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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+ ,0
+ ,190
+ ,0
+ ,70
+ ,0
+ ,133
+ ,0
+ ,220
+ ,0
+ ,40
+ ,0
+ ,88
+ ,0
+ ,80
+ ,0
+ ,30
+ ,0
+ ,24
+ ,0
+ ,279
+ ,0
+ ,414
+ ,0
+ ,1426
+ ,0
+ ,72
+ ,0
+ ,248
+ ,0
+ ,279
+ ,0
+ ,144
+ ,0
+ ,168
+ ,0
+ ,378
+ ,0
+ ,63
+ ,0
+ ,49
+ ,0
+ ,63
+ ,0
+ ,35
+ ,0
+ ,203
+ ,0
+ ,145
+ ,0
+ ,44
+ ,0
+ ,20
+ ,0
+ ,55
+ ,0)
+ ,dim=c(6
+ ,269)
+ ,dimnames=list(c('hours_blogs'
+ ,'hours_uk'
+ ,'hours_spr'
+ ,'blogs_uk'
+ ,'blogs_spr'
+ ,'uk_spr')
+ ,1:269))
> y <- array(NA,dim=c(6,269),dimnames=list(c('hours_blogs','hours_uk','hours_spr','blogs_uk','blogs_spr','uk_spr'),1:269))
> 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'
> 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
hours_uk hours_blogs hours_spr blogs_uk blogs_spr uk_spr
1 102 8976 918 88 792 9
2 99 10494 297 106 318 3
3 97 6790 194 70 140 2
4 82 5740 1722 70 1470 21
5 77 4312 770 56 560 10
6 65 3250 130 50 100 2
7 64 3072 256 48 192 4
8 62 4402 248 71 284 4
9 62 3782 248 61 244 4
10 62 4092 310 66 330 5
11 61 4880 122 80 160 2
12 59 2183 177 37 111 3
13 57 3021 171 53 159 3
14 56 2184 336 39 234 6
15 54 2160 162 40 120 3
16 54 3186 216 59 236 4
17 53 2226 159 42 126 3
18 52 1716 676 33 429 13
19 51 1836 153 36 108 3
20 51 2907 204 57 228 4
21 51 1938 408 38 304 8
22 50 4900 400 98 784 8
23 50 2150 150 43 129 3
24 50 3650 200 73 292 4
25 49 2548 98 52 104 2
26 49 2597 245 53 265 5
27 49 2499 196 51 204 4
28 48 1536 144 32 96 3
29 48 2064 144 43 129 3
30 47 2491 94 53 106 2
31 47 2350 141 50 150 3
32 46 2300 138 50 150 3
33 46 2576 138 56 168 3
34 45 2385 225 53 265 5
35 45 2115 135 47 141 3
36 45 1890 180 42 168 4
37 44 1276 132 29 87 3
38 43 2322 172 54 216 4
39 42 1680 336 40 320 8
40 42 1722 126 41 123 3
41 42 1554 168 37 148 4
42 42 1050 84 25 50 2
43 42 1134 210 27 135 5
44 42 2562 168 61 244 4
45 41 2214 287 54 378 7
46 41 1435 123 35 105 3
47 41 2255 164 55 220 4
48 41 1927 246 47 282 6
49 41 2009 287 49 343 7
50 41 1558 820 38 760 20
51 41 2132 2009 52 2548 49
52 40 1400 120 35 105 3
53 40 2080 120 52 156 3
54 40 2160 240 54 324 6
55 40 1600 240 40 240 6
56 40 2080 160 52 208 4
57 39 1326 195 34 170 5
58 39 1989 156 51 204 4
59 38 1634 152 43 172 4
60 38 1520 1178 40 1240 31
61 36 1368 108 38 114 3
62 36 1188 108 33 99 3
63 35 945 140 27 108 4
64 35 1190 210 34 204 6
65 35 1540 175 44 220 5
66 35 1610 105 46 138 3
67 34 1700 102 50 150 3
68 34 1054 68 31 62 2
69 34 1122 102 33 99 3
70 33 1221 99 37 111 3
71 33 1584 528 48 768 16
72 33 1089 99 33 99 3
73 32 1280 96 40 120 3
74 32 672 96 21 63 3
75 32 1056 64 33 66 2
76 31 1271 155 41 205 5
77 31 1085 93 35 105 3
78 30 1800 120 60 240 4
79 30 900 150 30 150 5
80 30 1350 60 45 90 2
81 30 780 90 26 78 3
82 29 1189 87 41 123 3
83 28 1344 392 48 672 14
84 28 280 224 10 80 8
85 27 945 108 35 140 4
86 27 621 108 23 92 4
87 27 783 81 29 87 3
88 26 442 104 17 68 4
89 25 875 75 35 105 3
90 25 1250 125 50 250 5
91 25 825 75 33 99 3
92 24 552 72 23 69 3
93 24 528 48 22 44 2
94 23 1196 92 52 208 4
95 23 874 713 38 1178 31
96 23 736 46 32 64 2
97 23 644 115 28 140 5
98 23 989 115 43 215 5
99 22 704 44 32 64 2
100 22 770 44 35 70 2
101 22 550 66 25 75 3
102 22 308 176 14 112 8
103 20 340 120 17 102 6
104 19 342 57 18 54 3
105 19 228 38 12 24 2
106 17 459 85 27 135 5
107 17 476 51 28 84 3
108 16 192 80 12 60 5
109 16 336 80 21 105 5
110 5 45 20 9 36 4
111 4 44 8 11 22 2
112 3 9 12 3 12 4
113 0 17316 624 0 444 0
114 0 14933 872 0 1096 0
115 0 11648 312 0 336 0
116 0 7154 392 0 292 0
117 0 7722 234 0 297 0
118 0 8855 231 0 345 0
119 0 6935 219 0 285 0
120 0 4260 284 0 240 0
121 0 6298 268 0 376 0
122 0 4480 320 0 350 0
123 0 5394 124 0 174 0
124 0 6222 183 0 306 0
125 0 4002 232 0 276 0
126 0 6438 406 0 777 0
127 0 3080 168 0 165 0
128 0 6608 224 0 472 0
129 0 4680 156 0 270 0
130 0 4131 204 0 324 0
131 0 4488 204 0 352 0
132 0 3150 150 0 189 0
133 0 4116 294 0 504 0
134 0 4263 196 0 348 0
135 0 3744 192 0 312 0
136 0 4371 188 0 372 0
137 0 3243 188 0 276 0
138 0 3082 138 0 201 0
139 0 2745 270 0 366 0
140 0 5535 180 0 492 0
141 0 4095 270 0 546 0
142 0 4410 270 0 588 0
143 0 1672 88 0 76 0
144 0 3168 132 0 216 0
145 0 2596 132 0 177 0
146 0 3354 86 0 156 0
147 0 2494 172 0 232 0
148 0 4074 210 0 485 0
149 0 2829 123 0 207 0
150 0 2050 287 0 350 0
151 0 2640 160 0 264 0
152 0 2730 117 0 210 0
153 0 2535 156 0 260 0
154 0 2691 156 0 276 0
155 0 1911 117 0 147 0
156 0 2808 117 0 216 0
157 0 2886 156 0 296 0
158 0 3198 195 0 410 0
159 0 2318 114 0 183 0
160 0 2736 152 0 288 0
161 0 2926 228 0 462 0
162 0 2432 190 0 320 0
163 0 851 259 0 161 0
164 0 1443 185 0 195 0
165 0 3219 185 0 435 0
166 0 1656 108 0 138 0
167 0 2376 180 0 330 0
168 0 2052 144 0 228 0
169 0 1728 324 0 432 0
170 0 2700 108 0 225 0
171 0 1260 108 0 105 0
172 0 1855 105 0 159 0
173 0 2100 105 0 180 0
174 0 680 510 0 300 0
175 0 2244 136 0 264 0
176 0 1156 204 0 204 0
177 0 2720 102 0 240 0
178 0 2142 102 0 189 0
179 0 1518 99 0 138 0
180 0 660 165 0 100 0
181 0 2409 99 0 219 0
182 0 1881 132 0 228 0
183 0 2145 231 0 455 0
184 0 2310 132 0 280 0
185 0 1696 160 0 265 0
186 0 1920 224 0 420 0
187 0 1088 448 0 476 0
188 0 576 800 0 450 0
189 0 1568 192 0 294 0
190 0 837 124 0 108 0
191 0 1395 93 0 135 0
192 0 279 1922 0 558 0
193 0 690 150 0 115 0
194 0 1830 300 0 610 0
195 0 1943 116 0 268 0
196 0 2088 145 0 360 0
197 0 1682 145 0 290 0
198 0 1540 112 0 220 0
199 0 924 280 0 330 0
200 0 1120 140 0 200 0
201 0 1596 84 0 171 0
202 0 1708 84 0 183 0
203 0 2436 476 0 1479 0
204 0 1755 108 0 260 0
205 0 2295 162 0 510 0
206 0 2295 81 0 255 0
207 0 1404 104 0 216 0
208 0 624 208 0 192 0
209 0 806 78 0 93 0
210 0 1664 104 0 256 0
211 0 1750 100 0 280 0
212 0 50 1175 0 94 0
213 0 648 192 0 216 0
214 0 696 72 0 87 0
215 0 1632 120 0 340 0
216 0 1008 72 0 126 0
217 0 1872 96 0 312 0
218 0 312 720 0 390 0
219 0 1248 96 0 208 0
220 0 575 276 0 300 0
221 0 874 184 0 304 0
222 0 920 69 0 120 0
