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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+ ,12
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+ ,0
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+ ,39
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21
+ ,15)
+ ,dim=c(15
+ ,173)
+ ,dimnames=list(c('pageviews'
+ ,'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(15,173),dimnames=list(c('pageviews','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 = '15'
> #'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
I1 pageviews logins compendium_views_info compendium_views_pr
1 11 1418 56 396 81
2 15 869 56 297 55
3 19 1530 54 559 50
4 23 3201 92 1562 63
5 16 1583 44 656 66
6 21 1439 33 511 57
7 24 1764 84 655 74
8 15 1373 55 525 52
9 17 4041 154 1436 108
10 19 1706 53 612 43
11 19 2152 119 865 75
12 25 1036 41 385 32
13 19 1929 58 639 85
14 28 2242 75 963 86
15 24 1220 33 398 56
16 26 2515 92 966 135
17 15 2147 100 801 63
18 21 2352 112 892 81
19 26 1638 73 513 52
20 16 1222 40 469 44
21 16 1677 60 643 39
22 20 1579 62 535 73
23 24 2452 77 992 59
24 10 2662 99 937 64
25 19 186 17 70 1
26 25 865 30 260 32
27 22 1793 76 503 129
28 15 2527 146 927 37
29 21 1324 56 537 65
30 22 2702 107 910 107
31 27 1383 58 532 74
32 26 1179 34 345 54
33 26 4308 119 1635 715
34 22 1831 66 557 66
35 20 1438 66 452 32
36 22 496 24 218 20
37 21 2253 259 764 71
38 22 2352 41 866 112
39 20 2144 68 574 66
40 21 4691 168 1276 190
41 20 1112 43 379 66
42 25 1973 105 798 56
43 18 2474 94 921 127
44 22 1226 57 503 50
45 25 1389 53 382 52
46 21 1496 103 464 42
47 20 2269 121 717 76
48 20 1833 62 690 67
49 18 893 32 385 39
50 8 1403 45 619 77
51 22 1425 46 479 57
52 26 1840 75 752 34
53 18 1502 88 430 39
54 20 1420 53 537 63
55 24 2970 90 1000 106
56 17 1644 78 465 47
57 20 1654 45 711 162
58 23 1054 46 299 57
59 20 937 41 248 36
60 22 3004 144 1162 263
61 20 2547 91 905 63
62 19 1626 63 512 63
63 15 1468 53 472 77
64 20 2445 62 905 79
65 22 1964 63 786 110
66 13 1381 32 489 56
67 20 1659 62 617 43
68 17 2888 117 925 111
69 14 1290 34 351 71
70 22 2845 92 1144 62
71 24 1982 93 669 56
72 22 1904 54 707 74
73 23 1391 144 458 60
74 17 1559 109 572 53
75 23 2146 75 720 105
76 25 874 50 273 32
77 16 1590 61 508 133
78 18 1590 55 506 79
79 20 1210 77 451 51
80 18 1281 72 407 67
81 24 1105 53 370 66
82 23 1272 42 316 76
83 24 1944 71 603 65
84 23 391 10 154 9
85 23 1605 65 577 45
86 13 1988 66 617 115
87 20 1386 41 411 97
88 18 2395 86 975 53
89 21 387 16 146 2
90 17 1742 42 705 52
91 20 620 19 184 44
92 19 449 19 200 22
93 18 800 45 274 35
94 19 1684 65 502 74
95 22 2699 95 964 144
96 22 1204 64 369 89
97 15 1138 38 417 42
98 17 2158 65 822 99
99 19 1111 52 389 52
100 20 1421 62 466 29
101 22 2833 65 1255 125
102 21 2922 95 1024 95
103 19 1002 29 400 40
104 21 2186 247 719 128
105 18 1035 29 356 73
106 16 1417 118 457 72
107 20 3261 110 1402 128
108 21 1587 67 600 61
109 15 1424 42 480 73
110 20 946 64 230 45
111 23 1926 81 651 58
112 15 3352 95 1367 97
113 18 1641 67 564 50
114 22 2035 63 716 37
115 16 2312 83 747 50
116 17 961 32 319 57
117 24 1900 83 612 52
118 13 1254 31 433 98
119 23 1335 67 434 61
120 5 1597 66 503 89
121 19 1645 70 564 48
122 24 2429 103 824 91
123 19 872 34 239 70
124 20 1018 40 459 37
125 22 1314 31 288 247
126 15 1335 42 498 46
127 19 1403 46 454 72
128 25 910 33 376 41
129 21 616 18 225 24
130 19 771 35 252 33
131 17 1376 66 481 87
132 15 1232 60 389 90
133 21 1544 54 609 69
134 24 1230 53 422 51
135 22 1255 39 339 45
136 19 721 45 245 25
137 20 1109 36 384 38
138 21 740 28 212 52
139 19 728 30 229 74
140 22 689 22 224 38
141 14 995 31 333 26
142 25 1613 55 384 67
143 11 2048 54 636 132
144 16 301 14 93 35
145 19 1803 81 581 118
146 17 861 43 304 43
147 20 1451 30 407 64
148 22 628 23 170 48
149 20 1161 38 312 64
150 22 979 53 340 75
151 15 675 45 168 39
152 23 1241 39 443 42
153 20 1049 24 367 93
154 17 1081 35 335 60
155 20 1688 151 364 71
156 25 617 30 206 27
157 22 1656 57 490 79
158 16 705 40 238 44
159 25 1597 77 530 124
160 18 982 35 291 81
161 19 1212 63 397 92
162 25 1143 44 467 42
163 23 435 19 178 10
164 24 532 13 175 24
165 21 882 42 299 64
166 21 830 42 260 48
167 22 652 49 227 49
168 21 707 30 239 48
169 18 954 49 333 62
170 13 285 12 75 19
171 22 733 20 261 45
172 23 642 27 238 36
173 15 894 14 329 44
shared_compendiums blogged_computations compendiums_reviewed
1 3 79 30
2 4 58 28
3 12 60 38
4 0 121 25
5 5 43 26
6 0 69 25
7 0 78 38
8 7 44 30
9 0 158 47
10 4 102 30
11 3 77 31
12 0 82 23
13 0 101 36
14 1 80 30
15 5 50 25
16 0 123 34
17 0 73 31
18 5 81 31
19 0 105 33
20 0 47 25
21 3 94 35
22 4 44 42
23 2 107 33
24 0 84 36
25 0 0 0
26 1 33 14
27 0 42 17
28 2 96 32
29 6 56 35
30 0 57 20
31 5 59 28
32 4 39 28
33 2 76 34
34 0 91 39
35 0 76 28
36 0 8 4
37 8 79 39
38 1 76 29
39 5 101 44
40 1 94 21
41 1 27 16
42 0 123 35
43 8 105 23
44 2 41 29
45 0 72 25
46 5 67 27
47 8 75 36
48 2 114 28
49 6 22 23
50 2 69 28
51 0 105 34
52 3 88 28
53 6 73 34
54 0 62 33
55 0 118 38
