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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Type 'contributors()' for more information and
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,'logins'
+ ,'logins_t'
+ ,'compendium_views_info'
+ ,'compendium_views_info_t'
+ ,'shared_compendiums'
+ ,'shared_compendiums_t'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p1_t'
+ ,'totsize'
+ ,'totsize_t')
+ ,1:197))
> y <- array(NA,dim=c(15,197),dimnames=list(c('Pop','pageviews','pageviews_t','time_in_rfc','time_in_rfc_t','logins','logins_t','compendium_views_info','compendium_views_info_t','shared_compendiums','shared_compendiums_t','feedback_messages_p1','feedback_messages_p1_t','totsize','totsize_t'),1:197))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
pageviews Pop pageviews_t time_in_rfc time_in_rfc_t logins logins_t
1 1418 1 1418 210907 210907 56 56
2 869 1 869 120982 120982 56 56
3 1530 1 1530 176508 176508 54 54
4 2172 1 2172 179321 179321 89 89
5 901 1 901 123185 123185 40 40
6 463 1 463 52746 52746 25 25
7 3201 1 3201 385534 385534 92 92
8 371 1 371 33170 33170 18 18
9 1192 0 0 101645 0 63 0
10 1583 1 1583 149061 149061 44 44
11 1439 1 1439 165446 165446 33 33
12 1764 1 1764 237213 237213 84 84
13 1495 1 1495 173326 173326 88 88
14 1373 1 1373 133131 133131 55 55
15 2187 1 2187 258873 258873 60 60
16 1491 1 1491 180083 180083 66 66
17 4041 1 4041 324799 324799 154 154
18 1706 1 1706 230964 230964 53 53
19 2152 1 2152 236785 236785 119 119
20 1036 1 1036 135473 135473 41 41
21 1882 1 1882 202925 202925 61 61
22 1929 1 1929 215147 215147 58 58
23 2242 1 2242 344297 344297 75 75
24 1220 1 1220 153935 153935 33 33
25 1289 1 1289 132943 132943 40 40
26 2515 1 2515 174724 174724 92 92
27 2147 1 2147 174415 174415 100 100
28 2352 1 2352 225548 225548 112 112
29 1638 1 1638 223632 223632 73 73
30 1222 1 1222 124817 124817 40 40
31 1812 1 1812 221698 221698 45 45
32 1677 1 1677 210767 210767 60 60
33 1579 1 1579 170266 170266 62 62
34 1731 1 1731 260561 260561 75 75
35 807 1 807 84853 84853 31 31
36 2452 1 2452 294424 294424 77 77
37 829 0 0 101011 0 34 0
38 1940 1 1940 215641 215641 46 46
39 2662 1 2662 325107 325107 99 99
40 186 0 0 7176 0 17 0
41 1499 1 1499 167542 167542 66 66
42 865 1 865 106408 106408 30 30
43 1793 0 0 96560 0 76 0
44 2527 1 2527 265769 265769 146 146
45 2747 1 2747 269651 269651 67 67
46 1324 1 1324 149112 149112 56 56
47 2702 0 0 175824 0 107 0
48 1383 1 1383 152871 152871 58 58
49 1179 1 1179 111665 111665 34 34
50 2099 1 2099 116408 116408 61 61
51 4308 1 4308 362301 362301 119 119
52 918 1 918 78800 78800 42 42
53 1831 1 1831 183167 183167 66 66
54 3373 1 3373 277965 277965 89 89
55 1713 1 1713 150629 150629 44 44
56 1438 1 1438 168809 168809 66 66
57 496 1 496 24188 24188 24 24
58 2253 1 2253 329267 329267 259 259
59 744 1 744 65029 65029 17 17
60 1161 1 1161 101097 101097 64 64
61 2352 1 2352 218946 218946 41 41
62 2144 1 2144 244052 244052 68 68
63 4691 0 0 341570 0 168 0
64 1112 0 0 103597 0 43 0
65 2694 1 2694 233328 233328 132 132
66 1973 1 1973 256462 256462 105 105
67 1769 1 1769 206161 206161 71 71
68 3148 1 3148 311473 311473 112 112
69 2474 1 2474 235800 235800 94 94
70 2084 1 2084 177939 177939 82 82
71 1954 1 1954 207176 207176 70 70
72 1226 1 1226 196553 196553 57 57
73 1389 1 1389 174184 174184 53 53
74 1496 1 1496 143246 143246 103 103
75 2269 1 2269 187559 187559 121 121
76 1833 1 1833 187681 187681 62 62
77 1268 1 1268 119016 119016 52 52
78 1943 1 1943 182192 182192 52 52
79 893 1 893 73566 73566 32 32
80 1762 1 1762 194979 194979 62 62
81 1403 1 1403 167488 167488 45 45
82 1425 1 1425 143756 143756 46 46
83 1857 1 1857 275541 275541 63 63
84 1840 1 1840 243199 243199 75 75
85 1502 1 1502 182999 182999 88 88
86 1441 1 1441 135649 135649 46 46
87 1420 1 1420 152299 152299 53 53
88 1416 1 1416 120221 120221 37 37
89 2970 1 2970 346485 346485 90 90
90 1317 1 1317 145790 145790 63 63
91 1644 1 1644 193339 193339 78 78
92 870 1 870 80953 80953 25 25
93 1654 1 1654 122774 122774 45 45
94 1054 1 1054 130585 130585 46 46
95 937 0 0 112611 0 41 0
96 3004 1 3004 286468 286468 144 144
97 2008 1 2008 241066 241066 82 82
98 2547 1 2547 148446 148446 91 91
99 1885 1 1885 204713 204713 71 71
100 1626 1 1626 182079 182079 63 63
101 1468 1 1468 140344 140344 53 53
102 2445 1 2445 220516 220516 62 62
103 1964 1 1964 243060 243060 63 63
104 1381 1 1381 162765 162765 32 32
105 1369 1 1369 182613 182613 39 39
106 1659 1 1659 232138 232138 62 62
107 2888 1 2888 265318 265318 117 117
108 1290 0 0 85574 0 34 0
109 2845 1 2845 310839 310839 92 92
110 1982 1 1982 225060 225060 93 93
111 1904 1 1904 232317 232317 54 54
112 1391 1 1391 144966 144966 144 144
113 602 1 602 43287 43287 14 14
114 1743 1 1743 155754 155754 61 61
115 1559 1 1559 164709 164709 109 109
116 2014 1 2014 201940 201940 38 38
117 2143 1 2143 235454 235454 73 73
118 2146 0 0 220801 0 75 0
119 874 1 874 99466 99466 50 50
120 1590 0 0 92661 0 61 0
121 1590 0 0 133328 0 55 0
122 1210 0 0 61361 0 77 0
123 2072 0 0 125930 0 75 0
124 1281 1 1281 100750 100750 72 72
125 1401 1 1401 224549 224549 50 50
126 834 0 0 82316 0 32 0
127 1105 0 0 102010 0 53 0
128 1272 0 0 101523 0 42 0
129 1944 1 1944 243511 243511 71 71
130 391 1 391 22938 22938 10 10
131 761 0 0 41566 0 35 0
132 1605 1 1605 152474 152474 65 65
133 530 1 530 61857 61857 25 25
134 1988 0 0 99923 0 66 0
135 1386 1 1386 132487 132487 41 41
136 2395 1 2395 317394 317394 86 86
137 387 1 387 21054 21054 16 16
138 1742 1 1742 209641 209641 42 42
139 620 0 0 22648 0 19 0
140 449 1 449 31414 31414 19 19
141 800 0 0 46698 0 45 0
142 1684 0 0 131698 0 65 0
143 1050 0 0 91735 0 35 0
144 2699 1 2699 244749 244749 95 95
145 1606 1 1606 184510 184510 49 49
146 1502 0 0 79863 0 37 0
147 1204 1 1204 128423 128423 64 64
148 1138 1 1138 97839 97839 38 38
149 568 1 568 38214 38214 34 34
150 1459 1 1459 151101 151101 32 32
151 2158 1 2158 272458 272458 65 65
152 1111 1 1111 172494 172494 52 52
153 1421 0 0 108043 0 62 0
154 2833 1 2833 328107 328107 65 65
