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)
+ ,dim=c(10
+ ,269)
+ ,dimnames=list(c('hours'
+ ,'lfm'
+ ,'blogs'
+ ,'uk'
+ ,'spr'
+ ,'hours_uk'
+ ,'hours_spr'
+ ,'blogs_uk'
+ ,'blogs_spr'
+ ,'uk_spr')
+ ,1:269))
> y <- array(NA,dim=c(10,269),dimnames=list(c('hours','lfm','blogs','uk','spr','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
lfm hours blogs uk spr hours_uk hours_spr blogs_uk blogs_spr uk_spr
1 122 102 88 1 9 102 918 88 792 9
2 114 99 106 1 3 99 297 106 318 3
3 140 97 70 1 2 97 194 70 140 2
4 143 82 70 1 21 82 1722 70 1470 21
5 122 77 56 1 10 77 770 56 560 10
6 127 65 50 1 2 65 130 50 100 2
7 113 64 48 1 4 64 256 48 192 4
8 118 62 71 1 4 62 248 71 284 4
9 161 62 61 1 4 62 248 61 244 4
10 134 62 66 1 5 62 310 66 330 5
11 96 61 80 1 2 61 122 80 160 2
12 104 59 37 1 3 59 177 37 111 3
13 135 57 53 1 3 57 171 53 159 3
14 110 56 39 1 6 56 336 39 234 6
15 128 54 40 1 3 54 162 40 120 3
16 142 54 59 1 4 54 216 59 236 4
17 117 53 42 1 3 53 159 42 126 3
18 94 52 33 1 13 52 676 33 429 13
19 135 51 36 1 3 51 153 36 108 3
20 121 51 57 1 4 51 204 57 228 4
21 103 51 38 1 8 51 408 38 304 8
22 118 50 98 1 8 50 400 98 784 8
23 127 50 43 1 3 50 150 43 129 3
24 116 50 73 1 4 50 200 73 292 4
25 129 49 52 1 2 49 98 52 104 2
26 115 49 53 1 5 49 245 53 265 5
27 135 49 51 1 4 49 196 51 204 4
28 133 48 32 1 3 48 144 32 96 3
29 113 48 43 1 3 48 144 43 129 3
30 111 47 53 1 2 47 94 53 106 2
31 92 47 50 1 3 47 141 50 150 3
32 118 46 50 1 3 46 138 50 150 3
33 134 46 56 1 3 46 138 56 168 3
34 106 45 53 1 5 45 225 53 265 5
35 137 45 47 1 3 45 135 47 141 3
36 100 45 42 1 4 45 180 42 168 4
37 102 44 29 1 3 44 132 29 87 3
38 134 43 54 1 4 43 172 54 216 4
39 130 42 40 1 8 42 336 40 320 8
40 144 42 41 1 3 42 126 41 123 3
41 120 42 37 1 4 42 168 37 148 4
42 91 42 25 1 2 42 84 25 50 2
43 100 42 27 1 5 42 210 27 135 5
44 134 42 61 1 4 42 168 61 244 4
45 161 41 54 1 7 41 287 54 378 7
46 128 41 35 1 3 41 123 35 105 3
47 124 41 55 1 4 41 164 55 220 4
48 115 41 47 1 6 41 246 47 282 6
49 123 41 49 1 7 41 287 49 343 7
50 117 41 38 1 20 41 820 38 760 20
51 111 41 52 1 49 41 2009 52 2548 49
52 146 40 35 1 3 40 120 35 105 3
53 101 40 52 1 3 40 120 52 156 3
54 131 40 54 1 6 40 240 54 324 6
55 122 40 40 1 6 40 240 40 240 6
56 78 40 52 1 4 40 160 52 208 4
57 120 39 34 1 5 39 195 34 170 5
58 115 39 51 1 4 39 156 51 204 4
59 142 38 43 1 4 38 152 43 172 4
60 94 38 40 1 31 38 1178 40 1240 31
61 114 36 38 1 3 36 108 38 114 3
62 108 36 33 1 3 36 108 33 99 3
63 119 35 27 1 4 35 140 27 108 4
64 117 35 34 1 6 35 210 34 204 6
65 86 35 44 1 5 35 175 44 220 5
66 138 35 46 1 3 35 105 46 138 3
67 119 34 50 1 3 34 102 50 150 3
68 117 34 31 1 2 34 68 31 62 2
69 117 34 33 1 3 34 102 33 99 3
70 76 33 37 1 3 33 99 37 111 3
71 119 33 48 1 16 33 528 48 768 16
72 119 33 33 1 3 33 99 33 99 3
73 124 32 40 1 3 32 96 40 120 3
74 116 32 21 1 3 32 96 21 63 3
75 118 32 33 1 2 32 64 33 66 2
76 102 31 41 1 5 31 155 41 205 5
77 116 31 35 1 3 31 93 35 105 3
78 103 30 60 1 4 30 120 60 240 4
79 117 30 30 1 5 30 150 30 150 5
80 108 30 45 1 2 30 60 45 90 2
81 122 30 26 1 3 30 90 26 78 3
82 90 29 41 1 3 29 87 41 123 3
83 133 28 48 1 14 28 392 48 672 14
84 116 28 10 1 8 28 224 10 80 8
85 110 27 35 1 4 27 108 35 140 4
86 90 27 23 1 4 27 108 23 92 4
87 74 27 29 1 3 27 81 29 87 3
88 75 26 17 1 4 26 104 17 68 4
89 107 25 35 1 3 25 75 35 105 3
90 90 25 50 1 5 25 125 50 250 5
91 96 25 33 1 3 25 75 33 99 3
92 115 24 23 1 3 24 72 23 69 3
93 91 24 22 1 2 24 48 22 44 2
94 77 23 52 1 4 23 92 52 208 4
95 108 23 38 1 31 23 713 38 1178 31
96 83 23 32 1 2 23 46 32 64 2
97 77 23 28 1 5 23 115 28 140 5
98 99 23 43 1 5 23 115 43 215 5
99 115 22 32 1 2 22 44 32 64 2
100 99 22 35 1 2 22 44 35 70 2
101 106 22 25 1 3 22 66 25 75 3
102 77 22 14 1 8 22 176 14 112 8
103 115 20 17 1 6 20 120 17 102 6
104 67 19 18 1 3 19 57 18 54 3
105 8 19 12 1 2 19 38 12 24 2
106 69 17 27 1 5 17 85 27 135 5
107 88 17 28 1 3 17 51 28 84 3
108 107 16 12 1 5 16 80 12 60 5
109 120 16 21 1 5 16 80 21 105 5
110 3 5 9 1 4 5 20 9 36 4
111 1 4 11 1 2 4 8 11 22 2
112 0 3 3 1 4 3 12 3 12 4
113 111 156 111 0 4 0 624 0 444 0
114 69 109 137 0 8 0 872 0 1096 0
115 116 104 112 0 3 0 312 0 336 0
116 103 98 73 0 4 0 392 0 292 0
117 139 78 99 0 3 0 234 0 297 0
118 135 77 115 0 3 0 231 0 345 0
119 113 73 95 0 3 0 219 0 285 0
120 99 71 60 0 4 0 284 0 240 0
121 76 67 94 0 4 0 268 0 376 0
122 110 64 70 0 5 0 320 0 350 0
123 121 62 87 0 2 0 124 0 174 0
124 95 61 102 0 3 0 183 0 306 0
125 66 58 69 0 4 0 232 0 276 0
126 111 58 111 0 7 0 406 0 777 0
127 77 56 55 0 3 0 168 0 165 0
128 101 56 118 0 4 0 224 0 472 0
129 108 52 90 0 3 0 156 0 270 0
130 135 51 81 0 4 0 204 0 324 0
131 70 51 88 0 4 0 204 0 352 0
132 124 50 63 0 3 0 150 0 189 0
133 92 49 84 0 6 0 294 0 504 0