56 2 100 35
57 0 24 24
58 5 67 29
59 0 46 20
60 1 57 29
61 1 135 37
62 2 124 33
63 6 33 25
64 1 98 32
65 4 58 29
66 2 68 28
67 0 131 31
68 10 110 52
69 0 37 21
70 9 130 24
71 7 93 41
72 0 118 33
73 0 39 32
74 0 81 31
75 1 51 18
76 0 28 23
77 1 40 17
78 0 56 20
79 0 27 12
80 0 83 30
81 3 28 13
82 0 59 22
83 0 133 42
84 0 12 1
85 0 106 32
86 0 44 25
87 0 71 36
88 1 116 31
89 0 4 0
90 5 62 24
91 0 12 13
92 0 18 8
93 0 14 13
94 0 60 19
95 2 98 33
96 8 32 38
97 2 25 24
98 0 100 43
99 0 46 43
100 1 45 14
101 3 129 41
102 3 136 45
103 0 59 31
104 4 63 31
105 11 14 30
106 0 36 16
107 0 113 37
108 4 47 30
109 0 92 35
110 0 50 20
111 0 41 18
112 9 91 31
113 1 111 31
114 3 41 21
115 10 120 39
116 0 25 18
117 1 131 39
118 2 45 14
119 4 29 7
120 0 58 17
121 2 47 30
122 1 109 37
123 0 37 32
124 0 15 17
125 6 7 24
126 0 54 22
127 2 54 12
128 0 14 19
129 2 16 13
130 1 32 15
131 0 38 15
132 1 22 17
133 0 32 16
134 0 32 18
135 0 37 17
136 0 32 16
137 7 0 23
138 2 5 22
139 7 10 13
140 3 27 16
141 0 29 20
142 6 25 22
143 2 55 17
144 0 5 17
145 3 43 12
146 1 34 17
147 0 35 23
148 1 0 17
149 0 37 14
150 0 26 21
151 0 38 18
152 0 23 18
153 0 30 17
154 0 18 15
155 0 28 21
156 2 21 14
157 1 50 15
158 0 12 15
159 0 27 22
160 0 41 21
161 1 12 18
162 0 21 17
163 0 8 4
164 0 26 10
165 0 27 16
166 0 37 18
167 0 29 12
168 0 32 16
169 0 35 21
170 1 10 2
171 0 17 17
172 0 10 16
173 0 17 16
feedback_messages_p1 feedback_messages_p120 totsize totrevisions totseconds
1 115 94 112285 24188 146283
2 109 103 84786 18273 98364
3 146 93 83123 14130 86146
4 96 91 119182 33251 195663
5 100 93 116174 27101 95757
6 93 60 57635 16373 85584
7 140 123 66198 19716 143983
8 99 90 57793 9028 59238
9 181 168 97668 29498 151511
10 116 115 133824 27563 136368
11 116 71 101481 18293 112642
12 88 66 99645 22530 94728
13 135 117 99052 35082 121527
14 108 108 67654 16116 127766
15 89 84 65553 15849 98958
16 129 120 69112 26569 85646
17 118 114 82753 24785 98579
18 118 94 85323 17569 130767
19 125 120 72654 23825 131741
20 95 81 30727 7869 53907
21 135 133 117478 37791 146761
22 154 122 74007 9605 82036
23 127 124 101494 34461 171975
24 136 126 79215 24919 159676
25 0 0 1423 603 1929
26 46 37 31081 12558 58391
27 54 38 22996 7784 31580
28 124 120 83122 28522 136815
29 128 95 60578 14459 69107
30 80 77 39992 14526 50495
31 97 90 79892 22240 108016
32 104 80 49810 11802 46341
33 125 110 100708 11912 79336
34 149 138 82875 18220 93176
35 118 100 72260 21884 127969
36 12 7 5950 2694 15049
37 144 140 115762 15808 155135
38 108 96 80670 25239 102996
39 166 164 143558 29801 160604
40 80 78 117105 18450 158051
41 60 49 23789 7132 44547
42 127 124 105195 35940 174141
43 84 62 149193 46230 184301
44 111 99 95260 30546 129847
45 98 70 55183 19746 117286
46 105 104 106671 15977 71180
47 135 116 73511 22583 109377
48 107 91 92945 17274 85298
49 88 67 22618 3007 23824
50 104 72 83737 21113 82981
51 132 120 69094 17401 73815
52 108 105 95536 23567 132190
53 129 104 225920 13065 128754
54 122 98 61370 14587 67808
55 147 111 106117 24021 131722
56 87 71 84651 20537 106175
57 90 69 15986 4527 25157
58 109 107 95364 30495 76669
59 78 73 26706 7117 57283
60 111 107 89691 17719 105805
61 141 129 126846 33473 72413
62 124 118 102860 21115 96971
63 93 73 51715 7236 71299
64 124 119 55801 13790 77494
65 112 104 111813 32902 120336
66 108 107 120293 25131 93913
67 117 90 161647 35947 181248
68 199 197 115929 29848 146123
69 78 36 24266 6943 32036
70 91 85 162901 42705 186646
71 158 139 109825 31808 102255
72 126 106 129838 26675 168237
73 122 50 37510 8435 64219
74 115 63 87771 36867 115338
75 72 63 44418 12607 84845
76 91 69 192565 22609 153197
77 45 41 35232 5892 29877
78 78 56 40909 17014 63506
79 39 25 13294 5394 22445
80 119 93 140867 6440 68370
81 50 44 44332 5818 42071
82 88 87 61056 18647 50517
83 155 110 101338 20556 103950
84 0 0 1168 238 5841
85 123 83 65567 22392 84396
86 99 80 32334 12237 35753
87 136 98 40735 8388 55515
88 117 82 91413 22120 209056
89 0 0 855 338 6622
90 88 60 97068 11727 115814
91 39 28 44339 3704 11609
92 25 9 14116 3988 13155
93 52 33 10288 3030 18274
94 75 59 65622 13520 72875
95 124 115 76643 20923 142775
96 145 120 92696 3769 20112
97 87 66 94785 12252 61023
98 162 152 93815 28864 132432
99 165 139 86687 21721 112494
100 54 38 34553 4821 45109
101 159 144 105547 33644 170875
102 170 160 213688 42935 214921
103 119 114 71220 18864 100226
104 120 119 91721 17939 78876
105 112 101 111194 325 6940
106 59 56 51009 13539 49025
107 136 133 135777 34538 122037
108 107 83 51513 12198 53782
109 130 116 74163 26924 127748
110 75 50 33416 10855 77395
111 71 61 83305 11932 89324
112 120 97 98952 14300 103300
113 116 98 102372 25515 112283
114 79 78 37238 2805 10901
115 150 117 103772 29402 120691
116 71 55 21399 5250 25899
117 144 132 130115 28608 139296
118 47 44 24874 8092 52678
119 28 21 34988 4473 23853
120 68 50 45549 1572 17306
121 110 73 64466 14817 89455
122 147 86 54990 16714 147866
123 111 48 34777 1669 14336
124 68 48 27114 7768 30059
