R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(144244
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+ ,2130
+ ,219475
+ ,138
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+ ,72
+ ,0
+ ,92
+ ,40
+ ,151
+ ,88
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+ ,139292
+ ,206
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+ ,5950
+ ,496
+ ,24188
+ ,24
+ ,218
+ ,20
+ ,0
+ ,8
+ ,4
+ ,12
+ ,7
+ ,2694
+ ,15049
+ ,7
+ ,7
+ ,3926
+ ,141
+ ,17547
+ ,5
+ ,69
+ ,3
+ ,0
+ ,0
+ ,1
+ ,4
+ ,4
+ ,2658
+ ,7670
+ ,3
+ ,3)
+ ,dim=c(15
+ ,144)
+ ,dimnames=list(c('#karakters'
+ ,'#PageViews'
+ ,'#SecRFC'
+ ,'#LogIns'
+ ,'#CourseCompViews'
+ ,'#CompViewsPR'
+ ,'#shared'
+ ,'#Blogs'
+ ,'#Reviews'
+ ,'#FBMinPR'
+ ,'#FBMinPR+120'
+ ,'#revisions'
+ ,'#seconden'
+ ,'#hyperlinks'
+ ,'#blogs')
+ ,1:144))
> y <- array(NA,dim=c(15,144),dimnames=list(c('#karakters','#PageViews','#SecRFC','#LogIns','#CourseCompViews','#CompViewsPR','#shared','#Blogs','#Reviews','#FBMinPR','#FBMinPR+120','#revisions','#seconden','#hyperlinks','#blogs'),1:144))
> 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 = '1'
> #'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
#karakters #PageViews #SecRFC #LogIns #CourseCompViews #CompViewsPR #shared
1 144244 1685 152043 53 670 47 3
2 197426 1109 121726 66 356 44 0
3 86652 1829 204039 75 615 83 6
4 65594 1808 185890 62 590 86 9
5 101382 2137 252805 52 866 67 5
6 76173 631 70849 28 179 46 3
7 124089 3873 366774 116 1548 117 9
8 66089 1121 102424 42 401 55 0
9 22618 893 73566 32 385 39 6
10 149695 2531 372238 308 833 81 8
11 56622 870 80953 25 437 31 0
12 150047 1915 168994 57 720 122 5
13 151911 3942 334657 116 1342 116 10
14 25162 530 61857 25 192 25 4
15 105079 1844 222373 69 673 59 16
16 69446 1937 220700 82 621 46 10
17 136588 2320 263411 85 683 62 8
18 128692 1807 217478 112 610 69 0
19 134047 2387 316105 97 820 120 0
20 31701 1161 101097 64 454 70 3
21 43750 602 43287 14 214 43 4
22 143592 1854 187965 126 562 54 6
23 100350 1757 199726 65 580 47 2
24 151715 3937 370483 139 1298 65 10
25 113344 2733 327474 64 1026 140 0
26 279488 3328 396725 123 1151 111 3
27 125081 2956 322896 157 1157 90 4
28 68788 1659 164107 67 633 60 8
29 103037 2768 425544 102 1203 101 1
30 102153 1907 198094 40 659 113 2
31 147172 1860 306952 67 606 58 4
32 146760 4023 401260 164 1429 155 4
33 127654 2135 254506 74 655 71 3
34 110459 1209 179444 64 429 63 4
35 131072 2277 232765 78 834 78 2
36 126817 1675 175699 68 621 62 5
37 108535 3687 361186 189 1456 278 1
38 82317 1441 179306 40 461 62 5
39 57224 1997 186856 177 640 116 0
40 135356 2089 240153 65 696 79 7
41 96125 2228 208051 76 648 80 0
42 1168 398 23623 11 156 9 0
43 102070 2861 283950 328 947 149 4
44 118906 1717 189897 54 601 65 9
45 59900 2670 233632 106 1073 72 11
46 79011 1577 166266 64 623 75 9
47 103297 2934 358752 77 1163 114 4
48 143372 1624 189252 36 555 64 3
49 109432 2295 297982 87 684 70 0
50 167949 2122 305704 68 746 58 6
51 8773 568 38214 34 276 52 0
52 45724 1596 163766 48 457 107 0
53 149959 2295 285330 64 838 85 0
54 81351 2537 239314 68 865 64 13
55 103129 2824 267198 126 1061 91 5
56 154451 2604 246745 86 1011 52 2
57 88977 2189 242585 83 716 58 7
58 140824 1723 270018 68 476 90 3
59 84601 2030 233143 83 699 58 2
60 169707 2432 302218 83 642 80 5
61 187326 4513 498732 117 2201 109 0
62 156349 2633 301703 105 818 64 1
63 108146 2314 359644 84 982 95 4
64 168553 1606 222504 50 552 72 5
65 144408 1997 207822 57 735 75 7
66 183500 2091 285198 78 771 61 0
67 104128 1288 196269 58 445 64 0
68 33032 918 78800 42 330 57 2
69 43929 1148 162874 50 348 48 1
70 56750 2312 251466 89 786 58 1
71 126372 2645 341637 90 1056 48 3
72 160141 3842 447353 114 1216 114 0
73 71571 1787 182231 64 644 67 0
74 125818 1438 176082 55 507 41 3
75 38692 1369 145943 69 653 45 5
76 95893 2175 252529 83 822 77 12
77 67150 2535 282399 94 897 192 1
78 110529 2949 384053 114 1162 63 1
79 59938 2958 261494 72 1061 89 1
80 81625 2128 237633 114 660 61 7
81 71154 2250 201783 61 690 58 2
82 104767 2610 264889 88 931 59 8
83 125386 2181 236660 88 759 67 1
84 165933 4060 383703 130 1698 146 0
85 64520 1714 173510 60 470 85 6
86 165986 3805 367807 145 1253 123 12
87 102812 2306 280343 103 656 57 2
88 81897 1940 191030 60 681 61 1
89 37110 1495 155915 51 559 53 0
90 146975 2473 314255 79 947 134 4
91 92059 1694 187167 52 705 94 2
