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.
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+ ,dim=c(4
+ ,289)
+ ,dimnames=list(c('UREN'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'totsize
')
+ ,1:289))
> y <- array(NA,dim=c(4,289),dimnames=list(c('UREN','blogged_computations','compendiums_reviewed','totsize
'),1:289))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
UREN blogged_computations compendiums_reviewed totsize\r
1 58.585278 79 30 112285
2 33.606111 58 28 84786
3 49.030000 60 38 83123
4 49.811389 108 30 101193
5 34.218056 49 22 38361
6 14.651667 0 26 68504
7 107.092778 121 25 119182
8 9.213889 1 18 22807
9 28.234722 20 11 17140
10 41.405833 43 26 116174
11 45.957222 69 25 57635
12 65.892500 78 38 66198
13 48.146111 86 44 71701
14 36.980833 44 30 57793
15 71.909167 104 40 80444
16 50.023056 63 34 53855
17 90.221944 158 47 97668
18 64.156667 102 30 133824
19 65.773611 77 31 101481
20 37.631389 82 23 99645
21 56.368056 115 36 114789
22 59.763056 101 36 99052
23 95.638056 80 30 67654
24 42.759722 50 25 65553
25 36.928611 83 39 97500
26 48.534444 123 34 69112
27 48.448611 73 31 82753
28 62.652222 81 31 85323
29 62.120000 105 33 72654
30 34.671389 47 25 30727
31 61.582778 105 33 77873
32 58.546389 94 35 117478
33 47.296111 44 42 74007
34 72.378056 114 43 90183
35 23.570278 38 30 61542
36 81.784444 107 33 101494
37 28.058611 30 13 27570
38 59.900278 71 32 55813
39 90.307500 84 36 79215
40 1.993333 0 0 1423
41 46.539444 59 28 55461
42 29.557778 33 14 31081
43 26.822222 42 17 22996
44 73.824722 96 32 83122
45 74.903056 106 30 70106
46 41.420000 56 35 60578
47 48.840000 57 20 39992
48 42.464167 59 28 79892
49 31.018056 39 28 49810
50 32.335556 34 39 71570
51 100.639167 76 34 100708
52 21.888889 20 26 33032
53 50.879722 91 39 82875
54 77.212500 115 39 139077
55 41.841389 85 33 71595
56 46.891389 76 28 72260
57 6.718889 8 4 5950
58 91.463056 79 39 115762
59 18.063611 21 18 32551
60 28.082500 30 14 31701
61 60.818333 76 29 80670
62 67.792222 101 44 143558
63 94.880556 94 21 117105
64 28.776944 27 16 23789
65 64.813333 92 28 120733
66 71.239444 123 35 105195
67 57.266944 75 28 73107
68 86.520278 128 38 132068
69 65.500000 105 23 149193
70 49.427500 55 36 46821
71 57.548889 56 32 87011
72 54.598056 41 29 95260
73 48.384444 72 25 55183
74 39.790556 67 27 106671
75 52.099722 75 36 73511
76 52.133611 114 28 92945
77 33.060000 118 23 78664
78 50.608889 77 40 70054
79 20.435000 22 23 22618
80 54.160833 66 40 74011
81 46.524444 69 28 83737
82 39.932222 105 34 69094
83 76.539167 116 33 93133
84 67.555278 88 28 95536
85 50.833056 73 34 225920
86 37.680278 99 30 62133
87 42.305278 62 33 61370
88 33.394722 53 22 43836
89 96.245833 118 38 106117
90 40.497222 30 26 38692
91 53.705278 100 35 84651
92 22.486944 49 8 56622
93 34.103889 24 24 15986
94 36.273611 67 29 95364
95 31.280833 46 20 26706
96 79.574444 57 29 89691
97 66.962778 75 45 67267
98 41.235000 135 37 126846
99 56.864722 68 33 41140
100 50.577500 124 33 102860
101 38.984444 33 25 51715
102 61.254444 98 32 55801
103 67.516667 58 29 111813
104 45.212500 68 28 120293
105 50.725833 81 28 138599
106 64.482778 131 31 161647
107 73.699444 110 52 115929
108 23.770556 37 21 24266
109 86.344167 130 24 162901
110 62.516667 93 41 109825
111 64.532500 118 33 129838
112 40.268333 39 32 37510
113 12.024167 13 19 43750
114 43.265000 74 20 40652
115 45.752500 81 31 87771
116 56.094444 109 31 85872
117 65.403889 151 32 89275
118 61.333611 51 18 44418
119 27.629444 28 23 192565
120 25.739167 40 17 35232
121 37.035556 56 20 40909
122 17.044722 27 12 13294
123 34.980556 37 17 32387
124 27.986111 83 30 140867
125 62.374722 54 31 120662
126 22.865556 27 10 21233
127 28.336111 28 13 44332
128 28.200833 59 22 61056
129 67.641944 133 42 101338
130 6.371667 12 1 1168
131 11.546111 0 9 13497
132 42.353889 106 32 65567
133 17.182500 23 11 25162
134 27.756389 44 25 32334
135 36.801944 71 36 40735
136 88.165000 116 31 91413
137 5.848333 4 0 855
138 58.233611 62 24 97068
139 6.291111 12 13 44339
140 8.726111 18 8 14116
141 12.971667 14 13 10288
142 36.582778 60 19 65622
143 25.481944 7 18 16563
144 67.985833 98 33 76643
145 51.252778 64 40 110681
146 22.184167 29 22 29011
147 35.673056 32 38 92696
148 27.177500 25 24 94785
149 10.615000 16 8 8773
150 41.972500 48 35 83209
151 75.682778 100 43 93815
152 47.915000 46 43 86687
153 30.011944 45 14 34553
154 91.140833 129 41 105547
155 69.605278 130 38 103487
156 97.518611 136 45 213688
157 43.893056 59 31 71220
158 27.462778 25 13 23517
159 23.733056 32 28 56926
160 63.678333 63 31 91721
161 97.671944 95 40 115168
162 23.390833 14 30 111194
163 33.456944 36 16 51009
164 90.166111 113 37 135777
165 36.408056 47 30 51513
166 56.741944 92 35 74163
167 45.984167 70 32 51633
168 39.367222 19 27 75345
169 32.235556 50 20 33416
170 69.457500 41 18 83305
171 83.270833 91 31 98952
172 54.399444 111 31 102372
173 48.127778 41 21 37238
174 70.691111 120 39 103772
175 28.996944 135 41 123969
176 37.801111 27 13 27142
177 55.410000 87 32 135400
178 25.694167 25 18 21399
179 62.313889 131 39 130115
180 37.716944 45 14 24874
181 20.668889 29 7 34988
182 22.566667 58 17 45549
183 4.080000 4 0 6023
184 50.453611 47 30 64466
185 75.515556 109 37 54990
186 1.999722 7 0 1644
187 12.961111 12 5 6179
188 4.874167 0 1 3926
189 37.046667 37 16 32755
190 26.451944 37 32 34777
191 42.389167 46 24 73224
192 27.262778 15 17 27114
193 22.116389 42 11 20760
194 16.442778 7 24 37636
195 38.872778 54 22 65461
196 32.947778 54 12 30080
197 20.244444 14 19 24094
198 18.187500 16 13 69008
199 27.678611 33 17 54968
200 19.990278 32 15 46090
201 21.464444 21 16 27507
202 13.691389 15 24 10672
203 37.536389 38 15 34029
204 30.123889 22 17 46300
205 24.929444 28 18 24760
206 12.304444 10 20 18779
207 21.568889 31 16 21280
208 50.424444 32 16 40662
209 37.227500 32 18 28987
210 34.462222 43 22 22827
211 25.730556 27 8 18513
212 33.846667 37 17 30594
213 14.698611 20 18 24006
214 22.742222 32 16 27913
215 16.383611 0 23 42744
216 14.865278 5 22 12934
217 16.892222 26 13 22574
218 15.659722 10 13 41385
219 18.191667 27 16 18653
220 22.485833 11 16 18472
221 21.195000 29 20 30976
222 28.891944 25 22 63339
223 27.251111 55 17 25568
224 18.885833 23 18 33747
225 8.608056 5 17 4154
226 37.627222 43 12 19474
227 20.417778 23 7 35130
228 17.534167 34 17 39067
229 17.015000 36 14 13310
230 20.809444 35 23 65892
231 8.826111 0 17 4143
232 22.621389 37 14 28579
233 24.218333 28 15 51776
