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(1418
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+ ,1
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+ ,0
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+ ,894
+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16)
+ ,dim=c(8
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed
')
+ ,1:289))
> y <- array(NA,dim=c(8,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed
'),1:289))
> 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 = '7'
> #'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
blogged_computations pageviews time_in_rfc logins compendium_views_info
1 79 1418 210907 56 396
2 58 869 120982 56 297
3 60 1530 176508 54 559
4 108 2172 179321 89 967
5 49 901 123185 40 270
6 0 463 52746 25 143
7 121 3201 385534 92 1562
8 1 371 33170 18 109
9 20 1192 101645 63 371
10 43 1583 149061 44 656
11 69 1439 165446 33 511
12 78 1764 237213 84 655
13 86 1495 173326 88 465
14 44 1373 133131 55 525
15 104 2187 258873 60 885
16 63 1491 180083 66 497
17 158 4041 324799 154 1436
18 102 1706 230964 53 612
19 77 2152 236785 119 865
20 82 1036 135473 41 385
21 115 1882 202925 61 567
22 101 1929 215147 58 639
23 80 2242 344297 75 963
24 50 1220 153935 33 398
25 83 1289 132943 40 410
26 123 2515 174724 92 966
27 73 2147 174415 100 801
28 81 2352 225548 112 892
29 105 1638 223632 73 513
30 47 1222 124817 40 469
31 105 1812 221698 45 683
32 94 1677 210767 60 643
33 44 1579 170266 62 535
34 114 1731 260561 75 625
35 38 807 84853 31 264
36 107 2452 294424 77 992
37 30 829 101011 34 238
38 71 1940 215641 46 818
39 84 2662 325107 99 937
40 0 186 7176 17 70
41 59 1499 167542 66 507
42 33 865 106408 30 260
43 42 1793 96560 76 503
44 96 2527 265769 146 927
45 106 2747 269651 67 1269
46 56 1324 149112 56 537
47 57 2702 175824 107 910
48 59 1383 152871 58 532
49 39 1179 111665 34 345
50 34 2099 116408 61 918
51 76 4308 362301 119 1635
52 20 918 78800 42 330
53 91 1831 183167 66 557
54 115 3373 277965 89 1178
55 85 1713 150629 44 740
56 76 1438 168809 66 452
57 8 496 24188 24 218
58 79 2253 329267 259 764
59 21 744 65029 17 255
60 30 1161 101097 64 454
61 76 2352 218946 41 866
62 101 2144 244052 68 574
63 94 4691 341570 168 1276
64 27 1112 103597 43 379
65 92 2694 233328 132 825
66 123 1973 256462 105 798
67 75 1769 206161 71 663
68 128 3148 311473 112 1069
69 105 2474 235800 94 921
70 55 2084 177939 82 858
71 56 1954 207176 70 711
72 41 1226 196553 57 503
73 72 1389 174184 53 382
74 67 1496 143246 103 464
75 75 2269 187559 121 717
76 114 1833 187681 62 690
77 118 1268 119016 52 462
78 77 1943 182192 52 657
79 22 893 73566 32 385
80 66 1762 194979 62 577
81 69 1403 167488 45 619
82 105 1425 143756 46 479
83 116 1857 275541 63 817
84 88 1840 243199 75 752
85 73 1502 182999 88 430
86 99 1441 135649 46 451
87 62 1420 152299 53 537
88 53 1416 120221 37 519
89 118 2970 346485 90 1000
90 30 1317 145790 63 637
91 100 1644 193339 78 465
92 49 870 80953 25 437
93 24 1654 122774 45 711
94 67 1054 130585 46 299
95 46 937 112611 41 248
96 57 3004 286468 144 1162
97 75 2008 241066 82 714
98 135 2547 148446 91 905
99 68 1885 204713 71 649
100 124 1626 182079 63 512
101 33 1468 140344 53 472
102 98 2445 220516 62 905
103 58 1964 243060 63 786
104 68 1381 162765 32 489
105 81 1369 182613 39 479
106 131 1659 232138 62 617
107 110 2888 265318 117 925
108 37 1290 85574 34 351
109 130 2845 310839 92 1144
110 93 1982 225060 93 669
111 118 1904 232317 54 707
112 39 1391 144966 144 458
113 13 602 43287 14 214
114 74 1743 155754 61 599
115 81 1559 164709 109 572
116 109 2014 201940 38 897
117 151 2143 235454 73 819
118 51 2146 220801 75 720
119 28 874 99466 50 273
120 40 1590 92661 61 508
121 56 1590 133328 55 506
122 27 1210 61361 77 451
123 37 2072 125930 75 699
124 83 1281 100750 72 407
125 54 1401 224549 50 465
126 27 834 82316 32 245
127 28 1105 102010 53 370
128 59 1272 101523 42 316
129 133 1944 243511 71 603
130 12 391 22938 10 154
131 0 761 41566 35 229
132 106 1605 152474 65 577
133 23 530 61857 25 192
134 44 1988 99923 66 617
135 71 1386 132487 41 411
136 116 2395 317394 86 975
137 4 387 21054 16 146
138 62 1742 209641 42 705
139 12 620 22648 19 184
140 18 449 31414 19 200
141 14 800 46698 45 274
142 60 1684 131698 65 502
143 7 1050 91735 35 382
144 98 2699 244749 95 964
145 64 1606 184510 49 537
146 29 1502 79863 37 438
147 32 1204 128423 64 369
148 25 1138 97839 38 417
149 16 568 38214 34 276
150 48 1459 151101 32 514
151 100 2158 272458 65 822
152 46 1111 172494 52 389
153 45 1421 108043 62 466
154 129 2833 328107 65 1255
155 130 1955 250579 83 694
156 136 2922 351067 95 1024
157 59 1002 158015 29 400
158 25 1060 98866 18 397
159 32 956 85439 33 350
160 63 2186 229242 247 719
161 95 3604 351619 139 1277
162 14 1035 84207 29 356
163 36 1417 120445 118 457
164 113 3261 324598 110 1402
165 47 1587 131069 67 600
166 92 1424 204271 42 480
167 70 1701 165543 65 595
168 19 1249 141722 94 436
169 50 946 116048 64 230
170 41 1926 250047 81 651
171 91 3352 299775 95 1367
172 111 1641 195838 67 564
173 41 2035 173260 63 716
174 120 2312 254488 83 747
175 135 1369 104389 45 467
176 27 1577 136084 30 671
177 87 2201 199476 70 861
178 25 961 92499 32 319
179 131 1900 224330 83 612
180 45 1254 135781 31 433
181 29 1335 74408 67 434
182 58 1597 81240 66 503
183 4 207 14688 10 85
184 47 1645 181633 70 564
185 109 2429 271856 103 824
186 7 151 7199 5 74
187 12 474 46660 20 259
188 0 141 17547 5 69
189 37 1639 133368 36 535
190 37 872 95227 34 239
191 46 1318 152601 48 438
192 15 1018 98146 40 459
193 42 1383 79619 43 426
194 7 1314 59194 31 288
195 54 1335 139942 42 498
196 54 1403 118612 46 454
197 14 910 72880 33 376
198 16 616 65475 18 225
199 33 1407 99643 55 555
200 32 771 71965 35 252
201 21 766 77272 59 208
202 15 473 49289 19 130
203 38 1376 135131 66 481
204 22 1232 108446 60 389
205 28 1521 89746 36 565
206 10 572 44296 25 173
207 31 1059 77648 47 278
208 32 1544 181528 54 609
209 32 1230 134019 53 422
210 43 1206 124064 40 445
211 27 1205 92630 40 387
212 37 1255 121848 39 339
213 20 613 52915 14 181
214 32 721 81872 45 245
215 0 1109 58981 36 384
216 5 740 53515 28 212
217 26 1126 60812 44 399
218 10 728 56375 30 229
219 27 689 65490 22 224
220 11 592 80949 17 203
221 29 995 76302 31 333
222 25 1613 104011 55 384
223 55 2048 98104 54 636
224 23 705 67989 21 185
225 5 301 30989 14 93
226 43 1803 135458 81 581
227 23 799 73504 35 248
228 34 861 63123 43 304
229 36 1186 61254 46 344
230 35 1451 74914 30 407
231 0 628 31774 23 170
232 37 1161 81437 38 312
233 28 1463 87186 54 507
234 16 742 50090 20 224
235 26 979 65745 53 340
236 38 675 56653 45 168
237 23 1241 158399 39 443
238 22 676 46455 20 204
