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(1818
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+ ,53
+ ,2
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+ ,125
+ ,71
+ ,100350
+ ,17372
+ ,75882
+ ,80
+ ,72)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('Pageviews'
+ ,'TimeRFC'
+ ,'Logins'
+ ,'CCviews'
+ ,'Cviews'
+ ,'Shares'
+ ,'BloggedC'
+ ,'RVC'
+ ,'Feedbackm'
+ ,'Feedback+120'
+ ,'Characters'
+ ,'Revisions'
+ ,'Seconds'
+ ,'Hyperlinks'
+ ,'Blogs')
+ ,1:164))
> y <- array(NA,dim=c(15,164),dimnames=list(c('Pageviews','TimeRFC','Logins','CCviews','Cviews','Shares','BloggedC','RVC','Feedbackm','Feedback+120','Characters','Revisions','Seconds','Hyperlinks','Blogs'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
TimeRFC Pageviews Logins CCviews Cviews Shares BloggedC RVC Feedbackm
1 279055 1818 73 504 95 3 96 42 159
2 212450 1439 76 513 68 4 75 38 149
3 233939 2059 83 710 64 16 70 46 178
4 222117 2733 106 1154 139 2 134 42 164
5 189911 1399 56 415 51 1 83 30 100
6 70849 631 28 179 46 3 8 35 129
7 605767 5460 135 2563 118 0 173 40 156
8 33186 381 19 111 46 0 1 18 67
9 227332 2150 62 763 79 7 88 38 148
10 267925 2042 49 661 76 0 104 37 132
11 372083 2541 123 983 82 0 114 46 169
12 279589 2449 133 754 66 7 132 60 230
13 212638 2100 87 785 60 10 57 37 122
14 368577 3020 85 1186 117 4 139 55 191
15 269455 2265 88 724 50 10 87 44 162
16 402600 5169 197 1783 133 0 176 63 237
17 335735 2375 78 850 63 8 114 40 156
18 432711 3564 173 1390 100 4 121 43 157
19 185822 1516 59 527 44 3 103 32 123
20 267365 2398 89 692 65 8 135 52 203
21 279452 2551 74 849 103 0 123 49 187
22 527853 3253 112 1443 103 1 110 41 152
23 227252 1761 50 587 62 5 84 25 89
24 200004 1787 58 636 70 9 103 57 227
25 257239 3796 134 1371 159 1 158 45 165
26 271341 3113 139 1094 78 0 116 42 162
27 324969 3230 134 1201 101 5 114 45 174
28 349753 2494 93 795 73 0 181 43 154
29 200723 1856 63 674 58 0 76 36 129
30 399591 3257 80 1230 147 0 155 45 174
31 327660 2692 89 1111 54 3 143 50 195
32 269239 2187 83 758 84 6 50 50 186
33 397689 2593 107 911 56 1 145 51 197
34 130446 1293 49 456 45 4 56 42 157
35 430118 3567 104 1293 87 4 141 44 168
36 273950 2764 56 1186 87 0 83 42 159
37 429837 3766 129 1353 77 0 112 44 161
38 254312 2075 93 695 72 2 79 40 153
39 120351 995 35 306 36 1 33 17 55
40 395658 3750 212 1319 51 2 152 43 166
41 349119 3437 87 1582 44 10 126 41 151
42 220517 2071 86 788 75 10 97 41 148
43 234780 2038 85 768 87 5 84 40 129
44 191784 1841 71 491 97 6 68 49 181
45 186112 2964 94 1211 90 1 64 52 93
46 459665 5578 158 2091 860 2 101 42 150
47 78800 918 42 330 57 2 20 26 82
48 259887 2704 87 770 99 1 107 59 229
49 368086 4145 123 1410 120 10 150 50 193
50 230299 2841 70 1187 76 3 129 50 176
51 256033 2222 82 706 56 0 102 47 179
52 24188 496 24 218 20 0 8 4 12
53 400109 2699 334 865 94 8 88 51 181
54 65029 744 17 255 21 5 21 18 67
55 101097 1161 64 454 70 3 30 14 52
56 319020 3405 68 1245 133 1 106 41 148
57 375638 2970 91 790 86 5 166 61 230
58 375011 4002 207 1233 224 6 132 40 148
59 387748 2919 156 1118 65 0 161 44 160
60 280106 2399 90 919 86 12 90 40 155
61 400971 4121 153 1352 70 10 160 51 198
62 322780 3330 124 1212 148 12 139 29 104
63 291391 3132 124 1257 72 11 104 43 169
64 295075 2868 93 1030 59 8 103 42 163
65 280018 1778 81 669 67 3 66 41 151
66 267432 2109 71 542 58 0 163 30 116
67 217181 2148 141 652 60 6 93 39 153
68 258166 3009 159 894 105 10 85 51 195
69 277891 2624 90 935 84 2 159 40 149
70 192894 1781 74 659 63 5 146 29 106
71 271853 2735 75 917 67 13 122 47 179
72 73566 893 32 385 39 6 22 23 88
73 276269 2411 96 789 60 7 85 48 185
74 242619 2291 64 949 94 2 105 38 133
75 230030 2235 71 784 67 5 131 42 164
76 371391 2370 91 1001 96 4 140 46 169
77 398698 3242 107 1268 54 3 156 40 153
78 243355 2080 114 616 54 6 89 45 166
79 233519 2537 73 775 62 2 137 42 164
80 219936 2149 74 746 71 0 102 41 146
81 206169 2150 54 795 50 1 74 37 141
82 483429 4232 133 1273 117 3 161 47 183
83 146100 1380 72 657 45 5 30 26 99
84 295224 2449 109 703 61 2 120 48 134
85 80953 870 25 437 31 0 49 8 28
86 217384 2700 63 1060 175 0 121 27 101
87 179344 1574 62 459 70 6 76 38 139
88 416097 4055 223 1591 284 1 85 41 159
89 395041 3327 131 1102 95 4 152 61 222
90 180679 3098 106 1051 72 1 165 45 171
91 311447 2673 105 850 63 1 89 41 154
92 292260 2404 84 732 75 3 168 42 154
93 199481 1932 68 632 90 10 48 35 129
