R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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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+ ,0
+ ,17
+ ,16
+ ,64
+ ,39
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21
+ ,15)
+ ,dim=c(16
+ ,173)
+ ,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'
+ ,'I1')
+ ,1:173))
> y <- array(NA,dim=c(16,173),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','I1'),1:173))
> 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 = '10'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
feedback_messages_p120 pageviews time_in_rfc logins compendium_views_info
1 94 1418 210907 56 396
2 103 869 120982 56 297
3 93 1530 176508 54 559
4 91 3201 385534 92 1562
5 93 1583 149061 44 656
6 60 1439 165446 33 511
7 123 1764 237213 84 655
8 90 1373 133131 55 525
9 168 4041 324799 154 1436
10 115 1706 230964 53 612
11 71 2152 236785 119 865
12 66 1036 135473 41 385
13 117 1929 215147 58 639
14 108 2242 344297 75 963
15 84 1220 153935 33 398
16 120 2515 174724 92 966
17 114 2147 174415 100 801
18 94 2352 225548 112 892
19 120 1638 223632 73 513
20 81 1222 124817 40 469
21 133 1677 210767 60 643
22 122 1579 170266 62 535
23 124 2452 294424 77 992
24 126 2662 325107 99 937
25 0 186 7176 17 70
26 37 865 106408 30 260
27 38 1793 96560 76 503
28 120 2527 265769 146 927
29 95 1324 149112 56 537
30 77 2702 175824 107 910
31 90 1383 152871 58 532
32 80 1179 111665 34 345
33 110 4308 362301 119 1635
34 138 1831 183167 66 557
35 100 1438 168809 66 452
36 7 496 24188 24 218
37 140 2253 329267 259 764
38 96 2352 218946 41 866
39 164 2144 244052 68 574
40 78 4691 341570 168 1276
41 49 1112 103597 43 379
42 124 1973 256462 105 798
43 62 2474 235800 94 921
44 99 1226 196553 57 503
45 70 1389 174184 53 382
46 104 1496 143246 103 464
47 116 2269 187559 121 717
48 91 1833 187681 62 690
49 67 893 73566 32 385
50 72 1403 167488 45 619
51 120 1425 143756 46 479
52 105 1840 243199 75 752
53 104 1502 182999 88 430
54 98 1420 152299 53 537
55 111 2970 346485 90 1000
56 71 1644 193339 78 465
57 69 1654 122774 45 711
58 107 1054 130585 46 299
59 73 937 112611 41 248
60 107 3004 286468 144 1162
61 129 2547 148446 91 905
62 118 1626 182079 63 512
63 73 1468 140344 53 472
64 119 2445 220516 62 905
65 104 1964 243060 63 786
66 107 1381 162765 32 489
67 90 1659 232138 62 617
68 197 2888 265318 117 925
69 36 1290 85574 34 351
70 85 2845 310839 92 1144
71 139 1982 225060 93 669
72 106 1904 232317 54 707
73 50 1391 144966 144 458
74 63 1559 164709 109 572
75 63 2146 220801 75 720
76 69 874 99466 50 273
77 41 1590 92661 61 508
78 56 1590 133328 55 506
79 25 1210 61361 77 451
80 93 1281 100750 72 407
81 44 1105 102010 53 370
82 87 1272 101523 42 316
83 110 1944 243511 71 603
84 0 391 22938 10 154
85 83 1605 152474 65 577
86 80 1988 99923 66 617
87 98 1386 132487 41 411
88 82 2395 317394 86 975
89 0 387 21054 16 146
90 60 1742 209641 42 705
91 28 620 22648 19 184
92 9 449 31414 19 200
93 33 800 46698 45 274
94 59 1684 131698 65 502
95 115 2699 244749 95 964
96 120 1204 128423 64 369
97 66 1138 97839 38 417
98 152 2158 272458 65 822
99 139 1111 172494 52 389
100 38 1421 108043 62 466
101 144 2833 328107 65 1255
102 160 2922 351067 95 1024
103 114 1002 158015 29 400
104 119 2186 229242 247 719
105 101 1035 84207 29 356
106 56 1417 120445 118 457
107 133 3261 324598 110 1402
108 83 1587 131069 67 600
109 116 1424 204271 42 480
110 50 946 116048 64 230
111 61 1926 250047 81 651
112 97 3352 299775 95 1367
113 98 1641 195838 67 564
114 78 2035 173260 63 716
115 117 2312 254488 83 747
116 55 961 92499 32 319
117 132 1900 224330 83 612
118 44 1254 135781 31 433
119 21 1335 74408 67 434
120 50 1597 81240 66 503
121 73 1645 181633 70 564
122 86 2429 271856 103 824
123 48 872 95227 34 239
124 48 1018 98146 40 459
125 68 1314 59194 31 288
126 87 1335 139942 42 498
127 43 1403 118612 46 454
128 67 910 72880 33 376
129 46 616 65475 18 225
130 56 771 71965 35 252
131 60 1376 135131 66 481
132 65 1232 108446 60 389
133 60 1544 181528 54 609
134 54 1230 134019 53 422
135 52 1255 121848 39 339
136 61 721 81872 45 245
137 61 1109 58981 36 384
138 81 740 53515 28 212
139 40 728 56375 30 229
140 40 689 65490 22 224
141 68 995 76302 31 333
142 79 1613 104011 55 384
143 47 2048 98104 54 636
144 41 301 30989 14 93
145 29 1803 135458 81 581
146 60 861 63123 43 304
147 79 1451 74914 30 407
148 47 628 31774 23 170
149 40 1161 81437 38 312
150 42 979 65745 53 340
151 49 675 56653 45 168
152 57 1241 158399 39 443
153 40 1049 73624 24 367
154 33 1081 91899 35 335
155 77 1688 139526 151 364
156 45 617 51567 30 206
157 45 1656 102538 57 490
158 50 705 86678 40 238
159 71 1597 150580 77 530
160 67 982 99611 35 291
161 62 1212 99373 63 397
162 54 1143 86230 44 467
163 4 435 30837 19 178
164 25 532 31706 13 175
165 40 882 89806 42 299
166 59 830 64175 42 260
167 24 652 59382 49 227
168 58 707 119308 30 239
169 42 954 76702 49 333
170 4 285 19764 12 75
171 63 733 84105 20 261
172 54 642 64187 27 238
173 39 894 72535 14 329
compendium_views_pr shared_compendiums blogged_computations
1 81 3 79
2 55 4 58
3 50 12 60
4 63 0 121
5 66 5 43
6 57 0 69
7 74 0 78
