R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,1
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+ ,467
+ ,0
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+ ,8
+ ,30837
+ ,178
+ ,0
+ ,15
+ ,4
+ ,26
+ ,31706
+ ,175
+ ,0
+ ,32
+ ,25
+ ,27
+ ,89806
+ ,299
+ ,0
+ ,62
+ ,40
+ ,13
+ ,62088
+ ,154
+ ,1
+ ,58
+ ,38
+ ,16
+ ,40151
+ ,106
+ ,0
+ ,36
+ ,19
+ ,2
+ ,27634
+ ,189
+ ,0
+ ,59
+ ,17
+ ,42
+ ,76990
+ ,194
+ ,0
+ ,68
+ ,67
+ ,5
+ ,37460
+ ,135
+ ,0
+ ,21
+ ,14
+ ,37
+ ,54157
+ ,201
+ ,0
+ ,55
+ ,30
+ ,17
+ ,49862
+ ,207
+ ,0
+ ,54
+ ,54
+ ,38
+ ,84337
+ ,280
+ ,0
+ ,55
+ ,35
+ ,37
+ ,64175
+ ,260
+ ,0
+ ,72
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+ ,29
+ ,59382
+ ,227
+ ,0
+ ,41
+ ,24
+ ,32
+ ,119308
+ ,239
+ ,0
+ ,61
+ ,58
+ ,35
+ ,76702
+ ,333
+ ,0
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+ ,17
+ ,103425
+ ,428
+ ,1
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+ ,46
+ ,20
+ ,70344
+ ,230
+ ,0
+ ,64
+ ,61
+ ,7
+ ,43410
+ ,292
+ ,0
+ ,3
+ ,3
+ ,46
+ ,104838
+ ,350
+ ,1
+ ,63
+ ,52
+ ,24
+ ,62215
+ ,186
+ ,0
+ ,40
+ ,25
+ ,40
+ ,69304
+ ,326
+ ,6
+ ,69
+ ,40
+ ,3
+ ,53117
+ ,155
+ ,3
+ ,48
+ ,32
+ ,10
+ ,19764
+ ,75
+ ,1
+ ,8
+ ,4
+ ,37
+ ,86680
+ ,361
+ ,2
+ ,52
+ ,49
+ ,17
+ ,84105
+ ,261
+ ,0
+ ,66
+ ,63
+ ,28
+ ,77945
+ ,299
+ ,0
+ ,76
+ ,67
+ ,19
+ ,89113
+ ,300
+ ,0
+ ,43
+ ,32
+ ,29
+ ,91005
+ ,450
+ ,3
+ ,39
+ ,23
+ ,8
+ ,40248
+ ,183
+ ,1
+ ,14
+ ,7
+ ,10
+ ,64187
+ ,238
+ ,0
+ ,61
+ ,54
+ ,15
+ ,50857
+ ,165
+ ,0
+ ,71
+ ,37
+ ,15
+ ,56613
+ ,234
+ ,1
+ ,44
+ ,35
+ ,28
+ ,62792
+ ,176
+ ,0
+ ,60
+ ,51
+ ,17
+ ,72535
+ ,329
+ ,0
+ ,64
+ ,39)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('blogged_computations'
+ ,'time_in_rfc'
+ ,'compendium_views_info'
+ ,'shared_compendiums'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120')
+ ,1:289))
> y <- array(NA,dim=c(6,289),dimnames=list(c('blogged_computations','time_in_rfc','compendium_views_info','shared_compendiums','feedback_messages_p1','feedback_messages_p120'),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'
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
blogged_computations time_in_rfc compendium_views_info shared_compendiums
1 79 210907 396 3
2 58 120982 297 4
3 60 176508 559 12
4 108 179321 967 2
5 49 123185 270 1
6 0 52746 143 3
7 121 385534 1562 0
8 1 33170 109 0
9 20 101645 371 0
10 43 149061 656 5
11 69 165446 511 0
12 78 237213 655 0
13 86 173326 465 7
14 44 133131 525 7
15 104 258873 885 3
16 63 180083 497 9
17 158 324799 1436 0
18 102 230964 612 4
19 77 236785 865 3
20 82 135473 385 0
21 115 202925 567 7
22 101 215147 639 0
23 80 344297 963 1
24 50 153935 398 5
25 83 132943 410 7
26 123 174724 966 0
27 73 174415 801 0
28 81 225548 892 5
29 105 223632 513 0
30 47 124817 469 0
31 105 221698 683 0
32 94 210767 643 3
33 44 170266 535 4
34 114 260561 625 1
35 38 84853 264 4
36 107 294424 992 2
37 30 101011 238 0
38 71 215641 818 0
39 84 325107 937 0
40 0 7176 70 0
41 59 167542 507 2
42 33 106408 260 1
43 42 96560 503 0
44 96 265769 927 2
45 106 269651 1269 10
46 56 149112 537 6
47 57 175824 910 0
48 59 152871 532 5
49 39 111665 345 4
50 34 116408 918 1
51 76 362301 1635 2
52 20 78800 330 2
53 91 183167 557 0
54 115 277965 1178 8
55 85 150629 740 3
56 76 168809 452 0
57 8 24188 218 0
58 79 329267 764 8
59 21 65029 255 5
60 30 101097 454 3
61 76 218946 866 1
62 101 244052 574 5
63 94 341570 1276 1
64 27 103597 379 1
65 92 233328 825 5
66 123 256462 798 0
67 75 206161 663 12
68 128 311473 1069 8
69 105 235800 921 8
70 55 177939 858 8
71 56 207176 711 8
72 41 196553 503 2
73 72 174184 382 0
74 67 143246 464 5
75 75 187559 717 8
76 114 187681 690 2
77 118 119016 462 5
78 77 182192 657 12
79 22 73566 385 6
80 66 194979 577 7
81 69 167488 619 2
82 105 143756 479 0
83 116 275541 817 4
84 88 243199 752 3
85 73 182999 430 6
86 99 135649 451 2
87 62 152299 537 0
88 53 120221 519 1
89 118 346485 1000 0
90 30 145790 637 5
91 100 193339 465 2
92 49 80953 437 0
93 24 122774 711 0
94 67 130585 299 5
95 46 112611 248 0
96 57 286468 1162 1
97 75 241066 714 0
98 135 148446 905 1
99 68 204713 649 1
100 124 182079 512 2
101 33 140344 472 6
102 98 220516 905 1
103 58 243060 786 4
104 68 162765 489 2
105 81 182613 479 3
106 131 232138 617 0
107 110 265318 925 10
108 37 85574 351 0
109 130 310839 1144 9
110 93 225060 669 7
111 118 232317 707 0
112 39 144966 458 0
113 13 43287 214 4
114 74 155754 599 4
115 81 164709 572 0
116 109 201940 897 0
117 151 235454 819 0
118 51 220801 720 1
119 28 99466 273 0
120 40 92661 508 1
121 56 133328 506 0
122 27 61361 451 0
123 37 125930 699 4
124 83 100750 407 0
125 54 224549 465 4
126 27 82316 245 4
127 28 102010 370 3
128 59 101523 316 0
129 133 243511 603 0
130 12 22938 154 0
131 0 41566 229 5
132 106 152474 577 0
133 23 61857 192 4
134 44 99923 617 0
135 71 132487 411 0
136 116 317394 975 1
137 4 21054 146 0
138 62 209641 705 5
139 12 22648 184 0
140 18 31414 200 0
141 14 46698 274 0
142 60 131698 502 0
143 7 91735 382 0
144 98 244749 964 2
145 64 184510 537 7
146 29 79863 438 1
147 32 128423 369 8
148 25 97839 417 2
149 16 38214 276 0
150 48 151101 514 2
151 100 272458 822 0
152 46 172494 389 0
153 45 108043 466 1
154 129 328107 1255 3
155 130 250579 694 0
156 136 351067 1024 3
157 59 158015 400 0
158 25 98866 397 0
159 32 85439 350 0
160 63 229242 719 4
161 95 351619 1277 4
162 14 84207 356 11
163 36 120445 457 0
164 113 324598 1402 0
165 47 131069 600 4
166 92 204271 480 0
167 70 165543 595 1
168 19 141722 436 0
169 50 116048 230 0
170 41 250047 651 0
171 91 299775 1367 9
172 111 195838 564 1
173 41 173260 716 3
174 120 254488 747 10
175 135 104389 467 5
176 27 136084 671 0
177 87 199476 861 2
178 25 92499 319 0
179 131 224330 612 1
