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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+ ,-2
+ ,2
+ ,-11
+ ,7
+ ,57
+ ,1
+ ,5
+ ,-11
+ ,6
+ ,57
+ ,1
+ ,6
+ ,-12
+ ,3
+ ,57
+ ,3
+ ,6
+ ,-10
+ ,10
+ ,55
+ ,3
+ ,3
+ ,-15
+ ,0
+ ,65
+ ,1
+ ,4
+ ,-15
+ ,-2
+ ,65
+ ,1
+ ,7
+ ,-15
+ ,-1
+ ,64
+ ,0
+ ,5
+ ,-13
+ ,2
+ ,60
+ ,2
+ ,6
+ ,-8
+ ,8
+ ,43
+ ,2
+ ,1
+ ,-13
+ ,-6
+ ,47
+ ,-1
+ ,3
+ ,-9
+ ,-4
+ ,40
+ ,1
+ ,6
+ ,-7
+ ,4
+ ,31
+ ,0
+ ,0
+ ,-4
+ ,7
+ ,27
+ ,1
+ ,3
+ ,-4
+ ,3
+ ,24
+ ,1
+ ,4
+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,-2
+ ,-5
+ ,5
+ ,-2
+ ,2
+ ,-1
+ ,-1
+ ,6
+ ,0
+ ,2
+ ,1
+ ,5
+ ,5
+ ,1
+ ,2
+ ,-3
+ ,0
+ ,12
+ ,-1
+ ,3
+ ,-4
+ ,-6
+ ,8
+ ,-2
+ ,-1
+ ,-9
+ ,-13
+ ,17
+ ,-1
+ ,-4
+ ,-9
+ ,-15
+ ,22
+ ,-1
+ ,4
+ ,-7
+ ,-8
+ ,24
+ ,1
+ ,5
+ ,-14
+ ,-20
+ ,36
+ ,-2
+ ,3
+ ,-12
+ ,-10
+ ,31
+ ,-5
+ ,-1
+ ,-16
+ ,-22
+ ,34
+ ,-5
+ ,-4
+ ,-20
+ ,-25
+ ,47
+ ,-6
+ ,0
+ ,-12
+ ,-10
+ ,33
+ ,-4
+ ,-1
+ ,-12
+ ,-8
+ ,35
+ ,-3
+ ,-1
+ ,-10
+ ,-9
+ ,31
+ ,-3
+ ,3
+ ,-10
+ ,-5
+ ,35
+ ,-1
+ ,2
+ ,-13
+ ,-7
+ ,39
+ ,-2
+ ,-4
+ ,-16
+ ,-11
+ ,46
+ ,-3
+ ,-3
+ ,-14
+ ,-11
+ ,40
+ ,-3
+ ,-1
+ ,-17
+ ,-16
+ ,50
+ ,-3
+ ,3
+ ,-24
+ ,-28
+ ,62
+ ,-5
+ ,-2)
+ ,dim=c(5
+ ,335)
+ ,dimnames=list(c('Csmvertr'
+ ,'econs'
+ ,'werkloosh'
+ ,'finsit'
+ ,'spaarverm')
+ ,1:335))
> y <- array(NA,dim=c(5,335),dimnames=list(c('Csmvertr','econs','werkloosh','finsit','spaarverm'),1:335))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Csmvertr econs werkloosh finsit spaarverm
1 -28 -25 37 -16 -33
2 -26 -23 33 -15 -32
3 -27 -24 36 -16 -32
4 -26 -24 37 -14 -31
5 -27 -25 39 -14 -31
6 -27 -25 39 -14 -32
7 -27 -24 37 -16 -32
8 -28 -24 37 -17 -33
9 -26 -22 36 -15 -31
10 -13 1 23 -9 -21
11 -13 -5 21 -9 -17
12 -14 -10 24 -7 -14
13 -12 -10 25 -4 -10
14 -16 -15 29 -9 -13
15 -16 -13 24 -8 -19
16 -12 -11 22 -6 -10
17 -15 -15 28 -5 -13
18 -18 -15 39 -7 -11
19 -17 -16 36 -6 -9
20 -10 -4 32 -1 -1
21 -9 -5 27 -2 -3
22 -13 -9 33 -1 -7
23 -15 -14 36 -3 -6
24 -12 -11 34 -2 -1
25 -13 -7 34 -2 -11
26 -10 -7 31 -1 -3
27 -13 -9 37 -2 -1
28 -11 -5 36 -1 -2
29 -12 -10 35 0 -2
30 -10 -9 32 1 -2
31 -13 -10 35 -1 -4
32 -12 -8 36 -1 -1
33 -11 -9 35 0 0
34 -11 -10 32 0 -3
35 -11 -10 28 1 -4
36 -8 -5 24 1 -4
37 -7 -6 25 2 -2
38 -10 -10 29 1 -3
39 -8 -10 28 2 4
40 -8 -9 25 1 3
41 -7 -10 22 0 3
42 -7 -8 22 2 -1
43 -6 -8 22 1 5
44 -8 -8 23 0 -2
45 -6 -4 22 1 2
46 -3 2 14 3 -1
47 1 3 7 2 6
48 0 2 9 4 4
49 -3 -3 12 1 -2
50 0 -1 9 4 4
51 0 1 6 2 3
52 -1 2 8 3 0
53 -1 -4 10 2 7
54 0 0 8 3 5
55 1 5 9 5 3
56 0 -1 11 5 9
57 2 3 6 3 7
58 3 6 6 4 8
59 2 7 9 5 8
60 4 7 7 5 10
61 3 3 8 4 11
62 4 8 2 6 5
63 3 3 2 5 9
64 1 0 7 4 7
65 2 1 6 4 8
66 4 4 4 7 12
67 3 4 8 8 10
68 2 1 9 5 10
69 -4 -17 11 4 8
70 -5 -16 14 1 11
71 -5 -13 18 2 10
72 -7 -15 23 0 8
73 -13 -31 25 -2 5
74 -11 -26 31 -1 12
75 -3 -5 18 2 10
76 -3 -5 19 3 8
77 -5 -6 23 2 8
78 -4 -5 24 2 10
79 -4 -5 25 5 12
80 -4 -7 26 4 13
81 -5 -6 27 5 7
82 -4 -8 23 2 13
83 -5 -6 27 6 11
84 -6 -12 34 7 13
85 -9 -15 34 1 11
86 -10 -15 37 1 10
87 -11 -16 41 0 15
88 -13 -19 43 -2 11
89 -13 -23 38 -1 10
90 -13 -23 39 -1 12
91 -11 -21 35 1 14
92 -12 -21 38 0 11
93 -14 -25 40 0 8
94 -20 -34 49 -1 3
95 -17 -30 51 -1 15
96 -16 -27 48 -1 11
97 -24 -40 54 -4 0
98 -24 -40 56 -6 4
99 -22 -34 56 -3 7
100 -25 -43 61 -7 12
101 -24 -39 57 -4 5
102 -25 -40 57 -5 2
103 -24 -40 52 -3 0
104 -25 -40 58 -5 5
105 -24 -35 60 -6 4
106 -26 -43 62 -7 7
107 -25 -44 48 -6 0
108 -24 -38 50 -8 -1
109 -22 -37 50 -5 3
110 -20 -31 48 -5 2
111 -14 -20 40 -3 7
112 -13 -22 35 -2 6
113 -10 -9 33 -1 3
114 -10 -11 34 1 3
115 -11 -8 34 -1 1
116 -6 -3 28 -1 8
117 -2 3 26 3 10
118 -3 6 23 2 6
119 -2 -3 20 4 11
120 -4 -8 20 3 6
121 -7 -8 26 1 6
122 -8 -10 28 0 3
123 -7 -9 29 2 10
124 -4 -7 25 2 12
125 -7 -12 27 2 9
126 -5 -9 24 3 12
127 -6 -8 26 2 10
128 -12 -19 38 1 6
129 -12 -21 38 0 8
130 -16 -24 45 -4 11
131 -20 -30 53 -9 11
132 -16 -28 44 -6 11
133 -16 -27 43 -7 14
134 -18 -26 47 -6 8
135 -15 -27 40 -6 12
136 -12 -23 34 -3 11
137 -13 -26 38 -3 14
138 -13 -23 39 -4 15
139 -12 -21 35 -5 15
140 -11 -20 35 -4 14
141 -9 -14 36 -3 16
142 -9 -16 25 -5 9
143 -8 -17 24 -3 13
144 -8 -18 29 -2 15
145 -15 -25 44 -3 14
146 -16 -26 43 -5 11
147 -21 -36 57 -3 14
148 -21 -35 56 -3 10
149 -16 -27 47 -4 13
150 -13 -22 41 -2 15
151 -12 -25 38 -3 20
152 -8 -17 33 -2 19
153 -9 -14 36 -3 16
154 -1 -7 22 2 22
155 -5 -12 27 1 19
156 -9 -17 32 -1 16
157 -1 -8 21 2 23
158 3 -2 14 5 23
159 2 -1 10 3 16
160 3 1 14 3 23
161 5 0 12 3 30
162 5 -2 10 1 31
163 3 -5 12 3 24
164 2 -4 9 1 20
165 1 -9 14 2 24
166 -4 -16 23 2 23
167 1 -7 17 1 25
168 1 -7 16 2 25
169 6 3 7 4 23
170 3 -2 9 3 21
171 2 -3 9 3 16
172 2 -6 14 3 26
173 2 -7 12 2 23
174 -8 -24 23 -1 15
175 0 -13 12 1 23
176 -2 -14 15 3 20
177 3 -7 6 4 22
178 5 -1 6 4 24
179 8 5 1 6 22
180 8 6 3 4 24
181 9 5 -1 6 24
182 11 5 -4 6 29
183 13 9 -6 8 29
184 12 10 -9 4 25
185 13 14 -13 8 16
186 15 19 -13 10 18
187 13 18 -10 9 13
188 16 16 -12 12 22
189 10 8 -9 9 15
190 14 10 -15 11 20
191 14 12 -14 11 19
192 15 13 -18 11 18
193 13 15 -13 11 13
194 8 3 -2 11 17
195 7 2 -1 9 17
196 3 -2 5 8 13
197 3 1 8 6 14
198 4 1 6 7 13
199 4 -1 7 8 17
200 0 -6 15 6 17
201 -4 -13 23 5 15
202 -14 -25 43 2 9
203 -18 -26 60 3 10
204 -8 -9 36 3 9
205 -1 1 28 7 14
206 1 3 23 8 18
207 2 6 23 7 18
208 0 2 22 7 12
209 1 5 22 6 16
210 0 5 24 6 12
211 -1 0 32 7 19
212 -3 -5 27 5 13
213 -3 -4 27 5 12
214 -3 -2 27 5 13
215 -4 -1 29 4 11
216 -8 -8 38 4 10
217 -9 -16 40 4 16
218 -13 -19 45 1 12
219 -18 -28 50 -1 6
220 -11 -11 43 3 8
221 -9 -4 44 4 6
222 -10 -9 44 3 8
223 -13 -12 49 2 8
224 -11 -10 42 1 9
225 -5 -2 36 4 13
226 -15 -13 57 3 8
227 -6 0 42 5 11
228 -6 0 39 6 8
229 -3 4 33 6 10
230 -1 7 32 6 15
231 -3 5 34 6 12
232 -4 2 37 6 13
233 -6 -2 38 5 12
234 0 6 28 6 15
235 -4 -3 31 5 13
236 -2 1 28 6 13
237 -2 0 30 5 16
238 -6 -7 39 7 14
239 -7 -6 38 4 12
240 -6 -4 39 5 15
241 -6 -4 38 6 14
242 -3 -2 37 6 19
243 -2 2 32 5 16
244 -5 -5 32 3 16
245 -11 -15 44 2 11
246 -11 -16 43 3 13
247 -11 -18 42 3 12
248 -10 -13 38 2 11
249 -14 -23 37 0 6
250 -8 -10 35 4 9
251 -9 -10 37 4 6
252 -5 -6 33 5 15
253 -1 -3 24 6 17
254 -2 -4 24 6 13
255 -5 -7 31 5 12
256 -4 -7 25 5 13
257 -6 -7 28 3 10
258 -2 -3 24 5 14
259 -2 0 25 5 13
260 -2 -5 16 5 10
261 -2 -3 17 3 11
262 2 3 11 6 12
263 1 2 12 6 7
264 -8 -7 39 4 11
265 -1 -1 19 6 9
266 1 0 14 5 13
267 -1 -3 15 4 12
268 2 4 7 5 5
269 2 2 12 5 13
270 1 3 12 4 11
271 -1 0 14 3 8
272 -2 -10 9 2 8
273 -2 -10 8 3 8
274 -1 -9 4 2 8
275 -8 -22 7 -1 0
276 -4 -16 3 0 3
277 -6 -18 5 -2 0
278 -3 -14 0 1 -1
279 -3 -12 -2 -2 -1
280 -7 -17 6 -2 -4
281 -9 -23 11 -2 1
282 -11 -28 9 -6 -1
283 -13 -31 17 -4 0
284 -11 -21 21 -2 -1
285 -9 -19 21 0 6
286 -17 -22 41 -5 0
287 -22 -22 57 -4 -3
288 -25 -25 65 -5 -3
289 -20 -16 68 -1 4
290 -24 -22 73 -2 1
291 -24 -21 71 -4 0
292 -22 -10 71 -1 -4
293 -19 -7 70 1 -2
294 -18 -5 69 1 3
295 -17 -4 65 -2 2
296 -11 7 57 1 5
297 -11 6 57 1 6
298 -12 3 57 3 6
299 -10 10 55 3 3
300 -15 0 65 1 4
301 -15 -2 65 1 7
302 -15 -1 64 0 5
303 -13 2 60 2 6
304 -8 8 43 2 1
305 -13 -6 47 -1 3
306 -9 -4 40 1 6
307 -7 4 31 0 0
308 -4 7 27 1 3
309 -4 3 24 1 4
310 -2 3 23 3 7
311 0 8 17 2 6
312 -2 3 16 0 6
313 -3 -3 15 0 6
314 1 4 8 3 6
315 -2 -5 5 -2 2
316 -1 -1 6 0 2
317 1 5 5 1 2
318 -3 0 12 -1 3
319 -4 -6 8 -2 -1
320 -9 -13 17 -1 -4
321 -9 -15 22 -1 4
322 -7 -8 24 1 5
323 -14 -20 36 -2 3
324 -12 -10 31 -5 -1
325 -16 -22 34 -5 -4
326 -20 -25 47 -6 0
327 -12 -10 33 -4 -1
328 -12 -8 35 -3 -1
329 -10 -9 31 -3 3
330 -10 -5 35 -1 2
331 -13 -7 39 -2 -4
332 -16 -11 46 -3 -3
333 -14 -11 40 -3 -1
334 -17 -16 50 -3 3
335 -24 -28 62 -5 -2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) econs werkloosh finsit spaarverm
0.02541 0.24854 -0.25171 0.24939 0.24828
