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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+ ,-4
+ ,-1
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+ ,-3
+ ,-1
+ ,5
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+ ,-3
+ ,3
+ ,6
+ ,-10
+ ,-5
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+ ,-1
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+ ,-2
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+ ,-3
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+ ,-1
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+ ,-3
+ ,3
+ ,11
+ ,-24
+ ,-28
+ ,62
+ ,-5
+ ,-2)
+ ,dim=c(6
+ ,335)
+ ,dimnames=list(c('Maand'
+ ,'Csmvert'
+ ,'econs'
+ ,'werkloosh'
+ ,'finsit'
+ ,'spaarverm')
+ ,1:335))
> y <- array(NA,dim=c(6,335),dimnames=list(c('Maand','Csmvert','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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
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
Csmvert Maand econs werkloosh finsit spaarverm
1 -28 1 -25 37 -16 -33
2 -26 2 -23 33 -15 -32
3 -27 3 -24 36 -16 -32
4 -26 4 -24 37 -14 -31
5 -27 5 -25 39 -14 -31
6 -27 6 -25 39 -14 -32
7 -27 7 -24 37 -16 -32
8 -28 8 -24 37 -17 -33
9 -26 9 -22 36 -15 -31
10 -13 10 1 23 -9 -21
11 -13 11 -5 21 -9 -17
12 -14 12 -10 24 -7 -14
13 -12 1 -10 25 -4 -10
14 -16 2 -15 29 -9 -13
15 -16 3 -13 24 -8 -19
16 -12 4 -11 22 -6 -10
17 -15 5 -15 28 -5 -13
18 -18 6 -15 39 -7 -11
19 -17 7 -16 36 -6 -9
20 -10 8 -4 32 -1 -1
21 -9 9 -5 27 -2 -3
22 -13 10 -9 33 -1 -7
23 -15 11 -14 36 -3 -6
24 -12 12 -11 34 -2 -1
25 -13 1 -7 34 -2 -11
26 -10 2 -7 31 -1 -3
27 -13 3 -9 37 -2 -1
28 -11 4 -5 36 -1 -2
29 -12 5 -10 35 0 -2
30 -10 6 -9 32 1 -2
31 -13 7 -10 35 -1 -4
32 -12 8 -8 36 -1 -1
33 -11 9 -9 35 0 0
34 -11 10 -10 32 0 -3
35 -11 11 -10 28 1 -4
36 -8 12 -5 24 1 -4
37 -7 1 -6 25 2 -2
38 -10 2 -10 29 1 -3
39 -8 3 -10 28 2 4
40 -8 4 -9 25 1 3
41 -7 5 -10 22 0 3
42 -7 6 -8 22 2 -1
43 -6 7 -8 22 1 5
44 -8 8 -8 23 0 -2
45 -6 9 -4 22 1 2
46 -3 10 2 14 3 -1
47 1 11 3 7 2 6
48 0 12 2 9 4 4
49 -3 1 -3 12 1 -2
50 0 2 -1 9 4 4
51 0 3 1 6 2 3
52 -1 4 2 8 3 0
53 -1 5 -4 10 2 7
54 0 6 0 8 3 5
55 1 7 5 9 5 3
56 0 8 -1 11 5 9
57 2 9 3 6 3 7
58 3 10 6 6 4 8
59 2 11 7 9 5 8
60 4 12 7 7 5 10
61 3 1 3 8 4 11
62 4 2 8 2 6 5
63 3 3 3 2 5 9
64 1 4 0 7 4 7
65 2 5 1 6 4 8
66 4 6 4 4 7 12
67 3 7 4 8 8 10
68 2 8 1 9 5 10
69 -4 9 -17 11 4 8
70 -5 10 -16 14 1 11
71 -5 11 -13 18 2 10
72 -7 12 -15 23 0 8
73 -13 1 -31 25 -2 5
74 -11 2 -26 31 -1 12
75 -3 3 -5 18 2 10
76 -3 4 -5 19 3 8
77 -5 5 -6 23 2 8
78 -4 6 -5 24 2 10
79 -4 7 -5 25 5 12
80 -4 8 -7 26 4 13
81 -5 9 -6 27 5 7
82 -4 10 -8 23 2 13
83 -5 11 -6 27 6 11
84 -6 12 -12 34 7 13
85 -9 1 -15 34 1 11
86 -10 2 -15 37 1 10
87 -11 3 -16 41 0 15
88 -13 4 -19 43 -2 11
89 -13 5 -23 38 -1 10
90 -13 6 -23 39 -1 12