180 45 135781 433 2
181 29 74408 434 4
182 58 81240 503 0
183 4 14688 85 0
184 47 181633 564 2
185 109 271856 824 1
186 7 7199 74 0
187 12 46660 259 0
188 0 17547 69 0
189 37 133368 535 1
190 37 95227 239 0
191 46 152601 438 2
192 15 98146 459 0
193 42 79619 426 3
194 7 59194 288 6
195 54 139942 498 0
196 54 118612 454 2
197 14 72880 376 0
198 16 65475 225 2
199 33 99643 555 1
200 32 71965 252 1
201 21 77272 208 2
202 15 49289 130 1
203 38 135131 481 0
204 22 108446 389 1
205 28 89746 565 3
206 10 44296 173 0
207 31 77648 278 0
208 32 181528 609 0
209 32 134019 422 0
210 43 124064 445 1
211 27 92630 387 4
212 37 121848 339 0
213 20 52915 181 0
214 32 81872 245 0
215 0 58981 384 7
216 5 53515 212 2
217 26 60812 399 0
218 10 56375 229 7
219 27 65490 224 3
220 11 80949 203 0
221 29 76302 333 0
222 25 104011 384 6
223 55 98104 636 2
224 23 67989 185 0
225 5 30989 93 0
226 43 135458 581 3
227 23 73504 248 0
228 34 63123 304 1
229 36 61254 344 1
230 35 74914 407 0
231 0 31774 170 1
232 37 81437 312 0
233 28 87186 507 0
234 16 50090 224 0
235 26 65745 340 0
236 38 56653 168 0
237 23 158399 443 0
238 22 46455 204 0
239 30 73624 367 0
240 16 38395 210 0
241 18 91899 335 0
242 28 139526 364 0
243 32 52164 178 0
244 21 51567 206 2
245 23 70551 279 0
246 29 84856 387 1
247 50 102538 490 1
248 12 86678 238 0
249 21 85709 343 0
250 18 34662 232 0
251 27 150580 530 0
252 41 99611 291 0
253 13 19349 67 0
254 12 99373 397 1
255 21 86230 467 0
256 8 30837 178 0
257 26 31706 175 0
258 27 89806 299 0
259 13 62088 154 1
260 16 40151 106 0
261 2 27634 189 0
262 42 76990 194 0
263 5 37460 135 0
264 37 54157 201 0
265 17 49862 207 0
266 38 84337 280 0
267 37 64175 260 0
268 29 59382 227 0
269 32 119308 239 0
270 35 76702 333 0
271 17 103425 428 1
272 20 70344 230 0
273 7 43410 292 0
274 46 104838 350 1
275 24 62215 186 0
276 40 69304 326 6
277 3 53117 155 3
278 10 19764 75 1
279 37 86680 361 2
280 17 84105 261 0
281 28 77945 299 0
282 19 89113 300 0
283 29 91005 450 3
284 8 40248 183 1
285 10 64187 238 0
286 15 50857 165 0
287 15 56613 234 1
288 28 62792 176 0
289 17 72535 329 0
feedback_messages_p1 feedback_messages_p120
1 115 94
2 109 103
3 146 93
4 116 103
5 68 51
6 101 70
7 96 91
8 67 22
9 44 38
10 100 93
11 93 60
12 140 123
13 166 148
14 99 90
15 139 124
16 130 70
17 181 168
18 116 115
19 116 71
20 88 66
21 139 134
22 135 117
23 108 108
24 89 84
25 156 156
26 129 120
27 118 114
28 118 94
29 125 120
30 95 81
31 126 110
32 135 133
33 154 122
34 165 158
35 113 109
36 127 124
37 52 39
38 121 92
39 136 126
40 0 0
41 108 70
42 46 37
43 54 38
44 124 120
45 115 93
46 128 95
47 80 77
48 97 90
49 104 80
50 59 31
51 125 110
52 82 66
53 149 138
54 149 133
55 122 113
56 118 100
57 12 7
58 144 140
59 67 61
60 52 41
61 108 96
62 166 164
63 80 78
64 60 49
65 107 102
66 127 124
67 107 99
68 146 129
69 84 62
70 141 73
71 123 114
72 111 99
73 98 70
74 105 104
75 135 116
76 107 91
77 85 74
78 155 138
79 88 67
80 155 151
81 104 72
82 132 120
83 127 115
84 108 105
85 129 104
86 116 108
87 122 98
88 85 69
89 147 111
90 99 99
91 87 71
92 28 27
93 90 69
94 109 107
95 78 73
96 111 107
97 158 93
98 141 129
99 122 69
100 124 118
101 93 73
102 124 119
103 112 104
104 108 107
105 99 99
106 117 90
107 199 197
108 78 36
109 91 85
110 158 139
111 126 106
112 122 50
113 71 64
114 75 31
115 115 63
116 119 92
117 124 106
118 72 63
119 91 69
120 45 41
121 78 56
122 39 25
123 68 65
124 119 93
125 117 114
126 39 38
127 50 44
128 88 87
129 155 110
130 0 0
131 36 27
132 123 83
133 32 30
134 99 80
135 136 98
136 117 82
137 0 0
138 88 60
139 39 28
140 25 9
141 52 33
142 75 59
143 71 49
144 124 115
145 151 140
146 71 49
147 145 120
148 87 66
149 27 21
150 131 124
151 162 152
152 165 139
153 54 38
154 159 144
155 147 120
156 170 160
157 119 114
158 49 39
159 104 78
160 120 119
161 150 141
162 112 101
163 59 56
164 136 133
165 107 83
166 130 116
167 115 90
168 107 36
169 75 50
170 71 61
171 120 97
172 116 98
173 79 78
174 150 117
175 156 148
176 51 41
177 118 105
178 71 55
179 144 132
180 47 44
181 28 21
182 68 50
183 0 0
184 110 73
185 147 86
186 0 0
187 15 13
188 4 4
189 64 57
190 111 48
191 85 46
192 68 48
193 40 32
194 80 68
195 88 87
196 48 43
197 76 67
198 51 46
199 67 46
200 59 56
201 61 48
202 76 44
203 60 60
204 68 65
205 71 55
206 76 38
207 62 52
208 61 60
209 67 54
210 88 86
211 30 24
212 64 52
213 68 49
214 64 61
215 91 61
216 88 81
217 52 43
218 49 40
219 62 40
220 61 56
221 76 68
222 88 79
223 66 47
224 71 57
225 68 41
226 48 29
227 25 3
228 68 60
229 41 30
230 90 79
231 66 47
232 54 40
233 59 48
234 60 36
235 77 42
236 68 49
237 72 57
238 67 12
239 64 40
240 63 43
241 59 33
242 84 77
243 64 43
244 56 45
245 54 47
246 67 43
247 58 45
248 59 50
249 40 35
250 22 7
251 83 71
252 81 67
253 2 0
254 72 62
255 61 54
256 15 4
257 32 25
258 62 40
259 58 38
260 36 19
261 59 17
262 68 67
263 21 14
264 55 30
265 54 54
266 55 35
267 72 59
268 41 24
269 61 58
270 67 42
271 76 46
272 64 61
273 3 3
274 63 52
275 40 25
276 69 40
277 48 32
278 8 4
279 52 49
280 66 63
281 76 67
282 43 32
283 39 23
284 14 7
285 61 54
286 71 37
287 44 35
288 60 51
289 64 39
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc compendium_views_info
-10.051701 0.000189 0.017705
shared_compendiums feedback_messages_p1 feedback_messages_p120
-1.171165 0.159628 0.220293
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-53.229 -10.932 -0.856 9.642 73.359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.005e+01 2.854e+00 -3.523 0.000498 ***
time_in_rfc 1.890e-04 3.681e-05 5.135 5.26e-07 ***
compendium_views_info 1.770e-02 8.704e-03 2.034 0.042884 *
shared_compendiums -1.171e+00 4.445e-01 -2.635 0.008879 **
feedback_messages_p1 1.596e-01 8.207e-02 1.945 0.052752 .