239 30 1049 73624 24 367
240 16 620 38395 31 210
241 18 1081 91899 35 335
242 28 1688 139526 151 364
243 32 736 52164 52 178
244 21 617 51567 30 206
245 23 812 70551 31 279
246 29 1051 84856 29 387
247 50 1656 102538 57 490
248 12 705 86678 40 238
249 21 945 85709 44 343
250 18 554 34662 25 232
251 27 1597 150580 77 530
252 41 982 99611 35 291
253 13 222 19349 11 67
254 12 1212 99373 63 397
255 21 1143 86230 44 467
256 8 435 30837 19 178
257 26 532 31706 13 175
258 27 882 89806 42 299
259 13 608 62088 38 154
260 16 459 40151 29 106
261 2 578 27634 20 189
262 42 826 76990 27 194
263 5 509 37460 20 135
264 37 717 54157 19 201
265 17 637 49862 37 207
266 38 857 84337 26 280
267 37 830 64175 42 260
268 29 652 59382 49 227
269 32 707 119308 30 239
270 35 954 76702 49 333
271 17 1461 103425 67 428
272 20 672 70344 28 230
273 7 778 43410 19 292
274 46 1141 104838 49 350
275 24 680 62215 27 186
276 40 1090 69304 30 326
277 3 616 53117 22 155
278 10 285 19764 12 75
279 37 1145 86680 31 361
280 17 733 84105 20 261
281 28 888 77945 20 299
282 19 849 89113 39 300
283 29 1182 91005 29 450
284 8 528 40248 16 183
285 10 642 64187 27 238
286 15 947 50857 21 165
287 15 819 56613 19 234
288 28 757 62792 35 176
289 17 894 72535 14 329
compendium_views_pr shared_compendiums compendiums_reviewed\r
1 81 3 30
2 55 4 28
3 50 12 38
4 125 2 30
5 40 1 22
6 37 3 26
7 63 0 25
8 44 0 18
9 88 0 11
10 66 5 26
11 57 0 25
12 74 0 38
13 49 7 44
14 52 7 30
15 88 3 40
16 36 9 34
17 108 0 47
18 43 4 30
19 75 3 31
20 32 0 23
21 44 7 36
22 85 0 36
23 86 1 30
24 56 5 25
25 50 7 39
26 135 0 34
27 63 0 31
28 81 5 31
29 52 0 33
30 44 0 25
31 113 0 33
32 39 3 35
33 73 4 42
34 48 1 43
35 33 4 30
36 59 2 33
37 41 0 13
38 69 0 32
39 64 0 36
40 1 0 0
41 59 2 28
42 32 1 14
43 129 0 17
44 37 2 32
45 31 10 30
46 65 6 35
47 107 0 20
48 74 5 28
49 54 4 28
50 76 1 39
51 715 2 34
52 57 2 26
53 66 0 39
54 106 8 39
55 54 3 33
56 32 0 28
57 20 0 4
58 71 8 39
59 21 5 18
60 70 3 14
61 112 1 29
62 66 5 44
63 190 1 21
64 66 1 16
65 165 5 28
66 56 0 35
67 61 12 28
68 53 8 38
69 127 8 23
70 63 8 36
71 38 8 32
72 50 2 29
73 52 0 25
74 42 5 27
75 76 8 36
76 67 2 28
77 50 5 23
78 53 12 40
79 39 6 23
80 50 7 40
81 77 2 28
82 57 0 34
83 73 4 33
84 34 3 28
85 39 6 34
86 46 2 30
87 63 0 33
88 35 1 22
89 106 0 38
90 43 5 26
91 47 2 35
92 31 0 8
93 162 0 24
94 57 5 29
95 36 0 20
96 263 1 29
97 78 0 45
98 63 1 37
99 54 1 33
100 63 2 33
101 77 6 25
102 79 1 32
103 110 4 29
104 56 2 28
105 56 3 28
106 43 0 31
107 111 10 52
108 71 0 21
109 62 9 24
110 56 7 41
111 74 0 33
112 60 0 32
113 43 4 19
114 68 4 20
115 53 0 31
116 87 0 31
117 46 0 32
118 105 1 18
119 32 0 23
120 133 1 17
121 79 0 20
122 51 0 12
123 207 4 17
124 67 0 30
125 47 4 31
126 34 4 10
127 66 3 13
128 76 0 22
129 65 0 42
130 9 0 1
131 42 5 9
132 45 0 32
133 25 4 11
134 115 0 25
135 97 0 36
136 53 1 31
137 2 0 0
138 52 5 24
139 44 0 13
140 22 0 8
141 35 0 13
142 74 0 19
143 103 0 18
144 144 2 33
145 60 7 40
146 134 1 22
147 89 8 38
148 42 2 24
149 52 0 8
150 98 2 35
151 99 0 43
152 52 0 43
153 29 1 14
154 125 3 41
155 106 0 38
156 95 3 45
157 40 0 31
158 140 0 13
159 43 0 28
160 128 4 31
161 142 4 40
162 73 11 30
163 72 0 16
164 128 0 37
165 61 4 30
166 73 0 35
167 148 1 32
168 64 0 27
169 45 0 20
170 58 0 18
171 97 9 31
172 50 1 31
173 37 3 21
174 50 10 39
175 105 5 41
176 69 0 13
177 46 2 32
178 57 0 18
179 52 1 39
180 98 2 14
181 61 4 7
182 89 0 17
183 0 0 0
184 48 2 30
185 91 1 37
186 0 0 0
187 7 0 5
188 3 0 1
189 54 1 16
190 70 0 32
191 36 2 24
192 37 0 17
193 123 3 11
194 247 6 24
195 46 0 22
196 72 2 12
197 41 0 19
198 24 2 13
199 45 1 17
200 33 1 15
201 27 2 16
202 36 1 24
203 87 0 15
204 90 1 17
205 114 3 18
206 31 0 20
207 45 0 16
208 69 0 16
209 51 0 18
210 34 1 22
211 60 4 8
212 45 0 17
213 54 0 18
214 25 0 16
215 38 7 23
216 52 2 22
217 67 0 13
218 74 7 13
219 38 3 16
220 30 0 16
221 26 0 20
222 67 6 22
223 132 2 17
224 42 0 18
225 35 0 17
226 118 3 12
227 68 0 7
228 43 1 17
229 76 1 14
230 64 0 23
231 48 1 17
232 64 0 14
233 56 0 15
234 71 0 17
235 75 0 21
236 39 0 18
237 42 0 18
238 39 0 17
239 93 0 17
240 38 0 16
241 60 0 15
242 71 0 21
243 52 0 16
244 27 2 14
245 59 0 15
246 40 1 17
247 79 1 15
248 44 0 15
249 65 0 10
250 10 0 6
251 124 0 22
252 81 0 21
253 15 0 1
254 92 1 18
255 42 0 17
256 10 0 4
257 24 0 10
258 64 0 16
259 45 1 16
260 22 0 9
261 56 0 16
262 94 0 17
263 19 0 7
264 35 0 15
265 32 0 14
266 35 0 14
267 48 0 18
268 49 0 12
269 48 0 16
270 62 0 21
271 96 1 19
272 45 0 16
273 63 0 1
274 71 1 16
275 26 0 10
276 48 6 19
277 29 3 12
278 19 1 2
279 45 2 14
280 45 0 17
281 67 0 19
282 30 0 14
283 36 3 11
284 34 1 4
285 36 0 16
286 34 0 20
287 37 1 12
288 46 0 15
289 44 0 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc
-1.063e+01 2.309e-02 1.858e-04
logins compendium_views_info compendium_views_pr
-1.287e-01 -9.586e-03 -1.343e-01
shared_compendiums `compendiums_reviewed\r`
-1.218e+00 1.182e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-43.490 -11.953 -1.138 8.785 76.624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.063e+01 2.823e+00 -3.767 0.000201 ***
pageviews 2.309e-02 6.572e-03 3.514 0.000514 ***
time_in_rfc 1.858e-04 3.637e-05 5.110 5.95e-07 ***
logins -1.287e-01 5.060e-02 -2.544 0.011502 *
compendium_views_info -9.586e-03 1.499e-02 -0.640 0.522938
compendium_views_pr -1.343e-01 2.592e-02 -5.183 4.17e-07 ***
shared_compendiums -1.218e+00 4.281e-01 -2.845 0.004761 **
`compendiums_reviewed\r` 1.182e+00 1.627e-01 7.263 3.73e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.08 on 281 degrees of freedom
Multiple R-squared: 0.7657, Adjusted R-squared: 0.7599
F-statistic: 131.2 on 7 and 281 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,] 0.3336574 6.673149e-01 6.663426e-01
[2,] 0.7425132 5.149736e-01 2.574868e-01
[3,] 0.6789712 6.420576e-01 3.210288e-01
[4,] 0.5661160 8.677681e-01 4.338840e-01
[5,] 0.4592814 9.185628e-01 5.407186e-01
[6,] 0.3540934 7.081868e-01 6.459066e-01
[7,] 0.2673249 5.346497e-01 7.326751e-01
[8,] 0.3947197 7.894393e-01 6.052803e-01
[9,] 0.3464287 6.928574e-01 6.535713e-01
[10,] 0.6365546 7.268907e-01 3.634454e-01
[11,] 0.7176457 5.647087e-01 2.823543e-01
[12,] 0.6488988 7.022024e-01 3.511012e-01
[13,] 0.7284535 5.430930e-01 2.715465e-01
[14,] 0.6686755 6.626490e-01 3.313245e-01
[15,] 0.6300621 7.398758e-01 3.699379e-01
[16,] 0.6710363 6.579275e-01 3.289637e-01
[17,] 0.6365029 7.269942e-01 3.634971e-01
[18,] 0.5750906 8.498188e-01 4.249094e-01
[19,] 0.5744940 8.510119e-01 4.255060e-01
[20,] 0.5248592 9.502816e-01 4.751408e-01
[21,] 0.4756382 9.512763e-01 5.243618e-01
[22,] 0.4316126 8.632252e-01 5.683874e-01
[23,] 0.6949512 6.100976e-01 3.050488e-01