8 52 7 44
9 108 0 158
10 43 4 102
11 75 3 77
12 32 0 82
13 85 0 101
14 86 1 80
15 56 5 50
16 135 0 123
17 63 0 73
18 81 5 81
19 52 0 105
20 44 0 47
21 39 3 94
22 73 4 44
23 59 2 107
24 64 0 84
25 1 0 0
26 32 1 33
27 129 0 42
28 37 2 96
29 65 6 56
30 107 0 57
31 74 5 59
32 54 4 39
33 715 2 76
34 66 0 91
35 32 0 76
36 20 0 8
37 71 8 79
38 112 1 76
39 66 5 101
40 190 1 94
41 66 1 27
42 56 0 123
43 127 8 105
44 50 2 41
45 52 0 72
46 42 5 67
47 76 8 75
48 67 2 114
49 39 6 22
50 77 2 69
51 57 0 105
52 34 3 88
53 39 6 73
54 63 0 62
55 106 0 118
56 47 2 100
57 162 0 24
58 57 5 67
59 36 0 46
60 263 1 57
61 63 1 135
62 63 2 124
63 77 6 33
64 79 1 98
65 110 4 58
66 56 2 68
67 43 0 131
68 111 10 110
69 71 0 37
70 62 9 130
71 56 7 93
72 74 0 118
73 60 0 39
74 53 0 81
75 105 1 51
76 32 0 28
77 133 1 40
78 79 0 56
79 51 0 27
80 67 0 83
81 66 3 28
82 76 0 59
83 65 0 133
84 9 0 12
85 45 0 106
86 115 0 44
87 97 0 71
88 53 1 116
89 2 0 4
90 52 5 62
91 44 0 12
92 22 0 18
93 35 0 14
94 74 0 60
95 144 2 98
96 89 8 32
97 42 2 25
98 99 0 100
99 52 0 46
100 29 1 45
101 125 3 129
102 95 3 136
103 40 0 59
104 128 4 63
105 73 11 14
106 72 0 36
107 128 0 113
108 61 4 47
109 73 0 92
110 45 0 50
111 58 0 41
112 97 9 91
113 50 1 111
114 37 3 41
115 50 10 120
116 57 0 25
117 52 1 131
118 98 2 45
119 61 4 29
120 89 0 58
121 48 2 47
122 91 1 109
123 70 0 37
124 37 0 15
125 247 6 7
126 46 0 54
127 72 2 54
128 41 0 14
129 24 2 16
130 33 1 32
131 87 0 38
132 90 1 22
133 69 0 32
134 51 0 32
135 45 0 37
136 25 0 32
137 38 7 0
138 52 2 5
139 74 7 10
140 38 3 27
141 26 0 29
142 67 6 25
143 132 2 55
144 35 0 5
145 118 3 43
146 43 1 34
147 64 0 35
148 48 1 0
149 64 0 37
150 75 0 26
151 39 0 38
152 42 0 23
153 93 0 30
154 60 0 18
155 71 0 28
156 27 2 21
157 79 1 50
158 44 0 12
159 124 0 27
160 81 0 41
161 92 1 12
162 42 0 21
163 10 0 8
164 24 0 26
165 64 0 27
166 48 0 37
167 49 0 29
168 48 0 32
169 62 0 35
170 19 1 10
171 45 0 17
172 36 0 10
173 44 0 17
compendiums_reviewed feedback_messages_p1 totsize totrevisions totseconds
1 30 115 112285 24188 146283
2 28 109 84786 18273 98364
3 38 146 83123 14130 86146
4 25 96 119182 33251 195663
5 26 100 116174 27101 95757
6 25 93 57635 16373 85584
7 38 140 66198 19716 143983
8 30 99 57793 9028 59238
9 47 181 97668 29498 151511
10 30 116 133824 27563 136368
11 31 116 101481 18293 112642
12 23 88 99645 22530 94728
13 36 135 99052 35082 121527
14 30 108 67654 16116 127766
15 25 89 65553 15849 98958
16 34 129 69112 26569 85646
17 31 118 82753 24785 98579
18 31 118 85323 17569 130767
19 33 125 72654 23825 131741
20 25 95 30727 7869 53907
21 35 135 117478 37791 146761
22 42 154 74007 9605 82036
23 33 127 101494 34461 171975
24 36 136 79215 24919 159676
25 0 0 1423 603 1929
26 14 46 31081 12558 58391
27 17 54 22996 7784 31580
28 32 124 83122 28522 136815
29 35 128 60578 14459 69107
30 20 80 39992 14526 50495
31 28 97 79892 22240 108016
32 28 104 49810 11802 46341
33 34 125 100708 11912 79336
34 39 149 82875 18220 93176
35 28 118 72260 21884 127969
36 4 12 5950 2694 15049
37 39 144 115762 15808 155135
38 29 108 80670 25239 102996
39 44 166 143558 29801 160604
40 21 80 117105 18450 158051
41 16 60 23789 7132 44547
42 35 127 105195 35940 174141
43 23 84 149193 46230 184301
44 29 111 95260 30546 129847
45 25 98 55183 19746 117286
46 27 105 106671 15977 71180
47 36 135 73511 22583 109377
48 28 107 92945 17274 85298
49 23 88 22618 3007 23824
50 28 104 83737 21113 82981
51 34 132 69094 17401 73815
52 28 108 95536 23567 132190
53 34 129 225920 13065 128754
54 33 122 61370 14587 67808
55 38 147 106117 24021 131722
56 35 87 84651 20537 106175
57 24 90 15986 4527 25157
58 29 109 95364 30495 76669
59 20 78 26706 7117 57283
60 29 111 89691 17719 105805
61 37 141 126846 33473 72413
62 33 124 102860 21115 96971
63 25 93 51715 7236 71299
64 32 124 55801 13790 77494
65 29 112 111813 32902 120336
66 28 108 120293 25131 93913
67 31 117 161647 35947 181248
68 52 199 115929 29848 146123
69 21 78 24266 6943 32036
70 24 91 162901 42705 186646
71 41 158 109825 31808 102255
72 33 126 129838 26675 168237
73 32 122 37510 8435 64219
74 31 115 87771 36867 115338
75 18 72 44418 12607 84845
76 23 91 192565 22609 153197
77 17 45 35232 5892 29877
78 20 78 40909 17014 63506
79 12 39 13294 5394 22445
80 30 119 140867 6440 68370
81 13 50 44332 5818 42071
82 22 88 61056 18647 50517
83 42 155 101338 20556 103950
84 1 0 1168 238 5841
85 32 123 65567 22392 84396
86 25 99 32334 12237 35753
87 36 136 40735 8388 55515
88 31 117 91413 22120 209056
89 0 0 855 338 6622
90 24 88 97068 11727 115814
91 13 39 44339 3704 11609
92 8 25 14116 3988 13155
93 13 52 10288 3030 18274
94 19 75 65622 13520 72875
95 33 124 76643 20923 142775
96 38 145 92696 3769 20112
97 24 87 94785 12252 61023
98 43 162 93815 28864 132432
99 43 165 86687 21721 112494
100 14 54 34553 4821 45109
101 41 159 105547 33644 170875
102 45 170 213688 42935 214921
103 31 119 71220 18864 100226
104 31 120 91721 17939 78876
105 30 112 111194 325 6940
106 16 59 51009 13539 49025
107 37 136 135777 34538 122037
108 30 107 51513 12198 53782
109 35 130 74163 26924 127748
110 20 75 33416 10855 77395
111 18 71 83305 11932 89324
