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(30
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+ ,1212
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+ ,1143
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+ ,12
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+ ,616
+ ,2
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+ ,12
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+ ,285
+ ,14
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+ ,31
+ ,37
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+ ,17
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+ ,20
+ ,17
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+ ,733
+ ,19
+ ,77945
+ ,20
+ ,28
+ ,25272
+ ,888
+ ,14
+ ,89113
+ ,39
+ ,19
+ ,82206
+ ,849
+ ,11
+ ,91005
+ ,29
+ ,29
+ ,32073
+ ,1182
+ ,4
+ ,40248
+ ,16
+ ,8
+ ,5444
+ ,528
+ ,16
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+ ,27
+ ,10
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+ ,20
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+ ,21
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+ ,12
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+ ,19
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+ ,819
+ ,15
+ ,62792
+ ,35
+ ,28
+ ,30884
+ ,757
+ ,16
+ ,72535
+ ,14
+ ,17
+ ,19540
+ ,894)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'totsize'
+ ,'pageviews')
+ ,1:289))
> y <- array(NA,dim=c(6,289),dimnames=list(c('compendiums_reviewed','time_in_rfc','logins','blogged_computations','totsize','pageviews'),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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
compendiums_reviewed time_in_rfc logins blogged_computations totsize
1 30 210907 56 79 112285
2 28 120982 56 58 84786
3 38 176508 54 60 83123
4 30 179321 89 108 101193
5 22 123185 40 49 38361
6 26 52746 25 0 68504
7 25 385534 92 121 119182
8 18 33170 18 1 22807
9 11 101645 63 20 17140
10 26 149061 44 43 116174
11 25 165446 33 69 57635
12 38 237213 84 78 66198
13 44 173326 88 86 71701
14 30 133131 55 44 57793
15 40 258873 60 104 80444
16 34 180083 66 63 53855
17 47 324799 154 158 97668
18 30 230964 53 102 133824
19 31 236785 119 77 101481
20 23 135473 41 82 99645
21 36 202925 61 115 114789
22 36 215147 58 101 99052
23 30 344297 75 80 67654
24 25 153935 33 50 65553
25 39 132943 40 83 97500
26 34 174724 92 123 69112
27 31 174415 100 73 82753
28 31 225548 112 81 85323
29 33 223632 73 105 72654
30 25 124817 40 47 30727
31 33 221698 45 105 77873
32 35 210767 60 94 117478
33 42 170266 62 44 74007
34 43 260561 75 114 90183
35 30 84853 31 38 61542
36 33 294424 77 107 101494
37 13 101011 34 30 27570
38 32 215641 46 71 55813
39 36 325107 99 84 79215
40 0 7176 17 0 1423
41 28 167542 66 59 55461
42 14 106408 30 33 31081
43 17 96560 76 42 22996
44 32 265769 146 96 83122
45 30 269651 67 106 70106
46 35 149112 56 56 60578
47 20 175824 107 57 39992
48 28 152871 58 59 79892
49 28 111665 34 39 49810
50 39 116408 61 34 71570
51 34 362301 119 76 100708
52 26 78800 42 20 33032
53 39 183167 66 91 82875
54 39 277965 89 115 139077
55 33 150629 44 85 71595
56 28 168809 66 76 72260
57 4 24188 24 8 5950
58 39 329267 259 79 115762
59 18 65029 17 21 32551
60 14 101097 64 30 31701
61 29 218946 41 76 80670
62 44 244052 68 101 143558
63 21 341570 168 94 117105
64 16 103597 43 27 23789
65 28 233328 132 92 120733
66 35 256462 105 123 105195
67 28 206161 71 75 73107
68 38 311473 112 128 132068
69 23 235800 94 105 149193
70 36 177939 82 55 46821
71 32 207176 70 56 87011
72 29 196553 57 41 95260
73 25 174184 53 72 55183
74 27 143246 103 67 106671
75 36 187559 121 75 73511
76 28 187681 62 114 92945
77 23 119016 52 118 78664
78 40 182192 52 77 70054
79 23 73566 32 22 22618
80 40 194979 62 66 74011
81 28 167488 45 69 83737
82 34 143756 46 105 69094
83 33 275541 63 116 93133
84 28 243199 75 88 95536
85 34 182999 88 73 225920
86 30 135649 46 99 62133
87 33 152299 53 62 61370
88 22 120221 37 53 43836
89 38 346485 90 118 106117
90 26 145790 63 30 38692
91 35 193339 78 100 84651
92 8 80953 25 49 56622
93 24 122774 45 24 15986
94 29 130585 46 67 95364
95 20 112611 41 46 26706
96 29 286468 144 57 89691
97 45 241066 82 75 67267
98 37 148446 91 135 126846
99 33 204713 71 68 41140
100 33 182079 63 124 102860
101 25 140344 53 33 51715
102 32 220516 62 98 55801
103 29 243060 63 58 111813
104 28 162765 32 68 120293
105 28 182613 39 81 138599
106 31 232138 62 131 161647
107 52 265318 117 110 115929
108 21 85574 34 37 24266
109 24 310839 92 130 162901
110 41 225060 93 93 109825
111 33 232317 54 118 129838
112 32 144966 144 39 37510
113 19 43287 14 13 43750
114 20 155754 61 74 40652
115 31 164709 109 81 87771
116 31 201940 38 109 85872
117 32 235454 73 151 89275
118 18 220801 75 51 44418
119 23 99466 50 28 192565
120 17 92661 61 40 35232
121 20 133328 55 56 40909
122 12 61361 77 27 13294
123 17 125930 75 37 32387
124 30 100750 72 83 140867
125 31 224549 50 54 120662
126 10 82316 32 27 21233
127 13 102010 53 28 44332
128 22 101523 42 59 61056
129 42 243511 71 133 101338
130 1 22938 10 12 1168
131 9 41566 35 0 13497
132 32 152474 65 106 65567
133 11 61857 25 23 25162
134 25 99923 66 44 32334
135 36 132487 41 71 40735
136 31 317394 86 116 91413
137 0 21054 16 4 855
138 24 209641 42 62 97068
139 13 22648 19 12 44339
140 8 31414 19 18 14116
141 13 46698 45 14 10288
142 19 131698 65 60 65622
143 18 91735 35 7 16563
144 33 244749 95 98 76643
145 40 184510 49 64 110681
146 22 79863 37 29 29011
147 38 128423 64 32 92696
148 24 97839 38 25 94785
149 8 38214 34 16 8773
150 35 151101 32 48 83209
151 43 272458 65 100 93815
152 43 172494 52 46 86687
153 14 108043 62 45 34553
154 41 328107 65 129 105547
155 38 250579 83 130 103487
156 45 351067 95 136 213688
157 31 158015 29 59 71220
158 13 98866 18 25 23517
159 28 85439 33 32 56926
160 31 229242 247 63 91721
161 40 351619 139 95 115168
162 30 84207 29 14 111194
163 16 120445 118 36 51009
164 37 324598 110 113 135777
165 30 131069 67 47 51513
166 35 204271 42 92 74163
167 32 165543 65 70 51633
168 27 141722 94 19 75345
169 20 116048 64 50 33416
170 18 250047 81 41 83305
171 31 299775 95 91 98952
172 31 195838 67 111 102372
173 21 173260 63 41 37238
174 39 254488 83 120 103772
175 41 104389 45 135 123969
176 13 136084 30 27 27142
177 32 199476 70 87 135400
178 18 92499 32 25 21399
179 39 224330 83 131 130115
180 14 135781 31 45 24874
181 7 74408 67 29 34988
182 17 81240 66 58 45549
183 0 14688 10 4 6023
184 30 181633 70 47 64466
185 37 271856 103 109 54990
186 0 7199 5 7 1644
187 5 46660 20 12 6179
188 1 17547 5 0 3926
189 16 133368 36 37 32755
190 32 95227 34 37 34777
191 24 152601 48 46 73224
192 17 98146 40 15 27114
193 11 79619 43 42 20760
194 24 59194 31 7 37636
195 22 139942 42 54 65461
196 12 118612 46 54 30080
197 19 72880 33 14 24094
198 13 65475 18 16 69008
199 17 99643 55 33 54968
200 15 71965 35 32 46090
