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(30
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+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'time_in_rfc'
+ ,'blogged_computations'
+ ,'totsize'
+ ,'compendium_views_pr')
+ ,1:289))
> y <- array(NA,dim=c(5,289),dimnames=list(c('compendiums_reviewed','time_in_rfc','blogged_computations','totsize','compendium_views_pr'),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
compendiums_reviewed time_in_rfc blogged_computations totsize
1 30 210907 79 112285
2 28 120982 58 84786
3 38 176508 60 83123
4 30 179321 108 101193
5 22 123185 49 38361
6 26 52746 0 68504
7 25 385534 121 119182
8 18 33170 1 22807
9 11 101645 20 17140
10 26 149061 43 116174
11 25 165446 69 57635
12 38 237213 78 66198
13 44 173326 86 71701
14 30 133131 44 57793
15 40 258873 104 80444
16 34 180083 63 53855
17 47 324799 158 97668
18 30 230964 102 133824
19 31 236785 77 101481
20 23 135473 82 99645
21 36 202925 115 114789
22 36 215147 101 99052
23 30 344297 80 67654
24 25 153935 50 65553
25 39 132943 83 97500
26 34 174724 123 69112
27 31 174415 73 82753
28 31 225548 81 85323
29 33 223632 105 72654
30 25 124817 47 30727
31 33 221698 105 77873
32 35 210767 94 117478
33 42 170266 44 74007
34 43 260561 114 90183
35 30 84853 38 61542
36 33 294424 107 101494
37 13 101011 30 27570
38 32 215641 71 55813
39 36 325107 84 79215
40 0 7176 0 1423
41 28 167542 59 55461
42 14 106408 33 31081
43 17 96560 42 22996
44 32 265769 96 83122
45 30 269651 106 70106
46 35 149112 56 60578
47 20 175824 57 39992
48 28 152871 59 79892
49 28 111665 39 49810
50 39 116408 34 71570
51 34 362301 76 100708
52 26 78800 20 33032
53 39 183167 91 82875
54 39 277965 115 139077
55 33 150629 85 71595
56 28 168809 76 72260
57 4 24188 8 5950
58 39 329267 79 115762
59 18 65029 21 32551
60 14 101097 30 31701
61 29 218946 76 80670
62 44 244052 101 143558
63 21 341570 94 117105
64 16 103597 27 23789
65 28 233328 92 120733
66 35 256462 123 105195
67 28 206161 75 73107
68 38 311473 128 132068
69 23 235800 105 149193
70 36 177939 55 46821
71 32 207176 56 87011
72 29 196553 41 95260
73 25 174184 72 55183
74 27 143246 67 106671
75 36 187559 75 73511
76 28 187681 114 92945
77 23 119016 118 78664
78 40 182192 77 70054
79 23 73566 22 22618
80 40 194979 66 74011
81 28 167488 69 83737
82 34 143756 105 69094
83 33 275541 116 93133
84 28 243199 88 95536
85 34 182999 73 225920
86 30 135649 99 62133
87 33 152299 62 61370
88 22 120221 53 43836
89 38 346485 118 106117
90 26 145790 30 38692
91 35 193339 100 84651
92 8 80953 49 56622
93 24 122774 24 15986
94 29 130585 67 95364
95 20 112611 46 26706
96 29 286468 57 89691
97 45 241066 75 67267
98 37 148446 135 126846
99 33 204713 68 41140
100 33 182079 124 102860
101 25 140344 33 51715
102 32 220516 98 55801
103 29 243060 58 111813
104 28 162765 68 120293
105 28 182613 81 138599
106 31 232138 131 161647
107 52 265318 110 115929
108 21 85574 37 24266
109 24 310839 130 162901
110 41 225060 93 109825
111 33 232317 118 129838
112 32 144966 39 37510
113 19 43287 13 43750
114 20 155754 74 40652
115 31 164709 81 87771
116 31 201940 109 85872
117 32 235454 151 89275
118 18 220801 51 44418
119 23 99466 28 192565
120 17 92661 40 35232
121 20 133328 56 40909
122 12 61361 27 13294
123 17 125930 37 32387
124 30 100750 83 140867
125 31 224549 54 120662
126 10 82316 27 21233
127 13 102010 28 44332
128 22 101523 59 61056
129 42 243511 133 101338
130 1 22938 12 1168
131 9 41566 0 13497
132 32 152474 106 65567
133 11 61857 23 25162
134 25 99923 44 32334
135 36 132487 71 40735
136 31 317394 116 91413
137 0 21054 4 855
138 24 209641 62 97068
139 13 22648 12 44339
140 8 31414 18 14116
141 13 46698 14 10288
142 19 131698 60 65622
143 18 91735 7 16563
144 33 244749 98 76643
145 40 184510 64 110681
146 22 79863 29 29011
147 38 128423 32 92696
148 24 97839 25 94785
149 8 38214 16 8773
150 35 151101 48 83209
151 43 272458 100 93815
152 43 172494 46 86687
153 14 108043 45 34553
154 41 328107 129 105547
155 38 250579 130 103487
156 45 351067 136 213688
157 31 158015 59 71220
158 13 98866 25 23517
159 28 85439 32 56926
160 31 229242 63 91721
161 40 351619 95 115168
162 30 84207 14 111194
163 16 120445 36 51009
164 37 324598 113 135777
165 30 131069 47 51513
166 35 204271 92 74163
167 32 165543 70 51633
168 27 141722 19 75345
169 20 116048 50 33416
170 18 250047 41 83305
171 31 299775 91 98952
172 31 195838 111 102372
173 21 173260 41 37238
174 39 254488 120 103772
175 41 104389 135 123969
176 13 136084 27 27142
177 32 199476 87 135400
178 18 92499 25 21399
179 39 224330 131 130115
180 14 135781 45 24874
181 7 74408 29 34988
182 17 81240 58 45549
183 0 14688 4 6023
184 30 181633 47 64466
185 37 271856 109 54990
186 0 7199 7 1644
187 5 46660 12 6179
188 1 17547 0 3926
189 16 133368 37 32755
190 32 95227 37 34777
191 24 152601 46 73224
192 17 98146 15 27114
193 11 79619 42 20760
194 24 59194 7 37636
195 22 139942 54 65461
196 12 118612 54 30080
197 19 72880 14 24094
198 13 65475 16 69008
199 17 99643 33 54968
200 15 71965 32 46090
201 16 77272 21 27507
202 24 49289 15 10672
203 15 135131 38 34029
204 17 108446 22 46300
205 18 89746 28 24760
206 20 44296 10 18779
207 16 77648 31 21280
208 16 181528 32 40662
209 18 134019 32 28987
210 22 124064 43 22827
211 8 92630 27 18513
212 17 121848 37 30594
213 18 52915 20 24006
214 16 81872 32 27913
215 23 58981 0 42744
216 22 53515 5 12934
217 13 60812 26 22574
218 13 56375 10 41385
219 16 65490 27 18653
220 16 80949 11 18472
221 20 76302 29 30976
222 22 104011 25 63339
223 17 98104 55 25568
224 18 67989 23 33747
225 17 30989 5 4154
226 12 135458 43 19474
227 7 73504 23 35130
228 17 63123 34 39067
229 14 61254 36 13310
230 23 74914 35 65892
231 17 31774 0 4143
232 14 81437 37 28579
233 15 87186 28 51776
234 17 50090 16 21152
235 21 65745 26 38084
236 18 56653 38 27717
237 18 158399 23 32928
238 17 46455 22 11342
239 17 73624 30 19499
240 16 38395 16 16380
241 15 91899 18 36874
242 21 139526 28 48259
243 16 52164 32 16734
244 14 51567 21 28207
245 15 70551 23 30143
246 17 84856 29 41369
247 15 102538 50 45833
248 15 86678 12 29156
249 10 85709 21 35944
250 6 34662 18 36278
251 22 150580 27 45588
252 21 99611 41 45097
253 1 19349 13 3895
254 18 99373 12 28394
255 17 86230 21 18632
256 4 30837 8 2325
257 10 31706 26 25139
258 16 89806 27 27975
259 16 62088 13 14483
260 9 40151 16 13127
261 16 27634 2 5839
262 17 76990 42 24069
263 7 37460 5 3738
264 15 54157 37 18625
265 14 49862 17 36341
266 14 84337 38 24548
267 18 64175 37 21792
268 12 59382 29 26263
