R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1418
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')
+ ,1:197))
> y <- array(NA,dim=c(15,197),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120','totsize','totrevisions','totseconds','tothyperlinks','totblogs
'),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 = '11'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
totsize pageviews time_in_rfc logins compendium_views_info
1 112285 1418 210907 56 396
2 84786 869 120982 56 297
3 83123 1530 176508 54 559
4 101193 2172 179321 89 967
5 38361 901 123185 40 270
6 68504 463 52746 25 143
7 119182 3201 385534 92 1562
8 22807 371 33170 18 109
9 17140 1192 101645 63 371
10 116174 1583 149061 44 656
11 57635 1439 165446 33 511
12 66198 1764 237213 84 655
13 71701 1495 173326 88 465
14 57793 1373 133131 55 525
15 80444 2187 258873 60 885
16 53855 1491 180083 66 497
17 97668 4041 324799 154 1436
18 133824 1706 230964 53 612
19 101481 2152 236785 119 865
20 99645 1036 135473 41 385
21 114789 1882 202925 61 567
22 99052 1929 215147 58 639
23 67654 2242 344297 75 963
24 65553 1220 153935 33 398
25 97500 1289 132943 40 410
26 69112 2515 174724 92 966
27 82753 2147 174415 100 801
28 85323 2352 225548 112 892
29 72654 1638 223632 73 513
30 30727 1222 124817 40 469
31 77873 1812 221698 45 683
32 117478 1677 210767 60 643
33 74007 1579 170266 62 535
34 90183 1731 260561 75 625
35 61542 807 84853 31 264
36 101494 2452 294424 77 992
37 27570 829 101011 34 238
38 55813 1940 215641 46 818
39 79215 2662 325107 99 937
40 1423 186 7176 17 70
41 55461 1499 167542 66 507
42 31081 865 106408 30 260
43 22996 1793 96560 76 503
44 83122 2527 265769 146 927
45 70106 2747 269651 67 1269
46 60578 1324 149112 56 537
47 39992 2702 175824 107 910
48 79892 1383 152871 58 532
49 49810 1179 111665 34 345
50 71570 2099 116408 61 918
51 100708 4308 362301 119 1635
52 33032 918 78800 42 330
53 82875 1831 183167 66 557
54 139077 3373 277965 89 1178
55 71595 1713 150629 44 740
56 72260 1438 168809 66 452
57 5950 496 24188 24 218
58 115762 2253 329267 259 764
59 32551 744 65029 17 255
60 31701 1161 101097 64 454
61 80670 2352 218946 41 866
62 143558 2144 244052 68 574
63 117105 4691 341570 168 1276
64 23789 1112 103597 43 379
65 120733 2694 233328 132 825
66 105195 1973 256462 105 798
67 73107 1769 206161 71 663
68 132068 3148 311473 112 1069
69 149193 2474 235800 94 921
70 46821 2084 177939 82 858
71 87011 1954 207176 70 711
72 95260 1226 196553 57 503
73 55183 1389 174184 53 382
74 106671 1496 143246 103 464
75 73511 2269 187559 121 717
76 92945 1833 187681 62 690
77 78664 1268 119016 52 462
78 70054 1943 182192 52 657
79 22618 893 73566 32 385
80 74011 1762 194979 62 577
81 83737 1403 167488 45 619
82 69094 1425 143756 46 479
83 93133 1857 275541 63 817
84 95536 1840 243199 75 752
85 225920 1502 182999 88 430
86 62133 1441 135649 46 451
87 61370 1420 152299 53 537
88 43836 1416 120221 37 519
89 106117 2970 346485 90 1000
90 38692 1317 145790 63 637
91 84651 1644 193339 78 465
92 56622 870 80953 25 437
93 15986 1654 122774 45 711
94 95364 1054 130585 46 299
95 26706 937 112611 41 248
96 89691 3004 286468 144 1162
97 67267 2008 241066 82 714
98 126846 2547 148446 91 905
99 41140 1885 204713 71 649
100 102860 1626 182079 63 512
101 51715 1468 140344 53 472
102 55801 2445 220516 62 905
103 111813 1964 243060 63 786
104 120293 1381 162765 32 489
105 138599 1369 182613 39 479
106 161647 1659 232138 62 617
107 115929 2888 265318 117 925
108 24266 1290 85574 34 351
109 162901 2845 310839 92 1144
110 109825 1982 225060 93 669
111 129838 1904 232317 54 707
112 37510 1391 144966 144 458
113 43750 602 43287 14 214
114 40652 1743 155754 61 599
115 87771 1559 164709 109 572
116 85872 2014 201940 38 897
117 89275 2143 235454 73 819
118 44418 2146 220801 75 720
119 192565 874 99466 50 273
120 35232 1590 92661 61 508
121 40909 1590 133328 55 506
122 13294 1210 61361 77 451
123 32387 2072 125930 75 699
124 140867 1281 100750 72 407
125 120662 1401 224549 50 465
126 21233 834 82316 32 245
127 44332 1105 102010 53 370
128 61056 1272 101523 42 316
129 101338 1944 243511 71 603
130 1168 391 22938 10 154
131 13497 761 41566 35 229
132 65567 1605 152474 65 577
133 25162 530 61857 25 192
134 32334 1988 99923 66 617
135 40735 1386 132487 41 411
136 91413 2395 317394 86 975
137 855 387 21054 16 146
138 97068 1742 209641 42 705
139 44339 620 22648 19 184
140 14116 449 31414 19 200
141 10288 800 46698 45 274
142 65622 1684 131698 65 502
143 16563 1050 91735 35 382
144 76643 2699 244749 95 964
145 110681 1606 184510 49 537
146 29011 1502 79863 37 438
147 92696 1204 128423 64 369
148 94785 1138 97839 38 417
149 8773 568 38214 34 276
150 83209 1459 151101 32 514
151 93815 2158 272458 65 822
152 86687 1111 172494 52 389
153 34553 1421 108043 62 466
154 105547 2833 328107 65 1255
155 103487 1955 250579 83 694
156 213688 2922 351067 95 1024
157 71220 1002 158015 29 400
158 23517 1060 98866 18 397
159 56926 956 85439 33 350
160 91721 2186 229242 247 719
161 115168 3604 351619 139 1277
162 111194 1035 84207 29 356
163 51009 1417 120445 118 457
164 135777 3261 324598 110 1402
165 51513 1587 131069 67 600
166 74163 1424 204271 42 480
167 51633 1701 165543 65 595
168 75345 1249 141722 94 436
169 33416 946 116048 64 230
170 83305 1926 250047 81 651
171 98952 3352 299775 95 1367
172 102372 1641 195838 67 564
173 37238 2035 173260 63 716
174 103772 2312 254488 83 747
175 123969 1369 104389 45 467
176 27142 1577 136084 30 671
177 135400 2201 199476 70 861
178 21399 961 92499 32 319
179 130115 1900 224330 83 612
180 24874 1254 135781 31 433
181 34988 1335 74408 67 434
182 45549 1597 81240 66 503