223 0 966 184 0 336 0
224 0 920 92 0 160 0
225 0 1702 69 0 222 0
226 0 1679 92 0 292 0
227 0 1232 88 0 224 0
228 0 66 462 0 63 0
229 0 189 252 0 108 0
230 0 1428 63 0 204 0
231 0 588 63 0 84 0
232 0 756 84 0 144 0
233 0 760 120 0 228 0
234 0 1100 340 0 935 0
235 0 720 60 0 108 0
236 0 340 360 0 306 0
237 0 1080 100 0 270 0
238 0 1083 95 0 285 0
239 0 570 304 0 480 0
240 0 760 190 0 400 0
241 0 666 90 0 185 0
242 0 828 72 0 184 0
243 0 576 54 0 96 0
244 0 612 90 0 170 0
245 0 396 72 0 88 0
246 0 1003 85 0 295 0
247 0 544 170 0 320 0
248 0 288 192 0 216 0
249 0 448 64 0 112 0
250 0 510 135 0 306 0
251 0 435 180 0 348 0
252 0 360 150 0 240 0
253 0 360 135 0 216 0
254 0 322 238 0 391 0
255 0 559 78 0 258 0
256 0 364 39 0 84 0
257 0 228 48 0 76 0
258 0 176 209 0 304 0
259 0 440 33 0 120 0
260 0 140 90 0 126 0
261 0 190 70 0 133 0
262 0 220 40 0 88 0
263 0 80 30 0 24 0
264 0 279 414 0 1426 0
265 0 72 248 0 279 0
266 0 144 168 0 378 0
267 0 63 49 0 63 0
268 0 35 203 0 145 0
269 0 44 20 0 55 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) hours_blogs hours_spr blogs_uk blogs_spr uk_spr
1.4011664 0.0005169 0.0110944 0.8573112 -0.0144429 0.2790182
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.297 -2.054 -0.560 1.398 31.389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4011664 0.7816842 1.792 0.07420 .
hours_blogs 0.0005169 0.0002370 2.181 0.03009 *
hours_spr 0.0110944 0.0028420 3.904 0.00012 ***
blogs_uk 0.8573112 0.0252008 34.019 < 2e-16 ***
blogs_spr -0.0144429 0.0028929 -4.992 1.09e-06 ***
uk_spr 0.2790182 0.1574969 1.772 0.07762 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.21 on 263 degrees of freedom
Multiple R-squared: 0.8984, Adjusted R-squared: 0.8965
F-statistic: 465.3 on 5 and 263 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,] 0.27616993 5.523399e-01 7.238301e-01
[2,] 0.14857053 2.971411e-01 8.514295e-01
[3,] 0.08082894 1.616579e-01 9.191711e-01
[4,] 0.10070335 2.014067e-01 8.992966e-01
[5,] 0.06585624 1.317125e-01 9.341438e-01
[6,] 0.06894028 1.378806e-01 9.310597e-01
[7,] 0.06529888 1.305978e-01 9.347011e-01
[8,] 0.03743845 7.487690e-02 9.625615e-01
[9,] 0.03262954 6.525908e-02 9.673705e-01
[10,] 0.02718300 5.436601e-02 9.728170e-01
[11,] 0.03111871 6.223742e-02 9.688813e-01
[12,] 0.01843788 3.687576e-02 9.815621e-01
[13,] 0.01304996 2.609992e-02 9.869500e-01
[14,] 0.06489699 1.297940e-01 9.351030e-01
[15,] 0.06743819 1.348764e-01 9.325618e-01
[16,] 0.05204120 1.040824e-01 9.479588e-01
[17,] 0.04870027 9.740055e-02 9.512997e-01
[18,] 0.03749328 7.498657e-02 9.625067e-01
[19,] 0.03054574 6.109148e-02 9.694543e-01
[20,] 0.04709563 9.419126e-02 9.529044e-01
[21,] 0.04817286 9.634572e-02 9.518271e-01
[22,] 0.04405780 8.811560e-02 9.559422e-01
[23,] 0.03892117 7.784234e-02 9.610788e-01
[24,] 0.03508601 7.017201e-02 9.649140e-01
[25,] 0.02833039 5.666078e-02 9.716696e-01
[26,] 0.02250590 4.501181e-02 9.774941e-01
[27,] 0.02161873 4.323747e-02 9.783813e-01
[28,] 0.02292254 4.584508e-02 9.770775e-01
[29,] 0.04433099 8.866198e-02 9.556690e-01
[30,] 0.03785099 7.570198e-02 9.621490e-01
[31,] 0.03617603 7.235206e-02 9.638240e-01
[32,] 0.04327663 8.655326e-02 9.567234e-01
[33,] 0.05349840 1.069968e-01 9.465016e-01
[34,] 0.10948379 2.189676e-01 8.905162e-01
[35,] 0.17980272 3.596054e-01 8.201973e-01
[36,] 0.16561195 3.312239e-01 8.343881e-01
[37,] 0.13597719 2.719544e-01 8.640228e-01
[38,] 0.17822319 3.564464e-01 8.217768e-01
[39,] 0.16298346 3.259669e-01 8.370165e-01
[40,] 0.14305262 2.861052e-01 8.569474e-01
[41,] 0.11916939 2.383388e-01 8.808306e-01
[42,] 0.11918364 2.383673e-01 8.808164e-01
[43,] 0.24630622 4.926124e-01 7.536938e-01
[44,] 0.31652095 6.330419e-01 6.834790e-01
[45,] 0.29686499 5.937300e-01 7.031350e-01
[46,] 0.26969341 5.393868e-01 7.303066e-01
[47,] 0.29546656 5.909331e-01 7.045334e-01
[48,] 0.27205962 5.441192e-01 7.279404e-01
[49,] 0.35562504 7.112501e-01 6.443750e-01
[50,] 0.33455026 6.691005e-01 6.654497e-01
[51,] 0.34765288 6.953058e-01 6.523471e-01
[52,] 0.33030775 6.606155e-01 6.696922e-01
[53,] 0.39087870 7.817574e-01 6.091213e-01
[54,] 0.50390812 9.921838e-01 4.960919e-01
[55,] 0.69204113 6.159177e-01 3.079589e-01
[56,] 0.76674080 4.665184e-01 2.332592e-01
[57,] 0.77205965 4.558807e-01 2.279404e-01
[58,] 0.77457277 4.508545e-01 2.254272e-01
[59,] 0.77528542 4.494292e-01 2.247146e-01
[60,] 0.86137234 2.772553e-01 1.386277e-01
[61,] 0.91068152 1.786370e-01 8.931848e-02
[62,] 0.93213866 1.357227e-01 6.786134e-02
[63,] 0.92028393 1.594321e-01 7.971607e-02
[64,] 0.95140905 9.718191e-02 4.859095e-02
[65,] 0.95931302 8.137397e-02 4.068698e-02
[66,] 0.99381731 1.236538e-02 6.182688e-03
[67,] 0.99722430 5.551404e-03 2.775702e-03
[68,] 0.99759217 4.815669e-03 2.407835e-03
[69,] 0.99870442 2.591168e-03 1.295584e-03
[70,] 0.99940541 1.189182e-03 5.945911e-04
[71,] 0.99980837 3.832526e-04 1.916263e-04
[72,] 0.99982966 3.406731e-04 1.703365e-04
[73,] 0.99997912 4.176437e-05 2.088219e-05
[74,] 0.99998289 3.422989e-05 1.711494e-05
[75,] 0.99998546 2.908757e-05 1.454378e-05
[76,] 1.00000000 4.751820e-09 2.375910e-09
[77,] 1.00000000 2.457679e-09 1.228839e-09
[78,] 1.00000000 4.090773e-11 2.045387e-11
[79,] 1.00000000 4.066199e-12 2.033100e-12
[80,] 1.00000000 4.669614e-16 2.334807e-16
[81,] 1.00000000 1.970909e-16 9.854543e-17
[82,] 1.00000000 7.355071e-18 3.677535e-18
[83,] 1.00000000 2.878595e-18 1.439298e-18
[84,] 1.00000000 1.729386e-20 8.646931e-21
[85,] 1.00000000 1.373106e-23 6.865530e-24
[86,] 1.00000000 6.481563e-29 3.240782e-29
[87,] 1.00000000 2.311108e-41 1.155554e-41
[88,] 1.00000000 1.518593e-42 7.592965e-43
[89,] 1.00000000 5.840030e-43 2.920015e-43
[90,] 1.00000000 2.750006e-48 1.375003e-48
[91,] 1.00000000 1.273447e-48 6.367233e-49
[92,] 1.00000000 7.848998e-49 3.924499e-49
[93,] 1.00000000 6.206674e-51 3.103337e-51
[94,] 1.00000000 9.041811e-54 4.520906e-54
[95,] 1.00000000 9.110855e-57 4.555428e-57
[96,] 1.00000000 1.647442e-62 8.237208e-63
[97,] 1.00000000 1.077600e-98 5.387999e-99