feedback_messages_p120 2.203e-01 8.573e-02 2.570 0.010690 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.74 on 283 degrees of freedom
Multiple R-squared: 0.7465, Adjusted R-squared: 0.742
F-statistic: 166.7 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,] 0.77398627 4.520275e-01 2.260137e-01
[2,] 0.73872705 5.225459e-01 2.612729e-01
[3,] 0.63949796 7.210041e-01 3.605020e-01
[4,] 0.71459024 5.708195e-01 2.854098e-01
[5,] 0.61846297 7.630741e-01 3.815370e-01
[6,] 0.51869793 9.626041e-01 4.813021e-01
[7,] 0.41629916 8.325983e-01 5.837008e-01
[8,] 0.33458964 6.691793e-01 6.654104e-01
[9,] 0.25781205 5.156241e-01 7.421879e-01
[10,] 0.25565324 5.113065e-01 7.443468e-01
[11,] 0.19363812 3.872762e-01 8.063619e-01
[12,] 0.35490826 7.098165e-01 6.450917e-01
[13,] 0.43954550 8.790910e-01 5.604545e-01
[14,] 0.36974856 7.394971e-01 6.302514e-01
[15,] 0.56730530 8.653894e-01 4.326947e-01
[16,] 0.49737295 9.947459e-01 5.026271e-01
[17,] 0.43147894 8.629579e-01 5.685211e-01
[18,] 0.46632968 9.326594e-01 5.336703e-01
[19,] 0.46680409 9.336082e-01 5.331959e-01
[20,] 0.40441496 8.088299e-01 5.955850e-01
[21,] 0.37539212 7.507842e-01 6.246079e-01
[22,] 0.33899455 6.779891e-01 6.610054e-01
[23,] 0.30495528 6.099106e-01 6.950447e-01
[24,] 0.25536899 5.107380e-01 7.446310e-01
[25,] 0.47008977 9.401795e-01 5.299102e-01
[26,] 0.41572834 8.314567e-01 5.842717e-01
[27,] 0.39083178 7.816636e-01 6.091682e-01
[28,] 0.34254509 6.850902e-01 6.574549e-01
[29,] 0.29549058 5.909812e-01 7.045094e-01
[30,] 0.28117086 5.623417e-01 7.188291e-01
[31,] 0.39160367 7.832073e-01 6.083963e-01
[32,] 0.35112510 7.022502e-01 6.488749e-01
[33,] 0.30442848 6.088570e-01 6.955715e-01
[34,] 0.26485393 5.297079e-01 7.351461e-01
[35,] 0.22840763 4.568153e-01 7.715924e-01
[36,] 0.19549052 3.909810e-01 8.045095e-01
[37,] 0.17526061 3.505212e-01 8.247394e-01
[38,] 0.14896750 2.979350e-01 8.510325e-01
[39,] 0.14780339 2.956068e-01 8.521966e-01
[40,] 0.12126114 2.425223e-01 8.787389e-01
[41,] 0.10253694 2.050739e-01 8.974631e-01
[42,] 0.09007315 1.801463e-01 9.099268e-01
[43,] 0.28929032 5.785806e-01 7.107097e-01
[44,] 0.28620323 5.724065e-01 7.137968e-01
[45,] 0.24828921 4.965784e-01 7.517108e-01
[46,] 0.21975403 4.395081e-01 7.802460e-01
[47,] 0.19544266 3.908853e-01 8.045573e-01
[48,] 0.16835041 3.367008e-01 8.316496e-01
[49,] 0.14423092 2.884618e-01 8.557691e-01
[50,] 0.17967141 3.593428e-01 8.203286e-01
[51,] 0.15597786 3.119557e-01 8.440221e-01
[52,] 0.13093457 2.618691e-01 8.690654e-01
[53,] 0.11157430 2.231486e-01 8.884257e-01
[54,] 0.09283955 1.856791e-01 9.071604e-01
[55,] 0.07866373 1.573275e-01 9.213363e-01
[56,] 0.06654978 1.330996e-01 9.334502e-01
[57,] 0.05923284 1.184657e-01 9.407672e-01
[58,] 0.06959966 1.391993e-01 9.304003e-01
[59,] 0.06110143 1.222029e-01 9.388986e-01
[60,] 0.06531798 1.306360e-01 9.346820e-01
[61,] 0.12981573 2.596315e-01 8.701843e-01
[62,] 0.11505408 2.301082e-01 8.849459e-01
[63,] 0.13005871 2.601174e-01 8.699413e-01
[64,] 0.17390017 3.478003e-01 8.260998e-01
[65,] 0.16823489 3.364698e-01 8.317651e-01
[66,] 0.14608844 2.921769e-01 8.539116e-01
[67,] 0.12476365 2.495273e-01 8.752364e-01
[68,] 0.23236247 4.647249e-01 7.676375e-01
[69,] 0.71291907 5.741619e-01 2.870809e-01
[70,] 0.68397028 6.320594e-01 3.160297e-01
[71,] 0.66876683 6.624663e-01 3.312332e-01
[72,] 0.68733777 6.253245e-01 3.126622e-01
[73,] 0.65839401 6.832120e-01 3.416060e-01
[74,] 0.70984439 5.803112e-01 2.901556e-01
[75,] 0.71318942 5.736212e-01 2.868106e-01
[76,] 0.68050272 6.389946e-01 3.194973e-01
[77,] 0.64860876 7.027825e-01 3.513912e-01
[78,] 0.71618137 5.676373e-01 2.838186e-01
[79,] 0.68997127 6.200575e-01 3.100287e-01
[80,] 0.65704758 6.859048e-01 3.429524e-01
[81,] 0.62631742 7.473652e-01 3.736826e-01
[82,] 0.69880836 6.023833e-01 3.011916e-01
[83,] 0.78783585 4.243283e-01 2.121641e-01
[84,] 0.79768053 4.046389e-01 2.023195e-01
[85,] 0.84362955 3.127409e-01 1.563705e-01
[86,] 0.82880647 3.423871e-01 1.711935e-01
[87,] 0.80571891 3.885622e-01 1.942811e-01
[88,] 0.91022570 1.795486e-01 8.977430e-02
[89,] 0.90623975 1.875205e-01 9.376025e-02
[90,] 0.96658844 6.682312e-02 3.341156e-02
[91,] 0.96036177 7.927646e-02 3.963823e-02
[92,] 0.98593382 2.813235e-02 1.406618e-02
[93,] 0.98504388 2.991223e-02 1.495612e-02
[94,] 0.98169007 3.661987e-02 1.830993e-02
[95,] 0.98581351 2.837298e-02 1.418649e-02
[96,] 0.98260214 3.479571e-02 1.739786e-02
[97,] 0.98073193 3.853615e-02 1.926807e-02
[98,] 0.99465019 1.069962e-02 5.349812e-03
[99,] 0.99365354 1.269291e-02 6.346455e-03
[100,] 0.99195235 1.609530e-02 8.047651e-03
[101,] 0.99653511 6.929779e-03 3.464889e-03
[102,] 0.99561459 8.770813e-03 4.385407e-03
[103,] 0.99687318 6.253636e-03 3.126818e-03
[104,] 0.99682230 6.355406e-03 3.177703e-03
[105,] 0.99630907 7.381857e-03 3.690928e-03
[106,] 0.99743455 5.130907e-03 2.565454e-03
[107,] 0.99728970 5.420593e-03 2.710296e-03
[108,] 0.99781118 4.377643e-03 2.188822e-03
[109,] 0.99986270 2.746009e-04 1.373005e-04
[110,] 0.99985861 2.827752e-04 1.413876e-04
[111,] 0.99984762 3.047584e-04 1.523792e-04
[112,] 0.99980078 3.984442e-04 1.992221e-04
[113,] 0.99973601 5.279818e-04 2.639909e-04
[114,] 0.99964459 7.108285e-04 3.554143e-04
[115,] 0.99955746 8.850825e-04 4.425413e-04
[116,] 0.99970538 5.892308e-04 2.946154e-04
[117,] 0.99976556 4.688777e-04 2.344389e-04
[118,] 0.99969016 6.196790e-04 3.098395e-04
[119,] 0.99958175 8.364907e-04 4.182453e-04
[120,] 0.99951500 9.699905e-04 4.849952e-04
[121,] 0.99983845 3.231013e-04 1.615506e-04
[122,] 0.99980664 3.867274e-04 1.933637e-04
[123,] 0.99975383 4.923317e-04 2.461659e-04
[124,] 0.99993470 1.306019e-04 6.530096e-05
[125,] 0.99991578 1.684491e-04 8.422454e-05
[126,] 0.99989404 2.119139e-04 1.059569e-04
[127,] 0.99986930 2.614037e-04 1.307018e-04
[128,] 0.99985654 2.869272e-04 1.434636e-04
[129,] 0.99980468 3.906390e-04 1.953195e-04
[130,] 0.99973092 5.381673e-04 2.690836e-04
[131,] 0.99963628 7.274317e-04 3.637158e-04
[132,] 0.99955437 8.912509e-04 4.456255e-04
[133,] 0.99942449 1.151015e-03 5.755075e-04
[134,] 0.99932959 1.340811e-03 6.704056e-04
[135,] 0.99958959 8.208209e-04 4.104105e-04
[136,] 0.99947272 1.054570e-03 5.272848e-04
[137,] 0.99940551 1.188975e-03 5.944874e-04
[138,] 0.99922208 1.555849e-03 7.779245e-04
[139,] 0.99945894 1.082112e-03 5.410558e-04
[140,] 0.99942822 1.143567e-03 5.717835e-04
[141,] 0.99924534 1.509315e-03 7.546577e-04
[142,] 0.99937178 1.256434e-03 6.282170e-04
[143,] 0.99925830 1.483398e-03 7.416990e-04
[144,] 0.99973029 5.394127e-04 2.697064e-04
[145,] 0.99967477 6.504574e-04 3.252287e-04
[146,] 0.99958411 8.317787e-04 4.158893e-04
[147,] 0.99983469 3.306123e-04 1.653061e-04
[148,] 0.99980265 3.946991e-04 1.973496e-04
[149,] 0.99975044 4.991270e-04 2.495635e-04
[150,] 0.99967231 6.553725e-04 3.276863e-04
[151,] 0.99961551 7.689834e-04 3.844917e-04
[152,] 0.99964657 7.068536e-04 3.534268e-04
[153,] 0.99978217 4.356617e-04 2.178309e-04
[154,] 0.99989595 2.080905e-04 1.040453e-04
[155,] 0.99985859 2.828182e-04 1.414091e-04
[156,] 0.99981536 3.692703e-04 1.846352e-04
[157,] 0.99976566 4.686836e-04 2.343418e-04
[158,] 0.99972569 5.486219e-04 2.743109e-04
[159,] 0.99963250 7.349967e-04 3.674983e-04
[160,] 0.99977318 4.536474e-04 2.268237e-04
[161,] 0.99973385 5.323095e-04 2.661547e-04
[162,] 0.99984993 3.001404e-04 1.500702e-04
