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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> x <- array(list(56
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+ ,46
+ ,0
+ ,28
+ ,15
+ ,60
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16
+ ,64)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1')
+ ,1:289))
> y <- array(NA,dim=c(7,289),dimnames=list(c('logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1'),1:289))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '7'
> 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
feedback_messages_p1 logins compendium_views_info compendium_views_pr
1 115 56 396 81
2 109 56 297 55
3 146 54 559 50
4 116 89 967 125
5 68 40 270 40
6 101 25 143 37
7 96 92 1562 63
8 67 18 109 44
9 44 63 371 88
10 100 44 656 66
11 93 33 511 57
12 140 84 655 74
13 166 88 465 49
14 99 55 525 52
15 139 60 885 88
16 130 66 497 36
17 181 154 1436 108
18 116 53 612 43
19 116 119 865 75
20 88 41 385 32
21 139 61 567 44
22 135 58 639 85
23 108 75 963 86
24 89 33 398 56
25 156 40 410 50
26 129 92 966 135
27 118 100 801 63
28 118 112 892 81
29 125 73 513 52
30 95 40 469 44
31 126 45 683 113
32 135 60 643 39
33 154 62 535 73
34 165 75 625 48
35 113 31 264 33
36 127 77 992 59
37 52 34 238 41
38 121 46 818 69
39 136 99 937 64
40 0 17 70 1
41 108 66 507 59
42 46 30 260 32
43 54 76 503 129
44 124 146 927 37
45 115 67 1269 31
46 128 56 537 65
47 80 107 910 107
48 97 58 532 74
49 104 34 345 54
50 59 61 918 76
51 125 119 1635 715
52 82 42 330 57
53 149 66 557 66
54 149 89 1178 106
55 122 44 740 54
56 118 66 452 32
57 12 24 218 20
58 144 259 764 71
59 67 17 255 21
60 52 64 454 70
61 108 41 866 112
62 166 68 574 66
63 80 168 1276 190
64 60 43 379 66
65 107 132 825 165
66 127 105 798 56
67 107 71 663 61
68 146 112 1069 53
69 84 94 921 127
70 141 82 858 63
71 123 70 711 38
72 111 57 503 50
73 98 53 382 52
74 105 103 464 42
75 135 121 717 76
76 107 62 690 67
77 85 52 462 50
78 155 52 657 53
79 88 32 385 39
80 155 62 577 50
81 104 45 619 77
82 132 46 479 57
83 127 63 817 73
84 108 75 752 34
85 129 88 430 39
86 116 46 451 46
87 122 53 537 63
88 85 37 519 35
89 147 90 1000 106
90 99 63 637 43
91 87 78 465 47
92 28 25 437 31
93 90 45 711 162
94 109 46 299 57
95 78 41 248 36
96 111 144 1162 263
97 158 82 714 78
98 141 91 905 63
99 122 71 649 54
100 124 63 512 63
101 93 53 472 77
102 124 62 905 79
103 112 63 786 110
104 108 32 489 56
105 99 39 479 56
106 117 62 617 43
107 199 117 925 111
108 78 34 351 71
109 91 92 1144 62
110 158 93 669 56
111 126 54 707 74
112 122 144 458 60
113 71 14 214 43
114 75 61 599 68
115 115 109 572 53
116 119 38 897 87
117 124 73 819 46
118 72 75 720 105
119 91 50 273 32
120 45 61 508 133
121 78 55 506 79
122 39 77 451 51
123 68 75 699 207
124 119 72 407 67
125 117 50 465 47
126 39 32 245 34
127 50 53 370 66
128 88 42 316 76
129 155 71 603 65
130 0 10 154 9
131 36 35 229 42
132 123 65 577 45
133 32 25 192 25
134 99 66 617 115
135 136 41 411 97
136 117 86 975 53
137 0 16 146 2
138 88 42 705 52
139 39 19 184 44
140 25 19 200 22
141 52 45 274 35
142 75 65 502 74
143 71 35 382 103
144 124 95 964 144
145 151 49 537 60
146 71 37 438 134
147 145 64 369 89
148 87 38 417 42
149 27 34 276 52
150 131 32 514 98
151 162 65 822 99
152 165 52 389 52
153 54 62 466 29
154 159 65 1255 125
155 147 83 694 106
156 170 95 1024 95
157 119 29 400 40
158 49 18 397 140
159 104 33 350 43
160 120 247 719 128
161 150 139 1277 142
162 112 29 356 73
163 59 118 457 72
164 136 110 1402 128
165 107 67 600 61
166 130 42 480 73
167 115 65 595 148
168 107 94 436 64
169 75 64 230 45
170 71 81 651 58
171 120 95 1367 97
172 116 67 564 50
173 79 63 716 37
174 150 83 747 50
175 156 45 467 105
176 51 30 671 69
177 118 70 861 46
178 71 32 319 57
179 144 83 612 52
180 47 31 433 98
181 28 67 434 61
182 68 66 503 89
183 0 10 85 0
184 110 70 564 48
185 147 103 824 91
186 0 5 74 0
187 15 20 259 7
188 4 5 69 3
189 64 36 535 54
190 111 34 239 70
191 85 48 438 36
192 68 40 459 37
193 40 43 426 123
194 80 31 288 247
195 88 42 498 46
196 48 46 454 72
197 76 33 376 41
198 51 18 225 24
199 67 55 555 45
200 59 35 252 33
201 61 59 208 27
202 76 19 130 36
203 60 66 481 87
204 68 60 389 90
205 71 36 565 114
206 76 25 173 31
207 62 47 278 45
208 61 54 609 69
209 67 53 422 51
210 88 40 445 34
211 30 40 387 60
212 64 39 339 45
213 68 14 181 54
214 64 45 245 25
215 91 36 384 38
216 88 28 212 52
217 52 44 399 67
218 49 30 229 74
219 62 22 224 38
220 61 17 203 30
221 76 31 333 26
222 88 55 384 67
223 66 54 636 132
224 71 21 185 42
225 68 14 93 35
226 48 81 581 118
227 25 35 248 68
228 68 43 304 43
229 41 46 344 76
230 90 30 407 64
231 66 23 170 48
232 54 38 312 64
233 59 54 507 56
234 60 20 224 71
235 77 53 340 75
236 68 45 168 39
237 72 39 443 42
238 67 20 204 39
239 64 24 367 93
240 63 31 210 38
241 59 35 335 60
242 84 151 364 71
243 64 52 178 52
244 56 30 206 27
245 54 31 279 59
246 67 29 387 40
247 58 57 490 79
248 59 40 238 44
249 40 44 343 65
250 22 25 232 10
251 83 77 530 124
252 81 35 291 81
253 2 11 67 15
254 72 63 397 92
255 61 44 467 42
256 15 19 178 10
257 32 13 175 24
258 62 42 299 64
259 58 38 154 45
260 36 29 106 22
261 59 20 189 56
262 68 27 194 94
263 21 20 135 19
264 55 19 201 35
265 54 37 207 32
266 55 26 280 35
267 72 42 260 48
268 41 49 227 49
269 61 30 239 48
270 67 49 333 62
271 76 67 428 96
272 64 28 230 45
273 3 19 292 63
274 63 49 350 71
275 40 27 186 26
276 69 30 326 48
277 48 22 155 29
278 8 12 75 19
279 52 31 361 45
280 66 20 261 45
281 76 20 299 67
282 43 39 300 30
283 39 29 450 36
284 14 16 183 34
285 61 27 238 36
286 71 21 165 34
287 44 19 234 37
288 60 35 176 46
289 64 14 329 44
shared_compendiums blogged_computations compendiums_reviewed
1 3 79 30
2 4 58 28
3 12 60 38
4 2 108 30
5 1 49 22
6 3 0 26
7 0 121 25
8 0 1 18
9 0 20 11
10 5 43 26
11 0 69 25
12 0 78 38
13 7 86 44
14 7 44 30
15 3 104 40
16 9 63 34
17 0 158 47
18 4 102 30
19 3 77 31
20 0 82 23
21 7 115 36
22 0 101 36
23 1 80 30
24 5 50 25
25 7 83 39
26 0 123 34
27 0 73 31
28 5 81 31
29 0 105 33
30 0 47 25
31 0 105 33
32 3 94 35
33 4 44 42
34 1 114 43
35 4 38 30
36 2 107 33
37 0 30 13
38 0 71 32
39 0 84 36
40 0 0 0
41 2 59 28
42 1 33 14
43 0 42 17
44 2 96 32
45 10 106 30
46 6 56 35
47 0 57 20
48 5 59 28
49 4 39 28
50 1 34 39
51 2 76 34
52 2 20 26
53 0 91 39
54 8 115 39
55 3 85 33
56 0 76 28
57 0 8 4
58 8 79 39
59 5 21 18
60 3 30 14
61 1 76 29
62 5 101 44
63 1 94 21
64 1 27 16
65 5 92 28
66 0 123 35
67 12 75 28
68 8 128 38
69 8 105 23
70 8 55 36
71 8 56 32
72 2 41 29
73 0 72 25
74 5 67 27
75 8 75 36
76 2 114 28
77 5 118 23
78 12 77 40
79 6 22 23
80 7 66 40
81 2 69 28
82 0 105 34
83 4 116 33
84 3 88 28
85 6 73 34
86 2 99 30
87 0 62 33
88 1 53 22
89 0 118 38
90 5 30 26
91 2 100 35
92 0 49 8
93 0 24 24
94 5 67 29
95 0 46 20
96 1 57 29
97 0 75 45
98 1 135 37
99 1 68 33
100 2 124 33
101 6 33 25
102 1 98 32
103 4 58 29
104 2 68 28