[98,] 1.00000000 1.484684e-99 7.423419e-100
[99,] 1.00000000 1.512043e-99 7.560217e-100
[100,] 1.00000000 1.188369e-140 5.941843e-141
[101,] 1.00000000 8.889657e-312 4.444829e-312
[102,] 1.00000000 0.000000e+00 0.000000e+00
[103,] 1.00000000 0.000000e+00 0.000000e+00
[104,] 1.00000000 0.000000e+00 0.000000e+00
[105,] 1.00000000 0.000000e+00 0.000000e+00
[106,] 1.00000000 0.000000e+00 0.000000e+00
[107,] 1.00000000 0.000000e+00 0.000000e+00
[108,] 1.00000000 0.000000e+00 0.000000e+00
[109,] 1.00000000 0.000000e+00 0.000000e+00
[110,] 1.00000000 0.000000e+00 0.000000e+00
[111,] 1.00000000 0.000000e+00 0.000000e+00
[112,] 1.00000000 0.000000e+00 0.000000e+00
[113,] 1.00000000 0.000000e+00 0.000000e+00
[114,] 1.00000000 0.000000e+00 0.000000e+00
[115,] 1.00000000 0.000000e+00 0.000000e+00
[116,] 1.00000000 0.000000e+00 0.000000e+00
[117,] 1.00000000 0.000000e+00 0.000000e+00
[118,] 1.00000000 0.000000e+00 0.000000e+00
[119,] 1.00000000 0.000000e+00 0.000000e+00
[120,] 1.00000000 0.000000e+00 0.000000e+00
[121,] 1.00000000 0.000000e+00 0.000000e+00
[122,] 1.00000000 0.000000e+00 0.000000e+00
[123,] 1.00000000 0.000000e+00 0.000000e+00
[124,] 1.00000000 0.000000e+00 0.000000e+00
[125,] 1.00000000 0.000000e+00 0.000000e+00
[126,] 1.00000000 0.000000e+00 0.000000e+00
[127,] 1.00000000 0.000000e+00 0.000000e+00
[128,] 1.00000000 0.000000e+00 0.000000e+00
[129,] 1.00000000 0.000000e+00 0.000000e+00
[130,] 1.00000000 0.000000e+00 0.000000e+00
[131,] 1.00000000 0.000000e+00 0.000000e+00
[132,] 1.00000000 0.000000e+00 0.000000e+00
[133,] 1.00000000 0.000000e+00 0.000000e+00
[134,] 1.00000000 0.000000e+00 0.000000e+00
[135,] 1.00000000 0.000000e+00 0.000000e+00
[136,] 1.00000000 0.000000e+00 0.000000e+00
[137,] 1.00000000 0.000000e+00 0.000000e+00
[138,] 1.00000000 0.000000e+00 0.000000e+00
[139,] 1.00000000 0.000000e+00 0.000000e+00
[140,] 1.00000000 0.000000e+00 0.000000e+00
[141,] 1.00000000 0.000000e+00 0.000000e+00
[142,] 1.00000000 0.000000e+00 0.000000e+00
[143,] 1.00000000 0.000000e+00 0.000000e+00
[144,] 1.00000000 0.000000e+00 0.000000e+00
[145,] 1.00000000 0.000000e+00 0.000000e+00
[146,] 1.00000000 0.000000e+00 0.000000e+00
[147,] 1.00000000 0.000000e+00 0.000000e+00
[148,] 1.00000000 0.000000e+00 0.000000e+00
[149,] 1.00000000 0.000000e+00 0.000000e+00
[150,] 1.00000000 0.000000e+00 0.000000e+00
[151,] 1.00000000 0.000000e+00 0.000000e+00
[152,] 1.00000000 0.000000e+00 0.000000e+00
[153,] 1.00000000 0.000000e+00 0.000000e+00
[154,] 1.00000000 0.000000e+00 0.000000e+00
[155,] 1.00000000 0.000000e+00 0.000000e+00
[156,] 1.00000000 0.000000e+00 0.000000e+00
[157,] 1.00000000 0.000000e+00 0.000000e+00
[158,] 1.00000000 0.000000e+00 0.000000e+00
[159,] 1.00000000 0.000000e+00 0.000000e+00
[160,] 1.00000000 0.000000e+00 0.000000e+00
[161,] 1.00000000 0.000000e+00 0.000000e+00
[162,] 1.00000000 0.000000e+00 0.000000e+00
[163,] 1.00000000 0.000000e+00 0.000000e+00
[164,] 1.00000000 0.000000e+00 0.000000e+00
[165,] 1.00000000 0.000000e+00 0.000000e+00
[166,] 1.00000000 0.000000e+00 0.000000e+00
[167,] 1.00000000 0.000000e+00 0.000000e+00
[168,] 1.00000000 0.000000e+00 0.000000e+00
[169,] 1.00000000 0.000000e+00 0.000000e+00
[170,] 1.00000000 0.000000e+00 0.000000e+00
[171,] 1.00000000 0.000000e+00 0.000000e+00
[172,] 1.00000000 0.000000e+00 0.000000e+00
[173,] 1.00000000 0.000000e+00 0.000000e+00
[174,] 1.00000000 0.000000e+00 0.000000e+00
[175,] 1.00000000 0.000000e+00 0.000000e+00
[176,] 1.00000000 0.000000e+00 0.000000e+00
[177,] 1.00000000 0.000000e+00 0.000000e+00
[178,] 1.00000000 0.000000e+00 0.000000e+00
[179,] 1.00000000 0.000000e+00 0.000000e+00
[180,] 1.00000000 0.000000e+00 0.000000e+00
[181,] 1.00000000 0.000000e+00 0.000000e+00
[182,] 1.00000000 0.000000e+00 0.000000e+00
[183,] 1.00000000 0.000000e+00 0.000000e+00
[184,] 1.00000000 0.000000e+00 0.000000e+00
[185,] 1.00000000 0.000000e+00 0.000000e+00
[186,] 1.00000000 0.000000e+00 0.000000e+00
[187,] 1.00000000 0.000000e+00 0.000000e+00
[188,] 1.00000000 0.000000e+00 0.000000e+00
[189,] 1.00000000 0.000000e+00 0.000000e+00
[190,] 1.00000000 0.000000e+00 0.000000e+00
[191,] 1.00000000 0.000000e+00 0.000000e+00
[192,] 1.00000000 0.000000e+00 0.000000e+00
[193,] 1.00000000 0.000000e+00 0.000000e+00
[194,] 1.00000000 0.000000e+00 0.000000e+00
[195,] 1.00000000 0.000000e+00 0.000000e+00
[196,] 1.00000000 0.000000e+00 0.000000e+00
[197,] 1.00000000 0.000000e+00 0.000000e+00
[198,] 1.00000000 0.000000e+00 0.000000e+00
[199,] 1.00000000 0.000000e+00 0.000000e+00
[200,] 1.00000000 0.000000e+00 0.000000e+00
[201,] 1.00000000 0.000000e+00 0.000000e+00
[202,] 1.00000000 0.000000e+00 0.000000e+00
[203,] 1.00000000 0.000000e+00 0.000000e+00
[204,] 1.00000000 0.000000e+00 0.000000e+00
[205,] 1.00000000 0.000000e+00 0.000000e+00
[206,] 1.00000000 0.000000e+00 0.000000e+00
[207,] 1.00000000 0.000000e+00 0.000000e+00
[208,] 1.00000000 0.000000e+00 0.000000e+00
[209,] 1.00000000 0.000000e+00 0.000000e+00
[210,] 1.00000000 0.000000e+00 0.000000e+00
[211,] 1.00000000 0.000000e+00 0.000000e+00
[212,] 1.00000000 0.000000e+00 0.000000e+00
[213,] 1.00000000 0.000000e+00 0.000000e+00
[214,] 1.00000000 0.000000e+00 0.000000e+00
[215,] 1.00000000 0.000000e+00 0.000000e+00
[216,] 1.00000000 0.000000e+00 0.000000e+00
[217,] 1.00000000 0.000000e+00 0.000000e+00
[218,] 1.00000000 0.000000e+00 0.000000e+00
[219,] 1.00000000 0.000000e+00 0.000000e+00
[220,] 1.00000000 0.000000e+00 0.000000e+00
[221,] 1.00000000 0.000000e+00 0.000000e+00
[222,] 1.00000000 0.000000e+00 0.000000e+00
[223,] 1.00000000 0.000000e+00 0.000000e+00
[224,] 1.00000000 0.000000e+00 0.000000e+00
[225,] 1.00000000 0.000000e+00 0.000000e+00
[226,] 1.00000000 0.000000e+00 0.000000e+00
[227,] 1.00000000 0.000000e+00 0.000000e+00
[228,] 1.00000000 0.000000e+00 0.000000e+00
[229,] 1.00000000 0.000000e+00 0.000000e+00
[230,] 1.00000000 0.000000e+00 0.000000e+00
[231,] 1.00000000 0.000000e+00 0.000000e+00
[232,] 1.00000000 0.000000e+00 0.000000e+00
[233,] 1.00000000 0.000000e+00 0.000000e+00
