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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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'q()' to quit R.
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+ ,0
+ ,1597
+ ,0
+ ,81240
+ ,66
+ ,0
+ ,503
+ ,0
+ ,0
+ ,0
+ ,68
+ ,0
+ ,45549
+ ,0
+ ,0
+ ,191
+ ,0
+ ,1639
+ ,0
+ ,133368
+ ,36
+ ,0
+ ,535
+ ,0
+ ,1
+ ,0
+ ,64
+ ,0
+ ,32755
+ ,0
+ ,0
+ ,192
+ ,0
+ ,1018
+ ,0
+ ,98146
+ ,40
+ ,0
+ ,459
+ ,0
+ ,0
+ ,0
+ ,68
+ ,0
+ ,27114
+ ,0
+ ,0
+ ,193
+ ,0
+ ,1383
+ ,0
+ ,79619
+ ,43
+ ,0
+ ,426
+ ,0
+ ,3
+ ,0
+ ,40
+ ,0
+ ,20760
+ ,0
+ ,0
+ ,194
+ ,0
+ ,1314
+ ,0
+ ,59194
+ ,31
+ ,0
+ ,288
+ ,0
+ ,6
+ ,0
+ ,80
+ ,0
+ ,37636
+ ,0
+ ,0
+ ,195
+ ,0
+ ,1335
+ ,0
+ ,139942
+ ,42
+ ,0
+ ,498
+ ,0
+ ,0
+ ,0
+ ,88
+ ,0
+ ,65461
+ ,0
+ ,0
+ ,196
+ ,0
+ ,1403
+ ,0
+ ,118612
+ ,46
+ ,0
+ ,454
+ ,0
+ ,2
+ ,0
+ ,48
+ ,0
+ ,30080
+ ,0
+ ,0
+ ,197
+ ,0
+ ,910
+ ,0
+ ,72880
+ ,33
+ ,0
+ ,376
+ ,0
+ ,0
+ ,0
+ ,76
+ ,0
+ ,24094
+ ,0)
+ ,dim=c(16
+ ,197)
+ ,dimnames=list(c('Pop'
+ ,'t'
+ ,'pop_t'
+ ,'pageviews'
+ ,'pageviews_p'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'logins_p'
+ ,'compendium_views_info'
+ ,'compendium_views_info_p'
+ ,'shared_compendiums'
+ ,'shared_compendiums_p'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p1_p'
+ ,'totsize'
+ ,'totsize_p')
+ ,1:197))
> y <- array(NA,dim=c(16,197),dimnames=list(c('Pop','t','pop_t','pageviews','pageviews_p','time_in_rfc','logins','logins_p','compendium_views_info','compendium_views_info_p','shared_compendiums','shared_compendiums_p','feedback_messages_p1','feedback_messages_p1_p','totsize','totsize_p'),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 = '6'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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
time_in_rfc Pop t pop_t pageviews pageviews_p logins logins_p
1 210907 1 1 1 1418 1418 56 56
2 120982 1 2 2 869 869 56 56
3 176508 1 3 3 1530 1530 54 54
4 179321 1 4 4 2172 2172 89 89
5 123185 1 5 5 901 901 40 40
6 52746 1 6 6 463 463 25 25
7 385534 1 7 7 3201 3201 92 92
8 33170 1 8 8 371 371 18 18
9 149061 1 9 9 1583 1583 44 44
10 165446 1 10 10 1439 1439 33 33
11 237213 1 11 11 1764 1764 84 84
12 173326 1 12 12 1495 1495 88 88
13 133131 1 13 13 1373 1373 55 55
14 258873 1 14 14 2187 2187 60 60
15 180083 1 15 15 1491 1491 66 66
16 324799 1 16 16 4041 4041 154 154
17 230964 1 17 17 1706 1706 53 53
18 236785 1 18 18 2152 2152 119 119
19 135473 1 19 19 1036 1036 41 41
20 202925 1 20 20 1882 1882 61 61
21 215147 1 21 21 1929 1929 58 58
22 344297 1 22 22 2242 2242 75 75
23 153935 1 23 23 1220 1220 33 33
24 132943 1 24 24 1289 1289 40 40
25 174724 1 25 25 2515 2515 92 92
26 174415 1 26 26 2147 2147 100 100
27 225548 1 27 27 2352 2352 112 112
28 223632 1 28 28 1638 1638 73 73
29 124817 1 29 29 1222 1222 40 40
30 221698 1 30 30 1812 1812 45 45
31 210767 1 31 31 1677 1677 60 60
32 170266 1 32 32 1579 1579 62 62
33 260561 1 33 33 1731 1731 75 75
34 84853 1 34 34 807 807 31 31
35 294424 1 35 35 2452 2452 77 77
36 215641 1 36 36 1940 1940 46 46
37 325107 1 37 37 2662 2662 99 99
38 167542 1 38 38 1499 1499 66 66
39 106408 1 39 39 865 865 30 30
40 265769 1 40 40 2527 2527 146 146
41 269651 1 41 41 2747 2747 67 67
42 149112 1 42 42 1324 1324 56 56
43 152871 1 43 43 1383 1383 58 58
44 111665 1 44 44 1179 1179 34 34
45 116408 1 45 45 2099 2099 61 61
46 362301 1 46 46 4308 4308 119 119
47 78800 1 47 47 918 918 42 42
48 183167 1 48 48 1831 1831 66 66
49 277965 1 49 49 3373 3373 89 89
50 150629 1 50 50 1713 1713 44 44
51 168809 1 51 51 1438 1438 66 66
52 24188 1 52 52 496 496 24 24
53 329267 1 53 53 2253 2253 259 259
54 65029 1 54 54 744 744 17 17
55 101097 1 55 55 1161 1161 64 64
56 218946 1 56 56 2352 2352 41 41
57 244052 1 57 57 2144 2144 68 68
58 233328 1 58 58 2694 2694 132 132
59 256462 1 59 59 1973 1973 105 105
60 206161 1 60 60 1769 1769 71 71
61 311473 1 61 61 3148 3148 112 112
62 235800 1 62 62 2474 2474 94 94
63 177939 1 63 63 2084 2084 82 82
64 207176 1 64 64 1954 1954 70 70
65 196553 1 65 65 1226 1226 57 57
66 174184 1 66 66 1389 1389 53 53
67 143246 1 67 67 1496 1496 103 103
68 187559 1 68 68 2269 2269 121 121
69 187681 1 69 69 1833 1833 62 62
70 119016 1 70 70 1268 1268 52 52
71 182192 1 71 71 1943 1943 52 52
72 73566 1 72 72 893 893 32 32
73 194979 1 73 73 1762 1762 62 62
74 167488 1 74 74 1403 1403 45 45
75 143756 1 75 75 1425 1425 46 46
76 275541 1 76 76 1857 1857 63 63
77 243199 1 77 77 1840 1840 75 75
78 182999 1 78 78 1502 1502 88 88
79 135649 1 79 79 1441 1441 46 46
80 152299 1 80 80 1420 1420 53 53
81 120221 1 81 81 1416 1416 37 37
82 346485 1 82 82 2970 2970 90 90
83 145790 1 83 83 1317 1317 63 63
84 193339 1 84 84 1644 1644 78 78
85 80953 1 85 85 870 870 25 25
86 122774 1 86 86 1654 1654 45 45
87 130585 1 87 87 1054 1054 46 46
88 286468 1 88 88 3004 3004 144 144
89 241066 1 89 89 2008 2008 82 82
90 148446 1 90 90 2547 2547 91 91
91 204713 1 91 91 1885 1885 71 71
92 182079 1 92 92 1626 1626 63 63
93 140344 1 93 93 1468 1468 53 53
94 220516 1 94 94 2445 2445 62 62
95 243060 1 95 95 1964 1964 63 63
96 162765 1 96 96 1381 1381 32 32
97 182613 1 97 97 1369 1369 39 39
98 232138 1 98 98 1659 1659 62 62
99 265318 1 99 99 2888 2888 117 117
100 310839 1 100 100 2845 2845 92 92
101 225060 1 101 101 1982 1982 93 93
102 232317 1 102 102 1904 1904 54 54
103 144966 1 103 103 1391 1391 144 144
104 43287 1 104 104 602 602 14 14
105 155754 1 105 105 1743 1743 61 61
106 164709 1 106 106 1559 1559 109 109
107 201940 1 107 107 2014 2014 38 38
108 235454 1 108 108 2143 2143 73 73
109 99466 1 109 109 874 874 50 50
110 100750 1 110 110 1281 1281 72 72
111 224549 1 111 111 1401 1401 50 50
112 243511 1 112 112 1944 1944 71 71
113 22938 1 113 113 391 391 10 10
114 152474 1 114 114 1605 1605 65 65
115 61857 1 115 115 530 530 25 25
116 132487 1 116 116 1386 1386 41 41
117 317394 1 117 117 2395 2395 86 86
118 21054 1 118 118 387 387 16 16
119 209641 1 119 119 1742 1742 42 42
120 31414 1 120 120 449 449 19 19
121 244749 1 121 121 2699 2699 95 95
122 184510 1 122 122 1606 1606 49 49
123 128423 1 123 123 1204 1204 64 64
124 97839 1 124 124 1138 1138 38 38
