R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(210907
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+ ,888
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+ ,32
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+ ,849
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+ ,20
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+ ,1182
+ ,29
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+ ,32073
+ ,34
+ ,40248
+ ,528
+ ,16
+ ,8
+ ,4
+ ,5444
+ ,6
+ ,64187
+ ,642
+ ,27
+ ,10
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+ ,12
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+ ,947
+ ,21
+ ,15
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+ ,24
+ ,56613
+ ,819
+ ,19
+ ,15
+ ,12
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+ ,16
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+ ,757
+ ,35
+ ,28
+ ,15
+ ,30884
+ ,72
+ ,72535
+ ,894
+ ,14
+ ,17
+ ,16
+ ,19540
+ ,27)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'pageviews'
+ ,'logins'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'totsize'
+ ,'tothyperlinks
')
+ ,1:289))
> y <- array(NA,dim=c(7,289),dimnames=list(c('time_in_rfc','pageviews','logins','blogged_computations','compendiums_reviewed','totsize','tothyperlinks
'),1:289))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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 pageviews logins blogged_computations
1 30 210907 1418 56 79
2 28 120982 869 56 58
3 38 176508 1530 54 60
4 30 179321 2172 89 108
5 22 123185 901 40 49
6 26 52746 463 25 0
7 25 385534 3201 92 121
8 18 33170 371 18 1
9 11 101645 1192 63 20
10 26 149061 1583 44 43
11 25 165446 1439 33 69
12 38 237213 1764 84 78
13 44 173326 1495 88 86
14 30 133131 1373 55 44
15 40 258873 2187 60 104
16 34 180083 1491 66 63
17 47 324799 4041 154 158
18 30 230964 1706 53 102
19 31 236785 2152 119 77
20 23 135473 1036 41 82
21 36 202925 1882 61 115
22 36 215147 1929 58 101
23 30 344297 2242 75 80
24 25 153935 1220 33 50
25 39 132943 1289 40 83
26 34 174724 2515 92 123
27 31 174415 2147 100 73
28 31 225548 2352 112 81
29 33 223632 1638 73 105
30 25 124817 1222 40 47
31 33 221698 1812 45 105
32 35 210767 1677 60 94
33 42 170266 1579 62 44
34 43 260561 1731 75 114
35 30 84853 807 31 38
36 33 294424 2452 77 107
37 13 101011 829 34 30
38 32 215641 1940 46 71
39 36 325107 2662 99 84
40 0 7176 186 17 0
41 28 167542 1499 66 59
42 14 106408 865 30 33
43 17 96560 1793 76 42
44 32 265769 2527 146 96
45 30 269651 2747 67 106
46 35 149112 1324 56 56
47 20 175824 2702 107 57
48 28 152871 1383 58 59
49 28 111665 1179 34 39
50 39 116408 2099 61 34
51 34 362301 4308 119 76
52 26 78800 918 42 20
53 39 183167 1831 66 91
54 39 277965 3373 89 115
55 33 150629 1713 44 85
56 28 168809 1438 66 76
57 4 24188 496 24 8
58 39 329267 2253 259 79
59 18 65029 744 17 21
60 14 101097 1161 64 30
61 29 218946 2352 41 76
62 44 244052 2144 68 101
63 21 341570 4691 168 94
64 16 103597 1112 43 27
65 28 233328 2694 132 92
66 35 256462 1973 105 123
67 28 206161 1769 71 75
68 38 311473 3148 112 128
69 23 235800 2474 94 105
70 36 177939 2084 82 55
71 32 207176 1954 70 56
72 29 196553 1226 57 41
73 25 174184 1389 53 72
74 27 143246 1496 103 67
75 36 187559 2269 121 75
76 28 187681 1833 62 114
77 23 119016 1268 52 118
78 40 182192 1943 52 77
79 23 73566 893 32 22
80 40 194979 1762 62 66
81 28 167488 1403 45 69
82 34 143756 1425 46 105
83 33 275541 1857 63 116
84 28 243199 1840 75 88
85 34 182999 1502 88 73
86 30 135649 1441 46 99
87 33 152299 1420 53 62
88 22 120221 1416 37 53
89 38 346485 2970 90 118
90 26 145790 1317 63 30
91 35 193339 1644 78 100
92 8 80953 870 25 49
93 24 122774 1654 45 24
94 29 130585 1054 46 67
95 20 112611 937 41 46
96 29 286468 3004 144 57
97 45 241066 2008 82 75
98 37 148446 2547 91 135
99 33 204713 1885 71 68
100 33 182079 1626 63 124
101 25 140344 1468 53 33
102 32 220516 2445 62 98
103 29 243060 1964 63 58
104 28 162765 1381 32 68
105 28 182613 1369 39 81
106 31 232138 1659 62 131
107 52 265318 2888 117 110
108 21 85574 1290 34 37
109 24 310839 2845 92 130
110 41 225060 1982 93 93
111 33 232317 1904 54 118
112 32 144966 1391 144 39
113 19 43287 602 14 13
114 20 155754 1743 61 74
115 31 164709 1559 109 81
116 31 201940 2014 38 109
117 32 235454 2143 73 151
118 18 220801 2146 75 51
119 23 99466 874 50 28
120 17 92661 1590 61 40
121 20 133328 1590 55 56
122 12 61361 1210 77 27
123 17 125930 2072 75 37
124 30 100750 1281 72 83
125 31 224549 1401 50 54
126 10 82316 834 32 27
127 13 102010 1105 53 28
128 22 101523 1272 42 59
129 42 243511 1944 71 133
130 1 22938 391 10 12
131 9 41566 761 35 0
132 32 152474 1605 65 106
133 11 61857 530 25 23
134 25 99923 1988 66 44
135 36 132487 1386 41 71
136 31 317394 2395 86 116
137 0 21054 387 16 4
138 24 209641 1742 42 62
139 13 22648 620 19 12
140 8 31414 449 19 18
141 13 46698 800 45 14
142 19 131698 1684 65 60
143 18 91735 1050 35 7
144 33 244749 2699 95 98
145 40 184510 1606 49 64
146 22 79863 1502 37 29
147 38 128423 1204 64 32
148 24 97839 1138 38 25
149 8 38214 568 34 16
150 35 151101 1459 32 48
151 43 272458 2158 65 100
152 43 172494 1111 52 46
153 14 108043 1421 62 45
154 41 328107 2833 65 129
155 38 250579 1955 83 130
156 45 351067 2922 95 136
157 31 158015 1002 29 59
158 13 98866 1060 18 25
159 28 85439 956 33 32
160 31 229242 2186 247 63
161 40 351619 3604 139 95
162 30 84207 1035 29 14
163 16 120445 1417 118 36
164 37 324598 3261 110 113
165 30 131069 1587 67 47
166 35 204271 1424 42 92
167 32 165543 1701 65 70
168 27 141722 1249 94 19
169 20 116048 946 64 50
170 18 250047 1926 81 41
171 31 299775 3352 95 91
172 31 195838 1641 67 111
173 21 173260 2035 63 41
174 39 254488 2312 83 120
175 41 104389 1369 45 135
176 13 136084 1577 30 27
177 32 199476 2201 70 87
178 18 92499 961 32 25
179 39 224330 1900 83 131
180 14 135781 1254 31 45
181 7 74408 1335 67 29
182 17 81240 1597 66 58
183 0 14688 207 10 4
184 30 181633 1645 70 47
185 37 271856 2429 103 109
186 0 7199 151 5 7
187 5 46660 474 20 12
188 1 17547 141 5 0
189 16 133368 1639 36 37
190 32 95227 872 34 37
191 24 152601 1318 48 46
192 17 98146 1018 40 15
193 11 79619 1383 43 42
194 24 59194 1314 31 7
195 22 139942 1335 42 54
196 12 118612 1403 46 54
197 19 72880 910 33 14
198 13 65475 616 18 16
199 17 99643 1407 55 33
200 15 71965 771 35 32
201 16 77272 766 59 21
202 24 49289 473 19 15
203 15 135131 1376 66 38
204 17 108446 1232 60 22
205 18 89746 1521 36 28
206 20 44296 572 25 10
207 16 77648 1059 47 31
208 16 181528 1544 54 32
209 18 134019 1230 53 32
210 22 124064 1206 40 43
211 8 92630 1205 40 27
212 17 121848 1255 39 37
213 18 52915 613 14 20
214 16 81872 721 45 32
215 23 58981 1109 36 0
216 22 53515 740 28 5
217 13 60812 1126 44 26
218 13 56375 728 30 10
219 16 65490 689 22 27
220 16 80949 592 17 11
221 20 76302 995 31 29
222 22 104011 1613 55 25
223 17 98104 2048 54 55
224 18 67989 705 21 23
225 17 30989 301 14 5
226 12 135458 1803 81 43
227 7 73504 799 35 23