134 104 49 87 0 4 0 196 0 348 0
135 113 48 78 0 4 0 192 0 312 0
136 95 47 93 0 4 0 188 0 372 0
137 89 47 69 0 4 0 188 0 276 0
138 83 46 67 0 3 0 138 0 201 0
139 96 45 61 0 6 0 270 0 366 0
140 95 45 123 0 4 0 180 0 492 0
141 110 45 91 0 6 0 270 0 546 0
142 106 45 98 0 6 0 270 0 588 0
143 78 44 38 0 2 0 88 0 76 0
144 115 44 72 0 3 0 132 0 216 0
145 74 44 59 0 3 0 132 0 177 0
146 93 43 78 0 2 0 86 0 156 0
147 88 43 58 0 4 0 172 0 232 0
148 104 42 97 0 5 0 210 0 485 0
149 86 41 69 0 3 0 123 0 207 0
150 104 41 50 0 7 0 287 0 350 0
151 99 40 66 0 4 0 160 0 264 0
152 101 39 70 0 3 0 117 0 210 0
153 53 39 65 0 4 0 156 0 260 0
154 96 39 69 0 4 0 156 0 276 0
155 58 39 49 0 3 0 117 0 147 0
156 117 39 72 0 3 0 117 0 216 0
157 82 39 74 0 4 0 156 0 296 0
158 57 39 82 0 5 0 195 0 410 0
159 71 38 61 0 3 0 114 0 183 0
160 105 38 72 0 4 0 152 0 288 0
161 60 38 77 0 6 0 228 0 462 0
162 77 38 64 0 5 0 190 0 320 0
163 73 37 23 0 7 0 259 0 161 0
164 78 37 39 0 5 0 185 0 195 0
165 81 37 87 0 5 0 185 0 435 0
166 101 36 46 0 3 0 108 0 138 0
167 118 36 66 0 5 0 180 0 330 0
168 59 36 57 0 4 0 144 0 228 0
169 101 36 48 0 9 0 324 0 432 0
170 22 36 75 0 3 0 108 0 225 0
171 77 36 35 0 3 0 108 0 105 0
172 100 35 53 0 3 0 105 0 159 0
173 39 35 60 0 3 0 105 0 180 0
174 42 34 20 0 15 0 510 0 300 0
175 80 34 66 0 4 0 136 0 264 0
176 48 34 34 0 6 0 204 0 204 0
177 131 34 80 0 3 0 102 0 240 0
178 46 34 63 0 3 0 102 0 189 0
179 89 33 46 0 3 0 99 0 138 0
180 51 33 20 0 5 0 165 0 100 0
181 108 33 73 0 3 0 99 0 219 0
182 86 33 57 0 4 0 132 0 228 0
183 105 33 65 0 7 0 231 0 455 0
184 85 33 70 0 4 0 132 0 280 0
185 103 32 53 0 5 0 160 0 265 0
186 83 32 60 0 7 0 224 0 420 0
187 77 32 34 0 14 0 448 0 476 0
188 26 32 18 0 25 0 800 0 450 0
189 73 32 49 0 6 0 192 0 294 0
190 42 31 27 0 4 0 124 0 108 0
191 71 31 45 0 3 0 93 0 135 0
192 105 31 9 0 62 0 1922 0 558 0
193 73 30 23 0 5 0 150 0 115 0
194 98 30 61 0 10 0 300 0 610 0
195 108 29 67 0 4 0 116 0 268 0
196 57 29 72 0 5 0 145 0 360 0
197 37 29 58 0 5 0 145 0 290 0
198 70 28 55 0 4 0 112 0 220 0
199 73 28 33 0 10 0 280 0 330 0
200 47 28 40 0 5 0 140 0 200 0
201 73 28 57 0 3 0 84 0 171 0
202 91 28 61 0 3 0 84 0 183 0
203 110 28 87 0 17 0 476 0 1479 0
204 78 27 65 0 4 0 108 0 260 0
205 92 27 85 0 6 0 162 0 510 0
206 52 27 85 0 3 0 81 0 255 0
207 88 26 54 0 4 0 104 0 216 0
208 100 26 24 0 8 0 208 0 192 0
209 33 26 31 0 3 0 78 0 93 0
210 42 26 64 0 4 0 104 0 256 0
211 81 25 70 0 4 0 100 0 280 0
212 67 25 2 0 47 0 1175 0 94 0
213 8 24 27 0 8 0 192 0 216 0
214 46 24 29 0 3 0 72 0 87 0
215 83 24 68 0 5 0 120 0 340 0
216 87 24 42 0 3 0 72 0 126 0
217 82 24 78 0 4 0 96 0 312 0
218 63 24 13 0 30 0 720 0 390 0
219 27 24 52 0 4 0 96 0 208 0
220 14 23 25 0 12 0 276 0 300 0
221 83 23 38 0 8 0 184 0 304 0
222 168 23 40 0 3 0 69 0 120 0
223 67 23 42 0 8 0 184 0 336 0
224 21 23 40 0 4 0 92 0 160 0
225 55 23 74 0 3 0 69 0 222 0
226 54 23 73 0 4 0 92 0 292 0
227 118 22 56 0 4 0 88 0 224 0
228 69 22 3 0 21 0 462 0 63 0
229 77 21 9 0 12 0 252 0 108 0
230 72 21 68 0 3 0 63 0 204 0
231 53 21 28 0 3 0 63 0 84 0
232 40 21 36 0 4 0 84 0 144 0
233 102 20 38 0 6 0 120 0 228 0
234 25 20 55 0 17 0 340 0 935 0
235 31 20 36 0 3 0 60 0 108 0
236 77 20 17 0 18 0 360 0 306 0
237 38 20 54 0 5 0 100 0 270 0
238 23 19 57 0 5 0 95 0 285 0
239 91 19 30 0 16 0 304 0 480 0
240 58 19 40 0 10 0 190 0 400 0
241 42 18 37 0 5 0 90 0 185 0
242 44 18 46 0 4 0 72 0 184 0
243 58 18 32 0 3 0 54 0 96 0
244 35 18 34 0 5 0 90 0 170 0
245 88 18 22 0 4 0 72 0 88 0
246 25 17 59 0 5 0 85 0 295 0
247 39 17 32 0 10 0 170 0 320 0
248 48 16 18 0 12 0 192 0 216 0
249 64 16 28 0 4 0 64 0 112 0
250 65 15 34 0 9 0 135 0 306 0
251 95 15 29 0 12 0 180 0 348 0
252 29 15 24 0 10 0 150 0 240 0
253 2 15 24 0 9 0 135 0 216 0
254 83 14 23 0 17 0 238 0 391 0
255 11 13 43 0 6 0 78 0 258 0
256 16 13 28 0 3 0 39 0 84 0
257 9 12 19 0 4 0 48 0 76 0
258 46 11 16 0 19 0 209 0 304 0
259 41 11 40 0 3 0 33 0 120 0
260 14 10 14 0 9 0 90 0 126 0
261 63 10 19 0 7 0 70 0 133 0
262 9 10 22 0 4 0 40 0 88 0
263 0 10 8 0 3 0 30 0 24 0
264 58 9 31 0 46 0 414 0 1426 0
265 18 8 9 0 31 0 248 0 279 0
266 42 8 18 0 21 0 168 0 378 0
267 26 7 9 0 7 0 49 0 63 0
268 38 7 5 0 29 0 203 0 145 0
269 1 4 11 0 5 0 20 0 55 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) hours blogs uk spr hours_uk
25.268897 0.464773 0.587249 39.742992 0.991210 0.232474
hours_spr blogs_uk blogs_spr uk_spr
-0.008381 -0.201812 -0.019363 0.322641
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-76.729 -15.285 2.652 17.187 108.480
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.268897 6.462281 3.910 0.000118 ***
hours 0.464773 0.175330 2.651 0.008524 **
blogs 0.587249 0.142811 4.112 5.27e-05 ***
uk 39.742992 9.161311 4.338 2.06e-05 ***
spr 0.991210 0.517464 1.916 0.056528 .
