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
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+ ,0)
+ ,dim=c(10
+ ,269)
+ ,dimnames=list(c('lfm'
+ ,'blogs'
+ ,'uk'
+ ,'spr'
+ ,'hours_blogs'
+ ,'hours_uk'
+ ,'hours_spr'
+ ,'blogs_uk'
+ ,'blogs_spr'
+ ,'uk_spr')
+ ,1:269))
> y <- array(NA,dim=c(10,269),dimnames=list(c('lfm','blogs','uk','spr','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 = '1'
> 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
lfm blogs uk spr hours_blogs hours_uk hours_spr blogs_uk blogs_spr uk_spr
1 122 88 1 9 8976 102 918 88 792 9
2 114 106 1 3 10494 99 297 106 318 3
3 140 70 1 2 6790 97 194 70 140 2
4 143 70 1 21 5740 82 1722 70 1470 21
5 122 56 1 10 4312 77 770 56 560 10
6 127 50 1 2 3250 65 130 50 100 2
7 113 48 1 4 3072 64 256 48 192 4
8 118 71 1 4 4402 62 248 71 284 4
9 161 61 1 4 3782 62 248 61 244 4
10 134 66 1 5 4092 62 310 66 330 5
11 96 80 1 2 4880 61 122 80 160 2
12 104 37 1 3 2183 59 177 37 111 3
13 135 53 1 3 3021 57 171 53 159 3
14 110 39 1 6 2184 56 336 39 234 6
15 128 40 1 3 2160 54 162 40 120 3
16 142 59 1 4 3186 54 216 59 236 4
17 117 42 1 3 2226 53 159 42 126 3
18 94 33 1 13 1716 52 676 33 429 13
19 135 36 1 3 1836 51 153 36 108 3
20 121 57 1 4 2907 51 204 57 228 4
21 103 38 1 8 1938 51 408 38 304 8
22 118 98 1 8 4900 50 400 98 784 8
23 127 43 1 3 2150 50 150 43 129 3
24 116 73 1 4 3650 50 200 73 292 4
25 129 52 1 2 2548 49 98 52 104 2
26 115 53 1 5 2597 49 245 53 265 5
27 135 51 1 4 2499 49 196 51 204 4
28 133 32 1 3 1536 48 144 32 96 3
29 113 43 1 3 2064 48 144 43 129 3
30 111 53 1 2 2491 47 94 53 106 2
31 92 50 1 3 2350 47 141 50 150 3
32 118 50 1 3 2300 46 138 50 150 3
33 134 56 1 3 2576 46 138 56 168 3
34 106 53 1 5 2385 45 225 53 265 5
35 137 47 1 3 2115 45 135 47 141 3
36 100 42 1 4 1890 45 180 42 168 4
37 102 29 1 3 1276 44 132 29 87 3
38 134 54 1 4 2322 43 172 54 216 4
39 130 40 1 8 1680 42 336 40 320 8
40 144 41 1 3 1722 42 126 41 123 3
41 120 37 1 4 1554 42 168 37 148 4
42 91 25 1 2 1050 42 84 25 50 2
43 100 27 1 5 1134 42 210 27 135 5
44 134 61 1 4 2562 42 168 61 244 4
45 161 54 1 7 2214 41 287 54 378 7
46 128 35 1 3 1435 41 123 35 105 3
47 124 55 1 4 2255 41 164 55 220 4
48 115 47 1 6 1927 41 246 47 282 6
49 123 49 1 7 2009 41 287 49 343 7
50 117 38 1 20 1558 41 820 38 760 20
51 111 52 1 49 2132 41 2009 52 2548 49
52 146 35 1 3 1400 40 120 35 105 3
53 101 52 1 3 2080 40 120 52 156 3
54 131 54 1 6 2160 40 240 54 324 6
55 122 40 1 6 1600 40 240 40 240 6
56 78 52 1 4 2080 40 160 52 208 4
57 120 34 1 5 1326 39 195 34 170 5
58 115 51 1 4 1989 39 156 51 204 4
59 142 43 1 4 1634 38 152 43 172 4
60 94 40 1 31 1520 38 1178 40 1240 31
61 114 38 1 3 1368 36 108 38 114 3
62 108 33 1 3 1188 36 108 33 99 3
63 119 27 1 4 945 35 140 27 108 4
64 117 34 1 6 1190 35 210 34 204 6
65 86 44 1 5 1540 35 175 44 220 5
66 138 46 1 3 1610 35 105 46 138 3
67 119 50 1 3 1700 34 102 50 150 3
68 117 31 1 2 1054 34 68 31 62 2
69 117 33 1 3 1122 34 102 33 99 3
70 76 37 1 3 1221 33 99 37 111 3
71 119 48 1 16 1584 33 528 48 768 16
72 119 33 1 3 1089 33 99 33 99 3
73 124 40 1 3 1280 32 96 40 120 3
74 116 21 1 3 672 32 96 21 63 3
75 118 33 1 2 1056 32 64 33 66 2
76 102 41 1 5 1271 31 155 41 205 5
77 116 35 1 3 1085 31 93 35 105 3
78 103 60 1 4 1800 30 120 60 240 4
79 117 30 1 5 900 30 150 30 150 5
80 108 45 1 2 1350 30 60 45 90 2
81 122 26 1 3 780 30 90 26 78 3
82 90 41 1 3 1189 29 87 41 123 3
83 133 48 1 14 1344 28 392 48 672 14
84 116 10 1 8 280 28 224 10 80 8
85 110 35 1 4 945 27 108 35 140 4
86 90 23 1 4 621 27 108 23 92 4
87 74 29 1 3 783 27 81 29 87 3
88 75 17 1 4 442 26 104 17 68 4
89 107 35 1 3 875 25 75 35 105 3
90 90 50 1 5 1250 25 125 50 250 5
91 96 33 1 3 825 25 75 33 99 3
92 115 23 1 3 552 24 72 23 69 3
93 91 22 1 2 528 24 48 22 44 2
94 77 52 1 4 1196 23 92 52 208 4
95 108 38 1 31 874 23 713 38 1178 31
96 83 32 1 2 736 23 46 32 64 2
97 77 28 1 5 644 23 115 28 140 5
98 99 43 1 5 989 23 115 43 215 5
99 115 32 1 2 704 22 44 32 64 2
100 99 35 1 2 770 22 44 35 70 2
101 106 25 1 3 550 22 66 25 75 3
102 77 14 1 8 308 22 176 14 112 8
103 115 17 1 6 340 20 120 17 102 6
104 67 18 1 3 342 19 57 18 54 3
105 8 12 1 2 228 19 38 12 24 2
106 69 27 1 5 459 17 85 27 135 5
107 88 28 1 3 476 17 51 28 84 3
108 107 12 1 5 192 16 80 12 60 5
109 120 21 1 5 336 16 80 21 105 5
110 3 9 1 4 45 5 20 9 36 4
111 1 11 1 2 44 4 8 11 22 2
112 0 3 1 4 9 3 12 3 12 4
113 111 111 0 4 17316 0 624 0 444 0
114 69 137 0 8 14933 0 872 0 1096 0
115 116 112 0 3 11648 0 312 0 336 0
116 103 73 0 4 7154 0 392 0 292 0
117 139 99 0 3 7722 0 234 0 297 0
118 135 115 0 3 8855 0 231 0 345 0
119 113 95 0 3 6935 0 219 0 285 0
120 99 60 0 4 4260 0 284 0 240 0
121 76 94 0 4 6298 0 268 0 376 0
122 110 70 0 5 4480 0 320 0 350 0
123 121 87 0 2 5394 0 124 0 174 0
124 95 102 0 3 6222 0 183 0 306 0
125 66 69 0 4 4002 0 232 0 276 0
126 111 111 0 7 6438 0 406 0 777 0
127 77 55 0 3 3080 0 168 0 165 0
128 101 118 0 4 6608 0 224 0 472 0
129 108 90 0 3 4680 0 156 0 270 0
130 135 81 0 4 4131 0 204 0 324 0
131 70 88 0 4 4488 0 204 0 352 0
132 124 63 0 3 3150 0 150 0 189 0
133 92 84 0 6 4116 0 294 0 504 0
