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(210907
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+ ,16
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+ ,1
+ ,8
+ ,4
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+ ,27
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+ ,0
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+ ,21
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+ ,34
+ ,0
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+ ,19
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+ ,1
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+ ,35
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+ ,46
+ ,0
+ ,28
+ ,15
+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed')
+ ,1:289))
> y <- array(NA,dim=c(7,289),dimnames=list(c('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 = '5'
> 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
shared_compendiums time_in_rfc logins compendium_views_info
1 3 210907 56 396
2 4 120982 56 297
3 12 176508 54 559
4 2 179321 89 967
5 1 123185 40 270
6 3 52746 25 143
7 0 385534 92 1562
8 0 33170 18 109
9 0 101645 63 371
10 5 149061 44 656
11 0 165446 33 511
12 0 237213 84 655
13 7 173326 88 465
14 7 133131 55 525
15 3 258873 60 885
16 9 180083 66 497
17 0 324799 154 1436
18 4 230964 53 612
19 3 236785 119 865
20 0 135473 41 385
21 7 202925 61 567
22 0 215147 58 639
23 1 344297 75 963
24 5 153935 33 398
25 7 132943 40 410
26 0 174724 92 966
27 0 174415 100 801
28 5 225548 112 892
29 0 223632 73 513
30 0 124817 40 469
31 0 221698 45 683
32 3 210767 60 643
33 4 170266 62 535
34 1 260561 75 625
35 4 84853 31 264
36 2 294424 77 992
37 0 101011 34 238
38 0 215641 46 818
39 0 325107 99 937
40 0 7176 17 70
41 2 167542 66 507
42 1 106408 30 260
43 0 96560 76 503
44 2 265769 146 927
45 10 269651 67 1269
46 6 149112 56 537
47 0 175824 107 910
48 5 152871 58 532
49 4 111665 34 345
50 1 116408 61 918
51 2 362301 119 1635
52 2 78800 42 330
53 0 183167 66 557
54 8 277965 89 1178
55 3 150629 44 740
56 0 168809 66 452
57 0 24188 24 218
58 8 329267 259 764
59 5 65029 17 255
60 3 101097 64 454
61 1 218946 41 866
62 5 244052 68 574
63 1 341570 168 1276
64 1 103597 43 379
65 5 233328 132 825
66 0 256462 105 798
67 12 206161 71 663
68 8 311473 112 1069
69 8 235800 94 921
70 8 177939 82 858
71 8 207176 70 711
72 2 196553 57 503
73 0 174184 53 382
74 5 143246 103 464
75 8 187559 121 717
76 2 187681 62 690
77 5 119016 52 462
78 12 182192 52 657
79 6 73566 32 385
80 7 194979 62 577
81 2 167488 45 619
82 0 143756 46 479
83 4 275541 63 817
84 3 243199 75 752
85 6 182999 88 430
86 2 135649 46 451
87 0 152299 53 537
88 1 120221 37 519
89 0 346485 90 1000
90 5 145790 63 637
91 2 193339 78 465
92 0 80953 25 437
93 0 122774 45 711
94 5 130585 46 299
95 0 112611 41 248
96 1 286468 144 1162
97 0 241066 82 714
98 1 148446 91 905
99 1 204713 71 649
100 2 182079 63 512
101 6 140344 53 472
102 1 220516 62 905
103 4 243060 63 786
104 2 162765 32 489
105 3 182613 39 479
106 0 232138 62 617
107 10 265318 117 925
108 0 85574 34 351
109 9 310839 92 1144
110 7 225060 93 669
111 0 232317 54 707
112 0 144966 144 458
113 4 43287 14 214
114 4 155754 61 599
115 0 164709 109 572
116 0 201940 38 897
117 0 235454 73 819
118 1 220801 75 720
119 0 99466 50 273
120 1 92661 61 508
121 0 133328 55 506
122 0 61361 77 451
123 4 125930 75 699
124 0 100750 72 407
125 4 224549 50 465
126 4 82316 32 245
127 3 102010 53 370
128 0 101523 42 316
129 0 243511 71 603
130 0 22938 10 154
131 5 41566 35 229
132 0 152474 65 577
133 4 61857 25 192
134 0 99923 66 617
135 0 132487 41 411
136 1 317394 86 975
137 0 21054 16 146
138 5 209641 42 705
139 0 22648 19 184
140 0 31414 19 200
141 0 46698 45 274
142 0 131698 65 502
143 0 91735 35 382
144 2 244749 95 964
145 7 184510 49 537
146 1 79863 37 438
147 8 128423 64 369
148 2 97839 38 417
149 0 38214 34 276
150 2 151101 32 514
151 0 272458 65 822
152 0 172494 52 389
153 1 108043 62 466
154 3 328107 65 1255
155 0 250579 83 694
156 3 351067 95 1024
157 0 158015 29 400
158 0 98866 18 397
159 0 85439 33 350
160 4 229242 247 719
161 4 351619 139 1277
162 11 84207 29 356
163 0 120445 118 457
164 0 324598 110 1402
165 4 131069 67 600
166 0 204271 42 480
167 1 165543 65 595
168 0 141722 94 436
169 0 116048 64 230
170 0 250047 81 651
171 9 299775 95 1367
172 1 195838 67 564
173 3 173260 63 716
174 10 254488 83 747
175 5 104389 45 467
176 0 136084 30 671
177 2 199476 70 861
178 0 92499 32 319
179 1 224330 83 612
180 2 135781 31 433
181 4 74408 67 434
182 0 81240 66 503
183 0 14688 10 85
184 2 181633 70 564
185 1 271856 103 824
186 0 7199 5 74
187 0 46660 20 259
188 0 17547 5 69
189 1 133368 36 535
190 0 95227 34 239
191 2 152601 48 438
192 0 98146 40 459
193 3 79619 43 426
194 6 59194 31 288
195 0 139942 42 498
196 2 118612 46 454
197 0 72880 33 376
198 2 65475 18 225
199 1 99643 55 555
200 1 71965 35 252
201 2 77272 59 208
202 1 49289 19 130
203 0 135131 66 481
204 1 108446 60 389
205 3 89746 36 565
206 0 44296 25 173
207 0 77648 47 278
208 0 181528 54 609
209 0 134019 53 422
210 1 124064 40 445
211 4 92630 40 387
212 0 121848 39 339
213 0 52915 14 181
214 0 81872 45 245
215 7 58981 36 384
216 2 53515 28 212
217 0 60812 44 399
218 7 56375 30 229
219 3 65490 22 224
220 0 80949 17 203
221 0 76302 31 333
222 6 104011 55 384
223 2 98104 54 636
224 0 67989 21 185
225 0 30989 14 93
226 3 135458 81 581
227 0 73504 35 248
228 1 63123 43 304
229 1 61254 46 344
230 0 74914 30 407
231 1 31774 23 170
232 0 81437 38 312
233 0 87186 54 507
234 0 50090 20 224
235 0 65745 53 340
236 0 56653 45 168
237 0 158399 39 443
238 0 46455 20 204
239 0 73624 24 367
240 0 38395 31 210
241 0 91899 35 335
242 0 139526 151 364
243 0 52164 52 178
244 2 51567 30 206
245 0 70551 31 279
246 1 84856 29 387
247 1 102538 57 490
248 0 86678 40 238
249 0 85709 44 343
250 0 34662 25 232
251 0 150580 77 530
252 0 99611 35 291
253 0 19349 11 67
254 1 99373 63 397
255 0 86230 44 467
256 0 30837 19 178
257 0 31706 13 175
258 0 89806 42 299
259 1 62088 38 154
260 0 40151 29 106
261 0 27634 20 189
262 0 76990 27 194
263 0 37460 20 135
264 0 54157 19 201
265 0 49862 37 207
266 0 84337 26 280
267 0 64175 42 260
268 0 59382 49 227
269 0 119308 30 239
270 0 76702 49 333
271 1 103425 67 428
272 0 70344 28 230
273 0 43410 19 292
274 1 104838 49 350
275 0 62215 27 186
276 6 69304 30 326
277 3 53117 22 155
278 1 19764 12 75
279 2 86680 31 361
280 0 84105 20 261
281 0 77945 20 299
282 0 89113 39 300
283 3 91005 29 450
284 1 40248 16 183
285 0 64187 27 238
286 0 50857 21 165
287 1 56613 19 234
288 0 62792 35 176
289 0 72535 14 329
compendium_views_pr blogged_computations compendiums_reviewed
1 81 79 30
2 55 58 28
3 50 60 38
4 125 108 30
5 40 49 22
6 37 0 26
7 63 121 25
8 44 1 18
9 88 20 11
10 66 43 26
11 57 69 25
12 74 78 38
13 49 86 44
14 52 44 30
15 88 104 40
16 36 63 34
17 108 158 47
18 43 102 30
19 75 77 31
20 32 82 23
21 44 115 36
22 85 101 36
23 86 80 30
24 56 50 25
25 50 83 39
26 135 123 34
27 63 73 31