hours_uk 0.232474 0.260179 0.894 0.372410
hours_spr -0.008381 0.018055 -0.464 0.642909
blogs_uk -0.201812 0.241768 -0.835 0.404637
blogs_spr -0.019363 0.014270 -1.357 0.175993
uk_spr 0.322641 0.678778 0.475 0.634955
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25.52 on 259 degrees of freedom
Multiple R-squared: 0.5103, Adjusted R-squared: 0.4933
F-statistic: 29.99 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,] 6.891228e-01 6.217544e-01 0.3108772
[2,] 5.935690e-01 8.128620e-01 0.4064310
[3,] 4.575506e-01 9.151011e-01 0.5424494
[4,] 3.659428e-01 7.318857e-01 0.6340572
[5,] 2.630797e-01 5.261595e-01 0.7369203
[6,] 1.989517e-01 3.979033e-01 0.8010483
[7,] 1.434824e-01 2.869648e-01 0.8565176
[8,] 9.888002e-02 1.977600e-01 0.9011200
[9,] 8.979177e-02 1.795835e-01 0.9102082
[10,] 1.321921e-01 2.643843e-01 0.8678079
[11,] 8.960552e-02 1.792110e-01 0.9103945
[12,] 6.011072e-02 1.202214e-01 0.9398893
[13,] 3.928805e-02 7.857609e-02 0.9607120
[14,] 2.542804e-02 5.085607e-02 0.9745720
[15,] 1.899206e-02 3.798412e-02 0.9810079
[16,] 1.162388e-02 2.324776e-02 0.9883761
[17,] 8.638321e-03 1.727664e-02 0.9913617
[18,] 6.281875e-03 1.256375e-02 0.9937181
[19,] 1.141276e-02 2.282551e-02 0.9885872
[20,] 7.101729e-03 1.420346e-02 0.9928983
[21,] 5.891860e-03 1.178372e-02 0.9941081
[22,] 4.355365e-03 8.710729e-03 0.9956446
[23,] 3.575026e-03 7.150051e-03 0.9964250
[24,] 3.798634e-03 7.597267e-03 0.9962014
[25,] 4.246326e-03 8.492652e-03 0.9957537
[26,] 3.608661e-03 7.217322e-03 0.9963913
[27,] 3.259481e-03 6.518962e-03 0.9967405
[28,] 3.533258e-03 7.066517e-03 0.9964667
[29,] 2.214092e-03 4.428185e-03 0.9977859
[30,] 5.042818e-03 1.008564e-02 0.9949572
[31,] 4.370439e-03 8.740878e-03 0.9956296
[32,] 3.422535e-03 6.845071e-03 0.9965775
[33,] 9.603670e-03 1.920734e-02 0.9903963
[34,] 6.858155e-03 1.371631e-02 0.9931418
[35,] 4.646499e-03 9.292998e-03 0.9953535
[36,] 3.235401e-03 6.470801e-03 0.9967646
[37,] 2.135029e-03 4.270058e-03 0.9978650
[38,] 1.424277e-03 2.848554e-03 0.9985757
[39,] 1.053149e-03 2.106297e-03 0.9989469
[40,] 1.236513e-03 2.473026e-03 0.9987635
[41,] 1.312659e-03 2.625319e-03 0.9986873
[42,] 9.130519e-04 1.826104e-03 0.9990869
[43,] 5.915294e-04 1.183059e-03 0.9994085
[44,] 2.557058e-03 5.114116e-03 0.9974429
[45,] 1.740794e-03 3.481589e-03 0.9982592
[46,] 1.229072e-03 2.458143e-03 0.9987709
[47,] 1.225528e-03 2.451055e-03 0.9987745
[48,] 1.030997e-03 2.061994e-03 0.9989690
[49,] 7.169204e-04 1.433841e-03 0.9992831
[50,] 5.525989e-04 1.105198e-03 0.9994474
[51,] 3.600838e-04 7.201676e-04 0.9996399
[52,] 2.338304e-04 4.676609e-04 0.9997662
[53,] 4.634538e-04 9.269076e-04 0.9995365
[54,] 4.145552e-04 8.291105e-04 0.9995854
[55,] 2.718029e-04 5.436058e-04 0.9997282
[56,] 1.783045e-04 3.566090e-04 0.9998217
[57,] 1.151291e-04 2.302583e-04 0.9998849
[58,] 4.397641e-04 8.795281e-04 0.9995602
[59,] 3.048282e-04 6.096565e-04 0.9996952
[60,] 2.010594e-04 4.021189e-04 0.9997989
[61,] 1.355454e-04 2.710908e-04 0.9998645
[62,] 8.769769e-05 1.753954e-04 0.9999123
[63,] 5.606347e-05 1.121269e-04 0.9999439
[64,] 4.466515e-05 8.933031e-05 0.9999553
[65,] 2.820766e-05 5.641532e-05 0.9999718
[66,] 2.202759e-05 4.405518e-05 0.9999780
[67,] 1.379963e-05 2.759927e-05 0.9999862
[68,] 9.220365e-06 1.844073e-05 0.9999908
[69,] 5.874876e-06 1.174975e-05 0.9999941
[70,] 7.486360e-06 1.497272e-05 0.9999925
[71,] 6.395632e-06 1.279126e-05 0.9999936
[72,] 4.384796e-06 8.769593e-06 0.9999956
[73,] 2.727361e-06 5.454723e-06 0.9999973
[74,] 3.036430e-06 6.072860e-06 0.9999970
[75,] 9.765297e-06 1.953059e-05 0.9999902
[76,] 2.123141e-05 4.246282e-05 0.9999788
[77,] 1.377054e-05 2.754108e-05 0.9999862
[78,] 1.345579e-05 2.691159e-05 0.9999865
[79,] 1.022123e-05 2.044246e-05 0.9999898
[80,] 6.892101e-06 1.378420e-05 0.9999931
[81,] 5.984406e-06 1.196881e-05 0.9999940
[82,] 9.867615e-06 1.973523e-05 0.9999901
[83,] 7.839152e-06 1.567830e-05 0.9999922
[84,] 8.148945e-06 1.629789e-05 0.9999919
[85,] 1.161475e-05 2.322950e-05 0.9999884
[86,] 1.104398e-05 2.208795e-05 0.9999890
[87,] 7.929689e-06 1.585938e-05 0.9999921
[88,] 5.266650e-06 1.053330e-05 0.9999947
[89,] 3.516143e-06 7.032285e-06 0.9999965
[90,] 5.657056e-06 1.131411e-05 0.9999943
[91,] 4.187167e-06 8.374333e-06 0.9999958
[92,] 7.567866e-06 1.513573e-05 0.9999924
[93,] 1.281566e-03 2.563133e-03 0.9987184
[94,] 1.495546e-03 2.991092e-03 0.9985045
[95,] 1.192443e-03 2.384886e-03 0.9988076
[96,] 9.599777e-04 1.919955e-03 0.9990400
[97,] 8.905118e-04 1.781024e-03 0.9991095
[98,] 8.761655e-03 1.752331e-02 0.9912383
[99,] 3.545090e-02 7.090181e-02 0.9645491
[100,] 8.637699e-02 1.727540e-01 0.9136230
[101,] 9.100793e-02 1.820159e-01 0.9089921
[102,] 1.639508e-01 3.279016e-01 0.8360492
[103,] 1.584475e-01 3.168951e-01 0.8415525
[104,] 1.545273e-01 3.090547e-01 0.8454727
[105,] 1.406871e-01 2.813742e-01 0.8593129
[106,] 1.213846e-01 2.427691e-01 0.8786154
[107,] 1.083493e-01 2.166985e-01 0.8916507
[108,] 9.436757e-02 1.887351e-01 0.9056324
[109,] 1.181147e-01 2.362295e-01 0.8818853
[110,] 1.084605e-01 2.169210e-01 0.8915395
[111,] 9.338966e-02 1.867793e-01 0.9066103
[112,] 9.053493e-02 1.810699e-01 0.9094651
[113,] 1.090635e-01 2.181269e-01 0.8909365
[114,] 1.084540e-01 2.169080e-01 0.8915460
[115,] 1.060548e-01 2.121096e-01 0.8939452
[116,] 9.789478e-02 1.957896e-01 0.9021052
[117,] 8.329591e-02 1.665918e-01 0.9167041
[118,] 9.466092e-02 1.893218e-01 0.9053391
[119,] 1.113804e-01 2.227608e-01 0.8886196
[120,] 1.105765e-01 2.211531e-01 0.8894235
[121,] 9.742357e-02 1.948471e-01 0.9025764
[122,] 8.294584e-02 1.658917e-01 0.9170542
[123,] 7.282046e-02 1.456409e-01 0.9271795
[124,] 6.295778e-02 1.259156e-01 0.9370422
[125,] 5.391573e-02 1.078315e-01 0.9460843
[126,] 4.868562e-02 9.737125e-02 0.9513144
[127,] 4.127967e-02 8.255934e-02 0.9587203
[128,] 3.700454e-02 7.400907e-02 0.9629955
[129,] 3.203199e-02 6.406399e-02 0.9679680
[130,] 2.663334e-02 5.326667e-02 0.9733667
[131,] 2.317293e-02 4.634587e-02 0.9768271
[132,] 2.063143e-02 4.126286e-02 0.9793686
[133,] 1.927453e-02 3.854905e-02 0.9807255
[134,] 1.578325e-02 3.156650e-02 0.9842168
[135,] 1.261488e-02 2.522975e-02 0.9873851
[136,] 9.988994e-03 1.997799e-02 0.9900110
[137,] 8.083468e-03 1.616694e-02 0.9919165
[138,] 7.094628e-03 1.418926e-02 0.9929054
[139,] 5.624326e-03 1.124865e-02 0.9943757
[140,] 4.496383e-03 8.992766e-03 0.9955036
[141,] 6.392075e-03 1.278415e-02 0.9936079
[142,] 4.989573e-03 9.979146e-03 0.9950104
[143,] 5.317080e-03 1.063416e-02 0.9946829
[144,] 5.283301e-03 1.056660e-02 0.9947167
[145,] 4.275743e-03 8.551487e-03 0.9957243
[146,] 5.995361e-03 1.199072e-02 0.9940046
[147,] 5.243603e-03 1.048721e-02 0.9947564
[148,] 4.359500e-03 8.719000e-03 0.9956405
[149,] 5.124198e-03 1.024840e-02 0.9948758
[150,] 4.140343e-03 8.280686e-03 0.9958597