105 3 81 28
106 0 131 31
107 10 110 52
108 0 37 21
109 9 130 24
110 7 93 41
111 0 118 33
112 0 39 32
113 4 13 19
114 4 74 20
115 0 81 31
116 0 109 31
117 0 151 32
118 1 51 18
119 0 28 23
120 1 40 17
121 0 56 20
122 0 27 12
123 4 37 17
124 0 83 30
125 4 54 31
126 4 27 10
127 3 28 13
128 0 59 22
129 0 133 42
130 0 12 1
131 5 0 9
132 0 106 32
133 4 23 11
134 0 44 25
135 0 71 36
136 1 116 31
137 0 4 0
138 5 62 24
139 0 12 13
140 0 18 8
141 0 14 13
142 0 60 19
143 0 7 18
144 2 98 33
145 7 64 40
146 1 29 22
147 8 32 38
148 2 25 24
149 0 16 8
150 2 48 35
151 0 100 43
152 0 46 43
153 1 45 14
154 3 129 41
155 0 130 38
156 3 136 45
157 0 59 31
158 0 25 13
159 0 32 28
160 4 63 31
161 4 95 40
162 11 14 30
163 0 36 16
164 0 113 37
165 4 47 30
166 0 92 35
167 1 70 32
168 0 19 27
169 0 50 20
170 0 41 18
171 9 91 31
172 1 111 31
173 3 41 21
174 10 120 39
175 5 135 41
176 0 27 13
177 2 87 32
178 0 25 18
179 1 131 39
180 2 45 14
181 4 29 7
182 0 58 17
183 0 4 0
184 2 47 30
185 1 109 37
186 0 7 0
187 0 12 5
188 0 0 1
189 1 37 16
190 0 37 32
191 2 46 24
192 0 15 17
193 3 42 11
194 6 7 24
195 0 54 22
196 2 54 12
197 0 14 19
198 2 16 13
199 1 33 17
200 1 32 15
201 2 21 16
202 1 15 24
203 0 38 15
204 1 22 17
205 3 28 18
206 0 10 20
207 0 31 16
208 0 32 16
209 0 32 18
210 1 43 22
211 4 27 8
212 0 37 17
213 0 20 18
214 0 32 16
215 7 0 23
216 2 5 22
217 0 26 13
218 7 10 13
219 3 27 16
220 0 11 16
221 0 29 20
222 6 25 22
223 2 55 17
224 0 23 18
225 0 5 17
226 3 43 12
227 0 23 7
228 1 34 17
229 1 36 14
230 0 35 23
231 1 0 17
232 0 37 14
233 0 28 15
234 0 16 17
235 0 26 21
236 0 38 18
237 0 23 18
238 0 22 17
239 0 30 17
240 0 16 16
241 0 18 15
242 0 28 21
243 0 32 16
244 2 21 14
245 0 23 15
246 1 29 17
247 1 50 15
248 0 12 15
249 0 21 10
250 0 18 6
251 0 27 22
252 0 41 21
253 0 13 1
254 1 12 18
255 0 21 17
256 0 8 4
257 0 26 10
258 0 27 16
259 1 13 16
260 0 16 9
261 0 2 16
262 0 42 17
263 0 5 7
264 0 37 15
265 0 17 14
266 0 38 14
267 0 37 18
268 0 29 12
269 0 32 16
270 0 35 21
271 1 17 19
272 0 20 16
273 0 7 1
274 1 46 16
275 0 24 10
276 6 40 19
277 3 3 12
278 1 10 2
279 2 37 14
280 0 17 17
281 0 28 19
282 0 19 14
283 3 29 11
284 1 8 4
285 0 10 16
286 0 15 20
287 1 15 12
288 0 28 15
289 0 17 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins compendium_views_info
1.1990852 0.0229634 -0.0038525
compendium_views_pr shared_compendiums blogged_computations
-0.0003721 0.2280429 0.0593608
compendiums_reviewed
3.5809120
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-81.937 -1.352 1.163 3.278 12.262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1990852 1.0922535 1.098 0.27322
logins 0.0229634 0.0174805 1.314 0.19003
compendium_views_info -0.0038525 0.0028720 -1.341 0.18087
compendium_views_pr -0.0003721 0.0098012 -0.038 0.96974
shared_compendiums 0.2280429 0.1694785 1.346 0.17953
blogged_computations 0.0593608 0.0217383 2.731 0.00672 **
compendiums_reviewed 3.5809120 0.0660298 54.232 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.096 on 282 degrees of freedom
Multiple R-squared: 0.9695, Adjusted R-squared: 0.9689
F-statistic: 1495 on 6 and 282 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,] 0.4706476798 9.412954e-01 5.293523e-01
[2,] 0.4007505749 8.015011e-01 5.992494e-01
[3,] 0.3431048987 6.862098e-01 6.568951e-01
[4,] 0.2377719881 4.755440e-01 7.622280e-01
[5,] 0.6415957343 7.168085e-01 3.584043e-01
[6,] 0.7290183933 5.419632e-01 2.709816e-01
[7,] 0.6516008145 6.967984e-01 3.483992e-01
[8,] 0.5686734032 8.626532e-01 4.313266e-01
[9,] 0.5360215709 9.279569e-01 4.639784e-01
[10,] 0.4752526546 9.505053e-01 5.247473e-01
[11,] 0.4037720406 8.075441e-01 5.962280e-01
[12,] 0.3340949739 6.681899e-01 6.659050e-01
[13,] 0.2686132515 5.372265e-01 7.313867e-01
[14,] 0.2209130733 4.418261e-01 7.790869e-01
[15,] 0.1864323969 3.728648e-01 8.135676e-01
[16,] 0.2538253749 5.076507e-01 7.461746e-01
[17,] 0.1996251167 3.992502e-01 8.003749e-01
[18,] 0.1589694227 3.179388e-01 8.410306e-01
[19,] 0.1206820335 2.413641e-01 8.793180e-01
[20,] 0.0896909180 1.793818e-01 9.103091e-01
[21,] 0.0718096782 1.436194e-01 9.281903e-01
[22,] 0.0543663800 1.087328e-01 9.456336e-01
[23,] 0.0421134645 8.422693e-02 9.578865e-01
[24,] 0.0296888035 5.937761e-02 9.703112e-01
[25,] 0.0217035090 4.340702e-02 9.782965e-01
[26,] 0.0151353799 3.027076e-02 9.848646e-01
[27,] 0.0108032239 2.160645e-02 9.891968e-01
[28,] 0.0079971978 1.599440e-02 9.920028e-01
[29,] 0.0058328501 1.166570e-02 9.941671e-01
[30,] 0.0038466653 7.693331e-03 9.961533e-01
[31,] 0.0024654651 4.930930e-03 9.975345e-01
[32,] 0.0016984372 3.396874e-03 9.983016e-01
[33,] 0.0018858352 3.771670e-03 9.981142e-01
[34,] 0.0025450044 5.090009e-03 9.974550e-01
[35,] 0.0016455306 3.291061e-03 9.983545e-01
[36,] 0.0010464717 2.092943e-03 9.989535e-01
[37,] 0.0007136664 1.427333e-03 9.992863e-01
[38,] 0.0007662861 1.532572e-03 9.992337e-01
[39,] 0.0009834821 1.966964e-03 9.990165e-01
[40,] 0.0006270755 1.254151e-03 9.993729e-01
[41,] 1.0000000000 1.361735e-16 6.808673e-17
[42,] 1.0000000000 1.772841e-16 8.864207e-17
[43,] 1.0000000000 4.394862e-17 2.197431e-17
[44,] 1.0000000000 7.094413e-17 3.547207e-17
[45,] 1.0000000000 1.608447e-16 8.042236e-17
[46,] 1.0000000000 3.107297e-16 1.553649e-16
[47,] 1.0000000000 8.140592e-17 4.070296e-17
[48,] 1.0000000000 1.531908e-16 7.659540e-17
[49,] 1.0000000000 6.145680e-17 3.072840e-17
[50,] 1.0000000000 1.324147e-16 6.620736e-17
[51,] 1.0000000000 2.842323e-16 1.421161e-16
[52,] 1.0000000000 4.971762e-16 2.485881e-16
[53,] 1.0000000000 1.069911e-15 5.349557e-16
[54,] 1.0000000000 2.028402e-15 1.014201e-15
[55,] 1.0000000000 4.051389e-15 2.025694e-15
[56,] 1.0000000000 4.918980e-15 2.459490e-15
[57,] 1.0000000000 3.005967e-15 1.502984e-15
[58,] 1.0000000000 4.609393e-15 2.304697e-15
[59,] 1.0000000000 8.824729e-15 4.412364e-15
[60,] 1.0000000000 2.923699e-15 1.461850e-15
[61,] 1.0000000000 7.830696e-16 3.915348e-16
[62,] 1.0000000000 1.053623e-15 5.268114e-16
[63,] 1.0000000000 1.119845e-15 5.599226e-16
[64,] 1.0000000000 1.987428e-15 9.937142e-16
[65,] 1.0000000000 3.866258e-15 1.933129e-15
[66,] 1.0000000000 7.757181e-15 3.878590e-15
[67,] 1.0000000000 1.112839e-14 5.564197e-15
[68,] 1.0000000000 3.020495e-15 1.510247e-15
[69,] 1.0000000000 4.603064e-15 2.301532e-15
[70,] 1.0000000000 7.363906e-15 3.681953e-15
[71,] 1.0000000000 8.190581e-15 4.095290e-15
[72,] 1.0000000000 1.619348e-14 8.096740e-15
[73,] 1.0000000000 2.590931e-14 1.295465e-14
[74,] 1.0000000000 4.797728e-14 2.398864e-14
[75,] 1.0000000000 9.144170e-14 4.572085e-14
[76,] 1.0000000000 1.743313e-13 8.716564e-14
[77,] 1.0000000000 2.834051e-13 1.417025e-13
[78,] 1.0000000000 4.881294e-13 2.440647e-13
[79,] 1.0000000000 7.995801e-13 3.997900e-13
[80,] 1.0000000000 1.189096e-12 5.945481e-13
[81,] 1.0000000000 1.539856e-12 7.699282e-13
[82,] 1.0000000000 2.057018e-29 1.028509e-29