28 81 81 31
29 52 105 33
30 44 47 25
31 113 105 33
32 39 94 35
33 73 44 42
34 48 114 43
35 33 38 30
36 59 107 33
37 41 30 13
38 69 71 32
39 64 84 36
40 1 0 0
41 59 59 28
42 32 33 14
43 129 42 17
44 37 96 32
45 31 106 30
46 65 56 35
47 107 57 20
48 74 59 28
49 54 39 28
50 76 34 39
51 715 76 34
52 57 20 26
53 66 91 39
54 106 115 39
55 54 85 33
56 32 76 28
57 20 8 4
58 71 79 39
59 21 21 18
60 70 30 14
61 112 76 29
62 66 101 44
63 190 94 21
64 66 27 16
65 165 92 28
66 56 123 35
67 61 75 28
68 53 128 38
69 127 105 23
70 63 55 36
71 38 56 32
72 50 41 29
73 52 72 25
74 42 67 27
75 76 75 36
76 67 114 28
77 50 118 23
78 53 77 40
79 39 22 23
80 50 66 40
81 77 69 28
82 57 105 34
83 73 116 33
84 34 88 28
85 39 73 34
86 46 99 30
87 63 62 33
88 35 53 22
89 106 118 38
90 43 30 26
91 47 100 35
92 31 49 8
93 162 24 24
94 57 67 29
95 36 46 20
96 263 57 29
97 78 75 45
98 63 135 37
99 54 68 33
100 63 124 33
101 77 33 25
102 79 98 32
103 110 58 29
104 56 68 28
105 56 81 28
106 43 131 31
107 111 110 52
108 71 37 21
109 62 130 24
110 56 93 41
111 74 118 33
112 60 39 32
113 43 13 19
114 68 74 20
115 53 81 31
116 87 109 31
117 46 151 32
118 105 51 18
119 32 28 23
120 133 40 17
121 79 56 20
122 51 27 12
123 207 37 17
124 67 83 30
125 47 54 31
126 34 27 10
127 66 28 13
128 76 59 22
129 65 133 42
130 9 12 1
131 42 0 9
132 45 106 32
133 25 23 11
134 115 44 25
135 97 71 36
136 53 116 31
137 2 4 0
138 52 62 24
139 44 12 13
140 22 18 8
141 35 14 13
142 74 60 19
143 103 7 18
144 144 98 33
145 60 64 40
146 134 29 22
147 89 32 38
148 42 25 24
149 52 16 8
150 98 48 35
151 99 100 43
152 52 46 43
153 29 45 14
154 125 129 41
155 106 130 38
156 95 136 45
157 40 59 31
158 140 25 13
159 43 32 28
160 128 63 31
161 142 95 40
162 73 14 30
163 72 36 16
164 128 113 37
165 61 47 30
166 73 92 35
167 148 70 32
168 64 19 27
169 45 50 20
170 58 41 18
171 97 91 31
172 50 111 31
173 37 41 21
174 50 120 39
175 105 135 41
176 69 27 13
177 46 87 32
178 57 25 18
179 52 131 39
180 98 45 14
181 61 29 7
182 89 58 17
183 0 4 0
184 48 47 30
185 91 109 37
186 0 7 0
187 7 12 5
188 3 0 1
189 54 37 16
190 70 37 32
191 36 46 24
192 37 15 17
193 123 42 11
194 247 7 24
195 46 54 22
196 72 54 12
197 41 14 19
198 24 16 13
199 45 33 17
200 33 32 15
201 27 21 16
202 36 15 24
203 87 38 15
204 90 22 17
205 114 28 18
206 31 10 20
207 45 31 16
208 69 32 16
209 51 32 18
210 34 43 22
211 60 27 8
212 45 37 17
213 54 20 18
214 25 32 16
215 38 0 23
216 52 5 22
217 67 26 13
218 74 10 13
219 38 27 16
220 30 11 16
221 26 29 20
222 67 25 22
223 132 55 17
224 42 23 18
225 35 5 17
226 118 43 12
227 68 23 7
228 43 34 17
229 76 36 14
230 64 35 23
231 48 0 17
232 64 37 14
233 56 28 15
234 71 16 17
235 75 26 21
236 39 38 18
237 42 23 18
238 39 22 17
239 93 30 17
240 38 16 16
241 60 18 15
242 71 28 21
243 52 32 16
244 27 21 14
245 59 23 15
246 40 29 17
247 79 50 15
248 44 12 15
249 65 21 10
250 10 18 6
251 124 27 22
252 81 41 21
253 15 13 1
254 92 12 18
255 42 21 17
256 10 8 4
257 24 26 10
258 64 27 16
259 45 13 16
260 22 16 9
261 56 2 16
262 94 42 17
263 19 5 7
264 35 37 15
265 32 17 14
266 35 38 14
267 48 37 18
268 49 29 12
269 48 32 16
270 62 35 21
271 96 17 19
272 45 20 16
273 63 7 1
274 71 46 16
275 26 24 10
276 48 40 19
277 29 3 12
278 19 10 2
279 45 37 14
280 45 17 17
281 67 28 19
282 30 19 14
283 36 29 11
284 34 8 4
285 36 10 16
286 34 15 20
287 37 15 12
288 46 28 15
289 44 17 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc logins
-6.519e-01 -3.555e-06 4.666e-03
compendium_views_info compendium_views_pr blogged_computations
2.885e-03 -7.291e-03 -2.049e-02
compendiums_reviewed
1.273e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.0129 -1.5772 -0.6172 1.2533 9.5581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.519e-01 3.861e-01 -1.688 0.0924 .
time_in_rfc -3.555e-06 5.217e-06 -0.681 0.4961
logins 4.666e-03 6.360e-03 0.734 0.4638
compendium_views_info 2.885e-03 1.283e-03 2.250 0.0252 *
compendium_views_pr -7.291e-03 3.430e-03 -2.126 0.0344 *
blogged_computations -2.049e-02 7.951e-03 -2.577 0.0105 *
compendiums_reviewed 1.273e-01 2.323e-02 5.480 9.43e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.491 on 282 degrees of freedom
Multiple R-squared: 0.1858, Adjusted R-squared: 0.1685
F-statistic: 10.73 on 6 and 282 DF, p-value: 9.576e-11
> 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,] 0.6072277 7.855446e-01 3.927723e-01
[2,] 0.4639084 9.278167e-01 5.360916e-01
[3,] 0.9648429 7.031414e-02 3.515707e-02
[4,] 0.9387217 1.225567e-01 6.127834e-02
[5,] 0.9238451 1.523097e-01 7.615486e-02
[6,] 0.9011086 1.977827e-01 9.889136e-02
[7,] 0.9225689 1.548621e-01 7.743107e-02
[8,] 0.9632984 7.340321e-02 3.670160e-02
[9,] 0.9474470 1.051061e-01 5.255304e-02
[10,] 0.9255275 1.489450e-01 7.447250e-02
[11,] 0.9092591 1.814818e-01 9.074092e-02
[12,] 0.9182412 1.635176e-01 8.175878e-02
[13,] 0.9212348 1.575303e-01 7.876515e-02
[14,] 0.8933344 2.133312e-01 1.066656e-01
[15,] 0.8860172 2.279657e-01 1.139828e-01
[16,] 0.8606665 2.786670e-01 1.393335e-01
[17,] 0.8266114 3.467771e-01 1.733886e-01
[18,] 0.8524715 2.950570e-01 1.475285e-01
[19,] 0.8659401 2.681198e-01 1.340599e-01
[20,] 0.8657699 2.684602e-01 1.342301e-01
[21,] 0.8899190 2.201620e-01 1.100810e-01
[22,] 0.8643381 2.713239e-01 1.356619e-01
[23,] 0.8407196 3.185609e-01 1.592804e-01
[24,] 0.8478138 3.043725e-01 1.521862e-01
[25,] 0.8765815 2.468370e-01 1.234185e-01
[26,] 0.8546123 2.907753e-01 1.453877e-01
[27,] 0.8231072 3.537856e-01 1.768928e-01
[28,] 0.7877311 4.245378e-01 2.122689e-01
[29,] 0.8085616 3.828767e-01 1.914384e-01
[30,] 0.8196352 3.607295e-01 1.803648e-01
[31,] 0.7845663 4.308674e-01 2.154337e-01
[32,] 0.7464257 5.071486e-01 2.535743e-01
[33,] 0.7040079 5.919843e-01 2.959921e-01
[34,] 0.6705084 6.589832e-01 3.294916e-01
[35,] 0.6308293 7.383413e-01 3.691707e-01
[36,] 0.8088165 3.823669e-01 1.911835e-01
[37,] 0.7900581 4.198839e-01 2.099419e-01
[38,] 0.7633658 4.732684e-01 2.366342e-01
[39,] 0.7680363 4.639274e-01 2.319637e-01
[40,] 0.7345537 5.308926e-01 2.654463e-01
[41,] 0.8723079 2.553843e-01 1.276921e-01
[42,] 0.9418993 1.162013e-01 5.810066e-02
[43,] 0.9316458 1.367084e-01 6.835420e-02
[44,] 0.9444662 1.110676e-01 5.553379e-02
[45,] 0.9637673 7.246538e-02 3.623269e-02
[46,] 0.9549907 9.001855e-02 4.500928e-02
[47,] 0.9505849 9.883027e-02 4.941514e-02
[48,] 0.9386145 1.227709e-01 6.138546e-02
[49,] 0.9622955 7.540903e-02 3.770452e-02
[50,] 0.9658033 6.839346e-02 3.419673e-02
[51,] 0.9615050 7.699008e-02 3.849504e-02
[52,] 0.9550051 8.998985e-02 4.499493e-02
[53,] 0.9465493 1.069014e-01 5.345070e-02
[54,] 0.9364375 1.271250e-01 6.356252e-02
[55,] 0.9230128 1.539744e-01 7.698722e-02
[56,] 0.9292064 1.415872e-01 7.079360e-02
[57,] 0.9289627 1.420747e-01 7.103735e-02
[58,] 0.9965498 6.900417e-03 3.450208e-03
[59,] 0.9981232 3.753570e-03 1.876785e-03
[60,] 0.9996848 6.304934e-04 3.152467e-04
[61,] 0.9997455 5.090401e-04 2.545201e-04
[62,] 0.9998501 2.997091e-04 1.498545e-04
[63,] 0.9998064 3.872424e-04 1.936212e-04