92 144551 3085 179797 104 1044 72 1
93 184923 3705 397681 108 1415 73 13
94 79756 1250 187992 35 473 49 0
95 140015 2960 323545 99 955 56 11
96 89506 3397 311281 113 1211 153 3
97 64593 1830 157429 76 689 76 4
98 70168 1840 215710 81 611 67 2
99 134238 3553 403932 79 1564 134 4
100 101047 2649 301614 88 1030 55 0
101 92622 3246 324178 79 1490 42 10
102 14116 492 31961 22 200 22 0
103 15986 1966 150216 54 822 175 0
104 89256 2081 175523 54 868 68 3
105 150491 3574 323485 179 1079 220 5
106 140358 2676 287015 134 846 83 8
107 114948 4763 369889 176 1650 127 0
108 95671 1816 213060 73 559 48 0
109 176225 3160 303406 116 1143 146 12
110 93487 1735 195153 73 635 82 5
111 89626 2711 237323 143 824 89 9
112 66485 1758 213274 44 595 72 0
113 79089 2063 296074 106 776 79 0
114 55918 1678 153613 46 622 41 1
115 112302 2090 318563 83 779 53 1
116 104581 2591 207280 114 960 72 0
117 117440 2758 353021 81 1010 115 0
118 101629 3657 422946 119 1317 74 0
119 112098 2683 218443 104 1137 135 2
120 68946 3145 366745 142 1047 113 3
121 114799 1426 228595 66 557 58 2
122 119442 3091 369331 92 1198 72 4
123 100087 3027 279012 58 1108 125 1
124 139165 2272 278019 74 908 49 3
125 83243 1968 270750 85 617 73 0
126 123534 1337 156923 57 390 68 6
127 6179 474 46660 20 259 7 0
128 1644 151 7199 5 74 0 0
129 6023 207 14688 10 85 0 0
130 120192 2588 338543 137 1039 63 0
131 83248 2205 195817 73 779 54 0
132 103925 3314 336047 189 1174 45 2
133 72128 1646 216027 65 436 58 0
134 112431 2471 271965 69 828 99 0
135 92280 1627 236370 46 528 83 1
136 83515 3202 219420 114 1196 150 1
137 48029 2146 185468 80 716 85 4
138 93879 2616 318651 112 907 91 0
139 855 387 21054 16 146 2 0
140 100046 2549 259692 47 1140 110 0
141 31081 934 115469 32 276 36 1
142 104978 2130 219475 138 749 72 0
143 5950 496 24188 24 218 20 0
144 3926 141 17547 5 69 3 0
#Blogs #Reviews #FBMinPR #FBMinPR+120 #revisions #seconden #hyperlinks
1 44 29 107 84 17416 88229 111
2 28 30 112 83 24797 178377 60
3 44 49 182 146 10971 114198 165
4 48 31 114 90 8589 92795 96
5 81 30 111 77 13326 127097 102
6 8 35 129 89 10024 47552 49
7 95 39 145 120 16378 130332 125
8 42 36 131 100 8728 61394 50
9 22 23 88 67 3007 23824 26
10 85 44 163 157 20867 191179 165
11 49 8 28 27 7905 55792 59
12 147 50 192 179 21166 75767 132
13 142 47 181 165 22938 191889 174
14 23 11 32 30 3913 24610 31
15 70 42 162 106 16346 99776 121
16 71 43 162 87 11034 113713 100
17 135 49 191 185 21983 134163 200
18 38 32 125 45 20954 100187 141
19 154 48 187 145 21849 231257 172
20 30 14 52 41 5296 45824 35
21 13 19 71 64 7409 19630 49
22 79 35 137 128 24634 113963 158
23 48 32 117 66 17372 75882 80
24 154 46 178 161 26719 197765 171
25 125 41 158 135 20190 230054 144
26 137 50 186 175 49809 258287 254
27 98 39 144 92 22355 135213 114
28 50 37 122 113 12347 72591 70
29 86 39 144 144 18507 150773 140
30 66 43 163 151 18700 80716 70
31 76 40 149 143 27259 178303 112
32 103 46 172 163 27195 195791 187
33 160 38 141 131 23841 105590 205
34 71 34 133 127 20654 113854 135
35 135 36 135 116 25270 114268 200
36 143 28 102 89 24634 94333 104
37 69 37 143 137 21261 118845 88
38 66 25 89 84 16278 111848 74
39 73 36 138 59 11338 80684 73
40 76 48 183 163 27111 91502 121
41 101 52 201 180 19499 106314 155
42 12 1 0 0 238 5841 11
43 84 40 155 150 20920 86480 113
44 103 52 208 208 24662 102509 128
45 77 43 169 97 12526 96252 83
46 67 41 148 111 16637 80238 104
47 139 52 179 162 21857 101345 148
48 83 36 140 139 30391 111542 123
49 147 45 164 115 23201 116938 142
50 113 36 140 139 35902 164263 197
51 16 8 27 21 1888 13983 16
52 83 45 169 119 9935 74151 132
53 145 41 155 130 32616 195894 208
54 99 45 173 154 17828 102204 111
55 99 41 158 127 22883 158376 147
56 116 36 132 118 34672 134969 154
57 78 45 175 171 20065 111563 115
58 96 38 144 116 32033 186099 165
59 65 37 133 88 19354 105406 125
60 122 56 210 208 38975 210012 174
61 149 33 128 122 43068 250931 185
62 185 44 159 147 36171 169216 198
63 140 45 166 143 25217 100125 121
64 94 35 127 127 39932 162519 176
65 74 34 132 117 34416 115466 125
66 158 39 141 104 43840 211381 213
67 50 53 205 171 24959 122975 173
68 20 26 82 66 7935 56968 21
69 61 27 88 64 10621 100792 73
70 76 37 138 85 13841 115750 98
71 130 36 140 133 30927 165278 183
72 147 45 175 138 39361 175721 166
73 83 37 134 110 17696 75881 103
74 103 29 111 85 31219 111669 169