[83,] 1.0000000000 4.146996e-29 2.073498e-29
[84,] 1.0000000000 6.896919e-29 3.448460e-29
[85,] 1.0000000000 1.796658e-28 8.983291e-29
[86,] 1.0000000000 3.884533e-28 1.942267e-28
[87,] 1.0000000000 6.885564e-28 3.442782e-28
[88,] 1.0000000000 7.137171e-29 3.568585e-29
[89,] 1.0000000000 1.905492e-28 9.527459e-29
[90,] 1.0000000000 3.563392e-28 1.781696e-28
[91,] 1.0000000000 7.867002e-28 3.933501e-28
[92,] 1.0000000000 1.980089e-27 9.900443e-28
[93,] 1.0000000000 3.870036e-27 1.935018e-27
[94,] 1.0000000000 7.259430e-27 3.629715e-27
[95,] 1.0000000000 1.495812e-26 7.479062e-27
[96,] 1.0000000000 1.169387e-26 5.846933e-27
[97,] 1.0000000000 2.720391e-26 1.360196e-26
[98,] 1.0000000000 5.821415e-26 2.910708e-26
[99,] 1.0000000000 1.407385e-25 7.036927e-26
[100,] 1.0000000000 2.296683e-25 1.148342e-25
[101,] 1.0000000000 4.846584e-25 2.423292e-25
[102,] 1.0000000000 1.166298e-24 5.831488e-25
[103,] 1.0000000000 2.192344e-24 1.096172e-24
[104,] 1.0000000000 5.247435e-24 2.623717e-24
[105,] 1.0000000000 1.184887e-23 5.924433e-24
[106,] 1.0000000000 2.104456e-23 1.052228e-23
[107,] 1.0000000000 4.380830e-23 2.190415e-23
[108,] 1.0000000000 9.613303e-23 4.806652e-23
[109,] 1.0000000000 1.565355e-22 7.826777e-23
[110,] 1.0000000000 2.086111e-22 1.043056e-22
[111,] 1.0000000000 6.237983e-26 3.118991e-26
[112,] 1.0000000000 1.331519e-25 6.657596e-26
[113,] 1.0000000000 7.183573e-26 3.591786e-26
[114,] 1.0000000000 1.038359e-25 5.191793e-26
[115,] 1.0000000000 1.126533e-25 5.632665e-26
[116,] 1.0000000000 2.739888e-25 1.369944e-25
[117,] 1.0000000000 6.576733e-25 3.288366e-25
[118,] 1.0000000000 1.622322e-24 8.111609e-25
[119,] 1.0000000000 1.724477e-24 8.622387e-25
[120,] 1.0000000000 2.972321e-24 1.486161e-24
[121,] 1.0000000000 4.694875e-24 2.347438e-24
[122,] 1.0000000000 1.021604e-23 5.108018e-24
[123,] 1.0000000000 2.338770e-23 1.169385e-23
[124,] 1.0000000000 4.354965e-24 2.177483e-24
[125,] 1.0000000000 4.398777e-24 2.199388e-24
[126,] 1.0000000000 9.755955e-24 4.877977e-24
[127,] 1.0000000000 2.194283e-23 1.097142e-23
[128,] 1.0000000000 5.283629e-23 2.641814e-23
[129,] 1.0000000000 9.890503e-23 4.945252e-23
[130,] 1.0000000000 1.932696e-23 9.663481e-24
[131,] 1.0000000000 1.997772e-23 9.988858e-24
[132,] 1.0000000000 3.801983e-23 1.900992e-23
[133,] 1.0000000000 7.425476e-23 3.712738e-23
[134,] 1.0000000000 9.018816e-23 4.509408e-23
[135,] 1.0000000000 2.179556e-22 1.089778e-22
[136,] 1.0000000000 5.058361e-22 2.529180e-22
[137,] 1.0000000000 5.508806e-23 2.754403e-23
[138,] 1.0000000000 1.061043e-22 5.305217e-23
[139,] 1.0000000000 1.702372e-22 8.511859e-23
[140,] 1.0000000000 2.888572e-22 1.444286e-22
[141,] 1.0000000000 6.478857e-22 3.239429e-22
[142,] 1.0000000000 1.461296e-21 7.306479e-22
[143,] 1.0000000000 1.845545e-21 9.227724e-22
[144,] 1.0000000000 4.363013e-21 2.181507e-21
[145,] 1.0000000000 5.861679e-21 2.930839e-21
[146,] 1.0000000000 8.786779e-21 4.393390e-21
[147,] 1.0000000000 2.092425e-20 1.046213e-20
[148,] 1.0000000000 3.702509e-20 1.851255e-20
[149,] 1.0000000000 8.105382e-20 4.052691e-20
[150,] 1.0000000000 1.743643e-19 8.718214e-20
[151,] 1.0000000000 3.798597e-19 1.899298e-19
[152,] 1.0000000000 7.135739e-19 3.567870e-19
[153,] 1.0000000000 1.622450e-18 8.112249e-19
[154,] 1.0000000000 2.252511e-18 1.126255e-18
[155,] 1.0000000000 2.114681e-18 1.057341e-18
[156,] 1.0000000000 1.145308e-18 5.726538e-19
[157,] 1.0000000000 2.589196e-18 1.294598e-18
[158,] 1.0000000000 2.954145e-18 1.477072e-18
[159,] 1.0000000000 4.106768e-18 2.053384e-18
[160,] 1.0000000000 9.097903e-18 4.548951e-18
[161,] 1.0000000000 1.864982e-17 9.324911e-18
[162,] 1.0000000000 3.811700e-17 1.905850e-17
[163,] 1.0000000000 7.690190e-17 3.845095e-17
[164,] 1.0000000000 1.262721e-16 6.313604e-17
[165,] 1.0000000000 2.725502e-16 1.362751e-16
[166,] 1.0000000000 3.302503e-16 1.651251e-16
[167,] 1.0000000000 6.623931e-16 3.311965e-16
[168,] 1.0000000000 6.351152e-16 3.175576e-16
[169,] 1.0000000000 1.046830e-15 5.234152e-16
[170,] 1.0000000000 1.350228e-15 6.751142e-16
[171,] 1.0000000000 1.260606e-15 6.303030e-16
[172,] 1.0000000000 2.736010e-15 1.368005e-15
[173,] 1.0000000000 5.192891e-15 2.596445e-15
[174,] 1.0000000000 1.131398e-14 5.656991e-15
[175,] 1.0000000000 9.187409e-15 4.593705e-15
[176,] 1.0000000000 1.353534e-14 6.767671e-15
[177,] 1.0000000000 2.937456e-14 1.468728e-14
[178,] 1.0000000000 3.034768e-14 1.517384e-14
[179,] 1.0000000000 6.538895e-14 3.269448e-14
[180,] 1.0000000000 1.230010e-13 6.150050e-14
[181,] 1.0000000000 3.859791e-14 1.929895e-14
[182,] 1.0000000000 1.485701e-14 7.428507e-15
[183,] 1.0000000000 2.695270e-14 1.347635e-14
[184,] 1.0000000000 5.762713e-14 2.881356e-14
[185,] 1.0000000000 2.581371e-14 1.290686e-14
[186,] 1.0000000000 3.749909e-14 1.874955e-14
[187,] 1.0000000000 7.540864e-14 3.770432e-14
[188,] 1.0000000000 1.085398e-13 5.426988e-14
[189,] 1.0000000000 2.125210e-13 1.062605e-13
[190,] 1.0000000000 4.286911e-13 2.143456e-13
[191,] 1.0000000000 8.092893e-13 4.046446e-13
[192,] 1.0000000000 1.733645e-12 8.668223e-13
[193,] 1.0000000000 2.646145e-15 1.323073e-15
[194,] 1.0000000000 4.977798e-15 2.488899e-15
[195,] 1.0000000000 9.268689e-15 4.634344e-15
[196,] 1.0000000000 1.878216e-14 9.391082e-15
[197,] 1.0000000000 4.137627e-14 2.068813e-14
[198,] 1.0000000000 9.296595e-14 4.648297e-14
[199,] 1.0000000000 2.125498e-13 1.062749e-13
[200,] 1.0000000000 4.293409e-13 2.146705e-13
[201,] 1.0000000000 5.841728e-13 2.920864e-13
[202,] 1.0000000000 1.325796e-12 6.628978e-13
[203,] 1.0000000000 2.968725e-12 1.484362e-12
[204,] 1.0000000000 6.491143e-12 3.245572e-12
[205,] 1.0000000000 1.037792e-11 5.188960e-12
[206,] 1.0000000000 1.986233e-11 9.931165e-12
[207,] 1.0000000000 2.952119e-11 1.476060e-11
[208,] 1.0000000000 5.163142e-11 2.581571e-11
[209,] 0.9999999999 1.040826e-10 5.204128e-11
[210,] 0.9999999999 2.185738e-10 1.092869e-10
[211,] 0.9999999998 4.609229e-10 2.304615e-10
[212,] 0.9999999995 9.675632e-10 4.837816e-10
[213,] 0.9999999992 1.529410e-09 7.647052e-10
[214,] 0.9999999985 3.048103e-09 1.524052e-09
[215,] 0.9999999974 5.171080e-09 2.585540e-09
[216,] 0.9999999962 7.570566e-09 3.785283e-09
[217,] 0.9999999931 1.387713e-08 6.938565e-09
[218,] 0.9999999863 2.740122e-08 1.370061e-08
[219,] 0.9999999803 3.937757e-08 1.968878e-08
[220,] 0.9999999995 1.033939e-09 5.169697e-10
[221,] 0.9999999991 1.715912e-09 8.579559e-10
[222,] 0.9999999983 3.399359e-09 1.699680e-09
[223,] 0.9999999963 7.407511e-09 3.703755e-09
[224,] 0.9999999931 1.376456e-08 6.882279e-09
[225,] 0.9999999929 1.426776e-08 7.133882e-09
[226,] 0.9999999896 2.078519e-08 1.039260e-08
[227,] 0.9999999777 4.460562e-08 2.230281e-08
[228,] 0.9999999783 4.333055e-08 2.166528e-08
[229,] 0.9999999645 7.099917e-08 3.549959e-08
[230,] 0.9999999294 1.412266e-07 7.061329e-08
[231,] 0.9999998804 2.392527e-07 1.196264e-07