94 282361 3147 78 1128 89 1 149 36 140
95 329281 2598 89 971 138 4 75 40 156
96 234577 2108 48 711 68 5 107 40 156
97 310685 2240 68 750 80 7 121 38 138
98 352078 2506 91 904 65 0 184 43 153
99 416463 4198 163 1369 130 12 155 65 251
100 429565 4165 120 1538 85 13 165 33 126
101 297080 2842 142 893 83 9 121 51 198
102 331792 2562 71 926 89 0 176 45 168
103 242507 2564 205 833 116 0 92 36 138
104 43287 602 14 214 43 4 13 19 71
105 238089 2579 87 833 87 4 120 25 90
106 285479 2665 164 931 80 0 124 44 167
107 310383 3004 63 1304 132 0 133 45 172
108 321797 2786 95 1079 59 0 169 44 162
109 193926 1477 96 490 50 0 39 35 129
110 175737 3358 106 992 87 0 126 46 179
111 354041 2107 78 677 62 5 82 44 163
112 303566 2338 92 698 70 1 148 45 164
113 23668 400 13 156 9 0 12 1 0
114 196743 2233 79 785 54 0 146 40 155
115 61857 530 25 192 25 4 23 11 32
116 217543 2033 54 641 113 0 87 51 189
117 440711 3246 128 1251 63 1 164 38 140
118 21054 387 16 146 2 0 4 0 0
119 252805 2137 52 866 67 5 81 30 111
120 31961 492 22 200 22 0 18 8 25
121 360436 3838 125 1351 157 3 118 43 159
122 251948 2193 77 740 79 7 76 48 183
123 187320 1796 97 524 113 14 55 49 184
124 180842 1907 58 724 50 3 62 32 119
125 38214 568 34 276 52 0 16 8 27
126 289296 2647 58 869 113 3 98 43 163
127 358276 2819 84 1031 115 0 137 52 198
128 211775 1464 67 511 78 0 50 53 205
129 447335 3946 90 1716 135 4 152 49 191
130 348017 2554 99 884 120 0 163 48 187
131 441946 3506 133 1201 122 3 142 56 210
132 215177 1552 43 575 54 0 80 45 166
133 140328 1476 48 507 63 0 65 40 145
134 318092 3105 366 1033 162 4 94 48 187
135 466139 4541 198 1574 162 5 128 50 186
136 162406 1876 63 576 107 16 63 43 164
137 417354 4469 142 1834 146 6 127 46 172
138 178322 2113 86 790 77 5 60 40 147
139 292443 2046 54 668 87 2 118 45 167
140 283913 2564 100 905 192 1 110 46 158
141 253950 2209 131 723 75 2 48 37 144
142 389698 4123 126 1619 131 9 96 45 169
143 246963 2340 93 811 67 1 128 39 145
144 173260 2035 63 716 37 3 41 21 79
145 346748 3241 108 1034 61 11 146 50 194
146 188437 2056 62 782 127 5 147 55 212
147 279125 2872 98 1103 58 2 121 40 148
148 314070 2749 113 852 71 1 185 48 171
149 1 2 0 0 0 9 0 0 0
150 14688 207 10 85 0 0 4 0 0
151 98 5 1 0 0 0 0 0 0
152 455 8 2 0 0 0 0 0 0
153 0 0 0 0 0 1 0 0 0
154 0 0 0 0 0 0 0 0 0
155 291847 2449 95 816 72 2 85 46 141
156 415839 3497 170 1145 123 3 164 52 204
157 0 0 0 0 0 0 0 0 0
158 203 4 4 0 0 0 0 0 0
159 7199 151 5 74 0 0 7 0 0
160 46660 475 21 259 7 0 12 5 15
161 17547 141 5 69 3 0 0 1 4
162 121550 1145 46 309 106 0 37 48 172
163 969 29 2 0 0 0 0 0 0
164 242774 2080 75 695 53 2 62 34 125
Feedback+120 Characters Revisions Seconds Hyperlinks Blogs
1 130 140824 32033 186099 165 165
2 143 110459 20654 113854 135 132
3 118 105079 16346 99776 121 121
4 146 112098 35926 106194 148 145
5 73 43929 10621 100792 73 71
6 89 76173 10024 47552 49 47
7 146 187326 43068 250931 185 177
8 22 22807 1271 6853 5 5
9 132 144408 34416 115466 125 124
10 92 66485 20318 110896 93 92
11 147 79089 24409 169351 154 149
12 203 81625 20648 94853 98 93
13 113 68788 12347 72591 70 70
14 171 103297 21857 101345 148 148
15 87 69446 11034 113713 100 100
16 208 114948 33433 165354 150 142
17 153 167949 35902 164263 197 194
18 97 125081 22355 135213 114 113
19 95 125818 31219 111669 169 162
20 197 136588 21983 134163 200 186
21 160 112431 40085 140303 148 147
22 148 103037 18507 150773 140 137
23 84 82317 16278 111848 74 71
24 227 118906 24662 102509 128 123
25 154 83515 31452 96785 140 134
26 151 104581 32580 116136 116 115
27 142 103129 22883 158376 147 138
28 148 83243 27652 153990 132 125
29 110 37110 9845 64057 70 66
30 149 113344 20190 230054 144 137
31 179 139165 46201 184531 155 152
32 149 86652 10971 114198 165 159
33 187 112302 34811 198299 161 159
34 153 69652 3029 33750 31 31
35 163 119442 38941 189723 199 185
36 127 69867 4958 100826 78 78
37 151 101629 32344 188355 121 117
38 100 70168 19433 104470 112 109
39 46 31081 12558 58391 41 41
40 156 103925 36524 164808 158 149
41 128 92622 26041 134097 123 123
42 111 79011 16637 80238 104 103
43 119 93487 28395 133252 94 87
44 148 64520 16747 54518 73 71
45 65 93473 9105 121850 52 51
46 134 114360 11941 79367 71 70
47 66 33032 7935 56968 21 21
48 201 96125 19499 106314 155 155
49 177 151911 22938 191889 174 172
50 156 89256 25314 104864 136 133
51 158 95676 28527 160792 128 125
52 7 5950 2694 15049 7 7