75 30 26 99 99 9648 68580 38
76 89 36 139 127 23923 81180 103
77 110 46 158 124 16786 114651 145
78 135 38 140 102 27738 232241 219
79 128 36 140 135 15049 82390 107
80 117 55 210 184 20648 94853 98
81 133 38 148 134 18050 80906 112
82 92 42 163 142 26584 124527 130
83 121 39 145 126 31852 134218 241
84 127 46 172 161 42570 147581 175
85 68 42 157 126 16747 54518 73
86 151 58 223 221 43146 189944 208
87 117 44 122 92 26759 136323 163
88 127 38 148 133 21588 89770 140
89 57 32 117 99 9845 64057 70
90 73 36 140 132 39038 135599 150
91 79 38 133 90 24648 91313 127
92 165 45 171 159 39039 81716 170
93 165 30 114 106 50099 226168 307
94 71 40 151 137 21654 122531 90
95 145 45 174 136 38086 145758 175
96 106 39 146 137 24347 160501 125
97 55 39 143 112 17672 72558 90
98 79 36 139 89 19433 104470 112
99 137 48 187 167 37343 191469 171
100 169 40 150 124 28317 135848 139
101 123 39 145 122 26041 134097 123
102 18 8 25 9 3988 13155 39
103 52 27 101 77 4527 25157 27
104 99 45 164 146 25314 104864 136
105 115 39 145 137 42721 194679 257
106 117 51 198 176 40312 117495 125
107 168 59 223 199 33433 165354 150
108 94 40 158 137 28524 160791 128
109 139 28 101 73 53405 214738 279
110 75 36 115 108 28395 133252 94
111 85 44 167 148 27269 134904 138
112 82 33 120 82 20318 110896 93
113 92 43 158 139 24409 169351 154
114 62 28 109 89 17326 83963 63
115 133 48 185 178 34811 198299 161
116 86 38 146 139 32580 116136 116
117 137 52 198 187 36809 157384 162
118 112 43 158 148 32344 188355 121
119 134 38 148 133 35926 106194 148
120 130 47 185 115 22124 174586 174
121 52 37 139 125 37062 153242 131
122 132 39 151 148 38941 189723 199
123 97 37 134 120 32982 129711 137
124 123 45 175 165 46201 184531 155
125 117 43 154 148 27652 153990 132
126 71 36 132 130 41517 100922 108
127 12 5 15 13 2089 21509 13
128 7 0 0 0 556 4245 6
129 4 0 0 0 2065 7953 5
130 146 42 153 150 41455 197680 207
131 146 40 155 103 28830 106020 130
132 124 39 151 143 36524 164808 158
133 96 30 116 87 25696 145707 126
134 123 45 171 148 40085 140303 148
135 104 41 151 135 34245 147341 140
136 138 41 153 144 31452 96785 140
137 89 23 84 36 18213 88634 82
138 130 57 206 122 36099 170492 92
139 4 0 0 0 338 6622 4
140 128 40 155 125 39844 128602 112
141 33 17 55 46 12558 58391 41
142 92 40 151 88 45873 139292 206
143 8 4 12 7 2694 15049 7
144 0 1 4 4 2658 7670 3
#blogs
1 107
2 59
3 159
4 94
5 97
6 47
7 123
8 49
9 25
10 158
11 58
12 123
13 172
14 28
15 121
16 100
17 186
18 126
19 165
20 35
21 49
22 156
23 72
24 171
25 137
26 246
27 113
28 70
29 137
30 69
31 108
32 182
33 192
34 132
35 199
36 98
37 83
38 71
39 70
40 118
41 155
42 11
43 107
44 123
45 80
46 103
47 148
48 123
49 141
50 194
51 16
52 101
53 204
54 109
55 138
56 151
57 113
58 165
59 122
60 169
61 177
62 194
63 121
64 170
65 124
66 210
67 158
68 21
69 71
70 96
71 178
72 157
73 101
74 162
75 38
76 100
77 142
78 216
79 107
80 93
81 109
82 126
83 239
84 173
85 71
86 193
87 159
88 137
89 66
90 144
91 126
92 164
93 297
94 89
95 170
96 119
97 90
98 109
99 161
100 135
101 123
102 39
103 27
104 133
105 256
106 125
107 142
108 125
109 267
110 87
111 133
112 92
113 149
114 61
115 159
116 115
117 160
118 117
119 145
120 173
121 132
122 185
123 133
124 152
125 125
126 108
127 13
128 6
129 5
130 190
131 130
132 149
133 121
134 147
135 140
136 134
137 82
138 88
139 4
140 111
141 41
142 205
143 7
144 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `#PageViews` `#SecRFC` `#LogIns`
4756.88338 -5.46229 -0.02627 13.71361
`#CourseCompViews` `#CompViewsPR` `#shared` `#Blogs`
7.86855 29.78751 1504.55655 -94.27016
`#Reviews` `#FBMinPR` `#FBMinPR+120` `#revisions`
-516.58485 63.57889 196.29651 1.53302
`#seconden` `#hyperlinks` `#blogs`
0.26752 180.04305 25.96359
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-41304 -14672 -5357 12534 93564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4756.88338 6693.65409 0.711 0.47858
`#PageViews` -5.46229 11.05058 -0.494 0.62194
`#SecRFC` -0.02627 0.06574 -0.400 0.69009
`#LogIns` 13.71361 63.05740 0.217 0.82818
`#CourseCompViews` 7.86855 22.53480 0.349 0.72753
`#CompViewsPR` 29.78751 69.45007 0.429 0.66871
`#shared` 1504.55655 610.66874 2.464 0.01506 *
`#Blogs` -94.27016 98.70631 -0.955 0.34134
`#Reviews` -516.58485 1241.61068 -0.416 0.67806
`#FBMinPR` 63.57889 339.62597 0.187 0.85180
`#FBMinPR+120` 196.29651 115.11546 1.705 0.09056 .