201 16 77272 59 21 27507
202 24 49289 19 15 10672
203 15 135131 66 38 34029
204 17 108446 60 22 46300
205 18 89746 36 28 24760
206 20 44296 25 10 18779
207 16 77648 47 31 21280
208 16 181528 54 32 40662
209 18 134019 53 32 28987
210 22 124064 40 43 22827
211 8 92630 40 27 18513
212 17 121848 39 37 30594
213 18 52915 14 20 24006
214 16 81872 45 32 27913
215 23 58981 36 0 42744
216 22 53515 28 5 12934
217 13 60812 44 26 22574
218 13 56375 30 10 41385
219 16 65490 22 27 18653
220 16 80949 17 11 18472
221 20 76302 31 29 30976
222 22 104011 55 25 63339
223 17 98104 54 55 25568
224 18 67989 21 23 33747
225 17 30989 14 5 4154
226 12 135458 81 43 19474
227 7 73504 35 23 35130
228 17 63123 43 34 39067
229 14 61254 46 36 13310
230 23 74914 30 35 65892
231 17 31774 23 0 4143
232 14 81437 38 37 28579
233 15 87186 54 28 51776
234 17 50090 20 16 21152
235 21 65745 53 26 38084
236 18 56653 45 38 27717
237 18 158399 39 23 32928
238 17 46455 20 22 11342
239 17 73624 24 30 19499
240 16 38395 31 16 16380
241 15 91899 35 18 36874
242 21 139526 151 28 48259
243 16 52164 52 32 16734
244 14 51567 30 21 28207
245 15 70551 31 23 30143
246 17 84856 29 29 41369
247 15 102538 57 50 45833
248 15 86678 40 12 29156
249 10 85709 44 21 35944
250 6 34662 25 18 36278
251 22 150580 77 27 45588
252 21 99611 35 41 45097
253 1 19349 11 13 3895
254 18 99373 63 12 28394
255 17 86230 44 21 18632
256 4 30837 19 8 2325
257 10 31706 13 26 25139
258 16 89806 42 27 27975
259 16 62088 38 13 14483
260 9 40151 29 16 13127
261 16 27634 20 2 5839
262 17 76990 27 42 24069
263 7 37460 20 5 3738
264 15 54157 19 37 18625
265 14 49862 37 17 36341
266 14 84337 26 38 24548
267 18 64175 42 37 21792
268 12 59382 49 29 26263
269 16 119308 30 32 23686
270 21 76702 49 35 49303
271 19 103425 67 17 25659
272 16 70344 28 20 28904
273 1 43410 19 7 2781
274 16 104838 49 46 29236
275 10 62215 27 24 19546
276 19 69304 30 40 22818
277 12 53117 22 3 32689
278 2 19764 12 10 5752
279 14 86680 31 37 22197
280 17 84105 20 17 20055
281 19 77945 20 28 25272
282 14 89113 39 19 82206
283 11 91005 29 29 32073
284 4 40248 16 8 5444
285 16 64187 27 10 20154
286 20 50857 21 15 36944
287 12 56613 19 15 8019
288 15 62792 35 28 30884
289 16 72535 14 17 19540
pageviews
1 1418
2 869
3 1530
4 2172
5 901
6 463
7 3201
8 371
9 1192
10 1583
11 1439
12 1764
13 1495
14 1373
15 2187
16 1491
17 4041
18 1706
19 2152
20 1036
21 1882
22 1929
23 2242
24 1220
25 1289
26 2515
27 2147
28 2352
29 1638
30 1222
31 1812
32 1677
33 1579
34 1731
35 807
36 2452
37 829
38 1940
39 2662
40 186
41 1499
42 865
43 1793
44 2527
45 2747
46 1324
47 2702
48 1383
49 1179
50 2099
51 4308
52 918
53 1831
54 3373
55 1713
56 1438
57 496
58 2253
59 744
60 1161
61 2352
62 2144
63 4691
64 1112
65 2694
66 1973
67 1769
68 3148
69 2474
70 2084
71 1954
72 1226
73 1389
74 1496
75 2269
76 1833
77 1268
78 1943
79 893
80 1762
81 1403
82 1425
83 1857
84 1840
85 1502
86 1441
87 1420
88 1416
89 2970
90 1317
91 1644
92 870
93 1654
94 1054
95 937
96 3004
97 2008
98 2547
99 1885
100 1626
101 1468
102 2445
103 1964
104 1381
105 1369
106 1659
107 2888
108 1290
109 2845
110 1982
111 1904
112 1391
113 602
114 1743
115 1559
116 2014
117 2143
118 2146
119 874
120 1590
121 1590
122 1210
123 2072
124 1281
125 1401
126 834
127 1105
128 1272
129 1944
130 391
131 761
132 1605
133 530
134 1988
135 1386
136 2395
137 387
138 1742
139 620
140 449
141 800
142 1684
143 1050
144 2699
145 1606
146 1502
147 1204
148 1138
149 568
150 1459
151 2158
152 1111
153 1421
154 2833
155 1955
156 2922
157 1002
158 1060
159 956
160 2186
161 3604
162 1035
163 1417
164 3261
165 1587
166 1424
167 1701
168 1249
169 946
170 1926
171 3352
172 1641
173 2035
174 2312
175 1369
176 1577
177 2201
178 961
179 1900
180 1254
181 1335
182 1597
183 207
184 1645
185 2429
186 151
187 474
188 141
189 1639
190 872
191 1318
192 1018
193 1383
194 1314
195 1335
196 1403
197 910
198 616
199 1407
200 771
201 766
202 473
203 1376
204 1232
205 1521
206 572
207 1059
208 1544
209 1230
210 1206
211 1205
212 1255
213 613
214 721
215 1109
216 740
217 1126
218 728
219 689
220 592
221 995
222 1613
223 2048
224 705
225 301
226 1803
227 799
228 861
229 1186
230 1451
231 628
232 1161
233 1463
234 742
235 979
236 675
237 1241
238 676
239 1049
240 620
241 1081
242 1688
243 736
244 617
245 812
246 1051
247 1656
248 705
249 945
250 554
251 1597
252 982
253 222
254 1212
255 1143
256 435
257 532
258 882
259 608
260 459
261 578
262 826
263 509
264 717
265 637
266 857
267 830
268 652
269 707
270 954
271 1461
272 672
273 778
274 1141
275 680
276 1090
277 616
278 285
279 1145
280 733
281 888
282 849
283 1182
284 528
285 642
286 947
287 819
288 757
289 894
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc logins
9.396e+00 4.263e-05 1.578e-02
blogged_computations totsize pageviews
8.950e-02 7.281e-05 -1.220e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.123 -3.990 -0.586 3.589 17.986
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.396e+00 8.082e-01 11.627 < 2e-16 ***
time_in_rfc 4.263e-05 1.218e-05 3.499 0.000542 ***
logins 1.578e-02 1.686e-02 0.936 0.350082
blogged_computations 8.950e-02 1.952e-02 4.586 6.80e-06 ***
totsize 7.281e-05 1.481e-05 4.917 1.49e-06 ***
pageviews -1.220e-03 1.209e-03 -1.009 0.313715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.195 on 283 degrees of freedom
Multiple R-squared: 0.664, Adjusted R-squared: 0.6581
F-statistic: 111.9 on 5 and 283 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,] 0.6849158 6.301683e-01 3.150842e-01
[2,] 0.6643855 6.712290e-01 3.356145e-01
[3,] 0.5373929 9.252141e-01 4.626071e-01
[4,] 0.7873818 4.252363e-01 2.126182e-01
[5,] 0.8644046 2.711908e-01 1.355954e-01
[6,] 0.8584788 2.830423e-01 1.415212e-01
[7,] 0.9106870 1.786259e-01 8.931295e-02
[8,] 0.8938149 2.123701e-01 1.061851e-01
[9,] 0.8993687 2.012627e-01 1.006313e-01
[10,] 0.8896679 2.206641e-01 1.103321e-01
[11,] 0.8682507 2.634987e-01 1.317493e-01
[12,] 0.8983424 2.033151e-01 1.016576e-01
[13,] 0.8631533 2.736934e-01 1.368467e-01
[14,] 0.8318195 3.363611e-01 1.681805e-01
[15,] 0.7864831 4.270338e-01 2.135169e-01
[16,] 0.7331072 5.337856e-01 2.668928e-01
[17,] 0.7615547 4.768905e-01 2.384453e-01
[18,] 0.7287464 5.425073e-01 2.712536e-01
[19,] 0.6748103 6.503793e-01 3.251897e-01
[20,] 0.6217448 7.565104e-01 3.782552e-01
[21,] 0.5737408 8.525185e-01 4.262592e-01
[22,] 0.5158850 9.682299e-01 4.841150e-01
[23,] 0.4554345 9.108690e-01 5.445655e-01
[24,] 0.3996142 7.992283e-01 6.003858e-01
[25,] 0.6982876 6.034248e-01 3.017124e-01
[26,] 0.6964569 6.070862e-01 3.035431e-01
[27,] 0.6800661 6.398678e-01 3.199339e-01
[28,] 0.6343279 7.313442e-01 3.656721e-01
[29,] 0.7157772 5.684457e-01 2.842228e-01
[30,] 0.6977295 6.045410e-01 3.022705e-01
[31,] 0.6628760 6.742480e-01 3.371240e-01
[32,] 0.8656841 2.686318e-01 1.343159e-01
[33,] 0.8377688 3.244624e-01 1.622312e-01