134 104 87 0 4 4263 0 196 0 348 0
135 113 78 0 4 3744 0 192 0 312 0
136 95 93 0 4 4371 0 188 0 372 0
137 89 69 0 4 3243 0 188 0 276 0
138 83 67 0 3 3082 0 138 0 201 0
139 96 61 0 6 2745 0 270 0 366 0
140 95 123 0 4 5535 0 180 0 492 0
141 110 91 0 6 4095 0 270 0 546 0
142 106 98 0 6 4410 0 270 0 588 0
143 78 38 0 2 1672 0 88 0 76 0
144 115 72 0 3 3168 0 132 0 216 0
145 74 59 0 3 2596 0 132 0 177 0
146 93 78 0 2 3354 0 86 0 156 0
147 88 58 0 4 2494 0 172 0 232 0
148 104 97 0 5 4074 0 210 0 485 0
149 86 69 0 3 2829 0 123 0 207 0
150 104 50 0 7 2050 0 287 0 350 0
151 99 66 0 4 2640 0 160 0 264 0
152 101 70 0 3 2730 0 117 0 210 0
153 53 65 0 4 2535 0 156 0 260 0
154 96 69 0 4 2691 0 156 0 276 0
155 58 49 0 3 1911 0 117 0 147 0
156 117 72 0 3 2808 0 117 0 216 0
157 82 74 0 4 2886 0 156 0 296 0
158 57 82 0 5 3198 0 195 0 410 0
159 71 61 0 3 2318 0 114 0 183 0
160 105 72 0 4 2736 0 152 0 288 0
161 60 77 0 6 2926 0 228 0 462 0
162 77 64 0 5 2432 0 190 0 320 0
163 73 23 0 7 851 0 259 0 161 0
164 78 39 0 5 1443 0 185 0 195 0
165 81 87 0 5 3219 0 185 0 435 0
166 101 46 0 3 1656 0 108 0 138 0
167 118 66 0 5 2376 0 180 0 330 0
168 59 57 0 4 2052 0 144 0 228 0
169 101 48 0 9 1728 0 324 0 432 0
170 22 75 0 3 2700 0 108 0 225 0
171 77 35 0 3 1260 0 108 0 105 0
172 100 53 0 3 1855 0 105 0 159 0
173 39 60 0 3 2100 0 105 0 180 0
174 42 20 0 15 680 0 510 0 300 0
175 80 66 0 4 2244 0 136 0 264 0
176 48 34 0 6 1156 0 204 0 204 0
177 131 80 0 3 2720 0 102 0 240 0
178 46 63 0 3 2142 0 102 0 189 0
179 89 46 0 3 1518 0 99 0 138 0
180 51 20 0 5 660 0 165 0 100 0
181 108 73 0 3 2409 0 99 0 219 0
182 86 57 0 4 1881 0 132 0 228 0
183 105 65 0 7 2145 0 231 0 455 0
184 85 70 0 4 2310 0 132 0 280 0
185 103 53 0 5 1696 0 160 0 265 0
186 83 60 0 7 1920 0 224 0 420 0
187 77 34 0 14 1088 0 448 0 476 0
188 26 18 0 25 576 0 800 0 450 0
189 73 49 0 6 1568 0 192 0 294 0
190 42 27 0 4 837 0 124 0 108 0
191 71 45 0 3 1395 0 93 0 135 0
192 105 9 0 62 279 0 1922 0 558 0
193 73 23 0 5 690 0 150 0 115 0
194 98 61 0 10 1830 0 300 0 610 0
195 108 67 0 4 1943 0 116 0 268 0
196 57 72 0 5 2088 0 145 0 360 0
197 37 58 0 5 1682 0 145 0 290 0
198 70 55 0 4 1540 0 112 0 220 0
199 73 33 0 10 924 0 280 0 330 0
200 47 40 0 5 1120 0 140 0 200 0
201 73 57 0 3 1596 0 84 0 171 0
202 91 61 0 3 1708 0 84 0 183 0
203 110 87 0 17 2436 0 476 0 1479 0
204 78 65 0 4 1755 0 108 0 260 0
205 92 85 0 6 2295 0 162 0 510 0
206 52 85 0 3 2295 0 81 0 255 0
207 88 54 0 4 1404 0 104 0 216 0
208 100 24 0 8 624 0 208 0 192 0
209 33 31 0 3 806 0 78 0 93 0
210 42 64 0 4 1664 0 104 0 256 0
211 81 70 0 4 1750 0 100 0 280 0
212 67 2 0 47 50 0 1175 0 94 0
213 8 27 0 8 648 0 192 0 216 0
214 46 29 0 3 696 0 72 0 87 0
215 83 68 0 5 1632 0 120 0 340 0
216 87 42 0 3 1008 0 72 0 126 0
217 82 78 0 4 1872 0 96 0 312 0
218 63 13 0 30 312 0 720 0 390 0
219 27 52 0 4 1248 0 96 0 208 0
220 14 25 0 12 575 0 276 0 300 0
221 83 38 0 8 874 0 184 0 304 0
222 168 40 0 3 920 0 69 0 120 0
223 67 42 0 8 966 0 184 0 336 0
224 21 40 0 4 920 0 92 0 160 0
225 55 74 0 3 1702 0 69 0 222 0
226 54 73 0 4 1679 0 92 0 292 0
227 118 56 0 4 1232 0 88 0 224 0
228 69 3 0 21 66 0 462 0 63 0
229 77 9 0 12 189 0 252 0 108 0
230 72 68 0 3 1428 0 63 0 204 0
231 53 28 0 3 588 0 63 0 84 0
232 40 36 0 4 756 0 84 0 144 0
233 102 38 0 6 760 0 120 0 228 0
234 25 55 0 17 1100 0 340 0 935 0
235 31 36 0 3 720 0 60 0 108 0
236 77 17 0 18 340 0 360 0 306 0
237 38 54 0 5 1080 0 100 0 270 0
238 23 57 0 5 1083 0 95 0 285 0
239 91 30 0 16 570 0 304 0 480 0
240 58 40 0 10 760 0 190 0 400 0
241 42 37 0 5 666 0 90 0 185 0
242 44 46 0 4 828 0 72 0 184 0
243 58 32 0 3 576 0 54 0 96 0
244 35 34 0 5 612 0 90 0 170 0
245 88 22 0 4 396 0 72 0 88 0
246 25 59 0 5 1003 0 85 0 295 0
247 39 32 0 10 544 0 170 0 320 0
248 48 18 0 12 288 0 192 0 216 0
249 64 28 0 4 448 0 64 0 112 0
250 65 34 0 9 510 0 135 0 306 0
251 95 29 0 12 435 0 180 0 348 0
252 29 24 0 10 360 0 150 0 240 0
253 2 24 0 9 360 0 135 0 216 0
254 83 23 0 17 322 0 238 0 391 0
255 11 43 0 6 559 0 78 0 258 0
256 16 28 0 3 364 0 39 0 84 0
257 9 19 0 4 228 0 48 0 76 0
258 46 16 0 19 176 0 209 0 304 0
259 41 40 0 3 440 0 33 0 120 0
260 14 14 0 9 140 0 90 0 126 0
261 63 19 0 7 190 0 70 0 133 0
262 9 22 0 4 220 0 40 0 88 0
263 0 8 0 3 80 0 30 0 24 0
264 58 31 0 46 279 0 414 0 1426 0
265 18 9 0 31 72 0 248 0 279 0
266 42 18 0 21 144 0 168 0 378 0
267 26 9 0 7 63 0 49 0 63 0
268 38 5 0 29 35 0 203 0 145 0
269 1 11 0 5 44 0 20 0 55 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) blogs uk spr hours_blogs hours_uk
24.746719 0.973199 40.858713 0.505388 -0.002276 0.590185
hours_spr blogs_uk blogs_spr uk_spr
0.028675 -0.405407 -0.031979 -0.091705
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-76.263 -15.157 1.706 17.864 106.762
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.746719 6.910851 3.581 0.000409 ***
blogs 0.973199 0.156287 6.227 1.91e-09 ***
uk 40.858713 9.279100 4.403 1.56e-05 ***
spr 0.505388 0.510633 0.990 0.323231
hours_blogs -0.002276 0.001530 -1.488 0.138090
hours_uk 0.590185 0.238789 2.472 0.014096 *
hours_spr 0.028675 0.016798 1.707 0.089014 .
blogs_uk -0.405407 0.237928 -1.704 0.089598 .