`#revisions` 1.53302 0.31741 4.830 3.81e-06 ***
`#seconden` 0.26752 0.08728 3.065 0.00265 **
`#hyperlinks` 180.04305 495.96958 0.363 0.71719
`#blogs` 25.96359 514.88816 0.050 0.95986
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23450 on 129 degrees of freedom
Multiple R-squared: 0.7765, Adjusted R-squared: 0.7522
F-statistic: 32.01 on 14 and 129 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,] 0.1123155988 2.246312e-01 8.876844e-01
[2,] 0.0469610968 9.392219e-02 9.530389e-01
[3,] 0.0167080064 3.341601e-02 9.832920e-01
[4,] 0.0060459927 1.209199e-02 9.939540e-01
[5,] 0.0026951049 5.390210e-03 9.973049e-01
[6,] 0.0015492005 3.098401e-03 9.984508e-01
[7,] 0.0005712945 1.142589e-03 9.994287e-01
[8,] 0.0042891992 8.578398e-03 9.957108e-01
[9,] 0.0321279600 6.425592e-02 9.678720e-01
[10,] 0.0340008598 6.800172e-02 9.659991e-01
[11,] 0.1310577091 2.621154e-01 8.689423e-01
[12,] 0.1230762147 2.461524e-01 8.769238e-01
[13,] 0.0933501636 1.867003e-01 9.066498e-01
[14,] 0.0783020631 1.566041e-01 9.216979e-01
[15,] 0.0780989303 1.561979e-01 9.219011e-01
[16,] 0.0856606829 1.713214e-01 9.143393e-01
[17,] 0.0835676620 1.671353e-01 9.164323e-01
[18,] 0.0643504323 1.287009e-01 9.356496e-01
[19,] 0.0764565291 1.529131e-01 9.235435e-01
[20,] 0.0629874794 1.259750e-01 9.370125e-01
[21,] 0.0473264227 9.465285e-02 9.526736e-01
[22,] 0.0416526994 8.330540e-02 9.583473e-01
[23,] 0.0437879603 8.757592e-02 9.562120e-01
[24,] 0.0309697963 6.193959e-02 9.690302e-01
[25,] 0.0213503848 4.270077e-02 9.786496e-01
[26,] 0.0427713135 8.554263e-02 9.572287e-01
[27,] 0.1665158254 3.330317e-01 8.334842e-01
[28,] 0.3939221777 7.878444e-01 6.060778e-01
[29,] 0.4627131191 9.254262e-01 5.372869e-01
[30,] 0.4057471718 8.114943e-01 5.942528e-01
[31,] 0.4271609738 8.543219e-01 5.728390e-01
[32,] 0.4365619619 8.731239e-01 5.634380e-01
[33,] 0.4454925633 8.909851e-01 5.545074e-01
[34,] 0.4027366431 8.054733e-01 5.972634e-01
[35,] 0.5744362613 8.511275e-01 4.255637e-01
[36,] 0.7149474081 5.701052e-01 2.850526e-01
[37,] 0.7171204705 5.657591e-01 2.828795e-01
[38,] 0.8758167250 2.483666e-01 1.241833e-01
[39,] 0.9424527365 1.150945e-01 5.754726e-02
[40,] 0.9389343209 1.221314e-01 6.106568e-02
[41,] 0.9600539605 7.989208e-02 3.994604e-02
[42,] 0.9630298956 7.394021e-02 3.697010e-02
[43,] 0.9814877910 3.702442e-02 1.851221e-02
[44,] 0.9976672298 4.665540e-03 2.332770e-03
[45,] 0.9985759096 2.848181e-03 1.424090e-03
[46,] 0.9981493753 3.701249e-03 1.850625e-03
[47,] 0.9994367144 1.126571e-03 5.632856e-04
[48,] 0.9997422124 5.155752e-04 2.577876e-04
[49,] 0.9999988288 2.342370e-06 1.171185e-06
[50,] 0.9999990334 1.933153e-06 9.665763e-07
[51,] 0.9999991267 1.746590e-06 8.732950e-07
[52,] 0.9999990251 1.949898e-06 9.749492e-07
[53,] 0.9999987734 2.453174e-06 1.226587e-06
[54,] 0.9999987831 2.433882e-06 1.216941e-06
[55,] 0.9999997440 5.119164e-07 2.559582e-07
[56,] 0.9999995947 8.106904e-07 4.053452e-07
[57,] 0.9999999165 1.669770e-07 8.348850e-08
[58,] 0.9999998808 2.383332e-07 1.191666e-07
[59,] 0.9999997862 4.275403e-07 2.137701e-07
[60,] 0.9999997033 5.933992e-07 2.966996e-07
[61,] 0.9999998997 2.005657e-07 1.002829e-07
[62,] 0.9999998606 2.788008e-07 1.394004e-07
[63,] 0.9999997720 4.559442e-07 2.279721e-07
[64,] 0.9999995524 8.952209e-07 4.476105e-07
[65,] 0.9999993968 1.206428e-06 6.032142e-07
[66,] 0.9999991951 1.609789e-06 8.048945e-07
[67,] 0.9999999211 1.578144e-07 7.890722e-08
[68,] 0.9999998775 2.450363e-07 1.225182e-07
[69,] 0.9999998954 2.092776e-07 1.046388e-07
[70,] 0.9999998592 2.815103e-07 1.407552e-07
[71,] 0.9999997173 5.654149e-07 2.827074e-07
[72,] 0.9999994705 1.059013e-06 5.295066e-07
[73,] 0.9999999158 1.684760e-07 8.423802e-08
[74,] 0.9999999582 8.359739e-08 4.179869e-08
[75,] 0.9999999906 1.883185e-08 9.415924e-09
[76,] 0.9999999903 1.940397e-08 9.701987e-09
[77,] 0.9999999884 2.326592e-08 1.163296e-08
[78,] 0.9999999821 3.588117e-08 1.794058e-08
[79,] 0.9999999702 5.952956e-08 2.976478e-08
[80,] 0.9999999403 1.194409e-07 5.972046e-08