228 17 63123 861 43 34
229 14 61254 1186 46 36
230 23 74914 1451 30 35
231 17 31774 628 23 0
232 14 81437 1161 38 37
233 15 87186 1463 54 28
234 17 50090 742 20 16
235 21 65745 979 53 26
236 18 56653 675 45 38
237 18 158399 1241 39 23
238 17 46455 676 20 22
239 17 73624 1049 24 30
240 16 38395 620 31 16
241 15 91899 1081 35 18
242 21 139526 1688 151 28
243 16 52164 736 52 32
244 14 51567 617 30 21
245 15 70551 812 31 23
246 17 84856 1051 29 29
247 15 102538 1656 57 50
248 15 86678 705 40 12
249 10 85709 945 44 21
250 6 34662 554 25 18
251 22 150580 1597 77 27
252 21 99611 982 35 41
253 1 19349 222 11 13
254 18 99373 1212 63 12
255 17 86230 1143 44 21
256 4 30837 435 19 8
257 10 31706 532 13 26
258 16 89806 882 42 27
259 16 62088 608 38 13
260 9 40151 459 29 16
261 16 27634 578 20 2
262 17 76990 826 27 42
263 7 37460 509 20 5
264 15 54157 717 19 37
265 14 49862 637 37 17
266 14 84337 857 26 38
267 18 64175 830 42 37
268 12 59382 652 49 29
269 16 119308 707 30 32
270 21 76702 954 49 35
271 19 103425 1461 67 17
272 16 70344 672 28 20
273 1 43410 778 19 7
274 16 104838 1141 49 46
275 10 62215 680 27 24
276 19 69304 1090 30 40
277 12 53117 616 22 3
278 2 19764 285 12 10
279 14 86680 1145 31 37
280 17 84105 733 20 17
281 19 77945 888 20 28
282 14 89113 849 39 19
283 11 91005 1182 29 29
284 4 40248 528 16 8
285 16 64187 642 27 10
286 20 50857 947 21 15
287 12 56613 819 19 15
288 15 62792 757 35 28
289 16 72535 894 14 17
totsize tothyperlinks\r
1 112285 144
2 84786 103
3 83123 98
4 101193 135
5 38361 61
6 68504 39
7 119182 150
8 22807 5
9 17140 28
10 116174 84
11 57635 80
12 66198 130
13 71701 82
14 57793 60
15 80444 131
16 53855 84
17 97668 140
18 133824 151
19 101481 91
20 99645 138
21 114789 150
22 99052 124
23 67654 119
24 65553 73
25 97500 110
26 69112 123
27 82753 90
28 85323 116
29 72654 113
30 30727 56
31 77873 115
32 117478 119
33 74007 129
34 90183 127
35 61542 27
36 101494 175
37 27570 35
38 55813 64
39 79215 96
40 1423 0
41 55461 84
42 31081 41
43 22996 47
44 83122 126
45 70106 105
46 60578 80
47 39992 70
48 79892 73
49 49810 57
50 71570 40
51 100708 68
52 33032 21
53 82875 127
54 139077 154
55 71595 116
56 72260 102
57 5950 7
58 115762 148
59 32551 21
60 31701 35
61 80670 112
62 143558 137
63 117105 135
64 23789 26
65 120733 230
66 105195 181
67 73107 71
68 132068 147
69 149193 190
70 46821 64
71 87011 105
72 95260 107
73 55183 94
74 106671 116
75 73511 106
76 92945 143
77 78664 81
78 70054 89
79 22618 26
80 74011 84
81 83737 113
82 69094 120
83 93133 110
84 95536 134
85 225920 54
86 62133 96
87 61370 78
88 43836 51
89 106117 121
90 38692 38
91 84651 145
92 56622 59
93 15986 27
94 95364 91
95 26706 48
96 89691 68
97 67267 58
98 126846 150
99 41140 74
100 102860 181
101 51715 65
102 55801 97
103 111813 121
104 120293 99
105 138599 152
106 161647 188
107 115929 138
108 24266 40
109 162901 254
110 109825 87
111 129838 178
112 37510 51
113 43750 49
114 40652 73
115 87771 176
116 85872 94
117 89275 120
118 44418 66
119 192565 56
120 35232 39
121 40909 66
122 13294 27
123 32387 65
124 140867 58
125 120662 98
126 21233 25
127 44332 26
128 61056 77
129 101338 130
130 1168 11
131 13497 2
132 65567 101
133 25162 31
134 32334 36
135 40735 120
136 91413 195
137 855 4
138 97068 89
139 44339 24
140 14116 39
141 10288 14
142 65622 78
143 16563 15
144 76643 106
145 110681 83
146 29011 24
147 92696 37
148 94785 77
149 8773 16
150 83209 56
151 93815 132
152 86687 144
153 34553 40
154 105547 153
155 103487 143
156 213688 220
157 71220 79
158 23517 50
159 56926 39
160 91721 95
161 115168 169
162 111194 12
163 51009 63
164 135777 134
165 51513 69
166 74163 119
167 51633 119
168 75345 75
169 33416 63
170 83305 55
171 98952 103
172 102372 197
173 37238 16
174 103772 140
175 123969 89
176 27142 40
177 135400 125
178 21399 21
179 130115 167
180 24874 32
181 34988 36
182 45549 13
183 6023 5
184 64466 96
185 54990 151
186 1644 6
187 6179 13
188 3926 3
189 32755 57
190 34777 23
191 73224 61
192 27114 21
193 20760 43
194 37636 20
195 65461 82
196 30080 90
197 24094 25
198 69008 60
199 54968 61
200 46090 85
201 27507 43
202 10672 25
203 34029 41
204 46300 26
205 24760 38
206 18779 12
207 21280 29
208 40662 49
209 28987 46
210 22827 41
211 18513 31
212 30594 41
213 24006 26
214 27913 23
215 42744 14
216 12934 16
217 22574 25
218 41385 21
219 18653 32
220 18472 9
221 30976 35
222 63339 42
223 25568 68
224 33747 32
225 4154 6
226 19474 68
227 35130 33
228 39067 84
229 13310 46
230 65892 30
231 4143 0
232 28579 36
233 51776 47
234 21152 20
235 38084 50
236 27717 30
237 32928 30
238 11342 34
239 19499 33
240 16380 34
241 36874 37
242 48259 83
243 16734 32
244 28207 30
245 30143 43
246 41369 41
247 45833 51
248 29156 19
249 35944 37
250 36278 33
251 45588 41
252 45097 54
253 3895 14
254 28394 25
255 18632 25
256 2325 8
257 25139 26
258 27975 20
259 14483 11
260 13127 14
261 5839 3
262 24069 40
263 3738 5
264 18625 38
265 36341 32
266 24548 41
267 21792 46
268 26263 47
269 23686 37
270 49303 51
271 25659 49
272 28904 21
273 2781 1
274 29236 44
275 19546 26
276 22818 21
277 32689 4
278 5752 10
279 22197 43
280 20055 34
281 25272 32
282 82206 20
283 32073 34
284 5444 6
285 20154 12
286 36944 24
287 8019 16
288 30884 72
289 19540 27
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc pageviews
9.400e+00 4.623e-05 -1.505e-03
logins blogged_computations totsize
1.851e-02 1.064e-01 7.899e-05
`tothyperlinks\\r`
-2.145e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-18.4401 -4.2600 -0.6224 3.5604 18.1980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.400e+00 8.075e-01 11.640 < 2e-16 ***
time_in_rfc 4.623e-05 1.254e-05 3.688 0.000271 ***
pageviews -1.505e-03 1.231e-03 -1.222 0.222541
logins 1.851e-02 1.700e-02 1.089 0.277094
blogged_computations 1.064e-01 2.403e-02 4.427 1.37e-05 ***
totsize 7.899e-05 1.566e-05 5.043 8.21e-07 ***
`tothyperlinks\\r` -2.145e-02 1.783e-02 -1.203 0.229979
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.191 on 282 degrees of freedom
Multiple R-squared: 0.6657, Adjusted R-squared: 0.6586
F-statistic: 93.61 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,] 0.6928183 6.143635e-01 3.071817e-01
[2,] 0.6014157 7.971686e-01 3.985843e-01
[3,] 0.5909105 8.181789e-01 4.090895e-01
[4,] 0.9019651 1.960699e-01 9.803494e-02
[5,] 0.8930984 2.138032e-01 1.069016e-01
[6,] 0.9402468 1.195063e-01 5.975316e-02
[7,] 0.9264584 1.470832e-01 7.354161e-02
[8,] 0.9279261 1.441479e-01 7.207393e-02
[9,] 0.9213574 1.572852e-01 7.864259e-02
[10,] 0.9043994 1.912013e-01 9.560065e-02
[11,] 0.9255135 1.489731e-01 7.448654e-02
[12,] 0.8962559 2.074883e-01 1.037441e-01
[13,] 0.8675288 2.649425e-01 1.324712e-01
[14,] 0.8273893 3.452214e-01 1.726107e-01
[15,] 0.7789735 4.420530e-01 2.210265e-01
[16,] 0.7987082 4.025836e-01 2.012918e-01
[17,] 0.7612298 4.775403e-01 2.387702e-01
[18,] 0.7085311 5.829379e-01 2.914689e-01
[19,] 0.6514145 6.971710e-01 3.485855e-01
[20,] 0.6154344 7.691313e-01 3.845656e-01
