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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+ ,668
+ ,2
+ ,34
+ ,71
+ ,100350
+ ,17372
+ ,80
+ ,72)
+ ,dim=c(9
+ ,164)
+ ,dimnames=list(c('TotalTime'
+ ,'CourseCompendiumViews'
+ ,'SharedbyotherAuthors'
+ ,'ReviewedCompendiums'
+ ,'PeerReviews'
+ ,'CWnumberOfCharacters'
+ ,'CWNumberOfRevisions'
+ ,'CWNumberOfHyperlinks'
+ ,'CWNumberOfBlogs')
+ ,1:164))
> y <- array(NA,dim=c(9,164),dimnames=list(c('TotalTime','CourseCompendiumViews','SharedbyotherAuthors','ReviewedCompendiums','PeerReviews','CWnumberOfCharacters','CWNumberOfRevisions','CWNumberOfHyperlinks','CWNumberOfBlogs'),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 = '6'
> #'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
CWnumberOfCharacters TotalTime CourseCompendiumViews SharedbyotherAuthors
1 140824 276257 492 3
2 110459 180480 436 4
3 105079 229040 694 16
4 112098 218443 1137 2
5 43929 171533 380 1
6 76173 70849 179 3
7 187326 536497 2354 0
8 22807 33186 111 0
9 144408 217320 740 7
10 66485 213274 595 0
11 79089 310843 809 0
12 81625 242788 693 7
13 68788 195022 738 10
14 103297 367785 1184 4
15 69446 261990 713 10
16 114948 392509 1729 0
17 167949 335528 844 8
18 125081 376673 1298 4
19 125818 181980 514 3
20 136588 266736 689 8
21 112431 278265 837 0
22 103037 461287 1330 1
23 82317 195786 491 5
24 118906 197058 622 9
25 83515 250284 1332 1
26 104581 245373 1043 0
27 103129 274121 1082 5
28 83243 278027 636 0
29 37110 165597 586 0
30 113344 371452 1170 0
31 139165 295296 973 3
32 86652 248320 721 6
33 112302 351980 863 1
34 69652 101014 343 4
35 119442 412722 1278 4
36 69867 273950 1186 0
37 101629 425531 1334 0
38 70168 231912 652 2
39 31081 115658 284 1
40 103925 376008 1273 2
41 92622 335039 1518 10
42 79011 194127 715 10
43 93487 206947 671 5
44 64520 182286 486 6
45 93473 153778 1022 1
46 114360 457592 2084 2
47 33032 78800 330 2
48 96125 208277 658 0
49 151911 359144 1385 10
50 89256 184648 930 3
51 95676 234078 620 0
52 5950 24188 218 0
53 149695 380576 840 8
54 32551 65029 255 5
55 31701 101097 454 3
56 100087 288327 1149 1
57 169707 334829 684 5
58 150491 359400 1190 6
59 120192 369577 1079 0
60 95893 269961 883 12
61 151715 389738 1331 10
62 176225 309474 1159 12
63 59900 282769 1217 11
64 104767 269324 946 8
65 114799 234773 579 2
66 72128 237561 474 0
67 143592 211396 626 6
68 89626 240376 843 10
69 131072 247447 893 2
70 126817 181550 633 5
71 81351 242152 873 13
72 22618 73566 385 6
73 88977 246125 729 7
74 92059 199565 774 2
75 81897 222676 769 4
76 108146 363558 996 4
77 126372 365442 1194 3
78 249771 217036 575 6
79 71154 213466 725 2
80 71571 204477 706 0
81 55918 169080 665 1
82 160141 478396 1259 0
83 38692 145943 653 5
84 102812 288626 694 2
85 56622 80953 437 0
86 15986 150221 822 0
87 123534 179317 458 6
88 108535 395368 1545 1
89 93879 349460 987 0
90 144551 180679 1051 1
91 56750 286578 838 1
92 127654 274477 703 3
93 65594 195623 613 10
94 59938 282361 1128 1
95 146975 329121 967 4
96 165904 198658 617 4
97 169265 262630 654 5
98 183500 300481 805 0
99 165986 403404 1355 12
100 184923 406613 1456 13
101 140358 294371 878 8
102 149959 313267 887 0
103 57224 189276 663 0
104 43750 43287 214 4
105 48029 189520 733 4
106 104978 250254 830 0
107 100046 268886 1174 0
108 101047 314153 1068 0
109 197426 160308 413 0
110 160902 162843 946 0
111 147172 344925 657 5
112 109432 300526 690 0
113 1168 23623 156 0
114 83248 195817 779 0
115 25162 61857 192 4
116 45724 163931 461 0
117 110529 428191 1213 1
118 855 21054 146 0
119 101382 252805 866 5
120 14116 31961 200 0
121 89506 335888 1290 3
122 135356 246100 715 7
123 116066 180591 514 13
124 144244 163400 697 3
125 8773 38214 276 0
126 102153 224597 752 3
127 117440 357602 1021 0
128 104128 198104 481 0
129 134238 424398 1626 4
130 134047 348017 884 0
131 279488 421610 1187 3
132 79756 192170 488 0
133 66089 102510 403 0
134 102070 302158 977 4
135 146760 444599 1525 5
136 154771 148707 551 15
137 165933 407736 1807 5
138 64593 164406 723 5
139 92280 278077 632 2
140 67150 282461 898 1
141 128692 219544 621 0
142 124089 384177 1606 9
143 125386 246963 811 1
144 37238 173260 716 3
145 140015 336715 1001 11
146 150047 176654 732 5
147 154451 253341 1024 2
148 156349 307133 831 1
149 0 1 0 9