[81,] 0.9999999540 9.209456e-08 4.604728e-08
[82,] 0.9999999748 5.037638e-08 2.518819e-08
[83,] 0.9999999918 1.637059e-08 8.185293e-09
[84,] 0.9999999843 3.135357e-08 1.567678e-08
[85,] 0.9999999700 6.001457e-08 3.000728e-08
[86,] 0.9999999144 1.711898e-07 8.559489e-08
[87,] 0.9999998206 3.587877e-07 1.793939e-07
[88,] 0.9999998206 3.588150e-07 1.794075e-07
[89,] 0.9999999657 6.850238e-08 3.425119e-08
[90,] 0.9999999464 1.071472e-07 5.357362e-08
[91,] 0.9999999107 1.785847e-07 8.929234e-08
[92,] 0.9999999716 5.676037e-08 2.838019e-08
[93,] 0.9999999400 1.199050e-07 5.995248e-08
[94,] 0.9999999142 1.716517e-07 8.582586e-08
[95,] 0.9999999095 1.810808e-07 9.054041e-08
[96,] 0.9999997545 4.910060e-07 2.455030e-07
[97,] 0.9999992207 1.558680e-06 7.793401e-07
[98,] 0.9999980264 3.947169e-06 1.973585e-06
[99,] 0.9999952914 9.417106e-06 4.708553e-06
[100,] 0.9999873593 2.528149e-05 1.264074e-05
[101,] 0.9999693863 6.122741e-05 3.061371e-05
[102,] 0.9999915606 1.687878e-05 8.439390e-06
[103,] 0.9999709759 5.804814e-05 2.902407e-05
[104,] 0.9999979510 4.097959e-06 2.048980e-06
[105,] 0.9999977961 4.407797e-06 2.203899e-06
[106,] 0.9999816268 3.674642e-05 1.837321e-05
[107,] 0.9998593566 2.812867e-04 1.406434e-04
[108,] 0.9990311732 1.937654e-03 9.688268e-04
[109,] 0.9983496083 3.300783e-03 1.650392e-03
> postscript(file="/var/wessaorg/rcomp/tmp/175hs1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2e9nf1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32m6n1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4nyjh1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5n54s1324115073.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 = 144
Frequency = 1
1 2 3 4 5 6 7
63545.391 93563.690 -12095.372 -8090.461 23542.708 24080.015 29981.275
8 9 10 11 12 13 14
19296.040 -10090.338 15933.275 16538.709 55253.035 18305.137 -2654.661
15 16 17 18 19 20 21
4675.567 -7766.337 10152.400 46878.168 7626.091 -3988.710 1503.440
22 23 24 25 26 27 28
28057.479 38030.551 15361.433 -6562.275 79763.105 33267.157 -1197.795
29 30 31 32 33 34 35
2564.920 24684.020 25033.807 8470.807 21792.544 5889.760 21389.973
36 37 38 39 40 41 42
40357.505 16821.290 3543.082 8562.940 26506.597 -1513.262 -4986.366
43 44 45 46 47 48 49
8948.880 -1380.988 -15011.959 -6389.667 10048.248 29644.129 24404.921
50 51 52 53 54 55 56
18833.357 -6193.266 -17942.646 9555.116 -18601.777 -11452.571 33778.385
57 58 59 60 61 62 63
-14818.910 4571.406 1652.787 4680.474 26104.444 22413.904 15200.191
64 65 66 67 68 69 70
16860.702 20494.605 26808.966 -15558.510 -7102.886 -13328.489 -10910.262
71 72 73 74 75 76 77
-3800.601 31171.725 1653.398 11645.613 -20133.922 -4615.640 -18362.751
78 79 80 81 82 83 84
-28373.129 -7174.241 -11486.288 -2868.910 -9712.395 -8649.257 26396.831
85 86 87 88 89 90 91
-4249.150 -14669.885 3712.476 -6534.616 -13574.701 9238.218 2007.303
92 93 94 95 96 97 98
28223.219 -20791.271 -12521.761 -1307.842 -20559.072 -11565.579 -10791.484
99 100 101 102 103 104 105
-14294.744 2168.248 -17550.087 -4838.208 -8425.320 -14938.115 -26879.523
106 107 108 109 110 111 112
6012.336 -6360.898 -20844.788 -25952.644 -13833.198 -33843.497 -9391.384
113 114 115 116 117 118 119
-37297.272 -9928.836 -31918.922 -5554.480 -16832.484 -17360.761 -6817.482
120 121 122 123 124 125 126
-41304.287 -20370.997 -34724.089 -12745.336 -20729.217 -28043.442 -6214.532
127 128 129 130 131 132 133
-8709.611 -5313.905 -3969.468 -35813.231 -8045.629 -28311.140 -28116.365
134 135 136 137 138 139 140
-16623.694 -32013.963 -21532.097 -16074.657 -14744.013 -5399.249 -17049.582
141 142 143 144
-14680.102 -39845.695 -7016.731 -7568.118
> postscript(file="/var/wessaorg/rcomp/tmp/60qmc1324115073.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 63545.391 NA
1 93563.690 63545.391
2 -12095.372 93563.690
3 -8090.461 -12095.372
4 23542.708 -8090.461
5 24080.015 23542.708
6 29981.275 24080.015
7 19296.040 29981.275
8 -10090.338 19296.040
9 15933.275 -10090.338
10 16538.709 15933.275
11 55253.035 16538.709
12 18305.137 55253.035
