R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(210907
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+ ,91005
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+ ,40248
+ ,8
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+ ,15
+ ,8019
+ ,62792
+ ,28
+ ,30884
+ ,72535
+ ,17
+ ,19540)
+ ,dim=c(3
+ ,289)
+ ,dimnames=list(c('time'
+ ,'blogged'
+ ,'size')
+ ,1:289))
> y <- array(NA,dim=c(3,289),dimnames=list(c('time','blogged','size'),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 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
blogged time size
1 79 210907 112285
2 58 120982 84786
3 60 176508 83123
4 108 179321 101193
5 49 123185 38361
6 0 52746 68504
7 121 385534 119182
8 1 33170 22807
9 20 101645 17140
10 43 149061 116174
11 69 165446 57635
12 78 237213 66198
13 86 173326 71701
14 44 133131 57793
15 104 258873 80444
16 63 180083 53855
17 158 324799 97668
18 102 230964 133824
19 77 236785 101481
20 82 135473 99645
21 115 202925 114789
22 101 215147 99052
23 80 344297 67654
24 50 153935 65553
25 83 132943 97500
26 123 174724 69112
27 73 174415 82753
28 81 225548 85323
29 105 223632 72654
30 47 124817 30727
31 105 221698 77873
32 94 210767 117478
33 44 170266 74007
34 114 260561 90183
35 38 84853 61542
36 107 294424 101494
37 30 101011 27570
38 71 215641 55813
39 84 325107 79215
40 0 7176 1423
41 59 167542 55461
42 33 106408 31081
43 42 96560 22996
44 96 265769 83122
45 106 269651 70106
46 56 149112 60578
47 57 175824 39992
48 59 152871 79892
49 39 111665 49810
50 34 116408 71570
51 76 362301 100708
52 20 78800 33032
53 91 183167 82875
54 115 277965 139077
55 85 150629 71595
56 76 168809 72260
57 8 24188 5950
58 79 329267 115762
59 21 65029 32551
60 30 101097 31701
61 76 218946 80670
62 101 244052 143558
63 94 341570 117105
64 27 103597 23789
65 92 233328 120733
66 123 256462 105195
67 75 206161 73107
68 128 311473 132068
69 105 235800 149193
70 55 177939 46821
71 56 207176 87011
72 41 196553 95260
73 72 174184 55183
74 67 143246 106671
75 75 187559 73511
76 114 187681 92945
77 118 119016 78664
78 77 182192 70054
79 22 73566 22618
80 66 194979 74011
81 69 167488 83737
82 105 143756 69094
83 116 275541 93133
84 88 243199 95536
85 73 182999 225920
86 99 135649 62133
87 62 152299 61370
88 53 120221 43836
89 118 346485 106117
90 30 145790 38692
91 100 193339 84651
92 49 80953 56622
93 24 122774 15986
94 67 130585 95364
95 46 112611 26706
96 57 286468 89691
97 75 241066 67267
98 135 148446 126846
99 68 204713 41140
100 124 182079 102860
101 33 140344 51715
102 98 220516 55801
103 58 243060 111813
104 68 162765 120293
105 81 182613 138599
106 131 232138 161647
107 110 265318 115929
108 37 85574 24266
109 130 310839 162901
110 93 225060 109825
111 118 232317 129838
112 39 144966 37510
113 13 43287 43750
114 74 155754 40652
115 81 164709 87771
116 109 201940 85872
117 151 235454 89275
118 51 220801 44418
119 28 99466 192565
120 40 92661 35232
121 56 133328 40909
122 27 61361 13294
123 37 125930 32387
124 83 100750 140867
125 54 224549 120662
126 27 82316 21233
127 28 102010 44332
128 59 101523 61056
129 133 243511 101338
130 12 22938 1168
131 0 41566 13497
132 106 152474 65567
133 23 61857 25162
134 44 99923 32334
135 71 132487 40735
136 116 317394 91413
137 4 21054 855
138 62 209641 97068
139 12 22648 44339
140 18 31414 14116
141 14 46698 10288
142 60 131698 65622
143 7 91735 16563
144 98 244749 76643
145 64 184510 110681
146 29 79863 29011
147 32 128423 92696
148 25 97839 94785
149 16 38214 8773
150 48 151101 83209
151 100 272458 93815
152 46 172494 86687
153 45 108043 34553
154 129 328107 105547
155 130 250579 103487
156 136 351067 213688
157 59 158015 71220
158 25 98866 23517
159 32 85439 56926
160 63 229242 91721
161 95 351619 115168
162 14 84207 111194
163 36 120445 51009
164 113 324598 135777
165 47 131069 51513
166 92 204271 74163
167 70 165543 51633
168 19 141722 75345
169 50 116048 33416
170 41 250047 83305
171 91 299775 98952
172 111 195838 102372
173 41 173260 37238
174 120 254488 103772
175 135 104389 123969
176 27 136084 27142
177 87 199476 135400
178 25 92499 21399
179 131 224330 130115
180 45 135781 24874
181 29 74408 34988
182 58 81240 45549
183 4 14688 6023
184 47 181633 64466
185 109 271856 54990
186 7 7199 1644
187 12 46660 6179
188 0 17547 3926
189 37 133368 32755
190 37 95227 34777
191 46 152601 73224
192 15 98146 27114
193 42 79619 20760
194 7 59194 37636
195 54 139942 65461
196 54 118612 30080
197 14 72880 24094
198 16 65475 69008
199 33 99643 54968
200 32 71965 46090
201 21 77272 27507
202 15 49289 10672
203 38 135131 34029
204 22 108446 46300
205 28 89746 24760
206 10 44296 18779
207 31 77648 21280
208 32 181528 40662
209 32 134019 28987
210 43 124064 22827
211 27 92630 18513
212 37 121848 30594
213 20 52915 24006
214 32 81872 27913
215 0 58981 42744
216 5 53515 12934
217 26 60812 22574
218 10 56375 41385
219 27 65490 18653
220 11 80949 18472
221 29 76302 30976
222 25 104011 63339
223 55 98104 25568
224 23 67989 33747
225 5 30989 4154
226 43 135458 19474
227 23 73504 35130
228 34 63123 39067
229 36 61254 13310
230 35 74914 65892
231 0 31774 4143
232 37 81437 28579
233 28 87186 51776
234 16 50090 21152
235 26 65745 38084
236 38 56653 27717
237 23 158399 32928
238 22 46455 11342
239 30 73624 19499
240 16 38395 16380
241 18 91899 36874
242 28 139526 48259
243 32 52164 16734
244 21 51567 28207
245 23 70551 30143
246 29 84856 41369
247 50 102538 45833
248 12 86678 29156
249 21 85709 35944
250 18 34662 36278
251 27 150580 45588
252 41 99611 45097
253 13 19349 3895
254 12 99373 28394
255 21 86230 18632
256 8 30837 2325
257 26 31706 25139
258 27 89806 27975
259 13 62088 14483
260 16 40151 13127
261 2 27634 5839
262 42 76990 24069
263 5 37460 3738
264 37 54157 18625
265 17 49862 36341
266 38 84337 24548
267 37 64175 21792
268 29 59382 26263
269 32 119308 23686
270 35 76702 49303
271 17 103425 25659
272 20 70344 28904
273 7 43410 2781
274 46 104838 29236
275 24 62215 19546
276 40 69304 22818
277 3 53117 32689
278 10 19764 5752
279 37 86680 22197
280 17 84105 20055
281 28 77945 25272
282 19 89113 82206
283 29 91005 32073
284 8 40248 5444
285 10 64187 20154
286 15 50857 36944
287 15 56613 8019
288 28 62792 30884
289 17 72535 19540
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time size
-1.1548117 0.0002664 0.0002893