[234,] 1.00000000 0.000000e+00 0.000000e+00
[235,] 1.00000000 0.000000e+00 0.000000e+00
[236,] 1.00000000 0.000000e+00 0.000000e+00
[237,] 1.00000000 0.000000e+00 0.000000e+00
[238,] 1.00000000 0.000000e+00 0.000000e+00
[239,] 1.00000000 0.000000e+00 0.000000e+00
[240,] 1.00000000 0.000000e+00 0.000000e+00
[241,] 1.00000000 0.000000e+00 0.000000e+00
[242,] 1.00000000 0.000000e+00 0.000000e+00
[243,] 1.00000000 0.000000e+00 0.000000e+00
[244,] 1.00000000 0.000000e+00 0.000000e+00
[245,] 1.00000000 0.000000e+00 0.000000e+00
[246,] 1.00000000 0.000000e+00 0.000000e+00
[247,] 1.00000000 0.000000e+00 0.000000e+00
[248,] 1.00000000 0.000000e+00 0.000000e+00
[249,] 1.00000000 0.000000e+00 0.000000e+00
[250,] 1.00000000 0.000000e+00 0.000000e+00
[251,] 1.00000000 0.000000e+00 0.000000e+00
[252,] 1.00000000 0.000000e+00 0.000000e+00
> postscript(file="/var/wessaorg/rcomp/tmp/1uwd41358158465.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/2gfl81358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ryld1358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ii2o1358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wvl11358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 269
Frequency = 1
1 2 3 4 5 6
19.25902714 1.76058999 31.38917584 13.88741814 22.11585458 18.49743683
7 8 9 10 11 12
18.67688311 -2.31120903 6.00464589 1.83308131 -11.10906711 23.55240031
13 14 15 16 17 18
8.16210994 18.01268003 16.28875590 0.26679796 13.65996054 16.48959833
19 20 21 22 23 24
16.81200061 -0.85678425 13.65131354 -33.29698031 9.98510914 -14.98907453
25 26 27 28 29 30
1.55844667 0.53319023 2.24009079 17.32284049 8.09612593 -1.19613989
31 32 33 34 35 36
1.28370677 0.34283324 -4.68371774 -3.13534664 1.91368459 5.92823016
37 38 39 40 41 42
16.03230614 -5.80077805 4.09992284 4.10055833 7.23272844 17.85552344
43 44 45 46 47 48
15.09018679 -12.47722666 -7.51811272 8.16607792 -8.47693272 -2.02122400
49 50 51 52 53 54
-3.63109984 2.51459091 -5.24326834 7.21745137 -6.97172253 -8.46966530
55 56 57 58 59 60
2.60893515 -6.94348438 6.66168602 -7.05253230 -1.42834935 -2.28878028
61 62 63 64 65 66
0.92517571 5.08812478 8.85353972 2.77760497 -5.07800441 -6.67848382
67 68 69 70 71 72
-10.94764914 5.06041289 3.18880421 -1.08501293 -9.60079266 2.23914392
73 74 75 76 77 78
-4.52417277 11.25575174 1.44690565 -6.36178948 -1.32018761 -22.75130448
79 80 81 82 83 84
1.52150487 -10.60178165 5.19658330 -8.19127115 -13.79639360 14.31914854
85 86 87 88 89 90
-5.18775829 4.57418300 -0.14706581 8.50831782 -7.01194693 -19.08397558
91 92 93 94 95 96
-5.35813840 1.95607529 3.01000172 -22.73215687 -10.97684574 -6.35957271
97 98 99 100 101 102
-3.38768095 -15.34245205 -7.32084423 -9.84023390 -1.60428997 5.87013288
103 104 105 106 107 108
2.31655069 1.30094573 6.56026028 -8.17413294 -8.84157411 2.79579421
109 110 111 112 113 114
-4.34450576 -4.95824243 -7.18337990 -2.05364234 -10.86148653 -2.96442216
115 116 117 118 119 120
-6.03027213 -5.23050250 -3.69896115 -3.55803071 -3.29908536 -3.28752642
121 122 123 124 125 126
-2.19916029 -2.21191572 -3.05178926 -2.22786084 -2.05732381 1.98905815
127 128 129 130 131 132
-2.47389406 -0.48471991 -1.65124685 -1.12009893 -0.90021979 -1.96374698
133 134 135 136 137 138
0.48888330 -0.75294121 -0.96025345 -0.37337865 -1.17686932 -1.62215296
139 140 141 142 143 144
-0.52934550 0.84688907 1.37260361 1.81639174 -2.14401305 -1.38339407
145 146 147 148 149 150
-1.65101840 -1.83576494 -1.24771280 1.16810251 -1.23831264 -0.58981249
151 152 153 154 155 156
-0.72787066 -1.07724789 -0.68699367 -0.53653878 -1.56383527 -1.03090623
157 158 159 160 161 162
-0.34847017 0.70407718 -1.22097332 -0.34210569 1.22958216 -0.14439060
163 164 165 166 167 168
-2.38915283 -1.38309812 1.16523887 -1.46217077 0.13992650 -0.76638675
169 170 171 172 173 174
0.35044532 -0.74524923 -1.73410652 -1.22844367 -1.05177556 -3.07788890
175 176 177 178 179 180
-0.25692638 -1.31556365 -0.47237704 -0.91021491 -1.29099378 -2.12857831
181 182 183 184 185 186
-0.58164879 -0.54487005 1.49887283 -0.01557594 -0.22550426 1.18732732
187 188 189 190 191 192
-0.05897163 -4.07506311 -0.09552052 -1.64965229 -1.20418156 -14.80958607
193 194 195 196 197 198
-1.76102562 3.13482456 0.17830959 1.11037377 0.30921958 -0.26227410
199 200 201 202 203 204
-0.21901641 -0.64468930 -0.68827864 -0.57285314 13.41987180 0.24869247
205 206 207 208 209 210
2.98121228 0.19691677 -0.16099678 -1.25828196 -1.33993250 0.28233330
211 212 213 214 215 216
0.62888954 -13.10524390 -0.74654765 -1.30316825 1.33456659 -0.90115821
217 218 219 220 221 222
1.07238168 -3.91763528 -0.10715368 -0.42753886 0.49636974 -0.90904812
223 224 225 226 227 228
0.91099051 -0.58650247 0.15993629 0.92765675 0.22095737 -5.65096896
229 230 231 232 233 234
-2.73479908 0.10815217 -1.19082599 -0.64406758 0.16767136 7.76230496
235 236 237 238 239 240
-0.87914005 -1.15134324 0.83076223 1.10132682 1.86412464 1.87524449
241 242 243 244 245 246
-0.07195689 0.02956596 -0.91145965 -0.26068943 -1.13366492 1.39804877
247 248 249 250 251 252
1.05334350 -0.56047513 -0.72515775 1.25701888 1.40313964 0.21490273
253 254 255 256 257 258
0.03468856 1.43911767 1.17081158 -0.80878303 -0.95388131 0.57978483
259 260 261 262 263 264
-0.26155467 -0.65221505 -0.35507111 -0.68767680 -1.42871699 14.45713288
265 266 267 268 269
-0.16021307 2.11996736 -1.06745006 -1.57719092 -0.85143645
> postscript(file="/var/wessaorg/rcomp/tmp/6s95t1358158465.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 = 269
Frequency = 1
lag(myerror, k = 1) myerror
0 19.25902714 NA
1 1.76058999 19.25902714
2 31.38917584 1.76058999
3 13.88741814 31.38917584
4 22.11585458 13.88741814
5 18.49743683 22.11585458
6 18.67688311 18.49743683
7 -2.31120903 18.67688311
8 6.00464589 -2.31120903
9 1.83308131 6.00464589
10 -11.10906711 1.83308131
11 23.55240031 -11.10906711
12 8.16210994 23.55240031
13 18.01268003 8.16210994
14 16.28875590 18.01268003
15 0.26679796 16.28875590
16 13.65996054 0.26679796
17 16.48959833 13.65996054
