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(1724
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+ ,1
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+ ,4
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+ ,3
+ ,3
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+ ,89
+ ,0
+ ,37
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+ ,152
+ ,76
+ ,52789
+ ,1813
+ ,15673
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+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
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+ ,117
+ ,66
+ ,100350
+ ,17372
+ ,75882
+ ,80
+ ,72)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('pageviews'
+ ,'timeinrfc'
+ ,'logins'
+ ,'compendiumviewsinfo'
+ ,'compendiumviewspr'
+ ,'sharedcompendiums'
+ ,'bloggedcomputations'
+ ,'compendiumsreviewed'
+ ,'feedbackmessagesp1'
+ ,'feedbackmessagesp120'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:164))
> y <- array(NA,dim=c(15,164),dimnames=list(c('pageviews','timeinrfc','logins','compendiumviewsinfo','compendiumviewspr','sharedcompendiums','bloggedcomputations','compendiumsreviewed','feedbackmessagesp1','feedbackmessagesp120','totsize','totrevisions','totseconds','tothyperlinks','totblogs'),1:164))
> 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 timeinrfc logins compendiumviewsinfo compendiumviewspr
1 140824 1724 270018 69 476 90
2 110459 1209 179444 64 429 63
3 105079 1844 222373 69 673 59
4 112098 2683 218443 104 1137 135
5 43929 1149 162874 51 348 48
6 76173 631 70849 28 179 46
7 187326 4513 498732 117 2201 109
8 22807 381 33186 19 111 46
9 144408 1997 207822 57 735 75
10 66485 1758 213274 44 595 72
11 79089 2064 296074 107 776 79
12 81625 2128 237633 114 660 61
13 68788 1659 164107 67 633 60
14 103297 2934 358752 77 1163 114
15 69446 1944 222781 83 622 46
16 114948 4763 369889 176 1650 127
17 167949 2122 305704 68 746 58
18 125081 2956 322896 157 1157 90
19 125818 1438 176082 55 507 41
20 136588 2320 263411 85 683 62
21 112431 2471 271965 69 828 99
22 103037 2769 425544 103 1203 101
23 82317 1442 179306 41 461 62
24 118906 1717 189897 54 601 65
25 83515 3202 219420 114 1196 150
26 104581 2592 207280 115 960 72
27 103129 2824 267198 126 1061 91
28 83243 1968 270750 85 617 73
29 37110 1495 155915 51 559 53
30 113344 2733 327474 64 1026 140
31 139165 2272 278019 74 908 49
32 86652 1830 204039 76 615 83
33 112302 2090 318563 83 779 53
34 69652 945 97717 36 310 40
35 119442 3092 369331 93 1198 72
36 69867 2764 273950 56 1186 87
37 101629 3657 422946 119 1317 74
38 70168 1842 215710 82 611 67
39 31081 934 115469 32 276 36
40 103925 3315 336047 190 1174 45
41 92622 3246 324178 79 1490 42
42 79011 1578 166266 65 623 75
43 93487 1735 195153 73 635 82
44 64520 1714 173510 60 470 85
45 93473 2496 153778 82 1022 82
46 114360 5501 455168 151 2068 848
47 33032 918 78800 42 330 57
48 96125 2228 208051 76 648 80
49 151911 3942 334657 116 1342 116
50 89256 2081 175523 54 868 68
51 95671 1816 213060 73 559 48
52 5950 496 24188 24 218 20
53 149695 2531 372238 308 833 81
54 32551 744 65029 17 255 21
55 31701 1161 101097 64 454 70
56 100087 3027 279012 58 1108 125
57 169707 2433 302218 84 642 80
58 150491 3574 323485 179 1079 220
59 120192 2606 339837 138 1046 63
60 95893 2175 252529 83 822 77
61 151715 3937 370483 139 1298 65
62 176225 3161 303406 117 1143 146
63 59900 2670 233632 106 1073 72
64 104767 2610 264889 88 931 59
65 114799 1426 228595 66 557 58
66 72128 1646 216027 65 436 58
67 143592 1855 187965 127 562 54
68 89626 2712 237323 144 824 89
69 131072 2277 232765 78 834 78
70 126817 1675 175699 68 621 62
71 81351 2537 239314 68 865 64
72 22618 893 73566 32 385 39
73 88977 2189 242585 83 716 58
74 92059 1694 187167 52 705 94
75 81897 1948 191920 62 683 61
76 108146 2314 359644 84 982 95
77 126372 2645 341637 90 1056 48
78 249771 1804 206059 106 522 50
79 71154 2250 201783 61 690 58
80 71571 1787 182231 64 644 67
81 55918 1678 153613 46 622 41
82 160141 3843 447353 115 1216 114
83 38692 1369 145943 69 653 45
84 102812 2306 280343 103 656 57
85 56622 870 80953 25 437 31
86 15986 1966 150216 54 822 175
87 123534 1337 156923 57 390 68
88 108535 3727 365370 195 1467 278
89 93879 2616 318651 112 907 91
90 144551 3085 179797 104 1044 72
91 56750 2312 251466 89 786 58
92 127654 2136 254506 75 655 71
93 65594 1808 185890 62 590 86
94 59938 2992 263577 74 1072 89
95 146975 2474 314255 80 947 134
96 143372 1624 189252 36 555 64
97 168553 1606 222504 50 552 72
98 183500 2091 285198 78 771 61
99 165986 3845 368878 147 1263 126
100 184923 3705 397681 108 1415 73
101 140358 2676 287015 134 846 83
102 149959 2295 285330 64 838 85
103 57224 1997 186856 177 640 116
104 43750 602 43287 14 214 43
105 48029 2146 185468 80 716 85
106 104978 2131 219475 139 749 72
107 100046 2549 259692 47 1140 110
108 101047 2649 301614 88 1030 55
109 197426 1110 121726 67 356 44
110 160902 3102 154165 88 906 79
111 147172 1860 306952 67 606 58
