269 270
-1.73486682 2.85343006 2.87344894 -2.21617845 -3.23548053 -6.07874977
271 272 273 274 275 276
6.74508663 0.40717298 -2.42893813 0.71193684 1.76585502 0.90728667
277 278 279 280 281 282
2.44491350 -1.39801334 -3.06781768 -1.23795358 2.21488897 -7.41407619
283 284 285 286 287 288
-0.67245610 -1.84209798 -0.92909488 0.68195819 -2.26684900 1.28217604
289
6.00031020
> postscript(file="/var/fisher/rcomp/tmp/69zun1354987363.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 1.68990576 NA
1 0.70454994 1.68990576
2 9.99524882 0.70454994
3 4.45935721 9.99524882
4 -11.88316006 4.45935721
5 7.12688175 -11.88316006
6 1.65892811 7.12688175
7 6.89262213 1.65892811
8 -0.24696136 6.89262213
9 2.34340118 -0.24696136
10 2.80896871 2.34340118
11 -3.28487429 2.80896871
12 -2.28342423 -3.28487429
13 -12.08170006 -2.28342423
14 -10.30133718 -12.08170006
15 10.00951167 -10.30133718
16 1.36071703 10.00951167
17 -1.59993310 1.36071703
18 6.63469950 -1.59993310
19 2.94774447 6.63469950
20 -2.25280177 2.94774447
21 -0.90291023 -2.25280177
22 -9.40136603 -0.90291023
23 -6.84356398 -9.40136603
24 2.58084831 -6.84356398
25 0.41521288 2.58084831
26 -0.61447365 0.41521288
27 3.45429090 -0.61447365
28 -4.60399248 3.45429090
29 0.17137792 -4.60399248
30 2.17066385 0.17137792
31 -0.73714313 2.17066385
32 -0.23234016 -0.73714313
33 -2.04586852 -0.23234016
34 -3.02552646 -2.04586852
35 -1.23931850 -3.02552646
36 1.57036316 -1.23931850
37 3.81569642 1.57036316
38 -3.34650542 3.81569642
39 -2.26602344 -3.34650542
40 6.29566144 -2.26602344
41 -7.05739709 6.29566144
42 -7.18220062 -7.05739709
43 -1.48673090 -7.18220062
44 4.42413890 -1.48673090
45 0.90896982 4.42413890
46 1.04119622 0.90896982
47 -7.69665985 1.04119622
48 0.03411801 -7.69665985
49 -60.15217063 0.03411801
50 1.86944878 -60.15217063
51 -11.16533549 1.86944878
52 -0.88744342 -11.16533549
53 2.93900965 -0.88744342
54 -1.80482643 2.93900965
55 9.50811806 -1.80482643
56 -2.83966310 9.50811806
57 -9.22179538 -2.83966310
58 -2.44150558 -9.22179538
59 -0.43184374 -2.44150558
60 -1.02827479 -0.43184374
61 -5.10320705 -1.02827479
62 -3.98513514 -5.10320705
63 -0.91190499 -3.98513514
64 -0.50709189 -0.91190499
65 -7.46191063 -0.50709189
66 -2.53423840 -7.46191063
67 1.49140611 -2.53423840
68 1.67403575 1.49140611
69 16.82103364 1.67403575
70 -0.30840181 16.82103364
71 0.98443991 -0.30840181
72 4.16482027 0.98443991
73 -1.28522421 4.16482027
74 -0.71218995 -1.28522421
75 1.15582689 -0.71218995
76 -2.42131497 1.15582689
77 2.27787452 -2.42131497
78 3.07637549 2.27787452
79 -1.17590107 3.07637549
80 3.79162135 -1.17590107
81 1.18603410 3.79162135
82 -0.57040719 1.18603410
83 -1.41838956 -0.57040719
84 3.84859974 -1.41838956
85 -0.78513331 3.84859974
86 -0.03942466 -0.78513331
87 2.30275282 -0.03942466
88 4.57458620 2.30275282
89 -1.56761353 4.57458620
90 -36.87637093 -1.56761353
91 -2.14319237 -36.87637093
92 3.18185304 -2.14319237
93 -4.27041592 3.18185304
94 -1.66122063 -4.27041592
95 0.12683783 -1.66122063
96 0.83416526 0.12683783
97 1.61163114 0.83416526
98 5.24690041 1.61163114
99 -4.38573079 5.24690041
100 0.10279057 -4.38573079
101 -0.37067145 0.10279057
102 0.95807947 -0.37067145
103 -0.71106120 0.95807947
104 -7.81615748 -0.71106120
105 3.43829810 -7.81615748
106 -2.99669621 3.43829810