[163,] 0.99983304 3.339130e-04 1.669565e-04
[164,] 0.99995569 8.861251e-05 4.430626e-05
[165,] 0.99996286 7.428242e-05 3.714121e-05
[166,] 0.99997869 4.261088e-05 2.130544e-05
[167,] 1.00000000 3.479859e-09 1.739930e-09
[168,] 1.00000000 2.383391e-09 1.191696e-09
[169,] 1.00000000 2.769999e-09 1.385000e-09
[170,] 1.00000000 4.167492e-09 2.083746e-09
[171,] 1.00000000 6.025694e-13 3.012847e-13
[172,] 1.00000000 9.955480e-13 4.977740e-13
[173,] 1.00000000 1.745138e-12 8.725692e-13
[174,] 1.00000000 4.942220e-13 2.471110e-13
[175,] 1.00000000 9.814227e-13 4.907113e-13
[176,] 1.00000000 1.421240e-12 7.106200e-13
[177,] 1.00000000 1.480295e-13 7.401475e-14
[178,] 1.00000000 2.943618e-13 1.471809e-13
[179,] 1.00000000 5.327817e-13 2.663909e-13
[180,] 1.00000000 8.242078e-13 4.121039e-13
[181,] 1.00000000 1.591846e-12 7.959230e-13
[182,] 1.00000000 2.257705e-12 1.128853e-12
[183,] 1.00000000 3.435597e-12 1.717799e-12
[184,] 1.00000000 1.987938e-12 9.939691e-13
[185,] 1.00000000 1.722384e-12 8.611921e-13
[186,] 1.00000000 2.061012e-12 1.030506e-12
[187,] 1.00000000 2.111078e-12 1.055539e-12
[188,] 1.00000000 7.958401e-13 3.979201e-13
[189,] 1.00000000 6.315071e-13 3.157535e-13
[190,] 1.00000000 1.239974e-12 6.199870e-13
[191,] 1.00000000 2.553309e-12 1.276654e-12
[192,] 1.00000000 3.969855e-12 1.984927e-12
[193,] 1.00000000 8.119380e-12 4.059690e-12
[194,] 1.00000000 1.623374e-11 8.116868e-12
[195,] 1.00000000 3.193335e-11 1.596668e-11
[196,] 1.00000000 4.364482e-11 2.182241e-11
[197,] 1.00000000 7.904676e-11 3.952338e-11
[198,] 1.00000000 1.204806e-10 6.024031e-11
[199,] 1.00000000 2.163943e-10 1.081972e-10
[200,] 1.00000000 1.834932e-10 9.174660e-11
[201,] 1.00000000 3.293973e-10 1.646987e-10
[202,] 1.00000000 5.363352e-10 2.681676e-10
[203,] 1.00000000 1.040754e-09 5.203769e-10
[204,] 1.00000000 1.864045e-09 9.320225e-10
[205,] 1.00000000 3.611145e-09 1.805573e-09
[206,] 1.00000000 5.813019e-09 2.906510e-09
[207,] 1.00000000 1.616275e-09 8.081376e-10
[208,] 1.00000000 9.383084e-10 4.691542e-10
[209,] 1.00000000 1.873714e-09 9.368570e-10
[210,] 1.00000000 2.410131e-09 1.205066e-09
[211,] 1.00000000 4.535060e-09 2.267530e-09
[212,] 1.00000000 5.411835e-09 2.705918e-09
[213,] 0.99999999 1.058285e-08 5.291424e-09
[214,] 0.99999999 1.179555e-08 5.897774e-09
[215,] 1.00000000 9.551462e-09 4.775731e-09
[216,] 0.99999999 1.890573e-08 9.452863e-09
[217,] 0.99999999 2.270274e-08 1.135137e-08
[218,] 0.99999998 3.671920e-08 1.835960e-08
[219,] 0.99999997 6.343100e-08 3.171550e-08
[220,] 0.99999995 1.067471e-07 5.337353e-08
[221,] 0.99999995 1.083536e-07 5.417680e-08
[222,] 0.99999990 1.976658e-07 9.883292e-08
[223,] 0.99999996 7.419450e-08 3.709725e-08
[224,] 0.99999996 7.886592e-08 3.943296e-08
[225,] 0.99999992 1.575718e-07 7.878592e-08
[226,] 0.99999986 2.818028e-07 1.409014e-07
[227,] 0.99999972 5.571775e-07 2.785888e-07
[228,] 0.99999975 5.017912e-07 2.508956e-07
[229,] 0.99999970 5.917715e-07 2.958858e-07
[230,] 0.99999944 1.120978e-06 5.604892e-07
[231,] 0.99999901 1.983032e-06 9.915158e-07
[232,] 0.99999817 3.661729e-06 1.830864e-06
[233,] 0.99999701 5.971455e-06 2.985727e-06
[234,] 0.99999584 8.326408e-06 4.163204e-06
[235,] 0.99999476 1.048568e-05 5.242838e-06
[236,] 0.99999008 1.984007e-05 9.920035e-06
[237,] 0.99998141 3.718981e-05 1.859490e-05
[238,] 0.99996586 6.828073e-05 3.414037e-05
[239,] 0.99998481 3.037556e-05 1.518778e-05
[240,] 0.99998578 2.844676e-05 1.422338e-05
[241,] 0.99997289 5.421635e-05 2.710818e-05
[242,] 0.99995685 8.629098e-05 4.314549e-05
[243,] 0.99995438 9.123135e-05 4.561568e-05
[244,] 0.99993105 1.378907e-04 6.894536e-05
[245,] 0.99988523 2.295390e-04 1.147695e-04
[246,] 0.99994627 1.074679e-04 5.373397e-05
[247,] 0.99991729 1.654228e-04 8.271141e-05
[248,] 0.99984057 3.188669e-04 1.594335e-04
[249,] 0.99984714 3.057174e-04 1.528587e-04
[250,] 0.99970585 5.882969e-04 2.941484e-04
[251,] 0.99958086 8.382869e-04 4.191435e-04
[252,] 0.99923690 1.526199e-03 7.630993e-04
[253,] 0.99898708 2.025849e-03 1.012924e-03
[254,] 0.99911922 1.761562e-03 8.807810e-04
[255,] 0.99850449 2.991019e-03 1.495510e-03
[256,] 0.99894938 2.101250e-03 1.050625e-03
[257,] 0.99796399 4.072023e-03 2.036011e-03
[258,] 0.99787224 4.255518e-03 2.127759e-03
[259,] 0.99808243 3.835144e-03 1.917572e-03
[260,] 0.99805833 3.883339e-03 1.941669e-03
[261,] 0.99606624 7.867516e-03 3.933758e-03
[262,] 0.99671602 6.567965e-03 3.283982e-03
[263,] 0.99721434 5.571313e-03 2.785657e-03
[264,] 0.99393464 1.213072e-02 6.065359e-03
[265,] 0.98719456 2.561088e-02 1.280544e-02
[266,] 0.98750635 2.498729e-02 1.249365e-02
[267,] 0.98617749 2.764503e-02 1.382251e-02
[268,] 0.98174686 3.650628e-02 1.825314e-02
[269,] 0.99271958 1.456083e-02 7.280416e-03
[270,] 0.98111010 3.777980e-02 1.888990e-02
[271,] 0.96143164 7.713673e-02 3.856836e-02
[272,] 0.93840907 1.231819e-01 6.159093e-02
> postscript(file="/var/fisher/rcomp/tmp/1wuxh1352152219.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2vwmn1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3djdy1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/44kfp1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5l28n1352152219.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
6.61906337 4.51777365 -2.95158147 28.16746760 10.06580076 -30.48069969
7 8 9 10 11 12
-4.85603622 -12.69014591 -11.12666485 -17.33553168 10.66538462 -17.83203914
13 14 15 16 17 18
4.14970010 -7.84195591 3.45408921 4.57755280 15.32533516 18.38865274
19 20 21 22 23 24
-3.66929476 31.03871335 33.14241620 11.74254461 -31.94482821 -2.95062273
25 26 27 28 29 30
9.59149799 35.89173446 -8.05094505 -1.06669982 17.30468643 -7.85581065
31 32 33 34 35 36
16.70382725 5.48854620 -34.38149588 3.75555210 -10.02832111 -1.41657966
37 38 39 40 41 42
-0.14942864 -13.77784271 -33.46279536 7.45581480 -1.91504643 4.01032633
43 44 45 46 47 48
7.90134328 -4.48871384 15.47635139 -5.97733932 -12.03040939 1.27933589
49 50 51 52 53 54
-7.70586209 -9.28307273 -53.22887600 -15.97382445 2.37896398 7.93437679
55 56 57 58 59 60
12.62087472 5.27179721 6.16197005 -31.17813550 -4.03330314 -0.91695040
61 62 63 64 65 66
-7.88728764 -2.01733664 -11.89245858 -8.44341310 9.64231620 22.85220407
67 68 69 70 71 72
9.50520038 17.88977910 36.47201112 -12.99641590 -21.07958929 -32.19591575
73 74 75 76 77 78
11.29634490 7.94165834 -0.83348597 41.57138123 73.35890899 -0.11096043
79 80 81 82 83 84
-10.45152390 -20.83151676 6.31018380 31.88921928 18.57636516 1.90586926
85 86 87 88 89 90
4.36869117 35.45740238 -7.30991719 3.53875973 -3.07105234 -30.54291509
91 92 93 94 95 96
38.08370210 25.59377509 -31.31243574 11.95698186 1.84035602 -47.79446530
97 98 99 100 101 102
-18.86951111 51.21236962 -5.64165584 47.12022517 -15.73560501 5.50466271
103 104 105 106 107 108
-27.91686135 0.15586523 13.95094766 47.74122228 -9.93335424 4.27876607
109 110 111 112 113 114
38.32537007 1.01770258 28.15264505 -16.95096598 -9.66780621 29.88603748
115 116 117 118 119 120
17.55204191 25.73283136 58.89596935 -17.63684246 -15.31128295 8.49683660
121 122 123 124 125 126
7.10117021 5.73431466 -9.61895647 27.31697781 -25.73541134 7.24090799
127 128 129 130 131 132
-1.94398990 11.05217936 37.36738905 14.98894522 -7.69906242 39.09358129
133 134 135 136 137 138
10.92660789 -9.18826744 5.43137351 13.21949002 7.48673772 -1.47006837