234 13.913889 16 17 21152
235 18.262500 26 21 38084
236 15.736944 38 18 27717
237 43.999722 23 18 32928
238 12.904167 22 17 11342
239 20.451111 30 17 19499
240 10.665278 16 16 16380
241 25.527500 18 15 36874
242 38.757222 28 21 48259
243 14.490000 32 16 16734
244 14.324167 21 14 28207
245 19.597500 23 15 30143
246 23.571111 29 17 41369
247 28.482778 50 15 45833
248 24.077222 12 15 29156
249 23.808056 21 10 35944
250 9.628333 18 6 36278
251 41.827778 27 22 45588
252 27.669722 41 21 45097
253 5.374722 13 1 3895
254 27.603611 12 18 28394
255 23.952778 21 17 18632
256 8.565833 8 4 2325
257 8.807222 26 10 25139
258 24.946111 27 16 27975
259 17.246667 13 16 14483
260 11.153056 16 9 13127
261 7.676111 2 16 5839
262 21.386111 42 17 24069
263 10.405556 5 7 3738
264 15.043611 37 15 18625
265 13.850556 17 14 36341
266 23.426944 38 14 24548
267 17.826389 37 18 21792
268 16.495000 29 12 26263
269 33.141111 32 16 23686
270 21.306111 35 21 49303
271 28.729167 17 19 25659
272 19.540000 20 16 28904
273 12.058333 7 1 2781
274 29.121667 46 16 29236
275 17.281944 24 10 19546
276 19.251111 40 19 22818
277 14.754722 3 12 32689
278 5.490000 10 2 5752
279 24.077778 37 14 22197
280 23.362500 17 17 20055
281 21.651389 28 19 25272
282 24.753611 19 14 82206
283 25.279167 29 11 32073
284 11.180000 8 4 5444
285 17.829722 10 16 20154
286 14.126944 15 20 36944
287 15.725833 15 12 8019
288 17.442222 28 15 30884
289 20.148611 17 16 19540
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) blogged_computations compendiums_reviewed
4.083548 0.318850 0.492732
`totsize\\r`
0.000106
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-51.475 -6.234 -1.660 5.081 44.894
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.084e+00 1.692e+00 2.413 0.016437 *
blogged_computations 3.189e-01 3.250e-02 9.811 < 2e-16 ***
compendiums_reviewed 4.927e-01 1.083e-01 4.549 7.97e-06 ***
`totsize\\r` 1.060e-04 2.829e-05 3.747 0.000217 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.72 on 285 degrees of freedom
Multiple R-squared: 0.7402, Adjusted R-squared: 0.7375
F-statistic: 270.7 on 3 and 285 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,] 0.9633273 7.334541e-02 3.667270e-02
[2,] 0.9257279 1.485443e-01 7.427213e-02
[3,] 0.8768804 2.462392e-01 1.231196e-01
[4,] 0.8200441 3.599118e-01 1.799559e-01
[5,] 0.7383030 5.233941e-01 2.616970e-01
[6,] 0.8112905 3.774190e-01 1.887095e-01
[7,] 0.7594717 4.810566e-01 2.405283e-01
[8,] 0.6868835 6.262331e-01 3.131165e-01
[9,] 0.6200817 7.598367e-01 3.799183e-01
[10,] 0.5574093 8.851814e-01 4.425907e-01
[11,] 0.4975217 9.950434e-01 5.024783e-01
[12,] 0.4600968 9.201937e-01 5.399032e-01
[13,] 0.4376141 8.752281e-01 5.623859e-01
[14,] 0.6120534 7.758932e-01 3.879466e-01
[15,] 0.6763612 6.472777e-01 3.236388e-01
[16,] 0.6224309 7.551381e-01 3.775691e-01
[17,] 0.9700739 5.985224e-02 2.992612e-02
[18,] 0.9581349 8.373013e-02 4.186507e-02
[19,] 0.9710654 5.786914e-02 2.893457e-02
[20,] 0.9904115 1.917696e-02 9.588478e-03
[21,] 0.9861807 2.763861e-02 1.381930e-02
[22,] 0.9827731 3.445381e-02 1.722691e-02
[23,] 0.9761684 4.766321e-02 2.383160e-02
[24,] 0.9675975 6.480504e-02 3.240252e-02
[25,] 0.9570187 8.596255e-02 4.298127e-02
[26,] 0.9441225 1.117549e-01 5.587747e-02
[27,] 0.9387014 1.225971e-01 6.129856e-02
[28,] 0.9223384 1.553233e-01 7.766164e-02
[29,] 0.9193413 1.613174e-01 8.065870e-02
[30,] 0.9272756 1.454487e-01 7.272436e-02
[31,] 0.9087922 1.824156e-01 9.120778e-02
[32,] 0.9046413 1.907174e-01 9.535870e-02
[33,] 0.9805305 3.893892e-02 1.946946e-02
[34,] 0.9775209 4.495829e-02 2.247914e-02
[35,] 0.9707514 5.849715e-02 2.924858e-02
[36,] 0.9622262 7.554764e-02 3.777382e-02
[37,] 0.9535637 9.287251e-02 4.643625e-02
[38,] 0.9528895 9.422097e-02 4.711048e-02
[39,] 0.9493447 1.013107e-01 5.065533e-02
[40,] 0.9369971 1.260059e-01 6.300293e-02
[41,] 0.9299416 1.401169e-01 7.005844e-02
[42,] 0.9142877 1.714246e-01 8.571230e-02
[43,] 0.8971648 2.056705e-01 1.028352e-01
[44,] 0.8797056 2.405888e-01 1.202944e-01
[45,] 0.9940341 1.193187e-02 5.965935e-03
[46,] 0.9921493 1.570142e-02 7.850710e-03
[47,] 0.9916068 1.678635e-02 8.393177e-03
[48,] 0.9889503 2.209941e-02 1.104971e-02
[49,] 0.9902236 1.955285e-02 9.776423e-03
[50,] 0.9877638 2.447239e-02 1.223619e-02
[51,] 0.9849806 3.003879e-02 1.501939e-02
[52,] 0.9964427 7.114664e-03 3.557332e-03
[53,] 0.9954115 9.177044e-03 4.588522e-03
[54,] 0.9939692 1.206169e-02 6.030843e-03
[55,] 0.9928776 1.424489e-02 7.122443e-03
[56,] 0.9914190 1.716195e-02 8.580977e-03
[57,] 0.9984559 3.088292e-03 1.544146e-03
[58,] 0.9979859 4.028297e-03 2.014148e-03
[59,] 0.9974259 5.148169e-03 2.574085e-03
[60,] 0.9968412 6.317583e-03 3.158792e-03
[61,] 0.9960796 7.840718e-03 3.920359e-03
[62,] 0.9952936 9.412861e-03 4.706431e-03
[63,] 0.9949850 1.003003e-02 5.015017e-03
[64,] 0.9939767 1.204652e-02 6.023260e-03
[65,] 0.9935483 1.290335e-02 6.451673e-03
[66,] 0.9936621 1.267576e-02 6.337879e-03
[67,] 0.9918799 1.624014e-02 8.120070e-03
[68,] 0.9922839 1.543223e-02 7.716117e-03
[69,] 0.9900850 1.982993e-02 9.914966e-03
[70,] 0.9920044 1.599126e-02 7.995630e-03
[71,] 0.9983734 3.253165e-03 1.626582e-03
[72,] 0.9979053 4.189477e-03 2.094739e-03
[73,] 0.9972958 5.408388e-03 2.704194e-03
[74,] 0.9964460 7.107905e-03 3.553952e-03
[75,] 0.9954686 9.062800e-03 4.531400e-03
[76,] 0.9975266 4.946870e-03 2.473435e-03
[77,] 0.9971925 5.615033e-03 2.807516e-03
[78,] 0.9969937 6.012553e-03 3.006277e-03
[79,] 0.9982258 3.548397e-03 1.774198e-03
[80,] 0.9989451 2.109741e-03 1.054870e-03
[81,] 0.9986283 2.743328e-03 1.371664e-03
[82,] 0.9982163 3.567464e-03 1.783732e-03
[83,] 0.9992817 1.436545e-03 7.182723e-04
[84,] 0.9992102 1.579503e-03 7.897516e-04
[85,] 0.9990926 1.814731e-03 9.073656e-04
[86,] 0.9989594 2.081250e-03 1.040625e-03
[87,] 0.9988095 2.380928e-03 1.190464e-03
[88,] 0.9989193 2.161383e-03 1.080691e-03
[89,] 0.9985568 2.886433e-03 1.443216e-03
[90,] 0.9998211 3.577044e-04 1.788522e-04
[91,] 0.9998047 3.906925e-04 1.953463e-04
[92,] 0.9999918 1.648890e-05 8.244452e-06
[93,] 0.9999911 1.785267e-05 8.926334e-06
[94,] 0.9999956 8.884210e-06 4.442105e-06
[95,] 0.9999941 1.184290e-05 5.921452e-06
[96,] 0.9999917 1.652321e-05 8.261606e-06
[97,] 0.9999954 9.242968e-06 4.621484e-06
[98,] 0.9999941 1.175476e-05 5.877380e-06
[99,] 0.9999926 1.470804e-05 7.354018e-06
[100,] 0.9999934 1.328948e-05 6.644742e-06
[101,] 0.9999904 1.923762e-05 9.618808e-06
[102,] 0.9999871 2.579673e-05 1.289837e-05