107 5.04610377 -2.99669621
108 -0.53655666 5.04610377
109 1.39305963 -0.53655666
110 3.10690764 1.39305963
111 12.77383429 3.10690764
112 -1.14437673 12.77383429
113 6.07268314 -1.14437673
114 7.36763461 6.07268314
115 6.05653430 7.36763461
116 1.93595655 6.05653430
117 0.71639527 1.93595655
118 9.29130822 0.71639527
119 -16.02570733 9.29130822
120 3.44601791 -16.02570733
121 -3.62001062 3.44601791
122 2.09152325 -3.62001062
123 8.68331559 2.09152325
124 -3.45074613 8.68331559
125 -2.45099865 -3.45074613
126 -0.80673769 -2.45099865
127 0.08524510 -0.80673769
128 1.01545912 0.08524510
129 -5.15009359 1.01545912
130 0.82611903 -5.15009359
131 6.99310990 0.82611903
132 -10.17659049 6.99310990
133 5.47234034 -10.17659049
134 3.00398573 5.47234034
135 4.66860596 3.00398573
136 -2.20101537 4.66860596
137 1.94803734 -2.20101537
138 -6.87595265 1.94803734
139 -2.32540682 -6.87595265
140 4.11103028 -2.32540682
141 3.19010173 4.11103028
142 4.59412835 3.19010173
143 -1.36329897 4.59412835
144 -2.12685719 -1.36329897
145 -6.74646819 -2.12685719
146 2.47544172 -6.74646819
147 0.68756651 2.47544172
148 -2.68596604 0.68756651
149 -2.36892780 -2.68596604
150 -2.54885539 -2.36892780
151 3.57804453 -2.54885539
152 1.33617412 3.57804453
153 4.03952142 1.33617412
154 4.98204479 4.03952142
155 -1.31830853 4.98204479
156 -0.92203515 -1.31830853
157 0.19337371 -0.92203515
158 2.43491008 0.19337371
159 -3.26960220 2.43491008
160 -2.44038811 -3.26960220
161 -0.71963010 -2.44038811
162 -3.99263659 -0.71963010
163 -3.77072092 -3.99263659
164 -2.15312600 -3.77072092
165 -3.24674569 -2.15312600
166 -1.83608331 -3.24674569
167 16.50590135 -1.83608331
168 0.63797287 16.50590135
169 -0.40304841 0.63797287
170 5.37993724 -0.40304841
171 -0.88021813 5.37993724
172 -4.60578639 -0.88021813
173 3.37565584 -4.60578639
174 -0.26940474 3.37565584
175 2.43758629 -0.26940474
176 -0.49465481 2.43758629
177 2.49544432 -0.49465481
178 -4.63202380 2.49544432
179 -7.55997019 -4.63202380
180 0.52870948 -7.55997019
181 4.58282599 0.52870948
182 -2.20893461 4.58282599
183 2.07563420 -2.20893461
184 14.31459426 2.07563420
185 -2.13854849 14.31459426
186 -4.36213848 -2.13854849
187 -2.15804976 -4.36213848
188 1.40904935 -2.15804976
189 2.26700987 1.40904935
190 0.34302076 2.26700987
191 5.09717900 0.34302076
192 -1.81967714 5.09717900
193 -7.56108800 -1.81967714
194 1.24965406 -7.56108800
195 -0.38876200 1.24965406
196 3.00565390 -0.38876200
197 0.59554089 3.00565390
198 5.56753601 0.59554089
199 -0.53504200 5.56753601
200 -0.48507610 -0.53504200
201 -7.61716461 -0.48507610
202 -1.13788162 -7.61716461
203 0.82056921 -1.13788162
204 4.49420743 0.82056921
205 6.07198460 4.49420743
206 -0.40554359 6.07198460
207 -2.63384521 -0.40554359
208 -1.69721352 -2.63384521
209 0.48490208 -1.69721352
210 -1.67901219 0.48490208
211 -2.00882552 -1.67901219
212 1.36812880 -2.00882552
213 -0.03647451 1.36812880
214 7.88734992 -0.03647451
215 2.11643758 7.88734992
216 2.62168301 2.11643758
217 -0.25094520 2.62168301
218 2.91518357 -0.25094520
219 -1.88119556 2.91518357
220 -0.68828858 -1.88119556
221 3.05119635 -0.68828858
222 3.83992548 3.05119635
223 2.36693883 3.83992548
224 5.92397801 2.36693883
225 3.13417406 5.92397801
226 1.50573430 3.13417406
227 2.01164277 1.50573430