269 16 119308 32 23686
270 21 76702 35 49303
271 19 103425 17 25659
272 16 70344 20 28904
273 1 43410 7 2781
274 16 104838 46 29236
275 10 62215 24 19546
276 19 69304 40 22818
277 12 53117 3 32689
278 2 19764 10 5752
279 14 86680 37 22197
280 17 84105 17 20055
281 19 77945 28 25272
282 14 89113 19 82206
283 11 91005 29 32073
284 4 40248 8 5444
285 16 64187 10 20154
286 20 50857 15 36944
287 12 56613 15 8019
288 15 62792 28 30884
289 16 72535 17 19540
compendium_views_pr
1 81
2 55
3 50
4 125
5 40
6 37
7 63
8 44
9 88
10 66
11 57
12 74
13 49
14 52
15 88
16 36
17 108
18 43
19 75
20 32
21 44
22 85
23 86
24 56
25 50
26 135
27 63
28 81
29 52
30 44
31 113
32 39
33 73
34 48
35 33
36 59
37 41
38 69
39 64
40 1
41 59
42 32
43 129
44 37
45 31
46 65
47 107
48 74
49 54
50 76
51 715
52 57
53 66
54 106
55 54
56 32
57 20
58 71
59 21
60 70
61 112
62 66
63 190
64 66
65 165
66 56
67 61
68 53
69 127
70 63
71 38
72 50
73 52
74 42
75 76
76 67
77 50
78 53
79 39
80 50
81 77
82 57
83 73
84 34
85 39
86 46
87 63
88 35
89 106
90 43
91 47
92 31
93 162
94 57
95 36
96 263
97 78
98 63
99 54
100 63
101 77
102 79
103 110
104 56
105 56
106 43
107 111
108 71
109 62
110 56
111 74
112 60
113 43
114 68
115 53
116 87
117 46
118 105
119 32
120 133
121 79
122 51
123 207
124 67
125 47
126 34
127 66
128 76
129 65
130 9
131 42
132 45
133 25
134 115
135 97
136 53
137 2
138 52
139 44
140 22
141 35
142 74
143 103
144 144
145 60
146 134
147 89
148 42
149 52
150 98
151 99
152 52
153 29
154 125
155 106
156 95
157 40
158 140
159 43
160 128
161 142
162 73
163 72
164 128
165 61
166 73
167 148
168 64
169 45
170 58
171 97
172 50
173 37
174 50
175 105
176 69
177 46
178 57
179 52
180 98
181 61
182 89
183 0
184 48
185 91
186 0
187 7
188 3
189 54
190 70
191 36
192 37
193 123
194 247
195 46
196 72
197 41
198 24
199 45
200 33
201 27
202 36
203 87
204 90
205 114
206 31
207 45
208 69
209 51
210 34
211 60
212 45
213 54
214 25
215 38
216 52
217 67
218 74
219 38
220 30
221 26
222 67
223 132
224 42
225 35
226 118
227 68
228 43
229 76
230 64
231 48
232 64
233 56
234 71
235 75
236 39
237 42
238 39
239 93
240 38
241 60
242 71
243 52
244 27
245 59
246 40
247 79
248 44
249 65
250 10
251 124
252 81
253 15
254 92
255 42
256 10
257 24
258 64
259 45
260 22
261 56
262 94
263 19
264 35
265 32
266 35
267 48
268 49
269 48
270 62
271 96
272 45
273 63
274 71
275 26
276 48
277 29
278 19
279 45
280 45
281 67
282 30
283 36
284 34
285 36
286 34
287 37
288 46
289 44
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc blogged_computations
9.063e+00 3.598e-05 8.735e-02
totsize compendium_views_pr
7.586e-05 4.990e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.2674 -3.9986 -0.3646 3.2893 16.9901
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.063e+00 7.608e-01 11.912 < 2e-16 ***
time_in_rfc 3.598e-05 9.051e-06 3.975 8.94e-05 ***
blogged_computations 8.735e-02 1.942e-02 4.498 1.00e-05 ***
totsize 7.585e-05 1.470e-05 5.160 4.63e-07 ***
compendium_views_pr 4.990e-03 7.688e-03 0.649 0.517
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.195 on 284 degrees of freedom
Multiple R-squared: 0.6629, Adjusted R-squared: 0.6582
F-statistic: 139.6 on 4 and 284 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,] 0.6328614 7.342771e-01 3.671386e-01
[2,] 0.4716054 9.432109e-01 5.283946e-01
[3,] 0.4116961 8.233922e-01 5.883039e-01
[4,] 0.2881501 5.763002e-01 7.118499e-01
[5,] 0.7862699 4.274603e-01 2.137301e-01
[6,] 0.8567660 2.864680e-01 1.432340e-01
[7,] 0.8158681 3.682638e-01 1.841319e-01
[8,] 0.8594342 2.811316e-01 1.405658e-01
[9,] 0.8158119 3.683762e-01 1.841881e-01
[10,] 0.7842333 4.315334e-01 2.157667e-01
[11,] 0.8621564 2.756873e-01 1.378436e-01
[12,] 0.8221812 3.556376e-01 1.778188e-01
[13,] 0.9089960 1.820079e-01 9.100396e-02
[14,] 0.8776784 2.446433e-01 1.223216e-01
[15,] 0.8436723 3.126553e-01 1.563277e-01
[16,] 0.8011182 3.977636e-01 1.988818e-01
[17,] 0.7557527 4.884947e-01 2.442473e-01
[18,] 0.7596803 4.806394e-01 2.403197e-01
[19,] 0.7174044 5.651913e-01 2.825956e-01
[20,] 0.6631368 6.737264e-01 3.368632e-01
[21,] 0.6048854 7.902292e-01 3.951146e-01
[22,] 0.5586993 8.826014e-01 4.413007e-01
[23,] 0.5091259 9.817482e-01 4.908741e-01
[24,] 0.4499615 8.999230e-01 5.500385e-01
[25,] 0.3928948 7.857896e-01 6.071052e-01
[26,] 0.7104193 5.791615e-01 2.895807e-01
[27,] 0.7128094 5.743813e-01 2.871906e-01
[28,] 0.6897090 6.205820e-01 3.102910e-01
[29,] 0.6518175 6.963651e-01 3.481825e-01
[30,] 0.7471261 5.057478e-01 2.528739e-01
[31,] 0.7128013 5.743974e-01 2.871987e-01
[32,] 0.6818356 6.363287e-01 3.181644e-01
[33,] 0.8768863 2.462274e-01 1.231137e-01
[34,] 0.8515899 2.968203e-01 1.484101e-01
[35,] 0.8553273 2.893454e-01 1.446727e-01
[36,] 0.8441443 3.117113e-01 1.558557e-01
[37,] 0.8157722 3.684556e-01 1.842278e-01
[38,] 0.7919784 4.160432e-01 2.080216e-01
[39,] 0.8227582 3.544835e-01 1.772418e-01
[40,] 0.8132036 3.735929e-01 1.867964e-01
[41,] 0.7814468 4.371064e-01 2.185532e-01
[42,] 0.7711649 4.576703e-01 2.288351e-01
[43,] 0.8832250 2.335501e-01 1.167750e-01
[44,] 0.8747518 2.504964e-01 1.252482e-01
[45,] 0.8769409 2.461183e-01 1.230591e-01
[46,] 0.8818753 2.362494e-01 1.181247e-01
[47,] 0.8608812 2.782376e-01 1.391188e-01
[48,] 0.8427878 3.144245e-01 1.572122e-01
[49,] 0.8187884 3.624231e-01 1.812116e-01
[50,] 0.8726496 2.547008e-01 1.273504e-01
[51,] 0.8536097 2.927806e-01 1.463903e-01
[52,] 0.8309803 3.380394e-01 1.690197e-01
[53,] 0.8293103 3.413795e-01 1.706897e-01
[54,] 0.8047879 3.904243e-01 1.952121e-01
[55,] 0.7893114 4.213772e-01 2.106886e-01
[56,] 0.9327726 1.344547e-01 6.722736e-02
[57,] 0.9214548 1.570904e-01 7.854519e-02
[58,] 0.9342858 1.314284e-01 6.571419e-02
[59,] 0.9238253 1.523494e-01 7.617470e-02
[60,] 0.9093367 1.813266e-01 9.066331e-02
[61,] 0.8973554 2.052892e-01 1.026446e-01
[62,] 0.9678789 6.424214e-02 3.212107e-02
[63,] 0.9799138 4.017233e-02 2.008616e-02
[64,] 0.9765423 4.691537e-02 2.345769e-02
[65,] 0.9710413 5.791739e-02 2.895869e-02
[66,] 0.9651972 6.960564e-02 3.480282e-02
[67,] 0.9592919 8.141625e-02 4.070812e-02
[68,] 0.9609761 7.804785e-02 3.902393e-02
[69,] 0.9606787 7.864267e-02 3.932133e-02
[70,] 0.9661837 6.763254e-02 3.381627e-02
[71,] 0.9796465 4.070703e-02 2.035351e-02
[72,] 0.9787837 4.243268e-02 2.121634e-02
[73,] 0.9881606 2.367880e-02 1.183940e-02
[74,] 0.9850894 2.982126e-02 1.491063e-02
[75,] 0.9832903 3.341941e-02 1.670970e-02
[76,] 0.9800977 3.980453e-02 1.990226e-02
[77,] 0.9783144 4.337112e-02 2.168556e-02
[78,] 0.9760509 4.789824e-02 2.394912e-02
[79,] 0.9712372 5.752565e-02 2.876282e-02