[24,] 0.6633760 6.732479e-01 3.366240e-01
[25,] 0.6113107 7.773785e-01 3.886893e-01
[26,] 0.5573782 8.852436e-01 4.426218e-01
[27,] 0.5012128 9.975745e-01 4.987872e-01
[28,] 0.5078299 9.843401e-01 4.921701e-01
[29,] 0.7103506 5.792987e-01 2.896494e-01
[30,] 0.6896806 6.206389e-01 3.103194e-01
[31,] 0.6483991 7.032017e-01 3.516009e-01
[32,] 0.5989829 8.020343e-01 4.010171e-01
[33,] 0.5781832 8.436335e-01 4.218168e-01
[34,] 0.5285635 9.428730e-01 4.714365e-01
[35,] 0.4858690 9.717380e-01 5.141310e-01
[36,] 0.4408231 8.816461e-01 5.591769e-01
[37,] 0.4693755 9.387511e-01 5.306245e-01
[38,] 0.4222172 8.444345e-01 5.777828e-01
[39,] 0.4037996 8.075992e-01 5.962004e-01
[40,] 0.6119910 7.760181e-01 3.880090e-01
[41,] 0.6152994 7.694011e-01 3.847006e-01
[42,] 0.6013057 7.973886e-01 3.986943e-01
[43,] 0.5563879 8.872242e-01 4.436121e-01
[44,] 0.5174300 9.651399e-01 4.825700e-01
[45,] 0.5094376 9.811248e-01 4.905624e-01
[46,] 0.4692543 9.385086e-01 5.307457e-01
[47,] 0.4310031 8.620063e-01 5.689969e-01
[48,] 0.3966982 7.933965e-01 6.033018e-01
[49,] 0.3586802 7.173604e-01 6.413198e-01
[50,] 0.3205321 6.410643e-01 6.794679e-01
[51,] 0.3051169 6.102337e-01 6.948831e-01
[52,] 0.2743392 5.486783e-01 7.256608e-01
[53,] 0.3319773 6.639545e-01 6.680227e-01
[54,] 0.2983414 5.966829e-01 7.016586e-01
[55,] 0.3035107 6.070214e-01 6.964893e-01
[56,] 0.3574865 7.149731e-01 6.425135e-01
[57,] 0.3365657 6.731314e-01 6.634343e-01
[58,] 0.3066651 6.133302e-01 6.933349e-01
[59,] 0.4074038 8.148076e-01 5.925962e-01
[60,] 0.4265512 8.531024e-01 5.734488e-01
[61,] 0.4575754 9.151507e-01 5.424246e-01
[62,] 0.5026924 9.946153e-01 4.973076e-01
[63,] 0.4686917 9.373833e-01 5.313083e-01
[64,] 0.4508153 9.016305e-01 5.491847e-01
[65,] 0.4119430 8.238861e-01 5.880570e-01
[66,] 0.5706044 8.587913e-01 4.293956e-01
[67,] 0.9424792 1.150415e-01 5.752075e-02
[68,] 0.9311899 1.376203e-01 6.881013e-02
[69,] 0.9221559 1.556883e-01 7.784414e-02
[70,] 0.9216252 1.567495e-01 7.837477e-02
[71,] 0.9091377 1.817245e-01 9.086226e-02
[72,] 0.9400754 1.198493e-01 5.992463e-02
[73,] 0.9475658 1.048683e-01 5.243416e-02
[74,] 0.9369380 1.261240e-01 6.306199e-02
[75,] 0.9246458 1.507084e-01 7.535420e-02
[76,] 0.9503742 9.925166e-02 4.962583e-02
[77,] 0.9420298 1.159404e-01 5.797018e-02
[78,] 0.9307860 1.384281e-01 6.921405e-02
[79,] 0.9243851 1.512298e-01 7.561491e-02
[80,] 0.9301394 1.397212e-01 6.986059e-02
[81,] 0.9291170 1.417660e-01 7.088301e-02
[82,] 0.9378045 1.243909e-01 6.219546e-02
[83,] 0.9413922 1.172155e-01 5.860777e-02
[84,] 0.9390256 1.219488e-01 6.097438e-02
[85,] 0.9274345 1.451309e-01 7.256547e-02
[86,] 0.9333329 1.333341e-01 6.666706e-02
[87,] 0.9495035 1.009930e-01 5.049649e-02
[88,] 0.9814873 3.702543e-02 1.851272e-02
[89,] 0.9813225 3.735504e-02 1.867752e-02
[90,] 0.9950013 9.997483e-03 4.998742e-03
[91,] 0.9949176 1.016479e-02 5.082393e-03
[92,] 0.9934688 1.306237e-02 6.531187e-03
[93,] 0.9938471 1.230578e-02 6.152888e-03
[94,] 0.9921499 1.570013e-02 7.850064e-03
[95,] 0.9911980 1.760399e-02 8.801995e-03
[96,] 0.9971356 5.728883e-03 2.864442e-03
[97,] 0.9963031 7.393852e-03 3.696926e-03
[98,] 0.9954455 9.108905e-03 4.554452e-03
[99,] 0.9973404 5.319252e-03 2.659626e-03
[100,] 0.9966010 6.797925e-03 3.398963e-03
[101,] 0.9974354 5.129290e-03 2.564645e-03
[102,] 0.9972632 5.473692e-03 2.736846e-03
[103,] 0.9966071 6.785824e-03 3.392912e-03
[104,] 0.9966956 6.608753e-03 3.304377e-03
[105,] 0.9964143 7.171414e-03 3.585707e-03
[106,] 0.9971071 5.785759e-03 2.892880e-03
[107,] 0.9997498 5.004311e-04 2.502156e-04
[108,] 0.9997613 4.773157e-04 2.386579e-04
[109,] 0.9997314 5.371751e-04 2.685875e-04
[110,] 0.9996450 7.099811e-04 3.549906e-04
[111,] 0.9995221 9.558477e-04 4.779238e-04
[112,] 0.9993640 1.271982e-03 6.359911e-04
[113,] 0.9991517 1.696568e-03 8.482841e-04
[114,] 0.9995637 8.726063e-04 4.363032e-04
[115,] 0.9996436 7.128270e-04 3.564135e-04
[116,] 0.9995308 9.383826e-04 4.691913e-04
[117,] 0.9993670 1.266074e-03 6.330370e-04
[118,] 0.9992646 1.470727e-03 7.353633e-04
[119,] 0.9995765 8.469773e-04 4.234886e-04
[120,] 0.9994810 1.038062e-03 5.190311e-04
[121,] 0.9993444 1.311172e-03 6.555860e-04
[122,] 0.9997611 4.778030e-04 2.389015e-04
[123,] 0.9996987 6.025731e-04 3.012866e-04
[124,] 0.9996283 7.434260e-04 3.717130e-04
[125,] 0.9995222 9.556611e-04 4.778305e-04
[126,] 0.9994216 1.156880e-03 5.784400e-04
[127,] 0.9992371 1.525700e-03 7.628502e-04
[128,] 0.9990415 1.916997e-03 9.584987e-04
[129,] 0.9987438 2.512439e-03 1.256219e-03
[130,] 0.9984826 3.034803e-03 1.517402e-03
[131,] 0.9980679 3.864253e-03 1.932126e-03
[132,] 0.9976306 4.738771e-03 2.369386e-03
[133,] 0.9981713 3.657308e-03 1.828654e-03
[134,] 0.9977248 4.550415e-03 2.275208e-03
[135,] 0.9974156 5.168813e-03 2.584407e-03
[136,] 0.9968725 6.254904e-03 3.127452e-03
[137,] 0.9970732 5.853531e-03 2.926766e-03
[138,] 0.9971809 5.638234e-03 2.819117e-03
[139,] 0.9966294 6.741214e-03 3.370607e-03
[140,] 0.9967483 6.503397e-03 3.251698e-03
[141,] 0.9960409 7.918295e-03 3.959148e-03
[142,] 0.9978851 4.229867e-03 2.114934e-03
[143,] 0.9973948 5.210373e-03 2.605187e-03
[144,] 0.9969969 6.006159e-03 3.003080e-03
[145,] 0.9989840 2.032093e-03 1.016047e-03
[146,] 0.9987709 2.458128e-03 1.229064e-03
[147,] 0.9984063 3.187423e-03 1.593711e-03
[148,] 0.9978952 4.209581e-03 2.104790e-03
[149,] 0.9976351 4.729719e-03 2.364860e-03
[150,] 0.9969708 6.058351e-03 3.029176e-03
[151,] 0.9981850 3.630020e-03 1.815010e-03
[152,] 0.9986201 2.759863e-03 1.379931e-03
[153,] 0.9981997 3.600617e-03 1.800308e-03
[154,] 0.9978168 4.366454e-03 2.183227e-03
[155,] 0.9973861 5.227785e-03 2.613893e-03
[156,] 0.9971996 5.600738e-03 2.800369e-03
[157,] 0.9966321 6.735711e-03 3.367855e-03
[158,] 0.9981974 3.605131e-03 1.802565e-03
[159,] 0.9978513 4.297441e-03 2.148721e-03
[160,] 0.9988696 2.260839e-03 1.130419e-03
[161,] 0.9988439 2.312208e-03 1.156104e-03
[162,] 0.9996338 7.324471e-04 3.662235e-04
[163,] 0.9997646 4.708645e-04 2.354323e-04
[164,] 0.9998036 3.928802e-04 1.964401e-04
[165,] 1.0000000 2.957663e-09 1.478832e-09
[166,] 1.0000000 2.410369e-09 1.205185e-09
[167,] 1.0000000 2.734401e-09 1.367201e-09
[168,] 1.0000000 4.233377e-09 2.116688e-09
[169,] 1.0000000 7.329123e-13 3.664562e-13
[170,] 1.0000000 1.098679e-12 5.493394e-13
[171,] 1.0000000 1.932470e-12 9.662351e-13
[172,] 1.0000000 7.948534e-13 3.974267e-13
[173,] 1.0000000 1.585601e-12 7.928004e-13
[174,] 1.0000000 2.105172e-12 1.052586e-12
[175,] 1.0000000 5.998778e-14 2.999389e-14
[176,] 1.0000000 1.215610e-13 6.078052e-14
[177,] 1.0000000 2.568458e-13 1.284229e-13
[178,] 1.0000000 4.327987e-13 2.163993e-13
[179,] 1.0000000 6.077994e-13 3.038997e-13
[180,] 1.0000000 7.366492e-13 3.683246e-13