13 -2654.661 18305.137
14 4675.567 -2654.661
15 -7766.337 4675.567
16 10152.400 -7766.337
17 46878.168 10152.400
18 7626.091 46878.168
19 -3988.710 7626.091
20 1503.440 -3988.710
21 28057.479 1503.440
22 38030.551 28057.479
23 15361.433 38030.551
24 -6562.275 15361.433
25 79763.105 -6562.275
26 33267.157 79763.105
27 -1197.795 33267.157
28 2564.920 -1197.795
29 24684.020 2564.920
30 25033.807 24684.020
31 8470.807 25033.807
32 21792.544 8470.807
33 5889.760 21792.544
34 21389.973 5889.760
35 40357.505 21389.973
36 16821.290 40357.505
37 3543.082 16821.290
38 8562.940 3543.082
39 26506.597 8562.940
40 -1513.262 26506.597
41 -4986.366 -1513.262
42 8948.880 -4986.366
43 -1380.988 8948.880
44 -15011.959 -1380.988
45 -6389.667 -15011.959
46 10048.248 -6389.667
47 29644.129 10048.248
48 24404.921 29644.129
49 18833.357 24404.921
50 -6193.266 18833.357
51 -17942.646 -6193.266
52 9555.116 -17942.646
53 -18601.777 9555.116
54 -11452.571 -18601.777
55 33778.385 -11452.571
56 -14818.910 33778.385
57 4571.406 -14818.910
58 1652.787 4571.406
59 4680.474 1652.787
60 26104.444 4680.474
61 22413.904 26104.444
62 15200.191 22413.904
63 16860.702 15200.191
64 20494.605 16860.702
65 26808.966 20494.605
66 -15558.510 26808.966
67 -7102.886 -15558.510
68 -13328.489 -7102.886
69 -10910.262 -13328.489
70 -3800.601 -10910.262
71 31171.725 -3800.601
72 1653.398 31171.725
73 11645.613 1653.398
74 -20133.922 11645.613
75 -4615.640 -20133.922
76 -18362.751 -4615.640
77 -28373.129 -18362.751
78 -7174.241 -28373.129
79 -11486.288 -7174.241
80 -2868.910 -11486.288
81 -9712.395 -2868.910
82 -8649.257 -9712.395
83 26396.831 -8649.257
84 -4249.150 26396.831
85 -14669.885 -4249.150
86 3712.476 -14669.885
87 -6534.616 3712.476
88 -13574.701 -6534.616
89 9238.218 -13574.701
90 2007.303 9238.218
91 28223.219 2007.303
92 -20791.271 28223.219
93 -12521.761 -20791.271
94 -1307.842 -12521.761
95 -20559.072 -1307.842
96 -11565.579 -20559.072
97 -10791.484 -11565.579
98 -14294.744 -10791.484
99 2168.248 -14294.744
100 -17550.087 2168.248
101 -4838.208 -17550.087
102 -8425.320 -4838.208
103 -14938.115 -8425.320
104 -26879.523 -14938.115
105 6012.336 -26879.523
106 -6360.898 6012.336
107 -20844.788 -6360.898
108 -25952.644 -20844.788
109 -13833.198 -25952.644
110 -33843.497 -13833.198
111 -9391.384 -33843.497
112 -37297.272 -9391.384
113 -9928.836 -37297.272
114 -31918.922 -9928.836
115 -5554.480 -31918.922
116 -16832.484 -5554.480
117 -17360.761 -16832.484
118 -6817.482 -17360.761
119 -41304.287 -6817.482
120 -20370.997 -41304.287
121 -34724.089 -20370.997
122 -12745.336 -34724.089
123 -20729.217 -12745.336
124 -28043.442 -20729.217
125 -6214.532 -28043.442
126 -8709.611 -6214.532
127 -5313.905 -8709.611
128 -3969.468 -5313.905
129 -35813.231 -3969.468
130 -8045.629 -35813.231
131 -28311.140 -8045.629
132 -28116.365 -28311.140
133 -16623.694 -28116.365
134 -32013.963 -16623.694
135 -21532.097 -32013.963
136 -16074.657 -21532.097
137 -14744.013 -16074.657
138 -5399.249 -14744.013
139 -17049.582 -5399.249
140 -14680.102 -17049.582
141 -39845.695 -14680.102
142 -7016.731 -39845.695
143 -7568.118 -7016.731
144 NA -7568.118
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 93563.690 63545.391
[2,] -12095.372 93563.690
[3,] -8090.461 -12095.372
[4,] 23542.708 -8090.461
[5,] 24080.015 23542.708
[6,] 29981.275 24080.015
[7,] 19296.040 29981.275
[8,] -10090.338 19296.040
[9,] 15933.275 -10090.338
[10,] 16538.709 15933.275
[11,] 55253.035 16538.709
[12,] 18305.137 55253.035
[13,] -2654.661 18305.137
[14,] 4675.567 -2654.661
[15,] -7766.337 4675.567
[16,] 10152.400 -7766.337
[17,] 46878.168 10152.400
[18,] 7626.091 46878.168
[19,] -3988.710 7626.091
[20,] 1503.440 -3988.710
[21,] 28057.479 1503.440
[22,] 38030.551 28057.479
[23,] 15361.433 38030.551
[24,] -6562.275 15361.433
[25,] 79763.105 -6562.275
[26,] 33267.157 79763.105
[27,] -1197.795 33267.157
[28,] 2564.920 -1197.795
[29,] 24684.020 2564.920
[30,] 25033.807 24684.020
[31,] 8470.807 25033.807
[32,] 21792.544 8470.807