183 6023 207 14688 10 85
184 64466 1645 181633 70 564
185 54990 2429 271856 103 824
186 1644 151 7199 5 74
187 6179 474 46660 20 259
188 3926 141 17547 5 69
189 32755 1639 133368 36 535
190 34777 872 95227 34 239
191 73224 1318 152601 48 438
192 27114 1018 98146 40 459
193 20760 1383 79619 43 426
194 37636 1314 59194 31 288
195 65461 1335 139942 42 498
196 30080 1403 118612 46 454
197 24094 910 72880 33 376
compendium_views_pr shared_compendiums blogged_computations
1 81 3 79
2 55 4 58
3 50 12 60
4 125 2 108
5 40 1 49
6 37 3 0
7 63 0 121
8 44 0 1
9 88 0 20
10 66 5 43
11 57 0 69
12 74 0 78
13 49 7 86
14 52 7 44
15 88 3 104
16 36 9 63
17 108 0 158
18 43 4 102
19 75 3 77
20 32 0 82
21 44 7 115
22 85 0 101
23 86 1 80
24 56 5 50
25 50 7 83
26 135 0 123
27 63 0 73
28 81 5 81
29 52 0 105
30 44 0 47
31 113 0 105
32 39 3 94
33 73 4 44
34 48 1 114
35 33 4 38
36 59 2 107
37 41 0 30
38 69 0 71
39 64 0 84
40 1 0 0
41 59 2 59
42 32 1 33
43 129 0 42
44 37 2 96
45 31 10 106
46 65 6 56
47 107 0 57
48 74 5 59
49 54 4 39
50 76 1 34
51 715 2 76
52 57 2 20
53 66 0 91
54 106 8 115
55 54 3 85
56 32 0 76
57 20 0 8
58 71 8 79
59 21 5 21
60 70 3 30
61 112 1 76
62 66 5 101
63 190 1 94
64 66 1 27
65 165 5 92
66 56 0 123
67 61 12 75
68 53 8 128
69 127 8 105
70 63 8 55
71 38 8 56
72 50 2 41
73 52 0 72
74 42 5 67
75 76 8 75
76 67 2 114
77 50 5 118
78 53 12 77
79 39 6 22
80 50 7 66
81 77 2 69
82 57 0 105
83 73 4 116
84 34 3 88
85 39 6 73
86 46 2 99
87 63 0 62
88 35 1 53
89 106 0 118
90 43 5 30
91 47 2 100
92 31 0 49
93 162 0 24
94 57 5 67
95 36 0 46
96 263 1 57
97 78 0 75
98 63 1 135
99 54 1 68
100 63 2 124
101 77 6 33
102 79 1 98
103 110 4 58
104 56 2 68
105 56 3 81
106 43 0 131
107 111 10 110
108 71 0 37
109 62 9 130
110 56 7 93
111 74 0 118
112 60 0 39
113 43 4 13
114 68 4 74
115 53 0 81
116 87 0 109
117 46 0 151
118 105 1 51
119 32 0 28
120 133 1 40
121 79 0 56
122 51 0 27
123 207 4 37
124 67 0 83
125 47 4 54
126 34 4 27
127 66 3 28
128 76 0 59
129 65 0 133
130 9 0 12
131 42 5 0
132 45 0 106
133 25 4 23
134 115 0 44
135 97 0 71
136 53 1 116
137 2 0 4
138 52 5 62
139 44 0 12
140 22 0 18
141 35 0 14
142 74 0 60
143 103 0 7
144 144 2 98
145 60 7 64
146 134 1 29
147 89 8 32
148 42 2 25
149 52 0 16
150 98 2 48
151 99 0 100
152 52 0 46
153 29 1 45
154 125 3 129
155 106 0 130
156 95 3 136
157 40 0 59
158 140 0 25
159 43 0 32
160 128 4 63
161 142 4 95
162 73 11 14
163 72 0 36
164 128 0 113
165 61 4 47
166 73 0 92
167 148 1 70
168 64 0 19
169 45 0 50
170 58 0 41
171 97 9 91
172 50 1 111
173 37 3 41
174 50 10 120
175 105 5 135
176 69 0 27
177 46 2 87
178 57 0 25
179 52 1 131
180 98 2 45
181 61 4 29
182 89 0 58
183 0 0 4
184 48 2 47
185 91 1 109
186 0 0 7
187 7 0 12
188 3 0 0
189 54 1 37
190 70 0 37
191 36 2 46
192 37 0 15
193 123 3 42
194 247 6 7
195 46 0 54
196 72 2 54
197 41 0 14
compendiums_reviewed feedback_messages_p1 feedback_messages_p120
1 30 115 94
2 28 109 103
3 38 146 93
4 30 116 103
5 22 68 51
6 26 101 70
7 25 96 91
8 18 67 22
9 11 44 38
10 26 100 93
11 25 93 60
12 38 140 123
13 44 166 148
14 30 99 90
15 40 139 124
16 34 130 70
17 47 181 168
18 30 116 115
19 31 116 71
20 23 88 66
21 36 139 134
22 36 135 117
23 30 108 108
24 25 89 84
25 39 156 156
26 34 129 120
27 31 118 114
28 31 118 94
29 33 125 120
30 25 95 81
31 33 126 110
32 35 135 133
33 42 154 122
34 43 165 158
35 30 113 109
36 33 127 124
37 13 52 39
38 32 121 92
39 36 136 126
40 0 0 0
41 28 108 70
42 14 46 37
43 17 54 38
44 32 124 120
45 30 115 93
46 35 128 95
47 20 80 77
48 28 97 90
49 28 104 80
50 39 59 31
51 34 125 110
52 26 82 66
53 39 149 138
54 39 149 133
55 33 122 113
56 28 118 100
57 4 12 7
58 39 144 140
59 18 67 61
60 14 52 41
61 29 108 96
62 44 166 164
63 21 80 78
64 16 60 49
65 28 107 102
66 35 127 124
67 28 107 99
68 38 146 129
69 23 84 62
70 36 141 73
71 32 123 114
72 29 111 99
73 25 98 70
74 27 105 104
75 36 135 116
76 28 107 91
77 23 85 74
78 40 155 138
79 23 88 67
80 40 155 151
81 28 104 72
82 34 132 120
83 33 127 115
84 28 108 105
85 34 129 104
86 30 116 108
87 33 122 98
88 22 85 69
89 38 147 111
90 26 99 99
91 35 87 71
92 8 28 27
93 24 90 69
94 29 109 107
95 20 78 73
96 29 111 107
97 45 158 93
98 37 141 129
99 33 122 69
100 33 124 118
101 25 93 73
102 32 124 119
103 29 112 104
104 28 108 107
105 28 99 99
106 31 117 90
107 52 199 197
108 21 78 36
109 24 91 85
110 41 158 139
111 33 126 106
112 32 122 50
113 19 71 64
114 20 75 31
115 31 115 63
116 31 119 92
117 32 124 106
118 18 72 63
119 23 91 69
120 17 45 41
121 20 78 56
122 12 39 25
123 17 68 65
124 30 119 93
125 31 117 114
126 10 39 38
127 13 50 44
128 22 88 87
129 42 155 110
130 1 0 0
131 9 36 27
132 32 123 83
133 11 32 30
134 25 99 80
135 36 136 98
136 31 117 82
137 0 0 0
138 24 88 60
139 13 39 28
140 8 25 9
141 13 52 33
142 19 75 59
143 18 71 49
144 33 124 115
145 40 151 140
146 22 71 49
147 38 145 120
148 24 87 66
149 8 27 21
150 35 131 124
151 43 162 152
152 43 165 139
153 14 54 38
154 41 159 144
155 38 147 120
156 45 170 160
157 31 119 114
158 13 49 39
159 28 104 78
160 31 120 119
161 40 150 141
162 30 112 101
163 16 59 56
164 37 136 133
165 30 107 83
166 35 130 116
167 32 115 90
168 27 107 36
169 20 75 50
170 18 71 61
171 31 120 97
172 31 116 98
173 21 79 78
174 39 150 117
175 41 156 148
176 13 51 41
177 32 118 105
178 18 71 55
179 39 144 132
180 14 47 44
181 7 28 21
182 17 68 50
183 0 0 0
184 30 110 73
185 37 147 86
186 0 0 0
187 5 15 13
188 1 4 4
189 16 64 57
190 32 111 48
191 24 85 46
192 17 68 48
193 11 40 32
194 24 80 68
195 22 88 87
196 12 48 43
197 19 76 67
totrevisions totseconds tothyperlinks totblogs\r
1 24188 146283 144 145
2 18273 98364 103 101
3 14130 86146 98 98
4 32287 96933 135 132
5 8654 79234 61 60
6 9245 42551 39 38