228 -9.62416501 2.01164277
229 2.69184794 -9.62416501
230 2.79929529 2.69184794
231 0.87974818 2.79929529
232 3.34405889 0.87974818
233 -0.30235144 3.34405889
234 3.94996987 -0.30235144
235 0.47358506 3.94996987
236 2.12589530 0.47358506
237 12.05425704 2.12589530
238 3.16339433 12.05425704
239 3.96563153 3.16339433
240 4.90442564 3.96563153
241 -0.39332436 4.90442564
242 3.85283104 -0.39332436
243 2.00920584 3.85283104
244 -2.94279762 2.00920584
245 5.50182972 -2.94279762
246 1.34449164 5.50182972
247 0.68942991 1.34449164
248 0.25710498 0.68942991
249 1.40479263 0.25710498
250 -0.42885677 1.40479263
251 1.17178361 -0.42885677
252 -3.52124186 1.17178361
253 2.27308666 -3.52124186
254 -2.98677937 2.27308666
255 0.02762243 -2.98677937
256 -5.09430395 0.02762243
257 2.77324715 -5.09430395
258 -1.05890479 2.77324715
259 2.21322444 -1.05890479
260 6.13910667 2.21322444
261 -0.70850926 6.13910667
262 -5.77572289 -0.70850926
263 1.10086359 -5.77572289
264 -1.73486682 1.10086359
265 2.85343006 -1.73486682
266 2.87344894 2.85343006
267 -2.21617845 2.87344894
268 -3.23548053 -2.21617845
269 -6.07874977 -3.23548053
270 6.74508663 -6.07874977
271 0.40717298 6.74508663
272 -2.42893813 0.40717298
273 0.71193684 -2.42893813
274 1.76585502 0.71193684
275 0.90728667 1.76585502
276 2.44491350 0.90728667
277 -1.39801334 2.44491350
278 -3.06781768 -1.39801334
279 -1.23795358 -3.06781768
280 2.21488897 -1.23795358
281 -7.41407619 2.21488897
282 -0.67245610 -7.41407619
283 -1.84209798 -0.67245610
284 -0.92909488 -1.84209798
285 0.68195819 -0.92909488
286 -2.26684900 0.68195819
287 1.28217604 -2.26684900
288 6.00031020 1.28217604
289 NA 6.00031020
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.70454994 1.68990576
[2,] 9.99524882 0.70454994
[3,] 4.45935721 9.99524882
[4,] -11.88316006 4.45935721
[5,] 7.12688175 -11.88316006
[6,] 1.65892811 7.12688175
[7,] 6.89262213 1.65892811
[8,] -0.24696136 6.89262213
[9,] 2.34340118 -0.24696136
[10,] 2.80896871 2.34340118
[11,] -3.28487429 2.80896871
[12,] -2.28342423 -3.28487429
[13,] -12.08170006 -2.28342423
[14,] -10.30133718 -12.08170006
[15,] 10.00951167 -10.30133718
[16,] 1.36071703 10.00951167
[17,] -1.59993310 1.36071703
[18,] 6.63469950 -1.59993310
[19,] 2.94774447 6.63469950
[20,] -2.25280177 2.94774447
[21,] -0.90291023 -2.25280177
[22,] -9.40136603 -0.90291023
[23,] -6.84356398 -9.40136603
[24,] 2.58084831 -6.84356398
[25,] 0.41521288 2.58084831
[26,] -0.61447365 0.41521288
[27,] 3.45429090 -0.61447365
[28,] -4.60399248 3.45429090
[29,] 0.17137792 -4.60399248
[30,] 2.17066385 0.17137792
[31,] -0.73714313 2.17066385
[32,] -0.23234016 -0.73714313
[33,] -2.04586852 -0.23234016
[34,] -3.02552646 -2.04586852
[35,] -1.23931850 -3.02552646
[36,] 1.57036316 -1.23931850
[37,] 3.81569642 1.57036316
[38,] -3.34650542 3.81569642
[39,] -2.26602344 -3.34650542
[40,] 6.29566144 -2.26602344
[41,] -7.05739709 6.29566144
[42,] -7.18220062 -7.05739709
[43,] -1.48673090 -7.18220062
[44,] 4.42413890 -1.48673090
[45,] 0.90896982 4.42413890
[46,] 1.04119622 0.90896982
[47,] -7.69665985 1.04119622
[48,] 0.03411801 -7.69665985
[49,] -60.15217063 0.03411801
[50,] 1.86944878 -60.15217063
[51,] -11.16533549 1.86944878
[52,] -0.88744342 -11.16533549
[53,] 2.93900965 -0.88744342
[54,] -1.80482643 2.93900965