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.96527 -0.23870 0.02281 0.27336 0.98795
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.025409 0.043449 0.585 0.559
econs 0.248542 0.002722 91.314 <2e-16 ***
werkloosh -0.251714 0.001356 -185.653 <2e-16 ***
finsit 0.249389 0.008402 29.683 <2e-16 ***
spaarverm 0.248278 0.002843 87.330 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3535 on 330 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 5.362e+04 on 4 and 330 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.19140131 0.382802629 0.8085986853
[2,] 0.20204710 0.404094196 0.7979529020
[3,] 0.10743475 0.214869503 0.8925652487
[4,] 0.06126928 0.122538561 0.9387307197
[5,] 0.14819895 0.296397908 0.8518010460
[6,] 0.08906427 0.178128539 0.9109357304
[7,] 0.09377341 0.187546816 0.9062265921
[8,] 0.07101374 0.142027480 0.9289862599
[9,] 0.05445831 0.108916627 0.9455416865
[10,] 0.03943442 0.078868847 0.9605655763
[11,] 0.12020811 0.240416229 0.8797918857
[12,] 0.15949344 0.318986877 0.8405065614
[13,] 0.20362197 0.407243931 0.7963780344
[14,] 0.19051600 0.381031995 0.8094840027
[15,] 0.24162736 0.483254711 0.7583726444
[16,] 0.19881572 0.397631447 0.8011842766
[17,] 0.15675883 0.313517654 0.8432411729
[18,] 0.27112893 0.542257856 0.7288710722
[19,] 0.33891032 0.677820636 0.6610896819
[20,] 0.44606847 0.892136935 0.5539315323
[21,] 0.40262620 0.805252397 0.5973738013
[22,] 0.37000862 0.740017246 0.6299913769
[23,] 0.38859133 0.777182669 0.6114086656
[24,] 0.44323365 0.886467305 0.5567663474
[25,] 0.43014957 0.860299149 0.5698504257
[26,] 0.38147576 0.762951514 0.6185242431
[27,] 0.34193064 0.683861273 0.6580693633
[28,] 0.64628530 0.707429402 0.3537147011
[29,] 0.59679661 0.806406779 0.4032033896
[30,] 0.70610436 0.587791286 0.2938956429
[31,] 0.66752375 0.664952503 0.3324762514
[32,] 0.61988679 0.760226426 0.3801132128
[33,] 0.66334657 0.673306859 0.3366534297
[34,] 0.64053749 0.718925020 0.3594625100
[35,] 0.59657601 0.806847973 0.4034239867
[36,] 0.54826919 0.903461628 0.4517308142
[37,] 0.50802326 0.983953475 0.4919767374
[38,] 0.48843933 0.976878658 0.5115606710
[39,] 0.60394028 0.792119433 0.3960597164
[40,] 0.55793400 0.884132003 0.4420660015
[41,] 0.54769316 0.904613682 0.4523068410
[42,] 0.74892369 0.502152612 0.2510763061
[43,] 0.73841524 0.523169511 0.2615847557
[44,] 0.71024427 0.579511459 0.2897557294
[45,] 0.72573912 0.548521754 0.2742608770
[46,] 0.69609060 0.607818807 0.3039094037
[47,] 0.66138778 0.677224445 0.3386122224
[48,] 0.62392525 0.752149491 0.3760747456
[49,] 0.68232931 0.635341386 0.3176706929
[50,] 0.66090265 0.678194702 0.3390973508
[51,] 0.62123951 0.757520976 0.3787604880
[52,] 0.72235297 0.555294052 0.2776470261
[53,] 0.71598417 0.568031660 0.2840158299
[54,] 0.75252843 0.494943147 0.2474715733
[55,] 0.74448407 0.511031863 0.2555159315
[56,] 0.85661121 0.286777583 0.1433887915
[57,] 0.83297578 0.334048447 0.1670242234
[58,] 0.81704107 0.365917860 0.1829589301
[59,] 0.88929822 0.221403561 0.1107017805
[60,] 0.90046615 0.199067710 0.0995338548
[61,] 0.89326015 0.213479693 0.1067398466
[62,] 0.87953853 0.240922938 0.1204614688
[63,] 0.89638058 0.207238837 0.1036194186
[64,] 0.88284340 0.234313207 0.1171566037
[65,] 0.90664284 0.186714310 0.0933571551
[66,] 0.89337269 0.213254612 0.1066273060
[67,] 0.90270300 0.194594004 0.0972970018
[68,] 0.89128339 0.217433216 0.1087166078
[69,] 0.88452525 0.230949497 0.1154747484
[70,] 0.87128197 0.257436059 0.1287180296
[71,] 0.86663650 0.266726997 0.1333634986
[72,] 0.90916879 0.181662428 0.0908312140
[73,] 0.89408176 0.211836477 0.1059182386
[74,] 0.88971588 0.220568232 0.1102841161
[75,] 0.87194558 0.256108840 0.1280544198
[76,] 0.94742955 0.105140901 0.0525704505
[77,] 0.95973157 0.080536850 0.0402684252
[78,] 0.95624643 0.087507139 0.0437535697
[79,] 0.95237863 0.095242742 0.0476213710
[80,] 0.95636107 0.087277859 0.0436389295
[81,] 0.95338699 0.093226023 0.0466130116
[82,] 0.94439864 0.111202728 0.0556013641
[83,] 0.93967740 0.120645197 0.0603225985
[84,] 0.96584387 0.068312270 0.0341561350
[85,] 0.95877982 0.082440360 0.0412201801
[86,] 0.95423990 0.091520191 0.0457600957
[87,] 0.94775969 0.104480610 0.0522403051
[88,] 0.94146525 0.117069506 0.0585347532
[89,] 0.93715830 0.125683393 0.0628416967
[90,] 0.93944009 0.121119824 0.0605599120
[91,] 0.94367526 0.112649488 0.0563247442
[92,] 0.95410540 0.091789206 0.0458946028
[93,] 0.94968779 0.100624429 0.0503122147
[94,] 0.94667031 0.106659385 0.0533296927
[95,] 0.93742107 0.125157863 0.0625789314
[96,] 0.93674818 0.126503645 0.0632518223
[97,] 0.94513660 0.109726796 0.0548633981
[98,] 0.94273301 0.114533976 0.0572669881
[99,] 0.93857180 0.122856400 0.0614281998
[100,] 0.95275189 0.094496220 0.0472481099
[101,] 0.94795486 0.104090282 0.0520451412
[102,] 0.94304360 0.113912802 0.0569564009
[103,] 0.95220392 0.095592167 0.0477960835
[104,] 0.94357646 0.112847072 0.0564235361
[105,] 0.93922369 0.121552614 0.0607763068
[106,] 0.92904289 0.141914218 0.0709571092
[107,] 0.92574633 0.148507342 0.0742536711
[108,] 0.93070054 0.138598920 0.0692994599
[109,] 0.92013943 0.159721145 0.0798605725
[110,] 0.93986145 0.120277101 0.0601385504
[111,] 0.95970467 0.080590668 0.0402953338
[112,] 0.95240385 0.095192301 0.0475961505
[113,] 0.97489790 0.050204208 0.0251021040
[114,] 0.97153627 0.056927466 0.0284637330
[115,] 0.98641105 0.027177899 0.0135889493
[116,] 0.98781637 0.024367267 0.0121836334
[117,] 0.99093789 0.018124222 0.0090621111
[118,] 0.98879147 0.022417056 0.0112085279
[119,] 0.99024425 0.019511497 0.0097557483
[120,] 0.99138829 0.017223422 0.0086117108
[121,] 0.99344034 0.013119324 0.0065596618
[122,] 0.99733028 0.005339450 0.0026697249
[123,] 0.99761461 0.004770782 0.0023853911
[124,] 0.99754758 0.004904839 0.0024524194
[125,] 0.99911986 0.001760277 0.0008801386
[126,] 0.99895841 0.002083174 0.0010415869
[127,] 0.99874953 0.002500950 0.0012504749
[128,] 0.99864652 0.002706968 0.0013534838
[129,] 0.99851727 0.002965467 0.0014827336
[130,] 0.99839779 0.003204420 0.0016022102
[131,] 0.99810066 0.003798671 0.0018993356
[132,] 0.99835903 0.003281933 0.0016409665
[133,] 0.99826299 0.003474011 0.0017370057
[134,] 0.99821141 0.003577188 0.0017885941
[135,] 0.99805172 0.003896562 0.0019482809
[136,] 0.99775000 0.004500003 0.0022500017
[137,] 0.99842494 0.003150124 0.0015750620
[138,] 0.99861579 0.002768422 0.0013842112
[139,] 0.99834674 0.003306514 0.0016532572
[140,] 0.99852900 0.002941990 0.0014709952
[141,] 0.99814164 0.003716721 0.0018583606
[142,] 0.99810512 0.003789751 0.0018948756
[143,] 0.99830764 0.003384724 0.0016923621
[144,] 0.99851854 0.002962910 0.0014814550
[145,] 0.99845931 0.003081376 0.0015406881
[146,] 0.99843944 0.003121122 0.0015605609
[147,] 0.99835504 0.003289921 0.0016449604
[148,] 0.99803302 0.003933958 0.0019669789
[149,] 0.99829331 0.003413371 0.0017066857
[150,] 0.99782247 0.004355065 0.0021775326
[151,] 0.99723410 0.005531807 0.0027659037
[152,] 0.99649167 0.007016661 0.0035083306
[153,] 0.99589140 0.008217196 0.0041085979
[154,] 0.99522361 0.009552783 0.0047763915
[155,] 0.99405433 0.011891347 0.0059456737
[156,] 0.99553724 0.008925524 0.0044627620
[157,] 0.99440208 0.011195848 0.0055979240
[158,] 0.99393393 0.012132132 0.0060660659
[159,] 0.99490320 0.010193592 0.0050967959
[160,] 0.99641823 0.007163549 0.0035817746
[161,] 0.99551078 0.008978441 0.0044892207
[162,] 0.99521195 0.009576107 0.0047880537
[163,] 0.99448011 0.011039778 0.0055198891
[164,] 0.99398190 0.012036191 0.0060180954
[165,] 0.99299350 0.014013006 0.0070065031
[166,] 0.99507441 0.009851187 0.0049255937
[167,] 0.99494164 0.010116711 0.0050583557
[168,] 0.99506269 0.009874627 0.0049373136