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-53.050 -9.391 -0.701 8.645 72.484
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.155e+00 2.213e+00 -0.522 0.602
time 2.664e-04 2.050e-05 12.996 < 2e-16 ***
size 2.893e-04 4.175e-05 6.930 2.79e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19.07 on 286 degrees of freedom
Multiple R-squared: 0.7349, Adjusted R-squared: 0.733
F-statistic: 396.4 on 2 and 286 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,] 0.7873671 4.252659e-01 2.126329e-01
[2,] 0.8461730 3.076540e-01 1.538270e-01
[3,] 0.7619867 4.760265e-01 2.380133e-01
[4,] 0.6574910 6.850180e-01 3.425090e-01
[5,] 0.6828567 6.342866e-01 3.171433e-01
[6,] 0.6329627 7.340746e-01 3.670373e-01
[7,] 0.5356388 9.287224e-01 4.643612e-01
[8,] 0.5873516 8.252969e-01 4.126484e-01
[9,] 0.4995244 9.990488e-01 5.004756e-01
[10,] 0.4426969 8.853938e-01 5.573031e-01
[11,] 0.3600153 7.200305e-01 6.399847e-01
[12,] 0.5515254 8.969492e-01 4.484746e-01
[13,] 0.4805857 9.611714e-01 5.194143e-01
[14,] 0.4644031 9.288062e-01 5.355969e-01
[15,] 0.5235178 9.529645e-01 4.764822e-01
[16,] 0.6010305 7.979390e-01 3.989695e-01
[17,] 0.5607936 8.784128e-01 4.392064e-01
[18,] 0.7277874 5.444253e-01 2.722126e-01
[19,] 0.6798038 6.403925e-01 3.201962e-01
[20,] 0.6723787 6.552426e-01 3.276213e-01
[21,] 0.9313922 1.372157e-01 6.860783e-02
[22,] 0.9100342 1.799315e-01 8.996577e-02
[23,] 0.8869696 2.260608e-01 1.130304e-01
[24,] 0.8936494 2.127013e-01 1.063506e-01
[25,] 0.8690603 2.618793e-01 1.309397e-01
[26,] 0.8696615 2.606769e-01 1.303385e-01
[27,] 0.8394917 3.210166e-01 1.605083e-01
[28,] 0.8554092 2.891817e-01 1.445908e-01
[29,] 0.8402238 3.195523e-01 1.597762e-01
[30,] 0.8064919 3.870162e-01 1.935081e-01
[31,] 0.7745897 4.508206e-01 2.254103e-01
[32,] 0.7348251 5.303499e-01 2.651749e-01
[33,] 0.6934337 6.131326e-01 3.065663e-01
[34,] 0.7403555 5.192890e-01 2.596445e-01
[35,] 0.6995586 6.008828e-01 3.004414e-01
[36,] 0.6555170 6.889660e-01 3.444830e-01
[37,] 0.6098639 7.802723e-01 3.901361e-01
[38,] 0.5819862 8.360277e-01 4.180138e-01
[39,] 0.5348302 9.303396e-01 4.651698e-01
[40,] 0.5092203 9.815594e-01 4.907797e-01
[41,] 0.4624124 9.248247e-01 5.375876e-01
[42,] 0.4159892 8.319784e-01 5.840108e-01
[43,] 0.3763712 7.527424e-01 6.236288e-01
[44,] 0.3356046 6.712092e-01 6.643954e-01
[45,] 0.3328060 6.656120e-01 6.671940e-01
[46,] 0.6015672 7.968657e-01 3.984328e-01
[47,] 0.5681601 8.636799e-01 4.318399e-01
[48,] 0.5607850 8.784300e-01 4.392150e-01
[49,] 0.5187392 9.625216e-01 4.812608e-01
[50,] 0.5406370 9.187260e-01 4.593630e-01
[51,] 0.5082242 9.835516e-01 4.917758e-01
[52,] 0.4653372 9.306744e-01 5.346628e-01
[53,] 0.6217816 7.564369e-01 3.782184e-01
[54,] 0.5854085 8.291830e-01 4.145915e-01
[55,] 0.5471176 9.057648e-01 4.528824e-01
[56,] 0.5080957 9.838085e-01 4.919043e-01
[57,] 0.4744128 9.488257e-01 5.255872e-01
[58,] 0.5228738 9.542523e-01 4.771262e-01
[59,] 0.4864294 9.728589e-01 5.135706e-01
[60,] 0.4489862 8.979725e-01 5.510138e-01
[61,] 0.4797754 9.595508e-01 5.202246e-01
[62,] 0.4395282 8.790564e-01 5.604718e-01
[63,] 0.4061222 8.122445e-01 5.938778e-01
[64,] 0.3696302 7.392605e-01 6.303698e-01
[65,] 0.3340976 6.681952e-01 6.659024e-01
[66,] 0.3552056 7.104111e-01 6.447944e-01
[67,] 0.4753211 9.506422e-01 5.246789e-01
[68,] 0.4498936 8.997871e-01 5.501064e-01
[69,] 0.4128250 8.256499e-01 5.871750e-01
[70,] 0.3777459 7.554918e-01 6.222541e-01
[71,] 0.4848228 9.696455e-01 5.151772e-01
[72,] 0.8030188 3.939624e-01 1.969812e-01
[73,] 0.7812656 4.374687e-01 2.187344e-01
[74,] 0.7540779 4.918441e-01 2.459221e-01
[75,] 0.7272325 5.455349e-01 2.727675e-01
[76,] 0.6954718 6.090563e-01 3.045282e-01
[77,] 0.8274059 3.451882e-01 1.725941e-01
[78,] 0.8233329 3.533343e-01 1.766671e-01
[79,] 0.7993040 4.013920e-01 2.006960e-01
[80,] 0.8867900 2.264200e-01 1.132100e-01
[81,] 0.9435190 1.129620e-01 5.648102e-02
[82,] 0.9328232 1.343536e-01 6.717679e-02
[83,] 0.9220902 1.558197e-01 7.790983e-02
[84,] 0.9082900 1.834201e-01 9.171003e-02
[85,] 0.9109584 1.780833e-01 8.904164e-02
[86,] 0.9188572 1.622856e-01 8.114279e-02
[87,] 0.9083931 1.832138e-01 9.160690e-02
[88,] 0.9021834 1.956332e-01 9.781662e-02
[89,] 0.8868926 2.262147e-01 1.131074e-01
[90,] 0.8715990 2.568021e-01 1.284010e-01
[91,] 0.9318864 1.362272e-01 6.811362e-02
[92,] 0.9215209 1.569583e-01 7.847914e-02
[93,] 0.9832466 3.350678e-02 1.675339e-02
[94,] 0.9793183 4.136340e-02 2.068170e-02
[95,] 0.9927167 1.456651e-02 7.283256e-03
[96,] 0.9928338 1.433230e-02 7.166150e-03
[97,] 0.9937349 1.253024e-02 6.265122e-03
[98,] 0.9969856 6.028761e-03 3.014381e-03
[99,] 0.9964049 7.190254e-03 3.595127e-03
[100,] 0.9955647 8.870585e-03 4.435293e-03
[101,] 0.9962003 7.599333e-03 3.799666e-03
[102,] 0.9952922 9.415622e-03 4.707811e-03
[103,] 0.9941470 1.170597e-02 5.852986e-03
[104,] 0.9926040 1.479207e-02 7.396037e-03
[105,] 0.9907192 1.856155e-02 9.280777e-03
[106,] 0.9910523 1.789545e-02 8.947724e-03
[107,] 0.9895259 2.094816e-02 1.047408e-02
[108,] 0.9882751 2.344988e-02 1.172494e-02
[109,] 0.9887674 2.246518e-02 1.123259e-02
[110,] 0.9873586 2.528280e-02 1.264140e-02
[111,] 0.9915366 1.692689e-02 8.463443e-03
[112,] 0.9994994 1.001296e-03 5.006479e-04
[113,] 0.9995076 9.848320e-04 4.924160e-04
[114,] 0.9999377 1.245495e-04 6.227476e-05
[115,] 0.9999154 1.692610e-04 8.463051e-05
[116,] 0.9998916 2.168659e-04 1.084329e-04
[117,] 0.9998559 2.881571e-04 1.440786e-04
[118,] 0.9998080 3.839231e-04 1.919616e-04
[119,] 0.9998053 3.894613e-04 1.947307e-04
[120,] 0.9999278 1.443720e-04 7.218601e-05
[121,] 0.9999001 1.998581e-04 9.992906e-05
[122,] 0.9998782 2.436010e-04 1.218005e-04
[123,] 0.9998650 2.700969e-04 1.350485e-04
[124,] 0.9999660 6.808713e-05 3.404356e-05
[125,] 0.9999532 9.359174e-05 4.679587e-05
[126,] 0.9999482 1.036285e-04 5.181423e-05
[127,] 0.9999940 1.193111e-05 5.965553e-06
[128,] 0.9999914 1.729676e-05 8.648381e-06
[129,] 0.9999886 2.285518e-05 1.142759e-05
[130,] 0.9999920 1.598821e-05 7.994104e-06
[131,] 0.9999897 2.069824e-05 1.034912e-05
[132,] 0.9999852 2.951426e-05 1.475713e-05
[133,] 0.9999853 2.937782e-05 1.468891e-05
[134,] 0.9999799 4.010668e-05 2.005334e-05
[135,] 0.9999724 5.512746e-05 2.756373e-05
[136,] 0.9999611 7.778909e-05 3.889455e-05
[137,] 0.9999492 1.016453e-04 5.082266e-05