18 16.81200061 16.48959833
19 -0.85678425 16.81200061
20 13.65131354 -0.85678425
21 -33.29698031 13.65131354
22 9.98510914 -33.29698031
23 -14.98907453 9.98510914
24 1.55844667 -14.98907453
25 0.53319023 1.55844667
26 2.24009079 0.53319023
27 17.32284049 2.24009079
28 8.09612593 17.32284049
29 -1.19613989 8.09612593
30 1.28370677 -1.19613989
31 0.34283324 1.28370677
32 -4.68371774 0.34283324
33 -3.13534664 -4.68371774
34 1.91368459 -3.13534664
35 5.92823016 1.91368459
36 16.03230614 5.92823016
37 -5.80077805 16.03230614
38 4.09992284 -5.80077805
39 4.10055833 4.09992284
40 7.23272844 4.10055833
41 17.85552344 7.23272844
42 15.09018679 17.85552344
43 -12.47722666 15.09018679
44 -7.51811272 -12.47722666
45 8.16607792 -7.51811272
46 -8.47693272 8.16607792
47 -2.02122400 -8.47693272
48 -3.63109984 -2.02122400
49 2.51459091 -3.63109984
50 -5.24326834 2.51459091
51 7.21745137 -5.24326834
52 -6.97172253 7.21745137
53 -8.46966530 -6.97172253
54 2.60893515 -8.46966530
55 -6.94348438 2.60893515
56 6.66168602 -6.94348438
57 -7.05253230 6.66168602
58 -1.42834935 -7.05253230
59 -2.28878028 -1.42834935
60 0.92517571 -2.28878028
61 5.08812478 0.92517571
62 8.85353972 5.08812478
63 2.77760497 8.85353972
64 -5.07800441 2.77760497
65 -6.67848382 -5.07800441
66 -10.94764914 -6.67848382
67 5.06041289 -10.94764914
68 3.18880421 5.06041289
69 -1.08501293 3.18880421
70 -9.60079266 -1.08501293
71 2.23914392 -9.60079266
72 -4.52417277 2.23914392
73 11.25575174 -4.52417277
74 1.44690565 11.25575174
75 -6.36178948 1.44690565
76 -1.32018761 -6.36178948
77 -22.75130448 -1.32018761
78 1.52150487 -22.75130448
79 -10.60178165 1.52150487
80 5.19658330 -10.60178165
81 -8.19127115 5.19658330
82 -13.79639360 -8.19127115
83 14.31914854 -13.79639360
84 -5.18775829 14.31914854
85 4.57418300 -5.18775829
86 -0.14706581 4.57418300
87 8.50831782 -0.14706581
88 -7.01194693 8.50831782
89 -19.08397558 -7.01194693
90 -5.35813840 -19.08397558
91 1.95607529 -5.35813840
92 3.01000172 1.95607529
93 -22.73215687 3.01000172
94 -10.97684574 -22.73215687
95 -6.35957271 -10.97684574
96 -3.38768095 -6.35957271
97 -15.34245205 -3.38768095
98 -7.32084423 -15.34245205
99 -9.84023390 -7.32084423
100 -1.60428997 -9.84023390
101 5.87013288 -1.60428997
102 2.31655069 5.87013288
103 1.30094573 2.31655069
104 6.56026028 1.30094573
105 -8.17413294 6.56026028
106 -8.84157411 -8.17413294
107 2.79579421 -8.84157411
108 -4.34450576 2.79579421
109 -4.95824243 -4.34450576
110 -7.18337990 -4.95824243
111 -2.05364234 -7.18337990
112 -10.86148653 -2.05364234
113 -2.96442216 -10.86148653
114 -6.03027213 -2.96442216
115 -5.23050250 -6.03027213
116 -3.69896115 -5.23050250
117 -3.55803071 -3.69896115
118 -3.29908536 -3.55803071
119 -3.28752642 -3.29908536
120 -2.19916029 -3.28752642
121 -2.21191572 -2.19916029
122 -3.05178926 -2.21191572
123 -2.22786084 -3.05178926
124 -2.05732381 -2.22786084
125 1.98905815 -2.05732381
126 -2.47389406 1.98905815
127 -0.48471991 -2.47389406
128 -1.65124685 -0.48471991
129 -1.12009893 -1.65124685
130 -0.90021979 -1.12009893
131 -1.96374698 -0.90021979
132 0.48888330 -1.96374698
133 -0.75294121 0.48888330
134 -0.96025345 -0.75294121
135 -0.37337865 -0.96025345
136 -1.17686932 -0.37337865
137 -1.62215296 -1.17686932
138 -0.52934550 -1.62215296
139 0.84688907 -0.52934550
140 1.37260361 0.84688907
141 1.81639174 1.37260361
142 -2.14401305 1.81639174
143 -1.38339407 -2.14401305
144 -1.65101840 -1.38339407
145 -1.83576494 -1.65101840
146 -1.24771280 -1.83576494
147 1.16810251 -1.24771280
148 -1.23831264 1.16810251
149 -0.58981249 -1.23831264
150 -0.72787066 -0.58981249
151 -1.07724789 -0.72787066
152 -0.68699367 -1.07724789
153 -0.53653878 -0.68699367
154 -1.56383527 -0.53653878
155 -1.03090623 -1.56383527
156 -0.34847017 -1.03090623
157 0.70407718 -0.34847017
158 -1.22097332 0.70407718
159 -0.34210569 -1.22097332
160 1.22958216 -0.34210569
161 -0.14439060 1.22958216
162 -2.38915283 -0.14439060
163 -1.38309812 -2.38915283
164 1.16523887 -1.38309812
165 -1.46217077 1.16523887
166 0.13992650 -1.46217077
167 -0.76638675 0.13992650
168 0.35044532 -0.76638675
169 -0.74524923 0.35044532
170 -1.73410652 -0.74524923
171 -1.22844367 -1.73410652
172 -1.05177556 -1.22844367
173 -3.07788890 -1.05177556
174 -0.25692638 -3.07788890
175 -1.31556365 -0.25692638
176 -0.47237704 -1.31556365
177 -0.91021491 -0.47237704
178 -1.29099378 -0.91021491
179 -2.12857831 -1.29099378
180 -0.58164879 -2.12857831
181 -0.54487005 -0.58164879
182 1.49887283 -0.54487005
183 -0.01557594 1.49887283
184 -0.22550426 -0.01557594
185 1.18732732 -0.22550426
186 -0.05897163 1.18732732
187 -4.07506311 -0.05897163
188 -0.09552052 -4.07506311
189 -1.64965229 -0.09552052
190 -1.20418156 -1.64965229
191 -14.80958607 -1.20418156
192 -1.76102562 -14.80958607
193 3.13482456 -1.76102562
194 0.17830959 3.13482456
195 1.11037377 0.17830959
196 0.30921958 1.11037377
197 -0.26227410 0.30921958
198 -0.21901641 -0.26227410
199 -0.64468930 -0.21901641
200 -0.68827864 -0.64468930
201 -0.57285314 -0.68827864
202 13.41987180 -0.57285314
203 0.24869247 13.41987180
204 2.98121228 0.24869247
205 0.19691677 2.98121228
206 -0.16099678 0.19691677
207 -1.25828196 -0.16099678
208 -1.33993250 -1.25828196
209 0.28233330 -1.33993250
210 0.62888954 0.28233330
211 -13.10524390 0.62888954
212 -0.74654765 -13.10524390
213 -1.30316825 -0.74654765
214 1.33456659 -1.30316825
215 -0.90115821 1.33456659
216 1.07238168 -0.90115821
217 -3.91763528 1.07238168
218 -0.10715368 -3.91763528
219 -0.42753886 -0.10715368
220 0.49636974 -0.42753886
221 -0.90904812 0.49636974
222 0.91099051 -0.90904812
223 -0.58650247 0.91099051
224 0.15993629 -0.58650247
225 0.92765675 0.15993629
226 0.22095737 0.92765675
227 -5.65096896 0.22095737
228 -2.73479908 -5.65096896
229 0.10815217 -2.73479908
230 -1.19082599 0.10815217
231 -0.64406758 -1.19082599
232 0.16767136 -0.64406758
233 7.76230496 0.16767136
234 -0.87914005 7.76230496
235 -1.15134324 -0.87914005
236 0.83076223 -1.15134324
237 1.10132682 0.83076223