125 80 68 37636 6387 22097
126 88 87 65461 18715 96841
127 48 43 30080 7936 41907
128 76 67 24094 8643 27080
129 51 46 69008 7294 35885
130 59 56 46090 7185 28313
131 60 60 34029 8509 36134
132 68 65 46300 13275 55764
133 61 60 40662 10737 66956
134 67 54 28987 8033 47487
135 64 52 30594 5401 35619
136 64 61 27913 10856 45608
137 91 61 42744 2154 7721
138 88 81 12934 6117 20634
139 49 40 41385 4820 31931
140 62 40 18653 5615 37754
141 76 68 30976 8702 40557
142 88 79 63339 15340 94238
143 66 47 25568 8030 44197
144 68 41 4154 1278 4103
145 48 29 19474 4236 44144
146 68 60 39067 7196 27640
147 90 79 65892 6371 28990
148 66 47 4143 1574 4694
149 54 40 28579 9620 42648
150 77 42 38084 8645 25836
151 68 49 27717 8987 22779
152 72 57 32928 5544 40820
153 64 40 19499 6909 32378
154 59 33 36874 6745 39613
155 84 77 48259 16724 60865
156 56 45 28207 7025 20107
157 58 45 45833 9078 48231
158 59 50 29156 4605 39725
159 83 71 45588 9653 62991
160 81 67 45097 8914 49363
161 72 62 28394 6700 24552
162 61 54 18632 5788 31493
163 15 4 2325 593 3439
164 32 25 25139 4506 19555
165 62 40 27975 6382 21228
166 72 59 21792 6928 28893
167 41 24 26263 1514 21425
168 61 58 23686 9238 50276
169 67 42 49303 8204 37643
170 8 4 5752 2416 9927
171 66 63 20055 5432 27184
172 61 54 20154 5576 18475
173 64 39 19540 6095 35873
tothyperlinks totblogs
1 144 145
2 103 101
3 98 98
4 150 144
5 84 84
6 80 79
7 130 127
8 60 60
9 140 133
10 151 150
11 91 91
12 138 132
13 124 124
14 119 118
15 73 70
16 123 119
17 90 89
18 116 112
19 113 108
20 56 52
21 119 116
22 129 123
23 175 162
24 96 92
25 0 0
26 41 41
27 47 47
28 126 120
29 80 79
30 70 65
31 73 70
32 57 55
33 68 67
34 127 127
35 102 99
36 7 7
37 148 141
38 112 109
39 137 133
40 135 123
41 26 26
42 181 166
43 190 179
44 107 108
45 94 90
46 116 114
47 106 103
48 143 142
49 26 25
50 113 113
51 120 118
52 134 129
53 54 51
54 78 76
55 121 118
56 145 141
57 27 27
58 91 91
59 48 48
60 68 63
61 150 144
62 181 168
63 65 64
64 97 97
65 121 117
66 99 100
67 188 187
68 138 127
69 40 37
70 254 245
71 87 87
72 178 177
73 51 49
74 176 177
75 66 60
76 56 55
77 39 39
78 66 64
79 27 26
80 58 58
81 26 26
82 77 76
83 130 129
84 11 11
85 101 101
86 36 36
87 120 89
88 195 193
89 4 4
90 89 84
91 24 23
92 39 39
93 14 14
94 78 78
95 106 101
96 37 36
97 77 75
98 132 131
99 144 131
100 40 39
101 153 144
102 220 211
103 79 78
104 95 90
105 12 12
106 63 57
107 134 133
108 69 69
109 119 119
110 63 61
111 55 49
112 103 101
113 197 196
114 16 15
115 140 136
116 21 21
117 167 163
118 32 29
119 36 35
120 13 13
121 96 96
122 151 151
123 23 23
124 21 14
125 20 20
126 82 72
127 90 87
128 25 21
129 60 56
130 85 82
131 41 38
132 26 25
133 49 47
134 46 45
135 41 41
136 23 23
137 14 14
138 16 16
139 21 21
140 32 27
141 35 33
142 42 42
143 68 68
144 6 6
145 68 67
146 84 77
147 30 30
148 0 0
149 36 36
150 50 48
151 30 29
152 30 28
153 33 33
154 37 33
155 83 80
156 30 30
157 51 51
158 19 18
159 41 39
160 54 54
161 25 24
162 25 24
163 8 8
164 26 26
165 20 19
166 46 47
167 47 47
168 37 37
169 51 51
170 10 10
171 34 34
172 12 11
173 27 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews logins
1.992e+01 -1.910e-03 7.982e-03
compendium_views_info compendium_views_pr shared_compendiums
2.403e-03 9.572e-03 -5.392e-02
blogged_computations compendiums_reviewed feedback_messages_p1
7.446e-03 1.296e-04 -1.946e-02
feedback_messages_p120 totsize totrevisions
1.945e-02 -7.589e-06 -1.592e-05
totseconds tothyperlinks totblogs
1.817e-05 4.729e-02 -5.194e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.4218 -1.9931 0.3918 2.5648 7.0336
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.992e+01 8.929e-01 22.307 <2e-16 ***
pageviews -1.910e-03 1.717e-03 -1.113 0.268
logins 7.982e-03 1.192e-02 0.670 0.504
compendium_views_info 2.403e-03 3.688e-03 0.651 0.516
compendium_views_pr 9.572e-03 6.829e-03 1.402 0.163
shared_compendiums -5.392e-02 1.305e-01 -0.413 0.680
blogged_computations 7.446e-03 2.403e-02 0.310 0.757
compendiums_reviewed 1.296e-04 2.205e-01 0.001 1.000
feedback_messages_p1 -1.946e-02 6.188e-02 -0.314 0.754
feedback_messages_p120 1.945e-02 2.581e-02 0.754 0.452
totsize -7.589e-06 1.358e-05 -0.559 0.577
totrevisions -1.591e-05 7.064e-05 -0.225 0.822
totseconds 1.817e-05 1.583e-05 1.148 0.253
tothyperlinks 4.729e-02 8.640e-02 0.547 0.585
totblogs -5.194e-02 9.068e-02 -0.573 0.568
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.837 on 158 degrees of freedom
Multiple R-squared: 0.03523, Adjusted R-squared: -0.05026
F-statistic: 0.4121 on 14 and 158 DF, p-value: 0.9695
> 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,] 0.9630902 0.07381968 0.03690984
[2,] 0.9600146 0.07997084 0.03998542
[3,] 0.9693232 0.06135350 0.03067675
[4,] 0.9547921 0.09041574 0.04520787
[5,] 0.9235253 0.15294944 0.07647472
[6,] 0.8983254 0.20334915 0.10167458
[7,] 0.9157402 0.16851958 0.08425979
[8,] 0.8786188 0.24276237 0.12138118
[9,] 0.8819159 0.23616829 0.11808414
[10,] 0.8382715 0.32345691 0.16172845
[11,] 0.8124606 0.37507881 0.18753940