150 6023 14688 85 0
151 0 98 0 0
152 0 455 0 0
153 0 0 0 1
154 0 0 0 0
155 84601 260901 773 2
156 68946 409280 1128 3
157 0 0 0 0
158 0 203 0 0
159 1644 7199 74 0
160 6179 46660 259 0
161 3926 17547 69 0
162 52789 118589 301 0
163 0 969 0 0
164 100350 233108 668 2
ReviewedCompendiums PeerReviews CWNumberOfRevisions CWNumberOfHyperlinks
1 41 126 32033 165
2 34 127 20654 135
3 44 111 16346 121
4 38 133 35926 148
5 27 64 10621 73
6 35 89 10024 49
7 33 122 43068 185
8 18 22 1271 5
9 34 117 34416 125
10 33 82 20318 93
11 46 147 24409 154
12 57 192 20648 98
13 37 113 12347 70
14 55 171 21857 148
15 44 87 11034 100
16 62 207 33433 150
17 40 153 35902 197
18 39 92 22355 114
19 32 95 31219 169
20 51 193 21983 200
21 49 160 40085 148
22 39 144 18507 140
23 25 84 16278 74
24 56 223 24662 128
25 45 154 31452 140
26 38 139 32580 116
27 45 142 22883 147
28 43 148 27652 132
29 32 99 9845 70
30 41 135 20190 144
31 50 179 46201 155
32 50 149 10971 165
33 51 187 34811 161
34 37 137 3029 31
35 44 163 38941 199
36 42 127 4958 78
37 44 151 32344 121
38 36 89 19433 112
39 17 46 12558 41
40 43 156 36524 158
41 41 128 26041 123
42 41 111 16637 104
43 38 114 28395 94
44 49 148 16747 73
45 45 45 9105 52
46 42 134 11941 71
47 26 66 7935 21
48 52 180 19499 155
49 50 177 22938 174
50 45 146 25314 136
51 40 137 28527 128
52 4 7 2694 7
53 44 157 20867 165
54 18 61 3597 21
55 14 41 5296 35
56 38 123 32982 137
57 61 228 38975 174
58 39 137 42721 257
59 42 150 41455 207
60 40 141 23923 103
61 51 181 26719 171
62 28 73 53405 279
63 43 97 12526 83
64 42 142 26584 130
65 37 125 37062 131
66 30 87 25696 126
67 39 140 24634 158
68 44 148 27269 138
69 36 116 25270 200
70 28 89 24634 104
71 47 160 17828 111
72 23 67 3007 26
73 48 179 20065 115
74 38 90 24648 127
75 42 144 21588 140
76 46 144 25217 121
77 37 135 30927 183
78 41 125 18487 68
79 42 146 18050 112
80 41 121 17696 103
81 36 109 17326 63
82 45 138 39361 166
83 26 99 9648 38
84 44 92 26759 163
85 8 27 7905 59
86 27 77 4527 27
87 38 137 41517 108
88 38 140 21261 88
89 57 122 36099 92
90 45 159 39039 170
91 40 97 13841 98
92 42 144 23841 205
93 31 90 8589 96
94 36 135 15049 107
95 40 147 39038 150
96 40 155 36774 138
97 35 127 40076 177
98 39 104 43840 213
99 65 248 43146 208
100 33 116 50099 307
101 51 176 40312 125
102 42 133 32616 208
103 36 59 11338 73
104 19 64 7409 49
105 25 40 18213 82
106 44 98 45873 206
107 40 125 39844 112
108 44 135 28317 139
109 30 83 24797 60
110 45 138 7471 70
111 42 149 27259 112
112 45 115 23201 142
113 1 0 238 11
114 40 103 28830 130
115 11 30 3913 31
116 45 119 9935 132
117 38 102 27738 219
118 0 0 338 4
119 30 77 13326 102
120 8 9 3988 39
121 41 143 24347 125
122 48 163 27111 121
123 48 146 3938 42
124 32 94 17416 111
125 8 21 1888 16
126 43 151 18700 70
127 52 187 36809 162
128 53 171 24959 173
129 49 170 37343 171
130 48 145 21849 172
131 56 198 49809 254
132 40 137 21654 90
133 36 100 8728 50
134 44 162 20920 113
135 46 163 27195 187
136 43 153 1037 16
137 46 161 42570 175
138 39 112 17672 90
139 41 135 34245 140
140 46 124 16786 145
141 32 45 20954 141
142 45 144 16378 125
143 39 126 31852 241
144 21 78 2805 16
145 49 149 38086 175
146 55 196 21166 132
147 36 118 34672 154
148 48 159 36171 198
149 0 0 0 0
150 0 0 2065 5
151 0 0 0 0
152 0 0 0 0
153 0 0 0 0
154 0 0 0 0
155 43 88 19354 125
156 52 129 22124 174
157 0 0 0 0
158 0 0 0 0
159 0 0 556 6
160 5 13 2089 13
161 1 4 2658 3
162 45 82 1813 35
163 0 0 0 0
164 34 71 17372 80
CWNumberOfBlogs
1 165
2 132
3 121
4 145
5 71
6 47
7 177
8 5
9 124
10 92
11 149
12 93
13 70
14 148
15 100
16 142
17 194
18 113
19 162
20 186
21 147
22 137
23 71
24 123
25 134
26 115
27 138
28 125
29 66
30 137
31 152
32 159
33 159
34 31
35 185
36 78
37 117
38 109
39 41
40 149
41 123
42 103
43 87
44 71
45 51
46 70
47 21
48 155
49 172
50 133
51 125
52 7
53 158
54 21
55 35
56 133
57 169
58 256
59 190
60 100
61 171
62 267
63 80
64 126
65 132
66 121
67 156
68 133
69 199
70 98
71 109
72 25
73 113
74 126
75 137
76 121
77 178
78 63
79 109
80 101
81 61
82 157
83 38
84 159
85 58
86 27
87 108
88 83
89 88
90 164
91 96
92 192
93 94
94 107
95 144
96 136
97 171
98 210
99 193
100 297
101 125
102 204
103 70
104 49
105 82
106 205
107 111
108 135
109 59
110 70
111 108
112 141
113 11
114 130
115 28
116 101
117 216
118 4
119 97