238 1.86412464 1.10132682
239 1.87524449 1.86412464
240 -0.07195689 1.87524449
241 0.02956596 -0.07195689
242 -0.91145965 0.02956596
243 -0.26068943 -0.91145965
244 -1.13366492 -0.26068943
245 1.39804877 -1.13366492
246 1.05334350 1.39804877
247 -0.56047513 1.05334350
248 -0.72515775 -0.56047513
249 1.25701888 -0.72515775
250 1.40313964 1.25701888
251 0.21490273 1.40313964
252 0.03468856 0.21490273
253 1.43911767 0.03468856
254 1.17081158 1.43911767
255 -0.80878303 1.17081158
256 -0.95388131 -0.80878303
257 0.57978483 -0.95388131
258 -0.26155467 0.57978483
259 -0.65221505 -0.26155467
260 -0.35507111 -0.65221505
261 -0.68767680 -0.35507111
262 -1.42871699 -0.68767680
263 14.45713288 -1.42871699
264 -0.16021307 14.45713288
265 2.11996736 -0.16021307
266 -1.06745006 2.11996736
267 -1.57719092 -1.06745006
268 -0.85143645 -1.57719092
269 NA -0.85143645
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.76058999 19.25902714
[2,] 31.38917584 1.76058999
[3,] 13.88741814 31.38917584
[4,] 22.11585458 13.88741814
[5,] 18.49743683 22.11585458
[6,] 18.67688311 18.49743683
[7,] -2.31120903 18.67688311
[8,] 6.00464589 -2.31120903
[9,] 1.83308131 6.00464589
[10,] -11.10906711 1.83308131
[11,] 23.55240031 -11.10906711
[12,] 8.16210994 23.55240031
[13,] 18.01268003 8.16210994
[14,] 16.28875590 18.01268003
[15,] 0.26679796 16.28875590
[16,] 13.65996054 0.26679796
[17,] 16.48959833 13.65996054
[18,] 16.81200061 16.48959833
[19,] -0.85678425 16.81200061
[20,] 13.65131354 -0.85678425
[21,] -33.29698031 13.65131354
[22,] 9.98510914 -33.29698031
[23,] -14.98907453 9.98510914
[24,] 1.55844667 -14.98907453
[25,] 0.53319023 1.55844667
[26,] 2.24009079 0.53319023
[27,] 17.32284049 2.24009079
[28,] 8.09612593 17.32284049
[29,] -1.19613989 8.09612593
[30,] 1.28370677 -1.19613989
[31,] 0.34283324 1.28370677
[32,] -4.68371774 0.34283324
[33,] -3.13534664 -4.68371774
[34,] 1.91368459 -3.13534664
[35,] 5.92823016 1.91368459
[36,] 16.03230614 5.92823016
[37,] -5.80077805 16.03230614
[38,] 4.09992284 -5.80077805
[39,] 4.10055833 4.09992284
[40,] 7.23272844 4.10055833
[41,] 17.85552344 7.23272844
[42,] 15.09018679 17.85552344
[43,] -12.47722666 15.09018679
[44,] -7.51811272 -12.47722666
[45,] 8.16607792 -7.51811272
[46,] -8.47693272 8.16607792
[47,] -2.02122400 -8.47693272
[48,] -3.63109984 -2.02122400
[49,] 2.51459091 -3.63109984
[50,] -5.24326834 2.51459091
[51,] 7.21745137 -5.24326834
[52,] -6.97172253 7.21745137
[53,] -8.46966530 -6.97172253
[54,] 2.60893515 -8.46966530
[55,] -6.94348438 2.60893515
[56,] 6.66168602 -6.94348438
[57,] -7.05253230 6.66168602
[58,] -1.42834935 -7.05253230
[59,] -2.28878028 -1.42834935
[60,] 0.92517571 -2.28878028
[61,] 5.08812478 0.92517571
[62,] 8.85353972 5.08812478
[63,] 2.77760497 8.85353972
[64,] -5.07800441 2.77760497
[65,] -6.67848382 -5.07800441
[66,] -10.94764914 -6.67848382
[67,] 5.06041289 -10.94764914
[68,] 3.18880421 5.06041289
[69,] -1.08501293 3.18880421
[70,] -9.60079266 -1.08501293
[71,] 2.23914392 -9.60079266
[72,] -4.52417277 2.23914392
[73,] 11.25575174 -4.52417277
[74,] 1.44690565 11.25575174
[75,] -6.36178948 1.44690565
[76,] -1.32018761 -6.36178948
[77,] -22.75130448 -1.32018761
[78,] 1.52150487 -22.75130448
[79,] -10.60178165 1.52150487
[80,] 5.19658330 -10.60178165
[81,] -8.19127115 5.19658330
[82,] -13.79639360 -8.19127115
[83,] 14.31914854 -13.79639360
[84,] -5.18775829 14.31914854
[85,] 4.57418300 -5.18775829
[86,] -0.14706581 4.57418300
[87,] 8.50831782 -0.14706581
[88,] -7.01194693 8.50831782
[89,] -19.08397558 -7.01194693
[90,] -5.35813840 -19.08397558
[91,] 1.95607529 -5.35813840
[92,] 3.01000172 1.95607529
[93,] -22.73215687 3.01000172
[94,] -10.97684574 -22.73215687
[95,] -6.35957271 -10.97684574
[96,] -3.38768095 -6.35957271
[97,] -15.34245205 -3.38768095
[98,] -7.32084423 -15.34245205
[99,] -9.84023390 -7.32084423
[100,] -1.60428997 -9.84023390
[101,] 5.87013288 -1.60428997
[102,] 2.31655069 5.87013288
[103,] 1.30094573 2.31655069
[104,] 6.56026028 1.30094573
[105,] -8.17413294 6.56026028
[106,] -8.84157411 -8.17413294
[107,] 2.79579421 -8.84157411
[108,] -4.34450576 2.79579421
[109,] -4.95824243 -4.34450576
[110,] -7.18337990 -4.95824243
[111,] -2.05364234 -7.18337990
[112,] -10.86148653 -2.05364234
[113,] -2.96442216 -10.86148653
[114,] -6.03027213 -2.96442216
[115,] -5.23050250 -6.03027213
[116,] -3.69896115 -5.23050250
[117,] -3.55803071 -3.69896115
[118,] -3.29908536 -3.55803071
[119,] -3.28752642 -3.29908536
[120,] -2.19916029 -3.28752642
[121,] -2.21191572 -2.19916029
[122,] -3.05178926 -2.21191572
[123,] -2.22786084 -3.05178926
[124,] -2.05732381 -2.22786084
[125,] 1.98905815 -2.05732381
[126,] -2.47389406 1.98905815
[127,] -0.48471991 -2.47389406
[128,] -1.65124685 -0.48471991
[129,] -1.12009893 -1.65124685
[130,] -0.90021979 -1.12009893
[131,] -1.96374698 -0.90021979
[132,] 0.48888330 -1.96374698
[133,] -0.75294121 0.48888330
[134,] -0.96025345 -0.75294121
[135,] -0.37337865 -0.96025345
[136,] -1.17686932 -0.37337865
[137,] -1.62215296 -1.17686932
[138,] -0.52934550 -1.62215296
[139,] 0.84688907 -0.52934550
[140,] 1.37260361 0.84688907
[141,] 1.81639174 1.37260361
[142,] -2.14401305 1.81639174
[143,] -1.38339407 -2.14401305
[144,] -1.65101840 -1.38339407
[145,] -1.83576494 -1.65101840
[146,] -1.24771280 -1.83576494
[147,] 1.16810251 -1.24771280
[148,] -1.23831264 1.16810251
[149,] -0.58981249 -1.23831264
[150,] -0.72787066 -0.58981249
[151,] -1.07724789 -0.72787066
[152,] -0.68699367 -1.07724789
[153,] -0.53653878 -0.68699367
[154,] -1.56383527 -0.53653878
[155,] -1.03090623 -1.56383527
[156,] -0.34847017 -1.03090623
[157,] 0.70407718 -0.34847017
[158,] -1.22097332 0.70407718
[159,] -0.34210569 -1.22097332
[160,] 1.22958216 -0.34210569
[161,] -0.14439060 1.22958216
[162,] -2.38915283 -0.14439060
[163,] -1.38309812 -2.38915283
[164,] 1.16523887 -1.38309812
[165,] -1.46217077 1.16523887
[166,] 0.13992650 -1.46217077
[167,] -0.76638675 0.13992650
[168,] 0.35044532 -0.76638675
[169,] -0.74524923 0.35044532
