R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1418
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+ ,947
+ ,50857
+ ,15
+ ,37
+ ,1352
+ ,819
+ ,56613
+ ,15
+ ,35
+ ,3080
+ ,757
+ ,62792
+ ,28
+ ,51
+ ,10205
+ ,894
+ ,72535
+ ,17
+ ,39
+ ,6095)
+ ,dim=c(5
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'blogged_comp.'
+ ,'feedb.mess.long'
+ ,'tot.revisions')
+ ,1:289))
> y <- array(NA,dim=c(5,289),dimnames=list(c('pageviews','time_in_rfc','blogged_comp.','feedb.mess.long','tot.revisions'),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 = '4'
> #'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
> 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
feedb.mess.long pageviews time_in_rfc blogged_comp. tot.revisions
1 94 1418 210907 79 24188
2 103 869 120982 58 18273
3 93 1530 176508 60 14130
4 103 2172 179321 108 32287
5 51 901 123185 49 8654
6 70 463 52746 0 9245
7 91 3201 385534 121 33251
8 22 371 33170 1 1271
9 38 1192 101645 20 5279
10 93 1583 149061 43 27101
11 60 1439 165446 69 16373
12 123 1764 237213 78 19716
13 148 1495 173326 86 17753
14 90 1373 133131 44 9028
15 124 2187 258873 104 18653
16 70 1491 180083 63 8828
17 168 4041 324799 158 29498
18 115 1706 230964 102 27563
19 71 2152 236785 77 18293
20 66 1036 135473 82 22530
21 134 1882 202925 115 15977
22 117 1929 215147 101 35082
23 108 2242 344297 80 16116
24 84 1220 153935 50 15849
25 156 1289 132943 83 16026
26 120 2515 174724 123 26569
27 114 2147 174415 73 24785
28 94 2352 225548 81 17569
29 120 1638 223632 105 23825
30 81 1222 124817 47 7869
31 110 1812 221698 105 14975
32 133 1677 210767 94 37791
33 122 1579 170266 44 9605
34 158 1731 260561 114 27295
35 109 807 84853 38 2746
36 124 2452 294424 107 34461
37 39 829 101011 30 8098
38 92 1940 215641 71 4787
39 126 2662 325107 84 24919
40 0 186 7176 0 603
41 70 1499 167542 59 16329
42 37 865 106408 33 12558
43 38 1793 96560 42 7784
44 120 2527 265769 96 28522
45 93 2747 269651 106 22265
46 95 1324 149112 56 14459
47 77 2702 175824 57 14526
48 90 1383 152871 59 22240
49 80 1179 111665 39 11802
50 31 2099 116408 34 7623
51 110 4308 362301 76 11912
52 66 918 78800 20 7935
53 138 1831 183167 91 18220
54 133 3373 277965 115 19199
55 113 1713 150629 85 19918
56 100 1438 168809 76 21884
57 7 496 24188 8 2694
58 140 2253 329267 79 15808
59 61 744 65029 21 3597
60 41 1161 101097 30 5296
61 96 2352 218946 76 25239
62 164 2144 244052 101 29801
63 78 4691 341570 94 18450
64 49 1112 103597 27 7132
65 102 2694 233328 92 34861
66 124 1973 256462 123 35940
67 99 1769 206161 75 16688
68 129 3148 311473 128 24683
69 62 2474 235800 105 46230
70 73 2084 177939 55 10387
71 114 1954 207176 56 21436
72 99 1226 196553 41 30546
73 70 1389 174184 72 19746
74 104 1496 143246 67 15977
75 116 2269 187559 75 22583
76 91 1833 187681 114 17274
77 74 1268 119016 118 16469
78 138 1943 182192 77 14251
79 67 893 73566 22 3007
80 151 1762 194979 66 16851
81 72 1403 167488 69 21113
82 120 1425 143756 105 17401
83 115 1857 275541 116 23958
84 105 1840 243199 88 23567
85 104 1502 182999 73 13065
86 108 1441 135649 99 15358
87 98 1420 152299 62 14587
88 69 1416 120221 53 12770
89 111 2970 346485 118 24021
90 99 1317 145790 30 9648
91 71 1644 193339 100 20537
92 27 870 80953 49 7905
93 69 1654 122774 24 4527
94 107 1054 130585 67 30495
95 73 937 112611 46 7117
96 107 3004 286468 57 17719
97 93 2008 241066 75 27056
98 129 2547 148446 135 33473
99 69 1885 204713 68 9758
100 118 1626 182079 124 21115
101 73 1468 140344 33 7236
102 119 2445 220516 98 13790
103 104 1964 243060 58 32902
104 107 1381 162765 68 25131
105 99 1369 182613 81 30910
106 90 1659 232138 131 35947
107 197 2888 265318 110 29848
108 36 1290 85574 37 6943
109 85 2845 310839 130 42705
110 139 1982 225060 93 31808
111 106 1904 232317 118 26675
112 50 1391 144966 39 8435
113 64 602 43287 13 7409
114 31 1743 155754 74 14993
115 63 1559 164709 81 36867
116 92 2014 201940 109 33835
117 106 2143 235454 151 24164
118 63 2146 220801 51 12607
119 69 874 99466 28 22609
120 41 1590 92661 40 5892
121 56 1590 133328 56 17014
122 25 1210 61361 27 5394
123 65 2072 125930 37 9178
124 93 1281 100750 83 6440
125 114 1401 224549 54 21916
126 38 834 82316 27 4011
127 44 1105 102010 28 5818
128 87 1272 101523 59 18647
129 110 1944 243511 133 20556
130 0 391 22938 12 238
131 27 761 41566 0 70
132 83 1605 152474 106 22392
133 30 530 61857 23 3913
134 80 1988 99923 44 12237
135 98 1386 132487 71 8388
136 82 2395 317394 116 22120
137 0 387 21054 4 338
138 60 1742 209641 62 11727
139 28 620 22648 12 3704
140 9 449 31414 18 3988
141 33 800 46698 14 3030
142 59 1684 131698 60 13520
143 49 1050 91735 7 1421
144 115 2699 244749 98 20923
145 140 1606 184510 64 20237
146 49 1502 79863 29 3219
147 120 1204 128423 32 3769
148 66 1138 97839 25 12252
149 21 568 38214 16 1888
150 124 1459 151101 48 14497
151 152 2158 272458 100 28864
152 139 1111 172494 46 21721
153 38 1421 108043 45 4821
154 144 2833 328107 129 33644
155 120 1955 250579 130 15923
156 160 2922 351067 136 42935
157 114 1002 158015 59 18864
158 39 1060 98866 25 4977
159 78 956 85439 32 7785
160 119 2186 229242 63 17939
161 141 3604 351619 95 23436
162 101 1035 84207 14 325
163 56 1417 120445 36 13539
164 133 3261 324598 113 34538
165 83 1587 131069 47 12198
166 116 1424 204271 92 26924
167 90 1701 165543 70 12716
168 36 1249 141722 19 8172
169 50 946 116048 50 10855
170 61 1926 250047 41 11932
171 97 3352 299775 91 14300
172 98 1641 195838 111 25515
173 78 2035 173260 41 2805
174 117 2312 254488 120 29402
175 148 1369 104389 135 16440
176 41 1577 136084 27 11221
177 105 2201 199476 87 28732
178 55 961 92499 25 5250
179 132 1900 224330 131 28608
180 44 1254 135781 45 8092
181 21 1335 74408 29 4473
182 50 1597 81240 58 1572
183 0 207 14688 4 2065
184 73 1645 181633 47 14817
185 86 2429 271856 109 16714
186 0 151 7199 7 556
187 13 474 46660 12 2089
188 4 141 17547 0 2658
189 57 1639 133368 37 10695
190 48 872 95227 37 1669
191 46 1318 152601 46 16267
192 48 1018 98146 15 7768
193 32 1383 79619 42 7252
194 68 1314 59194 7 6387
195 87 1335 139942 54 18715
196 43 1403 118612 54 7936
197 67 910 72880 14 8643
198 46 616 65475 16 7294
199 46 1407 99643 33 4570
200 56 771 71965 32 7185
201 48 766 77272 21 10058
202 44 473 49289 15 2342
203 60 1376 135131 38 8509
204 65 1232 108446 22 13275
205 55 1521 89746 28 6816
206 38 572 44296 10 1930
207 52 1059 77648 31 8086
208 60 1544 181528 32 10737
209 54 1230 134019 32 8033
210 86 1206 124064 43 7058
211 24 1205 92630 27 6782
212 52 1255 121848 37 5401
213 49 613 52915 20 6521
214 61 721 81872 32 10856
215 61 1109 58981 0 2154
