R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(101645
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+ ,dim=c(4
+ ,287)
+ ,dimnames=list(c('TimeRfc'
+ ,'Logins'
+ ,'Bloggedcomp'
+ ,'Totsize')
+ ,1:287))
> y <- array(NA,dim=c(4,287),dimnames=list(c('TimeRfc','Logins','Bloggedcomp','Totsize'),1:287))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
TimeRfc Logins Bloggedcomp Totsize
1 101645 63 20 17140
2 101011 34 30 27570
3 7176 17 0 1423
4 96560 76 42 22996
5 175824 107 57 39992
6 341570 168 94 117105
7 103597 43 27 23789
8 112611 41 46 26706
9 85574 34 37 24266
10 220801 75 51 44418
11 92661 61 40 35232
12 133328 55 56 40909
13 61361 77 27 13294
14 125930 75 37 32387
15 82316 32 27 21233
16 102010 53 28 44332
17 101523 42 59 61056
18 41566 35 0 13497
19 99923 66 44 32334
20 22648 19 12 44339
21 46698 45 14 10288
22 131698 65 60 65622
23 91735 35 7 16563
24 79863 37 29 29011
25 108043 62 45 34553
26 98866 18 25 23517
27 120445 118 36 51009
28 116048 64 50 33416
29 250047 81 41 83305
30 136084 30 27 27142
31 92499 32 25 21399
32 135781 31 45 24874
33 74408 67 29 34988
34 81240 66 58 45549
35 133368 36 37 32755
36 98146 40 15 27114
37 79619 43 42 20760
38 59194 31 7 37636
39 139942 42 54 65461
40 118612 46 54 30080
41 72880 33 14 24094
42 65475 18 16 69008
43 99643 55 33 54968
44 71965 35 32 46090
45 77272 59 21 27507
46 49289 19 15 10672
47 135131 66 38 34029
48 108446 60 22 46300
49 89746 36 28 24760
50 44296 25 10 18779
51 77648 47 31 21280
52 181528 54 32 40662
53 134019 53 32 28987
54 124064 40 43 22827
55 92630 40 27 18513
56 121848 39 37 30594
57 52915 14 20 24006
58 81872 45 32 27913
59 58981 36 0 42744
60 53515 28 5 12934
61 60812 44 26 22574
62 56375 30 10 41385
63 65490 22 27 18653
64 80949 17 11 18472
65 76302 31 29 30976
66 104011 55 25 63339
67 98104 54 55 25568
68 67989 21 23 33747
69 30989 14 5 4154
70 135458 81 43 19474
71 73504 35 23 35130
72 63123 43 34 39067
73 61254 46 36 13310
74 74914 30 35 65892
75 31774 23 0 4143
76 81437 38 37 28579
77 87186 54 28 51776
78 50090 20 16 21152
79 65745 53 26 38084
80 56653 45 38 27717
81 158399 39 23 32928
82 46455 20 22 11342
83 73624 24 30 19499
84 38395 31 16 16380
85 91899 35 18 36874
86 139526 151 28 48259
87 52164 52 32 16734
88 51567 30 21 28207
89 70551 31 23 30143
90 84856 29 29 41369
91 102538 57 50 45833
92 86678 40 12 29156
93 85709 44 21 35944
94 34662 25 18 36278
95 150580 77 27 45588
96 99611 35 41 45097
97 19349 11 13 3895
98 99373 63 12 28394
99 86230 44 21 18632
100 30837 19 8 2325
101 31706 13 26 25139
102 89806 42 27 27975
103 62088 38 13 14483
104 40151 29 16 13127
105 27634 20 2 5839
106 76990 27 42 24069
107 37460 20 5 3738
108 54157 19 37 18625
109 49862 37 17 36341
110 84337 26 38 24548
111 64175 42 37 21792
112 59382 49 29 26263
113 119308 30 32 23686
114 76702 49 35 49303
115 103425 67 17 25659
116 70344 28 20 28904
117 43410 19 7 2781
118 104838 49 46 29236
119 62215 27 24 19546
120 69304 30 40 22818
121 53117 22 3 32689
122 19764 12 10 5752
123 86680 31 37 22197
124 84105 20 17 20055
125 77945 20 28 25272
126 89113 39 19 82206
127 91005 29 29 32073
128 40248 16 8 5444
129 64187 27 10 20154
130 50857 21 15 36944
131 56613 19 15 8019
132 210907 56 79 112285
133 120982 56 58 84786
134 176508 54 60 83123
135 179321 89 108 101193
136 123185 40 49 38361
137 52746 25 0 68504
138 385534 92 121 119182
139 33170 18 1 22807
140 149061 44 43 116174
141 165446 33 69 57635
142 237213 84 78 66198
143 173326 88 86 71701
144 133131 55 44 57793
145 258873 60 104 80444
146 180083 66 63 53855
147 324799 154 158 97668
148 230964 53 102 133824
149 236785 119 77 101481
150 135473 41 82 99645
151 202925 61 115 114789
152 215147 58 101 99052
153 344297 75 80 67654
154 153935 33 50 65553
155 132943 40 83 97500
156 174724 92 123 69112
157 174415 100 73 82753
158 225548 112 81 85323
159 223632 73 105 72654
160 124817 40 47 30727
161 221698 45 105 77873
162 210767 60 94 117478
163 170266 62 44 74007
164 260561 75 114 90183
165 84853 31 38 61542
166 294424 77 107 101494
167 215641 46 71 55813
168 325107 99 84 79215
169 167542 66 59 55461
170 106408 30 33 31081
171 265769 146 96 83122
172 269651 67 106 70106
173 149112 56 56 60578
174 152871 58 59 79892
175 111665 34 39 49810
176 116408 61 34 71570
177 362301 119 76 100708
178 78800 42 20 33032
179 183167 66 91 82875
180 277965 89 115 139077
181 150629 44 85 71595
182 168809 66 76 72260
183 24188 24 8 5950
184 329267 259 79 115762
185 65029 17 21 32551
186 101097 64 30 31701
187 218946 41 76 80670
188 244052 68 101 143558
189 233328 132 92 120733
190 256462 105 123 105195
191 206161 71 75 73107
192 311473 112 128 132068
193 235800 94 105 149193
194 177939 82 55 46821
195 207176 70 56 87011
196 196553 57 41 95260
197 174184 53 72 55183
198 143246 103 67 106671
199 187559 121 75 73511
200 187681 62 114 92945
201 119016 52 118 78664
202 182192 52 77 70054
203 73566 32 22 22618
204 194979 62 66 74011
205 167488 45 69 83737
206 143756 46 105 69094
207 275541 63 116 93133
208 243199 75 88 95536
209 182999 88 73 225920
210 135649 46 99 62133
211 152299 53 62 61370
212 120221 37 53 43836
213 346485 90 118 106117
214 145790 63 30 38692
215 193339 78 100 84651
216 80953 25 49 56622
217 122774 45 24 15986
218 130585 46 67 95364
219 286468 144 57 89691
220 241066 82 75 67267
221 148446 91 135 126846
222 204713 71 68 41140
223 182079 63 124 102860
224 140344 53 33 51715
225 220516 62 98 55801
226 243060 63 58 111813
227 162765 32 68 120293
228 182613 39 81 138599
229 232138 62 131 161647
230 265318 117 110 115929
231 310839 92 130 162901
232 225060 93 93 109825
233 232317 54 118 129838
234 144966 144 39 37510
235 43287 14 13 43750
236 155754 61 74 40652
237 164709 109 81 87771
238 201940 38 109 85872
239 235454 73 151 89275
240 99466 50 28 192565
241 100750 72 83 140867
242 224549 50 54 120662
243 243511 71 133 101338
244 22938 10 12 1168
245 152474 65 106 65567
246 61857 25 23 25162
247 132487 41 71 40735
248 317394 86 116 91413
249 21054 16 4 855
250 209641 42 62 97068
251 31414 19 18 14116
252 244749 95 98 76643
253 184510 49 64 110681
254 128423 64 32 92696
255 97839 38 25 94785
256 38214 34 16 8773
257 151101 32 48 83209
258 272458 65 100 93815
259 172494 52 46 86687
260 328107 65 129 105547
261 250579 83 130 103487
262 351067 95 136 213688
263 158015 29 59 71220
264 85439 33 32 56926
265 229242 247 63 91721
266 351619 139 95 115168
267 84207 29 14 111194
268 324598 110 113 135777
269 131069 67 47 51513
270 204271 42 92 74163
271 165543 65 70 51633
272 141722 94 19 75345
273 299775 95 91 98952
274 195838 67 111 102372
275 173260 63 41 37238
276 254488 83 120 103772
277 104389 45 135 123969
278 199476 70 87 135400
279 224330 83 131 130115
280 14688 10 4 6023
281 181633 70 47 64466
282 271856 103 109 54990
283 7199 5 7 1644
284 46660 20 12 6179
285 17547 5 0 3926