[103,] 0.9999872 2.555532e-05 1.277766e-05
[104,] 0.9999817 3.652449e-05 1.826224e-05
[105,] 0.9999765 4.703084e-05 2.351542e-05
[106,] 0.9999677 6.456489e-05 3.228244e-05
[107,] 0.9999660 6.803971e-05 3.401985e-05
[108,] 0.9999517 9.654824e-05 4.827412e-05
[109,] 0.9999429 1.141246e-04 5.706231e-05
[110,] 0.9999281 1.438702e-04 7.193509e-05
[111,] 0.9999277 1.446899e-04 7.234493e-05
[112,] 0.9999843 3.132663e-05 1.566331e-05
[113,] 0.9999907 1.850725e-05 9.253624e-06
[114,] 0.9999871 2.586945e-05 1.293473e-05
[115,] 0.9999813 3.732450e-05 1.866225e-05
[116,] 0.9999742 5.162130e-05 2.581065e-05
[117,] 0.9999675 6.494151e-05 3.247075e-05
[118,] 0.9999981 3.764178e-06 1.882089e-06
[119,] 0.9999982 3.671061e-06 1.835531e-06
[120,] 0.9999973 5.403397e-06 2.701699e-06
[121,] 0.9999961 7.820873e-06 3.910437e-06
[122,] 0.9999964 7.256887e-06 3.628443e-06
[123,] 0.9999959 8.128161e-06 4.064080e-06
[124,] 0.9999942 1.160523e-05 5.802613e-06
[125,] 0.9999915 1.691928e-05 8.459638e-06
[126,] 0.9999953 9.371661e-06 4.685831e-06
[127,] 0.9999933 1.345890e-05 6.729449e-06
[128,] 0.9999911 1.781234e-05 8.906169e-06
[129,] 0.9999910 1.797880e-05 8.989398e-06
[130,] 0.9999967 6.635661e-06 3.317831e-06
[131,] 0.9999951 9.828142e-06 4.914071e-06
[132,] 0.9999951 9.811005e-06 4.905502e-06
[133,] 0.9999958 8.416433e-06 4.208217e-06
[134,] 0.9999946 1.079116e-05 5.395581e-06
[135,] 0.9999923 1.546216e-05 7.731081e-06
[136,] 0.9999891 2.186850e-05 1.093425e-05
[137,] 0.9999874 2.514843e-05 1.257421e-05
[138,] 0.9999852 2.962176e-05 1.481088e-05
[139,] 0.9999799 4.024987e-05 2.012494e-05
[140,] 0.9999728 5.443902e-05 2.721951e-05
[141,] 0.9999663 6.730285e-05 3.365143e-05
[142,] 0.9999603 7.940902e-05 3.970451e-05
[143,] 0.9999453 1.093132e-04 5.465661e-05
[144,] 0.9999256 1.488948e-04 7.444742e-05
[145,] 0.9999165 1.670369e-04 8.351843e-05
[146,] 0.9998829 2.341095e-04 1.170547e-04
[147,] 0.9998372 3.256524e-04 1.628262e-04
[148,] 0.9998808 2.383041e-04 1.191520e-04
[149,] 0.9998417 3.165333e-04 1.582666e-04
[150,] 0.9997895 4.210215e-04 2.105107e-04
[151,] 0.9997111 5.777521e-04 2.888760e-04
[152,] 0.9996440 7.119719e-04 3.559859e-04
[153,] 0.9996270 7.459820e-04 3.729910e-04
[154,] 0.9996886 6.227038e-04 3.113519e-04
[155,] 0.9999771 4.576605e-05 2.288302e-05
[156,] 0.9999818 3.640790e-05 1.820395e-05
[157,] 0.9999749 5.024912e-05 2.512456e-05
[158,] 0.9999891 2.189695e-05 1.094848e-05
[159,] 0.9999840 3.193549e-05 1.596774e-05
[160,] 0.9999776 4.479504e-05 2.239752e-05
[161,] 0.9999684 6.321353e-05 3.160677e-05
[162,] 0.9999612 7.769866e-05 3.884933e-05
[163,] 0.9999445 1.109972e-04 5.549862e-05
[164,] 0.9999987 2.506967e-06 1.253484e-06
[165,] 1.0000000 9.460124e-08 4.730062e-08
[166,] 0.9999999 1.331074e-07 6.655368e-08
[167,] 1.0000000 4.346300e-08 2.173150e-08
[168,] 1.0000000 4.908674e-08 2.454337e-08
[169,] 1.0000000 8.367739e-13 4.183870e-13
[170,] 1.0000000 2.237062e-13 1.118531e-13
[171,] 1.0000000 3.804879e-13 1.902439e-13
[172,] 1.0000000 6.934349e-13 3.467174e-13
[173,] 1.0000000 1.239825e-13 6.199126e-14
[174,] 1.0000000 1.141113e-13 5.705567e-14
[175,] 1.0000000 2.330708e-13 1.165354e-13
[176,] 1.0000000 8.937278e-14 4.468639e-14
[177,] 1.0000000 1.812949e-13 9.064746e-14
[178,] 1.0000000 1.750911e-13 8.754557e-14
[179,] 1.0000000 7.559650e-14 3.779825e-14
[180,] 1.0000000 1.258091e-13 6.290453e-14
[181,] 1.0000000 2.529540e-13 1.264770e-13
[182,] 1.0000000 5.195727e-13 2.597864e-13
[183,] 1.0000000 3.959381e-13 1.979690e-13
[184,] 1.0000000 6.174988e-13 3.087494e-13
[185,] 1.0000000 1.062105e-12 5.310523e-13
[186,] 1.0000000 1.201499e-12 6.007495e-13
[187,] 1.0000000 2.355531e-12 1.177766e-12
[188,] 1.0000000 4.160587e-12 2.080294e-12
[189,] 1.0000000 8.378632e-12 4.189316e-12
[190,] 1.0000000 1.508320e-11 7.541601e-12
[191,] 1.0000000 2.950234e-11 1.475117e-11
[192,] 1.0000000 4.255835e-11 2.127918e-11
[193,] 1.0000000 8.318434e-11 4.159217e-11
[194,] 1.0000000 1.160628e-10 5.803142e-11
[195,] 1.0000000 2.248085e-10 1.124043e-10
[196,] 1.0000000 3.532603e-10 1.766301e-10
[197,] 1.0000000 2.474879e-10 1.237440e-10
[198,] 1.0000000 3.657206e-10 1.828603e-10
[199,] 1.0000000 6.794805e-10 3.397403e-10
[200,] 1.0000000 1.051239e-09 5.256193e-10
[201,] 1.0000000 1.997308e-09 9.986542e-10
[202,] 1.0000000 1.834332e-11 9.171661e-12
[203,] 1.0000000 6.875563e-12 3.437781e-12
[204,] 1.0000000 8.130955e-12 4.065478e-12
[205,] 1.0000000 8.190545e-12 4.095272e-12
[206,] 1.0000000 6.823110e-12 3.411555e-12
[207,] 1.0000000 1.097848e-11 5.489242e-12
[208,] 1.0000000 2.335513e-11 1.167756e-11
[209,] 1.0000000 4.166897e-11 2.083448e-11
[210,] 1.0000000 8.416908e-11 4.208454e-11
[211,] 1.0000000 1.675959e-10 8.379795e-11
[212,] 1.0000000 3.183636e-10 1.591818e-10
[213,] 1.0000000 6.344778e-10 3.172389e-10
[214,] 1.0000000 9.473124e-10 4.736562e-10
[215,] 1.0000000 1.801723e-09 9.008616e-10
[216,] 1.0000000 3.666680e-09 1.833340e-09
[217,] 1.0000000 7.174948e-09 3.587474e-09
[218,] 1.0000000 1.286365e-08 6.431824e-09
[219,] 1.0000000 1.999768e-08 9.998842e-09
[220,] 1.0000000 2.686172e-09 1.343086e-09
[221,] 1.0000000 5.010112e-09 2.505056e-09
[222,] 1.0000000 6.400808e-09 3.200404e-09
[223,] 1.0000000 1.242744e-08 6.213718e-09
[224,] 1.0000000 5.774308e-09 2.887154e-09
[225,] 1.0000000 9.135014e-09 4.567507e-09
[226,] 1.0000000 1.864724e-08 9.323622e-09
[227,] 1.0000000 3.791390e-08 1.895695e-08
[228,] 1.0000000 5.610118e-08 2.805059e-08
[229,] 1.0000000 5.719475e-08 2.859738e-08
[230,] 1.0000000 5.508622e-08 2.754311e-08
[231,] 1.0000000 8.407517e-10 4.203759e-10
[232,] 1.0000000 1.225375e-09 6.126876e-10
[233,] 1.0000000 2.817013e-09 1.408507e-09
[234,] 1.0000000 2.782753e-09 1.391377e-09
[235,] 1.0000000 5.178761e-09 2.589380e-09
[236,] 1.0000000 2.294107e-09 1.147053e-09
[237,] 1.0000000 2.974851e-09 1.487426e-09
[238,] 1.0000000 4.743892e-09 2.371946e-09
[239,] 1.0000000 1.115982e-08 5.579910e-09
[240,] 1.0000000 2.623947e-08 1.311974e-08
[241,] 1.0000000 5.701166e-08 2.850583e-08
[242,] 1.0000000 9.470399e-08 4.735200e-08
[243,] 0.9999999 1.275306e-07 6.376529e-08
[244,] 0.9999999 2.144321e-07 1.072160e-07
[245,] 1.0000000 1.441051e-08 7.205254e-09
[246,] 1.0000000 3.671126e-08 1.835563e-08
[247,] 1.0000000 7.811165e-08 3.905582e-08
[248,] 1.0000000 5.583792e-08 2.791896e-08
[249,] 1.0000000 9.224648e-08 4.612324e-08
[250,] 0.9999999 2.340378e-07 1.170189e-07
[251,] 0.9999999 1.249486e-07 6.247432e-08
[252,] 0.9999999 2.321923e-07 1.160961e-07