112 109432 2295 297982 87 684 70
113 1168 398 23623 11 156 9
114 83248 2205 195817 73 779 54
115 25162 530 61857 25 192 25
116 45724 1596 163766 48 457 107
117 110529 2949 384053 114 1162 63
118 855 387 21054 16 146 2
119 101382 2137 252805 52 866 67
120 14116 492 31961 22 200 22
121 89506 3397 311281 113 1211 153
122 135356 2089 240153 65 696 79
123 116066 1638 174892 88 485 112
124 144244 1685 152043 53 670 47
125 8773 568 38214 34 276 52
126 102153 1907 198094 40 659 113
127 117440 2758 353021 81 1010 115
128 104128 1288 196269 58 445 64
129 134238 3554 403932 80 1564 134
130 134047 2387 316105 97 820 120
131 279488 3328 396725 123 1151 111
132 79756 1250 187992 35 473 49
133 66089 1121 102424 42 401 55
134 102070 2862 283950 329 947 149
135 146760 4023 401260 164 1429 155
136 154771 1721 137843 52 534 104
137 165933 4060 383703 130 1698 146
138 64593 1830 157429 76 689 76
139 92280 1627 236370 46 528 83
140 67150 2535 282399 94 897 192
141 128692 1807 217478 112 610 69
142 124089 3873 366774 116 1548 117
143 125386 2181 236660 88 759 67
144 37238 2035 173260 63 716 37
145 140015 2960 323545 99 955 56
146 150047 1915 168994 57 720 122
147 154451 2604 246745 86 1011 52
148 156349 2633 301703 105 818 64
149 0 2 1 0 0 0
150 6023 207 14688 10 85 0
151 0 5 98 1 0 0
152 0 8 455 2 0 0
153 0 0 0 0 0 0
154 0 0 0 0 0 0
155 84601 2030 233143 83 699 58
156 68946 3161 368328 145 1049 115
157 0 0 0 0 0 0
158 0 4 203 4 0 0
159 1644 151 7199 5 74 0
160 6179 474 46660 20 259 7
161 3926 141 17547 5 69 3
162 52789 1047 116678 42 285 89
163 0 29 969 2 0 0
164 100350 1757 199726 65 580 47
sharedcompendiums bloggedcomputations compendiumsreviewed
1 3 96 38
2 4 71 34
3 16 70 42
4 2 134 38
5 1 61 27
6 3 8 35
7 0 149 33
8 0 1 18
9 7 74 34
10 0 82 33
11 0 92 43
12 7 117 55
13 8 50 37
14 4 139 52
15 10 73 43
16 0 168 59
17 6 113 36
18 4 98 39
19 3 103 29
20 8 135 49
21 0 123 45
22 1 86 39
23 5 66 25
24 9 103 52
25 1 138 41
26 0 86 38
27 5 99 41
28 0 117 43
29 0 57 32
30 0 125 41
31 3 123 45
32 6 44 49
33 1 133 48
34 4 43 37
35 4 132 39
36 0 83 42
37 0 112 43
38 2 79 36
39 1 33 17
40 2 124 39
41 10 123 39
42 9 67 41
43 5 75 36
44 6 68 42
45 1 50 45
46 2 101 41
47 2 20 26
48 0 101 52
49 10 142 47
50 3 99 45
51 0 94 40
52 0 8 4
53 8 85 44
54 5 21 18
55 3 30 14
56 1 97 37
57 5 122 56
58 5 115 39
59 0 148 42
60 12 89 36
61 10 154 46
62 12 139 28
63 11 77 43
64 8 92 42
65 2 52 37
66 0 96 30
67 6 79 35
68 9 85 44
69 2 135 36
70 5 143 28
71 13 99 45
72 6 22 23
73 7 78 45
74 2 79 38
75 1 127 38
76 4 140 45
77 3 130 36
78 6 78 41
79 2 133 38
80 0 83 37
81 1 62 28
82 0 147 45
83 5 30 26
84 2 117 44
85 0 49 8
86 0 52 27
87 6 71 36
88 1 72 37
89 0 130 57
90 1 165 45
91 1 76 37
92 3 160 38
93 9 48 31
94 1 132 36
95 4 73 36
96 3 83 36
97 5 94 35
98 0 158 39
99 12 151 60
100 13 165 30
101 8 117 51
102 0 145 41
103 0 73 36
104 4 13 19
105 4 89 23
106 0 92 40
107 0 128 40
108 0 169 40
109 0 28 30
110 0 116 41
111 4 76 40
112 0 147 45
113 0 12 1
114 0 146 40
115 4 23 11
116 0 83 45
117 1 135 38
118 0 4 0
119 5 81 30
120 0 18 8
121 3 106 39
122 7 76 48
123 13 55 48
124 3 44 29
125 0 16 8
126 2 66 43
127 0 137 52
128 0 50 53
129 4 137 48
130 0 154 48
131 3 137 50
132 0 71 40
133 0 42 36
134 4 84 40
135 4 103 46
136 15 63 40
137 0 127 46
138 4 55 39
139 1 104 41
140 1 110 46
141 0 38 32
142 9 95 39
143 1 121 39
144 3 41 21
145 11 145 45
146 5 147 50
147 2 116 36
148 1 185 44
149 9 0 0
150 0 4 0
151 0 0 0
152 0 0 0
153 1 0 0
154 0 0 0
155 2 65 37
156 3 130 47
157 0 0 0
158 0 0 0
159 0 7 0
160 0 12 5
161 0 0 1
162 0 37 43
163 0 0 0
164 2 48 32
feedbackmessagesp1 feedbackmessagesp120 totrevisions totseconds
1 144 116 32033 186099
2 133 127 20654 113854
3 162 106 16346 99776
4 148 133 35926 106194
5 88 64 10621 100792
6 129 89 10024 47552
7 128 122 43068 250931
8 67 22 1271 6853
9 132 117 34416 115466
10 120 82 20318 110896
11 158 139 24409 169351
12 210 184 20648 94853
13 122 113 12347 72591
14 179 162 21857 101345
15 162 87 11034 113713
16 223 199 33433 165354
17 140 139 35902 164263
18 144 92 22355 135213
19 111 85 31219 111669
20 191 185 21983 134163
21 171 148 40085 140303
22 144 144 18507 150773
23 89 84 16278 111848
24 208 208 24662 102509
25 153 144 31452 96785
26 146 139 32580 116136
27 158 127 22883 158376
28 154 148 27652 153990
29 117 99 9845 64057
30 158 135 20190 230054
31 175 165 46201 184531
32 182 146 10971 114198
33 185 178 34811 198299
34 141 137 3029 33750
35 151 148 38941 189723
36 159 127 4958 100826
37 158 148 32344 188355
38 139 89 19433 104470
39 55 46 12558 58391
40 151 143 36524 164808
41 145 122 26041 134097
42 148 111 16637 80238
43 115 108 28395 133252
44 157 126 16747 54518
45 73 45 9105 121850
46 147 131 11941 79367
47 82 66 7935 56968
48 201 180 19499 106314
49 181 165 22938 191889
50 164 146 25314 104864
51 158 137 28524 160791
52 12 7 2694 15049
53 163 157 20867 191179
54 67 61 3597 25109
55 52 41 5296 45824
56 134 120 32982 129711
57 210 208 38975 210012