[151,] 3.156048e-03 6.312096e-03 0.9968440
[152,] 2.385676e-03 4.771353e-03 0.9976143
[153,] 1.958481e-03 3.916962e-03 0.9980415
[154,] 1.741177e-03 3.482353e-03 0.9982588
[155,] 2.024004e-03 4.048008e-03 0.9979760
[156,] 2.054200e-03 4.108400e-03 0.9979458
[157,] 1.859081e-03 3.718161e-03 0.9981409
[158,] 1.058410e-02 2.116820e-02 0.9894159
[159,] 8.276042e-03 1.655208e-02 0.9917240
[160,] 7.250535e-03 1.450107e-02 0.9927495
[161,] 1.286611e-02 2.573222e-02 0.9871339
[162,] 1.397620e-02 2.795241e-02 0.9860238
[163,] 1.118933e-02 2.237866e-02 0.9888107
[164,] 1.174846e-02 2.349691e-02 0.9882515
[165,] 1.622767e-02 3.245535e-02 0.9837723
[166,] 2.305486e-02 4.610972e-02 0.9769451
[167,] 1.891547e-02 3.783094e-02 0.9810845
[168,] 1.736394e-02 3.472789e-02 0.9826361
[169,] 1.569846e-02 3.139692e-02 0.9843015
[170,] 1.235963e-02 2.471926e-02 0.9876404
[171,] 1.128553e-02 2.257107e-02 0.9887145
[172,] 8.763922e-03 1.752784e-02 0.9912361
[173,] 8.330101e-03 1.666020e-02 0.9916699
[174,] 6.390839e-03 1.278168e-02 0.9936092
[175,] 4.971377e-03 9.942754e-03 0.9950286
[176,] 8.876818e-03 1.775364e-02 0.9911232
[177,] 6.963616e-03 1.392723e-02 0.9930364
[178,] 8.180598e-03 1.636120e-02 0.9918194
[179,] 6.420981e-03 1.284196e-02 0.9935790
[180,] 6.834063e-03 1.366813e-02 0.9931659
[181,] 5.241069e-03 1.048214e-02 0.9947589
[182,] 4.352600e-03 8.705201e-03 0.9956474
[183,] 4.536845e-03 9.073690e-03 0.9954632
[184,] 4.476911e-03 8.953821e-03 0.9955231
[185,] 7.247204e-03 1.449441e-02 0.9927528
[186,] 5.594812e-03 1.118962e-02 0.9944052
[187,] 4.208259e-03 8.416517e-03 0.9957917
[188,] 4.484705e-03 8.969410e-03 0.9955153
[189,] 3.368415e-03 6.736830e-03 0.9966316
[190,] 2.624648e-03 5.249296e-03 0.9973754
[191,] 2.407499e-03 4.814997e-03 0.9975925
[192,] 1.749040e-03 3.498080e-03 0.9982510
[193,] 1.383144e-03 2.766288e-03 0.9986169
[194,] 1.512280e-03 3.024560e-03 0.9984877
[195,] 1.180274e-03 2.360548e-03 0.9988197
[196,] 1.337671e-03 2.675343e-03 0.9986623
[197,] 1.841577e-03 3.683155e-03 0.9981584
[198,] 2.348792e-03 4.697585e-03 0.9976512
[199,] 1.710224e-03 3.420448e-03 0.9982898
[200,] 1.284882e-03 2.569764e-03 0.9987151
[201,] 5.409117e-03 1.081823e-02 0.9945909
[202,] 5.594344e-03 1.118869e-02 0.9944057
[203,] 4.343288e-03 8.686577e-03 0.9956567
[204,] 3.401792e-03 6.803585e-03 0.9965982
[205,] 2.711954e-03 5.423909e-03 0.9972880
[206,] 1.952465e-03 3.904929e-03 0.9980475
[207,] 3.775280e-03 7.550559e-03 0.9962247
[208,] 1.378971e-02 2.757941e-02 0.9862103
[209,] 1.055674e-02 2.111349e-02 0.9894433
[210,] 2.421756e-01 4.843511e-01 0.7578244
[211,] 2.050380e-01 4.100760e-01 0.7949620
[212,] 2.978944e-01 5.957887e-01 0.7021056
[213,] 2.643995e-01 5.287990e-01 0.7356005
[214,] 2.349474e-01 4.698948e-01 0.7650526
[215,] 4.213334e-01 8.426667e-01 0.5786666
[216,] 4.029991e-01 8.059981e-01 0.5970009
[217,] 3.546302e-01 7.092604e-01 0.6453698
[218,] 3.704272e-01 7.408544e-01 0.6295728
[219,] 3.238256e-01 6.476511e-01 0.6761744
[220,] 3.065134e-01 6.130267e-01 0.6934866
[221,] 4.301563e-01 8.603126e-01 0.5698437
[222,] 5.756976e-01 8.486047e-01 0.4243024
[223,] 5.497574e-01 9.004852e-01 0.4502426
[224,] 5.135398e-01 9.729205e-01 0.4864602
[225,] 4.664314e-01 9.328628e-01 0.5335686
[226,] 4.627900e-01 9.255801e-01 0.5372100
[227,] 4.107239e-01 8.214478e-01 0.5892761
[228,] 3.506401e-01 7.012802e-01 0.6493599
[229,] 2.973809e-01 5.947619e-01 0.7026191
[230,] 2.435738e-01 4.871477e-01 0.7564262
[231,] 2.021513e-01 4.043026e-01 0.7978487
[232,] 1.703537e-01 3.407075e-01 0.8296463
[233,] 2.897704e-01 5.795408e-01 0.7102296
[234,] 2.714214e-01 5.428428e-01 0.7285786
[235,] 2.395824e-01 4.791649e-01 0.7604176
[236,] 1.889119e-01 3.778239e-01 0.8110881
[237,] 2.809499e-01 5.618998e-01 0.7190501
[238,] 2.501535e-01 5.003071e-01 0.7498465
[239,] 4.048994e-01 8.097987e-01 0.5951006
[240,] 3.133179e-01 6.266358e-01 0.6866821
[241,] 3.795169e-01 7.590338e-01 0.6204831
[242,] 3.245903e-01 6.491807e-01 0.6754097
[243,] 3.239359e-01 6.478719e-01 0.6760641
[244,] 2.136127e-01 4.272255e-01 0.7863873
> postscript(file="/var/wessaorg/rcomp/tmp/13k4u1358278485.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/24xsy1358278485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3hf3v1358278485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4t2q21358278485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5xcgx1358278485.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-36.84475830 -56.19058213 -17.91642551 9.13849596 -14.12612995 -2.20667733
7 8 9 10 11 12
-14.52882978 -15.28499619 30.79483619 2.73867194 -40.88604143 -16.71946194
13 14 15 16 17 18
10.38720005 -9.62588356 9.65902228 17.72059495 -1.32356631 -23.09586068
19 20 21 22 23 24
19.98472388 -0.67226647 -13.42304633 -11.62460046 10.36540161 -9.93627425
25 26 27 28 29 30
9.98767394 -3.98981002 16.50308103 21.31042548 -2.29038862 -6.99806483
31 32 33 34 35 36
-24.90971089 1.76239399 15.79831358 -10.36843728 22.41653909 -13.07017368
37 38 39 40 41 42
-6.01911478 18.56147553 18.78770092 33.39693416 10.46091461 -13.88774960
43 44 45 46 47 48
-6.89829447 17.06931690 47.11509998 20.03311946 9.58094036 2.92450155
49 50 51 52 53 54
10.36456370 4.06587396 -0.84517900 38.70522435 -11.85967017 17.68667019
55 56 57 58 59 60
12.45625758 -34.83138589 13.04739690 3.14032138 33.26791133 -19.77080257
61 62 63 64 65 66
8.41160367 4.04833736 17.18681524 12.30660672 -21.21742776 30.46493467
67 68 69 70 71 72
10.82765261 15.47587487 14.39254713 -27.24473494 10.75258745 17.06465201
73 74 75 76 77 78
20.44532974 18.66491773 17.14342587 -1.73019579 14.75416831 -5.65801144
79 80 81 82 83 84
17.09996454 4.34387453 24.37239382 -11.86570234 27.86783647 20.52638723
85 86 87 88 89 90
11.03274162 -5.27145952 -22.59333174 -17.75983606 9.78679761 -12.39571434
91 92 93 94 95 96
-0.55850891 22.38706333 -0.59887568 -24.54807729 0.36115157 -11.38549229
97 98 99 100 101 102
-17.73539368 -0.06468978 21.29499344 4.25486274 14.07657963 -15.61452592
103 104 105 106 107 108
24.58839788 -20.61567856 -76.72934467 -21.51471482 -1.54493615 21.46992446
109 110 111 112 113 114
31.87234680 -73.35776541 -73.17534842 -73.18241791 -42.09592615 -66.78144355
115 116 117 118 119 120
-17.22984120 -5.71123680 24.07961306 12.05271150 2.39439934 8.55983969
121 122 123 124 125 126
-30.04818311 18.38124953 18.25057590 -14.03414072 -23.42206581 5.09923575
127 128 129 130 131 132
-4.96554915 -12.53950967 9.27243426 42.47914560 -26.08941775 40.43896864
133 134 135 136 137 138
0.90423117 9.28287661 23.30227929 -1.91339238 4.32168197 -0.91914182
139 140 141 142 143 144
17.39674224 -16.34473571 17.26470919 9.96723481 10.19234911 29.31432816
145 146 147 148 149 150
-4.80661443 3.69949019 10.65442357 8.44272222 3.22069489 32.55701950
151 152 153 154 155 156