[55,] 9.50811806 -1.80482643
[56,] -2.83966310 9.50811806
[57,] -9.22179538 -2.83966310
[58,] -2.44150558 -9.22179538
[59,] -0.43184374 -2.44150558
[60,] -1.02827479 -0.43184374
[61,] -5.10320705 -1.02827479
[62,] -3.98513514 -5.10320705
[63,] -0.91190499 -3.98513514
[64,] -0.50709189 -0.91190499
[65,] -7.46191063 -0.50709189
[66,] -2.53423840 -7.46191063
[67,] 1.49140611 -2.53423840
[68,] 1.67403575 1.49140611
[69,] 16.82103364 1.67403575
[70,] -0.30840181 16.82103364
[71,] 0.98443991 -0.30840181
[72,] 4.16482027 0.98443991
[73,] -1.28522421 4.16482027
[74,] -0.71218995 -1.28522421
[75,] 1.15582689 -0.71218995
[76,] -2.42131497 1.15582689
[77,] 2.27787452 -2.42131497
[78,] 3.07637549 2.27787452
[79,] -1.17590107 3.07637549
[80,] 3.79162135 -1.17590107
[81,] 1.18603410 3.79162135
[82,] -0.57040719 1.18603410
[83,] -1.41838956 -0.57040719
[84,] 3.84859974 -1.41838956
[85,] -0.78513331 3.84859974
[86,] -0.03942466 -0.78513331
[87,] 2.30275282 -0.03942466
[88,] 4.57458620 2.30275282
[89,] -1.56761353 4.57458620
[90,] -36.87637093 -1.56761353
[91,] -2.14319237 -36.87637093
[92,] 3.18185304 -2.14319237
[93,] -4.27041592 3.18185304
[94,] -1.66122063 -4.27041592
[95,] 0.12683783 -1.66122063
[96,] 0.83416526 0.12683783
[97,] 1.61163114 0.83416526
[98,] 5.24690041 1.61163114
[99,] -4.38573079 5.24690041
[100,] 0.10279057 -4.38573079
[101,] -0.37067145 0.10279057
[102,] 0.95807947 -0.37067145
[103,] -0.71106120 0.95807947
[104,] -7.81615748 -0.71106120
[105,] 3.43829810 -7.81615748
[106,] -2.99669621 3.43829810
[107,] 5.04610377 -2.99669621
[108,] -0.53655666 5.04610377
[109,] 1.39305963 -0.53655666
[110,] 3.10690764 1.39305963
[111,] 12.77383429 3.10690764
[112,] -1.14437673 12.77383429
[113,] 6.07268314 -1.14437673
[114,] 7.36763461 6.07268314
[115,] 6.05653430 7.36763461
[116,] 1.93595655 6.05653430
[117,] 0.71639527 1.93595655
[118,] 9.29130822 0.71639527
[119,] -16.02570733 9.29130822
[120,] 3.44601791 -16.02570733
[121,] -3.62001062 3.44601791
[122,] 2.09152325 -3.62001062
[123,] 8.68331559 2.09152325
[124,] -3.45074613 8.68331559
[125,] -2.45099865 -3.45074613
[126,] -0.80673769 -2.45099865
[127,] 0.08524510 -0.80673769
[128,] 1.01545912 0.08524510
[129,] -5.15009359 1.01545912
[130,] 0.82611903 -5.15009359
[131,] 6.99310990 0.82611903
[132,] -10.17659049 6.99310990
[133,] 5.47234034 -10.17659049
[134,] 3.00398573 5.47234034
[135,] 4.66860596 3.00398573
[136,] -2.20101537 4.66860596
[137,] 1.94803734 -2.20101537
[138,] -6.87595265 1.94803734
[139,] -2.32540682 -6.87595265
[140,] 4.11103028 -2.32540682
[141,] 3.19010173 4.11103028
[142,] 4.59412835 3.19010173
[143,] -1.36329897 4.59412835
[144,] -2.12685719 -1.36329897
[145,] -6.74646819 -2.12685719
[146,] 2.47544172 -6.74646819
[147,] 0.68756651 2.47544172
[148,] -2.68596604 0.68756651
[149,] -2.36892780 -2.68596604
[150,] -2.54885539 -2.36892780
[151,] 3.57804453 -2.54885539
[152,] 1.33617412 3.57804453
[153,] 4.03952142 1.33617412
[154,] 4.98204479 4.03952142
[155,] -1.31830853 4.98204479
[156,] -0.92203515 -1.31830853
[157,] 0.19337371 -0.92203515
[158,] 2.43491008 0.19337371
[159,] -3.26960220 2.43491008
[160,] -2.44038811 -3.26960220
[161,] -0.71963010 -2.44038811
[162,] -3.99263659 -0.71963010
[163,] -3.77072092 -3.99263659
[164,] -2.15312600 -3.77072092