58 145 137 42721 194679
59 153 150 41455 197680
60 139 127 23923 81180
61 178 161 26719 197765
62 101 73 53405 214738
63 169 97 12526 96252
64 163 142 26584 124527
65 139 125 37062 153242
66 116 87 25696 145707
67 137 128 24634 113963
68 167 148 27269 134904
69 135 116 25270 114268
70 102 89 24634 94333
71 173 154 17828 102204
72 88 67 3007 23824
73 175 171 20065 111563
74 133 90 24648 91313
75 148 133 21588 89770
76 166 143 25217 100125
77 140 133 30927 165278
78 154 125 18487 181712
79 148 134 18050 80906
80 134 110 17696 75881
81 109 89 17326 83963
82 175 138 39361 175721
83 99 99 9648 68580
84 122 92 26759 136323
85 28 27 7905 55792
86 101 77 4527 25157
87 132 130 41517 100922
88 143 137 21261 118845
89 206 122 36099 170492
90 171 159 39039 81716
91 138 85 13841 115750
92 141 131 23841 105590
93 114 90 8589 92795
94 140 135 15049 82390
95 140 132 39038 135599
96 140 139 30391 111542
97 127 127 39932 162519
98 141 104 43840 211381
99 231 229 43146 189944
100 114 106 50099 226168
101 198 176 40312 117495
102 155 130 32616 195894
103 138 59 11338 80684
104 71 64 7409 19630
105 84 36 18213 88634
106 151 88 45873 139292
107 155 125 39844 128602
108 150 124 28317 135848
109 112 83 24797 178377
110 161 127 7471 106330
111 149 143 27259 178303
112 164 115 23201 116938
113 0 0 238 5841
114 155 103 28830 106020
115 32 30 3913 24610
116 169 119 9935 74151
117 140 102 27738 232241
118 0 0 338 6622
119 111 77 13326 127097
120 25 9 3988 13155
121 146 137 24347 160501
122 183 163 27111 91502
123 181 146 3938 24469
124 107 84 17416 88229
125 27 21 1888 13983
126 163 151 18700 80716
127 198 187 36809 157384
128 205 171 24959 122975
129 187 167 37343 191469
130 187 145 21849 231257
131 186 175 49809 258287
132 151 137 21654 122531
133 131 100 8728 61394
134 155 150 20920 86480
135 172 163 27195 195791
136 152 141 1037 18284
137 172 161 42570 147581
138 143 112 17672 72558
139 151 135 34245 147341
140 158 124 16786 114651
141 125 45 20954 100187
142 145 120 16378 130332
143 145 126 31852 134218
144 79 78 2805 10901
145 174 136 38086 145758
146 192 179 21166 75767
147 132 118 34672 134969
148 159 147 36171 169216
149 0 0 0 0
150 0 0 2065 7953
151 0 0 0 0
152 0 0 0 0
153 0 0 0 0
154 0 0 0 0
155 133 88 19354 105406
156 185 115 22124 174586
157 0 0 0 0
158 0 0 0 0
159 0 0 556 4245
160 15 13 2089 21509
161 4 4 2658 7670
162 152 76 1813 15673
163 0 0 0 0
164 117 66 17372 75882
tothyperlinks totblogs
1 165 165
2 135 132
3 121 121
4 148 145
5 73 71
6 49 47
7 185 177
8 5 5
9 125 124
10 93 92
11 154 149
12 98 93
13 70 70
14 148 148
15 100 100
16 150 142
17 197 194
18 114 113
19 169 162
20 200 186
21 148 147
22 140 137
23 74 71
24 128 123
25 140 134
26 116 115
27 147 138
28 132 125
29 70 66
30 144 137
31 155 152
32 165 159
33 161 159
34 31 31
35 199 185
36 78 78
37 121 117
38 112 109
39 41 41
40 158 149
41 123 123
42 104 103
43 94 87
44 73 71
45 52 51
46 71 70
47 21 21
48 155 155
49 174 172
50 136 133
51 128 125
52 7 7
53 165 158
54 21 21
55 35 35
56 137 133
57 174 169
58 257 256
59 207 190
60 103 100
61 171 171
62 279 267
63 83 80
64 130 126
65 131 132
66 126 121
67 158 156
68 138 133
69 200 199
70 104 98
71 111 109
72 26 25
73 115 113
74 127 126
75 140 137
76 121 121
77 183 178
78 68 63
79 112 109
80 103 101
81 63 61
82 166 157
83 38 38
84 163 159
85 59 58
86 27 27
87 108 108
88 88 83
89 92 88
90 170 164
91 98 96
92 205 192
93 96 94
94 107 107
95 150 144
96 123 123
97 176 170
98 213 210
99 208 193
100 307 297
101 125 125
102 208 204
103 73 70
104 49 49
105 82 82
106 206 205
107 112 111
108 139 135
109 60 59
110 70 70
111 112 108
112 142 141
113 11 11
114 130 130
115 31 28
116 132 101
117 219 216
118 4 4
119 102 97
120 39 39
121 125 119
122 121 118
123 42 41
124 111 107
125 16 16
126 70 69
127 162 160
128 173 158
129 171 161
130 172 165
131 254 246
132 90 89
133 50 49
134 113 107
135 187 182
136 16 16
137 175 173
138 90 90
139 140 140
140 145 142
141 141 126
142 125 123
143 241 239
144 16 15
145 175 170
146 132 123
147 154 151
148 198 194
149 0 0
150 5 5
151 0 0
152 0 0
153 0 0
154 0 0
155 125 122
156 174 173
157 0 0
158 0 0
159 6 6
160 13 13
161 3 3
162 35 35
163 0 0
164 80 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews timeinrfc
2307.6247 7.5654 -0.1361
logins compendiumviewsinfo compendiumviewspr
6.6562 -10.9886 76.0490
sharedcompendiums bloggedcomputations compendiumsreviewed
2288.7219 2.3584 327.8774
feedbackmessagesp1 feedbackmessagesp120 totrevisions
-77.8978 204.4055 1.1101
totseconds tothyperlinks totblogs
0.4489 27.1901 7.2381
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-47586 -18016 -5025 13263 117778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2307.62467 5872.12460 0.393 0.694896
pageviews 7.56541 12.13575 0.623 0.533976
timeinrfc -0.13607 0.06866 -1.982 0.049335 *
logins 6.65624 71.45222 0.093 0.925904
compendiumviewsinfo -10.98863 25.06820 -0.438 0.661768
compendiumviewspr 76.04903 41.31839 1.841 0.067675 .