18.86981485 18.57079793 -26.18914059 14.77168036 -13.31687856 33.51248140
157 158 159 160 161 162
-1.77729347 -29.93220194 -6.22714707 22.67354586 -23.23892535 -0.68157019
163 164 165 166 167 168
15.37746379 13.00208041 -7.53862037 32.58948817 40.18330517 -14.81703308
169 170 171 172 173 174
32.97086061 -61.75610144 14.41022905 28.32501098 -36.37909686 -15.60103350
175 176 177 178 179 180
2.45731364 -13.32505892 45.47736854 -30.52694100 21.90838011 -2.98824973
181 182 183 184 185 186
26.62110702 13.47671632 30.03031344 5.84938438 33.25038743 10.69494205
187 188 189 190 191 192
15.98660362 -34.07412178 5.43790076 -14.36694115 5.31679967 25.49567912
193 194 195 196 197 198
18.80906332 27.37961790 32.10376787 -20.79919978 -33.93316206 0.65255484
199 200 201 202 203 204
14.16273303 -14.68253031 2.28579246 18.16915941 36.40413365 3.98585699
205 206 207 208 209 210
9.55190160 -33.09100824 20.02484920 46.08435499 -23.07683142 -31.07309844
211 212 213 214 215 216
5.29938277 -5.98194315 -46.41721452 -8.13925360 9.27687003 25.98168899
217 218 219 220 221 222
2.65227445 2.79197813 -39.09306167 -40.41225991 24.22476338 108.47963616
223 224 225 226 227 228
6.49540030 -38.54427470 -22.51174476 -22.36750193 50.73025886 16.02078199
229 230 231 232 233 234
28.99433457 -1.45753389 0.70879660 -16.64259605 44.59350340 -37.75925901
235 236 237 238 239 240
-25.08483885 23.55296014 -27.16561645 -43.21404130 35.26584123 -0.16388108
241 242 243 244 245 246
-13.98254702 -16.44679430 4.91105550 -19.51125322 39.78828005 -41.34916593
247 248 249 250 251 252
-15.25304146 -1.37863143 13.59201106 10.92879904 42.08181699 -21.34220185
253 254 255 256 257 258
-47.94142822 30.43275081 -45.86041874 -32.77415821 -35.09483687 -4.97227791
259 260 261 262 263 264
-13.24479607 -29.86492772 18.14917663 -35.76172157 -36.87210102 -4.17022260
265 266 267 268 269
-39.51897520 -9.64559245 -13.11545891 -17.69463329 -36.31116881
> postscript(file="/var/wessaorg/rcomp/tmp/6jryo1358278485.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 -36.84475830 NA
1 -56.19058213 -36.84475830
2 -17.91642551 -56.19058213
3 9.13849596 -17.91642551
4 -14.12612995 9.13849596
5 -2.20667733 -14.12612995
6 -14.52882978 -2.20667733
7 -15.28499619 -14.52882978
8 30.79483619 -15.28499619
9 2.73867194 30.79483619
10 -40.88604143 2.73867194
11 -16.71946194 -40.88604143
12 10.38720005 -16.71946194
13 -9.62588356 10.38720005
14 9.65902228 -9.62588356
15 17.72059495 9.65902228
16 -1.32356631 17.72059495
17 -23.09586068 -1.32356631
18 19.98472388 -23.09586068
19 -0.67226647 19.98472388
20 -13.42304633 -0.67226647
21 -11.62460046 -13.42304633
22 10.36540161 -11.62460046
23 -9.93627425 10.36540161
24 9.98767394 -9.93627425
25 -3.98981002 9.98767394
26 16.50308103 -3.98981002
27 21.31042548 16.50308103
28 -2.29038862 21.31042548
29 -6.99806483 -2.29038862
30 -24.90971089 -6.99806483
31 1.76239399 -24.90971089
32 15.79831358 1.76239399
33 -10.36843728 15.79831358
34 22.41653909 -10.36843728
35 -13.07017368 22.41653909
36 -6.01911478 -13.07017368
37 18.56147553 -6.01911478
38 18.78770092 18.56147553
39 33.39693416 18.78770092
40 10.46091461 33.39693416
41 -13.88774960 10.46091461
42 -6.89829447 -13.88774960
43 17.06931690 -6.89829447
44 47.11509998 17.06931690
45 20.03311946 47.11509998
46 9.58094036 20.03311946
47 2.92450155 9.58094036
48 10.36456370 2.92450155
49 4.06587396 10.36456370
50 -0.84517900 4.06587396
51 38.70522435 -0.84517900
52 -11.85967017 38.70522435
53 17.68667019 -11.85967017
54 12.45625758 17.68667019
55 -34.83138589 12.45625758
56 13.04739690 -34.83138589
57 3.14032138 13.04739690
58 33.26791133 3.14032138
59 -19.77080257 33.26791133
60 8.41160367 -19.77080257
61 4.04833736 8.41160367
62 17.18681524 4.04833736
63 12.30660672 17.18681524
64 -21.21742776 12.30660672
65 30.46493467 -21.21742776
66 10.82765261 30.46493467
67 15.47587487 10.82765261
68 14.39254713 15.47587487
69 -27.24473494 14.39254713
70 10.75258745 -27.24473494
71 17.06465201 10.75258745
72 20.44532974 17.06465201
73 18.66491773 20.44532974
74 17.14342587 18.66491773
75 -1.73019579 17.14342587
76 14.75416831 -1.73019579
77 -5.65801144 14.75416831
78 17.09996454 -5.65801144
79 4.34387453 17.09996454
80 24.37239382 4.34387453
81 -11.86570234 24.37239382
82 27.86783647 -11.86570234
83 20.52638723 27.86783647
84 11.03274162 20.52638723
85 -5.27145952 11.03274162
86 -22.59333174 -5.27145952
87 -17.75983606 -22.59333174
88 9.78679761 -17.75983606
89 -12.39571434 9.78679761
90 -0.55850891 -12.39571434
91 22.38706333 -0.55850891
92 -0.59887568 22.38706333
93 -24.54807729 -0.59887568
94 0.36115157 -24.54807729
95 -11.38549229 0.36115157
96 -17.73539368 -11.38549229
97 -0.06468978 -17.73539368
98 21.29499344 -0.06468978
99 4.25486274 21.29499344
100 14.07657963 4.25486274
101 -15.61452592 14.07657963
102 24.58839788 -15.61452592
103 -20.61567856 24.58839788
104 -76.72934467 -20.61567856
105 -21.51471482 -76.72934467
106 -1.54493615 -21.51471482
107 21.46992446 -1.54493615
108 31.87234680 21.46992446
109 -73.35776541 31.87234680
110 -73.17534842 -73.35776541
111 -73.18241791 -73.17534842
112 -42.09592615 -73.18241791
113 -66.78144355 -42.09592615
114 -17.22984120 -66.78144355
115 -5.71123680 -17.22984120
116 24.07961306 -5.71123680
117 12.05271150 24.07961306
118 2.39439934 12.05271150
119 8.55983969 2.39439934
120 -30.04818311 8.55983969
121 18.38124953 -30.04818311
122 18.25057590 18.38124953
123 -14.03414072 18.25057590
124 -23.42206581 -14.03414072
125 5.09923575 -23.42206581
126 -4.96554915 5.09923575
127 -12.53950967 -4.96554915
128 9.27243426 -12.53950967
129 42.47914560 9.27243426
130 -26.08941775 42.47914560
131 40.43896864 -26.08941775
132 0.90423117 40.43896864
133 9.28287661 0.90423117
134 23.30227929 9.28287661
135 -1.91339238 23.30227929
136 4.32168197 -1.91339238
137 -0.91914182 4.32168197
138 17.39674224 -0.91914182
139 -16.34473571 17.39674224
140 17.26470919 -16.34473571
141 9.96723481 17.26470919
142 10.19234911 9.96723481
143 29.31432816 10.19234911
144 -4.80661443 29.31432816
145 3.69949019 -4.80661443
146 10.65442357 3.69949019
147 8.44272222 10.65442357
148 3.22069489 8.44272222
149 32.55701950 3.22069489
150 18.86981485 32.55701950
151 18.57079793 18.86981485
152 -26.18914059 18.57079793
153 14.77168036 -26.18914059
154 -13.31687856 14.77168036
155 33.51248140 -13.31687856
156 -1.77729347 33.51248140
157 -29.93220194 -1.77729347
158 -6.22714707 -29.93220194
159 22.67354586 -6.22714707
160 -23.23892535 22.67354586
161 -0.68157019 -23.23892535
162 15.37746379 -0.68157019
163 13.00208041 15.37746379
164 -7.53862037 13.00208041
165 32.58948817 -7.53862037
166 40.18330517 32.58948817
167 -14.81703308 40.18330517
168 32.97086061 -14.81703308
169 -61.75610144 32.97086061
170 14.41022905 -61.75610144
171 28.32501098 14.41022905