[165,] -3.24674569 -2.15312600
[166,] -1.83608331 -3.24674569
[167,] 16.50590135 -1.83608331
[168,] 0.63797287 16.50590135
[169,] -0.40304841 0.63797287
[170,] 5.37993724 -0.40304841
[171,] -0.88021813 5.37993724
[172,] -4.60578639 -0.88021813
[173,] 3.37565584 -4.60578639
[174,] -0.26940474 3.37565584
[175,] 2.43758629 -0.26940474
[176,] -0.49465481 2.43758629
[177,] 2.49544432 -0.49465481
[178,] -4.63202380 2.49544432
[179,] -7.55997019 -4.63202380
[180,] 0.52870948 -7.55997019
[181,] 4.58282599 0.52870948
[182,] -2.20893461 4.58282599
[183,] 2.07563420 -2.20893461
[184,] 14.31459426 2.07563420
[185,] -2.13854849 14.31459426
[186,] -4.36213848 -2.13854849
[187,] -2.15804976 -4.36213848
[188,] 1.40904935 -2.15804976
[189,] 2.26700987 1.40904935
[190,] 0.34302076 2.26700987
[191,] 5.09717900 0.34302076
[192,] -1.81967714 5.09717900
[193,] -7.56108800 -1.81967714
[194,] 1.24965406 -7.56108800
[195,] -0.38876200 1.24965406
[196,] 3.00565390 -0.38876200
[197,] 0.59554089 3.00565390
[198,] 5.56753601 0.59554089
[199,] -0.53504200 5.56753601
[200,] -0.48507610 -0.53504200
[201,] -7.61716461 -0.48507610
[202,] -1.13788162 -7.61716461
[203,] 0.82056921 -1.13788162
[204,] 4.49420743 0.82056921
[205,] 6.07198460 4.49420743
[206,] -0.40554359 6.07198460
[207,] -2.63384521 -0.40554359
[208,] -1.69721352 -2.63384521
[209,] 0.48490208 -1.69721352
[210,] -1.67901219 0.48490208
[211,] -2.00882552 -1.67901219
[212,] 1.36812880 -2.00882552
[213,] -0.03647451 1.36812880
[214,] 7.88734992 -0.03647451
[215,] 2.11643758 7.88734992
[216,] 2.62168301 2.11643758
[217,] -0.25094520 2.62168301
[218,] 2.91518357 -0.25094520
[219,] -1.88119556 2.91518357
[220,] -0.68828858 -1.88119556
[221,] 3.05119635 -0.68828858
[222,] 3.83992548 3.05119635
[223,] 2.36693883 3.83992548
[224,] 5.92397801 2.36693883
[225,] 3.13417406 5.92397801
[226,] 1.50573430 3.13417406
[227,] 2.01164277 1.50573430
[228,] -9.62416501 2.01164277
[229,] 2.69184794 -9.62416501
[230,] 2.79929529 2.69184794
[231,] 0.87974818 2.79929529
[232,] 3.34405889 0.87974818
[233,] -0.30235144 3.34405889
[234,] 3.94996987 -0.30235144
[235,] 0.47358506 3.94996987
[236,] 2.12589530 0.47358506
[237,] 12.05425704 2.12589530
[238,] 3.16339433 12.05425704
[239,] 3.96563153 3.16339433
[240,] 4.90442564 3.96563153
[241,] -0.39332436 4.90442564
[242,] 3.85283104 -0.39332436
[243,] 2.00920584 3.85283104
[244,] -2.94279762 2.00920584
[245,] 5.50182972 -2.94279762
[246,] 1.34449164 5.50182972
[247,] 0.68942991 1.34449164
[248,] 0.25710498 0.68942991
[249,] 1.40479263 0.25710498
[250,] -0.42885677 1.40479263
[251,] 1.17178361 -0.42885677
[252,] -3.52124186 1.17178361
[253,] 2.27308666 -3.52124186
[254,] -2.98677937 2.27308666
[255,] 0.02762243 -2.98677937
[256,] -5.09430395 0.02762243
[257,] 2.77324715 -5.09430395
[258,] -1.05890479 2.77324715
[259,] 2.21322444 -1.05890479
[260,] 6.13910667 2.21322444
[261,] -0.70850926 6.13910667
[262,] -5.77572289 -0.70850926
[263,] 1.10086359 -5.77572289
[264,] -1.73486682 1.10086359
[265,] 2.85343006 -1.73486682
[266,] 2.87344894 2.85343006
[267,] -2.21617845 2.87344894
[268,] -3.23548053 -2.21617845
[269,] -6.07874977 -3.23548053
[270,] 6.74508663 -6.07874977
[271,] 0.40717298 6.74508663
[272,] -2.42893813 0.40717298
[273,] 0.71193684 -2.42893813