125 38214 1 125 125 568 568 34 34
126 151101 1 126 126 1459 1459 32 32
127 272458 1 127 127 2158 2158 65 65
128 172494 1 128 128 1111 1111 52 52
129 328107 1 129 129 2833 2833 65 65
130 250579 1 130 130 1955 1955 83 83
131 351067 1 131 131 2922 2922 95 95
132 158015 1 132 132 1002 1002 29 29
133 85439 1 133 133 956 956 33 33
134 229242 1 134 134 2186 2186 247 247
135 351619 1 135 135 3604 3604 139 139
136 84207 1 136 136 1035 1035 29 29
137 324598 1 137 137 3261 3261 110 110
138 131069 1 138 138 1587 1587 67 67
139 204271 1 139 139 1424 1424 42 42
140 165543 1 140 140 1701 1701 65 65
141 141722 1 141 141 1249 1249 94 94
142 299775 1 142 142 3352 3352 95 95
143 195838 1 143 143 1641 1641 67 67
144 173260 1 144 144 2035 2035 63 63
145 254488 1 145 145 2312 2312 83 83
146 104389 1 146 146 1369 1369 45 45
147 199476 1 147 147 2201 2201 70 70
148 224330 1 148 148 1900 1900 83 83
149 14688 1 149 149 207 207 10 10
150 181633 1 150 150 1645 1645 70 70
151 271856 1 151 151 2429 2429 103 103
152 7199 1 152 152 151 151 5 5
153 46660 1 153 153 474 474 20 20
154 17547 1 154 154 141 141 5 5
155 95227 1 155 155 872 872 34 34
156 152601 1 156 156 1318 1318 48 48
157 101645 0 157 0 1192 0 63 0
158 101011 0 158 0 829 0 34 0
159 7176 0 159 0 186 0 17 0
160 96560 0 160 0 1793 0 76 0
161 175824 0 161 0 2702 0 107 0
162 341570 0 162 0 4691 0 168 0
163 103597 0 163 0 1112 0 43 0
164 112611 0 164 0 937 0 41 0
165 85574 0 165 0 1290 0 34 0
166 220801 0 166 0 2146 0 75 0
167 92661 0 167 0 1590 0 61 0
168 133328 0 168 0 1590 0 55 0
169 61361 0 169 0 1210 0 77 0
170 125930 0 170 0 2072 0 75 0
171 82316 0 171 0 834 0 32 0
172 102010 0 172 0 1105 0 53 0
173 101523 0 173 0 1272 0 42 0
174 41566 0 174 0 761 0 35 0
175 99923 0 175 0 1988 0 66 0
176 22648 0 176 0 620 0 19 0
177 46698 0 177 0 800 0 45 0
178 131698 0 178 0 1684 0 65 0
179 91735 0 179 0 1050 0 35 0
180 79863 0 180 0 1502 0 37 0
181 108043 0 181 0 1421 0 62 0
182 98866 0 182 0 1060 0 18 0
183 120445 0 183 0 1417 0 118 0
184 116048 0 184 0 946 0 64 0
185 250047 0 185 0 1926 0 81 0
186 136084 0 186 0 1577 0 30 0
187 92499 0 187 0 961 0 32 0
188 135781 0 188 0 1254 0 31 0
189 74408 0 189 0 1335 0 67 0
190 81240 0 190 0 1597 0 66 0
191 133368 0 191 0 1639 0 36 0
192 98146 0 192 0 1018 0 40 0
193 79619 0 193 0 1383 0 43 0
194 59194 0 194 0 1314 0 31 0
195 139942 0 195 0 1335 0 42 0
196 118612 0 196 0 1403 0 46 0
197 72880 0 197 0 910 0 33 0
compendium_views_info compendium_views_info_p 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
9 656 656 5
10 511 511 0
11 655 655 0
12 465 465 7
13 525 525 7
14 885 885 3
15 497 497 9
16 1436 1436 0
17 612 612 4
18 865 865 3
19 385 385 0
20 567 567 7
21 639 639 0
22 963 963 1
23 398 398 5
24 410 410 7
25 966 966 0
26 801 801 0
27 892 892 5
28 513 513 0
29 469 469 0
30 683 683 0
31 643 643 3
32 535 535 4
33 625 625 1
34 264 264 4
35 992 992 2
36 818 818 0
37 937 937 0
38 507 507 2
39 260 260 1
40 927 927 2
41 1269 1269 10
42 537 537 6
43 532 532 5
44 345 345 4
45 918 918 1
46 1635 1635 2
47 330 330 2
48 557 557 0
49 1178 1178 8
50 740 740 3
51 452 452 0
52 218 218 0
53 764 764 8
54 255 255 5
55 454 454 3
56 866 866 1
57 574 574 5
58 825 825 5
59 798 798 0
60 663 663 12
61 1069 1069 8
62 921 921 8
63 858 858 8
64 711 711 8
65 503 503 2
66 382 382 0
67 464 464 5
68 717 717 8
69 690 690 2
70 462 462 5
71 657 657 12
72 385 385 6
73 577 577 7
74 619 619 2
75 479 479 0
76 817 817 4
77 752 752 3
78 430 430 6
79 451 451 2
80 537 537 0
81 519 519 1
82 1000 1000 0
83 637 637 5
84 465 465 2
85 437 437 0
86 711 711 0
87 299 299 5
88 1162 1162 1
89 714 714 0
90 905 905 1
91 649 649 1
92 512 512 2
93 472 472 6
94 905 905 1
95 786 786 4
96 489 489 2
97 479 479 3
98 617 617 0
99 925 925 10
100 1144 1144 9
101 669 669 7
102 707 707 0
103 458 458 0
104 214 214 4
105 599 599 4
106 572 572 0
107 897 897 0
108 819 819 0
109 273 273 0
110 407 407 0
111 465 465 4
112 603 603 0
113 154 154 0
114 577 577 0
115 192 192 4
116 411 411 0
117 975 975 1
118 146 146 0
119 705 705 5
120 200 200 0
121 964 964 2
122 537 537 7
123 369 369 8
124 417 417 2
125 276 276 0
126 514 514 2
127 822 822 0
128 389 389 0
129 1255 1255 3
130 694 694 0
131 1024 1024 3
132 400 400 0
133 350 350 0
134 719 719 4
135 1277 1277 4
136 356 356 11
137 1402 1402 0
138 600 600 4
139 480 480 0
140 595 595 1
141 436 436 0
142 1367 1367 9
143 564 564 1
144 716 716 3
145 747 747 10
146 467 467 5
147 861 861 2
148 612 612 1
149 85 85 0
150 564 564 2
151 824 824 1
152 74 74 0
153 259 259 0
154 69 69 0
155 239 239 0
156 438 438 2
157 371 0 0
158 238 0 0
159 70 0 0
160 503 0 0
161 910 0 0
162 1276 0 1
163 379 0 1
164 248 0 0
165 351 0 0
166 720 0 1
167 508 0 1
168 506 0 0
169 451 0 0
170 699 0 4
171 245 0 4
172 370 0 3
173 316 0 0
174 229 0 5
175 617 0 0
176 184 0 0
177 274 0 0
178 502 0 0
179 382 0 0
180 438 0 1
181 466 0 1
182 397 0 0
183 457 0 0
184 230 0 0
185 651 0 0
186 671 0 0
187 319 0 0
188 433 0 2
189 434 0 4
190 503 0 0
191 535 0 1
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_p feedback_messages_p1 feedback_messages_p1_p 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 5 100 100 116174
10 0 93 93 57635
11 0 140 140 66198
12 7 166 166 71701
13 7 99 99 57793
14 3 139 139 80444
15 9 130 130 53855
16 0 181 181 97668
17 4 116 116 133824
18 3 116 116 101481
19 0 88 88 99645
20 7 139 139 114789
21 0 135 135 99052
22 1 108 108 67654
23 5 89 89 65553
24 7 156 156 97500
25 0 129 129 69112
26 0 118 118 82753
27 5 118 118 85323
28 0 125 125 72654
29 0 95 95 30727
30 0 126 126 77873
31 3 135 135 117478
32 4 154 154 74007
33 1 165 165 90183
34 4 113 113 61542
35 2 127 127 101494
36 0 121 121 55813
37 0 136 136 79215
38 2 108 108 55461
39 1 46 46 31081
40 2 124 124 83122
41 10 115 115 70106
42 6 128 128 60578
43 5 97 97 79892
44 4 104 104 49810
45 1 59 59 71570
46 2 125 125 100708
47 2 82 82 33032
48 0 149 149 82875
49 8 149 149 139077
50 3 122 122 71595
51 0 118 118 72260
52 0 12 12 5950
53 8 144 144 115762
54 5 67 67 32551
55 3 52 52 31701
56 1 108 108 80670
57 5 166 166 143558
58 5 107 107 120733
59 0 127 127 105195
60 12 107 107 73107
61 8 146 146 132068
62 8 84 84 149193