[80,] 0.9729574 5.408514e-02 2.704257e-02
[81,] 0.9679954 6.400915e-02 3.200458e-02
[82,] 0.9614587 7.708256e-02 3.854128e-02
[83,] 0.9584147 8.317066e-02 4.158533e-02
[84,] 0.9529014 9.419715e-02 4.709858e-02
[85,] 0.9815054 3.698921e-02 1.849460e-02
[86,] 0.9789156 4.216887e-02 2.108444e-02
[87,] 0.9742916 5.141677e-02 2.570838e-02
[88,] 0.9697520 6.049596e-02 3.024798e-02
[89,] 0.9695661 6.086790e-02 3.043395e-02
[90,] 0.9895971 2.080580e-02 1.040290e-02
[91,] 0.9870735 2.585298e-02 1.292649e-02
[92,] 0.9879502 2.409953e-02 1.204977e-02
[93,] 0.9850487 2.990255e-02 1.495127e-02
[94,] 0.9821266 3.574677e-02 1.787338e-02
[95,] 0.9784672 4.306559e-02 2.153280e-02
[96,] 0.9750809 4.983813e-02 2.491906e-02
[97,] 0.9703662 5.926762e-02 2.963381e-02
[98,] 0.9684789 6.304217e-02 3.152109e-02
[99,] 0.9743789 5.124218e-02 2.562109e-02
[100,] 0.9916861 1.662788e-02 8.313941e-03
[101,] 0.9900207 1.995862e-02 9.979310e-03
[102,] 0.9989043 2.191487e-03 1.095744e-03
[103,] 0.9990772 1.845561e-03 9.227807e-04
[104,] 0.9989502 2.099683e-03 1.049841e-03
[105,] 0.9994257 1.148675e-03 5.743373e-04
[106,] 0.9992824 1.435164e-03 7.175822e-04
[107,] 0.9992717 1.456672e-03 7.283358e-04
[108,] 0.9990546 1.890740e-03 9.453700e-04
[109,] 0.9987754 2.449187e-03 1.224593e-03
[110,] 0.9986365 2.727010e-03 1.363505e-03
[111,] 0.9989812 2.037559e-03 1.018780e-03
[112,] 0.9992882 1.423580e-03 7.117902e-04
[113,] 0.9991949 1.610257e-03 8.051284e-04
[114,] 0.9990372 1.925688e-03 9.628438e-04
[115,] 0.9989782 2.043629e-03 1.021814e-03
[116,] 0.9990331 1.933769e-03 9.668846e-04
[117,] 0.9988821 2.235894e-03 1.117947e-03
[118,] 0.9985295 2.940902e-03 1.470451e-03
[119,] 0.9987683 2.463485e-03 1.231742e-03
[120,] 0.9989247 2.150502e-03 1.075251e-03
[121,] 0.9986235 2.752946e-03 1.376473e-03
[122,] 0.9985515 2.897016e-03 1.448508e-03
[123,] 0.9992857 1.428674e-03 7.143370e-04
[124,] 0.9991780 1.644066e-03 8.220330e-04
[125,] 0.9990383 1.923302e-03 9.616509e-04
[126,] 0.9989609 2.078108e-03 1.039054e-03
[127,] 0.9988042 2.391687e-03 1.195843e-03
[128,] 0.9995412 9.176024e-04 4.588012e-04
[129,] 0.9995101 9.797756e-04 4.898878e-04
[130,] 0.9997522 4.955993e-04 2.477996e-04
[131,] 0.9997521 4.958034e-04 2.479017e-04
[132,] 0.9996831 6.337528e-04 3.168764e-04
[133,] 0.9996644 6.712739e-04 3.356370e-04
[134,] 0.9995615 8.769868e-04 4.384934e-04
[135,] 0.9995595 8.809319e-04 4.404659e-04
[136,] 0.9994328 1.134349e-03 5.671747e-04
[137,] 0.9992378 1.524448e-03 7.622240e-04
[138,] 0.9994983 1.003443e-03 5.017213e-04
[139,] 0.9993780 1.244091e-03 6.220455e-04
[140,] 0.9997804 4.392879e-04 2.196439e-04
[141,] 0.9996996 6.008538e-04 3.004269e-04
[142,] 0.9996776 6.448845e-04 3.224422e-04
[143,] 0.9997671 4.658902e-04 2.329451e-04
[144,] 0.9998290 3.419356e-04 1.709678e-04
[145,] 0.9999875 2.503377e-05 1.251688e-05
[146,] 0.9999864 2.710908e-05 1.355454e-05
[147,] 0.9999805 3.892911e-05 1.946456e-05
[148,] 0.9999719 5.610925e-05 2.805463e-05
[149,] 0.9999748 5.041858e-05 2.520929e-05
[150,] 0.9999756 4.877986e-05 2.438993e-05
[151,] 0.9999748 5.039853e-05 2.519926e-05
[152,] 0.9999835 3.299166e-05 1.649583e-05
[153,] 0.9999760 4.798527e-05 2.399263e-05
[154,] 0.9999653 6.936769e-05 3.468384e-05
[155,] 0.9999661 6.788238e-05 3.394119e-05
[156,] 0.9999629 7.423488e-05 3.711744e-05
[157,] 0.9999607 7.852162e-05 3.926081e-05
[158,] 0.9999734 5.315543e-05 2.657771e-05
[159,] 0.9999720 5.594244e-05 2.797122e-05
[160,] 0.9999706 5.885542e-05 2.942771e-05
[161,] 0.9999674 6.525372e-05 3.262686e-05
[162,] 0.9999546 9.076843e-05 4.538422e-05
[163,] 0.9999781 4.371571e-05 2.185785e-05
[164,] 0.9999762 4.763308e-05 2.381654e-05
[165,] 0.9999673 6.545040e-05 3.272520e-05
[166,] 0.9999538 9.230651e-05 4.615325e-05
[167,] 0.9999407 1.185398e-04 5.926992e-05
[168,] 0.9999508 9.838990e-05 4.919495e-05
[169,] 0.9999532 9.356656e-05 4.678328e-05
[170,] 0.9999355 1.289507e-04 6.447535e-05
[171,] 0.9999127 1.746673e-04 8.733365e-05
[172,] 0.9998864 2.271476e-04 1.135738e-04
[173,] 0.9998967 2.065499e-04 1.032750e-04
[174,] 0.9999528 9.436544e-05 4.718272e-05
[175,] 0.9999396 1.207242e-04 6.036209e-05
[176,] 0.9999679 6.410892e-05 3.205446e-05
[177,] 0.9999683 6.347187e-05 3.173594e-05
[178,] 0.9999791 4.173630e-05 2.086815e-05
[179,] 0.9999901 1.985841e-05 9.929205e-06
[180,] 0.9999915 1.702773e-05 8.513866e-06
[181,] 0.9999961 7.797555e-06 3.898777e-06
[182,] 0.9999945 1.108108e-05 5.540542e-06
[183,] 0.9999997 6.056394e-07 3.028197e-07
[184,] 0.9999996 8.486177e-07 4.243089e-07
[185,] 0.9999993 1.342862e-06 6.714308e-07
[186,] 0.9999994 1.113607e-06 5.568033e-07
[187,] 0.9999992 1.513200e-06 7.566002e-07
[188,] 0.9999989 2.221611e-06 1.110805e-06
[189,] 0.9999990 1.945892e-06 9.729460e-07
[190,] 0.9999988 2.328745e-06 1.164373e-06
[191,] 0.9999987 2.548827e-06 1.274413e-06
[192,] 0.9999980 3.905796e-06 1.952898e-06
[193,] 0.9999971 5.872276e-06 2.936138e-06
[194,] 0.9999954 9.104840e-06 4.552420e-06
[195,] 0.9999992 1.676636e-06 8.383181e-07
[196,] 0.9999990 2.066193e-06 1.033096e-06
[197,] 0.9999985 3.073514e-06 1.536757e-06
[198,] 0.9999975 5.001999e-06 2.501000e-06
[199,] 0.9999983 3.307262e-06 1.653631e-06
[200,] 0.9999974 5.184708e-06 2.592354e-06
[201,] 0.9999972 5.627692e-06 2.813846e-06
[202,] 0.9999955 9.050121e-06 4.525061e-06
[203,] 0.9999962 7.537169e-06 3.768585e-06
[204,] 0.9999977 4.675895e-06 2.337948e-06
[205,] 0.9999962 7.541285e-06 3.770642e-06
[206,] 0.9999951 9.779096e-06 4.889548e-06
[207,] 0.9999925 1.500957e-05 7.504786e-06
[208,] 0.9999956 8.757648e-06 4.378824e-06
[209,] 0.9999987 2.661083e-06 1.330542e-06
[210,] 0.9999979 4.185279e-06 2.092640e-06
[211,] 0.9999972 5.687189e-06 2.843594e-06
[212,] 0.9999958 8.340941e-06 4.170470e-06
[213,] 0.9999942 1.161538e-05 5.807691e-06
[214,] 0.9999949 1.018533e-05 5.092663e-06
[215,] 0.9999921 1.580993e-05 7.904963e-06
[216,] 0.9999897 2.065060e-05 1.032530e-05
[217,] 0.9999860 2.795177e-05 1.397589e-05
[218,] 0.9999912 1.761918e-05 8.809592e-06
[219,] 0.9999971 5.877249e-06 2.938625e-06
[220,] 0.9999995 1.044665e-06 5.223324e-07
[221,] 0.9999991 1.793508e-06 8.967542e-07
[222,] 0.9999985 2.913193e-06 1.456596e-06
[223,] 0.9999980 3.944731e-06 1.972365e-06
[224,] 0.9999989 2.238723e-06 1.119361e-06
[225,] 0.9999984 3.178948e-06 1.589474e-06
[226,] 0.9999977 4.622116e-06 2.311058e-06
[227,] 0.9999964 7.104276e-06 3.552138e-06
[228,] 0.9999956 8.758771e-06 4.379386e-06
[229,] 0.9999944 1.122143e-05 5.610714e-06
[230,] 0.9999900 1.996833e-05 9.984165e-06
[231,] 0.9999918 1.630713e-05 8.153567e-06