[64,] 0.9997626 4.747084e-04 2.373542e-04
[65,] 0.9997453 5.094814e-04 2.547407e-04
[66,] 0.9998369 3.262282e-04 1.631141e-04
[67,] 0.9997679 4.642652e-04 2.321326e-04
[68,] 0.9998650 2.699736e-04 1.349868e-04
[69,] 0.9999946 1.089398e-05 5.446988e-06
[70,] 0.9999957 8.502014e-06 4.251007e-06
[71,] 0.9999964 7.191730e-06 3.595865e-06
[72,] 0.9999947 1.069181e-05 5.345905e-06
[73,] 0.9999947 1.064591e-05 5.322957e-06
[74,] 0.9999934 1.329681e-05 6.648403e-06
[75,] 0.9999903 1.945846e-05 9.729229e-06
[76,] 0.9999922 1.557703e-05 7.788514e-06
[77,] 0.9999887 2.265893e-05 1.132946e-05
[78,] 0.9999916 1.677253e-05 8.386267e-06
[79,] 0.9999884 2.310126e-05 1.155063e-05
[80,] 0.9999900 1.993969e-05 9.969843e-06
[81,] 0.9999878 2.446855e-05 1.223427e-05
[82,] 0.9999831 3.372341e-05 1.686170e-05
[83,] 0.9999754 4.920074e-05 2.460037e-05
[84,] 0.9999787 4.265469e-05 2.132735e-05
[85,] 0.9999826 3.470758e-05 1.735379e-05
[86,] 0.9999777 4.464727e-05 2.232363e-05
[87,] 0.9999783 4.331374e-05 2.165687e-05
[88,] 0.9999912 1.753822e-05 8.769110e-06
[89,] 0.9999906 1.887307e-05 9.436534e-06
[90,] 0.9999897 2.059649e-05 1.029824e-05
[91,] 0.9999854 2.915693e-05 1.457847e-05
[92,] 0.9999899 2.026026e-05 1.013013e-05
[93,] 0.9999879 2.414297e-05 1.207149e-05
[94,] 0.9999836 3.270010e-05 1.635005e-05
[95,] 0.9999764 4.711572e-05 2.355786e-05
[96,] 0.9999697 6.052416e-05 3.026208e-05
[97,] 0.9999606 7.878562e-05 3.939281e-05
[98,] 0.9999895 2.108372e-05 1.054186e-05
[99,] 0.9999874 2.518783e-05 1.259391e-05
[100,] 0.9999996 8.827434e-07 4.413717e-07
[101,] 0.9999998 4.043996e-07 2.021998e-07
[102,] 0.9999997 5.006993e-07 2.503497e-07
[103,] 0.9999998 3.060917e-07 1.530458e-07
[104,] 0.9999998 3.197054e-07 1.598527e-07
[105,] 0.9999999 2.805190e-07 1.402595e-07
[106,] 0.9999999 2.758900e-07 1.379450e-07
[107,] 0.9999999 2.875887e-07 1.437944e-07
[108,] 0.9999998 3.919375e-07 1.959687e-07
[109,] 0.9999997 5.939214e-07 2.969607e-07
[110,] 0.9999997 6.750186e-07 3.375093e-07
[111,] 0.9999995 1.019159e-06 5.095796e-07
[112,] 0.9999993 1.345061e-06 6.725306e-07
[113,] 0.9999991 1.785092e-06 8.925462e-07
[114,] 0.9999990 1.945051e-06 9.725253e-07
[115,] 0.9999989 2.295824e-06 1.147912e-06
[116,] 0.9999988 2.438081e-06 1.219041e-06
[117,] 0.9999993 1.423952e-06 7.119759e-07
[118,] 0.9999992 1.616412e-06 8.082058e-07
[119,] 0.9999989 2.240604e-06 1.120302e-06
[120,] 0.9999988 2.479632e-06 1.239816e-06
[121,] 0.9999981 3.753437e-06 1.876719e-06
[122,] 0.9999991 1.800282e-06 9.001411e-07
[123,] 0.9999990 2.026136e-06 1.013068e-06
[124,] 0.9999994 1.232348e-06 6.161741e-07
[125,] 0.9999995 9.838461e-07 4.919230e-07
[126,] 0.9999995 9.231409e-07 4.615705e-07
[127,] 0.9999994 1.284152e-06 6.420759e-07
[128,] 0.9999990 1.934230e-06 9.671151e-07
[129,] 0.9999993 1.335905e-06 6.679526e-07
[130,] 0.9999991 1.821837e-06 9.109183e-07
[131,] 0.9999986 2.705416e-06 1.352708e-06
[132,] 0.9999982 3.525010e-06 1.762505e-06
[133,] 0.9999977 4.679703e-06 2.339851e-06
[134,] 0.9999973 5.367707e-06 2.683853e-06
[135,] 0.9999963 7.496255e-06 3.748128e-06
[136,] 0.9999984 3.266029e-06 1.633015e-06
[137,] 0.9999979 4.266349e-06 2.133174e-06
[138,] 0.9999996 8.753877e-07 4.376938e-07
[139,] 0.9999993 1.301391e-06 6.506953e-07
[140,] 0.9999991 1.874459e-06 9.372296e-07
[141,] 0.9999987 2.653681e-06 1.326841e-06
[142,] 0.9999990 1.975484e-06 9.877420e-07
[143,] 0.9999993 1.349669e-06 6.748347e-07
[144,] 0.9999990 2.067856e-06 1.033928e-06
[145,] 0.9999985 3.043662e-06 1.521831e-06
[146,] 0.9999983 3.428693e-06 1.714347e-06
[147,] 0.9999974 5.116082e-06 2.558041e-06
[148,] 0.9999971 5.878120e-06 2.939060e-06
[149,] 0.9999960 7.917239e-06 3.958619e-06
[150,] 0.9999962 7.630359e-06 3.815179e-06
[151,] 0.9999960 8.085983e-06 4.042992e-06
[152,] 0.9999940 1.197420e-05 5.987100e-06
[153,] 1.0000000 3.387790e-08 1.693895e-08
[154,] 1.0000000 4.819859e-08 2.409929e-08
[155,] 1.0000000 6.266589e-09 3.133295e-09
[156,] 1.0000000 8.931936e-09 4.465968e-09
[157,] 1.0000000 1.023341e-08 5.116707e-09
[158,] 1.0000000 1.162756e-08 5.813780e-09
[159,] 1.0000000 1.260083e-08 6.300414e-09
[160,] 1.0000000 2.024314e-08 1.012157e-08
[161,] 1.0000000 2.781033e-08 1.390517e-08
[162,] 1.0000000 4.890464e-09 2.445232e-09
[163,] 1.0000000 7.568561e-09 3.784281e-09
[164,] 1.0000000 1.044788e-08 5.223941e-09
[165,] 1.0000000 1.804084e-12 9.020420e-13
[166,] 1.0000000 8.783864e-13 4.391932e-13
[167,] 1.0000000 9.916173e-13 4.958087e-13
[168,] 1.0000000 1.748227e-12 8.741136e-13
[169,] 1.0000000 2.807128e-12 1.403564e-12
[170,] 1.0000000 3.741769e-12 1.870885e-12
[171,] 1.0000000 6.200073e-12 3.100036e-12
[172,] 1.0000000 2.657632e-12 1.328816e-12
[173,] 1.0000000 3.939846e-12 1.969923e-12
[174,] 1.0000000 7.794389e-12 3.897195e-12
[175,] 1.0000000 1.109496e-11 5.547478e-12
[176,] 1.0000000 1.772465e-11 8.862326e-12
[177,] 1.0000000 3.451793e-11 1.725897e-11
[178,] 1.0000000 6.389840e-11 3.194920e-11
[179,] 1.0000000 1.219978e-10 6.099888e-11
[180,] 1.0000000 2.298154e-10 1.149077e-10
[181,] 1.0000000 3.289075e-10 1.644538e-10
[182,] 1.0000000 3.520591e-10 1.760295e-10
[183,] 1.0000000 4.797298e-10 2.398649e-10
[184,] 1.0000000 6.326681e-10 3.163340e-10
[185,] 1.0000000 2.933163e-10 1.466582e-10
[186,] 1.0000000 4.887944e-10 2.443972e-10
[187,] 1.0000000 6.306676e-10 3.153338e-10
[188,] 1.0000000 7.318552e-10 3.659276e-10
[189,] 1.0000000 1.056387e-09 5.281935e-10
[190,] 1.0000000 1.759026e-09 8.795129e-10
[191,] 1.0000000 3.134917e-09 1.567459e-09
[192,] 1.0000000 3.884401e-09 1.942200e-09
[193,] 1.0000000 6.796946e-09 3.398473e-09
[194,] 1.0000000 1.133353e-08 5.666766e-09
[195,] 1.0000000 2.065835e-08 1.032918e-08
[196,] 1.0000000 3.489859e-08 1.744930e-08
[197,] 1.0000000 5.173865e-08 2.586933e-08
[198,] 1.0000000 8.739489e-08 4.369744e-08
[199,] 0.9999999 1.269958e-07 6.349788e-08
[200,] 0.9999999 1.995954e-07 9.979769e-08
[201,] 0.9999998 3.456087e-07 1.728043e-07
[202,] 0.9999999 1.414187e-07 7.070933e-08
[203,] 0.9999999 2.457932e-07 1.228966e-07
[204,] 0.9999998 4.012779e-07 2.006390e-07
[205,] 0.9999997 6.846297e-07 3.423148e-07
[206,] 1.0000000 2.719367e-08 1.359684e-08
[207,] 1.0000000 3.964535e-08 1.982268e-08
[208,] 1.0000000 5.993214e-08 2.996607e-08
[209,] 1.0000000 2.096131e-11 1.048065e-11
[210,] 1.0000000 8.921447e-12 4.460724e-12
[211,] 1.0000000 1.939967e-11 9.699837e-12
[212,] 1.0000000 3.269147e-11 1.634573e-11
[213,] 1.0000000 6.655857e-15 3.327929e-15
[214,] 1.0000000 1.702450e-14 8.512251e-15
[215,] 1.0000000 4.370664e-14 2.185332e-14
[216,] 1.0000000 1.102233e-13 5.511164e-14
[217,] 1.0000000 4.321925e-14 2.160963e-14
[218,] 1.0000000 1.164162e-13 5.820809e-14
[219,] 1.0000000 3.017932e-13 1.508966e-13
[220,] 1.0000000 7.406406e-13 3.703203e-13
[221,] 1.0000000 1.324254e-12 6.621271e-13
[222,] 1.0000000 2.658155e-12 1.329077e-12
[223,] 1.0000000 6.201899e-12 3.100950e-12
[224,] 1.0000000 9.017941e-12 4.508970e-12
[225,] 1.0000000 2.259131e-11 1.129565e-11