216 81 740 53515 5 6117
217 43 1126 60812 26 5238
218 40 728 56375 10 4820
219 40 689 65490 27 5615
220 56 592 80949 11 4272
221 68 995 76302 29 8702
222 79 1613 104011 25 15340
223 47 2048 98104 55 8030
224 57 705 67989 23 9526
225 41 301 30989 5 1278
226 29 1803 135458 43 4236
227 3 799 73504 23 3023
228 60 861 63123 34 7196
229 30 1186 61254 36 3394
230 79 1451 74914 35 6371
231 47 628 31774 0 1574
232 40 1161 81437 37 9620
233 48 1463 87186 28 6978
234 36 742 50090 16 4911
235 42 979 65745 26 8645
236 49 675 56653 38 8987
237 57 1241 158399 23 5544
238 12 676 46455 22 3083
239 40 1049 73624 30 6909
240 43 620 38395 16 3189
241 33 1081 91899 18 6745
242 77 1688 139526 28 16724
243 43 736 52164 32 4850
244 45 617 51567 21 7025
245 47 812 70551 23 6047
246 43 1051 84856 29 7377
247 45 1656 102538 50 9078
248 50 705 86678 12 4605
249 35 945 85709 21 3238
250 7 554 34662 18 8100
251 71 1597 150580 27 9653
252 67 982 99611 41 8914
253 0 222 19349 13 786
254 62 1212 99373 12 6700
255 54 1143 86230 21 5788
256 4 435 30837 8 593
257 25 532 31706 26 4506
258 40 882 89806 27 6382
259 38 608 62088 13 5621
260 19 459 40151 16 3997
261 17 578 27634 2 520
262 67 826 76990 42 8891
263 14 509 37460 5 999
264 30 717 54157 37 7067
265 54 637 49862 17 4639
266 35 857 84337 38 5654
267 59 830 64175 37 6928
268 24 652 59382 29 1514
269 58 707 119308 32 9238
270 42 954 76702 35 8204
271 46 1461 103425 17 5926
272 61 672 70344 20 5785
273 3 778 43410 7 4
274 52 1141 104838 46 5930
275 25 680 62215 24 3710
276 40 1090 69304 40 705
277 32 616 53117 3 443
278 4 285 19764 10 2416
279 49 1145 86680 37 7747
280 63 733 84105 17 5432
281 67 888 77945 28 4913
282 32 849 89113 19 2650
283 23 1182 91005 29 2370
284 7 528 40248 8 775
285 54 642 64187 10 5576
286 37 947 50857 15 1352
287 35 819 56613 15 3080
288 51 757 62792 28 10205
289 39 894 72535 17 6095
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews time_in_rfc blogged_comp. tot.revisions
22.5690406 -0.0039088 0.0001917 0.3432887 0.0008034
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-69.278 -15.080 -1.693 13.851 73.128
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.257e+01 2.911e+00 7.752 1.62e-13 ***
pageviews -3.909e-03 4.113e-03 -0.950 0.34270
time_in_rfc 1.916e-04 4.482e-05 4.276 2.60e-05 ***
blogged_comp. 3.433e-01 7.654e-02 4.485 1.06e-05 ***
tot.revisions 8.034e-04 2.561e-04 3.137 0.00188 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.73 on 284 degrees of freedom
Multiple R-squared: 0.6644, Adjusted R-squared: 0.6596
F-statistic: 140.5 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,] 0.6190528 7.618944e-01 3.809472e-01
[2,] 0.4541443 9.082887e-01 5.458557e-01
[3,] 0.3153107 6.306214e-01 6.846893e-01
[4,] 0.2489037 4.978074e-01 7.510963e-01
[5,] 0.4934849 9.869698e-01 5.065151e-01
[6,] 0.8749468 2.501063e-01 1.250532e-01
[7,] 0.8903725 2.192550e-01 1.096275e-01
[8,] 0.8709207 2.581586e-01 1.290793e-01
[9,] 0.8285096 3.429808e-01 1.714904e-01
[10,] 0.8401357 3.197285e-01 1.598643e-01
[11,] 0.7886778 4.226445e-01 2.113222e-01
[12,] 0.7692218 4.615563e-01 2.307782e-01
[13,] 0.8520216 2.959568e-01 1.479784e-01
[14,] 0.8231251 3.537498e-01 1.768749e-01
[15,] 0.7740708 4.518585e-01 2.259292e-01
[16,] 0.7544096 4.911808e-01 2.455904e-01
[17,] 0.7035354 5.929293e-01 2.964646e-01
[18,] 0.8783384 2.433231e-01 1.216616e-01
[19,] 0.8591243 2.817515e-01 1.408757e-01
[20,] 0.8486856 3.026288e-01 1.513144e-01
[21,] 0.8120374 3.759252e-01 1.879626e-01
[22,] 0.7693955 4.612089e-01 2.306045e-01
[23,] 0.7255061 5.489879e-01 2.744939e-01
[24,] 0.6814809 6.370382e-01 3.185191e-01
[25,] 0.6601659 6.796683e-01 3.398341e-01
[26,] 0.8078988 3.842024e-01 1.921012e-01
[27,] 0.8344981 3.310037e-01 1.655019e-01
[28,] 0.8804622 2.390757e-01 1.195378e-01
[29,] 0.8535932 2.928137e-01 1.464068e-01
[30,] 0.8707078 2.585844e-01 1.292922e-01
[31,] 0.8423173 3.153654e-01 1.576827e-01
[32,] 0.8380993 3.238014e-01 1.619007e-01
[33,] 0.8951648 2.096704e-01 1.048352e-01
[34,] 0.8825609 2.348782e-01 1.174391e-01
[35,] 0.8926411 2.147179e-01 1.073589e-01
[36,] 0.8959976 2.080047e-01 1.040024e-01
[37,] 0.8739809 2.520382e-01 1.260191e-01
[38,] 0.8776275 2.447450e-01 1.223725e-01
[39,] 0.8615495 2.769010e-01 1.384505e-01
[40,] 0.8354697 3.290606e-01 1.645303e-01
[41,] 0.8060748 3.878505e-01 1.939252e-01
[42,] 0.7843646 4.312708e-01 2.156354e-01
[43,] 0.7817358 4.365283e-01 2.182642e-01
[44,] 0.7805021 4.389958e-01 2.194979e-01
[45,] 0.7608825 4.782349e-01 2.391175e-01
[46,] 0.7936055 4.127891e-01 2.063945e-01
[47,] 0.7685889 4.628221e-01 2.314111e-01
[48,] 0.7473112 5.053775e-01 2.526888e-01
[49,] 0.7124802 5.750396e-01 2.875198e-01
[50,] 0.7438258 5.123483e-01 2.561742e-01
[51,] 0.7605151 4.789698e-01 2.394849e-01
[52,] 0.7372162 5.255676e-01 2.627838e-01
[53,] 0.7190019 5.619962e-01 2.809981e-01
[54,] 0.6830326 6.339349e-01 3.169674e-01
[55,] 0.7668767 4.662465e-01 2.331233e-01
[56,] 0.7866132 4.267735e-01 2.133868e-01
[57,] 0.7576772 4.846457e-01 2.423228e-01
[58,] 0.7314572 5.370856e-01 2.685428e-01
[59,] 0.7212125 5.575750e-01 2.787875e-01
[60,] 0.6870219 6.259561e-01 3.129781e-01
[61,] 0.6569820 6.860360e-01 3.430180e-01
[62,] 0.8420760 3.158480e-01 1.579240e-01
[63,] 0.8177810 3.644379e-01 1.822190e-01
[64,] 0.8378033 3.243933e-01 1.621967e-01
[65,] 0.8297159 3.405683e-01 1.702841e-01
[66,] 0.8368340 3.263320e-01 1.631660e-01
[67,] 0.8296070 3.407861e-01 1.703930e-01
[68,] 0.8322689 3.354622e-01 1.677311e-01
[69,] 0.8471672 3.056657e-01 1.528328e-01
[70,] 0.8814599 2.370802e-01 1.185401e-01
[71,] 0.9299776 1.400449e-01 7.002243e-02
[72,] 0.9257123 1.485755e-01 7.428774e-02
[73,] 0.9762607 4.747865e-02 2.373933e-02
[74,] 0.9750895 4.982105e-02 2.491053e-02
[75,] 0.9741353 5.172943e-02 2.586472e-02
[76,] 0.9719642 5.607156e-02 2.803578e-02
[77,] 0.9662147 6.757053e-02 3.378526e-02
[78,] 0.9619926 7.601479e-02 3.800740e-02
[79,] 0.9582030 8.359396e-02 4.179698e-02
[80,] 0.9540245 9.195107e-02 4.597553e-02
[81,] 0.9451012 1.097977e-01 5.489884e-02
[82,] 0.9469706 1.060587e-01 5.302936e-02
[83,] 0.9571919 8.561627e-02 4.280813e-02
[84,] 0.9690127 6.197454e-02 3.098727e-02
[85,] 0.9786126 4.277475e-02 2.138737e-02
[86,] 0.9754935 4.901308e-02 2.450654e-02
[87,] 0.9727223 5.455533e-02 2.727766e-02
[88,] 0.9681413 6.371748e-02 3.185874e-02
[89,] 0.9628388 7.432235e-02 3.716117e-02
[90,] 0.9581330 8.373394e-02 4.186697e-02
[91,] 0.9523846 9.523085e-02 4.761543e-02
[92,] 0.9502950 9.941006e-02 4.970503e-02
[93,] 0.9434099 1.131801e-01 5.659006e-02
[94,] 0.9345815 1.308371e-01 6.541855e-02
[95,] 0.9302809 1.394381e-01 6.971905e-02