[169,] 0.99580144 0.008397113 0.0041985566
[170,] 0.99515769 0.009684612 0.0048423059
[171,] 0.99429343 0.011413144 0.0057065719
[172,] 0.99288960 0.014220794 0.0071103971
[173,] 0.99290256 0.014194885 0.0070974425
[174,] 0.99127824 0.017443527 0.0087217637
[175,] 0.98955370 0.020892603 0.0104463013
[176,] 0.98741811 0.025163777 0.0125818885
[177,] 0.98560812 0.028783753 0.0143918767
[178,] 0.98415650 0.031687004 0.0158435020
[179,] 0.98072832 0.038543365 0.0192716827
[180,] 0.98378474 0.032430514 0.0162152570
[181,] 0.98707766 0.025844673 0.0129223365
[182,] 0.98621423 0.027571534 0.0137857672
[183,] 0.98316936 0.033661277 0.0168306386
[184,] 0.97954580 0.040908393 0.0204541966
[185,] 0.97533026 0.049339482 0.0246697410
[186,] 0.97078440 0.058431190 0.0292155950
[187,] 0.97030536 0.059389283 0.0296946417
[188,] 0.96858085 0.062838294 0.0314191469
[189,] 0.97771255 0.044574907 0.0222874536
[190,] 0.97542184 0.049156313 0.0245781565
[191,] 0.97240968 0.055180632 0.0275903161
[192,] 0.97052574 0.058948524 0.0294742620
[193,] 0.97498290 0.050034207 0.0250171037
[194,] 0.96986252 0.060274968 0.0301374839
[195,] 0.96795244 0.064095127 0.0320475636
[196,] 0.96693240 0.066135209 0.0330676045
[197,] 0.96549114 0.069017718 0.0345088592
[198,] 0.97257501 0.054849971 0.0274249857
[199,] 0.97705435 0.045891294 0.0229456470
[200,] 0.97241537 0.055169258 0.0275846290
[201,] 0.96943223 0.061135540 0.0305677698
[202,] 0.96537295 0.069254103 0.0346270515
[203,] 0.96269407 0.074611865 0.0373059324
[204,] 0.97321015 0.053579693 0.0267898466
[205,] 0.97989883 0.040202335 0.0201011675
[206,] 0.98496049 0.030079016 0.0150395082
[207,] 0.98276761 0.034464775 0.0172323875
[208,] 0.98024964 0.039500718 0.0197503589
[209,] 0.97592546 0.048149077 0.0240745383
[210,] 0.97148033 0.057039330 0.0285196652
[211,] 0.96640987 0.067180252 0.0335901260
[212,] 0.96764933 0.064701344 0.0323506721
[213,] 0.96344995 0.073100100 0.0365500498
[214,] 0.97062039 0.058759219 0.0293796094
[215,] 0.97892822 0.042143561 0.0210717806
[216,] 0.97543343 0.049133138 0.0245665688
[217,] 0.97689661 0.046206783 0.0231033913
[218,] 0.97695781 0.046084379 0.0230421893
[219,] 0.97329556 0.053408886 0.0267044428
[220,] 0.98099728 0.038005438 0.0190027192
[221,] 0.97892933 0.042141335 0.0210706674
[222,] 0.97716669 0.045666622 0.0228333109
[223,] 0.97238053 0.055238948 0.0276194742
[224,] 0.96872084 0.062558313 0.0312791565
[225,] 0.96229213 0.075415745 0.0377078725
[226,] 0.95691991 0.086160183 0.0430800917
[227,] 0.95627146 0.087457080 0.0437285399
[228,] 0.94804418 0.103911645 0.0519558226
[229,] 0.93827307 0.123453856 0.0617269282
[230,] 0.94086634 0.118267314 0.0591336572
[231,] 0.94001686 0.119966287 0.0599831434
[232,] 0.93040853 0.139182949 0.0695914745
[233,] 0.91964483 0.160710335 0.0803551674
[234,] 0.92483282 0.150334353 0.0751671764
[235,] 0.95790733 0.084185338 0.0420926691
[236,] 0.96409739 0.071805214 0.0359026072
[237,] 0.96188496 0.076230076 0.0381150382
[238,] 0.98173033 0.036539335 0.0182696674
[239,] 0.97774184 0.044516327 0.0222581637
[240,] 0.98306990 0.033860207 0.0169301037
[241,] 0.98218588 0.035628239 0.0178141194
[242,] 0.98267012 0.034659761 0.0173298805
[243,] 0.97884525 0.042309491 0.0211547454
[244,] 0.97817331 0.043653373 0.0218266866
[245,] 0.97330813 0.053383741 0.0266918707
[246,] 0.97116681 0.057666376 0.0288331878
[247,] 0.97545028 0.049099435 0.0245497176
[248,] 0.98136842 0.037263163 0.0186315817
[249,] 0.97997324 0.040053518 0.0200267588
[250,] 0.97960884 0.040782312 0.0203911559
[251,] 0.97800214 0.043995714 0.0219978569
[252,] 0.97283809 0.054323819 0.0271619093
[253,] 0.97448837 0.051023251 0.0255116253
[254,] 0.97463268 0.050734632 0.0253673162
[255,] 0.97762926 0.044741484 0.0223707421
[256,] 0.97461811 0.050763786 0.0253818928
[257,] 0.96854616 0.062907680 0.0314538400
[258,] 0.96833305 0.063333902 0.0316669510
[259,] 0.96327018 0.073459641 0.0367298206
[260,] 0.96309859 0.073802819 0.0369014093
[261,] 0.95835917 0.083281652 0.0416408262
[262,] 0.95158801 0.096823972 0.0484119859
[263,] 0.95435609 0.091287817 0.0456439085
[264,] 0.94671419 0.106571624 0.0532858122
[265,] 0.94892066 0.102158678 0.0510793388
[266,] 0.93892561 0.122148774 0.0610743872
[267,] 0.92836349 0.143273018 0.0716365090
[268,] 0.94691817 0.106163668 0.0530818340
[269,] 0.93405129 0.131897419 0.0659487097
[270,] 0.92559371 0.148812581 0.0744062906
[271,] 0.93500956 0.129980872 0.0649904360
[272,] 0.92599410 0.148011802 0.0740059009
[273,] 0.91743150 0.165136998 0.0825684992
[274,] 0.90384689 0.192306229 0.0961531144
[275,] 0.88369757 0.232604864 0.1163024322
[276,] 0.86338989 0.273220224 0.1366101119
[277,] 0.86791705 0.264165894 0.1320829470
[278,] 0.86657714 0.266845714 0.1334228569
[279,] 0.84482363 0.310352735 0.1551763676
[280,] 0.84883082 0.302338358 0.1511691788
[281,] 0.85364255 0.292714894 0.1463574469
[282,] 0.87585918 0.248281642 0.1241408211
[283,] 0.86499957 0.270000855 0.1350004277
[284,] 0.85179996 0.296400075 0.1482000373
[285,] 0.86588170 0.268236596 0.1341182978
[286,] 0.93388720 0.132225592 0.0661127959
[287,] 0.92630540 0.147389202 0.0736946011
[288,] 0.93621588 0.127568245 0.0637841224
[289,] 0.92192632 0.156147367 0.0780736836
[290,] 0.90839189 0.183216216 0.0916081082
[291,] 0.94341497 0.113170060 0.0565850302
[292,] 0.93118012 0.137639770 0.0688198849
[293,] 0.91763861 0.164722777 0.0823613887
[294,] 0.89388974 0.212220512 0.1061102560
[295,] 0.88485684 0.230286315 0.1151431576
[296,] 0.87605282 0.247894366 0.1239471830
[297,] 0.84227197 0.315456061 0.1577280306
[298,] 0.80616912 0.387661754 0.1938308769
[299,] 0.83250412 0.334991751 0.1674958754
[300,] 0.81607659 0.367846822 0.1839234112
[301,] 0.76960855 0.460782907 0.2303914533
[302,] 0.71698006 0.566039878 0.2830199392
[303,] 0.87521726 0.249565471 0.1247827357
[304,] 0.91562522 0.168749563 0.0843747813
[305,] 0.88428603 0.231427948 0.1157139739
[306,] 0.86614058 0.267718835 0.1338594174
[307,] 0.82168621 0.356627582 0.1783137908
[308,] 0.88101860 0.237962802 0.1189814008
[309,] 0.89616095 0.207678106 0.1038390529
[310,] 0.94741702 0.105165952 0.0525829762
[311,] 0.93195076 0.136098488 0.0680492440
[312,] 0.95263651 0.094726971 0.0473634857
[313,] 0.94411730 0.111765396 0.0558826979
[314,] 0.92511652 0.149766967 0.0748834834
[315,] 0.90055564 0.198888721 0.0994443607
[316,] 0.87653193 0.246936146 0.1234680731
[317,] 0.79916032 0.401679361 0.2008396807
[318,] 0.70326265 0.593474697 0.2967373487
[319,] 0.98104234 0.037915316 0.0189576580
[320,] 0.93893421 0.122131580 0.0610657900
> postscript(file="/var/fisher/rcomp/tmp/1uxo41355586930.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ruis1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xubs1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4lsge1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5makr1355586930.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 = 335
Frequency = 1
1 2 3 4 5
-0.3150430888 -0.3166505605 -0.0635770770 0.4410808309 0.1930511481
6 7 8 9 10
0.4413287749 0.1881368787 -0.3141962839 -0.0583287256 -0.0261970115
11 12 13 14 15
-0.0314809965 -0.2772384025 0.2331974145 0.4745441988 -0.0408338410
16 17 18 19 20
0.2253763738 0.2252734009 -0.0036499180 -0.2561938439 -0.4787256016
21 22 23 24 25
0.2571914892 -0.4946338568 -0.2462791671 0.0138883599 0.5024950055
26 27 28 29 30
0.5117429131 -0.7280545840 0.0249502540 -0.2334408842 0.5134856323
31 32 33 34 35
-0.4874964201 -0.4777001560 0.0214614565 0.2596948753 -0.7482725316
36 37 38 39 40
0.0021596171 0.7564715144 0.2551637974 0.0161172434 -0.4899001922
41 42 43 44 45
0.2528895566 0.2501368315 0.0098602812 0.2489068351 -0.2394764609
46 47 48 49 50
-0.4983880816 0.0025176452 -0.2477352051 0.9879520829 0.4978920118