139 140 141 142 143 144
2.11895005 12.59888020 -5.19755868 11.29813383 -29.18117406 1.93136018
145 146 147 148 149 150
-17.08251805 -4.75714660 -28.97053840 -16.91144016 5.00508024 -25.49817006
151 152 153 154 155 156
-15.35180185 -40.40336587 10.55679734 1.21682871 30.49422262 2.68555993
157 158 159 160 161 162
-12.01076597 -7.08006458 -14.08062441 -23.69987995 -34.34874853 -25.41489193
163 164 165 166 167 168
-6.56294110 -14.14121584 -9.02845724 8.63202170 1.21044105 -30.46969683
169 170 171 172 173 174
11.05491109 -32.51492619 -9.80388747 35.11026314 -20.65820026 30.71055749
175 176 177 178 179 180
65.40027796 -17.72673156 4.47414834 -11.53197450 36.91471133 6.86418408
181 182 183 184 185 186
12.89058779 21.91917519 9.77015841 -18.56813769 11.83148865 14.38064880
187 188 189 190 191 192
3.38727819 3.99327524 -9.23415893 -3.47439167 -1.91048346 -23.05719736
193 194 195 196 197 198
19.53720365 -19.96053425 -4.43287234 18.79872836 -23.27395339 -6.24153926
199 200 201 202 203 204
-5.26844889 3.40243764 -5.20756402 -7.22104389 -8.80492878 -19.33883141
205 206 207 208 209 210
-8.85335396 -11.88785650 0.09891267 -26.00174783 -13.34578737 -10.10145611
211 212 213 214 215 216
7.29782966 -3.65600563 -4.80504684 -1.41724437 -27.66259493 -28.36690469
217 218 219 220 221 222
-0.28169667 -3.09523336 5.51034273 -19.91882189 -8.37985641 -15.83273586
223 224 225 226 227 228
16.69892298 -6.96671927 -12.33975914 6.62090400 10.11401207 3.83549715
229 230 231 232 233 234
16.39937016 -8.08560827 -18.68273959 8.70121143 -7.39845415 -4.89146757
235 236 237 238 239 240
-3.94008426 12.71847010 -28.78546065 6.31941980 0.60817496 -4.45368209
241 242 243 244 245 246
-11.93984841 -25.14038907 9.35030513 1.14620524 -4.19866924 -2.83783968
247 248 249 250 251 252
13.99200933 -18.98049374 -5.31896458 12.33779433 -29.68763145 -0.62051069
253 254 255 256 257 258
17.88845559 -27.74291690 -15.15056062 5.79519932 16.34421512 -3.92775188
259 260 261 262 263 264
-7.87045117 6.65260424 -9.68147941 8.44832452 -0.85623046 17.86679468
265 266 267 268 269 270
-6.55488856 10.66136685 5.82623857 11.97527611 -7.24825847 4.70878568
271 272 273 274 275 276
-21.17158764 -10.97239247 2.53587958 9.69565393 7.10493817 18.37957305
277 278 279 280 281 282
-10.93188907 14.00059355 7.52149168 -17.88248990 -8.86820779 -7.01917820
283 284 285 286 287 288
6.10211667 4.59751076 -17.92916052 -6.96809738 -3.35613389 2.25273914
289
-11.29289372
> postscript(file="/var/fisher/rcomp/tmp/66y9z1352152219.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 6.61906337 NA
1 4.51777365 6.61906337
2 -2.95158147 4.51777365
3 28.16746760 -2.95158147
4 10.06580076 28.16746760
5 -30.48069969 10.06580076
6 -4.85603622 -30.48069969
7 -12.69014591 -4.85603622
8 -11.12666485 -12.69014591
9 -17.33553168 -11.12666485
10 10.66538462 -17.33553168
11 -17.83203914 10.66538462
12 4.14970010 -17.83203914
13 -7.84195591 4.14970010
14 3.45408921 -7.84195591
15 4.57755280 3.45408921
16 15.32533516 4.57755280
17 18.38865274 15.32533516
18 -3.66929476 18.38865274
19 31.03871335 -3.66929476
20 33.14241620 31.03871335
21 11.74254461 33.14241620
22 -31.94482821 11.74254461
23 -2.95062273 -31.94482821
24 9.59149799 -2.95062273
25 35.89173446 9.59149799
26 -8.05094505 35.89173446
27 -1.06669982 -8.05094505
28 17.30468643 -1.06669982
29 -7.85581065 17.30468643
30 16.70382725 -7.85581065
31 5.48854620 16.70382725
32 -34.38149588 5.48854620
33 3.75555210 -34.38149588
34 -10.02832111 3.75555210
35 -1.41657966 -10.02832111
36 -0.14942864 -1.41657966
37 -13.77784271 -0.14942864
38 -33.46279536 -13.77784271
39 7.45581480 -33.46279536
40 -1.91504643 7.45581480
41 4.01032633 -1.91504643
42 7.90134328 4.01032633
43 -4.48871384 7.90134328
44 15.47635139 -4.48871384
45 -5.97733932 15.47635139
46 -12.03040939 -5.97733932
47 1.27933589 -12.03040939
48 -7.70586209 1.27933589
49 -9.28307273 -7.70586209
50 -53.22887600 -9.28307273
51 -15.97382445 -53.22887600
52 2.37896398 -15.97382445
53 7.93437679 2.37896398
54 12.62087472 7.93437679
55 5.27179721 12.62087472
56 6.16197005 5.27179721
57 -31.17813550 6.16197005
58 -4.03330314 -31.17813550
59 -0.91695040 -4.03330314
60 -7.88728764 -0.91695040
61 -2.01733664 -7.88728764
62 -11.89245858 -2.01733664
63 -8.44341310 -11.89245858
64 9.64231620 -8.44341310
65 22.85220407 9.64231620
66 9.50520038 22.85220407
67 17.88977910 9.50520038
68 36.47201112 17.88977910
69 -12.99641590 36.47201112
70 -21.07958929 -12.99641590
71 -32.19591575 -21.07958929
72 11.29634490 -32.19591575
73 7.94165834 11.29634490
74 -0.83348597 7.94165834
75 41.57138123 -0.83348597
76 73.35890899 41.57138123
77 -0.11096043 73.35890899
78 -10.45152390 -0.11096043
79 -20.83151676 -10.45152390
80 6.31018380 -20.83151676
81 31.88921928 6.31018380
82 18.57636516 31.88921928
83 1.90586926 18.57636516
84 4.36869117 1.90586926
85 35.45740238 4.36869117
86 -7.30991719 35.45740238
87 3.53875973 -7.30991719
88 -3.07105234 3.53875973
89 -30.54291509 -3.07105234
90 38.08370210 -30.54291509
91 25.59377509 38.08370210
92 -31.31243574 25.59377509
93 11.95698186 -31.31243574
94 1.84035602 11.95698186
95 -47.79446530 1.84035602
96 -18.86951111 -47.79446530
97 51.21236962 -18.86951111
98 -5.64165584 51.21236962
99 47.12022517 -5.64165584
100 -15.73560501 47.12022517
101 5.50466271 -15.73560501
102 -27.91686135 5.50466271
103 0.15586523 -27.91686135
104 13.95094766 0.15586523
105 47.74122228 13.95094766
106 -9.93335424 47.74122228
107 4.27876607 -9.93335424
108 38.32537007 4.27876607
109 1.01770258 38.32537007
110 28.15264505 1.01770258
111 -16.95096598 28.15264505
112 -9.66780621 -16.95096598
113 29.88603748 -9.66780621
114 17.55204191 29.88603748
115 25.73283136 17.55204191
116 58.89596935 25.73283136
117 -17.63684246 58.89596935
118 -15.31128295 -17.63684246
119 8.49683660 -15.31128295
120 7.10117021 8.49683660
121 5.73431466 7.10117021
122 -9.61895647 5.73431466
123 27.31697781 -9.61895647
124 -25.73541134 27.31697781
125 7.24090799 -25.73541134
126 -1.94398990 7.24090799
127 11.05217936 -1.94398990
128 37.36738905 11.05217936
129 14.98894522 37.36738905
130 -7.69906242 14.98894522
131 39.09358129 -7.69906242
132 10.92660789 39.09358129
133 -9.18826744 10.92660789
134 5.43137351 -9.18826744
135 13.21949002 5.43137351
136 7.48673772 13.21949002
137 -1.47006837 7.48673772
138 2.11895005 -1.47006837
139 12.59888020 2.11895005
140 -5.19755868 12.59888020
141 11.29813383 -5.19755868
142 -29.18117406 11.29813383
143 1.93136018 -29.18117406
144 -17.08251805 1.93136018
145 -4.75714660 -17.08251805
146 -28.97053840 -4.75714660
147 -16.91144016 -28.97053840
148 5.00508024 -16.91144016
149 -25.49817006 5.00508024
150 -15.35180185 -25.49817006
151 -40.40336587 -15.35180185
152 10.55679734 -40.40336587
153 1.21682871 10.55679734
154 30.49422262 1.21682871
155 2.68555993 30.49422262
156 -12.01076597 2.68555993
157 -7.08006458 -12.01076597
158 -14.08062441 -7.08006458
159 -23.69987995 -14.08062441
160 -34.34874853 -23.69987995
161 -25.41489193 -34.34874853
162 -6.56294110 -25.41489193
163 -14.14121584 -6.56294110
164 -9.02845724 -14.14121584
165 8.63202170 -9.02845724
166 1.21044105 8.63202170
167 -30.46969683 1.21044105
168 11.05491109 -30.46969683
169 -32.51492619 11.05491109
170 -9.80388747 -32.51492619
171 35.11026314 -9.80388747
172 -20.65820026 35.11026314