[96,] 0.9194638 1.610723e-01 8.053617e-02
[97,] 0.9112825 1.774350e-01 8.871749e-02
[98,] 0.8973043 2.053913e-01 1.026957e-01
[99,] 0.9320775 1.358450e-01 6.792249e-02
[100,] 0.9904309 1.913812e-02 9.569061e-03
[101,] 0.9901543 1.969130e-02 9.845652e-03
[102,] 0.9981629 3.674109e-03 1.837054e-03
[103,] 0.9982209 3.558210e-03 1.779105e-03
[104,] 0.9979262 4.147636e-03 2.073818e-03
[105,] 0.9977164 4.567214e-03 2.283607e-03
[106,] 0.9976304 4.739250e-03 2.369625e-03
[107,] 0.9993814 1.237164e-03 6.185818e-04
[108,] 0.9997873 4.253589e-04 2.126794e-04
[109,] 0.9998444 3.112452e-04 1.556226e-04
[110,] 0.9998521 2.957445e-04 1.478722e-04
[111,] 0.9998499 3.002490e-04 1.501245e-04
[112,] 0.9998034 3.932153e-04 1.966076e-04
[113,] 0.9997682 4.635413e-04 2.317707e-04
[114,] 0.9997767 4.466163e-04 2.233081e-04
[115,] 0.9997773 4.453095e-04 2.226548e-04
[116,] 0.9996955 6.090495e-04 3.045247e-04
[117,] 0.9997393 5.214467e-04 2.607233e-04
[118,] 0.9996933 6.133317e-04 3.066658e-04
[119,] 0.9996246 7.507103e-04 3.753552e-04
[120,] 0.9995260 9.480977e-04 4.740489e-04
[121,] 0.9993951 1.209753e-03 6.048767e-04
[122,] 0.9992597 1.480602e-03 7.403010e-04
[123,] 0.9994628 1.074415e-03 5.372077e-04
[124,] 0.9992913 1.417409e-03 7.087047e-04
[125,] 0.9992375 1.524971e-03 7.624854e-04
[126,] 0.9991079 1.784299e-03 8.921495e-04
[127,] 0.9989802 2.039679e-03 1.019840e-03
[128,] 0.9991420 1.716072e-03 8.580361e-04
[129,] 0.9996931 6.138900e-04 3.069450e-04
[130,] 0.9997491 5.017801e-04 2.508901e-04
[131,] 0.9997757 4.485044e-04 2.242522e-04
[132,] 0.9997007 5.985202e-04 2.992601e-04
[133,] 0.9997563 4.874297e-04 2.437148e-04
[134,] 0.9996709 6.581016e-04 3.290508e-04
[135,] 0.9996146 7.708799e-04 3.854399e-04
[136,] 0.9995141 9.718103e-04 4.859051e-04
[137,] 0.9993515 1.297055e-03 6.485274e-04
[138,] 0.9997805 4.389810e-04 2.194905e-04
[139,] 0.9997064 5.871837e-04 2.935919e-04
[140,] 0.9999845 3.104758e-05 1.552379e-05
[141,] 0.9999782 4.364551e-05 2.182276e-05
[142,] 0.9999729 5.416513e-05 2.708256e-05
[143,] 0.9999942 1.162966e-05 5.814828e-06
[144,] 0.9999951 9.837740e-06 4.918870e-06
[145,] 0.9999993 1.313604e-06 6.568020e-07
[146,] 0.9999992 1.504390e-06 7.521950e-07
[147,] 0.9999989 2.284039e-06 1.142020e-06
[148,] 0.9999985 3.004165e-06 1.502083e-06
[149,] 0.9999979 4.245199e-06 2.122600e-06
[150,] 0.9999987 2.660393e-06 1.330196e-06
[151,] 0.9999982 3.697629e-06 1.848815e-06
[152,] 0.9999986 2.885796e-06 1.442898e-06
[153,] 0.9999989 2.287426e-06 1.143713e-06
[154,] 0.9999987 2.695851e-06 1.347926e-06
[155,] 1.0000000 3.141194e-08 1.570597e-08
[156,] 1.0000000 4.540188e-08 2.270094e-08
[157,] 1.0000000 6.183850e-08 3.091925e-08
[158,] 1.0000000 7.892746e-08 3.946373e-08
[159,] 0.9999999 1.235745e-07 6.178724e-08
[160,] 0.9999999 1.627126e-07 8.135628e-08
[161,] 0.9999999 1.738149e-07 8.690743e-08
[162,] 0.9999999 2.243674e-07 1.121837e-07
[163,] 0.9999999 2.517908e-07 1.258954e-07
[164,] 0.9999998 3.841532e-07 1.920766e-07
[165,] 0.9999998 4.741410e-07 2.370705e-07
[166,] 0.9999998 3.486417e-07 1.743208e-07
[167,] 0.9999998 4.304325e-07 2.152163e-07
[168,] 1.0000000 4.102847e-09 2.051423e-09
[169,] 1.0000000 2.561302e-09 1.280651e-09
[170,] 1.0000000 3.345455e-09 1.672727e-09
[171,] 1.0000000 4.927167e-09 2.463584e-09
[172,] 1.0000000 5.313713e-09 2.656857e-09
[173,] 1.0000000 6.649536e-09 3.324768e-09
[174,] 1.0000000 5.192277e-09 2.596139e-09
[175,] 1.0000000 5.643844e-09 2.821922e-09
[176,] 1.0000000 3.410496e-09 1.705248e-09
[177,] 1.0000000 5.424853e-09 2.712426e-09
[178,] 1.0000000 7.206130e-09 3.603065e-09
[179,] 1.0000000 6.273282e-09 3.136641e-09
[180,] 1.0000000 6.431076e-09 3.215538e-09
[181,] 1.0000000 4.080538e-09 2.040269e-09
[182,] 1.0000000 6.515222e-09 3.257611e-09
[183,] 1.0000000 7.569186e-09 3.784593e-09
[184,] 1.0000000 1.752284e-09 8.761418e-10
[185,] 1.0000000 2.856956e-09 1.428478e-09
[186,] 1.0000000 3.354736e-09 1.677368e-09
[187,] 1.0000000 2.877717e-09 1.438859e-09
[188,] 1.0000000 5.140060e-09 2.570030e-09
[189,] 1.0000000 7.112783e-09 3.556392e-09
[190,] 1.0000000 9.099194e-09 4.549597e-09
[191,] 1.0000000 1.632949e-08 8.164746e-09
[192,] 1.0000000 2.858367e-08 1.429184e-08
[193,] 1.0000000 4.332647e-08 2.166324e-08
[194,] 1.0000000 6.753070e-08 3.376535e-08
[195,] 1.0000000 8.852709e-08 4.426354e-08
[196,] 0.9999999 1.540754e-07 7.703771e-08
[197,] 0.9999999 2.363610e-07 1.181805e-07
[198,] 0.9999998 3.938047e-07 1.969023e-07
[199,] 0.9999997 5.957414e-07 2.978707e-07
[200,] 0.9999995 1.009107e-06 5.045537e-07
[201,] 0.9999995 1.036066e-06 5.180330e-07
[202,] 0.9999992 1.577892e-06 7.889460e-07
[203,] 0.9999997 5.840821e-07 2.920410e-07
[204,] 0.9999998 3.216824e-07 1.608412e-07
[205,] 0.9999997 5.451828e-07 2.725914e-07
[206,] 0.9999996 8.852649e-07 4.426324e-07
[207,] 0.9999992 1.509618e-06 7.548089e-07
[208,] 0.9999996 8.740271e-07 4.370136e-07
[209,] 0.9999999 1.137831e-07 5.689153e-08
[210,] 0.9999999 1.968965e-07 9.844823e-08
[211,] 0.9999998 3.525456e-07 1.762728e-07
[212,] 0.9999997 6.224882e-07 3.112441e-07
[213,] 0.9999996 7.521774e-07 3.760887e-07
[214,] 0.9999996 8.209777e-07 4.104889e-07
[215,] 0.9999993 1.358854e-06 6.794271e-07
[216,] 0.9999989 2.259141e-06 1.129571e-06
[217,] 0.9999981 3.787025e-06 1.893513e-06
[218,] 0.9999983 3.327174e-06 1.663587e-06
[219,] 0.9999990 1.954479e-06 9.772394e-07
[220,] 0.9999998 4.372727e-07 2.186364e-07
[221,] 0.9999998 4.671732e-07 2.335866e-07
[222,] 0.9999996 7.941219e-07 3.970609e-07
[223,] 0.9999999 1.072923e-07 5.364616e-08
[224,] 1.0000000 3.003889e-08 1.501945e-08
[225,] 1.0000000 4.066201e-08 2.033101e-08
[226,] 1.0000000 8.185829e-08 4.092915e-08
[227,] 0.9999999 1.564945e-07 7.824726e-08
[228,] 0.9999998 3.040881e-07 1.520441e-07
[229,] 0.9999997 5.851290e-07 2.925645e-07
[230,] 0.9999996 7.944672e-07 3.972336e-07
[231,] 0.9999996 8.597440e-07 4.298720e-07
[232,] 0.9999992 1.625560e-06 8.127801e-07
[233,] 0.9999993 1.322008e-06 6.610040e-07
[234,] 0.9999993 1.371272e-06 6.856362e-07
[235,] 0.9999991 1.865510e-06 9.327551e-07
[236,] 0.9999986 2.713035e-06 1.356517e-06
[237,] 0.9999976 4.710064e-06 2.355032e-06
[238,] 0.9999957 8.652478e-06 4.326239e-06
[239,] 0.9999925 1.505053e-05 7.525266e-06
[240,] 0.9999916 1.680588e-05 8.402940e-06
[241,] 0.9999852 2.967419e-05 1.483710e-05
[242,] 0.9999738 5.232627e-05 2.616313e-05
[243,] 0.9999950 9.927773e-06 4.963887e-06
[244,] 0.9999931 1.377669e-05 6.888346e-06
[245,] 0.9999869 2.623541e-05 1.311771e-05
[246,] 0.9999798 4.037343e-05 2.018672e-05
[247,] 0.9999623 7.534297e-05 3.767148e-05
[248,] 0.9999305 1.390564e-04 6.952822e-05