[170,] -1.73410652 -0.74524923
[171,] -1.22844367 -1.73410652
[172,] -1.05177556 -1.22844367
[173,] -3.07788890 -1.05177556
[174,] -0.25692638 -3.07788890
[175,] -1.31556365 -0.25692638
[176,] -0.47237704 -1.31556365
[177,] -0.91021491 -0.47237704
[178,] -1.29099378 -0.91021491
[179,] -2.12857831 -1.29099378
[180,] -0.58164879 -2.12857831
[181,] -0.54487005 -0.58164879
[182,] 1.49887283 -0.54487005
[183,] -0.01557594 1.49887283
[184,] -0.22550426 -0.01557594
[185,] 1.18732732 -0.22550426
[186,] -0.05897163 1.18732732
[187,] -4.07506311 -0.05897163
[188,] -0.09552052 -4.07506311
[189,] -1.64965229 -0.09552052
[190,] -1.20418156 -1.64965229
[191,] -14.80958607 -1.20418156
[192,] -1.76102562 -14.80958607
[193,] 3.13482456 -1.76102562
[194,] 0.17830959 3.13482456
[195,] 1.11037377 0.17830959
[196,] 0.30921958 1.11037377
[197,] -0.26227410 0.30921958
[198,] -0.21901641 -0.26227410
[199,] -0.64468930 -0.21901641
[200,] -0.68827864 -0.64468930
[201,] -0.57285314 -0.68827864
[202,] 13.41987180 -0.57285314
[203,] 0.24869247 13.41987180
[204,] 2.98121228 0.24869247
[205,] 0.19691677 2.98121228
[206,] -0.16099678 0.19691677
[207,] -1.25828196 -0.16099678
[208,] -1.33993250 -1.25828196
[209,] 0.28233330 -1.33993250
[210,] 0.62888954 0.28233330
[211,] -13.10524390 0.62888954
[212,] -0.74654765 -13.10524390
[213,] -1.30316825 -0.74654765
[214,] 1.33456659 -1.30316825
[215,] -0.90115821 1.33456659
[216,] 1.07238168 -0.90115821
[217,] -3.91763528 1.07238168
[218,] -0.10715368 -3.91763528
[219,] -0.42753886 -0.10715368
[220,] 0.49636974 -0.42753886
[221,] -0.90904812 0.49636974
[222,] 0.91099051 -0.90904812
[223,] -0.58650247 0.91099051
[224,] 0.15993629 -0.58650247
[225,] 0.92765675 0.15993629
[226,] 0.22095737 0.92765675
[227,] -5.65096896 0.22095737
[228,] -2.73479908 -5.65096896
[229,] 0.10815217 -2.73479908
[230,] -1.19082599 0.10815217
[231,] -0.64406758 -1.19082599
[232,] 0.16767136 -0.64406758
[233,] 7.76230496 0.16767136
[234,] -0.87914005 7.76230496
[235,] -1.15134324 -0.87914005
[236,] 0.83076223 -1.15134324
[237,] 1.10132682 0.83076223
[238,] 1.86412464 1.10132682
[239,] 1.87524449 1.86412464
[240,] -0.07195689 1.87524449
[241,] 0.02956596 -0.07195689
[242,] -0.91145965 0.02956596
[243,] -0.26068943 -0.91145965
[244,] -1.13366492 -0.26068943
[245,] 1.39804877 -1.13366492
[246,] 1.05334350 1.39804877
[247,] -0.56047513 1.05334350
[248,] -0.72515775 -0.56047513
[249,] 1.25701888 -0.72515775
[250,] 1.40313964 1.25701888
[251,] 0.21490273 1.40313964
[252,] 0.03468856 0.21490273
[253,] 1.43911767 0.03468856
[254,] 1.17081158 1.43911767
[255,] -0.80878303 1.17081158
[256,] -0.95388131 -0.80878303
[257,] 0.57978483 -0.95388131
[258,] -0.26155467 0.57978483
[259,] -0.65221505 -0.26155467
[260,] -0.35507111 -0.65221505
[261,] -0.68767680 -0.35507111
[262,] -1.42871699 -0.68767680
[263,] 14.45713288 -1.42871699
[264,] -0.16021307 14.45713288
[265,] 2.11996736 -0.16021307
[266,] -1.06745006 2.11996736
[267,] -1.57719092 -1.06745006
[268,] -0.85143645 -1.57719092
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.76058999 19.25902714
2 31.38917584 1.76058999
3 13.88741814 31.38917584
4 22.11585458 13.88741814
5 18.49743683 22.11585458
6 18.67688311 18.49743683
7 -2.31120903 18.67688311
8 6.00464589 -2.31120903
9 1.83308131 6.00464589
10 -11.10906711 1.83308131
11 23.55240031 -11.10906711
12 8.16210994 23.55240031
13 18.01268003 8.16210994
14 16.28875590 18.01268003
15 0.26679796 16.28875590
16 13.65996054 0.26679796
17 16.48959833 13.65996054
18 16.81200061 16.48959833
19 -0.85678425 16.81200061
20 13.65131354 -0.85678425
21 -33.29698031 13.65131354
22 9.98510914 -33.29698031
23 -14.98907453 9.98510914
24 1.55844667 -14.98907453
25 0.53319023 1.55844667
26 2.24009079 0.53319023
27 17.32284049 2.24009079
28 8.09612593 17.32284049
29 -1.19613989 8.09612593
30 1.28370677 -1.19613989
31 0.34283324 1.28370677
32 -4.68371774 0.34283324
33 -3.13534664 -4.68371774
34 1.91368459 -3.13534664
35 5.92823016 1.91368459
36 16.03230614 5.92823016
37 -5.80077805 16.03230614
38 4.09992284 -5.80077805
39 4.10055833 4.09992284
40 7.23272844 4.10055833
41 17.85552344 7.23272844
42 15.09018679 17.85552344
43 -12.47722666 15.09018679
44 -7.51811272 -12.47722666
45 8.16607792 -7.51811272
46 -8.47693272 8.16607792
47 -2.02122400 -8.47693272
48 -3.63109984 -2.02122400
49 2.51459091 -3.63109984
50 -5.24326834 2.51459091
51 7.21745137 -5.24326834
52 -6.97172253 7.21745137
53 -8.46966530 -6.97172253
54 2.60893515 -8.46966530
55 -6.94348438 2.60893515
56 6.66168602 -6.94348438
57 -7.05253230 6.66168602
58 -1.42834935 -7.05253230
59 -2.28878028 -1.42834935
60 0.92517571 -2.28878028
61 5.08812478 0.92517571
62 8.85353972 5.08812478
63 2.77760497 8.85353972
64 -5.07800441 2.77760497
65 -6.67848382 -5.07800441
66 -10.94764914 -6.67848382
67 5.06041289 -10.94764914
68 3.18880421 5.06041289
69 -1.08501293 3.18880421
70 -9.60079266 -1.08501293
71 2.23914392 -9.60079266
72 -4.52417277 2.23914392
73 11.25575174 -4.52417277
74 1.44690565 11.25575174
75 -6.36178948 1.44690565
76 -1.32018761 -6.36178948
77 -22.75130448 -1.32018761
78 1.52150487 -22.75130448
79 -10.60178165 1.52150487
80 5.19658330 -10.60178165
81 -8.19127115 5.19658330
82 -13.79639360 -8.19127115
83 14.31914854 -13.79639360
84 -5.18775829 14.31914854
85 4.57418300 -5.18775829
86 -0.14706581 4.57418300
87 8.50831782 -0.14706581
88 -7.01194693 8.50831782
89 -19.08397558 -7.01194693
90 -5.35813840 -19.08397558
91 1.95607529 -5.35813840
92 3.01000172 1.95607529
93 -22.73215687 3.01000172
94 -10.97684574 -22.73215687
95 -6.35957271 -10.97684574
96 -3.38768095 -6.35957271
97 -15.34245205 -3.38768095
98 -7.32084423 -15.34245205
99 -9.84023390 -7.32084423
100 -1.60428997 -9.84023390
101 5.87013288 -1.60428997
102 2.31655069 5.87013288
103 1.30094573 2.31655069
104 6.56026028 1.30094573
105 -8.17413294 6.56026028
106 -8.84157411 -8.17413294
107 2.79579421 -8.84157411
108 -4.34450576 2.79579421
109 -4.95824243 -4.34450576
110 -7.18337990 -4.95824243