120 39
121 119
122 118
123 41
124 107
125 16
126 69
127 160
128 158
129 161
130 165
131 246
132 89
133 49
134 107
135 182
136 16
137 173
138 90
139 140
140 142
141 126
142 123
143 239
144 15
145 170
146 123
147 151
148 194
149 0
150 5
151 0
152 0
153 0
154 0
155 122
156 173
157 0
158 0
159 6
160 13
161 3
162 35
163 0
164 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TotalTime CourseCompendiumViews
4353.69251 0.05294 -3.64842
SharedbyotherAuthors ReviewedCompendiums PeerReviews
1841.09715 295.74197 135.06071
CWNumberOfRevisions CWNumberOfHyperlinks CWNumberOfBlogs
1.56632 220.45446 -90.60576
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-48621 -18469 -6170 12597 157732
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4353.69251 6187.34385 0.704 0.48271
TotalTime 0.05294 0.05579 0.949 0.34415
CourseCompendiumViews -3.64842 12.03108 -0.303 0.76211
SharedbyotherAuthors 1841.09715 650.74674 2.829 0.00528 **
ReviewedCompendiums 295.74197 414.90204 0.713 0.47704
PeerReviews 135.06071 115.95258 1.165 0.24589
CWNumberOfRevisions 1.56632 0.35498 4.412 1.9e-05 ***
CWNumberOfHyperlinks 220.45446 611.16829 0.361 0.71881
CWNumberOfBlogs -90.60576 634.71590 -0.143 0.88667
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 29230 on 155 degrees of freedom
Multiple R-squared: 0.7016, Adjusted R-squared: 0.6862
F-statistic: 45.56 on 8 and 155 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 4.739958e-01 9.479915e-01 5.260042e-01
[2,] 3.131446e-01 6.262892e-01 6.868554e-01
[3,] 3.078810e-01 6.157621e-01 6.921190e-01
[4,] 1.985725e-01 3.971449e-01 8.014275e-01
[5,] 1.254173e-01 2.508347e-01 8.745827e-01
[6,] 7.328852e-02 1.465770e-01 9.267115e-01
[7,] 5.855049e-02 1.171010e-01 9.414495e-01
[8,] 3.307173e-02 6.614346e-02 9.669283e-01
[9,] 2.346009e-02 4.692018e-02 9.765399e-01
[10,] 1.497382e-02 2.994765e-02 9.850262e-01
[11,] 8.357266e-03 1.671453e-02 9.916427e-01
[12,] 5.001381e-03 1.000276e-02 9.949986e-01
[13,] 2.946695e-03 5.893389e-03 9.970533e-01
[14,] 3.633799e-03 7.267597e-03 9.963662e-01
[15,] 1.846320e-03 3.692640e-03 9.981537e-01
[16,] 9.409800e-04 1.881960e-03 9.990590e-01
[17,] 1.001761e-03 2.003523e-03 9.989982e-01
[18,] 6.085687e-04 1.217137e-03 9.993914e-01
[19,] 4.367321e-04 8.734642e-04 9.995633e-01
[20,] 2.304059e-04 4.608118e-04 9.997696e-01
[21,] 1.144154e-04 2.288308e-04 9.998856e-01
[22,] 6.749301e-05 1.349860e-04 9.999325e-01
[23,] 1.535662e-04 3.071325e-04 9.998464e-01
[24,] 3.024657e-04 6.049314e-04 9.996975e-01
[25,] 1.748529e-04 3.497057e-04 9.998251e-01
[26,] 9.884812e-05 1.976962e-04 9.999012e-01
[27,] 5.627065e-05 1.125413e-04 9.999437e-01
[28,] 8.396996e-05 1.679399e-04 9.999160e-01
[29,] 7.525463e-05 1.505093e-04 9.999247e-01
[30,] 2.094158e-04 4.188317e-04 9.997906e-01
[31,] 1.366044e-04 2.732088e-04 9.998634e-01
[32,] 8.425155e-05 1.685031e-04 9.999157e-01
[33,] 5.182757e-05 1.036551e-04 9.999482e-01
[34,] 4.824761e-04 9.649522e-04 9.995175e-01
[35,] 5.837490e-04 1.167498e-03 9.994163e-01
[36,] 3.725937e-04 7.451875e-04 9.996274e-01
[37,] 2.253443e-04 4.506887e-04 9.997747e-01
[38,] 1.539755e-04 3.079511e-04 9.998460e-01
[39,] 1.186346e-04 2.372692e-04 9.998814e-01
[40,] 7.040680e-05 1.408136e-04 9.999296e-01
[41,] 6.055554e-05 1.211111e-04 9.999394e-01
[42,] 8.432712e-05 1.686542e-04 9.999157e-01
[43,] 4.986833e-05 9.973665e-05 9.999501e-01
[44,] 3.225194e-05 6.450388e-05 9.999677e-01
[45,] 2.074469e-05 4.148937e-05 9.999793e-01
[46,] 3.427400e-05 6.854800e-05 9.999657e-01
[47,] 2.938302e-05 5.876604e-05 9.999706e-01
[48,] 2.141402e-05 4.282805e-05 9.999786e-01
[49,] 1.627749e-05 3.255499e-05 9.999837e-01
[50,] 9.608172e-06 1.921634e-05 9.999904e-01
[51,] 5.334957e-06 1.066991e-05 9.999947e-01
[52,] 6.928469e-06 1.385694e-05 9.999931e-01
[53,] 4.221144e-06 8.442288e-06 9.999958e-01
[54,] 2.392535e-06 4.785071e-06 9.999976e-01
[55,] 1.745579e-06 3.491157e-06 9.999983e-01
[56,] 2.341519e-06 4.683037e-06 9.999977e-01
[57,] 2.982330e-06 5.964659e-06 9.999970e-01
[58,] 1.856432e-06 3.712865e-06 9.999981e-01
[59,] 4.899044e-06 9.798088e-06 9.999951e-01
[60,] 5.973141e-06 1.194628e-05 9.999940e-01
[61,] 4.475475e-06 8.950951e-06 9.999955e-01
[62,] 3.672342e-06 7.344683e-06 9.999963e-01
[63,] 2.051004e-06 4.102008e-06 9.999979e-01
[64,] 1.834928e-06 3.669857e-06 9.999982e-01