91 -11 7 -21 35 1 14
92 -12 8 -21 38 0 11
93 -14 9 -25 40 0 8
94 -20 10 -34 49 -1 3
95 -17 11 -30 51 -1 15
96 -16 12 -27 48 -1 11
97 -24 1 -40 54 -4 0
98 -24 2 -40 56 -6 4
99 -22 3 -34 56 -3 7
100 -25 4 -43 61 -7 12
101 -24 5 -39 57 -4 5
102 -25 6 -40 57 -5 2
103 -24 7 -40 52 -3 0
104 -25 8 -40 58 -5 5
105 -24 9 -35 60 -6 4
106 -26 10 -43 62 -7 7
107 -25 11 -44 48 -6 0
108 -24 12 -38 50 -8 -1
109 -22 1 -37 50 -5 3
110 -20 2 -31 48 -5 2
111 -14 3 -20 40 -3 7
112 -13 4 -22 35 -2 6
113 -10 5 -9 33 -1 3
114 -10 6 -11 34 1 3
115 -11 7 -8 34 -1 1
116 -6 8 -3 28 -1 8
117 -2 9 3 26 3 10
118 -3 10 6 23 2 6
119 -2 11 -3 20 4 11
120 -4 12 -8 20 3 6
121 -7 1 -8 26 1 6
122 -8 2 -10 28 0 3
123 -7 3 -9 29 2 10
124 -4 4 -7 25 2 12
125 -7 5 -12 27 2 9
126 -5 6 -9 24 3 12
127 -6 7 -8 26 2 10
128 -12 8 -19 38 1 6
129 -12 9 -21 38 0 8
130 -16 10 -24 45 -4 11
131 -20 11 -30 53 -9 11
132 -16 12 -28 44 -6 11
133 -16 1 -27 43 -7 14
134 -18 2 -26 47 -6 8
135 -15 3 -27 40 -6 12
136 -12 4 -23 34 -3 11
137 -13 5 -26 38 -3 14
138 -13 6 -23 39 -4 15
139 -12 7 -21 35 -5 15
140 -11 8 -20 35 -4 14
141 -9 9 -14 36 -3 16
142 -9 10 -16 25 -5 9
143 -8 11 -17 24 -3 13
144 -8 12 -18 29 -2 15
145 -15 1 -25 44 -3 14
146 -16 2 -26 43 -5 11
147 -21 3 -36 57 -3 14
148 -21 4 -35 56 -3 10
149 -16 5 -27 47 -4 13
150 -13 6 -22 41 -2 15
151 -12 7 -25 38 -3 20
152 -8 8 -17 33 -2 19
153 -9 9 -14 36 -3 16
154 -1 10 -7 22 2 22
155 -5 11 -12 27 1 19
156 -9 12 -17 32 -1 16
157 -1 1 -8 21 2 23
158 3 2 -2 14 5 23
159 2 3 -1 10 3 16
160 3 4 1 14 3 23
161 5 5 0 12 3 30
162 5 6 -2 10 1 31
163 3 7 -5 12 3 24
164 2 8 -4 9 1 20
165 1 9 -9 14 2 24
166 -4 10 -16 23 2 23
167 1 11 -7 17 1 25
168 1 12 -7 16 2 25
169 6 1 3 7 4 23
170 3 2 -2 9 3 21
171 2 3 -3 9 3 16
172 2 4 -6 14 3 26
173 2 5 -7 12 2 23
174 -8 6 -24 23 -1 15
175 0 7 -13 12 1 23
176 -2 8 -14 15 3 20
177 3 9 -7 6 4 22
178 5 10 -1 6 4 24
179 8 11 5 1 6 22
180 8 12 6 3 4 24
181 9 1 5 -1 6 24
182 11 2 5 -4 6 29
183 13 3 9 -6 8 29
184 12 4 10 -9 4 25
185 13 5 14 -13 8 16
186 15 6 19 -13 10 18
187 13 7 18 -10 9 13
188 16 8 16 -12 12 22
189 10 9 8 -9 9 15
190 14 10 10 -15 11 20
191 14 11 12 -14 11 19
192 15 12 13 -18 11 18
193 13 1 15 -13 11 13
194 8 2 3 -2 11 17
195 7 3 2 -1 9 17
196 3 4 -2 5 8 13
197 3 5 1 8 6 14
198 4 6 1 6 7 13
199 4 7 -1 7 8 17
200 0 8 -6 15 6 17
201 -4 9 -13 23 5 15
202 -14 10 -25 43 2 9
203 -18 11 -26 60 3 10
204 -8 12 -9 36 3 9
205 -1 1 1 28 7 14
206 1 2 3 23 8 18
207 2 3 6 23 7 18
208 0 4 2 22 7 12
209 1 5 5 22 6 16
210 0 6 5 24 6 12
211 -1 7 0 32 7 19
212 -3 8 -5 27 5 13
213 -3 9 -4 27 5 12
214 -3 10 -2 27 5 13
215 -4 11 -1 29 4 11
216 -8 12 -8 38 4 10
217 -9 1 -16 40 4 16
218 -13 2 -19 45 1 12
219 -18 3 -28 50 -1 6
220 -11 4 -11 43 3 8
221 -9 5 -4 44 4 6
222 -10 6 -9 44 3 8
223 -13 7 -12 49 2 8
224 -11 8 -10 42 1 9