53 175 149695 20867 191179 165 158
54 61 32551 3597 25109 21 21
55 41 31701 5296 45824 35 35
56 133 100087 32982 129711 137 133
57 228 169707 38975 210012 174 169
58 140 150491 42721 194679 257 256
59 155 120192 41455 197680 207 190
60 141 95893 23923 81180 103 100
61 181 151715 26719 197765 171 171
62 75 176225 53405 214738 279 267
63 97 59900 12526 96252 83 80
64 142 104767 26584 124527 130 126
65 136 114799 37062 153242 131 132
66 87 72128 25696 145707 126 121
67 140 143592 24634 113963 158 156
68 169 89626 27269 134904 138 133
69 129 131072 25270 114268 200 199
70 92 126817 24634 94333 104 98
71 160 81351 17828 102204 111 109
72 67 22618 3007 23824 26 25
73 179 88977 20065 111563 115 113
74 90 92059 24648 91313 127 126
75 144 81897 21588 89770 140 137
76 144 108146 25217 100125 121 121
77 144 126372 30927 165278 183 178
78 134 249771 18487 181712 68 63
79 146 71154 18050 80906 112 109
80 121 71571 17696 75881 103 101
81 112 55918 17326 83963 63 61
82 145 160141 39361 175721 166 157
83 99 38692 9648 68580 38 38
84 96 102812 26759 136323 163 159
85 27 56622 7905 55792 59 58
86 77 15986 4527 25157 27 27
87 137 123534 41517 100922 108 108
88 151 108535 21261 118845 88 83
89 126 93879 36099 170492 92 88
90 159 144551 39039 81716 170 164
91 101 56750 13841 115750 98 96
92 144 127654 23841 105590 205 192
93 102 65594 8589 92795 96 94
94 135 59938 15049 82390 107 107
95 147 146975 39038 135599 150 144
96 155 165904 36774 127667 138 136
97 138 169265 40076 163073 177 171
98 113 183500 43840 211381 213 210
99 248 165986 43146 189944 208 193
100 116 184923 50099 226168 307 297
101 176 140358 40312 117495 125 125
102 140 149959 32616 195894 208 204
103 59 57224 11338 80684 73 70
104 64 43750 7409 19630 49 49
105 40 48029 18213 88634 82 82
106 98 104978 45873 139292 206 205
107 139 100046 39844 128602 112 111
108 135 101047 28317 135848 139 135
109 97 197426 24797 178377 60 59
110 142 160902 7471 106330 70 70
111 155 147172 27259 178303 112 108
112 115 109432 23201 116938 142 141
113 0 1168 238 5841 11 11
114 103 83248 28830 106020 130 130
115 30 25162 3913 24610 31 28
116 130 45724 9935 74151 132 101
117 102 110529 27738 232241 219 216
118 0 855 338 6622 4 4
119 77 101382 13326 127097 102 97
120 9 14116 3988 13155 39 39
121 150 89506 24347 160501 125 119
122 163 135356 27111 91502 121 118
123 148 116066 3938 24469 42 41
124 94 144244 17416 88229 111 107
125 21 8773 1888 13983 16 16
126 151 102153 18700 80716 70 69
127 187 117440 36809 157384 162 160
128 171 104128 24959 122975 173 158
129 170 134238 37343 191469 171 161
130 145 134047 21849 231257 172 165
131 198 279488 49809 258287 254 246
132 152 79756 21654 122531 90 89
133 112 66089 8728 61394 50 49
134 173 102070 20920 86480 113 107
135 177 146760 27195 195791 187 182
136 153 154771 1037 18284 16 16
137 161 165933 42570 147581 175 173
138 115 64593 17672 72558 90 90
139 147 92280 34245 147341 140 140
140 124 67150 16786 114651 145 142
141 57 128692 20954 100187 141 126
142 144 124089 16378 130332 125 123
143 126 125386 31852 134218 241 239
144 78 37238 2805 10901 16 15
145 153 140015 38086 145758 175 170
146 196 150047 21166 75767 132 123
147 130 154451 34672 134969 154 151
148 159 156349 36171 169216 198 194
149 0 0 0 0 0 0
150 0 6023 2065 7953 5 5
151 0 0 0 0 0 0
152 0 0 0 0 0 0
153 0 0 0 0 0 0
154 0 0 0 0 0 0
155 94 84601 19354 105406 125 122
156 129 68946 22124 174586 174 173
157 0 0 0 0 0 0
158 0 0 0 0 0 0
159 0 1644 556 4245 6 6
160 13 6179 2089 21509 13 13
161 4 3926 2658 7670 3 3
162 89 52789 1813 15673 35 35
163 0 0 0 0 0 0
164 71 100350 17372 75882 80 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pageviews Logins CCviews Cviews
-4359.4098 6.2542 163.6075 102.4789 87.7266
Shares BloggedC RVC Feedbackm `Feedback+120`
668.8757 157.4432 -1033.3026 551.1757 -11.3184
Characters Revisions Seconds Hyperlinks Blogs
-0.2323 -0.3883 0.9933 98.3101 -84.2636
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-103216 -18153 31 14142 158047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4359.4098 7643.1534 -0.570 0.569287
Pageviews 6.2542 14.7846 0.423 0.672891
Logins 163.6075 83.0746 1.969 0.050761 .
CCviews 102.4789 29.1153 3.520 0.000573 ***
Cviews 87.7266 52.4932 1.671 0.096782 .
Shares 668.8757 879.4677 0.761 0.448130
BloggedC 157.4432 131.7199 1.195 0.233873
RVC -1033.3026 1052.3939 -0.982 0.327761
Feedbackm 551.1757 323.7104 1.703 0.090713 .