[65,] 1.054011e-06 2.108022e-06 9.999989e-01
[66,] 5.785421e-07 1.157084e-06 9.999994e-01
[67,] 2.431364e-01 4.862729e-01 7.568636e-01
[68,] 2.301911e-01 4.603822e-01 7.698089e-01
[69,] 2.023449e-01 4.046898e-01 7.976551e-01
[70,] 1.830567e-01 3.661134e-01 8.169433e-01
[71,] 1.692039e-01 3.384078e-01 8.307961e-01
[72,] 1.594394e-01 3.188788e-01 8.405606e-01
[73,] 1.344439e-01 2.688878e-01 8.655561e-01
[74,] 1.232030e-01 2.464059e-01 8.767970e-01
[75,] 1.150660e-01 2.301321e-01 8.849340e-01
[76,] 1.006350e-01 2.012700e-01 8.993650e-01
[77,] 8.587667e-02 1.717533e-01 9.141233e-01
[78,] 8.060593e-02 1.612119e-01 9.193941e-01
[79,] 7.367540e-02 1.473508e-01 9.263246e-01
[80,] 6.598585e-02 1.319717e-01 9.340141e-01
[81,] 5.336255e-02 1.067251e-01 9.466375e-01
[82,] 4.501423e-02 9.002846e-02 9.549858e-01
[83,] 4.279782e-02 8.559564e-02 9.572022e-01
[84,] 3.403764e-02 6.807528e-02 9.659624e-01
[85,] 3.860810e-02 7.721621e-02 9.613919e-01
[86,] 3.797170e-02 7.594340e-02 9.620283e-01
[87,] 5.181778e-02 1.036356e-01 9.481822e-01
[88,] 5.776258e-02 1.155252e-01 9.422374e-01
[89,] 4.694847e-02 9.389694e-02 9.530515e-01
[90,] 4.217598e-02 8.435197e-02 9.578240e-01
[91,] 3.819302e-02 7.638603e-02 9.618070e-01
[92,] 2.920031e-02 5.840063e-02 9.707997e-01
[93,] 2.268493e-02 4.536986e-02 9.773151e-01
[94,] 2.055053e-02 4.110107e-02 9.794495e-01
[95,] 2.509089e-02 5.018178e-02 9.749091e-01
[96,] 2.800214e-02 5.600429e-02 9.719979e-01
[97,] 2.300330e-02 4.600660e-02 9.769967e-01
[98,] 4.097297e-01 8.194594e-01 5.902703e-01
[99,] 8.310047e-01 3.379907e-01 1.689953e-01
[100,] 8.217525e-01 3.564950e-01 1.782475e-01
[101,] 7.939516e-01 4.120968e-01 2.060484e-01
[102,] 7.585734e-01 4.828532e-01 2.414266e-01
[103,] 7.428224e-01 5.143552e-01 2.571776e-01
[104,] 7.052758e-01 5.894483e-01 2.947242e-01
[105,] 8.217293e-01 3.565414e-01 1.782707e-01
[106,] 7.881163e-01 4.237673e-01 2.118837e-01
[107,] 7.487571e-01 5.024857e-01 2.512429e-01
[108,] 7.224884e-01 5.550233e-01 2.775116e-01
[109,] 6.765088e-01 6.469824e-01 3.234912e-01
[110,] 6.722691e-01 6.554619e-01 3.277309e-01
[111,] 6.259485e-01 7.481030e-01 3.740515e-01
[112,] 6.012628e-01 7.974744e-01 3.987372e-01
[113,] 7.318444e-01 5.363112e-01 2.681556e-01
[114,] 6.830834e-01 6.338332e-01 3.169166e-01
[115,] 6.330326e-01 7.339347e-01 3.669674e-01
[116,] 6.037311e-01 7.925379e-01 3.962689e-01
[117,] 7.745510e-01 4.508980e-01 2.254490e-01
[118,] 8.519503e-01 2.960994e-01 1.480497e-01
[119,] 8.226108e-01 3.547784e-01 1.773892e-01
[120,] 9.935687e-01 1.286268e-02 6.431338e-03
[121,] 9.897478e-01 2.050438e-02 1.025219e-02
[122,] 9.838708e-01 3.225845e-02 1.612923e-02
[123,] 9.855021e-01 2.899580e-02 1.449790e-02
[124,] 9.786019e-01 4.279626e-02 2.139813e-02
[125,] 9.999778e-01 4.447273e-05 2.223637e-05
[126,] 9.999700e-01 5.992260e-05 2.996130e-05
[127,] 9.999970e-01 5.975278e-06 2.987639e-06
[128,] 9.999934e-01 1.323726e-05 6.618630e-06
[129,] 9.999982e-01 3.601712e-06 1.800856e-06
[130,] 9.999949e-01 1.011875e-05 5.059374e-06
[131,] 9.999994e-01 1.147761e-06 5.738805e-07
[132,] 9.999989e-01 2.201903e-06 1.100951e-06
[133,] 9.999991e-01 1.718409e-06 8.592043e-07
[134,] 1.000000e+00 6.926223e-09 3.463112e-09
[135,] 1.000000e+00 2.386354e-09 1.193177e-09
[136,] 1.000000e+00 3.423542e-10 1.711771e-10
[137,] 1.000000e+00 2.796728e-12 1.398364e-12
[138,] 1.000000e+00 1.867174e-10 9.335872e-11
[139,] 1.000000e+00 1.157611e-10 5.788057e-11
[140,] 1.000000e+00 1.437942e-08 7.189711e-09
[141,] 9.999990e-01 1.983941e-06 9.919707e-07
> postscript(file="/var/wessaorg/rcomp/tmp/1ckmv1324379792.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2x5i41324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lmqn1324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4nz641324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5jevu1324379792.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
17375.6930 13417.3955 -7644.1807 -8315.7085 -12884.8823 18582.5679
7 8 9 10 11 12
44717.9008 6166.7703 22276.6430 -11813.5395 -30908.4382 -34249.1117
13 14 15 16 17 18
-16241.1753 -16384.8136 -19617.0123 -22738.4830 19603.8298 24290.4881
19 20 21 22 23 24
14410.6639 3078.7855 -21794.7779 -1145.9774 6068.9638 -12562.3075
25 26 27 28 29 30