[34,] 0.8387258 3.225483e-01 1.612742e-01
[35,] 0.8217571 3.564858e-01 1.782429e-01
[36,] 0.8127050 3.745901e-01 1.872950e-01
[37,] 0.7817717 4.364566e-01 2.182283e-01
[38,] 0.8117131 3.765737e-01 1.882869e-01
[39,] 0.7912341 4.175318e-01 2.087659e-01
[40,] 0.7569507 4.860985e-01 2.430493e-01
[41,] 0.7548916 4.902169e-01 2.451084e-01
[42,] 0.8946710 2.106579e-01 1.053290e-01
[43,] 0.8732849 2.534301e-01 1.267151e-01
[44,] 0.8744694 2.510613e-01 1.255306e-01
[45,] 0.8797345 2.405311e-01 1.202655e-01
[46,] 0.8579027 2.841945e-01 1.420973e-01
[47,] 0.8414146 3.171708e-01 1.585854e-01
[48,] 0.8172833 3.654334e-01 1.827167e-01
[49,] 0.8706650 2.586700e-01 1.293350e-01
[50,] 0.8488331 3.023339e-01 1.511669e-01
[51,] 0.8245523 3.508955e-01 1.754477e-01
[52,] 0.8262714 3.474572e-01 1.737286e-01
[53,] 0.7989919 4.020163e-01 2.010081e-01
[54,] 0.7839117 4.321767e-01 2.160883e-01
[55,] 0.9126067 1.747865e-01 8.739327e-02
[56,] 0.8984332 2.031336e-01 1.015668e-01
[57,] 0.9100331 1.799337e-01 8.996685e-02
[58,] 0.9006212 1.987575e-01 9.937875e-02
[59,] 0.8834556 2.330889e-01 1.165444e-01
[60,] 0.8689687 2.620626e-01 1.310313e-01
[61,] 0.9512623 9.747538e-02 4.873769e-02
[62,] 0.9692604 6.147921e-02 3.073961e-02
[63,] 0.9641447 7.171055e-02 3.585527e-02
[64,] 0.9559947 8.801061e-02 4.400531e-02
[65,] 0.9484151 1.031698e-01 5.158489e-02
[66,] 0.9406989 1.186023e-01 5.930114e-02
[67,] 0.9424485 1.151030e-01 5.755152e-02
[68,] 0.9424710 1.150579e-01 5.752896e-02
[69,] 0.9516891 9.662182e-02 4.831091e-02
[70,] 0.9701005 5.979910e-02 2.989955e-02
[71,] 0.9684026 6.319475e-02 3.159737e-02
[72,] 0.9807332 3.853351e-02 1.926675e-02
[73,] 0.9762160 4.756805e-02 2.378402e-02
[74,] 0.9732279 5.354421e-02 2.677210e-02
[75,] 0.9691477 6.170459e-02 3.085230e-02
[76,] 0.9675430 6.491399e-02 3.245700e-02
[77,] 0.9648096 7.038088e-02 3.519044e-02
[78,] 0.9579418 8.411639e-02 4.205819e-02
[79,] 0.9596517 8.069651e-02 4.034826e-02
[80,] 0.9522152 9.556966e-02 4.778483e-02
[81,] 0.9431138 1.137724e-01 5.688619e-02
[82,] 0.9369635 1.260730e-01 6.303651e-02
[83,] 0.9275208 1.449584e-01 7.247919e-02
[84,] 0.9691360 6.172800e-02 3.086400e-02
[85,] 0.9677162 6.456751e-02 3.228375e-02
[86,] 0.9610864 7.782728e-02 3.891364e-02
[87,] 0.9545715 9.085696e-02 4.542848e-02
[88,] 0.9477414 1.045172e-01 5.225862e-02
[89,] 0.9785175 4.296500e-02 2.148250e-02
[90,] 0.9739335 5.213301e-02 2.606651e-02
[91,] 0.9740157 5.196852e-02 2.598426e-02
[92,] 0.9686837 6.263253e-02 3.131626e-02
[93,] 0.9634974 7.300516e-02 3.650258e-02
[94,] 0.9568072 8.638564e-02 4.319282e-02
[95,] 0.9496139 1.007723e-01 5.038614e-02
[96,] 0.9409717 1.180567e-01 5.902833e-02
[97,] 0.9372099 1.255802e-01 6.279010e-02
[98,] 0.9501413 9.971748e-02 4.985874e-02
[99,] 0.9818688 3.626244e-02 1.813122e-02
[100,] 0.9789763 4.204749e-02 2.102375e-02
[101,] 0.9971755 5.649037e-03 2.824519e-03
[102,] 0.9973569 5.286131e-03 2.643065e-03
[103,] 0.9970154 5.969157e-03 2.984579e-03
[104,] 0.9978364 4.327149e-03 2.163575e-03
[105,] 0.9973374 5.325280e-03 2.662640e-03
[106,] 0.9973079 5.384151e-03 2.692075e-03
[107,] 0.9965470 6.905914e-03 3.452957e-03
[108,] 0.9956049 8.790208e-03 4.395104e-03
[109,] 0.9954955 9.009009e-03 4.504504e-03
[110,] 0.9963062 7.387690e-03 3.693845e-03
[111,] 0.9972757 5.448659e-03 2.724329e-03
[112,] 0.9968235 6.353028e-03 3.176514e-03
[113,] 0.9962458 7.508469e-03 3.754235e-03
[114,] 0.9961914 7.617246e-03 3.808623e-03
[115,] 0.9955419 8.916236e-03 4.458118e-03
[116,] 0.9947315 1.053692e-02 5.268459e-03
[117,] 0.9935047 1.299061e-02 6.495304e-03
[118,] 0.9945469 1.090627e-02 5.453135e-03
[119,] 0.9950743 9.851393e-03 4.925697e-03
[120,] 0.9938311 1.233786e-02 6.168931e-03
[121,] 0.9930722 1.385562e-02 6.927810e-03
[122,] 0.9964359 7.128283e-03 3.564141e-03
[123,] 0.9959454 8.109202e-03 4.054601e-03
[124,] 0.9950805 9.839031e-03 4.919515e-03
[125,] 0.9949208 1.015843e-02 5.079217e-03
[126,] 0.9950534 9.893204e-03 4.946602e-03
[127,] 0.9980354 3.929121e-03 1.964561e-03
[128,] 0.9981746 3.650737e-03 1.825369e-03
[129,] 0.9991119 1.776259e-03 8.881297e-04
[130,] 0.9991748 1.650451e-03 8.252255e-04
[131,] 0.9989476 2.104781e-03 1.052391e-03
[132,] 0.9989278 2.144313e-03 1.072156e-03
[133,] 0.9986321 2.735862e-03 1.367931e-03
[134,] 0.9985581 2.883862e-03 1.441931e-03
[135,] 0.9982895 3.421078e-03 1.710539e-03
[136,] 0.9977882 4.423562e-03 2.211781e-03
[137,] 0.9984221 3.155848e-03 1.577924e-03
[138,] 0.9984161 3.167874e-03 1.583937e-03
[139,] 0.9994299 1.140209e-03 5.701047e-04
[140,] 0.9992399 1.520257e-03 7.601287e-04
[141,] 0.9991940 1.612060e-03 8.060300e-04
[142,] 0.9994670 1.065931e-03 5.329657e-04
[143,] 0.9995803 8.394684e-04 4.197342e-04
[144,] 0.9999484 1.031358e-04 5.156789e-05
[145,] 0.9999475 1.049114e-04 5.245568e-05
[146,] 0.9999277 1.446352e-04 7.231759e-05
[147,] 0.9998984 2.032993e-04 1.016496e-04
[148,] 0.9999109 1.782436e-04 8.912181e-05
[149,] 0.9999027 1.946659e-04 9.733297e-05
[150,] 0.9998800 2.399041e-04 1.199521e-04
[151,] 0.9999129 1.742673e-04 8.713367e-05
[152,] 0.9998796 2.407315e-04 1.203657e-04
[153,] 0.9998345 3.309652e-04 1.654826e-04
[154,] 0.9998547 2.905285e-04 1.452643e-04
[155,] 0.9998486 3.027966e-04 1.513983e-04
[156,] 0.9998221 3.558199e-04 1.779099e-04
[157,] 0.9998688 2.623012e-04 1.311506e-04
[158,] 0.9998596 2.807847e-04 1.403923e-04
[159,] 0.9998865 2.269395e-04 1.134697e-04
[160,] 0.9998716 2.567562e-04 1.283781e-04
[161,] 0.9998253 3.494115e-04 1.747058e-04
[162,] 0.9999169 1.662269e-04 8.311345e-05
[163,] 0.9999064 1.871268e-04 9.356341e-05
[164,] 0.9998793 2.414488e-04 1.207244e-04
[165,] 0.9998314 3.371983e-04 1.685992e-04
[166,] 0.9997720 4.560971e-04 2.280486e-04
[167,] 0.9998409 3.182648e-04 1.591324e-04
[168,] 0.9998459 3.081373e-04 1.540687e-04
[169,] 0.9998003 3.994341e-04 1.997170e-04
[170,] 0.9997343 5.313767e-04 2.656883e-04
[171,] 0.9996459 7.082787e-04 3.541394e-04
[172,] 0.9996452 7.095365e-04 3.547682e-04
[173,] 0.9998238 3.523197e-04 1.761598e-04
[174,] 0.9997723 4.553314e-04 2.276657e-04
[175,] 0.9998884 2.232183e-04 1.116092e-04
[176,] 0.9998769 2.461551e-04 1.230776e-04
[177,] 0.9999060 1.880910e-04 9.404552e-05
[178,] 0.9999580 8.393060e-05 4.196530e-05
[179,] 0.9999679 6.415574e-05 3.207787e-05
[180,] 0.9999863 2.747709e-05 1.373854e-05
[181,] 0.9999815 3.696915e-05 1.848457e-05
[182,] 0.9999991 1.838997e-06 9.194985e-07
[183,] 0.9999987 2.626827e-06 1.313413e-06
[184,] 0.9999979 4.164170e-06 2.082085e-06
[185,] 0.9999978 4.320917e-06 2.160458e-06
[186,] 0.9999985 2.952563e-06 1.476281e-06
[187,] 0.9999978 4.378366e-06 2.189183e-06
[188,] 0.9999981 3.843115e-06 1.921557e-06
[189,] 0.9999976 4.814337e-06 2.407169e-06
[190,] 0.9999973 5.380079e-06 2.690039e-06
[191,] 0.9999961 7.873077e-06 3.936539e-06
[192,] 0.9999942 1.160851e-05 5.804253e-06
[193,] 0.9999910 1.795359e-05 8.976796e-06
[194,] 0.9999983 3.370503e-06 1.685252e-06