173 30.71055749 -20.65820026
174 65.40027796 30.71055749
175 -17.72673156 65.40027796
176 4.47414834 -17.72673156
177 -11.53197450 4.47414834
178 36.91471133 -11.53197450
179 6.86418408 36.91471133
180 12.89058779 6.86418408
181 21.91917519 12.89058779
182 9.77015841 21.91917519
183 -18.56813769 9.77015841
184 11.83148865 -18.56813769
185 14.38064880 11.83148865
186 3.38727819 14.38064880
187 3.99327524 3.38727819
188 -9.23415893 3.99327524
189 -3.47439167 -9.23415893
190 -1.91048346 -3.47439167
191 -23.05719736 -1.91048346
192 19.53720365 -23.05719736
193 -19.96053425 19.53720365
194 -4.43287234 -19.96053425
195 18.79872836 -4.43287234
196 -23.27395339 18.79872836
197 -6.24153926 -23.27395339
198 -5.26844889 -6.24153926
199 3.40243764 -5.26844889
200 -5.20756402 3.40243764
201 -7.22104389 -5.20756402
202 -8.80492878 -7.22104389
203 -19.33883141 -8.80492878
204 -8.85335396 -19.33883141
205 -11.88785650 -8.85335396
206 0.09891267 -11.88785650
207 -26.00174783 0.09891267
208 -13.34578737 -26.00174783
209 -10.10145611 -13.34578737
210 7.29782966 -10.10145611
211 -3.65600563 7.29782966
212 -4.80504684 -3.65600563
213 -1.41724437 -4.80504684
214 -27.66259493 -1.41724437
215 -28.36690469 -27.66259493
216 -0.28169667 -28.36690469
217 -3.09523336 -0.28169667
218 5.51034273 -3.09523336
219 -19.91882189 5.51034273
220 -8.37985641 -19.91882189
221 -15.83273586 -8.37985641
222 16.69892298 -15.83273586
223 -6.96671927 16.69892298
224 -12.33975914 -6.96671927
225 6.62090400 -12.33975914
226 10.11401207 6.62090400
227 3.83549715 10.11401207
228 16.39937016 3.83549715
229 -8.08560827 16.39937016
230 -18.68273959 -8.08560827
231 8.70121143 -18.68273959
232 -7.39845415 8.70121143
233 -4.89146757 -7.39845415
234 -3.94008426 -4.89146757
235 12.71847010 -3.94008426
236 -28.78546065 12.71847010
237 6.31941980 -28.78546065
238 0.60817496 6.31941980
239 -4.45368209 0.60817496
240 -11.93984841 -4.45368209
241 -25.14038907 -11.93984841
242 9.35030513 -25.14038907
243 1.14620524 9.35030513
244 -4.19866924 1.14620524
245 -2.83783968 -4.19866924
246 13.99200933 -2.83783968
247 -18.98049374 13.99200933
248 -5.31896458 -18.98049374
249 12.33779433 -5.31896458
250 -29.68763145 12.33779433
251 -0.62051069 -29.68763145
252 17.88845559 -0.62051069
253 -27.74291690 17.88845559
254 -15.15056062 -27.74291690
255 5.79519932 -15.15056062
256 16.34421512 5.79519932
257 -3.92775188 16.34421512
258 -7.87045117 -3.92775188
259 6.65260424 -7.87045117
260 -9.68147941 6.65260424
261 8.44832452 -9.68147941
262 -0.85623046 8.44832452
263 17.86679468 -0.85623046
264 -6.55488856 17.86679468
265 10.66136685 -6.55488856
266 5.82623857 10.66136685
267 11.97527611 5.82623857
268 -7.24825847 11.97527611
269 4.70878568 -7.24825847
270 -21.17158764 4.70878568
271 -10.97239247 -21.17158764
272 2.53587958 -10.97239247
273 9.69565393 2.53587958
274 7.10493817 9.69565393
275 18.37957305 7.10493817
276 -10.93188907 18.37957305
277 14.00059355 -10.93188907
278 7.52149168 14.00059355
279 -17.88248990 7.52149168
280 -8.86820779 -17.88248990
281 -7.01917820 -8.86820779
282 6.10211667 -7.01917820
283 4.59751076 6.10211667
284 -17.92916052 4.59751076
285 -6.96809738 -17.92916052
286 -3.35613389 -6.96809738
287 2.25273914 -3.35613389
288 -11.29289372 2.25273914
289 NA -11.29289372
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.51777365 6.61906337
[2,] -2.95158147 4.51777365
[3,] 28.16746760 -2.95158147
[4,] 10.06580076 28.16746760
[5,] -30.48069969 10.06580076
[6,] -4.85603622 -30.48069969
[7,] -12.69014591 -4.85603622
[8,] -11.12666485 -12.69014591
[9,] -17.33553168 -11.12666485
[10,] 10.66538462 -17.33553168
[11,] -17.83203914 10.66538462
[12,] 4.14970010 -17.83203914
[13,] -7.84195591 4.14970010
[14,] 3.45408921 -7.84195591
[15,] 4.57755280 3.45408921
[16,] 15.32533516 4.57755280
[17,] 18.38865274 15.32533516
[18,] -3.66929476 18.38865274
[19,] 31.03871335 -3.66929476
[20,] 33.14241620 31.03871335
[21,] 11.74254461 33.14241620
[22,] -31.94482821 11.74254461
[23,] -2.95062273 -31.94482821
[24,] 9.59149799 -2.95062273
[25,] 35.89173446 9.59149799
[26,] -8.05094505 35.89173446
[27,] -1.06669982 -8.05094505
[28,] 17.30468643 -1.06669982
[29,] -7.85581065 17.30468643
[30,] 16.70382725 -7.85581065
[31,] 5.48854620 16.70382725
[32,] -34.38149588 5.48854620
[33,] 3.75555210 -34.38149588
[34,] -10.02832111 3.75555210
[35,] -1.41657966 -10.02832111
[36,] -0.14942864 -1.41657966
[37,] -13.77784271 -0.14942864
[38,] -33.46279536 -13.77784271
[39,] 7.45581480 -33.46279536
[40,] -1.91504643 7.45581480
[41,] 4.01032633 -1.91504643
[42,] 7.90134328 4.01032633
[43,] -4.48871384 7.90134328
[44,] 15.47635139 -4.48871384
[45,] -5.97733932 15.47635139
[46,] -12.03040939 -5.97733932
[47,] 1.27933589 -12.03040939
[48,] -7.70586209 1.27933589
[49,] -9.28307273 -7.70586209
[50,] -53.22887600 -9.28307273
[51,] -15.97382445 -53.22887600
[52,] 2.37896398 -15.97382445
[53,] 7.93437679 2.37896398
[54,] 12.62087472 7.93437679
[55,] 5.27179721 12.62087472
[56,] 6.16197005 5.27179721
[57,] -31.17813550 6.16197005
[58,] -4.03330314 -31.17813550
[59,] -0.91695040 -4.03330314
[60,] -7.88728764 -0.91695040
[61,] -2.01733664 -7.88728764
[62,] -11.89245858 -2.01733664
[63,] -8.44341310 -11.89245858
[64,] 9.64231620 -8.44341310
[65,] 22.85220407 9.64231620
[66,] 9.50520038 22.85220407
[67,] 17.88977910 9.50520038
[68,] 36.47201112 17.88977910
[69,] -12.99641590 36.47201112
[70,] -21.07958929 -12.99641590
[71,] -32.19591575 -21.07958929
[72,] 11.29634490 -32.19591575
[73,] 7.94165834 11.29634490
[74,] -0.83348597 7.94165834
[75,] 41.57138123 -0.83348597
[76,] 73.35890899 41.57138123
[77,] -0.11096043 73.35890899
[78,] -10.45152390 -0.11096043
[79,] -20.83151676 -10.45152390
[80,] 6.31018380 -20.83151676
[81,] 31.88921928 6.31018380
[82,] 18.57636516 31.88921928
[83,] 1.90586926 18.57636516
[84,] 4.36869117 1.90586926
[85,] 35.45740238 4.36869117
[86,] -7.30991719 35.45740238
[87,] 3.53875973 -7.30991719
[88,] -3.07105234 3.53875973
[89,] -30.54291509 -3.07105234
[90,] 38.08370210 -30.54291509
[91,] 25.59377509 38.08370210
[92,] -31.31243574 25.59377509
[93,] 11.95698186 -31.31243574
[94,] 1.84035602 11.95698186
[95,] -47.79446530 1.84035602
[96,] -18.86951111 -47.79446530
[97,] 51.21236962 -18.86951111
[98,] -5.64165584 51.21236962
[99,] 47.12022517 -5.64165584
[100,] -15.73560501 47.12022517
[101,] 5.50466271 -15.73560501
[102,] -27.91686135 5.50466271
[103,] 0.15586523 -27.91686135
[104,] 13.95094766 0.15586523
[105,] 47.74122228 13.95094766
[106,] -9.93335424 47.74122228
[107,] 4.27876607 -9.93335424
[108,] 38.32537007 4.27876607
[109,] 1.01770258 38.32537007
[110,] 28.15264505 1.01770258
[111,] -16.95096598 28.15264505
[112,] -9.66780621 -16.95096598
[113,] 29.88603748 -9.66780621
[114,] 17.55204191 29.88603748
[115,] 25.73283136 17.55204191
[116,] 58.89596935 25.73283136
[117,] -17.63684246 58.89596935
[118,] -15.31128295 -17.63684246
[119,] 8.49683660 -15.31128295
[120,] 7.10117021 8.49683660
[121,] 5.73431466 7.10117021
[122,] -9.61895647 5.73431466
[123,] 27.31697781 -9.61895647
[124,] -25.73541134 27.31697781
[125,] 7.24090799 -25.73541134
[126,] -1.94398990 7.24090799
[127,] 11.05217936 -1.94398990
[128,] 37.36738905 11.05217936
[129,] 14.98894522 37.36738905
[130,] -7.69906242 14.98894522
[131,] 39.09358129 -7.69906242
[132,] 10.92660789 39.09358129