[21,] 0.5575616 8.848769e-01 4.424384e-01
[22,] 0.4963840 9.927679e-01 5.036160e-01
[23,] 0.4361284 8.722567e-01 5.638716e-01
[24,] 0.6948789 6.102422e-01 3.051211e-01
[25,] 0.7092361 5.815278e-01 2.907639e-01
[26,] 0.7138009 5.723981e-01 2.861991e-01
[27,] 0.6739714 6.520572e-01 3.260286e-01
[28,] 0.7461475 5.077049e-01 2.538525e-01
[29,] 0.7355834 5.288331e-01 2.644166e-01
[30,] 0.7053685 5.892630e-01 2.946315e-01
[31,] 0.8894492 2.211015e-01 1.105508e-01
[32,] 0.8647188 2.705625e-01 1.352812e-01
[33,] 0.8649445 2.701111e-01 1.350555e-01
[34,] 0.8508848 2.982305e-01 1.491152e-01
[35,] 0.8430572 3.138857e-01 1.569428e-01
[36,] 0.8153442 3.693116e-01 1.846558e-01
[37,] 0.8418847 3.162306e-01 1.581153e-01
[38,] 0.8238757 3.522487e-01 1.761243e-01
[39,] 0.7922544 4.154913e-01 2.077456e-01
[40,] 0.7903107 4.193785e-01 2.096893e-01
[41,] 0.9136994 1.726012e-01 8.630059e-02
[42,] 0.8957825 2.084351e-01 1.042175e-01
[43,] 0.8956738 2.086524e-01 1.043262e-01
[44,] 0.9016191 1.967618e-01 9.838089e-02
[45,] 0.8824132 2.351735e-01 1.175868e-01
[46,] 0.8688775 2.622451e-01 1.311225e-01
[47,] 0.8473787 3.052427e-01 1.526213e-01
[48,] 0.8941148 2.117703e-01 1.058852e-01
[49,] 0.8750119 2.499762e-01 1.249881e-01
[50,] 0.8532725 2.934549e-01 1.467275e-01
[51,] 0.8548457 2.903086e-01 1.451543e-01
[52,] 0.8304270 3.391460e-01 1.695730e-01
[53,] 0.8164341 3.671317e-01 1.835659e-01
[54,] 0.9270184 1.459633e-01 7.298164e-02
[55,] 0.9149088 1.701823e-01 8.509117e-02
[56,] 0.9079060 1.841879e-01 9.209397e-02
[57,] 0.8950994 2.098011e-01 1.049006e-01
[58,] 0.8803149 2.393703e-01 1.196851e-01
[59,] 0.8688858 2.622284e-01 1.311142e-01
[60,] 0.9414608 1.170784e-01 5.853920e-02
[61,] 0.9628086 7.438285e-02 3.719143e-02
[62,] 0.9579349 8.413012e-02 4.206506e-02
[63,] 0.9489475 1.021049e-01 5.105247e-02
[64,] 0.9397360 1.205280e-01 6.026402e-02
[65,] 0.9313858 1.372284e-01 6.861422e-02
[66,] 0.9347404 1.305191e-01 6.525957e-02
[67,] 0.9336218 1.327564e-01 6.637822e-02
[68,] 0.9529524 9.409522e-02 4.704761e-02
[69,] 0.9707908 5.841844e-02 2.920922e-02
[70,] 0.9690742 6.185156e-02 3.092578e-02
[71,] 0.9806652 3.866952e-02 1.933476e-02
[72,] 0.9760132 4.797358e-02 2.398679e-02
[73,] 0.9732823 5.343535e-02 2.671768e-02
[74,] 0.9705466 5.890680e-02 2.945340e-02
[75,] 0.9684689 6.306228e-02 3.153114e-02
[76,] 0.9706006 5.879884e-02 2.939942e-02
[77,] 0.9645124 7.097513e-02 3.548757e-02
[78,] 0.9658823 6.823544e-02 3.411772e-02
[79,] 0.9593708 8.125831e-02 4.062915e-02
[80,] 0.9519506 9.609883e-02 4.804942e-02
[81,] 0.9460217 1.079565e-01 5.397827e-02
[82,] 0.9383056 1.233887e-01 6.169436e-02
[83,] 0.9745861 5.082786e-02 2.541393e-02
[84,] 0.9733236 5.335280e-02 2.667640e-02
[85,] 0.9675939 6.481227e-02 3.240613e-02
[86,] 0.9619365 7.612699e-02 3.806349e-02
[87,] 0.9569127 8.617458e-02 4.308729e-02
[88,] 0.9802893 3.942149e-02 1.971075e-02
[89,] 0.9760105 4.797897e-02 2.398948e-02
[90,] 0.9757118 4.857648e-02 2.428824e-02
[91,] 0.9703900 5.922009e-02 2.961004e-02
[92,] 0.9656398 6.872034e-02 3.436017e-02
[93,] 0.9590784 8.184327e-02 4.092164e-02
[94,] 0.9517035 9.659310e-02 4.829655e-02
[95,] 0.9433838 1.132325e-01 5.661623e-02
[96,] 0.9380675 1.238650e-01 6.193251e-02
[97,] 0.9497097 1.005805e-01 5.029027e-02
[98,] 0.9814582 3.708361e-02 1.854180e-02
[99,] 0.9785032 4.299356e-02 2.149678e-02
[100,] 0.9960279 7.944225e-03 3.972112e-03
[101,] 0.9960224 7.955138e-03 3.977569e-03
[102,] 0.9955626 8.874764e-03 4.437382e-03
[103,] 0.9968377 6.324518e-03 3.162259e-03
[104,] 0.9960762 7.847579e-03 3.923790e-03
[105,] 0.9960913 7.817318e-03 3.908659e-03
[106,] 0.9950830 9.833970e-03 4.916985e-03
[107,] 0.9938585 1.228291e-02 6.141455e-03
[108,] 0.9940060 1.198793e-02 5.993964e-03
[109,] 0.9950104 9.979195e-03 4.989597e-03
[110,] 0.9964687 7.062631e-03 3.531315e-03
[111,] 0.9959289 8.142142e-03 4.071071e-03
[112,] 0.9951938 9.612340e-03 4.806170e-03
[113,] 0.9951539 9.692146e-03 4.846073e-03
[114,] 0.9942138 1.157238e-02 5.786188e-03
[115,] 0.9931652 1.366962e-02 6.834812e-03
[116,] 0.9916065 1.678701e-02 8.393506e-03
[117,] 0.9930476 1.390486e-02 6.952431e-03
[118,] 0.9938767 1.224656e-02 6.123281e-03
[119,] 0.9923496 1.530080e-02 7.650400e-03
[120,] 0.9912869 1.742611e-02 8.713053e-03
[121,] 0.9954409 9.118284e-03 4.559142e-03
[122,] 0.9948494 1.030114e-02 5.150571e-03
[123,] 0.9937799 1.244018e-02 6.220091e-03
[124,] 0.9935846 1.283074e-02 6.415371e-03
[125,] 0.9937302 1.253957e-02 6.269787e-03
[126,] 0.9976359 4.728250e-03 2.364125e-03
[127,] 0.9976837 4.632620e-03 2.316310e-03
[128,] 0.9988569 2.286183e-03 1.143091e-03
[129,] 0.9989390 2.121907e-03 1.060953e-03
[130,] 0.9986507 2.698550e-03 1.349275e-03
[131,] 0.9985931 2.813732e-03 1.406866e-03
[132,] 0.9982169 3.566164e-03 1.783082e-03
[133,] 0.9981171 3.765707e-03 1.882853e-03
[134,] 0.9977740 4.452076e-03 2.226038e-03
[135,] 0.9971402 5.719515e-03 2.859758e-03
[136,] 0.9978947 4.210699e-03 2.105349e-03
[137,] 0.9978836 4.232847e-03 2.116423e-03
[138,] 0.9992003 1.599401e-03 7.997007e-04
[139,] 0.9989568 2.086404e-03 1.043202e-03
[140,] 0.9988992 2.201574e-03 1.100787e-03
[141,] 0.9992652 1.469686e-03 7.348429e-04
[142,] 0.9994237 1.152566e-03 5.762832e-04
[143,] 0.9999326 1.348025e-04 6.740123e-05
[144,] 0.9999328 1.344857e-04 6.724287e-05
[145,] 0.9999074 1.851527e-04 9.257636e-05
[146,] 0.9998703 2.594141e-04 1.297070e-04
[147,] 0.9998846 2.307218e-04 1.153609e-04
[148,] 0.9998735 2.529322e-04 1.264661e-04
[149,] 0.9998423 3.154303e-04 1.577151e-04
[150,] 0.9998836 2.327469e-04 1.163735e-04
[151,] 0.9998413 3.173317e-04 1.586658e-04
[152,] 0.9997845 4.310477e-04 2.155239e-04
[153,] 0.9998047 3.906219e-04 1.953110e-04
[154,] 0.9997939 4.122591e-04 2.061296e-04
[155,] 0.9997590 4.819441e-04 2.409721e-04
[156,] 0.9998222 3.555287e-04 1.777644e-04
[157,] 0.9998096 3.807804e-04 1.903902e-04
[158,] 0.9998517 2.966094e-04 1.483047e-04
[159,] 0.9998354 3.292142e-04 1.646071e-04
[160,] 0.9997767 4.465502e-04 2.232751e-04
[161,] 0.9999035 1.930784e-04 9.653921e-05
[162,] 0.9998953 2.094697e-04 1.047348e-04
[163,] 0.9998554 2.892040e-04 1.446020e-04
[164,] 0.9998027 3.946112e-04 1.973056e-04
[165,] 0.9997330 5.340995e-04 2.670497e-04
[166,] 0.9997889 4.221628e-04 2.110814e-04
[167,] 0.9997943 4.113942e-04 2.056971e-04
[168,] 0.9997341 5.318914e-04 2.659457e-04
[169,] 0.9996483 7.034115e-04 3.517057e-04
[170,] 0.9995332 9.335146e-04 4.667573e-04
[171,] 0.9995409 9.181378e-04 4.590689e-04
[172,] 0.9997694 4.612834e-04 2.306417e-04
[173,] 0.9997181 5.638355e-04 2.819177e-04
[174,] 0.9998593 2.814224e-04 1.407112e-04
[175,] 0.9998529 2.942728e-04 1.471364e-04
[176,] 0.9998980 2.040890e-04 1.020445e-04
[177,] 0.9999540 9.207936e-05 4.603968e-05
[178,] 0.9999648 7.035086e-05 3.517543e-05
[179,] 0.9999844 3.119531e-05 1.559765e-05
[180,] 0.9999784 4.327840e-05 2.163920e-05
[181,] 0.9999987 2.694119e-06 1.347060e-06
[182,] 0.9999981 3.835180e-06 1.917590e-06