[195,] 0.9999978 4.329738e-06 2.164869e-06
[196,] 0.9999966 6.716118e-06 3.358059e-06
[197,] 0.9999947 1.052764e-05 5.263819e-06
[198,] 0.9999963 7.417996e-06 3.708998e-06
[199,] 0.9999941 1.173849e-05 5.869243e-06
[200,] 0.9999939 1.217638e-05 6.088191e-06
[201,] 0.9999903 1.931609e-05 9.658047e-06
[202,] 0.9999895 2.100293e-05 1.050147e-05
[203,] 0.9999939 1.215884e-05 6.079421e-06
[204,] 0.9999904 1.913054e-05 9.565272e-06
[205,] 0.9999888 2.243736e-05 1.121868e-05
[206,] 0.9999827 3.468726e-05 1.734363e-05
[207,] 0.9999876 2.472349e-05 1.236174e-05
[208,] 0.9999963 7.461218e-06 3.730609e-06
[209,] 0.9999941 1.172337e-05 5.861686e-06
[210,] 0.9999910 1.808904e-05 9.044520e-06
[211,] 0.9999865 2.693672e-05 1.346836e-05
[212,] 0.9999806 3.884708e-05 1.942354e-05
[213,] 0.9999773 4.544545e-05 2.272273e-05
[214,] 0.9999654 6.915314e-05 3.457657e-05
[215,] 0.9999480 1.040687e-04 5.203434e-05
[216,] 0.9999312 1.375749e-04 6.878745e-05
[217,] 0.9999597 8.063746e-05 4.031873e-05
[218,] 0.9999818 3.644061e-05 1.822030e-05
[219,] 0.9999919 1.618955e-05 8.094774e-06
[220,] 0.9999866 2.671843e-05 1.335921e-05
[221,] 0.9999787 4.255909e-05 2.127955e-05
[222,] 0.9999724 5.522704e-05 2.761352e-05
[223,] 0.9999859 2.823588e-05 1.411794e-05
[224,] 0.9999802 3.965927e-05 1.982964e-05
[225,] 0.9999762 4.768236e-05 2.384118e-05
[226,] 0.9999748 5.046659e-05 2.523330e-05
[227,] 0.9999759 4.821881e-05 2.410940e-05
[228,] 0.9999680 6.398368e-05 3.199184e-05
[229,] 0.9999513 9.747796e-05 4.873898e-05
[230,] 0.9999573 8.545271e-05 4.272636e-05
[231,] 0.9999336 1.327144e-04 6.635722e-05
[232,] 0.9999431 1.138419e-04 5.692093e-05
[233,] 0.9999068 1.863136e-04 9.315678e-05
[234,] 0.9998605 2.790848e-04 1.395424e-04
[235,] 0.9998144 3.712789e-04 1.856395e-04
[236,] 0.9997182 5.636018e-04 2.818009e-04
[237,] 0.9995442 9.116809e-04 4.558405e-04
[238,] 0.9992579 1.484267e-03 7.421335e-04
[239,] 0.9995583 8.834833e-04 4.417417e-04
[240,] 0.9993023 1.395303e-03 6.976517e-04
[241,] 0.9994716 1.056765e-03 5.283823e-04
[242,] 0.9995610 8.780098e-04 4.390049e-04
[243,] 0.9993708 1.258422e-03 6.292111e-04
[244,] 0.9990038 1.992454e-03 9.962272e-04
[245,] 0.9990892 1.821510e-03 9.107549e-04
[246,] 0.9984663 3.067357e-03 1.533678e-03
[247,] 0.9974942 5.011589e-03 2.505795e-03
[248,] 0.9970668 5.866434e-03 2.933217e-03
[249,] 0.9954631 9.073802e-03 4.536901e-03
[250,] 0.9927267 1.454662e-02 7.273310e-03
[251,] 0.9919596 1.608070e-02 8.040352e-03
[252,] 0.9876203 2.475938e-02 1.237969e-02
[253,] 0.9942934 1.141328e-02 5.706638e-03
[254,] 0.9907206 1.855876e-02 9.279382e-03
[255,] 0.9851765 2.964698e-02 1.482349e-02
[256,] 0.9775679 4.486419e-02 2.243209e-02
[257,] 0.9664201 6.715979e-02 3.357990e-02
[258,] 0.9520887 9.582265e-02 4.791132e-02
[259,] 0.9420113 1.159774e-01 5.798868e-02
[260,] 0.9147096 1.705808e-01 8.529041e-02
[261,] 0.8778657 2.442685e-01 1.221343e-01
[262,] 0.8606830 2.786341e-01 1.393170e-01
[263,] 0.8525927 2.948147e-01 1.474073e-01
[264,] 0.8130887 3.738225e-01 1.869113e-01
[265,] 0.8710549 2.578903e-01 1.289451e-01
[266,] 0.8082889 3.834222e-01 1.917111e-01
[267,] 0.7318094 5.363811e-01 2.681906e-01
[268,] 0.6815171 6.369658e-01 3.184829e-01
[269,] 0.5661412 8.677176e-01 4.338588e-01
[270,] 0.5764556 8.470889e-01 4.235444e-01
[271,] 0.4353507 8.707013e-01 5.646493e-01
[272,] 0.3205293 6.410585e-01 6.794707e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1fxau1324120639.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/27y631324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gdwh1324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4rr591324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/515u71324120639.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
-2.78652037 2.25885819 10.67184619 -2.82839097 0.64198405 9.53790752
7 8 9 10 11 12
-17.88474410 5.60824795 -5.30715279 -0.82044634 -0.58596452 7.51727811
13 14 15 16 17 18
14.73298738 7.58990363 6.12464734 8.14490502 5.00623955 -6.86942112
19 20 21 22 23 24
-2.02267439 -6.14818039 0.63692431 2.61925283 -4.60773448 0.76147969
25 26 27 28 29 30
10.35085824 2.73207999 2.65157673 -0.37066632 0.22969050 4.69890347
31 32 33 34 35 36
0.58633170 0.75185322 16.96714598 6.65552864 9.60022642 -4.13696722
37 38 39 40 41 42
-4.91977617 4.63393446 1.14446957 -9.84711808 2.93038159 -4.56692806
43 44 45 46 47 48
-0.95739623 -2.59057622 -3.18828990 10.55630100 -3.29649955 1.76180031
49 50 51 52 53 54
7.62841450 17.98596644 -1.59717806 9.50678946 8.80958145 0.04694980
55 56 57 58 59 60
5.75818328 0.05742636 -7.35013655 -1.27018727 2.22159553 -4.29249219
61 62 63 64 65 66
-0.18256246 6.25115598 -16.82396029 -1.28294884 -7.16317079 -3.24626021
67 68 69 70 71 72
-1.18217394 -3.67223417 -15.17288898 11.93551864 3.70418635 1.21572983
73 74 75 76 77 78
-1.42507176 -2.06573522 7.40258398 -5.10886263 -7.03133056 12.39513106
79 80 81 82 83 84
7.43644574 12.16767002 0.19328425 5.06039024 -4.03377186 -5.53412374
85 86 87 88 89 90
-5.73561398 2.46932090 7.99027792 0.68751544 -2.25057624 5.49925645
91 92 93 94 95 96
3.02360665 -12.68818869 7.36588776 1.65739089 0.23798391 -2.84736573
97 98 99 100 101 102
14.87296890 1.62984404 6.97499065 -1.75523727 3.85684503 2.37433542
103 104 105 106 107 108
-2.68773845 -1.99909358 -5.46662023 -10.73991747 14.68527530 3.91496774
109 110 111 112 113 114
-20.12317855 6.64061385 -4.84299460 9.62708751 3.92318836 -4.45465549
115 116 117 118 119 120
1.12453935 -1.15473818 -5.98500632 -7.17276445 -6.88525032 -1.51405018
121 122 123 124 125 126
-1.99837714 -3.13513592 -2.08955108 -0.94879852 -0.66669950 -6.35522729
127 128 129 130 131 132
-5.96679976 -0.56065470 4.19288068 -10.21386785 -2.77473990 2.77583021
133 134 135 136 137 138
-4.67156760 6.43611747 12.67970678 -7.39931656 -10.49435727 -5.48694134
139 140 141 142 143 144
-1.20723325 -5.12614815 -0.12304404 -5.12924096 3.58936129 0.61296574
145 146 147 148 149 150
10.13794424 5.74029125 13.97514586 2.08315733 -4.93954868 10.08323386
151 152 153 154 155 156
7.81567168 16.35670594 -5.78987398 0.81731449 -0.17236453 -5.02621365
157 158 159 160 161 162
5.16658930 -3.55140570 8.59854841 -1.71593861 0.93037212 8.47042990
163 164 165 166 167 168
-5.59983010 -3.98995329 7.93832335 4.33673714 6.57212907 4.41638668
169 170 171 172 173 174
-1.10702341 -10.71902861 -3.93391814 -3.18767144 -0.67432591 1.97081467
175 176 177 178 179 180
7.00603297 -5.13953279 -1.96354714 1.53251672 -0.14859946 -5.98232616
181 182 183 184 185 186
-10.13949844 -3.45956376 -10.72418396 4.86299466 3.59364145 -10.34400335
187 188 189 190 191 192
-7.64656473 -9.33704923 -3.34638666 13.22809680 -0.49928610 0.71394400
193 194 195 196 197 198
-6.05190820 9.82768919 -1.99493377 -8.48973776 4.07915665 -5.17623118
199 200 201 202 203 204
-2.75078465 -3.29538525 -0.56904537 10.66030566 -5.39812982 -1.80298729