7 33251 195663 150 144
8 1271 6853 5 5
9 5279 21529 28 28
10 27101 95757 84 84
11 16373 85584 80 79
12 19716 143983 130 127
13 17753 75851 82 78
14 9028 59238 60 60
15 18653 93163 131 131
16 8828 96037 84 84
17 29498 151511 140 133
18 27563 136368 151 150
19 18293 112642 91 91
20 22530 94728 138 132
21 15977 105499 150 136
22 35082 121527 124 124
23 16116 127766 119 118
24 15849 98958 73 70
25 16026 77900 110 107
26 26569 85646 123 119
27 24785 98579 90 89
28 17569 130767 116 112
29 23825 131741 113 108
30 7869 53907 56 52
31 14975 178812 115 112
32 37791 146761 119 116
33 9605 82036 129 123
34 27295 163253 127 125
35 2746 27032 27 27
36 34461 171975 175 162
37 8098 65990 35 32
38 4787 86572 64 64
39 24919 159676 96 92
40 603 1929 0 0
41 16329 85371 84 83
42 12558 58391 41 41
43 7784 31580 47 47
44 28522 136815 126 120
45 22265 120642 105 105
46 14459 69107 80 79
47 14526 50495 70 65
48 22240 108016 73 70
49 11802 46341 57 55
50 7623 78348 40 39
51 11912 79336 68 67
52 7935 56968 21 21
53 18220 93176 127 127
54 19199 161632 154 152
55 19918 87850 116 113
56 21884 127969 102 99
57 2694 15049 7 7
58 15808 155135 148 141
59 3597 25109 21 21
60 5296 45824 35 35
61 25239 102996 112 109
62 29801 160604 137 133
63 18450 158051 135 123
64 7132 44547 26 26
65 34861 162647 230 230
66 35940 174141 181 166
67 16688 60622 71 68
68 24683 179566 147 147
69 46230 184301 190 179
70 10387 75661 64 61
71 21436 96144 105 101
72 30546 129847 107 108
73 19746 117286 94 90
74 15977 71180 116 114
75 22583 109377 106 103
76 17274 85298 143 142
77 16469 73631 81 79
78 14251 86767 89 88
79 3007 23824 26 25
80 16851 93487 84 83
81 21113 82981 113 113
82 17401 73815 120 118
83 23958 94552 110 110
84 23567 132190 134 129
85 13065 128754 54 51
86 15358 66363 96 93
87 14587 67808 78 76
88 12770 61724 51 49
89 24021 131722 121 118
90 9648 68580 38 38
91 20537 106175 145 141
92 7905 55792 59 58
93 4527 25157 27 27
94 30495 76669 91 91
95 7117 57283 48 48
96 17719 105805 68 63
97 27056 129484 58 56
98 33473 72413 150 144
99 9758 87831 74 73
100 21115 96971 181 168
101 7236 71299 65 64
102 13790 77494 97 97
103 32902 120336 121 117
104 25131 93913 99 100
105 30910 136048 152 149
106 35947 181248 188 187
107 29848 146123 138 127
108 6943 32036 40 37
109 42705 186646 254 245
110 31808 102255 87 87
111 26675 168237 178 177
112 8435 64219 51 49
113 7409 19630 49 49
114 14993 76825 73 73
115 36867 115338 176 177
116 33835 109427 94 94
117 24164 118168 120 117
118 12607 84845 66 60
119 22609 153197 56 55
120 5892 29877 39 39
121 17014 63506 66 64
122 5394 22445 27 26
123 9178 47695 65 64
124 6440 68370 58 58
125 21916 146304 98 95
126 4011 38233 25 25
127 5818 42071 26 26
128 18647 50517 77 76
129 20556 103950 130 129
130 238 5841 11 11
131 70 2341 2 2
132 22392 84396 101 101
133 3913 24610 31 28
134 12237 35753 36 36
135 8388 55515 120 89
136 22120 209056 195 193
137 338 6622 4 4
138 11727 115814 89 84
139 3704 11609 24 23
140 3988 13155 39 39
141 3030 18274 14 14
142 13520 72875 78 78
143 1421 10112 15 14
144 20923 142775 106 101
145 20237 68847 83 82
146 3219 17659 24 24
147 3769 20112 37 36
148 12252 61023 77 75
149 1888 13983 16 16
150 14497 65176 56 55
151 28864 132432 132 131
152 21721 112494 144 131
153 4821 45109 40 39
154 33644 170875 153 144
155 15923 180759 143 139
156 42935 214921 220 211
157 18864 100226 79 78
158 4977 32043 50 50
159 7785 54454 39 39
160 17939 78876 95 90
161 23436 170745 169 166
162 325 6940 12 12
163 13539 49025 63 57
164 34538 122037 134 133
165 12198 53782 69 69
166 26924 127748 119 119
167 12716 86839 119 119
168 8172 44830 75 65
169 10855 77395 63 61
170 11932 89324 55 49
171 14300 103300 103 101
172 25515 112283 197 196
173 2805 10901 16 15
174 29402 120691 140 136
175 16440 58106 89 89
176 11221 57140 40 40
177 28732 122422 125 123
178 5250 25899 21 21
179 28608 139296 167 163
180 8092 52678 32 29
181 4473 23853 36 35
182 1572 17306 13 13
183 2065 7953 5 5
184 14817 89455 96 96
185 16714 147866 151 151
186 556 4245 6 6
187 2089 21509 13 13
188 2658 7670 3 3
189 10695 66675 57 56
190 1669 14336 23 23
191 16267 53608 61 57
192 7768 30059 21 14
193 7252 29668 43 43
194 6387 22097 20 20
195 18715 96841 82 72
196 7936 41907 90 87
197 8643 27080 25 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc
2099.9458 3.2962 -0.1055
logins compendium_views_info compendium_views_pr
46.7646 -16.7636 55.7978
shared_compendiums blogged_computations compendiums_reviewed
2262.1161 68.7547 597.3152
feedback_messages_p1 feedback_messages_p120 totrevisions
-130.3031 197.3850 1.1172
totseconds tothyperlinks `totblogs\r`
0.4068 -116.1658 144.1490
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-36477 -13948 -4711 10612 128425
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2099.94582 4904.76799 0.428 0.669053
pageviews 3.29618 9.35284 0.352 0.724927
time_in_rfc -0.10554 0.06991 -1.510 0.132880
logins 46.76455 69.47357 0.673 0.501720
compendium_views_info -16.76360 21.19232 -0.791 0.429961
compendium_views_pr 55.79783 36.30949 1.537 0.126097
shared_compendiums 2262.11609 611.28624 3.701 0.000285 ***
blogged_computations 68.75472 104.55285 0.658 0.511621
compendiums_reviewed 597.31522 769.61047 0.776 0.438682
feedback_messages_p1 -130.30314 235.70548 -0.553 0.581064
feedback_messages_p120 197.38503 120.69541 1.635 0.103694
totrevisions 1.11724 0.34710 3.219 0.001524 **
totseconds 0.40681 0.09492 4.286 2.95e-05 ***
tothyperlinks -116.16581 480.40094 -0.242 0.809200
`totblogs\r` 144.14902 497.54640 0.290 0.772360
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22730 on 182 degrees of freedom
Multiple R-squared: 0.7147, Adjusted R-squared: 0.6928