112 31 120 98952 14300 103300
113 31 116 102372 25515 112283
114 21 79 37238 2805 10901
115 39 150 103772 29402 120691
116 18 71 21399 5250 25899
117 39 144 130115 28608 139296
118 14 47 24874 8092 52678
119 7 28 34988 4473 23853
120 17 68 45549 1572 17306
121 30 110 64466 14817 89455
122 37 147 54990 16714 147866
123 32 111 34777 1669 14336
124 17 68 27114 7768 30059
125 24 80 37636 6387 22097
126 22 88 65461 18715 96841
127 12 48 30080 7936 41907
128 19 76 24094 8643 27080
129 13 51 69008 7294 35885
130 15 59 46090 7185 28313
131 15 60 34029 8509 36134
132 17 68 46300 13275 55764
133 16 61 40662 10737 66956
134 18 67 28987 8033 47487
135 17 64 30594 5401 35619
136 16 64 27913 10856 45608
137 23 91 42744 2154 7721
138 22 88 12934 6117 20634
139 13 49 41385 4820 31931
140 16 62 18653 5615 37754
141 20 76 30976 8702 40557
142 22 88 63339 15340 94238
143 17 66 25568 8030 44197
144 17 68 4154 1278 4103
145 12 48 19474 4236 44144
146 17 68 39067 7196 27640
147 23 90 65892 6371 28990
148 17 66 4143 1574 4694
149 14 54 28579 9620 42648
150 21 77 38084 8645 25836
151 18 68 27717 8987 22779
152 18 72 32928 5544 40820
153 17 64 19499 6909 32378
154 15 59 36874 6745 39613
155 21 84 48259 16724 60865
156 14 56 28207 7025 20107
157 15 58 45833 9078 48231
158 15 59 29156 4605 39725
159 22 83 45588 9653 62991
160 21 81 45097 8914 49363
161 18 72 28394 6700 24552
162 17 61 18632 5788 31493
163 4 15 2325 593 3439
164 10 32 25139 4506 19555
165 16 62 27975 6382 21228
166 18 72 21792 6928 28893
167 12 41 26263 1514 21425
168 16 61 23686 9238 50276
169 21 67 49303 8204 37643
170 2 8 5752 2416 9927
171 17 66 20055 5432 27184
172 16 61 20154 5576 18475
173 16 64 19540 6095 35873
tothyperlinks totblogs I1
1 144 145 11
2 103 101 15
3 98 98 19
4 150 144 23
5 84 84 16
6 80 79 21
7 130 127 24
8 60 60 15
9 140 133 17
10 151 150 19
11 91 91 19
12 138 132 25
13 124 124 19
14 119 118 28
15 73 70 24
16 123 119 26
17 90 89 15
18 116 112 21
19 113 108 26
20 56 52 16
21 119 116 16
22 129 123 20
23 175 162 24
24 96 92 10
25 0 0 19
26 41 41 25
27 47 47 22
28 126 120 15
29 80 79 21
30 70 65 22
31 73 70 27
32 57 55 26
33 68 67 26
34 127 127 22
35 102 99 20
36 7 7 22
37 148 141 21
38 112 109 22
39 137 133 20
40 135 123 21
41 26 26 20
42 181 166 25
43 190 179 18
44 107 108 22
45 94 90 25
46 116 114 21
47 106 103 20
48 143 142 20
49 26 25 18
50 113 113 8
51 120 118 22
52 134 129 26
53 54 51 18
54 78 76 20
55 121 118 24
56 145 141 17
57 27 27 20
58 91 91 23
59 48 48 20
60 68 63 22
61 150 144 20
62 181 168 19
63 65 64 15
64 97 97 20
65 121 117 22
66 99 100 13
67 188 187 20
68 138 127 17
69 40 37 14
70 254 245 22
71 87 87 24
72 178 177 22
73 51 49 23
74 176 177 17
75 66 60 23
76 56 55 25
77 39 39 16
78 66 64 18
79 27 26 20
80 58 58 18
81 26 26 24
82 77 76 23
83 130 129 24
84 11 11 23
85 101 101 23
86 36 36 13
87 120 89 20
88 195 193 18
89 4 4 21
90 89 84 17
91 24 23 20
92 39 39 19
93 14 14 18
94 78 78 19
95 106 101 22
96 37 36 22
97 77 75 15
98 132 131 17
99 144 131 19
100 40 39 20
101 153 144 22
102 220 211 21
103 79 78 19
104 95 90 21
105 12 12 18
106 63 57 16
107 134 133 20
108 69 69 21
109 119 119 15
110 63 61 20
111 55 49 23
112 103 101 15
113 197 196 18
114 16 15 22
115 140 136 16
116 21 21 17
117 167 163 24
118 32 29 13
119 36 35 23
120 13 13 5
121 96 96 19
122 151 151 24
123 23 23 19
124 21 14 20
125 20 20 22
126 82 72 15
127 90 87 19
128 25 21 25
129 60 56 21
130 85 82 19
131 41 38 17
132 26 25 15
133 49 47 21
134 46 45 24
135 41 41 22
136 23 23 19
137 14 14 20
138 16 16 21
139 21 21 19
140 32 27 22
141 35 33 14
142 42 42 25
143 68 68 11
144 6 6 16
145 68 67 19
146 84 77 17
147 30 30 20
148 0 0 22
149 36 36 20
150 50 48 22
151 30 29 15
152 30 28 23
153 33 33 20
154 37 33 17
155 83 80 20
156 30 30 25
157 51 51 22
158 19 18 16
159 41 39 25
160 54 54 18
161 25 24 19
162 25 24 25
163 8 8 23
164 26 26 24
165 20 19 21
166 46 47 21
167 47 47 22
168 37 37 21
169 51 51 18
170 10 10 13
171 34 34 22
172 12 11 23
173 27 21 15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc
-7.879e+00 2.867e-03 9.160e-05
logins compendium_views_info compendium_views_pr
-4.793e-03 -1.235e-02 -5.299e-03
shared_compendiums blogged_computations compendiums_reviewed
3.097e-01 1.940e-02 -5.950e-01
feedback_messages_p1 totsize totrevisions
9.134e-01 5.293e-05 8.592e-04
totseconds tothyperlinks totblogs
-1.227e-04 4.661e-01 -5.667e-01
I1
1.021e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-45.584 -5.363 1.967 7.551 20.864
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.879e+00 5.587e+00 -1.410 0.1604
pageviews 2.867e-03 5.239e-03 0.547 0.5850
time_in_rfc 9.160e-05 4.174e-05 2.194 0.0297 *
logins -4.793e-03 3.665e-02 -0.131 0.8961
compendium_views_info -1.235e-02 1.240e-02 -0.996 0.3208
compendium_views_pr -5.299e-03 2.090e-02 -0.254 0.8002
shared_compendiums 3.097e-01 3.968e-01 0.780 0.4363
blogged_computations 1.940e-02 7.306e-02 0.265 0.7910
compendiums_reviewed -5.950e-01 6.713e-01 -0.886 0.3768
feedback_messages_p1 9.134e-01 1.740e-01 5.249 4.88e-07 ***
totsize 5.293e-05 4.140e-05 1.279 0.2029
totrevisions 8.592e-04 2.141e-04 4.013 9.25e-05 ***
totseconds -1.226e-04 6.266e-05 -1.957 0.0521 .