155 1955 1 1955 250579 250579 83 83
156 2922 1 2922 351067 351067 95 95
157 1002 1 1002 158015 158015 29 29
158 1060 0 0 98866 0 18 0
159 956 1 956 85439 85439 33 33
160 2186 1 2186 229242 229242 247 247
161 3604 1 3604 351619 351619 139 139
162 1035 1 1035 84207 84207 29 29
163 1417 0 0 120445 0 118 0
164 3261 1 3261 324598 324598 110 110
165 1587 1 1587 131069 131069 67 67
166 1424 1 1424 204271 204271 42 42
167 1701 1 1701 165543 165543 65 65
168 1249 1 1249 141722 141722 94 94
169 946 0 0 116048 0 64 0
170 1926 0 0 250047 0 81 0
171 3352 1 3352 299775 299775 95 95
172 1641 1 1641 195838 195838 67 67
173 2035 1 2035 173260 173260 63 63
174 2312 1 2312 254488 254488 83 83
175 1369 1 1369 104389 104389 45 45
176 1577 0 0 136084 0 30 0
177 2201 1 2201 199476 199476 70 70
178 961 0 0 92499 0 32 0
179 1900 1 1900 224330 224330 83 83
180 1254 0 0 135781 0 31 0
181 1335 0 0 74408 0 67 0
182 1597 0 0 81240 0 66 0
183 207 1 207 14688 14688 10 10
184 1645 1 1645 181633 181633 70 70
185 2429 1 2429 271856 271856 103 103
186 151 1 151 7199 7199 5 5
187 474 1 474 46660 46660 20 20
188 141 1 141 17547 17547 5 5
189 1639 0 0 133368 0 36 0
190 872 1 872 95227 95227 34 34
191 1318 1 1318 152601 152601 48 48
192 1018 0 0 98146 0 40 0
193 1383 0 0 79619 0 43 0
194 1314 0 0 59194 0 31 0
195 1335 0 0 139942 0 42 0
196 1403 0 0 118612 0 46 0
197 910 0 0 72880 0 33 0
compendium_views_info compendium_views_info_t shared_compendiums
1 396 396 3
2 297 297 4
3 559 559 12
4 967 967 2
5 270 270 1
6 143 143 3
7 1562 1562 0
8 109 109 0
9 371 0 0
10 656 656 5
11 511 511 0
12 655 655 0
13 465 465 7
14 525 525 7
15 885 885 3
16 497 497 9
17 1436 1436 0
18 612 612 4
19 865 865 3
20 385 385 0
21 567 567 7
22 639 639 0
23 963 963 1
24 398 398 5
25 410 410 7
26 966 966 0
27 801 801 0
28 892 892 5
29 513 513 0
30 469 469 0
31 683 683 0
32 643 643 3
33 535 535 4
34 625 625 1
35 264 264 4
36 992 992 2
37 238 0 0
38 818 818 0
39 937 937 0
40 70 0 0
41 507 507 2
42 260 260 1
43 503 0 0
44 927 927 2
45 1269 1269 10
46 537 537 6
47 910 0 0
48 532 532 5
49 345 345 4
50 918 918 1
51 1635 1635 2
52 330 330 2
53 557 557 0
54 1178 1178 8
55 740 740 3
56 452 452 0
57 218 218 0
58 764 764 8
59 255 255 5
60 454 454 3
61 866 866 1
62 574 574 5
63 1276 0 1
64 379 0 1
65 825 825 5
66 798 798 0
67 663 663 12
68 1069 1069 8
69 921 921 8
70 858 858 8
71 711 711 8
72 503 503 2
73 382 382 0
74 464 464 5
75 717 717 8
76 690 690 2
77 462 462 5
78 657 657 12
79 385 385 6
80 577 577 7
81 619 619 2
82 479 479 0
83 817 817 4
84 752 752 3
85 430 430 6
86 451 451 2
87 537 537 0
88 519 519 1
89 1000 1000 0
90 637 637 5
91 465 465 2
92 437 437 0
93 711 711 0
94 299 299 5
95 248 0 0
96 1162 1162 1
97 714 714 0
98 905 905 1
99 649 649 1
100 512 512 2
101 472 472 6
102 905 905 1
103 786 786 4
104 489 489 2
105 479 479 3
106 617 617 0
107 925 925 10
108 351 0 0
109 1144 1144 9
110 669 669 7
111 707 707 0
112 458 458 0
113 214 214 4
114 599 599 4
115 572 572 0
116 897 897 0
117 819 819 0
118 720 0 1
119 273 273 0
120 508 0 1
121 506 0 0
122 451 0 0
123 699 0 4
124 407 407 0
125 465 465 4
126 245 0 4
127 370 0 3
128 316 0 0
129 603 603 0
130 154 154 0
131 229 0 5
132 577 577 0
133 192 192 4
134 617 0 0
135 411 411 0
136 975 975 1
137 146 146 0
138 705 705 5
139 184 0 0
140 200 200 0
141 274 0 0
142 502 0 0
143 382 0 0
144 964 964 2
145 537 537 7
146 438 0 1
147 369 369 8
148 417 417 2
149 276 276 0
150 514 514 2
151 822 822 0
152 389 389 0
153 466 0 1
154 1255 1255 3
155 694 694 0
156 1024 1024 3
157 400 400 0
158 397 0 0
159 350 350 0
160 719 719 4
161 1277 1277 4
162 356 356 11
163 457 0 0
164 1402 1402 0
165 600 600 4
166 480 480 0
167 595 595 1
168 436 436 0
169 230 0 0
170 651 0 0
171 1367 1367 9
172 564 564 1
173 716 716 3
174 747 747 10
175 467 467 5
176 671 0 0
177 861 861 2
178 319 0 0
179 612 612 1
180 433 0 2
181 434 0 4
182 503 0 0
183 85 85 0
184 564 564 2
185 824 824 1
186 74 74 0
187 259 259 0
188 69 69 0
189 535 0 1
190 239 239 0
191 438 438 2
192 459 0 0
193 426 0 3
194 288 0 6
195 498 0 0
196 454 0 2
197 376 0 0
shared_compendiums_t feedback_messages_p1 feedback_messages_p1_t totsize
1 3 115 115 112285
2 4 109 109 84786
3 12 146 146 83123
4 2 116 116 101193
5 1 68 68 38361
6 3 101 101 68504
7 0 96 96 119182
8 0 67 67 22807
9 0 44 0 17140
10 5 100 100 116174
11 0 93 93 57635
12 0 140 140 66198
13 7 166 166 71701
14 7 99 99 57793
15 3 139 139 80444
16 9 130 130 53855
17 0 181 181 97668
18 4 116 116 133824
19 3 116 116 101481
20 0 88 88 99645
21 7 139 139 114789
22 0 135 135 99052
23 1 108 108 67654
24 5 89 89 65553
25 7 156 156 97500
26 0 129 129 69112
27 0 118 118 82753
28 5 118 118 85323
29 0 125 125 72654
30 0 95 95 30727
31 0 126 126 77873
32 3 135 135 117478
33 4 154 154 74007
34 1 165 165 90183
35 4 113 113 61542
36 2 127 127 101494
37 0 52 0 27570
38 0 121 121 55813
39 0 136 136 79215
40 0 0 0 1423
41 2 108 108 55461
42 1 46 46 31081
43 0 54 0 22996
44 2 124 124 83122
45 10 115 115 70106
46 6 128 128 60578
47 0 80 0 39992
48 5 97 97 79892
49 4 104 104 49810
50 1 59 59 71570
51 2 125 125 100708
52 2 82 82 33032
53 0 149 149 82875
54 8 149 149 139077
55 3 122 122 71595
56 0 118 118 72260
57 0 12 12 5950
58 8 144 144 115762
59 5 67 67 32551
60 3 52 52 31701
61 1 108 108 80670
62 5 166 166 143558
63 0 80 0 117105
64 0 60 0 23789
65 5 107 107 120733
66 0 127 127 105195
67 12 107 107 73107
68 8 146 146 132068
69 8 84 84 149193
70 8 141 141 46821
71 8 123 123 87011
72 2 111 111 95260
73 0 98 98 55183
74 5 105 105 106671
75 8 135 135 73511
76 2 107 107 92945
77 5 85 85 78664
78 12 155 155 70054
79 6 88 88 22618
80 7 155 155 74011
81 2 104 104 83737
82 0 132 132 69094
83 4 127 127 93133
84 3 108 108 95536
85 6 129 129 225920
86 2 116 116 62133
87 0 122 122 61370
88 1 85 85 43836
89 0 147 147 106117
90 5 99 99 38692
91 2 87 87 84651
92 0 28 28 56622
93 0 90 90 15986
94 5 109 109 95364
95 0 78 0 26706
96 1 111 111 89691
97 0 158 158 67267
98 1 141 141 126846
99 1 122 122 41140
100 2 124 124 102860
101 6 93 93 51715
102 1 124 124 55801