51 52 53 54 55
-0.0072786189 -0.2569494431 0.2491787251 -0.0012527659 0.0055259944
56 57 58 59 60
-0.4894574212 0.2531368521 0.0098427979 -0.7329469509 0.2670698839
61 62 63 64 65
0.5140650459 -0.2480433772 -0.7490526457 0.0010888142 0.2525548260
66 67 68 69 70
-0.7377784413 -0.4837565750 0.2617522292 -0.0151120938 -0.5051778809
71 72 73 74 75
-0.2450608584 0.5059274064 0.2296451095 0.5098842179 -0.2334001034
76 77 78 79 80
0.2654798955 -0.2297326653 0.2768836313 -0.7161252981 0.0337850526
81 82 83 84 85
0.2772331531 0.0259640120 -0.9652665646 0.5420410955 0.2805588291
86 87 88 89 90
0.2839783233 -0.4526223715 0.2883216852 0.0228099451 -0.2220313527
91 92 93 94 95
-0.7213056618 0.0280582965 0.2704887110 0.2635733083 -0.2064999244
96 97 98 99 100
0.2858414987 0.5063980330 0.5154938583 -0.4687610874 -0.2171409498
101 102 103 104 105
-0.2283906393 0.0143738572 -0.2464190891 -0.4787450674 0.2796376533
106 107 108 109 110
0.2759611400 -0.5109376582 0.2482918675 0.2584713230 0.5120666045
111 112 113 114 115
0.0242219413 0.2616253899 0.0225898751 0.2726102211 -0.4776833212
116 117 118 119 120
0.0313775283 0.5425830873 -0.7156862792 0.0258869490 0.7593763217
121 122 123 124 125
-0.2315615225 0.7631732913 -0.4703769673 0.5291271447 0.0200999648
126 127 128 129 130
-0.4748912103 -0.4740612403 0.5229724088 0.7728911770 -0.4667599540
131 132 133 134 135
0.2851521786 0.7744741337 -0.2212258976 -0.2226359297 0.2707982782
136 137 138 139 140
0.2664549162 0.2741050758 -0.2186966016 -0.4732480255 0.2770979852
141 142 143 144 145
0.2916130431 0.2565661494 -0.2384943290 0.5226733914 -0.4641535951
146 147 148 149 150
-0.2237138438 -0.4579057080 0.0349484378 0.2857399208 -0.4625895167
151 152 153 154 155
-0.4641030907 0.2878763017 0.2916130431 0.2912090093 -0.2132870927
156 157 158 159 160
-0.4683939842 0.0397598323 0.0383400765 0.0196636564 -0.2085087194
161 162 163 164 165
-0.2013376130 0.0428200810 0.5310401760 0.0192448313 0.2780269206
166 167 168 169 170
-0.4684730111 0.5371955604 0.0360923941 0.2830195684 -0.2248960278
171 172 173 174 175
0.2650345119 -0.2135447604 0.5257918246 0.2542548799 0.2664354689
176 177 178 179 180
-0.4838257987 -0.2349927043 -0.2228023917 0.0251502282 0.2822589015
181 182 183 184 185
0.0251670630 0.0286370616 0.0322611065 0.0192441828 0.2551605364
186 187 188 189 190
0.0171148336 0.5115764511 0.5225670780 -0.2478407904 0.0046241085
191 192 193 194 195
0.0075308799 0.0004102779 0.0032833796 -0.2384647465 -0.2394299640
196 197 198 199 200
-0.4924768890 -0.2324614443 0.2629990604 -0.2307018903 -0.4754997949
201 202 203 204 205
0.0239531549 0.2785745305 0.3085873472 0.2905091395 0.5524284608
206 207 208 209 210
-0.4457258471 0.0580361465 0.2901575741 -0.1991909395 0.2973474793
211 212 213 214 215
0.5664385555 0.5390249866 0.5387602078 -0.2066022302 -0.2057722602
216 217 218 219 220
0.0477278081 0.0498292038 -0.2046956616 0.2791999499 -0.2021307320
221 222 223 224 225
0.5569524275 0.5524984126 -0.1939153811 -0.4518862991 0.3082125823
226 227 228 229 230
-0.1810505397 0.5685775489 0.3088793514 0.3078707406 0.0691414339
231 232 233 234 235
-0.1855129629 0.0669784946 -0.1894710898 0.3108280164 0.0487959985
236 237 238 239 240
0.0500952981 0.3066219454 0.3096212195 0.0540877432 -0.1855052032
241 242 243 244 245
-0.4383307427 0.5714823562 0.3129650457 -0.4484596938 0.5483091764
246 247 248 249 250
-0.2008068379 0.2928416444 -0.4590593695 -0.4851827140 0.0379483788
251 252 253 254 255
0.2862091708 -0.1987041266 0.0442985903 0.2859515031 0.2912432478
256 257 258 259 260
-0.4673181137 -0.4685649448 0.0385206812 -0.2071149531 -0.4849956465
261 262 263 264 265
-0.4798657078 -0.4778491346 0.2637953608 -0.1973782686 0.2748650146
266 267 268 269 270
0.0240315333 -0.4809604567 0.2540852348 0.0235188105 -0.4790791310
271 272 273 274 275
-0.2358019116 0.2404415763 -0.2606615900 -0.2666706083 -0.5460888217
276 277 278 279 280
-0.0384211696 0.2057028547 0.4530734485 0.2007283573 0.2019849121
281 282 283 284 285
-0.2895790092 -0.0561827970 -0.0438999817 0.2270309910 -0.5067756290
286 287 288 289 290
0.0097425170 -0.4673905205 -0.4586624469 0.3240975401 0.0681438437
291 292 293 294 295
0.0632295744 -0.4257940119 0.5815311408 -0.4086557603 0.3323912693
296 297 298 299 300
0.0917126492 0.0919774280 -0.6611737762 -0.1595656467 0.0934987617
301 302 303 304 305
-0.1542493075 0.0914387952 -0.4081002927 0.0628961593 -0.1990419608
306 307 308 309 310
0.2982642360 -0.2164456397 0.0368492293 0.0275993576 0.5322741004
311 312 313 314 315
0.2769451749 -0.2332783317 0.0062621463 -0.2437000152 0.4780963279
316 317 318 319 320
0.2368622402 0.2445046401 -0.5002848470 0.2266134814 -0.2727204073
321 322 323 324 325
-0.5032868316 -0.4867118073 -0.2389125852 -0.2416282816 0.2408553338
326 327 328 329 330
-0.4849573209 0.0124104195 -0.2306356907 0.0179403845 -0.2198742091
331 332 333 334 335
0.0231213967 -0.2195997066 -0.2264386949 -0.4596976162 0.2835452759
> postscript(file="/var/fisher/rcomp/tmp/6p32d1355586930.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 = 335
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.3150430888 NA
1 -0.3166505605 -0.3150430888
2 -0.0635770770 -0.3166505605
3 0.4410808309 -0.0635770770
4 0.1930511481 0.4410808309
5 0.4413287749 0.1930511481
6 0.1881368787 0.4413287749
7 -0.3141962839 0.1881368787
8 -0.0583287256 -0.3141962839
9 -0.0261970115 -0.0583287256
10 -0.0314809965 -0.0261970115
11 -0.2772384025 -0.0314809965
12 0.2331974145 -0.2772384025
13 0.4745441988 0.2331974145
14 -0.0408338410 0.4745441988
15 0.2253763738 -0.0408338410
16 0.2252734009 0.2253763738
17 -0.0036499180 0.2252734009
18 -0.2561938439 -0.0036499180
19 -0.4787256016 -0.2561938439
20 0.2571914892 -0.4787256016
21 -0.4946338568 0.2571914892
22 -0.2462791671 -0.4946338568
23 0.0138883599 -0.2462791671
24 0.5024950055 0.0138883599
25 0.5117429131 0.5024950055
26 -0.7280545840 0.5117429131
27 0.0249502540 -0.7280545840
28 -0.2334408842 0.0249502540
29 0.5134856323 -0.2334408842
30 -0.4874964201 0.5134856323
31 -0.4777001560 -0.4874964201
32 0.0214614565 -0.4777001560
33 0.2596948753 0.0214614565
34 -0.7482725316 0.2596948753
35 0.0021596171 -0.7482725316
36 0.7564715144 0.0021596171
37 0.2551637974 0.7564715144
38 0.0161172434 0.2551637974
39 -0.4899001922 0.0161172434
40 0.2528895566 -0.4899001922
41 0.2501368315 0.2528895566
42 0.0098602812 0.2501368315
43 0.2489068351 0.0098602812
44 -0.2394764609 0.2489068351
45 -0.4983880816 -0.2394764609
46 0.0025176452 -0.4983880816
47 -0.2477352051 0.0025176452
48 0.9879520829 -0.2477352051
49 0.4978920118 0.9879520829
50 -0.0072786189 0.4978920118
51 -0.2569494431 -0.0072786189
52 0.2491787251 -0.2569494431
53 -0.0012527659 0.2491787251
54 0.0055259944 -0.0012527659
55 -0.4894574212 0.0055259944
56 0.2531368521 -0.4894574212
57 0.0098427979 0.2531368521
58 -0.7329469509 0.0098427979
59 0.2670698839 -0.7329469509
60 0.5140650459 0.2670698839
61 -0.2480433772 0.5140650459
62 -0.7490526457 -0.2480433772
63 0.0010888142 -0.7490526457
64 0.2525548260 0.0010888142
65 -0.7377784413 0.2525548260
66 -0.4837565750 -0.7377784413
67 0.2617522292 -0.4837565750
68 -0.0151120938 0.2617522292
69 -0.5051778809 -0.0151120938
70 -0.2450608584 -0.5051778809
71 0.5059274064 -0.2450608584
72 0.2296451095 0.5059274064
73 0.5098842179 0.2296451095
74 -0.2334001034 0.5098842179
75 0.2654798955 -0.2334001034
76 -0.2297326653 0.2654798955
77 0.2768836313 -0.2297326653
78 -0.7161252981 0.2768836313
79 0.0337850526 -0.7161252981
80 0.2772331531 0.0337850526
81 0.0259640120 0.2772331531
82 -0.9652665646 0.0259640120
83 0.5420410955 -0.9652665646
84 0.2805588291 0.5420410955
85 0.2839783233 0.2805588291
86 -0.4526223715 0.2839783233