[232,] 0.9999997830 4.340122e-07 2.170061e-07
[233,] 0.9999996716 6.568483e-07 3.284241e-07
[234,] 0.9999995096 9.808987e-07 4.904494e-07
[235,] 0.9999993910 1.218090e-06 6.090448e-07
[236,] 0.9999990703 1.859428e-06 9.297140e-07
[237,] 0.9999988032 2.393612e-06 1.196806e-06
[238,] 0.9999976230 4.754039e-06 2.377019e-06
[239,] 0.9999963039 7.392111e-06 3.696055e-06
[240,] 0.9999931459 1.370829e-05 6.854143e-06
[241,] 0.9999878982 2.420360e-05 1.210180e-05
[242,] 0.9999796120 4.077593e-05 2.038796e-05
[243,] 0.9999602176 7.956473e-05 3.978236e-05
[244,] 0.9999251024 1.497953e-04 7.489763e-05
[245,] 0.9998818628 2.362744e-04 1.181372e-04
[246,] 0.9997752054 4.495891e-04 2.247946e-04
[247,] 0.9996272690 7.454619e-04 3.727310e-04
[248,] 0.9996288771 7.422458e-04 3.711229e-04
[249,] 0.9993365973 1.326805e-03 6.634027e-04
[250,] 0.9988643355 2.271329e-03 1.135664e-03
[251,] 0.9983591454 3.281709e-03 1.640855e-03
[252,] 0.9976106847 4.778631e-03 2.389315e-03
[253,] 0.9965867313 6.826537e-03 3.413269e-03
[254,] 0.9954633707 9.073259e-03 4.536629e-03
[255,] 0.9929196305 1.416074e-02 7.080369e-03
[256,] 0.9901737719 1.965246e-02 9.826228e-03
[257,] 0.9860569802 2.788604e-02 1.394302e-02
[258,] 0.9869886042 2.602279e-02 1.301140e-02
[259,] 0.9786517205 4.269656e-02 2.134828e-02
[260,] 0.9640827254 7.183455e-02 3.591727e-02
[261,] 0.9969500586 6.099883e-03 3.049941e-03
[262,] 0.9950438350 9.912330e-03 4.956165e-03
[263,] 0.9918460796 1.630784e-02 8.153920e-03
[264,] 0.9903282498 1.934350e-02 9.671750e-03
[265,] 0.9788402950 4.231941e-02 2.115970e-02
[266,] 0.9797670800 4.046584e-02 2.023292e-02
[267,] 0.9723098885 5.538022e-02 2.769011e-02
[268,] 0.9473190886 1.053618e-01 5.268091e-02
[269,] 0.8901791417 2.196417e-01 1.098209e-01
[270,] 0.7706713448 4.586573e-01 2.293287e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1l2gw1354810367.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/2c13b1354810367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ll601354810367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4sb4l1354810367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ls181354810367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.269713350 3.058996063 3.360243234 2.234670539 -14.979340878
6 7 8 9 10
6.003669161 2.023925631 1.308095286 2.239006056 3.545905565
11 12 13 14 15
-0.585719011 -1.281866653 0.328336110 -13.055678789 -10.228784929
16 17 18 19 20
1.670309173 4.155102103 1.563270329 -0.834565622 0.125984235
21 22 23 24 25
1.265279288 0.054160109 -3.583617081 -5.033786455 9.301706230
26 27 28 29 30
0.407673901 2.272283996 0.738900569 -0.282698033 2.392831315
31 32 33 34 35
2.037907964 3.318836113 -0.456898763 3.529960778 1.523154840
36 37 38 39 40
2.898611675 2.619637241 3.117845279 2.262035708 -1.319413835
41 42 43 44 45
3.036608099 -7.194139368 -10.327137794 2.289414155 1.162734597
46 47 48 49 50
-2.416421589 4.887644227 -8.361917982 -0.123402612 -81.936823727
51 52 53 54 55
0.914700086 -13.618016406 3.398346867 2.028495497 -1.238399579
56 57 58 59 60
12.261623342 -3.701446925 -6.346274177 -0.442459791 -1.491367299
61 62 63 64 65
0.651473544 0.779530576 -1.077517972 0.172780389 -0.857465301
66 67 68 69 70
-6.148385155 -0.706670722 0.869909531 -6.180409953 7.244812867
71 72 73 74 75
3.209035614 3.712103042 3.278091635 1.436872664 -1.376348744
76 77 78 79 80
-0.428392552 -6.100474631 4.613877080 2.528652770 5.868106694
81 82 83 84 85
-0.636585822 3.627277070 1.560781708 1.814999686 -0.001370129
86 87 88 89 90
1.739040154 -0.174359345 2.809527823 4.546952454 2.799534501
91 92 93 94 95
-45.905403132 -3.634053388 3.200447310 -1.046122592 2.479400768
96 97 98 99 100
3.610633272 -7.895453020 0.485732547 -0.743773674 -2.636764616
101 102 103 104 105
-0.419062028 4.258536728 4.221696946 3.212655622 -6.986347549
106 107 108 109 110
-2.014342691 3.701549822 0.003315327 -3.592528960 3.329251681
111 112 113 114 115
1.137495251 2.376711741 0.598680132 -2.189994497 -2.295226054
116 117 118 119 120
2.937801486 0.744259956 4.179693893 5.693313898 -19.071256268
121 122 123 124 125
2.574259535 -6.784484479 3.864579707 5.386152065 1.335733830
126 127 128 129 130
-0.301425737 0.135762552 4.799779095 -3.775516625 -5.125321675
131 132 133 134 135
1.526631740 1.666518465 -10.691683754 6.570459085 2.351449917
136 137 138 139 140
-0.520165714 -1.240728667 -2.190634371 -9.174336940 -5.572487523
141 142 143 144 145
3.453271528 2.670823128 5.635248720 -0.056725501 2.134972529
146 147 148 149 150
-10.041030088 3.987413008 -1.331553409 -3.494261568 2.445429954
151 152 153 154 155
2.596616914 7.414988662 0.151207226 6.030672095 2.816489623
156 157 158 159 160
0.701495587 4.180313640 0.933247916 1.242427697 0.286373172
161 162 163 164 165
0.793596406 0.766759259 -2.552951759 -1.477693394 -4.532904716
166 167 168 169 170
-1.080284207 -4.316862550 7.533392993 -1.352198541 3.580250617
171 172 173 174 175
3.459408906 -2.371565964 0.809328992 0.732104464 -0.365552453
176 177 178 179 180
3.568141636 -1.682035457 4.375817562 -4.387827568 -6.466427929
181 182 183 184 185
-0.742955368 2.937839352 -1.338697090 -1.289238013 7.451916014
186 187 188 189 190
-1.444340310 -4.274831666 -0.627872868 4.336447044 -6.818573047
191 192 193 194 195
-4.729094599 5.898543841 -3.066878921 -8.435183086 5.786582401
196 197 198 199 200
0.887925688 6.638552442 2.305609586 3.630374886 2.139044051
201 202 203 204 205
0.260191464 -12.181508099 3.201379950 4.545749805 4.390686925
206 207 208 209 210
2.693004924 1.674605641 1.738621475 -0.127361625 6.048785850
211 212 213 214 215
-1.766574195 0.156241060 1.553197009 3.526595937 6.510469845
216 217 218 219 220
7.461072157 3.257379489 -0.719985914 1.591364660 2.256203663
221 222 223 224 225
2.041914181 5.409889319 1.463785357 4.225315391 5.678428202
226 227 228 229 230
1.015520514 -2.453756368 3.878843877 -12.399651333 5.265204270
231 232 233 234 235
3.842001318 0.824991796 3.159180546 -2.594243468 -0.820910434
236 237 238 239 240
-0.282829170 5.805928492 3.960633603 1.041950335 3.667855871
241 242 243 244 245
3.527945965 3.900923148 3.117778392 3.080250181 -1.893118715
246 247 248 249 250
3.815780667 0.499374133 3.389643903 2.080433253 -1.429628124
251 252 253 254 255
1.737909364 2.515477198 -3.540584249 5.521120331 -1.516794123
256 257 258 259 260
-0.744452259 -6.166986800 2.114839372 -1.755986069 1.373549072
261 262 263 264 265
0.677299851 3.594612867 -5.494379467 -1.758037482 1.618748164
266 267 268 269 270
1.907120162 4.203204460 -5.123942701 0.856490593 -11.295109220
271 272 273 274 275
5.672467506 4.578958331 -1.483444792 1.797280766 1.673368807
276 277 278 279 280
-3.394218292 3.070498554 -1.162111333 -1.288645284 3.479265082
281 282 283 284 285
5.819054576 -9.188358459 -2.913611531 -1.875411732 2.223001533
286 287 288 289
-2.541649493 -0.809528045 3.316574558 5.459558275