225 -5 9 -2 36 4 13
226 -15 10 -13 57 3 8
227 -6 11 0 42 5 11
228 -6 12 0 39 6 8
229 -3 1 4 33 6 10
230 -1 2 7 32 6 15
231 -3 3 5 34 6 12
232 -4 4 2 37 6 13
233 -6 5 -2 38 5 12
234 0 6 6 28 6 15
235 -4 7 -3 31 5 13
236 -2 8 1 28 6 13
237 -2 9 0 30 5 16
238 -6 10 -7 39 7 14
239 -7 11 -6 38 4 12
240 -6 12 -4 39 5 15
241 -6 1 -4 38 6 14
242 -3 2 -2 37 6 19
243 -2 3 2 32 5 16
244 -5 4 -5 32 3 16
245 -11 5 -15 44 2 11
246 -11 6 -16 43 3 13
247 -11 7 -18 42 3 12
248 -10 8 -13 38 2 11
249 -14 9 -23 37 0 6
250 -8 10 -10 35 4 9
251 -9 11 -10 37 4 6
252 -5 12 -6 33 5 15
253 -1 1 -3 24 6 17
254 -2 2 -4 24 6 13
255 -5 3 -7 31 5 12
256 -4 4 -7 25 5 13
257 -6 5 -7 28 3 10
258 -2 6 -3 24 5 14
259 -2 7 0 25 5 13
260 -2 8 -5 16 5 10
261 -2 9 -3 17 3 11
262 2 10 3 11 6 12
263 1 11 2 12 6 7
264 -8 12 -7 39 4 11
265 -1 1 -1 19 6 9
266 1 2 0 14 5 13
267 -1 3 -3 15 4 12
268 2 4 4 7 5 5
269 2 5 2 12 5 13
270 1 6 3 12 4 11
271 -1 7 0 14 3 8
272 -2 8 -10 9 2 8
273 -2 9 -10 8 3 8
274 -1 10 -9 4 2 8
275 -8 11 -22 7 -1 0
276 -4 12 -16 3 0 3
277 -6 1 -18 5 -2 0
278 -3 2 -14 0 1 -1
279 -3 3 -12 -2 -2 -1
280 -7 4 -17 6 -2 -4
281 -9 5 -23 11 -2 1
282 -11 6 -28 9 -6 -1
283 -13 7 -31 17 -4 0
284 -11 8 -21 21 -2 -1
285 -9 9 -19 21 0 6
286 -17 10 -22 41 -5 0
287 -22 11 -22 57 -4 -3
288 -25 12 -25 65 -5 -3
289 -20 1 -16 68 -1 4
290 -24 2 -22 73 -2 1
291 -24 3 -21 71 -4 0
292 -22 4 -10 71 -1 -4
293 -19 5 -7 70 1 -2
294 -18 6 -5 69 1 3
295 -17 7 -4 65 -2 2
296 -11 8 7 57 1 5
297 -11 9 6 57 1 6
298 -12 10 3 57 3 6
299 -10 11 10 55 3 3
300 -15 12 0 65 1 4
301 -15 1 -2 65 1 7
302 -15 2 -1 64 0 5
303 -13 3 2 60 2 6
304 -8 4 8 43 2 1
305 -13 5 -6 47 -1 3
306 -9 6 -4 40 1 6
307 -7 7 4 31 0 0
308 -4 8 7 27 1 3
309 -4 9 3 24 1 4
310 -2 10 3 23 3 7
311 0 11 8 17 2 6
312 -2 12 3 16 0 6
313 -3 1 -3 15 0 6
314 1 2 4 8 3 6
315 -2 3 -5 5 -2 2
316 -1 4 -1 6 0 2
317 1 5 5 5 1 2
318 -3 6 0 12 -1 3
319 -4 7 -6 8 -2 -1
320 -9 8 -13 17 -1 -4
321 -9 9 -15 22 -1 4
322 -7 10 -8 24 1 5
323 -14 11 -20 36 -2 3
324 -12 12 -10 31 -5 -1
325 -16 1 -22 34 -5 -4
326 -20 2 -25 47 -6 0
327 -12 3 -10 33 -4 -1
328 -12 4 -8 35 -3 -1
329 -10 5 -9 31 -3 3
330 -10 6 -5 35 -1 2
331 -13 7 -7 39 -2 -4
332 -16 8 -11 46 -3 -3
333 -14 9 -11 40 -3 -1
334 -17 10 -16 50 -3 3
335 -24 11 -28 62 -5 -2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand econs werkloosh finsit spaarverm
0.09723 -0.01149 0.24853 -0.25164 0.24977 0.24832
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.91540 -0.24726 0.01519 0.26705 0.92644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.097228 0.055573 1.750 0.0811 .
Maand -0.011492 0.005587 -2.057 0.0405 *
econs 0.248532 0.002709 91.756 <2e-16 ***
werkloosh -0.251643 0.001350 -186.446 <2e-16 ***
finsit 0.249770 0.008363 29.866 <2e-16 ***