[232,] 0.9999854 2.925654e-05 1.462827e-05
[233,] 0.9999869 2.610330e-05 1.305165e-05
[234,] 0.9999778 4.446296e-05 2.223148e-05
[235,] 0.9999606 7.873638e-05 3.936819e-05
[236,] 0.9999443 1.113795e-04 5.568976e-05
[237,] 0.9999155 1.690073e-04 8.450367e-05
[238,] 0.9998552 2.896116e-04 1.448058e-04
[239,] 0.9997644 4.711445e-04 2.355722e-04
[240,] 0.9998150 3.700674e-04 1.850337e-04
[241,] 0.9996933 6.134324e-04 3.067162e-04
[242,] 0.9998139 3.722603e-04 1.861301e-04
[243,] 0.9998113 3.773751e-04 1.886875e-04
[244,] 0.9997778 4.444433e-04 2.222216e-04
[245,] 0.9996148 7.703595e-04 3.851798e-04
[246,] 0.9996920 6.160753e-04 3.080377e-04
[247,] 0.9994682 1.063560e-03 5.317800e-04
[248,] 0.9992868 1.426407e-03 7.132037e-04
[249,] 0.9989621 2.075731e-03 1.037866e-03
[250,] 0.9983300 3.340097e-03 1.670048e-03
[251,] 0.9972285 5.542908e-03 2.771454e-03
[252,] 0.9966772 6.645543e-03 3.322772e-03
[253,] 0.9946078 1.078435e-02 5.392175e-03
[254,] 0.9965903 6.819479e-03 3.409740e-03
[255,] 0.9947109 1.057825e-02 5.289123e-03
[256,] 0.9914313 1.713736e-02 8.568679e-03
[257,] 0.9868119 2.637614e-02 1.318807e-02
[258,] 0.9795806 4.083877e-02 2.041939e-02
[259,] 0.9689944 6.201126e-02 3.100563e-02
[260,] 0.9601822 7.963555e-02 3.981778e-02
[261,] 0.9432861 1.134278e-01 5.671391e-02
[262,] 0.9180597 1.638805e-01 8.194025e-02
[263,] 0.8899322 2.201355e-01 1.100678e-01
[264,] 0.8446759 3.106482e-01 1.553241e-01
[265,] 0.7961148 4.077703e-01 2.038852e-01
[266,] 0.9425664 1.148673e-01 5.743364e-02
[267,] 0.9541038 9.179242e-02 4.589621e-02
[268,] 0.9240238 1.519525e-01 7.597624e-02
[269,] 0.9178562 1.642876e-01 8.214381e-02
[270,] 0.8613205 2.773590e-01 1.386795e-01
[271,] 0.8033235 3.933530e-01 1.966765e-01
[272,] 0.6972705 6.054590e-01 3.027295e-01
[273,] 0.5761738 8.476524e-01 4.238262e-01
[274,] 0.4491924 8.983848e-01 5.508076e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1mxle1355757589.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2uvzw1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30b7m1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bua01355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rmai1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 289
Frequency = 1
1 2 3 4 5 6
-2.47269881 2.81257165 10.79126784 -3.24752382 1.11586034 9.65855602
7 8 9 10 11 12
-17.85732066 5.70693982 -5.20591543 -1.32328482 -0.69839562 8.19908380
13 14 15 16 17 18
15.50614539 7.66085963 5.99829806 8.69059698 4.50337366 -6.64750910
19 20 21 22 23 24
-1.37956143 -5.81746808 0.66468281 2.43696181 -3.99858811 0.77969985
25 26 27 28 29 30
10.25910043 1.99127983 2.69426089 -0.12893509 0.94939669 4.79095699
31 32 33 34 35 36
0.31870492 1.03779848 16.99011636 7.52494323 9.73230614 -3.99483666
37 38 39 40 41 42
-4.61321625 4.39933114 1.57525628 -9.43385015 3.25459059 -4.29084730
43 44 45 46 47 48
-1.59338850 -1.49968723 -3.49554841 10.76161196 -3.93479579 1.85435391
49 50 51 52 53 54
7.46546639 16.97116846 -5.94291925 9.56519476 8.78291904 -1.18684321
55 56 57 58 59 60
5.39317290 0.58454917 -7.18288375 2.05511063 2.18942251 -4.07437571
61 62 63 64 65 66
-1.25640874 6.11581630 -18.39339746 -1.28215418 -7.47484240 -2.29236135
67 68 69 70 71 72
-0.88090645 -3.73176145 -15.66850958 11.86534484 3.80223872 1.80902240
73 74 75 76 77 78
-1.06386358 -1.36978257 7.68285978 -5.15730988 -6.86815865 12.07825923
79 80 81 82 83 84
7.45858452 12.29383371 0.14836586 5.06826034 -3.53721927 -4.91559081
85 86 87 88 89 90
-5.35478270 2.46693010 8.07277313 0.48275431 -2.41389818 5.92208879
91 92 93 94 95 96
3.59092177 -12.70500554 6.40280523 1.86857985 0.66238274 -3.46388700
97 98 99 100 101 102
15.22145207 0.86839369 7.24240983 -1.56137328 3.69846465 1.81659102
103 104 105 106 107 108
-2.90411969 -2.26254012 -5.50074125 -10.33331994 14.43584970 3.43167616
109 110 111 112 113 114
-20.26739859 7.10660509 -4.94606153 11.17044728 3.71114564 -4.55307070
115 116 117 118 119 120
2.01394652 -1.79685925 -5.72472947 -7.35464823 -6.85376354 -2.22654188
121 122 123 124 125 126
-2.24837529 -2.89164336 -3.31489493 -0.95710664 -0.24562550 -6.16294598
127 128 129 130 131 132
-5.87069816 -0.87942479 4.54778620 -10.06966919 -2.79159165 2.99467896
133 134 135 136 137 138
-4.33061421 5.47244713 12.39506301 -6.81273196 -10.24444174 -5.64326144
139 140 141 142 143 144
-1.50862906 -4.94573994 0.07926585 -5.38877894 3.25505139 0.03942019
145 146 147 148 149 150
10.01366853 4.66162147 14.04620095 1.83403878 -4.76009892 9.50747808
151 152 153 154 155 156
7.78977577 16.87820802 -5.64623042 0.23491938 0.18793531 -5.25592341
157 158 159 160 161 162
5.49675730 -4.28587025 8.53555163 0.59055452 0.54416151 7.88590628
163 164 165 166 167 168
-4.76914822 -4.54936178 7.90446783 4.56221688 6.21196931 5.14414951
169 170 171 172 173 174
-0.36455760 -10.24864777 -4.78660061 -2.81900039 -0.88680338 2.17858034
175 176 177 178 179 180
6.46213016 -5.72027967 -2.33895591 1.51802473 0.29455358 -6.25428550
181 182 183 184 185 186
-10.23124264 -3.95092055 -10.39744667 5.16759857 4.01031375 -10.05787819
187 188 189 190 191 192
-7.29326708 -9.00682215 -3.84695696 13.29205723 -0.30500650 0.85460952
193 194 195 196 197 198
-6.78433559 8.10876290 -2.00937055 -8.68786587 4.06009627 -5.17031471
199 200 201 202 203 204
-2.92427996 -3.10781603 0.10161980 10.86458222 -5.25903917 -1.84721287
205 206 207 208 209 210
0.81565761 6.89094402 -0.40284202 -5.81755415 -1.13285310 2.81659037
211 212 213 214 215 216
-8.45743314 -2.22365951 3.19612112 -1.04550882 8.38327509 9.33458298
217 218 219 220 221 222
-2.56832006 -2.47298239 0.61816458 1.51317790 3.17959333 1.87257954
223 224 225 226 227 228
-2.99451401 1.71272377 5.89585802 -7.75815126 -9.72033963 -0.48156069
229 230 231 232 233 234
-1.79987486 2.86727264 6.24031415 -3.71171767 -3.85212950 2.77880756
235 236 237 238 239 240
4.03776702 1.28274623 -1.47791023 3.28932432 0.72486212 2.72621699
241 242 243 244 245 246
-2.03779169 0.45672290 0.73658001 -1.02666766 -1.19089770 -0.98638365
247 248 249 250 251 252
-5.99004799 -0.66058210 -7.03153274 -8.68382102 1.08451429 0.94722860
253 254 255 256 257 258
-10.26468794 1.70096486 1.37769665 -7.09722629 -4.50115070 -1.09353356
259 260 261 262 263 264
2.24480120 -4.01036450 5.04599951 -0.79604739 -4.22556047 -0.83047087
265 266 267 268 269 270
-1.25789493 -3.45292029 1.50400044 -3.96892323 -2.18650426 2.07128662
271 272 273 274 275 276
2.30595379 0.24243300 -10.76128462 -3.42452184 -5.00982780 1.97960254
277 278 279 280 281 282
-1.86015693 -9.17840167 -3.32143323 1.68062443 2.43592034 -6.31387650