172 -36.37909686 28.32501098
173 -15.60103350 -36.37909686
174 2.45731364 -15.60103350
175 -13.32505892 2.45731364
176 45.47736854 -13.32505892
177 -30.52694100 45.47736854
178 21.90838011 -30.52694100
179 -2.98824973 21.90838011
180 26.62110702 -2.98824973
181 13.47671632 26.62110702
182 30.03031344 13.47671632
183 5.84938438 30.03031344
184 33.25038743 5.84938438
185 10.69494205 33.25038743
186 15.98660362 10.69494205
187 -34.07412178 15.98660362
188 5.43790076 -34.07412178
189 -14.36694115 5.43790076
190 5.31679967 -14.36694115
191 25.49567912 5.31679967
192 18.80906332 25.49567912
193 27.37961790 18.80906332
194 32.10376787 27.37961790
195 -20.79919978 32.10376787
196 -33.93316206 -20.79919978
197 0.65255484 -33.93316206
198 14.16273303 0.65255484
199 -14.68253031 14.16273303
200 2.28579246 -14.68253031
201 18.16915941 2.28579246
202 36.40413365 18.16915941
203 3.98585699 36.40413365
204 9.55190160 3.98585699
205 -33.09100824 9.55190160
206 20.02484920 -33.09100824
207 46.08435499 20.02484920
208 -23.07683142 46.08435499
209 -31.07309844 -23.07683142
210 5.29938277 -31.07309844
211 -5.98194315 5.29938277
212 -46.41721452 -5.98194315
213 -8.13925360 -46.41721452
214 9.27687003 -8.13925360
215 25.98168899 9.27687003
216 2.65227445 25.98168899
217 2.79197813 2.65227445
218 -39.09306167 2.79197813
219 -40.41225991 -39.09306167
220 24.22476338 -40.41225991
221 108.47963616 24.22476338
222 6.49540030 108.47963616
223 -38.54427470 6.49540030
224 -22.51174476 -38.54427470
225 -22.36750193 -22.51174476
226 50.73025886 -22.36750193
227 16.02078199 50.73025886
228 28.99433457 16.02078199
229 -1.45753389 28.99433457
230 0.70879660 -1.45753389
231 -16.64259605 0.70879660
232 44.59350340 -16.64259605
233 -37.75925901 44.59350340
234 -25.08483885 -37.75925901
235 23.55296014 -25.08483885
236 -27.16561645 23.55296014
237 -43.21404130 -27.16561645
238 35.26584123 -43.21404130
239 -0.16388108 35.26584123
240 -13.98254702 -0.16388108
241 -16.44679430 -13.98254702
242 4.91105550 -16.44679430
243 -19.51125322 4.91105550
244 39.78828005 -19.51125322
245 -41.34916593 39.78828005
246 -15.25304146 -41.34916593
247 -1.37863143 -15.25304146
248 13.59201106 -1.37863143
249 10.92879904 13.59201106
250 42.08181699 10.92879904
251 -21.34220185 42.08181699
252 -47.94142822 -21.34220185
253 30.43275081 -47.94142822
254 -45.86041874 30.43275081
255 -32.77415821 -45.86041874
256 -35.09483687 -32.77415821
257 -4.97227791 -35.09483687
258 -13.24479607 -4.97227791
259 -29.86492772 -13.24479607
260 18.14917663 -29.86492772
261 -35.76172157 18.14917663
262 -36.87210102 -35.76172157
263 -4.17022260 -36.87210102
264 -39.51897520 -4.17022260
265 -9.64559245 -39.51897520
266 -13.11545891 -9.64559245
267 -17.69463329 -13.11545891
268 -36.31116881 -17.69463329
269 NA -36.31116881
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -56.19058213 -36.84475830
[2,] -17.91642551 -56.19058213
[3,] 9.13849596 -17.91642551
[4,] -14.12612995 9.13849596
[5,] -2.20667733 -14.12612995
[6,] -14.52882978 -2.20667733
[7,] -15.28499619 -14.52882978
[8,] 30.79483619 -15.28499619
[9,] 2.73867194 30.79483619
[10,] -40.88604143 2.73867194
[11,] -16.71946194 -40.88604143
[12,] 10.38720005 -16.71946194
[13,] -9.62588356 10.38720005
[14,] 9.65902228 -9.62588356
[15,] 17.72059495 9.65902228
[16,] -1.32356631 17.72059495
[17,] -23.09586068 -1.32356631
[18,] 19.98472388 -23.09586068
[19,] -0.67226647 19.98472388
[20,] -13.42304633 -0.67226647
[21,] -11.62460046 -13.42304633
[22,] 10.36540161 -11.62460046
[23,] -9.93627425 10.36540161
[24,] 9.98767394 -9.93627425
[25,] -3.98981002 9.98767394
[26,] 16.50308103 -3.98981002
[27,] 21.31042548 16.50308103
[28,] -2.29038862 21.31042548
[29,] -6.99806483 -2.29038862
[30,] -24.90971089 -6.99806483
[31,] 1.76239399 -24.90971089
[32,] 15.79831358 1.76239399
[33,] -10.36843728 15.79831358
[34,] 22.41653909 -10.36843728
[35,] -13.07017368 22.41653909
[36,] -6.01911478 -13.07017368
[37,] 18.56147553 -6.01911478
[38,] 18.78770092 18.56147553
[39,] 33.39693416 18.78770092
[40,] 10.46091461 33.39693416
[41,] -13.88774960 10.46091461
[42,] -6.89829447 -13.88774960
[43,] 17.06931690 -6.89829447
[44,] 47.11509998 17.06931690
[45,] 20.03311946 47.11509998
[46,] 9.58094036 20.03311946
[47,] 2.92450155 9.58094036
[48,] 10.36456370 2.92450155
[49,] 4.06587396 10.36456370
[50,] -0.84517900 4.06587396
[51,] 38.70522435 -0.84517900
[52,] -11.85967017 38.70522435
[53,] 17.68667019 -11.85967017
[54,] 12.45625758 17.68667019
[55,] -34.83138589 12.45625758
[56,] 13.04739690 -34.83138589
[57,] 3.14032138 13.04739690
[58,] 33.26791133 3.14032138
[59,] -19.77080257 33.26791133
[60,] 8.41160367 -19.77080257
[61,] 4.04833736 8.41160367
[62,] 17.18681524 4.04833736
[63,] 12.30660672 17.18681524
[64,] -21.21742776 12.30660672
[65,] 30.46493467 -21.21742776
[66,] 10.82765261 30.46493467
[67,] 15.47587487 10.82765261
[68,] 14.39254713 15.47587487
[69,] -27.24473494 14.39254713
[70,] 10.75258745 -27.24473494
[71,] 17.06465201 10.75258745
[72,] 20.44532974 17.06465201
[73,] 18.66491773 20.44532974
[74,] 17.14342587 18.66491773
[75,] -1.73019579 17.14342587
[76,] 14.75416831 -1.73019579
[77,] -5.65801144 14.75416831
[78,] 17.09996454 -5.65801144
[79,] 4.34387453 17.09996454
[80,] 24.37239382 4.34387453
[81,] -11.86570234 24.37239382
[82,] 27.86783647 -11.86570234
[83,] 20.52638723 27.86783647
[84,] 11.03274162 20.52638723
[85,] -5.27145952 11.03274162
[86,] -22.59333174 -5.27145952
[87,] -17.75983606 -22.59333174
[88,] 9.78679761 -17.75983606
[89,] -12.39571434 9.78679761
[90,] -0.55850891 -12.39571434
[91,] 22.38706333 -0.55850891
[92,] -0.59887568 22.38706333
[93,] -24.54807729 -0.59887568
[94,] 0.36115157 -24.54807729
[95,] -11.38549229 0.36115157
[96,] -17.73539368 -11.38549229
[97,] -0.06468978 -17.73539368
[98,] 21.29499344 -0.06468978
[99,] 4.25486274 21.29499344
[100,] 14.07657963 4.25486274
[101,] -15.61452592 14.07657963
[102,] 24.58839788 -15.61452592
[103,] -20.61567856 24.58839788
[104,] -76.72934467 -20.61567856
[105,] -21.51471482 -76.72934467
[106,] -1.54493615 -21.51471482
[107,] 21.46992446 -1.54493615
[108,] 31.87234680 21.46992446
[109,] -73.35776541 31.87234680
[110,] -73.17534842 -73.35776541
[111,] -73.18241791 -73.17534842
[112,] -42.09592615 -73.18241791
[113,] -66.78144355 -42.09592615
[114,] -17.22984120 -66.78144355
[115,] -5.71123680 -17.22984120
[116,] 24.07961306 -5.71123680
[117,] 12.05271150 24.07961306
[118,] 2.39439934 12.05271150
[119,] 8.55983969 2.39439934
[120,] -30.04818311 8.55983969
[121,] 18.38124953 -30.04818311
[122,] 18.25057590 18.38124953
[123,] -14.03414072 18.25057590
[124,] -23.42206581 -14.03414072
[125,] 5.09923575 -23.42206581
[126,] -4.96554915 5.09923575
[127,] -12.53950967 -4.96554915
[128,] 9.27243426 -12.53950967
[129,] 42.47914560 9.27243426