spaarverm 0.248320 0.002829 87.770 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3518 on 329 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9985
F-statistic: 4.332e+04 on 5 and 329 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,] 0.30650831 0.613016616 0.693491692
[2,] 0.18344480 0.366889596 0.816555202
[3,] 0.11330001 0.226600029 0.886699986
[4,] 0.22580433 0.451608664 0.774195668
[5,] 0.14545334 0.290906682 0.854546659
[6,] 0.14837874 0.296757480 0.851621260
[7,] 0.11084916 0.221698326 0.889150837
[8,] 0.08572006 0.171440123 0.914279938
[9,] 0.05985720 0.119714405 0.940142798
[10,] 0.16637942 0.332758849 0.833620575
[11,] 0.19952929 0.399058578 0.800470711
[12,] 0.25254624 0.505092477 0.747453762
[13,] 0.24387252 0.487745048 0.756127476
[14,] 0.27809661 0.556193215 0.721903393
[15,] 0.22079380 0.441587593 0.779206203
[16,] 0.18442178 0.368843553 0.815578223
[17,] 0.21553929 0.431078571 0.784460715
[18,] 0.21052247 0.421044945 0.789477527
[19,] 0.42724398 0.854487951 0.572756025
[20,] 0.37001350 0.740026996 0.629986502
[21,] 0.33955834 0.679116686 0.660441657
[22,] 0.37062674 0.741253484 0.629373258
[23,] 0.41310611 0.826212216 0.586893892
[24,] 0.39317684 0.786353682 0.606823159
[25,] 0.35441037 0.708820743 0.645589628
[26,] 0.33403409 0.668068178 0.665965911
[27,] 0.55108156 0.897836873 0.448918437
[28,] 0.49873492 0.997469843 0.501265079
[29,] 0.53308540 0.933829195 0.466914597
[30,] 0.48218248 0.964364962 0.517817519
[31,] 0.43720041 0.874400814 0.562799593
[32,] 0.51664545 0.966709107 0.483354553
[33,] 0.49093244 0.981864887 0.509067557
[34,] 0.44477289 0.889545775 0.555227112
[35,] 0.39608529 0.792170576 0.603914712
[36,] 0.36530802 0.730616043 0.634691978
[37,] 0.33598564 0.671971288 0.664014356
[38,] 0.42206527 0.844130537 0.577934732
[39,] 0.37784881 0.755697618 0.622151191
[40,] 0.34737634 0.694752681 0.652623660
[41,] 0.44937615 0.898752298 0.550623851
[42,] 0.41578773 0.831575457 0.584212271
[43,] 0.41754306 0.835086127 0.582456937
[44,] 0.47855651 0.957113029 0.521443486
[45,] 0.44131730 0.882634594 0.558682703
[46,] 0.40314268 0.806285360 0.596857320
[47,] 0.36399872 0.727997446 0.636001277
[48,] 0.40513300 0.810266002 0.594866999
[49,] 0.39585358 0.791707157 0.604146421
[50,] 0.35705087 0.714101740 0.642949130
[51,] 0.43816875 0.876337505 0.561831247
[52,] 0.46719067 0.934381345 0.532809328
[53,] 0.45391061 0.907821212 0.546089394
[54,] 0.50350427 0.992991461 0.496495730
[55,] 0.72319621 0.553607573 0.276803787
[56,] 0.69035029 0.619299416 0.309649708
[57,] 0.66549580 0.669008390 0.334504195
[58,] 0.77754720 0.444905598 0.222452799
[59,] 0.79109449 0.417811023 0.208905512
[60,] 0.78813818 0.423723649 0.211861825
[61,] 0.75868454 0.482630916 0.241315458
[62,] 0.76349064 0.473018714 0.236509357
[63,] 0.73535786 0.529284290 0.264642145