sharedcompendiums 2288.72188 639.35956 3.580 0.000465 ***
bloggedcomputations 2.35844 111.96963 0.021 0.983223
compendiumsreviewed 327.87736 880.49116 0.372 0.710139
feedbackmessagesp1 -77.89781 265.41116 -0.293 0.769549
feedbackmessagesp120 204.40551 128.56741 1.590 0.113984
totrevisions 1.11009 0.35585 3.120 0.002175 **
totseconds 0.44886 0.09153 4.904 2.43e-06 ***
tothyperlinks 27.19012 573.87959 0.047 0.962274
totblogs 7.23815 595.02264 0.012 0.990311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 27380 on 149 degrees of freedom
Multiple R-squared: 0.7467, Adjusted R-squared: 0.7229
F-statistic: 31.38 on 14 and 149 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 4.315704e-01 8.631407e-01 5.684296e-01
[2,] 3.226362e-01 6.452724e-01 6.773638e-01
[3,] 2.273377e-01 4.546753e-01 7.726623e-01
[4,] 1.624662e-01 3.249325e-01 8.375338e-01
[5,] 1.781403e-01 3.562807e-01 8.218597e-01
[6,] 1.097490e-01 2.194980e-01 8.902510e-01
[7,] 7.199137e-02 1.439827e-01 9.280086e-01
[8,] 5.858588e-02 1.171718e-01 9.414141e-01
[9,] 3.825642e-02 7.651284e-02 9.617436e-01
[10,] 2.446153e-02 4.892306e-02 9.755385e-01
[11,] 1.584006e-02 3.168012e-02 9.841599e-01
[12,] 1.165924e-02 2.331848e-02 9.883408e-01
[13,] 1.088478e-02 2.176956e-02 9.891152e-01
[14,] 6.999195e-03 1.399839e-02 9.930008e-01
[15,] 4.218539e-03 8.437079e-03 9.957815e-01
[16,] 2.541299e-03 5.082597e-03 9.974587e-01
[17,] 3.821341e-03 7.642683e-03 9.961787e-01
[18,] 7.298693e-03 1.459739e-02 9.927013e-01
[19,] 4.349270e-03 8.698539e-03 9.956507e-01
[20,] 2.982762e-03 5.965525e-03 9.970172e-01
[21,] 1.980757e-03 3.961513e-03 9.980192e-01
[22,] 2.034549e-03 4.069098e-03 9.979655e-01
[23,] 1.418543e-03 2.837086e-03 9.985815e-01
[24,] 2.468142e-03 4.936285e-03 9.975319e-01
[25,] 1.510060e-03 3.020120e-03 9.984899e-01
[26,] 1.217683e-03 2.435366e-03 9.987823e-01
[27,] 7.954080e-04 1.590816e-03 9.992046e-01
[28,] 3.018901e-03 6.037802e-03 9.969811e-01
[29,] 1.906525e-03 3.813050e-03 9.980935e-01
[30,] 1.501559e-03 3.003117e-03 9.984984e-01
[31,] 9.084217e-04 1.816843e-03 9.990916e-01
[32,] 5.645036e-04 1.129007e-03 9.994355e-01
[33,] 4.952797e-04 9.905594e-04 9.995047e-01
[34,] 3.473963e-04 6.947926e-04 9.996526e-01
[35,] 2.427111e-04 4.854222e-04 9.997573e-01
[36,] 2.036408e-04 4.072816e-04 9.997964e-01
[37,] 1.175557e-04 2.351113e-04 9.998824e-01
[38,] 7.607992e-05 1.521598e-04 9.999239e-01
[39,] 4.846342e-05 9.692684e-05 9.999515e-01
[40,] 4.430482e-05 8.860963e-05 9.999557e-01
[41,] 8.111171e-05 1.622234e-04 9.999189e-01
[42,] 6.463452e-05 1.292690e-04 9.999354e-01
[43,] 3.779325e-05 7.558651e-05 9.999622e-01
[44,] 2.471827e-05 4.943655e-05 9.999753e-01
[45,] 1.547665e-05 3.095330e-05 9.999845e-01
[46,] 1.562292e-05 3.124583e-05 9.999844e-01
[47,] 9.728220e-06 1.945644e-05 9.999903e-01
[48,] 6.339701e-06 1.267940e-05 9.999937e-01
[49,] 4.988966e-06 9.977932e-06 9.999950e-01
[50,] 8.305770e-06 1.661154e-05 9.999917e-01
[51,] 2.686095e-05 5.372189e-05 9.999731e-01
[52,] 2.513952e-05 5.027905e-05 9.999749e-01
[53,] 6.727000e-05 1.345400e-04 9.999327e-01
[54,] 1.085732e-04 2.171464e-04 9.998914e-01
[55,] 8.712478e-05 1.742496e-04 9.999129e-01
[56,] 8.726165e-05 1.745233e-04 9.999127e-01
[57,] 5.280990e-05 1.056198e-04 9.999472e-01
[58,] 3.404383e-05 6.808767e-05 9.999660e-01
[59,] 3.326402e-05 6.652804e-05 9.999667e-01
[60,] 1.978778e-05 3.957555e-05 9.999802e-01
[61,] 1.415184e-01 2.830369e-01 8.584816e-01
[62,] 1.241346e-01 2.482693e-01 8.758654e-01
[63,] 1.026514e-01 2.053029e-01 8.973486e-01
[64,] 9.170301e-02 1.834060e-01 9.082970e-01
[65,] 1.497432e-01 2.994864e-01 8.502568e-01
[66,] 1.611233e-01 3.222467e-01 8.388767e-01
[67,] 1.372927e-01 2.745854e-01 8.627073e-01
[68,] 1.218301e-01 2.436603e-01 8.781699e-01
[69,] 1.045499e-01 2.090999e-01 8.954501e-01
[70,] 9.290543e-02 1.858109e-01 9.070946e-01
[71,] 8.893200e-02 1.778640e-01 9.110680e-01
[72,] 1.065884e-01 2.131767e-01 8.934116e-01
[73,] 1.180007e-01 2.360015e-01 8.819993e-01
[74,] 1.106714e-01 2.213429e-01 8.893286e-01
[75,] 1.244844e-01 2.489688e-01 8.755156e-01
[76,] 1.253997e-01 2.507993e-01 8.746003e-01
[77,] 1.070662e-01 2.141325e-01 8.929338e-01
[78,] 1.321155e-01 2.642310e-01 8.678845e-01
[79,] 1.458609e-01 2.917218e-01 8.541391e-01
[80,] 1.332943e-01 2.665885e-01 8.667057e-01
[81,] 1.390877e-01 2.781753e-01 8.609123e-01
[82,] 2.537712e-01 5.075424e-01 7.462288e-01
[83,] 2.187346e-01 4.374693e-01 7.812654e-01
[84,] 2.108217e-01 4.216435e-01 7.891783e-01
[85,] 1.944748e-01 3.889495e-01 8.055252e-01
[86,] 1.616179e-01 3.232357e-01 8.383821e-01
[87,] 1.360865e-01 2.721730e-01 8.639135e-01
[88,] 1.392375e-01 2.784750e-01 8.607625e-01
[89,] 1.618994e-01 3.237987e-01 8.381006e-01
[90,] 1.418388e-01 2.836775e-01 8.581612e-01
[91,] 1.161331e-01 2.322662e-01 8.838669e-01
[92,] 2.179232e-01 4.358464e-01 7.820768e-01
[93,] 3.470592e-01 6.941185e-01 6.529408e-01
[94,] 3.171868e-01 6.343736e-01 6.828132e-01
[95,] 3.333563e-01 6.667126e-01 6.666437e-01
[96,] 2.869245e-01 5.738489e-01 7.130755e-01
[97,] 2.755544e-01 5.511088e-01 7.244456e-01
[98,] 2.409331e-01 4.818662e-01 7.590669e-01