[181,] 1.0000000 1.085461e-12 5.427306e-13
[182,] 1.0000000 1.182556e-12 5.912781e-13
[183,] 1.0000000 9.907082e-13 4.953541e-13
[184,] 1.0000000 1.368270e-12 6.841349e-13
[185,] 1.0000000 9.307197e-13 4.653598e-13
[186,] 1.0000000 4.075234e-13 2.037617e-13
[187,] 1.0000000 5.534208e-13 2.767104e-13
[188,] 1.0000000 1.166563e-12 5.832813e-13
[189,] 1.0000000 2.329571e-12 1.164786e-12
[190,] 1.0000000 2.970326e-12 1.485163e-12
[191,] 1.0000000 6.014566e-12 3.007283e-12
[192,] 1.0000000 1.152482e-11 5.762411e-12
[193,] 1.0000000 2.259989e-11 1.129995e-11
[194,] 1.0000000 3.559826e-11 1.779913e-11
[195,] 1.0000000 7.014590e-11 3.507295e-11
[196,] 1.0000000 1.055095e-10 5.275476e-11
[197,] 1.0000000 2.108756e-10 1.054378e-10
[198,] 1.0000000 2.757258e-10 1.378629e-10
[199,] 1.0000000 5.123825e-10 2.561912e-10
[200,] 1.0000000 5.970429e-10 2.985215e-10
[201,] 1.0000000 1.186853e-09 5.934265e-10
[202,] 1.0000000 2.275724e-09 1.137862e-09
[203,] 1.0000000 4.459444e-09 2.229722e-09
[204,] 1.0000000 5.262804e-09 2.631402e-09
[205,] 1.0000000 2.715913e-09 1.357956e-09
[206,] 1.0000000 1.914085e-09 9.570424e-10
[207,] 1.0000000 3.872112e-09 1.936056e-09
[208,] 1.0000000 6.260057e-09 3.130028e-09
[209,] 1.0000000 1.053954e-08 5.269771e-09
[210,] 1.0000000 1.446495e-08 7.232477e-09
[211,] 1.0000000 2.703951e-08 1.351976e-08
[212,] 1.0000000 1.987551e-08 9.937756e-09
[213,] 1.0000000 2.823160e-08 1.411580e-08
[214,] 1.0000000 5.517419e-08 2.758709e-08
[215,] 1.0000000 7.607669e-08 3.803834e-08
[216,] 0.9999999 1.306664e-07 6.533320e-08
[217,] 0.9999999 2.417375e-07 1.208688e-07
[218,] 0.9999999 2.876541e-07 1.438271e-07
[219,] 0.9999998 3.970702e-07 1.985351e-07
[220,] 0.9999996 7.300068e-07 3.650034e-07
[221,] 0.9999999 2.947442e-07 1.473721e-07
[222,] 0.9999998 4.525320e-07 2.262660e-07
[223,] 0.9999996 8.667868e-07 4.333934e-07
[224,] 0.9999993 1.338483e-06 6.692413e-07
[225,] 0.9999987 2.568447e-06 1.284224e-06
[226,] 0.9999990 2.048825e-06 1.024413e-06
[227,] 0.9999990 2.037941e-06 1.018970e-06
[228,] 0.9999980 3.961558e-06 1.980779e-06
[229,] 0.9999964 7.283447e-06 3.641723e-06
[230,] 0.9999931 1.376324e-05 6.881621e-06
[231,] 0.9999915 1.700347e-05 8.501733e-06
[232,] 0.9999881 2.387942e-05 1.193971e-05
[233,] 0.9999863 2.737519e-05 1.368760e-05
[234,] 0.9999751 4.988610e-05 2.494305e-05
[235,] 0.9999541 9.171310e-05 4.585655e-05
[236,] 0.9999171 1.658227e-04 8.291133e-05
[237,] 0.9999334 1.332625e-04 6.663123e-05
[238,] 0.9999275 1.449127e-04 7.245633e-05
[239,] 0.9998666 2.667388e-04 1.333694e-04
[240,] 0.9997973 4.054372e-04 2.027186e-04
[241,] 0.9997611 4.777187e-04 2.388593e-04
[242,] 0.9996390 7.220799e-04 3.610399e-04
[243,] 0.9994448 1.110476e-03 5.552382e-04
[244,] 0.9996558 6.884346e-04 3.442173e-04
[245,] 0.9994267 1.146573e-03 5.732867e-04
[246,] 0.9989664 2.067139e-03 1.033569e-03
[247,] 0.9990992 1.801543e-03 9.007717e-04
[248,] 0.9983969 3.206144e-03 1.603072e-03
[249,] 0.9980281 3.943855e-03 1.971928e-03
[250,] 0.9965507 6.898501e-03 3.449251e-03
[251,] 0.9960253 7.949384e-03 3.974692e-03
[252,] 0.9961794 7.641133e-03 3.820567e-03
[253,] 0.9941923 1.161540e-02 5.807698e-03
[254,] 0.9962765 7.447037e-03 3.723519e-03
[255,] 0.9934512 1.309756e-02 6.548778e-03
[256,] 0.9934657 1.306854e-02 6.534268e-03
[257,] 0.9927603 1.447943e-02 7.239714e-03
[258,] 0.9898871 2.022588e-02 1.011294e-02
[259,] 0.9810983 3.780344e-02 1.890172e-02
[260,] 0.9868100 2.638006e-02 1.319003e-02
[261,] 0.9973284 5.343137e-03 2.671568e-03
[262,] 0.9936270 1.274595e-02 6.372974e-03
[263,] 0.9965261 6.947755e-03 3.473878e-03
[264,] 0.9922748 1.545037e-02 7.725185e-03
[265,] 0.9922175 1.556506e-02 7.782530e-03
[266,] 0.9911668 1.766644e-02 8.833220e-03
[267,] 0.9860515 2.789704e-02 1.394852e-02
[268,] 0.9953974 9.205209e-03 4.602605e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1mudx1323861648.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2bhxe1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3g3pi1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4evzw1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5o7n81323861648.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 = 289
Frequency = 1
1 2 3 4 5 6
7.7721249 15.3031142 -8.7725177 39.6432065 4.2587379 -27.3779345
7 8 9 10 11 12
-8.2089939 -15.1008415 -5.2990898 -14.4474284 2.9093943 -14.0705418
13 14 15 16 17 18
8.7854815 -9.6492807 0.4225237 -5.3934643 7.4943218 18.1918765
19 20 21 22 23 24
-5.3711650 29.6138783 29.6338721 9.5615985 -28.9307420 -4.0207174
25 26 27 28 29 30
17.3865115 42.1323164 -5.9897826 -1.3013727 18.5405777 -7.7764964
31 32 33 34 35 36
21.1016070 8.1481896 -35.3277484 8.7224425 -5.4037391 -2.9307125
37 38 39 40 41 42
-0.4830880 -18.0343366 -39.4887689 7.9982798 -5.4975702 -0.7941301
43 44 45 46 47 48
5.1212579 -3.8528725 4.7587479 -4.6254631 -14.2110820 4.7871756
49 50 51 52 53 54
-11.6300857 -43.4898670 9.1000331 -17.2784308 1.9137153 -3.2826809
55 56 57 58 59 60
12.7425794 6.0852753 5.8219458 -9.7422013 -5.3635304 4.1337810
61 62 63 64 65 66
-12.8064549 -6.0283269 -31.4021489 -6.9585307 17.1159582 27.7271251
67 68 69 70 71 72
11.6790722 4.6639128 35.2260899 -21.1246003 -24.1402296 -26.1722307
73 74 75 76 77 78
6.1060191 13.9897830 -1.7705491 40.3605305 73.9772155 -3.6452972
79 80 81 82 83 84
-8.4877643 -18.8153254 7.5181517 33.9928464 24.1566200 2.9339170
85 86 87 88 89 90
2.7484168 34.5479613 -7.0345235 -1.7543297 -13.8510898 -21.5228973
91 92 93 94 95 96
18.6160620 26.6134300 -20.3771463 17.2831086 2.9187127 -23.0341821
97 98 99 100 101 102
-30.8493890 45.5642868 -18.1143604 48.1574132 -16.8994920 1.8522047
103 104 105 106 107 108
-20.8772503 2.1636550 13.7753280 43.2108902 -5.8105326 -5.6025608
109 110 111 112 113 114
30.8979010 2.0095785 26.1531496 -16.2679907 -6.2675915 19.3974013
115 116 117 118 119 120
15.0128367 24.1305134 53.9906551 -18.3566319 -13.8684994 8.4066243
121 122 123 124 125 126
4.0397617 5.1886212 5.3202288 32.0379779 -24.0123258 7.1635799
127 128 129 130 131 132
1.6809158 14.0329126 27.4938399 12.1318004 -6.8699175 33.3526205
133 134 135 136 137 138
10.1863317 -9.5371841 4.7016646 4.4518563 5.5114909 -9.6823082
139 140 141 142 143 144
-1.1383477 10.2892862 -4.7645716 7.9309172 -22.9349774 5.0633228
145 146 147 148 149 150
-15.9804850 -7.7179940 -20.4757149 -20.2292257 10.9664180 -19.8619483
151 152 153 154 155 156
-11.1154801 -34.4961240 3.7524708 5.6159185 35.5782322 -0.8171794
157 158 159 160 161 162
-6.5717499 2.3443317 -15.0371844 4.6579919 -36.1398815 -20.0243486
163 164 165 166 167 168
1.8554383 -10.9386846 -11.3900849 10.2315180 7.9332983 -32.5851812
169 170 171 172 173 174
10.0687893 -36.1321020 -18.8023996 32.6667050 -28.7845466 20.5926002