tothyperlinks 4.661e-01 2.612e-01 1.784 0.0763 .
totblogs -5.667e-01 2.735e-01 -2.072 0.0399 *
I1 1.021e-01 2.443e-01 0.418 0.6766
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.67 on 157 degrees of freedom
Multiple R-squared: 0.9078, Adjusted R-squared: 0.899
F-statistic: 103 on 15 and 157 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.9984691 0.0030618776 1.530939e-03
[2,] 0.9960040 0.0079920209 3.996010e-03
[3,] 0.9912666 0.0174667663 8.733383e-03
[4,] 0.9858428 0.0283144922 1.415725e-02
[5,] 0.9871681 0.0256637514 1.283188e-02
[6,] 0.9793483 0.0413034027 2.065170e-02
[7,] 0.9670286 0.0659428515 3.297143e-02
[8,] 0.9530937 0.0938125335 4.690627e-02
[9,] 0.9359782 0.1280436104 6.402181e-02
[10,] 0.9085932 0.1828135671 9.140678e-02
[11,] 0.8888682 0.2222635074 1.111318e-01
[12,] 0.8679555 0.2640889907 1.320445e-01
[13,] 0.8286980 0.3426040288 1.713020e-01
[14,] 0.7796803 0.4406394016 2.203197e-01
[15,] 0.7246108 0.5507783604 2.753892e-01
[16,] 0.7889673 0.4220653492 2.110327e-01
[17,] 0.7365241 0.5269518591 2.634759e-01
[18,] 0.6798117 0.6403765539 3.201883e-01
[19,] 0.6876907 0.6246185132 3.123093e-01
[20,] 0.6273666 0.7452668202 3.726334e-01
[21,] 0.6594016 0.6811967048 3.405984e-01
[22,] 0.6277966 0.7444067782 3.722034e-01
[23,] 0.5699007 0.8601985999 4.300993e-01
[24,] 0.5646190 0.8707620759 4.353810e-01
[25,] 0.6759524 0.6480951417 3.240476e-01
[26,] 0.6209701 0.7580598609 3.790299e-01
[27,] 0.6628886 0.6742227288 3.371114e-01
[28,] 0.6824030 0.6351939638 3.175970e-01
[29,] 0.6497314 0.7005372054 3.502686e-01
[30,] 0.5977832 0.8044336657 4.022168e-01
[31,] 0.5473544 0.9052912046 4.526456e-01
[32,] 0.5802334 0.8395332165 4.197666e-01
[33,] 0.5654028 0.8691944685 4.345972e-01
[34,] 0.5490399 0.9019202261 4.509601e-01
[35,] 0.5944496 0.8111007927 4.055504e-01
[36,] 0.5665516 0.8668967690 4.334484e-01
[37,] 0.7386602 0.5226795466 2.613398e-01
[38,] 0.7243992 0.5512016528 2.756008e-01
[39,] 0.6811211 0.6377577372 3.188789e-01
[40,] 0.6503625 0.6992749502 3.496375e-01
[41,] 0.6547206 0.6905587416 3.452794e-01
[42,] 0.6083646 0.7832707628 3.916354e-01
[43,] 0.5584529 0.8830941321 4.415471e-01
[44,] 0.5540209 0.8919582096 4.459791e-01
[45,] 0.5082918 0.9834164465 4.917082e-01
[46,] 0.5818455 0.8363089166 4.181545e-01
[47,] 0.5584727 0.8830545994 4.415273e-01
[48,] 0.5585315 0.8829369518 4.414685e-01
[49,] 0.5700926 0.8598148566 4.299074e-01
[50,] 0.7340694 0.5318611801 2.659306e-01
[51,] 0.8747541 0.2504918348 1.252459e-01
[52,] 0.8655844 0.2688311416 1.344156e-01
[53,] 0.8504990 0.2990020864 1.495010e-01
[54,] 0.8240130 0.3519740053 1.759870e-01
[55,] 0.9950680 0.0098639781 4.931989e-03
[56,] 0.9998656 0.0002688047 1.344023e-04
[57,] 0.9997981 0.0004037487 2.018743e-04
[58,] 0.9999060 0.0001880917 9.404586e-05
[59,] 0.9999281 0.0001437633 7.188166e-05
[60,] 0.9999411 0.0001178343 5.891715e-05
[61,] 0.9999055 0.0001889876 9.449382e-05
[62,] 0.9998501 0.0002998324 1.499162e-04
[63,] 0.9997718 0.0004563774 2.281887e-04
[64,] 0.9997155 0.0005690932 2.845466e-04
[65,] 0.9998509 0.0002981876 1.490938e-04
[66,] 0.9997993 0.0004013663 2.006832e-04
[67,] 0.9999357 0.0001286750 6.433751e-05
[68,] 0.9998964 0.0002071369 1.035685e-04
[69,] 0.9999291 0.0001417851 7.089254e-05
[70,] 0.9999109 0.0001782327 8.911637e-05
[71,] 0.9998676 0.0002648735 1.324367e-04
[72,] 0.9998531 0.0002937758 1.468879e-04
[73,] 0.9997750 0.0004499670 2.249835e-04
[74,] 0.9996625 0.0006749429 3.374715e-04
[75,] 0.9994955 0.0010090973 5.045486e-04
[76,] 0.9992803 0.0014393623 7.196811e-04
[77,] 0.9995381 0.0009237094 4.618547e-04
[78,] 0.9992981 0.0014037512 7.018756e-04
[79,] 0.9990787 0.0018426467 9.213234e-04
[80,] 0.9991887 0.0016225613 8.112807e-04
[81,] 0.9990720 0.0018559007 9.279503e-04
[82,] 0.9986146 0.0027707502 1.385375e-03
[83,] 0.9979697 0.0040606560 2.030328e-03
[84,] 0.9974630 0.0050740358 2.537018e-03
[85,] 0.9982083 0.0035833120 1.791656e-03
[86,] 0.9986822 0.0026356352 1.317818e-03
[87,] 0.9988492 0.0023016047 1.150802e-03
[88,] 0.9983842 0.0032316236 1.615812e-03
[89,] 0.9985262 0.0029475219 1.473761e-03
[90,] 0.9980411 0.0039178149 1.958907e-03
[91,] 0.9972764 0.0054472638 2.723632e-03
[92,] 0.9962069 0.0075861849 3.793092e-03
[93,] 0.9963868 0.0072264838 3.613242e-03
[94,] 0.9950682 0.0098635113 4.931756e-03
[95,] 0.9928855 0.0142290439 7.114522e-03
[96,] 0.9974265 0.0051469145 2.573457e-03
[97,] 0.9981940 0.0036119244 1.805962e-03
[98,] 0.9971975 0.0056049435 2.802472e-03
[99,] 0.9968065 0.0063869072 3.193454e-03
[100,] 0.9966725 0.0066550834 3.327542e-03
[101,] 0.9950669 0.0098661926 4.933096e-03
[102,] 0.9956997 0.0086005266 4.300263e-03
[103,] 0.9965937 0.0068125188 3.406259e-03
[104,] 0.9991935 0.0016130300 8.065150e-04
[105,] 0.9998091 0.0003817676 1.908838e-04
[106,] 0.9997109 0.0005781086 2.890543e-04
[107,] 0.9999120 0.0001760451 8.802255e-05