[253,] 0.9999997 5.987794e-07 2.993897e-07
[254,] 0.9999994 1.245337e-06 6.226685e-07
[255,] 0.9999992 1.678587e-06 8.392934e-07
[256,] 0.9999980 3.949302e-06 1.974651e-06
[257,] 0.9999951 9.808251e-06 4.904125e-06
[258,] 0.9999948 1.038637e-05 5.193186e-06
[259,] 0.9999918 1.645555e-05 8.227776e-06
[260,] 0.9999791 4.173944e-05 2.086972e-05
[261,] 0.9999733 5.331013e-05 2.665507e-05
[262,] 0.9999506 9.883743e-05 4.941872e-05
[263,] 0.9999869 2.616793e-05 1.308396e-05
[264,] 0.9999812 3.769588e-05 1.884794e-05
[265,] 0.9999944 1.118467e-05 5.592336e-06
[266,] 0.9999810 3.790612e-05 1.895306e-05
[267,] 0.9999483 1.034654e-04 5.173269e-05
[268,] 0.9998816 2.368044e-04 1.184022e-04
[269,] 0.9996250 7.499315e-04 3.749657e-04
[270,] 0.9995306 9.387744e-04 4.693872e-04
[271,] 0.9985531 2.893857e-03 1.446928e-03
[272,] 0.9983878 3.224401e-03 1.612200e-03
[273,] 0.9946113 1.077735e-02 5.388674e-03
[274,] 0.9932989 1.340220e-02 6.701101e-03
[275,] 0.9769704 4.605921e-02 2.302960e-02
[276,] 0.9370958 1.258085e-01 6.290424e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1h0l21352144422.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2rqse1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3km4w1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4l73v1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5yspi1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 289
Frequency = 1
1 2 3 4 5 6
2.62765311 -11.75511741 -1.71995530 -14.21706720 -0.39577136 -9.50478305
7 8 9 10 11 12
39.47597190 -6.47537160 10.53716743 -1.51451587 1.44502280 11.19740308
13 14 15 16 17 18
-12.63953185 -2.04051637 6.42832882 3.39007910 2.24821284 -1.41779180
19 20 21 22 23 24
11.10625157 -14.49374138 -14.29000836 -4.76287764 44.09276470 3.46631683
25 26 27 28 29 30
-23.17168969 -18.84689142 -2.95805432 8.42231756 0.59522468 0.02632286
31 32 33 34 35 36
-0.49524640 -5.20814003 0.64319067 1.19812147 -13.93538941 16.56473599
37 38 39 40 41 42
5.08144100 11.49440125 33.30487567 -2.24106241 3.96801151 4.75913591
43 44 45 46 47 48
-1.46720259 14.55265285 14.80772961 -4.18643909 12.48793720 -2.69711542
49 50 51 52 53 54
-4.57732846 -9.39232941 44.89440597 -4.88429710 -10.22103297 2.50155126
55 56 57 58 59 60
-13.19412020 -2.88130462 -2.51712731 30.70226183 -5.03558362 4.17468477
61 62 63 64 65 66
9.66139211 -5.39349652 38.06380866 5.67894175 4.80057395 -0.45966194
67 68 69 70 71 72
7.72331353 8.90000803 0.78889488 5.10551351 10.61856988 13.05423594
73 74 75 76 77 78
3.17562262 -10.26754991 -1.42858769 -11.94814037 -28.31961345 -5.16158260
79 80 81 82 83 84
-4.39373834 1.47824626 -2.23293592 -21.70790087 9.33612773 11.48896971
85 86 87 88 89 90
-17.22846263 -19.33790512 -4.31275708 -3.07489230 24.56504548 9.93553585
91 92 93 94 95 96
-8.48247145 -7.16442891 8.84775479 -13.57133992 -0.15547190 33.51937188
97 98 99 100 101 102
9.66178973 -37.57092315 10.47810092 -20.20746797 6.57841386 4.24088301
103 104 105 106 107 108
18.79766411 -7.10121024 -7.67348853 -13.78049353 -3.36892040 -5.03017259
109 110 111 112 113 114
11.71593400 -3.06413210 -7.19921459 4.00590530 -10.20412633 1.42251840
115 116 117 118 119 120
-8.73690901 -7.12146512 -12.05720378 27.41092737 -17.12791245 -3.20965534
121 122 123 124 125 126
0.90513489 -2.96981423 7.28987333 -32.27679748 13.00760452 2.99489516
127 128 129 130 131 132
4.21975757 -12.00731774 -10.28591752 -2.15463201 1.59720689 -18.24573582
133 134 135 136 137 138
-2.32199114 -6.10247910 -11.97648954 22.12975563 0.39874835 12.26593368
139 140 141 142 143 144
-12.72438034 -6.53498435 -3.07189188 -2.95005543 8.54148810 8.27014917
145 146 147 148 149 150
-4.67937368 -5.06149264 -7.16389445 -6.75070463 -3.44200127 -3.48217417
151 152 153 154 155 156
8.58173105 -1.21251718 1.01904429 14.53492183 -5.62291561 5.24615342
157 158 159 160 161 162
-1.82713123 6.50950403 -10.38472018 14.50950259 31.37978300 -11.72587127
163 164 165 166 167 168
4.60378763 17.42814800 -2.90412298 -1.78320468 -1.65975169 7.93469436
169 170 171 172 173 174
-1.18745552 34.60103714 24.40766106 -11.20327583 16.67652996 -1.87152314
175 176 177 178 179 180
-51.47492364 15.82586264 -6.53423419 2.50175738 -16.54863213 9.75008293
181 182 183 184 185 186
0.18060072 -13.21513089 -1.91742750 9.76832806 12.61695502 -4.49005281
187 188 189 190 191 192
1.93268681 -0.11829533 9.80970552 -8.88306682 4.05071662 7.14577444
193 194 195 196 197 198
-2.97961576 -5.68794652 -0.20808142 2.54485625 -0.21903154 -4.71847124
199 200 201 202 203 204
-1.13040690 -6.57329960 -0.11458953 -8.13177340 10.33825651 5.74108961
205 206 207 208 209 210
0.42419593 -6.81293655 -2.53854408 23.94353977 10.99876023 3.40820460
211 212 213 214 215 216
7.13369640 6.34605441 -7.17590630 -2.38720331 -3.56391196 -3.02370596
217 218 219 220 221 222
-4.27993749 -2.40492498 -4.36188572 5.05307228 -5.27349936 -0.71731110
223 224 225 226 227 228
-5.45601623 -4.97784591 -5.88653186 11.85596036 1.82753822 -9.90808940
229 230 231 232 233 234
-6.85634796 -12.75168027 -4.07305894 -3.18742530 -1.67259700 -5.88995289
235 236 237 238 239 240
-8.49567559 -12.27026840 20.22286245 -7.77285114 -3.64140524 -8.13996859
241 242 243 244 245 246
4.40478378 10.28272816 -9.45437687 -6.34360890 -2.40593656 -2.52092104
247 248 249 250 251 252
-3.79286054 5.68576710 4.29103246 -6.99661907 13.46254628 -4.61463208
253 254 255 256 257 258
-3.75950693 7.81473846 2.82182154 -0.28590865 -11.15865000 1.40436446
259 260 261 262 263 264
-0.40093425 -3.85823032 -5.54781524 -7.01705886 0.88238230 -10.20274414
265 266 267 268 269 270
-6.40407730 -2.27340709 -9.23388412 -5.53203758 8.45977581 -9.51100573
271 272 273 274 275 276
7.14323946 -1.86827504 4.95529717 -0.61190931 -1.45333192 -9.36720707
277 278 279 280 281 282
0.33659244 -3.37726432 -1.05450179 3.35609729 -3.40086658 -1.00071858
283 284 285 286 287 288
3.12896182 1.99762318 0.53750820 -8.51029916 0.09668521 -6.23401658
289
0.68953338
> postscript(file="/var/wessaorg/rcomp/tmp/6e5bk1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 2.62765311 NA
1 -11.75511741 2.62765311
2 -1.71995530 -11.75511741
3 -14.21706720 -1.71995530
4 -0.39577136 -14.21706720
5 -9.50478305 -0.39577136
6 39.47597190 -9.50478305
7 -6.47537160 39.47597190
8 10.53716743 -6.47537160
9 -1.51451587 10.53716743
10 1.44502280 -1.51451587
11 11.19740308 1.44502280
12 -12.63953185 11.19740308
13 -2.04051637 -12.63953185
14 6.42832882 -2.04051637
15 3.39007910 6.42832882
16 2.24821284 3.39007910
17 -1.41779180 2.24821284