[12,] 0.7586517 0.48269661 0.24134831
[13,] 0.7796273 0.44074531 0.22037266
[14,] 0.8060907 0.38781859 0.19390930
[15,] 0.8971685 0.20566299 0.10283150
[16,] 0.9042932 0.19141354 0.09570677
[17,] 0.9146808 0.17063838 0.08531919
[18,] 0.9121106 0.17577881 0.08788941
[19,] 0.8860008 0.22799847 0.11399923
[20,] 0.8882661 0.22346784 0.11173392
[21,] 0.8631321 0.27373579 0.13686790
[22,] 0.8677472 0.26450560 0.13225280
[23,] 0.8344873 0.33102533 0.16551267
[24,] 0.7944180 0.41116393 0.20558197
[25,] 0.7884363 0.42312732 0.21156366
[26,] 0.7982911 0.40341790 0.20170895
[27,] 0.8452284 0.30954317 0.15477158
[28,] 0.8467779 0.30644425 0.15322213
[29,] 0.8216895 0.35662096 0.17831048
[30,] 0.7866479 0.42670416 0.21335208
[31,] 0.7637275 0.47254509 0.23627254
[32,] 0.7326881 0.53462386 0.26731193
[33,] 0.9525434 0.09491325 0.04745662
[34,] 0.9403020 0.11939605 0.05969803
[35,] 0.9485019 0.10299617 0.05149809
[36,] 0.9409634 0.11807325 0.05903662
[37,] 0.9240269 0.15194615 0.07597308
[38,] 0.9348797 0.13024054 0.06512027
[39,] 0.9388813 0.12223747 0.06111873
[40,] 0.9241181 0.15176385 0.07588192
[41,] 0.9242520 0.15149608 0.07574804
[42,] 0.9081480 0.18370407 0.09185204
[43,] 0.8910142 0.21797168 0.10898584
[44,] 0.8685244 0.26295130 0.13147565
[45,] 0.8808098 0.23838050 0.11919025
[46,] 0.8960050 0.20798997 0.10399499
[47,] 0.8741360 0.25172800 0.12586400
[48,] 0.8590726 0.28185489 0.14092745
[49,] 0.8911061 0.21778788 0.10889394
[50,] 0.8680216 0.26395678 0.13197839
[51,] 0.8680020 0.26399600 0.13199800
[52,] 0.8912080 0.21758406 0.10879203
[53,] 0.8749432 0.25011358 0.12505679
[54,] 0.8906801 0.21863987 0.10931994
[55,] 0.8729678 0.25406431 0.12703215
[56,] 0.8655076 0.26898488 0.13449244
[57,] 0.8447795 0.31044103 0.15522051
[58,] 0.8318922 0.33621556 0.16810778
[59,] 0.8548538 0.29029233 0.14514617
[60,] 0.8574665 0.28506702 0.14253351
[61,] 0.8355083 0.32898332 0.16449166
[62,] 0.8045482 0.39090362 0.19545181
[63,] 0.7766227 0.44675470 0.22337735
[64,] 0.7815954 0.43680916 0.21840458
[65,] 0.7809696 0.43806073 0.21903037
[66,] 0.8039427 0.39211456 0.19605728
[67,] 0.7847639 0.43047222 0.21523611
[68,] 0.8040085 0.39198307 0.19599154
[69,] 0.8429957 0.31400860 0.15700430
[70,] 0.8387663 0.32246738 0.16123369
[71,] 0.8414957 0.31700851 0.15850426
[72,] 0.8125963 0.37480738 0.18740369
[73,] 0.8035445 0.39291097 0.19645548
[74,] 0.7693758 0.46124832 0.23062416
[75,] 0.7346008 0.53079843 0.26539921
[76,] 0.7045621 0.59087574 0.29543787
[77,] 0.6636052 0.67278970 0.33639485
[78,] 0.6265109 0.74697820 0.37348910
[79,] 0.6270981 0.74580381 0.37290190
[80,] 0.6808326 0.63833481 0.31916740
[81,] 0.6675786 0.66484280 0.33242140
[82,] 0.6465919 0.70681615 0.35340808
[83,] 0.6022696 0.79546077 0.39773039
[84,] 0.6227980 0.75440407 0.37720203
[85,] 0.5852185 0.82956300 0.41478150
[86,] 0.5472034 0.90559311 0.45279655
[87,] 0.5069903 0.98601933 0.49300967
[88,] 0.4630636 0.92612712 0.53693644
[89,] 0.4689893 0.93797858 0.53101071
[90,] 0.4384564 0.87691283 0.56154358
[91,] 0.4032887 0.80657730 0.59671135
[92,] 0.4514406 0.90288129 0.54855936
[93,] 0.4045190 0.80903807 0.59548096
[94,] 0.3966510 0.79330206 0.60334897
[95,] 0.4101437 0.82028744 0.58985628
[96,] 0.4037301 0.80746017 0.59626991
[97,] 0.3836479 0.76729575 0.61635213
[98,] 0.3653633 0.73072666 0.63463667
[99,] 0.3358098 0.67161968 0.66419016
[100,] 0.3750006 0.75000122 0.62499939
[101,] 0.4605107 0.92102136 0.53948932
[102,] 0.4820968 0.96419354 0.51790323
[103,] 0.8183968 0.36320648 0.18160324
[104,] 0.8373083 0.32538334 0.16269167
[105,] 0.8311651 0.33766984 0.16883492
[106,] 0.7963851 0.40722986 0.20361493
[107,] 0.7576708 0.48465841 0.24232920
[108,] 0.7505513 0.49889746 0.24944873
[109,] 0.7440823 0.51183534 0.25591767
[110,] 0.7019085 0.59618309 0.29809154
[111,] 0.7493000 0.50140001 0.25070001
[112,] 0.7074367 0.58512652 0.29256326
[113,] 0.6538175 0.69236498 0.34618249
[114,] 0.6045184 0.79096324 0.39548162
[115,] 0.6247819 0.75043621 0.37521810
[116,] 0.5854308 0.82913846 0.41456923
[117,] 0.5482088 0.90358232 0.45179116
[118,] 0.5120447 0.97591058 0.48795529
[119,] 0.4987944 0.99758873 0.50120563
[120,] 0.4890302 0.97806042 0.51096979
[121,] 0.4263004 0.85260083 0.57369958
[122,] 0.4550888 0.91017767 0.54491117
[123,] 0.5150235 0.96995291 0.48497645
[124,] 0.6516278 0.69674431 0.34837215
[125,] 0.6213204 0.75735923 0.37867961
[126,] 0.8338963 0.33220736 0.16610368
[127,] 0.7970668 0.40586632 0.20293316
[128,] 0.7631005 0.47379907 0.23689954
[129,] 0.6916964 0.61660725 0.30830363
[130,] 0.6216876 0.75662475 0.37831237
[131,] 0.8059159 0.38816824 0.19408412
[132,] 0.7962723 0.40745546 0.20372773
[133,] 0.7306246 0.53875079 0.26937539
[134,] 0.6404914 0.71901728 0.35950864
[135,] 0.5285362 0.94292754 0.47146377
[136,] 0.6570189 0.68596214 0.34298107
[137,] 0.5149462 0.97010757 0.48505379
[138,] 0.7604903 0.47901946 0.23950973