[133,] -9.18826744 10.92660789
[134,] 5.43137351 -9.18826744
[135,] 13.21949002 5.43137351
[136,] 7.48673772 13.21949002
[137,] -1.47006837 7.48673772
[138,] 2.11895005 -1.47006837
[139,] 12.59888020 2.11895005
[140,] -5.19755868 12.59888020
[141,] 11.29813383 -5.19755868
[142,] -29.18117406 11.29813383
[143,] 1.93136018 -29.18117406
[144,] -17.08251805 1.93136018
[145,] -4.75714660 -17.08251805
[146,] -28.97053840 -4.75714660
[147,] -16.91144016 -28.97053840
[148,] 5.00508024 -16.91144016
[149,] -25.49817006 5.00508024
[150,] -15.35180185 -25.49817006
[151,] -40.40336587 -15.35180185
[152,] 10.55679734 -40.40336587
[153,] 1.21682871 10.55679734
[154,] 30.49422262 1.21682871
[155,] 2.68555993 30.49422262
[156,] -12.01076597 2.68555993
[157,] -7.08006458 -12.01076597
[158,] -14.08062441 -7.08006458
[159,] -23.69987995 -14.08062441
[160,] -34.34874853 -23.69987995
[161,] -25.41489193 -34.34874853
[162,] -6.56294110 -25.41489193
[163,] -14.14121584 -6.56294110
[164,] -9.02845724 -14.14121584
[165,] 8.63202170 -9.02845724
[166,] 1.21044105 8.63202170
[167,] -30.46969683 1.21044105
[168,] 11.05491109 -30.46969683
[169,] -32.51492619 11.05491109
[170,] -9.80388747 -32.51492619
[171,] 35.11026314 -9.80388747
[172,] -20.65820026 35.11026314
[173,] 30.71055749 -20.65820026
[174,] 65.40027796 30.71055749
[175,] -17.72673156 65.40027796
[176,] 4.47414834 -17.72673156
[177,] -11.53197450 4.47414834
[178,] 36.91471133 -11.53197450
[179,] 6.86418408 36.91471133
[180,] 12.89058779 6.86418408
[181,] 21.91917519 12.89058779
[182,] 9.77015841 21.91917519
[183,] -18.56813769 9.77015841
[184,] 11.83148865 -18.56813769
[185,] 14.38064880 11.83148865
[186,] 3.38727819 14.38064880
[187,] 3.99327524 3.38727819
[188,] -9.23415893 3.99327524
[189,] -3.47439167 -9.23415893
[190,] -1.91048346 -3.47439167
[191,] -23.05719736 -1.91048346
[192,] 19.53720365 -23.05719736
[193,] -19.96053425 19.53720365
[194,] -4.43287234 -19.96053425
[195,] 18.79872836 -4.43287234
[196,] -23.27395339 18.79872836
[197,] -6.24153926 -23.27395339
[198,] -5.26844889 -6.24153926
[199,] 3.40243764 -5.26844889
[200,] -5.20756402 3.40243764
[201,] -7.22104389 -5.20756402
[202,] -8.80492878 -7.22104389
[203,] -19.33883141 -8.80492878
[204,] -8.85335396 -19.33883141
[205,] -11.88785650 -8.85335396
[206,] 0.09891267 -11.88785650
[207,] -26.00174783 0.09891267
[208,] -13.34578737 -26.00174783
[209,] -10.10145611 -13.34578737
[210,] 7.29782966 -10.10145611
[211,] -3.65600563 7.29782966
[212,] -4.80504684 -3.65600563
[213,] -1.41724437 -4.80504684
[214,] -27.66259493 -1.41724437
[215,] -28.36690469 -27.66259493
[216,] -0.28169667 -28.36690469
[217,] -3.09523336 -0.28169667
[218,] 5.51034273 -3.09523336
[219,] -19.91882189 5.51034273
[220,] -8.37985641 -19.91882189
[221,] -15.83273586 -8.37985641
[222,] 16.69892298 -15.83273586
[223,] -6.96671927 16.69892298
[224,] -12.33975914 -6.96671927
[225,] 6.62090400 -12.33975914
[226,] 10.11401207 6.62090400
[227,] 3.83549715 10.11401207
[228,] 16.39937016 3.83549715
[229,] -8.08560827 16.39937016
[230,] -18.68273959 -8.08560827
[231,] 8.70121143 -18.68273959
[232,] -7.39845415 8.70121143
[233,] -4.89146757 -7.39845415
[234,] -3.94008426 -4.89146757
[235,] 12.71847010 -3.94008426
[236,] -28.78546065 12.71847010
[237,] 6.31941980 -28.78546065
[238,] 0.60817496 6.31941980
[239,] -4.45368209 0.60817496
[240,] -11.93984841 -4.45368209
[241,] -25.14038907 -11.93984841
[242,] 9.35030513 -25.14038907
[243,] 1.14620524 9.35030513
[244,] -4.19866924 1.14620524
[245,] -2.83783968 -4.19866924
[246,] 13.99200933 -2.83783968
[247,] -18.98049374 13.99200933
[248,] -5.31896458 -18.98049374
[249,] 12.33779433 -5.31896458
[250,] -29.68763145 12.33779433
[251,] -0.62051069 -29.68763145
[252,] 17.88845559 -0.62051069
[253,] -27.74291690 17.88845559
[254,] -15.15056062 -27.74291690
[255,] 5.79519932 -15.15056062
[256,] 16.34421512 5.79519932
[257,] -3.92775188 16.34421512
[258,] -7.87045117 -3.92775188
[259,] 6.65260424 -7.87045117
[260,] -9.68147941 6.65260424
[261,] 8.44832452 -9.68147941
[262,] -0.85623046 8.44832452
[263,] 17.86679468 -0.85623046
[264,] -6.55488856 17.86679468
[265,] 10.66136685 -6.55488856
[266,] 5.82623857 10.66136685
[267,] 11.97527611 5.82623857
[268,] -7.24825847 11.97527611
[269,] 4.70878568 -7.24825847
[270,] -21.17158764 4.70878568
[271,] -10.97239247 -21.17158764
[272,] 2.53587958 -10.97239247
[273,] 9.69565393 2.53587958
[274,] 7.10493817 9.69565393
[275,] 18.37957305 7.10493817
[276,] -10.93188907 18.37957305
[277,] 14.00059355 -10.93188907
[278,] 7.52149168 14.00059355
[279,] -17.88248990 7.52149168
[280,] -8.86820779 -17.88248990
[281,] -7.01917820 -8.86820779
[282,] 6.10211667 -7.01917820
[283,] 4.59751076 6.10211667
[284,] -17.92916052 4.59751076
[285,] -6.96809738 -17.92916052
[286,] -3.35613389 -6.96809738
[287,] 2.25273914 -3.35613389
[288,] -11.29289372 2.25273914
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.51777365 6.61906337
2 -2.95158147 4.51777365
3 28.16746760 -2.95158147
4 10.06580076 28.16746760
5 -30.48069969 10.06580076
6 -4.85603622 -30.48069969
7 -12.69014591 -4.85603622
8 -11.12666485 -12.69014591
9 -17.33553168 -11.12666485
10 10.66538462 -17.33553168
11 -17.83203914 10.66538462
12 4.14970010 -17.83203914
13 -7.84195591 4.14970010
14 3.45408921 -7.84195591
15 4.57755280 3.45408921
16 15.32533516 4.57755280
17 18.38865274 15.32533516
18 -3.66929476 18.38865274
19 31.03871335 -3.66929476
20 33.14241620 31.03871335
21 11.74254461 33.14241620
22 -31.94482821 11.74254461
23 -2.95062273 -31.94482821
24 9.59149799 -2.95062273
25 35.89173446 9.59149799
26 -8.05094505 35.89173446
27 -1.06669982 -8.05094505
28 17.30468643 -1.06669982
29 -7.85581065 17.30468643
30 16.70382725 -7.85581065
31 5.48854620 16.70382725
32 -34.38149588 5.48854620
33 3.75555210 -34.38149588
34 -10.02832111 3.75555210
35 -1.41657966 -10.02832111
36 -0.14942864 -1.41657966
37 -13.77784271 -0.14942864
38 -33.46279536 -13.77784271
39 7.45581480 -33.46279536
40 -1.91504643 7.45581480
41 4.01032633 -1.91504643
42 7.90134328 4.01032633
43 -4.48871384 7.90134328
44 15.47635139 -4.48871384
45 -5.97733932 15.47635139
46 -12.03040939 -5.97733932
47 1.27933589 -12.03040939
48 -7.70586209 1.27933589
49 -9.28307273 -7.70586209
50 -53.22887600 -9.28307273
51 -15.97382445 -53.22887600
52 2.37896398 -15.97382445
53 7.93437679 2.37896398
54 12.62087472 7.93437679
55 5.27179721 12.62087472
56 6.16197005 5.27179721
57 -31.17813550 6.16197005
58 -4.03330314 -31.17813550
59 -0.91695040 -4.03330314
60 -7.88728764 -0.91695040
61 -2.01733664 -7.88728764
62 -11.89245858 -2.01733664
63 -8.44341310 -11.89245858
64 9.64231620 -8.44341310
65 22.85220407 9.64231620
66 9.50520038 22.85220407
67 17.88977910 9.50520038
68 36.47201112 17.88977910
69 -12.99641590 36.47201112
70 -21.07958929 -12.99641590
71 -32.19591575 -21.07958929
72 11.29634490 -32.19591575
73 7.94165834 11.29634490
74 -0.83348597 7.94165834
75 41.57138123 -0.83348597
76 73.35890899 41.57138123
77 -0.11096043 73.35890899
78 -10.45152390 -0.11096043
79 -20.83151676 -10.45152390
80 6.31018380 -20.83151676
81 31.88921928 6.31018380
82 18.57636516 31.88921928
83 1.90586926 18.57636516
84 4.36869117 1.90586926
85 35.45740238 4.36869117
86 -7.30991719 35.45740238
87 3.53875973 -7.30991719
88 -3.07105234 3.53875973