> postscript(file="/var/wessaorg/rcomp/tmp/6icxi1354810367.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 1.269713350 NA
1 3.058996063 1.269713350
2 3.360243234 3.058996063
3 2.234670539 3.360243234
4 -14.979340878 2.234670539
5 6.003669161 -14.979340878
6 2.023925631 6.003669161
7 1.308095286 2.023925631
8 2.239006056 1.308095286
9 3.545905565 2.239006056
10 -0.585719011 3.545905565
11 -1.281866653 -0.585719011
12 0.328336110 -1.281866653
13 -13.055678789 0.328336110
14 -10.228784929 -13.055678789
15 1.670309173 -10.228784929
16 4.155102103 1.670309173
17 1.563270329 4.155102103
18 -0.834565622 1.563270329
19 0.125984235 -0.834565622
20 1.265279288 0.125984235
21 0.054160109 1.265279288
22 -3.583617081 0.054160109
23 -5.033786455 -3.583617081
24 9.301706230 -5.033786455
25 0.407673901 9.301706230
26 2.272283996 0.407673901
27 0.738900569 2.272283996
28 -0.282698033 0.738900569
29 2.392831315 -0.282698033
30 2.037907964 2.392831315
31 3.318836113 2.037907964
32 -0.456898763 3.318836113
33 3.529960778 -0.456898763
34 1.523154840 3.529960778
35 2.898611675 1.523154840
36 2.619637241 2.898611675
37 3.117845279 2.619637241
38 2.262035708 3.117845279
39 -1.319413835 2.262035708
40 3.036608099 -1.319413835
41 -7.194139368 3.036608099
42 -10.327137794 -7.194139368
43 2.289414155 -10.327137794
44 1.162734597 2.289414155
45 -2.416421589 1.162734597
46 4.887644227 -2.416421589
47 -8.361917982 4.887644227
48 -0.123402612 -8.361917982
49 -81.936823727 -0.123402612
50 0.914700086 -81.936823727
51 -13.618016406 0.914700086
52 3.398346867 -13.618016406
53 2.028495497 3.398346867
54 -1.238399579 2.028495497
55 12.261623342 -1.238399579
56 -3.701446925 12.261623342
57 -6.346274177 -3.701446925
58 -0.442459791 -6.346274177
59 -1.491367299 -0.442459791
60 0.651473544 -1.491367299
61 0.779530576 0.651473544
62 -1.077517972 0.779530576
63 0.172780389 -1.077517972
64 -0.857465301 0.172780389
65 -6.148385155 -0.857465301
66 -0.706670722 -6.148385155
67 0.869909531 -0.706670722
68 -6.180409953 0.869909531
69 7.244812867 -6.180409953
70 3.209035614 7.244812867
71 3.712103042 3.209035614
72 3.278091635 3.712103042
73 1.436872664 3.278091635
74 -1.376348744 1.436872664
75 -0.428392552 -1.376348744
76 -6.100474631 -0.428392552
77 4.613877080 -6.100474631
78 2.528652770 4.613877080
79 5.868106694 2.528652770
80 -0.636585822 5.868106694
81 3.627277070 -0.636585822
82 1.560781708 3.627277070
83 1.814999686 1.560781708
84 -0.001370129 1.814999686
85 1.739040154 -0.001370129
86 -0.174359345 1.739040154
87 2.809527823 -0.174359345
88 4.546952454 2.809527823
89 2.799534501 4.546952454
90 -45.905403132 2.799534501
91 -3.634053388 -45.905403132
92 3.200447310 -3.634053388
93 -1.046122592 3.200447310
94 2.479400768 -1.046122592
95 3.610633272 2.479400768
96 -7.895453020 3.610633272
97 0.485732547 -7.895453020
98 -0.743773674 0.485732547
99 -2.636764616 -0.743773674
100 -0.419062028 -2.636764616
101 4.258536728 -0.419062028
102 4.221696946 4.258536728
103 3.212655622 4.221696946
104 -6.986347549 3.212655622
105 -2.014342691 -6.986347549
106 3.701549822 -2.014342691
107 0.003315327 3.701549822
108 -3.592528960 0.003315327
109 3.329251681 -3.592528960
110 1.137495251 3.329251681
111 2.376711741 1.137495251
112 0.598680132 2.376711741
113 -2.189994497 0.598680132
114 -2.295226054 -2.189994497
115 2.937801486 -2.295226054
116 0.744259956 2.937801486
117 4.179693893 0.744259956
118 5.693313898 4.179693893
119 -19.071256268 5.693313898
120 2.574259535 -19.071256268
121 -6.784484479 2.574259535
122 3.864579707 -6.784484479
123 5.386152065 3.864579707
124 1.335733830 5.386152065
125 -0.301425737 1.335733830
126 0.135762552 -0.301425737
127 4.799779095 0.135762552
128 -3.775516625 4.799779095
129 -5.125321675 -3.775516625
130 1.526631740 -5.125321675
131 1.666518465 1.526631740
132 -10.691683754 1.666518465
133 6.570459085 -10.691683754
134 2.351449917 6.570459085
135 -0.520165714 2.351449917
136 -1.240728667 -0.520165714
137 -2.190634371 -1.240728667
138 -9.174336940 -2.190634371
139 -5.572487523 -9.174336940
140 3.453271528 -5.572487523
141 2.670823128 3.453271528
142 5.635248720 2.670823128
143 -0.056725501 5.635248720
144 2.134972529 -0.056725501
145 -10.041030088 2.134972529
146 3.987413008 -10.041030088
147 -1.331553409 3.987413008
148 -3.494261568 -1.331553409
149 2.445429954 -3.494261568
150 2.596616914 2.445429954
151 7.414988662 2.596616914
152 0.151207226 7.414988662
153 6.030672095 0.151207226
154 2.816489623 6.030672095
155 0.701495587 2.816489623
156 4.180313640 0.701495587
157 0.933247916 4.180313640
158 1.242427697 0.933247916
159 0.286373172 1.242427697
160 0.793596406 0.286373172
161 0.766759259 0.793596406
162 -2.552951759 0.766759259
163 -1.477693394 -2.552951759
164 -4.532904716 -1.477693394
165 -1.080284207 -4.532904716
166 -4.316862550 -1.080284207
167 7.533392993 -4.316862550
168 -1.352198541 7.533392993
169 3.580250617 -1.352198541
170 3.459408906 3.580250617
171 -2.371565964 3.459408906
172 0.809328992 -2.371565964
173 0.732104464 0.809328992
174 -0.365552453 0.732104464
175 3.568141636 -0.365552453
176 -1.682035457 3.568141636
177 4.375817562 -1.682035457
178 -4.387827568 4.375817562
179 -6.466427929 -4.387827568
180 -0.742955368 -6.466427929
181 2.937839352 -0.742955368
182 -1.338697090 2.937839352
183 -1.289238013 -1.338697090
184 7.451916014 -1.289238013
185 -1.444340310 7.451916014
186 -4.274831666 -1.444340310
187 -0.627872868 -4.274831666
188 4.336447044 -0.627872868
189 -6.818573047 4.336447044
190 -4.729094599 -6.818573047
191 5.898543841 -4.729094599
192 -3.066878921 5.898543841
193 -8.435183086 -3.066878921
194 5.786582401 -8.435183086
195 0.887925688 5.786582401
196 6.638552442 0.887925688
197 2.305609586 6.638552442
198 3.630374886 2.305609586
199 2.139044051 3.630374886
200 0.260191464 2.139044051
201 -12.181508099 0.260191464
202 3.201379950 -12.181508099
203 4.545749805 3.201379950
204 4.390686925 4.545749805
205 2.693004924 4.390686925
206 1.674605641 2.693004924
207 1.738621475 1.674605641
208 -0.127361625 1.738621475
209 6.048785850 -0.127361625
210 -1.766574195 6.048785850
211 0.156241060 -1.766574195
212 1.553197009 0.156241060
213 3.526595937 1.553197009
214 6.510469845 3.526595937
215 7.461072157 6.510469845
216 3.257379489 7.461072157
217 -0.719985914 3.257379489
218 1.591364660 -0.719985914
219 2.256203663 1.591364660
220 2.041914181 2.256203663
221 5.409889319 2.041914181
222 1.463785357 5.409889319
223 4.225315391 1.463785357
224 5.678428202 4.225315391
225 1.015520514 5.678428202
226 -2.453756368 1.015520514
227 3.878843877 -2.453756368
228 -12.399651333 3.878843877
229 5.265204270 -12.399651333
230 3.842001318 5.265204270
231 0.824991796 3.842001318
232 3.159180546 0.824991796
233 -2.594243468 3.159180546
234 -0.820910434 -2.594243468
235 -0.282829170 -0.820910434
236 5.805928492 -0.282829170