[33,] 5889.760 21792.544
[34,] 21389.973 5889.760
[35,] 40357.505 21389.973
[36,] 16821.290 40357.505
[37,] 3543.082 16821.290
[38,] 8562.940 3543.082
[39,] 26506.597 8562.940
[40,] -1513.262 26506.597
[41,] -4986.366 -1513.262
[42,] 8948.880 -4986.366
[43,] -1380.988 8948.880
[44,] -15011.959 -1380.988
[45,] -6389.667 -15011.959
[46,] 10048.248 -6389.667
[47,] 29644.129 10048.248
[48,] 24404.921 29644.129
[49,] 18833.357 24404.921
[50,] -6193.266 18833.357
[51,] -17942.646 -6193.266
[52,] 9555.116 -17942.646
[53,] -18601.777 9555.116
[54,] -11452.571 -18601.777
[55,] 33778.385 -11452.571
[56,] -14818.910 33778.385
[57,] 4571.406 -14818.910
[58,] 1652.787 4571.406
[59,] 4680.474 1652.787
[60,] 26104.444 4680.474
[61,] 22413.904 26104.444
[62,] 15200.191 22413.904
[63,] 16860.702 15200.191
[64,] 20494.605 16860.702
[65,] 26808.966 20494.605
[66,] -15558.510 26808.966
[67,] -7102.886 -15558.510
[68,] -13328.489 -7102.886
[69,] -10910.262 -13328.489
[70,] -3800.601 -10910.262
[71,] 31171.725 -3800.601
[72,] 1653.398 31171.725
[73,] 11645.613 1653.398
[74,] -20133.922 11645.613
[75,] -4615.640 -20133.922
[76,] -18362.751 -4615.640
[77,] -28373.129 -18362.751
[78,] -7174.241 -28373.129
[79,] -11486.288 -7174.241
[80,] -2868.910 -11486.288
[81,] -9712.395 -2868.910
[82,] -8649.257 -9712.395
[83,] 26396.831 -8649.257
[84,] -4249.150 26396.831
[85,] -14669.885 -4249.150
[86,] 3712.476 -14669.885
[87,] -6534.616 3712.476
[88,] -13574.701 -6534.616
[89,] 9238.218 -13574.701
[90,] 2007.303 9238.218
[91,] 28223.219 2007.303
[92,] -20791.271 28223.219
[93,] -12521.761 -20791.271
[94,] -1307.842 -12521.761
[95,] -20559.072 -1307.842
[96,] -11565.579 -20559.072
[97,] -10791.484 -11565.579
[98,] -14294.744 -10791.484
[99,] 2168.248 -14294.744
[100,] -17550.087 2168.248
[101,] -4838.208 -17550.087
[102,] -8425.320 -4838.208
[103,] -14938.115 -8425.320
[104,] -26879.523 -14938.115
[105,] 6012.336 -26879.523
[106,] -6360.898 6012.336
[107,] -20844.788 -6360.898
[108,] -25952.644 -20844.788
[109,] -13833.198 -25952.644
[110,] -33843.497 -13833.198
[111,] -9391.384 -33843.497
[112,] -37297.272 -9391.384
[113,] -9928.836 -37297.272
[114,] -31918.922 -9928.836
[115,] -5554.480 -31918.922
[116,] -16832.484 -5554.480
[117,] -17360.761 -16832.484
[118,] -6817.482 -17360.761
[119,] -41304.287 -6817.482
[120,] -20370.997 -41304.287
[121,] -34724.089 -20370.997
[122,] -12745.336 -34724.089
[123,] -20729.217 -12745.336
[124,] -28043.442 -20729.217
[125,] -6214.532 -28043.442
[126,] -8709.611 -6214.532
[127,] -5313.905 -8709.611
[128,] -3969.468 -5313.905
[129,] -35813.231 -3969.468
[130,] -8045.629 -35813.231
[131,] -28311.140 -8045.629
[132,] -28116.365 -28311.140
[133,] -16623.694 -28116.365
[134,] -32013.963 -16623.694
[135,] -21532.097 -32013.963
[136,] -16074.657 -21532.097
[137,] -14744.013 -16074.657
[138,] -5399.249 -14744.013
[139,] -17049.582 -5399.249
[140,] -14680.102 -17049.582
[141,] -39845.695 -14680.102
[142,] -7016.731 -39845.695
[143,] -7568.118 -7016.731
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 93563.690 63545.391
2 -12095.372 93563.690
3 -8090.461 -12095.372
4 23542.708 -8090.461
5 24080.015 23542.708
6 29981.275 24080.015
7 19296.040 29981.275
8 -10090.338 19296.040
9 15933.275 -10090.338
10 16538.709 15933.275
11 55253.035 16538.709
12 18305.137 55253.035
13 -2654.661 18305.137
14 4675.567 -2654.661
15 -7766.337 4675.567
16 10152.400 -7766.337
17 46878.168 10152.400
18 7626.091 46878.168
19 -3988.710 7626.091
20 1503.440 -3988.710
21 28057.479 1503.440
22 38030.551 28057.479
23 15361.433 38030.551
24 -6562.275 15361.433
25 79763.105 -6562.275
26 33267.157 79763.105
27 -1197.795 33267.157
28 2564.920 -1197.795
29 24684.020 2564.920
30 25033.807 24684.020
31 8470.807 25033.807
32 21792.544 8470.807
33 5889.760 21792.544
34 21389.973 5889.760
35 40357.505 21389.973
36 16821.290 40357.505
37 3543.082 16821.290
38 8562.940 3543.082
39 26506.597 8562.940
40 -1513.262 26506.597
41 -4986.366 -1513.262
42 8948.880 -4986.366