[99,] 3.920535e-01 7.841070e-01 6.079465e-01
[100,] 3.594964e-01 7.189929e-01 6.405036e-01
[101,] 3.083613e-01 6.167227e-01 6.916387e-01
[102,] 2.745428e-01 5.490857e-01 7.254572e-01
[103,] 2.301567e-01 4.603134e-01 7.698433e-01
[104,] 5.485946e-01 9.028108e-01 4.514054e-01
[105,] 5.194460e-01 9.611080e-01 4.805540e-01
[106,] 6.084219e-01 7.831563e-01 3.915781e-01
[107,] 7.270627e-01 5.458747e-01 2.729373e-01
[108,] 6.751762e-01 6.496476e-01 3.248238e-01
[109,] 6.243362e-01 7.513276e-01 3.756638e-01
[110,] 5.834448e-01 8.331104e-01 4.165552e-01
[111,] 6.547807e-01 6.904386e-01 3.452193e-01
[112,] 6.437232e-01 7.125536e-01 3.562768e-01
[113,] 5.864741e-01 8.270518e-01 4.135259e-01
[114,] 9.221912e-01 1.556177e-01 7.780885e-02
[115,] 8.914974e-01 2.170052e-01 1.085026e-01
[116,] 8.566787e-01 2.866426e-01 1.433213e-01
[117,] 8.067552e-01 3.864897e-01 1.932448e-01
[118,] 7.605598e-01 4.788804e-01 2.394402e-01
[119,] 9.999624e-01 7.522203e-05 3.761102e-05
[120,] 9.999438e-01 1.124854e-04 5.624270e-05
[121,] 9.999303e-01 1.393481e-04 6.967404e-05
[122,] 9.999815e-01 3.693546e-05 1.846773e-05
[123,] 1.000000e+00 2.706707e-09 1.353354e-09
[124,] 1.000000e+00 1.503496e-08 7.517481e-09
[125,] 9.999999e-01 2.105076e-07 1.052538e-07
[126,] 9.999999e-01 2.506553e-07 1.253277e-07
[127,] 1.000000e+00 8.567278e-12 4.283639e-12
[128,] 1.000000e+00 1.451032e-09 7.255158e-10
[129,] 1.000000e+00 4.251523e-45 2.125761e-45
> postscript(file="/var/wessaorg/rcomp/tmp/154in1324132899.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/2pbbc1324132899.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3t7151324132899.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4x7om1324132899.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5412j1324132899.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 164
Frequency = 1
1 2 3 4 5 6
3324.20508 8191.51537 -5171.05372 -4879.24214 -22117.18282 16366.25918
7 8 9 10 11 12
40965.71905 9886.65619 21959.05833 -13285.11870 -35536.10868 -27008.75718
13 14 15 16 17 18
-14646.20438 10655.80491 -23524.94020 -25247.69095 30864.20027 29985.09184
19 20 21 22 23 24
22785.61195 5757.48553 -14991.95818 16023.90155 -7257.02631 -11214.66142
25 26 27 28 29 30
-28891.01071 -6806.45142 -20543.66453 -33389.58018 -17330.68122 -24484.71514
31 32 33 34 35 36
-18177.92394 -16439.02613 -31986.55987 15630.07233 -24812.13938 8982.84375
37 38 39 40 41 42
-19635.38571 -11880.64369 -17118.67033 -24541.17197 -20418.19850 -15318.15877
43 44 45 46 47 48
-26195.57690 -15592.77591 10398.73889 4698.66838 -21841.07327 -7323.00979
49 50 51 52 53 54
-5359.31197 -19656.77381 -26631.94558 -7916.87814 12896.07894 -6075.63705
55 56 57 58 59 60
-9903.86551 -13134.17016 -9208.49826 -25762.60416 -24419.61084 -7012.09538
61 62 63 64 65 66
-3263.07362 -17250.98708 -31270.02442 -14562.86606 -11919.10900 -30328.02465
67 68 69 70 71 72
29240.14203 -47585.77150 29809.35819 32968.03395 -35617.12410 -18468.64160
73 74 75 76 77 78
-22553.23701 4683.48883 -6341.13728 22637.21732 7856.61789 117778.19932
79 80 81 82 83 84
-11352.15988 486.76014 -15163.46840 35663.72883 -26184.43433 -1665.80797
85 86 87 88 89 90
19107.54804 -19484.05758 -5900.19641 10034.62220 -30184.17370 26236.30079
91 92 93 94 95 96
-17101.22143 28795.91165 -20436.19522 -13084.64505 21867.21520 31111.68268
97 98 99 100 101 102
22129.46568 33201.89328 -28413.14064 -2417.38563 1301.19536 11881.24452
103 104 105 106 107 108
-2312.24940 77.24384 -24952.31733 -21845.28358 -15057.10648 1946.79612
109 110 111 112 113 114
75608.89926 73900.88118 20170.21340 22964.49614 -3600.52746 -11635.23567
115 116 117 118 119 120
-5587.07348 -24550.98036 -22174.70459 -3664.53061 17500.79604 -1418.75518
121 122 123 124 125 126
-35088.78790 24817.46040 43232.17951 65271.03839 -7553.04028 14363.67744
127 128 129 130 131 132
-13123.24977 -6812.89254 -11526.58569 -9167.59722 84594.53813 -16841.63017
133 134 135 136 137 138
8082.87536 6690.79925 2567.31273 81328.51526 37408.50553 -17962.30254
139 140 141 142 143 144
-31769.02874 -27015.02788 59997.33165 21914.79982 7732.78513 15557.03376
145 146 147 148 149 150
-3294.16196 42700.75923 37757.31501 20889.03638 -22921.11638 -1028.25803
151 152 153 154 155 156
-2338.77281 -2319.54722 -4596.34655 -2307.62467 3809.15973 -45926.42096
157 158 159 160 161 162
-2307.62467 -2336.88845 -2792.23901 -6762.46619 -3894.46627 26388.02840
163 164
-2408.47936 38692.94030
> postscript(file="/var/wessaorg/rcomp/tmp/6tpex1324132899.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 3324.20508 NA
1 8191.51537 3324.20508
2 -5171.05372 8191.51537
3 -4879.24214 -5171.05372
4 -22117.18282 -4879.24214
5 16366.25918 -22117.18282
6 40965.71905 16366.25918
7 9886.65619 40965.71905
8 21959.05833 9886.65619
9 -13285.11870 21959.05833
10 -35536.10868 -13285.11870
11 -27008.75718 -35536.10868
12 -14646.20438 -27008.75718
13 10655.80491 -14646.20438
14 -23524.94020 10655.80491
15 -25247.69095 -23524.94020
16 30864.20027 -25247.69095
17 29985.09184 30864.20027
18 22785.61195 29985.09184
19 5757.48553 22785.61195
20 -14991.95818 5757.48553
21 16023.90155 -14991.95818
22 -7257.02631 16023.90155