237 3.960633603 5.805928492
238 1.041950335 3.960633603
239 3.667855871 1.041950335
240 3.527945965 3.667855871
241 3.900923148 3.527945965
242 3.117778392 3.900923148
243 3.080250181 3.117778392
244 -1.893118715 3.080250181
245 3.815780667 -1.893118715
246 0.499374133 3.815780667
247 3.389643903 0.499374133
248 2.080433253 3.389643903
249 -1.429628124 2.080433253
250 1.737909364 -1.429628124
251 2.515477198 1.737909364
252 -3.540584249 2.515477198
253 5.521120331 -3.540584249
254 -1.516794123 5.521120331
255 -0.744452259 -1.516794123
256 -6.166986800 -0.744452259
257 2.114839372 -6.166986800
258 -1.755986069 2.114839372
259 1.373549072 -1.755986069
260 0.677299851 1.373549072
261 3.594612867 0.677299851
262 -5.494379467 3.594612867
263 -1.758037482 -5.494379467
264 1.618748164 -1.758037482
265 1.907120162 1.618748164
266 4.203204460 1.907120162
267 -5.123942701 4.203204460
268 0.856490593 -5.123942701
269 -11.295109220 0.856490593
270 5.672467506 -11.295109220
271 4.578958331 5.672467506
272 -1.483444792 4.578958331
273 1.797280766 -1.483444792
274 1.673368807 1.797280766
275 -3.394218292 1.673368807
276 3.070498554 -3.394218292
277 -1.162111333 3.070498554
278 -1.288645284 -1.162111333
279 3.479265082 -1.288645284
280 5.819054576 3.479265082
281 -9.188358459 5.819054576
282 -2.913611531 -9.188358459
283 -1.875411732 -2.913611531
284 2.223001533 -1.875411732
285 -2.541649493 2.223001533
286 -0.809528045 -2.541649493
287 3.316574558 -0.809528045
288 5.459558275 3.316574558
289 NA 5.459558275
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.058996063 1.269713350
[2,] 3.360243234 3.058996063
[3,] 2.234670539 3.360243234
[4,] -14.979340878 2.234670539
[5,] 6.003669161 -14.979340878
[6,] 2.023925631 6.003669161
[7,] 1.308095286 2.023925631
[8,] 2.239006056 1.308095286
[9,] 3.545905565 2.239006056
[10,] -0.585719011 3.545905565
[11,] -1.281866653 -0.585719011
[12,] 0.328336110 -1.281866653
[13,] -13.055678789 0.328336110
[14,] -10.228784929 -13.055678789
[15,] 1.670309173 -10.228784929
[16,] 4.155102103 1.670309173
[17,] 1.563270329 4.155102103
[18,] -0.834565622 1.563270329
[19,] 0.125984235 -0.834565622
[20,] 1.265279288 0.125984235
[21,] 0.054160109 1.265279288
[22,] -3.583617081 0.054160109
[23,] -5.033786455 -3.583617081
[24,] 9.301706230 -5.033786455
[25,] 0.407673901 9.301706230
[26,] 2.272283996 0.407673901
[27,] 0.738900569 2.272283996
[28,] -0.282698033 0.738900569
[29,] 2.392831315 -0.282698033
[30,] 2.037907964 2.392831315
[31,] 3.318836113 2.037907964
[32,] -0.456898763 3.318836113
[33,] 3.529960778 -0.456898763
[34,] 1.523154840 3.529960778
[35,] 2.898611675 1.523154840
[36,] 2.619637241 2.898611675
[37,] 3.117845279 2.619637241
[38,] 2.262035708 3.117845279
[39,] -1.319413835 2.262035708
[40,] 3.036608099 -1.319413835
[41,] -7.194139368 3.036608099
[42,] -10.327137794 -7.194139368
[43,] 2.289414155 -10.327137794
[44,] 1.162734597 2.289414155
[45,] -2.416421589 1.162734597
[46,] 4.887644227 -2.416421589
[47,] -8.361917982 4.887644227
[48,] -0.123402612 -8.361917982
[49,] -81.936823727 -0.123402612
[50,] 0.914700086 -81.936823727
[51,] -13.618016406 0.914700086
[52,] 3.398346867 -13.618016406
[53,] 2.028495497 3.398346867
[54,] -1.238399579 2.028495497
[55,] 12.261623342 -1.238399579
[56,] -3.701446925 12.261623342
[57,] -6.346274177 -3.701446925
[58,] -0.442459791 -6.346274177
[59,] -1.491367299 -0.442459791
[60,] 0.651473544 -1.491367299
[61,] 0.779530576 0.651473544
[62,] -1.077517972 0.779530576
[63,] 0.172780389 -1.077517972
[64,] -0.857465301 0.172780389
[65,] -6.148385155 -0.857465301
[66,] -0.706670722 -6.148385155
[67,] 0.869909531 -0.706670722
[68,] -6.180409953 0.869909531
[69,] 7.244812867 -6.180409953
[70,] 3.209035614 7.244812867
[71,] 3.712103042 3.209035614
[72,] 3.278091635 3.712103042
[73,] 1.436872664 3.278091635
[74,] -1.376348744 1.436872664
[75,] -0.428392552 -1.376348744
[76,] -6.100474631 -0.428392552
[77,] 4.613877080 -6.100474631
[78,] 2.528652770 4.613877080
[79,] 5.868106694 2.528652770
[80,] -0.636585822 5.868106694
[81,] 3.627277070 -0.636585822
[82,] 1.560781708 3.627277070
[83,] 1.814999686 1.560781708
[84,] -0.001370129 1.814999686
[85,] 1.739040154 -0.001370129
[86,] -0.174359345 1.739040154
[87,] 2.809527823 -0.174359345
[88,] 4.546952454 2.809527823
[89,] 2.799534501 4.546952454
[90,] -45.905403132 2.799534501
[91,] -3.634053388 -45.905403132
[92,] 3.200447310 -3.634053388
[93,] -1.046122592 3.200447310
[94,] 2.479400768 -1.046122592
[95,] 3.610633272 2.479400768
[96,] -7.895453020 3.610633272
[97,] 0.485732547 -7.895453020
[98,] -0.743773674 0.485732547
[99,] -2.636764616 -0.743773674
[100,] -0.419062028 -2.636764616
[101,] 4.258536728 -0.419062028
[102,] 4.221696946 4.258536728
[103,] 3.212655622 4.221696946
[104,] -6.986347549 3.212655622
[105,] -2.014342691 -6.986347549
[106,] 3.701549822 -2.014342691
[107,] 0.003315327 3.701549822
[108,] -3.592528960 0.003315327
[109,] 3.329251681 -3.592528960
[110,] 1.137495251 3.329251681
[111,] 2.376711741 1.137495251
[112,] 0.598680132 2.376711741
[113,] -2.189994497 0.598680132
[114,] -2.295226054 -2.189994497
[115,] 2.937801486 -2.295226054
[116,] 0.744259956 2.937801486
[117,] 4.179693893 0.744259956
[118,] 5.693313898 4.179693893
[119,] -19.071256268 5.693313898
[120,] 2.574259535 -19.071256268
[121,] -6.784484479 2.574259535
[122,] 3.864579707 -6.784484479
[123,] 5.386152065 3.864579707
[124,] 1.335733830 5.386152065
[125,] -0.301425737 1.335733830
[126,] 0.135762552 -0.301425737
[127,] 4.799779095 0.135762552
[128,] -3.775516625 4.799779095
[129,] -5.125321675 -3.775516625
[130,] 1.526631740 -5.125321675
[131,] 1.666518465 1.526631740
[132,] -10.691683754 1.666518465
[133,] 6.570459085 -10.691683754
[134,] 2.351449917 6.570459085
[135,] -0.520165714 2.351449917
[136,] -1.240728667 -0.520165714
[137,] -2.190634371 -1.240728667
[138,] -9.174336940 -2.190634371
[139,] -5.572487523 -9.174336940
[140,] 3.453271528 -5.572487523
[141,] 2.670823128 3.453271528
[142,] 5.635248720 2.670823128
[143,] -0.056725501 5.635248720
[144,] 2.134972529 -0.056725501
[145,] -10.041030088 2.134972529
[146,] 3.987413008 -10.041030088
[147,] -1.331553409 3.987413008
[148,] -3.494261568 -1.331553409
[149,] 2.445429954 -3.494261568
[150,] 2.596616914 2.445429954
[151,] 7.414988662 2.596616914
[152,] 0.151207226 7.414988662
[153,] 6.030672095 0.151207226
[154,] 2.816489623 6.030672095
[155,] 0.701495587 2.816489623
[156,] 4.180313640 0.701495587
[157,] 0.933247916 4.180313640
[158,] 1.242427697 0.933247916
[159,] 0.286373172 1.242427697
[160,] 0.793596406 0.286373172
[161,] 0.766759259 0.793596406
[162,] -2.552951759 0.766759259
[163,] -1.477693394 -2.552951759
[164,] -4.532904716 -1.477693394
[165,] -1.080284207 -4.532904716
[166,] -4.316862550 -1.080284207
[167,] 7.533392993 -4.316862550
[168,] -1.352198541 7.533392993
[169,] 3.580250617 -1.352198541