18 11.10625157 -1.41779180
19 -14.49374138 11.10625157
20 -14.29000836 -14.49374138
21 -4.76287764 -14.29000836
22 44.09276470 -4.76287764
23 3.46631683 44.09276470
24 -23.17168969 3.46631683
25 -18.84689142 -23.17168969
26 -2.95805432 -18.84689142
27 8.42231756 -2.95805432
28 0.59522468 8.42231756
29 0.02632286 0.59522468
30 -0.49524640 0.02632286
31 -5.20814003 -0.49524640
32 0.64319067 -5.20814003
33 1.19812147 0.64319067
34 -13.93538941 1.19812147
35 16.56473599 -13.93538941
36 5.08144100 16.56473599
37 11.49440125 5.08144100
38 33.30487567 11.49440125
39 -2.24106241 33.30487567
40 3.96801151 -2.24106241
41 4.75913591 3.96801151
42 -1.46720259 4.75913591
43 14.55265285 -1.46720259
44 14.80772961 14.55265285
45 -4.18643909 14.80772961
46 12.48793720 -4.18643909
47 -2.69711542 12.48793720
48 -4.57732846 -2.69711542
49 -9.39232941 -4.57732846
50 44.89440597 -9.39232941
51 -4.88429710 44.89440597
52 -10.22103297 -4.88429710
53 2.50155126 -10.22103297
54 -13.19412020 2.50155126
55 -2.88130462 -13.19412020
56 -2.51712731 -2.88130462
57 30.70226183 -2.51712731
58 -5.03558362 30.70226183
59 4.17468477 -5.03558362
60 9.66139211 4.17468477
61 -5.39349652 9.66139211
62 38.06380866 -5.39349652
63 5.67894175 38.06380866
64 4.80057395 5.67894175
65 -0.45966194 4.80057395
66 7.72331353 -0.45966194
67 8.90000803 7.72331353
68 0.78889488 8.90000803
69 5.10551351 0.78889488
70 10.61856988 5.10551351
71 13.05423594 10.61856988
72 3.17562262 13.05423594
73 -10.26754991 3.17562262
74 -1.42858769 -10.26754991
75 -11.94814037 -1.42858769
76 -28.31961345 -11.94814037
77 -5.16158260 -28.31961345
78 -4.39373834 -5.16158260
79 1.47824626 -4.39373834
80 -2.23293592 1.47824626
81 -21.70790087 -2.23293592
82 9.33612773 -21.70790087
83 11.48896971 9.33612773
84 -17.22846263 11.48896971
85 -19.33790512 -17.22846263
86 -4.31275708 -19.33790512
87 -3.07489230 -4.31275708
88 24.56504548 -3.07489230
89 9.93553585 24.56504548
90 -8.48247145 9.93553585
91 -7.16442891 -8.48247145
92 8.84775479 -7.16442891
93 -13.57133992 8.84775479
94 -0.15547190 -13.57133992
95 33.51937188 -0.15547190
96 9.66178973 33.51937188
97 -37.57092315 9.66178973
98 10.47810092 -37.57092315
99 -20.20746797 10.47810092
100 6.57841386 -20.20746797
101 4.24088301 6.57841386
102 18.79766411 4.24088301
103 -7.10121024 18.79766411
104 -7.67348853 -7.10121024
105 -13.78049353 -7.67348853
106 -3.36892040 -13.78049353
107 -5.03017259 -3.36892040
108 11.71593400 -5.03017259
109 -3.06413210 11.71593400
110 -7.19921459 -3.06413210
111 4.00590530 -7.19921459
112 -10.20412633 4.00590530
113 1.42251840 -10.20412633
114 -8.73690901 1.42251840
115 -7.12146512 -8.73690901
116 -12.05720378 -7.12146512
117 27.41092737 -12.05720378
118 -17.12791245 27.41092737
119 -3.20965534 -17.12791245
120 0.90513489 -3.20965534
121 -2.96981423 0.90513489
122 7.28987333 -2.96981423
123 -32.27679748 7.28987333
124 13.00760452 -32.27679748
125 2.99489516 13.00760452
126 4.21975757 2.99489516
127 -12.00731774 4.21975757
128 -10.28591752 -12.00731774
129 -2.15463201 -10.28591752
130 1.59720689 -2.15463201
131 -18.24573582 1.59720689
132 -2.32199114 -18.24573582
133 -6.10247910 -2.32199114
134 -11.97648954 -6.10247910
135 22.12975563 -11.97648954
136 0.39874835 22.12975563
137 12.26593368 0.39874835
138 -12.72438034 12.26593368
139 -6.53498435 -12.72438034
140 -3.07189188 -6.53498435
141 -2.95005543 -3.07189188
142 8.54148810 -2.95005543
143 8.27014917 8.54148810
144 -4.67937368 8.27014917
145 -5.06149264 -4.67937368
146 -7.16389445 -5.06149264
147 -6.75070463 -7.16389445
148 -3.44200127 -6.75070463
149 -3.48217417 -3.44200127
150 8.58173105 -3.48217417
151 -1.21251718 8.58173105
152 1.01904429 -1.21251718
153 14.53492183 1.01904429
154 -5.62291561 14.53492183
155 5.24615342 -5.62291561
156 -1.82713123 5.24615342
157 6.50950403 -1.82713123
158 -10.38472018 6.50950403
159 14.50950259 -10.38472018
160 31.37978300 14.50950259
161 -11.72587127 31.37978300
162 4.60378763 -11.72587127
163 17.42814800 4.60378763
164 -2.90412298 17.42814800
165 -1.78320468 -2.90412298
166 -1.65975169 -1.78320468
167 7.93469436 -1.65975169
168 -1.18745552 7.93469436
169 34.60103714 -1.18745552
170 24.40766106 34.60103714
171 -11.20327583 24.40766106
172 16.67652996 -11.20327583
173 -1.87152314 16.67652996
174 -51.47492364 -1.87152314
175 15.82586264 -51.47492364
176 -6.53423419 15.82586264
177 2.50175738 -6.53423419
178 -16.54863213 2.50175738
179 9.75008293 -16.54863213
180 0.18060072 9.75008293
181 -13.21513089 0.18060072
182 -1.91742750 -13.21513089
183 9.76832806 -1.91742750
184 12.61695502 9.76832806
185 -4.49005281 12.61695502
186 1.93268681 -4.49005281
187 -0.11829533 1.93268681
188 9.80970552 -0.11829533
189 -8.88306682 9.80970552
190 4.05071662 -8.88306682
191 7.14577444 4.05071662
192 -2.97961576 7.14577444
193 -5.68794652 -2.97961576
194 -0.20808142 -5.68794652
195 2.54485625 -0.20808142
196 -0.21903154 2.54485625
197 -4.71847124 -0.21903154
198 -1.13040690 -4.71847124
199 -6.57329960 -1.13040690
200 -0.11458953 -6.57329960
201 -8.13177340 -0.11458953
202 10.33825651 -8.13177340
203 5.74108961 10.33825651
204 0.42419593 5.74108961
205 -6.81293655 0.42419593
206 -2.53854408 -6.81293655
207 23.94353977 -2.53854408
208 10.99876023 23.94353977
209 3.40820460 10.99876023
210 7.13369640 3.40820460
211 6.34605441 7.13369640
212 -7.17590630 6.34605441
213 -2.38720331 -7.17590630
214 -3.56391196 -2.38720331
215 -3.02370596 -3.56391196
216 -4.27993749 -3.02370596
217 -2.40492498 -4.27993749
218 -4.36188572 -2.40492498
219 5.05307228 -4.36188572
220 -5.27349936 5.05307228
221 -0.71731110 -5.27349936
222 -5.45601623 -0.71731110
223 -4.97784591 -5.45601623
224 -5.88653186 -4.97784591
225 11.85596036 -5.88653186
226 1.82753822 11.85596036
227 -9.90808940 1.82753822
228 -6.85634796 -9.90808940
229 -12.75168027 -6.85634796
230 -4.07305894 -12.75168027
231 -3.18742530 -4.07305894
232 -1.67259700 -3.18742530
233 -5.88995289 -1.67259700
234 -8.49567559 -5.88995289
235 -12.27026840 -8.49567559
236 20.22286245 -12.27026840
237 -7.77285114 20.22286245
238 -3.64140524 -7.77285114
239 -8.13996859 -3.64140524
240 4.40478378 -8.13996859
241 10.28272816 4.40478378
242 -9.45437687 10.28272816
243 -6.34360890 -9.45437687
244 -2.40593656 -6.34360890
245 -2.52092104 -2.40593656
246 -3.79286054 -2.52092104
247 5.68576710 -3.79286054
248 4.29103246 5.68576710
249 -6.99661907 4.29103246
250 13.46254628 -6.99661907
251 -4.61463208 13.46254628