283 284 285 286 287 288
-6.48250162 -7.79216436 2.04605766 4.82527983 -1.20266567 -1.33981848
289
1.14094377
> postscript(file="/var/wessaorg/rcomp/tmp/6bpre1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.47269881 NA
1 2.81257165 -2.47269881
2 10.79126784 2.81257165
3 -3.24752382 10.79126784
4 1.11586034 -3.24752382
5 9.65855602 1.11586034
6 -17.85732066 9.65855602
7 5.70693982 -17.85732066
8 -5.20591543 5.70693982
9 -1.32328482 -5.20591543
10 -0.69839562 -1.32328482
11 8.19908380 -0.69839562
12 15.50614539 8.19908380
13 7.66085963 15.50614539
14 5.99829806 7.66085963
15 8.69059698 5.99829806
16 4.50337366 8.69059698
17 -6.64750910 4.50337366
18 -1.37956143 -6.64750910
19 -5.81746808 -1.37956143
20 0.66468281 -5.81746808
21 2.43696181 0.66468281
22 -3.99858811 2.43696181
23 0.77969985 -3.99858811
24 10.25910043 0.77969985
25 1.99127983 10.25910043
26 2.69426089 1.99127983
27 -0.12893509 2.69426089
28 0.94939669 -0.12893509
29 4.79095699 0.94939669
30 0.31870492 4.79095699
31 1.03779848 0.31870492
32 16.99011636 1.03779848
33 7.52494323 16.99011636
34 9.73230614 7.52494323
35 -3.99483666 9.73230614
36 -4.61321625 -3.99483666
37 4.39933114 -4.61321625
38 1.57525628 4.39933114
39 -9.43385015 1.57525628
40 3.25459059 -9.43385015
41 -4.29084730 3.25459059
42 -1.59338850 -4.29084730
43 -1.49968723 -1.59338850
44 -3.49554841 -1.49968723
45 10.76161196 -3.49554841
46 -3.93479579 10.76161196
47 1.85435391 -3.93479579
48 7.46546639 1.85435391
49 16.97116846 7.46546639
50 -5.94291925 16.97116846
51 9.56519476 -5.94291925
52 8.78291904 9.56519476
53 -1.18684321 8.78291904
54 5.39317290 -1.18684321
55 0.58454917 5.39317290
56 -7.18288375 0.58454917
57 2.05511063 -7.18288375
58 2.18942251 2.05511063
59 -4.07437571 2.18942251
60 -1.25640874 -4.07437571
61 6.11581630 -1.25640874
62 -18.39339746 6.11581630
63 -1.28215418 -18.39339746
64 -7.47484240 -1.28215418
65 -2.29236135 -7.47484240
66 -0.88090645 -2.29236135
67 -3.73176145 -0.88090645
68 -15.66850958 -3.73176145
69 11.86534484 -15.66850958
70 3.80223872 11.86534484
71 1.80902240 3.80223872
72 -1.06386358 1.80902240
73 -1.36978257 -1.06386358
74 7.68285978 -1.36978257
75 -5.15730988 7.68285978
76 -6.86815865 -5.15730988
77 12.07825923 -6.86815865
78 7.45858452 12.07825923
79 12.29383371 7.45858452
80 0.14836586 12.29383371
81 5.06826034 0.14836586
82 -3.53721927 5.06826034
83 -4.91559081 -3.53721927
84 -5.35478270 -4.91559081
85 2.46693010 -5.35478270
86 8.07277313 2.46693010
87 0.48275431 8.07277313
88 -2.41389818 0.48275431
89 5.92208879 -2.41389818
90 3.59092177 5.92208879
91 -12.70500554 3.59092177
92 6.40280523 -12.70500554
93 1.86857985 6.40280523
94 0.66238274 1.86857985
95 -3.46388700 0.66238274
96 15.22145207 -3.46388700
97 0.86839369 15.22145207
98 7.24240983 0.86839369
99 -1.56137328 7.24240983
100 3.69846465 -1.56137328
101 1.81659102 3.69846465
102 -2.90411969 1.81659102
103 -2.26254012 -2.90411969
104 -5.50074125 -2.26254012
105 -10.33331994 -5.50074125
106 14.43584970 -10.33331994
107 3.43167616 14.43584970
108 -20.26739859 3.43167616
109 7.10660509 -20.26739859
110 -4.94606153 7.10660509
111 11.17044728 -4.94606153
112 3.71114564 11.17044728
113 -4.55307070 3.71114564
114 2.01394652 -4.55307070
115 -1.79685925 2.01394652
116 -5.72472947 -1.79685925
117 -7.35464823 -5.72472947
118 -6.85376354 -7.35464823
119 -2.22654188 -6.85376354
120 -2.24837529 -2.22654188
121 -2.89164336 -2.24837529
122 -3.31489493 -2.89164336
123 -0.95710664 -3.31489493
124 -0.24562550 -0.95710664
125 -6.16294598 -0.24562550
126 -5.87069816 -6.16294598
127 -0.87942479 -5.87069816
128 4.54778620 -0.87942479
129 -10.06966919 4.54778620
130 -2.79159165 -10.06966919
131 2.99467896 -2.79159165
132 -4.33061421 2.99467896
133 5.47244713 -4.33061421
134 12.39506301 5.47244713
135 -6.81273196 12.39506301
136 -10.24444174 -6.81273196
137 -5.64326144 -10.24444174
138 -1.50862906 -5.64326144
139 -4.94573994 -1.50862906
140 0.07926585 -4.94573994
141 -5.38877894 0.07926585
142 3.25505139 -5.38877894
143 0.03942019 3.25505139
144 10.01366853 0.03942019
145 4.66162147 10.01366853
146 14.04620095 4.66162147
147 1.83403878 14.04620095
148 -4.76009892 1.83403878
149 9.50747808 -4.76009892
150 7.78977577 9.50747808
151 16.87820802 7.78977577
152 -5.64623042 16.87820802
153 0.23491938 -5.64623042
154 0.18793531 0.23491938
155 -5.25592341 0.18793531
156 5.49675730 -5.25592341
157 -4.28587025 5.49675730
158 8.53555163 -4.28587025
159 0.59055452 8.53555163
160 0.54416151 0.59055452
161 7.88590628 0.54416151
162 -4.76914822 7.88590628
163 -4.54936178 -4.76914822
164 7.90446783 -4.54936178
165 4.56221688 7.90446783
166 6.21196931 4.56221688
167 5.14414951 6.21196931
168 -0.36455760 5.14414951
169 -10.24864777 -0.36455760
170 -4.78660061 -10.24864777
171 -2.81900039 -4.78660061
172 -0.88680338 -2.81900039
173 2.17858034 -0.88680338
174 6.46213016 2.17858034
175 -5.72027967 6.46213016
176 -2.33895591 -5.72027967
177 1.51802473 -2.33895591
178 0.29455358 1.51802473
179 -6.25428550 0.29455358
180 -10.23124264 -6.25428550
181 -3.95092055 -10.23124264
182 -10.39744667 -3.95092055
183 5.16759857 -10.39744667
184 4.01031375 5.16759857
185 -10.05787819 4.01031375
186 -7.29326708 -10.05787819
187 -9.00682215 -7.29326708
188 -3.84695696 -9.00682215
189 13.29205723 -3.84695696
190 -0.30500650 13.29205723
191 0.85460952 -0.30500650
192 -6.78433559 0.85460952
193 8.10876290 -6.78433559
194 -2.00937055 8.10876290
195 -8.68786587 -2.00937055
196 4.06009627 -8.68786587
197 -5.17031471 4.06009627
198 -2.92427996 -5.17031471
199 -3.10781603 -2.92427996
200 0.10161980 -3.10781603
201 10.86458222 0.10161980
202 -5.25903917 10.86458222
203 -1.84721287 -5.25903917
204 0.81565761 -1.84721287
205 6.89094402 0.81565761
206 -0.40284202 6.89094402
207 -5.81755415 -0.40284202
208 -1.13285310 -5.81755415
209 2.81659037 -1.13285310
210 -8.45743314 2.81659037
211 -2.22365951 -8.45743314
212 3.19612112 -2.22365951
213 -1.04550882 3.19612112
214 8.38327509 -1.04550882
215 9.33458298 8.38327509
216 -2.56832006 9.33458298
217 -2.47298239 -2.56832006
218 0.61816458 -2.47298239
219 1.51317790 0.61816458
220 3.17959333 1.51317790
221 1.87257954 3.17959333
222 -2.99451401 1.87257954
223 1.71272377 -2.99451401
224 5.89585802 1.71272377
225 -7.75815126 5.89585802
226 -9.72033963 -7.75815126
227 -0.48156069 -9.72033963
228 -1.79987486 -0.48156069
229 2.86727264 -1.79987486
230 6.24031415 2.86727264
231 -3.71171767 6.24031415
232 -3.85212950 -3.71171767
233 2.77880756 -3.85212950
234 4.03776702 2.77880756