[138,] 0.9999557 8.857030e-05 4.428515e-05
[139,] 0.9999503 9.933851e-05 4.966925e-05
[140,] 0.9999419 1.161555e-04 5.807776e-05
[141,] 0.9999189 1.622932e-04 8.114658e-05
[142,] 0.9999442 1.115748e-04 5.578740e-05
[143,] 0.9999629 7.428359e-05 3.714179e-05
[144,] 0.9999483 1.034665e-04 5.173326e-05
[145,] 0.9999404 1.192198e-04 5.960990e-05
[146,] 0.9999193 1.614282e-04 8.071408e-05
[147,] 0.9999322 1.356892e-04 6.784460e-05
[148,] 0.9999114 1.771670e-04 8.858349e-05
[149,] 0.9999120 1.759513e-04 8.797564e-05
[150,] 0.9999768 4.648997e-05 2.324498e-05
[151,] 0.9999716 5.680969e-05 2.840484e-05
[152,] 0.9999596 8.088048e-05 4.044024e-05
[153,] 0.9999450 1.100821e-04 5.504106e-05
[154,] 0.9999252 1.496985e-04 7.484923e-05
[155,] 0.9999301 1.398401e-04 6.992003e-05
[156,] 0.9999487 1.026496e-04 5.132478e-05
[157,] 0.9999920 1.594025e-05 7.970123e-06
[158,] 0.9999894 2.116014e-05 1.058007e-05
[159,] 0.9999860 2.807999e-05 1.403999e-05
[160,] 0.9999795 4.092298e-05 2.046149e-05
[161,] 0.9999814 3.727167e-05 1.863583e-05
[162,] 0.9999796 4.074582e-05 2.037291e-05
[163,] 0.9999962 7.617424e-06 3.808712e-06
[164,] 0.9999954 9.107787e-06 4.553894e-06
[165,] 0.9999997 6.835839e-07 3.417920e-07
[166,] 0.9999996 7.421096e-07 3.710548e-07
[167,] 0.9999998 3.710535e-07 1.855267e-07
[168,] 0.9999998 4.708532e-07 2.354266e-07
[169,] 0.9999998 3.047010e-07 1.523505e-07
[170,] 1.0000000 1.523346e-11 7.616730e-12
[171,] 1.0000000 1.674973e-11 8.374867e-12
[172,] 1.0000000 2.951040e-11 1.475520e-11
[173,] 1.0000000 5.293949e-11 2.646974e-11
[174,] 1.0000000 1.040251e-13 5.201255e-14
[175,] 1.0000000 2.016049e-13 1.008024e-13
[176,] 1.0000000 3.887769e-13 1.943885e-13
[177,] 1.0000000 4.392657e-14 2.196329e-14
[178,] 1.0000000 8.815374e-14 4.407687e-14
[179,] 1.0000000 1.312520e-13 6.562601e-14
[180,] 1.0000000 5.629545e-15 2.814772e-15
[181,] 1.0000000 1.214775e-14 6.073877e-15
[182,] 1.0000000 2.424074e-14 1.212037e-14
[183,] 1.0000000 3.555652e-14 1.777826e-14
[184,] 1.0000000 7.331990e-14 3.665995e-14
[185,] 1.0000000 1.258541e-13 6.292703e-14
[186,] 1.0000000 2.334793e-13 1.167397e-13
[187,] 1.0000000 2.281456e-13 1.140728e-13
[188,] 1.0000000 1.741895e-13 8.709474e-14
[189,] 1.0000000 1.570088e-13 7.850439e-14
[190,] 1.0000000 1.694711e-13 8.473556e-14
[191,] 1.0000000 7.070425e-14 3.535213e-14
[192,] 1.0000000 1.059744e-13 5.298721e-14
[193,] 1.0000000 1.415099e-13 7.075493e-14
[194,] 1.0000000 2.909822e-13 1.454911e-13
[195,] 1.0000000 5.269797e-13 2.634898e-13
[196,] 1.0000000 1.056295e-12 5.281475e-13
[197,] 1.0000000 2.162613e-12 1.081307e-12
[198,] 1.0000000 4.343941e-12 2.171971e-12
[199,] 1.0000000 5.883748e-12 2.941874e-12
[200,] 1.0000000 1.212883e-11 6.064413e-12
[201,] 1.0000000 2.013798e-11 1.006899e-11
[202,] 1.0000000 3.548876e-11 1.774438e-11
[203,] 1.0000000 3.326665e-11 1.663332e-11
[204,] 1.0000000 6.196174e-11 3.098087e-11
[205,] 1.0000000 9.573450e-11 4.786725e-11
[206,] 1.0000000 1.942238e-10 9.711188e-11
[207,] 1.0000000 3.745017e-10 1.872508e-10
[208,] 1.0000000 7.486054e-10 3.743027e-10
[209,] 1.0000000 1.306434e-09 6.532168e-10
[210,] 1.0000000 4.252668e-10 2.126334e-10
[211,] 1.0000000 4.430235e-10 2.215117e-10
[212,] 1.0000000 8.412577e-10 4.206289e-10
[213,] 1.0000000 9.156330e-10 4.578165e-10
[214,] 1.0000000 1.693501e-09 8.467507e-10
[215,] 1.0000000 1.813415e-09 9.067077e-10
[216,] 1.0000000 3.506149e-09 1.753075e-09
[217,] 1.0000000 4.794967e-09 2.397483e-09
[218,] 1.0000000 7.638083e-10 3.819042e-10
[219,] 1.0000000 1.575789e-09 7.878944e-10
[220,] 1.0000000 2.424772e-09 1.212386e-09
[221,] 1.0000000 3.074434e-09 1.537217e-09
[222,] 1.0000000 6.144746e-09 3.072373e-09
[223,] 1.0000000 9.388310e-09 4.694155e-09
[224,] 1.0000000 7.250839e-09 3.625419e-09
[225,] 1.0000000 1.409311e-08 7.046557e-09
[226,] 1.0000000 1.351268e-08 6.756340e-09
[227,] 1.0000000 1.638181e-08 8.190907e-09
[228,] 1.0000000 3.298858e-08 1.649429e-08
[229,] 1.0000000 6.378149e-08 3.189075e-08
[230,] 0.9999999 1.277232e-07 6.386161e-08
[231,] 1.0000000 9.733895e-08 4.866947e-08
[232,] 1.0000000 8.906519e-08 4.453259e-08
[233,] 0.9999999 1.676496e-07 8.382480e-08
[234,] 0.9999999 2.807111e-07 1.403556e-07
[235,] 0.9999997 5.646977e-07 2.823489e-07
[236,] 0.9999996 7.803702e-07 3.901851e-07
[237,] 0.9999995 1.016132e-06 5.080661e-07
[238,] 0.9999995 1.057867e-06 5.289335e-07
[239,] 0.9999989 2.103854e-06 1.051927e-06
[240,] 0.9999979 4.141872e-06 2.070936e-06
[241,] 0.9999960 8.005832e-06 4.002916e-06
[242,] 0.9999976 4.809272e-06 2.404636e-06
[243,] 0.9999976 4.707501e-06 2.353751e-06
[244,] 0.9999959 8.243658e-06 4.121829e-06
[245,] 0.9999919 1.624731e-05 8.123654e-06
[246,] 0.9999934 1.321430e-05 6.607148e-06
[247,] 0.9999901 1.986264e-05 9.931320e-06
[248,] 0.9999815 3.703016e-05 1.851508e-05
[249,] 0.9999902 1.958534e-05 9.792668e-06
[250,] 0.9999834 3.321727e-05 1.660864e-05
[251,] 0.9999680 6.407270e-05 3.203635e-05
[252,] 0.9999686 6.276885e-05 3.138443e-05
[253,] 0.9999394 1.212063e-04 6.060314e-05
[254,] 0.9999043 1.913621e-04 9.568105e-05
[255,] 0.9998163 3.674587e-04 1.837293e-04
[256,] 0.9997137 5.725319e-04 2.862660e-04
[257,] 0.9997665 4.669781e-04 2.334891e-04
[258,] 0.9996554 6.892803e-04 3.446402e-04
[259,] 0.9997953 4.093410e-04 2.046705e-04
[260,] 0.9995851 8.298614e-04 4.149307e-04
[261,] 0.9994588 1.082451e-03 5.412256e-04
[262,] 0.9996189 7.622007e-04 3.811003e-04
[263,] 0.9994991 1.001806e-03 5.009031e-04
[264,] 0.9990858 1.828400e-03 9.142000e-04
[265,] 0.9990006 1.998724e-03 9.993618e-04
[266,] 0.9995702 8.595594e-04 4.297797e-04
[267,] 0.9990252 1.949577e-03 9.747885e-04
[268,] 0.9982396 3.520855e-03 1.760428e-03
[269,] 0.9974378 5.124341e-03 2.562170e-03
[270,] 0.9948218 1.035636e-02 5.178179e-03
[271,] 0.9984686 3.062897e-03 1.531449e-03
[272,] 0.9983491 3.301808e-03 1.650904e-03
[273,] 0.9963661 7.267793e-03 3.633896e-03
[274,] 0.9967757 6.448547e-03 3.224273e-03
[275,] 0.9938498 1.230032e-02 6.150158e-03
[276,] 0.9862120 2.757608e-02 1.378804e-02
[277,] 0.9824739 3.505216e-02 1.752608e-02
[278,] 0.9424891 1.150218e-01 5.751091e-02
> postscript(file="/var/www/rcomp/tmp/13djx1324655439.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2fau61324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/384wk1324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lcn61324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5g1o61324655439.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