286 95227 34 37 34777
287 152601 48 46 73224
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Logins Bloggedcomp Totsize
1.328e+04 8.091e+02 1.116e+03 3.822e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-143319 -20301 -1699 16922 155212
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.328e+04 4.535e+03 2.929 0.00368 **
Logins 8.091e+02 8.128e+01 9.954 < 2e-16 ***
Bloggedcomp 1.116e+03 9.686e+01 11.520 < 2e-16 ***
Totsize 3.822e-01 8.637e-02 4.426 1.37e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 37710 on 283 degrees of freedom
Multiple R-squared: 0.7928, Adjusted R-squared: 0.7906
F-statistic: 361 on 3 and 283 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.598562e-01 3.197124e-01 0.8401437800
[2,] 6.721543e-02 1.344309e-01 0.9327845696
[3,] 3.010724e-02 6.021449e-02 0.9698927566
[4,] 2.528945e-01 5.057890e-01 0.7471054888
[5,] 2.809896e-01 5.619792e-01 0.7190103831
[6,] 2.067970e-01 4.135941e-01 0.7932029637
[7,] 1.769942e-01 3.539884e-01 0.8230058145
[8,] 1.170699e-01 2.341397e-01 0.8829301455
[9,] 7.439475e-02 1.487895e-01 0.9256052470
[10,] 6.007769e-02 1.201554e-01 0.9399223099
[11,] 1.483539e-01 2.967078e-01 0.8516460774
[12,] 1.035598e-01 2.071196e-01 0.8964401821
[13,] 8.621527e-02 1.724305e-01 0.9137847319
[14,] 9.477579e-02 1.895516e-01 0.9052242128
[15,] 6.737923e-02 1.347585e-01 0.9326207744
[16,] 6.306937e-02 1.261387e-01 0.9369306333
[17,] 8.763545e-02 1.752709e-01 0.9123645474
[18,] 6.217614e-02 1.243523e-01 0.9378238582
[19,] 4.594773e-02 9.189545e-02 0.9540522741
[20,] 6.079949e-02 1.215990e-01 0.9392005097
[21,] 8.233616e-02 1.646723e-01 0.9176638359
[22,] 6.318286e-02 1.263657e-01 0.9368171370
[23,] 1.714735e-01 3.429470e-01 0.8285265138
[24,] 2.429631e-01 4.859262e-01 0.7570369046
[25,] 2.108661e-01 4.217323e-01 0.7891338609
[26,] 2.227554e-01 4.455107e-01 0.7772446365
[27,] 2.268217e-01 4.536435e-01 0.7731782547
[28,] 3.433368e-01 6.866736e-01 0.6566632215
[29,] 3.359155e-01 6.718311e-01 0.6640844553
[30,] 3.020510e-01 6.041020e-01 0.6979489866
[31,] 2.628208e-01 5.256417e-01 0.7371791639
[32,] 2.335746e-01 4.671493e-01 0.7664253688
[33,] 2.007344e-01 4.014687e-01 0.7992656313
[34,] 1.661527e-01 3.323055e-01 0.8338472600
[35,] 1.360258e-01 2.720516e-01 0.8639741757
[36,] 1.418787e-01 2.837574e-01 0.8581212819
[37,] 1.270932e-01 2.541865e-01 0.8729067711
[38,] 1.189397e-01 2.378793e-01 0.8810603310
[39,] 9.890007e-02 1.978001e-01 0.9010999321
[40,] 7.917653e-02 1.583531e-01 0.9208234671
[41,] 6.619504e-02 1.323901e-01 0.9338049593
[42,] 5.181375e-02 1.036275e-01 0.9481862460
[43,] 4.074888e-02 8.149777e-02 0.9592511157
[44,] 3.149320e-02 6.298639e-02 0.9685068047
[45,] 2.456248e-02 4.912495e-02 0.9754375248
[46,] 5.610710e-02 1.122142e-01 0.9438928973
[47,] 5.457553e-02 1.091511e-01 0.9454244729
[48,] 4.838508e-02 9.677015e-02 0.9516149245
[49,] 3.909569e-02 7.819139e-02 0.9609043061
[50,] 3.367542e-02 6.735083e-02 0.9663245849
[51,] 2.630237e-02 5.260475e-02 0.9736976258
[52,] 2.100459e-02 4.200918e-02 0.9789954121
[53,] 1.616283e-02 3.232565e-02 0.9838371744
[54,] 1.238852e-02 2.477705e-02 0.9876114758
[55,] 1.061654e-02 2.123309e-02 0.9893834562
[56,] 8.351384e-03 1.670277e-02 0.9916486165
[57,] 6.157391e-03 1.231478e-02 0.9938426087
[58,] 6.046528e-03 1.209306e-02 0.9939534721
[59,] 4.522266e-03 9.044532e-03 0.9954777338
[60,] 3.508469e-03 7.016937e-03 0.9964915315
[61,] 3.079729e-03 6.159458e-03 0.9969202710
[62,] 2.233961e-03 4.467921e-03 0.9977660394
[63,] 1.590261e-03 3.180521e-03 0.9984097393
[64,] 1.164094e-03 2.328187e-03 0.9988359064
[65,] 8.419477e-04 1.683895e-03 0.9991580523
[66,] 9.714776e-04 1.942955e-03 0.9990285224
[67,] 8.725580e-04 1.745116e-03 0.9991274420
[68,] 9.308737e-04 1.861747e-03 0.9990691263
[69,] 6.527927e-04 1.305585e-03 0.9993472073
[70,] 4.787727e-04 9.575454e-04 0.9995212273
[71,] 3.983213e-04 7.966425e-04 0.9996016787
[72,] 2.757003e-04 5.514007e-04 0.9997242997
[73,] 2.749443e-04 5.498886e-04 0.9997250557
[74,] 3.443216e-04 6.886432e-04 0.9996556784
[75,] 1.303399e-03 2.606798e-03 0.9986966008
[76,] 9.552159e-04 1.910432e-03 0.9990447841
[77,] 6.814378e-04 1.362876e-03 0.9993185622
[78,] 5.534024e-04 1.106805e-03 0.9994465976
[79,] 4.125247e-04 8.250493e-04 0.9995874753
[80,] 4.316917e-04 8.633834e-04 0.9995683083
[81,] 4.754792e-04 9.509585e-04 0.9995245208
[82,] 3.790431e-04 7.580862e-04 0.9996209569
[83,] 2.677151e-04 5.354301e-04 0.9997322849
[84,] 1.867560e-04 3.735120e-04 0.9998132440
[85,] 1.694927e-04 3.389855e-04 0.9998305073
[86,] 1.282435e-04 2.564869e-04 0.9998717565
[87,] 8.786221e-05 1.757244e-04 0.9999121378
[88,] 9.117968e-05 1.823594e-04 0.9999088203
[89,] 8.304256e-05 1.660851e-04 0.9999169574
[90,] 5.739860e-05 1.147972e-04 0.9999426014
[91,] 4.203026e-05 8.406051e-05 0.9999579697
[92,] 3.026484e-05 6.052969e-05 0.9999697352
[93,] 2.120159e-05 4.240317e-05 0.9999787984
[94,] 1.421110e-05 2.842219e-05 0.9999857889
[95,] 1.313790e-05 2.627579e-05 0.9999868621
[96,] 8.729045e-06 1.745809e-05 0.9999912710
[97,] 5.748232e-06 1.149646e-05 0.9999942518
[98,] 4.132323e-06 8.264645e-06 0.9999958677
[99,] 2.696756e-06 5.393511e-06 0.9999973032
[100,] 1.813690e-06 3.627381e-06 0.9999981863
[101,] 1.177063e-06 2.354126e-06 0.9999988229
[102,] 8.641114e-07 1.728223e-06 0.9999991359
[103,] 7.499707e-07 1.499941e-06 0.9999992500
[104,] 4.758906e-07 9.517812e-07 0.9999995241
[105,] 4.132021e-07 8.264043e-07 0.9999995868
[106,] 4.041869e-07 8.083737e-07 0.9999995958
[107,] 4.941283e-07 9.882566e-07 0.9999995059
[108,] 5.213354e-07 1.042671e-06 0.9999994787
[109,] 3.468690e-07 6.937381e-07 0.9999996531
[110,] 2.177274e-07 4.354548e-07 0.9999997823
[111,] 1.423808e-07 2.847615e-07 0.9999998576
[112,] 9.051958e-08 1.810392e-07 0.9999999095
[113,] 5.670304e-08 1.134061e-07 0.9999999433
[114,] 4.016861e-08 8.033723e-08 0.9999999598
[115,] 2.436974e-08 4.873948e-08 0.9999999756
[116,] 1.639643e-08 3.279286e-08 0.9999999836
[117,] 1.003527e-08 2.007054e-08 0.9999999900
[118,] 8.743525e-09 1.748705e-08 0.9999999913
[119,] 5.404325e-09 1.080865e-08 0.9999999946
[120,] 3.858297e-09 7.716593e-09 0.9999999961
[121,] 2.385894e-09 4.771789e-09 0.9999999976
[122,] 1.432616e-09 2.865232e-09 0.9999999986
[123,] 8.775363e-10 1.755073e-09 0.9999999991
[124,] 5.567076e-10 1.113415e-09 0.9999999994
[125,] 3.417553e-10 6.835106e-10 0.9999999997
[126,] 2.174231e-10 4.348463e-10 0.9999999998
[127,] 2.626207e-10 5.252414e-10 0.9999999997
[128,] 1.753148e-10 3.506297e-10 0.9999999998
[129,] 5.599586e-10 1.119917e-09 0.9999999994
[130,] 3.533187e-10 7.066373e-10 0.9999999996
[131,] 2.359526e-10 4.719052e-10 0.9999999998
[132,] 5.752741e-08 1.150548e-07 0.9999999425
[133,] 3.669938e-08 7.339875e-08 0.9999999633
[134,] 2.351135e-08 4.702271e-08 0.9999999765
[135,] 1.737601e-08 3.475201e-08 0.9999999826