103 4 112 112 111813
104 2 108 108 120293
105 3 99 99 138599
106 0 117 117 161647
107 10 199 199 115929
108 0 78 0 24266
109 9 91 91 162901
110 7 158 158 109825
111 0 126 126 129838
112 0 122 122 37510
113 4 71 71 43750
114 4 75 75 40652
115 0 115 115 87771
116 0 119 119 85872
117 0 124 124 89275
118 0 72 0 44418
119 0 91 91 192565
120 0 45 0 35232
121 0 78 0 40909
122 0 39 0 13294
123 0 68 0 32387
124 0 119 119 140867
125 4 117 117 120662
126 0 39 0 21233
127 0 50 0 44332
128 0 88 0 61056
129 0 155 155 101338
130 0 0 0 1168
131 0 36 0 13497
132 0 123 123 65567
133 4 32 32 25162
134 0 99 0 32334
135 0 136 136 40735
136 1 117 117 91413
137 0 0 0 855
138 5 88 88 97068
139 0 39 0 44339
140 0 25 25 14116
141 0 52 0 10288
142 0 75 0 65622
143 0 71 0 16563
144 2 124 124 76643
145 7 151 151 110681
146 0 71 0 29011
147 8 145 145 92696
148 2 87 87 94785
149 0 27 27 8773
150 2 131 131 83209
151 0 162 162 93815
152 0 165 165 86687
153 0 54 0 34553
154 3 159 159 105547
155 0 147 147 103487
156 3 170 170 213688
157 0 119 119 71220
158 0 49 0 23517
159 0 104 104 56926
160 4 120 120 91721
161 4 150 150 115168
162 11 112 112 111194
163 0 59 0 51009
164 0 136 136 135777
165 4 107 107 51513
166 0 130 130 74163
167 1 115 115 51633
168 0 107 107 75345
169 0 75 0 33416
170 0 71 0 83305
171 9 120 120 98952
172 1 116 116 102372
173 3 79 79 37238
174 10 150 150 103772
175 5 156 156 123969
176 0 51 0 27142
177 2 118 118 135400
178 0 71 0 21399
179 1 144 144 130115
180 0 47 0 24874
181 0 28 0 34988
182 0 68 0 45549
183 0 0 0 6023
184 2 110 110 64466
185 1 147 147 54990
186 0 0 0 1644
187 0 15 15 6179
188 0 4 4 3926
189 0 64 0 32755
190 0 111 111 34777
191 2 85 85 73224
192 0 68 0 27114
193 0 40 0 20760
194 0 80 0 37636
195 0 88 0 65461
196 0 48 0 30080
197 0 76 0 24094
totsize_t
1 112285
2 84786
3 83123
4 101193
5 38361
6 68504
7 119182
8 22807
9 0
10 116174
11 57635
12 66198
13 71701
14 57793
15 80444
16 53855
17 97668
18 133824
19 101481
20 99645
21 114789
22 99052
23 67654
24 65553
25 97500
26 69112
27 82753
28 85323
29 72654
30 30727
31 77873
32 117478
33 74007
34 90183
35 61542
36 101494
37 0
38 55813
39 79215
40 0
41 55461
42 31081
43 0
44 83122
45 70106
46 60578
47 0
48 79892
49 49810
50 71570
51 100708
52 33032
53 82875
54 139077
55 71595
56 72260
57 5950
58 115762
59 32551
60 31701
61 80670
62 143558
63 0
64 0
65 120733
66 105195
67 73107
68 132068
69 149193
70 46821
71 87011
72 95260
73 55183
74 106671
75 73511
76 92945
77 78664
78 70054
79 22618
80 74011
81 83737
82 69094
83 93133
84 95536
85 225920
86 62133
87 61370
88 43836
89 106117
90 38692
91 84651
92 56622
93 15986
94 95364
95 0
96 89691
97 67267
98 126846
99 41140
100 102860
101 51715
102 55801
103 111813
104 120293
105 138599
106 161647
107 115929
108 0
109 162901
110 109825
111 129838
112 37510
113 43750
114 40652
115 87771
116 85872
117 89275
118 0
119 192565
120 0
121 0
122 0
123 0
124 140867
125 120662
126 0
127 0
128 0
129 101338
130 1168
131 0
132 65567
133 25162
134 0
135 40735
136 91413
137 855
138 97068
139 0
140 14116
141 0
142 0
143 0
144 76643
145 110681
146 0
147 92696
148 94785
149 8773
150 83209
151 93815
152 86687
153 0
154 105547
155 103487
156 213688
157 71220
158 0
159 56926
160 91721
161 115168
162 111194
163 0
164 135777
165 51513
166 74163
167 51633
168 75345
169 0
170 0
171 98952
172 102372
173 37238
174 103772
175 123969
176 0
177 135400
178 0
179 130115
180 0
181 0
182 0
183 6023
184 64466
185 54990
186 1644
187 6179
188 3926
189 0
190 34777
191 73224
192 0
193 0
194 0
195 0
196 0
197 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop pageviews_t
-2.345e+02 2.345e+02 1.000e+00
time_in_rfc time_in_rfc_t logins
1.092e-04 -1.092e-04 4.393e+00
logins_t compendium_views_info compendium_views_info_t
-4.393e+00 2.409e+00 -2.409e+00
shared_compendiums shared_compendiums_t feedback_messages_p1
3.884e+01 -3.884e+01 2.335e+00
feedback_messages_p1_t totsize totsize_t
-2.335e+00 3.718e-03 -3.718e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-308.4 0.0 0.0 0.0 415.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.345e+02 4.441e+01 -5.281 3.64e-07 ***
Pop 2.345e+02 4.844e+01 4.841 2.75e-06 ***
pageviews_t 1.000e+00 3.882e-02 25.760 < 2e-16 ***
time_in_rfc 1.092e-04 4.491e-04 0.243 0.80817
time_in_rfc_t -1.092e-04 4.858e-04 -0.225 0.82241
logins 4.393e+00 6.769e-01 6.489 7.93e-10 ***
logins_t -4.393e+00 7.157e-01 -6.138 5.11e-09 ***
compendium_views_info 2.409e+00 1.195e-01 20.168 < 2e-16 ***
compendium_views_info_t -2.409e+00 1.463e-01 -16.471 < 2e-16 ***
shared_compendiums 3.884e+01 7.587e+00 5.119 7.78e-07 ***
shared_compendiums_t -3.884e+01 7.843e+00 -4.951 1.67e-06 ***
feedback_messages_p1 2.335e+00 7.301e-01 3.198 0.00163 **
feedback_messages_p1_t -2.335e+00 7.705e-01 -3.030 0.00280 **
totsize 3.718e-03 9.556e-04 3.891 0.00014 ***
totsize_t -3.718e-03 9.759e-04 -3.810 0.00019 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 73.54 on 182 degrees of freedom
Multiple R-squared: 0.9911, Adjusted R-squared: 0.9905
F-statistic: 1454 on 14 and 182 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,] 4.814481e-43 9.628962e-43 1.0000000
[2,] 1.915814e-57 3.831628e-57 1.0000000
[3,] 2.791132e-71 5.582264e-71 1.0000000
[4,] 2.254025e-85 4.508050e-85 1.0000000
[5,] 3.619723e-100 7.239446e-100 1.0000000
[6,] 2.349644e-117 4.699288e-117 1.0000000
[7,] 1.606565e-130 3.213130e-130 1.0000000
[8,] 1.039502e-141 2.079004e-141 1.0000000
[9,] 8.140782e-162 1.628156e-161 1.0000000
[10,] 2.990634e-170 5.981267e-170 1.0000000
[11,] 9.936456e-185 1.987291e-184 1.0000000
[12,] 8.843940e-205 1.768788e-204 1.0000000
[13,] 3.649992e-216 7.299984e-216 1.0000000
[14,] 5.286032e-230 1.057206e-229 1.0000000
[15,] 7.394210e-242 1.478842e-241 1.0000000
[16,] 3.629365e-256 7.258731e-256 1.0000000
[17,] 3.854268e-272 7.708536e-272 1.0000000
[18,] 3.195089e-295 6.390178e-295 1.0000000
[19,] 1.203986e-309 2.407972e-309 1.0000000
[20,] 3.551773e-277 7.103546e-277 1.0000000
[21,] 1.199090e-295 2.398181e-295 1.0000000
[22,] 6.545296e-304 1.309059e-303 1.0000000