-8.50773934 2.40049076 -9.90832771 32.11455987 6.24493604 -32.71357074
7 8 9 10 11 12
-15.01687573 -13.27867476 -10.87819831 -29.15952660 9.41209870 -3.18117188
13 14 15 16 17 18
20.24373590 -7.02616777 12.92793960 0.60697130 44.38476378 2.91840713
19 20 21 22 23 24
-14.27491025 18.24179448 28.89391803 16.19133671 -30.12537233 -8.81258397
25 26 27 28 29 30
20.53625786 57.62038846 3.75621001 -2.60713725 25.56848390 6.01883145
31 32 33 34 35 36
24.57371415 5.02716196 -21.60835040 19.66072831 -1.25143293 0.36856689
37 38 39 40 41 42
-3.72683368 -1.43075076 -24.35862951 -1.16828114 -0.51723231 -3.18015114
43 44 45 46 47 48
10.78204361 2.31634313 15.04799593 -0.08860702 -0.24788785 -3.67759882
49 50 51 52 53 54
-3.99890459 -16.55764864 -48.48379730 -9.39091086 19.38972568 1.87944215
55 56 57 58 59 60
25.31999060 11.28516243 0.99068296 -41.04011898 -4.58369994 -4.94488218
61 62 63 64 65 66
-4.50246267 -4.38386908 -29.70569806 -6.32175951 -3.92390631 25.40941211
67 68 69 70 71 72
0.09101327 7.98200627 0.18387464 -4.78694173 -23.20191534 -37.75888802
73 74 75 76 77 78
10.79402852 -0.86132214 4.92897276 38.27401741 64.69531441 9.35867664
79 80 81 82 83 84
-2.98390012 -6.19207853 1.31660878 47.87425283 16.81717985 -3.26339685
85 86 87 88 89 90
-39.94993955 46.04752873 4.83336769 9.45045429 -3.83591742 -18.87190834
91 92 93 94 95 96
25.16648972 12.21078105 -12.17226833 5.78230341 9.43334695 -44.09753645
97 98 99 100 101 102
-7.51673236 59.91676405 2.72509077 46.89765666 -18.18899824 24.27421277
103 104 105 106 107 108
-37.93547897 -9.00140278 -6.58424623 23.55621498 6.94506374 8.34085750
109 110 111 112 113 114
1.23056972 2.43416201 19.71121263 -9.31046220 -10.03247434 21.90702560
115 116 117 118 119 120
12.88974668 31.52227366 63.61093918 -19.50847912 -53.05008474 6.28058110
121 122 123 124 125 126
9.80607688 7.96456235 -4.75788331 16.56468001 -39.56498507 0.08613912
127 128 129 130 131 132
-10.84234980 15.44893982 39.97491938 6.70711637 -13.82155850 47.57251861
133 134 135 136 137 138
0.39891100 9.18469171 25.08042621 6.16680132 -0.70050598 -20.76808958
139 140 141 142 143 144
-5.70546063 6.70345170 -0.26012414 7.09051551 -21.07163208 11.78968654
145 146 147 148 149 150
-16.01256315 0.48926510 -27.86994456 -27.32793981 4.43798500 -15.16578323
151 152 153 154 155 156
1.44106002 -23.87025850 7.37986329 12.22416567 34.47055410 -18.17779673
157 158 159 160 161 162
-2.53885778 -6.98291481 -6.07206322 -23.44208180 -30.82196194 -39.44420658
163 164 165 166 167 168
-9.68443438 -11.58702980 -1.66006245 17.28892307 12.12270306 -39.39244924
169 170 171 172 173 174
10.57659223 -48.54887846 -16.32130180 30.37398390 -14.76817814 23.34689682
175 176 177 178 179 180
72.48416162 -15.94507608 -4.15038182 -4.67423697 34.75849619 2.79178731
181 182 183 184 185 186
0.21305390 24.33787020 -0.50001047 -18.87576094 21.83388750 5.76165495
187 188 189 190 191 192
-1.06122578 -4.65485157 -6.84554011 2.72873591 -14.67655934 -17.83178483
193 194 195 196 197 198
15.94135765 -18.50062917 -1.05878275 14.85878318 -11.22819894 -20.24988800
199 200 201 202 203 204
-8.28898011 0.65184832 -6.38547152 -0.06135981 -6.68371546 -19.12600995
205 206 207 208 209 210
-1.91331852 -6.07686091 5.31591307 -26.96104704 -10.92881911 4.50495361
211 212 213 214 215 216
-1.87418025 -3.15186547 0.11515086 3.27180942 -26.92169215 -11.84142019
217 218 219 220 221 222
4.42599334 -15.83438337 5.31434448 -14.75095970 0.86928035 -19.87426126
223 224 225 226 227 228
22.62667627 -3.71814278 -3.30123428 2.44009825 -5.58723816 7.03883655
229 230 231 232 233 234
16.98843397 -2.86257485 -8.50714498 8.19499554 -9.04744645 -2.30668825
235 236 237 238 239 240
-1.37516891 16.04586364 -27.56286439 7.49966828 5.90301026 2.18898910
241 242 243 244 245 246
-15.99149985 -21.97125830 14.41905333 0.25881181 -3.35788391 -4.41597224
247 248 249 250 251 252
10.58275679 -18.36793278 -11.07366867 -0.57338598 -25.14286076 2.57532692
253 254 255 256 257 258
7.87413516 -21.52892674 -6.20390000 0.26840136 11.43660526 -3.85943327
259 260 261 262 263 264
-6.57307198 2.66238669 -5.89508240 15.68429152 -4.90450093 18.34111048
265 266 267 268 269 270
-5.64029450 9.58876008 14.75646323 6.73962281 -5.47675279 1.46054341
271 272 273 274 275 276
-16.81695603 -5.94429160 -4.21247673 10.77181288 2.92832139 16.09346690
277 278 279 280 281 282
-19.45073666 4.22634597 8.64484594 -10.04957238 1.08187662 -27.36444054
283 284 285 286 287 288
-3.36439393 -3.14067807 -11.77284258 -8.07977755 -1.24464462 3.49442937
289
-6.81878471
> postscript(file="/var/www/rcomp/tmp/6wiuj1324655439.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 -8.50773934 NA
1 2.40049076 -8.50773934
2 -9.90832771 2.40049076
3 32.11455987 -9.90832771
4 6.24493604 32.11455987
5 -32.71357074 6.24493604
6 -15.01687573 -32.71357074
7 -13.27867476 -15.01687573
8 -10.87819831 -13.27867476
9 -29.15952660 -10.87819831
10 9.41209870 -29.15952660
11 -3.18117188 9.41209870
12 20.24373590 -3.18117188
13 -7.02616777 20.24373590
14 12.92793960 -7.02616777
15 0.60697130 12.92793960
16 44.38476378 0.60697130
17 2.91840713 44.38476378
18 -14.27491025 2.91840713
19 18.24179448 -14.27491025
20 28.89391803 18.24179448
21 16.19133671 28.89391803
22 -30.12537233 16.19133671
23 -8.81258397 -30.12537233
24 20.53625786 -8.81258397
25 57.62038846 20.53625786
26 3.75621001 57.62038846
27 -2.60713725 3.75621001
28 25.56848390 -2.60713725
29 6.01883145 25.56848390
30 24.57371415 6.01883145
31 5.02716196 24.57371415
32 -21.60835040 5.02716196
33 19.66072831 -21.60835040
34 -1.25143293 19.66072831
35 0.36856689 -1.25143293
36 -3.72683368 0.36856689
37 -1.43075076 -3.72683368
38 -24.35862951 -1.43075076
39 -1.16828114 -24.35862951
40 -0.51723231 -1.16828114
41 -3.18015114 -0.51723231
42 10.78204361 -3.18015114
43 2.31634313 10.78204361
44 15.04799593 2.31634313
45 -0.08860702 15.04799593
46 -0.24788785 -0.08860702
47 -3.67759882 -0.24788785
48 -3.99890459 -3.67759882
49 -16.55764864 -3.99890459
50 -48.48379730 -16.55764864
51 -9.39091086 -48.48379730
52 19.38972568 -9.39091086
53 1.87944215 19.38972568
54 25.31999060 1.87944215
55 11.28516243 25.31999060