63 8 141 141 46821
64 8 123 123 87011
65 2 111 111 95260
66 0 98 98 55183
67 5 105 105 106671
68 8 135 135 73511
69 2 107 107 92945
70 5 85 85 78664
71 12 155 155 70054
72 6 88 88 22618
73 7 155 155 74011
74 2 104 104 83737
75 0 132 132 69094
76 4 127 127 93133
77 3 108 108 95536
78 6 129 129 225920
79 2 116 116 62133
80 0 122 122 61370
81 1 85 85 43836
82 0 147 147 106117
83 5 99 99 38692
84 2 87 87 84651
85 0 28 28 56622
86 0 90 90 15986
87 5 109 109 95364
88 1 111 111 89691
89 0 158 158 67267
90 1 141 141 126846
91 1 122 122 41140
92 2 124 124 102860
93 6 93 93 51715
94 1 124 124 55801
95 4 112 112 111813
96 2 108 108 120293
97 3 99 99 138599
98 0 117 117 161647
99 10 199 199 115929
100 9 91 91 162901
101 7 158 158 109825
102 0 126 126 129838
103 0 122 122 37510
104 4 71 71 43750
105 4 75 75 40652
106 0 115 115 87771
107 0 119 119 85872
108 0 124 124 89275
109 0 91 91 192565
110 0 119 119 140867
111 4 117 117 120662
112 0 155 155 101338
113 0 0 0 1168
114 0 123 123 65567
115 4 32 32 25162
116 0 136 136 40735
117 1 117 117 91413
118 0 0 0 855
119 5 88 88 97068
120 0 25 25 14116
121 2 124 124 76643
122 7 151 151 110681
123 8 145 145 92696
124 2 87 87 94785
125 0 27 27 8773
126 2 131 131 83209
127 0 162 162 93815
128 0 165 165 86687
129 3 159 159 105547
130 0 147 147 103487
131 3 170 170 213688
132 0 119 119 71220
133 0 104 104 56926
134 4 120 120 91721
135 4 150 150 115168
136 11 112 112 111194
137 0 136 136 135777
138 4 107 107 51513
139 0 130 130 74163
140 1 115 115 51633
141 0 107 107 75345
142 9 120 120 98952
143 1 116 116 102372
144 3 79 79 37238
145 10 150 150 103772
146 5 156 156 123969
147 2 118 118 135400
148 1 144 144 130115
149 0 0 0 6023
150 2 110 110 64466
151 1 147 147 54990
152 0 0 0 1644
153 0 15 15 6179
154 0 4 4 3926
155 0 111 111 34777
156 2 85 85 73224
157 0 44 0 17140
158 0 52 0 27570
159 0 0 0 1423
160 0 54 0 22996
161 0 80 0 39992
162 0 80 0 117105
163 0 60 0 23789
164 0 78 0 26706
165 0 78 0 24266
166 0 72 0 44418
167 0 45 0 35232
168 0 78 0 40909
169 0 39 0 13294
170 0 68 0 32387
171 0 39 0 21233
172 0 50 0 44332
173 0 88 0 61056
174 0 36 0 13497
175 0 99 0 32334
176 0 39 0 44339
177 0 52 0 10288
178 0 75 0 65622
179 0 71 0 16563
180 0 71 0 29011
181 0 54 0 34553
182 0 49 0 23517
183 0 59 0 51009
184 0 75 0 33416
185 0 71 0 83305
186 0 51 0 27142
187 0 71 0 21399
188 0 47 0 24874
189 0 28 0 34988
190 0 68 0 45549
191 0 64 0 32755
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_p
1 112285
2 84786
3 83123
4 101193
5 38361
6 68504
7 119182
8 22807
9 116174
10 57635
11 66198
12 71701
13 57793
14 80444
15 53855
16 97668
17 133824
18 101481
19 99645
20 114789
21 99052
22 67654
23 65553
24 97500
25 69112
26 82753
27 85323
28 72654
29 30727
30 77873
31 117478
32 74007
33 90183
34 61542
35 101494
36 55813
37 79215
38 55461
39 31081
40 83122
41 70106
42 60578
43 79892
44 49810
45 71570
46 100708
47 33032
48 82875
49 139077
50 71595
51 72260
52 5950
53 115762
54 32551
55 31701
56 80670
57 143558
58 120733
59 105195
60 73107
61 132068
62 149193
63 46821
64 87011
65 95260
66 55183
67 106671
68 73511
69 92945
70 78664
71 70054
72 22618
73 74011
74 83737
75 69094
76 93133
77 95536
78 225920
79 62133
80 61370
81 43836
82 106117
83 38692
84 84651
85 56622
86 15986
87 95364
88 89691
89 67267
90 126846
91 41140
92 102860
93 51715
94 55801
95 111813
96 120293
97 138599
98 161647
99 115929
100 162901
101 109825
102 129838
103 37510
104 43750
105 40652
106 87771
107 85872
108 89275
109 192565
110 140867
111 120662
112 101338
113 1168
114 65567
115 25162
116 40735
117 91413
118 855
119 97068
120 14116
121 76643
122 110681
123 92696
124 94785
125 8773
126 83209
127 93815
128 86687
129 105547
130 103487
131 213688
132 71220
133 56926
134 91721
135 115168
136 111194
137 135777
138 51513
139 74163
140 51633
141 75345
142 98952
143 102372
144 37238
145 103772
146 123969
147 135400
148 130115
149 6023
150 64466
151 54990
152 1644
153 6179
154 3926
155 34777
156 73224
157 0
158 0
159 0
160 0
161 0
162 0
163 0
164 0
165 0
166 0
167 0
168 0
169 0
170 0
171 0
172 0
173 0
174 0
175 0
176 0
177 0
178 0
179 0
180 0
181 0
182 0
183 0
184 0
185 0
186 0
187 0
188 0
189 0
190 0
191 0
192 0
193 0
194 0
195 0
196 0
197 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop t
8.507e+04 -9.228e+04 -5.170e+02
pop_t pageviews pageviews_p
4.656e+02 -1.281e+01 4.519e+01
logins logins_p compendium_views_info
-8.904e+01 2.786e+02 1.968e+02
compendium_views_info_p shared_compendiums shared_compendiums_p
-9.832e+01 -8.171e+02 -1.061e+03
feedback_messages_p1 feedback_messages_p1_p totsize
1.401e+02 2.717e+02 1.220e+00
totsize_p
-9.273e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-121879 -18646 -353 20246 108575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.507e+04 9.036e+04 0.941 0.34775
Pop -9.228e+04 9.092e+04 -1.015 0.31144
t -5.170e+02 5.293e+02 -0.977 0.33005
pop_t 4.656e+02 5.325e+02 0.874 0.38311
pageviews -1.281e+01 3.586e+01 -0.357 0.72128
pageviews_p 4.519e+01 3.953e+01 1.143 0.25443
logins -8.904e+01 3.286e+02 -0.271 0.78674
logins_p 2.786e+02 3.433e+02 0.812 0.41814
compendium_views_info 1.968e+02 9.793e+01 2.010 0.04590 *
compendium_views_info_p -9.832e+01 1.042e+02 -0.943 0.34676
shared_compendiums -8.171e+02 3.778e+03 -0.216 0.82901
shared_compendiums_p -1.061e+03 3.873e+03 -0.274 0.78451
feedback_messages_p1 1.401e+02 3.353e+02 0.418 0.67647
feedback_messages_p1_p 2.717e+02 3.503e+02 0.776 0.43901
totsize 1.220e+00 4.027e-01 3.029 0.00281 **
totsize_p -9.273e-01 4.110e-01 -2.256 0.02528 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 31760 on 181 degrees of freedom
Multiple R-squared: 0.8627, Adjusted R-squared: 0.8514
F-statistic: 75.85 on 15 and 181 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,] 0.99952416 9.516898e-04 4.758449e-04
[2,] 0.99866832 2.663351e-03 1.331675e-03
[3,] 0.99676547 6.469057e-03 3.234529e-03
[4,] 0.99984501 3.099771e-04 1.549886e-04
[5,] 0.99967098 6.580328e-04 3.290164e-04
[6,] 0.99963741 7.251809e-04 3.625905e-04
[7,] 0.99997508 4.983190e-05 2.491595e-05
[8,] 0.99997742 4.515104e-05 2.257552e-05
[9,] 0.99995169 9.661447e-05 4.830723e-05