[136,] 2.108344e-08 4.216688e-08 0.9999999789
[137,] 2.284268e-08 4.568536e-08 0.9999999772
[138,] 1.400247e-08 2.800494e-08 0.9999999860
[139,] 1.780889e-08 3.561777e-08 0.9999999822
[140,] 1.268220e-08 2.536441e-08 0.9999999873
[141,] 1.092303e-08 2.184606e-08 0.9999999891
[142,] 7.266654e-09 1.453331e-08 0.9999999927
[143,] 4.381683e-09 8.763366e-09 0.9999999956
[144,] 7.509797e-09 1.501959e-08 0.9999999925
[145,] 8.288162e-09 1.657632e-08 0.9999999917
[146,] 5.031578e-09 1.006316e-08 0.9999999950
[147,] 1.034305e-05 2.068610e-05 0.9999896570
[148,] 8.880055e-06 1.776011e-05 0.9999911199
[149,] 1.213083e-05 2.426166e-05 0.9999878692
[150,] 4.045104e-05 8.090209e-05 0.9999595490
[151,] 3.845045e-05 7.690090e-05 0.9999615495
[152,] 2.690247e-05 5.380493e-05 0.9999730975
[153,] 1.891555e-05 3.783109e-05 0.9999810845
[154,] 1.390288e-05 2.780576e-05 0.9999860971
[155,] 1.105633e-05 2.211265e-05 0.9999889437
[156,] 7.677797e-06 1.535559e-05 0.9999923222
[157,] 6.326914e-06 1.265383e-05 0.9999936731
[158,] 4.997543e-06 9.995086e-06 0.9999950025
[159,] 3.938329e-06 7.876657e-06 0.9999960617
[160,] 6.871091e-06 1.374218e-05 0.9999931289
[161,] 1.354700e-05 2.709399e-05 0.9999864530
[162,] 1.573015e-04 3.146029e-04 0.9998426985
[163,] 1.163998e-04 2.327995e-04 0.9998836002
[164,] 8.995886e-05 1.799177e-04 0.9999100411
[165,] 6.428772e-05 1.285754e-04 0.9999357123
[166,] 9.239970e-05 1.847994e-04 0.9999076003
[167,] 6.556692e-05 1.311338e-04 0.9999344331
[168,] 4.702346e-05 9.404691e-05 0.9999529765
[169,] 3.308806e-05 6.617611e-05 0.9999669119
[170,] 2.431672e-05 4.863344e-05 0.9999756833
[171,] 6.985726e-04 1.397145e-03 0.9993014274
[172,] 5.215834e-04 1.043167e-03 0.9994784166
[173,] 4.246895e-04 8.493789e-04 0.9995753105
[174,] 3.288665e-04 6.577330e-04 0.9996711335
[175,] 2.722147e-04 5.444295e-04 0.9997277853
[176,] 2.055627e-04 4.111255e-04 0.9997944373
[177,] 1.671854e-04 3.343707e-04 0.9998328146
[178,] 1.416501e-04 2.833003e-04 0.9998583499
[179,] 1.012327e-04 2.024655e-04 0.9998987673
[180,] 7.448246e-05 1.489649e-04 0.9999255175
[181,] 1.022529e-04 2.045058e-04 0.9998977471
[182,] 7.712978e-05 1.542596e-04 0.9999228702
[183,] 7.863782e-05 1.572756e-04 0.9999213622
[184,] 6.277104e-05 1.255421e-04 0.9999372290
[185,] 4.902038e-05 9.804077e-05 0.9999509796
[186,] 3.660663e-05 7.321327e-05 0.9999633934
[187,] 3.362513e-05 6.725025e-05 0.9999663749
[188,] 2.502503e-05 5.005006e-05 0.9999749750
[189,] 2.519747e-05 5.039494e-05 0.9999748025
[190,] 3.393399e-05 6.786799e-05 0.9999660660
[191,] 2.465264e-05 4.930527e-05 0.9999753474
[192,] 5.953991e-05 1.190798e-04 0.9999404601
[193,] 5.735951e-05 1.147190e-04 0.9999426405
[194,] 5.942886e-05 1.188577e-04 0.9999405711
[195,] 3.833985e-04 7.667969e-04 0.9996166015
[196,] 2.852723e-04 5.705446e-04 0.9997147277
[197,] 2.045607e-04 4.091215e-04 0.9997954393
[198,] 1.720707e-04 3.441415e-04 0.9998279293
[199,] 1.230272e-04 2.460544e-04 0.9998769728
[200,] 1.559539e-04 3.119077e-04 0.9998440461
[201,] 1.781804e-04 3.563607e-04 0.9998218196
[202,] 1.624227e-04 3.248453e-04 0.9998375773
[203,] 3.508482e-04 7.016965e-04 0.9996491518
[204,] 4.227764e-04 8.455527e-04 0.9995772236
[205,] 3.018292e-04 6.036584e-04 0.9996981708
[206,] 2.134732e-04 4.269464e-04 0.9997865268
[207,] 8.283855e-04 1.656771e-03 0.9991716145
[208,] 7.270345e-04 1.454069e-03 0.9992729655
[209,] 6.058899e-04 1.211780e-03 0.9993941101
[210,] 5.278537e-04 1.055707e-03 0.9994721463
[211,] 5.033744e-04 1.006749e-03 0.9994966256
[212,] 4.414436e-04 8.828872e-04 0.9995585564
[213,] 6.635648e-04 1.327130e-03 0.9993364352
[214,] 8.478457e-04 1.695691e-03 0.9991521543
[215,] 1.678477e-02 3.356953e-02 0.9832152337
[216,] 1.734710e-02 3.469420e-02 0.9826528990
[217,] 2.354404e-02 4.708807e-02 0.9764559647
[218,] 2.085963e-02 4.171926e-02 0.9791403703
[219,] 1.810425e-02 3.620851e-02 0.9818957455
[220,] 3.176285e-02 6.352570e-02 0.9682371512
[221,] 2.517263e-02 5.034525e-02 0.9748273732
[222,] 1.980214e-02 3.960427e-02 0.9801978647
[223,] 1.969975e-02 3.939951e-02 0.9803002455
[224,] 1.538684e-02 3.077368e-02 0.9846131589
[225,] 1.234702e-02 2.469404e-02 0.9876529817
[226,] 9.453126e-03 1.890625e-02 0.9905468740
[227,] 7.159125e-03 1.431825e-02 0.9928408755
[228,] 6.876701e-03 1.375340e-02 0.9931232990
[229,] 5.232165e-03 1.046433e-02 0.9947678354
[230,] 3.853624e-03 7.707249e-03 0.9961463756
[231,] 5.528044e-03 1.105609e-02 0.9944719562
[232,] 4.035033e-03 8.070065e-03 0.9959649675
[233,] 4.297741e-03 8.595481e-03 0.9957022595
[234,] 5.719412e-03 1.143882e-02 0.9942805884
[235,] 5.783682e-02 1.156736e-01 0.9421631791
[236,] 7.064825e-02 1.412965e-01 0.9293517539
[237,] 5.967098e-02 1.193420e-01 0.9403290188
[238,] 4.783598e-02 9.567197e-02 0.9521640160
[239,] 6.662198e-02 1.332440e-01 0.9333780218
[240,] 5.308207e-02 1.061641e-01 0.9469179292
[241,] 4.277496e-02 8.554992e-02 0.9572250390
[242,] 6.089460e-02 1.217892e-01 0.9391054039
[243,] 4.826884e-02 9.653767e-02 0.9517311637
[244,] 5.548886e-02 1.109777e-01 0.9445111409
[245,] 4.734980e-02 9.469959e-02 0.9526502035
[246,] 3.692351e-02 7.384703e-02 0.9630764864
[247,] 2.853250e-02 5.706500e-02 0.9714675004
[248,] 2.142126e-02 4.284251e-02 0.9785787447
[249,] 1.622141e-02 3.244283e-02 0.9837785859
[250,] 1.345162e-02 2.690325e-02 0.9865483755
[251,] 1.028230e-02 2.056461e-02 0.9897176955
[252,] 1.313299e-02 2.626598e-02 0.9868670123
[253,] 1.086988e-02 2.173976e-02 0.9891301176
[254,] 2.627592e-02 5.255184e-02 0.9737240812
[255,] 1.887310e-02 3.774621e-02 0.9811268970
[256,] 1.744989e-02 3.489978e-02 0.9825501109
[257,] 1.445387e-02 2.890774e-02 0.9855461311
[258,] 1.005484e-02 2.010967e-02 0.9899451641
[259,] 6.875683e-01 6.248633e-01 0.3124316645
[260,] 6.410199e-01 7.179601e-01 0.3589800556
[261,] 5.967938e-01 8.064124e-01 0.4032061862
[262,] 6.002791e-01 7.994418e-01 0.3997209124
[263,] 5.895241e-01 8.209517e-01 0.4104758604
[264,] 7.697447e-01 4.605107e-01 0.2302553457
[265,] 7.061967e-01 5.876067e-01 0.2938033453
[266,] 9.986782e-01 2.643615e-03 0.0013218076
[267,] 9.992683e-01 1.463397e-03 0.0007316987
[268,] 9.985713e-01 2.857495e-03 0.0014287475
[269,] 9.961028e-01 7.794455e-03 0.0038972275
[270,] 9.992730e-01 1.454033e-03 0.0007270167
[271,] 9.985581e-01 2.883745e-03 0.0014418726
[272,] 9.946796e-01 1.064086e-02 0.0053204308
[273,] 9.983683e-01 3.263454e-03 0.0016317270
[274,] 9.940994e-01 1.180115e-02 0.0059005773
> postscript(file="/var/wessaorg/rcomp/tmp/15ywp1356081533.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/28u7n1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dvmn1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4mnkg1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58lcl1356081533.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 = 287
Frequency = 1
1 2 3 4 5