`Feedback+120` -11.3184 161.9336 -0.070 0.944371
Characters -0.2323 0.1078 -2.155 0.032756 *
Revisions -0.3883 0.4741 -0.819 0.414108
Seconds 0.9933 0.1108 8.966 1.2e-15 ***
Hyperlinks 98.3101 755.6472 0.130 0.896662
Blogs -84.2636 785.2957 -0.107 0.914694
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35940 on 149 degrees of freedom
Multiple R-squared: 0.9264, Adjusted R-squared: 0.9194
F-statistic: 133.9 on 14 and 149 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.3221677 6.443355e-01 6.778323e-01
[2,] 0.1785543 3.571086e-01 8.214457e-01
[3,] 0.4223137 8.446274e-01 5.776863e-01
[4,] 0.3406440 6.812880e-01 6.593560e-01
[5,] 0.8362361 3.275278e-01 1.637639e-01
[6,] 0.8056181 3.887637e-01 1.943819e-01
[7,] 0.7928983 4.142035e-01 2.071017e-01
[8,] 0.7640060 4.719879e-01 2.359940e-01
[9,] 0.8144128 3.711743e-01 1.855872e-01
[10,] 0.7635896 4.728207e-01 2.364104e-01
[11,] 0.7201410 5.597180e-01 2.798590e-01
[12,] 0.6510646 6.978709e-01 3.489354e-01
[13,] 0.6980004 6.039992e-01 3.019996e-01
[14,] 0.6578393 6.843214e-01 3.421607e-01
[15,] 0.5918022 8.163957e-01 4.081978e-01
[16,] 0.5371286 9.257428e-01 4.628714e-01
[17,] 0.6682688 6.634625e-01 3.317312e-01
[18,] 0.8876018 2.247964e-01 1.123982e-01
[19,] 0.8996183 2.007633e-01 1.003817e-01
[20,] 0.8811314 2.377372e-01 1.188686e-01
[21,] 0.8681634 2.636733e-01 1.318366e-01
[22,] 0.8347036 3.305928e-01 1.652964e-01
[23,] 0.8018253 3.963494e-01 1.981747e-01
[24,] 0.8408393 3.183214e-01 1.591607e-01
[25,] 0.8035448 3.929103e-01 1.964552e-01
[26,] 0.7866225 4.267550e-01 2.133775e-01
[27,] 0.8542425 2.915149e-01 1.457575e-01
[28,] 0.9803187 3.936258e-02 1.968129e-02
[29,] 0.9819520 3.609607e-02 1.804803e-02
[30,] 0.9838619 3.227621e-02 1.613811e-02
[31,] 0.9803212 3.935755e-02 1.967878e-02
[32,] 0.9925304 1.493911e-02 7.469557e-03
[33,] 0.9965293 6.941397e-03 3.470698e-03
[34,] 0.9963093 7.381411e-03 3.690705e-03
[35,] 0.9964668 7.066352e-03 3.533176e-03
[36,] 0.9962620 7.475951e-03 3.737975e-03
[37,] 0.9946402 1.071956e-02 5.359782e-03
[38,] 0.9942385 1.152290e-02 5.761451e-03
[39,] 0.9926165 1.476701e-02 7.383507e-03
[40,] 0.9908630 1.827395e-02 9.136975e-03
[41,] 0.9921070 1.578597e-02 7.892984e-03
[42,] 0.9889464 2.210729e-02 1.105365e-02
[43,] 0.9905503 1.889935e-02 9.449676e-03
[44,] 0.9890353 2.192935e-02 1.096468e-02
[45,] 0.9945096 1.098072e-02 5.490359e-03
[46,] 0.9931325 1.373491e-02 6.867453e-03
[47,] 0.9905249 1.895026e-02 9.475131e-03
[48,] 0.9879277 2.414461e-02 1.207231e-02
[49,] 0.9838062 3.238762e-02 1.619381e-02
[50,] 0.9792920 4.141597e-02 2.070799e-02
[51,] 0.9885471 2.290572e-02 1.145286e-02
[52,] 0.9846989 3.060214e-02 1.530107e-02
[53,] 0.9797251 4.054978e-02 2.027489e-02
[54,] 0.9744338 5.113236e-02 2.556618e-02
[55,] 0.9709615 5.807705e-02 2.903852e-02
[56,] 0.9631451 7.370972e-02 3.685486e-02
[57,] 0.9526243 9.475133e-02 4.737566e-02
[58,] 0.9400660 1.198679e-01 5.993396e-02
[59,] 0.9930462 1.390761e-02 6.953803e-03
[60,] 0.9949455 1.010895e-02 5.054477e-03
[61,] 0.9937616 1.247690e-02 6.238449e-03
[62,] 0.9912556 1.748877e-02 8.744387e-03
[63,] 0.9882448 2.351047e-02 1.175523e-02
[64,] 0.9841577 3.168460e-02 1.584230e-02
[65,] 0.9988313 2.337318e-03 1.168659e-03
[66,] 0.9987144 2.571110e-03 1.285555e-03
[67,] 0.9987220 2.556025e-03 1.278013e-03
[68,] 0.9984243 3.151319e-03 1.575660e-03
[69,] 0.9976957 4.608552e-03 2.304276e-03
[70,] 0.9972279 5.544226e-03 2.772113e-03
[71,] 0.9970234 5.953190e-03 2.976595e-03
[72,] 0.9958828 8.234301e-03 4.117150e-03
[73,] 0.9990148 1.970442e-03 9.852210e-04
[74,] 0.9990465 1.907080e-03 9.535398e-04
[75,] 0.9993914 1.217227e-03 6.086135e-04
[76,] 0.9993101 1.379740e-03 6.898700e-04
[77,] 0.9991632 1.673595e-03 8.367974e-04
[78,] 0.9994763 1.047332e-03 5.236660e-04
[79,] 0.9991798 1.640491e-03 8.202456e-04
[80,] 0.9990537 1.892577e-03 9.462887e-04
[81,] 0.9986481 2.703849e-03 1.351924e-03
[82,] 0.9991089 1.782115e-03 8.910576e-04
[83,] 0.9991806 1.638773e-03 8.193867e-04
[84,] 0.9989288 2.142467e-03 1.071233e-03
[85,] 0.9985755 2.849051e-03 1.424525e-03
[86,] 0.9979137 4.172665e-03 2.086333e-03
[87,] 0.9973418 5.316400e-03 2.658200e-03
[88,] 0.9962473 7.505432e-03 3.752716e-03
[89,] 0.9961079 7.784237e-03 3.892119e-03
[90,] 0.9945347 1.093067e-02 5.465337e-03
[91,] 0.9929899 1.402023e-02 7.010115e-03
[92,] 0.9962034 7.593145e-03 3.796572e-03
[93,] 0.9999750 4.999005e-05 2.499502e-05
[94,] 0.9999871 2.571455e-05 1.285728e-05
[95,] 0.9999946 1.082019e-05 5.410093e-06
[96,] 0.9999891 2.179088e-05 1.089544e-05
[97,] 0.9999983 3.394004e-06 1.697002e-06
[98,] 0.9999964 7.134748e-06 3.567374e-06
[99,] 0.9999924 1.512931e-05 7.564655e-06
[100,] 0.9999980 4.092709e-06 2.046354e-06
[101,] 0.9999957 8.690321e-06 4.345161e-06