[10,] 0.99997163 5.674473e-05 2.837237e-05
[11,] 0.99994819 1.036285e-04 5.181424e-05
[12,] 0.99990645 1.870913e-04 9.354563e-05
[13,] 0.99982191 3.561766e-04 1.780883e-04
[14,] 0.99968095 6.381041e-04 3.190520e-04
[15,] 0.99980364 3.927127e-04 1.963564e-04
[16,] 0.99975943 4.811351e-04 2.405675e-04
[17,] 0.99968660 6.268015e-04 3.134008e-04
[18,] 0.99948292 1.034164e-03 5.170821e-04
[19,] 0.99976396 4.720710e-04 2.360355e-04
[20,] 0.99960297 7.940579e-04 3.970290e-04
[21,] 0.99943425 1.131495e-03 5.657475e-04
[22,] 0.99911662 1.766763e-03 8.833817e-04
[23,] 0.99871439 2.571229e-03 1.285614e-03
[24,] 0.99805049 3.899029e-03 1.949514e-03
[25,] 0.99713411 5.731790e-03 2.865895e-03
[26,] 0.99616919 7.661618e-03 3.830809e-03
[27,] 0.99968965 6.206998e-04 3.103499e-04
[28,] 0.99960190 7.962100e-04 3.981050e-04
[29,] 0.99943885 1.122307e-03 5.611534e-04
[30,] 0.99923110 1.537800e-03 7.689001e-04
[31,] 0.99922356 1.552882e-03 7.764412e-04
[32,] 0.99931213 1.375733e-03 6.878666e-04
[33,] 0.99897933 2.041350e-03 1.020675e-03
[34,] 0.99850045 2.999103e-03 1.499551e-03
[35,] 0.99948908 1.021845e-03 5.109226e-04
[36,] 0.99921598 1.568037e-03 7.840183e-04
[37,] 0.99882086 2.358277e-03 1.179139e-03
[38,] 0.99837312 3.253752e-03 1.626876e-03
[39,] 0.99776887 4.462258e-03 2.231129e-03
[40,] 0.99712068 5.758641e-03 2.879321e-03
[41,] 0.99633137 7.337253e-03 3.668627e-03
[42,] 0.99695570 6.088602e-03 3.044301e-03
[43,] 0.99581953 8.360930e-03 4.180465e-03
[44,] 0.99420033 1.159934e-02 5.799670e-03
[45,] 0.99389272 1.221456e-02 6.107282e-03
[46,] 0.99186457 1.627086e-02 8.135429e-03
[47,] 0.99225430 1.549141e-02 7.745704e-03
[48,] 0.99260188 1.479625e-02 7.398124e-03
[49,] 0.99219551 1.560899e-02 7.804493e-03
[50,] 0.99133092 1.733816e-02 8.669079e-03
[51,] 0.98843568 2.312863e-02 1.156432e-02
[52,] 0.98527614 2.944773e-02 1.472386e-02
[53,] 0.98064337 3.871326e-02 1.935663e-02
[54,] 0.97602785 4.794431e-02 2.397215e-02
[55,] 0.96951788 6.096424e-02 3.048212e-02
[56,] 0.96082629 7.834741e-02 3.917371e-02
[57,] 0.95457177 9.085647e-02 4.542823e-02
[58,] 0.97679504 4.640992e-02 2.320496e-02
[59,] 0.97999645 4.000710e-02 2.000355e-02
[60,] 0.97990602 4.018795e-02 2.009398e-02
[61,] 0.97494170 5.011661e-02 2.505830e-02
[62,] 0.96871892 6.256216e-02 3.128108e-02
[63,] 0.96244720 7.510560e-02 3.755280e-02
[64,] 0.97473120 5.053761e-02 2.526880e-02
[65,] 0.96811507 6.376986e-02 3.188493e-02
[66,] 0.96879792 6.240415e-02 3.120208e-02
[67,] 0.96071248 7.857503e-02 3.928752e-02
[68,] 0.96317432 7.365137e-02 3.682568e-02
[69,] 0.95455180 9.089640e-02 4.544820e-02
[70,] 0.94410546 1.117891e-01 5.589454e-02
[71,] 0.93507578 1.298484e-01 6.492422e-02
[72,] 0.99872009 2.559827e-03 1.279913e-03
[73,] 0.99834473 3.310537e-03 1.655269e-03
[74,] 0.99765028 4.699441e-03 2.349720e-03
[75,] 0.99677102 6.457956e-03 3.228978e-03
[76,] 0.99638019 7.239612e-03 3.619806e-03
[77,] 0.99634666 7.306687e-03 3.653344e-03
[78,] 0.99493930 1.012140e-02 5.060698e-03
[79,] 0.99394967 1.210066e-02 6.050331e-03
[80,] 0.99323831 1.352338e-02 6.761691e-03
[81,] 0.99296075 1.407850e-02 7.039248e-03
[82,] 0.99330280 1.339441e-02 6.697204e-03
[83,] 0.99125910 1.748180e-02 8.740898e-03
[84,] 0.98889668 2.220664e-02 1.110332e-02
[85,] 0.98633961 2.732078e-02 1.366039e-02
[86,] 0.98334490 3.331020e-02 1.665510e-02
[87,] 0.97888549 4.222902e-02 2.111451e-02
[88,] 0.97485889 5.028222e-02 2.514111e-02
[89,] 0.97120633 5.758734e-02 2.879367e-02
[90,] 0.96345981 7.308037e-02 3.654019e-02
[91,] 0.96651730 6.696540e-02 3.348270e-02
[92,] 0.98894594 2.210812e-02 1.105406e-02
[93,] 0.99523646 9.527089e-03 4.763545e-03
[94,] 0.99433649 1.132702e-02 5.663511e-03
[95,] 0.99232639 1.534723e-02 7.673615e-03
[96,] 0.99219994 1.560013e-02 7.800063e-03
[97,] 0.99109067 1.781867e-02 8.909335e-03
[98,] 0.99080970 1.838061e-02 9.190305e-03
[99,] 0.99686106 6.277875e-03 3.138938e-03
[100,] 0.99556311 8.873784e-03 4.436892e-03
[101,] 0.99681857 6.362859e-03 3.181430e-03
[102,] 0.99549464 9.010712e-03 4.505356e-03
[103,] 0.99514358 9.712849e-03 4.856425e-03
[104,] 0.99350022 1.299956e-02 6.499780e-03
[105,] 0.99129172 1.741656e-02 8.708281e-03
[106,] 0.99098719 1.802562e-02 9.012808e-03
[107,] 0.98878020 2.243959e-02 1.121980e-02
[108,] 0.98773095 2.453810e-02 1.226905e-02
[109,] 0.98502812 2.994377e-02 1.497188e-02
[110,] 0.98033259 3.933482e-02 1.966741e-02
[111,] 0.98489717 3.020567e-02 1.510283e-02
[112,] 0.98252332 3.495337e-02 1.747668e-02
[113,] 0.98017391 3.965218e-02 1.982609e-02
[114,] 0.98631754 2.736493e-02 1.368246e-02
[115,] 0.98344654 3.310693e-02 1.655346e-02
[116,] 0.97767920 4.464160e-02 2.232080e-02
[117,] 0.97360239 5.279521e-02 2.639761e-02
[118,] 0.96818921 6.362158e-02 3.181079e-02
[119,] 0.96124853 7.750293e-02 3.875147e-02
[120,] 0.95484014 9.031972e-02 4.515986e-02
[121,] 0.98202659 3.594683e-02 1.797341e-02
[122,] 0.97682335 4.635330e-02 2.317665e-02
[123,] 0.97516379 4.967241e-02 2.483621e-02
[124,] 0.96845268 6.309465e-02 3.154732e-02
[125,] 0.96597904 6.804193e-02 3.402096e-02
[126,] 0.95441586 9.116828e-02 4.558414e-02
[127,] 0.94517932 1.096414e-01 5.482068e-02
[128,] 0.94281737 1.143653e-01 5.718263e-02
[129,] 0.92904969 1.419006e-01 7.095031e-02
[130,] 0.90773147 1.845371e-01 9.226853e-02
[131,] 0.88252351 2.349530e-01 1.174765e-01
[132,] 0.85255144 2.948971e-01 1.474486e-01
[133,] 0.82248455 3.550309e-01 1.775154e-01
[134,] 0.78188391 4.362322e-01 2.181161e-01
[135,] 0.73577365 5.284527e-01 2.642264e-01
[136,] 0.68627221 6.274556e-01 3.137278e-01
[137,] 0.62983666 7.403267e-01 3.701633e-01
[138,] 0.57307252 8.538550e-01 4.269275e-01
[139,] 0.52988296 9.402341e-01 4.701170e-01
[140,] 0.50390710 9.921858e-01 4.960929e-01
[141,] 0.43933619 8.786724e-01 5.606638e-01
[142,] 0.37789370 7.557874e-01 6.221063e-01
[143,] 0.34208489 6.841698e-01 6.579151e-01
[144,] 0.28815715 5.763143e-01 7.118429e-01
[145,] 0.23684093 4.736819e-01 7.631591e-01
[146,] 0.26051173 5.210235e-01 7.394883e-01
[147,] 0.20873353 4.174671e-01 7.912665e-01
[148,] 0.32596643 6.519329e-01 6.740336e-01
[149,] 0.27850420 5.570084e-01 7.214958e-01
[150,] 0.23075056 4.615011e-01 7.692494e-01