F-statistic: 32.57 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,] 1.549030e-01 3.098059e-01 0.8450970269
[2,] 1.052987e-01 2.105974e-01 0.8947012868
[3,] 4.655693e-02 9.311385e-02 0.9534430749
[4,] 2.630583e-01 5.261167e-01 0.7369416749
[5,] 2.213400e-01 4.426800e-01 0.7786599786
[6,] 1.433885e-01 2.867771e-01 0.8566114600
[7,] 8.931389e-02 1.786278e-01 0.9106861079
[8,] 6.187050e-02 1.237410e-01 0.9381294988
[9,] 3.712267e-02 7.424534e-02 0.9628773314
[10,] 2.947910e-02 5.895820e-02 0.9705209009
[11,] 1.671632e-02 3.343265e-02 0.9832836771
[12,] 1.030114e-02 2.060228e-02 0.9896988600
[13,] 7.077243e-03 1.415449e-02 0.9929227565
[14,] 1.215046e-02 2.430092e-02 0.9878495395
[15,] 7.690571e-03 1.538114e-02 0.9923094290
[16,] 4.422158e-03 8.844317e-03 0.9955778416
[17,] 2.785964e-03 5.571927e-03 0.9972140363
[18,] 4.668541e-03 9.337081e-03 0.9953314593
[19,] 5.243960e-03 1.048792e-02 0.9947560399
[20,] 3.961118e-03 7.922235e-03 0.9960388823
[21,] 2.540352e-03 5.080703e-03 0.9974596485
[22,] 1.659954e-03 3.319909e-03 0.9983400456
[23,] 2.442722e-03 4.885444e-03 0.9975572782
[24,] 1.733500e-03 3.467000e-03 0.9982665000
[25,] 1.179996e-03 2.359992e-03 0.9988200041
[26,] 7.062325e-04 1.412465e-03 0.9992937675
[27,] 5.073328e-04 1.014666e-03 0.9994926672
[28,] 2.041985e-03 4.083969e-03 0.9979580154
[29,] 1.386226e-03 2.772452e-03 0.9986137740
[30,] 8.939268e-04 1.787854e-03 0.9991060732
[31,] 6.082174e-04 1.216435e-03 0.9993917826
[32,] 3.638509e-04 7.277018e-04 0.9996361491
[33,] 5.217880e-04 1.043576e-03 0.9994782120
[34,] 7.754269e-04 1.550854e-03 0.9992245731
[35,] 6.011417e-04 1.202283e-03 0.9993988583
[36,] 3.629996e-04 7.259992e-04 0.9996370004
[37,] 4.035512e-04 8.071024e-04 0.9995964488
[38,] 3.884629e-04 7.769257e-04 0.9996115371
[39,] 2.716165e-04 5.432331e-04 0.9997283835
[40,] 1.673645e-04 3.347291e-04 0.9998326355
[41,] 1.513635e-04 3.027271e-04 0.9998486365
[42,] 8.939738e-05 1.787948e-04 0.9999106026
[43,] 5.336031e-05 1.067206e-04 0.9999466397
[44,] 3.190582e-05 6.381165e-05 0.9999680942
[45,] 3.538904e-05 7.077808e-05 0.9999646110
[46,] 3.355114e-05 6.710227e-05 0.9999664489
[47,] 2.104522e-05 4.209044e-05 0.9999789548
[48,] 7.424829e-05 1.484966e-04 0.9999257517
[49,] 6.600557e-05 1.320111e-04 0.9999339944
[50,] 4.105953e-05 8.211907e-05 0.9999589405
[51,] 2.819919e-05 5.639839e-05 0.9999718008
[52,] 1.629385e-05 3.258770e-05 0.9999837062
[53,] 1.397506e-05 2.795012e-05 0.9999860249
[54,] 8.990942e-06 1.798188e-05 0.9999910091
[55,] 5.407649e-06 1.081530e-05 0.9999945924
[56,] 4.715375e-06 9.430750e-06 0.9999952846
[57,] 9.100684e-06 1.820137e-05 0.9999908993
[58,] 2.272304e-05 4.544609e-05 0.9999772770
[59,] 2.053395e-05 4.106789e-05 0.9999794661
[60,] 1.531362e-05 3.062724e-05 0.9999846864
[61,] 2.880867e-05 5.761735e-05 0.9999711913
[62,] 2.359696e-05 4.719392e-05 0.9999764030
[63,] 2.526864e-05 5.053727e-05 0.9999747314
[64,] 1.782866e-05 3.565732e-05 0.9999821713
[65,] 1.104068e-05 2.208137e-05 0.9999889593
[66,] 7.624011e-06 1.524802e-05 0.9999923760
[67,] 4.666726e-06 9.333453e-06 0.9999953333
[68,] 4.755698e-01 9.511397e-01 0.5244301713
[69,] 4.422731e-01 8.845462e-01 0.5577269125
[70,] 4.038093e-01 8.076185e-01 0.5961907333
[71,] 3.692007e-01 7.384013e-01 0.6307993483
[72,] 3.717207e-01 7.434414e-01 0.6282792873
[73,] 4.045917e-01 8.091833e-01 0.5954083322
[74,] 3.695343e-01 7.390686e-01 0.6304657016
[75,] 3.589022e-01 7.178044e-01 0.6410977967
[76,] 3.239118e-01 6.478236e-01 0.6760881859
[77,] 2.926028e-01 5.852056e-01 0.7073971882
[78,] 2.838710e-01 5.677420e-01 0.7161290045
[79,] 2.835133e-01 5.670266e-01 0.7164866798
[80,] 2.955826e-01 5.911652e-01 0.7044174227
[81,] 3.237884e-01 6.475768e-01 0.6762115965
[82,] 3.046236e-01 6.092472e-01 0.6953764192
[83,] 2.757087e-01 5.514174e-01 0.7242912842
[84,] 2.556262e-01 5.112524e-01 0.7443737835
[85,] 2.274824e-01 4.549647e-01 0.7725176378
[86,] 2.057047e-01 4.114094e-01 0.7942953039
[87,] 2.411050e-01 4.822099e-01 0.7588950471
[88,] 2.415555e-01 4.831109e-01 0.7584445268
[89,] 2.844132e-01 5.688263e-01 0.7155868439
[90,] 4.318716e-01 8.637432e-01 0.5681284178
[91,] 3.905608e-01 7.811216e-01 0.6094391802
[92,] 3.872866e-01 7.745731e-01 0.6127134395
[93,] 3.852442e-01 7.704885e-01 0.6147557629
[94,] 3.732573e-01 7.465147e-01 0.6267426586
[95,] 3.750719e-01 7.501439e-01 0.6249280681
[96,] 3.369861e-01 6.739722e-01 0.6630138929
[97,] 3.346223e-01 6.692446e-01 0.6653777006
[98,] 3.123230e-01 6.246461e-01 0.6876769553
[99,] 2.788953e-01 5.577906e-01 0.7211046845
[100,] 2.427980e-01 4.855960e-01 0.7572019913
[101,] 2.109485e-01 4.218970e-01 0.7890514984
[102,] 7.686052e-01 4.627897e-01 0.2313948268
[103,] 7.326551e-01 5.346899e-01 0.2673449432
[104,] 7.009813e-01 5.980375e-01 0.2990187373
[105,] 6.807487e-01 6.385025e-01 0.3192512677
[106,] 6.597735e-01 6.804529e-01 0.3402264609
[107,] 9.723128e-01 5.537436e-02 0.0276871803
[108,] 9.682710e-01 6.345806e-02 0.0317290288
[109,] 9.633616e-01 7.327682e-02 0.0366384086
[110,] 9.543516e-01 9.129677e-02 0.0456483834
[111,] 9.419971e-01 1.160058e-01 0.0580028975
[112,] 9.321659e-01 1.356682e-01 0.0678341201
[113,] 9.150396e-01 1.699208e-01 0.0849604112
[114,] 9.002431e-01 1.995138e-01 0.0997568969
[115,] 8.857539e-01 2.284921e-01 0.1142460660
[116,] 8.758571e-01 2.482859e-01 0.1241429495
[117,] 8.591696e-01 2.816608e-01 0.1408303798
[118,] 8.904522e-01 2.190957e-01 0.1095478447
[119,] 8.799366e-01 2.401268e-01 0.1200634156
[120,] 8.527756e-01 2.944489e-01 0.1472244254
[121,] 8.569404e-01 2.861192e-01 0.1430595768
[122,] 8.471145e-01 3.057709e-01 0.1528854671
[123,] 8.148693e-01 3.702615e-01 0.1851307344
[124,] 7.842424e-01 4.315153e-01 0.2157576325