[23,] 0.000000e+00 0.000000e+00 1.0000000
[24,] 0.000000e+00 0.000000e+00 1.0000000
[25,] 0.000000e+00 0.000000e+00 1.0000000
[26,] 0.000000e+00 0.000000e+00 1.0000000
[27,] 0.000000e+00 0.000000e+00 1.0000000
[28,] 0.000000e+00 0.000000e+00 1.0000000
[29,] 0.000000e+00 0.000000e+00 1.0000000
[30,] 0.000000e+00 0.000000e+00 1.0000000
[31,] 0.000000e+00 0.000000e+00 1.0000000
[32,] 0.000000e+00 0.000000e+00 1.0000000
[33,] 0.000000e+00 0.000000e+00 1.0000000
[34,] 0.000000e+00 0.000000e+00 1.0000000
[35,] 0.000000e+00 0.000000e+00 1.0000000
[36,] 0.000000e+00 0.000000e+00 1.0000000
[37,] 0.000000e+00 0.000000e+00 1.0000000
[38,] 0.000000e+00 0.000000e+00 1.0000000
[39,] 0.000000e+00 0.000000e+00 1.0000000
[40,] 0.000000e+00 0.000000e+00 1.0000000
[41,] 0.000000e+00 0.000000e+00 1.0000000
[42,] 0.000000e+00 0.000000e+00 1.0000000
[43,] 0.000000e+00 0.000000e+00 1.0000000
[44,] 0.000000e+00 0.000000e+00 1.0000000
[45,] 0.000000e+00 0.000000e+00 1.0000000
[46,] 0.000000e+00 0.000000e+00 1.0000000
[47,] 0.000000e+00 0.000000e+00 1.0000000
[48,] 0.000000e+00 0.000000e+00 1.0000000
[49,] 0.000000e+00 0.000000e+00 1.0000000
[50,] 0.000000e+00 0.000000e+00 1.0000000
[51,] 0.000000e+00 0.000000e+00 1.0000000
[52,] 0.000000e+00 0.000000e+00 1.0000000
[53,] 0.000000e+00 0.000000e+00 1.0000000
[54,] 0.000000e+00 0.000000e+00 1.0000000
[55,] 0.000000e+00 0.000000e+00 1.0000000
[56,] 0.000000e+00 0.000000e+00 1.0000000
[57,] 0.000000e+00 0.000000e+00 1.0000000
[58,] 0.000000e+00 0.000000e+00 1.0000000
[59,] 0.000000e+00 0.000000e+00 1.0000000
[60,] 0.000000e+00 0.000000e+00 1.0000000
[61,] 0.000000e+00 0.000000e+00 1.0000000
[62,] 0.000000e+00 0.000000e+00 1.0000000
[63,] 0.000000e+00 0.000000e+00 1.0000000
[64,] 0.000000e+00 0.000000e+00 1.0000000
[65,] 0.000000e+00 0.000000e+00 1.0000000
[66,] 0.000000e+00 0.000000e+00 1.0000000
[67,] 0.000000e+00 0.000000e+00 1.0000000
[68,] 0.000000e+00 0.000000e+00 1.0000000
[69,] 0.000000e+00 0.000000e+00 1.0000000
[70,] 0.000000e+00 0.000000e+00 1.0000000
[71,] 0.000000e+00 0.000000e+00 1.0000000
[72,] 0.000000e+00 0.000000e+00 1.0000000
[73,] 0.000000e+00 0.000000e+00 1.0000000
[74,] 0.000000e+00 0.000000e+00 1.0000000
[75,] 0.000000e+00 0.000000e+00 1.0000000
[76,] 0.000000e+00 0.000000e+00 1.0000000
[77,] 0.000000e+00 0.000000e+00 1.0000000
[78,] 4.524537e-83 9.049074e-83 1.0000000
[79,] 2.190899e-84 4.381797e-84 1.0000000
[80,] 1.048258e-85 2.096516e-85 1.0000000
[81,] 4.955799e-87 9.911597e-87 1.0000000
[82,] 2.315039e-88 4.630079e-88 1.0000000
[83,] 1.068565e-89 2.137130e-89 1.0000000
[84,] 4.873470e-91 9.746939e-91 1.0000000
[85,] 2.196170e-92 4.392340e-92 1.0000000
[86,] 9.778628e-94 1.955726e-93 1.0000000
[87,] 4.301963e-95 8.603926e-95 1.0000000
[88,] 1.869921e-96 3.739841e-96 1.0000000
[89,] 8.030392e-98 1.606078e-97 1.0000000
[90,] 3.407182e-99 6.814364e-99 1.0000000
[91,] 1.957248e-52 3.914495e-52 1.0000000
[92,] 2.721582e-53 5.443163e-53 1.0000000
[93,] 3.738520e-54 7.477039e-54 1.0000000
[94,] 5.072966e-55 1.014593e-54 1.0000000
[95,] 6.799667e-56 1.359933e-55 1.0000000
[96,] 9.002322e-57 1.800464e-56 1.0000000
[97,] 1.177168e-57 2.354337e-57 1.0000000
[98,] 1.520247e-58 3.040493e-58 1.0000000
[99,] 1.938890e-59 3.877780e-59 1.0000000
[100,] 2.441891e-60 4.883782e-60 1.0000000
[101,] 8.213160e-29 1.642632e-28 1.0000000
[102,] 2.237399e-29 4.474798e-29 1.0000000
[103,] 9.917888e-25 1.983578e-24 1.0000000
[104,] 2.714064e-20 5.428128e-20 1.0000000
[105,] 8.808739e-14 1.761748e-13 1.0000000
[106,] 2.224503e-13 4.449007e-13 1.0000000
[107,] 9.674956e-14 1.934991e-13 1.0000000
[108,] 4.152801e-14 8.305602e-14 1.0000000
[109,] 2.535971e-14 5.071943e-14 1.0000000
[110,] 1.531486e-07 3.062972e-07 0.9999998
[111,] 6.680274e-07 1.336055e-06 0.9999993
[112,] 3.762571e-07 7.525142e-07 0.9999996
[113,] 2.090525e-07 4.181051e-07 0.9999998
[114,] 3.092306e-06 6.184613e-06 0.9999969
[115,] 1.792254e-06 3.584508e-06 0.9999982
[116,] 1.024262e-06 2.048523e-06 0.9999990
[117,] 1.773512e-05 3.547024e-05 0.9999823
[118,] 1.064570e-05 2.129141e-05 0.9999894
[119,] 6.297936e-06 1.259587e-05 0.9999937
[120,] 3.671190e-06 7.342379e-06 0.9999963
[121,] 2.108127e-06 4.216255e-06 0.9999979
[122,] 2.108492e-06 4.216984e-06 0.9999979
[123,] 1.189032e-06 2.378064e-06 0.9999988
[124,] 7.188641e-07 1.437728e-06 0.9999993
[125,] 3.425577e-05 6.851154e-05 0.9999657
[126,] 2.437742e-05 4.875484e-05 0.9999756
[127,] 1.438641e-05 2.877281e-05 0.9999856
[128,] 8.348284e-06 1.669657e-05 0.9999917
[129,] 4.387078e-03 8.774157e-03 0.9956129
[130,] 2.999100e-03 5.998200e-03 0.9970009
[131,] 2.014869e-03 4.029737e-03 0.9979851
[132,] 1.329648e-03 2.659295e-03 0.9986704
[133,] 8.614772e-04 1.722954e-03 0.9991385
[134,] 5.476966e-04 1.095393e-03 0.9994523
[135,] 3.414942e-04 6.829884e-04 0.9996585
[136,] 4.036879e-04 8.073757e-04 0.9995963
[137,] 2.466636e-04 4.933271e-04 0.9997533
[138,] 1.475429e-04 2.950858e-04 0.9998525
[139,] 8.633172e-05 1.726634e-04 0.9999137
[140,] 4.937670e-05 9.875340e-05 0.9999506
[141,] 5.319335e-05 1.063867e-04 0.9999468
[142,] 2.953796e-05 5.907591e-05 0.9999705
[143,] 1.598932e-05 3.197864e-05 0.9999840
[144,] 8.428160e-06 1.685632e-05 0.9999916
[145,] 4.320856e-06 8.641711e-06 0.9999957
[146,] 7.841078e-04 1.568216e-03 0.9992159
[147,] 4.531961e-04 9.063922e-04 0.9995468
[148,] 2.537768e-04 5.075537e-04 0.9997462
[149,] 1.374232e-04 2.748464e-04 0.9998626
[150,] 7.181129e-05 1.436226e-04 0.9999282
[151,] 3.612481e-05 7.224963e-05 0.9999639
[152,] 2.168369e-05 4.336737e-05 0.9999783
[153,] 4.014926e-03 8.029852e-03 0.9959851
[154,] 2.239464e-03 4.478929e-03 0.9977605
[155,] 1.186738e-03 2.373476e-03 0.9988133
[156,] 5.941078e-04 1.188216e-03 0.9994059
[157,] 2.790226e-04 5.580451e-04 0.9997210
[158,] 1.218419e-04 2.436837e-04 0.9998782
[159,] 1.995584e-04 3.991168e-04 0.9998004
[160,] 7.675702e-05 1.535140e-04 0.9999232
[161,] 4.985001e-04 9.970002e-04 0.9995015
[162,] 1.644956e-04 3.289911e-04 0.9998355