175 176 177 178 179 180
76.6235600 -19.8779851 -2.2095915 -10.1907921 34.7230908 8.6345641
181 182 183 184 185 186
12.5526197 21.8345542 9.2257511 -26.2681862 3.8831809 14.1618037
187 188 189 190 191 192
3.1036808 4.6427284 -15.6790595 -11.9526052 -12.8809609 -21.6894897
193 194 195 196 197 198
22.6925157 -4.8394135 -1.8471468 18.3876774 -19.0229448 -4.9911657
199 200 201 202 203 204
-7.8074013 6.2970223 -3.6763612 -13.0700344 -1.1929505 -11.3050778
205 206 207 208 209 210
-5.4273203 -15.4059933 -1.4044625 -23.6126477 -14.2350071 -8.0764981
211 212 213 214 215 216
4.9263656 -9.7720209 -3.8403408 3.3557965 -31.1763262 -22.3457003
217 218 219 220 221 222
2.4519912 2.5035199 4.3784332 -17.8284044 -10.4883270 -19.8793770
223 224 225 226 227 228
13.2281552 -6.4390300 -9.7742067 8.1389700 9.2647603 8.3694469
229 230 231 232 233 234
11.9595786 -12.6216263 -17.6107448 5.6194734 -9.7510863 -5.6444286
235 236 237 238 239 240
-2.8580294 13.8835613 -30.8295194 -1.9349285 1.7330475 -2.6228744
241 242 243 244 245 246
-15.3622466 -18.6371526 12.4160494 3.1539067 -1.3686388 -6.4665444
247 248 249 250 251 252
9.4695977 -14.1443735 -0.2548302 9.0917575 -21.5855646 3.7983139
253 254 255 256 257 258
17.8022694 -19.6074760 -15.0984673 3.6246846 13.2111671 -1.4664311
259 260 261 262 263 264
-7.2264556 5.6386501 -12.8522452 17.1193546 -4.9354148 12.3559155
265 266 267 268 269 270
-1.8452019 7.3541733 9.6109032 14.4227752 -2.1768330 2.3547888
271 272 273 274 275 276
-20.9434089 -5.0154472 4.1249621 12.3070051 4.2993507 10.8685326
277 278 279 280 281 282
-12.7789795 14.0491841 4.4674065 -13.8959917 -4.3750170 -11.1553973
283 284 285 286 287 288
-1.0401129 3.8318129 -14.4393546 -20.4739262 -7.1071434 4.1246627
289
-14.5365801
> postscript(file="/var/wessaorg/rcomp/tmp/6sh5t1323861648.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 7.7721249 NA
1 15.3031142 7.7721249
2 -8.7725177 15.3031142
3 39.6432065 -8.7725177
4 4.2587379 39.6432065
5 -27.3779345 4.2587379
6 -8.2089939 -27.3779345
7 -15.1008415 -8.2089939
8 -5.2990898 -15.1008415
9 -14.4474284 -5.2990898
10 2.9093943 -14.4474284
11 -14.0705418 2.9093943
12 8.7854815 -14.0705418
13 -9.6492807 8.7854815
14 0.4225237 -9.6492807
15 -5.3934643 0.4225237
16 7.4943218 -5.3934643
17 18.1918765 7.4943218
18 -5.3711650 18.1918765
19 29.6138783 -5.3711650
20 29.6338721 29.6138783
21 9.5615985 29.6338721
22 -28.9307420 9.5615985
23 -4.0207174 -28.9307420
24 17.3865115 -4.0207174
25 42.1323164 17.3865115
26 -5.9897826 42.1323164
27 -1.3013727 -5.9897826
28 18.5405777 -1.3013727
29 -7.7764964 18.5405777
30 21.1016070 -7.7764964
31 8.1481896 21.1016070
32 -35.3277484 8.1481896
33 8.7224425 -35.3277484
34 -5.4037391 8.7224425
35 -2.9307125 -5.4037391
36 -0.4830880 -2.9307125
37 -18.0343366 -0.4830880
38 -39.4887689 -18.0343366
39 7.9982798 -39.4887689
40 -5.4975702 7.9982798
41 -0.7941301 -5.4975702
42 5.1212579 -0.7941301
43 -3.8528725 5.1212579
44 4.7587479 -3.8528725
45 -4.6254631 4.7587479
46 -14.2110820 -4.6254631
47 4.7871756 -14.2110820
48 -11.6300857 4.7871756
49 -43.4898670 -11.6300857
50 9.1000331 -43.4898670
51 -17.2784308 9.1000331
52 1.9137153 -17.2784308
53 -3.2826809 1.9137153
54 12.7425794 -3.2826809
55 6.0852753 12.7425794
56 5.8219458 6.0852753
57 -9.7422013 5.8219458
58 -5.3635304 -9.7422013
59 4.1337810 -5.3635304
60 -12.8064549 4.1337810
61 -6.0283269 -12.8064549
62 -31.4021489 -6.0283269
63 -6.9585307 -31.4021489
64 17.1159582 -6.9585307
65 27.7271251 17.1159582
66 11.6790722 27.7271251
67 4.6639128 11.6790722
68 35.2260899 4.6639128
69 -21.1246003 35.2260899
70 -24.1402296 -21.1246003
71 -26.1722307 -24.1402296
72 6.1060191 -26.1722307
73 13.9897830 6.1060191
74 -1.7705491 13.9897830
75 40.3605305 -1.7705491
76 73.9772155 40.3605305
77 -3.6452972 73.9772155
78 -8.4877643 -3.6452972
79 -18.8153254 -8.4877643
80 7.5181517 -18.8153254
81 33.9928464 7.5181517
82 24.1566200 33.9928464
83 2.9339170 24.1566200
84 2.7484168 2.9339170
85 34.5479613 2.7484168
86 -7.0345235 34.5479613
87 -1.7543297 -7.0345235
88 -13.8510898 -1.7543297
89 -21.5228973 -13.8510898
90 18.6160620 -21.5228973
91 26.6134300 18.6160620
92 -20.3771463 26.6134300
93 17.2831086 -20.3771463
94 2.9187127 17.2831086
95 -23.0341821 2.9187127
96 -30.8493890 -23.0341821
97 45.5642868 -30.8493890
98 -18.1143604 45.5642868
99 48.1574132 -18.1143604
100 -16.8994920 48.1574132
101 1.8522047 -16.8994920
102 -20.8772503 1.8522047
103 2.1636550 -20.8772503
104 13.7753280 2.1636550
105 43.2108902 13.7753280
106 -5.8105326 43.2108902
107 -5.6025608 -5.8105326
108 30.8979010 -5.6025608
109 2.0095785 30.8979010
110 26.1531496 2.0095785
111 -16.2679907 26.1531496
112 -6.2675915 -16.2679907
113 19.3974013 -6.2675915
114 15.0128367 19.3974013
115 24.1305134 15.0128367
116 53.9906551 24.1305134
117 -18.3566319 53.9906551
118 -13.8684994 -18.3566319
119 8.4066243 -13.8684994
120 4.0397617 8.4066243
121 5.1886212 4.0397617
122 5.3202288 5.1886212
123 32.0379779 5.3202288
124 -24.0123258 32.0379779
125 7.1635799 -24.0123258
126 1.6809158 7.1635799
127 14.0329126 1.6809158
128 27.4938399 14.0329126
129 12.1318004 27.4938399
130 -6.8699175 12.1318004
131 33.3526205 -6.8699175
132 10.1863317 33.3526205
133 -9.5371841 10.1863317
134 4.7016646 -9.5371841
135 4.4518563 4.7016646
136 5.5114909 4.4518563
137 -9.6823082 5.5114909
138 -1.1383477 -9.6823082
139 10.2892862 -1.1383477
140 -4.7645716 10.2892862
141 7.9309172 -4.7645716
142 -22.9349774 7.9309172
143 5.0633228 -22.9349774
144 -15.9804850 5.0633228
145 -7.7179940 -15.9804850
146 -20.4757149 -7.7179940
147 -20.2292257 -20.4757149
148 10.9664180 -20.2292257
149 -19.8619483 10.9664180
150 -11.1154801 -19.8619483
151 -34.4961240 -11.1154801
152 3.7524708 -34.4961240
153 5.6159185 3.7524708
154 35.5782322 5.6159185
155 -0.8171794 35.5782322
156 -6.5717499 -0.8171794
157 2.3443317 -6.5717499
158 -15.0371844 2.3443317
159 4.6579919 -15.0371844
160 -36.1398815 4.6579919
161 -20.0243486 -36.1398815
162 1.8554383 -20.0243486
163 -10.9386846 1.8554383
164 -11.3900849 -10.9386846
165 10.2315180 -11.3900849
166 7.9332983 10.2315180
167 -32.5851812 7.9332983
168 10.0687893 -32.5851812
169 -36.1321020 10.0687893
170 -18.8023996 -36.1321020
171 32.6667050 -18.8023996
172 -28.7845466 32.6667050
173 20.5926002 -28.7845466
174 76.6235600 20.5926002
175 -19.8779851 76.6235600
176 -2.2095915 -19.8779851
177 -10.1907921 -2.2095915
178 34.7230908 -10.1907921
179 8.6345641 34.7230908
180 12.5526197 8.6345641
181 21.8345542 12.5526197
182 9.2257511 21.8345542
183 -26.2681862 9.2257511
184 3.8831809 -26.2681862
185 14.1618037 3.8831809
186 3.1036808 14.1618037
187 4.6427284 3.1036808