23 -11214.66142 -7257.02631
24 -28891.01071 -11214.66142
25 -6806.45142 -28891.01071
26 -20543.66453 -6806.45142
27 -33389.58018 -20543.66453
28 -17330.68122 -33389.58018
29 -24484.71514 -17330.68122
30 -18177.92394 -24484.71514
31 -16439.02613 -18177.92394
32 -31986.55987 -16439.02613
33 15630.07233 -31986.55987
34 -24812.13938 15630.07233
35 8982.84375 -24812.13938
36 -19635.38571 8982.84375
37 -11880.64369 -19635.38571
38 -17118.67033 -11880.64369
39 -24541.17197 -17118.67033
40 -20418.19850 -24541.17197
41 -15318.15877 -20418.19850
42 -26195.57690 -15318.15877
43 -15592.77591 -26195.57690
44 10398.73889 -15592.77591
45 4698.66838 10398.73889
46 -21841.07327 4698.66838
47 -7323.00979 -21841.07327
48 -5359.31197 -7323.00979
49 -19656.77381 -5359.31197
50 -26631.94558 -19656.77381
51 -7916.87814 -26631.94558
52 12896.07894 -7916.87814
53 -6075.63705 12896.07894
54 -9903.86551 -6075.63705
55 -13134.17016 -9903.86551
56 -9208.49826 -13134.17016
57 -25762.60416 -9208.49826
58 -24419.61084 -25762.60416
59 -7012.09538 -24419.61084
60 -3263.07362 -7012.09538
61 -17250.98708 -3263.07362
62 -31270.02442 -17250.98708
63 -14562.86606 -31270.02442
64 -11919.10900 -14562.86606
65 -30328.02465 -11919.10900
66 29240.14203 -30328.02465
67 -47585.77150 29240.14203
68 29809.35819 -47585.77150
69 32968.03395 29809.35819
70 -35617.12410 32968.03395
71 -18468.64160 -35617.12410
72 -22553.23701 -18468.64160
73 4683.48883 -22553.23701
74 -6341.13728 4683.48883
75 22637.21732 -6341.13728
76 7856.61789 22637.21732
77 117778.19932 7856.61789
78 -11352.15988 117778.19932
79 486.76014 -11352.15988
80 -15163.46840 486.76014
81 35663.72883 -15163.46840
82 -26184.43433 35663.72883
83 -1665.80797 -26184.43433
84 19107.54804 -1665.80797
85 -19484.05758 19107.54804
86 -5900.19641 -19484.05758
87 10034.62220 -5900.19641
88 -30184.17370 10034.62220
89 26236.30079 -30184.17370
90 -17101.22143 26236.30079
91 28795.91165 -17101.22143
92 -20436.19522 28795.91165
93 -13084.64505 -20436.19522
94 21867.21520 -13084.64505
95 31111.68268 21867.21520
96 22129.46568 31111.68268
97 33201.89328 22129.46568
98 -28413.14064 33201.89328
99 -2417.38563 -28413.14064
100 1301.19536 -2417.38563
101 11881.24452 1301.19536
102 -2312.24940 11881.24452
103 77.24384 -2312.24940
104 -24952.31733 77.24384
105 -21845.28358 -24952.31733
106 -15057.10648 -21845.28358
107 1946.79612 -15057.10648
108 75608.89926 1946.79612
109 73900.88118 75608.89926
110 20170.21340 73900.88118
111 22964.49614 20170.21340
112 -3600.52746 22964.49614
113 -11635.23567 -3600.52746
114 -5587.07348 -11635.23567
115 -24550.98036 -5587.07348
116 -22174.70459 -24550.98036
117 -3664.53061 -22174.70459
118 17500.79604 -3664.53061
119 -1418.75518 17500.79604
120 -35088.78790 -1418.75518
121 24817.46040 -35088.78790
122 43232.17951 24817.46040
123 65271.03839 43232.17951
124 -7553.04028 65271.03839
125 14363.67744 -7553.04028
126 -13123.24977 14363.67744
127 -6812.89254 -13123.24977
128 -11526.58569 -6812.89254
129 -9167.59722 -11526.58569
130 84594.53813 -9167.59722
131 -16841.63017 84594.53813
132 8082.87536 -16841.63017
133 6690.79925 8082.87536
134 2567.31273 6690.79925
135 81328.51526 2567.31273
136 37408.50553 81328.51526
137 -17962.30254 37408.50553
138 -31769.02874 -17962.30254
139 -27015.02788 -31769.02874
140 59997.33165 -27015.02788
141 21914.79982 59997.33165
142 7732.78513 21914.79982
143 15557.03376 7732.78513
144 -3294.16196 15557.03376
145 42700.75923 -3294.16196
146 37757.31501 42700.75923
147 20889.03638 37757.31501
148 -22921.11638 20889.03638
149 -1028.25803 -22921.11638
150 -2338.77281 -1028.25803
151 -2319.54722 -2338.77281
152 -4596.34655 -2319.54722
153 -2307.62467 -4596.34655
154 3809.15973 -2307.62467
155 -45926.42096 3809.15973
156 -2307.62467 -45926.42096
157 -2336.88845 -2307.62467
158 -2792.23901 -2336.88845
159 -6762.46619 -2792.23901
160 -3894.46627 -6762.46619
161 26388.02840 -3894.46627
162 -2408.47936 26388.02840
163 38692.94030 -2408.47936
164 NA 38692.94030
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8191.51537 3324.20508
[2,] -5171.05372 8191.51537
[3,] -4879.24214 -5171.05372
[4,] -22117.18282 -4879.24214
[5,] 16366.25918 -22117.18282
[6,] 40965.71905 16366.25918
[7,] 9886.65619 40965.71905
[8,] 21959.05833 9886.65619
[9,] -13285.11870 21959.05833
[10,] -35536.10868 -13285.11870
[11,] -27008.75718 -35536.10868
[12,] -14646.20438 -27008.75718
[13,] 10655.80491 -14646.20438
[14,] -23524.94020 10655.80491
[15,] -25247.69095 -23524.94020
[16,] 30864.20027 -25247.69095
[17,] 29985.09184 30864.20027
[18,] 22785.61195 29985.09184
[19,] 5757.48553 22785.61195
[20,] -14991.95818 5757.48553
[21,] 16023.90155 -14991.95818
[22,] -7257.02631 16023.90155
[23,] -11214.66142 -7257.02631
[24,] -28891.01071 -11214.66142
[25,] -6806.45142 -28891.01071
[26,] -20543.66453 -6806.45142
[27,] -33389.58018 -20543.66453
[28,] -17330.68122 -33389.58018
[29,] -24484.71514 -17330.68122
[30,] -18177.92394 -24484.71514
[31,] -16439.02613 -18177.92394
[32,] -31986.55987 -16439.02613
[33,] 15630.07233 -31986.55987
[34,] -24812.13938 15630.07233
[35,] 8982.84375 -24812.13938