> postscript(file="/var/wessaorg/rcomp/tmp/1x9ot1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2h01v1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3jc0c1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ujin1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5bqsb1353075219.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 = 197
Frequency = 1
1 2 3 4 5
1.437682e-12 2.559065e-13 -1.343845e-12 4.523491e-13 3.074276e-14
6 7 8 9 10
-2.073456e-13 -5.819132e-13 3.259170e-13 7.841891e+01 4.077897e-13
11 12 13 14 15
3.658904e-14 -7.496263e-15 4.067327e-14 1.134606e-13 1.572058e-13
16 17 18 19 20
1.250125e-13 1.623161e-13 -7.350161e-14 1.258405e-13 -1.444635e-13
21 22 23 24 25
1.179718e-13 -2.199094e-14 2.120933e-13 -3.094138e-14 -4.472316e-14
26 27 28 29 30
1.086934e-14 4.949268e-14 8.250211e-15 -4.333795e-15 -5.163332e-14
31 32 33 34 35
-6.878042e-14 -3.680739e-14 -3.975966e-14 -7.535085e-14 2.179561e-13
36 37 38 39 40
1.441693e-14 1.058437e+02 -1.294194e-15 -1.272063e-13 1.711161e+02
41 42 43 44 45
-2.731925e-14 -7.196513e-14 2.597396e+02 3.986687e-14 1.499032e-13
46 47 48 49 50
5.821234e-14 -8.051267e+01 -3.531438e-14 -5.707473e-14 2.436968e-14
51 52 53 54 55
4.132254e-13 9.537605e-14 -7.861292e-14 -3.415332e-13 -2.490645e-14
56 57 58 59 60
-4.197785e-15 1.432578e-14 -5.968100e-14 1.989848e-13 -1.878429e-14
61 62 63 64 65
-3.229957e-14 -8.516721e-14 4.151343e+02 -3.412058e+01 -2.149834e-13
66 67 68 69 70
-2.766154e-14 1.376825e-13 -1.560981e-13 2.235150e-13 1.157262e-13
71 72 73 74 75
-2.899972e-14 3.932461e-14 -3.843579e-14 2.358519e-14 2.496152e-14
76 77 78 79 80
-7.390453e-14 4.058148e-14 2.813098e-13 4.529113e-14 -5.955933e-14
81 82 83 84 85
6.148353e-14 3.559375e-14 3.310058e-14 -8.295188e-15 -1.480382e-13
86 87 88 89 90
7.481352e-14 1.905421e-14 -2.033339e-14 -1.284912e-13 4.369206e-14
91 92 93 94 95
-9.015971e-14 -2.296978e-13 -1.011259e-13 -5.030371e-15 1.002512e+02
96 97 98 99 100
9.085398e-15 -7.369291e-14 2.013879e-14 -3.731028e-14 2.981486e-15
101 102 103 104 105
9.178043e-14 2.868528e-14 6.225667e-14 -8.823171e-15 2.742374e-14
106 107 108 109 110
-4.944591e-14 -1.373933e-13 2.478794e+02 1.304657e-13 5.443054e-14
111 112 113 114 115
-8.653360e-14 3.569791e-14 3.920802e-14 -1.451525e-13 -1.157260e-13
116 117 118 119 120
1.303902e-13 -2.095122e-14 -7.972694e+01 -1.457968e-13 4.769316e+01
121 122 123 124 125
1.511700e+01 -1.274433e+02 -1.552117e+02 -5.271314e-14 1.254600e-14
126 127 128 129 130
3.375434e+00 -1.938957e+02 1.171876e+02 1.170228e-14 -1.757497e-13
131 132 133 134 135
-4.287591e+01 -7.883968e-14 6.467199e-14 8.388262e+01 -1.371681e-13
136 137 138 139 140
1.056481e-13 -4.794163e-15 -9.375005e-14 6.938867e+01 7.652255e-14
141 142 143 144 145
1.197752e+01 -9.874731e+00 -2.688930e+01 -1.704954e-13 3.149784e-15
146 147 148 149 150
1.975912e+02 9.225112e-15 2.932288e-14 -5.469511e-14 3.046401e-14
151 152 153 154 155
-1.142913e-14 2.089005e-14 -4.467986e+01 3.009188e-14 5.737743e-14
156 157 158 159 160
3.187808e-14 -1.057789e-13 4.637571e+01 -1.102004e-13 4.916454e-14
161 162 163 164 165
-1.832383e-13 -4.885557e-14 -3.083663e+02 -4.842569e-14 3.225863e-14
166 167 168 169 170
5.645661e-14 -7.557566e-14 -1.049220e-13 3.326358e+01 -2.664551e+02
171 172 173 174 175
5.729498e-14 -1.862997e-14 -1.399573e-13 -2.429287e-14 9.272158e-14
176 177 178 179 180
-1.716617e+02 4.440522e-14 3.100343e+01 -6.681785e-14 1.446249e+01
181 182 183 184 185
-1.293028e+02 -7.194740e+00 3.762914e-14 -4.643100e-14 4.111420e-14
186 187 188 189 190
-2.064032e-13 -1.798549e-14 -1.206113e-13 1.018734e+02 -5.622658e-14
191 192 193 194 195
-2.767087e-14 -2.992844e+02 1.065481e+02 1.523273e+02 -2.788548e+02
196 197
2.716282e+01 -1.812626e+02
> postscript(file="/var/wessaorg/rcomp/tmp/6qjf21353075219.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 = 197
Frequency = 1
lag(myerror, k = 1) myerror
0 1.437682e-12 NA
1 2.559065e-13 1.437682e-12
2 -1.343845e-12 2.559065e-13
3 4.523491e-13 -1.343845e-12
4 3.074276e-14 4.523491e-13
5 -2.073456e-13 3.074276e-14
6 -5.819132e-13 -2.073456e-13
7 3.259170e-13 -5.819132e-13
8 7.841891e+01 3.259170e-13
9 4.077897e-13 7.841891e+01
10 3.658904e-14 4.077897e-13
11 -7.496263e-15 3.658904e-14
12 4.067327e-14 -7.496263e-15
13 1.134606e-13 4.067327e-14
14 1.572058e-13 1.134606e-13
15 1.250125e-13 1.572058e-13
16 1.623161e-13 1.250125e-13
17 -7.350161e-14 1.623161e-13
18 1.258405e-13 -7.350161e-14
19 -1.444635e-13 1.258405e-13
20 1.179718e-13 -1.444635e-13
21 -2.199094e-14 1.179718e-13
22 2.120933e-13 -2.199094e-14
23 -3.094138e-14 2.120933e-13
24 -4.472316e-14 -3.094138e-14
25 1.086934e-14 -4.472316e-14
26 4.949268e-14 1.086934e-14
27 8.250211e-15 4.949268e-14
28 -4.333795e-15 8.250211e-15
29 -5.163332e-14 -4.333795e-15
30 -6.878042e-14 -5.163332e-14
31 -3.680739e-14 -6.878042e-14
32 -3.975966e-14 -3.680739e-14
33 -7.535085e-14 -3.975966e-14
34 2.179561e-13 -7.535085e-14
35 1.441693e-14 2.179561e-13
36 1.058437e+02 1.441693e-14
37 -1.294194e-15 1.058437e+02
38 -1.272063e-13 -1.294194e-15
39 1.711161e+02 -1.272063e-13
40 -2.731925e-14 1.711161e+02
41 -7.196513e-14 -2.731925e-14
42 2.597396e+02 -7.196513e-14
43 3.986687e-14 2.597396e+02
44 1.499032e-13 3.986687e-14
45 5.821234e-14 1.499032e-13
46 -8.051267e+01 5.821234e-14
47 -3.531438e-14 -8.051267e+01
48 -5.707473e-14 -3.531438e-14
49 2.436968e-14 -5.707473e-14
50 4.132254e-13 2.436968e-14
51 9.537605e-14 4.132254e-13
52 -7.861292e-14 9.537605e-14
53 -3.415332e-13 -7.861292e-14
54 -2.490645e-14 -3.415332e-13
55 -4.197785e-15 -2.490645e-14
56 1.432578e-14 -4.197785e-15
57 -5.968100e-14 1.432578e-14
58 1.989848e-13 -5.968100e-14
59 -1.878429e-14 1.989848e-13
60 -3.229957e-14 -1.878429e-14
61 -8.516721e-14 -3.229957e-14
62 4.151343e+02 -8.516721e-14
63 -3.412058e+01 4.151343e+02
64 -2.149834e-13 -3.412058e+01
65 -2.766154e-14 -2.149834e-13
66 1.376825e-13 -2.766154e-14
67 -1.560981e-13 1.376825e-13
68 2.235150e-13 -1.560981e-13
69 1.157262e-13 2.235150e-13
70 -2.899972e-14 1.157262e-13
71 3.932461e-14 -2.899972e-14
72 -3.843579e-14 3.932461e-14
73 2.358519e-14 -3.843579e-14