188 -15.6790595 4.6427284
189 -11.9526052 -15.6790595
190 -12.8809609 -11.9526052
191 -21.6894897 -12.8809609
192 22.6925157 -21.6894897
193 -4.8394135 22.6925157
194 -1.8471468 -4.8394135
195 18.3876774 -1.8471468
196 -19.0229448 18.3876774
197 -4.9911657 -19.0229448
198 -7.8074013 -4.9911657
199 6.2970223 -7.8074013
200 -3.6763612 6.2970223
201 -13.0700344 -3.6763612
202 -1.1929505 -13.0700344
203 -11.3050778 -1.1929505
204 -5.4273203 -11.3050778
205 -15.4059933 -5.4273203
206 -1.4044625 -15.4059933
207 -23.6126477 -1.4044625
208 -14.2350071 -23.6126477
209 -8.0764981 -14.2350071
210 4.9263656 -8.0764981
211 -9.7720209 4.9263656
212 -3.8403408 -9.7720209
213 3.3557965 -3.8403408
214 -31.1763262 3.3557965
215 -22.3457003 -31.1763262
216 2.4519912 -22.3457003
217 2.5035199 2.4519912
218 4.3784332 2.5035199
219 -17.8284044 4.3784332
220 -10.4883270 -17.8284044
221 -19.8793770 -10.4883270
222 13.2281552 -19.8793770
223 -6.4390300 13.2281552
224 -9.7742067 -6.4390300
225 8.1389700 -9.7742067
226 9.2647603 8.1389700
227 8.3694469 9.2647603
228 11.9595786 8.3694469
229 -12.6216263 11.9595786
230 -17.6107448 -12.6216263
231 5.6194734 -17.6107448
232 -9.7510863 5.6194734
233 -5.6444286 -9.7510863
234 -2.8580294 -5.6444286
235 13.8835613 -2.8580294
236 -30.8295194 13.8835613
237 -1.9349285 -30.8295194
238 1.7330475 -1.9349285
239 -2.6228744 1.7330475
240 -15.3622466 -2.6228744
241 -18.6371526 -15.3622466
242 12.4160494 -18.6371526
243 3.1539067 12.4160494
244 -1.3686388 3.1539067
245 -6.4665444 -1.3686388
246 9.4695977 -6.4665444
247 -14.1443735 9.4695977
248 -0.2548302 -14.1443735
249 9.0917575 -0.2548302
250 -21.5855646 9.0917575
251 3.7983139 -21.5855646
252 17.8022694 3.7983139
253 -19.6074760 17.8022694
254 -15.0984673 -19.6074760
255 3.6246846 -15.0984673
256 13.2111671 3.6246846
257 -1.4664311 13.2111671
258 -7.2264556 -1.4664311
259 5.6386501 -7.2264556
260 -12.8522452 5.6386501
261 17.1193546 -12.8522452
262 -4.9354148 17.1193546
263 12.3559155 -4.9354148
264 -1.8452019 12.3559155
265 7.3541733 -1.8452019
266 9.6109032 7.3541733
267 14.4227752 9.6109032
268 -2.1768330 14.4227752
269 2.3547888 -2.1768330
270 -20.9434089 2.3547888
271 -5.0154472 -20.9434089
272 4.1249621 -5.0154472
273 12.3070051 4.1249621
274 4.2993507 12.3070051
275 10.8685326 4.2993507
276 -12.7789795 10.8685326
277 14.0491841 -12.7789795
278 4.4674065 14.0491841
279 -13.8959917 4.4674065
280 -4.3750170 -13.8959917
281 -11.1553973 -4.3750170
282 -1.0401129 -11.1553973
283 3.8318129 -1.0401129
284 -14.4393546 3.8318129
285 -20.4739262 -14.4393546
286 -7.1071434 -20.4739262
287 4.1246627 -7.1071434
288 -14.5365801 4.1246627
289 NA -14.5365801
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 15.3031142 7.7721249
[2,] -8.7725177 15.3031142
[3,] 39.6432065 -8.7725177
[4,] 4.2587379 39.6432065
[5,] -27.3779345 4.2587379
[6,] -8.2089939 -27.3779345
[7,] -15.1008415 -8.2089939
[8,] -5.2990898 -15.1008415
[9,] -14.4474284 -5.2990898
[10,] 2.9093943 -14.4474284
[11,] -14.0705418 2.9093943
[12,] 8.7854815 -14.0705418
[13,] -9.6492807 8.7854815
[14,] 0.4225237 -9.6492807
[15,] -5.3934643 0.4225237
[16,] 7.4943218 -5.3934643
[17,] 18.1918765 7.4943218
[18,] -5.3711650 18.1918765
[19,] 29.6138783 -5.3711650
[20,] 29.6338721 29.6138783
[21,] 9.5615985 29.6338721
[22,] -28.9307420 9.5615985
[23,] -4.0207174 -28.9307420
[24,] 17.3865115 -4.0207174
[25,] 42.1323164 17.3865115
[26,] -5.9897826 42.1323164
[27,] -1.3013727 -5.9897826
[28,] 18.5405777 -1.3013727
[29,] -7.7764964 18.5405777
[30,] 21.1016070 -7.7764964
[31,] 8.1481896 21.1016070
[32,] -35.3277484 8.1481896
[33,] 8.7224425 -35.3277484
[34,] -5.4037391 8.7224425
[35,] -2.9307125 -5.4037391
[36,] -0.4830880 -2.9307125
[37,] -18.0343366 -0.4830880
[38,] -39.4887689 -18.0343366
[39,] 7.9982798 -39.4887689
[40,] -5.4975702 7.9982798
[41,] -0.7941301 -5.4975702
[42,] 5.1212579 -0.7941301
[43,] -3.8528725 5.1212579
[44,] 4.7587479 -3.8528725
[45,] -4.6254631 4.7587479
[46,] -14.2110820 -4.6254631
[47,] 4.7871756 -14.2110820
[48,] -11.6300857 4.7871756
[49,] -43.4898670 -11.6300857
[50,] 9.1000331 -43.4898670
[51,] -17.2784308 9.1000331
[52,] 1.9137153 -17.2784308
[53,] -3.2826809 1.9137153
[54,] 12.7425794 -3.2826809
[55,] 6.0852753 12.7425794
[56,] 5.8219458 6.0852753
[57,] -9.7422013 5.8219458
[58,] -5.3635304 -9.7422013
[59,] 4.1337810 -5.3635304
[60,] -12.8064549 4.1337810
[61,] -6.0283269 -12.8064549
[62,] -31.4021489 -6.0283269
[63,] -6.9585307 -31.4021489
[64,] 17.1159582 -6.9585307
[65,] 27.7271251 17.1159582
[66,] 11.6790722 27.7271251
[67,] 4.6639128 11.6790722
[68,] 35.2260899 4.6639128
[69,] -21.1246003 35.2260899
[70,] -24.1402296 -21.1246003
[71,] -26.1722307 -24.1402296
[72,] 6.1060191 -26.1722307
[73,] 13.9897830 6.1060191
[74,] -1.7705491 13.9897830
[75,] 40.3605305 -1.7705491
[76,] 73.9772155 40.3605305
[77,] -3.6452972 73.9772155
[78,] -8.4877643 -3.6452972
[79,] -18.8153254 -8.4877643
[80,] 7.5181517 -18.8153254
[81,] 33.9928464 7.5181517
[82,] 24.1566200 33.9928464
[83,] 2.9339170 24.1566200
[84,] 2.7484168 2.9339170
[85,] 34.5479613 2.7484168
[86,] -7.0345235 34.5479613
[87,] -1.7543297 -7.0345235
[88,] -13.8510898 -1.7543297
[89,] -21.5228973 -13.8510898
[90,] 18.6160620 -21.5228973
[91,] 26.6134300 18.6160620
[92,] -20.3771463 26.6134300
[93,] 17.2831086 -20.3771463
[94,] 2.9187127 17.2831086
[95,] -23.0341821 2.9187127
[96,] -30.8493890 -23.0341821
[97,] 45.5642868 -30.8493890
[98,] -18.1143604 45.5642868
[99,] 48.1574132 -18.1143604
[100,] -16.8994920 48.1574132
[101,] 1.8522047 -16.8994920
[102,] -20.8772503 1.8522047
[103,] 2.1636550 -20.8772503
[104,] 13.7753280 2.1636550
[105,] 43.2108902 13.7753280
[106,] -5.8105326 43.2108902
[107,] -5.6025608 -5.8105326
[108,] 30.8979010 -5.6025608
[109,] 2.0095785 30.8979010
[110,] 26.1531496 2.0095785
[111,] -16.2679907 26.1531496
[112,] -6.2675915 -16.2679907
[113,] 19.3974013 -6.2675915
[114,] 15.0128367 19.3974013
[115,] 24.1305134 15.0128367
[116,] 53.9906551 24.1305134
[117,] -18.3566319 53.9906551
[118,] -13.8684994 -18.3566319
[119,] 8.4066243 -13.8684994
[120,] 4.0397617 8.4066243
[121,] 5.1886212 4.0397617
[122,] 5.3202288 5.1886212
[123,] 32.0379779 5.3202288
[124,] -24.0123258 32.0379779
[125,] 7.1635799 -24.0123258
[126,] 1.6809158 7.1635799
[127,] 14.0329126 1.6809158
[128,] 27.4938399 14.0329126
[129,] 12.1318004 27.4938399
[130,] -6.8699175 12.1318004
[131,] 33.3526205 -6.8699175
[132,] 10.1863317 33.3526205
[133,] -9.5371841 10.1863317
[134,] 4.7016646 -9.5371841
[135,] 4.4518563 4.7016646
[136,] 5.5114909 4.4518563
[137,] -9.6823082 5.5114909
[138,] -1.1383477 -9.6823082
[139,] 10.2892862 -1.1383477
[140,] -4.7645716 10.2892862
[141,] 7.9309172 -4.7645716