[102,] 0.9999918 1.633786e-05 8.168929e-06
[103,] 0.9999837 3.262247e-05 1.631124e-05
[104,] 0.9999998 4.573748e-07 2.286874e-07
[105,] 0.9999996 8.243214e-07 4.121607e-07
[106,] 0.9999997 6.991168e-07 3.495584e-07
[107,] 0.9999991 1.848853e-06 9.244263e-07
[108,] 0.9999982 3.688791e-06 1.844396e-06
[109,] 0.9999967 6.653512e-06 3.326756e-06
[110,] 0.9999978 4.380825e-06 2.190412e-06
[111,] 0.9999945 1.099495e-05 5.497476e-06
[112,] 0.9999973 5.375355e-06 2.687678e-06
[113,] 0.9999950 9.948493e-06 4.974246e-06
[114,] 0.9999878 2.449929e-05 1.224964e-05
[115,] 0.9999853 2.932946e-05 1.466473e-05
[116,] 0.9999784 4.320338e-05 2.160169e-05
[117,] 0.9999931 1.384129e-05 6.920643e-06
[118,] 0.9999844 3.115755e-05 1.557877e-05
[119,] 0.9999970 6.063494e-06 3.031747e-06
[120,] 0.9999997 6.590971e-07 3.295485e-07
[121,] 1.0000000 8.498491e-08 4.249246e-08
[122,] 1.0000000 3.924222e-08 1.962111e-08
[123,] 0.9999998 3.206223e-07 1.603112e-07
[124,] 1.0000000 3.938888e-09 1.969444e-09
[125,] 1.0000000 4.900841e-09 2.450420e-09
[126,] 1.0000000 7.309489e-08 3.654744e-08
[127,] 1.0000000 2.525944e-13 1.262972e-13
[128,] 1.0000000 1.167070e-10 5.835350e-11
[129,] 1.0000000 5.284423e-08 2.642212e-08
> postscript(file="/var/wessaorg/rcomp/tmp/1hder1324679073.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/23fw91324679073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/35opl1324679073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ryzm1324679073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5tjj41324679073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 164
Frequency = 1
1 2 3 4 5 6
-1785.5192 -444.7565 -7545.4556 -74011.6199 5346.1249 -19394.1385
7 8 9 10 11 12
18370.4192 -2679.4537 -5991.8139 39026.6268 27645.9349 6839.2463
13 14 15 16 17 18
8212.3781 67010.9022 9505.9315 -70213.5433 40402.8987 74274.1373
19 20 21 22 23 24
-11228.1018 -13969.4934 -10124.3135 158046.6538 21036.1995 -40346.1212
25 26 27 28 29 30
-73753.9803 -28035.5101 -41118.8457 42826.9217 11881.0786 -37529.4160
31 32 33 34 35 36
-31732.5189 8375.6512 32996.0766 -1473.7785 35296.3827 -16269.0958
37 38 39 40 41 42
31970.0776 15548.8054 13673.3695 3313.5060 -17469.2877 -5821.5171
43 44 45 46 47 48
-20366.3787 18157.0246 -79576.4557 9848.3350 -36468.1817 -12862.6544
49 50 51 52 53 54
-60654.8388 -64524.0035 -37010.1987 -18855.5382 21837.8032 -6586.6517
55 56 57 58 59 60
-21989.9689 1599.2742 10483.2043 -34504.1928 5062.3123 37545.3184
61 62 63 64 65 66
-29653.0108 -64132.7969 -28853.7473 -2044.2817 20938.5345 8550.0322
67 68 69 70 71 72
-20806.3144 -61023.4054 6091.0252 -9440.8418 -6626.6795 -24918.4534
73 74 75 76 77 78
11179.0685 4177.5576 -10738.5323 100094.6967 36864.4448 -29979.9230
79 80 81 82 83 84
-1691.6616 9785.3168 -12880.9459 95681.7023 -31794.1717 41875.8563
85 86 87 88 89 90
-26404.4432 4725.3649 1818.3085 26803.4833 16231.9731 -73621.7708
91 92 93 94 95 96
35261.5496 47994.5142 -15173.8731 4734.9849 45747.1875 -112.3540
97 98 99 100 101 102
36977.9786 6389.0296 -22143.8960 -12929.0449 11922.5500 -16752.2386
103 104 105 106 107 108
-14655.1117 -14756.1517 3236.7762 -20217.3432 -17919.0888 5777.2332
109 110 111 112 113 114
-43851.3932 -103216.2253 64524.7822 50216.0183 173.9576 -53493.3571
115 116 117 118 119 120
6945.7283 -2589.2899 9300.1767 -1695.7757 -4111.6622 -9813.5639
121 122 123 124 125 126
-25412.3907 30291.9487 37346.9502 -7284.1243 -19627.1071 57258.4030
127 128 129 130 131 132
23466.1949 -19317.9048 -2475.9777 -51493.3614 12307.8963 -12860.7879
133 134 135 136 137 138
-20461.1780 -2066.0686 8971.1814 29434.5894 6180.9962 -34965.0317
139 140 141 142 143 144
21698.7215 -3903.0704 28276.8870 7901.8771 -21907.7058 47145.1583
145 146 147 148 149 150
17634.1537 -40566.9891 -14927.6285 -4272.5188 -1671.9798 1007.3857
151 152 153 154 155 156
4262.5314 4437.1614 3690.5341 4359.4098 52029.1030 5973.6344
157 158 159 160 161 162
4359.4098 3882.9631 -2592.5652 -6687.3702 6029.5860 17144.3108
163 164
4819.8235 54809.2159
> postscript(file="/var/wessaorg/rcomp/tmp/6gsax1324679073.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 -1785.5192 NA
1 -444.7565 -1785.5192
2 -7545.4556 -444.7565
3 -74011.6199 -7545.4556
4 5346.1249 -74011.6199
5 -19394.1385 5346.1249
6 18370.4192 -19394.1385
7 -2679.4537 18370.4192
8 -5991.8139 -2679.4537
9 39026.6268 -5991.8139
10 27645.9349 39026.6268
11 6839.2463 27645.9349
12 8212.3781 6839.2463
13 67010.9022 8212.3781
14 9505.9315 67010.9022
15 -70213.5433 9505.9315
16 40402.8987 -70213.5433
17 74274.1373 40402.8987
18 -11228.1018 74274.1373
19 -13969.4934 -11228.1018
20 -10124.3135 -13969.4934
21 158046.6538 -10124.3135
22 21036.1995 158046.6538
23 -40346.1212 21036.1995
24 -73753.9803 -40346.1212