87 0.2883216852 -0.4526223715
88 0.0228099451 0.2883216852
89 -0.2220313527 0.0228099451
90 -0.7213056618 -0.2220313527
91 0.0280582965 -0.7213056618
92 0.2704887110 0.0280582965
93 0.2635733083 0.2704887110
94 -0.2064999244 0.2635733083
95 0.2858414987 -0.2064999244
96 0.5063980330 0.2858414987
97 0.5154938583 0.5063980330
98 -0.4687610874 0.5154938583
99 -0.2171409498 -0.4687610874
100 -0.2283906393 -0.2171409498
101 0.0143738572 -0.2283906393
102 -0.2464190891 0.0143738572
103 -0.4787450674 -0.2464190891
104 0.2796376533 -0.4787450674
105 0.2759611400 0.2796376533
106 -0.5109376582 0.2759611400
107 0.2482918675 -0.5109376582
108 0.2584713230 0.2482918675
109 0.5120666045 0.2584713230
110 0.0242219413 0.5120666045
111 0.2616253899 0.0242219413
112 0.0225898751 0.2616253899
113 0.2726102211 0.0225898751
114 -0.4776833212 0.2726102211
115 0.0313775283 -0.4776833212
116 0.5425830873 0.0313775283
117 -0.7156862792 0.5425830873
118 0.0258869490 -0.7156862792
119 0.7593763217 0.0258869490
120 -0.2315615225 0.7593763217
121 0.7631732913 -0.2315615225
122 -0.4703769673 0.7631732913
123 0.5291271447 -0.4703769673
124 0.0200999648 0.5291271447
125 -0.4748912103 0.0200999648
126 -0.4740612403 -0.4748912103
127 0.5229724088 -0.4740612403
128 0.7728911770 0.5229724088
129 -0.4667599540 0.7728911770
130 0.2851521786 -0.4667599540
131 0.7744741337 0.2851521786
132 -0.2212258976 0.7744741337
133 -0.2226359297 -0.2212258976
134 0.2707982782 -0.2226359297
135 0.2664549162 0.2707982782
136 0.2741050758 0.2664549162
137 -0.2186966016 0.2741050758
138 -0.4732480255 -0.2186966016
139 0.2770979852 -0.4732480255
140 0.2916130431 0.2770979852
141 0.2565661494 0.2916130431
142 -0.2384943290 0.2565661494
143 0.5226733914 -0.2384943290
144 -0.4641535951 0.5226733914
145 -0.2237138438 -0.4641535951
146 -0.4579057080 -0.2237138438
147 0.0349484378 -0.4579057080
148 0.2857399208 0.0349484378
149 -0.4625895167 0.2857399208
150 -0.4641030907 -0.4625895167
151 0.2878763017 -0.4641030907
152 0.2916130431 0.2878763017
153 0.2912090093 0.2916130431
154 -0.2132870927 0.2912090093
155 -0.4683939842 -0.2132870927
156 0.0397598323 -0.4683939842
157 0.0383400765 0.0397598323
158 0.0196636564 0.0383400765
159 -0.2085087194 0.0196636564
160 -0.2013376130 -0.2085087194
161 0.0428200810 -0.2013376130
162 0.5310401760 0.0428200810
163 0.0192448313 0.5310401760
164 0.2780269206 0.0192448313
165 -0.4684730111 0.2780269206
166 0.5371955604 -0.4684730111
167 0.0360923941 0.5371955604
168 0.2830195684 0.0360923941
169 -0.2248960278 0.2830195684
170 0.2650345119 -0.2248960278
171 -0.2135447604 0.2650345119
172 0.5257918246 -0.2135447604
173 0.2542548799 0.5257918246
174 0.2664354689 0.2542548799
175 -0.4838257987 0.2664354689
176 -0.2349927043 -0.4838257987
177 -0.2228023917 -0.2349927043
178 0.0251502282 -0.2228023917
179 0.2822589015 0.0251502282
180 0.0251670630 0.2822589015
181 0.0286370616 0.0251670630
182 0.0322611065 0.0286370616
183 0.0192441828 0.0322611065
184 0.2551605364 0.0192441828
185 0.0171148336 0.2551605364
186 0.5115764511 0.0171148336
187 0.5225670780 0.5115764511
188 -0.2478407904 0.5225670780
189 0.0046241085 -0.2478407904
190 0.0075308799 0.0046241085
191 0.0004102779 0.0075308799
192 0.0032833796 0.0004102779
193 -0.2384647465 0.0032833796
194 -0.2394299640 -0.2384647465
195 -0.4924768890 -0.2394299640
196 -0.2324614443 -0.4924768890
197 0.2629990604 -0.2324614443
198 -0.2307018903 0.2629990604
199 -0.4754997949 -0.2307018903
200 0.0239531549 -0.4754997949
201 0.2785745305 0.0239531549
202 0.3085873472 0.2785745305
203 0.2905091395 0.3085873472
204 0.5524284608 0.2905091395
205 -0.4457258471 0.5524284608
206 0.0580361465 -0.4457258471
207 0.2901575741 0.0580361465
208 -0.1991909395 0.2901575741
209 0.2973474793 -0.1991909395
210 0.5664385555 0.2973474793
211 0.5390249866 0.5664385555
212 0.5387602078 0.5390249866
213 -0.2066022302 0.5387602078
214 -0.2057722602 -0.2066022302
215 0.0477278081 -0.2057722602
216 0.0498292038 0.0477278081
217 -0.2046956616 0.0498292038
218 0.2791999499 -0.2046956616
219 -0.2021307320 0.2791999499
220 0.5569524275 -0.2021307320
221 0.5524984126 0.5569524275
222 -0.1939153811 0.5524984126
223 -0.4518862991 -0.1939153811
224 0.3082125823 -0.4518862991
225 -0.1810505397 0.3082125823
226 0.5685775489 -0.1810505397
227 0.3088793514 0.5685775489
228 0.3078707406 0.3088793514
229 0.0691414339 0.3078707406
230 -0.1855129629 0.0691414339
231 0.0669784946 -0.1855129629
232 -0.1894710898 0.0669784946
233 0.3108280164 -0.1894710898
234 0.0487959985 0.3108280164
235 0.0500952981 0.0487959985
236 0.3066219454 0.0500952981
237 0.3096212195 0.3066219454
238 0.0540877432 0.3096212195
239 -0.1855052032 0.0540877432
240 -0.4383307427 -0.1855052032
241 0.5714823562 -0.4383307427
242 0.3129650457 0.5714823562
243 -0.4484596938 0.3129650457
244 0.5483091764 -0.4484596938
245 -0.2008068379 0.5483091764
246 0.2928416444 -0.2008068379
247 -0.4590593695 0.2928416444
248 -0.4851827140 -0.4590593695
249 0.0379483788 -0.4851827140
250 0.2862091708 0.0379483788
251 -0.1987041266 0.2862091708
252 0.0442985903 -0.1987041266
253 0.2859515031 0.0442985903
254 0.2912432478 0.2859515031
255 -0.4673181137 0.2912432478
256 -0.4685649448 -0.4673181137
257 0.0385206812 -0.4685649448
258 -0.2071149531 0.0385206812
259 -0.4849956465 -0.2071149531
260 -0.4798657078 -0.4849956465
261 -0.4778491346 -0.4798657078
262 0.2637953608 -0.4778491346
263 -0.1973782686 0.2637953608
264 0.2748650146 -0.1973782686
265 0.0240315333 0.2748650146
266 -0.4809604567 0.0240315333
267 0.2540852348 -0.4809604567
268 0.0235188105 0.2540852348
269 -0.4790791310 0.0235188105
270 -0.2358019116 -0.4790791310
271 0.2404415763 -0.2358019116
272 -0.2606615900 0.2404415763
273 -0.2666706083 -0.2606615900
274 -0.5460888217 -0.2666706083
275 -0.0384211696 -0.5460888217
276 0.2057028547 -0.0384211696
277 0.4530734485 0.2057028547
278 0.2007283573 0.4530734485
279 0.2019849121 0.2007283573
280 -0.2895790092 0.2019849121
281 -0.0561827970 -0.2895790092
282 -0.0438999817 -0.0561827970
283 0.2270309910 -0.0438999817
284 -0.5067756290 0.2270309910
285 0.0097425170 -0.5067756290
286 -0.4673905205 0.0097425170
287 -0.4586624469 -0.4673905205
288 0.3240975401 -0.4586624469
289 0.0681438437 0.3240975401
290 0.0632295744 0.0681438437
291 -0.4257940119 0.0632295744
292 0.5815311408 -0.4257940119
293 -0.4086557603 0.5815311408
294 0.3323912693 -0.4086557603
295 0.0917126492 0.3323912693
296 0.0919774280 0.0917126492
297 -0.6611737762 0.0919774280
298 -0.1595656467 -0.6611737762
299 0.0934987617 -0.1595656467
300 -0.1542493075 0.0934987617
301 0.0914387952 -0.1542493075
302 -0.4081002927 0.0914387952
303 0.0628961593 -0.4081002927
304 -0.1990419608 0.0628961593
305 0.2982642360 -0.1990419608
306 -0.2164456397 0.2982642360
307 0.0368492293 -0.2164456397
308 0.0275993576 0.0368492293
309 0.5322741004 0.0275993576
310 0.2769451749 0.5322741004
311 -0.2332783317 0.2769451749
312 0.0062621463 -0.2332783317
313 -0.2437000152 0.0062621463
314 0.4780963279 -0.2437000152
315 0.2368622402 0.4780963279
316 0.2445046401 0.2368622402
317 -0.5002848470 0.2445046401
318 0.2266134814 -0.5002848470
319 -0.2727204073 0.2266134814
320 -0.5032868316 -0.2727204073
321 -0.4867118073 -0.5032868316
322 -0.2389125852 -0.4867118073
323 -0.2416282816 -0.2389125852
324 0.2408553338 -0.2416282816
325 -0.4849573209 0.2408553338
326 0.0124104195 -0.4849573209
327 -0.2306356907 0.0124104195
328 0.0179403845 -0.2306356907
329 -0.2198742091 0.0179403845
330 0.0231213967 -0.2198742091
331 -0.2195997066 0.0231213967
332 -0.2264386949 -0.2195997066
333 -0.4596976162 -0.2264386949
334 0.2835452759 -0.4596976162
335 NA 0.2835452759