74 2.496152e-14 2.358519e-14
75 -7.390453e-14 2.496152e-14
76 4.058148e-14 -7.390453e-14
77 2.813098e-13 4.058148e-14
78 4.529113e-14 2.813098e-13
79 -5.955933e-14 4.529113e-14
80 6.148353e-14 -5.955933e-14
81 3.559375e-14 6.148353e-14
82 3.310058e-14 3.559375e-14
83 -8.295188e-15 3.310058e-14
84 -1.480382e-13 -8.295188e-15
85 7.481352e-14 -1.480382e-13
86 1.905421e-14 7.481352e-14
87 -2.033339e-14 1.905421e-14
88 -1.284912e-13 -2.033339e-14
89 4.369206e-14 -1.284912e-13
90 -9.015971e-14 4.369206e-14
91 -2.296978e-13 -9.015971e-14
92 -1.011259e-13 -2.296978e-13
93 -5.030371e-15 -1.011259e-13
94 1.002512e+02 -5.030371e-15
95 9.085398e-15 1.002512e+02
96 -7.369291e-14 9.085398e-15
97 2.013879e-14 -7.369291e-14
98 -3.731028e-14 2.013879e-14
99 2.981486e-15 -3.731028e-14
100 9.178043e-14 2.981486e-15
101 2.868528e-14 9.178043e-14
102 6.225667e-14 2.868528e-14
103 -8.823171e-15 6.225667e-14
104 2.742374e-14 -8.823171e-15
105 -4.944591e-14 2.742374e-14
106 -1.373933e-13 -4.944591e-14
107 2.478794e+02 -1.373933e-13
108 1.304657e-13 2.478794e+02
109 5.443054e-14 1.304657e-13
110 -8.653360e-14 5.443054e-14
111 3.569791e-14 -8.653360e-14
112 3.920802e-14 3.569791e-14
113 -1.451525e-13 3.920802e-14
114 -1.157260e-13 -1.451525e-13
115 1.303902e-13 -1.157260e-13
116 -2.095122e-14 1.303902e-13
117 -7.972694e+01 -2.095122e-14
118 -1.457968e-13 -7.972694e+01
119 4.769316e+01 -1.457968e-13
120 1.511700e+01 4.769316e+01
121 -1.274433e+02 1.511700e+01
122 -1.552117e+02 -1.274433e+02
123 -5.271314e-14 -1.552117e+02
124 1.254600e-14 -5.271314e-14
125 3.375434e+00 1.254600e-14
126 -1.938957e+02 3.375434e+00
127 1.171876e+02 -1.938957e+02
128 1.170228e-14 1.171876e+02
129 -1.757497e-13 1.170228e-14
130 -4.287591e+01 -1.757497e-13
131 -7.883968e-14 -4.287591e+01
132 6.467199e-14 -7.883968e-14
133 8.388262e+01 6.467199e-14
134 -1.371681e-13 8.388262e+01
135 1.056481e-13 -1.371681e-13
136 -4.794163e-15 1.056481e-13
137 -9.375005e-14 -4.794163e-15
138 6.938867e+01 -9.375005e-14
139 7.652255e-14 6.938867e+01
140 1.197752e+01 7.652255e-14
141 -9.874731e+00 1.197752e+01
142 -2.688930e+01 -9.874731e+00
143 -1.704954e-13 -2.688930e+01
144 3.149784e-15 -1.704954e-13
145 1.975912e+02 3.149784e-15
146 9.225112e-15 1.975912e+02
147 2.932288e-14 9.225112e-15
148 -5.469511e-14 2.932288e-14
149 3.046401e-14 -5.469511e-14
150 -1.142913e-14 3.046401e-14
151 2.089005e-14 -1.142913e-14
152 -4.467986e+01 2.089005e-14
153 3.009188e-14 -4.467986e+01
154 5.737743e-14 3.009188e-14
155 3.187808e-14 5.737743e-14
156 -1.057789e-13 3.187808e-14
157 4.637571e+01 -1.057789e-13
158 -1.102004e-13 4.637571e+01
159 4.916454e-14 -1.102004e-13
160 -1.832383e-13 4.916454e-14
161 -4.885557e-14 -1.832383e-13
162 -3.083663e+02 -4.885557e-14
163 -4.842569e-14 -3.083663e+02
164 3.225863e-14 -4.842569e-14
165 5.645661e-14 3.225863e-14
166 -7.557566e-14 5.645661e-14
167 -1.049220e-13 -7.557566e-14
168 3.326358e+01 -1.049220e-13
169 -2.664551e+02 3.326358e+01
170 5.729498e-14 -2.664551e+02
171 -1.862997e-14 5.729498e-14
172 -1.399573e-13 -1.862997e-14
173 -2.429287e-14 -1.399573e-13
174 9.272158e-14 -2.429287e-14
175 -1.716617e+02 9.272158e-14
176 4.440522e-14 -1.716617e+02
177 3.100343e+01 4.440522e-14
178 -6.681785e-14 3.100343e+01
179 1.446249e+01 -6.681785e-14
180 -1.293028e+02 1.446249e+01
181 -7.194740e+00 -1.293028e+02
182 3.762914e-14 -7.194740e+00
183 -4.643100e-14 3.762914e-14
184 4.111420e-14 -4.643100e-14
185 -2.064032e-13 4.111420e-14
186 -1.798549e-14 -2.064032e-13
187 -1.206113e-13 -1.798549e-14
188 1.018734e+02 -1.206113e-13
189 -5.622658e-14 1.018734e+02
190 -2.767087e-14 -5.622658e-14
191 -2.992844e+02 -2.767087e-14
192 1.065481e+02 -2.992844e+02
193 1.523273e+02 1.065481e+02
194 -2.788548e+02 1.523273e+02
195 2.716282e+01 -2.788548e+02
196 -1.812626e+02 2.716282e+01
197 NA -1.812626e+02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.559065e-13 1.437682e-12
[2,] -1.343845e-12 2.559065e-13
[3,] 4.523491e-13 -1.343845e-12
[4,] 3.074276e-14 4.523491e-13
[5,] -2.073456e-13 3.074276e-14
[6,] -5.819132e-13 -2.073456e-13
[7,] 3.259170e-13 -5.819132e-13
[8,] 7.841891e+01 3.259170e-13
[9,] 4.077897e-13 7.841891e+01
[10,] 3.658904e-14 4.077897e-13
[11,] -7.496263e-15 3.658904e-14
[12,] 4.067327e-14 -7.496263e-15
[13,] 1.134606e-13 4.067327e-14
[14,] 1.572058e-13 1.134606e-13
[15,] 1.250125e-13 1.572058e-13
[16,] 1.623161e-13 1.250125e-13
[17,] -7.350161e-14 1.623161e-13
[18,] 1.258405e-13 -7.350161e-14
[19,] -1.444635e-13 1.258405e-13
[20,] 1.179718e-13 -1.444635e-13
[21,] -2.199094e-14 1.179718e-13
[22,] 2.120933e-13 -2.199094e-14
[23,] -3.094138e-14 2.120933e-13
[24,] -4.472316e-14 -3.094138e-14
[25,] 1.086934e-14 -4.472316e-14
[26,] 4.949268e-14 1.086934e-14
[27,] 8.250211e-15 4.949268e-14
[28,] -4.333795e-15 8.250211e-15
[29,] -5.163332e-14 -4.333795e-15
[30,] -6.878042e-14 -5.163332e-14
[31,] -3.680739e-14 -6.878042e-14
[32,] -3.975966e-14 -3.680739e-14
[33,] -7.535085e-14 -3.975966e-14
[34,] 2.179561e-13 -7.535085e-14
[35,] 1.441693e-14 2.179561e-13
[36,] 1.058437e+02 1.441693e-14
[37,] -1.294194e-15 1.058437e+02
[38,] -1.272063e-13 -1.294194e-15
[39,] 1.711161e+02 -1.272063e-13
[40,] -2.731925e-14 1.711161e+02
[41,] -7.196513e-14 -2.731925e-14
[42,] 2.597396e+02 -7.196513e-14
[43,] 3.986687e-14 2.597396e+02
[44,] 1.499032e-13 3.986687e-14
[45,] 5.821234e-14 1.499032e-13
[46,] -8.051267e+01 5.821234e-14
[47,] -3.531438e-14 -8.051267e+01
[48,] -5.707473e-14 -3.531438e-14
[49,] 2.436968e-14 -5.707473e-14
[50,] 4.132254e-13 2.436968e-14
[51,] 9.537605e-14 4.132254e-13
[52,] -7.861292e-14 9.537605e-14
[53,] -3.415332e-13 -7.861292e-14
[54,] -2.490645e-14 -3.415332e-13
[55,] -4.197785e-15 -2.490645e-14
[56,] 1.432578e-14 -4.197785e-15
[57,] -5.968100e-14 1.432578e-14
[58,] 1.989848e-13 -5.968100e-14
[59,] -1.878429e-14 1.989848e-13
[60,] -3.229957e-14 -1.878429e-14
[61,] -8.516721e-14 -3.229957e-14
[62,] 4.151343e+02 -8.516721e-14
[63,] -3.412058e+01 4.151343e+02
[64,] -2.149834e-13 -3.412058e+01
[65,] -2.766154e-14 -2.149834e-13