[249,] 0.9999006 1.988009e-04 9.940043e-05
[250,] 0.9998165 3.670661e-04 1.835331e-04
[251,] 0.9997264 5.472578e-04 2.736289e-04
[252,] 0.9994956 1.008852e-03 5.044258e-04
[253,] 0.9992753 1.449349e-03 7.246746e-04
[254,] 0.9986887 2.622516e-03 1.311258e-03
[255,] 0.9982210 3.558050e-03 1.779025e-03
[256,] 0.9970961 5.807864e-03 2.903932e-03
[257,] 0.9962934 7.413218e-03 3.706609e-03
[258,] 0.9965918 6.816365e-03 3.408182e-03
[259,] 0.9953979 9.204113e-03 4.602057e-03
[260,] 0.9943114 1.137719e-02 5.688593e-03
[261,] 0.9901240 1.975203e-02 9.876013e-03
[262,] 0.9892551 2.148987e-02 1.074494e-02
[263,] 0.9844241 3.115178e-02 1.557589e-02
[264,] 0.9755513 4.889739e-02 2.444870e-02
[265,] 0.9714131 5.717374e-02 2.858687e-02
[266,] 0.9666869 6.662623e-02 3.331311e-02
[267,] 0.9428157 1.143686e-01 5.718432e-02
[268,] 0.9162786 1.674428e-01 8.372140e-02
[269,] 0.9161710 1.676581e-01 8.382905e-02
[270,] 0.8647916 2.704168e-01 1.352084e-01
[271,] 0.8087746 3.824508e-01 1.912254e-01
[272,] 0.7055369 5.889263e-01 2.944631e-01
[273,] 0.6306650 7.386700e-01 3.693350e-01
[274,] 0.8670052 2.659897e-01 1.329948e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1o1bv1324665280.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/221pp1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3olpp1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4mmjo1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5m7z01324665280.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
-10.00047060 26.04941636 10.63324921 -8.46183500 -15.43010455 31.70415667
7 8 9 10 11 12
-61.19867277 -6.84049969 -10.49751560 11.51562347 -25.49401360 19.24644000
13 14 15 16 17 18
54.27002241 24.92460987 9.67713204 -9.97453769 21.03839235 -2.32591633
19 20 21 22 23 24
-29.66837948 -24.73413879 27.58141617 -2.12041400 -12.20263207 6.79957435
25 26 27 28 29 30
71.62177111 10.20449362 21.42300107 -4.52448059 5.78678658 16.82909027
31 32 33 34 35 36
3.94776825 13.96059925 50.14914564 31.19524287 58.07171694 -9.83075988
37 38 39 40 41 42
-16.49259944 7.46591816 2.67132939 -23.70177937 -12.19295846 -23.99932632
43 44 45 46 47 48
-16.73866651 0.50190746 -24.78810455 18.18744989 0.05715319 5.41642606
49 50 51 52 53 54
17.76814967 -23.47088645 -0.82693894 18.67597274 41.60566183 15.43903824
55 56 57 58 59 60
23.07603219 7.02692037 -23.17673220 23.31167752 18.77703466 -10.96020567
61 62 63 64 65 66
-5.70489814 44.42300580 -38.78857639 -4.07607113 -14.34751911 -11.10832830
67 68 69 70 71 72
4.67984885 -4.73107339 -69.27796159 -2.75192886 22.91633095 4.93693572
73 74 75 76 77 78
-21.10390680 23.98825483 22.46329554 -13.38726279 -20.16214970 50.22509113
79 80 81 82 83 84
23.85400982 61.75421458 -17.83426004 25.42395985 -12.18883384 -6.13056319
85 86 87 88 89 90
16.67265181 18.74134868 18.78932044 0.47098874 -26.17231445 35.58748425
91 92 93 94 95 96
-33.02589755 -30.85555085 17.48987740 16.02318546 11.00188047 7.46684587
97 98 99 100 101 102
-15.40543755 14.69966555 -16.61855293 7.35847902 12.12942633 19.00362913
103 104 105 106 107 108
-3.82042080 15.10011389 -5.85642649 -44.42580583 73.12805054 -16.20715236
109 110 111 112 113 114
-64.95958674 23.56355512 -15.59047495 -15.08040167 25.07265287 -52.05595852
115 116 117 118 119 120
-42.46829007 -26.00143567 -24.56865601 -21.13471656 3.00767496 -11.57825549
121 122 123 124 125 126
-18.80044827 -18.20194341 6.31946718 22.46195105 17.72609113 -9.57665765
127 128 129 130 131 132
-8.08688213 14.71030566 -13.81276332 -29.74756191 -0.61702202 -16.89642209
133 134 135 136 137 138
-13.39197146 21.11484222 24.34423941 -49.63057019 -26.73615104 -26.64417017
139 140 141 142 143 144
-3.58149449 -27.21784479 -2.63229192 -13.68665953 9.40905151 5.62135228
145 146 147 148 149 150
50.11710518 4.45426546 63.51094177 10.70221650 -13.68221117 50.04971398
151 152 153 154 155 156
28.12966429 54.47211490 -19.04286588 -1.69298861 -0.37231195 0.38697882
157 158 159 160 161 162
29.65362314 -10.95467931 25.55315736 25.00062129 13.68753435 61.27071644
163 164 165 166 167 168
-7.34994944 -5.57314250 15.57960187 6.63391757 8.10628518 -21.93665022
169 170 171 172 173 174
-16.99796549 -25.62459997 -12.64819908 -14.29212758 13.85080173 -10.12235232
175 176 177 178 179 180
51.22336596 -19.76992913 -0.14609526 5.65928645 5.90889066 -21.63971664
181 182 183 184 185 186
-24.16050226 -3.07046104 -27.60714901 -5.98866306 -30.02382597 -26.20825522
187 188 189 190 191 192
-22.45668562 -23.51633872 -6.01734176 -3.45387726 -29.52436835 -0.79029182
193 194 195 196 197 198
-20.66702767 31.68787208 9.25517992 -21.73104988 22.27021619 1.93746274
199 200 201 202 203 204
-5.16656728 5.89443737 -1.67424539 6.80241569 -2.97027106 8.24473453
205 206 207 208 209 210
6.08782404 4.19376740 1.55040760 -10.93607759 -6.88567765 23.93558727
211 212 213 214 215 216
-26.32948043 -6.05718999 6.58080474 5.85094890 29.73125524 44.43613577
217 218 219 220 221 222
0.04355800 2.16667874 -6.20731797 13.02233661 17.74988864 21.89506068
223 224 225 226 227 228
-11.69823316 8.60728897 10.92510616 -30.64735637 -40.85767669 11.24542610
229 230 231 232 233 234
-14.75802202 30.61133240 19.53146350 -14.06924205 -0.77840160 -2.70688947
235 236 237 238 239 240
-5.21369045 -2.05366545 -3.42593876 -26.85929931 -8.42855677 7.44111771
241 242 243 244 245 246
-14.55474595 11.23980737 -1.57144497 2.10658263 1.32957796 -7.60611527
247 248 249 250 251 252
-15.20580716 5.75521364 -10.11229848 -32.73354581 8.78976612 7.94200259
253 254 255 256 257 258
-30.50385539 15.62076157 7.51308942 -26.00151405 -14.11185528 -10.72942013
259 260 261 262 263 264
-3.07069879 -18.17389940 -9.71030594 11.34284787 -16.27791357 -18.52528690
265 266 267 268 269 270
14.80162462 -17.97027064 9.10808107 -18.57310128 -3.07864797 -10.14666656
271 272 273 274 275 276
-1.27713927 16.06238032 -27.25397981 -6.75734396 -18.05446216 -5.88886856
277 278 279 280 281 282
0.27285950 -26.61683353 -4.63183954 16.97692310 19.40417457 -12.98095197
283 284 285 286 287 288
-24.24986877 -24.58788237 13.72593574 2.15004271 -2.84174281 1.54461673
289
-4.70897676
> postscript(file="/var/wessaorg/rcomp/tmp/6j4g31324665280.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 -10.00047060 NA
1 26.04941636 -10.00047060
2 10.63324921 26.04941636
3 -8.46183500 10.63324921
4 -15.43010455 -8.46183500
5 31.70415667 -15.43010455
6 -61.19867277 31.70415667
7 -6.84049969 -61.19867277
8 -10.49751560 -6.84049969
9 11.51562347 -10.49751560
10 -25.49401360 11.51562347
11 19.24644000 -25.49401360
12 54.27002241 19.24644000
13 24.92460987 54.27002241
14 9.67713204 24.92460987
15 -9.97453769 9.67713204
16 21.03839235 -9.97453769