8.525352e+03 1.620932e+04 -2.040314e+04 -3.386379e+04 -2.914154e+03
6 7 8 9 10
4.271982e+04 1.630642e+04 4.622973e+03 -5.775592e+03 7.295600e+04
11 12 13 14 15
-2.807207e+04 -2.573726e+03 -4.942623e+04 -1.695024e+03 4.902022e+03
16 17 18 19 20
-2.338895e+03 -3.490896e+04 -5.191102e+03 -2.821099e+04 -3.634238e+04
21 22 23 24 25
-2.254468e+04 -2.620336e+04 3.599526e+04 -6.800810e+03 -1.881872e+04
26 27 28 29 30
3.413752e+04 -4.797142e+04 -1.757637e+04 9.364257e+04 5.802962e+04
31 32 33 34 35
1.725322e+04 3.769961e+04 -3.881211e+04 -6.756652e+04 3.715564e+04
36 37 38 39 40
2.540160e+04 -2.325119e+04 -1.363973e+03 7.405490e+03 -3.637510e+03
41 42 43 44 45
8.069137e+03 -6.598534e+03 -1.596839e+04 -2.295605e+04 -1.768968e+04
46 47 48 49 50
-1.807284e+02 1.304409e+04 4.376422e+03 6.631880e+03 -7.547609e+03
51 52 53 54 55
-1.638313e+04 7.330949e+04 3.107194e+04 2.171512e+04 9.783177e+03
56 57 58 59 60
2.403444e+04 -3.185034e+03 -1.419208e+04 2.361221e+02 7.057389e+03
61 62 63 64 65
-2.570742e+04 -8.154316e+03 -2.847261e+03 3.457939e+04 -6.258510e+03
66 67 68 69 70
-5.873354e+03 -3.000924e+04 -8.451007e+02 -7.859071e+02 1.219256e+03
71 72 73 74 75
-7.185542e+03 -3.781786e+04 -3.450091e+04 -2.687789e+04 -1.699124e+03
76 77 78 79 80
-1.479733e+04 -2.081718e+04 -5.310251e+03 -3.398415e+04 -4.603110e+04
81 82 83 84 85
7.531486e+04 -1.189063e+04 -2.204762e+00 -2.408096e+04 1.612199e+04
86 87 88 89 90
-4.561170e+04 -4.529069e+04 -2.019951e+04 -4.996185e+03 -5.877713e+01
91 92 93 94 95
-3.016895e+04 1.650058e+04 -3.415573e+02 -3.279662e+04 2.744943e+04
96 97 98 99 100
-4.972917e+03 -1.882632e+04 1.087846e+04 6.796393e+03 -7.631598e+03
101 102 103 104 105
-3.071296e+04 1.724516e+03 -1.978846e+03 -1.946349e+04 -6.291832e+03
106 107 108 109 110
-1.419999e+04 9.897378e+02 -2.290061e+04 -2.621359e+04 -1.763724e+03
111 112 113 114 115
-3.270146e+04 -3.594019e+04 3.699545e+04 -3.412142e+04 7.160471e+03
116 117 118 119 120
1.045031e+03 5.882934e+03 -1.058951e+04 -7.161410e+03 -2.160337e+04
121 122 123 124 125
6.194685e+03 -1.658269e+04 -1.451605e+03 2.800822e+04 7.580138e+03
126 127 128 129 130
-8.342761e+03 9.643317e+03 3.014444e+03 1.019972e+04 -1.027247e+04
131 132 133 134 135
8.157295e+03 2.125113e+04 -3.473099e+04 2.081711e+04 -6.515315e+04
136 137 138 139 140
8.203819e+03 -6.945146e+03 1.172513e+05 -4.507357e+03 7.797025e+03
141 142 143 144 145
2.644492e+04 4.363454e+04 -3.451862e+04 4.165789e+03 5.025550e+04
146 147 148 149 150
2.252267e+04 -2.670785e+04 9.838796e+03 2.519498e+03 -4.056322e+04
151 152 153 154 155
-3.190287e+04 4.382789e+03 1.552119e+05 3.310815e+04 -4.258012e+04
156 157 158 159 160
-7.665275e+04 -3.285691e+04 -1.341506e+03 6.358381e+03 1.498531e+04
161 162 163 164 165
2.508326e+04 -8.473101e+02 2.944010e+04 2.492696e+04 -1.943278e+04
166 167 168 169 170
6.065934e+04 6.458690e+04 1.077224e+05 1.383112e+04 2.015313e+04
171 172 173 174 175
-4.524615e+03 5.708981e+04 4.883378e+03 -3.705360e+03 8.320397e+03
176 177 178 179 180
-1.151915e+04 1.294468e+05 -3.403598e+03 -1.672833e+04 1.120016e+04
181 182 183 184 185
-2.046066e+04 -1.029174e+04 -1.971143e+04 -2.595694e+04 2.119916e+03
186 187 188 189 190
-9.555407e+03 5.685730e+04 8.186219e+03 -3.555107e+04 -1.922408e+04
191 192 193 194 195
2.380704e+04 1.427306e+04 -2.771842e+04 1.904882e+04 4.151739e+04
196 197 198 199 200
5.499659e+04 1.659145e+04 -6.890003e+04 -3.540235e+04 -3.849095e+04
201 202 203 204 205
-9.807019e+04 1.414544e+04 1.201766e+03 2.960346e+04 8.801559e+03
206 207 208 209 210
-5.031232e+04 4.625649e+04 3.453035e+04 -6.928513e+04 -4.906376e+04
211 212 213 214 215
3.499903e+03 1.111065e+03 8.816152e+04 3.327457e+04 -2.698627e+04
216 217 218 219 220
-2.887196e+04 4.019521e+04 -3.112290e+04 5.879881e+04 5.204454e+04
221 222 223 224 225
-1.375785e+05 4.238815e+04 -5.984992e+04 2.759408e+04 2.639431e+04
226 227 228 229 230
7.135340e+04 1.739813e+03 -5.578177e+03 -3.926204e+04 -9.673819e+03
231 232 233 234 235
1.580369e+04 -9.212343e+03 -5.946912e+03 -4.267388e+04 -1.254878e+04
236 237 238 239 240
-4.988667e+03 -6.068900e+04 3.468024e+03 -3.950031e+04 -5.911297e+04
241 242 243 244 245
-1.172387e+05 6.444122e+04 -1.435107e+04 -1.227013e+04 -5.673419e+04
246 247 248 249 250
-6.932002e+03 -8.758730e+03 7.015854e+04 -9.962268e+03 5.609716e+04
251 252 253 254 255
-2.271955e+04 1.596217e+04 1.786797e+04 -7.774411e+03 -1.031055e+04
256 257 258 259 260
-2.378161e+04 2.656643e+04 5.914787e+04 3.268053e+04 7.795383e+04
261 262 263 264 265
-1.446570e+04 2.749789e+04 2.821596e+04 -1.200564e+04 -8.923117e+04
266 267 268 269 270
7.585608e+04 -1.065877e+04 4.433588e+04 -8.552069e+03 2.600716e+04
271 272 273 274 275
1.830256e+03 2.390356e+03 7.027204e+04 -3.463494e+04 4.902626e+04
276 277 278 279 280
4.925961e+02 -1.433191e+05 -1.926825e+04 -5.200821e+04 -1.344921e+04
281 282 283 284 285
3.463389e+04 3.259879e+04 -1.856665e+04 1.445984e+03 -1.280110e+03
286 287
-1.400807e+02 2.116956e+04
> postscript(file="/var/wessaorg/rcomp/tmp/6g3a21356081533.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 = 287
Frequency = 1
lag(myerror, k = 1) myerror
0 8.525352e+03 NA
1 1.620932e+04 8.525352e+03
2 -2.040314e+04 1.620932e+04
3 -3.386379e+04 -2.040314e+04
4 -2.914154e+03 -3.386379e+04
5 4.271982e+04 -2.914154e+03
6 1.630642e+04 4.271982e+04
7 4.622973e+03 1.630642e+04
8 -5.775592e+03 4.622973e+03
9 7.295600e+04 -5.775592e+03
10 -2.807207e+04 7.295600e+04
11 -2.573726e+03 -2.807207e+04
12 -4.942623e+04 -2.573726e+03
13 -1.695024e+03 -4.942623e+04
14 4.902022e+03 -1.695024e+03
15 -2.338895e+03 4.902022e+03
16 -3.490896e+04 -2.338895e+03
17 -5.191102e+03 -3.490896e+04
18 -2.821099e+04 -5.191102e+03
19 -3.634238e+04 -2.821099e+04
20 -2.254468e+04 -3.634238e+04
21 -2.620336e+04 -2.254468e+04
22 3.599526e+04 -2.620336e+04
23 -6.800810e+03 3.599526e+04
24 -1.881872e+04 -6.800810e+03
25 3.413752e+04 -1.881872e+04
26 -4.797142e+04 3.413752e+04
27 -1.757637e+04 -4.797142e+04
28 9.364257e+04 -1.757637e+04
29 5.802962e+04 9.364257e+04
30 1.725322e+04 5.802962e+04
31 3.769961e+04 1.725322e+04
32 -3.881211e+04 3.769961e+04
33 -6.756652e+04 -3.881211e+04
34 3.715564e+04 -6.756652e+04
35 2.540160e+04 3.715564e+04
36 -2.325119e+04 2.540160e+04
37 -1.363973e+03 -2.325119e+04
38 7.405490e+03 -1.363973e+03
39 -3.637510e+03 7.405490e+03
40 8.069137e+03 -3.637510e+03
41 -6.598534e+03 8.069137e+03
42 -1.596839e+04 -6.598534e+03
43 -2.295605e+04 -1.596839e+04
44 -1.768968e+04 -2.295605e+04
45 -1.807284e+02 -1.768968e+04
46 1.304409e+04 -1.807284e+02
47 4.376422e+03 1.304409e+04
48 6.631880e+03 4.376422e+03
49 -7.547609e+03 6.631880e+03
50 -1.638313e+04 -7.547609e+03
51 7.330949e+04 -1.638313e+04
52 3.107194e+04 7.330949e+04
53 2.171512e+04 3.107194e+04
54 9.783177e+03 2.171512e+04
55 2.403444e+04 9.783177e+03
56 -3.185034e+03 2.403444e+04
57 -1.419208e+04 -3.185034e+03