[108,] 0.9998515 0.0002970635 1.485317e-04
[109,] 0.9997467 0.0005065651 2.532826e-04
[110,] 0.9995612 0.0008776520 4.388260e-04
[111,] 0.9992473 0.0015054255 7.527128e-04
[112,] 0.9988536 0.0022927627 1.146381e-03
[113,] 0.9992655 0.0014689162 7.344581e-04
[114,] 0.9987960 0.0024080047 1.204002e-03
[115,] 0.9979113 0.0041774014 2.088701e-03
[116,] 0.9965020 0.0069960288 3.498014e-03
[117,] 0.9942170 0.0115659891 5.782995e-03
[118,] 0.9905489 0.0189022590 9.451129e-03
[119,] 0.9939884 0.0120231485 6.011574e-03
[120,] 0.9933816 0.0132367412 6.618371e-03
[121,] 0.9902284 0.0195431838 9.771592e-03
[122,] 0.9848255 0.0303490471 1.517452e-02
[123,] 0.9775973 0.0448054804 2.240274e-02
[124,] 0.9727139 0.0545722549 2.728613e-02
[125,] 0.9601608 0.0796784545 3.983923e-02
[126,] 0.9514620 0.0970759493 4.853797e-02
[127,] 0.9247863 0.1504274301 7.521372e-02
[128,] 0.9872620 0.0254759172 1.273796e-02
[129,] 0.9847034 0.0305932413 1.529662e-02
[130,] 0.9921275 0.0157450187 7.872509e-03
[131,] 0.9914818 0.0170364850 8.518243e-03
[132,] 0.9804850 0.0390299038 1.951495e-02
[133,] 0.9573064 0.0853872375 4.269362e-02
[134,] 0.9484267 0.1031465585 5.157328e-02
[135,] 0.9780064 0.0439871398 2.199357e-02
[136,] 0.9335834 0.1328332747 6.641664e-02
> postscript(file="/var/www/rcomp/tmp/1fcxv1323886663.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/21ki81323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3mdpg1323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47ina1323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5oojm1323886663.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 = 173
Frequency = 1
1 2 3 4 5 6
-0.4229791 15.8416112 -25.9316695 -2.2382307 2.2234962 -17.3240185
7 8 9 10 11 12
12.3416369 14.1262485 9.6136572 12.1954461 -27.3193162 -9.7470869
13 14 15 16 17 18
-5.5322152 14.7545020 8.9580626 9.2375635 13.0719850 -2.3107247
19 20 21 22 23 24
10.8042915 7.3064724 7.9135984 4.7071370 5.6533106 4.7731893
25 26 27 28 29 30
5.3425564 -2.1610984 -2.4194269 7.5510973 -8.1829612 4.6269752
31 32 33 34 35 36
5.3161857 -6.4972222 -1.3581843 18.6559533 1.3528734 3.0926733
37 38 39 40 41 42
18.0740482 -0.6109699 18.1567438 -2.0219702 1.9702839 7.8919189
43 44 45 46 47 48
-27.8368788 0.2863538 -13.7103965 15.9738859 1.2517039 4.1804783
49 50 51 52 53 54
1.0663984 -13.3513672 13.8253173 9.4319971 -9.6229637 -0.7196012
55 56 57 58 59 60
-24.0559851 3.1039989 1.3778799 3.9166336 13.3732075 5.6811960
61 62 63 64 65 66
1.2790960 12.0563349 1.9671898 17.5500522 -4.6012926 11.3315184
67 68 69 70 71 72
-11.8652031 20.8644334 -24.6790229 -7.2113706 -8.6088152 4.7189273
73 74 75 76 77 78
-45.5837898 -34.6152234 -3.9290846 -9.2499846 10.0575427 -11.7186364
79 80 81 82 83 84
-1.4855299 0.6411326 3.1124444 9.7787770 -21.2505463 6.2251354
85 86 87 88 89 90
-19.9239710 -0.3800142 -18.7167625 -5.8622823 5.3884950 -12.6087078
91 92 93 94 95 96
2.2023994 -3.6089716 -3.2775309 -1.7172641 10.7617862 0.2956124
97 98 99 100 101 102
-2.0597583 12.8516423 2.9283864 -3.0849460 0.4205243 1.9289700
103 104 105 106 107 108
16.1839550 10.7168927 9.0442878 3.2905991 4.8983752 -1.4707883
109 110 111 112 113 114
6.9494102 -8.0933118 -9.6685233 -7.2089684 4.6609798 7.8790030
115 116 117 118 119 120
-20.1702395 -0.6716036 8.6407176 4.1535527 -1.0338674 -1.1048438
121 122 123 124 125 126
-14.5280922 -27.9073366 -36.0687187 -10.5802275 5.9119548 9.8753049
127 128 129 130 131 132
5.9751116 4.5426461 5.4358963 12.4060886 7.6663574 5.7487685
133 134 135 136 137 138
6.6394507 -0.2976578 0.6581000 8.2896694 -9.9912722 12.9312497
139 140 141 142 143 144
2.3645409 -8.6285296 9.1701386 5.1528514 -0.1350861 -7.3616827
145 146 147 148 149 150
-5.8961357 8.7384547 7.7346610 -1.0914437 -3.2143068 -17.0661220
151 152 153 154 155 156
-5.3634109 -4.7725564 -7.3766781 -14.4992957 3.2294772 0.7718764
157 158 159 160 161 162
-1.2752312 5.1663362 2.2903289 4.4465447 3.1559221 8.1932391
163 164 165 166 167 168
-3.0812693 4.3779633 -11.3377468 6.0019950 -0.5849330 8.0079622
169 170 171 172 173
-6.3371028 2.2306961 12.9105870 5.7259546 -11.3199127
> postscript(file="/var/www/rcomp/tmp/6hwvv1323886663.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 = 173
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.4229791 NA
1 15.8416112 -0.4229791
2 -25.9316695 15.8416112
3 -2.2382307 -25.9316695
4 2.2234962 -2.2382307
5 -17.3240185 2.2234962
6 12.3416369 -17.3240185
7 14.1262485 12.3416369
8 9.6136572 14.1262485
9 12.1954461 9.6136572
10 -27.3193162 12.1954461
11 -9.7470869 -27.3193162
12 -5.5322152 -9.7470869
13 14.7545020 -5.5322152
14 8.9580626 14.7545020
15 9.2375635 8.9580626
16 13.0719850 9.2375635
17 -2.3107247 13.0719850
18 10.8042915 -2.3107247
19 7.3064724 10.8042915
20 7.9135984 7.3064724
21 4.7071370 7.9135984
22 5.6533106 4.7071370
23 4.7731893 5.6533106
24 5.3425564 4.7731893