235 1.28274623 4.03776702
236 -1.47791023 1.28274623
237 3.28932432 -1.47791023
238 0.72486212 3.28932432
239 2.72621699 0.72486212
240 -2.03779169 2.72621699
241 0.45672290 -2.03779169
242 0.73658001 0.45672290
243 -1.02666766 0.73658001
244 -1.19089770 -1.02666766
245 -0.98638365 -1.19089770
246 -5.99004799 -0.98638365
247 -0.66058210 -5.99004799
248 -7.03153274 -0.66058210
249 -8.68382102 -7.03153274
250 1.08451429 -8.68382102
251 0.94722860 1.08451429
252 -10.26468794 0.94722860
253 1.70096486 -10.26468794
254 1.37769665 1.70096486
255 -7.09722629 1.37769665
256 -4.50115070 -7.09722629
257 -1.09353356 -4.50115070
258 2.24480120 -1.09353356
259 -4.01036450 2.24480120
260 5.04599951 -4.01036450
261 -0.79604739 5.04599951
262 -4.22556047 -0.79604739
263 -0.83047087 -4.22556047
264 -1.25789493 -0.83047087
265 -3.45292029 -1.25789493
266 1.50400044 -3.45292029
267 -3.96892323 1.50400044
268 -2.18650426 -3.96892323
269 2.07128662 -2.18650426
270 2.30595379 2.07128662
271 0.24243300 2.30595379
272 -10.76128462 0.24243300
273 -3.42452184 -10.76128462
274 -5.00982780 -3.42452184
275 1.97960254 -5.00982780
276 -1.86015693 1.97960254
277 -9.17840167 -1.86015693
278 -3.32143323 -9.17840167
279 1.68062443 -3.32143323
280 2.43592034 1.68062443
281 -6.31387650 2.43592034
282 -6.48250162 -6.31387650
283 -7.79216436 -6.48250162
284 2.04605766 -7.79216436
285 4.82527983 2.04605766
286 -1.20266567 4.82527983
287 -1.33981848 -1.20266567
288 1.14094377 -1.33981848
289 NA 1.14094377
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.81257165 -2.47269881
[2,] 10.79126784 2.81257165
[3,] -3.24752382 10.79126784
[4,] 1.11586034 -3.24752382
[5,] 9.65855602 1.11586034
[6,] -17.85732066 9.65855602
[7,] 5.70693982 -17.85732066
[8,] -5.20591543 5.70693982
[9,] -1.32328482 -5.20591543
[10,] -0.69839562 -1.32328482
[11,] 8.19908380 -0.69839562
[12,] 15.50614539 8.19908380
[13,] 7.66085963 15.50614539
[14,] 5.99829806 7.66085963
[15,] 8.69059698 5.99829806
[16,] 4.50337366 8.69059698
[17,] -6.64750910 4.50337366
[18,] -1.37956143 -6.64750910
[19,] -5.81746808 -1.37956143
[20,] 0.66468281 -5.81746808
[21,] 2.43696181 0.66468281
[22,] -3.99858811 2.43696181
[23,] 0.77969985 -3.99858811
[24,] 10.25910043 0.77969985
[25,] 1.99127983 10.25910043
[26,] 2.69426089 1.99127983
[27,] -0.12893509 2.69426089
[28,] 0.94939669 -0.12893509
[29,] 4.79095699 0.94939669
[30,] 0.31870492 4.79095699
[31,] 1.03779848 0.31870492
[32,] 16.99011636 1.03779848
[33,] 7.52494323 16.99011636
[34,] 9.73230614 7.52494323
[35,] -3.99483666 9.73230614
[36,] -4.61321625 -3.99483666
[37,] 4.39933114 -4.61321625
[38,] 1.57525628 4.39933114
[39,] -9.43385015 1.57525628
[40,] 3.25459059 -9.43385015
[41,] -4.29084730 3.25459059
[42,] -1.59338850 -4.29084730
[43,] -1.49968723 -1.59338850
[44,] -3.49554841 -1.49968723
[45,] 10.76161196 -3.49554841
[46,] -3.93479579 10.76161196
[47,] 1.85435391 -3.93479579
[48,] 7.46546639 1.85435391
[49,] 16.97116846 7.46546639
[50,] -5.94291925 16.97116846
[51,] 9.56519476 -5.94291925
[52,] 8.78291904 9.56519476
[53,] -1.18684321 8.78291904
[54,] 5.39317290 -1.18684321
[55,] 0.58454917 5.39317290
[56,] -7.18288375 0.58454917
[57,] 2.05511063 -7.18288375
[58,] 2.18942251 2.05511063
[59,] -4.07437571 2.18942251
[60,] -1.25640874 -4.07437571
[61,] 6.11581630 -1.25640874
[62,] -18.39339746 6.11581630
[63,] -1.28215418 -18.39339746
[64,] -7.47484240 -1.28215418
[65,] -2.29236135 -7.47484240
[66,] -0.88090645 -2.29236135
[67,] -3.73176145 -0.88090645
[68,] -15.66850958 -3.73176145
[69,] 11.86534484 -15.66850958
[70,] 3.80223872 11.86534484
[71,] 1.80902240 3.80223872
[72,] -1.06386358 1.80902240
[73,] -1.36978257 -1.06386358
[74,] 7.68285978 -1.36978257
[75,] -5.15730988 7.68285978
[76,] -6.86815865 -5.15730988
[77,] 12.07825923 -6.86815865
[78,] 7.45858452 12.07825923
[79,] 12.29383371 7.45858452
[80,] 0.14836586 12.29383371
[81,] 5.06826034 0.14836586
[82,] -3.53721927 5.06826034
[83,] -4.91559081 -3.53721927
[84,] -5.35478270 -4.91559081
[85,] 2.46693010 -5.35478270
[86,] 8.07277313 2.46693010
[87,] 0.48275431 8.07277313
[88,] -2.41389818 0.48275431
[89,] 5.92208879 -2.41389818
[90,] 3.59092177 5.92208879
[91,] -12.70500554 3.59092177
[92,] 6.40280523 -12.70500554
[93,] 1.86857985 6.40280523
[94,] 0.66238274 1.86857985
[95,] -3.46388700 0.66238274
[96,] 15.22145207 -3.46388700
[97,] 0.86839369 15.22145207
[98,] 7.24240983 0.86839369
[99,] -1.56137328 7.24240983
[100,] 3.69846465 -1.56137328
[101,] 1.81659102 3.69846465
[102,] -2.90411969 1.81659102
[103,] -2.26254012 -2.90411969
[104,] -5.50074125 -2.26254012
[105,] -10.33331994 -5.50074125
[106,] 14.43584970 -10.33331994
[107,] 3.43167616 14.43584970
[108,] -20.26739859 3.43167616
[109,] 7.10660509 -20.26739859
[110,] -4.94606153 7.10660509
[111,] 11.17044728 -4.94606153
[112,] 3.71114564 11.17044728
[113,] -4.55307070 3.71114564
[114,] 2.01394652 -4.55307070
[115,] -1.79685925 2.01394652
[116,] -5.72472947 -1.79685925
[117,] -7.35464823 -5.72472947
[118,] -6.85376354 -7.35464823
[119,] -2.22654188 -6.85376354
[120,] -2.24837529 -2.22654188
[121,] -2.89164336 -2.24837529
[122,] -3.31489493 -2.89164336
[123,] -0.95710664 -3.31489493
[124,] -0.24562550 -0.95710664
[125,] -6.16294598 -0.24562550
[126,] -5.87069816 -6.16294598
[127,] -0.87942479 -5.87069816
[128,] 4.54778620 -0.87942479
[129,] -10.06966919 4.54778620
[130,] -2.79159165 -10.06966919
[131,] 2.99467896 -2.79159165
[132,] -4.33061421 2.99467896
[133,] 5.47244713 -4.33061421
[134,] 12.39506301 5.47244713
[135,] -6.81273196 12.39506301
[136,] -10.24444174 -6.81273196
[137,] -5.64326144 -10.24444174
[138,] -1.50862906 -5.64326144
[139,] -4.94573994 -1.50862906
[140,] 0.07926585 -4.94573994
[141,] -5.38877894 0.07926585
[142,] 3.25505139 -5.38877894
[143,] 0.03942019 3.25505139
[144,] 10.01366853 0.03942019
[145,] 4.66162147 10.01366853
[146,] 14.04620095 4.66162147
[147,] 1.83403878 14.04620095
[148,] -4.76009892 1.83403878
[149,] 9.50747808 -4.76009892
[150,] 7.78977577 9.50747808
[151,] 16.87820802 7.78977577
[152,] -5.64623042 16.87820802
[153,] 0.23491938 -5.64623042
[154,] 0.18793531 0.23491938
[155,] -5.25592341 0.18793531
[156,] 5.49675730 -5.25592341
[157,] -4.28587025 5.49675730
[158,] 8.53555163 -4.28587025
[159,] 0.59055452 8.53555163
[160,] 0.54416151 0.59055452
[161,] 7.88590628 0.54416151
[162,] -4.76914822 7.88590628
[163,] -4.54936178 -4.76914822
[164,] 7.90446783 -4.54936178
[165,] 4.56221688 7.90446783
[166,] 6.21196931 4.56221688
[167,] 5.14414951 6.21196931
[168,] -0.36455760 5.14414951