25 -28035.5101 -73753.9803
26 -41118.8457 -28035.5101
27 42826.9217 -41118.8457
28 11881.0786 42826.9217
29 -37529.4160 11881.0786
30 -31732.5189 -37529.4160
31 8375.6512 -31732.5189
32 32996.0766 8375.6512
33 -1473.7785 32996.0766
34 35296.3827 -1473.7785
35 -16269.0958 35296.3827
36 31970.0776 -16269.0958
37 15548.8054 31970.0776
38 13673.3695 15548.8054
39 3313.5060 13673.3695
40 -17469.2877 3313.5060
41 -5821.5171 -17469.2877
42 -20366.3787 -5821.5171
43 18157.0246 -20366.3787
44 -79576.4557 18157.0246
45 9848.3350 -79576.4557
46 -36468.1817 9848.3350
47 -12862.6544 -36468.1817
48 -60654.8388 -12862.6544
49 -64524.0035 -60654.8388
50 -37010.1987 -64524.0035
51 -18855.5382 -37010.1987
52 21837.8032 -18855.5382
53 -6586.6517 21837.8032
54 -21989.9689 -6586.6517
55 1599.2742 -21989.9689
56 10483.2043 1599.2742
57 -34504.1928 10483.2043
58 5062.3123 -34504.1928
59 37545.3184 5062.3123
60 -29653.0108 37545.3184
61 -64132.7969 -29653.0108
62 -28853.7473 -64132.7969
63 -2044.2817 -28853.7473
64 20938.5345 -2044.2817
65 8550.0322 20938.5345
66 -20806.3144 8550.0322
67 -61023.4054 -20806.3144
68 6091.0252 -61023.4054
69 -9440.8418 6091.0252
70 -6626.6795 -9440.8418
71 -24918.4534 -6626.6795
72 11179.0685 -24918.4534
73 4177.5576 11179.0685
74 -10738.5323 4177.5576
75 100094.6967 -10738.5323
76 36864.4448 100094.6967
77 -29979.9230 36864.4448
78 -1691.6616 -29979.9230
79 9785.3168 -1691.6616
80 -12880.9459 9785.3168
81 95681.7023 -12880.9459
82 -31794.1717 95681.7023
83 41875.8563 -31794.1717
84 -26404.4432 41875.8563
85 4725.3649 -26404.4432
86 1818.3085 4725.3649
87 26803.4833 1818.3085
88 16231.9731 26803.4833
89 -73621.7708 16231.9731
90 35261.5496 -73621.7708
91 47994.5142 35261.5496
92 -15173.8731 47994.5142
93 4734.9849 -15173.8731
94 45747.1875 4734.9849
95 -112.3540 45747.1875
96 36977.9786 -112.3540
97 6389.0296 36977.9786
98 -22143.8960 6389.0296
99 -12929.0449 -22143.8960
100 11922.5500 -12929.0449
101 -16752.2386 11922.5500
102 -14655.1117 -16752.2386
103 -14756.1517 -14655.1117
104 3236.7762 -14756.1517
105 -20217.3432 3236.7762
106 -17919.0888 -20217.3432
107 5777.2332 -17919.0888
108 -43851.3932 5777.2332
109 -103216.2253 -43851.3932
110 64524.7822 -103216.2253
111 50216.0183 64524.7822
112 173.9576 50216.0183
113 -53493.3571 173.9576
114 6945.7283 -53493.3571
115 -2589.2899 6945.7283
116 9300.1767 -2589.2899
117 -1695.7757 9300.1767
118 -4111.6622 -1695.7757
119 -9813.5639 -4111.6622
120 -25412.3907 -9813.5639
121 30291.9487 -25412.3907
122 37346.9502 30291.9487
123 -7284.1243 37346.9502
124 -19627.1071 -7284.1243
125 57258.4030 -19627.1071
126 23466.1949 57258.4030
127 -19317.9048 23466.1949
128 -2475.9777 -19317.9048
129 -51493.3614 -2475.9777
130 12307.8963 -51493.3614
131 -12860.7879 12307.8963
132 -20461.1780 -12860.7879
133 -2066.0686 -20461.1780
134 8971.1814 -2066.0686
135 29434.5894 8971.1814
136 6180.9962 29434.5894
137 -34965.0317 6180.9962
138 21698.7215 -34965.0317
139 -3903.0704 21698.7215
140 28276.8870 -3903.0704
141 7901.8771 28276.8870
142 -21907.7058 7901.8771
143 47145.1583 -21907.7058
144 17634.1537 47145.1583
145 -40566.9891 17634.1537
146 -14927.6285 -40566.9891
147 -4272.5188 -14927.6285
148 -1671.9798 -4272.5188
149 1007.3857 -1671.9798
150 4262.5314 1007.3857
151 4437.1614 4262.5314
152 3690.5341 4437.1614
153 4359.4098 3690.5341
154 52029.1030 4359.4098
155 5973.6344 52029.1030
156 4359.4098 5973.6344
157 3882.9631 4359.4098
158 -2592.5652 3882.9631
159 -6687.3702 -2592.5652
160 6029.5860 -6687.3702
161 17144.3108 6029.5860
162 4819.8235 17144.3108
163 54809.2159 4819.8235
164 NA 54809.2159
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -444.7565 -1785.5192
[2,] -7545.4556 -444.7565
[3,] -74011.6199 -7545.4556
[4,] 5346.1249 -74011.6199
[5,] -19394.1385 5346.1249
[6,] 18370.4192 -19394.1385
[7,] -2679.4537 18370.4192
[8,] -5991.8139 -2679.4537
[9,] 39026.6268 -5991.8139
[10,] 27645.9349 39026.6268
[11,] 6839.2463 27645.9349
[12,] 8212.3781 6839.2463
[13,] 67010.9022 8212.3781
[14,] 9505.9315 67010.9022
[15,] -70213.5433 9505.9315
[16,] 40402.8987 -70213.5433
[17,] 74274.1373 40402.8987
[18,] -11228.1018 74274.1373
[19,] -13969.4934 -11228.1018
[20,] -10124.3135 -13969.4934
[21,] 158046.6538 -10124.3135
[22,] 21036.1995 158046.6538
[23,] -40346.1212 21036.1995
[24,] -73753.9803 -40346.1212
[25,] -28035.5101 -73753.9803
[26,] -41118.8457 -28035.5101
[27,] 42826.9217 -41118.8457
[28,] 11881.0786 42826.9217
[29,] -37529.4160 11881.0786
[30,] -31732.5189 -37529.4160
[31,] 8375.6512 -31732.5189
[32,] 32996.0766 8375.6512
[33,] -1473.7785 32996.0766
[34,] 35296.3827 -1473.7785
[35,] -16269.0958 35296.3827
[36,] 31970.0776 -16269.0958
[37,] 15548.8054 31970.0776
[38,] 13673.3695 15548.8054