111 -2.05364234 -7.18337990
112 -10.86148653 -2.05364234
113 -2.96442216 -10.86148653
114 -6.03027213 -2.96442216
115 -5.23050250 -6.03027213
116 -3.69896115 -5.23050250
117 -3.55803071 -3.69896115
118 -3.29908536 -3.55803071
119 -3.28752642 -3.29908536
120 -2.19916029 -3.28752642
121 -2.21191572 -2.19916029
122 -3.05178926 -2.21191572
123 -2.22786084 -3.05178926
124 -2.05732381 -2.22786084
125 1.98905815 -2.05732381
126 -2.47389406 1.98905815
127 -0.48471991 -2.47389406
128 -1.65124685 -0.48471991
129 -1.12009893 -1.65124685
130 -0.90021979 -1.12009893
131 -1.96374698 -0.90021979
132 0.48888330 -1.96374698
133 -0.75294121 0.48888330
134 -0.96025345 -0.75294121
135 -0.37337865 -0.96025345
136 -1.17686932 -0.37337865
137 -1.62215296 -1.17686932
138 -0.52934550 -1.62215296
139 0.84688907 -0.52934550
140 1.37260361 0.84688907
141 1.81639174 1.37260361
142 -2.14401305 1.81639174
143 -1.38339407 -2.14401305
144 -1.65101840 -1.38339407
145 -1.83576494 -1.65101840
146 -1.24771280 -1.83576494
147 1.16810251 -1.24771280
148 -1.23831264 1.16810251
149 -0.58981249 -1.23831264
150 -0.72787066 -0.58981249
151 -1.07724789 -0.72787066
152 -0.68699367 -1.07724789
153 -0.53653878 -0.68699367
154 -1.56383527 -0.53653878
155 -1.03090623 -1.56383527
156 -0.34847017 -1.03090623
157 0.70407718 -0.34847017
158 -1.22097332 0.70407718
159 -0.34210569 -1.22097332
160 1.22958216 -0.34210569
161 -0.14439060 1.22958216
162 -2.38915283 -0.14439060
163 -1.38309812 -2.38915283
164 1.16523887 -1.38309812
165 -1.46217077 1.16523887
166 0.13992650 -1.46217077
167 -0.76638675 0.13992650
168 0.35044532 -0.76638675
169 -0.74524923 0.35044532
170 -1.73410652 -0.74524923
171 -1.22844367 -1.73410652
172 -1.05177556 -1.22844367
173 -3.07788890 -1.05177556
174 -0.25692638 -3.07788890
175 -1.31556365 -0.25692638
176 -0.47237704 -1.31556365
177 -0.91021491 -0.47237704
178 -1.29099378 -0.91021491
179 -2.12857831 -1.29099378
180 -0.58164879 -2.12857831
181 -0.54487005 -0.58164879
182 1.49887283 -0.54487005
183 -0.01557594 1.49887283
184 -0.22550426 -0.01557594
185 1.18732732 -0.22550426
186 -0.05897163 1.18732732
187 -4.07506311 -0.05897163
188 -0.09552052 -4.07506311
189 -1.64965229 -0.09552052
190 -1.20418156 -1.64965229
191 -14.80958607 -1.20418156
192 -1.76102562 -14.80958607
193 3.13482456 -1.76102562
194 0.17830959 3.13482456
195 1.11037377 0.17830959
196 0.30921958 1.11037377
197 -0.26227410 0.30921958
198 -0.21901641 -0.26227410
199 -0.64468930 -0.21901641
200 -0.68827864 -0.64468930
201 -0.57285314 -0.68827864
202 13.41987180 -0.57285314
203 0.24869247 13.41987180
204 2.98121228 0.24869247
205 0.19691677 2.98121228
206 -0.16099678 0.19691677
207 -1.25828196 -0.16099678
208 -1.33993250 -1.25828196
209 0.28233330 -1.33993250
210 0.62888954 0.28233330
211 -13.10524390 0.62888954
212 -0.74654765 -13.10524390
213 -1.30316825 -0.74654765
214 1.33456659 -1.30316825
215 -0.90115821 1.33456659
216 1.07238168 -0.90115821
217 -3.91763528 1.07238168
218 -0.10715368 -3.91763528
219 -0.42753886 -0.10715368
220 0.49636974 -0.42753886
221 -0.90904812 0.49636974
222 0.91099051 -0.90904812
223 -0.58650247 0.91099051
224 0.15993629 -0.58650247
225 0.92765675 0.15993629
226 0.22095737 0.92765675
227 -5.65096896 0.22095737
228 -2.73479908 -5.65096896
229 0.10815217 -2.73479908
230 -1.19082599 0.10815217
231 -0.64406758 -1.19082599
232 0.16767136 -0.64406758
233 7.76230496 0.16767136
234 -0.87914005 7.76230496
235 -1.15134324 -0.87914005
236 0.83076223 -1.15134324
237 1.10132682 0.83076223
238 1.86412464 1.10132682
239 1.87524449 1.86412464
240 -0.07195689 1.87524449
241 0.02956596 -0.07195689
242 -0.91145965 0.02956596
243 -0.26068943 -0.91145965
244 -1.13366492 -0.26068943
245 1.39804877 -1.13366492
246 1.05334350 1.39804877
247 -0.56047513 1.05334350
248 -0.72515775 -0.56047513
249 1.25701888 -0.72515775
250 1.40313964 1.25701888
251 0.21490273 1.40313964
252 0.03468856 0.21490273
253 1.43911767 0.03468856
254 1.17081158 1.43911767
255 -0.80878303 1.17081158
256 -0.95388131 -0.80878303
257 0.57978483 -0.95388131
258 -0.26155467 0.57978483
259 -0.65221505 -0.26155467
260 -0.35507111 -0.65221505
261 -0.68767680 -0.35507111
262 -1.42871699 -0.68767680
263 14.45713288 -1.42871699
264 -0.16021307 14.45713288
265 2.11996736 -0.16021307
266 -1.06745006 2.11996736
267 -1.57719092 -1.06745006
268 -0.85143645 -1.57719092
> 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/7ss001358158465.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/802m11358158465.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/941u01358158465.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/105zzg1358158465.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11yy031358158465.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12xvt61358158465.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13wp9v1358158465.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14mtog1358158465.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/15h30e1358158465.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/16l8rs1358158465.tab")
+ }
>
> try(system("convert tmp/1uwd41358158465.ps tmp/1uwd41358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gfl81358158465.ps tmp/2gfl81358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ryld1358158465.ps tmp/3ryld1358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ii2o1358158465.ps tmp/4ii2o1358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wvl11358158465.ps tmp/5wvl11358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/6s95t1358158465.ps tmp/6s95t1358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ss001358158465.ps tmp/7ss001358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/802m11358158465.ps tmp/802m11358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/941u01358158465.ps tmp/941u01358158465.png",intern=TRUE))
character(0)
> try(system("convert tmp/105zzg1358158465.ps tmp/105zzg1358158465.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.628 0.879 11.562