[125,] 7.685254e-01 4.629492e-01 0.2314746068
[126,] 7.291373e-01 5.417253e-01 0.2708626509
[127,] 7.327137e-01 5.345726e-01 0.2672863215
[128,] 7.092202e-01 5.815596e-01 0.2907798157
[129,] 6.695208e-01 6.609584e-01 0.3304792196
[130,] 6.823983e-01 6.352035e-01 0.3176017327
[131,] 7.500886e-01 4.998228e-01 0.2499113988
[132,] 7.046277e-01 5.907447e-01 0.2953723416
[133,] 6.643394e-01 6.713211e-01 0.3356605524
[134,] 6.329893e-01 7.340215e-01 0.3670107451
[135,] 6.715230e-01 6.569540e-01 0.3284769870
[136,] 6.173657e-01 7.652685e-01 0.3826342626
[137,] 7.142220e-01 5.715559e-01 0.2857779706
[138,] 6.745283e-01 6.509435e-01 0.3254717478
[139,] 8.727919e-01 2.544161e-01 0.1272080607
[140,] 8.382380e-01 3.235240e-01 0.1617620032
[141,] 8.030194e-01 3.939611e-01 0.1969805724
[142,] 7.603113e-01 4.793774e-01 0.2396886890
[143,] 7.489350e-01 5.021301e-01 0.2510650283
[144,] 6.917816e-01 6.164368e-01 0.3082183867
[145,] 9.323351e-01 1.353299e-01 0.0676649348
[146,] 9.444874e-01 1.110251e-01 0.0555125661
[147,] 9.522623e-01 9.547541e-02 0.0477377042
[148,] 9.601377e-01 7.972469e-02 0.0398623428
[149,] 9.515891e-01 9.682174e-02 0.0484108724
[150,] 9.507761e-01 9.844775e-02 0.0492238738
[151,] 9.653374e-01 6.932518e-02 0.0346625894
[152,] 9.593790e-01 8.124198e-02 0.0406209886
[153,] 9.991063e-01 1.787416e-03 0.0008937079
[154,] 9.996902e-01 6.196943e-04 0.0003098471
[155,] 9.991067e-01 1.786660e-03 0.0008933301
[156,] 9.976981e-01 4.603724e-03 0.0023018620
[157,] 9.985355e-01 2.928946e-03 0.0014644730
[158,] 9.960094e-01 7.981114e-03 0.0039905568
[159,] 9.887266e-01 2.254690e-02 0.0112734498
[160,] 9.984374e-01 3.125180e-03 0.0015625902
[161,] 9.926758e-01 1.464834e-02 0.0073241722
[162,] 9.736209e-01 5.275820e-02 0.0263790979
> postscript(file="/var/wessaorg/rcomp/tmp/1h05o1324644314.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/29nql1324644314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3omoy1324644314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zfip1324644314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5h4ih1324644314.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
2854.9782 -7033.1386 -8212.0769 1996.1903 -16727.6964 18048.7745
7 8 9 10 11 12
16800.2321 10734.7479 -4951.4712 23136.1959 -2510.8927 -31663.0106
13 14 15 16 17 18
-24110.8138 -8963.5690 -1023.3544 -24077.6797 -18643.8885 23940.0102
19 20 21 22 23 24
23722.7650 23536.8252 15568.2836 -9498.2694 -3758.0249 -16558.1623
25 26 27 28 29 30
-148.8000 -21513.5111 -3611.3195 -9601.3184 -25942.1260 -12177.6682
31 32 33 34 35 36
-28246.9538 -9749.3586 -1357.2141 -31478.2863 15679.8257 -18885.1601
37 38 39 40 41 42
-13680.2082 9041.4175 -19178.9787 -1668.4930 -12007.3973 -14604.8392
43 44 45 46 47 48
-13508.7014 -20564.0823 -24273.6729 -14157.1952 -6826.6799 -16173.6971
49 50 51 52 53 54
-7249.6920 15441.1836 21766.8628 -19790.6327 -2552.5874 17503.9414
55 56 57 58 59 60
-12475.0329 -19303.3354 -5901.2215 -1880.0780 -4467.7848 -6911.1584
61 62 63 64 65 66
-2211.0772 5741.4916 25323.9826 -12730.5138 -19274.9852 -22189.4248
67 68 69 70 71 72
-8162.4285 1414.3242 -2923.4866 -19497.4034 -6099.5127 -6992.9805
73 74 75 76 77 78
-24847.9138 26719.8201 -36476.5520 18575.4796 443.1837 -30538.4445
79 80 81 82 83 84
-16096.7489 -22274.5057 11502.7868 -6387.3784 9471.7080 889.0179
85 86 87 88 89 90
128425.2903 -9421.1294 472.9897 -7333.6587 20490.8898 -22316.3421
91 92 93 94 95 96
-5541.6423 21760.6326 -11758.9804 -4711.1854 -18319.2810 11811.0670
97 98 99 100 101 102
-26271.6665 28494.1451 -13953.4404 10612.0879 -11104.9121 -8314.2941
103 104 105 106 107 108
8167.5363 32226.0325 24917.5349 34566.8184 -33115.8481 -6228.8440
109 110 111 112 113 114
17638.6341 -4453.0066 15767.9223 -8735.8173 2460.5404 -19836.9151
115 116 117 118 119 120
-15266.6949 -6762.0919 452.8522 -7906.3515 92103.4480 -1311.9720
121 122 123 124 125 126
-13947.6091 -9850.4093 -23640.1865 81785.8823 15628.1223 -14246.5453
127 128 129 130 131 132
6407.7315 -1727.8397 17195.7722 -2560.4966 -5277.4654 -10544.6098
133 134 135 136 137 138
-5272.8681 -15006.1157 -11243.7236 -20342.6735 -2169.4633 26031.6952
139 140 141 142 143 144
23902.9497 -345.5140 -8661.4110 8835.1305 2072.9548 -26060.6279
145 146 147 148 149 150
21240.7247 -745.3446 37336.2589 39479.8280 -5782.0204 13627.7657
151 152 153 154 155 156
-12554.3021 -6654.0364 3570.7559 -18177.8236 -7691.9119 53722.4723
157 158 159 160 161 162
-7873.7261 -4433.6367 9905.3015 6152.0054 -2419.8559 62478.3649
163 164 165 166 167 168
3708.6374 38161.1781 -8542.3580 -27632.5909 -21759.4513 44804.7917
169 170 171 172 173 174
-21771.3423 38431.4928 21264.2061 6870.0578 18584.4107 -16244.5696
175 176 177 178 179 180
29774.4449 -7768.9291 35607.5081 -4500.3967 14321.9244 -16164.0215
181 182 183 184 185 186
5794.3207 22827.2266 -209.3108 -1767.5291 -35237.5716 -2184.5275
187 188 189 190 191 192
-5414.0932 -3070.8744 -14199.1025 13354.0864 24834.1493 3490.5647
193 194 195 196 197
-17952.1884 -21024.2039 -6171.0627 -8323.1986 -7270.9641
> postscript(file="/var/wessaorg/rcomp/tmp/6iq0e1324644314.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 2854.9782 NA
1 -7033.1386 2854.9782
2 -8212.0769 -7033.1386
3 1996.1903 -8212.0769
4 -16727.6964 1996.1903
5 18048.7745 -16727.6964
6 16800.2321 18048.7745
7 10734.7479 16800.2321
8 -4951.4712 10734.7479
9 23136.1959 -4951.4712
10 -2510.8927 23136.1959
11 -31663.0106 -2510.8927
12 -24110.8138 -31663.0106
13 -8963.5690 -24110.8138
14 -1023.3544 -8963.5690
15 -24077.6797 -1023.3544
16 -18643.8885 -24077.6797
17 23940.0102 -18643.8885
18 23722.7650 23940.0102
19 23536.8252 23722.7650
20 15568.2836 23536.8252
21 -9498.2694 15568.2836