56 0.99068296 11.28516243
57 -41.04011898 0.99068296
58 -4.58369994 -41.04011898
59 -4.94488218 -4.58369994
60 -4.50246267 -4.94488218
61 -4.38386908 -4.50246267
62 -29.70569806 -4.38386908
63 -6.32175951 -29.70569806
64 -3.92390631 -6.32175951
65 25.40941211 -3.92390631
66 0.09101327 25.40941211
67 7.98200627 0.09101327
68 0.18387464 7.98200627
69 -4.78694173 0.18387464
70 -23.20191534 -4.78694173
71 -37.75888802 -23.20191534
72 10.79402852 -37.75888802
73 -0.86132214 10.79402852
74 4.92897276 -0.86132214
75 38.27401741 4.92897276
76 64.69531441 38.27401741
77 9.35867664 64.69531441
78 -2.98390012 9.35867664
79 -6.19207853 -2.98390012
80 1.31660878 -6.19207853
81 47.87425283 1.31660878
82 16.81717985 47.87425283
83 -3.26339685 16.81717985
84 -39.94993955 -3.26339685
85 46.04752873 -39.94993955
86 4.83336769 46.04752873
87 9.45045429 4.83336769
88 -3.83591742 9.45045429
89 -18.87190834 -3.83591742
90 25.16648972 -18.87190834
91 12.21078105 25.16648972
92 -12.17226833 12.21078105
93 5.78230341 -12.17226833
94 9.43334695 5.78230341
95 -44.09753645 9.43334695
96 -7.51673236 -44.09753645
97 59.91676405 -7.51673236
98 2.72509077 59.91676405
99 46.89765666 2.72509077
100 -18.18899824 46.89765666
101 24.27421277 -18.18899824
102 -37.93547897 24.27421277
103 -9.00140278 -37.93547897
104 -6.58424623 -9.00140278
105 23.55621498 -6.58424623
106 6.94506374 23.55621498
107 8.34085750 6.94506374
108 1.23056972 8.34085750
109 2.43416201 1.23056972
110 19.71121263 2.43416201
111 -9.31046220 19.71121263
112 -10.03247434 -9.31046220
113 21.90702560 -10.03247434
114 12.88974668 21.90702560
115 31.52227366 12.88974668
116 63.61093918 31.52227366
117 -19.50847912 63.61093918
118 -53.05008474 -19.50847912
119 6.28058110 -53.05008474
120 9.80607688 6.28058110
121 7.96456235 9.80607688
122 -4.75788331 7.96456235
123 16.56468001 -4.75788331
124 -39.56498507 16.56468001
125 0.08613912 -39.56498507
126 -10.84234980 0.08613912
127 15.44893982 -10.84234980
128 39.97491938 15.44893982
129 6.70711637 39.97491938
130 -13.82155850 6.70711637
131 47.57251861 -13.82155850
132 0.39891100 47.57251861
133 9.18469171 0.39891100
134 25.08042621 9.18469171
135 6.16680132 25.08042621
136 -0.70050598 6.16680132
137 -20.76808958 -0.70050598
138 -5.70546063 -20.76808958
139 6.70345170 -5.70546063
140 -0.26012414 6.70345170
141 7.09051551 -0.26012414
142 -21.07163208 7.09051551
143 11.78968654 -21.07163208
144 -16.01256315 11.78968654
145 0.48926510 -16.01256315
146 -27.86994456 0.48926510
147 -27.32793981 -27.86994456
148 4.43798500 -27.32793981
149 -15.16578323 4.43798500
150 1.44106002 -15.16578323
151 -23.87025850 1.44106002
152 7.37986329 -23.87025850
153 12.22416567 7.37986329
154 34.47055410 12.22416567
155 -18.17779673 34.47055410
156 -2.53885778 -18.17779673
157 -6.98291481 -2.53885778
158 -6.07206322 -6.98291481
159 -23.44208180 -6.07206322
160 -30.82196194 -23.44208180
161 -39.44420658 -30.82196194
162 -9.68443438 -39.44420658
163 -11.58702980 -9.68443438
164 -1.66006245 -11.58702980
165 17.28892307 -1.66006245
166 12.12270306 17.28892307
167 -39.39244924 12.12270306
168 10.57659223 -39.39244924
169 -48.54887846 10.57659223
170 -16.32130180 -48.54887846
171 30.37398390 -16.32130180
172 -14.76817814 30.37398390
173 23.34689682 -14.76817814
174 72.48416162 23.34689682
175 -15.94507608 72.48416162
176 -4.15038182 -15.94507608
177 -4.67423697 -4.15038182
178 34.75849619 -4.67423697
179 2.79178731 34.75849619
180 0.21305390 2.79178731
181 24.33787020 0.21305390
182 -0.50001047 24.33787020
183 -18.87576094 -0.50001047
184 21.83388750 -18.87576094
185 5.76165495 21.83388750
186 -1.06122578 5.76165495
187 -4.65485157 -1.06122578
188 -6.84554011 -4.65485157
189 2.72873591 -6.84554011
190 -14.67655934 2.72873591
191 -17.83178483 -14.67655934
192 15.94135765 -17.83178483
193 -18.50062917 15.94135765
194 -1.05878275 -18.50062917
195 14.85878318 -1.05878275
196 -11.22819894 14.85878318
197 -20.24988800 -11.22819894
198 -8.28898011 -20.24988800
199 0.65184832 -8.28898011
200 -6.38547152 0.65184832
201 -0.06135981 -6.38547152
202 -6.68371546 -0.06135981
203 -19.12600995 -6.68371546
204 -1.91331852 -19.12600995
205 -6.07686091 -1.91331852
206 5.31591307 -6.07686091
207 -26.96104704 5.31591307
208 -10.92881911 -26.96104704
209 4.50495361 -10.92881911
210 -1.87418025 4.50495361
211 -3.15186547 -1.87418025
212 0.11515086 -3.15186547
213 3.27180942 0.11515086
214 -26.92169215 3.27180942
215 -11.84142019 -26.92169215
216 4.42599334 -11.84142019
217 -15.83438337 4.42599334
218 5.31434448 -15.83438337
219 -14.75095970 5.31434448
220 0.86928035 -14.75095970
221 -19.87426126 0.86928035
222 22.62667627 -19.87426126
223 -3.71814278 22.62667627
224 -3.30123428 -3.71814278
225 2.44009825 -3.30123428
226 -5.58723816 2.44009825
227 7.03883655 -5.58723816
228 16.98843397 7.03883655
229 -2.86257485 16.98843397
230 -8.50714498 -2.86257485
231 8.19499554 -8.50714498
232 -9.04744645 8.19499554
233 -2.30668825 -9.04744645
234 -1.37516891 -2.30668825
235 16.04586364 -1.37516891
236 -27.56286439 16.04586364
237 7.49966828 -27.56286439
238 5.90301026 7.49966828
239 2.18898910 5.90301026
240 -15.99149985 2.18898910
241 -21.97125830 -15.99149985
242 14.41905333 -21.97125830
243 0.25881181 14.41905333
244 -3.35788391 0.25881181
245 -4.41597224 -3.35788391
246 10.58275679 -4.41597224
247 -18.36793278 10.58275679
248 -11.07366867 -18.36793278
249 -0.57338598 -11.07366867
250 -25.14286076 -0.57338598
251 2.57532692 -25.14286076
252 7.87413516 2.57532692
253 -21.52892674 7.87413516
254 -6.20390000 -21.52892674
255 0.26840136 -6.20390000
256 11.43660526 0.26840136
257 -3.85943327 11.43660526
258 -6.57307198 -3.85943327
259 2.66238669 -6.57307198
260 -5.89508240 2.66238669
261 15.68429152 -5.89508240
262 -4.90450093 15.68429152
263 18.34111048 -4.90450093
264 -5.64029450 18.34111048
265 9.58876008 -5.64029450
266 14.75646323 9.58876008
267 6.73962281 14.75646323
268 -5.47675279 6.73962281
269 1.46054341 -5.47675279
270 -16.81695603 1.46054341
271 -5.94429160 -16.81695603
272 -4.21247673 -5.94429160
273 10.77181288 -4.21247673
274 2.92832139 10.77181288
275 16.09346690 2.92832139
276 -19.45073666 16.09346690
277 4.22634597 -19.45073666
278 8.64484594 4.22634597
279 -10.04957238 8.64484594
280 1.08187662 -10.04957238