[274,] 1.76585502 0.71193684
[275,] 0.90728667 1.76585502
[276,] 2.44491350 0.90728667
[277,] -1.39801334 2.44491350
[278,] -3.06781768 -1.39801334
[279,] -1.23795358 -3.06781768
[280,] 2.21488897 -1.23795358
[281,] -7.41407619 2.21488897
[282,] -0.67245610 -7.41407619
[283,] -1.84209798 -0.67245610
[284,] -0.92909488 -1.84209798
[285,] 0.68195819 -0.92909488
[286,] -2.26684900 0.68195819
[287,] 1.28217604 -2.26684900
[288,] 6.00031020 1.28217604
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.70454994 1.68990576
2 9.99524882 0.70454994
3 4.45935721 9.99524882
4 -11.88316006 4.45935721
5 7.12688175 -11.88316006
6 1.65892811 7.12688175
7 6.89262213 1.65892811
8 -0.24696136 6.89262213
9 2.34340118 -0.24696136
10 2.80896871 2.34340118
11 -3.28487429 2.80896871
12 -2.28342423 -3.28487429
13 -12.08170006 -2.28342423
14 -10.30133718 -12.08170006
15 10.00951167 -10.30133718
16 1.36071703 10.00951167
17 -1.59993310 1.36071703
18 6.63469950 -1.59993310
19 2.94774447 6.63469950
20 -2.25280177 2.94774447
21 -0.90291023 -2.25280177
22 -9.40136603 -0.90291023
23 -6.84356398 -9.40136603
24 2.58084831 -6.84356398
25 0.41521288 2.58084831
26 -0.61447365 0.41521288
27 3.45429090 -0.61447365
28 -4.60399248 3.45429090
29 0.17137792 -4.60399248
30 2.17066385 0.17137792
31 -0.73714313 2.17066385
32 -0.23234016 -0.73714313
33 -2.04586852 -0.23234016
34 -3.02552646 -2.04586852
35 -1.23931850 -3.02552646
36 1.57036316 -1.23931850
37 3.81569642 1.57036316
38 -3.34650542 3.81569642
39 -2.26602344 -3.34650542
40 6.29566144 -2.26602344
41 -7.05739709 6.29566144
42 -7.18220062 -7.05739709
43 -1.48673090 -7.18220062
44 4.42413890 -1.48673090
45 0.90896982 4.42413890
46 1.04119622 0.90896982
47 -7.69665985 1.04119622
48 0.03411801 -7.69665985
49 -60.15217063 0.03411801
50 1.86944878 -60.15217063
51 -11.16533549 1.86944878
52 -0.88744342 -11.16533549
53 2.93900965 -0.88744342
54 -1.80482643 2.93900965
55 9.50811806 -1.80482643
56 -2.83966310 9.50811806
57 -9.22179538 -2.83966310
58 -2.44150558 -9.22179538
59 -0.43184374 -2.44150558
60 -1.02827479 -0.43184374
61 -5.10320705 -1.02827479
62 -3.98513514 -5.10320705
63 -0.91190499 -3.98513514
64 -0.50709189 -0.91190499
65 -7.46191063 -0.50709189
66 -2.53423840 -7.46191063
67 1.49140611 -2.53423840
68 1.67403575 1.49140611
69 16.82103364 1.67403575
70 -0.30840181 16.82103364
71 0.98443991 -0.30840181
72 4.16482027 0.98443991
73 -1.28522421 4.16482027
74 -0.71218995 -1.28522421
75 1.15582689 -0.71218995
76 -2.42131497 1.15582689
77 2.27787452 -2.42131497
78 3.07637549 2.27787452
79 -1.17590107 3.07637549
80 3.79162135 -1.17590107
81 1.18603410 3.79162135
82 -0.57040719 1.18603410
83 -1.41838956 -0.57040719
84 3.84859974 -1.41838956
85 -0.78513331 3.84859974
86 -0.03942466 -0.78513331
87 2.30275282 -0.03942466
88 4.57458620 2.30275282
89 -1.56761353 4.57458620
90 -36.87637093 -1.56761353
91 -2.14319237 -36.87637093
92 3.18185304 -2.14319237
93 -4.27041592 3.18185304
94 -1.66122063 -4.27041592
95 0.12683783 -1.66122063
96 0.83416526 0.12683783
97 1.61163114 0.83416526
98 5.24690041 1.61163114
99 -4.38573079 5.24690041
100 0.10279057 -4.38573079
101 -0.37067145 0.10279057
102 0.95807947 -0.37067145
103 -0.71106120 0.95807947
104 -7.81615748 -0.71106120
105 3.43829810 -7.81615748