[170,] 3.459408906 3.580250617
[171,] -2.371565964 3.459408906
[172,] 0.809328992 -2.371565964
[173,] 0.732104464 0.809328992
[174,] -0.365552453 0.732104464
[175,] 3.568141636 -0.365552453
[176,] -1.682035457 3.568141636
[177,] 4.375817562 -1.682035457
[178,] -4.387827568 4.375817562
[179,] -6.466427929 -4.387827568
[180,] -0.742955368 -6.466427929
[181,] 2.937839352 -0.742955368
[182,] -1.338697090 2.937839352
[183,] -1.289238013 -1.338697090
[184,] 7.451916014 -1.289238013
[185,] -1.444340310 7.451916014
[186,] -4.274831666 -1.444340310
[187,] -0.627872868 -4.274831666
[188,] 4.336447044 -0.627872868
[189,] -6.818573047 4.336447044
[190,] -4.729094599 -6.818573047
[191,] 5.898543841 -4.729094599
[192,] -3.066878921 5.898543841
[193,] -8.435183086 -3.066878921
[194,] 5.786582401 -8.435183086
[195,] 0.887925688 5.786582401
[196,] 6.638552442 0.887925688
[197,] 2.305609586 6.638552442
[198,] 3.630374886 2.305609586
[199,] 2.139044051 3.630374886
[200,] 0.260191464 2.139044051
[201,] -12.181508099 0.260191464
[202,] 3.201379950 -12.181508099
[203,] 4.545749805 3.201379950
[204,] 4.390686925 4.545749805
[205,] 2.693004924 4.390686925
[206,] 1.674605641 2.693004924
[207,] 1.738621475 1.674605641
[208,] -0.127361625 1.738621475
[209,] 6.048785850 -0.127361625
[210,] -1.766574195 6.048785850
[211,] 0.156241060 -1.766574195
[212,] 1.553197009 0.156241060
[213,] 3.526595937 1.553197009
[214,] 6.510469845 3.526595937
[215,] 7.461072157 6.510469845
[216,] 3.257379489 7.461072157
[217,] -0.719985914 3.257379489
[218,] 1.591364660 -0.719985914
[219,] 2.256203663 1.591364660
[220,] 2.041914181 2.256203663
[221,] 5.409889319 2.041914181
[222,] 1.463785357 5.409889319
[223,] 4.225315391 1.463785357
[224,] 5.678428202 4.225315391
[225,] 1.015520514 5.678428202
[226,] -2.453756368 1.015520514
[227,] 3.878843877 -2.453756368
[228,] -12.399651333 3.878843877
[229,] 5.265204270 -12.399651333
[230,] 3.842001318 5.265204270
[231,] 0.824991796 3.842001318
[232,] 3.159180546 0.824991796
[233,] -2.594243468 3.159180546
[234,] -0.820910434 -2.594243468
[235,] -0.282829170 -0.820910434
[236,] 5.805928492 -0.282829170
[237,] 3.960633603 5.805928492
[238,] 1.041950335 3.960633603
[239,] 3.667855871 1.041950335
[240,] 3.527945965 3.667855871
[241,] 3.900923148 3.527945965
[242,] 3.117778392 3.900923148
[243,] 3.080250181 3.117778392
[244,] -1.893118715 3.080250181
[245,] 3.815780667 -1.893118715
[246,] 0.499374133 3.815780667
[247,] 3.389643903 0.499374133
[248,] 2.080433253 3.389643903
[249,] -1.429628124 2.080433253
[250,] 1.737909364 -1.429628124
[251,] 2.515477198 1.737909364
[252,] -3.540584249 2.515477198
[253,] 5.521120331 -3.540584249
[254,] -1.516794123 5.521120331
[255,] -0.744452259 -1.516794123
[256,] -6.166986800 -0.744452259
[257,] 2.114839372 -6.166986800
[258,] -1.755986069 2.114839372
[259,] 1.373549072 -1.755986069
[260,] 0.677299851 1.373549072
[261,] 3.594612867 0.677299851
[262,] -5.494379467 3.594612867
[263,] -1.758037482 -5.494379467
[264,] 1.618748164 -1.758037482
[265,] 1.907120162 1.618748164
[266,] 4.203204460 1.907120162
[267,] -5.123942701 4.203204460
[268,] 0.856490593 -5.123942701
[269,] -11.295109220 0.856490593
[270,] 5.672467506 -11.295109220
[271,] 4.578958331 5.672467506
[272,] -1.483444792 4.578958331
[273,] 1.797280766 -1.483444792
[274,] 1.673368807 1.797280766
[275,] -3.394218292 1.673368807
[276,] 3.070498554 -3.394218292
[277,] -1.162111333 3.070498554
[278,] -1.288645284 -1.162111333
[279,] 3.479265082 -1.288645284
[280,] 5.819054576 3.479265082
[281,] -9.188358459 5.819054576
[282,] -2.913611531 -9.188358459
[283,] -1.875411732 -2.913611531
[284,] 2.223001533 -1.875411732
[285,] -2.541649493 2.223001533
[286,] -0.809528045 -2.541649493
[287,] 3.316574558 -0.809528045
[288,] 5.459558275 3.316574558
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.058996063 1.269713350
2 3.360243234 3.058996063
3 2.234670539 3.360243234
4 -14.979340878 2.234670539
5 6.003669161 -14.979340878
6 2.023925631 6.003669161
7 1.308095286 2.023925631
8 2.239006056 1.308095286
9 3.545905565 2.239006056
10 -0.585719011 3.545905565
11 -1.281866653 -0.585719011
12 0.328336110 -1.281866653
13 -13.055678789 0.328336110
14 -10.228784929 -13.055678789
15 1.670309173 -10.228784929
16 4.155102103 1.670309173
17 1.563270329 4.155102103
18 -0.834565622 1.563270329
19 0.125984235 -0.834565622
20 1.265279288 0.125984235
21 0.054160109 1.265279288
22 -3.583617081 0.054160109
23 -5.033786455 -3.583617081
24 9.301706230 -5.033786455
25 0.407673901 9.301706230
26 2.272283996 0.407673901
27 0.738900569 2.272283996
28 -0.282698033 0.738900569
29 2.392831315 -0.282698033
30 2.037907964 2.392831315
31 3.318836113 2.037907964
32 -0.456898763 3.318836113
33 3.529960778 -0.456898763
34 1.523154840 3.529960778
35 2.898611675 1.523154840
36 2.619637241 2.898611675
37 3.117845279 2.619637241
38 2.262035708 3.117845279
39 -1.319413835 2.262035708
40 3.036608099 -1.319413835
41 -7.194139368 3.036608099
42 -10.327137794 -7.194139368
43 2.289414155 -10.327137794
44 1.162734597 2.289414155
45 -2.416421589 1.162734597
46 4.887644227 -2.416421589
47 -8.361917982 4.887644227
48 -0.123402612 -8.361917982
49 -81.936823727 -0.123402612
50 0.914700086 -81.936823727
51 -13.618016406 0.914700086
52 3.398346867 -13.618016406
53 2.028495497 3.398346867
54 -1.238399579 2.028495497
55 12.261623342 -1.238399579
56 -3.701446925 12.261623342
57 -6.346274177 -3.701446925
58 -0.442459791 -6.346274177
59 -1.491367299 -0.442459791
60 0.651473544 -1.491367299
61 0.779530576 0.651473544
62 -1.077517972 0.779530576
63 0.172780389 -1.077517972
64 -0.857465301 0.172780389
65 -6.148385155 -0.857465301
66 -0.706670722 -6.148385155
67 0.869909531 -0.706670722
68 -6.180409953 0.869909531
69 7.244812867 -6.180409953
70 3.209035614 7.244812867
71 3.712103042 3.209035614
72 3.278091635 3.712103042
73 1.436872664 3.278091635
74 -1.376348744 1.436872664
75 -0.428392552 -1.376348744
76 -6.100474631 -0.428392552
77 4.613877080 -6.100474631
78 2.528652770 4.613877080
79 5.868106694 2.528652770
80 -0.636585822 5.868106694
81 3.627277070 -0.636585822
82 1.560781708 3.627277070
83 1.814999686 1.560781708
84 -0.001370129 1.814999686
85 1.739040154 -0.001370129
86 -0.174359345 1.739040154
87 2.809527823 -0.174359345
88 4.546952454 2.809527823
89 2.799534501 4.546952454
90 -45.905403132 2.799534501
91 -3.634053388 -45.905403132
92 3.200447310 -3.634053388
93 -1.046122592 3.200447310
94 2.479400768 -1.046122592
95 3.610633272 2.479400768
96 -7.895453020 3.610633272
97 0.485732547 -7.895453020
98 -0.743773674 0.485732547
99 -2.636764616 -0.743773674
100 -0.419062028 -2.636764616
101 4.258536728 -0.419062028
102 4.221696946 4.258536728
103 3.212655622 4.221696946
104 -6.986347549 3.212655622
105 -2.014342691 -6.986347549
106 3.701549822 -2.014342691
107 0.003315327 3.701549822