blogs_spr -0.031979 0.014041 -2.278 0.023570 *
uk_spr -0.091705 0.689415 -0.133 0.894282
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25.75 on 259 degrees of freedom
Multiple R-squared: 0.5013, Adjusted R-squared: 0.484
F-statistic: 28.93 on 9 and 259 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,] 6.464844e-01 7.070311e-01 0.3535156
[2,] 4.981008e-01 9.962015e-01 0.5018992
[3,] 3.879245e-01 7.758490e-01 0.6120755
[4,] 3.053405e-01 6.106810e-01 0.6946595
[5,] 2.051065e-01 4.102131e-01 0.7948935
[6,] 1.369578e-01 2.739155e-01 0.8630422
[7,] 1.118364e-01 2.236728e-01 0.8881636
[8,] 7.372018e-02 1.474404e-01 0.9262798
[9,] 5.939914e-02 1.187983e-01 0.9406009
[10,] 1.057321e-01 2.114642e-01 0.8942679
[11,] 7.080625e-02 1.416125e-01 0.9291937
[12,] 4.650466e-02 9.300932e-02 0.9534953
[13,] 3.002953e-02 6.005906e-02 0.9699705
[14,] 1.867212e-02 3.734425e-02 0.9813279
[15,] 1.443267e-02 2.886535e-02 0.9855673
[16,] 9.067193e-03 1.813439e-02 0.9909328
[17,] 6.291527e-03 1.258305e-02 0.9937085
[18,] 4.435578e-03 8.871155e-03 0.9955644
[19,] 7.637099e-03 1.527420e-02 0.9923629
[20,] 4.603482e-03 9.206964e-03 0.9953965
[21,] 3.834361e-03 7.668722e-03 0.9961656
[22,] 2.645732e-03 5.291464e-03 0.9973543
[23,] 2.348887e-03 4.697773e-03 0.9976511
[24,] 2.157385e-03 4.314769e-03 0.9978426
[25,] 1.768434e-03 3.536868e-03 0.9982316
[26,] 1.610689e-03 3.221379e-03 0.9983893
[27,] 1.918332e-03 3.836664e-03 0.9980817
[28,] 2.412946e-03 4.825892e-03 0.9975871
[29,] 1.483056e-03 2.966111e-03 0.9985169
[30,] 2.507460e-03 5.014920e-03 0.9974925
[31,] 1.825803e-03 3.651605e-03 0.9981742
[32,] 1.388200e-03 2.776401e-03 0.9986118
[33,] 5.242085e-03 1.048417e-02 0.9947579
[34,] 3.873617e-03 7.747233e-03 0.9961264
[35,] 2.557153e-03 5.114306e-03 0.9974428
[36,] 1.700120e-03 3.400241e-03 0.9982999
[37,] 1.100033e-03 2.200067e-03 0.9989000
[38,] 7.421679e-04 1.484336e-03 0.9992578
[39,] 5.477705e-04 1.095541e-03 0.9994522
[40,] 7.649331e-04 1.529866e-03 0.9992351
[41,] 7.866725e-04 1.573345e-03 0.9992133
[42,] 5.422047e-04 1.084409e-03 0.9994578
[43,] 3.486236e-04 6.972472e-04 0.9996514
[44,] 1.521811e-03 3.043622e-03 0.9984782
[45,] 1.029305e-03 2.058609e-03 0.9989707
[46,] 7.074909e-04 1.414982e-03 0.9992925
[47,] 7.540735e-04 1.508147e-03 0.9992459
[48,] 6.843082e-04 1.368616e-03 0.9993157
[49,] 4.573507e-04 9.147015e-04 0.9995426
[50,] 3.262208e-04 6.524416e-04 0.9996738
[51,] 2.129076e-04 4.258151e-04 0.9997871
[52,] 1.372514e-04 2.745028e-04 0.9998627
[53,] 2.533618e-04 5.067236e-04 0.9997466
[54,] 2.380156e-04 4.760311e-04 0.9997620
[55,] 1.531374e-04 3.062749e-04 0.9998469
[56,] 9.732247e-05 1.946449e-04 0.9999027
[57,] 6.135451e-05 1.227090e-04 0.9999386
[58,] 1.990365e-04 3.980730e-04 0.9998010
[59,] 1.381158e-04 2.762317e-04 0.9998619
[60,] 9.111206e-05 1.822241e-04 0.9999089
[61,] 6.248891e-05 1.249778e-04 0.9999375
[62,] 4.008300e-05 8.016599e-05 0.9999599
[63,] 2.521720e-05 5.043439e-05 0.9999748
[64,] 1.878290e-05 3.756580e-05 0.9999812
[65,] 1.162225e-05 2.324450e-05 0.9999884
[66,] 9.258355e-06 1.851671e-05 0.9999907
[67,] 5.895646e-06 1.179129e-05 0.9999941
[68,] 3.783120e-06 7.566239e-06 0.9999962
[69,] 2.525863e-06 5.051727e-06 0.9999975
[70,] 2.947036e-06 5.894073e-06 0.9999971
[71,] 2.616837e-06 5.233673e-06 0.9999974
[72,] 2.088992e-06 4.177983e-06 0.9999979
[73,] 1.256303e-06 2.512606e-06 0.9999987
[74,] 1.157453e-06 2.314906e-06 0.9999988
[75,] 2.945266e-06 5.890532e-06 0.9999971
[76,] 4.329421e-06 8.658843e-06 0.9999957
[77,] 2.662456e-06 5.324912e-06 0.9999973
[78,] 2.570015e-06 5.140030e-06 0.9999974
[79,] 1.725522e-06 3.451044e-06 0.9999983
[80,] 1.276752e-06 2.553503e-06 0.9999987
[81,] 8.906569e-07 1.781314e-06 0.9999991
[82,] 1.603431e-06 3.206863e-06 0.9999984
[83,] 1.279465e-06 2.558929e-06 0.9999987
[84,] 1.109902e-06 2.219804e-06 0.9999989
[85,] 1.245913e-06 2.491826e-06 0.9999988
[86,] 1.133047e-06 2.266093e-06 0.9999989
[87,] 8.903550e-07 1.780710e-06 0.9999991
[88,] 5.497358e-07 1.099472e-06 0.9999995
[89,] 3.705805e-07 7.411610e-07 0.9999996
[90,] 4.516396e-07 9.032791e-07 0.9999995
[91,] 4.605672e-07 9.211345e-07 0.9999995
[92,] 5.409028e-07 1.081806e-06 0.9999995
[93,] 6.292412e-05 1.258482e-04 0.9999371
[94,] 5.941941e-05 1.188388e-04 0.9999406
[95,] 4.241457e-05 8.482914e-05 0.9999576
[96,] 5.060800e-05 1.012160e-04 0.9999494
[97,] 6.960512e-05 1.392102e-04 0.9999304
[98,] 2.894470e-04 5.788939e-04 0.9997106
[99,] 6.956907e-04 1.391381e-03 0.9993043
[100,] 8.713025e-04 1.742605e-03 0.9991287
[101,] 6.423847e-04 1.284769e-03 0.9993576
[102,] 1.962306e-03 3.924612e-03 0.9980377
[103,] 1.731820e-03 3.463640e-03 0.9982682
[104,] 1.782575e-02 3.565150e-02 0.9821742
[105,] 1.739937e-02 3.479873e-02 0.9826006
[106,] 1.724266e-02 3.448532e-02 0.9827573
[107,] 1.745628e-02 3.491257e-02 0.9825437
[108,] 1.406649e-02 2.813297e-02 0.9859335
[109,] 2.762699e-02 5.525397e-02 0.9723730
[110,] 2.305950e-02 4.611900e-02 0.9769405
[111,] 2.016910e-02 4.033820e-02 0.9798309
[112,] 2.543868e-02 5.087737e-02 0.9745613
[113,] 3.122436e-02 6.244872e-02 0.9687756
[114,] 2.834255e-02 5.668510e-02 0.9716574
[115,] 2.425046e-02 4.850092e-02 0.9757495
[116,] 3.057594e-02 6.115188e-02 0.9694241
[117,] 2.545976e-02 5.091951e-02 0.9745402
[118,] 2.695417e-02 5.390834e-02 0.9730458
[119,] 3.921334e-02 7.842669e-02 0.9607867
[120,] 4.135841e-02 8.271682e-02 0.9586416
[121,] 3.608176e-02 7.216352e-02 0.9639182
[122,] 2.993224e-02 5.986448e-02 0.9700678
[123,] 2.496081e-02 4.992161e-02 0.9750392
[124,] 2.272529e-02 4.545057e-02 0.9772747
[125,] 1.883403e-02 3.766805e-02 0.9811660
[126,] 1.649099e-02 3.298197e-02 0.9835090
[127,] 1.351165e-02 2.702330e-02 0.9864884
[128,] 1.697059e-02 3.394119e-02 0.9830294
[129,] 1.401195e-02 2.802389e-02 0.9859881
[130,] 1.181534e-02 2.363067e-02 0.9881847
[131,] 9.643973e-03 1.928795e-02 0.9903560
[132,] 8.208408e-03 1.641682e-02 0.9917916
[133,] 7.407603e-03 1.481521e-02 0.9925924
[134,] 6.162571e-03 1.232514e-02 0.9938374
[135,] 4.782158e-03 9.564317e-03 0.9952178
[136,] 3.807331e-03 7.614662e-03 0.9961927
[137,] 3.071696e-03 6.143391e-03 0.9969283
[138,] 2.702591e-03 5.405183e-03 0.9972974
[139,] 2.081705e-03 4.163409e-03 0.9979183
[140,] 1.613989e-03 3.227977e-03 0.9983860
[141,] 2.464811e-03 4.929622e-03 0.9975352
[142,] 1.867811e-03 3.735622e-03 0.9981322
[143,] 1.807192e-03 3.614384e-03 0.9981928
[144,] 1.693226e-03 3.386451e-03 0.9983068
[145,] 1.393881e-03 2.787762e-03 0.9986061