[36,] -19635.38571 8982.84375
[37,] -11880.64369 -19635.38571
[38,] -17118.67033 -11880.64369
[39,] -24541.17197 -17118.67033
[40,] -20418.19850 -24541.17197
[41,] -15318.15877 -20418.19850
[42,] -26195.57690 -15318.15877
[43,] -15592.77591 -26195.57690
[44,] 10398.73889 -15592.77591
[45,] 4698.66838 10398.73889
[46,] -21841.07327 4698.66838
[47,] -7323.00979 -21841.07327
[48,] -5359.31197 -7323.00979
[49,] -19656.77381 -5359.31197
[50,] -26631.94558 -19656.77381
[51,] -7916.87814 -26631.94558
[52,] 12896.07894 -7916.87814
[53,] -6075.63705 12896.07894
[54,] -9903.86551 -6075.63705
[55,] -13134.17016 -9903.86551
[56,] -9208.49826 -13134.17016
[57,] -25762.60416 -9208.49826
[58,] -24419.61084 -25762.60416
[59,] -7012.09538 -24419.61084
[60,] -3263.07362 -7012.09538
[61,] -17250.98708 -3263.07362
[62,] -31270.02442 -17250.98708
[63,] -14562.86606 -31270.02442
[64,] -11919.10900 -14562.86606
[65,] -30328.02465 -11919.10900
[66,] 29240.14203 -30328.02465
[67,] -47585.77150 29240.14203
[68,] 29809.35819 -47585.77150
[69,] 32968.03395 29809.35819
[70,] -35617.12410 32968.03395
[71,] -18468.64160 -35617.12410
[72,] -22553.23701 -18468.64160
[73,] 4683.48883 -22553.23701
[74,] -6341.13728 4683.48883
[75,] 22637.21732 -6341.13728
[76,] 7856.61789 22637.21732
[77,] 117778.19932 7856.61789
[78,] -11352.15988 117778.19932
[79,] 486.76014 -11352.15988
[80,] -15163.46840 486.76014
[81,] 35663.72883 -15163.46840
[82,] -26184.43433 35663.72883
[83,] -1665.80797 -26184.43433
[84,] 19107.54804 -1665.80797
[85,] -19484.05758 19107.54804
[86,] -5900.19641 -19484.05758
[87,] 10034.62220 -5900.19641
[88,] -30184.17370 10034.62220
[89,] 26236.30079 -30184.17370
[90,] -17101.22143 26236.30079
[91,] 28795.91165 -17101.22143
[92,] -20436.19522 28795.91165
[93,] -13084.64505 -20436.19522
[94,] 21867.21520 -13084.64505
[95,] 31111.68268 21867.21520
[96,] 22129.46568 31111.68268
[97,] 33201.89328 22129.46568
[98,] -28413.14064 33201.89328
[99,] -2417.38563 -28413.14064
[100,] 1301.19536 -2417.38563
[101,] 11881.24452 1301.19536
[102,] -2312.24940 11881.24452
[103,] 77.24384 -2312.24940
[104,] -24952.31733 77.24384
[105,] -21845.28358 -24952.31733
[106,] -15057.10648 -21845.28358
[107,] 1946.79612 -15057.10648
[108,] 75608.89926 1946.79612
[109,] 73900.88118 75608.89926
[110,] 20170.21340 73900.88118
[111,] 22964.49614 20170.21340
[112,] -3600.52746 22964.49614
[113,] -11635.23567 -3600.52746
[114,] -5587.07348 -11635.23567
[115,] -24550.98036 -5587.07348
[116,] -22174.70459 -24550.98036
[117,] -3664.53061 -22174.70459
[118,] 17500.79604 -3664.53061
[119,] -1418.75518 17500.79604
[120,] -35088.78790 -1418.75518
[121,] 24817.46040 -35088.78790
[122,] 43232.17951 24817.46040
[123,] 65271.03839 43232.17951
[124,] -7553.04028 65271.03839
[125,] 14363.67744 -7553.04028
[126,] -13123.24977 14363.67744
[127,] -6812.89254 -13123.24977
[128,] -11526.58569 -6812.89254
[129,] -9167.59722 -11526.58569
[130,] 84594.53813 -9167.59722
[131,] -16841.63017 84594.53813
[132,] 8082.87536 -16841.63017
[133,] 6690.79925 8082.87536
[134,] 2567.31273 6690.79925
[135,] 81328.51526 2567.31273
[136,] 37408.50553 81328.51526
[137,] -17962.30254 37408.50553
[138,] -31769.02874 -17962.30254
[139,] -27015.02788 -31769.02874
[140,] 59997.33165 -27015.02788
[141,] 21914.79982 59997.33165
[142,] 7732.78513 21914.79982
[143,] 15557.03376 7732.78513
[144,] -3294.16196 15557.03376
[145,] 42700.75923 -3294.16196
[146,] 37757.31501 42700.75923
[147,] 20889.03638 37757.31501
[148,] -22921.11638 20889.03638
[149,] -1028.25803 -22921.11638
[150,] -2338.77281 -1028.25803
[151,] -2319.54722 -2338.77281
[152,] -4596.34655 -2319.54722
[153,] -2307.62467 -4596.34655
[154,] 3809.15973 -2307.62467
[155,] -45926.42096 3809.15973
[156,] -2307.62467 -45926.42096
[157,] -2336.88845 -2307.62467
[158,] -2792.23901 -2336.88845
[159,] -6762.46619 -2792.23901
[160,] -3894.46627 -6762.46619
[161,] 26388.02840 -3894.46627
[162,] -2408.47936 26388.02840
[163,] 38692.94030 -2408.47936
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8191.51537 3324.20508
2 -5171.05372 8191.51537
3 -4879.24214 -5171.05372
4 -22117.18282 -4879.24214
5 16366.25918 -22117.18282
6 40965.71905 16366.25918
7 9886.65619 40965.71905
8 21959.05833 9886.65619
9 -13285.11870 21959.05833
10 -35536.10868 -13285.11870
11 -27008.75718 -35536.10868
12 -14646.20438 -27008.75718
13 10655.80491 -14646.20438
14 -23524.94020 10655.80491
15 -25247.69095 -23524.94020
16 30864.20027 -25247.69095
17 29985.09184 30864.20027
18 22785.61195 29985.09184
19 5757.48553 22785.61195
20 -14991.95818 5757.48553
21 16023.90155 -14991.95818
22 -7257.02631 16023.90155
23 -11214.66142 -7257.02631
24 -28891.01071 -11214.66142
25 -6806.45142 -28891.01071
26 -20543.66453 -6806.45142
27 -33389.58018 -20543.66453
28 -17330.68122 -33389.58018
29 -24484.71514 -17330.68122
30 -18177.92394 -24484.71514
31 -16439.02613 -18177.92394
32 -31986.55987 -16439.02613
33 15630.07233 -31986.55987
34 -24812.13938 15630.07233
35 8982.84375 -24812.13938
36 -19635.38571 8982.84375
37 -11880.64369 -19635.38571
38 -17118.67033 -11880.64369
39 -24541.17197 -17118.67033