[66,] 1.376825e-13 -2.766154e-14
[67,] -1.560981e-13 1.376825e-13
[68,] 2.235150e-13 -1.560981e-13
[69,] 1.157262e-13 2.235150e-13
[70,] -2.899972e-14 1.157262e-13
[71,] 3.932461e-14 -2.899972e-14
[72,] -3.843579e-14 3.932461e-14
[73,] 2.358519e-14 -3.843579e-14
[74,] 2.496152e-14 2.358519e-14
[75,] -7.390453e-14 2.496152e-14
[76,] 4.058148e-14 -7.390453e-14
[77,] 2.813098e-13 4.058148e-14
[78,] 4.529113e-14 2.813098e-13
[79,] -5.955933e-14 4.529113e-14
[80,] 6.148353e-14 -5.955933e-14
[81,] 3.559375e-14 6.148353e-14
[82,] 3.310058e-14 3.559375e-14
[83,] -8.295188e-15 3.310058e-14
[84,] -1.480382e-13 -8.295188e-15
[85,] 7.481352e-14 -1.480382e-13
[86,] 1.905421e-14 7.481352e-14
[87,] -2.033339e-14 1.905421e-14
[88,] -1.284912e-13 -2.033339e-14
[89,] 4.369206e-14 -1.284912e-13
[90,] -9.015971e-14 4.369206e-14
[91,] -2.296978e-13 -9.015971e-14
[92,] -1.011259e-13 -2.296978e-13
[93,] -5.030371e-15 -1.011259e-13
[94,] 1.002512e+02 -5.030371e-15
[95,] 9.085398e-15 1.002512e+02
[96,] -7.369291e-14 9.085398e-15
[97,] 2.013879e-14 -7.369291e-14
[98,] -3.731028e-14 2.013879e-14
[99,] 2.981486e-15 -3.731028e-14
[100,] 9.178043e-14 2.981486e-15
[101,] 2.868528e-14 9.178043e-14
[102,] 6.225667e-14 2.868528e-14
[103,] -8.823171e-15 6.225667e-14
[104,] 2.742374e-14 -8.823171e-15
[105,] -4.944591e-14 2.742374e-14
[106,] -1.373933e-13 -4.944591e-14
[107,] 2.478794e+02 -1.373933e-13
[108,] 1.304657e-13 2.478794e+02
[109,] 5.443054e-14 1.304657e-13
[110,] -8.653360e-14 5.443054e-14
[111,] 3.569791e-14 -8.653360e-14
[112,] 3.920802e-14 3.569791e-14
[113,] -1.451525e-13 3.920802e-14
[114,] -1.157260e-13 -1.451525e-13
[115,] 1.303902e-13 -1.157260e-13
[116,] -2.095122e-14 1.303902e-13
[117,] -7.972694e+01 -2.095122e-14
[118,] -1.457968e-13 -7.972694e+01
[119,] 4.769316e+01 -1.457968e-13
[120,] 1.511700e+01 4.769316e+01
[121,] -1.274433e+02 1.511700e+01
[122,] -1.552117e+02 -1.274433e+02
[123,] -5.271314e-14 -1.552117e+02
[124,] 1.254600e-14 -5.271314e-14
[125,] 3.375434e+00 1.254600e-14
[126,] -1.938957e+02 3.375434e+00
[127,] 1.171876e+02 -1.938957e+02
[128,] 1.170228e-14 1.171876e+02
[129,] -1.757497e-13 1.170228e-14
[130,] -4.287591e+01 -1.757497e-13
[131,] -7.883968e-14 -4.287591e+01
[132,] 6.467199e-14 -7.883968e-14
[133,] 8.388262e+01 6.467199e-14
[134,] -1.371681e-13 8.388262e+01
[135,] 1.056481e-13 -1.371681e-13
[136,] -4.794163e-15 1.056481e-13
[137,] -9.375005e-14 -4.794163e-15
[138,] 6.938867e+01 -9.375005e-14
[139,] 7.652255e-14 6.938867e+01
[140,] 1.197752e+01 7.652255e-14
[141,] -9.874731e+00 1.197752e+01
[142,] -2.688930e+01 -9.874731e+00
[143,] -1.704954e-13 -2.688930e+01
[144,] 3.149784e-15 -1.704954e-13
[145,] 1.975912e+02 3.149784e-15
[146,] 9.225112e-15 1.975912e+02
[147,] 2.932288e-14 9.225112e-15
[148,] -5.469511e-14 2.932288e-14
[149,] 3.046401e-14 -5.469511e-14
[150,] -1.142913e-14 3.046401e-14
[151,] 2.089005e-14 -1.142913e-14
[152,] -4.467986e+01 2.089005e-14
[153,] 3.009188e-14 -4.467986e+01
[154,] 5.737743e-14 3.009188e-14
[155,] 3.187808e-14 5.737743e-14
[156,] -1.057789e-13 3.187808e-14
[157,] 4.637571e+01 -1.057789e-13
[158,] -1.102004e-13 4.637571e+01
[159,] 4.916454e-14 -1.102004e-13
[160,] -1.832383e-13 4.916454e-14
[161,] -4.885557e-14 -1.832383e-13
[162,] -3.083663e+02 -4.885557e-14
[163,] -4.842569e-14 -3.083663e+02
[164,] 3.225863e-14 -4.842569e-14
[165,] 5.645661e-14 3.225863e-14
[166,] -7.557566e-14 5.645661e-14
[167,] -1.049220e-13 -7.557566e-14
[168,] 3.326358e+01 -1.049220e-13
[169,] -2.664551e+02 3.326358e+01
[170,] 5.729498e-14 -2.664551e+02
[171,] -1.862997e-14 5.729498e-14
[172,] -1.399573e-13 -1.862997e-14
[173,] -2.429287e-14 -1.399573e-13
[174,] 9.272158e-14 -2.429287e-14
[175,] -1.716617e+02 9.272158e-14
[176,] 4.440522e-14 -1.716617e+02
[177,] 3.100343e+01 4.440522e-14
[178,] -6.681785e-14 3.100343e+01
[179,] 1.446249e+01 -6.681785e-14
[180,] -1.293028e+02 1.446249e+01
[181,] -7.194740e+00 -1.293028e+02
[182,] 3.762914e-14 -7.194740e+00
[183,] -4.643100e-14 3.762914e-14
[184,] 4.111420e-14 -4.643100e-14
[185,] -2.064032e-13 4.111420e-14
[186,] -1.798549e-14 -2.064032e-13
[187,] -1.206113e-13 -1.798549e-14
[188,] 1.018734e+02 -1.206113e-13
[189,] -5.622658e-14 1.018734e+02
[190,] -2.767087e-14 -5.622658e-14
[191,] -2.992844e+02 -2.767087e-14
[192,] 1.065481e+02 -2.992844e+02
[193,] 1.523273e+02 1.065481e+02
[194,] -2.788548e+02 1.523273e+02
[195,] 2.716282e+01 -2.788548e+02
[196,] -1.812626e+02 2.716282e+01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.559065e-13 1.437682e-12
2 -1.343845e-12 2.559065e-13
3 4.523491e-13 -1.343845e-12
4 3.074276e-14 4.523491e-13
5 -2.073456e-13 3.074276e-14
6 -5.819132e-13 -2.073456e-13
7 3.259170e-13 -5.819132e-13
8 7.841891e+01 3.259170e-13
9 4.077897e-13 7.841891e+01
10 3.658904e-14 4.077897e-13
11 -7.496263e-15 3.658904e-14
12 4.067327e-14 -7.496263e-15
13 1.134606e-13 4.067327e-14
14 1.572058e-13 1.134606e-13
15 1.250125e-13 1.572058e-13
16 1.623161e-13 1.250125e-13
17 -7.350161e-14 1.623161e-13
18 1.258405e-13 -7.350161e-14
19 -1.444635e-13 1.258405e-13
20 1.179718e-13 -1.444635e-13
21 -2.199094e-14 1.179718e-13
22 2.120933e-13 -2.199094e-14
23 -3.094138e-14 2.120933e-13
24 -4.472316e-14 -3.094138e-14
25 1.086934e-14 -4.472316e-14
26 4.949268e-14 1.086934e-14
27 8.250211e-15 4.949268e-14
28 -4.333795e-15 8.250211e-15
29 -5.163332e-14 -4.333795e-15
30 -6.878042e-14 -5.163332e-14
31 -3.680739e-14 -6.878042e-14
32 -3.975966e-14 -3.680739e-14
33 -7.535085e-14 -3.975966e-14
34 2.179561e-13 -7.535085e-14
35 1.441693e-14 2.179561e-13
36 1.058437e+02 1.441693e-14
37 -1.294194e-15 1.058437e+02
38 -1.272063e-13 -1.294194e-15
39 1.711161e+02 -1.272063e-13
40 -2.731925e-14 1.711161e+02
41 -7.196513e-14 -2.731925e-14
42 2.597396e+02 -7.196513e-14
43 3.986687e-14 2.597396e+02
44 1.499032e-13 3.986687e-14
45 5.821234e-14 1.499032e-13
46 -8.051267e+01 5.821234e-14
47 -3.531438e-14 -8.051267e+01
48 -5.707473e-14 -3.531438e-14
49 2.436968e-14 -5.707473e-14
50 4.132254e-13 2.436968e-14
51 9.537605e-14 4.132254e-13
52 -7.861292e-14 9.537605e-14