17 -2.32591633 21.03839235
18 -29.66837948 -2.32591633
19 -24.73413879 -29.66837948
20 27.58141617 -24.73413879
21 -2.12041400 27.58141617
22 -12.20263207 -2.12041400
23 6.79957435 -12.20263207
24 71.62177111 6.79957435
25 10.20449362 71.62177111
26 21.42300107 10.20449362
27 -4.52448059 21.42300107
28 5.78678658 -4.52448059
29 16.82909027 5.78678658
30 3.94776825 16.82909027
31 13.96059925 3.94776825
32 50.14914564 13.96059925
33 31.19524287 50.14914564
34 58.07171694 31.19524287
35 -9.83075988 58.07171694
36 -16.49259944 -9.83075988
37 7.46591816 -16.49259944
38 2.67132939 7.46591816
39 -23.70177937 2.67132939
40 -12.19295846 -23.70177937
41 -23.99932632 -12.19295846
42 -16.73866651 -23.99932632
43 0.50190746 -16.73866651
44 -24.78810455 0.50190746
45 18.18744989 -24.78810455
46 0.05715319 18.18744989
47 5.41642606 0.05715319
48 17.76814967 5.41642606
49 -23.47088645 17.76814967
50 -0.82693894 -23.47088645
51 18.67597274 -0.82693894
52 41.60566183 18.67597274
53 15.43903824 41.60566183
54 23.07603219 15.43903824
55 7.02692037 23.07603219
56 -23.17673220 7.02692037
57 23.31167752 -23.17673220
58 18.77703466 23.31167752
59 -10.96020567 18.77703466
60 -5.70489814 -10.96020567
61 44.42300580 -5.70489814
62 -38.78857639 44.42300580
63 -4.07607113 -38.78857639
64 -14.34751911 -4.07607113
65 -11.10832830 -14.34751911
66 4.67984885 -11.10832830
67 -4.73107339 4.67984885
68 -69.27796159 -4.73107339
69 -2.75192886 -69.27796159
70 22.91633095 -2.75192886
71 4.93693572 22.91633095
72 -21.10390680 4.93693572
73 23.98825483 -21.10390680
74 22.46329554 23.98825483
75 -13.38726279 22.46329554
76 -20.16214970 -13.38726279
77 50.22509113 -20.16214970
78 23.85400982 50.22509113
79 61.75421458 23.85400982
80 -17.83426004 61.75421458
81 25.42395985 -17.83426004
82 -12.18883384 25.42395985
83 -6.13056319 -12.18883384
84 16.67265181 -6.13056319
85 18.74134868 16.67265181
86 18.78932044 18.74134868
87 0.47098874 18.78932044
88 -26.17231445 0.47098874
89 35.58748425 -26.17231445
90 -33.02589755 35.58748425
91 -30.85555085 -33.02589755
92 17.48987740 -30.85555085
93 16.02318546 17.48987740
94 11.00188047 16.02318546
95 7.46684587 11.00188047
96 -15.40543755 7.46684587
97 14.69966555 -15.40543755
98 -16.61855293 14.69966555
99 7.35847902 -16.61855293
100 12.12942633 7.35847902
101 19.00362913 12.12942633
102 -3.82042080 19.00362913
103 15.10011389 -3.82042080
104 -5.85642649 15.10011389
105 -44.42580583 -5.85642649
106 73.12805054 -44.42580583
107 -16.20715236 73.12805054
108 -64.95958674 -16.20715236
109 23.56355512 -64.95958674
110 -15.59047495 23.56355512
111 -15.08040167 -15.59047495
112 25.07265287 -15.08040167
113 -52.05595852 25.07265287
114 -42.46829007 -52.05595852
115 -26.00143567 -42.46829007
116 -24.56865601 -26.00143567
117 -21.13471656 -24.56865601
118 3.00767496 -21.13471656
119 -11.57825549 3.00767496
120 -18.80044827 -11.57825549
121 -18.20194341 -18.80044827
122 6.31946718 -18.20194341
123 22.46195105 6.31946718
124 17.72609113 22.46195105
125 -9.57665765 17.72609113
126 -8.08688213 -9.57665765
127 14.71030566 -8.08688213
128 -13.81276332 14.71030566
129 -29.74756191 -13.81276332
130 -0.61702202 -29.74756191
131 -16.89642209 -0.61702202
132 -13.39197146 -16.89642209
133 21.11484222 -13.39197146
134 24.34423941 21.11484222
135 -49.63057019 24.34423941
136 -26.73615104 -49.63057019
137 -26.64417017 -26.73615104
138 -3.58149449 -26.64417017
139 -27.21784479 -3.58149449
140 -2.63229192 -27.21784479
141 -13.68665953 -2.63229192
142 9.40905151 -13.68665953
143 5.62135228 9.40905151
144 50.11710518 5.62135228
145 4.45426546 50.11710518
146 63.51094177 4.45426546
147 10.70221650 63.51094177
148 -13.68221117 10.70221650
149 50.04971398 -13.68221117
150 28.12966429 50.04971398
151 54.47211490 28.12966429
152 -19.04286588 54.47211490
153 -1.69298861 -19.04286588
154 -0.37231195 -1.69298861
155 0.38697882 -0.37231195
156 29.65362314 0.38697882
157 -10.95467931 29.65362314
158 25.55315736 -10.95467931
159 25.00062129 25.55315736
160 13.68753435 25.00062129
161 61.27071644 13.68753435
162 -7.34994944 61.27071644
163 -5.57314250 -7.34994944
164 15.57960187 -5.57314250
165 6.63391757 15.57960187
166 8.10628518 6.63391757
167 -21.93665022 8.10628518
168 -16.99796549 -21.93665022
169 -25.62459997 -16.99796549
170 -12.64819908 -25.62459997
171 -14.29212758 -12.64819908
172 13.85080173 -14.29212758
173 -10.12235232 13.85080173
174 51.22336596 -10.12235232
175 -19.76992913 51.22336596
176 -0.14609526 -19.76992913
177 5.65928645 -0.14609526
178 5.90889066 5.65928645
179 -21.63971664 5.90889066
180 -24.16050226 -21.63971664
181 -3.07046104 -24.16050226
182 -27.60714901 -3.07046104
183 -5.98866306 -27.60714901
184 -30.02382597 -5.98866306
185 -26.20825522 -30.02382597
186 -22.45668562 -26.20825522
187 -23.51633872 -22.45668562
188 -6.01734176 -23.51633872
189 -3.45387726 -6.01734176
190 -29.52436835 -3.45387726
191 -0.79029182 -29.52436835
192 -20.66702767 -0.79029182
193 31.68787208 -20.66702767
194 9.25517992 31.68787208
195 -21.73104988 9.25517992
196 22.27021619 -21.73104988
197 1.93746274 22.27021619
198 -5.16656728 1.93746274
199 5.89443737 -5.16656728
200 -1.67424539 5.89443737
201 6.80241569 -1.67424539
202 -2.97027106 6.80241569
203 8.24473453 -2.97027106
204 6.08782404 8.24473453
205 4.19376740 6.08782404
206 1.55040760 4.19376740
207 -10.93607759 1.55040760
208 -6.88567765 -10.93607759
209 23.93558727 -6.88567765
210 -26.32948043 23.93558727
211 -6.05718999 -26.32948043
212 6.58080474 -6.05718999
213 5.85094890 6.58080474
214 29.73125524 5.85094890
215 44.43613577 29.73125524
216 0.04355800 44.43613577
217 2.16667874 0.04355800
218 -6.20731797 2.16667874
219 13.02233661 -6.20731797
220 17.74988864 13.02233661
221 21.89506068 17.74988864
222 -11.69823316 21.89506068
223 8.60728897 -11.69823316
224 10.92510616 8.60728897
225 -30.64735637 10.92510616
226 -40.85767669 -30.64735637
227 11.24542610 -40.85767669
228 -14.75802202 11.24542610
229 30.61133240 -14.75802202
230 19.53146350 30.61133240
231 -14.06924205 19.53146350
232 -0.77840160 -14.06924205
233 -2.70688947 -0.77840160
234 -5.21369045 -2.70688947
235 -2.05366545 -5.21369045
236 -3.42593876 -2.05366545
237 -26.85929931 -3.42593876
238 -8.42855677 -26.85929931
239 7.44111771 -8.42855677
240 -14.55474595 7.44111771
241 11.23980737 -14.55474595
242 -1.57144497 11.23980737
243 2.10658263 -1.57144497
244 1.32957796 2.10658263
245 -7.60611527 1.32957796
246 -15.20580716 -7.60611527
247 5.75521364 -15.20580716
248 -10.11229848 5.75521364
249 -32.73354581 -10.11229848
250 8.78976612 -32.73354581
251 7.94200259 8.78976612