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3166505605 -0.3150430888
[2,] -0.0635770770 -0.3166505605
[3,] 0.4410808309 -0.0635770770
[4,] 0.1930511481 0.4410808309
[5,] 0.4413287749 0.1930511481
[6,] 0.1881368787 0.4413287749
[7,] -0.3141962839 0.1881368787
[8,] -0.0583287256 -0.3141962839
[9,] -0.0261970115 -0.0583287256
[10,] -0.0314809965 -0.0261970115
[11,] -0.2772384025 -0.0314809965
[12,] 0.2331974145 -0.2772384025
[13,] 0.4745441988 0.2331974145
[14,] -0.0408338410 0.4745441988
[15,] 0.2253763738 -0.0408338410
[16,] 0.2252734009 0.2253763738
[17,] -0.0036499180 0.2252734009
[18,] -0.2561938439 -0.0036499180
[19,] -0.4787256016 -0.2561938439
[20,] 0.2571914892 -0.4787256016
[21,] -0.4946338568 0.2571914892
[22,] -0.2462791671 -0.4946338568
[23,] 0.0138883599 -0.2462791671
[24,] 0.5024950055 0.0138883599
[25,] 0.5117429131 0.5024950055
[26,] -0.7280545840 0.5117429131
[27,] 0.0249502540 -0.7280545840
[28,] -0.2334408842 0.0249502540
[29,] 0.5134856323 -0.2334408842
[30,] -0.4874964201 0.5134856323
[31,] -0.4777001560 -0.4874964201
[32,] 0.0214614565 -0.4777001560
[33,] 0.2596948753 0.0214614565
[34,] -0.7482725316 0.2596948753
[35,] 0.0021596171 -0.7482725316
[36,] 0.7564715144 0.0021596171
[37,] 0.2551637974 0.7564715144
[38,] 0.0161172434 0.2551637974
[39,] -0.4899001922 0.0161172434
[40,] 0.2528895566 -0.4899001922
[41,] 0.2501368315 0.2528895566
[42,] 0.0098602812 0.2501368315
[43,] 0.2489068351 0.0098602812
[44,] -0.2394764609 0.2489068351
[45,] -0.4983880816 -0.2394764609
[46,] 0.0025176452 -0.4983880816
[47,] -0.2477352051 0.0025176452
[48,] 0.9879520829 -0.2477352051
[49,] 0.4978920118 0.9879520829
[50,] -0.0072786189 0.4978920118
[51,] -0.2569494431 -0.0072786189
[52,] 0.2491787251 -0.2569494431
[53,] -0.0012527659 0.2491787251
[54,] 0.0055259944 -0.0012527659
[55,] -0.4894574212 0.0055259944
[56,] 0.2531368521 -0.4894574212
[57,] 0.0098427979 0.2531368521
[58,] -0.7329469509 0.0098427979
[59,] 0.2670698839 -0.7329469509
[60,] 0.5140650459 0.2670698839
[61,] -0.2480433772 0.5140650459
[62,] -0.7490526457 -0.2480433772
[63,] 0.0010888142 -0.7490526457
[64,] 0.2525548260 0.0010888142
[65,] -0.7377784413 0.2525548260
[66,] -0.4837565750 -0.7377784413
[67,] 0.2617522292 -0.4837565750
[68,] -0.0151120938 0.2617522292
[69,] -0.5051778809 -0.0151120938
[70,] -0.2450608584 -0.5051778809
[71,] 0.5059274064 -0.2450608584
[72,] 0.2296451095 0.5059274064
[73,] 0.5098842179 0.2296451095
[74,] -0.2334001034 0.5098842179
[75,] 0.2654798955 -0.2334001034
[76,] -0.2297326653 0.2654798955
[77,] 0.2768836313 -0.2297326653
[78,] -0.7161252981 0.2768836313
[79,] 0.0337850526 -0.7161252981
[80,] 0.2772331531 0.0337850526
[81,] 0.0259640120 0.2772331531
[82,] -0.9652665646 0.0259640120
[83,] 0.5420410955 -0.9652665646
[84,] 0.2805588291 0.5420410955
[85,] 0.2839783233 0.2805588291
[86,] -0.4526223715 0.2839783233
[87,] 0.2883216852 -0.4526223715
[88,] 0.0228099451 0.2883216852
[89,] -0.2220313527 0.0228099451
[90,] -0.7213056618 -0.2220313527
[91,] 0.0280582965 -0.7213056618
[92,] 0.2704887110 0.0280582965
[93,] 0.2635733083 0.2704887110
[94,] -0.2064999244 0.2635733083
[95,] 0.2858414987 -0.2064999244
[96,] 0.5063980330 0.2858414987
[97,] 0.5154938583 0.5063980330
[98,] -0.4687610874 0.5154938583
[99,] -0.2171409498 -0.4687610874
[100,] -0.2283906393 -0.2171409498
[101,] 0.0143738572 -0.2283906393
[102,] -0.2464190891 0.0143738572
[103,] -0.4787450674 -0.2464190891
[104,] 0.2796376533 -0.4787450674
[105,] 0.2759611400 0.2796376533
[106,] -0.5109376582 0.2759611400
[107,] 0.2482918675 -0.5109376582
[108,] 0.2584713230 0.2482918675
[109,] 0.5120666045 0.2584713230
[110,] 0.0242219413 0.5120666045
[111,] 0.2616253899 0.0242219413
[112,] 0.0225898751 0.2616253899
[113,] 0.2726102211 0.0225898751
[114,] -0.4776833212 0.2726102211
[115,] 0.0313775283 -0.4776833212
[116,] 0.5425830873 0.0313775283
[117,] -0.7156862792 0.5425830873
[118,] 0.0258869490 -0.7156862792
[119,] 0.7593763217 0.0258869490
[120,] -0.2315615225 0.7593763217
[121,] 0.7631732913 -0.2315615225
[122,] -0.4703769673 0.7631732913
[123,] 0.5291271447 -0.4703769673
[124,] 0.0200999648 0.5291271447
[125,] -0.4748912103 0.0200999648
[126,] -0.4740612403 -0.4748912103
[127,] 0.5229724088 -0.4740612403
[128,] 0.7728911770 0.5229724088
[129,] -0.4667599540 0.7728911770
[130,] 0.2851521786 -0.4667599540
[131,] 0.7744741337 0.2851521786
[132,] -0.2212258976 0.7744741337
[133,] -0.2226359297 -0.2212258976
[134,] 0.2707982782 -0.2226359297
[135,] 0.2664549162 0.2707982782
[136,] 0.2741050758 0.2664549162
[137,] -0.2186966016 0.2741050758
[138,] -0.4732480255 -0.2186966016
[139,] 0.2770979852 -0.4732480255
[140,] 0.2916130431 0.2770979852
[141,] 0.2565661494 0.2916130431
[142,] -0.2384943290 0.2565661494
[143,] 0.5226733914 -0.2384943290
[144,] -0.4641535951 0.5226733914
[145,] -0.2237138438 -0.4641535951
[146,] -0.4579057080 -0.2237138438
[147,] 0.0349484378 -0.4579057080
[148,] 0.2857399208 0.0349484378
[149,] -0.4625895167 0.2857399208
[150,] -0.4641030907 -0.4625895167
[151,] 0.2878763017 -0.4641030907
[152,] 0.2916130431 0.2878763017
[153,] 0.2912090093 0.2916130431
[154,] -0.2132870927 0.2912090093
[155,] -0.4683939842 -0.2132870927
[156,] 0.0397598323 -0.4683939842
[157,] 0.0383400765 0.0397598323
[158,] 0.0196636564 0.0383400765
[159,] -0.2085087194 0.0196636564
[160,] -0.2013376130 -0.2085087194
[161,] 0.0428200810 -0.2013376130
[162,] 0.5310401760 0.0428200810
[163,] 0.0192448313 0.5310401760
[164,] 0.2780269206 0.0192448313
[165,] -0.4684730111 0.2780269206
[166,] 0.5371955604 -0.4684730111
[167,] 0.0360923941 0.5371955604
[168,] 0.2830195684 0.0360923941
[169,] -0.2248960278 0.2830195684
[170,] 0.2650345119 -0.2248960278
[171,] -0.2135447604 0.2650345119
[172,] 0.5257918246 -0.2135447604
[173,] 0.2542548799 0.5257918246
[174,] 0.2664354689 0.2542548799
[175,] -0.4838257987 0.2664354689
[176,] -0.2349927043 -0.4838257987
[177,] -0.2228023917 -0.2349927043
[178,] 0.0251502282 -0.2228023917
[179,] 0.2822589015 0.0251502282
[180,] 0.0251670630 0.2822589015
[181,] 0.0286370616 0.0251670630
[182,] 0.0322611065 0.0286370616
[183,] 0.0192441828 0.0322611065
[184,] 0.2551605364 0.0192441828
[185,] 0.0171148336 0.2551605364
[186,] 0.5115764511 0.0171148336
[187,] 0.5225670780 0.5115764511
[188,] -0.2478407904 0.5225670780
[189,] 0.0046241085 -0.2478407904
[190,] 0.0075308799 0.0046241085
[191,] 0.0004102779 0.0075308799
[192,] 0.0032833796 0.0004102779
[193,] -0.2384647465 0.0032833796
[194,] -0.2394299640 -0.2384647465
[195,] -0.4924768890 -0.2394299640
[196,] -0.2324614443 -0.4924768890
[197,] 0.2629990604 -0.2324614443
[198,] -0.2307018903 0.2629990604
[199,] -0.4754997949 -0.2307018903
[200,] 0.0239531549 -0.4754997949
[201,] 0.2785745305 0.0239531549
[202,] 0.3085873472 0.2785745305
[203,] 0.2905091395 0.3085873472
[204,] 0.5524284608 0.2905091395
[205,] -0.4457258471 0.5524284608
[206,] 0.0580361465 -0.4457258471
[207,] 0.2901575741 0.0580361465
[208,] -0.1991909395 0.2901575741
[209,] 0.2973474793 -0.1991909395
[210,] 0.5664385555 0.2973474793
[211,] 0.5390249866 0.5664385555
[212,] 0.5387602078 0.5390249866
[213,] -0.2066022302 0.5387602078
[214,] -0.2057722602 -0.2066022302
[215,] 0.0477278081 -0.2057722602
[216,] 0.0498292038 0.0477278081
[217,] -0.2046956616 0.0498292038
[218,] 0.2791999499 -0.2046956616
[219,] -0.2021307320 0.2791999499
[220,] 0.5569524275 -0.2021307320
[221,] 0.5524984126 0.5569524275
[222,] -0.1939153811 0.5524984126
[223,] -0.4518862991 -0.1939153811
[224,] 0.3082125823 -0.4518862991
[225,] -0.1810505397 0.3082125823
[226,] 0.5685775489 -0.1810505397
[227,] 0.3088793514 0.5685775489
[228,] 0.3078707406 0.3088793514
[229,] 0.0691414339 0.3078707406
[230,] -0.1855129629 0.0691414339