53 -3.415332e-13 -7.861292e-14
54 -2.490645e-14 -3.415332e-13
55 -4.197785e-15 -2.490645e-14
56 1.432578e-14 -4.197785e-15
57 -5.968100e-14 1.432578e-14
58 1.989848e-13 -5.968100e-14
59 -1.878429e-14 1.989848e-13
60 -3.229957e-14 -1.878429e-14
61 -8.516721e-14 -3.229957e-14
62 4.151343e+02 -8.516721e-14
63 -3.412058e+01 4.151343e+02
64 -2.149834e-13 -3.412058e+01
65 -2.766154e-14 -2.149834e-13
66 1.376825e-13 -2.766154e-14
67 -1.560981e-13 1.376825e-13
68 2.235150e-13 -1.560981e-13
69 1.157262e-13 2.235150e-13
70 -2.899972e-14 1.157262e-13
71 3.932461e-14 -2.899972e-14
72 -3.843579e-14 3.932461e-14
73 2.358519e-14 -3.843579e-14
74 2.496152e-14 2.358519e-14
75 -7.390453e-14 2.496152e-14
76 4.058148e-14 -7.390453e-14
77 2.813098e-13 4.058148e-14
78 4.529113e-14 2.813098e-13
79 -5.955933e-14 4.529113e-14
80 6.148353e-14 -5.955933e-14
81 3.559375e-14 6.148353e-14
82 3.310058e-14 3.559375e-14
83 -8.295188e-15 3.310058e-14
84 -1.480382e-13 -8.295188e-15
85 7.481352e-14 -1.480382e-13
86 1.905421e-14 7.481352e-14
87 -2.033339e-14 1.905421e-14
88 -1.284912e-13 -2.033339e-14
89 4.369206e-14 -1.284912e-13
90 -9.015971e-14 4.369206e-14
91 -2.296978e-13 -9.015971e-14
92 -1.011259e-13 -2.296978e-13
93 -5.030371e-15 -1.011259e-13
94 1.002512e+02 -5.030371e-15
95 9.085398e-15 1.002512e+02
96 -7.369291e-14 9.085398e-15
97 2.013879e-14 -7.369291e-14
98 -3.731028e-14 2.013879e-14
99 2.981486e-15 -3.731028e-14
100 9.178043e-14 2.981486e-15
101 2.868528e-14 9.178043e-14
102 6.225667e-14 2.868528e-14
103 -8.823171e-15 6.225667e-14
104 2.742374e-14 -8.823171e-15
105 -4.944591e-14 2.742374e-14
106 -1.373933e-13 -4.944591e-14
107 2.478794e+02 -1.373933e-13
108 1.304657e-13 2.478794e+02
109 5.443054e-14 1.304657e-13
110 -8.653360e-14 5.443054e-14
111 3.569791e-14 -8.653360e-14
112 3.920802e-14 3.569791e-14
113 -1.451525e-13 3.920802e-14
114 -1.157260e-13 -1.451525e-13
115 1.303902e-13 -1.157260e-13
116 -2.095122e-14 1.303902e-13
117 -7.972694e+01 -2.095122e-14
118 -1.457968e-13 -7.972694e+01
119 4.769316e+01 -1.457968e-13
120 1.511700e+01 4.769316e+01
121 -1.274433e+02 1.511700e+01
122 -1.552117e+02 -1.274433e+02
123 -5.271314e-14 -1.552117e+02
124 1.254600e-14 -5.271314e-14
125 3.375434e+00 1.254600e-14
126 -1.938957e+02 3.375434e+00
127 1.171876e+02 -1.938957e+02
128 1.170228e-14 1.171876e+02
129 -1.757497e-13 1.170228e-14
130 -4.287591e+01 -1.757497e-13
131 -7.883968e-14 -4.287591e+01
132 6.467199e-14 -7.883968e-14
133 8.388262e+01 6.467199e-14
134 -1.371681e-13 8.388262e+01
135 1.056481e-13 -1.371681e-13
136 -4.794163e-15 1.056481e-13
137 -9.375005e-14 -4.794163e-15
138 6.938867e+01 -9.375005e-14
139 7.652255e-14 6.938867e+01
140 1.197752e+01 7.652255e-14
141 -9.874731e+00 1.197752e+01
142 -2.688930e+01 -9.874731e+00
143 -1.704954e-13 -2.688930e+01
144 3.149784e-15 -1.704954e-13
145 1.975912e+02 3.149784e-15
146 9.225112e-15 1.975912e+02
147 2.932288e-14 9.225112e-15
148 -5.469511e-14 2.932288e-14
149 3.046401e-14 -5.469511e-14
150 -1.142913e-14 3.046401e-14
151 2.089005e-14 -1.142913e-14
152 -4.467986e+01 2.089005e-14
153 3.009188e-14 -4.467986e+01
154 5.737743e-14 3.009188e-14
155 3.187808e-14 5.737743e-14
156 -1.057789e-13 3.187808e-14
157 4.637571e+01 -1.057789e-13
158 -1.102004e-13 4.637571e+01
159 4.916454e-14 -1.102004e-13
160 -1.832383e-13 4.916454e-14
161 -4.885557e-14 -1.832383e-13
162 -3.083663e+02 -4.885557e-14
163 -4.842569e-14 -3.083663e+02
164 3.225863e-14 -4.842569e-14
165 5.645661e-14 3.225863e-14
166 -7.557566e-14 5.645661e-14
167 -1.049220e-13 -7.557566e-14
168 3.326358e+01 -1.049220e-13
169 -2.664551e+02 3.326358e+01
170 5.729498e-14 -2.664551e+02
171 -1.862997e-14 5.729498e-14
172 -1.399573e-13 -1.862997e-14
173 -2.429287e-14 -1.399573e-13
174 9.272158e-14 -2.429287e-14
175 -1.716617e+02 9.272158e-14
176 4.440522e-14 -1.716617e+02
177 3.100343e+01 4.440522e-14
178 -6.681785e-14 3.100343e+01
179 1.446249e+01 -6.681785e-14
180 -1.293028e+02 1.446249e+01
181 -7.194740e+00 -1.293028e+02
182 3.762914e-14 -7.194740e+00
183 -4.643100e-14 3.762914e-14
184 4.111420e-14 -4.643100e-14
185 -2.064032e-13 4.111420e-14
186 -1.798549e-14 -2.064032e-13
187 -1.206113e-13 -1.798549e-14
188 1.018734e+02 -1.206113e-13
189 -5.622658e-14 1.018734e+02
190 -2.767087e-14 -5.622658e-14
191 -2.992844e+02 -2.767087e-14
192 1.065481e+02 -2.992844e+02
193 1.523273e+02 1.065481e+02
194 -2.788548e+02 1.523273e+02
195 2.716282e+01 -2.788548e+02
196 -1.812626e+02 2.716282e+01
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/71x7n1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8dvqz1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9o5nc1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1040tr1353075219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11wbzl1353075220.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12txlh1353075220.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ybn41353075220.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/147xpl1353075220.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15p8151353075220.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16e6po1353075220.tab")
+ }
>
> try(system("convert tmp/1x9ot1353075219.ps tmp/1x9ot1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h01v1353075219.ps tmp/2h01v1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jc0c1353075219.ps tmp/3jc0c1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ujin1353075219.ps tmp/4ujin1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bqsb1353075219.ps tmp/5bqsb1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qjf21353075219.ps tmp/6qjf21353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/71x7n1353075219.ps tmp/71x7n1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dvqz1353075219.ps tmp/8dvqz1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/9o5nc1353075219.ps tmp/9o5nc1353075219.png",intern=TRUE))
character(0)
> try(system("convert tmp/1040tr1353075219.ps tmp/1040tr1353075219.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.234 1.288 13.520