[64,] 0.81363241 0.372735173 0.186367587
[65,] 0.78914577 0.421708464 0.210854232
[66,] 0.78587716 0.428245685 0.214122842
[67,] 0.78154945 0.436901095 0.218450548
[68,] 0.76327559 0.473448825 0.236724412
[69,] 0.74835415 0.503291706 0.251645853
[70,] 0.73681651 0.526366971 0.263183486
[71,] 0.80763279 0.384734414 0.192367207
[72,] 0.78388223 0.432235535 0.216117768
[73,] 0.78834464 0.423310717 0.211655358
[74,] 0.76411857 0.471762854 0.235881427
[75,] 0.86491402 0.270171968 0.135085984
[76,] 0.91334424 0.173311520 0.086655760
[77,] 0.90104958 0.197900832 0.098950416
[78,] 0.88833319 0.223333611 0.111666806
[79,] 0.90691701 0.186165970 0.093082985
[80,] 0.89903950 0.201921006 0.100960503
[81,] 0.88292316 0.234153673 0.117076836
[82,] 0.87367145 0.252657099 0.126328549
[83,] 0.91834068 0.163318646 0.081659323
[84,] 0.90532796 0.189344080 0.094672040
[85,] 0.90198205 0.196035891 0.098017946
[86,] 0.89488427 0.210231455 0.105115727
[87,] 0.88036188 0.239276245 0.119638123
[88,] 0.88328959 0.233420812 0.116710406
[89,] 0.88041713 0.239165732 0.119582866
[90,] 0.88187432 0.236251359 0.118125679
[91,] 0.91012732 0.179745354 0.089872677
[92,] 0.90451147 0.190977055 0.095488527
[93,] 0.90039408 0.199211849 0.099605925
[94,] 0.88523261 0.229534780 0.114767390
[95,] 0.88122970 0.237540591 0.118770295
[96,] 0.89016337 0.219673263 0.109836631
[97,] 0.89136217 0.217275657 0.108637828
[98,] 0.89080916 0.218381685 0.109190843
[99,] 0.90144172 0.197116568 0.098558284
[100,] 0.90083747 0.198325055 0.099162527
[101,] 0.88931174 0.221376513 0.110688256
[102,] 0.89755168 0.204896636 0.102448318
[103,] 0.88216144 0.235677120 0.117838560
[104,] 0.87233054 0.255338912 0.127669456
[105,] 0.85430997 0.291380056 0.145690028
[106,] 0.84768379 0.304632417 0.152316208
[107,] 0.85641496 0.287170086 0.143585043
[108,] 0.83969209 0.320615829 0.160307915
[109,] 0.87979806 0.240403881 0.120201940
[110,] 0.91001640 0.179967198 0.089983599
[111,] 0.89879667 0.202406669 0.101203334
[112,] 0.95246528 0.095069433 0.047534717
[113,] 0.94999528 0.100009438 0.050004719
[114,] 0.97079840 0.058403208 0.029201604
[115,] 0.97570716 0.048585671 0.024292836
[116,] 0.98031786 0.039364271 0.019682135
[117,] 0.97614380 0.047712392 0.023856196
[118,] 0.97934106 0.041317874 0.020658937
[119,] 0.98152889 0.036942225 0.018471112
[120,] 0.98613657 0.027726855 0.013863427
[121,] 0.99456504 0.010869928 0.005434964
[122,] 0.99477982 0.010440362 0.005220181
[123,] 0.99506886 0.009862278 0.004931139
[124,] 0.99848933 0.003021336 0.001510668
[125,] 0.99837180 0.003256399 0.001628199
[126,] 0.99816105 0.003677897 0.001838948
[127,] 0.99794558 0.004108847 0.002054423
[128,] 0.99770631 0.004587381 0.002293691
[129,] 0.99749763 0.005004747 0.002502374
[130,] 0.99706033 0.005879343 0.002939671