252 -3.75950693 -4.61463208
253 7.81473846 -3.75950693
254 2.82182154 7.81473846
255 -0.28590865 2.82182154
256 -11.15865000 -0.28590865
257 1.40436446 -11.15865000
258 -0.40093425 1.40436446
259 -3.85823032 -0.40093425
260 -5.54781524 -3.85823032
261 -7.01705886 -5.54781524
262 0.88238230 -7.01705886
263 -10.20274414 0.88238230
264 -6.40407730 -10.20274414
265 -2.27340709 -6.40407730
266 -9.23388412 -2.27340709
267 -5.53203758 -9.23388412
268 8.45977581 -5.53203758
269 -9.51100573 8.45977581
270 7.14323946 -9.51100573
271 -1.86827504 7.14323946
272 4.95529717 -1.86827504
273 -0.61190931 4.95529717
274 -1.45333192 -0.61190931
275 -9.36720707 -1.45333192
276 0.33659244 -9.36720707
277 -3.37726432 0.33659244
278 -1.05450179 -3.37726432
279 3.35609729 -1.05450179
280 -3.40086658 3.35609729
281 -1.00071858 -3.40086658
282 3.12896182 -1.00071858
283 1.99762318 3.12896182
284 0.53750820 1.99762318
285 -8.51029916 0.53750820
286 0.09668521 -8.51029916
287 -6.23401658 0.09668521
288 0.68953338 -6.23401658
289 NA 0.68953338
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.75511741 2.62765311
[2,] -1.71995530 -11.75511741
[3,] -14.21706720 -1.71995530
[4,] -0.39577136 -14.21706720
[5,] -9.50478305 -0.39577136
[6,] 39.47597190 -9.50478305
[7,] -6.47537160 39.47597190
[8,] 10.53716743 -6.47537160
[9,] -1.51451587 10.53716743
[10,] 1.44502280 -1.51451587
[11,] 11.19740308 1.44502280
[12,] -12.63953185 11.19740308
[13,] -2.04051637 -12.63953185
[14,] 6.42832882 -2.04051637
[15,] 3.39007910 6.42832882
[16,] 2.24821284 3.39007910
[17,] -1.41779180 2.24821284
[18,] 11.10625157 -1.41779180
[19,] -14.49374138 11.10625157
[20,] -14.29000836 -14.49374138
[21,] -4.76287764 -14.29000836
[22,] 44.09276470 -4.76287764
[23,] 3.46631683 44.09276470
[24,] -23.17168969 3.46631683
[25,] -18.84689142 -23.17168969
[26,] -2.95805432 -18.84689142
[27,] 8.42231756 -2.95805432
[28,] 0.59522468 8.42231756
[29,] 0.02632286 0.59522468
[30,] -0.49524640 0.02632286
[31,] -5.20814003 -0.49524640
[32,] 0.64319067 -5.20814003
[33,] 1.19812147 0.64319067
[34,] -13.93538941 1.19812147
[35,] 16.56473599 -13.93538941
[36,] 5.08144100 16.56473599
[37,] 11.49440125 5.08144100
[38,] 33.30487567 11.49440125
[39,] -2.24106241 33.30487567
[40,] 3.96801151 -2.24106241
[41,] 4.75913591 3.96801151
[42,] -1.46720259 4.75913591
[43,] 14.55265285 -1.46720259
[44,] 14.80772961 14.55265285
[45,] -4.18643909 14.80772961
[46,] 12.48793720 -4.18643909
[47,] -2.69711542 12.48793720
[48,] -4.57732846 -2.69711542
[49,] -9.39232941 -4.57732846
[50,] 44.89440597 -9.39232941
[51,] -4.88429710 44.89440597
[52,] -10.22103297 -4.88429710
[53,] 2.50155126 -10.22103297
[54,] -13.19412020 2.50155126
[55,] -2.88130462 -13.19412020
[56,] -2.51712731 -2.88130462
[57,] 30.70226183 -2.51712731
[58,] -5.03558362 30.70226183
[59,] 4.17468477 -5.03558362
[60,] 9.66139211 4.17468477
[61,] -5.39349652 9.66139211
[62,] 38.06380866 -5.39349652
[63,] 5.67894175 38.06380866
[64,] 4.80057395 5.67894175
[65,] -0.45966194 4.80057395
[66,] 7.72331353 -0.45966194
[67,] 8.90000803 7.72331353
[68,] 0.78889488 8.90000803
[69,] 5.10551351 0.78889488
[70,] 10.61856988 5.10551351
[71,] 13.05423594 10.61856988
[72,] 3.17562262 13.05423594
[73,] -10.26754991 3.17562262
[74,] -1.42858769 -10.26754991
[75,] -11.94814037 -1.42858769
[76,] -28.31961345 -11.94814037
[77,] -5.16158260 -28.31961345
[78,] -4.39373834 -5.16158260
[79,] 1.47824626 -4.39373834
[80,] -2.23293592 1.47824626
[81,] -21.70790087 -2.23293592
[82,] 9.33612773 -21.70790087
[83,] 11.48896971 9.33612773
[84,] -17.22846263 11.48896971
[85,] -19.33790512 -17.22846263
[86,] -4.31275708 -19.33790512
[87,] -3.07489230 -4.31275708
[88,] 24.56504548 -3.07489230
[89,] 9.93553585 24.56504548
[90,] -8.48247145 9.93553585
[91,] -7.16442891 -8.48247145
[92,] 8.84775479 -7.16442891
[93,] -13.57133992 8.84775479
[94,] -0.15547190 -13.57133992
[95,] 33.51937188 -0.15547190
[96,] 9.66178973 33.51937188
[97,] -37.57092315 9.66178973
[98,] 10.47810092 -37.57092315
[99,] -20.20746797 10.47810092
[100,] 6.57841386 -20.20746797
[101,] 4.24088301 6.57841386
[102,] 18.79766411 4.24088301
[103,] -7.10121024 18.79766411
[104,] -7.67348853 -7.10121024
[105,] -13.78049353 -7.67348853
[106,] -3.36892040 -13.78049353
[107,] -5.03017259 -3.36892040
[108,] 11.71593400 -5.03017259
[109,] -3.06413210 11.71593400
[110,] -7.19921459 -3.06413210
[111,] 4.00590530 -7.19921459
[112,] -10.20412633 4.00590530
[113,] 1.42251840 -10.20412633
[114,] -8.73690901 1.42251840
[115,] -7.12146512 -8.73690901
[116,] -12.05720378 -7.12146512
[117,] 27.41092737 -12.05720378
[118,] -17.12791245 27.41092737
[119,] -3.20965534 -17.12791245
[120,] 0.90513489 -3.20965534
[121,] -2.96981423 0.90513489
[122,] 7.28987333 -2.96981423
[123,] -32.27679748 7.28987333
[124,] 13.00760452 -32.27679748
[125,] 2.99489516 13.00760452
[126,] 4.21975757 2.99489516
[127,] -12.00731774 4.21975757
[128,] -10.28591752 -12.00731774
[129,] -2.15463201 -10.28591752
[130,] 1.59720689 -2.15463201
[131,] -18.24573582 1.59720689
[132,] -2.32199114 -18.24573582
[133,] -6.10247910 -2.32199114
[134,] -11.97648954 -6.10247910
[135,] 22.12975563 -11.97648954
[136,] 0.39874835 22.12975563
[137,] 12.26593368 0.39874835
[138,] -12.72438034 12.26593368
[139,] -6.53498435 -12.72438034
[140,] -3.07189188 -6.53498435
[141,] -2.95005543 -3.07189188
[142,] 8.54148810 -2.95005543
[143,] 8.27014917 8.54148810
[144,] -4.67937368 8.27014917
[145,] -5.06149264 -4.67937368
[146,] -7.16389445 -5.06149264
[147,] -6.75070463 -7.16389445
[148,] -3.44200127 -6.75070463
[149,] -3.48217417 -3.44200127
[150,] 8.58173105 -3.48217417
[151,] -1.21251718 8.58173105
[152,] 1.01904429 -1.21251718
[153,] 14.53492183 1.01904429
[154,] -5.62291561 14.53492183
[155,] 5.24615342 -5.62291561
[156,] -1.82713123 5.24615342
[157,] 6.50950403 -1.82713123
[158,] -10.38472018 6.50950403
[159,] 14.50950259 -10.38472018
[160,] 31.37978300 14.50950259
[161,] -11.72587127 31.37978300
[162,] 4.60378763 -11.72587127
[163,] 17.42814800 4.60378763
[164,] -2.90412298 17.42814800
[165,] -1.78320468 -2.90412298
[166,] -1.65975169 -1.78320468
[167,] 7.93469436 -1.65975169
[168,] -1.18745552 7.93469436
[169,] 34.60103714 -1.18745552
[170,] 24.40766106 34.60103714
[171,] -11.20327583 24.40766106
[172,] 16.67652996 -11.20327583
[173,] -1.87152314 16.67652996
[174,] -51.47492364 -1.87152314
[175,] 15.82586264 -51.47492364
[176,] -6.53423419 15.82586264
[177,] 2.50175738 -6.53423419
[178,] -16.54863213 2.50175738