281 -27.36444054 1.08187662
282 -3.36439393 -27.36444054
283 -3.14067807 -3.36439393
284 -11.77284258 -3.14067807
285 -8.07977755 -11.77284258
286 -1.24464462 -8.07977755
287 3.49442937 -1.24464462
288 -6.81878471 3.49442937
289 NA -6.81878471
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.40049076 -8.50773934
[2,] -9.90832771 2.40049076
[3,] 32.11455987 -9.90832771
[4,] 6.24493604 32.11455987
[5,] -32.71357074 6.24493604
[6,] -15.01687573 -32.71357074
[7,] -13.27867476 -15.01687573
[8,] -10.87819831 -13.27867476
[9,] -29.15952660 -10.87819831
[10,] 9.41209870 -29.15952660
[11,] -3.18117188 9.41209870
[12,] 20.24373590 -3.18117188
[13,] -7.02616777 20.24373590
[14,] 12.92793960 -7.02616777
[15,] 0.60697130 12.92793960
[16,] 44.38476378 0.60697130
[17,] 2.91840713 44.38476378
[18,] -14.27491025 2.91840713
[19,] 18.24179448 -14.27491025
[20,] 28.89391803 18.24179448
[21,] 16.19133671 28.89391803
[22,] -30.12537233 16.19133671
[23,] -8.81258397 -30.12537233
[24,] 20.53625786 -8.81258397
[25,] 57.62038846 20.53625786
[26,] 3.75621001 57.62038846
[27,] -2.60713725 3.75621001
[28,] 25.56848390 -2.60713725
[29,] 6.01883145 25.56848390
[30,] 24.57371415 6.01883145
[31,] 5.02716196 24.57371415
[32,] -21.60835040 5.02716196
[33,] 19.66072831 -21.60835040
[34,] -1.25143293 19.66072831
[35,] 0.36856689 -1.25143293
[36,] -3.72683368 0.36856689
[37,] -1.43075076 -3.72683368
[38,] -24.35862951 -1.43075076
[39,] -1.16828114 -24.35862951
[40,] -0.51723231 -1.16828114
[41,] -3.18015114 -0.51723231
[42,] 10.78204361 -3.18015114
[43,] 2.31634313 10.78204361
[44,] 15.04799593 2.31634313
[45,] -0.08860702 15.04799593
[46,] -0.24788785 -0.08860702
[47,] -3.67759882 -0.24788785
[48,] -3.99890459 -3.67759882
[49,] -16.55764864 -3.99890459
[50,] -48.48379730 -16.55764864
[51,] -9.39091086 -48.48379730
[52,] 19.38972568 -9.39091086
[53,] 1.87944215 19.38972568
[54,] 25.31999060 1.87944215
[55,] 11.28516243 25.31999060
[56,] 0.99068296 11.28516243
[57,] -41.04011898 0.99068296
[58,] -4.58369994 -41.04011898
[59,] -4.94488218 -4.58369994
[60,] -4.50246267 -4.94488218
[61,] -4.38386908 -4.50246267
[62,] -29.70569806 -4.38386908
[63,] -6.32175951 -29.70569806
[64,] -3.92390631 -6.32175951
[65,] 25.40941211 -3.92390631
[66,] 0.09101327 25.40941211
[67,] 7.98200627 0.09101327
[68,] 0.18387464 7.98200627
[69,] -4.78694173 0.18387464
[70,] -23.20191534 -4.78694173
[71,] -37.75888802 -23.20191534
[72,] 10.79402852 -37.75888802
[73,] -0.86132214 10.79402852
[74,] 4.92897276 -0.86132214
[75,] 38.27401741 4.92897276
[76,] 64.69531441 38.27401741
[77,] 9.35867664 64.69531441
[78,] -2.98390012 9.35867664
[79,] -6.19207853 -2.98390012
[80,] 1.31660878 -6.19207853
[81,] 47.87425283 1.31660878
[82,] 16.81717985 47.87425283
[83,] -3.26339685 16.81717985
[84,] -39.94993955 -3.26339685
[85,] 46.04752873 -39.94993955
[86,] 4.83336769 46.04752873
[87,] 9.45045429 4.83336769
[88,] -3.83591742 9.45045429
[89,] -18.87190834 -3.83591742
[90,] 25.16648972 -18.87190834
[91,] 12.21078105 25.16648972
[92,] -12.17226833 12.21078105
[93,] 5.78230341 -12.17226833
[94,] 9.43334695 5.78230341
[95,] -44.09753645 9.43334695
[96,] -7.51673236 -44.09753645
[97,] 59.91676405 -7.51673236
[98,] 2.72509077 59.91676405
[99,] 46.89765666 2.72509077
[100,] -18.18899824 46.89765666
[101,] 24.27421277 -18.18899824
[102,] -37.93547897 24.27421277
[103,] -9.00140278 -37.93547897
[104,] -6.58424623 -9.00140278
[105,] 23.55621498 -6.58424623
[106,] 6.94506374 23.55621498
[107,] 8.34085750 6.94506374
[108,] 1.23056972 8.34085750
[109,] 2.43416201 1.23056972
[110,] 19.71121263 2.43416201
[111,] -9.31046220 19.71121263
[112,] -10.03247434 -9.31046220
[113,] 21.90702560 -10.03247434
[114,] 12.88974668 21.90702560
[115,] 31.52227366 12.88974668
[116,] 63.61093918 31.52227366
[117,] -19.50847912 63.61093918
[118,] -53.05008474 -19.50847912
[119,] 6.28058110 -53.05008474
[120,] 9.80607688 6.28058110
[121,] 7.96456235 9.80607688
[122,] -4.75788331 7.96456235
[123,] 16.56468001 -4.75788331
[124,] -39.56498507 16.56468001
[125,] 0.08613912 -39.56498507
[126,] -10.84234980 0.08613912
[127,] 15.44893982 -10.84234980
[128,] 39.97491938 15.44893982
[129,] 6.70711637 39.97491938
[130,] -13.82155850 6.70711637
[131,] 47.57251861 -13.82155850
[132,] 0.39891100 47.57251861
[133,] 9.18469171 0.39891100
[134,] 25.08042621 9.18469171
[135,] 6.16680132 25.08042621
[136,] -0.70050598 6.16680132
[137,] -20.76808958 -0.70050598
[138,] -5.70546063 -20.76808958
[139,] 6.70345170 -5.70546063
[140,] -0.26012414 6.70345170
[141,] 7.09051551 -0.26012414
[142,] -21.07163208 7.09051551
[143,] 11.78968654 -21.07163208
[144,] -16.01256315 11.78968654
[145,] 0.48926510 -16.01256315
[146,] -27.86994456 0.48926510
[147,] -27.32793981 -27.86994456
[148,] 4.43798500 -27.32793981
[149,] -15.16578323 4.43798500
[150,] 1.44106002 -15.16578323
[151,] -23.87025850 1.44106002
[152,] 7.37986329 -23.87025850
[153,] 12.22416567 7.37986329
[154,] 34.47055410 12.22416567
[155,] -18.17779673 34.47055410
[156,] -2.53885778 -18.17779673
[157,] -6.98291481 -2.53885778
[158,] -6.07206322 -6.98291481
[159,] -23.44208180 -6.07206322
[160,] -30.82196194 -23.44208180
[161,] -39.44420658 -30.82196194
[162,] -9.68443438 -39.44420658
[163,] -11.58702980 -9.68443438
[164,] -1.66006245 -11.58702980
[165,] 17.28892307 -1.66006245
[166,] 12.12270306 17.28892307
[167,] -39.39244924 12.12270306
[168,] 10.57659223 -39.39244924
[169,] -48.54887846 10.57659223
[170,] -16.32130180 -48.54887846
[171,] 30.37398390 -16.32130180
[172,] -14.76817814 30.37398390
[173,] 23.34689682 -14.76817814
[174,] 72.48416162 23.34689682
[175,] -15.94507608 72.48416162
[176,] -4.15038182 -15.94507608
[177,] -4.67423697 -4.15038182
[178,] 34.75849619 -4.67423697
[179,] 2.79178731 34.75849619
[180,] 0.21305390 2.79178731
[181,] 24.33787020 0.21305390
[182,] -0.50001047 24.33787020
[183,] -18.87576094 -0.50001047
[184,] 21.83388750 -18.87576094
[185,] 5.76165495 21.83388750
[186,] -1.06122578 5.76165495
[187,] -4.65485157 -1.06122578
[188,] -6.84554011 -4.65485157
[189,] 2.72873591 -6.84554011
[190,] -14.67655934 2.72873591
[191,] -17.83178483 -14.67655934
[192,] 15.94135765 -17.83178483
[193,] -18.50062917 15.94135765
[194,] -1.05878275 -18.50062917
[195,] 14.85878318 -1.05878275
[196,] -11.22819894 14.85878318