205 206 207 208 209 210
1.75691126 6.75652766 -0.47976801 -5.92776532 -1.41967045 2.64463787
211 212 213 214 215 216
-8.27044057 -2.21383312 3.33717013 -1.61315503 8.76228994 9.39418745
217 218 219 220 221 222
-2.27963652 -2.29285028 0.53082384 1.27740255 3.22505940 2.42086233
223 224 225 226 227 228
-1.71559515 1.71862370 5.67901802 -7.51550341 -9.72340888 -0.60251231
229 230 231 232 233 234
-1.47729813 3.77734995 6.35082732 -3.44320754 -3.45571478 3.08611118
235 236 237 238 239 240
4.05942351 0.88317310 -1.70619059 3.33780828 1.26169351 2.60968653
241 242 243 244 245 246
-1.84303215 -0.68718834 0.37513622 -1.24831950 -1.15544207 -0.79641060
247 248 249 250 251 252
-5.45825910 -1.05926221 -7.08788470 -8.84472039 1.18220054 1.05029219
253 254 255 256 257 258
-10.57089905 1.71070992 1.39194347 -7.36521267 -4.46114873 -1.26464228
259 260 261 262 263 264
1.88107923 -4.39323537 5.21118509 -0.60793670 -4.40744293 -0.79742472
265 266 267 268 269 270
-1.49591554 -3.54444636 1.31984183 -4.41303863 -2.68179059 2.00262956
271 272 273 274 275 276
2.53033040 0.08858335 -10.42639826 -3.49214266 -5.21596634 2.26461671
277 278 279 280 281 282
-1.90478155 -9.39421249 -3.11114188 1.61534075 2.70281486 -6.46044810
283 284 285 286 287 288
-6.22189682 -7.83269314 1.86226178 5.22751167 -1.03657758 -1.45631379
289
1.43724062
> postscript(file="/var/wessaorg/rcomp/tmp/6jzxq1324120639.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 -2.78652037 NA
1 2.25885819 -2.78652037
2 10.67184619 2.25885819
3 -2.82839097 10.67184619
4 0.64198405 -2.82839097
5 9.53790752 0.64198405
6 -17.88474410 9.53790752
7 5.60824795 -17.88474410
8 -5.30715279 5.60824795
9 -0.82044634 -5.30715279
10 -0.58596452 -0.82044634
11 7.51727811 -0.58596452
12 14.73298738 7.51727811
13 7.58990363 14.73298738
14 6.12464734 7.58990363
15 8.14490502 6.12464734
16 5.00623955 8.14490502
17 -6.86942112 5.00623955
18 -2.02267439 -6.86942112
19 -6.14818039 -2.02267439
20 0.63692431 -6.14818039
21 2.61925283 0.63692431
22 -4.60773448 2.61925283
23 0.76147969 -4.60773448
24 10.35085824 0.76147969
25 2.73207999 10.35085824
26 2.65157673 2.73207999
27 -0.37066632 2.65157673
28 0.22969050 -0.37066632
29 4.69890347 0.22969050
30 0.58633170 4.69890347
31 0.75185322 0.58633170
32 16.96714598 0.75185322
33 6.65552864 16.96714598
34 9.60022642 6.65552864
35 -4.13696722 9.60022642
36 -4.91977617 -4.13696722
37 4.63393446 -4.91977617
38 1.14446957 4.63393446
39 -9.84711808 1.14446957
40 2.93038159 -9.84711808
41 -4.56692806 2.93038159
42 -0.95739623 -4.56692806
43 -2.59057622 -0.95739623
44 -3.18828990 -2.59057622
45 10.55630100 -3.18828990
46 -3.29649955 10.55630100
47 1.76180031 -3.29649955
48 7.62841450 1.76180031
49 17.98596644 7.62841450
50 -1.59717806 17.98596644
51 9.50678946 -1.59717806
52 8.80958145 9.50678946
53 0.04694980 8.80958145
54 5.75818328 0.04694980
55 0.05742636 5.75818328
56 -7.35013655 0.05742636
57 -1.27018727 -7.35013655
58 2.22159553 -1.27018727
59 -4.29249219 2.22159553
60 -0.18256246 -4.29249219
61 6.25115598 -0.18256246
62 -16.82396029 6.25115598
63 -1.28294884 -16.82396029
64 -7.16317079 -1.28294884
65 -3.24626021 -7.16317079
66 -1.18217394 -3.24626021
67 -3.67223417 -1.18217394
68 -15.17288898 -3.67223417
69 11.93551864 -15.17288898
70 3.70418635 11.93551864
71 1.21572983 3.70418635
72 -1.42507176 1.21572983
73 -2.06573522 -1.42507176
74 7.40258398 -2.06573522
75 -5.10886263 7.40258398
76 -7.03133056 -5.10886263
77 12.39513106 -7.03133056
78 7.43644574 12.39513106
79 12.16767002 7.43644574
80 0.19328425 12.16767002
81 5.06039024 0.19328425
82 -4.03377186 5.06039024
83 -5.53412374 -4.03377186
84 -5.73561398 -5.53412374
85 2.46932090 -5.73561398
86 7.99027792 2.46932090
87 0.68751544 7.99027792
88 -2.25057624 0.68751544
89 5.49925645 -2.25057624
90 3.02360665 5.49925645
91 -12.68818869 3.02360665
92 7.36588776 -12.68818869
93 1.65739089 7.36588776
94 0.23798391 1.65739089
95 -2.84736573 0.23798391
96 14.87296890 -2.84736573
97 1.62984404 14.87296890
98 6.97499065 1.62984404
99 -1.75523727 6.97499065
100 3.85684503 -1.75523727
101 2.37433542 3.85684503
102 -2.68773845 2.37433542
103 -1.99909358 -2.68773845
104 -5.46662023 -1.99909358
105 -10.73991747 -5.46662023
106 14.68527530 -10.73991747
107 3.91496774 14.68527530
108 -20.12317855 3.91496774
109 6.64061385 -20.12317855
110 -4.84299460 6.64061385
111 9.62708751 -4.84299460
112 3.92318836 9.62708751
113 -4.45465549 3.92318836
114 1.12453935 -4.45465549
115 -1.15473818 1.12453935
116 -5.98500632 -1.15473818
117 -7.17276445 -5.98500632
118 -6.88525032 -7.17276445
119 -1.51405018 -6.88525032
120 -1.99837714 -1.51405018
121 -3.13513592 -1.99837714
122 -2.08955108 -3.13513592
123 -0.94879852 -2.08955108
124 -0.66669950 -0.94879852
125 -6.35522729 -0.66669950
126 -5.96679976 -6.35522729
127 -0.56065470 -5.96679976
128 4.19288068 -0.56065470
129 -10.21386785 4.19288068
130 -2.77473990 -10.21386785
131 2.77583021 -2.77473990
132 -4.67156760 2.77583021
133 6.43611747 -4.67156760
134 12.67970678 6.43611747
135 -7.39931656 12.67970678
136 -10.49435727 -7.39931656
137 -5.48694134 -10.49435727
138 -1.20723325 -5.48694134
139 -5.12614815 -1.20723325
140 -0.12304404 -5.12614815
141 -5.12924096 -0.12304404
142 3.58936129 -5.12924096
143 0.61296574 3.58936129
144 10.13794424 0.61296574
145 5.74029125 10.13794424
146 13.97514586 5.74029125
147 2.08315733 13.97514586
148 -4.93954868 2.08315733
149 10.08323386 -4.93954868
150 7.81567168 10.08323386
151 16.35670594 7.81567168
152 -5.78987398 16.35670594
153 0.81731449 -5.78987398
154 -0.17236453 0.81731449
155 -5.02621365 -0.17236453
156 5.16658930 -5.02621365
157 -3.55140570 5.16658930
158 8.59854841 -3.55140570
159 -1.71593861 8.59854841
160 0.93037212 -1.71593861
161 8.47042990 0.93037212
162 -5.59983010 8.47042990
163 -3.98995329 -5.59983010
164 7.93832335 -3.98995329
165 4.33673714 7.93832335
166 6.57212907 4.33673714
167 4.41638668 6.57212907
168 -1.10702341 4.41638668
169 -10.71902861 -1.10702341
170 -3.93391814 -10.71902861
171 -3.18767144 -3.93391814
172 -0.67432591 -3.18767144
173 1.97081467 -0.67432591
174 7.00603297 1.97081467
175 -5.13953279 7.00603297
176 -1.96354714 -5.13953279
177 1.53251672 -1.96354714
178 -0.14859946 1.53251672
179 -5.98232616 -0.14859946
180 -10.13949844 -5.98232616
181 -3.45956376 -10.13949844
182 -10.72418396 -3.45956376
183 4.86299466 -10.72418396
184 3.59364145 4.86299466
185 -10.34400335 3.59364145
186 -7.64656473 -10.34400335
187 -9.33704923 -7.64656473
188 -3.34638666 -9.33704923
189 13.22809680 -3.34638666
190 -0.49928610 13.22809680
191 0.71394400 -0.49928610
192 -6.05190820 0.71394400
193 9.82768919 -6.05190820
194 -1.99493377 9.82768919
195 -8.48973776 -1.99493377
196 4.07915665 -8.48973776
197 -5.17623118 4.07915665
198 -2.75078465 -5.17623118
199 -3.29538525 -2.75078465
200 -0.56904537 -3.29538525