[39,] 3313.5060 13673.3695
[40,] -17469.2877 3313.5060
[41,] -5821.5171 -17469.2877
[42,] -20366.3787 -5821.5171
[43,] 18157.0246 -20366.3787
[44,] -79576.4557 18157.0246
[45,] 9848.3350 -79576.4557
[46,] -36468.1817 9848.3350
[47,] -12862.6544 -36468.1817
[48,] -60654.8388 -12862.6544
[49,] -64524.0035 -60654.8388
[50,] -37010.1987 -64524.0035
[51,] -18855.5382 -37010.1987
[52,] 21837.8032 -18855.5382
[53,] -6586.6517 21837.8032
[54,] -21989.9689 -6586.6517
[55,] 1599.2742 -21989.9689
[56,] 10483.2043 1599.2742
[57,] -34504.1928 10483.2043
[58,] 5062.3123 -34504.1928
[59,] 37545.3184 5062.3123
[60,] -29653.0108 37545.3184
[61,] -64132.7969 -29653.0108
[62,] -28853.7473 -64132.7969
[63,] -2044.2817 -28853.7473
[64,] 20938.5345 -2044.2817
[65,] 8550.0322 20938.5345
[66,] -20806.3144 8550.0322
[67,] -61023.4054 -20806.3144
[68,] 6091.0252 -61023.4054
[69,] -9440.8418 6091.0252
[70,] -6626.6795 -9440.8418
[71,] -24918.4534 -6626.6795
[72,] 11179.0685 -24918.4534
[73,] 4177.5576 11179.0685
[74,] -10738.5323 4177.5576
[75,] 100094.6967 -10738.5323
[76,] 36864.4448 100094.6967
[77,] -29979.9230 36864.4448
[78,] -1691.6616 -29979.9230
[79,] 9785.3168 -1691.6616
[80,] -12880.9459 9785.3168
[81,] 95681.7023 -12880.9459
[82,] -31794.1717 95681.7023
[83,] 41875.8563 -31794.1717
[84,] -26404.4432 41875.8563
[85,] 4725.3649 -26404.4432
[86,] 1818.3085 4725.3649
[87,] 26803.4833 1818.3085
[88,] 16231.9731 26803.4833
[89,] -73621.7708 16231.9731
[90,] 35261.5496 -73621.7708
[91,] 47994.5142 35261.5496
[92,] -15173.8731 47994.5142
[93,] 4734.9849 -15173.8731
[94,] 45747.1875 4734.9849
[95,] -112.3540 45747.1875
[96,] 36977.9786 -112.3540
[97,] 6389.0296 36977.9786
[98,] -22143.8960 6389.0296
[99,] -12929.0449 -22143.8960
[100,] 11922.5500 -12929.0449
[101,] -16752.2386 11922.5500
[102,] -14655.1117 -16752.2386
[103,] -14756.1517 -14655.1117
[104,] 3236.7762 -14756.1517
[105,] -20217.3432 3236.7762
[106,] -17919.0888 -20217.3432
[107,] 5777.2332 -17919.0888
[108,] -43851.3932 5777.2332
[109,] -103216.2253 -43851.3932
[110,] 64524.7822 -103216.2253
[111,] 50216.0183 64524.7822
[112,] 173.9576 50216.0183
[113,] -53493.3571 173.9576
[114,] 6945.7283 -53493.3571
[115,] -2589.2899 6945.7283
[116,] 9300.1767 -2589.2899
[117,] -1695.7757 9300.1767
[118,] -4111.6622 -1695.7757
[119,] -9813.5639 -4111.6622
[120,] -25412.3907 -9813.5639
[121,] 30291.9487 -25412.3907
[122,] 37346.9502 30291.9487
[123,] -7284.1243 37346.9502
[124,] -19627.1071 -7284.1243
[125,] 57258.4030 -19627.1071
[126,] 23466.1949 57258.4030
[127,] -19317.9048 23466.1949
[128,] -2475.9777 -19317.9048
[129,] -51493.3614 -2475.9777
[130,] 12307.8963 -51493.3614
[131,] -12860.7879 12307.8963
[132,] -20461.1780 -12860.7879
[133,] -2066.0686 -20461.1780
[134,] 8971.1814 -2066.0686
[135,] 29434.5894 8971.1814
[136,] 6180.9962 29434.5894
[137,] -34965.0317 6180.9962
[138,] 21698.7215 -34965.0317
[139,] -3903.0704 21698.7215
[140,] 28276.8870 -3903.0704
[141,] 7901.8771 28276.8870
[142,] -21907.7058 7901.8771
[143,] 47145.1583 -21907.7058
[144,] 17634.1537 47145.1583
[145,] -40566.9891 17634.1537
[146,] -14927.6285 -40566.9891
[147,] -4272.5188 -14927.6285
[148,] -1671.9798 -4272.5188
[149,] 1007.3857 -1671.9798
[150,] 4262.5314 1007.3857
[151,] 4437.1614 4262.5314
[152,] 3690.5341 4437.1614
[153,] 4359.4098 3690.5341
[154,] 52029.1030 4359.4098
[155,] 5973.6344 52029.1030
[156,] 4359.4098 5973.6344
[157,] 3882.9631 4359.4098
[158,] -2592.5652 3882.9631
[159,] -6687.3702 -2592.5652
[160,] 6029.5860 -6687.3702
[161,] 17144.3108 6029.5860
[162,] 4819.8235 17144.3108
[163,] 54809.2159 4819.8235
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -444.7565 -1785.5192
2 -7545.4556 -444.7565
3 -74011.6199 -7545.4556
4 5346.1249 -74011.6199
5 -19394.1385 5346.1249
6 18370.4192 -19394.1385
7 -2679.4537 18370.4192
8 -5991.8139 -2679.4537
9 39026.6268 -5991.8139
10 27645.9349 39026.6268
11 6839.2463 27645.9349
12 8212.3781 6839.2463
13 67010.9022 8212.3781
14 9505.9315 67010.9022
15 -70213.5433 9505.9315
16 40402.8987 -70213.5433
17 74274.1373 40402.8987
18 -11228.1018 74274.1373
19 -13969.4934 -11228.1018
20 -10124.3135 -13969.4934
21 158046.6538 -10124.3135
22 21036.1995 158046.6538
23 -40346.1212 21036.1995
24 -73753.9803 -40346.1212
25 -28035.5101 -73753.9803
26 -41118.8457 -28035.5101
27 42826.9217 -41118.8457
28 11881.0786 42826.9217
29 -37529.4160 11881.0786
30 -31732.5189 -37529.4160
31 8375.6512 -31732.5189
32 32996.0766 8375.6512
33 -1473.7785 32996.0766
34 35296.3827 -1473.7785
35 -16269.0958 35296.3827
36 31970.0776 -16269.0958
37 15548.8054 31970.0776
38 13673.3695 15548.8054
39 3313.5060 13673.3695
40 -17469.2877 3313.5060
41 -5821.5171 -17469.2877
42 -20366.3787 -5821.5171
43 18157.0246 -20366.3787