[151,] 0.18985173 3.797035e-01 8.101483e-01
[152,] 0.14504474 2.900895e-01 8.549553e-01
[153,] 0.13297957 2.659591e-01 8.670204e-01
[154,] 0.09420148 1.884030e-01 9.057985e-01
[155,] 0.06219002 1.243800e-01 9.378100e-01
[156,] 0.04081668 8.163336e-02 9.591833e-01
[157,] 0.03851515 7.703030e-02 9.614849e-01
[158,] 0.05845756 1.169151e-01 9.415424e-01
[159,] 0.03665568 7.331137e-02 9.633443e-01
[160,] 0.03184641 6.369281e-02 9.681536e-01
> postscript(file="/var/fisher/rcomp/tmp/10mo71353078686.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2typq1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xevq1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/46awv1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5r09k1353078686.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
4.806257e+04 -1.889081e+03 7.125250e+03 -6.933607e+04 2.995760e+04
6 7 8 9 10
-2.954663e+04 4.374463e+04 -1.962746e+04 -3.326064e+04 1.482835e+04
11 12 13 14 15
3.041095e+04 -5.927142e+03 -1.011741e+04 2.229593e+04 2.593221e+04
16 17 18 19 20
-7.177086e+04 3.407028e+04 -4.347860e+03 -9.678309e+02 5.134880e+03
21 22 23 24 25
2.468597e+03 1.085754e+05 3.092872e+04 -2.793695e+04 -8.415206e+04
26 27 28 29 30
-5.721604e+04 -1.527180e+04 4.214687e+04 -7.938355e+03 2.130165e+04
31 32 33 34 35
6.231108e+03 -1.401328e+04 4.518552e+04 -2.122298e+04 3.348881e+04
36 37 38 39 40
6.431806e+03 5.778068e+04 8.775401e+03 3.016454e+04 2.599286e+03
41 42 43 44 45
3.224381e+03 -7.065667e+03 1.860934e+02 -7.351153e+03 -8.738117e+04
46 47 48 49 50
-2.841559e+04 -2.143720e+04 -1.942036e+04 -4.145698e+04 -4.184280e+04
51 52 53 54 55
5.316276e+03 -1.468896e+04 6.376031e+04 -5.136257e+03 -8.361235e+03
56 57 58 59 60
-6.396083e+03 1.437445e+04 -1.998966e+04 2.123736e+04 3.749370e+04
61 62 63 64 65
9.618396e+03 -5.666366e+03 -3.589170e+04 1.001757e+04 3.723449e+04
66 67 68 69 70
3.564161e+04 -2.482158e+04 -3.084174e+04 -8.131090e+03 -1.522261e+04
71 72 73 74 75
-6.224781e+03 -2.001297e+04 7.965296e+03 1.224211e+00 -2.179042e+04
76 77 78 79 80
6.207424e+04 3.970729e+04 -2.139718e+04 -1.507021e+04 -1.349162e+04
81 82 83 84 85
-1.833840e+04 5.459920e+04 -2.764142e+03 3.421497e+04 -1.151512e+04
86 87 88 89 90
-3.945550e+04 6.576171e+03 -1.089680e+04 1.721563e+04 -1.218789e+05
91 92 93 94 95
1.778307e+04 1.597354e+03 6.105282e+03 -1.301912e+04 3.087233e+04
96 97 98 99 100
5.156019e+01 2.022654e+04 2.266993e+04 -2.627430e+04 3.270824e+04
101 102 103 104 105
5.710739e+03 1.337121e+04 -2.118671e+04 -2.190321e+04 6.093905e+03
106 107 108 109 110
-2.314808e+04 -2.025164e+04 7.133756e+03 -4.619317e+04 -7.181277e+04
111 112 113 114 115
6.085352e+04 2.720390e+04 5.896635e+03 -2.541295e+04 2.114480e+04
116 117 118 119 120
-1.539213e+04 6.767928e+04 4.141797e+03 3.391029e+04 -7.466339e+03
121 122 123 124 125
-1.190842e+04 2.390533e+03 -1.731076e+04 -3.350032e+04 -1.385316e+04
126 127 128 129 130
-1.368086e+04 2.887269e+04 8.832051e+03 2.354683e+04 2.626722e+04
131 132 133 134 135
2.463371e+04 2.483148e+04 -3.167686e+04 -1.381694e+04 8.981769e+03
136 137 138 139 140
-3.366105e+04 -2.142503e+04 -2.943467e+04 4.204798e+04 -6.639912e+03
141 142 143 144 145
-1.112703e+04 -8.383967e+03 1.317129e+04 1.718956e+03 3.162605e+04
146 147 148 149 150
-7.087057e+04 -3.955385e+04 6.126273e+03 1.083262e+04 1.406632e+04
151 152 153 154 155
3.274490e+04 8.623442e+03 9.104849e+03 1.757580e+04 -3.694508e+03
156 157 158 159 160
2.024644e+04 1.852199e+04 2.350888e+04 -7.309880e+03 -1.068261e+04
161 162 163 164 165
-2.098679e+04 1.089918e+04 9.659807e+03 3.565986e+04 -4.259543e+03
166 167 168 169 170
5.054222e+04 -2.873092e+04 -5.365702e+01 -2.443687e+04 -2.138787e+04
171 172 173 174 175
2.286256e+04 -6.725263e+03 -2.308162e+04 -3.181218e+03 -3.809387e+04
176 177 178 179 180
-5.756567e+04 -6.379472e+03 -2.335665e+04 1.042799e+04 -2.034832e+04
181 182 183 184 185
-3.529014e+02 9.371885e+03 -1.801351e+03 4.737818e+04 5.279680e+04
186 187 188 189 190
-2.287814e+03 2.042267e+04 4.620451e+04 -1.864572e+04 -4.336729e+04
191 192 193 194 195
1.782941e+04 -4.012224e+03 3.542846e+03 -1.489222e+04 -1.368142e+04
196 197
2.579165e+04 -9.799294e+03
> postscript(file="/var/fisher/rcomp/tmp/6g7pm1353078686.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 4.806257e+04 NA
1 -1.889081e+03 4.806257e+04
2 7.125250e+03 -1.889081e+03
3 -6.933607e+04 7.125250e+03
4 2.995760e+04 -6.933607e+04
5 -2.954663e+04 2.995760e+04
6 4.374463e+04 -2.954663e+04
7 -1.962746e+04 4.374463e+04
8 -3.326064e+04 -1.962746e+04
9 1.482835e+04 -3.326064e+04
10 3.041095e+04 1.482835e+04
11 -5.927142e+03 3.041095e+04
12 -1.011741e+04 -5.927142e+03
13 2.229593e+04 -1.011741e+04
14 2.593221e+04 2.229593e+04
15 -7.177086e+04 2.593221e+04
16 3.407028e+04 -7.177086e+04
17 -4.347860e+03 3.407028e+04
18 -9.678309e+02 -4.347860e+03
19 5.134880e+03 -9.678309e+02
20 2.468597e+03 5.134880e+03
21 1.085754e+05 2.468597e+03
22 3.092872e+04 1.085754e+05
23 -2.793695e+04 3.092872e+04
24 -8.415206e+04 -2.793695e+04
25 -5.721604e+04 -8.415206e+04
26 -1.527180e+04 -5.721604e+04
27 4.214687e+04 -1.527180e+04
28 -7.938355e+03 4.214687e+04
29 2.130165e+04 -7.938355e+03
30 6.231108e+03 2.130165e+04
31 -1.401328e+04 6.231108e+03
32 4.518552e+04 -1.401328e+04
33 -2.122298e+04 4.518552e+04
34 3.348881e+04 -2.122298e+04
35 6.431806e+03 3.348881e+04
36 5.778068e+04 6.431806e+03
37 8.775401e+03 5.778068e+04
38 3.016454e+04 8.775401e+03
39 2.599286e+03 3.016454e+04
40 3.224381e+03 2.599286e+03
41 -7.065667e+03 3.224381e+03
42 1.860934e+02 -7.065667e+03
43 -7.351153e+03 1.860934e+02
44 -8.738117e+04 -7.351153e+03
45 -2.841559e+04 -8.738117e+04
46 -2.143720e+04 -2.841559e+04
47 -1.942036e+04 -2.143720e+04
48 -4.145698e+04 -1.942036e+04
49 -4.184280e+04 -4.145698e+04
50 5.316276e+03 -4.184280e+04
51 -1.468896e+04 5.316276e+03
52 6.376031e+04 -1.468896e+04
53 -5.136257e+03 6.376031e+04
54 -8.361235e+03 -5.136257e+03
55 -6.396083e+03 -8.361235e+03
56 1.437445e+04 -6.396083e+03
57 -1.998966e+04 1.437445e+04
58 2.123736e+04 -1.998966e+04
59 3.749370e+04 2.123736e+04
60 9.618396e+03 3.749370e+04
61 -5.666366e+03 9.618396e+03
62 -3.589170e+04 -5.666366e+03