22 -3758.0249 -9498.2694
23 -16558.1623 -3758.0249
24 -148.8000 -16558.1623
25 -21513.5111 -148.8000
26 -3611.3195 -21513.5111
27 -9601.3184 -3611.3195
28 -25942.1260 -9601.3184
29 -12177.6682 -25942.1260
30 -28246.9538 -12177.6682
31 -9749.3586 -28246.9538
32 -1357.2141 -9749.3586
33 -31478.2863 -1357.2141
34 15679.8257 -31478.2863
35 -18885.1601 15679.8257
36 -13680.2082 -18885.1601
37 9041.4175 -13680.2082
38 -19178.9787 9041.4175
39 -1668.4930 -19178.9787
40 -12007.3973 -1668.4930
41 -14604.8392 -12007.3973
42 -13508.7014 -14604.8392
43 -20564.0823 -13508.7014
44 -24273.6729 -20564.0823
45 -14157.1952 -24273.6729
46 -6826.6799 -14157.1952
47 -16173.6971 -6826.6799
48 -7249.6920 -16173.6971
49 15441.1836 -7249.6920
50 21766.8628 15441.1836
51 -19790.6327 21766.8628
52 -2552.5874 -19790.6327
53 17503.9414 -2552.5874
54 -12475.0329 17503.9414
55 -19303.3354 -12475.0329
56 -5901.2215 -19303.3354
57 -1880.0780 -5901.2215
58 -4467.7848 -1880.0780
59 -6911.1584 -4467.7848
60 -2211.0772 -6911.1584
61 5741.4916 -2211.0772
62 25323.9826 5741.4916
63 -12730.5138 25323.9826
64 -19274.9852 -12730.5138
65 -22189.4248 -19274.9852
66 -8162.4285 -22189.4248
67 1414.3242 -8162.4285
68 -2923.4866 1414.3242
69 -19497.4034 -2923.4866
70 -6099.5127 -19497.4034
71 -6992.9805 -6099.5127
72 -24847.9138 -6992.9805
73 26719.8201 -24847.9138
74 -36476.5520 26719.8201
75 18575.4796 -36476.5520
76 443.1837 18575.4796
77 -30538.4445 443.1837
78 -16096.7489 -30538.4445
79 -22274.5057 -16096.7489
80 11502.7868 -22274.5057
81 -6387.3784 11502.7868
82 9471.7080 -6387.3784
83 889.0179 9471.7080
84 128425.2903 889.0179
85 -9421.1294 128425.2903
86 472.9897 -9421.1294
87 -7333.6587 472.9897
88 20490.8898 -7333.6587
89 -22316.3421 20490.8898
90 -5541.6423 -22316.3421
91 21760.6326 -5541.6423
92 -11758.9804 21760.6326
93 -4711.1854 -11758.9804
94 -18319.2810 -4711.1854
95 11811.0670 -18319.2810
96 -26271.6665 11811.0670
97 28494.1451 -26271.6665
98 -13953.4404 28494.1451
99 10612.0879 -13953.4404
100 -11104.9121 10612.0879
101 -8314.2941 -11104.9121
102 8167.5363 -8314.2941
103 32226.0325 8167.5363
104 24917.5349 32226.0325
105 34566.8184 24917.5349
106 -33115.8481 34566.8184
107 -6228.8440 -33115.8481
108 17638.6341 -6228.8440
109 -4453.0066 17638.6341
110 15767.9223 -4453.0066
111 -8735.8173 15767.9223
112 2460.5404 -8735.8173
113 -19836.9151 2460.5404
114 -15266.6949 -19836.9151
115 -6762.0919 -15266.6949
116 452.8522 -6762.0919
117 -7906.3515 452.8522
118 92103.4480 -7906.3515
119 -1311.9720 92103.4480
120 -13947.6091 -1311.9720
121 -9850.4093 -13947.6091
122 -23640.1865 -9850.4093
123 81785.8823 -23640.1865
124 15628.1223 81785.8823
125 -14246.5453 15628.1223
126 6407.7315 -14246.5453
127 -1727.8397 6407.7315
128 17195.7722 -1727.8397
129 -2560.4966 17195.7722
130 -5277.4654 -2560.4966
131 -10544.6098 -5277.4654
132 -5272.8681 -10544.6098
133 -15006.1157 -5272.8681
134 -11243.7236 -15006.1157
135 -20342.6735 -11243.7236
136 -2169.4633 -20342.6735
137 26031.6952 -2169.4633
138 23902.9497 26031.6952
139 -345.5140 23902.9497
140 -8661.4110 -345.5140
141 8835.1305 -8661.4110
142 2072.9548 8835.1305
143 -26060.6279 2072.9548
144 21240.7247 -26060.6279
145 -745.3446 21240.7247
146 37336.2589 -745.3446
147 39479.8280 37336.2589
148 -5782.0204 39479.8280
149 13627.7657 -5782.0204
150 -12554.3021 13627.7657
151 -6654.0364 -12554.3021
152 3570.7559 -6654.0364
153 -18177.8236 3570.7559
154 -7691.9119 -18177.8236
155 53722.4723 -7691.9119
156 -7873.7261 53722.4723
157 -4433.6367 -7873.7261
158 9905.3015 -4433.6367
159 6152.0054 9905.3015
160 -2419.8559 6152.0054
161 62478.3649 -2419.8559
162 3708.6374 62478.3649
163 38161.1781 3708.6374
164 -8542.3580 38161.1781
165 -27632.5909 -8542.3580
166 -21759.4513 -27632.5909
167 44804.7917 -21759.4513
168 -21771.3423 44804.7917
169 38431.4928 -21771.3423
170 21264.2061 38431.4928
171 6870.0578 21264.2061
172 18584.4107 6870.0578
173 -16244.5696 18584.4107
174 29774.4449 -16244.5696
175 -7768.9291 29774.4449
176 35607.5081 -7768.9291
177 -4500.3967 35607.5081
178 14321.9244 -4500.3967
179 -16164.0215 14321.9244
180 5794.3207 -16164.0215
181 22827.2266 5794.3207
182 -209.3108 22827.2266
183 -1767.5291 -209.3108
184 -35237.5716 -1767.5291
185 -2184.5275 -35237.5716
186 -5414.0932 -2184.5275
187 -3070.8744 -5414.0932
188 -14199.1025 -3070.8744
189 13354.0864 -14199.1025
190 24834.1493 13354.0864
191 3490.5647 24834.1493
192 -17952.1884 3490.5647
193 -21024.2039 -17952.1884
194 -6171.0627 -21024.2039
195 -8323.1986 -6171.0627
196 -7270.9641 -8323.1986
197 NA -7270.9641
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7033.1386 2854.9782
[2,] -8212.0769 -7033.1386
[3,] 1996.1903 -8212.0769
[4,] -16727.6964 1996.1903
[5,] 18048.7745 -16727.6964
[6,] 16800.2321 18048.7745
[7,] 10734.7479 16800.2321
[8,] -4951.4712 10734.7479
[9,] 23136.1959 -4951.4712
[10,] -2510.8927 23136.1959
[11,] -31663.0106 -2510.8927
[12,] -24110.8138 -31663.0106
[13,] -8963.5690 -24110.8138
[14,] -1023.3544 -8963.5690
[15,] -24077.6797 -1023.3544
[16,] -18643.8885 -24077.6797
[17,] 23940.0102 -18643.8885
[18,] 23722.7650 23940.0102
[19,] 23536.8252 23722.7650
[20,] 15568.2836 23536.8252
[21,] -9498.2694 15568.2836
[22,] -3758.0249 -9498.2694
[23,] -16558.1623 -3758.0249
[24,] -148.8000 -16558.1623
[25,] -21513.5111 -148.8000
[26,] -3611.3195 -21513.5111
[27,] -9601.3184 -3611.3195
[28,] -25942.1260 -9601.3184
[29,] -12177.6682 -25942.1260
[30,] -28246.9538 -12177.6682
[31,] -9749.3586 -28246.9538
[32,] -1357.2141 -9749.3586
[33,] -31478.2863 -1357.2141
[34,] 15679.8257 -31478.2863
[35,] -18885.1601 15679.8257
[36,] -13680.2082 -18885.1601
[37,] 9041.4175 -13680.2082