[146,] 2.542159e-03 5.084317e-03 0.9974578
[147,] 2.199906e-03 4.399811e-03 0.9978001
[148,] 1.723561e-03 3.447122e-03 0.9982764
[149,] 2.460949e-03 4.921899e-03 0.9975391
[150,] 2.014471e-03 4.028941e-03 0.9979855
[151,] 1.546537e-03 3.093073e-03 0.9984535
[152,] 1.156518e-03 2.313036e-03 0.9988435
[153,] 1.137850e-03 2.275701e-03 0.9988621
[154,] 1.083655e-03 2.167310e-03 0.9989163
[155,] 1.150659e-03 2.301317e-03 0.9988493
[156,] 1.180768e-03 2.361537e-03 0.9988192
[157,] 1.048071e-03 2.096142e-03 0.9989519
[158,] 9.897587e-03 1.979517e-02 0.9901024
[159,] 7.862027e-03 1.572405e-02 0.9921380
[160,] 6.872077e-03 1.374415e-02 0.9931279
[161,] 1.348269e-02 2.696537e-02 0.9865173
[162,] 1.478377e-02 2.956754e-02 0.9852162
[163,] 1.234060e-02 2.468120e-02 0.9876594
[164,] 1.165218e-02 2.330436e-02 0.9883478
[165,] 1.222603e-02 2.445207e-02 0.9877740
[166,] 1.998163e-02 3.996326e-02 0.9800184
[167,] 1.661359e-02 3.322719e-02 0.9833864
[168,] 1.344040e-02 2.688081e-02 0.9865596
[169,] 1.099736e-02 2.199472e-02 0.9890026
[170,] 8.539811e-03 1.707962e-02 0.9914602
[171,] 7.384943e-03 1.476989e-02 0.9926151
[172,] 5.844432e-03 1.168886e-02 0.9941556
[173,] 5.474665e-03 1.094933e-02 0.9945253
[174,] 4.170039e-03 8.340079e-03 0.9958300
[175,] 3.199957e-03 6.399913e-03 0.9968000
[176,] 5.344240e-03 1.068848e-02 0.9946558
[177,] 4.101523e-03 8.203046e-03 0.9958985
[178,] 3.799095e-03 7.598190e-03 0.9962009
[179,] 2.865142e-03 5.730283e-03 0.9971349
[180,] 2.443937e-03 4.887873e-03 0.9975561
[181,] 1.937331e-03 3.874662e-03 0.9980627
[182,] 1.587539e-03 3.175078e-03 0.9984125
[183,] 1.531435e-03 3.062870e-03 0.9984686
[184,] 1.705273e-03 3.410547e-03 0.9982947
[185,] 2.936345e-03 5.872690e-03 0.9970637
[186,] 2.219906e-03 4.439813e-03 0.9977801
[187,] 1.638598e-03 3.277195e-03 0.9983614
[188,] 1.494237e-03 2.988474e-03 0.9985058
[189,] 1.098554e-03 2.197109e-03 0.9989014
[190,] 8.267349e-04 1.653470e-03 0.9991733
[191,] 7.437244e-04 1.487449e-03 0.9992563
[192,] 5.308554e-04 1.061711e-03 0.9994691
[193,] 3.713771e-04 7.427542e-04 0.9996286
[194,] 6.517982e-04 1.303596e-03 0.9993482
[195,] 4.888877e-04 9.777753e-04 0.9995111
[196,] 7.307234e-04 1.461447e-03 0.9992693
[197,] 7.442123e-04 1.488425e-03 0.9992558
[198,] 1.168080e-03 2.336160e-03 0.9988319
[199,] 8.252725e-04 1.650545e-03 0.9991747
[200,] 6.396819e-04 1.279364e-03 0.9993603
[201,] 1.810986e-03 3.621972e-03 0.9981890
[202,] 1.383795e-03 2.767591e-03 0.9986162
[203,] 9.665629e-04 1.933126e-03 0.9990334
[204,] 8.352672e-04 1.670534e-03 0.9991647
[205,] 5.835088e-04 1.167018e-03 0.9994165
[206,] 5.062492e-04 1.012498e-03 0.9994938
[207,] 9.522999e-04 1.904600e-03 0.9990477
[208,] 2.918315e-03 5.836630e-03 0.9970817
[209,] 2.197911e-03 4.395822e-03 0.9978021
[210,] 1.554558e-01 3.109115e-01 0.8445442
[211,] 1.278997e-01 2.557995e-01 0.8721003
[212,] 1.574321e-01 3.148642e-01 0.8425679
[213,] 1.463129e-01 2.926257e-01 0.8536871
[214,] 1.467827e-01 2.935654e-01 0.8532173
[215,] 2.605906e-01 5.211813e-01 0.7394094
[216,] 2.565167e-01 5.130334e-01 0.7434833
[217,] 2.226265e-01 4.452529e-01 0.7773735
[218,] 2.145257e-01 4.290513e-01 0.7854743
[219,] 1.864591e-01 3.729183e-01 0.8135409
[220,] 1.560108e-01 3.120217e-01 0.8439892
[221,] 3.127850e-01 6.255700e-01 0.6872150
[222,] 5.106939e-01 9.786122e-01 0.4893061
[223,] 4.640584e-01 9.281169e-01 0.5359416
[224,] 4.441569e-01 8.883139e-01 0.5558431
[225,] 3.988472e-01 7.976945e-01 0.6011528
[226,] 4.101120e-01 8.202240e-01 0.5898880
[227,] 3.556522e-01 7.113045e-01 0.6443478
[228,] 3.057071e-01 6.114141e-01 0.6942929
[229,] 2.541355e-01 5.082711e-01 0.7458645
[230,] 2.064587e-01 4.129174e-01 0.7935413
[231,] 1.870684e-01 3.741369e-01 0.8129316
[232,] 1.489797e-01 2.979594e-01 0.8510203
[233,] 3.284211e-01 6.568422e-01 0.6715789
[234,] 2.921716e-01 5.843432e-01 0.7078284
[235,] 2.616967e-01 5.233934e-01 0.7383033
[236,] 2.072365e-01 4.144731e-01 0.7927635
[237,] 2.521800e-01 5.043599e-01 0.7478200
[238,] 2.565798e-01 5.131597e-01 0.7434202
[239,] 5.658931e-01 8.682138e-01 0.4341069
[240,] 4.601910e-01 9.203820e-01 0.5398090
[241,] 4.612062e-01 9.224124e-01 0.5387938
[242,] 4.373793e-01 8.747585e-01 0.5626207
[243,] 3.557418e-01 7.114837e-01 0.6442582
[244,] 2.290142e-01 4.580284e-01 0.7709858
> postscript(file="/var/fisher/rcomp/tmp/1x8p81358278677.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/fisher/rcomp/tmp/2m1iz1358278677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3deo01358278677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ljfd1358278677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5537r1358278677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-38.0589586 -45.9223056 -9.0572300 -8.7376334 -19.3397289 0.6831036
7 8 9 10 11 12
-13.4946198 -14.1747621 31.8128051 3.2381044 -39.1317044 -15.2327458
13 14 15 16 17 18
11.4773802 -10.4624497 10.6803876 17.9750683 -0.4368888 -28.1695430
19 20 21 22 23 24
20.8589752 -0.6655623 -15.1570915 -11.3132566 10.9469006 -10.3075395
25 26 27 28 29 30
10.4381583 -4.3257547 16.4548075 22.0921898 -1.8964258 -6.9003448
31 32 33 34 35 36
-24.8722319 1.6901720 15.4872500 -10.8740531 22.3608653 -13.1529243
37 38 39 40 41 42
-5.3791960 17.9616146 18.0080108 33.3261110 10.3963351 -12.8352021
43 44 45 46 47 48
-6.9154715 16.1336347 46.5380901 20.1802057 8.7790080 2.3787626
49 50 51 52 53 54
9.7911820 1.6839730 0.1284679 38.7767515 -12.6970254 17.0399054
55 56 57 58 59 60
12.0281341 -35.5947995 12.8669423 2.3428347 32.7587068 -20.2337841
61 62 63 64 65 66
7.9931898 3.9427643 17.3431346 12.1616084 -21.7907013 29.4453790
67 68 69 70 71 72
9.4390188 15.3311775 14.1449603 -27.8409148 13.0703590 16.7460583
73 74 75 76 77 78
19.5540205 19.1353890 16.6231314 -2.2450466 14.1456632 -7.7022749
79 80 81 82 83 84
17.1309428 2.5413701 24.3743537 -13.0963319 31.1321923 21.6544385
85 86 87 88 89 90
10.4632038 -4.9957438 -23.0057537 -17.0590267 8.7249377 -13.5625620
91 92 93 94 95 96
-1.4451578 22.3282240 -0.8562033 -26.6238474 13.6353546 -12.7735777
97 98 99 100 101 102
-17.5010303 -0.8342286 19.8011215 2.4398427 13.7323812 -13.6121632
103 104 105 106 107 108
26.0510398 -20.4094349 -76.2630215 -21.1128561 -2.4705676 23.1314397
109 110 111 112 113 114
32.7881250 -71.6410517 -73.4649670 -70.6739639 11.9254605 -49.0843685
115 116 117 118 119 120
9.0494344 19.5688500 36.7544302 21.3831050 12.9022947 23.0673267
121 122 123 124 125 126
-23.5747700 26.8160822 24.8602241 -11.8291200 -16.6363667 2.5497624