-33163.5467 -5152.1704 -9226.1667 -27300.3576 -21579.0108 12280.1542
31 32 33 34 35 36
-14521.1119 -13327.1172 -25328.3138 15622.4313 -32591.8085 7870.2011
37 38 39 40 41 42
-20526.2034 -15686.2214 -16434.3170 -31697.5227 -28513.5877 -18192.5209
43 44 45 46 47 48
-12530.0934 -29128.5591 42376.0740 31171.0330 -9730.2437 -7211.4062
49 50 51 52 53 54
17791.2479 -17611.0368 -10714.9940 -6145.7857 24569.6147 -5443.2372
55 56 57 58 59 60
-4389.2346 -14841.7453 7990.2572 -10007.0672 -25820.5189 -23613.8842
61 62 63 64 65 66
9591.1857 -1480.6147 -31721.4974 -15602.9607 -6348.2556 -20756.8583
67 68 69 70 71 72
29560.4239 -36873.6757 21239.9520 33022.7002 -34599.1482 -19299.7641
73 74 75 76 77 78
-23547.4018 -2299.1523 -22937.5882 -7446.5449 -326.8936 157731.9339
79 80 81 82 83 84
-20764.0947 -10772.3840 -17669.3100 19086.5942 -21317.0190 -6850.1421
85 86 87 88 89 90
23431.2131 -22302.3750 -8481.8969 11719.2932 -27558.9842 14077.5523
91 92 93 94 95 96
-21074.5226 8802.3556 -12713.2507 -23434.1210 8511.6086 37456.3256
97 98 99 100 101 102
30386.6019 43999.3303 -25538.7367 -4245.0633 -9330.4666 23415.6419
103 104 105 106 107 108
-855.5842 -1709.2467 -23018.4406 -34535.0794 -20012.9207 -10051.3421
109 110 111 112 113 114
119288.9765 98641.0583 27603.3890 7976.9612 -5963.9455 -16407.8600
115 116 117 118 119 120
-6861.2573 -30516.6724 -11077.2539 -5129.3818 23757.1816 -6092.0040
121 122 123 124 125 126
-19794.5915 13036.8424 34466.3885 64045.8915 -6833.6842 11548.7719
127 128 129 130 131 132
-21625.4752 -10644.2956 -13067.2306 23525.2136 96595.9596 -9017.3123
133 134 135 136 137 138
13372.3428 -4955.4825 12278.8823 79855.8928 12450.1632 -21058.7173
139 140 141 142 143 144
-30346.8014 -26465.0647 46951.9926 13864.8025 -840.2381 -2505.7081
145 146 147 148 149 150
-16210.3098 35960.9561 35580.0685 18529.1497 -20923.6198 -2681.8140
151 152 153 154 155 156
-4358.8804 -4377.7791 -6194.7897 -4353.6925 -5845.7980 -48620.7768
157 158 159 160 161 162
-4353.6925 -4364.4388 -4470.7722 -7894.3905 -6493.6512 11487.8588
163 164
-4404.9890 24443.7270
> postscript(file="/var/wessaorg/rcomp/tmp/6j9rn1324379792.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 17375.6930 NA
1 13417.3955 17375.6930
2 -7644.1807 13417.3955
3 -8315.7085 -7644.1807
4 -12884.8823 -8315.7085
5 18582.5679 -12884.8823
6 44717.9008 18582.5679
7 6166.7703 44717.9008
8 22276.6430 6166.7703
9 -11813.5395 22276.6430
10 -30908.4382 -11813.5395
11 -34249.1117 -30908.4382
12 -16241.1753 -34249.1117
13 -16384.8136 -16241.1753
14 -19617.0123 -16384.8136
15 -22738.4830 -19617.0123
16 19603.8298 -22738.4830
17 24290.4881 19603.8298
18 14410.6639 24290.4881
19 3078.7855 14410.6639
20 -21794.7779 3078.7855
21 -1145.9774 -21794.7779
22 6068.9638 -1145.9774
23 -12562.3075 6068.9638
24 -33163.5467 -12562.3075
25 -5152.1704 -33163.5467
26 -9226.1667 -5152.1704
27 -27300.3576 -9226.1667
28 -21579.0108 -27300.3576
29 12280.1542 -21579.0108
30 -14521.1119 12280.1542
31 -13327.1172 -14521.1119
32 -25328.3138 -13327.1172
33 15622.4313 -25328.3138
34 -32591.8085 15622.4313
35 7870.2011 -32591.8085
36 -20526.2034 7870.2011
37 -15686.2214 -20526.2034
38 -16434.3170 -15686.2214
39 -31697.5227 -16434.3170
40 -28513.5877 -31697.5227
41 -18192.5209 -28513.5877
42 -12530.0934 -18192.5209
43 -29128.5591 -12530.0934
44 42376.0740 -29128.5591
45 31171.0330 42376.0740
46 -9730.2437 31171.0330
47 -7211.4062 -9730.2437
48 17791.2479 -7211.4062
49 -17611.0368 17791.2479
50 -10714.9940 -17611.0368
51 -6145.7857 -10714.9940
52 24569.6147 -6145.7857
53 -5443.2372 24569.6147
54 -4389.2346 -5443.2372
55 -14841.7453 -4389.2346
56 7990.2572 -14841.7453
57 -10007.0672 7990.2572
58 -25820.5189 -10007.0672
59 -23613.8842 -25820.5189
60 9591.1857 -23613.8842
61 -1480.6147 9591.1857
62 -31721.4974 -1480.6147
63 -15602.9607 -31721.4974
64 -6348.2556 -15602.9607
65 -20756.8583 -6348.2556
66 29560.4239 -20756.8583
67 -36873.6757 29560.4239
68 21239.9520 -36873.6757
69 33022.7002 21239.9520
70 -34599.1482 33022.7002
71 -19299.7641 -34599.1482
72 -23547.4018 -19299.7641
73 -2299.1523 -23547.4018
74 -22937.5882 -2299.1523
75 -7446.5449 -22937.5882
76 -326.8936 -7446.5449
77 157731.9339 -326.8936
78 -20764.0947 157731.9339
79 -10772.3840 -20764.0947
80 -17669.3100 -10772.3840
81 19086.5942 -17669.3100
82 -21317.0190 19086.5942