[226,] 1.0000000 4.826388e-11 2.413194e-11
[227,] 1.0000000 1.120989e-10 5.604947e-11
[228,] 1.0000000 2.320865e-10 1.160433e-10
[229,] 1.0000000 5.114651e-10 2.557326e-10
[230,] 1.0000000 1.110338e-09 5.551692e-10
[231,] 1.0000000 2.362167e-09 1.181084e-09
[232,] 1.0000000 5.212163e-09 2.606082e-09
[233,] 1.0000000 1.150484e-08 5.752419e-09
[234,] 1.0000000 2.655982e-08 1.327991e-08
[235,] 1.0000000 3.373670e-08 1.686835e-08
[236,] 1.0000000 7.231468e-08 3.615734e-08
[237,] 0.9999999 1.658782e-07 8.293909e-08
[238,] 0.9999998 3.735363e-07 1.867682e-07
[239,] 0.9999996 8.306470e-07 4.153235e-07
[240,] 0.9999991 1.720239e-06 8.601193e-07
[241,] 0.9999985 2.989140e-06 1.494570e-06
[242,] 0.9999970 5.965795e-06 2.982898e-06
[243,] 0.9999940 1.203712e-05 6.018562e-06
[244,] 0.9999874 2.529743e-05 1.264872e-05
[245,] 0.9999763 4.748883e-05 2.374442e-05
[246,] 0.9999752 4.954650e-05 2.477325e-05
[247,] 0.9999556 8.873771e-05 4.436885e-05
[248,] 0.9999279 1.441360e-04 7.206798e-05
[249,] 0.9998591 2.818596e-04 1.409298e-04
[250,] 0.9998069 3.862737e-04 1.931369e-04
[251,] 0.9996132 7.735533e-04 3.867766e-04
[252,] 0.9992640 1.472092e-03 7.360460e-04
[253,] 0.9986737 2.652642e-03 1.326321e-03
[254,] 0.9975683 4.863319e-03 2.431659e-03
[255,] 0.9962759 7.448182e-03 3.724091e-03
[256,] 0.9939550 1.209008e-02 6.045042e-03
[257,] 0.9917740 1.645197e-02 8.225986e-03
[258,] 0.9892646 2.147079e-02 1.073540e-02
[259,] 0.9843041 3.139189e-02 1.569595e-02
[260,] 0.9768782 4.624352e-02 2.312176e-02
[261,] 0.9875903 2.481942e-02 1.240971e-02
[262,] 0.9773304 4.533929e-02 2.266964e-02
[263,] 0.9597375 8.052495e-02 4.026248e-02
[264,] 0.9397774 1.204452e-01 6.022261e-02
[265,] 0.8969291 2.061418e-01 1.030709e-01
[266,] 0.8419860 3.160280e-01 1.580140e-01
[267,] 0.9563984 8.720325e-02 4.360163e-02
[268,] 0.9988122 2.375688e-03 1.187844e-03
[269,] 0.9954919 9.016117e-03 4.508059e-03
[270,] 0.9781957 4.360867e-02 2.180434e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1sxzs1323610269.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/2z6y11323610269.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3rf6e1323610269.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a3v61323610269.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mlf81323610269.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.388542717 1.989211329 8.171646374 -0.610261140 -0.380472177 0.270504611
7 8 9 10 11 12
-3.157254846 -1.578520871 -0.699937074 2.136569370 -1.741043486 -3.485717423
13 14 15 16 17 18
3.034677883 3.815879839 -0.580102636 5.775787384 -5.012893167 2.044820062
19 20 21 22 23 24
-0.378709964 -1.182693677 4.547625774 -2.590612627 -1.805006014 3.147470834
25 26 27 28 29 30
3.856034872 -2.766578139 -3.496562849 1.661810730 -2.043673013 -2.342536466
31 32 33 34 35 36
-1.965655352 0.021399430 -0.487916229 -2.362475374 1.247862206 -1.100770713
37 38 39 40 41 42
-0.575449799 -3.271597893 -3.752338811 0.403367205 -0.448215001 0.267491031
43 44 45 46 47 48
-1.173519286 -1.595579022 6.215774424 2.537655972 -2.445651812 2.574191826
49 50 51 52 53 54
1.523631534 -4.581010389 1.109691195 -0.700135732 -3.230316451 3.990942148
55 56 57 58 59 60
-0.218096300 -2.133529110 -0.202538633 3.581753998 3.360252718 1.745776147
61 62 63 64 65 66
-1.577194465 1.496231788 -0.961099753 -0.276142635 3.009168639 -2.755196740
67 68 69 70 71 72
9.558052669 4.324482738 6.544012260 3.430236066 4.361671509 -0.853282459
73 74 75 76 77 78
-1.406070480 2.584095670 4.193893969 0.299335950 4.354205695 8.034064356
79 80 81 82 83 84
3.460815378 3.016419632 -0.337433451 -2.194362649 1.688869695 0.483774477
85 86 87 88 89 90
3.103599715 0.163596683 -3.074085490 -1.049970958 -3.067778793 1.657028538
91 92 93 94 95 96
-0.429671618 -0.226123286 -2.555055392 3.135957383 -1.195318864 -1.960235241
97 98 99 100 101 102
-4.556221013 -2.340239659 -2.237591020 0.327684046 3.597078518 -1.953796088
103 104 105 106 107 108
1.253287747 -0.092061889 1.241067548 -1.540480104 4.824650345 -1.612480104
109 110 111 112 113 114
7.087721716 3.182958077 -2.057124506 -3.662716255 2.284456047 2.659004181
115 116 117 118 119 120
-2.821288158 -2.473747206 -1.858469003 -0.471170497 -2.136030819 -0.143630066
121 122 123 124 125 126
-1.413010277 -1.392924945 2.836266890 -2.129572375 1.378491601 3.616565923
127 128 129 130 131 132
2.099863464 -1.132153126 -2.700384977 0.426624377 4.136230030 -2.347235794
133 134 135 136 137 138
3.454559251 -2.523201974 -2.674494458 -1.616819905 0.327331885 2.761692848
139 140 141 142 143 144
-0.975181078 -0.391226068 -1.295333618 -1.281134606 -1.684250100 -0.845237424
145 146 147 148 149 150
3.187210613 -0.729682773 4.212883113 -0.617164215 -0.478586586 -1.200237803
151 152 153 154 155 156
-3.756971005 -4.251477652 -0.246396829 -0.770156611 -2.247228994 -0.746424778
157 158 159 160 161 162
-2.521073236 -0.347829990 -2.802999309 0.518018853 -0.540811859 7.789304843
163 164 165 166 167 168
-1.563118471 -4.213614696 0.663240395 -2.240461624 -1.339398732 -3.121688622
169 170 171 172 173 174
-1.090904285 -1.743696706 4.956004394 -0.898707753 0.344805991 6.873246332
175 176 177 178 179 180
2.778692704 -1.538808197 -1.404969802 -1.452310433 -1.604451593 1.595170412
181 182 183 184 185 186
3.499482583 -1.145109624 0.494126895 -1.161915375 -2.052410046 0.584047300
187 188 189 190 191 192
-0.362393533 0.386414937 -0.470354193 -2.662409360 -0.143184086 -2.096970706
193 194 195 196 197 198
2.862376134 4.776179981 -1.841908445 1.652963376 -2.160530390 0.999572129
199 200 201 202 203 204
-1.011479537 0.004316367 0.641720667 -1.121638248 -1.059881758 -0.421836797
205 206 207 208 209 210
1.286491093 -1.921233532 -1.166778248 -1.589749042 -1.600209565 -1.048986641
211 212 213 214 215 216
3.650308638 -1.152668305 -1.235176573 -1.172542223 3.935097660 -0.218898667
217 218 219 220 221 222
-1.121964429 6.141295580 1.929469150 -1.317831638 -1.944248016 3.857513958
223 224 225 226 227 228
0.839121441 -1.251815750 -1.377815465 2.293074449 0.110370128 -0.355163048
229 230 231 232 233 234
0.172216588 -2.139880800 -0.646868031 -0.693372770 -1.680281819 -1.228020589
235 236 237 238 239 240
-1.936125562 -1.069564501 -1.758842944 -1.293605124 -1.128362118 -1.393851941
241 242 243 244 245 246
-1.254320200 -2.188566245 -0.920633898 0.946001081 -1.054799922 -0.576392231
247 248 249 250 251 252
0.027840444 -1.255929342 -0.607058759 -0.332936504 -2.044289582 -1.239246257
253 254 255 256 257 258
0.724467997 -0.808782667 -2.021679521 -0.113064818 -0.366113583 -1.104251654
259 260 261 262 263 264
-0.191139882 -0.303865554 -1.475815377 -0.378037221 -0.347817338 -0.720137184
265 266 267 268 269 270
-1.141117223 -0.725686066 -1.249154375 -0.596571774 -0.784456833 -1.768678006
271 272 273 274 275 276
-0.898156201 -1.190983252 0.350483254 0.209729445 -0.381122531 4.568825690
277 278 279 280 281 282
2.036312644 1.538601992 1.078013385 -1.382937330 -1.383245639 -1.252858645
283 284 285 286 287 288