252 -30.50385539 7.94200259
253 15.62076157 -30.50385539
254 7.51308942 15.62076157
255 -26.00151405 7.51308942
256 -14.11185528 -26.00151405
257 -10.72942013 -14.11185528
258 -3.07069879 -10.72942013
259 -18.17389940 -3.07069879
260 -9.71030594 -18.17389940
261 11.34284787 -9.71030594
262 -16.27791357 11.34284787
263 -18.52528690 -16.27791357
264 14.80162462 -18.52528690
265 -17.97027064 14.80162462
266 9.10808107 -17.97027064
267 -18.57310128 9.10808107
268 -3.07864797 -18.57310128
269 -10.14666656 -3.07864797
270 -1.27713927 -10.14666656
271 16.06238032 -1.27713927
272 -27.25397981 16.06238032
273 -6.75734396 -27.25397981
274 -18.05446216 -6.75734396
275 -5.88886856 -18.05446216
276 0.27285950 -5.88886856
277 -26.61683353 0.27285950
278 -4.63183954 -26.61683353
279 16.97692310 -4.63183954
280 19.40417457 16.97692310
281 -12.98095197 19.40417457
282 -24.24986877 -12.98095197
283 -24.58788237 -24.24986877
284 13.72593574 -24.58788237
285 2.15004271 13.72593574
286 -2.84174281 2.15004271
287 1.54461673 -2.84174281
288 -4.70897676 1.54461673
289 NA -4.70897676
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 26.04941636 -10.00047060
[2,] 10.63324921 26.04941636
[3,] -8.46183500 10.63324921
[4,] -15.43010455 -8.46183500
[5,] 31.70415667 -15.43010455
[6,] -61.19867277 31.70415667
[7,] -6.84049969 -61.19867277
[8,] -10.49751560 -6.84049969
[9,] 11.51562347 -10.49751560
[10,] -25.49401360 11.51562347
[11,] 19.24644000 -25.49401360
[12,] 54.27002241 19.24644000
[13,] 24.92460987 54.27002241
[14,] 9.67713204 24.92460987
[15,] -9.97453769 9.67713204
[16,] 21.03839235 -9.97453769
[17,] -2.32591633 21.03839235
[18,] -29.66837948 -2.32591633
[19,] -24.73413879 -29.66837948
[20,] 27.58141617 -24.73413879
[21,] -2.12041400 27.58141617
[22,] -12.20263207 -2.12041400
[23,] 6.79957435 -12.20263207
[24,] 71.62177111 6.79957435
[25,] 10.20449362 71.62177111
[26,] 21.42300107 10.20449362
[27,] -4.52448059 21.42300107
[28,] 5.78678658 -4.52448059
[29,] 16.82909027 5.78678658
[30,] 3.94776825 16.82909027
[31,] 13.96059925 3.94776825
[32,] 50.14914564 13.96059925
[33,] 31.19524287 50.14914564
[34,] 58.07171694 31.19524287
[35,] -9.83075988 58.07171694
[36,] -16.49259944 -9.83075988
[37,] 7.46591816 -16.49259944
[38,] 2.67132939 7.46591816
[39,] -23.70177937 2.67132939
[40,] -12.19295846 -23.70177937
[41,] -23.99932632 -12.19295846
[42,] -16.73866651 -23.99932632
[43,] 0.50190746 -16.73866651
[44,] -24.78810455 0.50190746
[45,] 18.18744989 -24.78810455
[46,] 0.05715319 18.18744989
[47,] 5.41642606 0.05715319
[48,] 17.76814967 5.41642606
[49,] -23.47088645 17.76814967
[50,] -0.82693894 -23.47088645
[51,] 18.67597274 -0.82693894
[52,] 41.60566183 18.67597274
[53,] 15.43903824 41.60566183
[54,] 23.07603219 15.43903824
[55,] 7.02692037 23.07603219
[56,] -23.17673220 7.02692037
[57,] 23.31167752 -23.17673220
[58,] 18.77703466 23.31167752
[59,] -10.96020567 18.77703466
[60,] -5.70489814 -10.96020567
[61,] 44.42300580 -5.70489814
[62,] -38.78857639 44.42300580
[63,] -4.07607113 -38.78857639
[64,] -14.34751911 -4.07607113
[65,] -11.10832830 -14.34751911
[66,] 4.67984885 -11.10832830
[67,] -4.73107339 4.67984885
[68,] -69.27796159 -4.73107339
[69,] -2.75192886 -69.27796159
[70,] 22.91633095 -2.75192886
[71,] 4.93693572 22.91633095
[72,] -21.10390680 4.93693572
[73,] 23.98825483 -21.10390680
[74,] 22.46329554 23.98825483
[75,] -13.38726279 22.46329554
[76,] -20.16214970 -13.38726279
[77,] 50.22509113 -20.16214970
[78,] 23.85400982 50.22509113
[79,] 61.75421458 23.85400982
[80,] -17.83426004 61.75421458
[81,] 25.42395985 -17.83426004
[82,] -12.18883384 25.42395985
[83,] -6.13056319 -12.18883384
[84,] 16.67265181 -6.13056319
[85,] 18.74134868 16.67265181
[86,] 18.78932044 18.74134868
[87,] 0.47098874 18.78932044
[88,] -26.17231445 0.47098874
[89,] 35.58748425 -26.17231445
[90,] -33.02589755 35.58748425
[91,] -30.85555085 -33.02589755
[92,] 17.48987740 -30.85555085
[93,] 16.02318546 17.48987740
[94,] 11.00188047 16.02318546
[95,] 7.46684587 11.00188047
[96,] -15.40543755 7.46684587
[97,] 14.69966555 -15.40543755
[98,] -16.61855293 14.69966555
[99,] 7.35847902 -16.61855293
[100,] 12.12942633 7.35847902
[101,] 19.00362913 12.12942633
[102,] -3.82042080 19.00362913
[103,] 15.10011389 -3.82042080
[104,] -5.85642649 15.10011389
[105,] -44.42580583 -5.85642649
[106,] 73.12805054 -44.42580583
[107,] -16.20715236 73.12805054
[108,] -64.95958674 -16.20715236
[109,] 23.56355512 -64.95958674
[110,] -15.59047495 23.56355512
[111,] -15.08040167 -15.59047495
[112,] 25.07265287 -15.08040167
[113,] -52.05595852 25.07265287
[114,] -42.46829007 -52.05595852
[115,] -26.00143567 -42.46829007
[116,] -24.56865601 -26.00143567
[117,] -21.13471656 -24.56865601
[118,] 3.00767496 -21.13471656
[119,] -11.57825549 3.00767496
[120,] -18.80044827 -11.57825549
[121,] -18.20194341 -18.80044827
[122,] 6.31946718 -18.20194341
[123,] 22.46195105 6.31946718
[124,] 17.72609113 22.46195105
[125,] -9.57665765 17.72609113
[126,] -8.08688213 -9.57665765
[127,] 14.71030566 -8.08688213
[128,] -13.81276332 14.71030566
[129,] -29.74756191 -13.81276332
[130,] -0.61702202 -29.74756191
[131,] -16.89642209 -0.61702202
[132,] -13.39197146 -16.89642209
[133,] 21.11484222 -13.39197146
[134,] 24.34423941 21.11484222
[135,] -49.63057019 24.34423941
[136,] -26.73615104 -49.63057019
[137,] -26.64417017 -26.73615104
[138,] -3.58149449 -26.64417017
[139,] -27.21784479 -3.58149449
[140,] -2.63229192 -27.21784479
[141,] -13.68665953 -2.63229192
[142,] 9.40905151 -13.68665953
[143,] 5.62135228 9.40905151
[144,] 50.11710518 5.62135228
[145,] 4.45426546 50.11710518
[146,] 63.51094177 4.45426546
[147,] 10.70221650 63.51094177
[148,] -13.68221117 10.70221650
[149,] 50.04971398 -13.68221117
[150,] 28.12966429 50.04971398
[151,] 54.47211490 28.12966429
[152,] -19.04286588 54.47211490
[153,] -1.69298861 -19.04286588
[154,] -0.37231195 -1.69298861
[155,] 0.38697882 -0.37231195
[156,] 29.65362314 0.38697882
[157,] -10.95467931 29.65362314
[158,] 25.55315736 -10.95467931
[159,] 25.00062129 25.55315736
[160,] 13.68753435 25.00062129
[161,] 61.27071644 13.68753435
[162,] -7.34994944 61.27071644
[163,] -5.57314250 -7.34994944
[164,] 15.57960187 -5.57314250
[165,] 6.63391757 15.57960187
[166,] 8.10628518 6.63391757
[167,] -21.93665022 8.10628518
[168,] -16.99796549 -21.93665022
[169,] -25.62459997 -16.99796549
[170,] -12.64819908 -25.62459997
[171,] -14.29212758 -12.64819908
[172,] 13.85080173 -14.29212758
[173,] -10.12235232 13.85080173
[174,] 51.22336596 -10.12235232
[175,] -19.76992913 51.22336596
[176,] -0.14609526 -19.76992913
[177,] 5.65928645 -0.14609526