58 2.361221e+02 -1.419208e+04
59 7.057389e+03 2.361221e+02
60 -2.570742e+04 7.057389e+03
61 -8.154316e+03 -2.570742e+04
62 -2.847261e+03 -8.154316e+03
63 3.457939e+04 -2.847261e+03
64 -6.258510e+03 3.457939e+04
65 -5.873354e+03 -6.258510e+03
66 -3.000924e+04 -5.873354e+03
67 -8.451007e+02 -3.000924e+04
68 -7.859071e+02 -8.451007e+02
69 1.219256e+03 -7.859071e+02
70 -7.185542e+03 1.219256e+03
71 -3.781786e+04 -7.185542e+03
72 -3.450091e+04 -3.781786e+04
73 -2.687789e+04 -3.450091e+04
74 -1.699124e+03 -2.687789e+04
75 -1.479733e+04 -1.699124e+03
76 -2.081718e+04 -1.479733e+04
77 -5.310251e+03 -2.081718e+04
78 -3.398415e+04 -5.310251e+03
79 -4.603110e+04 -3.398415e+04
80 7.531486e+04 -4.603110e+04
81 -1.189063e+04 7.531486e+04
82 -2.204762e+00 -1.189063e+04
83 -2.408096e+04 -2.204762e+00
84 1.612199e+04 -2.408096e+04
85 -4.561170e+04 1.612199e+04
86 -4.529069e+04 -4.561170e+04
87 -2.019951e+04 -4.529069e+04
88 -4.996185e+03 -2.019951e+04
89 -5.877713e+01 -4.996185e+03
90 -3.016895e+04 -5.877713e+01
91 1.650058e+04 -3.016895e+04
92 -3.415573e+02 1.650058e+04
93 -3.279662e+04 -3.415573e+02
94 2.744943e+04 -3.279662e+04
95 -4.972917e+03 2.744943e+04
96 -1.882632e+04 -4.972917e+03
97 1.087846e+04 -1.882632e+04
98 6.796393e+03 1.087846e+04
99 -7.631598e+03 6.796393e+03
100 -3.071296e+04 -7.631598e+03
101 1.724516e+03 -3.071296e+04
102 -1.978846e+03 1.724516e+03
103 -1.946349e+04 -1.978846e+03
104 -6.291832e+03 -1.946349e+04
105 -1.419999e+04 -6.291832e+03
106 9.897378e+02 -1.419999e+04
107 -2.290061e+04 9.897378e+02
108 -2.621359e+04 -2.290061e+04
109 -1.763724e+03 -2.621359e+04
110 -3.270146e+04 -1.763724e+03
111 -3.594019e+04 -3.270146e+04
112 3.699545e+04 -3.594019e+04
113 -3.412142e+04 3.699545e+04
114 7.160471e+03 -3.412142e+04
115 1.045031e+03 7.160471e+03
116 5.882934e+03 1.045031e+03
117 -1.058951e+04 5.882934e+03
118 -7.161410e+03 -1.058951e+04
119 -2.160337e+04 -7.161410e+03
120 6.194685e+03 -2.160337e+04
121 -1.658269e+04 6.194685e+03
122 -1.451605e+03 -1.658269e+04
123 2.800822e+04 -1.451605e+03
124 7.580138e+03 2.800822e+04
125 -8.342761e+03 7.580138e+03
126 9.643317e+03 -8.342761e+03
127 3.014444e+03 9.643317e+03
128 1.019972e+04 3.014444e+03
129 -1.027247e+04 1.019972e+04
130 8.157295e+03 -1.027247e+04
131 2.125113e+04 8.157295e+03
132 -3.473099e+04 2.125113e+04
133 2.081711e+04 -3.473099e+04
134 -6.515315e+04 2.081711e+04
135 8.203819e+03 -6.515315e+04
136 -6.945146e+03 8.203819e+03
137 1.172513e+05 -6.945146e+03
138 -4.507357e+03 1.172513e+05
139 7.797025e+03 -4.507357e+03
140 2.644492e+04 7.797025e+03
141 4.363454e+04 2.644492e+04
142 -3.451862e+04 4.363454e+04
143 4.165789e+03 -3.451862e+04
144 5.025550e+04 4.165789e+03
145 2.252267e+04 5.025550e+04
146 -2.670785e+04 2.252267e+04
147 9.838796e+03 -2.670785e+04
148 2.519498e+03 9.838796e+03
149 -4.056322e+04 2.519498e+03
150 -3.190287e+04 -4.056322e+04
151 4.382789e+03 -3.190287e+04
152 1.552119e+05 4.382789e+03
153 3.310815e+04 1.552119e+05
154 -4.258012e+04 3.310815e+04
155 -7.665275e+04 -4.258012e+04
156 -3.285691e+04 -7.665275e+04
157 -1.341506e+03 -3.285691e+04
158 6.358381e+03 -1.341506e+03
159 1.498531e+04 6.358381e+03
160 2.508326e+04 1.498531e+04
161 -8.473101e+02 2.508326e+04
162 2.944010e+04 -8.473101e+02
163 2.492696e+04 2.944010e+04
164 -1.943278e+04 2.492696e+04
165 6.065934e+04 -1.943278e+04
166 6.458690e+04 6.065934e+04
167 1.077224e+05 6.458690e+04
168 1.383112e+04 1.077224e+05
169 2.015313e+04 1.383112e+04
170 -4.524615e+03 2.015313e+04
171 5.708981e+04 -4.524615e+03
172 4.883378e+03 5.708981e+04
173 -3.705360e+03 4.883378e+03
174 8.320397e+03 -3.705360e+03
175 -1.151915e+04 8.320397e+03
176 1.294468e+05 -1.151915e+04
177 -3.403598e+03 1.294468e+05
178 -1.672833e+04 -3.403598e+03
179 1.120016e+04 -1.672833e+04
180 -2.046066e+04 1.120016e+04
181 -1.029174e+04 -2.046066e+04
182 -1.971143e+04 -1.029174e+04
183 -2.595694e+04 -1.971143e+04
184 2.119916e+03 -2.595694e+04
185 -9.555407e+03 2.119916e+03
186 5.685730e+04 -9.555407e+03
187 8.186219e+03 5.685730e+04
188 -3.555107e+04 8.186219e+03
189 -1.922408e+04 -3.555107e+04
190 2.380704e+04 -1.922408e+04
191 1.427306e+04 2.380704e+04
192 -2.771842e+04 1.427306e+04
193 1.904882e+04 -2.771842e+04
194 4.151739e+04 1.904882e+04
195 5.499659e+04 4.151739e+04
196 1.659145e+04 5.499659e+04
197 -6.890003e+04 1.659145e+04
198 -3.540235e+04 -6.890003e+04
199 -3.849095e+04 -3.540235e+04
200 -9.807019e+04 -3.849095e+04
201 1.414544e+04 -9.807019e+04
202 1.201766e+03 1.414544e+04
203 2.960346e+04 1.201766e+03
204 8.801559e+03 2.960346e+04
205 -5.031232e+04 8.801559e+03
206 4.625649e+04 -5.031232e+04
207 3.453035e+04 4.625649e+04
208 -6.928513e+04 3.453035e+04
209 -4.906376e+04 -6.928513e+04
210 3.499903e+03 -4.906376e+04
211 1.111065e+03 3.499903e+03
212 8.816152e+04 1.111065e+03
213 3.327457e+04 8.816152e+04
214 -2.698627e+04 3.327457e+04
215 -2.887196e+04 -2.698627e+04
216 4.019521e+04 -2.887196e+04
217 -3.112290e+04 4.019521e+04
218 5.879881e+04 -3.112290e+04
219 5.204454e+04 5.879881e+04
220 -1.375785e+05 5.204454e+04
221 4.238815e+04 -1.375785e+05
222 -5.984992e+04 4.238815e+04
223 2.759408e+04 -5.984992e+04
224 2.639431e+04 2.759408e+04
225 7.135340e+04 2.639431e+04
226 1.739813e+03 7.135340e+04
227 -5.578177e+03 1.739813e+03
228 -3.926204e+04 -5.578177e+03
229 -9.673819e+03 -3.926204e+04
230 1.580369e+04 -9.673819e+03
231 -9.212343e+03 1.580369e+04
232 -5.946912e+03 -9.212343e+03
233 -4.267388e+04 -5.946912e+03
234 -1.254878e+04 -4.267388e+04
235 -4.988667e+03 -1.254878e+04
236 -6.068900e+04 -4.988667e+03
237 3.468024e+03 -6.068900e+04
238 -3.950031e+04 3.468024e+03
239 -5.911297e+04 -3.950031e+04
240 -1.172387e+05 -5.911297e+04
241 6.444122e+04 -1.172387e+05
242 -1.435107e+04 6.444122e+04
243 -1.227013e+04 -1.435107e+04
244 -5.673419e+04 -1.227013e+04
245 -6.932002e+03 -5.673419e+04
246 -8.758730e+03 -6.932002e+03
247 7.015854e+04 -8.758730e+03
248 -9.962268e+03 7.015854e+04
249 5.609716e+04 -9.962268e+03
250 -2.271955e+04 5.609716e+04
251 1.596217e+04 -2.271955e+04
252 1.786797e+04 1.596217e+04
253 -7.774411e+03 1.786797e+04
254 -1.031055e+04 -7.774411e+03
255 -2.378161e+04 -1.031055e+04
256 2.656643e+04 -2.378161e+04
257 5.914787e+04 2.656643e+04
258 3.268053e+04 5.914787e+04
259 7.795383e+04 3.268053e+04
260 -1.446570e+04 7.795383e+04
261 2.749789e+04 -1.446570e+04
262 2.821596e+04 2.749789e+04
263 -1.200564e+04 2.821596e+04
264 -8.923117e+04 -1.200564e+04
265 7.585608e+04 -8.923117e+04
266 -1.065877e+04 7.585608e+04
267 4.433588e+04 -1.065877e+04
268 -8.552069e+03 4.433588e+04
269 2.600716e+04 -8.552069e+03
270 1.830256e+03 2.600716e+04
271 2.390356e+03 1.830256e+03
272 7.027204e+04 2.390356e+03
273 -3.463494e+04 7.027204e+04
274 4.902626e+04 -3.463494e+04
275 4.925961e+02 4.902626e+04
276 -1.433191e+05 4.925961e+02
277 -1.926825e+04 -1.433191e+05