1.998223708 1.094949214 -1.501816260 -1.731833639 0.139010428 -0.796180459
289
-1.472286086
> postscript(file="/var/wessaorg/rcomp/tmp/63z841323610269.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 1.388542717 NA
1 1.989211329 1.388542717
2 8.171646374 1.989211329
3 -0.610261140 8.171646374
4 -0.380472177 -0.610261140
5 0.270504611 -0.380472177
6 -3.157254846 0.270504611
7 -1.578520871 -3.157254846
8 -0.699937074 -1.578520871
9 2.136569370 -0.699937074
10 -1.741043486 2.136569370
11 -3.485717423 -1.741043486
12 3.034677883 -3.485717423
13 3.815879839 3.034677883
14 -0.580102636 3.815879839
15 5.775787384 -0.580102636
16 -5.012893167 5.775787384
17 2.044820062 -5.012893167
18 -0.378709964 2.044820062
19 -1.182693677 -0.378709964
20 4.547625774 -1.182693677
21 -2.590612627 4.547625774
22 -1.805006014 -2.590612627
23 3.147470834 -1.805006014
24 3.856034872 3.147470834
25 -2.766578139 3.856034872
26 -3.496562849 -2.766578139
27 1.661810730 -3.496562849
28 -2.043673013 1.661810730
29 -2.342536466 -2.043673013
30 -1.965655352 -2.342536466
31 0.021399430 -1.965655352
32 -0.487916229 0.021399430
33 -2.362475374 -0.487916229
34 1.247862206 -2.362475374
35 -1.100770713 1.247862206
36 -0.575449799 -1.100770713
37 -3.271597893 -0.575449799
38 -3.752338811 -3.271597893
39 0.403367205 -3.752338811
40 -0.448215001 0.403367205
41 0.267491031 -0.448215001
42 -1.173519286 0.267491031
43 -1.595579022 -1.173519286
44 6.215774424 -1.595579022
45 2.537655972 6.215774424
46 -2.445651812 2.537655972
47 2.574191826 -2.445651812
48 1.523631534 2.574191826
49 -4.581010389 1.523631534
50 1.109691195 -4.581010389
51 -0.700135732 1.109691195
52 -3.230316451 -0.700135732
53 3.990942148 -3.230316451
54 -0.218096300 3.990942148
55 -2.133529110 -0.218096300
56 -0.202538633 -2.133529110
57 3.581753998 -0.202538633
58 3.360252718 3.581753998
59 1.745776147 3.360252718
60 -1.577194465 1.745776147
61 1.496231788 -1.577194465
62 -0.961099753 1.496231788
63 -0.276142635 -0.961099753
64 3.009168639 -0.276142635
65 -2.755196740 3.009168639
66 9.558052669 -2.755196740
67 4.324482738 9.558052669
68 6.544012260 4.324482738
69 3.430236066 6.544012260
70 4.361671509 3.430236066
71 -0.853282459 4.361671509
72 -1.406070480 -0.853282459
73 2.584095670 -1.406070480
74 4.193893969 2.584095670
75 0.299335950 4.193893969
76 4.354205695 0.299335950
77 8.034064356 4.354205695
78 3.460815378 8.034064356
79 3.016419632 3.460815378
80 -0.337433451 3.016419632
81 -2.194362649 -0.337433451
82 1.688869695 -2.194362649
83 0.483774477 1.688869695
84 3.103599715 0.483774477
85 0.163596683 3.103599715
86 -3.074085490 0.163596683
87 -1.049970958 -3.074085490
88 -3.067778793 -1.049970958
89 1.657028538 -3.067778793
90 -0.429671618 1.657028538
91 -0.226123286 -0.429671618
92 -2.555055392 -0.226123286
93 3.135957383 -2.555055392
94 -1.195318864 3.135957383
95 -1.960235241 -1.195318864
96 -4.556221013 -1.960235241
97 -2.340239659 -4.556221013
98 -2.237591020 -2.340239659
99 0.327684046 -2.237591020
100 3.597078518 0.327684046
101 -1.953796088 3.597078518
102 1.253287747 -1.953796088
103 -0.092061889 1.253287747
104 1.241067548 -0.092061889
105 -1.540480104 1.241067548
106 4.824650345 -1.540480104
107 -1.612480104 4.824650345
108 7.087721716 -1.612480104
109 3.182958077 7.087721716
110 -2.057124506 3.182958077
111 -3.662716255 -2.057124506
112 2.284456047 -3.662716255
113 2.659004181 2.284456047
114 -2.821288158 2.659004181
115 -2.473747206 -2.821288158
116 -1.858469003 -2.473747206
117 -0.471170497 -1.858469003
118 -2.136030819 -0.471170497
119 -0.143630066 -2.136030819
120 -1.413010277 -0.143630066
121 -1.392924945 -1.413010277
122 2.836266890 -1.392924945
123 -2.129572375 2.836266890
124 1.378491601 -2.129572375
125 3.616565923 1.378491601
126 2.099863464 3.616565923
127 -1.132153126 2.099863464
128 -2.700384977 -1.132153126
129 0.426624377 -2.700384977
130 4.136230030 0.426624377
131 -2.347235794 4.136230030
132 3.454559251 -2.347235794
133 -2.523201974 3.454559251
134 -2.674494458 -2.523201974
135 -1.616819905 -2.674494458
136 0.327331885 -1.616819905
137 2.761692848 0.327331885
138 -0.975181078 2.761692848
139 -0.391226068 -0.975181078
140 -1.295333618 -0.391226068
141 -1.281134606 -1.295333618
142 -1.684250100 -1.281134606
143 -0.845237424 -1.684250100
144 3.187210613 -0.845237424
145 -0.729682773 3.187210613
146 4.212883113 -0.729682773
147 -0.617164215 4.212883113
148 -0.478586586 -0.617164215
149 -1.200237803 -0.478586586
150 -3.756971005 -1.200237803
151 -4.251477652 -3.756971005
152 -0.246396829 -4.251477652
153 -0.770156611 -0.246396829
154 -2.247228994 -0.770156611
155 -0.746424778 -2.247228994
156 -2.521073236 -0.746424778
157 -0.347829990 -2.521073236
158 -2.802999309 -0.347829990
159 0.518018853 -2.802999309
160 -0.540811859 0.518018853
161 7.789304843 -0.540811859
162 -1.563118471 7.789304843
163 -4.213614696 -1.563118471
164 0.663240395 -4.213614696
165 -2.240461624 0.663240395
166 -1.339398732 -2.240461624
167 -3.121688622 -1.339398732
168 -1.090904285 -3.121688622
169 -1.743696706 -1.090904285
170 4.956004394 -1.743696706
171 -0.898707753 4.956004394
172 0.344805991 -0.898707753
173 6.873246332 0.344805991
174 2.778692704 6.873246332
175 -1.538808197 2.778692704
176 -1.404969802 -1.538808197
177 -1.452310433 -1.404969802
178 -1.604451593 -1.452310433
179 1.595170412 -1.604451593
180 3.499482583 1.595170412
181 -1.145109624 3.499482583
182 0.494126895 -1.145109624
183 -1.161915375 0.494126895
184 -2.052410046 -1.161915375
185 0.584047300 -2.052410046
186 -0.362393533 0.584047300
187 0.386414937 -0.362393533
188 -0.470354193 0.386414937
189 -2.662409360 -0.470354193
190 -0.143184086 -2.662409360
191 -2.096970706 -0.143184086
192 2.862376134 -2.096970706
193 4.776179981 2.862376134
194 -1.841908445 4.776179981
195 1.652963376 -1.841908445
196 -2.160530390 1.652963376
197 0.999572129 -2.160530390
198 -1.011479537 0.999572129
199 0.004316367 -1.011479537
200 0.641720667 0.004316367
201 -1.121638248 0.641720667
202 -1.059881758 -1.121638248
203 -0.421836797 -1.059881758
204 1.286491093 -0.421836797
205 -1.921233532 1.286491093
206 -1.166778248 -1.921233532
207 -1.589749042 -1.166778248
208 -1.600209565 -1.589749042
209 -1.048986641 -1.600209565
210 3.650308638 -1.048986641
211 -1.152668305 3.650308638
212 -1.235176573 -1.152668305
213 -1.172542223 -1.235176573
214 3.935097660 -1.172542223
215 -0.218898667 3.935097660
216 -1.121964429 -0.218898667
217 6.141295580 -1.121964429
218 1.929469150 6.141295580
219 -1.317831638 1.929469150
220 -1.944248016 -1.317831638
221 3.857513958 -1.944248016
222 0.839121441 3.857513958
223 -1.251815750 0.839121441
224 -1.377815465 -1.251815750
225 2.293074449 -1.377815465
226 0.110370128 2.293074449
227 -0.355163048 0.110370128
228 0.172216588 -0.355163048
229 -2.139880800 0.172216588
230 -0.646868031 -2.139880800
231 -0.693372770 -0.646868031
232 -1.680281819 -0.693372770
233 -1.228020589 -1.680281819
234 -1.936125562 -1.228020589
235 -1.069564501 -1.936125562