[183,] 0.9999970 6.047175e-06 3.023588e-06
[184,] 0.9999968 6.307956e-06 3.153978e-06
[185,] 0.9999979 4.298101e-06 2.149050e-06
[186,] 0.9999968 6.341304e-06 3.170652e-06
[187,] 0.9999971 5.783545e-06 2.891772e-06
[188,] 0.9999964 7.240198e-06 3.620099e-06
[189,] 0.9999961 7.866971e-06 3.933486e-06
[190,] 0.9999943 1.138382e-05 5.691910e-06
[191,] 0.9999921 1.589343e-05 7.946714e-06
[192,] 0.9999877 2.462196e-05 1.231098e-05
[193,] 0.9999975 5.078901e-06 2.539451e-06
[194,] 0.9999967 6.582262e-06 3.291131e-06
[195,] 0.9999949 1.020152e-05 5.100759e-06
[196,] 0.9999920 1.590414e-05 7.952072e-06
[197,] 0.9999944 1.117698e-05 5.588492e-06
[198,] 0.9999913 1.742746e-05 8.713728e-06
[199,] 0.9999912 1.765444e-05 8.827218e-06
[200,] 0.9999861 2.783451e-05 1.391725e-05
[201,] 0.9999851 2.971281e-05 1.485640e-05
[202,] 0.9999912 1.769368e-05 8.846840e-06
[203,] 0.9999862 2.765836e-05 1.382918e-05
[204,] 0.9999835 3.294775e-05 1.647388e-05
[205,] 0.9999753 4.930842e-05 2.465421e-05
[206,] 0.9999823 3.545949e-05 1.772974e-05
[207,] 0.9999944 1.129940e-05 5.649700e-06
[208,] 0.9999911 1.782572e-05 8.912862e-06
[209,] 0.9999863 2.731839e-05 1.365919e-05
[210,] 0.9999797 4.064631e-05 2.032316e-05
[211,] 0.9999710 5.801493e-05 2.900747e-05
[212,] 0.9999661 6.785326e-05 3.392663e-05
[213,] 0.9999488 1.024436e-04 5.122181e-05
[214,] 0.9999236 1.528387e-04 7.641937e-05
[215,] 0.9998983 2.034135e-04 1.017068e-04
[216,] 0.9999396 1.207139e-04 6.035697e-05
[217,] 0.9999735 5.306256e-05 2.653128e-05
[218,] 0.9999881 2.386495e-05 1.193248e-05
[219,] 0.9999799 4.029278e-05 2.014639e-05
[220,] 0.9999683 6.342096e-05 3.171048e-05
[221,] 0.9999616 7.676221e-05 3.838111e-05
[222,] 0.9999820 3.603924e-05 1.801962e-05
[223,] 0.9999738 5.242868e-05 2.621434e-05
[224,] 0.9999688 6.237777e-05 3.118888e-05
[225,] 0.9999671 6.589883e-05 3.294941e-05
[226,] 0.9999657 6.859655e-05 3.429827e-05
[227,] 0.9999604 7.914281e-05 3.957140e-05
[228,] 0.9999400 1.200797e-04 6.003985e-05
[229,] 0.9999404 1.192422e-04 5.962110e-05
[230,] 0.9999070 1.860610e-04 9.303050e-05
[231,] 0.9999115 1.769882e-04 8.849408e-05
[232,] 0.9998591 2.817830e-04 1.408915e-04
[233,] 0.9998006 3.987662e-04 1.993831e-04
[234,] 0.9997512 4.976867e-04 2.488434e-04
[235,] 0.9996205 7.589157e-04 3.794579e-04
[236,] 0.9993780 1.243977e-03 6.219887e-04
[237,] 0.9989918 2.016398e-03 1.008199e-03
[238,] 0.9993199 1.360198e-03 6.800990e-04
[239,] 0.9989413 2.117463e-03 1.058732e-03
[240,] 0.9992254 1.549167e-03 7.745833e-04
[241,] 0.9994079 1.184290e-03 5.921448e-04
[242,] 0.9991233 1.753413e-03 8.767066e-04
[243,] 0.9985896 2.820765e-03 1.410383e-03
[244,] 0.9987373 2.525455e-03 1.262728e-03
[245,] 0.9978943 4.211361e-03 2.105681e-03
[246,] 0.9966304 6.739146e-03 3.369573e-03
[247,] 0.9960149 7.970209e-03 3.985105e-03
[248,] 0.9939135 1.217290e-02 6.086450e-03
[249,] 0.9905148 1.897039e-02 9.485196e-03
[250,] 0.9911726 1.765480e-02 8.827401e-03
[251,] 0.9862044 2.759114e-02 1.379557e-02
[252,] 0.9945792 1.084165e-02 5.420827e-03
[253,] 0.9911617 1.767660e-02 8.838298e-03
[254,] 0.9857419 2.851626e-02 1.425813e-02
[255,] 0.9776803 4.463936e-02 2.231968e-02
[256,] 0.9657179 6.856411e-02 3.428206e-02
[257,] 0.9513328 9.733436e-02 4.866718e-02
[258,] 0.9410462 1.179076e-01 5.895382e-02
[259,] 0.9121448 1.757103e-01 8.785517e-02
[260,] 0.8723673 2.552654e-01 1.276327e-01
[261,] 0.8468615 3.062769e-01 1.531385e-01
[262,] 0.8176348 3.647304e-01 1.823652e-01
[263,] 0.7789161 4.421678e-01 2.210839e-01
[264,] 0.8312896 3.374208e-01 1.687104e-01
[265,] 0.7545381 4.909238e-01 2.454619e-01
[266,] 0.6593337 6.813326e-01 3.406663e-01
[267,] 0.7559967 4.880066e-01 2.440033e-01
[268,] 0.6382689 7.234622e-01 3.617311e-01
[269,] 0.5458556 9.082888e-01 4.541444e-01
[270,] 0.3897286 7.794571e-01 6.102714e-01
> postscript(file="/var/fisher/rcomp/tmp/1yihy1386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ebn81386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3pmkw1386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4xojo1386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5ohkd1386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-2.23735261 2.62035150 10.89654630 -2.65533121 0.58646578 9.82127484
7 8 9 10 11 12
-18.17759348 5.49126274 -5.35202723 -0.67218306 -0.67057629 7.99542268
13 14 15 16 17 18
14.15458788 7.53488924 6.20509941 8.14285493 4.29528614 -6.67368266
19 20 21 22 23 24
-2.56596696 -5.49672907 0.83845400 2.57442664 -4.63280272 0.77770134
25 26 27 28 29 30
10.48191276 2.69874403 2.54497595 -0.22884963 -0.10977146 4.70263931
31 32 33 34 35 36
0.39046378 0.54260710 18.19799542 6.24418920 8.99349775 -3.39231758
37 38 39 40 41 42
-5.06976340 4.11020296 0.61004489 -9.87870140 3.03334394 -4.65882359
43 44 45 46 47 48
-0.84856849 -2.66166462 -3.53386143 10.63625464 -3.16364850 1.51932066
49 50 51 52 53 54
7.72217066 17.83611364 -2.44974134 9.27498883 9.16330376 0.26228424
55 56 57 58 59 60
6.19053381 0.13358900 -7.38670590 -1.39901717 2.04413586 -4.45572532
61 62 63 64 65 66
0.20430063 6.13995514 -16.59488275 -1.50524952 -4.96585855 -2.74233536
67 68 69 70 71 72
-1.81310029 -4.03067772 -14.19693167 11.81591631 4.08929505 1.71250710
73 74 75 76 77 78
-1.34515336 -1.74260018 7.59268562 -4.86739178 -7.98569873 12.32306279
79 80 81 82 83 84
7.38152866 12.02483130 0.60480794 5.19347035 -4.84703602 -5.29578733
85 86 87 88 89 90
-7.68144727 2.26566304 7.94515704 0.48156064 -2.95392636 5.24355396
91 92 93 94 95 96
3.47786392 -12.71549164 7.34384431 1.58949878 0.07188351 -3.47771570
97 98 99 100 101 102
13.91175564 1.72239054 6.76254177 -0.97034198 4.13902473 2.18510948
103 104 105 106 107 108
-2.25364329 -2.05080820 -4.80809051 -10.45450121 14.61589649 3.96121998
109 110 111 112 113 114
-18.44013360 5.75445433 -4.26488139 9.30839104 4.45819761 -4.62391204
115 116 117 118 119 120
2.53951726 -1.77040862 -6.95274134 -7.28443765 -7.59650029 -1.62152582
121 122 123 124 125 126
-1.96188607 -3.18412815 -1.59168026 -2.17523604 -0.77136558 -6.55575072
127 128 129 130 131 132
-6.35660496 -0.40409346 3.58890928 -10.18979258 -2.84720019 2.47426402
133 134 135 136 137 138
-4.69397607 6.28819724 13.60546938 -6.43873778 -10.49413524 -5.60134293
139 140 141 142 143 144
-1.12955670 -4.72138282 -0.18936527 -5.05037947 3.56042838 0.38297859
145 146 147 148 149 150
9.80943184 5.62181224 13.35785280 2.59143783 -4.99288380 9.74036628
151 152 153 154 155 156
7.83168694 17.68318371 -6.06232279 0.71350483 -0.51514585 -4.61883293
157 158 159 160 161 162
5.05864830 -3.15293329 8.41397152 -2.18935351 1.61743068 7.71298171
163 164 165 166 167 168
-5.52731084 -4.40642063 8.10011084 4.42957751 7.33120526 4.82415260
169 170 171 172 173 174
-1.13301008 -11.32242769 -4.25995249 -1.89310895 -1.47311874 1.81840439
175 176 177 178 179 180
5.75650075 -5.03119179 -1.87417175 1.27847715 -0.07933663 -6.42919731
181 182 183 184 185 186
-10.14737290 -4.46316560 -10.74643565 5.35033757 4.08084282 -10.34375237