[130,] -26.08941775 42.47914560
[131,] 40.43896864 -26.08941775
[132,] 0.90423117 40.43896864
[133,] 9.28287661 0.90423117
[134,] 23.30227929 9.28287661
[135,] -1.91339238 23.30227929
[136,] 4.32168197 -1.91339238
[137,] -0.91914182 4.32168197
[138,] 17.39674224 -0.91914182
[139,] -16.34473571 17.39674224
[140,] 17.26470919 -16.34473571
[141,] 9.96723481 17.26470919
[142,] 10.19234911 9.96723481
[143,] 29.31432816 10.19234911
[144,] -4.80661443 29.31432816
[145,] 3.69949019 -4.80661443
[146,] 10.65442357 3.69949019
[147,] 8.44272222 10.65442357
[148,] 3.22069489 8.44272222
[149,] 32.55701950 3.22069489
[150,] 18.86981485 32.55701950
[151,] 18.57079793 18.86981485
[152,] -26.18914059 18.57079793
[153,] 14.77168036 -26.18914059
[154,] -13.31687856 14.77168036
[155,] 33.51248140 -13.31687856
[156,] -1.77729347 33.51248140
[157,] -29.93220194 -1.77729347
[158,] -6.22714707 -29.93220194
[159,] 22.67354586 -6.22714707
[160,] -23.23892535 22.67354586
[161,] -0.68157019 -23.23892535
[162,] 15.37746379 -0.68157019
[163,] 13.00208041 15.37746379
[164,] -7.53862037 13.00208041
[165,] 32.58948817 -7.53862037
[166,] 40.18330517 32.58948817
[167,] -14.81703308 40.18330517
[168,] 32.97086061 -14.81703308
[169,] -61.75610144 32.97086061
[170,] 14.41022905 -61.75610144
[171,] 28.32501098 14.41022905
[172,] -36.37909686 28.32501098
[173,] -15.60103350 -36.37909686
[174,] 2.45731364 -15.60103350
[175,] -13.32505892 2.45731364
[176,] 45.47736854 -13.32505892
[177,] -30.52694100 45.47736854
[178,] 21.90838011 -30.52694100
[179,] -2.98824973 21.90838011
[180,] 26.62110702 -2.98824973
[181,] 13.47671632 26.62110702
[182,] 30.03031344 13.47671632
[183,] 5.84938438 30.03031344
[184,] 33.25038743 5.84938438
[185,] 10.69494205 33.25038743
[186,] 15.98660362 10.69494205
[187,] -34.07412178 15.98660362
[188,] 5.43790076 -34.07412178
[189,] -14.36694115 5.43790076
[190,] 5.31679967 -14.36694115
[191,] 25.49567912 5.31679967
[192,] 18.80906332 25.49567912
[193,] 27.37961790 18.80906332
[194,] 32.10376787 27.37961790
[195,] -20.79919978 32.10376787
[196,] -33.93316206 -20.79919978
[197,] 0.65255484 -33.93316206
[198,] 14.16273303 0.65255484
[199,] -14.68253031 14.16273303
[200,] 2.28579246 -14.68253031
[201,] 18.16915941 2.28579246
[202,] 36.40413365 18.16915941
[203,] 3.98585699 36.40413365
[204,] 9.55190160 3.98585699
[205,] -33.09100824 9.55190160
[206,] 20.02484920 -33.09100824
[207,] 46.08435499 20.02484920
[208,] -23.07683142 46.08435499
[209,] -31.07309844 -23.07683142
[210,] 5.29938277 -31.07309844
[211,] -5.98194315 5.29938277
[212,] -46.41721452 -5.98194315
[213,] -8.13925360 -46.41721452
[214,] 9.27687003 -8.13925360
[215,] 25.98168899 9.27687003
[216,] 2.65227445 25.98168899
[217,] 2.79197813 2.65227445
[218,] -39.09306167 2.79197813
[219,] -40.41225991 -39.09306167
[220,] 24.22476338 -40.41225991
[221,] 108.47963616 24.22476338
[222,] 6.49540030 108.47963616
[223,] -38.54427470 6.49540030
[224,] -22.51174476 -38.54427470
[225,] -22.36750193 -22.51174476
[226,] 50.73025886 -22.36750193
[227,] 16.02078199 50.73025886
[228,] 28.99433457 16.02078199
[229,] -1.45753389 28.99433457
[230,] 0.70879660 -1.45753389
[231,] -16.64259605 0.70879660
[232,] 44.59350340 -16.64259605
[233,] -37.75925901 44.59350340
[234,] -25.08483885 -37.75925901
[235,] 23.55296014 -25.08483885
[236,] -27.16561645 23.55296014
[237,] -43.21404130 -27.16561645
[238,] 35.26584123 -43.21404130
[239,] -0.16388108 35.26584123
[240,] -13.98254702 -0.16388108
[241,] -16.44679430 -13.98254702
[242,] 4.91105550 -16.44679430
[243,] -19.51125322 4.91105550
[244,] 39.78828005 -19.51125322
[245,] -41.34916593 39.78828005
[246,] -15.25304146 -41.34916593
[247,] -1.37863143 -15.25304146
[248,] 13.59201106 -1.37863143
[249,] 10.92879904 13.59201106
[250,] 42.08181699 10.92879904
[251,] -21.34220185 42.08181699
[252,] -47.94142822 -21.34220185
[253,] 30.43275081 -47.94142822
[254,] -45.86041874 30.43275081
[255,] -32.77415821 -45.86041874
[256,] -35.09483687 -32.77415821
[257,] -4.97227791 -35.09483687
[258,] -13.24479607 -4.97227791
[259,] -29.86492772 -13.24479607
[260,] 18.14917663 -29.86492772
[261,] -35.76172157 18.14917663
[262,] -36.87210102 -35.76172157
[263,] -4.17022260 -36.87210102
[264,] -39.51897520 -4.17022260
[265,] -9.64559245 -39.51897520
[266,] -13.11545891 -9.64559245
[267,] -17.69463329 -13.11545891
[268,] -36.31116881 -17.69463329
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -56.19058213 -36.84475830
2 -17.91642551 -56.19058213
3 9.13849596 -17.91642551
4 -14.12612995 9.13849596
5 -2.20667733 -14.12612995
6 -14.52882978 -2.20667733
7 -15.28499619 -14.52882978
8 30.79483619 -15.28499619
9 2.73867194 30.79483619
10 -40.88604143 2.73867194
11 -16.71946194 -40.88604143
12 10.38720005 -16.71946194
13 -9.62588356 10.38720005
14 9.65902228 -9.62588356
15 17.72059495 9.65902228
16 -1.32356631 17.72059495
17 -23.09586068 -1.32356631
18 19.98472388 -23.09586068
19 -0.67226647 19.98472388
20 -13.42304633 -0.67226647
21 -11.62460046 -13.42304633
22 10.36540161 -11.62460046
23 -9.93627425 10.36540161
24 9.98767394 -9.93627425
25 -3.98981002 9.98767394
26 16.50308103 -3.98981002
27 21.31042548 16.50308103
28 -2.29038862 21.31042548
29 -6.99806483 -2.29038862
30 -24.90971089 -6.99806483
31 1.76239399 -24.90971089
32 15.79831358 1.76239399
33 -10.36843728 15.79831358
34 22.41653909 -10.36843728
35 -13.07017368 22.41653909
36 -6.01911478 -13.07017368
37 18.56147553 -6.01911478
38 18.78770092 18.56147553
39 33.39693416 18.78770092
40 10.46091461 33.39693416
41 -13.88774960 10.46091461
42 -6.89829447 -13.88774960
43 17.06931690 -6.89829447
44 47.11509998 17.06931690
45 20.03311946 47.11509998
46 9.58094036 20.03311946
47 2.92450155 9.58094036
48 10.36456370 2.92450155
49 4.06587396 10.36456370
50 -0.84517900 4.06587396
51 38.70522435 -0.84517900
52 -11.85967017 38.70522435
53 17.68667019 -11.85967017
54 12.45625758 17.68667019
55 -34.83138589 12.45625758
56 13.04739690 -34.83138589
57 3.14032138 13.04739690
58 33.26791133 3.14032138
59 -19.77080257 33.26791133
60 8.41160367 -19.77080257
61 4.04833736 8.41160367
62 17.18681524 4.04833736
63 12.30660672 17.18681524
64 -21.21742776 12.30660672
65 30.46493467 -21.21742776
66 10.82765261 30.46493467
67 15.47587487 10.82765261
68 14.39254713 15.47587487
69 -27.24473494 14.39254713
70 10.75258745 -27.24473494
71 17.06465201 10.75258745
72 20.44532974 17.06465201
73 18.66491773 20.44532974
74 17.14342587 18.66491773
75 -1.73019579 17.14342587
76 14.75416831 -1.73019579
77 -5.65801144 14.75416831
78 17.09996454 -5.65801144
79 4.34387453 17.09996454
80 24.37239382 4.34387453
81 -11.86570234 24.37239382
82 27.86783647 -11.86570234
83 20.52638723 27.86783647
84 11.03274162 20.52638723
85 -5.27145952 11.03274162
86 -22.59333174 -5.27145952