278 -5.200821e+04 -1.926825e+04
279 -1.344921e+04 -5.200821e+04
280 3.463389e+04 -1.344921e+04
281 3.259879e+04 3.463389e+04
282 -1.856665e+04 3.259879e+04
283 1.445984e+03 -1.856665e+04
284 -1.280110e+03 1.445984e+03
285 -1.400807e+02 -1.280110e+03
286 2.116956e+04 -1.400807e+02
287 NA 2.116956e+04
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.620932e+04 8.525352e+03
[2,] -2.040314e+04 1.620932e+04
[3,] -3.386379e+04 -2.040314e+04
[4,] -2.914154e+03 -3.386379e+04
[5,] 4.271982e+04 -2.914154e+03
[6,] 1.630642e+04 4.271982e+04
[7,] 4.622973e+03 1.630642e+04
[8,] -5.775592e+03 4.622973e+03
[9,] 7.295600e+04 -5.775592e+03
[10,] -2.807207e+04 7.295600e+04
[11,] -2.573726e+03 -2.807207e+04
[12,] -4.942623e+04 -2.573726e+03
[13,] -1.695024e+03 -4.942623e+04
[14,] 4.902022e+03 -1.695024e+03
[15,] -2.338895e+03 4.902022e+03
[16,] -3.490896e+04 -2.338895e+03
[17,] -5.191102e+03 -3.490896e+04
[18,] -2.821099e+04 -5.191102e+03
[19,] -3.634238e+04 -2.821099e+04
[20,] -2.254468e+04 -3.634238e+04
[21,] -2.620336e+04 -2.254468e+04
[22,] 3.599526e+04 -2.620336e+04
[23,] -6.800810e+03 3.599526e+04
[24,] -1.881872e+04 -6.800810e+03
[25,] 3.413752e+04 -1.881872e+04
[26,] -4.797142e+04 3.413752e+04
[27,] -1.757637e+04 -4.797142e+04
[28,] 9.364257e+04 -1.757637e+04
[29,] 5.802962e+04 9.364257e+04
[30,] 1.725322e+04 5.802962e+04
[31,] 3.769961e+04 1.725322e+04
[32,] -3.881211e+04 3.769961e+04
[33,] -6.756652e+04 -3.881211e+04
[34,] 3.715564e+04 -6.756652e+04
[35,] 2.540160e+04 3.715564e+04
[36,] -2.325119e+04 2.540160e+04
[37,] -1.363973e+03 -2.325119e+04
[38,] 7.405490e+03 -1.363973e+03
[39,] -3.637510e+03 7.405490e+03
[40,] 8.069137e+03 -3.637510e+03
[41,] -6.598534e+03 8.069137e+03
[42,] -1.596839e+04 -6.598534e+03
[43,] -2.295605e+04 -1.596839e+04
[44,] -1.768968e+04 -2.295605e+04
[45,] -1.807284e+02 -1.768968e+04
[46,] 1.304409e+04 -1.807284e+02
[47,] 4.376422e+03 1.304409e+04
[48,] 6.631880e+03 4.376422e+03
[49,] -7.547609e+03 6.631880e+03
[50,] -1.638313e+04 -7.547609e+03
[51,] 7.330949e+04 -1.638313e+04
[52,] 3.107194e+04 7.330949e+04
[53,] 2.171512e+04 3.107194e+04
[54,] 9.783177e+03 2.171512e+04
[55,] 2.403444e+04 9.783177e+03
[56,] -3.185034e+03 2.403444e+04
[57,] -1.419208e+04 -3.185034e+03
[58,] 2.361221e+02 -1.419208e+04
[59,] 7.057389e+03 2.361221e+02
[60,] -2.570742e+04 7.057389e+03
[61,] -8.154316e+03 -2.570742e+04
[62,] -2.847261e+03 -8.154316e+03
[63,] 3.457939e+04 -2.847261e+03
[64,] -6.258510e+03 3.457939e+04
[65,] -5.873354e+03 -6.258510e+03
[66,] -3.000924e+04 -5.873354e+03
[67,] -8.451007e+02 -3.000924e+04
[68,] -7.859071e+02 -8.451007e+02
[69,] 1.219256e+03 -7.859071e+02
[70,] -7.185542e+03 1.219256e+03
[71,] -3.781786e+04 -7.185542e+03
[72,] -3.450091e+04 -3.781786e+04
[73,] -2.687789e+04 -3.450091e+04
[74,] -1.699124e+03 -2.687789e+04
[75,] -1.479733e+04 -1.699124e+03
[76,] -2.081718e+04 -1.479733e+04
[77,] -5.310251e+03 -2.081718e+04
[78,] -3.398415e+04 -5.310251e+03
[79,] -4.603110e+04 -3.398415e+04
[80,] 7.531486e+04 -4.603110e+04
[81,] -1.189063e+04 7.531486e+04
[82,] -2.204762e+00 -1.189063e+04
[83,] -2.408096e+04 -2.204762e+00
[84,] 1.612199e+04 -2.408096e+04
[85,] -4.561170e+04 1.612199e+04
[86,] -4.529069e+04 -4.561170e+04
[87,] -2.019951e+04 -4.529069e+04
[88,] -4.996185e+03 -2.019951e+04
[89,] -5.877713e+01 -4.996185e+03
[90,] -3.016895e+04 -5.877713e+01
[91,] 1.650058e+04 -3.016895e+04
[92,] -3.415573e+02 1.650058e+04
[93,] -3.279662e+04 -3.415573e+02
[94,] 2.744943e+04 -3.279662e+04
[95,] -4.972917e+03 2.744943e+04
[96,] -1.882632e+04 -4.972917e+03
[97,] 1.087846e+04 -1.882632e+04
[98,] 6.796393e+03 1.087846e+04
[99,] -7.631598e+03 6.796393e+03
[100,] -3.071296e+04 -7.631598e+03
[101,] 1.724516e+03 -3.071296e+04
[102,] -1.978846e+03 1.724516e+03
[103,] -1.946349e+04 -1.978846e+03
[104,] -6.291832e+03 -1.946349e+04
[105,] -1.419999e+04 -6.291832e+03
[106,] 9.897378e+02 -1.419999e+04
[107,] -2.290061e+04 9.897378e+02
[108,] -2.621359e+04 -2.290061e+04
[109,] -1.763724e+03 -2.621359e+04
[110,] -3.270146e+04 -1.763724e+03
[111,] -3.594019e+04 -3.270146e+04
[112,] 3.699545e+04 -3.594019e+04
[113,] -3.412142e+04 3.699545e+04
[114,] 7.160471e+03 -3.412142e+04
[115,] 1.045031e+03 7.160471e+03
[116,] 5.882934e+03 1.045031e+03
[117,] -1.058951e+04 5.882934e+03
[118,] -7.161410e+03 -1.058951e+04
[119,] -2.160337e+04 -7.161410e+03
[120,] 6.194685e+03 -2.160337e+04
[121,] -1.658269e+04 6.194685e+03
[122,] -1.451605e+03 -1.658269e+04
[123,] 2.800822e+04 -1.451605e+03
[124,] 7.580138e+03 2.800822e+04
[125,] -8.342761e+03 7.580138e+03
[126,] 9.643317e+03 -8.342761e+03
[127,] 3.014444e+03 9.643317e+03
[128,] 1.019972e+04 3.014444e+03
[129,] -1.027247e+04 1.019972e+04
[130,] 8.157295e+03 -1.027247e+04
[131,] 2.125113e+04 8.157295e+03
[132,] -3.473099e+04 2.125113e+04
[133,] 2.081711e+04 -3.473099e+04
[134,] -6.515315e+04 2.081711e+04
[135,] 8.203819e+03 -6.515315e+04
[136,] -6.945146e+03 8.203819e+03
[137,] 1.172513e+05 -6.945146e+03
[138,] -4.507357e+03 1.172513e+05
[139,] 7.797025e+03 -4.507357e+03
[140,] 2.644492e+04 7.797025e+03
[141,] 4.363454e+04 2.644492e+04
[142,] -3.451862e+04 4.363454e+04
[143,] 4.165789e+03 -3.451862e+04
[144,] 5.025550e+04 4.165789e+03
[145,] 2.252267e+04 5.025550e+04
[146,] -2.670785e+04 2.252267e+04
[147,] 9.838796e+03 -2.670785e+04
[148,] 2.519498e+03 9.838796e+03
[149,] -4.056322e+04 2.519498e+03
[150,] -3.190287e+04 -4.056322e+04
[151,] 4.382789e+03 -3.190287e+04
[152,] 1.552119e+05 4.382789e+03
[153,] 3.310815e+04 1.552119e+05
[154,] -4.258012e+04 3.310815e+04
[155,] -7.665275e+04 -4.258012e+04
[156,] -3.285691e+04 -7.665275e+04
[157,] -1.341506e+03 -3.285691e+04
[158,] 6.358381e+03 -1.341506e+03
[159,] 1.498531e+04 6.358381e+03
[160,] 2.508326e+04 1.498531e+04
[161,] -8.473101e+02 2.508326e+04
[162,] 2.944010e+04 -8.473101e+02
[163,] 2.492696e+04 2.944010e+04
[164,] -1.943278e+04 2.492696e+04
[165,] 6.065934e+04 -1.943278e+04
[166,] 6.458690e+04 6.065934e+04
[167,] 1.077224e+05 6.458690e+04
[168,] 1.383112e+04 1.077224e+05
[169,] 2.015313e+04 1.383112e+04
[170,] -4.524615e+03 2.015313e+04
[171,] 5.708981e+04 -4.524615e+03
[172,] 4.883378e+03 5.708981e+04
[173,] -3.705360e+03 4.883378e+03
[174,] 8.320397e+03 -3.705360e+03
[175,] -1.151915e+04 8.320397e+03
[176,] 1.294468e+05 -1.151915e+04
[177,] -3.403598e+03 1.294468e+05
[178,] -1.672833e+04 -3.403598e+03
[179,] 1.120016e+04 -1.672833e+04
[180,] -2.046066e+04 1.120016e+04
[181,] -1.029174e+04 -2.046066e+04
[182,] -1.971143e+04 -1.029174e+04
[183,] -2.595694e+04 -1.971143e+04
[184,] 2.119916e+03 -2.595694e+04
[185,] -9.555407e+03 2.119916e+03
[186,] 5.685730e+04 -9.555407e+03
[187,] 8.186219e+03 5.685730e+04
[188,] -3.555107e+04 8.186219e+03
[189,] -1.922408e+04 -3.555107e+04
[190,] 2.380704e+04 -1.922408e+04
[191,] 1.427306e+04 2.380704e+04
[192,] -2.771842e+04 1.427306e+04