[131,] 0.99740875 0.005182491 0.002591246
[132,] 0.99732584 0.005348321 0.002674160
[133,] 0.99734318 0.005313640 0.002656820
[134,] 0.99723611 0.005527778 0.002763889
[135,] 0.99668570 0.006628608 0.003314304
[136,] 0.99793858 0.004122839 0.002061419
[137,] 0.99839387 0.003212269 0.001606135
[138,] 0.99816673 0.003666532 0.001833266
[139,] 0.99847893 0.003042136 0.001521068
[140,] 0.99806385 0.003872303 0.001936152
[141,] 0.99797921 0.004041577 0.002020789
[142,] 0.99822275 0.003554494 0.001777247
[143,] 0.99844144 0.003117122 0.001558561
[144,] 0.99840441 0.003191180 0.001595590
[145,] 0.99843273 0.003134537 0.001567269
[146,] 0.99841730 0.003165397 0.001582699
[147,] 0.99805078 0.003898449 0.001949225
[148,] 0.99815604 0.003687927 0.001843963
[149,] 0.99763367 0.004732651 0.002366325
[150,] 0.99698332 0.006033363 0.003016681
[151,] 0.99617373 0.007652539 0.003826270
[152,] 0.99561344 0.008773119 0.004386560
[153,] 0.99496413 0.010071740 0.005035870
[154,] 0.99372750 0.012544993 0.006272496
[155,] 0.99533885 0.009322297 0.004661148
[156,] 0.99416429 0.011671429 0.005835715
[157,] 0.99384423 0.012311544 0.006155772
[158,] 0.99453091 0.010938186 0.005469093
[159,] 0.99654388 0.006912250 0.003456125
[160,] 0.99575366 0.008492675 0.004246337
[161,] 0.99521084 0.009578318 0.004789159
[162,] 0.99469704 0.010605923 0.005302962
[163,] 0.99402560 0.011948799 0.005974399
[164,] 0.99317060 0.013658796 0.006829398
[165,] 0.99508844 0.009823115 0.004911558
[166,] 0.99492645 0.010147100 0.005073550
[167,] 0.99509726 0.009805476 0.004902738
[168,] 0.99571829 0.008563412 0.004281706
[169,] 0.99495966 0.010080671 0.005040336
[170,] 0.99392428 0.012151436 0.006075718
[171,] 0.99253054 0.014938917 0.007469459
[172,] 0.99326427 0.013471455 0.006735728
[173,] 0.99165928 0.016681439 0.008340720
[174,] 0.98992086 0.020158277 0.010079138
[175,] 0.98776508 0.024469840 0.012234920
[176,] 0.98593825 0.028123505 0.014061753
[177,] 0.98436119 0.031277613 0.015638807
[178,] 0.98095301 0.038093989 0.019046995
[179,] 0.98418969 0.031620615 0.015810308
[180,] 0.98788607 0.024227863 0.012113932
[181,] 0.98670442 0.026591157 0.013295579
[182,] 0.98373068 0.032538637 0.016269318
[183,] 0.98019829 0.039603430 0.019801715
[184,] 0.97603451 0.047930989 0.023965495
[185,] 0.97213279 0.055734419 0.027867210
[186,] 0.97322838 0.053543230 0.026771615
[187,] 0.97270018 0.054599639 0.027299819
[188,] 0.98215163 0.035696730 0.017848365
[189,] 0.98052085 0.038958292 0.019479146
[190,] 0.97795932 0.044081352 0.022040676
[191,] 0.97624155 0.047516897 0.023758448
[192,] 0.97933677 0.041326455 0.020663227
[193,] 0.97502335 0.049953299 0.024976650
[194,] 0.97436640 0.051267204 0.025633602
[195,] 0.97487109 0.050257811 0.025128906
[196,] 0.97574841 0.048503182 0.024251591