187 188 189 190 191 192
-7.69956612 -9.33709922 -3.06594045 12.69094930 -0.72861307 0.56753822
193 194 195 196 197 198
-5.98075193 9.97884504 -1.79414584 -7.81341643 4.13335708 -4.69884157
199 200 201 202 203 204
-2.45096688 -2.43587552 -0.39585151 10.77926011 -5.64882103 -2.10972526
205 206 207 208 209 210
1.95452027 6.66069823 -0.62244456 -6.03279002 -1.43266124 2.44116291
211 212 213 214 215 216
-8.27874526 -2.33934147 3.35115494 -2.04841189 8.80012501 9.51123774
217 218 219 220 221 222
-2.34384786 -2.34807533 0.54295182 0.99794048 3.21522932 2.43940795
223 224 225 226 227 228
-1.26462947 1.70328009 5.63009659 -7.10201987 -9.75715473 0.28077200
229 230 231 232 233 234
-1.19261599 3.48059182 6.32342104 -3.54220276 -3.28855059 3.08709997
235 236 237 238 239 240
4.35141895 0.57555511 -1.98108554 3.59271567 1.30715847 2.91778700
241 242 243 244 245 246
-1.70317384 -0.11510301 0.29406797 -1.22910048 -0.91859345 -0.75115401
247 248 249 250 251 252
-5.54857363 -1.25840142 -7.03398263 -8.70386514 1.02314721 1.05958766
253 254 255 256 257 258
-10.55416298 1.68088032 1.34997590 -7.38554526 -4.49955910 -1.65468940
259 260 261 262 263 264
1.65045026 -4.54074305 5.21271255 -0.72704075 -4.45567840 -0.76838784
265 266 267 268 269 270
-1.42381759 -3.59236886 1.43424178 -4.22227293 -2.88826106 2.05909532
271 272 273 274 275 276
2.99319554 -0.11909205 -10.53037819 -3.69549657 -5.29183607 1.87407250
277 278 279 280 281 282
-2.15100686 -9.41037996 -3.02481398 1.78163845 2.67445301 -7.04940914
283 284 285 286 287 288
-6.25413667 -7.91438958 1.70085889 5.28643069 -1.02210629 -0.68508448
289
1.56040308
> postscript(file="/var/fisher/rcomp/tmp/6yo051386534962.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 -2.23735261 NA
1 2.62035150 -2.23735261
2 10.89654630 2.62035150
3 -2.65533121 10.89654630
4 0.58646578 -2.65533121
5 9.82127484 0.58646578
6 -18.17759348 9.82127484
7 5.49126274 -18.17759348
8 -5.35202723 5.49126274
9 -0.67218306 -5.35202723
10 -0.67057629 -0.67218306
11 7.99542268 -0.67057629
12 14.15458788 7.99542268
13 7.53488924 14.15458788
14 6.20509941 7.53488924
15 8.14285493 6.20509941
16 4.29528614 8.14285493
17 -6.67368266 4.29528614
18 -2.56596696 -6.67368266
19 -5.49672907 -2.56596696
20 0.83845400 -5.49672907
21 2.57442664 0.83845400
22 -4.63280272 2.57442664
23 0.77770134 -4.63280272
24 10.48191276 0.77770134
25 2.69874403 10.48191276
26 2.54497595 2.69874403
27 -0.22884963 2.54497595
28 -0.10977146 -0.22884963
29 4.70263931 -0.10977146
30 0.39046378 4.70263931
31 0.54260710 0.39046378
32 18.19799542 0.54260710
33 6.24418920 18.19799542
34 8.99349775 6.24418920
35 -3.39231758 8.99349775
36 -5.06976340 -3.39231758
37 4.11020296 -5.06976340
38 0.61004489 4.11020296
39 -9.87870140 0.61004489
40 3.03334394 -9.87870140
41 -4.65882359 3.03334394
42 -0.84856849 -4.65882359
43 -2.66166462 -0.84856849
44 -3.53386143 -2.66166462
45 10.63625464 -3.53386143
46 -3.16364850 10.63625464
47 1.51932066 -3.16364850
48 7.72217066 1.51932066
49 17.83611364 7.72217066
50 -2.44974134 17.83611364
51 9.27498883 -2.44974134
52 9.16330376 9.27498883
53 0.26228424 9.16330376
54 6.19053381 0.26228424
55 0.13358900 6.19053381
56 -7.38670590 0.13358900
57 -1.39901717 -7.38670590
58 2.04413586 -1.39901717
59 -4.45572532 2.04413586
60 0.20430063 -4.45572532
61 6.13995514 0.20430063
62 -16.59488275 6.13995514
63 -1.50524952 -16.59488275
64 -4.96585855 -1.50524952
65 -2.74233536 -4.96585855
66 -1.81310029 -2.74233536
67 -4.03067772 -1.81310029
68 -14.19693167 -4.03067772
69 11.81591631 -14.19693167
70 4.08929505 11.81591631
71 1.71250710 4.08929505
72 -1.34515336 1.71250710
73 -1.74260018 -1.34515336
74 7.59268562 -1.74260018
75 -4.86739178 7.59268562
76 -7.98569873 -4.86739178
77 12.32306279 -7.98569873
78 7.38152866 12.32306279
79 12.02483130 7.38152866
80 0.60480794 12.02483130
81 5.19347035 0.60480794
82 -4.84703602 5.19347035
83 -5.29578733 -4.84703602
84 -7.68144727 -5.29578733
85 2.26566304 -7.68144727
86 7.94515704 2.26566304
87 0.48156064 7.94515704
88 -2.95392636 0.48156064
89 5.24355396 -2.95392636
90 3.47786392 5.24355396
91 -12.71549164 3.47786392
92 7.34384431 -12.71549164
93 1.58949878 7.34384431
94 0.07188351 1.58949878
95 -3.47771570 0.07188351
96 13.91175564 -3.47771570
97 1.72239054 13.91175564
98 6.76254177 1.72239054
99 -0.97034198 6.76254177
100 4.13902473 -0.97034198
101 2.18510948 4.13902473
102 -2.25364329 2.18510948
103 -2.05080820 -2.25364329
104 -4.80809051 -2.05080820
105 -10.45450121 -4.80809051
106 14.61589649 -10.45450121
107 3.96121998 14.61589649
108 -18.44013360 3.96121998
109 5.75445433 -18.44013360
110 -4.26488139 5.75445433
111 9.30839104 -4.26488139
112 4.45819761 9.30839104
113 -4.62391204 4.45819761
114 2.53951726 -4.62391204
115 -1.77040862 2.53951726
116 -6.95274134 -1.77040862
117 -7.28443765 -6.95274134
118 -7.59650029 -7.28443765
119 -1.62152582 -7.59650029
120 -1.96188607 -1.62152582
121 -3.18412815 -1.96188607
122 -1.59168026 -3.18412815
123 -2.17523604 -1.59168026
124 -0.77136558 -2.17523604
125 -6.55575072 -0.77136558
126 -6.35660496 -6.55575072
127 -0.40409346 -6.35660496
128 3.58890928 -0.40409346
129 -10.18979258 3.58890928
130 -2.84720019 -10.18979258
131 2.47426402 -2.84720019
132 -4.69397607 2.47426402
133 6.28819724 -4.69397607
134 13.60546938 6.28819724
135 -6.43873778 13.60546938
136 -10.49413524 -6.43873778
137 -5.60134293 -10.49413524
138 -1.12955670 -5.60134293
139 -4.72138282 -1.12955670
140 -0.18936527 -4.72138282
141 -5.05037947 -0.18936527
142 3.56042838 -5.05037947
143 0.38297859 3.56042838
144 9.80943184 0.38297859
145 5.62181224 9.80943184
146 13.35785280 5.62181224
147 2.59143783 13.35785280
148 -4.99288380 2.59143783
149 9.74036628 -4.99288380
150 7.83168694 9.74036628
151 17.68318371 7.83168694
152 -6.06232279 17.68318371
153 0.71350483 -6.06232279
154 -0.51514585 0.71350483
155 -4.61883293 -0.51514585
156 5.05864830 -4.61883293
157 -3.15293329 5.05864830
158 8.41397152 -3.15293329
159 -2.18935351 8.41397152
160 1.61743068 -2.18935351
161 7.71298171 1.61743068
162 -5.52731084 7.71298171
163 -4.40642063 -5.52731084
164 8.10011084 -4.40642063
165 4.42957751 8.10011084
166 7.33120526 4.42957751
167 4.82415260 7.33120526
168 -1.13301008 4.82415260
169 -11.32242769 -1.13301008
170 -4.25995249 -11.32242769
171 -1.89310895 -4.25995249
172 -1.47311874 -1.89310895
173 1.81840439 -1.47311874
174 5.75650075 1.81840439
175 -5.03119179 5.75650075
176 -1.87417175 -5.03119179
177 1.27847715 -1.87417175
178 -0.07933663 1.27847715
179 -6.42919731 -0.07933663
180 -10.14737290 -6.42919731
181 -4.46316560 -10.14737290
182 -10.74643565 -4.46316560
183 5.35033757 -10.74643565
184 4.08084282 5.35033757
185 -10.34375237 4.08084282
186 -7.69956612 -10.34375237
187 -9.33709922 -7.69956612
188 -3.06594045 -9.33709922
189 12.69094930 -3.06594045
190 -0.72861307 12.69094930
191 0.56753822 -0.72861307
192 -5.98075193 0.56753822
193 9.97884504 -5.98075193