83 -6850.1421 -21317.0190
84 23431.2131 -6850.1421
85 -22302.3750 23431.2131
86 -8481.8969 -22302.3750
87 11719.2932 -8481.8969
88 -27558.9842 11719.2932
89 14077.5523 -27558.9842
90 -21074.5226 14077.5523
91 8802.3556 -21074.5226
92 -12713.2507 8802.3556
93 -23434.1210 -12713.2507
94 8511.6086 -23434.1210
95 37456.3256 8511.6086
96 30386.6019 37456.3256
97 43999.3303 30386.6019
98 -25538.7367 43999.3303
99 -4245.0633 -25538.7367
100 -9330.4666 -4245.0633
101 23415.6419 -9330.4666
102 -855.5842 23415.6419
103 -1709.2467 -855.5842
104 -23018.4406 -1709.2467
105 -34535.0794 -23018.4406
106 -20012.9207 -34535.0794
107 -10051.3421 -20012.9207
108 119288.9765 -10051.3421
109 98641.0583 119288.9765
110 27603.3890 98641.0583
111 7976.9612 27603.3890
112 -5963.9455 7976.9612
113 -16407.8600 -5963.9455
114 -6861.2573 -16407.8600
115 -30516.6724 -6861.2573
116 -11077.2539 -30516.6724
117 -5129.3818 -11077.2539
118 23757.1816 -5129.3818
119 -6092.0040 23757.1816
120 -19794.5915 -6092.0040
121 13036.8424 -19794.5915
122 34466.3885 13036.8424
123 64045.8915 34466.3885
124 -6833.6842 64045.8915
125 11548.7719 -6833.6842
126 -21625.4752 11548.7719
127 -10644.2956 -21625.4752
128 -13067.2306 -10644.2956
129 23525.2136 -13067.2306
130 96595.9596 23525.2136
131 -9017.3123 96595.9596
132 13372.3428 -9017.3123
133 -4955.4825 13372.3428
134 12278.8823 -4955.4825
135 79855.8928 12278.8823
136 12450.1632 79855.8928
137 -21058.7173 12450.1632
138 -30346.8014 -21058.7173
139 -26465.0647 -30346.8014
140 46951.9926 -26465.0647
141 13864.8025 46951.9926
142 -840.2381 13864.8025
143 -2505.7081 -840.2381
144 -16210.3098 -2505.7081
145 35960.9561 -16210.3098
146 35580.0685 35960.9561
147 18529.1497 35580.0685
148 -20923.6198 18529.1497
149 -2681.8140 -20923.6198
150 -4358.8804 -2681.8140
151 -4377.7791 -4358.8804
152 -6194.7897 -4377.7791
153 -4353.6925 -6194.7897
154 -5845.7980 -4353.6925
155 -48620.7768 -5845.7980
156 -4353.6925 -48620.7768
157 -4364.4388 -4353.6925
158 -4470.7722 -4364.4388
159 -7894.3905 -4470.7722
160 -6493.6512 -7894.3905
161 11487.8588 -6493.6512
162 -4404.9890 11487.8588
163 24443.7270 -4404.9890
164 NA 24443.7270
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13417.3955 17375.6930
[2,] -7644.1807 13417.3955
[3,] -8315.7085 -7644.1807
[4,] -12884.8823 -8315.7085
[5,] 18582.5679 -12884.8823
[6,] 44717.9008 18582.5679
[7,] 6166.7703 44717.9008
[8,] 22276.6430 6166.7703
[9,] -11813.5395 22276.6430
[10,] -30908.4382 -11813.5395
[11,] -34249.1117 -30908.4382
[12,] -16241.1753 -34249.1117
[13,] -16384.8136 -16241.1753
[14,] -19617.0123 -16384.8136
[15,] -22738.4830 -19617.0123
[16,] 19603.8298 -22738.4830
[17,] 24290.4881 19603.8298
[18,] 14410.6639 24290.4881
[19,] 3078.7855 14410.6639
[20,] -21794.7779 3078.7855
[21,] -1145.9774 -21794.7779
[22,] 6068.9638 -1145.9774
[23,] -12562.3075 6068.9638
[24,] -33163.5467 -12562.3075
[25,] -5152.1704 -33163.5467
[26,] -9226.1667 -5152.1704
[27,] -27300.3576 -9226.1667
[28,] -21579.0108 -27300.3576
[29,] 12280.1542 -21579.0108
[30,] -14521.1119 12280.1542
[31,] -13327.1172 -14521.1119
[32,] -25328.3138 -13327.1172
[33,] 15622.4313 -25328.3138
[34,] -32591.8085 15622.4313
[35,] 7870.2011 -32591.8085
[36,] -20526.2034 7870.2011
[37,] -15686.2214 -20526.2034
[38,] -16434.3170 -15686.2214
[39,] -31697.5227 -16434.3170
[40,] -28513.5877 -31697.5227
[41,] -18192.5209 -28513.5877
[42,] -12530.0934 -18192.5209
[43,] -29128.5591 -12530.0934
[44,] 42376.0740 -29128.5591
[45,] 31171.0330 42376.0740
[46,] -9730.2437 31171.0330
[47,] -7211.4062 -9730.2437
[48,] 17791.2479 -7211.4062
[49,] -17611.0368 17791.2479
[50,] -10714.9940 -17611.0368
[51,] -6145.7857 -10714.9940
[52,] 24569.6147 -6145.7857
[53,] -5443.2372 24569.6147
[54,] -4389.2346 -5443.2372
[55,] -14841.7453 -4389.2346
[56,] 7990.2572 -14841.7453
[57,] -10007.0672 7990.2572
[58,] -25820.5189 -10007.0672
[59,] -23613.8842 -25820.5189
[60,] 9591.1857 -23613.8842
[61,] -1480.6147 9591.1857
[62,] -31721.4974 -1480.6147
[63,] -15602.9607 -31721.4974
[64,] -6348.2556 -15602.9607
[65,] -20756.8583 -6348.2556
[66,] 29560.4239 -20756.8583
[67,] -36873.6757 29560.4239
[68,] 21239.9520 -36873.6757
[69,] 33022.7002 21239.9520
[70,] -34599.1482 33022.7002
[71,] -19299.7641 -34599.1482
[72,] -23547.4018 -19299.7641