[169,] -10.24864777 -0.36455760
[170,] -4.78660061 -10.24864777
[171,] -2.81900039 -4.78660061
[172,] -0.88680338 -2.81900039
[173,] 2.17858034 -0.88680338
[174,] 6.46213016 2.17858034
[175,] -5.72027967 6.46213016
[176,] -2.33895591 -5.72027967
[177,] 1.51802473 -2.33895591
[178,] 0.29455358 1.51802473
[179,] -6.25428550 0.29455358
[180,] -10.23124264 -6.25428550
[181,] -3.95092055 -10.23124264
[182,] -10.39744667 -3.95092055
[183,] 5.16759857 -10.39744667
[184,] 4.01031375 5.16759857
[185,] -10.05787819 4.01031375
[186,] -7.29326708 -10.05787819
[187,] -9.00682215 -7.29326708
[188,] -3.84695696 -9.00682215
[189,] 13.29205723 -3.84695696
[190,] -0.30500650 13.29205723
[191,] 0.85460952 -0.30500650
[192,] -6.78433559 0.85460952
[193,] 8.10876290 -6.78433559
[194,] -2.00937055 8.10876290
[195,] -8.68786587 -2.00937055
[196,] 4.06009627 -8.68786587
[197,] -5.17031471 4.06009627
[198,] -2.92427996 -5.17031471
[199,] -3.10781603 -2.92427996
[200,] 0.10161980 -3.10781603
[201,] 10.86458222 0.10161980
[202,] -5.25903917 10.86458222
[203,] -1.84721287 -5.25903917
[204,] 0.81565761 -1.84721287
[205,] 6.89094402 0.81565761
[206,] -0.40284202 6.89094402
[207,] -5.81755415 -0.40284202
[208,] -1.13285310 -5.81755415
[209,] 2.81659037 -1.13285310
[210,] -8.45743314 2.81659037
[211,] -2.22365951 -8.45743314
[212,] 3.19612112 -2.22365951
[213,] -1.04550882 3.19612112
[214,] 8.38327509 -1.04550882
[215,] 9.33458298 8.38327509
[216,] -2.56832006 9.33458298
[217,] -2.47298239 -2.56832006
[218,] 0.61816458 -2.47298239
[219,] 1.51317790 0.61816458
[220,] 3.17959333 1.51317790
[221,] 1.87257954 3.17959333
[222,] -2.99451401 1.87257954
[223,] 1.71272377 -2.99451401
[224,] 5.89585802 1.71272377
[225,] -7.75815126 5.89585802
[226,] -9.72033963 -7.75815126
[227,] -0.48156069 -9.72033963
[228,] -1.79987486 -0.48156069
[229,] 2.86727264 -1.79987486
[230,] 6.24031415 2.86727264
[231,] -3.71171767 6.24031415
[232,] -3.85212950 -3.71171767
[233,] 2.77880756 -3.85212950
[234,] 4.03776702 2.77880756
[235,] 1.28274623 4.03776702
[236,] -1.47791023 1.28274623
[237,] 3.28932432 -1.47791023
[238,] 0.72486212 3.28932432
[239,] 2.72621699 0.72486212
[240,] -2.03779169 2.72621699
[241,] 0.45672290 -2.03779169
[242,] 0.73658001 0.45672290
[243,] -1.02666766 0.73658001
[244,] -1.19089770 -1.02666766
[245,] -0.98638365 -1.19089770
[246,] -5.99004799 -0.98638365
[247,] -0.66058210 -5.99004799
[248,] -7.03153274 -0.66058210
[249,] -8.68382102 -7.03153274
[250,] 1.08451429 -8.68382102
[251,] 0.94722860 1.08451429
[252,] -10.26468794 0.94722860
[253,] 1.70096486 -10.26468794
[254,] 1.37769665 1.70096486
[255,] -7.09722629 1.37769665
[256,] -4.50115070 -7.09722629
[257,] -1.09353356 -4.50115070
[258,] 2.24480120 -1.09353356
[259,] -4.01036450 2.24480120
[260,] 5.04599951 -4.01036450
[261,] -0.79604739 5.04599951
[262,] -4.22556047 -0.79604739
[263,] -0.83047087 -4.22556047
[264,] -1.25789493 -0.83047087
[265,] -3.45292029 -1.25789493
[266,] 1.50400044 -3.45292029
[267,] -3.96892323 1.50400044
[268,] -2.18650426 -3.96892323
[269,] 2.07128662 -2.18650426
[270,] 2.30595379 2.07128662
[271,] 0.24243300 2.30595379
[272,] -10.76128462 0.24243300
[273,] -3.42452184 -10.76128462
[274,] -5.00982780 -3.42452184
[275,] 1.97960254 -5.00982780
[276,] -1.86015693 1.97960254
[277,] -9.17840167 -1.86015693
[278,] -3.32143323 -9.17840167
[279,] 1.68062443 -3.32143323
[280,] 2.43592034 1.68062443
[281,] -6.31387650 2.43592034
[282,] -6.48250162 -6.31387650
[283,] -7.79216436 -6.48250162
[284,] 2.04605766 -7.79216436
[285,] 4.82527983 2.04605766
[286,] -1.20266567 4.82527983
[287,] -1.33981848 -1.20266567
[288,] 1.14094377 -1.33981848
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.81257165 -2.47269881
2 10.79126784 2.81257165
3 -3.24752382 10.79126784
4 1.11586034 -3.24752382
5 9.65855602 1.11586034
6 -17.85732066 9.65855602
7 5.70693982 -17.85732066
8 -5.20591543 5.70693982
9 -1.32328482 -5.20591543
10 -0.69839562 -1.32328482
11 8.19908380 -0.69839562
12 15.50614539 8.19908380
13 7.66085963 15.50614539
14 5.99829806 7.66085963
15 8.69059698 5.99829806
16 4.50337366 8.69059698
17 -6.64750910 4.50337366
18 -1.37956143 -6.64750910
19 -5.81746808 -1.37956143
20 0.66468281 -5.81746808
21 2.43696181 0.66468281
22 -3.99858811 2.43696181
23 0.77969985 -3.99858811
24 10.25910043 0.77969985
25 1.99127983 10.25910043
26 2.69426089 1.99127983
27 -0.12893509 2.69426089
28 0.94939669 -0.12893509
29 4.79095699 0.94939669
30 0.31870492 4.79095699
31 1.03779848 0.31870492
32 16.99011636 1.03779848
33 7.52494323 16.99011636
34 9.73230614 7.52494323
35 -3.99483666 9.73230614
36 -4.61321625 -3.99483666
37 4.39933114 -4.61321625
38 1.57525628 4.39933114
39 -9.43385015 1.57525628
40 3.25459059 -9.43385015
41 -4.29084730 3.25459059
42 -1.59338850 -4.29084730
43 -1.49968723 -1.59338850
44 -3.49554841 -1.49968723
45 10.76161196 -3.49554841
46 -3.93479579 10.76161196
47 1.85435391 -3.93479579
48 7.46546639 1.85435391
49 16.97116846 7.46546639
50 -5.94291925 16.97116846
51 9.56519476 -5.94291925
52 8.78291904 9.56519476
53 -1.18684321 8.78291904
54 5.39317290 -1.18684321
55 0.58454917 5.39317290
56 -7.18288375 0.58454917
57 2.05511063 -7.18288375
58 2.18942251 2.05511063
59 -4.07437571 2.18942251
60 -1.25640874 -4.07437571
61 6.11581630 -1.25640874
62 -18.39339746 6.11581630
63 -1.28215418 -18.39339746
64 -7.47484240 -1.28215418
65 -2.29236135 -7.47484240
66 -0.88090645 -2.29236135
67 -3.73176145 -0.88090645
68 -15.66850958 -3.73176145
69 11.86534484 -15.66850958
70 3.80223872 11.86534484
71 1.80902240 3.80223872
72 -1.06386358 1.80902240
73 -1.36978257 -1.06386358
74 7.68285978 -1.36978257
75 -5.15730988 7.68285978
76 -6.86815865 -5.15730988
77 12.07825923 -6.86815865
78 7.45858452 12.07825923
79 12.29383371 7.45858452
80 0.14836586 12.29383371
81 5.06826034 0.14836586
82 -3.53721927 5.06826034
83 -4.91559081 -3.53721927
84 -5.35478270 -4.91559081
85 2.46693010 -5.35478270
86 8.07277313 2.46693010
87 0.48275431 8.07277313
88 -2.41389818 0.48275431
89 5.92208879 -2.41389818
90 3.59092177 5.92208879
91 -12.70500554 3.59092177
92 6.40280523 -12.70500554
93 1.86857985 6.40280523
94 0.66238274 1.86857985
95 -3.46388700 0.66238274
96 15.22145207 -3.46388700
97 0.86839369 15.22145207
98 7.24240983 0.86839369
99 -1.56137328 7.24240983
100 3.69846465 -1.56137328
101 1.81659102 3.69846465
102 -2.90411969 1.81659102
103 -2.26254012 -2.90411969
104 -5.50074125 -2.26254012
105 -10.33331994 -5.50074125
106 14.43584970 -10.33331994
107 3.43167616 14.43584970