106 -2.99669621 3.43829810
107 5.04610377 -2.99669621
108 -0.53655666 5.04610377
109 1.39305963 -0.53655666
110 3.10690764 1.39305963
111 12.77383429 3.10690764
112 -1.14437673 12.77383429
113 6.07268314 -1.14437673
114 7.36763461 6.07268314
115 6.05653430 7.36763461
116 1.93595655 6.05653430
117 0.71639527 1.93595655
118 9.29130822 0.71639527
119 -16.02570733 9.29130822
120 3.44601791 -16.02570733
121 -3.62001062 3.44601791
122 2.09152325 -3.62001062
123 8.68331559 2.09152325
124 -3.45074613 8.68331559
125 -2.45099865 -3.45074613
126 -0.80673769 -2.45099865
127 0.08524510 -0.80673769
128 1.01545912 0.08524510
129 -5.15009359 1.01545912
130 0.82611903 -5.15009359
131 6.99310990 0.82611903
132 -10.17659049 6.99310990
133 5.47234034 -10.17659049
134 3.00398573 5.47234034
135 4.66860596 3.00398573
136 -2.20101537 4.66860596
137 1.94803734 -2.20101537
138 -6.87595265 1.94803734
139 -2.32540682 -6.87595265
140 4.11103028 -2.32540682
141 3.19010173 4.11103028
142 4.59412835 3.19010173
143 -1.36329897 4.59412835
144 -2.12685719 -1.36329897
145 -6.74646819 -2.12685719
146 2.47544172 -6.74646819
147 0.68756651 2.47544172
148 -2.68596604 0.68756651
149 -2.36892780 -2.68596604
150 -2.54885539 -2.36892780
151 3.57804453 -2.54885539
152 1.33617412 3.57804453
153 4.03952142 1.33617412
154 4.98204479 4.03952142
155 -1.31830853 4.98204479
156 -0.92203515 -1.31830853
157 0.19337371 -0.92203515
158 2.43491008 0.19337371
159 -3.26960220 2.43491008
160 -2.44038811 -3.26960220
161 -0.71963010 -2.44038811
162 -3.99263659 -0.71963010
163 -3.77072092 -3.99263659
164 -2.15312600 -3.77072092
165 -3.24674569 -2.15312600
166 -1.83608331 -3.24674569
167 16.50590135 -1.83608331
168 0.63797287 16.50590135
169 -0.40304841 0.63797287
170 5.37993724 -0.40304841
171 -0.88021813 5.37993724
172 -4.60578639 -0.88021813
173 3.37565584 -4.60578639
174 -0.26940474 3.37565584
175 2.43758629 -0.26940474
176 -0.49465481 2.43758629
177 2.49544432 -0.49465481
178 -4.63202380 2.49544432
179 -7.55997019 -4.63202380
180 0.52870948 -7.55997019
181 4.58282599 0.52870948
182 -2.20893461 4.58282599
183 2.07563420 -2.20893461
184 14.31459426 2.07563420
185 -2.13854849 14.31459426
186 -4.36213848 -2.13854849
187 -2.15804976 -4.36213848
188 1.40904935 -2.15804976
189 2.26700987 1.40904935
190 0.34302076 2.26700987
191 5.09717900 0.34302076
192 -1.81967714 5.09717900
193 -7.56108800 -1.81967714
194 1.24965406 -7.56108800
195 -0.38876200 1.24965406
196 3.00565390 -0.38876200
197 0.59554089 3.00565390
198 5.56753601 0.59554089
199 -0.53504200 5.56753601
200 -0.48507610 -0.53504200
201 -7.61716461 -0.48507610
202 -1.13788162 -7.61716461
203 0.82056921 -1.13788162
204 4.49420743 0.82056921
205 6.07198460 4.49420743
206 -0.40554359 6.07198460
207 -2.63384521 -0.40554359
208 -1.69721352 -2.63384521
209 0.48490208 -1.69721352
210 -1.67901219 0.48490208
211 -2.00882552 -1.67901219
212 1.36812880 -2.00882552
213 -0.03647451 1.36812880
214 7.88734992 -0.03647451
215 2.11643758 7.88734992
216 2.62168301 2.11643758
217 -0.25094520 2.62168301
218 2.91518357 -0.25094520
219 -1.88119556 2.91518357
220 -0.68828858 -1.88119556
221 3.05119635 -0.68828858
222 3.83992548 3.05119635
223 2.36693883 3.83992548
224 5.92397801 2.36693883
225 3.13417406 5.92397801
226 1.50573430 3.13417406