201 10.66030566 -0.56904537
202 -5.39812982 10.66030566
203 -1.80298729 -5.39812982
204 1.75691126 -1.80298729
205 6.75652766 1.75691126
206 -0.47976801 6.75652766
207 -5.92776532 -0.47976801
208 -1.41967045 -5.92776532
209 2.64463787 -1.41967045
210 -8.27044057 2.64463787
211 -2.21383312 -8.27044057
212 3.33717013 -2.21383312
213 -1.61315503 3.33717013
214 8.76228994 -1.61315503
215 9.39418745 8.76228994
216 -2.27963652 9.39418745
217 -2.29285028 -2.27963652
218 0.53082384 -2.29285028
219 1.27740255 0.53082384
220 3.22505940 1.27740255
221 2.42086233 3.22505940
222 -1.71559515 2.42086233
223 1.71862370 -1.71559515
224 5.67901802 1.71862370
225 -7.51550341 5.67901802
226 -9.72340888 -7.51550341
227 -0.60251231 -9.72340888
228 -1.47729813 -0.60251231
229 3.77734995 -1.47729813
230 6.35082732 3.77734995
231 -3.44320754 6.35082732
232 -3.45571478 -3.44320754
233 3.08611118 -3.45571478
234 4.05942351 3.08611118
235 0.88317310 4.05942351
236 -1.70619059 0.88317310
237 3.33780828 -1.70619059
238 1.26169351 3.33780828
239 2.60968653 1.26169351
240 -1.84303215 2.60968653
241 -0.68718834 -1.84303215
242 0.37513622 -0.68718834
243 -1.24831950 0.37513622
244 -1.15544207 -1.24831950
245 -0.79641060 -1.15544207
246 -5.45825910 -0.79641060
247 -1.05926221 -5.45825910
248 -7.08788470 -1.05926221
249 -8.84472039 -7.08788470
250 1.18220054 -8.84472039
251 1.05029219 1.18220054
252 -10.57089905 1.05029219
253 1.71070992 -10.57089905
254 1.39194347 1.71070992
255 -7.36521267 1.39194347
256 -4.46114873 -7.36521267
257 -1.26464228 -4.46114873
258 1.88107923 -1.26464228
259 -4.39323537 1.88107923
260 5.21118509 -4.39323537
261 -0.60793670 5.21118509
262 -4.40744293 -0.60793670
263 -0.79742472 -4.40744293
264 -1.49591554 -0.79742472
265 -3.54444636 -1.49591554
266 1.31984183 -3.54444636
267 -4.41303863 1.31984183
268 -2.68179059 -4.41303863
269 2.00262956 -2.68179059
270 2.53033040 2.00262956
271 0.08858335 2.53033040
272 -10.42639826 0.08858335
273 -3.49214266 -10.42639826
274 -5.21596634 -3.49214266
275 2.26461671 -5.21596634
276 -1.90478155 2.26461671
277 -9.39421249 -1.90478155
278 -3.11114188 -9.39421249
279 1.61534075 -3.11114188
280 2.70281486 1.61534075
281 -6.46044810 2.70281486
282 -6.22189682 -6.46044810
283 -7.83269314 -6.22189682
284 1.86226178 -7.83269314
285 5.22751167 1.86226178
286 -1.03657758 5.22751167
287 -1.45631379 -1.03657758
288 1.43724062 -1.45631379
289 NA 1.43724062
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.25885819 -2.78652037
[2,] 10.67184619 2.25885819
[3,] -2.82839097 10.67184619
[4,] 0.64198405 -2.82839097
[5,] 9.53790752 0.64198405
[6,] -17.88474410 9.53790752
[7,] 5.60824795 -17.88474410
[8,] -5.30715279 5.60824795
[9,] -0.82044634 -5.30715279
[10,] -0.58596452 -0.82044634
[11,] 7.51727811 -0.58596452
[12,] 14.73298738 7.51727811
[13,] 7.58990363 14.73298738
[14,] 6.12464734 7.58990363
[15,] 8.14490502 6.12464734
[16,] 5.00623955 8.14490502
[17,] -6.86942112 5.00623955
[18,] -2.02267439 -6.86942112
[19,] -6.14818039 -2.02267439
[20,] 0.63692431 -6.14818039
[21,] 2.61925283 0.63692431
[22,] -4.60773448 2.61925283
[23,] 0.76147969 -4.60773448
[24,] 10.35085824 0.76147969
[25,] 2.73207999 10.35085824
[26,] 2.65157673 2.73207999
[27,] -0.37066632 2.65157673
[28,] 0.22969050 -0.37066632
[29,] 4.69890347 0.22969050
[30,] 0.58633170 4.69890347
[31,] 0.75185322 0.58633170
[32,] 16.96714598 0.75185322
[33,] 6.65552864 16.96714598
[34,] 9.60022642 6.65552864
[35,] -4.13696722 9.60022642
[36,] -4.91977617 -4.13696722
[37,] 4.63393446 -4.91977617
[38,] 1.14446957 4.63393446
[39,] -9.84711808 1.14446957
[40,] 2.93038159 -9.84711808
[41,] -4.56692806 2.93038159
[42,] -0.95739623 -4.56692806
[43,] -2.59057622 -0.95739623
[44,] -3.18828990 -2.59057622
[45,] 10.55630100 -3.18828990
[46,] -3.29649955 10.55630100
[47,] 1.76180031 -3.29649955
[48,] 7.62841450 1.76180031
[49,] 17.98596644 7.62841450
[50,] -1.59717806 17.98596644
[51,] 9.50678946 -1.59717806
[52,] 8.80958145 9.50678946
[53,] 0.04694980 8.80958145
[54,] 5.75818328 0.04694980
[55,] 0.05742636 5.75818328
[56,] -7.35013655 0.05742636
[57,] -1.27018727 -7.35013655
[58,] 2.22159553 -1.27018727
[59,] -4.29249219 2.22159553
[60,] -0.18256246 -4.29249219
[61,] 6.25115598 -0.18256246
[62,] -16.82396029 6.25115598
[63,] -1.28294884 -16.82396029
[64,] -7.16317079 -1.28294884
[65,] -3.24626021 -7.16317079
[66,] -1.18217394 -3.24626021
[67,] -3.67223417 -1.18217394
[68,] -15.17288898 -3.67223417
[69,] 11.93551864 -15.17288898
[70,] 3.70418635 11.93551864
[71,] 1.21572983 3.70418635
[72,] -1.42507176 1.21572983
[73,] -2.06573522 -1.42507176
[74,] 7.40258398 -2.06573522
[75,] -5.10886263 7.40258398
[76,] -7.03133056 -5.10886263
[77,] 12.39513106 -7.03133056
[78,] 7.43644574 12.39513106
[79,] 12.16767002 7.43644574
[80,] 0.19328425 12.16767002
[81,] 5.06039024 0.19328425
[82,] -4.03377186 5.06039024
[83,] -5.53412374 -4.03377186
[84,] -5.73561398 -5.53412374
[85,] 2.46932090 -5.73561398
[86,] 7.99027792 2.46932090
[87,] 0.68751544 7.99027792
[88,] -2.25057624 0.68751544
[89,] 5.49925645 -2.25057624
[90,] 3.02360665 5.49925645
[91,] -12.68818869 3.02360665
[92,] 7.36588776 -12.68818869
[93,] 1.65739089 7.36588776
[94,] 0.23798391 1.65739089
[95,] -2.84736573 0.23798391
[96,] 14.87296890 -2.84736573
[97,] 1.62984404 14.87296890
[98,] 6.97499065 1.62984404
[99,] -1.75523727 6.97499065
[100,] 3.85684503 -1.75523727
[101,] 2.37433542 3.85684503
[102,] -2.68773845 2.37433542
[103,] -1.99909358 -2.68773845
[104,] -5.46662023 -1.99909358
[105,] -10.73991747 -5.46662023
[106,] 14.68527530 -10.73991747
[107,] 3.91496774 14.68527530
[108,] -20.12317855 3.91496774
[109,] 6.64061385 -20.12317855
[110,] -4.84299460 6.64061385
[111,] 9.62708751 -4.84299460
[112,] 3.92318836 9.62708751
[113,] -4.45465549 3.92318836
[114,] 1.12453935 -4.45465549
[115,] -1.15473818 1.12453935
[116,] -5.98500632 -1.15473818
[117,] -7.17276445 -5.98500632
[118,] -6.88525032 -7.17276445
[119,] -1.51405018 -6.88525032
[120,] -1.99837714 -1.51405018
[121,] -3.13513592 -1.99837714
[122,] -2.08955108 -3.13513592
[123,] -0.94879852 -2.08955108
[124,] -0.66669950 -0.94879852
[125,] -6.35522729 -0.66669950
[126,] -5.96679976 -6.35522729
[127,] -0.56065470 -5.96679976
[128,] 4.19288068 -0.56065470
[129,] -10.21386785 4.19288068
[130,] -2.77473990 -10.21386785
[131,] 2.77583021 -2.77473990
[132,] -4.67156760 2.77583021
[133,] 6.43611747 -4.67156760
[134,] 12.67970678 6.43611747
[135,] -7.39931656 12.67970678
[136,] -10.49435727 -7.39931656
[137,] -5.48694134 -10.49435727
[138,] -1.20723325 -5.48694134
[139,] -5.12614815 -1.20723325
[140,] -0.12304404 -5.12614815
[141,] -5.12924096 -0.12304404
[142,] 3.58936129 -5.12924096
[143,] 0.61296574 3.58936129
[144,] 10.13794424 0.61296574
[145,] 5.74029125 10.13794424
[146,] 13.97514586 5.74029125
[147,] 2.08315733 13.97514586
[148,] -4.93954868 2.08315733