> postscript(file="/var/www/rcomp/tmp/1g46v1323885964.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/254p91323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vvmi1323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4tl9k1323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5uuz71323885964.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
-9.103739185 -5.525453383 0.724904889 1.563674224 -3.406985946
6 7 8 9 10
1.382216615 2.893302270 -4.487133831 -3.096344445 -0.865284774
11 12 13 14 15
-0.655568484 5.040929504 -0.772627663 7.033566745 3.783774081
16 17 18 19 20
5.549167497 -5.014111803 0.774653348 5.012228495 -4.099918513
21 22 23 24 25
-4.391002048 0.637046375 2.901130668 -10.587355533 -0.890732751
26 27 28 29 30
5.110949591 2.396476069 -5.497601767 1.550419621 2.492972557
31 32 33 34 35
6.418798699 6.990875279 2.194776812 2.431175699 -0.419139955
36 37 38 39 40
2.007050783 -0.644506711 2.320155656 0.250162277 1.522074450
41 42 43 44 45
-0.008945164 2.966695847 -2.464849737 1.713825900 4.733679044
46 47 48 49 50
1.593813415 0.505479752 0.399816179 -1.344759523 -11.618929249
51 52 53 54 55
2.102439867 5.489174985 -1.217074384 0.204261891 4.637712511
56 57 58 59 60
-2.790478059 -0.287962405 3.509783963 -0.145007522 0.161874025
61 62 63 64 65
1.286778019 -1.329288357 -4.481616016 0.430536515 1.866031749
66 67 68 69 70
-6.509741047 -0.263713180 -3.097019290 -4.966464990 2.309421964
71 72 73 74 75
4.918397257 1.516468580 3.406659297 -2.259318886 2.686002887
76 77 78 79 80
4.853232420 -4.029961016 -1.652779152 0.129253725 -1.581599445
81 82 83 84 85
4.102946889 3.415862640 4.797914775 3.161136235 3.587242481
86 87 88 89 90
-6.233735396 -0.925348031 -2.961979666 1.203990828 -2.794446509
91 92 93 94 95
0.618714135 -0.613838125 -1.619165416 -0.520569293 0.887644124
96 97 98 99 100
3.325794041 -4.064276628 -3.465302584 -1.530662488 0.586016403
101 102 103 104 105
0.726788917 0.829535987 -1.589792210 -0.173702779 -0.248336340
106 107 108 109 110
-4.458782026 -0.014335965 1.810946300 -5.578246206 -0.284228989
111 112 113 114 115
3.025402483 -3.758474242 -1.570821456 3.416817376 -2.648705857
116 117 118 119 120
-2.653725198 3.868786573 -7.565639669 3.624417632 -14.421809919
121 122 123 124 125
-0.128882153 4.172128860 0.315253402 0.043020889 1.869064529
126 127 128 129 130
-5.828079447 -0.727732244 5.066588748 1.488296883 -0.584407016
131 132 133 134 135
-3.317578572 -5.274790468 0.642732739 4.165854769 2.740261509
136 137 138 139 140
-1.246675220 1.868494119 1.364297205 -0.640529963 2.141525611
141 142 143 144 145
-5.680802739 5.677841071 -8.327842002 -3.521871300 -0.758925629
146 147 148 149 150
-2.868168859 1.188620518 2.622915451 0.391834428 2.564843877
151 152 153 154 155
-4.653056891 3.425320096 0.186826411 -2.509978111 0.228794928
156 157 158 159 160
5.531700609 2.642549582 -4.190358733 4.529228317 -1.993062528
161 162 163 164 165
-0.842869072 5.093476116 3.393649291 4.313815071 1.294846443
166 167 168 169 170
1.239969126 2.087985051 0.563029141 -1.599172957 -6.826102538
171 172 173
2.097441554 3.146217939 -4.768378451
> postscript(file="/var/www/rcomp/tmp/6dr021323885964.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 -9.103739185 NA
1 -5.525453383 -9.103739185
2 0.724904889 -5.525453383
3 1.563674224 0.724904889
4 -3.406985946 1.563674224
5 1.382216615 -3.406985946
6 2.893302270 1.382216615
7 -4.487133831 2.893302270
8 -3.096344445 -4.487133831
9 -0.865284774 -3.096344445
10 -0.655568484 -0.865284774
11 5.040929504 -0.655568484
12 -0.772627663 5.040929504
13 7.033566745 -0.772627663
14 3.783774081 7.033566745
15 5.549167497 3.783774081
16 -5.014111803 5.549167497
17 0.774653348 -5.014111803
18 5.012228495 0.774653348
19 -4.099918513 5.012228495
20 -4.391002048 -4.099918513
21 0.637046375 -4.391002048
22 2.901130668 0.637046375
23 -10.587355533 2.901130668
24 -0.890732751 -10.587355533
25 5.110949591 -0.890732751
26 2.396476069 5.110949591
27 -5.497601767 2.396476069
28 1.550419621 -5.497601767
29 2.492972557 1.550419621
30 6.418798699 2.492972557
31 6.990875279 6.418798699
32 2.194776812 6.990875279
33 2.431175699 2.194776812
34 -0.419139955 2.431175699
35 2.007050783 -0.419139955
36 -0.644506711 2.007050783
37 2.320155656 -0.644506711
38 0.250162277 2.320155656
39 1.522074450 0.250162277
40 -0.008945164 1.522074450
41 2.966695847 -0.008945164
42 -2.464849737 2.966695847
43 1.713825900 -2.464849737
44 4.733679044 1.713825900
45 1.593813415 4.733679044
46 0.505479752 1.593813415
47 0.399816179 0.505479752
48 -1.344759523 0.399816179
49 -11.618929249 -1.344759523
50 2.102439867 -11.618929249
51 5.489174985 2.102439867
52 -1.217074384 5.489174985
53 0.204261891 -1.217074384
54 4.637712511 0.204261891
55 -2.790478059 4.637712511
56 -0.287962405 -2.790478059
57 3.509783963 -0.287962405
58 -0.145007522 3.509783963
59 0.161874025 -0.145007522
60 1.286778019 0.161874025
61 -1.329288357 1.286778019
62 -4.481616016 -1.329288357
63 0.430536515 -4.481616016
64 1.866031749 0.430536515
65 -6.509741047 1.866031749
66 -0.263713180 -6.509741047
67 -3.097019290 -0.263713180
68 -4.966464990 -3.097019290