[38,] -19178.9787 9041.4175
[39,] -1668.4930 -19178.9787
[40,] -12007.3973 -1668.4930
[41,] -14604.8392 -12007.3973
[42,] -13508.7014 -14604.8392
[43,] -20564.0823 -13508.7014
[44,] -24273.6729 -20564.0823
[45,] -14157.1952 -24273.6729
[46,] -6826.6799 -14157.1952
[47,] -16173.6971 -6826.6799
[48,] -7249.6920 -16173.6971
[49,] 15441.1836 -7249.6920
[50,] 21766.8628 15441.1836
[51,] -19790.6327 21766.8628
[52,] -2552.5874 -19790.6327
[53,] 17503.9414 -2552.5874
[54,] -12475.0329 17503.9414
[55,] -19303.3354 -12475.0329
[56,] -5901.2215 -19303.3354
[57,] -1880.0780 -5901.2215
[58,] -4467.7848 -1880.0780
[59,] -6911.1584 -4467.7848
[60,] -2211.0772 -6911.1584
[61,] 5741.4916 -2211.0772
[62,] 25323.9826 5741.4916
[63,] -12730.5138 25323.9826
[64,] -19274.9852 -12730.5138
[65,] -22189.4248 -19274.9852
[66,] -8162.4285 -22189.4248
[67,] 1414.3242 -8162.4285
[68,] -2923.4866 1414.3242
[69,] -19497.4034 -2923.4866
[70,] -6099.5127 -19497.4034
[71,] -6992.9805 -6099.5127
[72,] -24847.9138 -6992.9805
[73,] 26719.8201 -24847.9138
[74,] -36476.5520 26719.8201
[75,] 18575.4796 -36476.5520
[76,] 443.1837 18575.4796
[77,] -30538.4445 443.1837
[78,] -16096.7489 -30538.4445
[79,] -22274.5057 -16096.7489
[80,] 11502.7868 -22274.5057
[81,] -6387.3784 11502.7868
[82,] 9471.7080 -6387.3784
[83,] 889.0179 9471.7080
[84,] 128425.2903 889.0179
[85,] -9421.1294 128425.2903
[86,] 472.9897 -9421.1294
[87,] -7333.6587 472.9897
[88,] 20490.8898 -7333.6587
[89,] -22316.3421 20490.8898
[90,] -5541.6423 -22316.3421
[91,] 21760.6326 -5541.6423
[92,] -11758.9804 21760.6326
[93,] -4711.1854 -11758.9804
[94,] -18319.2810 -4711.1854
[95,] 11811.0670 -18319.2810
[96,] -26271.6665 11811.0670
[97,] 28494.1451 -26271.6665
[98,] -13953.4404 28494.1451
[99,] 10612.0879 -13953.4404
[100,] -11104.9121 10612.0879
[101,] -8314.2941 -11104.9121
[102,] 8167.5363 -8314.2941
[103,] 32226.0325 8167.5363
[104,] 24917.5349 32226.0325
[105,] 34566.8184 24917.5349
[106,] -33115.8481 34566.8184
[107,] -6228.8440 -33115.8481
[108,] 17638.6341 -6228.8440
[109,] -4453.0066 17638.6341
[110,] 15767.9223 -4453.0066
[111,] -8735.8173 15767.9223
[112,] 2460.5404 -8735.8173
[113,] -19836.9151 2460.5404
[114,] -15266.6949 -19836.9151
[115,] -6762.0919 -15266.6949
[116,] 452.8522 -6762.0919
[117,] -7906.3515 452.8522
[118,] 92103.4480 -7906.3515
[119,] -1311.9720 92103.4480
[120,] -13947.6091 -1311.9720
[121,] -9850.4093 -13947.6091
[122,] -23640.1865 -9850.4093
[123,] 81785.8823 -23640.1865
[124,] 15628.1223 81785.8823
[125,] -14246.5453 15628.1223
[126,] 6407.7315 -14246.5453
[127,] -1727.8397 6407.7315
[128,] 17195.7722 -1727.8397
[129,] -2560.4966 17195.7722
[130,] -5277.4654 -2560.4966
[131,] -10544.6098 -5277.4654
[132,] -5272.8681 -10544.6098
[133,] -15006.1157 -5272.8681
[134,] -11243.7236 -15006.1157
[135,] -20342.6735 -11243.7236
[136,] -2169.4633 -20342.6735
[137,] 26031.6952 -2169.4633
[138,] 23902.9497 26031.6952
[139,] -345.5140 23902.9497
[140,] -8661.4110 -345.5140
[141,] 8835.1305 -8661.4110
[142,] 2072.9548 8835.1305
[143,] -26060.6279 2072.9548
[144,] 21240.7247 -26060.6279
[145,] -745.3446 21240.7247
[146,] 37336.2589 -745.3446
[147,] 39479.8280 37336.2589
[148,] -5782.0204 39479.8280
[149,] 13627.7657 -5782.0204
[150,] -12554.3021 13627.7657
[151,] -6654.0364 -12554.3021
[152,] 3570.7559 -6654.0364
[153,] -18177.8236 3570.7559
[154,] -7691.9119 -18177.8236
[155,] 53722.4723 -7691.9119
[156,] -7873.7261 53722.4723
[157,] -4433.6367 -7873.7261
[158,] 9905.3015 -4433.6367
[159,] 6152.0054 9905.3015
[160,] -2419.8559 6152.0054
[161,] 62478.3649 -2419.8559
[162,] 3708.6374 62478.3649
[163,] 38161.1781 3708.6374
[164,] -8542.3580 38161.1781
[165,] -27632.5909 -8542.3580
[166,] -21759.4513 -27632.5909
[167,] 44804.7917 -21759.4513
[168,] -21771.3423 44804.7917
[169,] 38431.4928 -21771.3423
[170,] 21264.2061 38431.4928
[171,] 6870.0578 21264.2061
[172,] 18584.4107 6870.0578
[173,] -16244.5696 18584.4107
[174,] 29774.4449 -16244.5696
[175,] -7768.9291 29774.4449
[176,] 35607.5081 -7768.9291
[177,] -4500.3967 35607.5081
[178,] 14321.9244 -4500.3967
[179,] -16164.0215 14321.9244
[180,] 5794.3207 -16164.0215
[181,] 22827.2266 5794.3207
[182,] -209.3108 22827.2266
[183,] -1767.5291 -209.3108
[184,] -35237.5716 -1767.5291
[185,] -2184.5275 -35237.5716
[186,] -5414.0932 -2184.5275
[187,] -3070.8744 -5414.0932
[188,] -14199.1025 -3070.8744
[189,] 13354.0864 -14199.1025
[190,] 24834.1493 13354.0864
[191,] 3490.5647 24834.1493
[192,] -17952.1884 3490.5647
[193,] -21024.2039 -17952.1884
[194,] -6171.0627 -21024.2039
[195,] -8323.1986 -6171.0627
[196,] -7270.9641 -8323.1986
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7033.1386 2854.9782
2 -8212.0769 -7033.1386
3 1996.1903 -8212.0769
4 -16727.6964 1996.1903
5 18048.7745 -16727.6964
6 16800.2321 18048.7745
7 10734.7479 16800.2321
8 -4951.4712 10734.7479
9 23136.1959 -4951.4712
10 -2510.8927 23136.1959
11 -31663.0106 -2510.8927
12 -24110.8138 -31663.0106
13 -8963.5690 -24110.8138
14 -1023.3544 -8963.5690
15 -24077.6797 -1023.3544
16 -18643.8885 -24077.6797
17 23940.0102 -18643.8885
18 23722.7650 23940.0102
19 23536.8252 23722.7650
20 15568.2836 23536.8252
21 -9498.2694 15568.2836
22 -3758.0249 -9498.2694
23 -16558.1623 -3758.0249
24 -148.8000 -16558.1623
25 -21513.5111 -148.8000
26 -3611.3195 -21513.5111
27 -9601.3184 -3611.3195
28 -25942.1260 -9601.3184
29 -12177.6682 -25942.1260
30 -28246.9538 -12177.6682
31 -9749.3586 -28246.9538
32 -1357.2141 -9749.3586
33 -31478.2863 -1357.2141
34 15679.8257 -31478.2863
35 -18885.1601 15679.8257
36 -13680.2082 -18885.1601
37 9041.4175 -13680.2082
38 -19178.9787 9041.4175
39 -1668.4930 -19178.9787
40 -12007.3973 -1668.4930