[149,] 10.08323386 -4.93954868
[150,] 7.81567168 10.08323386
[151,] 16.35670594 7.81567168
[152,] -5.78987398 16.35670594
[153,] 0.81731449 -5.78987398
[154,] -0.17236453 0.81731449
[155,] -5.02621365 -0.17236453
[156,] 5.16658930 -5.02621365
[157,] -3.55140570 5.16658930
[158,] 8.59854841 -3.55140570
[159,] -1.71593861 8.59854841
[160,] 0.93037212 -1.71593861
[161,] 8.47042990 0.93037212
[162,] -5.59983010 8.47042990
[163,] -3.98995329 -5.59983010
[164,] 7.93832335 -3.98995329
[165,] 4.33673714 7.93832335
[166,] 6.57212907 4.33673714
[167,] 4.41638668 6.57212907
[168,] -1.10702341 4.41638668
[169,] -10.71902861 -1.10702341
[170,] -3.93391814 -10.71902861
[171,] -3.18767144 -3.93391814
[172,] -0.67432591 -3.18767144
[173,] 1.97081467 -0.67432591
[174,] 7.00603297 1.97081467
[175,] -5.13953279 7.00603297
[176,] -1.96354714 -5.13953279
[177,] 1.53251672 -1.96354714
[178,] -0.14859946 1.53251672
[179,] -5.98232616 -0.14859946
[180,] -10.13949844 -5.98232616
[181,] -3.45956376 -10.13949844
[182,] -10.72418396 -3.45956376
[183,] 4.86299466 -10.72418396
[184,] 3.59364145 4.86299466
[185,] -10.34400335 3.59364145
[186,] -7.64656473 -10.34400335
[187,] -9.33704923 -7.64656473
[188,] -3.34638666 -9.33704923
[189,] 13.22809680 -3.34638666
[190,] -0.49928610 13.22809680
[191,] 0.71394400 -0.49928610
[192,] -6.05190820 0.71394400
[193,] 9.82768919 -6.05190820
[194,] -1.99493377 9.82768919
[195,] -8.48973776 -1.99493377
[196,] 4.07915665 -8.48973776
[197,] -5.17623118 4.07915665
[198,] -2.75078465 -5.17623118
[199,] -3.29538525 -2.75078465
[200,] -0.56904537 -3.29538525
[201,] 10.66030566 -0.56904537
[202,] -5.39812982 10.66030566
[203,] -1.80298729 -5.39812982
[204,] 1.75691126 -1.80298729
[205,] 6.75652766 1.75691126
[206,] -0.47976801 6.75652766
[207,] -5.92776532 -0.47976801
[208,] -1.41967045 -5.92776532
[209,] 2.64463787 -1.41967045
[210,] -8.27044057 2.64463787
[211,] -2.21383312 -8.27044057
[212,] 3.33717013 -2.21383312
[213,] -1.61315503 3.33717013
[214,] 8.76228994 -1.61315503
[215,] 9.39418745 8.76228994
[216,] -2.27963652 9.39418745
[217,] -2.29285028 -2.27963652
[218,] 0.53082384 -2.29285028
[219,] 1.27740255 0.53082384
[220,] 3.22505940 1.27740255
[221,] 2.42086233 3.22505940
[222,] -1.71559515 2.42086233
[223,] 1.71862370 -1.71559515
[224,] 5.67901802 1.71862370
[225,] -7.51550341 5.67901802
[226,] -9.72340888 -7.51550341
[227,] -0.60251231 -9.72340888
[228,] -1.47729813 -0.60251231
[229,] 3.77734995 -1.47729813
[230,] 6.35082732 3.77734995
[231,] -3.44320754 6.35082732
[232,] -3.45571478 -3.44320754
[233,] 3.08611118 -3.45571478
[234,] 4.05942351 3.08611118
[235,] 0.88317310 4.05942351
[236,] -1.70619059 0.88317310
[237,] 3.33780828 -1.70619059
[238,] 1.26169351 3.33780828
[239,] 2.60968653 1.26169351
[240,] -1.84303215 2.60968653
[241,] -0.68718834 -1.84303215
[242,] 0.37513622 -0.68718834
[243,] -1.24831950 0.37513622
[244,] -1.15544207 -1.24831950
[245,] -0.79641060 -1.15544207
[246,] -5.45825910 -0.79641060
[247,] -1.05926221 -5.45825910
[248,] -7.08788470 -1.05926221
[249,] -8.84472039 -7.08788470
[250,] 1.18220054 -8.84472039
[251,] 1.05029219 1.18220054
[252,] -10.57089905 1.05029219
[253,] 1.71070992 -10.57089905
[254,] 1.39194347 1.71070992
[255,] -7.36521267 1.39194347
[256,] -4.46114873 -7.36521267
[257,] -1.26464228 -4.46114873
[258,] 1.88107923 -1.26464228
[259,] -4.39323537 1.88107923
[260,] 5.21118509 -4.39323537
[261,] -0.60793670 5.21118509
[262,] -4.40744293 -0.60793670
[263,] -0.79742472 -4.40744293
[264,] -1.49591554 -0.79742472
[265,] -3.54444636 -1.49591554
[266,] 1.31984183 -3.54444636
[267,] -4.41303863 1.31984183
[268,] -2.68179059 -4.41303863
[269,] 2.00262956 -2.68179059
[270,] 2.53033040 2.00262956
[271,] 0.08858335 2.53033040
[272,] -10.42639826 0.08858335
[273,] -3.49214266 -10.42639826
[274,] -5.21596634 -3.49214266
[275,] 2.26461671 -5.21596634
[276,] -1.90478155 2.26461671
[277,] -9.39421249 -1.90478155
[278,] -3.11114188 -9.39421249
[279,] 1.61534075 -3.11114188
[280,] 2.70281486 1.61534075
[281,] -6.46044810 2.70281486
[282,] -6.22189682 -6.46044810
[283,] -7.83269314 -6.22189682
[284,] 1.86226178 -7.83269314
[285,] 5.22751167 1.86226178
[286,] -1.03657758 5.22751167
[287,] -1.45631379 -1.03657758
[288,] 1.43724062 -1.45631379
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.25885819 -2.78652037
2 10.67184619 2.25885819
3 -2.82839097 10.67184619
4 0.64198405 -2.82839097
5 9.53790752 0.64198405
6 -17.88474410 9.53790752
7 5.60824795 -17.88474410
8 -5.30715279 5.60824795
9 -0.82044634 -5.30715279
10 -0.58596452 -0.82044634
11 7.51727811 -0.58596452
12 14.73298738 7.51727811
13 7.58990363 14.73298738
14 6.12464734 7.58990363
15 8.14490502 6.12464734
16 5.00623955 8.14490502
17 -6.86942112 5.00623955
18 -2.02267439 -6.86942112
19 -6.14818039 -2.02267439
20 0.63692431 -6.14818039
21 2.61925283 0.63692431
22 -4.60773448 2.61925283
23 0.76147969 -4.60773448
24 10.35085824 0.76147969
25 2.73207999 10.35085824
26 2.65157673 2.73207999
27 -0.37066632 2.65157673
28 0.22969050 -0.37066632
29 4.69890347 0.22969050
30 0.58633170 4.69890347
31 0.75185322 0.58633170
32 16.96714598 0.75185322
33 6.65552864 16.96714598
34 9.60022642 6.65552864
35 -4.13696722 9.60022642
36 -4.91977617 -4.13696722
37 4.63393446 -4.91977617
38 1.14446957 4.63393446
39 -9.84711808 1.14446957
40 2.93038159 -9.84711808
41 -4.56692806 2.93038159
42 -0.95739623 -4.56692806
43 -2.59057622 -0.95739623
44 -3.18828990 -2.59057622
45 10.55630100 -3.18828990
46 -3.29649955 10.55630100
47 1.76180031 -3.29649955
48 7.62841450 1.76180031
49 17.98596644 7.62841450
50 -1.59717806 17.98596644
51 9.50678946 -1.59717806
52 8.80958145 9.50678946
53 0.04694980 8.80958145
54 5.75818328 0.04694980
55 0.05742636 5.75818328
56 -7.35013655 0.05742636
57 -1.27018727 -7.35013655
58 2.22159553 -1.27018727
59 -4.29249219 2.22159553
60 -0.18256246 -4.29249219
61 6.25115598 -0.18256246
62 -16.82396029 6.25115598
63 -1.28294884 -16.82396029
64 -7.16317079 -1.28294884
65 -3.24626021 -7.16317079
66 -1.18217394 -3.24626021
67 -3.67223417 -1.18217394
68 -15.17288898 -3.67223417
69 11.93551864 -15.17288898
70 3.70418635 11.93551864
71 1.21572983 3.70418635
72 -1.42507176 1.21572983
73 -2.06573522 -1.42507176
74 7.40258398 -2.06573522
75 -5.10886263 7.40258398
76 -7.03133056 -5.10886263
77 12.39513106 -7.03133056
78 7.43644574 12.39513106
79 12.16767002 7.43644574
80 0.19328425 12.16767002
81 5.06039024 0.19328425
82 -4.03377186 5.06039024
83 -5.53412374 -4.03377186
84 -5.73561398 -5.53412374
85 2.46932090 -5.73561398
86 7.99027792 2.46932090
87 0.68751544 7.99027792
88 -2.25057624 0.68751544
89 5.49925645 -2.25057624
90 3.02360665 5.49925645
91 -12.68818869 3.02360665
92 7.36588776 -12.68818869
93 1.65739089 7.36588776
94 0.23798391 1.65739089
95 -2.84736573 0.23798391
96 14.87296890 -2.84736573
97 1.62984404 14.87296890