127 128 129 130 131 132
4.6807557 -16.8942757 8.9624696 43.3167509 -26.7876568 45.3381344
133 134 135 136 137 138
-0.4722941 7.7748984 23.3158406 -5.8215811 5.8977531 1.0184078
139 140 141 142 143 144
19.0658243 -28.3012756 12.6988279 3.9465287 18.9736166 28.9998922
145 146 147 148 149 150
-1.8976396 1.4897523 12.9498530 1.0870006 2.1181480 34.6845850
151 152 153 154 155 156
17.8639992 16.1875990 -27.4150106 12.5589311 -10.2540363 30.6106119
157 158 159 160 161 162
-5.2236418 -35.2771932 -6.7687777 19.2402062 -27.8190567 -2.2378496
163 164 165 166 167 168
21.9907658 16.9870494 -15.0091673 35.0554138 37.2948281 -15.4080118
169 170 171 172 173 174
33.4485988 -67.0089220 19.8039549 28.4535506 -38.1296357 -13.2742915
175 176 177 178 179 180
-1.3491471 -9.5626028 37.8223383 -33.5798008 22.9993832 4.2311274
181 182 183 184 185 186
20.3413253 11.5468704 26.2664166 0.5346433 31.8935204 7.7017457
187 188 189 190 191 192
16.9411112 -36.1373782 4.9993778 -9.2415012 5.7687321 3.5264888
193 194 195 196 197 198
24.2896220 23.9042408 25.6939552 -28.2368990 -37.7747433 -2.9652220
199 200 201 202 203 204
15.7110386 -14.2710705 -2.0428410 12.7030353 31.1856138 -2.8139896
205 206 207 208 209 210
-1.6133479 -45.9291522 15.7999081 49.4492686 -20.8601074 -38.0611337
211 212 213 214 215 216
-3.8223877 -14.0195895 -44.1894078 -6.1839066 0.6952800 24.1218332
217 218 219 220 221 222
-9.1922680 2.9760375 -43.6352008 -38.1531800 23.6633533 106.7620839
223 224 225 226 227 228
5.0032867 -40.1236689 -34.2849081 -33.2904431 44.1766472 19.6376073
229 230 231 232 233 234
34.0876601 -12.4729420 1.7056090 -17.8864200 42.8194456 -39.2096810
235 236 237 238 239 240
-26.9260169 26.8483749 -33.6013796 -50.8910897 36.9012076 -1.6553676
241 242 243 244 245 246
-16.4307702 -21.8312737 3.4273205 -21.1137677 41.4722423 -50.4130390
247 248 249 250 251 252
-15.3462352 1.7284286 12.7482999 9.6912835 42.9231508 -19.9642669
253 254 255 256 257 258
-46.7962500 33.6901530 -51.3403348 -35.1160441 -34.6860977 0.2088600
259 260 261 262 263 264
-20.2981574 -27.1527397 18.9031980 -37.0107573 -33.9591413 14.2018792
265 266 267 268 269
-29.1979172 -3.2790340 -10.2902313 -7.3733708 -35.6933584
> postscript(file="/var/fisher/rcomp/tmp/6bte51358278677.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 -38.0589586 NA
1 -45.9223056 -38.0589586
2 -9.0572300 -45.9223056
3 -8.7376334 -9.0572300
4 -19.3397289 -8.7376334
5 0.6831036 -19.3397289
6 -13.4946198 0.6831036
7 -14.1747621 -13.4946198
8 31.8128051 -14.1747621
9 3.2381044 31.8128051
10 -39.1317044 3.2381044
11 -15.2327458 -39.1317044
12 11.4773802 -15.2327458
13 -10.4624497 11.4773802
14 10.6803876 -10.4624497
15 17.9750683 10.6803876
16 -0.4368888 17.9750683
17 -28.1695430 -0.4368888
18 20.8589752 -28.1695430
19 -0.6655623 20.8589752
20 -15.1570915 -0.6655623
21 -11.3132566 -15.1570915
22 10.9469006 -11.3132566
23 -10.3075395 10.9469006
24 10.4381583 -10.3075395
25 -4.3257547 10.4381583
26 16.4548075 -4.3257547
27 22.0921898 16.4548075
28 -1.8964258 22.0921898
29 -6.9003448 -1.8964258
30 -24.8722319 -6.9003448
31 1.6901720 -24.8722319
32 15.4872500 1.6901720
33 -10.8740531 15.4872500
34 22.3608653 -10.8740531
35 -13.1529243 22.3608653
36 -5.3791960 -13.1529243
37 17.9616146 -5.3791960
38 18.0080108 17.9616146
39 33.3261110 18.0080108
40 10.3963351 33.3261110
41 -12.8352021 10.3963351
42 -6.9154715 -12.8352021
43 16.1336347 -6.9154715
44 46.5380901 16.1336347
45 20.1802057 46.5380901
46 8.7790080 20.1802057
47 2.3787626 8.7790080
48 9.7911820 2.3787626
49 1.6839730 9.7911820
50 0.1284679 1.6839730
51 38.7767515 0.1284679
52 -12.6970254 38.7767515
53 17.0399054 -12.6970254
54 12.0281341 17.0399054
55 -35.5947995 12.0281341
56 12.8669423 -35.5947995
57 2.3428347 12.8669423
58 32.7587068 2.3428347
59 -20.2337841 32.7587068
60 7.9931898 -20.2337841
61 3.9427643 7.9931898
62 17.3431346 3.9427643
63 12.1616084 17.3431346
64 -21.7907013 12.1616084
65 29.4453790 -21.7907013
66 9.4390188 29.4453790
67 15.3311775 9.4390188
68 14.1449603 15.3311775
69 -27.8409148 14.1449603
70 13.0703590 -27.8409148
71 16.7460583 13.0703590
72 19.5540205 16.7460583
73 19.1353890 19.5540205
74 16.6231314 19.1353890
75 -2.2450466 16.6231314
76 14.1456632 -2.2450466
77 -7.7022749 14.1456632
78 17.1309428 -7.7022749
79 2.5413701 17.1309428
80 24.3743537 2.5413701
81 -13.0963319 24.3743537
82 31.1321923 -13.0963319
83 21.6544385 31.1321923
84 10.4632038 21.6544385
85 -4.9957438 10.4632038
86 -23.0057537 -4.9957438
87 -17.0590267 -23.0057537
88 8.7249377 -17.0590267
89 -13.5625620 8.7249377
90 -1.4451578 -13.5625620
91 22.3282240 -1.4451578
92 -0.8562033 22.3282240
93 -26.6238474 -0.8562033
94 13.6353546 -26.6238474
95 -12.7735777 13.6353546
96 -17.5010303 -12.7735777
97 -0.8342286 -17.5010303
98 19.8011215 -0.8342286
99 2.4398427 19.8011215
100 13.7323812 2.4398427
101 -13.6121632 13.7323812
102 26.0510398 -13.6121632
103 -20.4094349 26.0510398
104 -76.2630215 -20.4094349
105 -21.1128561 -76.2630215
106 -2.4705676 -21.1128561
107 23.1314397 -2.4705676
108 32.7881250 23.1314397
109 -71.6410517 32.7881250
110 -73.4649670 -71.6410517
111 -70.6739639 -73.4649670
112 11.9254605 -70.6739639
113 -49.0843685 11.9254605
114 9.0494344 -49.0843685
115 19.5688500 9.0494344
116 36.7544302 19.5688500
117 21.3831050 36.7544302
118 12.9022947 21.3831050
119 23.0673267 12.9022947
120 -23.5747700 23.0673267
121 26.8160822 -23.5747700
122 24.8602241 26.8160822
123 -11.8291200 24.8602241
124 -16.6363667 -11.8291200
125 2.5497624 -16.6363667
126 4.6807557 2.5497624
127 -16.8942757 4.6807557
128 8.9624696 -16.8942757
129 43.3167509 8.9624696
130 -26.7876568 43.3167509
131 45.3381344 -26.7876568
132 -0.4722941 45.3381344
133 7.7748984 -0.4722941
134 23.3158406 7.7748984
135 -5.8215811 23.3158406
136 5.8977531 -5.8215811
137 1.0184078 5.8977531
138 19.0658243 1.0184078
139 -28.3012756 19.0658243
140 12.6988279 -28.3012756
141 3.9465287 12.6988279
142 18.9736166 3.9465287
143 28.9998922 18.9736166
144 -1.8976396 28.9998922
145 1.4897523 -1.8976396
146 12.9498530 1.4897523
147 1.0870006 12.9498530
148 2.1181480 1.0870006
149 34.6845850 2.1181480
150 17.8639992 34.6845850
151 16.1875990 17.8639992
152 -27.4150106 16.1875990
153 12.5589311 -27.4150106
154 -10.2540363 12.5589311
155 30.6106119 -10.2540363
156 -5.2236418 30.6106119
157 -35.2771932 -5.2236418
158 -6.7687777 -35.2771932
159 19.2402062 -6.7687777
160 -27.8190567 19.2402062
161 -2.2378496 -27.8190567
162 21.9907658 -2.2378496
163 16.9870494 21.9907658