43 -1380.988 8948.880
44 -15011.959 -1380.988
45 -6389.667 -15011.959
46 10048.248 -6389.667
47 29644.129 10048.248
48 24404.921 29644.129
49 18833.357 24404.921
50 -6193.266 18833.357
51 -17942.646 -6193.266
52 9555.116 -17942.646
53 -18601.777 9555.116
54 -11452.571 -18601.777
55 33778.385 -11452.571
56 -14818.910 33778.385
57 4571.406 -14818.910
58 1652.787 4571.406
59 4680.474 1652.787
60 26104.444 4680.474
61 22413.904 26104.444
62 15200.191 22413.904
63 16860.702 15200.191
64 20494.605 16860.702
65 26808.966 20494.605
66 -15558.510 26808.966
67 -7102.886 -15558.510
68 -13328.489 -7102.886
69 -10910.262 -13328.489
70 -3800.601 -10910.262
71 31171.725 -3800.601
72 1653.398 31171.725
73 11645.613 1653.398
74 -20133.922 11645.613
75 -4615.640 -20133.922
76 -18362.751 -4615.640
77 -28373.129 -18362.751
78 -7174.241 -28373.129
79 -11486.288 -7174.241
80 -2868.910 -11486.288
81 -9712.395 -2868.910
82 -8649.257 -9712.395
83 26396.831 -8649.257
84 -4249.150 26396.831
85 -14669.885 -4249.150
86 3712.476 -14669.885
87 -6534.616 3712.476
88 -13574.701 -6534.616
89 9238.218 -13574.701
90 2007.303 9238.218
91 28223.219 2007.303
92 -20791.271 28223.219
93 -12521.761 -20791.271
94 -1307.842 -12521.761
95 -20559.072 -1307.842
96 -11565.579 -20559.072
97 -10791.484 -11565.579
98 -14294.744 -10791.484
99 2168.248 -14294.744
100 -17550.087 2168.248
101 -4838.208 -17550.087
102 -8425.320 -4838.208
103 -14938.115 -8425.320
104 -26879.523 -14938.115
105 6012.336 -26879.523
106 -6360.898 6012.336
107 -20844.788 -6360.898
108 -25952.644 -20844.788
109 -13833.198 -25952.644
110 -33843.497 -13833.198
111 -9391.384 -33843.497
112 -37297.272 -9391.384
113 -9928.836 -37297.272
114 -31918.922 -9928.836
115 -5554.480 -31918.922
116 -16832.484 -5554.480
117 -17360.761 -16832.484
118 -6817.482 -17360.761
119 -41304.287 -6817.482
120 -20370.997 -41304.287
121 -34724.089 -20370.997
122 -12745.336 -34724.089
123 -20729.217 -12745.336
124 -28043.442 -20729.217
125 -6214.532 -28043.442
126 -8709.611 -6214.532
127 -5313.905 -8709.611
128 -3969.468 -5313.905
129 -35813.231 -3969.468
130 -8045.629 -35813.231
131 -28311.140 -8045.629
132 -28116.365 -28311.140
133 -16623.694 -28116.365
134 -32013.963 -16623.694
135 -21532.097 -32013.963
136 -16074.657 -21532.097
137 -14744.013 -16074.657
138 -5399.249 -14744.013
139 -17049.582 -5399.249
140 -14680.102 -17049.582
141 -39845.695 -14680.102
142 -7016.731 -39845.695
143 -7568.118 -7016.731
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/749fi1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89nxh1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sf961324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10oxlr1324115073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11jre61324115073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12qyod1324115073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/133t641324115073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14jtho1324115073.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/153w7d1324115073.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16j4vc1324115073.tab")
+ }
>
> try(system("convert tmp/175hs1324115073.ps tmp/175hs1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e9nf1324115073.ps tmp/2e9nf1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/32m6n1324115073.ps tmp/32m6n1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nyjh1324115073.ps tmp/4nyjh1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n54s1324115073.ps tmp/5n54s1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/60qmc1324115073.ps tmp/60qmc1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/749fi1324115073.ps tmp/749fi1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/89nxh1324115073.ps tmp/89nxh1324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sf961324115073.ps tmp/9sf961324115073.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oxlr1324115073.ps tmp/10oxlr1324115073.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.521 0.642 6.187