[73,] -2299.1523 -23547.4018
[74,] -22937.5882 -2299.1523
[75,] -7446.5449 -22937.5882
[76,] -326.8936 -7446.5449
[77,] 157731.9339 -326.8936
[78,] -20764.0947 157731.9339
[79,] -10772.3840 -20764.0947
[80,] -17669.3100 -10772.3840
[81,] 19086.5942 -17669.3100
[82,] -21317.0190 19086.5942
[83,] -6850.1421 -21317.0190
[84,] 23431.2131 -6850.1421
[85,] -22302.3750 23431.2131
[86,] -8481.8969 -22302.3750
[87,] 11719.2932 -8481.8969
[88,] -27558.9842 11719.2932
[89,] 14077.5523 -27558.9842
[90,] -21074.5226 14077.5523
[91,] 8802.3556 -21074.5226
[92,] -12713.2507 8802.3556
[93,] -23434.1210 -12713.2507
[94,] 8511.6086 -23434.1210
[95,] 37456.3256 8511.6086
[96,] 30386.6019 37456.3256
[97,] 43999.3303 30386.6019
[98,] -25538.7367 43999.3303
[99,] -4245.0633 -25538.7367
[100,] -9330.4666 -4245.0633
[101,] 23415.6419 -9330.4666
[102,] -855.5842 23415.6419
[103,] -1709.2467 -855.5842
[104,] -23018.4406 -1709.2467
[105,] -34535.0794 -23018.4406
[106,] -20012.9207 -34535.0794
[107,] -10051.3421 -20012.9207
[108,] 119288.9765 -10051.3421
[109,] 98641.0583 119288.9765
[110,] 27603.3890 98641.0583
[111,] 7976.9612 27603.3890
[112,] -5963.9455 7976.9612
[113,] -16407.8600 -5963.9455
[114,] -6861.2573 -16407.8600
[115,] -30516.6724 -6861.2573
[116,] -11077.2539 -30516.6724
[117,] -5129.3818 -11077.2539
[118,] 23757.1816 -5129.3818
[119,] -6092.0040 23757.1816
[120,] -19794.5915 -6092.0040
[121,] 13036.8424 -19794.5915
[122,] 34466.3885 13036.8424
[123,] 64045.8915 34466.3885
[124,] -6833.6842 64045.8915
[125,] 11548.7719 -6833.6842
[126,] -21625.4752 11548.7719
[127,] -10644.2956 -21625.4752
[128,] -13067.2306 -10644.2956
[129,] 23525.2136 -13067.2306
[130,] 96595.9596 23525.2136
[131,] -9017.3123 96595.9596
[132,] 13372.3428 -9017.3123
[133,] -4955.4825 13372.3428
[134,] 12278.8823 -4955.4825
[135,] 79855.8928 12278.8823
[136,] 12450.1632 79855.8928
[137,] -21058.7173 12450.1632
[138,] -30346.8014 -21058.7173
[139,] -26465.0647 -30346.8014
[140,] 46951.9926 -26465.0647
[141,] 13864.8025 46951.9926
[142,] -840.2381 13864.8025
[143,] -2505.7081 -840.2381
[144,] -16210.3098 -2505.7081
[145,] 35960.9561 -16210.3098
[146,] 35580.0685 35960.9561
[147,] 18529.1497 35580.0685
[148,] -20923.6198 18529.1497
[149,] -2681.8140 -20923.6198
[150,] -4358.8804 -2681.8140
[151,] -4377.7791 -4358.8804
[152,] -6194.7897 -4377.7791
[153,] -4353.6925 -6194.7897
[154,] -5845.7980 -4353.6925
[155,] -48620.7768 -5845.7980
[156,] -4353.6925 -48620.7768
[157,] -4364.4388 -4353.6925
[158,] -4470.7722 -4364.4388
[159,] -7894.3905 -4470.7722
[160,] -6493.6512 -7894.3905
[161,] 11487.8588 -6493.6512
[162,] -4404.9890 11487.8588
[163,] 24443.7270 -4404.9890
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13417.3955 17375.6930
2 -7644.1807 13417.3955
3 -8315.7085 -7644.1807
4 -12884.8823 -8315.7085
5 18582.5679 -12884.8823
6 44717.9008 18582.5679
7 6166.7703 44717.9008
8 22276.6430 6166.7703
9 -11813.5395 22276.6430
10 -30908.4382 -11813.5395
11 -34249.1117 -30908.4382
12 -16241.1753 -34249.1117
13 -16384.8136 -16241.1753
14 -19617.0123 -16384.8136
15 -22738.4830 -19617.0123
16 19603.8298 -22738.4830
17 24290.4881 19603.8298
18 14410.6639 24290.4881
19 3078.7855 14410.6639
20 -21794.7779 3078.7855
21 -1145.9774 -21794.7779
22 6068.9638 -1145.9774
23 -12562.3075 6068.9638
24 -33163.5467 -12562.3075
25 -5152.1704 -33163.5467
26 -9226.1667 -5152.1704
27 -27300.3576 -9226.1667
28 -21579.0108 -27300.3576
29 12280.1542 -21579.0108
30 -14521.1119 12280.1542
31 -13327.1172 -14521.1119
32 -25328.3138 -13327.1172
33 15622.4313 -25328.3138
34 -32591.8085 15622.4313
35 7870.2011 -32591.8085
36 -20526.2034 7870.2011
37 -15686.2214 -20526.2034
38 -16434.3170 -15686.2214
39 -31697.5227 -16434.3170
40 -28513.5877 -31697.5227
41 -18192.5209 -28513.5877
42 -12530.0934 -18192.5209
43 -29128.5591 -12530.0934
44 42376.0740 -29128.5591
45 31171.0330 42376.0740
46 -9730.2437 31171.0330
47 -7211.4062 -9730.2437
48 17791.2479 -7211.4062
49 -17611.0368 17791.2479
50 -10714.9940 -17611.0368
51 -6145.7857 -10714.9940
52 24569.6147 -6145.7857
53 -5443.2372 24569.6147
54 -4389.2346 -5443.2372
55 -14841.7453 -4389.2346
56 7990.2572 -14841.7453
57 -10007.0672 7990.2572
58 -25820.5189 -10007.0672
59 -23613.8842 -25820.5189
60 9591.1857 -23613.8842