236 -1.758842944 -1.069564501
237 -1.293605124 -1.758842944
238 -1.128362118 -1.293605124
239 -1.393851941 -1.128362118
240 -1.254320200 -1.393851941
241 -2.188566245 -1.254320200
242 -0.920633898 -2.188566245
243 0.946001081 -0.920633898
244 -1.054799922 0.946001081
245 -0.576392231 -1.054799922
246 0.027840444 -0.576392231
247 -1.255929342 0.027840444
248 -0.607058759 -1.255929342
249 -0.332936504 -0.607058759
250 -2.044289582 -0.332936504
251 -1.239246257 -2.044289582
252 0.724467997 -1.239246257
253 -0.808782667 0.724467997
254 -2.021679521 -0.808782667
255 -0.113064818 -2.021679521
256 -0.366113583 -0.113064818
257 -1.104251654 -0.366113583
258 -0.191139882 -1.104251654
259 -0.303865554 -0.191139882
260 -1.475815377 -0.303865554
261 -0.378037221 -1.475815377
262 -0.347817338 -0.378037221
263 -0.720137184 -0.347817338
264 -1.141117223 -0.720137184
265 -0.725686066 -1.141117223
266 -1.249154375 -0.725686066
267 -0.596571774 -1.249154375
268 -0.784456833 -0.596571774
269 -1.768678006 -0.784456833
270 -0.898156201 -1.768678006
271 -1.190983252 -0.898156201
272 0.350483254 -1.190983252
273 0.209729445 0.350483254
274 -0.381122531 0.209729445
275 4.568825690 -0.381122531
276 2.036312644 4.568825690
277 1.538601992 2.036312644
278 1.078013385 1.538601992
279 -1.382937330 1.078013385
280 -1.383245639 -1.382937330
281 -1.252858645 -1.383245639
282 1.998223708 -1.252858645
283 1.094949214 1.998223708
284 -1.501816260 1.094949214
285 -1.731833639 -1.501816260
286 0.139010428 -1.731833639
287 -0.796180459 0.139010428
288 -1.472286086 -0.796180459
289 NA -1.472286086
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.989211329 1.388542717
[2,] 8.171646374 1.989211329
[3,] -0.610261140 8.171646374
[4,] -0.380472177 -0.610261140
[5,] 0.270504611 -0.380472177
[6,] -3.157254846 0.270504611
[7,] -1.578520871 -3.157254846
[8,] -0.699937074 -1.578520871
[9,] 2.136569370 -0.699937074
[10,] -1.741043486 2.136569370
[11,] -3.485717423 -1.741043486
[12,] 3.034677883 -3.485717423
[13,] 3.815879839 3.034677883
[14,] -0.580102636 3.815879839
[15,] 5.775787384 -0.580102636
[16,] -5.012893167 5.775787384
[17,] 2.044820062 -5.012893167
[18,] -0.378709964 2.044820062
[19,] -1.182693677 -0.378709964
[20,] 4.547625774 -1.182693677
[21,] -2.590612627 4.547625774
[22,] -1.805006014 -2.590612627
[23,] 3.147470834 -1.805006014
[24,] 3.856034872 3.147470834
[25,] -2.766578139 3.856034872
[26,] -3.496562849 -2.766578139
[27,] 1.661810730 -3.496562849
[28,] -2.043673013 1.661810730
[29,] -2.342536466 -2.043673013
[30,] -1.965655352 -2.342536466
[31,] 0.021399430 -1.965655352
[32,] -0.487916229 0.021399430
[33,] -2.362475374 -0.487916229
[34,] 1.247862206 -2.362475374
[35,] -1.100770713 1.247862206
[36,] -0.575449799 -1.100770713
[37,] -3.271597893 -0.575449799
[38,] -3.752338811 -3.271597893
[39,] 0.403367205 -3.752338811
[40,] -0.448215001 0.403367205
[41,] 0.267491031 -0.448215001
[42,] -1.173519286 0.267491031
[43,] -1.595579022 -1.173519286
[44,] 6.215774424 -1.595579022
[45,] 2.537655972 6.215774424
[46,] -2.445651812 2.537655972
[47,] 2.574191826 -2.445651812
[48,] 1.523631534 2.574191826
[49,] -4.581010389 1.523631534
[50,] 1.109691195 -4.581010389
[51,] -0.700135732 1.109691195
[52,] -3.230316451 -0.700135732
[53,] 3.990942148 -3.230316451
[54,] -0.218096300 3.990942148
[55,] -2.133529110 -0.218096300
[56,] -0.202538633 -2.133529110
[57,] 3.581753998 -0.202538633
[58,] 3.360252718 3.581753998
[59,] 1.745776147 3.360252718
[60,] -1.577194465 1.745776147
[61,] 1.496231788 -1.577194465
[62,] -0.961099753 1.496231788
[63,] -0.276142635 -0.961099753
[64,] 3.009168639 -0.276142635
[65,] -2.755196740 3.009168639
[66,] 9.558052669 -2.755196740
[67,] 4.324482738 9.558052669
[68,] 6.544012260 4.324482738
[69,] 3.430236066 6.544012260
[70,] 4.361671509 3.430236066
[71,] -0.853282459 4.361671509
[72,] -1.406070480 -0.853282459
[73,] 2.584095670 -1.406070480
[74,] 4.193893969 2.584095670
[75,] 0.299335950 4.193893969
[76,] 4.354205695 0.299335950
[77,] 8.034064356 4.354205695
[78,] 3.460815378 8.034064356
[79,] 3.016419632 3.460815378
[80,] -0.337433451 3.016419632
[81,] -2.194362649 -0.337433451
[82,] 1.688869695 -2.194362649
[83,] 0.483774477 1.688869695
[84,] 3.103599715 0.483774477
[85,] 0.163596683 3.103599715
[86,] -3.074085490 0.163596683
[87,] -1.049970958 -3.074085490
[88,] -3.067778793 -1.049970958
[89,] 1.657028538 -3.067778793
[90,] -0.429671618 1.657028538
[91,] -0.226123286 -0.429671618
[92,] -2.555055392 -0.226123286
[93,] 3.135957383 -2.555055392
[94,] -1.195318864 3.135957383
[95,] -1.960235241 -1.195318864
[96,] -4.556221013 -1.960235241
[97,] -2.340239659 -4.556221013
[98,] -2.237591020 -2.340239659
[99,] 0.327684046 -2.237591020
[100,] 3.597078518 0.327684046
[101,] -1.953796088 3.597078518
[102,] 1.253287747 -1.953796088
[103,] -0.092061889 1.253287747
[104,] 1.241067548 -0.092061889
[105,] -1.540480104 1.241067548
[106,] 4.824650345 -1.540480104
[107,] -1.612480104 4.824650345
[108,] 7.087721716 -1.612480104
[109,] 3.182958077 7.087721716
[110,] -2.057124506 3.182958077
[111,] -3.662716255 -2.057124506
[112,] 2.284456047 -3.662716255
[113,] 2.659004181 2.284456047
[114,] -2.821288158 2.659004181
[115,] -2.473747206 -2.821288158
[116,] -1.858469003 -2.473747206
[117,] -0.471170497 -1.858469003
[118,] -2.136030819 -0.471170497
[119,] -0.143630066 -2.136030819
[120,] -1.413010277 -0.143630066
[121,] -1.392924945 -1.413010277
[122,] 2.836266890 -1.392924945
[123,] -2.129572375 2.836266890
[124,] 1.378491601 -2.129572375
[125,] 3.616565923 1.378491601
[126,] 2.099863464 3.616565923
[127,] -1.132153126 2.099863464
[128,] -2.700384977 -1.132153126
[129,] 0.426624377 -2.700384977
[130,] 4.136230030 0.426624377
[131,] -2.347235794 4.136230030
[132,] 3.454559251 -2.347235794
[133,] -2.523201974 3.454559251
[134,] -2.674494458 -2.523201974
[135,] -1.616819905 -2.674494458
[136,] 0.327331885 -1.616819905
[137,] 2.761692848 0.327331885
[138,] -0.975181078 2.761692848
[139,] -0.391226068 -0.975181078
[140,] -1.295333618 -0.391226068
[141,] -1.281134606 -1.295333618
[142,] -1.684250100 -1.281134606
[143,] -0.845237424 -1.684250100
[144,] 3.187210613 -0.845237424
[145,] -0.729682773 3.187210613
[146,] 4.212883113 -0.729682773
[147,] -0.617164215 4.212883113
[148,] -0.478586586 -0.617164215
[149,] -1.200237803 -0.478586586
[150,] -3.756971005 -1.200237803
[151,] -4.251477652 -3.756971005
[152,] -0.246396829 -4.251477652
[153,] -0.770156611 -0.246396829
[154,] -2.247228994 -0.770156611
[155,] -0.746424778 -2.247228994
[156,] -2.521073236 -0.746424778
[157,] -0.347829990 -2.521073236
[158,] -2.802999309 -0.347829990
[159,] 0.518018853 -2.802999309
[160,] -0.540811859 0.518018853
[161,] 7.789304843 -0.540811859
[162,] -1.563118471 7.789304843
[163,] -4.213614696 -1.563118471
[164,] 0.663240395 -4.213614696
[165,] -2.240461624 0.663240395
[166,] -1.339398732 -2.240461624
[167,] -3.121688622 -1.339398732
[168,] -1.090904285 -3.121688622