[193,] 1.904882e+04 -2.771842e+04
[194,] 4.151739e+04 1.904882e+04
[195,] 5.499659e+04 4.151739e+04
[196,] 1.659145e+04 5.499659e+04
[197,] -6.890003e+04 1.659145e+04
[198,] -3.540235e+04 -6.890003e+04
[199,] -3.849095e+04 -3.540235e+04
[200,] -9.807019e+04 -3.849095e+04
[201,] 1.414544e+04 -9.807019e+04
[202,] 1.201766e+03 1.414544e+04
[203,] 2.960346e+04 1.201766e+03
[204,] 8.801559e+03 2.960346e+04
[205,] -5.031232e+04 8.801559e+03
[206,] 4.625649e+04 -5.031232e+04
[207,] 3.453035e+04 4.625649e+04
[208,] -6.928513e+04 3.453035e+04
[209,] -4.906376e+04 -6.928513e+04
[210,] 3.499903e+03 -4.906376e+04
[211,] 1.111065e+03 3.499903e+03
[212,] 8.816152e+04 1.111065e+03
[213,] 3.327457e+04 8.816152e+04
[214,] -2.698627e+04 3.327457e+04
[215,] -2.887196e+04 -2.698627e+04
[216,] 4.019521e+04 -2.887196e+04
[217,] -3.112290e+04 4.019521e+04
[218,] 5.879881e+04 -3.112290e+04
[219,] 5.204454e+04 5.879881e+04
[220,] -1.375785e+05 5.204454e+04
[221,] 4.238815e+04 -1.375785e+05
[222,] -5.984992e+04 4.238815e+04
[223,] 2.759408e+04 -5.984992e+04
[224,] 2.639431e+04 2.759408e+04
[225,] 7.135340e+04 2.639431e+04
[226,] 1.739813e+03 7.135340e+04
[227,] -5.578177e+03 1.739813e+03
[228,] -3.926204e+04 -5.578177e+03
[229,] -9.673819e+03 -3.926204e+04
[230,] 1.580369e+04 -9.673819e+03
[231,] -9.212343e+03 1.580369e+04
[232,] -5.946912e+03 -9.212343e+03
[233,] -4.267388e+04 -5.946912e+03
[234,] -1.254878e+04 -4.267388e+04
[235,] -4.988667e+03 -1.254878e+04
[236,] -6.068900e+04 -4.988667e+03
[237,] 3.468024e+03 -6.068900e+04
[238,] -3.950031e+04 3.468024e+03
[239,] -5.911297e+04 -3.950031e+04
[240,] -1.172387e+05 -5.911297e+04
[241,] 6.444122e+04 -1.172387e+05
[242,] -1.435107e+04 6.444122e+04
[243,] -1.227013e+04 -1.435107e+04
[244,] -5.673419e+04 -1.227013e+04
[245,] -6.932002e+03 -5.673419e+04
[246,] -8.758730e+03 -6.932002e+03
[247,] 7.015854e+04 -8.758730e+03
[248,] -9.962268e+03 7.015854e+04
[249,] 5.609716e+04 -9.962268e+03
[250,] -2.271955e+04 5.609716e+04
[251,] 1.596217e+04 -2.271955e+04
[252,] 1.786797e+04 1.596217e+04
[253,] -7.774411e+03 1.786797e+04
[254,] -1.031055e+04 -7.774411e+03
[255,] -2.378161e+04 -1.031055e+04
[256,] 2.656643e+04 -2.378161e+04
[257,] 5.914787e+04 2.656643e+04
[258,] 3.268053e+04 5.914787e+04
[259,] 7.795383e+04 3.268053e+04
[260,] -1.446570e+04 7.795383e+04
[261,] 2.749789e+04 -1.446570e+04
[262,] 2.821596e+04 2.749789e+04
[263,] -1.200564e+04 2.821596e+04
[264,] -8.923117e+04 -1.200564e+04
[265,] 7.585608e+04 -8.923117e+04
[266,] -1.065877e+04 7.585608e+04
[267,] 4.433588e+04 -1.065877e+04
[268,] -8.552069e+03 4.433588e+04
[269,] 2.600716e+04 -8.552069e+03
[270,] 1.830256e+03 2.600716e+04
[271,] 2.390356e+03 1.830256e+03
[272,] 7.027204e+04 2.390356e+03
[273,] -3.463494e+04 7.027204e+04
[274,] 4.902626e+04 -3.463494e+04
[275,] 4.925961e+02 4.902626e+04
[276,] -1.433191e+05 4.925961e+02
[277,] -1.926825e+04 -1.433191e+05
[278,] -5.200821e+04 -1.926825e+04
[279,] -1.344921e+04 -5.200821e+04
[280,] 3.463389e+04 -1.344921e+04
[281,] 3.259879e+04 3.463389e+04
[282,] -1.856665e+04 3.259879e+04
[283,] 1.445984e+03 -1.856665e+04
[284,] -1.280110e+03 1.445984e+03
[285,] -1.400807e+02 -1.280110e+03
[286,] 2.116956e+04 -1.400807e+02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.620932e+04 8.525352e+03
2 -2.040314e+04 1.620932e+04
3 -3.386379e+04 -2.040314e+04
4 -2.914154e+03 -3.386379e+04
5 4.271982e+04 -2.914154e+03
6 1.630642e+04 4.271982e+04
7 4.622973e+03 1.630642e+04
8 -5.775592e+03 4.622973e+03
9 7.295600e+04 -5.775592e+03
10 -2.807207e+04 7.295600e+04
11 -2.573726e+03 -2.807207e+04
12 -4.942623e+04 -2.573726e+03
13 -1.695024e+03 -4.942623e+04
14 4.902022e+03 -1.695024e+03
15 -2.338895e+03 4.902022e+03
16 -3.490896e+04 -2.338895e+03
17 -5.191102e+03 -3.490896e+04
18 -2.821099e+04 -5.191102e+03
19 -3.634238e+04 -2.821099e+04
20 -2.254468e+04 -3.634238e+04
21 -2.620336e+04 -2.254468e+04
22 3.599526e+04 -2.620336e+04
23 -6.800810e+03 3.599526e+04
24 -1.881872e+04 -6.800810e+03
25 3.413752e+04 -1.881872e+04
26 -4.797142e+04 3.413752e+04
27 -1.757637e+04 -4.797142e+04
28 9.364257e+04 -1.757637e+04
29 5.802962e+04 9.364257e+04
30 1.725322e+04 5.802962e+04
31 3.769961e+04 1.725322e+04
32 -3.881211e+04 3.769961e+04
33 -6.756652e+04 -3.881211e+04
34 3.715564e+04 -6.756652e+04
35 2.540160e+04 3.715564e+04
36 -2.325119e+04 2.540160e+04
37 -1.363973e+03 -2.325119e+04
38 7.405490e+03 -1.363973e+03
39 -3.637510e+03 7.405490e+03
40 8.069137e+03 -3.637510e+03
41 -6.598534e+03 8.069137e+03
42 -1.596839e+04 -6.598534e+03
43 -2.295605e+04 -1.596839e+04
44 -1.768968e+04 -2.295605e+04
45 -1.807284e+02 -1.768968e+04
46 1.304409e+04 -1.807284e+02
47 4.376422e+03 1.304409e+04
48 6.631880e+03 4.376422e+03
49 -7.547609e+03 6.631880e+03
50 -1.638313e+04 -7.547609e+03
51 7.330949e+04 -1.638313e+04
52 3.107194e+04 7.330949e+04
53 2.171512e+04 3.107194e+04
54 9.783177e+03 2.171512e+04
55 2.403444e+04 9.783177e+03
56 -3.185034e+03 2.403444e+04
57 -1.419208e+04 -3.185034e+03
58 2.361221e+02 -1.419208e+04
59 7.057389e+03 2.361221e+02
60 -2.570742e+04 7.057389e+03
61 -8.154316e+03 -2.570742e+04
62 -2.847261e+03 -8.154316e+03
63 3.457939e+04 -2.847261e+03
64 -6.258510e+03 3.457939e+04
65 -5.873354e+03 -6.258510e+03
66 -3.000924e+04 -5.873354e+03
67 -8.451007e+02 -3.000924e+04
68 -7.859071e+02 -8.451007e+02
69 1.219256e+03 -7.859071e+02
70 -7.185542e+03 1.219256e+03
71 -3.781786e+04 -7.185542e+03
72 -3.450091e+04 -3.781786e+04
73 -2.687789e+04 -3.450091e+04
74 -1.699124e+03 -2.687789e+04
75 -1.479733e+04 -1.699124e+03
76 -2.081718e+04 -1.479733e+04
77 -5.310251e+03 -2.081718e+04
78 -3.398415e+04 -5.310251e+03
79 -4.603110e+04 -3.398415e+04
80 7.531486e+04 -4.603110e+04
81 -1.189063e+04 7.531486e+04
82 -2.204762e+00 -1.189063e+04
83 -2.408096e+04 -2.204762e+00
84 1.612199e+04 -2.408096e+04
85 -4.561170e+04 1.612199e+04
86 -4.529069e+04 -4.561170e+04
87 -2.019951e+04 -4.529069e+04
88 -4.996185e+03 -2.019951e+04
89 -5.877713e+01 -4.996185e+03
90 -3.016895e+04 -5.877713e+01
91 1.650058e+04 -3.016895e+04
92 -3.415573e+02 1.650058e+04
93 -3.279662e+04 -3.415573e+02
94 2.744943e+04 -3.279662e+04
95 -4.972917e+03 2.744943e+04
96 -1.882632e+04 -4.972917e+03
97 1.087846e+04 -1.882632e+04
98 6.796393e+03 1.087846e+04
99 -7.631598e+03 6.796393e+03
100 -3.071296e+04 -7.631598e+03
101 1.724516e+03 -3.071296e+04
102 -1.978846e+03 1.724516e+03
103 -1.946349e+04 -1.978846e+03
104 -6.291832e+03 -1.946349e+04
105 -1.419999e+04 -6.291832e+03
106 9.897378e+02 -1.419999e+04
107 -2.290061e+04 9.897378e+02
108 -2.621359e+04 -2.290061e+04
109 -1.763724e+03 -2.621359e+04
110 -3.270146e+04 -1.763724e+03
111 -3.594019e+04 -3.270146e+04
112 3.699545e+04 -3.594019e+04
113 -3.412142e+04 3.699545e+04
114 7.160471e+03 -3.412142e+04
115 1.045031e+03 7.160471e+03
116 5.882934e+03 1.045031e+03
117 -1.058951e+04 5.882934e+03