98 6.97499065 1.62984404
99 -1.75523727 6.97499065
100 3.85684503 -1.75523727
101 2.37433542 3.85684503
102 -2.68773845 2.37433542
103 -1.99909358 -2.68773845
104 -5.46662023 -1.99909358
105 -10.73991747 -5.46662023
106 14.68527530 -10.73991747
107 3.91496774 14.68527530
108 -20.12317855 3.91496774
109 6.64061385 -20.12317855
110 -4.84299460 6.64061385
111 9.62708751 -4.84299460
112 3.92318836 9.62708751
113 -4.45465549 3.92318836
114 1.12453935 -4.45465549
115 -1.15473818 1.12453935
116 -5.98500632 -1.15473818
117 -7.17276445 -5.98500632
118 -6.88525032 -7.17276445
119 -1.51405018 -6.88525032
120 -1.99837714 -1.51405018
121 -3.13513592 -1.99837714
122 -2.08955108 -3.13513592
123 -0.94879852 -2.08955108
124 -0.66669950 -0.94879852
125 -6.35522729 -0.66669950
126 -5.96679976 -6.35522729
127 -0.56065470 -5.96679976
128 4.19288068 -0.56065470
129 -10.21386785 4.19288068
130 -2.77473990 -10.21386785
131 2.77583021 -2.77473990
132 -4.67156760 2.77583021
133 6.43611747 -4.67156760
134 12.67970678 6.43611747
135 -7.39931656 12.67970678
136 -10.49435727 -7.39931656
137 -5.48694134 -10.49435727
138 -1.20723325 -5.48694134
139 -5.12614815 -1.20723325
140 -0.12304404 -5.12614815
141 -5.12924096 -0.12304404
142 3.58936129 -5.12924096
143 0.61296574 3.58936129
144 10.13794424 0.61296574
145 5.74029125 10.13794424
146 13.97514586 5.74029125
147 2.08315733 13.97514586
148 -4.93954868 2.08315733
149 10.08323386 -4.93954868
150 7.81567168 10.08323386
151 16.35670594 7.81567168
152 -5.78987398 16.35670594
153 0.81731449 -5.78987398
154 -0.17236453 0.81731449
155 -5.02621365 -0.17236453
156 5.16658930 -5.02621365
157 -3.55140570 5.16658930
158 8.59854841 -3.55140570
159 -1.71593861 8.59854841
160 0.93037212 -1.71593861
161 8.47042990 0.93037212
162 -5.59983010 8.47042990
163 -3.98995329 -5.59983010
164 7.93832335 -3.98995329
165 4.33673714 7.93832335
166 6.57212907 4.33673714
167 4.41638668 6.57212907
168 -1.10702341 4.41638668
169 -10.71902861 -1.10702341
170 -3.93391814 -10.71902861
171 -3.18767144 -3.93391814
172 -0.67432591 -3.18767144
173 1.97081467 -0.67432591
174 7.00603297 1.97081467
175 -5.13953279 7.00603297
176 -1.96354714 -5.13953279
177 1.53251672 -1.96354714
178 -0.14859946 1.53251672
179 -5.98232616 -0.14859946
180 -10.13949844 -5.98232616
181 -3.45956376 -10.13949844
182 -10.72418396 -3.45956376
183 4.86299466 -10.72418396
184 3.59364145 4.86299466
185 -10.34400335 3.59364145
186 -7.64656473 -10.34400335
187 -9.33704923 -7.64656473
188 -3.34638666 -9.33704923
189 13.22809680 -3.34638666
190 -0.49928610 13.22809680
191 0.71394400 -0.49928610
192 -6.05190820 0.71394400
193 9.82768919 -6.05190820
194 -1.99493377 9.82768919
195 -8.48973776 -1.99493377
196 4.07915665 -8.48973776
197 -5.17623118 4.07915665
198 -2.75078465 -5.17623118
199 -3.29538525 -2.75078465
200 -0.56904537 -3.29538525
201 10.66030566 -0.56904537
202 -5.39812982 10.66030566
203 -1.80298729 -5.39812982
204 1.75691126 -1.80298729
205 6.75652766 1.75691126
206 -0.47976801 6.75652766
207 -5.92776532 -0.47976801
208 -1.41967045 -5.92776532
209 2.64463787 -1.41967045
210 -8.27044057 2.64463787
211 -2.21383312 -8.27044057
212 3.33717013 -2.21383312
213 -1.61315503 3.33717013
214 8.76228994 -1.61315503
215 9.39418745 8.76228994
216 -2.27963652 9.39418745
217 -2.29285028 -2.27963652
218 0.53082384 -2.29285028
219 1.27740255 0.53082384
220 3.22505940 1.27740255
221 2.42086233 3.22505940
222 -1.71559515 2.42086233
223 1.71862370 -1.71559515
224 5.67901802 1.71862370
225 -7.51550341 5.67901802
226 -9.72340888 -7.51550341
227 -0.60251231 -9.72340888
228 -1.47729813 -0.60251231
229 3.77734995 -1.47729813
230 6.35082732 3.77734995
231 -3.44320754 6.35082732
232 -3.45571478 -3.44320754
233 3.08611118 -3.45571478
234 4.05942351 3.08611118
235 0.88317310 4.05942351
236 -1.70619059 0.88317310
237 3.33780828 -1.70619059
238 1.26169351 3.33780828
239 2.60968653 1.26169351
240 -1.84303215 2.60968653
241 -0.68718834 -1.84303215
242 0.37513622 -0.68718834
243 -1.24831950 0.37513622
244 -1.15544207 -1.24831950
245 -0.79641060 -1.15544207
246 -5.45825910 -0.79641060
247 -1.05926221 -5.45825910
248 -7.08788470 -1.05926221
249 -8.84472039 -7.08788470
250 1.18220054 -8.84472039
251 1.05029219 1.18220054
252 -10.57089905 1.05029219
253 1.71070992 -10.57089905
254 1.39194347 1.71070992
255 -7.36521267 1.39194347
256 -4.46114873 -7.36521267
257 -1.26464228 -4.46114873
258 1.88107923 -1.26464228
259 -4.39323537 1.88107923
260 5.21118509 -4.39323537
261 -0.60793670 5.21118509
262 -4.40744293 -0.60793670
263 -0.79742472 -4.40744293
264 -1.49591554 -0.79742472
265 -3.54444636 -1.49591554
266 1.31984183 -3.54444636
267 -4.41303863 1.31984183
268 -2.68179059 -4.41303863
269 2.00262956 -2.68179059
270 2.53033040 2.00262956
271 0.08858335 2.53033040
272 -10.42639826 0.08858335
273 -3.49214266 -10.42639826
274 -5.21596634 -3.49214266
275 2.26461671 -5.21596634
276 -1.90478155 2.26461671
277 -9.39421249 -1.90478155
278 -3.11114188 -9.39421249
279 1.61534075 -3.11114188
280 2.70281486 1.61534075
281 -6.46044810 2.70281486
282 -6.22189682 -6.46044810
283 -7.83269314 -6.22189682
284 1.86226178 -7.83269314
285 5.22751167 1.86226178
286 -1.03657758 5.22751167
287 -1.45631379 -1.03657758
288 1.43724062 -1.45631379
> 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/7xhqr1324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8u7691324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94h4b1324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10fbfw1324120639.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ocdf1324120639.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/123jp51324120639.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/13pjc91324120639.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/14j9zg1324120639.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/15qp8k1324120639.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/16xfu11324120639.tab")
+ }
>
> try(system("convert tmp/1fxau1324120639.ps tmp/1fxau1324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/27y631324120639.ps tmp/27y631324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gdwh1324120639.ps tmp/3gdwh1324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rr591324120639.ps tmp/4rr591324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/515u71324120639.ps tmp/515u71324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jzxq1324120639.ps tmp/6jzxq1324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xhqr1324120639.ps tmp/7xhqr1324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u7691324120639.ps tmp/8u7691324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/94h4b1324120639.ps tmp/94h4b1324120639.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fbfw1324120639.ps tmp/10fbfw1324120639.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.210 0.662 9.041