[142,] -22.9349774 7.9309172
[143,] 5.0633228 -22.9349774
[144,] -15.9804850 5.0633228
[145,] -7.7179940 -15.9804850
[146,] -20.4757149 -7.7179940
[147,] -20.2292257 -20.4757149
[148,] 10.9664180 -20.2292257
[149,] -19.8619483 10.9664180
[150,] -11.1154801 -19.8619483
[151,] -34.4961240 -11.1154801
[152,] 3.7524708 -34.4961240
[153,] 5.6159185 3.7524708
[154,] 35.5782322 5.6159185
[155,] -0.8171794 35.5782322
[156,] -6.5717499 -0.8171794
[157,] 2.3443317 -6.5717499
[158,] -15.0371844 2.3443317
[159,] 4.6579919 -15.0371844
[160,] -36.1398815 4.6579919
[161,] -20.0243486 -36.1398815
[162,] 1.8554383 -20.0243486
[163,] -10.9386846 1.8554383
[164,] -11.3900849 -10.9386846
[165,] 10.2315180 -11.3900849
[166,] 7.9332983 10.2315180
[167,] -32.5851812 7.9332983
[168,] 10.0687893 -32.5851812
[169,] -36.1321020 10.0687893
[170,] -18.8023996 -36.1321020
[171,] 32.6667050 -18.8023996
[172,] -28.7845466 32.6667050
[173,] 20.5926002 -28.7845466
[174,] 76.6235600 20.5926002
[175,] -19.8779851 76.6235600
[176,] -2.2095915 -19.8779851
[177,] -10.1907921 -2.2095915
[178,] 34.7230908 -10.1907921
[179,] 8.6345641 34.7230908
[180,] 12.5526197 8.6345641
[181,] 21.8345542 12.5526197
[182,] 9.2257511 21.8345542
[183,] -26.2681862 9.2257511
[184,] 3.8831809 -26.2681862
[185,] 14.1618037 3.8831809
[186,] 3.1036808 14.1618037
[187,] 4.6427284 3.1036808
[188,] -15.6790595 4.6427284
[189,] -11.9526052 -15.6790595
[190,] -12.8809609 -11.9526052
[191,] -21.6894897 -12.8809609
[192,] 22.6925157 -21.6894897
[193,] -4.8394135 22.6925157
[194,] -1.8471468 -4.8394135
[195,] 18.3876774 -1.8471468
[196,] -19.0229448 18.3876774
[197,] -4.9911657 -19.0229448
[198,] -7.8074013 -4.9911657
[199,] 6.2970223 -7.8074013
[200,] -3.6763612 6.2970223
[201,] -13.0700344 -3.6763612
[202,] -1.1929505 -13.0700344
[203,] -11.3050778 -1.1929505
[204,] -5.4273203 -11.3050778
[205,] -15.4059933 -5.4273203
[206,] -1.4044625 -15.4059933
[207,] -23.6126477 -1.4044625
[208,] -14.2350071 -23.6126477
[209,] -8.0764981 -14.2350071
[210,] 4.9263656 -8.0764981
[211,] -9.7720209 4.9263656
[212,] -3.8403408 -9.7720209
[213,] 3.3557965 -3.8403408
[214,] -31.1763262 3.3557965
[215,] -22.3457003 -31.1763262
[216,] 2.4519912 -22.3457003
[217,] 2.5035199 2.4519912
[218,] 4.3784332 2.5035199
[219,] -17.8284044 4.3784332
[220,] -10.4883270 -17.8284044
[221,] -19.8793770 -10.4883270
[222,] 13.2281552 -19.8793770
[223,] -6.4390300 13.2281552
[224,] -9.7742067 -6.4390300
[225,] 8.1389700 -9.7742067
[226,] 9.2647603 8.1389700
[227,] 8.3694469 9.2647603
[228,] 11.9595786 8.3694469
[229,] -12.6216263 11.9595786
[230,] -17.6107448 -12.6216263
[231,] 5.6194734 -17.6107448
[232,] -9.7510863 5.6194734
[233,] -5.6444286 -9.7510863
[234,] -2.8580294 -5.6444286
[235,] 13.8835613 -2.8580294
[236,] -30.8295194 13.8835613
[237,] -1.9349285 -30.8295194
[238,] 1.7330475 -1.9349285
[239,] -2.6228744 1.7330475
[240,] -15.3622466 -2.6228744
[241,] -18.6371526 -15.3622466
[242,] 12.4160494 -18.6371526
[243,] 3.1539067 12.4160494
[244,] -1.3686388 3.1539067
[245,] -6.4665444 -1.3686388
[246,] 9.4695977 -6.4665444
[247,] -14.1443735 9.4695977
[248,] -0.2548302 -14.1443735
[249,] 9.0917575 -0.2548302
[250,] -21.5855646 9.0917575
[251,] 3.7983139 -21.5855646
[252,] 17.8022694 3.7983139
[253,] -19.6074760 17.8022694
[254,] -15.0984673 -19.6074760
[255,] 3.6246846 -15.0984673
[256,] 13.2111671 3.6246846
[257,] -1.4664311 13.2111671
[258,] -7.2264556 -1.4664311
[259,] 5.6386501 -7.2264556
[260,] -12.8522452 5.6386501
[261,] 17.1193546 -12.8522452
[262,] -4.9354148 17.1193546
[263,] 12.3559155 -4.9354148
[264,] -1.8452019 12.3559155
[265,] 7.3541733 -1.8452019
[266,] 9.6109032 7.3541733
[267,] 14.4227752 9.6109032
[268,] -2.1768330 14.4227752
[269,] 2.3547888 -2.1768330
[270,] -20.9434089 2.3547888
[271,] -5.0154472 -20.9434089
[272,] 4.1249621 -5.0154472
[273,] 12.3070051 4.1249621
[274,] 4.2993507 12.3070051
[275,] 10.8685326 4.2993507
[276,] -12.7789795 10.8685326
[277,] 14.0491841 -12.7789795
[278,] 4.4674065 14.0491841
[279,] -13.8959917 4.4674065
[280,] -4.3750170 -13.8959917
[281,] -11.1553973 -4.3750170
[282,] -1.0401129 -11.1553973
[283,] 3.8318129 -1.0401129
[284,] -14.4393546 3.8318129
[285,] -20.4739262 -14.4393546
[286,] -7.1071434 -20.4739262
[287,] 4.1246627 -7.1071434
[288,] -14.5365801 4.1246627
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 15.3031142 7.7721249
2 -8.7725177 15.3031142
3 39.6432065 -8.7725177
4 4.2587379 39.6432065
5 -27.3779345 4.2587379
6 -8.2089939 -27.3779345
7 -15.1008415 -8.2089939
8 -5.2990898 -15.1008415
9 -14.4474284 -5.2990898
10 2.9093943 -14.4474284
11 -14.0705418 2.9093943
12 8.7854815 -14.0705418
13 -9.6492807 8.7854815
14 0.4225237 -9.6492807
15 -5.3934643 0.4225237
16 7.4943218 -5.3934643
17 18.1918765 7.4943218
18 -5.3711650 18.1918765
19 29.6138783 -5.3711650
20 29.6338721 29.6138783
21 9.5615985 29.6338721
22 -28.9307420 9.5615985
23 -4.0207174 -28.9307420
24 17.3865115 -4.0207174
25 42.1323164 17.3865115
26 -5.9897826 42.1323164
27 -1.3013727 -5.9897826
28 18.5405777 -1.3013727
29 -7.7764964 18.5405777
30 21.1016070 -7.7764964
31 8.1481896 21.1016070
32 -35.3277484 8.1481896
33 8.7224425 -35.3277484
34 -5.4037391 8.7224425
35 -2.9307125 -5.4037391
36 -0.4830880 -2.9307125
37 -18.0343366 -0.4830880
38 -39.4887689 -18.0343366
39 7.9982798 -39.4887689
40 -5.4975702 7.9982798
41 -0.7941301 -5.4975702
42 5.1212579 -0.7941301
43 -3.8528725 5.1212579
44 4.7587479 -3.8528725
45 -4.6254631 4.7587479
46 -14.2110820 -4.6254631
47 4.7871756 -14.2110820
48 -11.6300857 4.7871756
49 -43.4898670 -11.6300857
50 9.1000331 -43.4898670
51 -17.2784308 9.1000331
52 1.9137153 -17.2784308
53 -3.2826809 1.9137153
54 12.7425794 -3.2826809
55 6.0852753 12.7425794
56 5.8219458 6.0852753
57 -9.7422013 5.8219458
58 -5.3635304 -9.7422013
59 4.1337810 -5.3635304
60 -12.8064549 4.1337810
61 -6.0283269 -12.8064549
62 -31.4021489 -6.0283269
63 -6.9585307 -31.4021489
64 17.1159582 -6.9585307
65 27.7271251 17.1159582
66 11.6790722 27.7271251
67 4.6639128 11.6790722
68 35.2260899 4.6639128
69 -21.1246003 35.2260899
70 -24.1402296 -21.1246003
71 -26.1722307 -24.1402296
72 6.1060191 -26.1722307
73 13.9897830 6.1060191
74 -1.7705491 13.9897830
75 40.3605305 -1.7705491
76 73.9772155 40.3605305
77 -3.6452972 73.9772155
78 -8.4877643 -3.6452972
79 -18.8153254 -8.4877643
80 7.5181517 -18.8153254
81 33.9928464 7.5181517
82 24.1566200 33.9928464
83 2.9339170 24.1566200
84 2.7484168 2.9339170
85 34.5479613 2.7484168
86 -7.0345235 34.5479613
87 -1.7543297 -7.0345235
88 -13.8510898 -1.7543297
89 -21.5228973 -13.8510898
90 18.6160620 -21.5228973
91 26.6134300 18.6160620
92 -20.3771463 26.6134300
93 17.2831086 -20.3771463