[179,] 9.75008293 -16.54863213
[180,] 0.18060072 9.75008293
[181,] -13.21513089 0.18060072
[182,] -1.91742750 -13.21513089
[183,] 9.76832806 -1.91742750
[184,] 12.61695502 9.76832806
[185,] -4.49005281 12.61695502
[186,] 1.93268681 -4.49005281
[187,] -0.11829533 1.93268681
[188,] 9.80970552 -0.11829533
[189,] -8.88306682 9.80970552
[190,] 4.05071662 -8.88306682
[191,] 7.14577444 4.05071662
[192,] -2.97961576 7.14577444
[193,] -5.68794652 -2.97961576
[194,] -0.20808142 -5.68794652
[195,] 2.54485625 -0.20808142
[196,] -0.21903154 2.54485625
[197,] -4.71847124 -0.21903154
[198,] -1.13040690 -4.71847124
[199,] -6.57329960 -1.13040690
[200,] -0.11458953 -6.57329960
[201,] -8.13177340 -0.11458953
[202,] 10.33825651 -8.13177340
[203,] 5.74108961 10.33825651
[204,] 0.42419593 5.74108961
[205,] -6.81293655 0.42419593
[206,] -2.53854408 -6.81293655
[207,] 23.94353977 -2.53854408
[208,] 10.99876023 23.94353977
[209,] 3.40820460 10.99876023
[210,] 7.13369640 3.40820460
[211,] 6.34605441 7.13369640
[212,] -7.17590630 6.34605441
[213,] -2.38720331 -7.17590630
[214,] -3.56391196 -2.38720331
[215,] -3.02370596 -3.56391196
[216,] -4.27993749 -3.02370596
[217,] -2.40492498 -4.27993749
[218,] -4.36188572 -2.40492498
[219,] 5.05307228 -4.36188572
[220,] -5.27349936 5.05307228
[221,] -0.71731110 -5.27349936
[222,] -5.45601623 -0.71731110
[223,] -4.97784591 -5.45601623
[224,] -5.88653186 -4.97784591
[225,] 11.85596036 -5.88653186
[226,] 1.82753822 11.85596036
[227,] -9.90808940 1.82753822
[228,] -6.85634796 -9.90808940
[229,] -12.75168027 -6.85634796
[230,] -4.07305894 -12.75168027
[231,] -3.18742530 -4.07305894
[232,] -1.67259700 -3.18742530
[233,] -5.88995289 -1.67259700
[234,] -8.49567559 -5.88995289
[235,] -12.27026840 -8.49567559
[236,] 20.22286245 -12.27026840
[237,] -7.77285114 20.22286245
[238,] -3.64140524 -7.77285114
[239,] -8.13996859 -3.64140524
[240,] 4.40478378 -8.13996859
[241,] 10.28272816 4.40478378
[242,] -9.45437687 10.28272816
[243,] -6.34360890 -9.45437687
[244,] -2.40593656 -6.34360890
[245,] -2.52092104 -2.40593656
[246,] -3.79286054 -2.52092104
[247,] 5.68576710 -3.79286054
[248,] 4.29103246 5.68576710
[249,] -6.99661907 4.29103246
[250,] 13.46254628 -6.99661907
[251,] -4.61463208 13.46254628
[252,] -3.75950693 -4.61463208
[253,] 7.81473846 -3.75950693
[254,] 2.82182154 7.81473846
[255,] -0.28590865 2.82182154
[256,] -11.15865000 -0.28590865
[257,] 1.40436446 -11.15865000
[258,] -0.40093425 1.40436446
[259,] -3.85823032 -0.40093425
[260,] -5.54781524 -3.85823032
[261,] -7.01705886 -5.54781524
[262,] 0.88238230 -7.01705886
[263,] -10.20274414 0.88238230
[264,] -6.40407730 -10.20274414
[265,] -2.27340709 -6.40407730
[266,] -9.23388412 -2.27340709
[267,] -5.53203758 -9.23388412
[268,] 8.45977581 -5.53203758
[269,] -9.51100573 8.45977581
[270,] 7.14323946 -9.51100573
[271,] -1.86827504 7.14323946
[272,] 4.95529717 -1.86827504
[273,] -0.61190931 4.95529717
[274,] -1.45333192 -0.61190931
[275,] -9.36720707 -1.45333192
[276,] 0.33659244 -9.36720707
[277,] -3.37726432 0.33659244
[278,] -1.05450179 -3.37726432
[279,] 3.35609729 -1.05450179
[280,] -3.40086658 3.35609729
[281,] -1.00071858 -3.40086658
[282,] 3.12896182 -1.00071858
[283,] 1.99762318 3.12896182
[284,] 0.53750820 1.99762318
[285,] -8.51029916 0.53750820
[286,] 0.09668521 -8.51029916
[287,] -6.23401658 0.09668521
[288,] 0.68953338 -6.23401658
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.75511741 2.62765311
2 -1.71995530 -11.75511741
3 -14.21706720 -1.71995530
4 -0.39577136 -14.21706720
5 -9.50478305 -0.39577136
6 39.47597190 -9.50478305
7 -6.47537160 39.47597190
8 10.53716743 -6.47537160
9 -1.51451587 10.53716743
10 1.44502280 -1.51451587
11 11.19740308 1.44502280
12 -12.63953185 11.19740308
13 -2.04051637 -12.63953185
14 6.42832882 -2.04051637
15 3.39007910 6.42832882
16 2.24821284 3.39007910
17 -1.41779180 2.24821284
18 11.10625157 -1.41779180
19 -14.49374138 11.10625157
20 -14.29000836 -14.49374138
21 -4.76287764 -14.29000836
22 44.09276470 -4.76287764
23 3.46631683 44.09276470
24 -23.17168969 3.46631683
25 -18.84689142 -23.17168969
26 -2.95805432 -18.84689142
27 8.42231756 -2.95805432
28 0.59522468 8.42231756
29 0.02632286 0.59522468
30 -0.49524640 0.02632286
31 -5.20814003 -0.49524640
32 0.64319067 -5.20814003
33 1.19812147 0.64319067
34 -13.93538941 1.19812147
35 16.56473599 -13.93538941
36 5.08144100 16.56473599
37 11.49440125 5.08144100
38 33.30487567 11.49440125
39 -2.24106241 33.30487567
40 3.96801151 -2.24106241
41 4.75913591 3.96801151
42 -1.46720259 4.75913591
43 14.55265285 -1.46720259
44 14.80772961 14.55265285
45 -4.18643909 14.80772961
46 12.48793720 -4.18643909
47 -2.69711542 12.48793720
48 -4.57732846 -2.69711542
49 -9.39232941 -4.57732846
50 44.89440597 -9.39232941
51 -4.88429710 44.89440597
52 -10.22103297 -4.88429710
53 2.50155126 -10.22103297
54 -13.19412020 2.50155126
55 -2.88130462 -13.19412020
56 -2.51712731 -2.88130462
57 30.70226183 -2.51712731
58 -5.03558362 30.70226183
59 4.17468477 -5.03558362
60 9.66139211 4.17468477
61 -5.39349652 9.66139211
62 38.06380866 -5.39349652
63 5.67894175 38.06380866
64 4.80057395 5.67894175
65 -0.45966194 4.80057395
66 7.72331353 -0.45966194
67 8.90000803 7.72331353
68 0.78889488 8.90000803
69 5.10551351 0.78889488
70 10.61856988 5.10551351
71 13.05423594 10.61856988
72 3.17562262 13.05423594
73 -10.26754991 3.17562262
74 -1.42858769 -10.26754991
75 -11.94814037 -1.42858769
76 -28.31961345 -11.94814037
77 -5.16158260 -28.31961345
78 -4.39373834 -5.16158260
79 1.47824626 -4.39373834
80 -2.23293592 1.47824626
81 -21.70790087 -2.23293592
82 9.33612773 -21.70790087
83 11.48896971 9.33612773
84 -17.22846263 11.48896971
85 -19.33790512 -17.22846263
86 -4.31275708 -19.33790512
87 -3.07489230 -4.31275708
88 24.56504548 -3.07489230
89 9.93553585 24.56504548
90 -8.48247145 9.93553585
91 -7.16442891 -8.48247145
92 8.84775479 -7.16442891
93 -13.57133992 8.84775479
94 -0.15547190 -13.57133992
95 33.51937188 -0.15547190
96 9.66178973 33.51937188
97 -37.57092315 9.66178973
98 10.47810092 -37.57092315
99 -20.20746797 10.47810092
100 6.57841386 -20.20746797
101 4.24088301 6.57841386
102 18.79766411 4.24088301
103 -7.10121024 18.79766411
104 -7.67348853 -7.10121024
105 -13.78049353 -7.67348853
106 -3.36892040 -13.78049353
107 -5.03017259 -3.36892040
108 11.71593400 -5.03017259
109 -3.06413210 11.71593400
110 -7.19921459 -3.06413210
111 4.00590530 -7.19921459
112 -10.20412633 4.00590530
113 1.42251840 -10.20412633