89 -30.54291509 -3.07105234
90 38.08370210 -30.54291509
91 25.59377509 38.08370210
92 -31.31243574 25.59377509
93 11.95698186 -31.31243574
94 1.84035602 11.95698186
95 -47.79446530 1.84035602
96 -18.86951111 -47.79446530
97 51.21236962 -18.86951111
98 -5.64165584 51.21236962
99 47.12022517 -5.64165584
100 -15.73560501 47.12022517
101 5.50466271 -15.73560501
102 -27.91686135 5.50466271
103 0.15586523 -27.91686135
104 13.95094766 0.15586523
105 47.74122228 13.95094766
106 -9.93335424 47.74122228
107 4.27876607 -9.93335424
108 38.32537007 4.27876607
109 1.01770258 38.32537007
110 28.15264505 1.01770258
111 -16.95096598 28.15264505
112 -9.66780621 -16.95096598
113 29.88603748 -9.66780621
114 17.55204191 29.88603748
115 25.73283136 17.55204191
116 58.89596935 25.73283136
117 -17.63684246 58.89596935
118 -15.31128295 -17.63684246
119 8.49683660 -15.31128295
120 7.10117021 8.49683660
121 5.73431466 7.10117021
122 -9.61895647 5.73431466
123 27.31697781 -9.61895647
124 -25.73541134 27.31697781
125 7.24090799 -25.73541134
126 -1.94398990 7.24090799
127 11.05217936 -1.94398990
128 37.36738905 11.05217936
129 14.98894522 37.36738905
130 -7.69906242 14.98894522
131 39.09358129 -7.69906242
132 10.92660789 39.09358129
133 -9.18826744 10.92660789
134 5.43137351 -9.18826744
135 13.21949002 5.43137351
136 7.48673772 13.21949002
137 -1.47006837 7.48673772
138 2.11895005 -1.47006837
139 12.59888020 2.11895005
140 -5.19755868 12.59888020
141 11.29813383 -5.19755868
142 -29.18117406 11.29813383
143 1.93136018 -29.18117406
144 -17.08251805 1.93136018
145 -4.75714660 -17.08251805
146 -28.97053840 -4.75714660
147 -16.91144016 -28.97053840
148 5.00508024 -16.91144016
149 -25.49817006 5.00508024
150 -15.35180185 -25.49817006
151 -40.40336587 -15.35180185
152 10.55679734 -40.40336587
153 1.21682871 10.55679734
154 30.49422262 1.21682871
155 2.68555993 30.49422262
156 -12.01076597 2.68555993
157 -7.08006458 -12.01076597
158 -14.08062441 -7.08006458
159 -23.69987995 -14.08062441
160 -34.34874853 -23.69987995
161 -25.41489193 -34.34874853
162 -6.56294110 -25.41489193
163 -14.14121584 -6.56294110
164 -9.02845724 -14.14121584
165 8.63202170 -9.02845724
166 1.21044105 8.63202170
167 -30.46969683 1.21044105
168 11.05491109 -30.46969683
169 -32.51492619 11.05491109
170 -9.80388747 -32.51492619
171 35.11026314 -9.80388747
172 -20.65820026 35.11026314
173 30.71055749 -20.65820026
174 65.40027796 30.71055749
175 -17.72673156 65.40027796
176 4.47414834 -17.72673156
177 -11.53197450 4.47414834
178 36.91471133 -11.53197450
179 6.86418408 36.91471133
180 12.89058779 6.86418408
181 21.91917519 12.89058779
182 9.77015841 21.91917519
183 -18.56813769 9.77015841
184 11.83148865 -18.56813769
185 14.38064880 11.83148865
186 3.38727819 14.38064880
187 3.99327524 3.38727819
188 -9.23415893 3.99327524
189 -3.47439167 -9.23415893
190 -1.91048346 -3.47439167
191 -23.05719736 -1.91048346
192 19.53720365 -23.05719736
193 -19.96053425 19.53720365
194 -4.43287234 -19.96053425
195 18.79872836 -4.43287234
196 -23.27395339 18.79872836
197 -6.24153926 -23.27395339
198 -5.26844889 -6.24153926
199 3.40243764 -5.26844889
200 -5.20756402 3.40243764
201 -7.22104389 -5.20756402
202 -8.80492878 -7.22104389
203 -19.33883141 -8.80492878
204 -8.85335396 -19.33883141
205 -11.88785650 -8.85335396
206 0.09891267 -11.88785650
207 -26.00174783 0.09891267
208 -13.34578737 -26.00174783
209 -10.10145611 -13.34578737
210 7.29782966 -10.10145611
211 -3.65600563 7.29782966
212 -4.80504684 -3.65600563
213 -1.41724437 -4.80504684
214 -27.66259493 -1.41724437
215 -28.36690469 -27.66259493
216 -0.28169667 -28.36690469
217 -3.09523336 -0.28169667
218 5.51034273 -3.09523336
219 -19.91882189 5.51034273
220 -8.37985641 -19.91882189
221 -15.83273586 -8.37985641
222 16.69892298 -15.83273586
223 -6.96671927 16.69892298
224 -12.33975914 -6.96671927
225 6.62090400 -12.33975914
226 10.11401207 6.62090400
227 3.83549715 10.11401207
228 16.39937016 3.83549715
229 -8.08560827 16.39937016
230 -18.68273959 -8.08560827
231 8.70121143 -18.68273959
232 -7.39845415 8.70121143
233 -4.89146757 -7.39845415
234 -3.94008426 -4.89146757
235 12.71847010 -3.94008426
236 -28.78546065 12.71847010
237 6.31941980 -28.78546065
238 0.60817496 6.31941980
239 -4.45368209 0.60817496
240 -11.93984841 -4.45368209
241 -25.14038907 -11.93984841
242 9.35030513 -25.14038907
243 1.14620524 9.35030513
244 -4.19866924 1.14620524
245 -2.83783968 -4.19866924
246 13.99200933 -2.83783968
247 -18.98049374 13.99200933
248 -5.31896458 -18.98049374
249 12.33779433 -5.31896458
250 -29.68763145 12.33779433
251 -0.62051069 -29.68763145
252 17.88845559 -0.62051069
253 -27.74291690 17.88845559
254 -15.15056062 -27.74291690
255 5.79519932 -15.15056062
256 16.34421512 5.79519932
257 -3.92775188 16.34421512
258 -7.87045117 -3.92775188
259 6.65260424 -7.87045117
260 -9.68147941 6.65260424
261 8.44832452 -9.68147941
262 -0.85623046 8.44832452
263 17.86679468 -0.85623046
264 -6.55488856 17.86679468
265 10.66136685 -6.55488856
266 5.82623857 10.66136685
267 11.97527611 5.82623857
268 -7.24825847 11.97527611
269 4.70878568 -7.24825847
270 -21.17158764 4.70878568
271 -10.97239247 -21.17158764
272 2.53587958 -10.97239247
273 9.69565393 2.53587958
274 7.10493817 9.69565393
275 18.37957305 7.10493817
276 -10.93188907 18.37957305
277 14.00059355 -10.93188907
278 7.52149168 14.00059355
279 -17.88248990 7.52149168
280 -8.86820779 -17.88248990
281 -7.01917820 -8.86820779
282 6.10211667 -7.01917820
283 4.59751076 6.10211667
284 -17.92916052 4.59751076
285 -6.96809738 -17.92916052
286 -3.35613389 -6.96809738
287 2.25273914 -3.35613389
288 -11.29289372 2.25273914
> 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/fisher/rcomp/tmp/7cr9w1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8gyaf1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9rfb51352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10jxqs1352152219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11d6gf1352152219.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/fisher/rcomp/tmp/12yfng1352152219.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/fisher/rcomp/tmp/13rd5n1352152219.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/fisher/rcomp/tmp/141fnt1352152219.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/fisher/rcomp/tmp/15zry31352152219.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/fisher/rcomp/tmp/16qpba1352152219.tab")
+ }
>
> try(system("convert tmp/1wuxh1352152219.ps tmp/1wuxh1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vwmn1352152219.ps tmp/2vwmn1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/3djdy1352152219.ps tmp/3djdy1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/44kfp1352152219.ps tmp/44kfp1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l28n1352152219.ps tmp/5l28n1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/66y9z1352152219.ps tmp/66y9z1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cr9w1352152219.ps tmp/7cr9w1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gyaf1352152219.ps tmp/8gyaf1352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rfb51352152219.ps tmp/9rfb51352152219.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jxqs1352152219.ps tmp/10jxqs1352152219.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.697 1.172 12.869