118 -7.161410e+03 -1.058951e+04
119 -2.160337e+04 -7.161410e+03
120 6.194685e+03 -2.160337e+04
121 -1.658269e+04 6.194685e+03
122 -1.451605e+03 -1.658269e+04
123 2.800822e+04 -1.451605e+03
124 7.580138e+03 2.800822e+04
125 -8.342761e+03 7.580138e+03
126 9.643317e+03 -8.342761e+03
127 3.014444e+03 9.643317e+03
128 1.019972e+04 3.014444e+03
129 -1.027247e+04 1.019972e+04
130 8.157295e+03 -1.027247e+04
131 2.125113e+04 8.157295e+03
132 -3.473099e+04 2.125113e+04
133 2.081711e+04 -3.473099e+04
134 -6.515315e+04 2.081711e+04
135 8.203819e+03 -6.515315e+04
136 -6.945146e+03 8.203819e+03
137 1.172513e+05 -6.945146e+03
138 -4.507357e+03 1.172513e+05
139 7.797025e+03 -4.507357e+03
140 2.644492e+04 7.797025e+03
141 4.363454e+04 2.644492e+04
142 -3.451862e+04 4.363454e+04
143 4.165789e+03 -3.451862e+04
144 5.025550e+04 4.165789e+03
145 2.252267e+04 5.025550e+04
146 -2.670785e+04 2.252267e+04
147 9.838796e+03 -2.670785e+04
148 2.519498e+03 9.838796e+03
149 -4.056322e+04 2.519498e+03
150 -3.190287e+04 -4.056322e+04
151 4.382789e+03 -3.190287e+04
152 1.552119e+05 4.382789e+03
153 3.310815e+04 1.552119e+05
154 -4.258012e+04 3.310815e+04
155 -7.665275e+04 -4.258012e+04
156 -3.285691e+04 -7.665275e+04
157 -1.341506e+03 -3.285691e+04
158 6.358381e+03 -1.341506e+03
159 1.498531e+04 6.358381e+03
160 2.508326e+04 1.498531e+04
161 -8.473101e+02 2.508326e+04
162 2.944010e+04 -8.473101e+02
163 2.492696e+04 2.944010e+04
164 -1.943278e+04 2.492696e+04
165 6.065934e+04 -1.943278e+04
166 6.458690e+04 6.065934e+04
167 1.077224e+05 6.458690e+04
168 1.383112e+04 1.077224e+05
169 2.015313e+04 1.383112e+04
170 -4.524615e+03 2.015313e+04
171 5.708981e+04 -4.524615e+03
172 4.883378e+03 5.708981e+04
173 -3.705360e+03 4.883378e+03
174 8.320397e+03 -3.705360e+03
175 -1.151915e+04 8.320397e+03
176 1.294468e+05 -1.151915e+04
177 -3.403598e+03 1.294468e+05
178 -1.672833e+04 -3.403598e+03
179 1.120016e+04 -1.672833e+04
180 -2.046066e+04 1.120016e+04
181 -1.029174e+04 -2.046066e+04
182 -1.971143e+04 -1.029174e+04
183 -2.595694e+04 -1.971143e+04
184 2.119916e+03 -2.595694e+04
185 -9.555407e+03 2.119916e+03
186 5.685730e+04 -9.555407e+03
187 8.186219e+03 5.685730e+04
188 -3.555107e+04 8.186219e+03
189 -1.922408e+04 -3.555107e+04
190 2.380704e+04 -1.922408e+04
191 1.427306e+04 2.380704e+04
192 -2.771842e+04 1.427306e+04
193 1.904882e+04 -2.771842e+04
194 4.151739e+04 1.904882e+04
195 5.499659e+04 4.151739e+04
196 1.659145e+04 5.499659e+04
197 -6.890003e+04 1.659145e+04
198 -3.540235e+04 -6.890003e+04
199 -3.849095e+04 -3.540235e+04
200 -9.807019e+04 -3.849095e+04
201 1.414544e+04 -9.807019e+04
202 1.201766e+03 1.414544e+04
203 2.960346e+04 1.201766e+03
204 8.801559e+03 2.960346e+04
205 -5.031232e+04 8.801559e+03
206 4.625649e+04 -5.031232e+04
207 3.453035e+04 4.625649e+04
208 -6.928513e+04 3.453035e+04
209 -4.906376e+04 -6.928513e+04
210 3.499903e+03 -4.906376e+04
211 1.111065e+03 3.499903e+03
212 8.816152e+04 1.111065e+03
213 3.327457e+04 8.816152e+04
214 -2.698627e+04 3.327457e+04
215 -2.887196e+04 -2.698627e+04
216 4.019521e+04 -2.887196e+04
217 -3.112290e+04 4.019521e+04
218 5.879881e+04 -3.112290e+04
219 5.204454e+04 5.879881e+04
220 -1.375785e+05 5.204454e+04
221 4.238815e+04 -1.375785e+05
222 -5.984992e+04 4.238815e+04
223 2.759408e+04 -5.984992e+04
224 2.639431e+04 2.759408e+04
225 7.135340e+04 2.639431e+04
226 1.739813e+03 7.135340e+04
227 -5.578177e+03 1.739813e+03
228 -3.926204e+04 -5.578177e+03
229 -9.673819e+03 -3.926204e+04
230 1.580369e+04 -9.673819e+03
231 -9.212343e+03 1.580369e+04
232 -5.946912e+03 -9.212343e+03
233 -4.267388e+04 -5.946912e+03
234 -1.254878e+04 -4.267388e+04
235 -4.988667e+03 -1.254878e+04
236 -6.068900e+04 -4.988667e+03
237 3.468024e+03 -6.068900e+04
238 -3.950031e+04 3.468024e+03
239 -5.911297e+04 -3.950031e+04
240 -1.172387e+05 -5.911297e+04
241 6.444122e+04 -1.172387e+05
242 -1.435107e+04 6.444122e+04
243 -1.227013e+04 -1.435107e+04
244 -5.673419e+04 -1.227013e+04
245 -6.932002e+03 -5.673419e+04
246 -8.758730e+03 -6.932002e+03
247 7.015854e+04 -8.758730e+03
248 -9.962268e+03 7.015854e+04
249 5.609716e+04 -9.962268e+03
250 -2.271955e+04 5.609716e+04
251 1.596217e+04 -2.271955e+04
252 1.786797e+04 1.596217e+04
253 -7.774411e+03 1.786797e+04
254 -1.031055e+04 -7.774411e+03
255 -2.378161e+04 -1.031055e+04
256 2.656643e+04 -2.378161e+04
257 5.914787e+04 2.656643e+04
258 3.268053e+04 5.914787e+04
259 7.795383e+04 3.268053e+04
260 -1.446570e+04 7.795383e+04
261 2.749789e+04 -1.446570e+04
262 2.821596e+04 2.749789e+04
263 -1.200564e+04 2.821596e+04
264 -8.923117e+04 -1.200564e+04
265 7.585608e+04 -8.923117e+04
266 -1.065877e+04 7.585608e+04
267 4.433588e+04 -1.065877e+04
268 -8.552069e+03 4.433588e+04
269 2.600716e+04 -8.552069e+03
270 1.830256e+03 2.600716e+04
271 2.390356e+03 1.830256e+03
272 7.027204e+04 2.390356e+03
273 -3.463494e+04 7.027204e+04
274 4.902626e+04 -3.463494e+04
275 4.925961e+02 4.902626e+04
276 -1.433191e+05 4.925961e+02
277 -1.926825e+04 -1.433191e+05
278 -5.200821e+04 -1.926825e+04
279 -1.344921e+04 -5.200821e+04
280 3.463389e+04 -1.344921e+04
281 3.259879e+04 3.463389e+04
282 -1.856665e+04 3.259879e+04
283 1.445984e+03 -1.856665e+04
284 -1.280110e+03 1.445984e+03
285 -1.400807e+02 -1.280110e+03
286 2.116956e+04 -1.400807e+02
> 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/7ubkb1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8czst1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/93ake1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wewg1356081533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117oaw1356081533.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/12bthi1356081533.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/13k9cz1356081533.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/14qhya1356081533.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/15ewqu1356081533.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/16atgh1356081533.tab")
+ }
>
> try(system("convert tmp/15ywp1356081533.ps tmp/15ywp1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/28u7n1356081533.ps tmp/28u7n1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dvmn1356081533.ps tmp/3dvmn1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mnkg1356081533.ps tmp/4mnkg1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/58lcl1356081533.ps tmp/58lcl1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g3a21356081533.ps tmp/6g3a21356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ubkb1356081533.ps tmp/7ubkb1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/8czst1356081533.ps tmp/8czst1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/93ake1356081533.ps tmp/93ake1356081533.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wewg1356081533.ps tmp/10wewg1356081533.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.837 1.305 13.132