63 1.001757e+04 -3.589170e+04
64 3.723449e+04 1.001757e+04
65 3.564161e+04 3.723449e+04
66 -2.482158e+04 3.564161e+04
67 -3.084174e+04 -2.482158e+04
68 -8.131090e+03 -3.084174e+04
69 -1.522261e+04 -8.131090e+03
70 -6.224781e+03 -1.522261e+04
71 -2.001297e+04 -6.224781e+03
72 7.965296e+03 -2.001297e+04
73 1.224211e+00 7.965296e+03
74 -2.179042e+04 1.224211e+00
75 6.207424e+04 -2.179042e+04
76 3.970729e+04 6.207424e+04
77 -2.139718e+04 3.970729e+04
78 -1.507021e+04 -2.139718e+04
79 -1.349162e+04 -1.507021e+04
80 -1.833840e+04 -1.349162e+04
81 5.459920e+04 -1.833840e+04
82 -2.764142e+03 5.459920e+04
83 3.421497e+04 -2.764142e+03
84 -1.151512e+04 3.421497e+04
85 -3.945550e+04 -1.151512e+04
86 6.576171e+03 -3.945550e+04
87 -1.089680e+04 6.576171e+03
88 1.721563e+04 -1.089680e+04
89 -1.218789e+05 1.721563e+04
90 1.778307e+04 -1.218789e+05
91 1.597354e+03 1.778307e+04
92 6.105282e+03 1.597354e+03
93 -1.301912e+04 6.105282e+03
94 3.087233e+04 -1.301912e+04
95 5.156019e+01 3.087233e+04
96 2.022654e+04 5.156019e+01
97 2.266993e+04 2.022654e+04
98 -2.627430e+04 2.266993e+04
99 3.270824e+04 -2.627430e+04
100 5.710739e+03 3.270824e+04
101 1.337121e+04 5.710739e+03
102 -2.118671e+04 1.337121e+04
103 -2.190321e+04 -2.118671e+04
104 6.093905e+03 -2.190321e+04
105 -2.314808e+04 6.093905e+03
106 -2.025164e+04 -2.314808e+04
107 7.133756e+03 -2.025164e+04
108 -4.619317e+04 7.133756e+03
109 -7.181277e+04 -4.619317e+04
110 6.085352e+04 -7.181277e+04
111 2.720390e+04 6.085352e+04
112 5.896635e+03 2.720390e+04
113 -2.541295e+04 5.896635e+03
114 2.114480e+04 -2.541295e+04
115 -1.539213e+04 2.114480e+04
116 6.767928e+04 -1.539213e+04
117 4.141797e+03 6.767928e+04
118 3.391029e+04 4.141797e+03
119 -7.466339e+03 3.391029e+04
120 -1.190842e+04 -7.466339e+03
121 2.390533e+03 -1.190842e+04
122 -1.731076e+04 2.390533e+03
123 -3.350032e+04 -1.731076e+04
124 -1.385316e+04 -3.350032e+04
125 -1.368086e+04 -1.385316e+04
126 2.887269e+04 -1.368086e+04
127 8.832051e+03 2.887269e+04
128 2.354683e+04 8.832051e+03
129 2.626722e+04 2.354683e+04
130 2.463371e+04 2.626722e+04
131 2.483148e+04 2.463371e+04
132 -3.167686e+04 2.483148e+04
133 -1.381694e+04 -3.167686e+04
134 8.981769e+03 -1.381694e+04
135 -3.366105e+04 8.981769e+03
136 -2.142503e+04 -3.366105e+04
137 -2.943467e+04 -2.142503e+04
138 4.204798e+04 -2.943467e+04
139 -6.639912e+03 4.204798e+04
140 -1.112703e+04 -6.639912e+03
141 -8.383967e+03 -1.112703e+04
142 1.317129e+04 -8.383967e+03
143 1.718956e+03 1.317129e+04
144 3.162605e+04 1.718956e+03
145 -7.087057e+04 3.162605e+04
146 -3.955385e+04 -7.087057e+04
147 6.126273e+03 -3.955385e+04
148 1.083262e+04 6.126273e+03
149 1.406632e+04 1.083262e+04
150 3.274490e+04 1.406632e+04
151 8.623442e+03 3.274490e+04
152 9.104849e+03 8.623442e+03
153 1.757580e+04 9.104849e+03
154 -3.694508e+03 1.757580e+04
155 2.024644e+04 -3.694508e+03
156 1.852199e+04 2.024644e+04
157 2.350888e+04 1.852199e+04
158 -7.309880e+03 2.350888e+04
159 -1.068261e+04 -7.309880e+03
160 -2.098679e+04 -1.068261e+04
161 1.089918e+04 -2.098679e+04
162 9.659807e+03 1.089918e+04
163 3.565986e+04 9.659807e+03
164 -4.259543e+03 3.565986e+04
165 5.054222e+04 -4.259543e+03
166 -2.873092e+04 5.054222e+04
167 -5.365702e+01 -2.873092e+04
168 -2.443687e+04 -5.365702e+01
169 -2.138787e+04 -2.443687e+04
170 2.286256e+04 -2.138787e+04
171 -6.725263e+03 2.286256e+04
172 -2.308162e+04 -6.725263e+03
173 -3.181218e+03 -2.308162e+04
174 -3.809387e+04 -3.181218e+03
175 -5.756567e+04 -3.809387e+04
176 -6.379472e+03 -5.756567e+04
177 -2.335665e+04 -6.379472e+03
178 1.042799e+04 -2.335665e+04
179 -2.034832e+04 1.042799e+04
180 -3.529014e+02 -2.034832e+04
181 9.371885e+03 -3.529014e+02
182 -1.801351e+03 9.371885e+03
183 4.737818e+04 -1.801351e+03
184 5.279680e+04 4.737818e+04
185 -2.287814e+03 5.279680e+04
186 2.042267e+04 -2.287814e+03
187 4.620451e+04 2.042267e+04
188 -1.864572e+04 4.620451e+04
189 -4.336729e+04 -1.864572e+04
190 1.782941e+04 -4.336729e+04
191 -4.012224e+03 1.782941e+04
192 3.542846e+03 -4.012224e+03
193 -1.489222e+04 3.542846e+03
194 -1.368142e+04 -1.489222e+04
195 2.579165e+04 -1.368142e+04
196 -9.799294e+03 2.579165e+04
197 NA -9.799294e+03
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.889081e+03 4.806257e+04
[2,] 7.125250e+03 -1.889081e+03
[3,] -6.933607e+04 7.125250e+03
[4,] 2.995760e+04 -6.933607e+04
[5,] -2.954663e+04 2.995760e+04
[6,] 4.374463e+04 -2.954663e+04
[7,] -1.962746e+04 4.374463e+04
[8,] -3.326064e+04 -1.962746e+04
[9,] 1.482835e+04 -3.326064e+04
[10,] 3.041095e+04 1.482835e+04
[11,] -5.927142e+03 3.041095e+04
[12,] -1.011741e+04 -5.927142e+03
[13,] 2.229593e+04 -1.011741e+04
[14,] 2.593221e+04 2.229593e+04
[15,] -7.177086e+04 2.593221e+04
[16,] 3.407028e+04 -7.177086e+04
[17,] -4.347860e+03 3.407028e+04
[18,] -9.678309e+02 -4.347860e+03
[19,] 5.134880e+03 -9.678309e+02
[20,] 2.468597e+03 5.134880e+03
[21,] 1.085754e+05 2.468597e+03
[22,] 3.092872e+04 1.085754e+05
[23,] -2.793695e+04 3.092872e+04
[24,] -8.415206e+04 -2.793695e+04
[25,] -5.721604e+04 -8.415206e+04
[26,] -1.527180e+04 -5.721604e+04
[27,] 4.214687e+04 -1.527180e+04
[28,] -7.938355e+03 4.214687e+04
[29,] 2.130165e+04 -7.938355e+03
[30,] 6.231108e+03 2.130165e+04
[31,] -1.401328e+04 6.231108e+03
[32,] 4.518552e+04 -1.401328e+04
[33,] -2.122298e+04 4.518552e+04
[34,] 3.348881e+04 -2.122298e+04
[35,] 6.431806e+03 3.348881e+04
[36,] 5.778068e+04 6.431806e+03
[37,] 8.775401e+03 5.778068e+04
[38,] 3.016454e+04 8.775401e+03
[39,] 2.599286e+03 3.016454e+04
[40,] 3.224381e+03 2.599286e+03
[41,] -7.065667e+03 3.224381e+03
[42,] 1.860934e+02 -7.065667e+03
[43,] -7.351153e+03 1.860934e+02
[44,] -8.738117e+04 -7.351153e+03
[45,] -2.841559e+04 -8.738117e+04
[46,] -2.143720e+04 -2.841559e+04
[47,] -1.942036e+04 -2.143720e+04
[48,] -4.145698e+04 -1.942036e+04
[49,] -4.184280e+04 -4.145698e+04
[50,] 5.316276e+03 -4.184280e+04
[51,] -1.468896e+04 5.316276e+03
[52,] 6.376031e+04 -1.468896e+04
[53,] -5.136257e+03 6.376031e+04
[54,] -8.361235e+03 -5.136257e+03
[55,] -6.396083e+03 -8.361235e+03
[56,] 1.437445e+04 -6.396083e+03
[57,] -1.998966e+04 1.437445e+04
[58,] 2.123736e+04 -1.998966e+04
[59,] 3.749370e+04 2.123736e+04