194 -1.79414584 9.97884504
195 -7.81341643 -1.79414584
196 4.13335708 -7.81341643
197 -4.69884157 4.13335708
198 -2.45096688 -4.69884157
199 -2.43587552 -2.45096688
200 -0.39585151 -2.43587552
201 10.77926011 -0.39585151
202 -5.64882103 10.77926011
203 -2.10972526 -5.64882103
204 1.95452027 -2.10972526
205 6.66069823 1.95452027
206 -0.62244456 6.66069823
207 -6.03279002 -0.62244456
208 -1.43266124 -6.03279002
209 2.44116291 -1.43266124
210 -8.27874526 2.44116291
211 -2.33934147 -8.27874526
212 3.35115494 -2.33934147
213 -2.04841189 3.35115494
214 8.80012501 -2.04841189
215 9.51123774 8.80012501
216 -2.34384786 9.51123774
217 -2.34807533 -2.34384786
218 0.54295182 -2.34807533
219 0.99794048 0.54295182
220 3.21522932 0.99794048
221 2.43940795 3.21522932
222 -1.26462947 2.43940795
223 1.70328009 -1.26462947
224 5.63009659 1.70328009
225 -7.10201987 5.63009659
226 -9.75715473 -7.10201987
227 0.28077200 -9.75715473
228 -1.19261599 0.28077200
229 3.48059182 -1.19261599
230 6.32342104 3.48059182
231 -3.54220276 6.32342104
232 -3.28855059 -3.54220276
233 3.08709997 -3.28855059
234 4.35141895 3.08709997
235 0.57555511 4.35141895
236 -1.98108554 0.57555511
237 3.59271567 -1.98108554
238 1.30715847 3.59271567
239 2.91778700 1.30715847
240 -1.70317384 2.91778700
241 -0.11510301 -1.70317384
242 0.29406797 -0.11510301
243 -1.22910048 0.29406797
244 -0.91859345 -1.22910048
245 -0.75115401 -0.91859345
246 -5.54857363 -0.75115401
247 -1.25840142 -5.54857363
248 -7.03398263 -1.25840142
249 -8.70386514 -7.03398263
250 1.02314721 -8.70386514
251 1.05958766 1.02314721
252 -10.55416298 1.05958766
253 1.68088032 -10.55416298
254 1.34997590 1.68088032
255 -7.38554526 1.34997590
256 -4.49955910 -7.38554526
257 -1.65468940 -4.49955910
258 1.65045026 -1.65468940
259 -4.54074305 1.65045026
260 5.21271255 -4.54074305
261 -0.72704075 5.21271255
262 -4.45567840 -0.72704075
263 -0.76838784 -4.45567840
264 -1.42381759 -0.76838784
265 -3.59236886 -1.42381759
266 1.43424178 -3.59236886
267 -4.22227293 1.43424178
268 -2.88826106 -4.22227293
269 2.05909532 -2.88826106
270 2.99319554 2.05909532
271 -0.11909205 2.99319554
272 -10.53037819 -0.11909205
273 -3.69549657 -10.53037819
274 -5.29183607 -3.69549657
275 1.87407250 -5.29183607
276 -2.15100686 1.87407250
277 -9.41037996 -2.15100686
278 -3.02481398 -9.41037996
279 1.78163845 -3.02481398
280 2.67445301 1.78163845
281 -7.04940914 2.67445301
282 -6.25413667 -7.04940914
283 -7.91438958 -6.25413667
284 1.70085889 -7.91438958
285 5.28643069 1.70085889
286 -1.02210629 5.28643069
287 -0.68508448 -1.02210629
288 1.56040308 -0.68508448
289 NA 1.56040308
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.62035150 -2.23735261
[2,] 10.89654630 2.62035150
[3,] -2.65533121 10.89654630
[4,] 0.58646578 -2.65533121
[5,] 9.82127484 0.58646578
[6,] -18.17759348 9.82127484
[7,] 5.49126274 -18.17759348
[8,] -5.35202723 5.49126274
[9,] -0.67218306 -5.35202723
[10,] -0.67057629 -0.67218306
[11,] 7.99542268 -0.67057629
[12,] 14.15458788 7.99542268
[13,] 7.53488924 14.15458788
[14,] 6.20509941 7.53488924
[15,] 8.14285493 6.20509941
[16,] 4.29528614 8.14285493
[17,] -6.67368266 4.29528614
[18,] -2.56596696 -6.67368266
[19,] -5.49672907 -2.56596696
[20,] 0.83845400 -5.49672907
[21,] 2.57442664 0.83845400
[22,] -4.63280272 2.57442664
[23,] 0.77770134 -4.63280272
[24,] 10.48191276 0.77770134
[25,] 2.69874403 10.48191276
[26,] 2.54497595 2.69874403
[27,] -0.22884963 2.54497595
[28,] -0.10977146 -0.22884963
[29,] 4.70263931 -0.10977146
[30,] 0.39046378 4.70263931
[31,] 0.54260710 0.39046378
[32,] 18.19799542 0.54260710
[33,] 6.24418920 18.19799542
[34,] 8.99349775 6.24418920
[35,] -3.39231758 8.99349775
[36,] -5.06976340 -3.39231758
[37,] 4.11020296 -5.06976340
[38,] 0.61004489 4.11020296
[39,] -9.87870140 0.61004489
[40,] 3.03334394 -9.87870140
[41,] -4.65882359 3.03334394
[42,] -0.84856849 -4.65882359
[43,] -2.66166462 -0.84856849
[44,] -3.53386143 -2.66166462
[45,] 10.63625464 -3.53386143
[46,] -3.16364850 10.63625464
[47,] 1.51932066 -3.16364850
[48,] 7.72217066 1.51932066
[49,] 17.83611364 7.72217066
[50,] -2.44974134 17.83611364
[51,] 9.27498883 -2.44974134
[52,] 9.16330376 9.27498883
[53,] 0.26228424 9.16330376
[54,] 6.19053381 0.26228424
[55,] 0.13358900 6.19053381
[56,] -7.38670590 0.13358900
[57,] -1.39901717 -7.38670590
[58,] 2.04413586 -1.39901717
[59,] -4.45572532 2.04413586
[60,] 0.20430063 -4.45572532
[61,] 6.13995514 0.20430063
[62,] -16.59488275 6.13995514
[63,] -1.50524952 -16.59488275
[64,] -4.96585855 -1.50524952
[65,] -2.74233536 -4.96585855
[66,] -1.81310029 -2.74233536
[67,] -4.03067772 -1.81310029
[68,] -14.19693167 -4.03067772
[69,] 11.81591631 -14.19693167
[70,] 4.08929505 11.81591631
[71,] 1.71250710 4.08929505
[72,] -1.34515336 1.71250710
[73,] -1.74260018 -1.34515336
[74,] 7.59268562 -1.74260018
[75,] -4.86739178 7.59268562
[76,] -7.98569873 -4.86739178
[77,] 12.32306279 -7.98569873
[78,] 7.38152866 12.32306279
[79,] 12.02483130 7.38152866
[80,] 0.60480794 12.02483130
[81,] 5.19347035 0.60480794
[82,] -4.84703602 5.19347035
[83,] -5.29578733 -4.84703602
[84,] -7.68144727 -5.29578733
[85,] 2.26566304 -7.68144727
[86,] 7.94515704 2.26566304
[87,] 0.48156064 7.94515704
[88,] -2.95392636 0.48156064
[89,] 5.24355396 -2.95392636
[90,] 3.47786392 5.24355396
[91,] -12.71549164 3.47786392
[92,] 7.34384431 -12.71549164
[93,] 1.58949878 7.34384431
[94,] 0.07188351 1.58949878
[95,] -3.47771570 0.07188351
[96,] 13.91175564 -3.47771570
[97,] 1.72239054 13.91175564
[98,] 6.76254177 1.72239054
[99,] -0.97034198 6.76254177
[100,] 4.13902473 -0.97034198
[101,] 2.18510948 4.13902473
[102,] -2.25364329 2.18510948
[103,] -2.05080820 -2.25364329
[104,] -4.80809051 -2.05080820
[105,] -10.45450121 -4.80809051
[106,] 14.61589649 -10.45450121
[107,] 3.96121998 14.61589649
[108,] -18.44013360 3.96121998
[109,] 5.75445433 -18.44013360
[110,] -4.26488139 5.75445433
[111,] 9.30839104 -4.26488139
[112,] 4.45819761 9.30839104
[113,] -4.62391204 4.45819761
[114,] 2.53951726 -4.62391204
[115,] -1.77040862 2.53951726
[116,] -6.95274134 -1.77040862
[117,] -7.28443765 -6.95274134
[118,] -7.59650029 -7.28443765
[119,] -1.62152582 -7.59650029
[120,] -1.96188607 -1.62152582
[121,] -3.18412815 -1.96188607
[122,] -1.59168026 -3.18412815
[123,] -2.17523604 -1.59168026
[124,] -0.77136558 -2.17523604
[125,] -6.55575072 -0.77136558
[126,] -6.35660496 -6.55575072
[127,] -0.40409346 -6.35660496
[128,] 3.58890928 -0.40409346
[129,] -10.18979258 3.58890928
[130,] -2.84720019 -10.18979258
[131,] 2.47426402 -2.84720019
[132,] -4.69397607 2.47426402
[133,] 6.28819724 -4.69397607
[134,] 13.60546938 6.28819724
[135,] -6.43873778 13.60546938
[136,] -10.49413524 -6.43873778
[137,] -5.60134293 -10.49413524
[138,] -1.12955670 -5.60134293
[139,] -4.72138282 -1.12955670
[140,] -0.18936527 -4.72138282
[141,] -5.05037947 -0.18936527
[142,] 3.56042838 -5.05037947
[143,] 0.38297859 3.56042838
[144,] 9.80943184 0.38297859