[231,] 0.0669784946 -0.1855129629
[232,] -0.1894710898 0.0669784946
[233,] 0.3108280164 -0.1894710898
[234,] 0.0487959985 0.3108280164
[235,] 0.0500952981 0.0487959985
[236,] 0.3066219454 0.0500952981
[237,] 0.3096212195 0.3066219454
[238,] 0.0540877432 0.3096212195
[239,] -0.1855052032 0.0540877432
[240,] -0.4383307427 -0.1855052032
[241,] 0.5714823562 -0.4383307427
[242,] 0.3129650457 0.5714823562
[243,] -0.4484596938 0.3129650457
[244,] 0.5483091764 -0.4484596938
[245,] -0.2008068379 0.5483091764
[246,] 0.2928416444 -0.2008068379
[247,] -0.4590593695 0.2928416444
[248,] -0.4851827140 -0.4590593695
[249,] 0.0379483788 -0.4851827140
[250,] 0.2862091708 0.0379483788
[251,] -0.1987041266 0.2862091708
[252,] 0.0442985903 -0.1987041266
[253,] 0.2859515031 0.0442985903
[254,] 0.2912432478 0.2859515031
[255,] -0.4673181137 0.2912432478
[256,] -0.4685649448 -0.4673181137
[257,] 0.0385206812 -0.4685649448
[258,] -0.2071149531 0.0385206812
[259,] -0.4849956465 -0.2071149531
[260,] -0.4798657078 -0.4849956465
[261,] -0.4778491346 -0.4798657078
[262,] 0.2637953608 -0.4778491346
[263,] -0.1973782686 0.2637953608
[264,] 0.2748650146 -0.1973782686
[265,] 0.0240315333 0.2748650146
[266,] -0.4809604567 0.0240315333
[267,] 0.2540852348 -0.4809604567
[268,] 0.0235188105 0.2540852348
[269,] -0.4790791310 0.0235188105
[270,] -0.2358019116 -0.4790791310
[271,] 0.2404415763 -0.2358019116
[272,] -0.2606615900 0.2404415763
[273,] -0.2666706083 -0.2606615900
[274,] -0.5460888217 -0.2666706083
[275,] -0.0384211696 -0.5460888217
[276,] 0.2057028547 -0.0384211696
[277,] 0.4530734485 0.2057028547
[278,] 0.2007283573 0.4530734485
[279,] 0.2019849121 0.2007283573
[280,] -0.2895790092 0.2019849121
[281,] -0.0561827970 -0.2895790092
[282,] -0.0438999817 -0.0561827970
[283,] 0.2270309910 -0.0438999817
[284,] -0.5067756290 0.2270309910
[285,] 0.0097425170 -0.5067756290
[286,] -0.4673905205 0.0097425170
[287,] -0.4586624469 -0.4673905205
[288,] 0.3240975401 -0.4586624469
[289,] 0.0681438437 0.3240975401
[290,] 0.0632295744 0.0681438437
[291,] -0.4257940119 0.0632295744
[292,] 0.5815311408 -0.4257940119
[293,] -0.4086557603 0.5815311408
[294,] 0.3323912693 -0.4086557603
[295,] 0.0917126492 0.3323912693
[296,] 0.0919774280 0.0917126492
[297,] -0.6611737762 0.0919774280
[298,] -0.1595656467 -0.6611737762
[299,] 0.0934987617 -0.1595656467
[300,] -0.1542493075 0.0934987617
[301,] 0.0914387952 -0.1542493075
[302,] -0.4081002927 0.0914387952
[303,] 0.0628961593 -0.4081002927
[304,] -0.1990419608 0.0628961593
[305,] 0.2982642360 -0.1990419608
[306,] -0.2164456397 0.2982642360
[307,] 0.0368492293 -0.2164456397
[308,] 0.0275993576 0.0368492293
[309,] 0.5322741004 0.0275993576
[310,] 0.2769451749 0.5322741004
[311,] -0.2332783317 0.2769451749
[312,] 0.0062621463 -0.2332783317
[313,] -0.2437000152 0.0062621463
[314,] 0.4780963279 -0.2437000152
[315,] 0.2368622402 0.4780963279
[316,] 0.2445046401 0.2368622402
[317,] -0.5002848470 0.2445046401
[318,] 0.2266134814 -0.5002848470
[319,] -0.2727204073 0.2266134814
[320,] -0.5032868316 -0.2727204073
[321,] -0.4867118073 -0.5032868316
[322,] -0.2389125852 -0.4867118073
[323,] -0.2416282816 -0.2389125852
[324,] 0.2408553338 -0.2416282816
[325,] -0.4849573209 0.2408553338
[326,] 0.0124104195 -0.4849573209
[327,] -0.2306356907 0.0124104195
[328,] 0.0179403845 -0.2306356907
[329,] -0.2198742091 0.0179403845
[330,] 0.0231213967 -0.2198742091
[331,] -0.2195997066 0.0231213967
[332,] -0.2264386949 -0.2195997066
[333,] -0.4596976162 -0.2264386949
[334,] 0.2835452759 -0.4596976162
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3166505605 -0.3150430888
2 -0.0635770770 -0.3166505605
3 0.4410808309 -0.0635770770
4 0.1930511481 0.4410808309
5 0.4413287749 0.1930511481
6 0.1881368787 0.4413287749
7 -0.3141962839 0.1881368787
8 -0.0583287256 -0.3141962839
9 -0.0261970115 -0.0583287256
10 -0.0314809965 -0.0261970115
11 -0.2772384025 -0.0314809965
12 0.2331974145 -0.2772384025
13 0.4745441988 0.2331974145
14 -0.0408338410 0.4745441988
15 0.2253763738 -0.0408338410
16 0.2252734009 0.2253763738
17 -0.0036499180 0.2252734009
18 -0.2561938439 -0.0036499180
19 -0.4787256016 -0.2561938439
20 0.2571914892 -0.4787256016
21 -0.4946338568 0.2571914892
22 -0.2462791671 -0.4946338568
23 0.0138883599 -0.2462791671
24 0.5024950055 0.0138883599
25 0.5117429131 0.5024950055
26 -0.7280545840 0.5117429131
27 0.0249502540 -0.7280545840
28 -0.2334408842 0.0249502540
29 0.5134856323 -0.2334408842
30 -0.4874964201 0.5134856323
31 -0.4777001560 -0.4874964201
32 0.0214614565 -0.4777001560
33 0.2596948753 0.0214614565
34 -0.7482725316 0.2596948753
35 0.0021596171 -0.7482725316
36 0.7564715144 0.0021596171
37 0.2551637974 0.7564715144
38 0.0161172434 0.2551637974
39 -0.4899001922 0.0161172434
40 0.2528895566 -0.4899001922
41 0.2501368315 0.2528895566
42 0.0098602812 0.2501368315
43 0.2489068351 0.0098602812
44 -0.2394764609 0.2489068351
45 -0.4983880816 -0.2394764609
46 0.0025176452 -0.4983880816
47 -0.2477352051 0.0025176452
48 0.9879520829 -0.2477352051
49 0.4978920118 0.9879520829
50 -0.0072786189 0.4978920118
51 -0.2569494431 -0.0072786189
52 0.2491787251 -0.2569494431
53 -0.0012527659 0.2491787251
54 0.0055259944 -0.0012527659
55 -0.4894574212 0.0055259944
56 0.2531368521 -0.4894574212
57 0.0098427979 0.2531368521
58 -0.7329469509 0.0098427979
59 0.2670698839 -0.7329469509
60 0.5140650459 0.2670698839
61 -0.2480433772 0.5140650459
62 -0.7490526457 -0.2480433772
63 0.0010888142 -0.7490526457
64 0.2525548260 0.0010888142
65 -0.7377784413 0.2525548260
66 -0.4837565750 -0.7377784413
67 0.2617522292 -0.4837565750
68 -0.0151120938 0.2617522292
69 -0.5051778809 -0.0151120938
70 -0.2450608584 -0.5051778809
71 0.5059274064 -0.2450608584
72 0.2296451095 0.5059274064
73 0.5098842179 0.2296451095
74 -0.2334001034 0.5098842179
75 0.2654798955 -0.2334001034
76 -0.2297326653 0.2654798955
77 0.2768836313 -0.2297326653
78 -0.7161252981 0.2768836313
79 0.0337850526 -0.7161252981
80 0.2772331531 0.0337850526
81 0.0259640120 0.2772331531
82 -0.9652665646 0.0259640120
83 0.5420410955 -0.9652665646
84 0.2805588291 0.5420410955
85 0.2839783233 0.2805588291
86 -0.4526223715 0.2839783233
87 0.2883216852 -0.4526223715
88 0.0228099451 0.2883216852
89 -0.2220313527 0.0228099451
90 -0.7213056618 -0.2220313527
91 0.0280582965 -0.7213056618
92 0.2704887110 0.0280582965
93 0.2635733083 0.2704887110
94 -0.2064999244 0.2635733083
95 0.2858414987 -0.2064999244
96 0.5063980330 0.2858414987
97 0.5154938583 0.5063980330
98 -0.4687610874 0.5154938583
99 -0.2171409498 -0.4687610874
100 -0.2283906393 -0.2171409498
101 0.0143738572 -0.2283906393
102 -0.2464190891 0.0143738572
103 -0.4787450674 -0.2464190891
104 0.2796376533 -0.4787450674
105 0.2759611400 0.2796376533
106 -0.5109376582 0.2759611400
107 0.2482918675 -0.5109376582
108 0.2584713230 0.2482918675
109 0.5120666045 0.2584713230
110 0.0242219413 0.5120666045
111 0.2616253899 0.0242219413
112 0.0225898751 0.2616253899
113 0.2726102211 0.0225898751
114 -0.4776833212 0.2726102211
115 0.0313775283 -0.4776833212
116 0.5425830873 0.0313775283
117 -0.7156862792 0.5425830873
118 0.0258869490 -0.7156862792
119 0.7593763217 0.0258869490
120 -0.2315615225 0.7593763217
121 0.7631732913 -0.2315615225
122 -0.4703769673 0.7631732913
123 0.5291271447 -0.4703769673
124 0.0200999648 0.5291271447
125 -0.4748912103 0.0200999648
126 -0.4740612403 -0.4748912103
127 0.5229724088 -0.4740612403
128 0.7728911770 0.5229724088
129 -0.4667599540 0.7728911770
130 0.2851521786 -0.4667599540
131 0.7744741337 0.2851521786
132 -0.2212258976 0.7744741337
133 -0.2226359297 -0.2212258976
134 0.2707982782 -0.2226359297
135 0.2664549162 0.2707982782
136 0.2741050758 0.2664549162