[197,] -20.24988800 -11.22819894
[198,] -8.28898011 -20.24988800
[199,] 0.65184832 -8.28898011
[200,] -6.38547152 0.65184832
[201,] -0.06135981 -6.38547152
[202,] -6.68371546 -0.06135981
[203,] -19.12600995 -6.68371546
[204,] -1.91331852 -19.12600995
[205,] -6.07686091 -1.91331852
[206,] 5.31591307 -6.07686091
[207,] -26.96104704 5.31591307
[208,] -10.92881911 -26.96104704
[209,] 4.50495361 -10.92881911
[210,] -1.87418025 4.50495361
[211,] -3.15186547 -1.87418025
[212,] 0.11515086 -3.15186547
[213,] 3.27180942 0.11515086
[214,] -26.92169215 3.27180942
[215,] -11.84142019 -26.92169215
[216,] 4.42599334 -11.84142019
[217,] -15.83438337 4.42599334
[218,] 5.31434448 -15.83438337
[219,] -14.75095970 5.31434448
[220,] 0.86928035 -14.75095970
[221,] -19.87426126 0.86928035
[222,] 22.62667627 -19.87426126
[223,] -3.71814278 22.62667627
[224,] -3.30123428 -3.71814278
[225,] 2.44009825 -3.30123428
[226,] -5.58723816 2.44009825
[227,] 7.03883655 -5.58723816
[228,] 16.98843397 7.03883655
[229,] -2.86257485 16.98843397
[230,] -8.50714498 -2.86257485
[231,] 8.19499554 -8.50714498
[232,] -9.04744645 8.19499554
[233,] -2.30668825 -9.04744645
[234,] -1.37516891 -2.30668825
[235,] 16.04586364 -1.37516891
[236,] -27.56286439 16.04586364
[237,] 7.49966828 -27.56286439
[238,] 5.90301026 7.49966828
[239,] 2.18898910 5.90301026
[240,] -15.99149985 2.18898910
[241,] -21.97125830 -15.99149985
[242,] 14.41905333 -21.97125830
[243,] 0.25881181 14.41905333
[244,] -3.35788391 0.25881181
[245,] -4.41597224 -3.35788391
[246,] 10.58275679 -4.41597224
[247,] -18.36793278 10.58275679
[248,] -11.07366867 -18.36793278
[249,] -0.57338598 -11.07366867
[250,] -25.14286076 -0.57338598
[251,] 2.57532692 -25.14286076
[252,] 7.87413516 2.57532692
[253,] -21.52892674 7.87413516
[254,] -6.20390000 -21.52892674
[255,] 0.26840136 -6.20390000
[256,] 11.43660526 0.26840136
[257,] -3.85943327 11.43660526
[258,] -6.57307198 -3.85943327
[259,] 2.66238669 -6.57307198
[260,] -5.89508240 2.66238669
[261,] 15.68429152 -5.89508240
[262,] -4.90450093 15.68429152
[263,] 18.34111048 -4.90450093
[264,] -5.64029450 18.34111048
[265,] 9.58876008 -5.64029450
[266,] 14.75646323 9.58876008
[267,] 6.73962281 14.75646323
[268,] -5.47675279 6.73962281
[269,] 1.46054341 -5.47675279
[270,] -16.81695603 1.46054341
[271,] -5.94429160 -16.81695603
[272,] -4.21247673 -5.94429160
[273,] 10.77181288 -4.21247673
[274,] 2.92832139 10.77181288
[275,] 16.09346690 2.92832139
[276,] -19.45073666 16.09346690
[277,] 4.22634597 -19.45073666
[278,] 8.64484594 4.22634597
[279,] -10.04957238 8.64484594
[280,] 1.08187662 -10.04957238
[281,] -27.36444054 1.08187662
[282,] -3.36439393 -27.36444054
[283,] -3.14067807 -3.36439393
[284,] -11.77284258 -3.14067807
[285,] -8.07977755 -11.77284258
[286,] -1.24464462 -8.07977755
[287,] 3.49442937 -1.24464462
[288,] -6.81878471 3.49442937
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.40049076 -8.50773934
2 -9.90832771 2.40049076
3 32.11455987 -9.90832771
4 6.24493604 32.11455987
5 -32.71357074 6.24493604
6 -15.01687573 -32.71357074
7 -13.27867476 -15.01687573
8 -10.87819831 -13.27867476
9 -29.15952660 -10.87819831
10 9.41209870 -29.15952660
11 -3.18117188 9.41209870
12 20.24373590 -3.18117188
13 -7.02616777 20.24373590
14 12.92793960 -7.02616777
15 0.60697130 12.92793960
16 44.38476378 0.60697130
17 2.91840713 44.38476378
18 -14.27491025 2.91840713
19 18.24179448 -14.27491025
20 28.89391803 18.24179448
21 16.19133671 28.89391803
22 -30.12537233 16.19133671
23 -8.81258397 -30.12537233
24 20.53625786 -8.81258397
25 57.62038846 20.53625786
26 3.75621001 57.62038846
27 -2.60713725 3.75621001
28 25.56848390 -2.60713725
29 6.01883145 25.56848390
30 24.57371415 6.01883145
31 5.02716196 24.57371415
32 -21.60835040 5.02716196
33 19.66072831 -21.60835040
34 -1.25143293 19.66072831
35 0.36856689 -1.25143293
36 -3.72683368 0.36856689
37 -1.43075076 -3.72683368
38 -24.35862951 -1.43075076
39 -1.16828114 -24.35862951
40 -0.51723231 -1.16828114
41 -3.18015114 -0.51723231
42 10.78204361 -3.18015114
43 2.31634313 10.78204361
44 15.04799593 2.31634313
45 -0.08860702 15.04799593
46 -0.24788785 -0.08860702
47 -3.67759882 -0.24788785
48 -3.99890459 -3.67759882
49 -16.55764864 -3.99890459
50 -48.48379730 -16.55764864
51 -9.39091086 -48.48379730
52 19.38972568 -9.39091086
53 1.87944215 19.38972568
54 25.31999060 1.87944215
55 11.28516243 25.31999060
56 0.99068296 11.28516243
57 -41.04011898 0.99068296
58 -4.58369994 -41.04011898
59 -4.94488218 -4.58369994
60 -4.50246267 -4.94488218
61 -4.38386908 -4.50246267
62 -29.70569806 -4.38386908
63 -6.32175951 -29.70569806
64 -3.92390631 -6.32175951
65 25.40941211 -3.92390631
66 0.09101327 25.40941211
67 7.98200627 0.09101327
68 0.18387464 7.98200627
69 -4.78694173 0.18387464
70 -23.20191534 -4.78694173
71 -37.75888802 -23.20191534
72 10.79402852 -37.75888802
73 -0.86132214 10.79402852
74 4.92897276 -0.86132214
75 38.27401741 4.92897276
76 64.69531441 38.27401741
77 9.35867664 64.69531441
78 -2.98390012 9.35867664
79 -6.19207853 -2.98390012
80 1.31660878 -6.19207853
81 47.87425283 1.31660878
82 16.81717985 47.87425283
83 -3.26339685 16.81717985
84 -39.94993955 -3.26339685
85 46.04752873 -39.94993955
86 4.83336769 46.04752873
87 9.45045429 4.83336769
88 -3.83591742 9.45045429
89 -18.87190834 -3.83591742
90 25.16648972 -18.87190834
91 12.21078105 25.16648972
92 -12.17226833 12.21078105
93 5.78230341 -12.17226833
94 9.43334695 5.78230341
95 -44.09753645 9.43334695
96 -7.51673236 -44.09753645
97 59.91676405 -7.51673236
98 2.72509077 59.91676405
99 46.89765666 2.72509077
100 -18.18899824 46.89765666
101 24.27421277 -18.18899824
102 -37.93547897 24.27421277
103 -9.00140278 -37.93547897
104 -6.58424623 -9.00140278
105 23.55621498 -6.58424623
106 6.94506374 23.55621498
107 8.34085750 6.94506374
108 1.23056972 8.34085750
109 2.43416201 1.23056972
110 19.71121263 2.43416201
111 -9.31046220 19.71121263
112 -10.03247434 -9.31046220
113 21.90702560 -10.03247434
114 12.88974668 21.90702560
115 31.52227366 12.88974668
116 63.61093918 31.52227366
117 -19.50847912 63.61093918
118 -53.05008474 -19.50847912
119 6.28058110 -53.05008474
120 9.80607688 6.28058110
121 7.96456235 9.80607688
122 -4.75788331 7.96456235
123 16.56468001 -4.75788331
124 -39.56498507 16.56468001
125 0.08613912 -39.56498507