164 -15.0091673 16.9870494
165 35.0554138 -15.0091673
166 37.2948281 35.0554138
167 -15.4080118 37.2948281
168 33.4485988 -15.4080118
169 -67.0089220 33.4485988
170 19.8039549 -67.0089220
171 28.4535506 19.8039549
172 -38.1296357 28.4535506
173 -13.2742915 -38.1296357
174 -1.3491471 -13.2742915
175 -9.5626028 -1.3491471
176 37.8223383 -9.5626028
177 -33.5798008 37.8223383
178 22.9993832 -33.5798008
179 4.2311274 22.9993832
180 20.3413253 4.2311274
181 11.5468704 20.3413253
182 26.2664166 11.5468704
183 0.5346433 26.2664166
184 31.8935204 0.5346433
185 7.7017457 31.8935204
186 16.9411112 7.7017457
187 -36.1373782 16.9411112
188 4.9993778 -36.1373782
189 -9.2415012 4.9993778
190 5.7687321 -9.2415012
191 3.5264888 5.7687321
192 24.2896220 3.5264888
193 23.9042408 24.2896220
194 25.6939552 23.9042408
195 -28.2368990 25.6939552
196 -37.7747433 -28.2368990
197 -2.9652220 -37.7747433
198 15.7110386 -2.9652220
199 -14.2710705 15.7110386
200 -2.0428410 -14.2710705
201 12.7030353 -2.0428410
202 31.1856138 12.7030353
203 -2.8139896 31.1856138
204 -1.6133479 -2.8139896
205 -45.9291522 -1.6133479
206 15.7999081 -45.9291522
207 49.4492686 15.7999081
208 -20.8601074 49.4492686
209 -38.0611337 -20.8601074
210 -3.8223877 -38.0611337
211 -14.0195895 -3.8223877
212 -44.1894078 -14.0195895
213 -6.1839066 -44.1894078
214 0.6952800 -6.1839066
215 24.1218332 0.6952800
216 -9.1922680 24.1218332
217 2.9760375 -9.1922680
218 -43.6352008 2.9760375
219 -38.1531800 -43.6352008
220 23.6633533 -38.1531800
221 106.7620839 23.6633533
222 5.0032867 106.7620839
223 -40.1236689 5.0032867
224 -34.2849081 -40.1236689
225 -33.2904431 -34.2849081
226 44.1766472 -33.2904431
227 19.6376073 44.1766472
228 34.0876601 19.6376073
229 -12.4729420 34.0876601
230 1.7056090 -12.4729420
231 -17.8864200 1.7056090
232 42.8194456 -17.8864200
233 -39.2096810 42.8194456
234 -26.9260169 -39.2096810
235 26.8483749 -26.9260169
236 -33.6013796 26.8483749
237 -50.8910897 -33.6013796
238 36.9012076 -50.8910897
239 -1.6553676 36.9012076
240 -16.4307702 -1.6553676
241 -21.8312737 -16.4307702
242 3.4273205 -21.8312737
243 -21.1137677 3.4273205
244 41.4722423 -21.1137677
245 -50.4130390 41.4722423
246 -15.3462352 -50.4130390
247 1.7284286 -15.3462352
248 12.7482999 1.7284286
249 9.6912835 12.7482999
250 42.9231508 9.6912835
251 -19.9642669 42.9231508
252 -46.7962500 -19.9642669
253 33.6901530 -46.7962500
254 -51.3403348 33.6901530
255 -35.1160441 -51.3403348
256 -34.6860977 -35.1160441
257 0.2088600 -34.6860977
258 -20.2981574 0.2088600
259 -27.1527397 -20.2981574
260 18.9031980 -27.1527397
261 -37.0107573 18.9031980
262 -33.9591413 -37.0107573
263 14.2018792 -33.9591413
264 -29.1979172 14.2018792
265 -3.2790340 -29.1979172
266 -10.2902313 -3.2790340
267 -7.3733708 -10.2902313
268 -35.6933584 -7.3733708
269 NA -35.6933584
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -45.9223056 -38.0589586
[2,] -9.0572300 -45.9223056
[3,] -8.7376334 -9.0572300
[4,] -19.3397289 -8.7376334
[5,] 0.6831036 -19.3397289
[6,] -13.4946198 0.6831036
[7,] -14.1747621 -13.4946198
[8,] 31.8128051 -14.1747621
[9,] 3.2381044 31.8128051
[10,] -39.1317044 3.2381044
[11,] -15.2327458 -39.1317044
[12,] 11.4773802 -15.2327458
[13,] -10.4624497 11.4773802
[14,] 10.6803876 -10.4624497
[15,] 17.9750683 10.6803876
[16,] -0.4368888 17.9750683
[17,] -28.1695430 -0.4368888
[18,] 20.8589752 -28.1695430
[19,] -0.6655623 20.8589752
[20,] -15.1570915 -0.6655623
[21,] -11.3132566 -15.1570915
[22,] 10.9469006 -11.3132566
[23,] -10.3075395 10.9469006
[24,] 10.4381583 -10.3075395
[25,] -4.3257547 10.4381583
[26,] 16.4548075 -4.3257547
[27,] 22.0921898 16.4548075
[28,] -1.8964258 22.0921898
[29,] -6.9003448 -1.8964258
[30,] -24.8722319 -6.9003448
[31,] 1.6901720 -24.8722319
[32,] 15.4872500 1.6901720
[33,] -10.8740531 15.4872500
[34,] 22.3608653 -10.8740531
[35,] -13.1529243 22.3608653
[36,] -5.3791960 -13.1529243
[37,] 17.9616146 -5.3791960
[38,] 18.0080108 17.9616146
[39,] 33.3261110 18.0080108
[40,] 10.3963351 33.3261110
[41,] -12.8352021 10.3963351
[42,] -6.9154715 -12.8352021
[43,] 16.1336347 -6.9154715
[44,] 46.5380901 16.1336347
[45,] 20.1802057 46.5380901
[46,] 8.7790080 20.1802057
[47,] 2.3787626 8.7790080
[48,] 9.7911820 2.3787626
[49,] 1.6839730 9.7911820
[50,] 0.1284679 1.6839730
[51,] 38.7767515 0.1284679
[52,] -12.6970254 38.7767515
[53,] 17.0399054 -12.6970254
[54,] 12.0281341 17.0399054
[55,] -35.5947995 12.0281341
[56,] 12.8669423 -35.5947995
[57,] 2.3428347 12.8669423
[58,] 32.7587068 2.3428347
[59,] -20.2337841 32.7587068
[60,] 7.9931898 -20.2337841
[61,] 3.9427643 7.9931898
[62,] 17.3431346 3.9427643
[63,] 12.1616084 17.3431346
[64,] -21.7907013 12.1616084
[65,] 29.4453790 -21.7907013
[66,] 9.4390188 29.4453790
[67,] 15.3311775 9.4390188
[68,] 14.1449603 15.3311775
[69,] -27.8409148 14.1449603
[70,] 13.0703590 -27.8409148
[71,] 16.7460583 13.0703590
[72,] 19.5540205 16.7460583
[73,] 19.1353890 19.5540205
[74,] 16.6231314 19.1353890
[75,] -2.2450466 16.6231314
[76,] 14.1456632 -2.2450466
[77,] -7.7022749 14.1456632
[78,] 17.1309428 -7.7022749
[79,] 2.5413701 17.1309428
[80,] 24.3743537 2.5413701
[81,] -13.0963319 24.3743537
[82,] 31.1321923 -13.0963319
[83,] 21.6544385 31.1321923
[84,] 10.4632038 21.6544385
[85,] -4.9957438 10.4632038
[86,] -23.0057537 -4.9957438
[87,] -17.0590267 -23.0057537
[88,] 8.7249377 -17.0590267
[89,] -13.5625620 8.7249377
[90,] -1.4451578 -13.5625620
[91,] 22.3282240 -1.4451578
[92,] -0.8562033 22.3282240
[93,] -26.6238474 -0.8562033
[94,] 13.6353546 -26.6238474
[95,] -12.7735777 13.6353546
[96,] -17.5010303 -12.7735777
[97,] -0.8342286 -17.5010303
[98,] 19.8011215 -0.8342286
[99,] 2.4398427 19.8011215
[100,] 13.7323812 2.4398427
[101,] -13.6121632 13.7323812
[102,] 26.0510398 -13.6121632
[103,] -20.4094349 26.0510398
[104,] -76.2630215 -20.4094349
[105,] -21.1128561 -76.2630215
[106,] -2.4705676 -21.1128561
[107,] 23.1314397 -2.4705676
[108,] 32.7881250 23.1314397
[109,] -71.6410517 32.7881250
[110,] -73.4649670 -71.6410517
[111,] -70.6739639 -73.4649670
[112,] 11.9254605 -70.6739639
[113,] -49.0843685 11.9254605
[114,] 9.0494344 -49.0843685
[115,] 19.5688500 9.0494344
[116,] 36.7544302 19.5688500
[117,] 21.3831050 36.7544302
[118,] 12.9022947 21.3831050
[119,] 23.0673267 12.9022947
[120,] -23.5747700 23.0673267
[121,] 26.8160822 -23.5747700
[122,] 24.8602241 26.8160822
[123,] -11.8291200 24.8602241
[124,] -16.6363667 -11.8291200
[125,] 2.5497624 -16.6363667
[126,] 4.6807557 2.5497624