137 -0.2186966016 0.2741050758
138 -0.4732480255 -0.2186966016
139 0.2770979852 -0.4732480255
140 0.2916130431 0.2770979852
141 0.2565661494 0.2916130431
142 -0.2384943290 0.2565661494
143 0.5226733914 -0.2384943290
144 -0.4641535951 0.5226733914
145 -0.2237138438 -0.4641535951
146 -0.4579057080 -0.2237138438
147 0.0349484378 -0.4579057080
148 0.2857399208 0.0349484378
149 -0.4625895167 0.2857399208
150 -0.4641030907 -0.4625895167
151 0.2878763017 -0.4641030907
152 0.2916130431 0.2878763017
153 0.2912090093 0.2916130431
154 -0.2132870927 0.2912090093
155 -0.4683939842 -0.2132870927
156 0.0397598323 -0.4683939842
157 0.0383400765 0.0397598323
158 0.0196636564 0.0383400765
159 -0.2085087194 0.0196636564
160 -0.2013376130 -0.2085087194
161 0.0428200810 -0.2013376130
162 0.5310401760 0.0428200810
163 0.0192448313 0.5310401760
164 0.2780269206 0.0192448313
165 -0.4684730111 0.2780269206
166 0.5371955604 -0.4684730111
167 0.0360923941 0.5371955604
168 0.2830195684 0.0360923941
169 -0.2248960278 0.2830195684
170 0.2650345119 -0.2248960278
171 -0.2135447604 0.2650345119
172 0.5257918246 -0.2135447604
173 0.2542548799 0.5257918246
174 0.2664354689 0.2542548799
175 -0.4838257987 0.2664354689
176 -0.2349927043 -0.4838257987
177 -0.2228023917 -0.2349927043
178 0.0251502282 -0.2228023917
179 0.2822589015 0.0251502282
180 0.0251670630 0.2822589015
181 0.0286370616 0.0251670630
182 0.0322611065 0.0286370616
183 0.0192441828 0.0322611065
184 0.2551605364 0.0192441828
185 0.0171148336 0.2551605364
186 0.5115764511 0.0171148336
187 0.5225670780 0.5115764511
188 -0.2478407904 0.5225670780
189 0.0046241085 -0.2478407904
190 0.0075308799 0.0046241085
191 0.0004102779 0.0075308799
192 0.0032833796 0.0004102779
193 -0.2384647465 0.0032833796
194 -0.2394299640 -0.2384647465
195 -0.4924768890 -0.2394299640
196 -0.2324614443 -0.4924768890
197 0.2629990604 -0.2324614443
198 -0.2307018903 0.2629990604
199 -0.4754997949 -0.2307018903
200 0.0239531549 -0.4754997949
201 0.2785745305 0.0239531549
202 0.3085873472 0.2785745305
203 0.2905091395 0.3085873472
204 0.5524284608 0.2905091395
205 -0.4457258471 0.5524284608
206 0.0580361465 -0.4457258471
207 0.2901575741 0.0580361465
208 -0.1991909395 0.2901575741
209 0.2973474793 -0.1991909395
210 0.5664385555 0.2973474793
211 0.5390249866 0.5664385555
212 0.5387602078 0.5390249866
213 -0.2066022302 0.5387602078
214 -0.2057722602 -0.2066022302
215 0.0477278081 -0.2057722602
216 0.0498292038 0.0477278081
217 -0.2046956616 0.0498292038
218 0.2791999499 -0.2046956616
219 -0.2021307320 0.2791999499
220 0.5569524275 -0.2021307320
221 0.5524984126 0.5569524275
222 -0.1939153811 0.5524984126
223 -0.4518862991 -0.1939153811
224 0.3082125823 -0.4518862991
225 -0.1810505397 0.3082125823
226 0.5685775489 -0.1810505397
227 0.3088793514 0.5685775489
228 0.3078707406 0.3088793514
229 0.0691414339 0.3078707406
230 -0.1855129629 0.0691414339
231 0.0669784946 -0.1855129629
232 -0.1894710898 0.0669784946
233 0.3108280164 -0.1894710898
234 0.0487959985 0.3108280164
235 0.0500952981 0.0487959985
236 0.3066219454 0.0500952981
237 0.3096212195 0.3066219454
238 0.0540877432 0.3096212195
239 -0.1855052032 0.0540877432
240 -0.4383307427 -0.1855052032
241 0.5714823562 -0.4383307427
242 0.3129650457 0.5714823562
243 -0.4484596938 0.3129650457
244 0.5483091764 -0.4484596938
245 -0.2008068379 0.5483091764
246 0.2928416444 -0.2008068379
247 -0.4590593695 0.2928416444
248 -0.4851827140 -0.4590593695
249 0.0379483788 -0.4851827140
250 0.2862091708 0.0379483788
251 -0.1987041266 0.2862091708
252 0.0442985903 -0.1987041266
253 0.2859515031 0.0442985903
254 0.2912432478 0.2859515031
255 -0.4673181137 0.2912432478
256 -0.4685649448 -0.4673181137
257 0.0385206812 -0.4685649448
258 -0.2071149531 0.0385206812
259 -0.4849956465 -0.2071149531
260 -0.4798657078 -0.4849956465
261 -0.4778491346 -0.4798657078
262 0.2637953608 -0.4778491346
263 -0.1973782686 0.2637953608
264 0.2748650146 -0.1973782686
265 0.0240315333 0.2748650146
266 -0.4809604567 0.0240315333
267 0.2540852348 -0.4809604567
268 0.0235188105 0.2540852348
269 -0.4790791310 0.0235188105
270 -0.2358019116 -0.4790791310
271 0.2404415763 -0.2358019116
272 -0.2606615900 0.2404415763
273 -0.2666706083 -0.2606615900
274 -0.5460888217 -0.2666706083
275 -0.0384211696 -0.5460888217
276 0.2057028547 -0.0384211696
277 0.4530734485 0.2057028547
278 0.2007283573 0.4530734485
279 0.2019849121 0.2007283573
280 -0.2895790092 0.2019849121
281 -0.0561827970 -0.2895790092
282 -0.0438999817 -0.0561827970
283 0.2270309910 -0.0438999817
284 -0.5067756290 0.2270309910
285 0.0097425170 -0.5067756290
286 -0.4673905205 0.0097425170
287 -0.4586624469 -0.4673905205
288 0.3240975401 -0.4586624469
289 0.0681438437 0.3240975401
290 0.0632295744 0.0681438437
291 -0.4257940119 0.0632295744
292 0.5815311408 -0.4257940119
293 -0.4086557603 0.5815311408
294 0.3323912693 -0.4086557603
295 0.0917126492 0.3323912693
296 0.0919774280 0.0917126492
297 -0.6611737762 0.0919774280
298 -0.1595656467 -0.6611737762
299 0.0934987617 -0.1595656467
300 -0.1542493075 0.0934987617
301 0.0914387952 -0.1542493075
302 -0.4081002927 0.0914387952
303 0.0628961593 -0.4081002927
304 -0.1990419608 0.0628961593
305 0.2982642360 -0.1990419608
306 -0.2164456397 0.2982642360
307 0.0368492293 -0.2164456397
308 0.0275993576 0.0368492293
309 0.5322741004 0.0275993576
310 0.2769451749 0.5322741004
311 -0.2332783317 0.2769451749
312 0.0062621463 -0.2332783317
313 -0.2437000152 0.0062621463
314 0.4780963279 -0.2437000152
315 0.2368622402 0.4780963279
316 0.2445046401 0.2368622402
317 -0.5002848470 0.2445046401
318 0.2266134814 -0.5002848470
319 -0.2727204073 0.2266134814
320 -0.5032868316 -0.2727204073
321 -0.4867118073 -0.5032868316
322 -0.2389125852 -0.4867118073
323 -0.2416282816 -0.2389125852
324 0.2408553338 -0.2416282816
325 -0.4849573209 0.2408553338
326 0.0124104195 -0.4849573209
327 -0.2306356907 0.0124104195
328 0.0179403845 -0.2306356907
329 -0.2198742091 0.0179403845
330 0.0231213967 -0.2198742091
331 -0.2195997066 0.0231213967
332 -0.2264386949 -0.2195997066
333 -0.4596976162 -0.2264386949
334 0.2835452759 -0.4596976162
> 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/70z7j1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8p1sd1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9wutz1355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10zez51355586930.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pjjp1355586931.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/12meuv1355586931.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/13omer1355586931.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/144aq11355586931.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/15n18z1355586931.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/1665sv1355586931.tab")
+ }
>
> try(system("convert tmp/1uxo41355586930.ps tmp/1uxo41355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ruis1355586930.ps tmp/2ruis1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xubs1355586930.ps tmp/3xubs1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lsge1355586930.ps tmp/4lsge1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/5makr1355586930.ps tmp/5makr1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p32d1355586930.ps tmp/6p32d1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/70z7j1355586930.ps tmp/70z7j1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p1sd1355586930.ps tmp/8p1sd1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wutz1355586930.ps tmp/9wutz1355586930.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zez51355586930.ps tmp/10zez51355586930.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.950 1.801 16.753