[60,] 9.618396e+03 3.749370e+04
[61,] -5.666366e+03 9.618396e+03
[62,] -3.589170e+04 -5.666366e+03
[63,] 1.001757e+04 -3.589170e+04
[64,] 3.723449e+04 1.001757e+04
[65,] 3.564161e+04 3.723449e+04
[66,] -2.482158e+04 3.564161e+04
[67,] -3.084174e+04 -2.482158e+04
[68,] -8.131090e+03 -3.084174e+04
[69,] -1.522261e+04 -8.131090e+03
[70,] -6.224781e+03 -1.522261e+04
[71,] -2.001297e+04 -6.224781e+03
[72,] 7.965296e+03 -2.001297e+04
[73,] 1.224211e+00 7.965296e+03
[74,] -2.179042e+04 1.224211e+00
[75,] 6.207424e+04 -2.179042e+04
[76,] 3.970729e+04 6.207424e+04
[77,] -2.139718e+04 3.970729e+04
[78,] -1.507021e+04 -2.139718e+04
[79,] -1.349162e+04 -1.507021e+04
[80,] -1.833840e+04 -1.349162e+04
[81,] 5.459920e+04 -1.833840e+04
[82,] -2.764142e+03 5.459920e+04
[83,] 3.421497e+04 -2.764142e+03
[84,] -1.151512e+04 3.421497e+04
[85,] -3.945550e+04 -1.151512e+04
[86,] 6.576171e+03 -3.945550e+04
[87,] -1.089680e+04 6.576171e+03
[88,] 1.721563e+04 -1.089680e+04
[89,] -1.218789e+05 1.721563e+04
[90,] 1.778307e+04 -1.218789e+05
[91,] 1.597354e+03 1.778307e+04
[92,] 6.105282e+03 1.597354e+03
[93,] -1.301912e+04 6.105282e+03
[94,] 3.087233e+04 -1.301912e+04
[95,] 5.156019e+01 3.087233e+04
[96,] 2.022654e+04 5.156019e+01
[97,] 2.266993e+04 2.022654e+04
[98,] -2.627430e+04 2.266993e+04
[99,] 3.270824e+04 -2.627430e+04
[100,] 5.710739e+03 3.270824e+04
[101,] 1.337121e+04 5.710739e+03
[102,] -2.118671e+04 1.337121e+04
[103,] -2.190321e+04 -2.118671e+04
[104,] 6.093905e+03 -2.190321e+04
[105,] -2.314808e+04 6.093905e+03
[106,] -2.025164e+04 -2.314808e+04
[107,] 7.133756e+03 -2.025164e+04
[108,] -4.619317e+04 7.133756e+03
[109,] -7.181277e+04 -4.619317e+04
[110,] 6.085352e+04 -7.181277e+04
[111,] 2.720390e+04 6.085352e+04
[112,] 5.896635e+03 2.720390e+04
[113,] -2.541295e+04 5.896635e+03
[114,] 2.114480e+04 -2.541295e+04
[115,] -1.539213e+04 2.114480e+04
[116,] 6.767928e+04 -1.539213e+04
[117,] 4.141797e+03 6.767928e+04
[118,] 3.391029e+04 4.141797e+03
[119,] -7.466339e+03 3.391029e+04
[120,] -1.190842e+04 -7.466339e+03
[121,] 2.390533e+03 -1.190842e+04
[122,] -1.731076e+04 2.390533e+03
[123,] -3.350032e+04 -1.731076e+04
[124,] -1.385316e+04 -3.350032e+04
[125,] -1.368086e+04 -1.385316e+04
[126,] 2.887269e+04 -1.368086e+04
[127,] 8.832051e+03 2.887269e+04
[128,] 2.354683e+04 8.832051e+03
[129,] 2.626722e+04 2.354683e+04
[130,] 2.463371e+04 2.626722e+04
[131,] 2.483148e+04 2.463371e+04
[132,] -3.167686e+04 2.483148e+04
[133,] -1.381694e+04 -3.167686e+04
[134,] 8.981769e+03 -1.381694e+04
[135,] -3.366105e+04 8.981769e+03
[136,] -2.142503e+04 -3.366105e+04
[137,] -2.943467e+04 -2.142503e+04
[138,] 4.204798e+04 -2.943467e+04
[139,] -6.639912e+03 4.204798e+04
[140,] -1.112703e+04 -6.639912e+03
[141,] -8.383967e+03 -1.112703e+04
[142,] 1.317129e+04 -8.383967e+03
[143,] 1.718956e+03 1.317129e+04
[144,] 3.162605e+04 1.718956e+03
[145,] -7.087057e+04 3.162605e+04
[146,] -3.955385e+04 -7.087057e+04
[147,] 6.126273e+03 -3.955385e+04
[148,] 1.083262e+04 6.126273e+03
[149,] 1.406632e+04 1.083262e+04
[150,] 3.274490e+04 1.406632e+04
[151,] 8.623442e+03 3.274490e+04
[152,] 9.104849e+03 8.623442e+03
[153,] 1.757580e+04 9.104849e+03
[154,] -3.694508e+03 1.757580e+04
[155,] 2.024644e+04 -3.694508e+03
[156,] 1.852199e+04 2.024644e+04
[157,] 2.350888e+04 1.852199e+04
[158,] -7.309880e+03 2.350888e+04
[159,] -1.068261e+04 -7.309880e+03
[160,] -2.098679e+04 -1.068261e+04
[161,] 1.089918e+04 -2.098679e+04
[162,] 9.659807e+03 1.089918e+04
[163,] 3.565986e+04 9.659807e+03
[164,] -4.259543e+03 3.565986e+04
[165,] 5.054222e+04 -4.259543e+03
[166,] -2.873092e+04 5.054222e+04
[167,] -5.365702e+01 -2.873092e+04
[168,] -2.443687e+04 -5.365702e+01
[169,] -2.138787e+04 -2.443687e+04
[170,] 2.286256e+04 -2.138787e+04
[171,] -6.725263e+03 2.286256e+04
[172,] -2.308162e+04 -6.725263e+03
[173,] -3.181218e+03 -2.308162e+04
[174,] -3.809387e+04 -3.181218e+03
[175,] -5.756567e+04 -3.809387e+04
[176,] -6.379472e+03 -5.756567e+04
[177,] -2.335665e+04 -6.379472e+03
[178,] 1.042799e+04 -2.335665e+04
[179,] -2.034832e+04 1.042799e+04
[180,] -3.529014e+02 -2.034832e+04
[181,] 9.371885e+03 -3.529014e+02
[182,] -1.801351e+03 9.371885e+03
[183,] 4.737818e+04 -1.801351e+03
[184,] 5.279680e+04 4.737818e+04
[185,] -2.287814e+03 5.279680e+04
[186,] 2.042267e+04 -2.287814e+03
[187,] 4.620451e+04 2.042267e+04
[188,] -1.864572e+04 4.620451e+04
[189,] -4.336729e+04 -1.864572e+04
[190,] 1.782941e+04 -4.336729e+04
[191,] -4.012224e+03 1.782941e+04
[192,] 3.542846e+03 -4.012224e+03
[193,] -1.489222e+04 3.542846e+03
[194,] -1.368142e+04 -1.489222e+04
[195,] 2.579165e+04 -1.368142e+04
[196,] -9.799294e+03 2.579165e+04
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.889081e+03 4.806257e+04
2 7.125250e+03 -1.889081e+03
3 -6.933607e+04 7.125250e+03
4 2.995760e+04 -6.933607e+04
5 -2.954663e+04 2.995760e+04
6 4.374463e+04 -2.954663e+04
7 -1.962746e+04 4.374463e+04
8 -3.326064e+04 -1.962746e+04
9 1.482835e+04 -3.326064e+04
10 3.041095e+04 1.482835e+04
11 -5.927142e+03 3.041095e+04
12 -1.011741e+04 -5.927142e+03
13 2.229593e+04 -1.011741e+04
14 2.593221e+04 2.229593e+04
15 -7.177086e+04 2.593221e+04
16 3.407028e+04 -7.177086e+04
17 -4.347860e+03 3.407028e+04
18 -9.678309e+02 -4.347860e+03
19 5.134880e+03 -9.678309e+02
20 2.468597e+03 5.134880e+03
21 1.085754e+05 2.468597e+03
22 3.092872e+04 1.085754e+05
23 -2.793695e+04 3.092872e+04
24 -8.415206e+04 -2.793695e+04
25 -5.721604e+04 -8.415206e+04
26 -1.527180e+04 -5.721604e+04
27 4.214687e+04 -1.527180e+04
28 -7.938355e+03 4.214687e+04
29 2.130165e+04 -7.938355e+03
30 6.231108e+03 2.130165e+04
31 -1.401328e+04 6.231108e+03
32 4.518552e+04 -1.401328e+04
33 -2.122298e+04 4.518552e+04
34 3.348881e+04 -2.122298e+04
35 6.431806e+03 3.348881e+04
36 5.778068e+04 6.431806e+03
37 8.775401e+03 5.778068e+04
38 3.016454e+04 8.775401e+03
39 2.599286e+03 3.016454e+04
40 3.224381e+03 2.599286e+03
41 -7.065667e+03 3.224381e+03
42 1.860934e+02 -7.065667e+03
43 -7.351153e+03 1.860934e+02
44 -8.738117e+04 -7.351153e+03
45 -2.841559e+04 -8.738117e+04
46 -2.143720e+04 -2.841559e+04
47 -1.942036e+04 -2.143720e+04
48 -4.145698e+04 -1.942036e+04