[178,] 5.90889066 5.65928645
[179,] -21.63971664 5.90889066
[180,] -24.16050226 -21.63971664
[181,] -3.07046104 -24.16050226
[182,] -27.60714901 -3.07046104
[183,] -5.98866306 -27.60714901
[184,] -30.02382597 -5.98866306
[185,] -26.20825522 -30.02382597
[186,] -22.45668562 -26.20825522
[187,] -23.51633872 -22.45668562
[188,] -6.01734176 -23.51633872
[189,] -3.45387726 -6.01734176
[190,] -29.52436835 -3.45387726
[191,] -0.79029182 -29.52436835
[192,] -20.66702767 -0.79029182
[193,] 31.68787208 -20.66702767
[194,] 9.25517992 31.68787208
[195,] -21.73104988 9.25517992
[196,] 22.27021619 -21.73104988
[197,] 1.93746274 22.27021619
[198,] -5.16656728 1.93746274
[199,] 5.89443737 -5.16656728
[200,] -1.67424539 5.89443737
[201,] 6.80241569 -1.67424539
[202,] -2.97027106 6.80241569
[203,] 8.24473453 -2.97027106
[204,] 6.08782404 8.24473453
[205,] 4.19376740 6.08782404
[206,] 1.55040760 4.19376740
[207,] -10.93607759 1.55040760
[208,] -6.88567765 -10.93607759
[209,] 23.93558727 -6.88567765
[210,] -26.32948043 23.93558727
[211,] -6.05718999 -26.32948043
[212,] 6.58080474 -6.05718999
[213,] 5.85094890 6.58080474
[214,] 29.73125524 5.85094890
[215,] 44.43613577 29.73125524
[216,] 0.04355800 44.43613577
[217,] 2.16667874 0.04355800
[218,] -6.20731797 2.16667874
[219,] 13.02233661 -6.20731797
[220,] 17.74988864 13.02233661
[221,] 21.89506068 17.74988864
[222,] -11.69823316 21.89506068
[223,] 8.60728897 -11.69823316
[224,] 10.92510616 8.60728897
[225,] -30.64735637 10.92510616
[226,] -40.85767669 -30.64735637
[227,] 11.24542610 -40.85767669
[228,] -14.75802202 11.24542610
[229,] 30.61133240 -14.75802202
[230,] 19.53146350 30.61133240
[231,] -14.06924205 19.53146350
[232,] -0.77840160 -14.06924205
[233,] -2.70688947 -0.77840160
[234,] -5.21369045 -2.70688947
[235,] -2.05366545 -5.21369045
[236,] -3.42593876 -2.05366545
[237,] -26.85929931 -3.42593876
[238,] -8.42855677 -26.85929931
[239,] 7.44111771 -8.42855677
[240,] -14.55474595 7.44111771
[241,] 11.23980737 -14.55474595
[242,] -1.57144497 11.23980737
[243,] 2.10658263 -1.57144497
[244,] 1.32957796 2.10658263
[245,] -7.60611527 1.32957796
[246,] -15.20580716 -7.60611527
[247,] 5.75521364 -15.20580716
[248,] -10.11229848 5.75521364
[249,] -32.73354581 -10.11229848
[250,] 8.78976612 -32.73354581
[251,] 7.94200259 8.78976612
[252,] -30.50385539 7.94200259
[253,] 15.62076157 -30.50385539
[254,] 7.51308942 15.62076157
[255,] -26.00151405 7.51308942
[256,] -14.11185528 -26.00151405
[257,] -10.72942013 -14.11185528
[258,] -3.07069879 -10.72942013
[259,] -18.17389940 -3.07069879
[260,] -9.71030594 -18.17389940
[261,] 11.34284787 -9.71030594
[262,] -16.27791357 11.34284787
[263,] -18.52528690 -16.27791357
[264,] 14.80162462 -18.52528690
[265,] -17.97027064 14.80162462
[266,] 9.10808107 -17.97027064
[267,] -18.57310128 9.10808107
[268,] -3.07864797 -18.57310128
[269,] -10.14666656 -3.07864797
[270,] -1.27713927 -10.14666656
[271,] 16.06238032 -1.27713927
[272,] -27.25397981 16.06238032
[273,] -6.75734396 -27.25397981
[274,] -18.05446216 -6.75734396
[275,] -5.88886856 -18.05446216
[276,] 0.27285950 -5.88886856
[277,] -26.61683353 0.27285950
[278,] -4.63183954 -26.61683353
[279,] 16.97692310 -4.63183954
[280,] 19.40417457 16.97692310
[281,] -12.98095197 19.40417457
[282,] -24.24986877 -12.98095197
[283,] -24.58788237 -24.24986877
[284,] 13.72593574 -24.58788237
[285,] 2.15004271 13.72593574
[286,] -2.84174281 2.15004271
[287,] 1.54461673 -2.84174281
[288,] -4.70897676 1.54461673
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 26.04941636 -10.00047060
2 10.63324921 26.04941636
3 -8.46183500 10.63324921
4 -15.43010455 -8.46183500
5 31.70415667 -15.43010455
6 -61.19867277 31.70415667
7 -6.84049969 -61.19867277
8 -10.49751560 -6.84049969
9 11.51562347 -10.49751560
10 -25.49401360 11.51562347
11 19.24644000 -25.49401360
12 54.27002241 19.24644000
13 24.92460987 54.27002241
14 9.67713204 24.92460987
15 -9.97453769 9.67713204
16 21.03839235 -9.97453769
17 -2.32591633 21.03839235
18 -29.66837948 -2.32591633
19 -24.73413879 -29.66837948
20 27.58141617 -24.73413879
21 -2.12041400 27.58141617
22 -12.20263207 -2.12041400
23 6.79957435 -12.20263207
24 71.62177111 6.79957435
25 10.20449362 71.62177111
26 21.42300107 10.20449362
27 -4.52448059 21.42300107
28 5.78678658 -4.52448059
29 16.82909027 5.78678658
30 3.94776825 16.82909027
31 13.96059925 3.94776825
32 50.14914564 13.96059925
33 31.19524287 50.14914564
34 58.07171694 31.19524287
35 -9.83075988 58.07171694
36 -16.49259944 -9.83075988
37 7.46591816 -16.49259944
38 2.67132939 7.46591816
39 -23.70177937 2.67132939
40 -12.19295846 -23.70177937
41 -23.99932632 -12.19295846
42 -16.73866651 -23.99932632
43 0.50190746 -16.73866651
44 -24.78810455 0.50190746
45 18.18744989 -24.78810455
46 0.05715319 18.18744989
47 5.41642606 0.05715319
48 17.76814967 5.41642606
49 -23.47088645 17.76814967
50 -0.82693894 -23.47088645
51 18.67597274 -0.82693894
52 41.60566183 18.67597274
53 15.43903824 41.60566183
54 23.07603219 15.43903824
55 7.02692037 23.07603219
56 -23.17673220 7.02692037
57 23.31167752 -23.17673220
58 18.77703466 23.31167752
59 -10.96020567 18.77703466
60 -5.70489814 -10.96020567
61 44.42300580 -5.70489814
62 -38.78857639 44.42300580
63 -4.07607113 -38.78857639
64 -14.34751911 -4.07607113
65 -11.10832830 -14.34751911
66 4.67984885 -11.10832830
67 -4.73107339 4.67984885
68 -69.27796159 -4.73107339
69 -2.75192886 -69.27796159
70 22.91633095 -2.75192886
71 4.93693572 22.91633095
72 -21.10390680 4.93693572
73 23.98825483 -21.10390680
74 22.46329554 23.98825483
75 -13.38726279 22.46329554
76 -20.16214970 -13.38726279
77 50.22509113 -20.16214970
78 23.85400982 50.22509113
79 61.75421458 23.85400982
80 -17.83426004 61.75421458
81 25.42395985 -17.83426004
82 -12.18883384 25.42395985
83 -6.13056319 -12.18883384
84 16.67265181 -6.13056319
85 18.74134868 16.67265181
86 18.78932044 18.74134868
87 0.47098874 18.78932044
88 -26.17231445 0.47098874
89 35.58748425 -26.17231445
90 -33.02589755 35.58748425
91 -30.85555085 -33.02589755
92 17.48987740 -30.85555085
93 16.02318546 17.48987740
94 11.00188047 16.02318546
95 7.46684587 11.00188047
96 -15.40543755 7.46684587
97 14.69966555 -15.40543755
98 -16.61855293 14.69966555
99 7.35847902 -16.61855293
100 12.12942633 7.35847902
101 19.00362913 12.12942633
102 -3.82042080 19.00362913
103 15.10011389 -3.82042080
104 -5.85642649 15.10011389
105 -44.42580583 -5.85642649
106 73.12805054 -44.42580583
107 -16.20715236 73.12805054
108 -64.95958674 -16.20715236
109 23.56355512 -64.95958674
110 -15.59047495 23.56355512
111 -15.08040167 -15.59047495