[145,] 5.62181224 9.80943184
[146,] 13.35785280 5.62181224
[147,] 2.59143783 13.35785280
[148,] -4.99288380 2.59143783
[149,] 9.74036628 -4.99288380
[150,] 7.83168694 9.74036628
[151,] 17.68318371 7.83168694
[152,] -6.06232279 17.68318371
[153,] 0.71350483 -6.06232279
[154,] -0.51514585 0.71350483
[155,] -4.61883293 -0.51514585
[156,] 5.05864830 -4.61883293
[157,] -3.15293329 5.05864830
[158,] 8.41397152 -3.15293329
[159,] -2.18935351 8.41397152
[160,] 1.61743068 -2.18935351
[161,] 7.71298171 1.61743068
[162,] -5.52731084 7.71298171
[163,] -4.40642063 -5.52731084
[164,] 8.10011084 -4.40642063
[165,] 4.42957751 8.10011084
[166,] 7.33120526 4.42957751
[167,] 4.82415260 7.33120526
[168,] -1.13301008 4.82415260
[169,] -11.32242769 -1.13301008
[170,] -4.25995249 -11.32242769
[171,] -1.89310895 -4.25995249
[172,] -1.47311874 -1.89310895
[173,] 1.81840439 -1.47311874
[174,] 5.75650075 1.81840439
[175,] -5.03119179 5.75650075
[176,] -1.87417175 -5.03119179
[177,] 1.27847715 -1.87417175
[178,] -0.07933663 1.27847715
[179,] -6.42919731 -0.07933663
[180,] -10.14737290 -6.42919731
[181,] -4.46316560 -10.14737290
[182,] -10.74643565 -4.46316560
[183,] 5.35033757 -10.74643565
[184,] 4.08084282 5.35033757
[185,] -10.34375237 4.08084282
[186,] -7.69956612 -10.34375237
[187,] -9.33709922 -7.69956612
[188,] -3.06594045 -9.33709922
[189,] 12.69094930 -3.06594045
[190,] -0.72861307 12.69094930
[191,] 0.56753822 -0.72861307
[192,] -5.98075193 0.56753822
[193,] 9.97884504 -5.98075193
[194,] -1.79414584 9.97884504
[195,] -7.81341643 -1.79414584
[196,] 4.13335708 -7.81341643
[197,] -4.69884157 4.13335708
[198,] -2.45096688 -4.69884157
[199,] -2.43587552 -2.45096688
[200,] -0.39585151 -2.43587552
[201,] 10.77926011 -0.39585151
[202,] -5.64882103 10.77926011
[203,] -2.10972526 -5.64882103
[204,] 1.95452027 -2.10972526
[205,] 6.66069823 1.95452027
[206,] -0.62244456 6.66069823
[207,] -6.03279002 -0.62244456
[208,] -1.43266124 -6.03279002
[209,] 2.44116291 -1.43266124
[210,] -8.27874526 2.44116291
[211,] -2.33934147 -8.27874526
[212,] 3.35115494 -2.33934147
[213,] -2.04841189 3.35115494
[214,] 8.80012501 -2.04841189
[215,] 9.51123774 8.80012501
[216,] -2.34384786 9.51123774
[217,] -2.34807533 -2.34384786
[218,] 0.54295182 -2.34807533
[219,] 0.99794048 0.54295182
[220,] 3.21522932 0.99794048
[221,] 2.43940795 3.21522932
[222,] -1.26462947 2.43940795
[223,] 1.70328009 -1.26462947
[224,] 5.63009659 1.70328009
[225,] -7.10201987 5.63009659
[226,] -9.75715473 -7.10201987
[227,] 0.28077200 -9.75715473
[228,] -1.19261599 0.28077200
[229,] 3.48059182 -1.19261599
[230,] 6.32342104 3.48059182
[231,] -3.54220276 6.32342104
[232,] -3.28855059 -3.54220276
[233,] 3.08709997 -3.28855059
[234,] 4.35141895 3.08709997
[235,] 0.57555511 4.35141895
[236,] -1.98108554 0.57555511
[237,] 3.59271567 -1.98108554
[238,] 1.30715847 3.59271567
[239,] 2.91778700 1.30715847
[240,] -1.70317384 2.91778700
[241,] -0.11510301 -1.70317384
[242,] 0.29406797 -0.11510301
[243,] -1.22910048 0.29406797
[244,] -0.91859345 -1.22910048
[245,] -0.75115401 -0.91859345
[246,] -5.54857363 -0.75115401
[247,] -1.25840142 -5.54857363
[248,] -7.03398263 -1.25840142
[249,] -8.70386514 -7.03398263
[250,] 1.02314721 -8.70386514
[251,] 1.05958766 1.02314721
[252,] -10.55416298 1.05958766
[253,] 1.68088032 -10.55416298
[254,] 1.34997590 1.68088032
[255,] -7.38554526 1.34997590
[256,] -4.49955910 -7.38554526
[257,] -1.65468940 -4.49955910
[258,] 1.65045026 -1.65468940
[259,] -4.54074305 1.65045026
[260,] 5.21271255 -4.54074305
[261,] -0.72704075 5.21271255
[262,] -4.45567840 -0.72704075
[263,] -0.76838784 -4.45567840
[264,] -1.42381759 -0.76838784
[265,] -3.59236886 -1.42381759
[266,] 1.43424178 -3.59236886
[267,] -4.22227293 1.43424178
[268,] -2.88826106 -4.22227293
[269,] 2.05909532 -2.88826106
[270,] 2.99319554 2.05909532
[271,] -0.11909205 2.99319554
[272,] -10.53037819 -0.11909205
[273,] -3.69549657 -10.53037819
[274,] -5.29183607 -3.69549657
[275,] 1.87407250 -5.29183607
[276,] -2.15100686 1.87407250
[277,] -9.41037996 -2.15100686
[278,] -3.02481398 -9.41037996
[279,] 1.78163845 -3.02481398
[280,] 2.67445301 1.78163845
[281,] -7.04940914 2.67445301
[282,] -6.25413667 -7.04940914
[283,] -7.91438958 -6.25413667
[284,] 1.70085889 -7.91438958
[285,] 5.28643069 1.70085889
[286,] -1.02210629 5.28643069
[287,] -0.68508448 -1.02210629
[288,] 1.56040308 -0.68508448
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.62035150 -2.23735261
2 10.89654630 2.62035150
3 -2.65533121 10.89654630
4 0.58646578 -2.65533121
5 9.82127484 0.58646578
6 -18.17759348 9.82127484
7 5.49126274 -18.17759348
8 -5.35202723 5.49126274
9 -0.67218306 -5.35202723
10 -0.67057629 -0.67218306
11 7.99542268 -0.67057629
12 14.15458788 7.99542268
13 7.53488924 14.15458788
14 6.20509941 7.53488924
15 8.14285493 6.20509941
16 4.29528614 8.14285493
17 -6.67368266 4.29528614
18 -2.56596696 -6.67368266
19 -5.49672907 -2.56596696
20 0.83845400 -5.49672907
21 2.57442664 0.83845400
22 -4.63280272 2.57442664
23 0.77770134 -4.63280272
24 10.48191276 0.77770134
25 2.69874403 10.48191276
26 2.54497595 2.69874403
27 -0.22884963 2.54497595
28 -0.10977146 -0.22884963
29 4.70263931 -0.10977146
30 0.39046378 4.70263931
31 0.54260710 0.39046378
32 18.19799542 0.54260710
33 6.24418920 18.19799542
34 8.99349775 6.24418920
35 -3.39231758 8.99349775
36 -5.06976340 -3.39231758
37 4.11020296 -5.06976340
38 0.61004489 4.11020296
39 -9.87870140 0.61004489
40 3.03334394 -9.87870140
41 -4.65882359 3.03334394
42 -0.84856849 -4.65882359
43 -2.66166462 -0.84856849
44 -3.53386143 -2.66166462
45 10.63625464 -3.53386143
46 -3.16364850 10.63625464
47 1.51932066 -3.16364850
48 7.72217066 1.51932066
49 17.83611364 7.72217066
50 -2.44974134 17.83611364
51 9.27498883 -2.44974134
52 9.16330376 9.27498883
53 0.26228424 9.16330376
54 6.19053381 0.26228424
55 0.13358900 6.19053381
56 -7.38670590 0.13358900
57 -1.39901717 -7.38670590
58 2.04413586 -1.39901717
59 -4.45572532 2.04413586
60 0.20430063 -4.45572532
61 6.13995514 0.20430063
62 -16.59488275 6.13995514
63 -1.50524952 -16.59488275
64 -4.96585855 -1.50524952
65 -2.74233536 -4.96585855
66 -1.81310029 -2.74233536
67 -4.03067772 -1.81310029
68 -14.19693167 -4.03067772
69 11.81591631 -14.19693167
70 4.08929505 11.81591631
71 1.71250710 4.08929505
72 -1.34515336 1.71250710
73 -1.74260018 -1.34515336
74 7.59268562 -1.74260018
75 -4.86739178 7.59268562
76 -7.98569873 -4.86739178
77 12.32306279 -7.98569873
78 7.38152866 12.32306279
79 12.02483130 7.38152866
80 0.60480794 12.02483130
81 5.19347035 0.60480794
82 -4.84703602 5.19347035
83 -5.29578733 -4.84703602
84 -7.68144727 -5.29578733
85 2.26566304 -7.68144727
86 7.94515704 2.26566304
87 0.48156064 7.94515704
88 -2.95392636 0.48156064
89 5.24355396 -2.95392636
90 3.47786392 5.24355396
91 -12.71549164 3.47786392
92 7.34384431 -12.71549164
93 1.58949878 7.34384431
94 0.07188351 1.58949878
95 -3.47771570 0.07188351