61 -1480.6147 9591.1857
62 -31721.4974 -1480.6147
63 -15602.9607 -31721.4974
64 -6348.2556 -15602.9607
65 -20756.8583 -6348.2556
66 29560.4239 -20756.8583
67 -36873.6757 29560.4239
68 21239.9520 -36873.6757
69 33022.7002 21239.9520
70 -34599.1482 33022.7002
71 -19299.7641 -34599.1482
72 -23547.4018 -19299.7641
73 -2299.1523 -23547.4018
74 -22937.5882 -2299.1523
75 -7446.5449 -22937.5882
76 -326.8936 -7446.5449
77 157731.9339 -326.8936
78 -20764.0947 157731.9339
79 -10772.3840 -20764.0947
80 -17669.3100 -10772.3840
81 19086.5942 -17669.3100
82 -21317.0190 19086.5942
83 -6850.1421 -21317.0190
84 23431.2131 -6850.1421
85 -22302.3750 23431.2131
86 -8481.8969 -22302.3750
87 11719.2932 -8481.8969
88 -27558.9842 11719.2932
89 14077.5523 -27558.9842
90 -21074.5226 14077.5523
91 8802.3556 -21074.5226
92 -12713.2507 8802.3556
93 -23434.1210 -12713.2507
94 8511.6086 -23434.1210
95 37456.3256 8511.6086
96 30386.6019 37456.3256
97 43999.3303 30386.6019
98 -25538.7367 43999.3303
99 -4245.0633 -25538.7367
100 -9330.4666 -4245.0633
101 23415.6419 -9330.4666
102 -855.5842 23415.6419
103 -1709.2467 -855.5842
104 -23018.4406 -1709.2467
105 -34535.0794 -23018.4406
106 -20012.9207 -34535.0794
107 -10051.3421 -20012.9207
108 119288.9765 -10051.3421
109 98641.0583 119288.9765
110 27603.3890 98641.0583
111 7976.9612 27603.3890
112 -5963.9455 7976.9612
113 -16407.8600 -5963.9455
114 -6861.2573 -16407.8600
115 -30516.6724 -6861.2573
116 -11077.2539 -30516.6724
117 -5129.3818 -11077.2539
118 23757.1816 -5129.3818
119 -6092.0040 23757.1816
120 -19794.5915 -6092.0040
121 13036.8424 -19794.5915
122 34466.3885 13036.8424
123 64045.8915 34466.3885
124 -6833.6842 64045.8915
125 11548.7719 -6833.6842
126 -21625.4752 11548.7719
127 -10644.2956 -21625.4752
128 -13067.2306 -10644.2956
129 23525.2136 -13067.2306
130 96595.9596 23525.2136
131 -9017.3123 96595.9596
132 13372.3428 -9017.3123
133 -4955.4825 13372.3428
134 12278.8823 -4955.4825
135 79855.8928 12278.8823
136 12450.1632 79855.8928
137 -21058.7173 12450.1632
138 -30346.8014 -21058.7173
139 -26465.0647 -30346.8014
140 46951.9926 -26465.0647
141 13864.8025 46951.9926
142 -840.2381 13864.8025
143 -2505.7081 -840.2381
144 -16210.3098 -2505.7081
145 35960.9561 -16210.3098
146 35580.0685 35960.9561
147 18529.1497 35580.0685
148 -20923.6198 18529.1497
149 -2681.8140 -20923.6198
150 -4358.8804 -2681.8140
151 -4377.7791 -4358.8804
152 -6194.7897 -4377.7791
153 -4353.6925 -6194.7897
154 -5845.7980 -4353.6925
155 -48620.7768 -5845.7980
156 -4353.6925 -48620.7768
157 -4364.4388 -4353.6925
158 -4470.7722 -4364.4388
159 -7894.3905 -4470.7722
160 -6493.6512 -7894.3905
161 11487.8588 -6493.6512
162 -4404.9890 11487.8588
163 24443.7270 -4404.9890
> 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/7jexi1324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8bbvo1324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9whjt1324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10m1y81324379792.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oowb1324379792.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/12n3f71324379792.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/13hb201324379792.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/14lto41324379792.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/158lnj1324379792.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/16wj8t1324379792.tab")
+ }
>
> try(system("convert tmp/1ckmv1324379792.ps tmp/1ckmv1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x5i41324379792.ps tmp/2x5i41324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lmqn1324379792.ps tmp/3lmqn1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nz641324379792.ps tmp/4nz641324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jevu1324379792.ps tmp/5jevu1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j9rn1324379792.ps tmp/6j9rn1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jexi1324379792.ps tmp/7jexi1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bbvo1324379792.ps tmp/8bbvo1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/9whjt1324379792.ps tmp/9whjt1324379792.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m1y81324379792.ps tmp/10m1y81324379792.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.163 0.659 6.832