44 -79576.4557 18157.0246
45 9848.3350 -79576.4557
46 -36468.1817 9848.3350
47 -12862.6544 -36468.1817
48 -60654.8388 -12862.6544
49 -64524.0035 -60654.8388
50 -37010.1987 -64524.0035
51 -18855.5382 -37010.1987
52 21837.8032 -18855.5382
53 -6586.6517 21837.8032
54 -21989.9689 -6586.6517
55 1599.2742 -21989.9689
56 10483.2043 1599.2742
57 -34504.1928 10483.2043
58 5062.3123 -34504.1928
59 37545.3184 5062.3123
60 -29653.0108 37545.3184
61 -64132.7969 -29653.0108
62 -28853.7473 -64132.7969
63 -2044.2817 -28853.7473
64 20938.5345 -2044.2817
65 8550.0322 20938.5345
66 -20806.3144 8550.0322
67 -61023.4054 -20806.3144
68 6091.0252 -61023.4054
69 -9440.8418 6091.0252
70 -6626.6795 -9440.8418
71 -24918.4534 -6626.6795
72 11179.0685 -24918.4534
73 4177.5576 11179.0685
74 -10738.5323 4177.5576
75 100094.6967 -10738.5323
76 36864.4448 100094.6967
77 -29979.9230 36864.4448
78 -1691.6616 -29979.9230
79 9785.3168 -1691.6616
80 -12880.9459 9785.3168
81 95681.7023 -12880.9459
82 -31794.1717 95681.7023
83 41875.8563 -31794.1717
84 -26404.4432 41875.8563
85 4725.3649 -26404.4432
86 1818.3085 4725.3649
87 26803.4833 1818.3085
88 16231.9731 26803.4833
89 -73621.7708 16231.9731
90 35261.5496 -73621.7708
91 47994.5142 35261.5496
92 -15173.8731 47994.5142
93 4734.9849 -15173.8731
94 45747.1875 4734.9849
95 -112.3540 45747.1875
96 36977.9786 -112.3540
97 6389.0296 36977.9786
98 -22143.8960 6389.0296
99 -12929.0449 -22143.8960
100 11922.5500 -12929.0449
101 -16752.2386 11922.5500
102 -14655.1117 -16752.2386
103 -14756.1517 -14655.1117
104 3236.7762 -14756.1517
105 -20217.3432 3236.7762
106 -17919.0888 -20217.3432
107 5777.2332 -17919.0888
108 -43851.3932 5777.2332
109 -103216.2253 -43851.3932
110 64524.7822 -103216.2253
111 50216.0183 64524.7822
112 173.9576 50216.0183
113 -53493.3571 173.9576
114 6945.7283 -53493.3571
115 -2589.2899 6945.7283
116 9300.1767 -2589.2899
117 -1695.7757 9300.1767
118 -4111.6622 -1695.7757
119 -9813.5639 -4111.6622
120 -25412.3907 -9813.5639
121 30291.9487 -25412.3907
122 37346.9502 30291.9487
123 -7284.1243 37346.9502
124 -19627.1071 -7284.1243
125 57258.4030 -19627.1071
126 23466.1949 57258.4030
127 -19317.9048 23466.1949
128 -2475.9777 -19317.9048
129 -51493.3614 -2475.9777
130 12307.8963 -51493.3614
131 -12860.7879 12307.8963
132 -20461.1780 -12860.7879
133 -2066.0686 -20461.1780
134 8971.1814 -2066.0686
135 29434.5894 8971.1814
136 6180.9962 29434.5894
137 -34965.0317 6180.9962
138 21698.7215 -34965.0317
139 -3903.0704 21698.7215
140 28276.8870 -3903.0704
141 7901.8771 28276.8870
142 -21907.7058 7901.8771
143 47145.1583 -21907.7058
144 17634.1537 47145.1583
145 -40566.9891 17634.1537
146 -14927.6285 -40566.9891
147 -4272.5188 -14927.6285
148 -1671.9798 -4272.5188
149 1007.3857 -1671.9798
150 4262.5314 1007.3857
151 4437.1614 4262.5314
152 3690.5341 4437.1614
153 4359.4098 3690.5341
154 52029.1030 4359.4098
155 5973.6344 52029.1030
156 4359.4098 5973.6344
157 3882.9631 4359.4098
158 -2592.5652 3882.9631
159 -6687.3702 -2592.5652
160 6029.5860 -6687.3702
161 17144.3108 6029.5860
162 4819.8235 17144.3108
163 54809.2159 4819.8235
> 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/7k32f1324679073.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/8oqf51324679073.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/93tx71324679073.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/109uge1324679073.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11j60b1324679073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/125zyy1324679073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ih2z1324679073.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/149cwt1324679073.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/15gy941324679073.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/16ppas1324679073.tab")
+ }
>
> try(system("convert tmp/1hder1324679073.ps tmp/1hder1324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/23fw91324679073.ps tmp/23fw91324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/35opl1324679073.ps tmp/35opl1324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ryzm1324679073.ps tmp/4ryzm1324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tjj41324679073.ps tmp/5tjj41324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gsax1324679073.ps tmp/6gsax1324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k32f1324679073.ps tmp/7k32f1324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oqf51324679073.ps tmp/8oqf51324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/93tx71324679073.ps tmp/93tx71324679073.png",intern=TRUE))
character(0)
> try(system("convert tmp/109uge1324679073.ps tmp/109uge1324679073.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.259 0.587 6.872