25 -2.1610984 5.3425564
26 -2.4194269 -2.1610984
27 7.5510973 -2.4194269
28 -8.1829612 7.5510973
29 4.6269752 -8.1829612
30 5.3161857 4.6269752
31 -6.4972222 5.3161857
32 -1.3581843 -6.4972222
33 18.6559533 -1.3581843
34 1.3528734 18.6559533
35 3.0926733 1.3528734
36 18.0740482 3.0926733
37 -0.6109699 18.0740482
38 18.1567438 -0.6109699
39 -2.0219702 18.1567438
40 1.9702839 -2.0219702
41 7.8919189 1.9702839
42 -27.8368788 7.8919189
43 0.2863538 -27.8368788
44 -13.7103965 0.2863538
45 15.9738859 -13.7103965
46 1.2517039 15.9738859
47 4.1804783 1.2517039
48 1.0663984 4.1804783
49 -13.3513672 1.0663984
50 13.8253173 -13.3513672
51 9.4319971 13.8253173
52 -9.6229637 9.4319971
53 -0.7196012 -9.6229637
54 -24.0559851 -0.7196012
55 3.1039989 -24.0559851
56 1.3778799 3.1039989
57 3.9166336 1.3778799
58 13.3732075 3.9166336
59 5.6811960 13.3732075
60 1.2790960 5.6811960
61 12.0563349 1.2790960
62 1.9671898 12.0563349
63 17.5500522 1.9671898
64 -4.6012926 17.5500522
65 11.3315184 -4.6012926
66 -11.8652031 11.3315184
67 20.8644334 -11.8652031
68 -24.6790229 20.8644334
69 -7.2113706 -24.6790229
70 -8.6088152 -7.2113706
71 4.7189273 -8.6088152
72 -45.5837898 4.7189273
73 -34.6152234 -45.5837898
74 -3.9290846 -34.6152234
75 -9.2499846 -3.9290846
76 10.0575427 -9.2499846
77 -11.7186364 10.0575427
78 -1.4855299 -11.7186364
79 0.6411326 -1.4855299
80 3.1124444 0.6411326
81 9.7787770 3.1124444
82 -21.2505463 9.7787770
83 6.2251354 -21.2505463
84 -19.9239710 6.2251354
85 -0.3800142 -19.9239710
86 -18.7167625 -0.3800142
87 -5.8622823 -18.7167625
88 5.3884950 -5.8622823
89 -12.6087078 5.3884950
90 2.2023994 -12.6087078
91 -3.6089716 2.2023994
92 -3.2775309 -3.6089716
93 -1.7172641 -3.2775309
94 10.7617862 -1.7172641
95 0.2956124 10.7617862
96 -2.0597583 0.2956124
97 12.8516423 -2.0597583
98 2.9283864 12.8516423
99 -3.0849460 2.9283864
100 0.4205243 -3.0849460
101 1.9289700 0.4205243
102 16.1839550 1.9289700
103 10.7168927 16.1839550
104 9.0442878 10.7168927
105 3.2905991 9.0442878
106 4.8983752 3.2905991
107 -1.4707883 4.8983752
108 6.9494102 -1.4707883
109 -8.0933118 6.9494102
110 -9.6685233 -8.0933118
111 -7.2089684 -9.6685233
112 4.6609798 -7.2089684
113 7.8790030 4.6609798
114 -20.1702395 7.8790030
115 -0.6716036 -20.1702395
116 8.6407176 -0.6716036
117 4.1535527 8.6407176
118 -1.0338674 4.1535527
119 -1.1048438 -1.0338674
120 -14.5280922 -1.1048438
121 -27.9073366 -14.5280922
122 -36.0687187 -27.9073366
123 -10.5802275 -36.0687187
124 5.9119548 -10.5802275
125 9.8753049 5.9119548
126 5.9751116 9.8753049
127 4.5426461 5.9751116
128 5.4358963 4.5426461
129 12.4060886 5.4358963
130 7.6663574 12.4060886
131 5.7487685 7.6663574
132 6.6394507 5.7487685
133 -0.2976578 6.6394507
134 0.6581000 -0.2976578
135 8.2896694 0.6581000
136 -9.9912722 8.2896694
137 12.9312497 -9.9912722
138 2.3645409 12.9312497
139 -8.6285296 2.3645409
140 9.1701386 -8.6285296
141 5.1528514 9.1701386
142 -0.1350861 5.1528514
143 -7.3616827 -0.1350861
144 -5.8961357 -7.3616827
145 8.7384547 -5.8961357
146 7.7346610 8.7384547
147 -1.0914437 7.7346610
148 -3.2143068 -1.0914437
149 -17.0661220 -3.2143068
150 -5.3634109 -17.0661220
151 -4.7725564 -5.3634109
152 -7.3766781 -4.7725564
153 -14.4992957 -7.3766781
154 3.2294772 -14.4992957
155 0.7718764 3.2294772
156 -1.2752312 0.7718764
157 5.1663362 -1.2752312
158 2.2903289 5.1663362
159 4.4465447 2.2903289
160 3.1559221 4.4465447
161 8.1932391 3.1559221
162 -3.0812693 8.1932391
163 4.3779633 -3.0812693
164 -11.3377468 4.3779633
165 6.0019950 -11.3377468
166 -0.5849330 6.0019950
167 8.0079622 -0.5849330
168 -6.3371028 8.0079622
169 2.2306961 -6.3371028
170 12.9105870 2.2306961
171 5.7259546 12.9105870
172 -11.3199127 5.7259546
173 NA -11.3199127
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 15.8416112 -0.4229791
[2,] -25.9316695 15.8416112
[3,] -2.2382307 -25.9316695
[4,] 2.2234962 -2.2382307
[5,] -17.3240185 2.2234962
[6,] 12.3416369 -17.3240185
[7,] 14.1262485 12.3416369
[8,] 9.6136572 14.1262485
[9,] 12.1954461 9.6136572
[10,] -27.3193162 12.1954461
[11,] -9.7470869 -27.3193162
[12,] -5.5322152 -9.7470869
[13,] 14.7545020 -5.5322152
[14,] 8.9580626 14.7545020
[15,] 9.2375635 8.9580626
[16,] 13.0719850 9.2375635
[17,] -2.3107247 13.0719850
[18,] 10.8042915 -2.3107247
[19,] 7.3064724 10.8042915
[20,] 7.9135984 7.3064724
[21,] 4.7071370 7.9135984
[22,] 5.6533106 4.7071370
[23,] 4.7731893 5.6533106
[24,] 5.3425564 4.7731893
[25,] -2.1610984 5.3425564
[26,] -2.4194269 -2.1610984
[27,] 7.5510973 -2.4194269
[28,] -8.1829612 7.5510973
[29,] 4.6269752 -8.1829612
[30,] 5.3161857 4.6269752
[31,] -6.4972222 5.3161857
[32,] -1.3581843 -6.4972222
[33,] 18.6559533 -1.3581843
[34,] 1.3528734 18.6559533
[35,] 3.0926733 1.3528734
[36,] 18.0740482 3.0926733
[37,] -0.6109699 18.0740482
[38,] 18.1567438 -0.6109699
[39,] -2.0219702 18.1567438
[40,] 1.9702839 -2.0219702