114 -8.73690901 1.42251840
115 -7.12146512 -8.73690901
116 -12.05720378 -7.12146512
117 27.41092737 -12.05720378
118 -17.12791245 27.41092737
119 -3.20965534 -17.12791245
120 0.90513489 -3.20965534
121 -2.96981423 0.90513489
122 7.28987333 -2.96981423
123 -32.27679748 7.28987333
124 13.00760452 -32.27679748
125 2.99489516 13.00760452
126 4.21975757 2.99489516
127 -12.00731774 4.21975757
128 -10.28591752 -12.00731774
129 -2.15463201 -10.28591752
130 1.59720689 -2.15463201
131 -18.24573582 1.59720689
132 -2.32199114 -18.24573582
133 -6.10247910 -2.32199114
134 -11.97648954 -6.10247910
135 22.12975563 -11.97648954
136 0.39874835 22.12975563
137 12.26593368 0.39874835
138 -12.72438034 12.26593368
139 -6.53498435 -12.72438034
140 -3.07189188 -6.53498435
141 -2.95005543 -3.07189188
142 8.54148810 -2.95005543
143 8.27014917 8.54148810
144 -4.67937368 8.27014917
145 -5.06149264 -4.67937368
146 -7.16389445 -5.06149264
147 -6.75070463 -7.16389445
148 -3.44200127 -6.75070463
149 -3.48217417 -3.44200127
150 8.58173105 -3.48217417
151 -1.21251718 8.58173105
152 1.01904429 -1.21251718
153 14.53492183 1.01904429
154 -5.62291561 14.53492183
155 5.24615342 -5.62291561
156 -1.82713123 5.24615342
157 6.50950403 -1.82713123
158 -10.38472018 6.50950403
159 14.50950259 -10.38472018
160 31.37978300 14.50950259
161 -11.72587127 31.37978300
162 4.60378763 -11.72587127
163 17.42814800 4.60378763
164 -2.90412298 17.42814800
165 -1.78320468 -2.90412298
166 -1.65975169 -1.78320468
167 7.93469436 -1.65975169
168 -1.18745552 7.93469436
169 34.60103714 -1.18745552
170 24.40766106 34.60103714
171 -11.20327583 24.40766106
172 16.67652996 -11.20327583
173 -1.87152314 16.67652996
174 -51.47492364 -1.87152314
175 15.82586264 -51.47492364
176 -6.53423419 15.82586264
177 2.50175738 -6.53423419
178 -16.54863213 2.50175738
179 9.75008293 -16.54863213
180 0.18060072 9.75008293
181 -13.21513089 0.18060072
182 -1.91742750 -13.21513089
183 9.76832806 -1.91742750
184 12.61695502 9.76832806
185 -4.49005281 12.61695502
186 1.93268681 -4.49005281
187 -0.11829533 1.93268681
188 9.80970552 -0.11829533
189 -8.88306682 9.80970552
190 4.05071662 -8.88306682
191 7.14577444 4.05071662
192 -2.97961576 7.14577444
193 -5.68794652 -2.97961576
194 -0.20808142 -5.68794652
195 2.54485625 -0.20808142
196 -0.21903154 2.54485625
197 -4.71847124 -0.21903154
198 -1.13040690 -4.71847124
199 -6.57329960 -1.13040690
200 -0.11458953 -6.57329960
201 -8.13177340 -0.11458953
202 10.33825651 -8.13177340
203 5.74108961 10.33825651
204 0.42419593 5.74108961
205 -6.81293655 0.42419593
206 -2.53854408 -6.81293655
207 23.94353977 -2.53854408
208 10.99876023 23.94353977
209 3.40820460 10.99876023
210 7.13369640 3.40820460
211 6.34605441 7.13369640
212 -7.17590630 6.34605441
213 -2.38720331 -7.17590630
214 -3.56391196 -2.38720331
215 -3.02370596 -3.56391196
216 -4.27993749 -3.02370596
217 -2.40492498 -4.27993749
218 -4.36188572 -2.40492498
219 5.05307228 -4.36188572
220 -5.27349936 5.05307228
221 -0.71731110 -5.27349936
222 -5.45601623 -0.71731110
223 -4.97784591 -5.45601623
224 -5.88653186 -4.97784591
225 11.85596036 -5.88653186
226 1.82753822 11.85596036
227 -9.90808940 1.82753822
228 -6.85634796 -9.90808940
229 -12.75168027 -6.85634796
230 -4.07305894 -12.75168027
231 -3.18742530 -4.07305894
232 -1.67259700 -3.18742530
233 -5.88995289 -1.67259700
234 -8.49567559 -5.88995289
235 -12.27026840 -8.49567559
236 20.22286245 -12.27026840
237 -7.77285114 20.22286245
238 -3.64140524 -7.77285114
239 -8.13996859 -3.64140524
240 4.40478378 -8.13996859
241 10.28272816 4.40478378
242 -9.45437687 10.28272816
243 -6.34360890 -9.45437687
244 -2.40593656 -6.34360890
245 -2.52092104 -2.40593656
246 -3.79286054 -2.52092104
247 5.68576710 -3.79286054
248 4.29103246 5.68576710
249 -6.99661907 4.29103246
250 13.46254628 -6.99661907
251 -4.61463208 13.46254628
252 -3.75950693 -4.61463208
253 7.81473846 -3.75950693
254 2.82182154 7.81473846
255 -0.28590865 2.82182154
256 -11.15865000 -0.28590865
257 1.40436446 -11.15865000
258 -0.40093425 1.40436446
259 -3.85823032 -0.40093425
260 -5.54781524 -3.85823032
261 -7.01705886 -5.54781524
262 0.88238230 -7.01705886
263 -10.20274414 0.88238230
264 -6.40407730 -10.20274414
265 -2.27340709 -6.40407730
266 -9.23388412 -2.27340709
267 -5.53203758 -9.23388412
268 8.45977581 -5.53203758
269 -9.51100573 8.45977581
270 7.14323946 -9.51100573
271 -1.86827504 7.14323946
272 4.95529717 -1.86827504
273 -0.61190931 4.95529717
274 -1.45333192 -0.61190931
275 -9.36720707 -1.45333192
276 0.33659244 -9.36720707
277 -3.37726432 0.33659244
278 -1.05450179 -3.37726432
279 3.35609729 -1.05450179
280 -3.40086658 3.35609729
281 -1.00071858 -3.40086658
282 3.12896182 -1.00071858
283 1.99762318 3.12896182
284 0.53750820 1.99762318
285 -8.51029916 0.53750820
286 0.09668521 -8.51029916
287 -6.23401658 0.09668521
288 0.68953338 -6.23401658
> 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/7iq1j1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8rn691352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/907gi1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10etmy1352144422.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11x6hy1352144422.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/12x0771352144422.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/1333au1352144422.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/148snn1352144422.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/15lp5h1352144422.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/164t7m1352144422.tab")
+ }
>
> try(system("convert tmp/1h0l21352144422.ps tmp/1h0l21352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rqse1352144422.ps tmp/2rqse1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/3km4w1352144422.ps tmp/3km4w1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l73v1352144422.ps tmp/4l73v1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yspi1352144422.ps tmp/5yspi1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e5bk1352144422.ps tmp/6e5bk1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/7iq1j1352144422.ps tmp/7iq1j1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rn691352144422.ps tmp/8rn691352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/907gi1352144422.ps tmp/907gi1352144422.png",intern=TRUE))
character(0)
> try(system("convert tmp/10etmy1352144422.ps tmp/10etmy1352144422.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.796 1.140 13.959