49 -4.184280e+04 -4.145698e+04
50 5.316276e+03 -4.184280e+04
51 -1.468896e+04 5.316276e+03
52 6.376031e+04 -1.468896e+04
53 -5.136257e+03 6.376031e+04
54 -8.361235e+03 -5.136257e+03
55 -6.396083e+03 -8.361235e+03
56 1.437445e+04 -6.396083e+03
57 -1.998966e+04 1.437445e+04
58 2.123736e+04 -1.998966e+04
59 3.749370e+04 2.123736e+04
60 9.618396e+03 3.749370e+04
61 -5.666366e+03 9.618396e+03
62 -3.589170e+04 -5.666366e+03
63 1.001757e+04 -3.589170e+04
64 3.723449e+04 1.001757e+04
65 3.564161e+04 3.723449e+04
66 -2.482158e+04 3.564161e+04
67 -3.084174e+04 -2.482158e+04
68 -8.131090e+03 -3.084174e+04
69 -1.522261e+04 -8.131090e+03
70 -6.224781e+03 -1.522261e+04
71 -2.001297e+04 -6.224781e+03
72 7.965296e+03 -2.001297e+04
73 1.224211e+00 7.965296e+03
74 -2.179042e+04 1.224211e+00
75 6.207424e+04 -2.179042e+04
76 3.970729e+04 6.207424e+04
77 -2.139718e+04 3.970729e+04
78 -1.507021e+04 -2.139718e+04
79 -1.349162e+04 -1.507021e+04
80 -1.833840e+04 -1.349162e+04
81 5.459920e+04 -1.833840e+04
82 -2.764142e+03 5.459920e+04
83 3.421497e+04 -2.764142e+03
84 -1.151512e+04 3.421497e+04
85 -3.945550e+04 -1.151512e+04
86 6.576171e+03 -3.945550e+04
87 -1.089680e+04 6.576171e+03
88 1.721563e+04 -1.089680e+04
89 -1.218789e+05 1.721563e+04
90 1.778307e+04 -1.218789e+05
91 1.597354e+03 1.778307e+04
92 6.105282e+03 1.597354e+03
93 -1.301912e+04 6.105282e+03
94 3.087233e+04 -1.301912e+04
95 5.156019e+01 3.087233e+04
96 2.022654e+04 5.156019e+01
97 2.266993e+04 2.022654e+04
98 -2.627430e+04 2.266993e+04
99 3.270824e+04 -2.627430e+04
100 5.710739e+03 3.270824e+04
101 1.337121e+04 5.710739e+03
102 -2.118671e+04 1.337121e+04
103 -2.190321e+04 -2.118671e+04
104 6.093905e+03 -2.190321e+04
105 -2.314808e+04 6.093905e+03
106 -2.025164e+04 -2.314808e+04
107 7.133756e+03 -2.025164e+04
108 -4.619317e+04 7.133756e+03
109 -7.181277e+04 -4.619317e+04
110 6.085352e+04 -7.181277e+04
111 2.720390e+04 6.085352e+04
112 5.896635e+03 2.720390e+04
113 -2.541295e+04 5.896635e+03
114 2.114480e+04 -2.541295e+04
115 -1.539213e+04 2.114480e+04
116 6.767928e+04 -1.539213e+04
117 4.141797e+03 6.767928e+04
118 3.391029e+04 4.141797e+03
119 -7.466339e+03 3.391029e+04
120 -1.190842e+04 -7.466339e+03
121 2.390533e+03 -1.190842e+04
122 -1.731076e+04 2.390533e+03
123 -3.350032e+04 -1.731076e+04
124 -1.385316e+04 -3.350032e+04
125 -1.368086e+04 -1.385316e+04
126 2.887269e+04 -1.368086e+04
127 8.832051e+03 2.887269e+04
128 2.354683e+04 8.832051e+03
129 2.626722e+04 2.354683e+04
130 2.463371e+04 2.626722e+04
131 2.483148e+04 2.463371e+04
132 -3.167686e+04 2.483148e+04
133 -1.381694e+04 -3.167686e+04
134 8.981769e+03 -1.381694e+04
135 -3.366105e+04 8.981769e+03
136 -2.142503e+04 -3.366105e+04
137 -2.943467e+04 -2.142503e+04
138 4.204798e+04 -2.943467e+04
139 -6.639912e+03 4.204798e+04
140 -1.112703e+04 -6.639912e+03
141 -8.383967e+03 -1.112703e+04
142 1.317129e+04 -8.383967e+03
143 1.718956e+03 1.317129e+04
144 3.162605e+04 1.718956e+03
145 -7.087057e+04 3.162605e+04
146 -3.955385e+04 -7.087057e+04
147 6.126273e+03 -3.955385e+04
148 1.083262e+04 6.126273e+03
149 1.406632e+04 1.083262e+04
150 3.274490e+04 1.406632e+04
151 8.623442e+03 3.274490e+04
152 9.104849e+03 8.623442e+03
153 1.757580e+04 9.104849e+03
154 -3.694508e+03 1.757580e+04
155 2.024644e+04 -3.694508e+03
156 1.852199e+04 2.024644e+04
157 2.350888e+04 1.852199e+04
158 -7.309880e+03 2.350888e+04
159 -1.068261e+04 -7.309880e+03
160 -2.098679e+04 -1.068261e+04
161 1.089918e+04 -2.098679e+04
162 9.659807e+03 1.089918e+04
163 3.565986e+04 9.659807e+03
164 -4.259543e+03 3.565986e+04
165 5.054222e+04 -4.259543e+03
166 -2.873092e+04 5.054222e+04
167 -5.365702e+01 -2.873092e+04
168 -2.443687e+04 -5.365702e+01
169 -2.138787e+04 -2.443687e+04
170 2.286256e+04 -2.138787e+04
171 -6.725263e+03 2.286256e+04
172 -2.308162e+04 -6.725263e+03
173 -3.181218e+03 -2.308162e+04
174 -3.809387e+04 -3.181218e+03
175 -5.756567e+04 -3.809387e+04
176 -6.379472e+03 -5.756567e+04
177 -2.335665e+04 -6.379472e+03
178 1.042799e+04 -2.335665e+04
179 -2.034832e+04 1.042799e+04
180 -3.529014e+02 -2.034832e+04
181 9.371885e+03 -3.529014e+02
182 -1.801351e+03 9.371885e+03
183 4.737818e+04 -1.801351e+03
184 5.279680e+04 4.737818e+04
185 -2.287814e+03 5.279680e+04
186 2.042267e+04 -2.287814e+03
187 4.620451e+04 2.042267e+04
188 -1.864572e+04 4.620451e+04
189 -4.336729e+04 -1.864572e+04
190 1.782941e+04 -4.336729e+04
191 -4.012224e+03 1.782941e+04
192 3.542846e+03 -4.012224e+03
193 -1.489222e+04 3.542846e+03
194 -1.368142e+04 -1.489222e+04
195 2.579165e+04 -1.368142e+04
196 -9.799294e+03 2.579165e+04
> 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/76y281353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8mc7w1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/98gsf1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10nute1353078686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112g651353078686.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/12a14g1353078686.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/13r70z1353078686.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/14aafu1353078686.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/15erqc1353078686.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/16myo91353078686.tab")
+ }
>
> try(system("convert tmp/10mo71353078686.ps tmp/10mo71353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/2typq1353078686.ps tmp/2typq1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xevq1353078686.ps tmp/3xevq1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/46awv1353078686.ps tmp/46awv1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r09k1353078686.ps tmp/5r09k1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g7pm1353078686.ps tmp/6g7pm1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/76y281353078686.ps tmp/76y281353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mc7w1353078686.ps tmp/8mc7w1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/98gsf1353078686.ps tmp/98gsf1353078686.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nute1353078686.ps tmp/10nute1353078686.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.979 1.368 13.345