[41,] 7.8919189 1.9702839
[42,] -27.8368788 7.8919189
[43,] 0.2863538 -27.8368788
[44,] -13.7103965 0.2863538
[45,] 15.9738859 -13.7103965
[46,] 1.2517039 15.9738859
[47,] 4.1804783 1.2517039
[48,] 1.0663984 4.1804783
[49,] -13.3513672 1.0663984
[50,] 13.8253173 -13.3513672
[51,] 9.4319971 13.8253173
[52,] -9.6229637 9.4319971
[53,] -0.7196012 -9.6229637
[54,] -24.0559851 -0.7196012
[55,] 3.1039989 -24.0559851
[56,] 1.3778799 3.1039989
[57,] 3.9166336 1.3778799
[58,] 13.3732075 3.9166336
[59,] 5.6811960 13.3732075
[60,] 1.2790960 5.6811960
[61,] 12.0563349 1.2790960
[62,] 1.9671898 12.0563349
[63,] 17.5500522 1.9671898
[64,] -4.6012926 17.5500522
[65,] 11.3315184 -4.6012926
[66,] -11.8652031 11.3315184
[67,] 20.8644334 -11.8652031
[68,] -24.6790229 20.8644334
[69,] -7.2113706 -24.6790229
[70,] -8.6088152 -7.2113706
[71,] 4.7189273 -8.6088152
[72,] -45.5837898 4.7189273
[73,] -34.6152234 -45.5837898
[74,] -3.9290846 -34.6152234
[75,] -9.2499846 -3.9290846
[76,] 10.0575427 -9.2499846
[77,] -11.7186364 10.0575427
[78,] -1.4855299 -11.7186364
[79,] 0.6411326 -1.4855299
[80,] 3.1124444 0.6411326
[81,] 9.7787770 3.1124444
[82,] -21.2505463 9.7787770
[83,] 6.2251354 -21.2505463
[84,] -19.9239710 6.2251354
[85,] -0.3800142 -19.9239710
[86,] -18.7167625 -0.3800142
[87,] -5.8622823 -18.7167625
[88,] 5.3884950 -5.8622823
[89,] -12.6087078 5.3884950
[90,] 2.2023994 -12.6087078
[91,] -3.6089716 2.2023994
[92,] -3.2775309 -3.6089716
[93,] -1.7172641 -3.2775309
[94,] 10.7617862 -1.7172641
[95,] 0.2956124 10.7617862
[96,] -2.0597583 0.2956124
[97,] 12.8516423 -2.0597583
[98,] 2.9283864 12.8516423
[99,] -3.0849460 2.9283864
[100,] 0.4205243 -3.0849460
[101,] 1.9289700 0.4205243
[102,] 16.1839550 1.9289700
[103,] 10.7168927 16.1839550
[104,] 9.0442878 10.7168927
[105,] 3.2905991 9.0442878
[106,] 4.8983752 3.2905991
[107,] -1.4707883 4.8983752
[108,] 6.9494102 -1.4707883
[109,] -8.0933118 6.9494102
[110,] -9.6685233 -8.0933118
[111,] -7.2089684 -9.6685233
[112,] 4.6609798 -7.2089684
[113,] 7.8790030 4.6609798
[114,] -20.1702395 7.8790030
[115,] -0.6716036 -20.1702395
[116,] 8.6407176 -0.6716036
[117,] 4.1535527 8.6407176
[118,] -1.0338674 4.1535527
[119,] -1.1048438 -1.0338674
[120,] -14.5280922 -1.1048438
[121,] -27.9073366 -14.5280922
[122,] -36.0687187 -27.9073366
[123,] -10.5802275 -36.0687187
[124,] 5.9119548 -10.5802275
[125,] 9.8753049 5.9119548
[126,] 5.9751116 9.8753049
[127,] 4.5426461 5.9751116
[128,] 5.4358963 4.5426461
[129,] 12.4060886 5.4358963
[130,] 7.6663574 12.4060886
[131,] 5.7487685 7.6663574
[132,] 6.6394507 5.7487685
[133,] -0.2976578 6.6394507
[134,] 0.6581000 -0.2976578
[135,] 8.2896694 0.6581000
[136,] -9.9912722 8.2896694
[137,] 12.9312497 -9.9912722
[138,] 2.3645409 12.9312497
[139,] -8.6285296 2.3645409
[140,] 9.1701386 -8.6285296
[141,] 5.1528514 9.1701386
[142,] -0.1350861 5.1528514
[143,] -7.3616827 -0.1350861
[144,] -5.8961357 -7.3616827
[145,] 8.7384547 -5.8961357
[146,] 7.7346610 8.7384547
[147,] -1.0914437 7.7346610
[148,] -3.2143068 -1.0914437
[149,] -17.0661220 -3.2143068
[150,] -5.3634109 -17.0661220
[151,] -4.7725564 -5.3634109
[152,] -7.3766781 -4.7725564
[153,] -14.4992957 -7.3766781
[154,] 3.2294772 -14.4992957
[155,] 0.7718764 3.2294772
[156,] -1.2752312 0.7718764
[157,] 5.1663362 -1.2752312
[158,] 2.2903289 5.1663362
[159,] 4.4465447 2.2903289
[160,] 3.1559221 4.4465447
[161,] 8.1932391 3.1559221
[162,] -3.0812693 8.1932391
[163,] 4.3779633 -3.0812693
[164,] -11.3377468 4.3779633
[165,] 6.0019950 -11.3377468
[166,] -0.5849330 6.0019950
[167,] 8.0079622 -0.5849330
[168,] -6.3371028 8.0079622
[169,] 2.2306961 -6.3371028
[170,] 12.9105870 2.2306961
[171,] 5.7259546 12.9105870
[172,] -11.3199127 5.7259546
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 15.8416112 -0.4229791
2 -25.9316695 15.8416112
3 -2.2382307 -25.9316695
4 2.2234962 -2.2382307
5 -17.3240185 2.2234962
6 12.3416369 -17.3240185
7 14.1262485 12.3416369
8 9.6136572 14.1262485
9 12.1954461 9.6136572
10 -27.3193162 12.1954461
11 -9.7470869 -27.3193162
12 -5.5322152 -9.7470869
13 14.7545020 -5.5322152
14 8.9580626 14.7545020
15 9.2375635 8.9580626
16 13.0719850 9.2375635
17 -2.3107247 13.0719850
18 10.8042915 -2.3107247
19 7.3064724 10.8042915
20 7.9135984 7.3064724
21 4.7071370 7.9135984
22 5.6533106 4.7071370
23 4.7731893 5.6533106
24 5.3425564 4.7731893
25 -2.1610984 5.3425564
26 -2.4194269 -2.1610984
27 7.5510973 -2.4194269
28 -8.1829612 7.5510973
29 4.6269752 -8.1829612
30 5.3161857 4.6269752
31 -6.4972222 5.3161857
32 -1.3581843 -6.4972222
33 18.6559533 -1.3581843
34 1.3528734 18.6559533
35 3.0926733 1.3528734
36 18.0740482 3.0926733
37 -0.6109699 18.0740482
38 18.1567438 -0.6109699
39 -2.0219702 18.1567438
40 1.9702839 -2.0219702
41 7.8919189 1.9702839
42 -27.8368788 7.8919189
43 0.2863538 -27.8368788
44 -13.7103965 0.2863538
45 15.9738859 -13.7103965