108 -3.592528960 0.003315327
109 3.329251681 -3.592528960
110 1.137495251 3.329251681
111 2.376711741 1.137495251
112 0.598680132 2.376711741
113 -2.189994497 0.598680132
114 -2.295226054 -2.189994497
115 2.937801486 -2.295226054
116 0.744259956 2.937801486
117 4.179693893 0.744259956
118 5.693313898 4.179693893
119 -19.071256268 5.693313898
120 2.574259535 -19.071256268
121 -6.784484479 2.574259535
122 3.864579707 -6.784484479
123 5.386152065 3.864579707
124 1.335733830 5.386152065
125 -0.301425737 1.335733830
126 0.135762552 -0.301425737
127 4.799779095 0.135762552
128 -3.775516625 4.799779095
129 -5.125321675 -3.775516625
130 1.526631740 -5.125321675
131 1.666518465 1.526631740
132 -10.691683754 1.666518465
133 6.570459085 -10.691683754
134 2.351449917 6.570459085
135 -0.520165714 2.351449917
136 -1.240728667 -0.520165714
137 -2.190634371 -1.240728667
138 -9.174336940 -2.190634371
139 -5.572487523 -9.174336940
140 3.453271528 -5.572487523
141 2.670823128 3.453271528
142 5.635248720 2.670823128
143 -0.056725501 5.635248720
144 2.134972529 -0.056725501
145 -10.041030088 2.134972529
146 3.987413008 -10.041030088
147 -1.331553409 3.987413008
148 -3.494261568 -1.331553409
149 2.445429954 -3.494261568
150 2.596616914 2.445429954
151 7.414988662 2.596616914
152 0.151207226 7.414988662
153 6.030672095 0.151207226
154 2.816489623 6.030672095
155 0.701495587 2.816489623
156 4.180313640 0.701495587
157 0.933247916 4.180313640
158 1.242427697 0.933247916
159 0.286373172 1.242427697
160 0.793596406 0.286373172
161 0.766759259 0.793596406
162 -2.552951759 0.766759259
163 -1.477693394 -2.552951759
164 -4.532904716 -1.477693394
165 -1.080284207 -4.532904716
166 -4.316862550 -1.080284207
167 7.533392993 -4.316862550
168 -1.352198541 7.533392993
169 3.580250617 -1.352198541
170 3.459408906 3.580250617
171 -2.371565964 3.459408906
172 0.809328992 -2.371565964
173 0.732104464 0.809328992
174 -0.365552453 0.732104464
175 3.568141636 -0.365552453
176 -1.682035457 3.568141636
177 4.375817562 -1.682035457
178 -4.387827568 4.375817562
179 -6.466427929 -4.387827568
180 -0.742955368 -6.466427929
181 2.937839352 -0.742955368
182 -1.338697090 2.937839352
183 -1.289238013 -1.338697090
184 7.451916014 -1.289238013
185 -1.444340310 7.451916014
186 -4.274831666 -1.444340310
187 -0.627872868 -4.274831666
188 4.336447044 -0.627872868
189 -6.818573047 4.336447044
190 -4.729094599 -6.818573047
191 5.898543841 -4.729094599
192 -3.066878921 5.898543841
193 -8.435183086 -3.066878921
194 5.786582401 -8.435183086
195 0.887925688 5.786582401
196 6.638552442 0.887925688
197 2.305609586 6.638552442
198 3.630374886 2.305609586
199 2.139044051 3.630374886
200 0.260191464 2.139044051
201 -12.181508099 0.260191464
202 3.201379950 -12.181508099
203 4.545749805 3.201379950
204 4.390686925 4.545749805
205 2.693004924 4.390686925
206 1.674605641 2.693004924
207 1.738621475 1.674605641
208 -0.127361625 1.738621475
209 6.048785850 -0.127361625
210 -1.766574195 6.048785850
211 0.156241060 -1.766574195
212 1.553197009 0.156241060
213 3.526595937 1.553197009
214 6.510469845 3.526595937
215 7.461072157 6.510469845
216 3.257379489 7.461072157
217 -0.719985914 3.257379489
218 1.591364660 -0.719985914
219 2.256203663 1.591364660
220 2.041914181 2.256203663
221 5.409889319 2.041914181
222 1.463785357 5.409889319
223 4.225315391 1.463785357
224 5.678428202 4.225315391
225 1.015520514 5.678428202
226 -2.453756368 1.015520514
227 3.878843877 -2.453756368
228 -12.399651333 3.878843877
229 5.265204270 -12.399651333
230 3.842001318 5.265204270
231 0.824991796 3.842001318
232 3.159180546 0.824991796
233 -2.594243468 3.159180546
234 -0.820910434 -2.594243468
235 -0.282829170 -0.820910434
236 5.805928492 -0.282829170
237 3.960633603 5.805928492
238 1.041950335 3.960633603
239 3.667855871 1.041950335
240 3.527945965 3.667855871
241 3.900923148 3.527945965
242 3.117778392 3.900923148
243 3.080250181 3.117778392
244 -1.893118715 3.080250181
245 3.815780667 -1.893118715
246 0.499374133 3.815780667
247 3.389643903 0.499374133
248 2.080433253 3.389643903
249 -1.429628124 2.080433253
250 1.737909364 -1.429628124
251 2.515477198 1.737909364
252 -3.540584249 2.515477198
253 5.521120331 -3.540584249
254 -1.516794123 5.521120331
255 -0.744452259 -1.516794123
256 -6.166986800 -0.744452259
257 2.114839372 -6.166986800
258 -1.755986069 2.114839372
259 1.373549072 -1.755986069
260 0.677299851 1.373549072
261 3.594612867 0.677299851
262 -5.494379467 3.594612867
263 -1.758037482 -5.494379467
264 1.618748164 -1.758037482
265 1.907120162 1.618748164
266 4.203204460 1.907120162
267 -5.123942701 4.203204460
268 0.856490593 -5.123942701
269 -11.295109220 0.856490593
270 5.672467506 -11.295109220
271 4.578958331 5.672467506
272 -1.483444792 4.578958331
273 1.797280766 -1.483444792
274 1.673368807 1.797280766
275 -3.394218292 1.673368807
276 3.070498554 -3.394218292
277 -1.162111333 3.070498554
278 -1.288645284 -1.162111333
279 3.479265082 -1.288645284
280 5.819054576 3.479265082
281 -9.188358459 5.819054576
282 -2.913611531 -9.188358459
283 -1.875411732 -2.913611531
284 2.223001533 -1.875411732
285 -2.541649493 2.223001533
286 -0.809528045 -2.541649493
287 3.316574558 -0.809528045
288 5.459558275 3.316574558
> 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/76ef61354810367.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/8te7r1354810367.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/92phv1354810367.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/10wf8f1354810367.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11fxdo1354810367.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12vdu41354810367.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ikwq1354810367.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14dwsq1354810367.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/15vx441354810367.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/16k4xj1354810367.tab")
+ }
>
> try(system("convert tmp/1l2gw1354810367.ps tmp/1l2gw1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/2c13b1354810367.ps tmp/2c13b1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ll601354810367.ps tmp/3ll601354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sb4l1354810367.ps tmp/4sb4l1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ls181354810367.ps tmp/5ls181354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/6icxi1354810367.ps tmp/6icxi1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/76ef61354810367.ps tmp/76ef61354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/8te7r1354810367.ps tmp/8te7r1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/92phv1354810367.ps tmp/92phv1354810367.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wf8f1354810367.ps tmp/10wf8f1354810367.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.799 0.925 12.720