[127,] -16.8942757 4.6807557
[128,] 8.9624696 -16.8942757
[129,] 43.3167509 8.9624696
[130,] -26.7876568 43.3167509
[131,] 45.3381344 -26.7876568
[132,] -0.4722941 45.3381344
[133,] 7.7748984 -0.4722941
[134,] 23.3158406 7.7748984
[135,] -5.8215811 23.3158406
[136,] 5.8977531 -5.8215811
[137,] 1.0184078 5.8977531
[138,] 19.0658243 1.0184078
[139,] -28.3012756 19.0658243
[140,] 12.6988279 -28.3012756
[141,] 3.9465287 12.6988279
[142,] 18.9736166 3.9465287
[143,] 28.9998922 18.9736166
[144,] -1.8976396 28.9998922
[145,] 1.4897523 -1.8976396
[146,] 12.9498530 1.4897523
[147,] 1.0870006 12.9498530
[148,] 2.1181480 1.0870006
[149,] 34.6845850 2.1181480
[150,] 17.8639992 34.6845850
[151,] 16.1875990 17.8639992
[152,] -27.4150106 16.1875990
[153,] 12.5589311 -27.4150106
[154,] -10.2540363 12.5589311
[155,] 30.6106119 -10.2540363
[156,] -5.2236418 30.6106119
[157,] -35.2771932 -5.2236418
[158,] -6.7687777 -35.2771932
[159,] 19.2402062 -6.7687777
[160,] -27.8190567 19.2402062
[161,] -2.2378496 -27.8190567
[162,] 21.9907658 -2.2378496
[163,] 16.9870494 21.9907658
[164,] -15.0091673 16.9870494
[165,] 35.0554138 -15.0091673
[166,] 37.2948281 35.0554138
[167,] -15.4080118 37.2948281
[168,] 33.4485988 -15.4080118
[169,] -67.0089220 33.4485988
[170,] 19.8039549 -67.0089220
[171,] 28.4535506 19.8039549
[172,] -38.1296357 28.4535506
[173,] -13.2742915 -38.1296357
[174,] -1.3491471 -13.2742915
[175,] -9.5626028 -1.3491471
[176,] 37.8223383 -9.5626028
[177,] -33.5798008 37.8223383
[178,] 22.9993832 -33.5798008
[179,] 4.2311274 22.9993832
[180,] 20.3413253 4.2311274
[181,] 11.5468704 20.3413253
[182,] 26.2664166 11.5468704
[183,] 0.5346433 26.2664166
[184,] 31.8935204 0.5346433
[185,] 7.7017457 31.8935204
[186,] 16.9411112 7.7017457
[187,] -36.1373782 16.9411112
[188,] 4.9993778 -36.1373782
[189,] -9.2415012 4.9993778
[190,] 5.7687321 -9.2415012
[191,] 3.5264888 5.7687321
[192,] 24.2896220 3.5264888
[193,] 23.9042408 24.2896220
[194,] 25.6939552 23.9042408
[195,] -28.2368990 25.6939552
[196,] -37.7747433 -28.2368990
[197,] -2.9652220 -37.7747433
[198,] 15.7110386 -2.9652220
[199,] -14.2710705 15.7110386
[200,] -2.0428410 -14.2710705
[201,] 12.7030353 -2.0428410
[202,] 31.1856138 12.7030353
[203,] -2.8139896 31.1856138
[204,] -1.6133479 -2.8139896
[205,] -45.9291522 -1.6133479
[206,] 15.7999081 -45.9291522
[207,] 49.4492686 15.7999081
[208,] -20.8601074 49.4492686
[209,] -38.0611337 -20.8601074
[210,] -3.8223877 -38.0611337
[211,] -14.0195895 -3.8223877
[212,] -44.1894078 -14.0195895
[213,] -6.1839066 -44.1894078
[214,] 0.6952800 -6.1839066
[215,] 24.1218332 0.6952800
[216,] -9.1922680 24.1218332
[217,] 2.9760375 -9.1922680
[218,] -43.6352008 2.9760375
[219,] -38.1531800 -43.6352008
[220,] 23.6633533 -38.1531800
[221,] 106.7620839 23.6633533
[222,] 5.0032867 106.7620839
[223,] -40.1236689 5.0032867
[224,] -34.2849081 -40.1236689
[225,] -33.2904431 -34.2849081
[226,] 44.1766472 -33.2904431
[227,] 19.6376073 44.1766472
[228,] 34.0876601 19.6376073
[229,] -12.4729420 34.0876601
[230,] 1.7056090 -12.4729420
[231,] -17.8864200 1.7056090
[232,] 42.8194456 -17.8864200
[233,] -39.2096810 42.8194456
[234,] -26.9260169 -39.2096810
[235,] 26.8483749 -26.9260169
[236,] -33.6013796 26.8483749
[237,] -50.8910897 -33.6013796
[238,] 36.9012076 -50.8910897
[239,] -1.6553676 36.9012076
[240,] -16.4307702 -1.6553676
[241,] -21.8312737 -16.4307702
[242,] 3.4273205 -21.8312737
[243,] -21.1137677 3.4273205
[244,] 41.4722423 -21.1137677
[245,] -50.4130390 41.4722423
[246,] -15.3462352 -50.4130390
[247,] 1.7284286 -15.3462352
[248,] 12.7482999 1.7284286
[249,] 9.6912835 12.7482999
[250,] 42.9231508 9.6912835
[251,] -19.9642669 42.9231508
[252,] -46.7962500 -19.9642669
[253,] 33.6901530 -46.7962500
[254,] -51.3403348 33.6901530
[255,] -35.1160441 -51.3403348
[256,] -34.6860977 -35.1160441
[257,] 0.2088600 -34.6860977
[258,] -20.2981574 0.2088600
[259,] -27.1527397 -20.2981574
[260,] 18.9031980 -27.1527397
[261,] -37.0107573 18.9031980
[262,] -33.9591413 -37.0107573
[263,] 14.2018792 -33.9591413
[264,] -29.1979172 14.2018792
[265,] -3.2790340 -29.1979172
[266,] -10.2902313 -3.2790340
[267,] -7.3733708 -10.2902313
[268,] -35.6933584 -7.3733708
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -45.9223056 -38.0589586
2 -9.0572300 -45.9223056
3 -8.7376334 -9.0572300
4 -19.3397289 -8.7376334
5 0.6831036 -19.3397289
6 -13.4946198 0.6831036
7 -14.1747621 -13.4946198
8 31.8128051 -14.1747621
9 3.2381044 31.8128051
10 -39.1317044 3.2381044
11 -15.2327458 -39.1317044
12 11.4773802 -15.2327458
13 -10.4624497 11.4773802
14 10.6803876 -10.4624497
15 17.9750683 10.6803876
16 -0.4368888 17.9750683
17 -28.1695430 -0.4368888
18 20.8589752 -28.1695430
19 -0.6655623 20.8589752
20 -15.1570915 -0.6655623
21 -11.3132566 -15.1570915
22 10.9469006 -11.3132566
23 -10.3075395 10.9469006
24 10.4381583 -10.3075395
25 -4.3257547 10.4381583
26 16.4548075 -4.3257547
27 22.0921898 16.4548075
28 -1.8964258 22.0921898
29 -6.9003448 -1.8964258
30 -24.8722319 -6.9003448
31 1.6901720 -24.8722319
32 15.4872500 1.6901720
33 -10.8740531 15.4872500
34 22.3608653 -10.8740531
35 -13.1529243 22.3608653
36 -5.3791960 -13.1529243
37 17.9616146 -5.3791960
38 18.0080108 17.9616146
39 33.3261110 18.0080108
40 10.3963351 33.3261110
41 -12.8352021 10.3963351
42 -6.9154715 -12.8352021
43 16.1336347 -6.9154715
44 46.5380901 16.1336347
45 20.1802057 46.5380901
46 8.7790080 20.1802057
47 2.3787626 8.7790080
48 9.7911820 2.3787626
49 1.6839730 9.7911820
50 0.1284679 1.6839730
51 38.7767515 0.1284679
52 -12.6970254 38.7767515
53 17.0399054 -12.6970254
54 12.0281341 17.0399054
55 -35.5947995 12.0281341
56 12.8669423 -35.5947995
57 2.3428347 12.8669423
58 32.7587068 2.3428347
59 -20.2337841 32.7587068
60 7.9931898 -20.2337841
61 3.9427643 7.9931898
62 17.3431346 3.9427643
63 12.1616084 17.3431346
64 -21.7907013 12.1616084
65 29.4453790 -21.7907013
66 9.4390188 29.4453790
67 15.3311775 9.4390188
68 14.1449603 15.3311775
69 -27.8409148 14.1449603
70 13.0703590 -27.8409148
71 16.7460583 13.0703590
72 19.5540205 16.7460583
73 19.1353890 19.5540205
74 16.6231314 19.1353890
75 -2.2450466 16.6231314
76 14.1456632 -2.2450466
77 -7.7022749 14.1456632
78 17.1309428 -7.7022749
79 2.5413701 17.1309428
80 24.3743537 2.5413701
81 -13.0963319 24.3743537
82 31.1321923 -13.0963319
83 21.6544385 31.1321923
84 10.4632038 21.6544385
85 -4.9957438 10.4632038
86 -23.0057537 -4.9957438
87 -17.0590267 -23.0057537
88 8.7249377 -17.0590267