108 -20.26739859 3.43167616
109 7.10660509 -20.26739859
110 -4.94606153 7.10660509
111 11.17044728 -4.94606153
112 3.71114564 11.17044728
113 -4.55307070 3.71114564
114 2.01394652 -4.55307070
115 -1.79685925 2.01394652
116 -5.72472947 -1.79685925
117 -7.35464823 -5.72472947
118 -6.85376354 -7.35464823
119 -2.22654188 -6.85376354
120 -2.24837529 -2.22654188
121 -2.89164336 -2.24837529
122 -3.31489493 -2.89164336
123 -0.95710664 -3.31489493
124 -0.24562550 -0.95710664
125 -6.16294598 -0.24562550
126 -5.87069816 -6.16294598
127 -0.87942479 -5.87069816
128 4.54778620 -0.87942479
129 -10.06966919 4.54778620
130 -2.79159165 -10.06966919
131 2.99467896 -2.79159165
132 -4.33061421 2.99467896
133 5.47244713 -4.33061421
134 12.39506301 5.47244713
135 -6.81273196 12.39506301
136 -10.24444174 -6.81273196
137 -5.64326144 -10.24444174
138 -1.50862906 -5.64326144
139 -4.94573994 -1.50862906
140 0.07926585 -4.94573994
141 -5.38877894 0.07926585
142 3.25505139 -5.38877894
143 0.03942019 3.25505139
144 10.01366853 0.03942019
145 4.66162147 10.01366853
146 14.04620095 4.66162147
147 1.83403878 14.04620095
148 -4.76009892 1.83403878
149 9.50747808 -4.76009892
150 7.78977577 9.50747808
151 16.87820802 7.78977577
152 -5.64623042 16.87820802
153 0.23491938 -5.64623042
154 0.18793531 0.23491938
155 -5.25592341 0.18793531
156 5.49675730 -5.25592341
157 -4.28587025 5.49675730
158 8.53555163 -4.28587025
159 0.59055452 8.53555163
160 0.54416151 0.59055452
161 7.88590628 0.54416151
162 -4.76914822 7.88590628
163 -4.54936178 -4.76914822
164 7.90446783 -4.54936178
165 4.56221688 7.90446783
166 6.21196931 4.56221688
167 5.14414951 6.21196931
168 -0.36455760 5.14414951
169 -10.24864777 -0.36455760
170 -4.78660061 -10.24864777
171 -2.81900039 -4.78660061
172 -0.88680338 -2.81900039
173 2.17858034 -0.88680338
174 6.46213016 2.17858034
175 -5.72027967 6.46213016
176 -2.33895591 -5.72027967
177 1.51802473 -2.33895591
178 0.29455358 1.51802473
179 -6.25428550 0.29455358
180 -10.23124264 -6.25428550
181 -3.95092055 -10.23124264
182 -10.39744667 -3.95092055
183 5.16759857 -10.39744667
184 4.01031375 5.16759857
185 -10.05787819 4.01031375
186 -7.29326708 -10.05787819
187 -9.00682215 -7.29326708
188 -3.84695696 -9.00682215
189 13.29205723 -3.84695696
190 -0.30500650 13.29205723
191 0.85460952 -0.30500650
192 -6.78433559 0.85460952
193 8.10876290 -6.78433559
194 -2.00937055 8.10876290
195 -8.68786587 -2.00937055
196 4.06009627 -8.68786587
197 -5.17031471 4.06009627
198 -2.92427996 -5.17031471
199 -3.10781603 -2.92427996
200 0.10161980 -3.10781603
201 10.86458222 0.10161980
202 -5.25903917 10.86458222
203 -1.84721287 -5.25903917
204 0.81565761 -1.84721287
205 6.89094402 0.81565761
206 -0.40284202 6.89094402
207 -5.81755415 -0.40284202
208 -1.13285310 -5.81755415
209 2.81659037 -1.13285310
210 -8.45743314 2.81659037
211 -2.22365951 -8.45743314
212 3.19612112 -2.22365951
213 -1.04550882 3.19612112
214 8.38327509 -1.04550882
215 9.33458298 8.38327509
216 -2.56832006 9.33458298
217 -2.47298239 -2.56832006
218 0.61816458 -2.47298239
219 1.51317790 0.61816458
220 3.17959333 1.51317790
221 1.87257954 3.17959333
222 -2.99451401 1.87257954
223 1.71272377 -2.99451401
224 5.89585802 1.71272377
225 -7.75815126 5.89585802
226 -9.72033963 -7.75815126
227 -0.48156069 -9.72033963
228 -1.79987486 -0.48156069
229 2.86727264 -1.79987486
230 6.24031415 2.86727264
231 -3.71171767 6.24031415
232 -3.85212950 -3.71171767
233 2.77880756 -3.85212950
234 4.03776702 2.77880756
235 1.28274623 4.03776702
236 -1.47791023 1.28274623
237 3.28932432 -1.47791023
238 0.72486212 3.28932432
239 2.72621699 0.72486212
240 -2.03779169 2.72621699
241 0.45672290 -2.03779169
242 0.73658001 0.45672290
243 -1.02666766 0.73658001
244 -1.19089770 -1.02666766
245 -0.98638365 -1.19089770
246 -5.99004799 -0.98638365
247 -0.66058210 -5.99004799
248 -7.03153274 -0.66058210
249 -8.68382102 -7.03153274
250 1.08451429 -8.68382102
251 0.94722860 1.08451429
252 -10.26468794 0.94722860
253 1.70096486 -10.26468794
254 1.37769665 1.70096486
255 -7.09722629 1.37769665
256 -4.50115070 -7.09722629
257 -1.09353356 -4.50115070
258 2.24480120 -1.09353356
259 -4.01036450 2.24480120
260 5.04599951 -4.01036450
261 -0.79604739 5.04599951
262 -4.22556047 -0.79604739
263 -0.83047087 -4.22556047
264 -1.25789493 -0.83047087
265 -3.45292029 -1.25789493
266 1.50400044 -3.45292029
267 -3.96892323 1.50400044
268 -2.18650426 -3.96892323
269 2.07128662 -2.18650426
270 2.30595379 2.07128662
271 0.24243300 2.30595379
272 -10.76128462 0.24243300
273 -3.42452184 -10.76128462
274 -5.00982780 -3.42452184
275 1.97960254 -5.00982780
276 -1.86015693 1.97960254
277 -9.17840167 -1.86015693
278 -3.32143323 -9.17840167
279 1.68062443 -3.32143323
280 2.43592034 1.68062443
281 -6.31387650 2.43592034
282 -6.48250162 -6.31387650
283 -7.79216436 -6.48250162
284 2.04605766 -7.79216436
285 4.82527983 2.04605766
286 -1.20266567 4.82527983
287 -1.33981848 -1.20266567
288 1.14094377 -1.33981848
> 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/7imzg1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8i2le1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9vxug1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wjef1355757589.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vmof1355757589.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/12s08u1355757589.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/13bqr31355757589.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/14ojy41355757589.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/15646i1355757589.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/16k3i91355757589.tab")
+ }
>
> try(system("convert tmp/1mxle1355757589.ps tmp/1mxle1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uvzw1355757589.ps tmp/2uvzw1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/30b7m1355757589.ps tmp/30b7m1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bua01355757589.ps tmp/4bua01355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rmai1355757589.ps tmp/5rmai1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bpre1355757589.ps tmp/6bpre1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/7imzg1355757589.ps tmp/7imzg1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/8i2le1355757589.ps tmp/8i2le1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vxug1355757589.ps tmp/9vxug1355757589.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wjef1355757589.ps tmp/10wjef1355757589.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.238 1.764 19.025