96 13.91175564 -3.47771570
97 1.72239054 13.91175564
98 6.76254177 1.72239054
99 -0.97034198 6.76254177
100 4.13902473 -0.97034198
101 2.18510948 4.13902473
102 -2.25364329 2.18510948
103 -2.05080820 -2.25364329
104 -4.80809051 -2.05080820
105 -10.45450121 -4.80809051
106 14.61589649 -10.45450121
107 3.96121998 14.61589649
108 -18.44013360 3.96121998
109 5.75445433 -18.44013360
110 -4.26488139 5.75445433
111 9.30839104 -4.26488139
112 4.45819761 9.30839104
113 -4.62391204 4.45819761
114 2.53951726 -4.62391204
115 -1.77040862 2.53951726
116 -6.95274134 -1.77040862
117 -7.28443765 -6.95274134
118 -7.59650029 -7.28443765
119 -1.62152582 -7.59650029
120 -1.96188607 -1.62152582
121 -3.18412815 -1.96188607
122 -1.59168026 -3.18412815
123 -2.17523604 -1.59168026
124 -0.77136558 -2.17523604
125 -6.55575072 -0.77136558
126 -6.35660496 -6.55575072
127 -0.40409346 -6.35660496
128 3.58890928 -0.40409346
129 -10.18979258 3.58890928
130 -2.84720019 -10.18979258
131 2.47426402 -2.84720019
132 -4.69397607 2.47426402
133 6.28819724 -4.69397607
134 13.60546938 6.28819724
135 -6.43873778 13.60546938
136 -10.49413524 -6.43873778
137 -5.60134293 -10.49413524
138 -1.12955670 -5.60134293
139 -4.72138282 -1.12955670
140 -0.18936527 -4.72138282
141 -5.05037947 -0.18936527
142 3.56042838 -5.05037947
143 0.38297859 3.56042838
144 9.80943184 0.38297859
145 5.62181224 9.80943184
146 13.35785280 5.62181224
147 2.59143783 13.35785280
148 -4.99288380 2.59143783
149 9.74036628 -4.99288380
150 7.83168694 9.74036628
151 17.68318371 7.83168694
152 -6.06232279 17.68318371
153 0.71350483 -6.06232279
154 -0.51514585 0.71350483
155 -4.61883293 -0.51514585
156 5.05864830 -4.61883293
157 -3.15293329 5.05864830
158 8.41397152 -3.15293329
159 -2.18935351 8.41397152
160 1.61743068 -2.18935351
161 7.71298171 1.61743068
162 -5.52731084 7.71298171
163 -4.40642063 -5.52731084
164 8.10011084 -4.40642063
165 4.42957751 8.10011084
166 7.33120526 4.42957751
167 4.82415260 7.33120526
168 -1.13301008 4.82415260
169 -11.32242769 -1.13301008
170 -4.25995249 -11.32242769
171 -1.89310895 -4.25995249
172 -1.47311874 -1.89310895
173 1.81840439 -1.47311874
174 5.75650075 1.81840439
175 -5.03119179 5.75650075
176 -1.87417175 -5.03119179
177 1.27847715 -1.87417175
178 -0.07933663 1.27847715
179 -6.42919731 -0.07933663
180 -10.14737290 -6.42919731
181 -4.46316560 -10.14737290
182 -10.74643565 -4.46316560
183 5.35033757 -10.74643565
184 4.08084282 5.35033757
185 -10.34375237 4.08084282
186 -7.69956612 -10.34375237
187 -9.33709922 -7.69956612
188 -3.06594045 -9.33709922
189 12.69094930 -3.06594045
190 -0.72861307 12.69094930
191 0.56753822 -0.72861307
192 -5.98075193 0.56753822
193 9.97884504 -5.98075193
194 -1.79414584 9.97884504
195 -7.81341643 -1.79414584
196 4.13335708 -7.81341643
197 -4.69884157 4.13335708
198 -2.45096688 -4.69884157
199 -2.43587552 -2.45096688
200 -0.39585151 -2.43587552
201 10.77926011 -0.39585151
202 -5.64882103 10.77926011
203 -2.10972526 -5.64882103
204 1.95452027 -2.10972526
205 6.66069823 1.95452027
206 -0.62244456 6.66069823
207 -6.03279002 -0.62244456
208 -1.43266124 -6.03279002
209 2.44116291 -1.43266124
210 -8.27874526 2.44116291
211 -2.33934147 -8.27874526
212 3.35115494 -2.33934147
213 -2.04841189 3.35115494
214 8.80012501 -2.04841189
215 9.51123774 8.80012501
216 -2.34384786 9.51123774
217 -2.34807533 -2.34384786
218 0.54295182 -2.34807533
219 0.99794048 0.54295182
220 3.21522932 0.99794048
221 2.43940795 3.21522932
222 -1.26462947 2.43940795
223 1.70328009 -1.26462947
224 5.63009659 1.70328009
225 -7.10201987 5.63009659
226 -9.75715473 -7.10201987
227 0.28077200 -9.75715473
228 -1.19261599 0.28077200
229 3.48059182 -1.19261599
230 6.32342104 3.48059182
231 -3.54220276 6.32342104
232 -3.28855059 -3.54220276
233 3.08709997 -3.28855059
234 4.35141895 3.08709997
235 0.57555511 4.35141895
236 -1.98108554 0.57555511
237 3.59271567 -1.98108554
238 1.30715847 3.59271567
239 2.91778700 1.30715847
240 -1.70317384 2.91778700
241 -0.11510301 -1.70317384
242 0.29406797 -0.11510301
243 -1.22910048 0.29406797
244 -0.91859345 -1.22910048
245 -0.75115401 -0.91859345
246 -5.54857363 -0.75115401
247 -1.25840142 -5.54857363
248 -7.03398263 -1.25840142
249 -8.70386514 -7.03398263
250 1.02314721 -8.70386514
251 1.05958766 1.02314721
252 -10.55416298 1.05958766
253 1.68088032 -10.55416298
254 1.34997590 1.68088032
255 -7.38554526 1.34997590
256 -4.49955910 -7.38554526
257 -1.65468940 -4.49955910
258 1.65045026 -1.65468940
259 -4.54074305 1.65045026
260 5.21271255 -4.54074305
261 -0.72704075 5.21271255
262 -4.45567840 -0.72704075
263 -0.76838784 -4.45567840
264 -1.42381759 -0.76838784
265 -3.59236886 -1.42381759
266 1.43424178 -3.59236886
267 -4.22227293 1.43424178
268 -2.88826106 -4.22227293
269 2.05909532 -2.88826106
270 2.99319554 2.05909532
271 -0.11909205 2.99319554
272 -10.53037819 -0.11909205
273 -3.69549657 -10.53037819
274 -5.29183607 -3.69549657
275 1.87407250 -5.29183607
276 -2.15100686 1.87407250
277 -9.41037996 -2.15100686
278 -3.02481398 -9.41037996
279 1.78163845 -3.02481398
280 2.67445301 1.78163845
281 -7.04940914 2.67445301
282 -6.25413667 -7.04940914
283 -7.91438958 -6.25413667
284 1.70085889 -7.91438958
285 5.28643069 1.70085889
286 -1.02210629 5.28643069
287 -0.68508448 -1.02210629
288 1.56040308 -0.68508448
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7wgbp1386534962.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8efc11386534963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/99cdx1386534963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10c8591386534963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/119lty1386534963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12c4mq1386534963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13e7gh1386534963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/146ydn1386534963.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15xwgp1386534963.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/1680iu1386534963.tab")
+ }
>
> try(system("convert tmp/1yihy1386534962.ps tmp/1yihy1386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ebn81386534962.ps tmp/2ebn81386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pmkw1386534962.ps tmp/3pmkw1386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xojo1386534962.ps tmp/4xojo1386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ohkd1386534962.ps tmp/5ohkd1386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yo051386534962.ps tmp/6yo051386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wgbp1386534962.ps tmp/7wgbp1386534962.png",intern=TRUE))
character(0)
> try(system("convert tmp/8efc11386534963.ps tmp/8efc11386534963.png",intern=TRUE))
character(0)
> try(system("convert tmp/99cdx1386534963.ps tmp/99cdx1386534963.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c8591386534963.ps tmp/10c8591386534963.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
22.478 3.362 25.863