126 -10.84234980 0.08613912
127 15.44893982 -10.84234980
128 39.97491938 15.44893982
129 6.70711637 39.97491938
130 -13.82155850 6.70711637
131 47.57251861 -13.82155850
132 0.39891100 47.57251861
133 9.18469171 0.39891100
134 25.08042621 9.18469171
135 6.16680132 25.08042621
136 -0.70050598 6.16680132
137 -20.76808958 -0.70050598
138 -5.70546063 -20.76808958
139 6.70345170 -5.70546063
140 -0.26012414 6.70345170
141 7.09051551 -0.26012414
142 -21.07163208 7.09051551
143 11.78968654 -21.07163208
144 -16.01256315 11.78968654
145 0.48926510 -16.01256315
146 -27.86994456 0.48926510
147 -27.32793981 -27.86994456
148 4.43798500 -27.32793981
149 -15.16578323 4.43798500
150 1.44106002 -15.16578323
151 -23.87025850 1.44106002
152 7.37986329 -23.87025850
153 12.22416567 7.37986329
154 34.47055410 12.22416567
155 -18.17779673 34.47055410
156 -2.53885778 -18.17779673
157 -6.98291481 -2.53885778
158 -6.07206322 -6.98291481
159 -23.44208180 -6.07206322
160 -30.82196194 -23.44208180
161 -39.44420658 -30.82196194
162 -9.68443438 -39.44420658
163 -11.58702980 -9.68443438
164 -1.66006245 -11.58702980
165 17.28892307 -1.66006245
166 12.12270306 17.28892307
167 -39.39244924 12.12270306
168 10.57659223 -39.39244924
169 -48.54887846 10.57659223
170 -16.32130180 -48.54887846
171 30.37398390 -16.32130180
172 -14.76817814 30.37398390
173 23.34689682 -14.76817814
174 72.48416162 23.34689682
175 -15.94507608 72.48416162
176 -4.15038182 -15.94507608
177 -4.67423697 -4.15038182
178 34.75849619 -4.67423697
179 2.79178731 34.75849619
180 0.21305390 2.79178731
181 24.33787020 0.21305390
182 -0.50001047 24.33787020
183 -18.87576094 -0.50001047
184 21.83388750 -18.87576094
185 5.76165495 21.83388750
186 -1.06122578 5.76165495
187 -4.65485157 -1.06122578
188 -6.84554011 -4.65485157
189 2.72873591 -6.84554011
190 -14.67655934 2.72873591
191 -17.83178483 -14.67655934
192 15.94135765 -17.83178483
193 -18.50062917 15.94135765
194 -1.05878275 -18.50062917
195 14.85878318 -1.05878275
196 -11.22819894 14.85878318
197 -20.24988800 -11.22819894
198 -8.28898011 -20.24988800
199 0.65184832 -8.28898011
200 -6.38547152 0.65184832
201 -0.06135981 -6.38547152
202 -6.68371546 -0.06135981
203 -19.12600995 -6.68371546
204 -1.91331852 -19.12600995
205 -6.07686091 -1.91331852
206 5.31591307 -6.07686091
207 -26.96104704 5.31591307
208 -10.92881911 -26.96104704
209 4.50495361 -10.92881911
210 -1.87418025 4.50495361
211 -3.15186547 -1.87418025
212 0.11515086 -3.15186547
213 3.27180942 0.11515086
214 -26.92169215 3.27180942
215 -11.84142019 -26.92169215
216 4.42599334 -11.84142019
217 -15.83438337 4.42599334
218 5.31434448 -15.83438337
219 -14.75095970 5.31434448
220 0.86928035 -14.75095970
221 -19.87426126 0.86928035
222 22.62667627 -19.87426126
223 -3.71814278 22.62667627
224 -3.30123428 -3.71814278
225 2.44009825 -3.30123428
226 -5.58723816 2.44009825
227 7.03883655 -5.58723816
228 16.98843397 7.03883655
229 -2.86257485 16.98843397
230 -8.50714498 -2.86257485
231 8.19499554 -8.50714498
232 -9.04744645 8.19499554
233 -2.30668825 -9.04744645
234 -1.37516891 -2.30668825
235 16.04586364 -1.37516891
236 -27.56286439 16.04586364
237 7.49966828 -27.56286439
238 5.90301026 7.49966828
239 2.18898910 5.90301026
240 -15.99149985 2.18898910
241 -21.97125830 -15.99149985
242 14.41905333 -21.97125830
243 0.25881181 14.41905333
244 -3.35788391 0.25881181
245 -4.41597224 -3.35788391
246 10.58275679 -4.41597224
247 -18.36793278 10.58275679
248 -11.07366867 -18.36793278
249 -0.57338598 -11.07366867
250 -25.14286076 -0.57338598
251 2.57532692 -25.14286076
252 7.87413516 2.57532692
253 -21.52892674 7.87413516
254 -6.20390000 -21.52892674
255 0.26840136 -6.20390000
256 11.43660526 0.26840136
257 -3.85943327 11.43660526
258 -6.57307198 -3.85943327
259 2.66238669 -6.57307198
260 -5.89508240 2.66238669
261 15.68429152 -5.89508240
262 -4.90450093 15.68429152
263 18.34111048 -4.90450093
264 -5.64029450 18.34111048
265 9.58876008 -5.64029450
266 14.75646323 9.58876008
267 6.73962281 14.75646323
268 -5.47675279 6.73962281
269 1.46054341 -5.47675279
270 -16.81695603 1.46054341
271 -5.94429160 -16.81695603
272 -4.21247673 -5.94429160
273 10.77181288 -4.21247673
274 2.92832139 10.77181288
275 16.09346690 2.92832139
276 -19.45073666 16.09346690
277 4.22634597 -19.45073666
278 8.64484594 4.22634597
279 -10.04957238 8.64484594
280 1.08187662 -10.04957238
281 -27.36444054 1.08187662
282 -3.36439393 -27.36444054
283 -3.14067807 -3.36439393
284 -11.77284258 -3.14067807
285 -8.07977755 -11.77284258
286 -1.24464462 -8.07977755
287 3.49442937 -1.24464462
288 -6.81878471 3.49442937
> 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/www/rcomp/tmp/7c8vu1324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8yii11324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9v1ya1324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10cshk1324655439.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/11xd5i1324655439.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/www/rcomp/tmp/12qso61324655439.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/www/rcomp/tmp/13uddq1324655439.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/www/rcomp/tmp/14v7a71324655439.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/www/rcomp/tmp/15g52e1324655439.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/www/rcomp/tmp/16nkfj1324655439.tab")
+ }
>
> try(system("convert tmp/13djx1324655439.ps tmp/13djx1324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fau61324655439.ps tmp/2fau61324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/384wk1324655439.ps tmp/384wk1324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lcn61324655439.ps tmp/4lcn61324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g1o61324655439.ps tmp/5g1o61324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wiuj1324655439.ps tmp/6wiuj1324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c8vu1324655439.ps tmp/7c8vu1324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yii11324655439.ps tmp/8yii11324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v1ya1324655439.ps tmp/9v1ya1324655439.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cshk1324655439.ps tmp/10cshk1324655439.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.630 0.300 7.878