69 2.309421964 -4.966464990
70 4.918397257 2.309421964
71 1.516468580 4.918397257
72 3.406659297 1.516468580
73 -2.259318886 3.406659297
74 2.686002887 -2.259318886
75 4.853232420 2.686002887
76 -4.029961016 4.853232420
77 -1.652779152 -4.029961016
78 0.129253725 -1.652779152
79 -1.581599445 0.129253725
80 4.102946889 -1.581599445
81 3.415862640 4.102946889
82 4.797914775 3.415862640
83 3.161136235 4.797914775
84 3.587242481 3.161136235
85 -6.233735396 3.587242481
86 -0.925348031 -6.233735396
87 -2.961979666 -0.925348031
88 1.203990828 -2.961979666
89 -2.794446509 1.203990828
90 0.618714135 -2.794446509
91 -0.613838125 0.618714135
92 -1.619165416 -0.613838125
93 -0.520569293 -1.619165416
94 0.887644124 -0.520569293
95 3.325794041 0.887644124
96 -4.064276628 3.325794041
97 -3.465302584 -4.064276628
98 -1.530662488 -3.465302584
99 0.586016403 -1.530662488
100 0.726788917 0.586016403
101 0.829535987 0.726788917
102 -1.589792210 0.829535987
103 -0.173702779 -1.589792210
104 -0.248336340 -0.173702779
105 -4.458782026 -0.248336340
106 -0.014335965 -4.458782026
107 1.810946300 -0.014335965
108 -5.578246206 1.810946300
109 -0.284228989 -5.578246206
110 3.025402483 -0.284228989
111 -3.758474242 3.025402483
112 -1.570821456 -3.758474242
113 3.416817376 -1.570821456
114 -2.648705857 3.416817376
115 -2.653725198 -2.648705857
116 3.868786573 -2.653725198
117 -7.565639669 3.868786573
118 3.624417632 -7.565639669
119 -14.421809919 3.624417632
120 -0.128882153 -14.421809919
121 4.172128860 -0.128882153
122 0.315253402 4.172128860
123 0.043020889 0.315253402
124 1.869064529 0.043020889
125 -5.828079447 1.869064529
126 -0.727732244 -5.828079447
127 5.066588748 -0.727732244
128 1.488296883 5.066588748
129 -0.584407016 1.488296883
130 -3.317578572 -0.584407016
131 -5.274790468 -3.317578572
132 0.642732739 -5.274790468
133 4.165854769 0.642732739
134 2.740261509 4.165854769
135 -1.246675220 2.740261509
136 1.868494119 -1.246675220
137 1.364297205 1.868494119
138 -0.640529963 1.364297205
139 2.141525611 -0.640529963
140 -5.680802739 2.141525611
141 5.677841071 -5.680802739
142 -8.327842002 5.677841071
143 -3.521871300 -8.327842002
144 -0.758925629 -3.521871300
145 -2.868168859 -0.758925629
146 1.188620518 -2.868168859
147 2.622915451 1.188620518
148 0.391834428 2.622915451
149 2.564843877 0.391834428
150 -4.653056891 2.564843877
151 3.425320096 -4.653056891
152 0.186826411 3.425320096
153 -2.509978111 0.186826411
154 0.228794928 -2.509978111
155 5.531700609 0.228794928
156 2.642549582 5.531700609
157 -4.190358733 2.642549582
158 4.529228317 -4.190358733
159 -1.993062528 4.529228317
160 -0.842869072 -1.993062528
161 5.093476116 -0.842869072
162 3.393649291 5.093476116
163 4.313815071 3.393649291
164 1.294846443 4.313815071
165 1.239969126 1.294846443
166 2.087985051 1.239969126
167 0.563029141 2.087985051
168 -1.599172957 0.563029141
169 -6.826102538 -1.599172957
170 2.097441554 -6.826102538
171 3.146217939 2.097441554
172 -4.768378451 3.146217939
173 NA -4.768378451
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.525453383 -9.103739185
[2,] 0.724904889 -5.525453383
[3,] 1.563674224 0.724904889
[4,] -3.406985946 1.563674224
[5,] 1.382216615 -3.406985946
[6,] 2.893302270 1.382216615
[7,] -4.487133831 2.893302270
[8,] -3.096344445 -4.487133831
[9,] -0.865284774 -3.096344445
[10,] -0.655568484 -0.865284774
[11,] 5.040929504 -0.655568484
[12,] -0.772627663 5.040929504
[13,] 7.033566745 -0.772627663
[14,] 3.783774081 7.033566745
[15,] 5.549167497 3.783774081
[16,] -5.014111803 5.549167497
[17,] 0.774653348 -5.014111803
[18,] 5.012228495 0.774653348
[19,] -4.099918513 5.012228495
[20,] -4.391002048 -4.099918513
[21,] 0.637046375 -4.391002048
[22,] 2.901130668 0.637046375
[23,] -10.587355533 2.901130668
[24,] -0.890732751 -10.587355533
[25,] 5.110949591 -0.890732751
[26,] 2.396476069 5.110949591
[27,] -5.497601767 2.396476069
[28,] 1.550419621 -5.497601767
[29,] 2.492972557 1.550419621
[30,] 6.418798699 2.492972557
[31,] 6.990875279 6.418798699
[32,] 2.194776812 6.990875279
[33,] 2.431175699 2.194776812
[34,] -0.419139955 2.431175699
[35,] 2.007050783 -0.419139955
[36,] -0.644506711 2.007050783
[37,] 2.320155656 -0.644506711
[38,] 0.250162277 2.320155656
[39,] 1.522074450 0.250162277
[40,] -0.008945164 1.522074450
[41,] 2.966695847 -0.008945164
[42,] -2.464849737 2.966695847
[43,] 1.713825900 -2.464849737
[44,] 4.733679044 1.713825900
[45,] 1.593813415 4.733679044
[46,] 0.505479752 1.593813415
[47,] 0.399816179 0.505479752
[48,] -1.344759523 0.399816179
[49,] -11.618929249 -1.344759523
[50,] 2.102439867 -11.618929249
[51,] 5.489174985 2.102439867
[52,] -1.217074384 5.489174985
[53,] 0.204261891 -1.217074384
[54,] 4.637712511 0.204261891
[55,] -2.790478059 4.637712511
[56,] -0.287962405 -2.790478059
[57,] 3.509783963 -0.287962405
[58,] -0.145007522 3.509783963
[59,] 0.161874025 -0.145007522
[60,] 1.286778019 0.161874025
[61,] -1.329288357 1.286778019
[62,] -4.481616016 -1.329288357
[63,] 0.430536515 -4.481616016