112 25.07265287 -15.08040167
113 -52.05595852 25.07265287
114 -42.46829007 -52.05595852
115 -26.00143567 -42.46829007
116 -24.56865601 -26.00143567
117 -21.13471656 -24.56865601
118 3.00767496 -21.13471656
119 -11.57825549 3.00767496
120 -18.80044827 -11.57825549
121 -18.20194341 -18.80044827
122 6.31946718 -18.20194341
123 22.46195105 6.31946718
124 17.72609113 22.46195105
125 -9.57665765 17.72609113
126 -8.08688213 -9.57665765
127 14.71030566 -8.08688213
128 -13.81276332 14.71030566
129 -29.74756191 -13.81276332
130 -0.61702202 -29.74756191
131 -16.89642209 -0.61702202
132 -13.39197146 -16.89642209
133 21.11484222 -13.39197146
134 24.34423941 21.11484222
135 -49.63057019 24.34423941
136 -26.73615104 -49.63057019
137 -26.64417017 -26.73615104
138 -3.58149449 -26.64417017
139 -27.21784479 -3.58149449
140 -2.63229192 -27.21784479
141 -13.68665953 -2.63229192
142 9.40905151 -13.68665953
143 5.62135228 9.40905151
144 50.11710518 5.62135228
145 4.45426546 50.11710518
146 63.51094177 4.45426546
147 10.70221650 63.51094177
148 -13.68221117 10.70221650
149 50.04971398 -13.68221117
150 28.12966429 50.04971398
151 54.47211490 28.12966429
152 -19.04286588 54.47211490
153 -1.69298861 -19.04286588
154 -0.37231195 -1.69298861
155 0.38697882 -0.37231195
156 29.65362314 0.38697882
157 -10.95467931 29.65362314
158 25.55315736 -10.95467931
159 25.00062129 25.55315736
160 13.68753435 25.00062129
161 61.27071644 13.68753435
162 -7.34994944 61.27071644
163 -5.57314250 -7.34994944
164 15.57960187 -5.57314250
165 6.63391757 15.57960187
166 8.10628518 6.63391757
167 -21.93665022 8.10628518
168 -16.99796549 -21.93665022
169 -25.62459997 -16.99796549
170 -12.64819908 -25.62459997
171 -14.29212758 -12.64819908
172 13.85080173 -14.29212758
173 -10.12235232 13.85080173
174 51.22336596 -10.12235232
175 -19.76992913 51.22336596
176 -0.14609526 -19.76992913
177 5.65928645 -0.14609526
178 5.90889066 5.65928645
179 -21.63971664 5.90889066
180 -24.16050226 -21.63971664
181 -3.07046104 -24.16050226
182 -27.60714901 -3.07046104
183 -5.98866306 -27.60714901
184 -30.02382597 -5.98866306
185 -26.20825522 -30.02382597
186 -22.45668562 -26.20825522
187 -23.51633872 -22.45668562
188 -6.01734176 -23.51633872
189 -3.45387726 -6.01734176
190 -29.52436835 -3.45387726
191 -0.79029182 -29.52436835
192 -20.66702767 -0.79029182
193 31.68787208 -20.66702767
194 9.25517992 31.68787208
195 -21.73104988 9.25517992
196 22.27021619 -21.73104988
197 1.93746274 22.27021619
198 -5.16656728 1.93746274
199 5.89443737 -5.16656728
200 -1.67424539 5.89443737
201 6.80241569 -1.67424539
202 -2.97027106 6.80241569
203 8.24473453 -2.97027106
204 6.08782404 8.24473453
205 4.19376740 6.08782404
206 1.55040760 4.19376740
207 -10.93607759 1.55040760
208 -6.88567765 -10.93607759
209 23.93558727 -6.88567765
210 -26.32948043 23.93558727
211 -6.05718999 -26.32948043
212 6.58080474 -6.05718999
213 5.85094890 6.58080474
214 29.73125524 5.85094890
215 44.43613577 29.73125524
216 0.04355800 44.43613577
217 2.16667874 0.04355800
218 -6.20731797 2.16667874
219 13.02233661 -6.20731797
220 17.74988864 13.02233661
221 21.89506068 17.74988864
222 -11.69823316 21.89506068
223 8.60728897 -11.69823316
224 10.92510616 8.60728897
225 -30.64735637 10.92510616
226 -40.85767669 -30.64735637
227 11.24542610 -40.85767669
228 -14.75802202 11.24542610
229 30.61133240 -14.75802202
230 19.53146350 30.61133240
231 -14.06924205 19.53146350
232 -0.77840160 -14.06924205
233 -2.70688947 -0.77840160
234 -5.21369045 -2.70688947
235 -2.05366545 -5.21369045
236 -3.42593876 -2.05366545
237 -26.85929931 -3.42593876
238 -8.42855677 -26.85929931
239 7.44111771 -8.42855677
240 -14.55474595 7.44111771
241 11.23980737 -14.55474595
242 -1.57144497 11.23980737
243 2.10658263 -1.57144497
244 1.32957796 2.10658263
245 -7.60611527 1.32957796
246 -15.20580716 -7.60611527
247 5.75521364 -15.20580716
248 -10.11229848 5.75521364
249 -32.73354581 -10.11229848
250 8.78976612 -32.73354581
251 7.94200259 8.78976612
252 -30.50385539 7.94200259
253 15.62076157 -30.50385539
254 7.51308942 15.62076157
255 -26.00151405 7.51308942
256 -14.11185528 -26.00151405
257 -10.72942013 -14.11185528
258 -3.07069879 -10.72942013
259 -18.17389940 -3.07069879
260 -9.71030594 -18.17389940
261 11.34284787 -9.71030594
262 -16.27791357 11.34284787
263 -18.52528690 -16.27791357
264 14.80162462 -18.52528690
265 -17.97027064 14.80162462
266 9.10808107 -17.97027064
267 -18.57310128 9.10808107
268 -3.07864797 -18.57310128
269 -10.14666656 -3.07864797
270 -1.27713927 -10.14666656
271 16.06238032 -1.27713927
272 -27.25397981 16.06238032
273 -6.75734396 -27.25397981
274 -18.05446216 -6.75734396
275 -5.88886856 -18.05446216
276 0.27285950 -5.88886856
277 -26.61683353 0.27285950
278 -4.63183954 -26.61683353
279 16.97692310 -4.63183954
280 19.40417457 16.97692310
281 -12.98095197 19.40417457
282 -24.24986877 -12.98095197
283 -24.58788237 -24.24986877
284 13.72593574 -24.58788237
285 2.15004271 13.72593574
286 -2.84174281 2.15004271
287 1.54461673 -2.84174281
288 -4.70897676 1.54461673
> 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/7fw9t1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8vc8k1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ekfg1324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10l5691324665280.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rvzd1324665280.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/12hi141324665280.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/13q15a1324665280.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/145ldl1324665280.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/15hned1324665280.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/160nxe1324665280.tab")
+ }
>
> try(system("convert tmp/1o1bv1324665280.ps tmp/1o1bv1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/221pp1324665280.ps tmp/221pp1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/3olpp1324665280.ps tmp/3olpp1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mmjo1324665280.ps tmp/4mmjo1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m7z01324665280.ps tmp/5m7z01324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j4g31324665280.ps tmp/6j4g31324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fw9t1324665280.ps tmp/7fw9t1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vc8k1324665280.ps tmp/8vc8k1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ekfg1324665280.ps tmp/9ekfg1324665280.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l5691324665280.ps tmp/10l5691324665280.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.205 0.909 9.167