87 -17.75983606 -22.59333174
88 9.78679761 -17.75983606
89 -12.39571434 9.78679761
90 -0.55850891 -12.39571434
91 22.38706333 -0.55850891
92 -0.59887568 22.38706333
93 -24.54807729 -0.59887568
94 0.36115157 -24.54807729
95 -11.38549229 0.36115157
96 -17.73539368 -11.38549229
97 -0.06468978 -17.73539368
98 21.29499344 -0.06468978
99 4.25486274 21.29499344
100 14.07657963 4.25486274
101 -15.61452592 14.07657963
102 24.58839788 -15.61452592
103 -20.61567856 24.58839788
104 -76.72934467 -20.61567856
105 -21.51471482 -76.72934467
106 -1.54493615 -21.51471482
107 21.46992446 -1.54493615
108 31.87234680 21.46992446
109 -73.35776541 31.87234680
110 -73.17534842 -73.35776541
111 -73.18241791 -73.17534842
112 -42.09592615 -73.18241791
113 -66.78144355 -42.09592615
114 -17.22984120 -66.78144355
115 -5.71123680 -17.22984120
116 24.07961306 -5.71123680
117 12.05271150 24.07961306
118 2.39439934 12.05271150
119 8.55983969 2.39439934
120 -30.04818311 8.55983969
121 18.38124953 -30.04818311
122 18.25057590 18.38124953
123 -14.03414072 18.25057590
124 -23.42206581 -14.03414072
125 5.09923575 -23.42206581
126 -4.96554915 5.09923575
127 -12.53950967 -4.96554915
128 9.27243426 -12.53950967
129 42.47914560 9.27243426
130 -26.08941775 42.47914560
131 40.43896864 -26.08941775
132 0.90423117 40.43896864
133 9.28287661 0.90423117
134 23.30227929 9.28287661
135 -1.91339238 23.30227929
136 4.32168197 -1.91339238
137 -0.91914182 4.32168197
138 17.39674224 -0.91914182
139 -16.34473571 17.39674224
140 17.26470919 -16.34473571
141 9.96723481 17.26470919
142 10.19234911 9.96723481
143 29.31432816 10.19234911
144 -4.80661443 29.31432816
145 3.69949019 -4.80661443
146 10.65442357 3.69949019
147 8.44272222 10.65442357
148 3.22069489 8.44272222
149 32.55701950 3.22069489
150 18.86981485 32.55701950
151 18.57079793 18.86981485
152 -26.18914059 18.57079793
153 14.77168036 -26.18914059
154 -13.31687856 14.77168036
155 33.51248140 -13.31687856
156 -1.77729347 33.51248140
157 -29.93220194 -1.77729347
158 -6.22714707 -29.93220194
159 22.67354586 -6.22714707
160 -23.23892535 22.67354586
161 -0.68157019 -23.23892535
162 15.37746379 -0.68157019
163 13.00208041 15.37746379
164 -7.53862037 13.00208041
165 32.58948817 -7.53862037
166 40.18330517 32.58948817
167 -14.81703308 40.18330517
168 32.97086061 -14.81703308
169 -61.75610144 32.97086061
170 14.41022905 -61.75610144
171 28.32501098 14.41022905
172 -36.37909686 28.32501098
173 -15.60103350 -36.37909686
174 2.45731364 -15.60103350
175 -13.32505892 2.45731364
176 45.47736854 -13.32505892
177 -30.52694100 45.47736854
178 21.90838011 -30.52694100
179 -2.98824973 21.90838011
180 26.62110702 -2.98824973
181 13.47671632 26.62110702
182 30.03031344 13.47671632
183 5.84938438 30.03031344
184 33.25038743 5.84938438
185 10.69494205 33.25038743
186 15.98660362 10.69494205
187 -34.07412178 15.98660362
188 5.43790076 -34.07412178
189 -14.36694115 5.43790076
190 5.31679967 -14.36694115
191 25.49567912 5.31679967
192 18.80906332 25.49567912
193 27.37961790 18.80906332
194 32.10376787 27.37961790
195 -20.79919978 32.10376787
196 -33.93316206 -20.79919978
197 0.65255484 -33.93316206
198 14.16273303 0.65255484
199 -14.68253031 14.16273303
200 2.28579246 -14.68253031
201 18.16915941 2.28579246
202 36.40413365 18.16915941
203 3.98585699 36.40413365
204 9.55190160 3.98585699
205 -33.09100824 9.55190160
206 20.02484920 -33.09100824
207 46.08435499 20.02484920
208 -23.07683142 46.08435499
209 -31.07309844 -23.07683142
210 5.29938277 -31.07309844
211 -5.98194315 5.29938277
212 -46.41721452 -5.98194315
213 -8.13925360 -46.41721452
214 9.27687003 -8.13925360
215 25.98168899 9.27687003
216 2.65227445 25.98168899
217 2.79197813 2.65227445
218 -39.09306167 2.79197813
219 -40.41225991 -39.09306167
220 24.22476338 -40.41225991
221 108.47963616 24.22476338
222 6.49540030 108.47963616
223 -38.54427470 6.49540030
224 -22.51174476 -38.54427470
225 -22.36750193 -22.51174476
226 50.73025886 -22.36750193
227 16.02078199 50.73025886
228 28.99433457 16.02078199
229 -1.45753389 28.99433457
230 0.70879660 -1.45753389
231 -16.64259605 0.70879660
232 44.59350340 -16.64259605
233 -37.75925901 44.59350340
234 -25.08483885 -37.75925901
235 23.55296014 -25.08483885
236 -27.16561645 23.55296014
237 -43.21404130 -27.16561645
238 35.26584123 -43.21404130
239 -0.16388108 35.26584123
240 -13.98254702 -0.16388108
241 -16.44679430 -13.98254702
242 4.91105550 -16.44679430
243 -19.51125322 4.91105550
244 39.78828005 -19.51125322
245 -41.34916593 39.78828005
246 -15.25304146 -41.34916593
247 -1.37863143 -15.25304146
248 13.59201106 -1.37863143
249 10.92879904 13.59201106
250 42.08181699 10.92879904
251 -21.34220185 42.08181699
252 -47.94142822 -21.34220185
253 30.43275081 -47.94142822
254 -45.86041874 30.43275081
255 -32.77415821 -45.86041874
256 -35.09483687 -32.77415821
257 -4.97227791 -35.09483687
258 -13.24479607 -4.97227791
259 -29.86492772 -13.24479607
260 18.14917663 -29.86492772
261 -35.76172157 18.14917663
262 -36.87210102 -35.76172157
263 -4.17022260 -36.87210102
264 -39.51897520 -4.17022260
265 -9.64559245 -39.51897520
266 -13.11545891 -9.64559245
267 -17.69463329 -13.11545891
268 -36.31116881 -17.69463329
> 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/7xoda1358278485.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/8dq6b1358278485.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/9tjwd1358278485.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/10y06h1358278485.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11rkox1358278485.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/129x221358278485.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/139m9j1358278485.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14pgev1358278485.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/15guyi1358278485.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/16i2i91358278485.tab")
+ }
>
> try(system("convert tmp/13k4u1358278485.ps tmp/13k4u1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/24xsy1358278485.ps tmp/24xsy1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hf3v1358278485.ps tmp/3hf3v1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/4t2q21358278485.ps tmp/4t2q21358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xcgx1358278485.ps tmp/5xcgx1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jryo1358278485.ps tmp/6jryo1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xoda1358278485.ps tmp/7xoda1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dq6b1358278485.ps tmp/8dq6b1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tjwd1358278485.ps tmp/9tjwd1358278485.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y06h1358278485.ps tmp/10y06h1358278485.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.405 1.256 15.746