94 2.9187127 17.2831086
95 -23.0341821 2.9187127
96 -30.8493890 -23.0341821
97 45.5642868 -30.8493890
98 -18.1143604 45.5642868
99 48.1574132 -18.1143604
100 -16.8994920 48.1574132
101 1.8522047 -16.8994920
102 -20.8772503 1.8522047
103 2.1636550 -20.8772503
104 13.7753280 2.1636550
105 43.2108902 13.7753280
106 -5.8105326 43.2108902
107 -5.6025608 -5.8105326
108 30.8979010 -5.6025608
109 2.0095785 30.8979010
110 26.1531496 2.0095785
111 -16.2679907 26.1531496
112 -6.2675915 -16.2679907
113 19.3974013 -6.2675915
114 15.0128367 19.3974013
115 24.1305134 15.0128367
116 53.9906551 24.1305134
117 -18.3566319 53.9906551
118 -13.8684994 -18.3566319
119 8.4066243 -13.8684994
120 4.0397617 8.4066243
121 5.1886212 4.0397617
122 5.3202288 5.1886212
123 32.0379779 5.3202288
124 -24.0123258 32.0379779
125 7.1635799 -24.0123258
126 1.6809158 7.1635799
127 14.0329126 1.6809158
128 27.4938399 14.0329126
129 12.1318004 27.4938399
130 -6.8699175 12.1318004
131 33.3526205 -6.8699175
132 10.1863317 33.3526205
133 -9.5371841 10.1863317
134 4.7016646 -9.5371841
135 4.4518563 4.7016646
136 5.5114909 4.4518563
137 -9.6823082 5.5114909
138 -1.1383477 -9.6823082
139 10.2892862 -1.1383477
140 -4.7645716 10.2892862
141 7.9309172 -4.7645716
142 -22.9349774 7.9309172
143 5.0633228 -22.9349774
144 -15.9804850 5.0633228
145 -7.7179940 -15.9804850
146 -20.4757149 -7.7179940
147 -20.2292257 -20.4757149
148 10.9664180 -20.2292257
149 -19.8619483 10.9664180
150 -11.1154801 -19.8619483
151 -34.4961240 -11.1154801
152 3.7524708 -34.4961240
153 5.6159185 3.7524708
154 35.5782322 5.6159185
155 -0.8171794 35.5782322
156 -6.5717499 -0.8171794
157 2.3443317 -6.5717499
158 -15.0371844 2.3443317
159 4.6579919 -15.0371844
160 -36.1398815 4.6579919
161 -20.0243486 -36.1398815
162 1.8554383 -20.0243486
163 -10.9386846 1.8554383
164 -11.3900849 -10.9386846
165 10.2315180 -11.3900849
166 7.9332983 10.2315180
167 -32.5851812 7.9332983
168 10.0687893 -32.5851812
169 -36.1321020 10.0687893
170 -18.8023996 -36.1321020
171 32.6667050 -18.8023996
172 -28.7845466 32.6667050
173 20.5926002 -28.7845466
174 76.6235600 20.5926002
175 -19.8779851 76.6235600
176 -2.2095915 -19.8779851
177 -10.1907921 -2.2095915
178 34.7230908 -10.1907921
179 8.6345641 34.7230908
180 12.5526197 8.6345641
181 21.8345542 12.5526197
182 9.2257511 21.8345542
183 -26.2681862 9.2257511
184 3.8831809 -26.2681862
185 14.1618037 3.8831809
186 3.1036808 14.1618037
187 4.6427284 3.1036808
188 -15.6790595 4.6427284
189 -11.9526052 -15.6790595
190 -12.8809609 -11.9526052
191 -21.6894897 -12.8809609
192 22.6925157 -21.6894897
193 -4.8394135 22.6925157
194 -1.8471468 -4.8394135
195 18.3876774 -1.8471468
196 -19.0229448 18.3876774
197 -4.9911657 -19.0229448
198 -7.8074013 -4.9911657
199 6.2970223 -7.8074013
200 -3.6763612 6.2970223
201 -13.0700344 -3.6763612
202 -1.1929505 -13.0700344
203 -11.3050778 -1.1929505
204 -5.4273203 -11.3050778
205 -15.4059933 -5.4273203
206 -1.4044625 -15.4059933
207 -23.6126477 -1.4044625
208 -14.2350071 -23.6126477
209 -8.0764981 -14.2350071
210 4.9263656 -8.0764981
211 -9.7720209 4.9263656
212 -3.8403408 -9.7720209
213 3.3557965 -3.8403408
214 -31.1763262 3.3557965
215 -22.3457003 -31.1763262
216 2.4519912 -22.3457003
217 2.5035199 2.4519912
218 4.3784332 2.5035199
219 -17.8284044 4.3784332
220 -10.4883270 -17.8284044
221 -19.8793770 -10.4883270
222 13.2281552 -19.8793770
223 -6.4390300 13.2281552
224 -9.7742067 -6.4390300
225 8.1389700 -9.7742067
226 9.2647603 8.1389700
227 8.3694469 9.2647603
228 11.9595786 8.3694469
229 -12.6216263 11.9595786
230 -17.6107448 -12.6216263
231 5.6194734 -17.6107448
232 -9.7510863 5.6194734
233 -5.6444286 -9.7510863
234 -2.8580294 -5.6444286
235 13.8835613 -2.8580294
236 -30.8295194 13.8835613
237 -1.9349285 -30.8295194
238 1.7330475 -1.9349285
239 -2.6228744 1.7330475
240 -15.3622466 -2.6228744
241 -18.6371526 -15.3622466
242 12.4160494 -18.6371526
243 3.1539067 12.4160494
244 -1.3686388 3.1539067
245 -6.4665444 -1.3686388
246 9.4695977 -6.4665444
247 -14.1443735 9.4695977
248 -0.2548302 -14.1443735
249 9.0917575 -0.2548302
250 -21.5855646 9.0917575
251 3.7983139 -21.5855646
252 17.8022694 3.7983139
253 -19.6074760 17.8022694
254 -15.0984673 -19.6074760
255 3.6246846 -15.0984673
256 13.2111671 3.6246846
257 -1.4664311 13.2111671
258 -7.2264556 -1.4664311
259 5.6386501 -7.2264556
260 -12.8522452 5.6386501
261 17.1193546 -12.8522452
262 -4.9354148 17.1193546
263 12.3559155 -4.9354148
264 -1.8452019 12.3559155
265 7.3541733 -1.8452019
266 9.6109032 7.3541733
267 14.4227752 9.6109032
268 -2.1768330 14.4227752
269 2.3547888 -2.1768330
270 -20.9434089 2.3547888
271 -5.0154472 -20.9434089
272 4.1249621 -5.0154472
273 12.3070051 4.1249621
274 4.2993507 12.3070051
275 10.8685326 4.2993507
276 -12.7789795 10.8685326
277 14.0491841 -12.7789795
278 4.4674065 14.0491841
279 -13.8959917 4.4674065
280 -4.3750170 -13.8959917
281 -11.1553973 -4.3750170
282 -1.0401129 -11.1553973
283 3.8318129 -1.0401129
284 -14.4393546 3.8318129
285 -20.4739262 -14.4393546
286 -7.1071434 -20.4739262
287 4.1246627 -7.1071434
288 -14.5365801 4.1246627
> 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/7qcmc1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/82afp1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/95tmg1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10bkus1323861648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nkb11323861648.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/12fay41323861648.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/13ilcb1323861648.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/143i4t1323861648.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/15vr6y1323861648.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/161mn61323861648.tab")
+ }
>
> try(system("convert tmp/1mudx1323861648.ps tmp/1mudx1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bhxe1323861648.ps tmp/2bhxe1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g3pi1323861648.ps tmp/3g3pi1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/4evzw1323861648.ps tmp/4evzw1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o7n81323861648.ps tmp/5o7n81323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sh5t1323861648.ps tmp/6sh5t1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qcmc1323861648.ps tmp/7qcmc1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/82afp1323861648.ps tmp/82afp1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/95tmg1323861648.ps tmp/95tmg1323861648.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bkus1323861648.ps tmp/10bkus1323861648.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.140 0.709 9.865