[64,] 1.866031749 0.430536515
[65,] -6.509741047 1.866031749
[66,] -0.263713180 -6.509741047
[67,] -3.097019290 -0.263713180
[68,] -4.966464990 -3.097019290
[69,] 2.309421964 -4.966464990
[70,] 4.918397257 2.309421964
[71,] 1.516468580 4.918397257
[72,] 3.406659297 1.516468580
[73,] -2.259318886 3.406659297
[74,] 2.686002887 -2.259318886
[75,] 4.853232420 2.686002887
[76,] -4.029961016 4.853232420
[77,] -1.652779152 -4.029961016
[78,] 0.129253725 -1.652779152
[79,] -1.581599445 0.129253725
[80,] 4.102946889 -1.581599445
[81,] 3.415862640 4.102946889
[82,] 4.797914775 3.415862640
[83,] 3.161136235 4.797914775
[84,] 3.587242481 3.161136235
[85,] -6.233735396 3.587242481
[86,] -0.925348031 -6.233735396
[87,] -2.961979666 -0.925348031
[88,] 1.203990828 -2.961979666
[89,] -2.794446509 1.203990828
[90,] 0.618714135 -2.794446509
[91,] -0.613838125 0.618714135
[92,] -1.619165416 -0.613838125
[93,] -0.520569293 -1.619165416
[94,] 0.887644124 -0.520569293
[95,] 3.325794041 0.887644124
[96,] -4.064276628 3.325794041
[97,] -3.465302584 -4.064276628
[98,] -1.530662488 -3.465302584
[99,] 0.586016403 -1.530662488
[100,] 0.726788917 0.586016403
[101,] 0.829535987 0.726788917
[102,] -1.589792210 0.829535987
[103,] -0.173702779 -1.589792210
[104,] -0.248336340 -0.173702779
[105,] -4.458782026 -0.248336340
[106,] -0.014335965 -4.458782026
[107,] 1.810946300 -0.014335965
[108,] -5.578246206 1.810946300
[109,] -0.284228989 -5.578246206
[110,] 3.025402483 -0.284228989
[111,] -3.758474242 3.025402483
[112,] -1.570821456 -3.758474242
[113,] 3.416817376 -1.570821456
[114,] -2.648705857 3.416817376
[115,] -2.653725198 -2.648705857
[116,] 3.868786573 -2.653725198
[117,] -7.565639669 3.868786573
[118,] 3.624417632 -7.565639669
[119,] -14.421809919 3.624417632
[120,] -0.128882153 -14.421809919
[121,] 4.172128860 -0.128882153
[122,] 0.315253402 4.172128860
[123,] 0.043020889 0.315253402
[124,] 1.869064529 0.043020889
[125,] -5.828079447 1.869064529
[126,] -0.727732244 -5.828079447
[127,] 5.066588748 -0.727732244
[128,] 1.488296883 5.066588748
[129,] -0.584407016 1.488296883
[130,] -3.317578572 -0.584407016
[131,] -5.274790468 -3.317578572
[132,] 0.642732739 -5.274790468
[133,] 4.165854769 0.642732739
[134,] 2.740261509 4.165854769
[135,] -1.246675220 2.740261509
[136,] 1.868494119 -1.246675220
[137,] 1.364297205 1.868494119
[138,] -0.640529963 1.364297205
[139,] 2.141525611 -0.640529963
[140,] -5.680802739 2.141525611
[141,] 5.677841071 -5.680802739
[142,] -8.327842002 5.677841071
[143,] -3.521871300 -8.327842002
[144,] -0.758925629 -3.521871300
[145,] -2.868168859 -0.758925629
[146,] 1.188620518 -2.868168859
[147,] 2.622915451 1.188620518
[148,] 0.391834428 2.622915451
[149,] 2.564843877 0.391834428
[150,] -4.653056891 2.564843877
[151,] 3.425320096 -4.653056891
[152,] 0.186826411 3.425320096
[153,] -2.509978111 0.186826411
[154,] 0.228794928 -2.509978111
[155,] 5.531700609 0.228794928
[156,] 2.642549582 5.531700609
[157,] -4.190358733 2.642549582
[158,] 4.529228317 -4.190358733
[159,] -1.993062528 4.529228317
[160,] -0.842869072 -1.993062528
[161,] 5.093476116 -0.842869072
[162,] 3.393649291 5.093476116
[163,] 4.313815071 3.393649291
[164,] 1.294846443 4.313815071
[165,] 1.239969126 1.294846443
[166,] 2.087985051 1.239969126
[167,] 0.563029141 2.087985051
[168,] -1.599172957 0.563029141
[169,] -6.826102538 -1.599172957
[170,] 2.097441554 -6.826102538
[171,] 3.146217939 2.097441554
[172,] -4.768378451 3.146217939
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.525453383 -9.103739185
2 0.724904889 -5.525453383
3 1.563674224 0.724904889
4 -3.406985946 1.563674224
5 1.382216615 -3.406985946
6 2.893302270 1.382216615
7 -4.487133831 2.893302270
8 -3.096344445 -4.487133831
9 -0.865284774 -3.096344445
10 -0.655568484 -0.865284774
11 5.040929504 -0.655568484
12 -0.772627663 5.040929504
13 7.033566745 -0.772627663
14 3.783774081 7.033566745
15 5.549167497 3.783774081
16 -5.014111803 5.549167497
17 0.774653348 -5.014111803
18 5.012228495 0.774653348
19 -4.099918513 5.012228495
20 -4.391002048 -4.099918513
21 0.637046375 -4.391002048
22 2.901130668 0.637046375
23 -10.587355533 2.901130668
24 -0.890732751 -10.587355533
25 5.110949591 -0.890732751
26 2.396476069 5.110949591
27 -5.497601767 2.396476069
28 1.550419621 -5.497601767
29 2.492972557 1.550419621
30 6.418798699 2.492972557
31 6.990875279 6.418798699
32 2.194776812 6.990875279
33 2.431175699 2.194776812
34 -0.419139955 2.431175699
35 2.007050783 -0.419139955
36 -0.644506711 2.007050783
37 2.320155656 -0.644506711
38 0.250162277 2.320155656
39 1.522074450 0.250162277
40 -0.008945164 1.522074450
41 2.966695847 -0.008945164
42 -2.464849737 2.966695847
43 1.713825900 -2.464849737
44 4.733679044 1.713825900
45 1.593813415 4.733679044
46 0.505479752 1.593813415
47 0.399816179 0.505479752
48 -1.344759523 0.399816179
49 -11.618929249 -1.344759523
50 2.102439867 -11.618929249
51 5.489174985 2.102439867
52 -1.217074384 5.489174985
53 0.204261891 -1.217074384
54 4.637712511 0.204261891