[169,] -1.743696706 -1.090904285
[170,] 4.956004394 -1.743696706
[171,] -0.898707753 4.956004394
[172,] 0.344805991 -0.898707753
[173,] 6.873246332 0.344805991
[174,] 2.778692704 6.873246332
[175,] -1.538808197 2.778692704
[176,] -1.404969802 -1.538808197
[177,] -1.452310433 -1.404969802
[178,] -1.604451593 -1.452310433
[179,] 1.595170412 -1.604451593
[180,] 3.499482583 1.595170412
[181,] -1.145109624 3.499482583
[182,] 0.494126895 -1.145109624
[183,] -1.161915375 0.494126895
[184,] -2.052410046 -1.161915375
[185,] 0.584047300 -2.052410046
[186,] -0.362393533 0.584047300
[187,] 0.386414937 -0.362393533
[188,] -0.470354193 0.386414937
[189,] -2.662409360 -0.470354193
[190,] -0.143184086 -2.662409360
[191,] -2.096970706 -0.143184086
[192,] 2.862376134 -2.096970706
[193,] 4.776179981 2.862376134
[194,] -1.841908445 4.776179981
[195,] 1.652963376 -1.841908445
[196,] -2.160530390 1.652963376
[197,] 0.999572129 -2.160530390
[198,] -1.011479537 0.999572129
[199,] 0.004316367 -1.011479537
[200,] 0.641720667 0.004316367
[201,] -1.121638248 0.641720667
[202,] -1.059881758 -1.121638248
[203,] -0.421836797 -1.059881758
[204,] 1.286491093 -0.421836797
[205,] -1.921233532 1.286491093
[206,] -1.166778248 -1.921233532
[207,] -1.589749042 -1.166778248
[208,] -1.600209565 -1.589749042
[209,] -1.048986641 -1.600209565
[210,] 3.650308638 -1.048986641
[211,] -1.152668305 3.650308638
[212,] -1.235176573 -1.152668305
[213,] -1.172542223 -1.235176573
[214,] 3.935097660 -1.172542223
[215,] -0.218898667 3.935097660
[216,] -1.121964429 -0.218898667
[217,] 6.141295580 -1.121964429
[218,] 1.929469150 6.141295580
[219,] -1.317831638 1.929469150
[220,] -1.944248016 -1.317831638
[221,] 3.857513958 -1.944248016
[222,] 0.839121441 3.857513958
[223,] -1.251815750 0.839121441
[224,] -1.377815465 -1.251815750
[225,] 2.293074449 -1.377815465
[226,] 0.110370128 2.293074449
[227,] -0.355163048 0.110370128
[228,] 0.172216588 -0.355163048
[229,] -2.139880800 0.172216588
[230,] -0.646868031 -2.139880800
[231,] -0.693372770 -0.646868031
[232,] -1.680281819 -0.693372770
[233,] -1.228020589 -1.680281819
[234,] -1.936125562 -1.228020589
[235,] -1.069564501 -1.936125562
[236,] -1.758842944 -1.069564501
[237,] -1.293605124 -1.758842944
[238,] -1.128362118 -1.293605124
[239,] -1.393851941 -1.128362118
[240,] -1.254320200 -1.393851941
[241,] -2.188566245 -1.254320200
[242,] -0.920633898 -2.188566245
[243,] 0.946001081 -0.920633898
[244,] -1.054799922 0.946001081
[245,] -0.576392231 -1.054799922
[246,] 0.027840444 -0.576392231
[247,] -1.255929342 0.027840444
[248,] -0.607058759 -1.255929342
[249,] -0.332936504 -0.607058759
[250,] -2.044289582 -0.332936504
[251,] -1.239246257 -2.044289582
[252,] 0.724467997 -1.239246257
[253,] -0.808782667 0.724467997
[254,] -2.021679521 -0.808782667
[255,] -0.113064818 -2.021679521
[256,] -0.366113583 -0.113064818
[257,] -1.104251654 -0.366113583
[258,] -0.191139882 -1.104251654
[259,] -0.303865554 -0.191139882
[260,] -1.475815377 -0.303865554
[261,] -0.378037221 -1.475815377
[262,] -0.347817338 -0.378037221
[263,] -0.720137184 -0.347817338
[264,] -1.141117223 -0.720137184
[265,] -0.725686066 -1.141117223
[266,] -1.249154375 -0.725686066
[267,] -0.596571774 -1.249154375
[268,] -0.784456833 -0.596571774
[269,] -1.768678006 -0.784456833
[270,] -0.898156201 -1.768678006
[271,] -1.190983252 -0.898156201
[272,] 0.350483254 -1.190983252
[273,] 0.209729445 0.350483254
[274,] -0.381122531 0.209729445
[275,] 4.568825690 -0.381122531
[276,] 2.036312644 4.568825690
[277,] 1.538601992 2.036312644
[278,] 1.078013385 1.538601992
[279,] -1.382937330 1.078013385
[280,] -1.383245639 -1.382937330
[281,] -1.252858645 -1.383245639
[282,] 1.998223708 -1.252858645
[283,] 1.094949214 1.998223708
[284,] -1.501816260 1.094949214
[285,] -1.731833639 -1.501816260
[286,] 0.139010428 -1.731833639
[287,] -0.796180459 0.139010428
[288,] -1.472286086 -0.796180459
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.989211329 1.388542717
2 8.171646374 1.989211329
3 -0.610261140 8.171646374
4 -0.380472177 -0.610261140
5 0.270504611 -0.380472177
6 -3.157254846 0.270504611
7 -1.578520871 -3.157254846
8 -0.699937074 -1.578520871
9 2.136569370 -0.699937074
10 -1.741043486 2.136569370
11 -3.485717423 -1.741043486
12 3.034677883 -3.485717423
13 3.815879839 3.034677883
14 -0.580102636 3.815879839
15 5.775787384 -0.580102636
16 -5.012893167 5.775787384
17 2.044820062 -5.012893167
18 -0.378709964 2.044820062
19 -1.182693677 -0.378709964
20 4.547625774 -1.182693677
21 -2.590612627 4.547625774
22 -1.805006014 -2.590612627
23 3.147470834 -1.805006014
24 3.856034872 3.147470834
25 -2.766578139 3.856034872
26 -3.496562849 -2.766578139
27 1.661810730 -3.496562849
28 -2.043673013 1.661810730
29 -2.342536466 -2.043673013
30 -1.965655352 -2.342536466
31 0.021399430 -1.965655352
32 -0.487916229 0.021399430
33 -2.362475374 -0.487916229
34 1.247862206 -2.362475374
35 -1.100770713 1.247862206
36 -0.575449799 -1.100770713
37 -3.271597893 -0.575449799
38 -3.752338811 -3.271597893
39 0.403367205 -3.752338811
40 -0.448215001 0.403367205
41 0.267491031 -0.448215001
42 -1.173519286 0.267491031
43 -1.595579022 -1.173519286
44 6.215774424 -1.595579022
45 2.537655972 6.215774424
46 -2.445651812 2.537655972
47 2.574191826 -2.445651812
48 1.523631534 2.574191826
49 -4.581010389 1.523631534
50 1.109691195 -4.581010389
51 -0.700135732 1.109691195
52 -3.230316451 -0.700135732
53 3.990942148 -3.230316451
54 -0.218096300 3.990942148
55 -2.133529110 -0.218096300
56 -0.202538633 -2.133529110
57 3.581753998 -0.202538633
58 3.360252718 3.581753998
59 1.745776147 3.360252718
60 -1.577194465 1.745776147
61 1.496231788 -1.577194465
62 -0.961099753 1.496231788
63 -0.276142635 -0.961099753
64 3.009168639 -0.276142635
65 -2.755196740 3.009168639
66 9.558052669 -2.755196740
67 4.324482738 9.558052669
68 6.544012260 4.324482738
69 3.430236066 6.544012260
70 4.361671509 3.430236066
71 -0.853282459 4.361671509
72 -1.406070480 -0.853282459
73 2.584095670 -1.406070480
74 4.193893969 2.584095670
75 0.299335950 4.193893969
76 4.354205695 0.299335950
77 8.034064356 4.354205695
78 3.460815378 8.034064356
79 3.016419632 3.460815378
80 -0.337433451 3.016419632
81 -2.194362649 -0.337433451
82 1.688869695 -2.194362649
83 0.483774477 1.688869695
84 3.103599715 0.483774477
85 0.163596683 3.103599715
86 -3.074085490 0.163596683
87 -1.049970958 -3.074085490
88 -3.067778793 -1.049970958
89 1.657028538 -3.067778793
90 -0.429671618 1.657028538
91 -0.226123286 -0.429671618
92 -2.555055392 -0.226123286
93 3.135957383 -2.555055392
94 -1.195318864 3.135957383
95 -1.960235241 -1.195318864
96 -4.556221013 -1.960235241
97 -2.340239659 -4.556221013
98 -2.237591020 -2.340239659
99 0.327684046 -2.237591020
100 3.597078518 0.327684046
101 -1.953796088 3.597078518
102 1.253287747 -1.953796088
103 -0.092061889 1.253287747
104 1.241067548 -0.092061889
105 -1.540480104 1.241067548
106 4.824650345 -1.540480104
107 -1.612480104 4.824650345