227 2.01164277 1.50573430
228 -9.62416501 2.01164277
229 2.69184794 -9.62416501
230 2.79929529 2.69184794
231 0.87974818 2.79929529
232 3.34405889 0.87974818
233 -0.30235144 3.34405889
234 3.94996987 -0.30235144
235 0.47358506 3.94996987
236 2.12589530 0.47358506
237 12.05425704 2.12589530
238 3.16339433 12.05425704
239 3.96563153 3.16339433
240 4.90442564 3.96563153
241 -0.39332436 4.90442564
242 3.85283104 -0.39332436
243 2.00920584 3.85283104
244 -2.94279762 2.00920584
245 5.50182972 -2.94279762
246 1.34449164 5.50182972
247 0.68942991 1.34449164
248 0.25710498 0.68942991
249 1.40479263 0.25710498
250 -0.42885677 1.40479263
251 1.17178361 -0.42885677
252 -3.52124186 1.17178361
253 2.27308666 -3.52124186
254 -2.98677937 2.27308666
255 0.02762243 -2.98677937
256 -5.09430395 0.02762243
257 2.77324715 -5.09430395
258 -1.05890479 2.77324715
259 2.21322444 -1.05890479
260 6.13910667 2.21322444
261 -0.70850926 6.13910667
262 -5.77572289 -0.70850926
263 1.10086359 -5.77572289
264 -1.73486682 1.10086359
265 2.85343006 -1.73486682
266 2.87344894 2.85343006
267 -2.21617845 2.87344894
268 -3.23548053 -2.21617845
269 -6.07874977 -3.23548053
270 6.74508663 -6.07874977
271 0.40717298 6.74508663
272 -2.42893813 0.40717298
273 0.71193684 -2.42893813
274 1.76585502 0.71193684
275 0.90728667 1.76585502
276 2.44491350 0.90728667
277 -1.39801334 2.44491350
278 -3.06781768 -1.39801334
279 -1.23795358 -3.06781768
280 2.21488897 -1.23795358
281 -7.41407619 2.21488897
282 -0.67245610 -7.41407619
283 -1.84209798 -0.67245610
284 -0.92909488 -1.84209798
285 0.68195819 -0.92909488
286 -2.26684900 0.68195819
287 1.28217604 -2.26684900
288 6.00031020 1.28217604
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7ibq31354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8sbiq1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9i6ka1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/108nez1354987363.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1113hi1354987363.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/123eaa1354987363.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13m69d1354987363.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14km9s1354987363.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/fisher/rcomp/tmp/15m9p11354987363.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/fisher/rcomp/tmp/16x5ta1354987363.tab")
+ }
>
> try(system("convert tmp/10qzz1354987363.ps tmp/10qzz1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gxnj1354987363.ps tmp/2gxnj1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/35wff1354987363.ps tmp/35wff1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z4sc1354987363.ps tmp/4z4sc1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jicn1354987363.ps tmp/5jicn1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/69zun1354987363.ps tmp/69zun1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ibq31354987363.ps tmp/7ibq31354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sbiq1354987363.ps tmp/8sbiq1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/9i6ka1354987363.ps tmp/9i6ka1354987363.png",intern=TRUE))
character(0)
> try(system("convert tmp/108nez1354987363.ps tmp/108nez1354987363.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.606 1.592 18.207