40 -20418.19850 -24541.17197
41 -15318.15877 -20418.19850
42 -26195.57690 -15318.15877
43 -15592.77591 -26195.57690
44 10398.73889 -15592.77591
45 4698.66838 10398.73889
46 -21841.07327 4698.66838
47 -7323.00979 -21841.07327
48 -5359.31197 -7323.00979
49 -19656.77381 -5359.31197
50 -26631.94558 -19656.77381
51 -7916.87814 -26631.94558
52 12896.07894 -7916.87814
53 -6075.63705 12896.07894
54 -9903.86551 -6075.63705
55 -13134.17016 -9903.86551
56 -9208.49826 -13134.17016
57 -25762.60416 -9208.49826
58 -24419.61084 -25762.60416
59 -7012.09538 -24419.61084
60 -3263.07362 -7012.09538
61 -17250.98708 -3263.07362
62 -31270.02442 -17250.98708
63 -14562.86606 -31270.02442
64 -11919.10900 -14562.86606
65 -30328.02465 -11919.10900
66 29240.14203 -30328.02465
67 -47585.77150 29240.14203
68 29809.35819 -47585.77150
69 32968.03395 29809.35819
70 -35617.12410 32968.03395
71 -18468.64160 -35617.12410
72 -22553.23701 -18468.64160
73 4683.48883 -22553.23701
74 -6341.13728 4683.48883
75 22637.21732 -6341.13728
76 7856.61789 22637.21732
77 117778.19932 7856.61789
78 -11352.15988 117778.19932
79 486.76014 -11352.15988
80 -15163.46840 486.76014
81 35663.72883 -15163.46840
82 -26184.43433 35663.72883
83 -1665.80797 -26184.43433
84 19107.54804 -1665.80797
85 -19484.05758 19107.54804
86 -5900.19641 -19484.05758
87 10034.62220 -5900.19641
88 -30184.17370 10034.62220
89 26236.30079 -30184.17370
90 -17101.22143 26236.30079
91 28795.91165 -17101.22143
92 -20436.19522 28795.91165
93 -13084.64505 -20436.19522
94 21867.21520 -13084.64505
95 31111.68268 21867.21520
96 22129.46568 31111.68268
97 33201.89328 22129.46568
98 -28413.14064 33201.89328
99 -2417.38563 -28413.14064
100 1301.19536 -2417.38563
101 11881.24452 1301.19536
102 -2312.24940 11881.24452
103 77.24384 -2312.24940
104 -24952.31733 77.24384
105 -21845.28358 -24952.31733
106 -15057.10648 -21845.28358
107 1946.79612 -15057.10648
108 75608.89926 1946.79612
109 73900.88118 75608.89926
110 20170.21340 73900.88118
111 22964.49614 20170.21340
112 -3600.52746 22964.49614
113 -11635.23567 -3600.52746
114 -5587.07348 -11635.23567
115 -24550.98036 -5587.07348
116 -22174.70459 -24550.98036
117 -3664.53061 -22174.70459
118 17500.79604 -3664.53061
119 -1418.75518 17500.79604
120 -35088.78790 -1418.75518
121 24817.46040 -35088.78790
122 43232.17951 24817.46040
123 65271.03839 43232.17951
124 -7553.04028 65271.03839
125 14363.67744 -7553.04028
126 -13123.24977 14363.67744
127 -6812.89254 -13123.24977
128 -11526.58569 -6812.89254
129 -9167.59722 -11526.58569
130 84594.53813 -9167.59722
131 -16841.63017 84594.53813
132 8082.87536 -16841.63017
133 6690.79925 8082.87536
134 2567.31273 6690.79925
135 81328.51526 2567.31273
136 37408.50553 81328.51526
137 -17962.30254 37408.50553
138 -31769.02874 -17962.30254
139 -27015.02788 -31769.02874
140 59997.33165 -27015.02788
141 21914.79982 59997.33165
142 7732.78513 21914.79982
143 15557.03376 7732.78513
144 -3294.16196 15557.03376
145 42700.75923 -3294.16196
146 37757.31501 42700.75923
147 20889.03638 37757.31501
148 -22921.11638 20889.03638
149 -1028.25803 -22921.11638
150 -2338.77281 -1028.25803
151 -2319.54722 -2338.77281
152 -4596.34655 -2319.54722
153 -2307.62467 -4596.34655
154 3809.15973 -2307.62467
155 -45926.42096 3809.15973
156 -2307.62467 -45926.42096
157 -2336.88845 -2307.62467
158 -2792.23901 -2336.88845
159 -6762.46619 -2792.23901
160 -3894.46627 -6762.46619
161 26388.02840 -3894.46627
162 -2408.47936 26388.02840
163 38692.94030 -2408.47936
> 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/7un311324132899.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/8ffck1324132899.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/9ju881324132899.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/105jlz1324132899.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11qn4o1324132899.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12utaz1324132899.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13vcio1324132899.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/141xmw1324132899.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/15lpx01324132899.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/164aln1324132899.tab")
+ }
>
> try(system("convert tmp/154in1324132899.ps tmp/154in1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pbbc1324132899.ps tmp/2pbbc1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t7151324132899.ps tmp/3t7151324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x7om1324132899.ps tmp/4x7om1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/5412j1324132899.ps tmp/5412j1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tpex1324132899.ps tmp/6tpex1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/7un311324132899.ps tmp/7un311324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ffck1324132899.ps tmp/8ffck1324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ju881324132899.ps tmp/9ju881324132899.png",intern=TRUE))
character(0)
> try(system("convert tmp/105jlz1324132899.ps tmp/105jlz1324132899.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.157 0.646 6.882