89 -13.5625620 8.7249377
90 -1.4451578 -13.5625620
91 22.3282240 -1.4451578
92 -0.8562033 22.3282240
93 -26.6238474 -0.8562033
94 13.6353546 -26.6238474
95 -12.7735777 13.6353546
96 -17.5010303 -12.7735777
97 -0.8342286 -17.5010303
98 19.8011215 -0.8342286
99 2.4398427 19.8011215
100 13.7323812 2.4398427
101 -13.6121632 13.7323812
102 26.0510398 -13.6121632
103 -20.4094349 26.0510398
104 -76.2630215 -20.4094349
105 -21.1128561 -76.2630215
106 -2.4705676 -21.1128561
107 23.1314397 -2.4705676
108 32.7881250 23.1314397
109 -71.6410517 32.7881250
110 -73.4649670 -71.6410517
111 -70.6739639 -73.4649670
112 11.9254605 -70.6739639
113 -49.0843685 11.9254605
114 9.0494344 -49.0843685
115 19.5688500 9.0494344
116 36.7544302 19.5688500
117 21.3831050 36.7544302
118 12.9022947 21.3831050
119 23.0673267 12.9022947
120 -23.5747700 23.0673267
121 26.8160822 -23.5747700
122 24.8602241 26.8160822
123 -11.8291200 24.8602241
124 -16.6363667 -11.8291200
125 2.5497624 -16.6363667
126 4.6807557 2.5497624
127 -16.8942757 4.6807557
128 8.9624696 -16.8942757
129 43.3167509 8.9624696
130 -26.7876568 43.3167509
131 45.3381344 -26.7876568
132 -0.4722941 45.3381344
133 7.7748984 -0.4722941
134 23.3158406 7.7748984
135 -5.8215811 23.3158406
136 5.8977531 -5.8215811
137 1.0184078 5.8977531
138 19.0658243 1.0184078
139 -28.3012756 19.0658243
140 12.6988279 -28.3012756
141 3.9465287 12.6988279
142 18.9736166 3.9465287
143 28.9998922 18.9736166
144 -1.8976396 28.9998922
145 1.4897523 -1.8976396
146 12.9498530 1.4897523
147 1.0870006 12.9498530
148 2.1181480 1.0870006
149 34.6845850 2.1181480
150 17.8639992 34.6845850
151 16.1875990 17.8639992
152 -27.4150106 16.1875990
153 12.5589311 -27.4150106
154 -10.2540363 12.5589311
155 30.6106119 -10.2540363
156 -5.2236418 30.6106119
157 -35.2771932 -5.2236418
158 -6.7687777 -35.2771932
159 19.2402062 -6.7687777
160 -27.8190567 19.2402062
161 -2.2378496 -27.8190567
162 21.9907658 -2.2378496
163 16.9870494 21.9907658
164 -15.0091673 16.9870494
165 35.0554138 -15.0091673
166 37.2948281 35.0554138
167 -15.4080118 37.2948281
168 33.4485988 -15.4080118
169 -67.0089220 33.4485988
170 19.8039549 -67.0089220
171 28.4535506 19.8039549
172 -38.1296357 28.4535506
173 -13.2742915 -38.1296357
174 -1.3491471 -13.2742915
175 -9.5626028 -1.3491471
176 37.8223383 -9.5626028
177 -33.5798008 37.8223383
178 22.9993832 -33.5798008
179 4.2311274 22.9993832
180 20.3413253 4.2311274
181 11.5468704 20.3413253
182 26.2664166 11.5468704
183 0.5346433 26.2664166
184 31.8935204 0.5346433
185 7.7017457 31.8935204
186 16.9411112 7.7017457
187 -36.1373782 16.9411112
188 4.9993778 -36.1373782
189 -9.2415012 4.9993778
190 5.7687321 -9.2415012
191 3.5264888 5.7687321
192 24.2896220 3.5264888
193 23.9042408 24.2896220
194 25.6939552 23.9042408
195 -28.2368990 25.6939552
196 -37.7747433 -28.2368990
197 -2.9652220 -37.7747433
198 15.7110386 -2.9652220
199 -14.2710705 15.7110386
200 -2.0428410 -14.2710705
201 12.7030353 -2.0428410
202 31.1856138 12.7030353
203 -2.8139896 31.1856138
204 -1.6133479 -2.8139896
205 -45.9291522 -1.6133479
206 15.7999081 -45.9291522
207 49.4492686 15.7999081
208 -20.8601074 49.4492686
209 -38.0611337 -20.8601074
210 -3.8223877 -38.0611337
211 -14.0195895 -3.8223877
212 -44.1894078 -14.0195895
213 -6.1839066 -44.1894078
214 0.6952800 -6.1839066
215 24.1218332 0.6952800
216 -9.1922680 24.1218332
217 2.9760375 -9.1922680
218 -43.6352008 2.9760375
219 -38.1531800 -43.6352008
220 23.6633533 -38.1531800
221 106.7620839 23.6633533
222 5.0032867 106.7620839
223 -40.1236689 5.0032867
224 -34.2849081 -40.1236689
225 -33.2904431 -34.2849081
226 44.1766472 -33.2904431
227 19.6376073 44.1766472
228 34.0876601 19.6376073
229 -12.4729420 34.0876601
230 1.7056090 -12.4729420
231 -17.8864200 1.7056090
232 42.8194456 -17.8864200
233 -39.2096810 42.8194456
234 -26.9260169 -39.2096810
235 26.8483749 -26.9260169
236 -33.6013796 26.8483749
237 -50.8910897 -33.6013796
238 36.9012076 -50.8910897
239 -1.6553676 36.9012076
240 -16.4307702 -1.6553676
241 -21.8312737 -16.4307702
242 3.4273205 -21.8312737
243 -21.1137677 3.4273205
244 41.4722423 -21.1137677
245 -50.4130390 41.4722423
246 -15.3462352 -50.4130390
247 1.7284286 -15.3462352
248 12.7482999 1.7284286
249 9.6912835 12.7482999
250 42.9231508 9.6912835
251 -19.9642669 42.9231508
252 -46.7962500 -19.9642669
253 33.6901530 -46.7962500
254 -51.3403348 33.6901530
255 -35.1160441 -51.3403348
256 -34.6860977 -35.1160441
257 0.2088600 -34.6860977
258 -20.2981574 0.2088600
259 -27.1527397 -20.2981574
260 18.9031980 -27.1527397
261 -37.0107573 18.9031980
262 -33.9591413 -37.0107573
263 14.2018792 -33.9591413
264 -29.1979172 14.2018792
265 -3.2790340 -29.1979172
266 -10.2902313 -3.2790340
267 -7.3733708 -10.2902313
268 -35.6933584 -7.3733708
> 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/fisher/rcomp/tmp/7uc7r1358278677.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/fisher/rcomp/tmp/8xi2t1358278677.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/fisher/rcomp/tmp/9sose1358278677.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/fisher/rcomp/tmp/10x44e1358278677.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11maze1358278677.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12xu6k1358278677.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13x0dd1358278677.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1480z91358278677.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/fisher/rcomp/tmp/15i0hj1358278677.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/fisher/rcomp/tmp/16ycp21358278677.tab")
+ }
>
> try(system("convert tmp/1x8p81358278677.ps tmp/1x8p81358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m1iz1358278677.ps tmp/2m1iz1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/3deo01358278677.ps tmp/3deo01358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ljfd1358278677.ps tmp/4ljfd1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/5537r1358278677.ps tmp/5537r1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bte51358278677.ps tmp/6bte51358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uc7r1358278677.ps tmp/7uc7r1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xi2t1358278677.ps tmp/8xi2t1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sose1358278677.ps tmp/9sose1358278677.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x44e1358278677.ps tmp/10x44e1358278677.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.023 1.715 14.754