108 7.087721716 -1.612480104
109 3.182958077 7.087721716
110 -2.057124506 3.182958077
111 -3.662716255 -2.057124506
112 2.284456047 -3.662716255
113 2.659004181 2.284456047
114 -2.821288158 2.659004181
115 -2.473747206 -2.821288158
116 -1.858469003 -2.473747206
117 -0.471170497 -1.858469003
118 -2.136030819 -0.471170497
119 -0.143630066 -2.136030819
120 -1.413010277 -0.143630066
121 -1.392924945 -1.413010277
122 2.836266890 -1.392924945
123 -2.129572375 2.836266890
124 1.378491601 -2.129572375
125 3.616565923 1.378491601
126 2.099863464 3.616565923
127 -1.132153126 2.099863464
128 -2.700384977 -1.132153126
129 0.426624377 -2.700384977
130 4.136230030 0.426624377
131 -2.347235794 4.136230030
132 3.454559251 -2.347235794
133 -2.523201974 3.454559251
134 -2.674494458 -2.523201974
135 -1.616819905 -2.674494458
136 0.327331885 -1.616819905
137 2.761692848 0.327331885
138 -0.975181078 2.761692848
139 -0.391226068 -0.975181078
140 -1.295333618 -0.391226068
141 -1.281134606 -1.295333618
142 -1.684250100 -1.281134606
143 -0.845237424 -1.684250100
144 3.187210613 -0.845237424
145 -0.729682773 3.187210613
146 4.212883113 -0.729682773
147 -0.617164215 4.212883113
148 -0.478586586 -0.617164215
149 -1.200237803 -0.478586586
150 -3.756971005 -1.200237803
151 -4.251477652 -3.756971005
152 -0.246396829 -4.251477652
153 -0.770156611 -0.246396829
154 -2.247228994 -0.770156611
155 -0.746424778 -2.247228994
156 -2.521073236 -0.746424778
157 -0.347829990 -2.521073236
158 -2.802999309 -0.347829990
159 0.518018853 -2.802999309
160 -0.540811859 0.518018853
161 7.789304843 -0.540811859
162 -1.563118471 7.789304843
163 -4.213614696 -1.563118471
164 0.663240395 -4.213614696
165 -2.240461624 0.663240395
166 -1.339398732 -2.240461624
167 -3.121688622 -1.339398732
168 -1.090904285 -3.121688622
169 -1.743696706 -1.090904285
170 4.956004394 -1.743696706
171 -0.898707753 4.956004394
172 0.344805991 -0.898707753
173 6.873246332 0.344805991
174 2.778692704 6.873246332
175 -1.538808197 2.778692704
176 -1.404969802 -1.538808197
177 -1.452310433 -1.404969802
178 -1.604451593 -1.452310433
179 1.595170412 -1.604451593
180 3.499482583 1.595170412
181 -1.145109624 3.499482583
182 0.494126895 -1.145109624
183 -1.161915375 0.494126895
184 -2.052410046 -1.161915375
185 0.584047300 -2.052410046
186 -0.362393533 0.584047300
187 0.386414937 -0.362393533
188 -0.470354193 0.386414937
189 -2.662409360 -0.470354193
190 -0.143184086 -2.662409360
191 -2.096970706 -0.143184086
192 2.862376134 -2.096970706
193 4.776179981 2.862376134
194 -1.841908445 4.776179981
195 1.652963376 -1.841908445
196 -2.160530390 1.652963376
197 0.999572129 -2.160530390
198 -1.011479537 0.999572129
199 0.004316367 -1.011479537
200 0.641720667 0.004316367
201 -1.121638248 0.641720667
202 -1.059881758 -1.121638248
203 -0.421836797 -1.059881758
204 1.286491093 -0.421836797
205 -1.921233532 1.286491093
206 -1.166778248 -1.921233532
207 -1.589749042 -1.166778248
208 -1.600209565 -1.589749042
209 -1.048986641 -1.600209565
210 3.650308638 -1.048986641
211 -1.152668305 3.650308638
212 -1.235176573 -1.152668305
213 -1.172542223 -1.235176573
214 3.935097660 -1.172542223
215 -0.218898667 3.935097660
216 -1.121964429 -0.218898667
217 6.141295580 -1.121964429
218 1.929469150 6.141295580
219 -1.317831638 1.929469150
220 -1.944248016 -1.317831638
221 3.857513958 -1.944248016
222 0.839121441 3.857513958
223 -1.251815750 0.839121441
224 -1.377815465 -1.251815750
225 2.293074449 -1.377815465
226 0.110370128 2.293074449
227 -0.355163048 0.110370128
228 0.172216588 -0.355163048
229 -2.139880800 0.172216588
230 -0.646868031 -2.139880800
231 -0.693372770 -0.646868031
232 -1.680281819 -0.693372770
233 -1.228020589 -1.680281819
234 -1.936125562 -1.228020589
235 -1.069564501 -1.936125562
236 -1.758842944 -1.069564501
237 -1.293605124 -1.758842944
238 -1.128362118 -1.293605124
239 -1.393851941 -1.128362118
240 -1.254320200 -1.393851941
241 -2.188566245 -1.254320200
242 -0.920633898 -2.188566245
243 0.946001081 -0.920633898
244 -1.054799922 0.946001081
245 -0.576392231 -1.054799922
246 0.027840444 -0.576392231
247 -1.255929342 0.027840444
248 -0.607058759 -1.255929342
249 -0.332936504 -0.607058759
250 -2.044289582 -0.332936504
251 -1.239246257 -2.044289582
252 0.724467997 -1.239246257
253 -0.808782667 0.724467997
254 -2.021679521 -0.808782667
255 -0.113064818 -2.021679521
256 -0.366113583 -0.113064818
257 -1.104251654 -0.366113583
258 -0.191139882 -1.104251654
259 -0.303865554 -0.191139882
260 -1.475815377 -0.303865554
261 -0.378037221 -1.475815377
262 -0.347817338 -0.378037221
263 -0.720137184 -0.347817338
264 -1.141117223 -0.720137184
265 -0.725686066 -1.141117223
266 -1.249154375 -0.725686066
267 -0.596571774 -1.249154375
268 -0.784456833 -0.596571774
269 -1.768678006 -0.784456833
270 -0.898156201 -1.768678006
271 -1.190983252 -0.898156201
272 0.350483254 -1.190983252
273 0.209729445 0.350483254
274 -0.381122531 0.209729445
275 4.568825690 -0.381122531
276 2.036312644 4.568825690
277 1.538601992 2.036312644
278 1.078013385 1.538601992
279 -1.382937330 1.078013385
280 -1.383245639 -1.382937330
281 -1.252858645 -1.383245639
282 1.998223708 -1.252858645
283 1.094949214 1.998223708
284 -1.501816260 1.094949214
285 -1.731833639 -1.501816260
286 0.139010428 -1.731833639
287 -0.796180459 0.139010428
288 -1.472286086 -0.796180459
> 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/76fbm1323610269.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/86cty1323610269.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/99tr91323610269.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/10zpar1323610269.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/114h5f1323610269.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12k1l71323610269.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13m1fq1323610269.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/140nu61323610269.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/1535t91323610269.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/16in7r1323610269.tab")
+ }
>
> try(system("convert tmp/1sxzs1323610269.ps tmp/1sxzs1323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z6y11323610269.ps tmp/2z6y11323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rf6e1323610269.ps tmp/3rf6e1323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a3v61323610269.ps tmp/4a3v61323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mlf81323610269.ps tmp/5mlf81323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/63z841323610269.ps tmp/63z841323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/76fbm1323610269.ps tmp/76fbm1323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/86cty1323610269.ps tmp/86cty1323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/99tr91323610269.ps tmp/99tr91323610269.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zpar1323610269.ps tmp/10zpar1323610269.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.402 0.550 9.457