46 1.2517039 15.9738859
47 4.1804783 1.2517039
48 1.0663984 4.1804783
49 -13.3513672 1.0663984
50 13.8253173 -13.3513672
51 9.4319971 13.8253173
52 -9.6229637 9.4319971
53 -0.7196012 -9.6229637
54 -24.0559851 -0.7196012
55 3.1039989 -24.0559851
56 1.3778799 3.1039989
57 3.9166336 1.3778799
58 13.3732075 3.9166336
59 5.6811960 13.3732075
60 1.2790960 5.6811960
61 12.0563349 1.2790960
62 1.9671898 12.0563349
63 17.5500522 1.9671898
64 -4.6012926 17.5500522
65 11.3315184 -4.6012926
66 -11.8652031 11.3315184
67 20.8644334 -11.8652031
68 -24.6790229 20.8644334
69 -7.2113706 -24.6790229
70 -8.6088152 -7.2113706
71 4.7189273 -8.6088152
72 -45.5837898 4.7189273
73 -34.6152234 -45.5837898
74 -3.9290846 -34.6152234
75 -9.2499846 -3.9290846
76 10.0575427 -9.2499846
77 -11.7186364 10.0575427
78 -1.4855299 -11.7186364
79 0.6411326 -1.4855299
80 3.1124444 0.6411326
81 9.7787770 3.1124444
82 -21.2505463 9.7787770
83 6.2251354 -21.2505463
84 -19.9239710 6.2251354
85 -0.3800142 -19.9239710
86 -18.7167625 -0.3800142
87 -5.8622823 -18.7167625
88 5.3884950 -5.8622823
89 -12.6087078 5.3884950
90 2.2023994 -12.6087078
91 -3.6089716 2.2023994
92 -3.2775309 -3.6089716
93 -1.7172641 -3.2775309
94 10.7617862 -1.7172641
95 0.2956124 10.7617862
96 -2.0597583 0.2956124
97 12.8516423 -2.0597583
98 2.9283864 12.8516423
99 -3.0849460 2.9283864
100 0.4205243 -3.0849460
101 1.9289700 0.4205243
102 16.1839550 1.9289700
103 10.7168927 16.1839550
104 9.0442878 10.7168927
105 3.2905991 9.0442878
106 4.8983752 3.2905991
107 -1.4707883 4.8983752
108 6.9494102 -1.4707883
109 -8.0933118 6.9494102
110 -9.6685233 -8.0933118
111 -7.2089684 -9.6685233
112 4.6609798 -7.2089684
113 7.8790030 4.6609798
114 -20.1702395 7.8790030
115 -0.6716036 -20.1702395
116 8.6407176 -0.6716036
117 4.1535527 8.6407176
118 -1.0338674 4.1535527
119 -1.1048438 -1.0338674
120 -14.5280922 -1.1048438
121 -27.9073366 -14.5280922
122 -36.0687187 -27.9073366
123 -10.5802275 -36.0687187
124 5.9119548 -10.5802275
125 9.8753049 5.9119548
126 5.9751116 9.8753049
127 4.5426461 5.9751116
128 5.4358963 4.5426461
129 12.4060886 5.4358963
130 7.6663574 12.4060886
131 5.7487685 7.6663574
132 6.6394507 5.7487685
133 -0.2976578 6.6394507
134 0.6581000 -0.2976578
135 8.2896694 0.6581000
136 -9.9912722 8.2896694
137 12.9312497 -9.9912722
138 2.3645409 12.9312497
139 -8.6285296 2.3645409
140 9.1701386 -8.6285296
141 5.1528514 9.1701386
142 -0.1350861 5.1528514
143 -7.3616827 -0.1350861
144 -5.8961357 -7.3616827
145 8.7384547 -5.8961357
146 7.7346610 8.7384547
147 -1.0914437 7.7346610
148 -3.2143068 -1.0914437
149 -17.0661220 -3.2143068
150 -5.3634109 -17.0661220
151 -4.7725564 -5.3634109
152 -7.3766781 -4.7725564
153 -14.4992957 -7.3766781
154 3.2294772 -14.4992957
155 0.7718764 3.2294772
156 -1.2752312 0.7718764
157 5.1663362 -1.2752312
158 2.2903289 5.1663362
159 4.4465447 2.2903289
160 3.1559221 4.4465447
161 8.1932391 3.1559221
162 -3.0812693 8.1932391
163 4.3779633 -3.0812693
164 -11.3377468 4.3779633
165 6.0019950 -11.3377468
166 -0.5849330 6.0019950
167 8.0079622 -0.5849330
168 -6.3371028 8.0079622
169 2.2306961 -6.3371028
170 12.9105870 2.2306961
171 5.7259546 12.9105870
172 -11.3199127 5.7259546
> 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/www/rcomp/tmp/7qc7x1323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8t6781323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9j44k1323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10pmtc1323886663.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/110js81323886663.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/www/rcomp/tmp/122giq1323886663.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/www/rcomp/tmp/1341dm1323886663.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/www/rcomp/tmp/14x8io1323886663.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/www/rcomp/tmp/15kqld1323886663.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/www/rcomp/tmp/16wsmg1323886663.tab")
+ }
>
> try(system("convert tmp/1fcxv1323886663.ps tmp/1fcxv1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/21ki81323886663.ps tmp/21ki81323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mdpg1323886663.ps tmp/3mdpg1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/47ina1323886663.ps tmp/47ina1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oojm1323886663.ps tmp/5oojm1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hwvv1323886663.ps tmp/6hwvv1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qc7x1323886663.ps tmp/7qc7x1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t6781323886663.ps tmp/8t6781323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j44k1323886663.ps tmp/9j44k1323886663.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pmtc1323886663.ps tmp/10pmtc1323886663.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.120 0.240 6.337