55 -2.790478059 4.637712511
56 -0.287962405 -2.790478059
57 3.509783963 -0.287962405
58 -0.145007522 3.509783963
59 0.161874025 -0.145007522
60 1.286778019 0.161874025
61 -1.329288357 1.286778019
62 -4.481616016 -1.329288357
63 0.430536515 -4.481616016
64 1.866031749 0.430536515
65 -6.509741047 1.866031749
66 -0.263713180 -6.509741047
67 -3.097019290 -0.263713180
68 -4.966464990 -3.097019290
69 2.309421964 -4.966464990
70 4.918397257 2.309421964
71 1.516468580 4.918397257
72 3.406659297 1.516468580
73 -2.259318886 3.406659297
74 2.686002887 -2.259318886
75 4.853232420 2.686002887
76 -4.029961016 4.853232420
77 -1.652779152 -4.029961016
78 0.129253725 -1.652779152
79 -1.581599445 0.129253725
80 4.102946889 -1.581599445
81 3.415862640 4.102946889
82 4.797914775 3.415862640
83 3.161136235 4.797914775
84 3.587242481 3.161136235
85 -6.233735396 3.587242481
86 -0.925348031 -6.233735396
87 -2.961979666 -0.925348031
88 1.203990828 -2.961979666
89 -2.794446509 1.203990828
90 0.618714135 -2.794446509
91 -0.613838125 0.618714135
92 -1.619165416 -0.613838125
93 -0.520569293 -1.619165416
94 0.887644124 -0.520569293
95 3.325794041 0.887644124
96 -4.064276628 3.325794041
97 -3.465302584 -4.064276628
98 -1.530662488 -3.465302584
99 0.586016403 -1.530662488
100 0.726788917 0.586016403
101 0.829535987 0.726788917
102 -1.589792210 0.829535987
103 -0.173702779 -1.589792210
104 -0.248336340 -0.173702779
105 -4.458782026 -0.248336340
106 -0.014335965 -4.458782026
107 1.810946300 -0.014335965
108 -5.578246206 1.810946300
109 -0.284228989 -5.578246206
110 3.025402483 -0.284228989
111 -3.758474242 3.025402483
112 -1.570821456 -3.758474242
113 3.416817376 -1.570821456
114 -2.648705857 3.416817376
115 -2.653725198 -2.648705857
116 3.868786573 -2.653725198
117 -7.565639669 3.868786573
118 3.624417632 -7.565639669
119 -14.421809919 3.624417632
120 -0.128882153 -14.421809919
121 4.172128860 -0.128882153
122 0.315253402 4.172128860
123 0.043020889 0.315253402
124 1.869064529 0.043020889
125 -5.828079447 1.869064529
126 -0.727732244 -5.828079447
127 5.066588748 -0.727732244
128 1.488296883 5.066588748
129 -0.584407016 1.488296883
130 -3.317578572 -0.584407016
131 -5.274790468 -3.317578572
132 0.642732739 -5.274790468
133 4.165854769 0.642732739
134 2.740261509 4.165854769
135 -1.246675220 2.740261509
136 1.868494119 -1.246675220
137 1.364297205 1.868494119
138 -0.640529963 1.364297205
139 2.141525611 -0.640529963
140 -5.680802739 2.141525611
141 5.677841071 -5.680802739
142 -8.327842002 5.677841071
143 -3.521871300 -8.327842002
144 -0.758925629 -3.521871300
145 -2.868168859 -0.758925629
146 1.188620518 -2.868168859
147 2.622915451 1.188620518
148 0.391834428 2.622915451
149 2.564843877 0.391834428
150 -4.653056891 2.564843877
151 3.425320096 -4.653056891
152 0.186826411 3.425320096
153 -2.509978111 0.186826411
154 0.228794928 -2.509978111
155 5.531700609 0.228794928
156 2.642549582 5.531700609
157 -4.190358733 2.642549582
158 4.529228317 -4.190358733
159 -1.993062528 4.529228317
160 -0.842869072 -1.993062528
161 5.093476116 -0.842869072
162 3.393649291 5.093476116
163 4.313815071 3.393649291
164 1.294846443 4.313815071
165 1.239969126 1.294846443
166 2.087985051 1.239969126
167 0.563029141 2.087985051
168 -1.599172957 0.563029141
169 -6.826102538 -1.599172957
170 2.097441554 -6.826102538
171 3.146217939 2.097441554
172 -4.768378451 3.146217939
> 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/7easx1323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8s3t81323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9804e1323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/104unz1323885964.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p9hi1323885964.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/12ur3p1323885964.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/13l3xz1323885964.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/14ux291323885964.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/157u6z1323885964.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/16yz871323885964.tab")
+ }
>
> try(system("convert tmp/1g46v1323885964.ps tmp/1g46v1323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/254p91323885964.ps tmp/254p91323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vvmi1323885964.ps tmp/3vvmi1323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tl9k1323885964.ps tmp/4tl9k1323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uuz71323885964.ps tmp/5uuz71323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dr021323885964.ps tmp/6dr021323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/7easx1323885964.ps tmp/7easx1323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/8s3t81323885964.ps tmp/8s3t81323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/9804e1323885964.ps tmp/9804e1323885964.png",intern=TRUE))
character(0)
> try(system("convert tmp/104unz1323885964.ps tmp/104unz1323885964.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.280 0.440 7.693