41 -14604.8392 -12007.3973
42 -13508.7014 -14604.8392
43 -20564.0823 -13508.7014
44 -24273.6729 -20564.0823
45 -14157.1952 -24273.6729
46 -6826.6799 -14157.1952
47 -16173.6971 -6826.6799
48 -7249.6920 -16173.6971
49 15441.1836 -7249.6920
50 21766.8628 15441.1836
51 -19790.6327 21766.8628
52 -2552.5874 -19790.6327
53 17503.9414 -2552.5874
54 -12475.0329 17503.9414
55 -19303.3354 -12475.0329
56 -5901.2215 -19303.3354
57 -1880.0780 -5901.2215
58 -4467.7848 -1880.0780
59 -6911.1584 -4467.7848
60 -2211.0772 -6911.1584
61 5741.4916 -2211.0772
62 25323.9826 5741.4916
63 -12730.5138 25323.9826
64 -19274.9852 -12730.5138
65 -22189.4248 -19274.9852
66 -8162.4285 -22189.4248
67 1414.3242 -8162.4285
68 -2923.4866 1414.3242
69 -19497.4034 -2923.4866
70 -6099.5127 -19497.4034
71 -6992.9805 -6099.5127
72 -24847.9138 -6992.9805
73 26719.8201 -24847.9138
74 -36476.5520 26719.8201
75 18575.4796 -36476.5520
76 443.1837 18575.4796
77 -30538.4445 443.1837
78 -16096.7489 -30538.4445
79 -22274.5057 -16096.7489
80 11502.7868 -22274.5057
81 -6387.3784 11502.7868
82 9471.7080 -6387.3784
83 889.0179 9471.7080
84 128425.2903 889.0179
85 -9421.1294 128425.2903
86 472.9897 -9421.1294
87 -7333.6587 472.9897
88 20490.8898 -7333.6587
89 -22316.3421 20490.8898
90 -5541.6423 -22316.3421
91 21760.6326 -5541.6423
92 -11758.9804 21760.6326
93 -4711.1854 -11758.9804
94 -18319.2810 -4711.1854
95 11811.0670 -18319.2810
96 -26271.6665 11811.0670
97 28494.1451 -26271.6665
98 -13953.4404 28494.1451
99 10612.0879 -13953.4404
100 -11104.9121 10612.0879
101 -8314.2941 -11104.9121
102 8167.5363 -8314.2941
103 32226.0325 8167.5363
104 24917.5349 32226.0325
105 34566.8184 24917.5349
106 -33115.8481 34566.8184
107 -6228.8440 -33115.8481
108 17638.6341 -6228.8440
109 -4453.0066 17638.6341
110 15767.9223 -4453.0066
111 -8735.8173 15767.9223
112 2460.5404 -8735.8173
113 -19836.9151 2460.5404
114 -15266.6949 -19836.9151
115 -6762.0919 -15266.6949
116 452.8522 -6762.0919
117 -7906.3515 452.8522
118 92103.4480 -7906.3515
119 -1311.9720 92103.4480
120 -13947.6091 -1311.9720
121 -9850.4093 -13947.6091
122 -23640.1865 -9850.4093
123 81785.8823 -23640.1865
124 15628.1223 81785.8823
125 -14246.5453 15628.1223
126 6407.7315 -14246.5453
127 -1727.8397 6407.7315
128 17195.7722 -1727.8397
129 -2560.4966 17195.7722
130 -5277.4654 -2560.4966
131 -10544.6098 -5277.4654
132 -5272.8681 -10544.6098
133 -15006.1157 -5272.8681
134 -11243.7236 -15006.1157
135 -20342.6735 -11243.7236
136 -2169.4633 -20342.6735
137 26031.6952 -2169.4633
138 23902.9497 26031.6952
139 -345.5140 23902.9497
140 -8661.4110 -345.5140
141 8835.1305 -8661.4110
142 2072.9548 8835.1305
143 -26060.6279 2072.9548
144 21240.7247 -26060.6279
145 -745.3446 21240.7247
146 37336.2589 -745.3446
147 39479.8280 37336.2589
148 -5782.0204 39479.8280
149 13627.7657 -5782.0204
150 -12554.3021 13627.7657
151 -6654.0364 -12554.3021
152 3570.7559 -6654.0364
153 -18177.8236 3570.7559
154 -7691.9119 -18177.8236
155 53722.4723 -7691.9119
156 -7873.7261 53722.4723
157 -4433.6367 -7873.7261
158 9905.3015 -4433.6367
159 6152.0054 9905.3015
160 -2419.8559 6152.0054
161 62478.3649 -2419.8559
162 3708.6374 62478.3649
163 38161.1781 3708.6374
164 -8542.3580 38161.1781
165 -27632.5909 -8542.3580
166 -21759.4513 -27632.5909
167 44804.7917 -21759.4513
168 -21771.3423 44804.7917
169 38431.4928 -21771.3423
170 21264.2061 38431.4928
171 6870.0578 21264.2061
172 18584.4107 6870.0578
173 -16244.5696 18584.4107
174 29774.4449 -16244.5696
175 -7768.9291 29774.4449
176 35607.5081 -7768.9291
177 -4500.3967 35607.5081
178 14321.9244 -4500.3967
179 -16164.0215 14321.9244
180 5794.3207 -16164.0215
181 22827.2266 5794.3207
182 -209.3108 22827.2266
183 -1767.5291 -209.3108
184 -35237.5716 -1767.5291
185 -2184.5275 -35237.5716
186 -5414.0932 -2184.5275
187 -3070.8744 -5414.0932
188 -14199.1025 -3070.8744
189 13354.0864 -14199.1025
190 24834.1493 13354.0864
191 3490.5647 24834.1493
192 -17952.1884 3490.5647
193 -21024.2039 -17952.1884
194 -6171.0627 -21024.2039
195 -8323.1986 -6171.0627
196 -7270.9641 -8323.1986
> 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/78abu1324644314.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/8avis1324644314.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/9wng51324644314.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/10b6q01324644314.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11seys1324644314.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12thhb1324644314.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lzs41324644314.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14ptjy1324644314.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/154vse1324644314.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/16qb7m1324644314.tab")
+ }
>
> try(system("convert tmp/1h05o1324644314.ps tmp/1h05o1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/29nql1324644314.ps tmp/29nql1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/3omoy1324644314.ps tmp/3omoy1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zfip1324644314.ps tmp/4zfip1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h4ih1324644314.ps tmp/5h4ih1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/6iq0e1324644314.ps tmp/6iq0e1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/78abu1324644314.ps tmp/78abu1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/8avis1324644314.ps tmp/8avis1324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wng51324644314.ps tmp/9wng51324644314.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b6q01324644314.ps tmp/10b6q01324644314.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.006 0.744 8.917