[197,] 0.97873446 0.042531073 0.021265536
[198,] 0.98426767 0.031464667 0.015732333
[199,] 0.98077297 0.038454052 0.019227026
[200,] 0.97804656 0.043906876 0.021953438
[201,] 0.97528500 0.049429993 0.024714997
[202,] 0.97307657 0.053846863 0.026923431
[203,] 0.98159200 0.036816001 0.018408000
[204,] 0.98716536 0.025669284 0.012834642
[205,] 0.99143271 0.017134577 0.008567289
[206,] 0.98971339 0.020573214 0.010286607
[207,] 0.98758325 0.024833494 0.012416747
[208,] 0.98504252 0.029914952 0.014957476
[209,] 0.98160156 0.036796876 0.018398438
[210,] 0.97867861 0.042642772 0.021321386
[211,] 0.97798081 0.044038374 0.022019187
[212,] 0.97578535 0.048429298 0.024214649
[213,] 0.97998705 0.040025910 0.020012955
[214,] 0.98570729 0.028585414 0.014292707
[215,] 0.98316189 0.033676223 0.016838111
[216,] 0.98382053 0.032358934 0.016179467
[217,] 0.98483092 0.030338157 0.015169079
[218,] 0.98181027 0.036379451 0.018189725
[219,] 0.98951431 0.020971385 0.010485692
[220,] 0.98954311 0.020913782 0.010456891
[221,] 0.98760168 0.024796634 0.012398317
[222,] 0.98450090 0.030998203 0.015499102
[223,] 0.98321796 0.033564078 0.016782039
[224,] 0.97918691 0.041626187 0.020813094
[225,] 0.97637422 0.047251558 0.023625779
[226,] 0.97591476 0.048170478 0.024085239
[227,] 0.97087257 0.058254868 0.029127434
[228,] 0.96491043 0.070179147 0.035089573
[229,] 0.96940139 0.061197217 0.030598608
[230,] 0.97182701 0.056345975 0.028172988
[231,] 0.96876483 0.062470340 0.031235170
[232,] 0.96287413 0.074251744 0.037125872
[233,] 0.97191715 0.056165705 0.028082853
[234,] 0.98306352 0.033872969 0.016936485
[235,] 0.98433788 0.031324237 0.015662118
[236,] 0.98407097 0.031858061 0.015929031
[237,] 0.99266309 0.014673830 0.007336915
[238,] 0.99078702 0.018425955 0.009212977
[239,] 0.99347196 0.013056087 0.006528044
[240,] 0.99289399 0.014212012 0.007106006
[241,] 0.99268020 0.014639608 0.007319804
[242,] 0.99135219 0.017295610 0.008647805
[243,] 0.99267560 0.014648803 0.007324401
[244,] 0.99129741 0.017405170 0.008702585
[245,] 0.98931054 0.021378915 0.010689458
[246,] 0.98951648 0.020967035 0.010483518
[247,] 0.99105577 0.017888470 0.008944235
[248,] 0.99104218 0.017915646 0.008957823
[249,] 0.99147231 0.017055371 0.008527686
[250,] 0.99043935 0.019121301 0.009560650
[251,] 0.98782262 0.024354765 0.012177382
[252,] 0.98832207 0.023355861 0.011677931
[253,] 0.98773181 0.024536383 0.012268192
[254,] 0.98807070 0.023858603 0.011929301
[255,] 0.98817237 0.023655261 0.011827630
[256,] 0.98533059 0.029338819 0.014669410
[257,] 0.98313176 0.033736478 0.016868239
[258,] 0.97874897 0.042502060 0.021251030
[259,] 0.98170639 0.036587229 0.018293614
[260,] 0.97796872 0.044062559 0.022031279
[261,] 0.97278104 0.054437930 0.027218965
[262,] 0.97638439 0.047231227 0.023615613
[263,] 0.97210218 0.055795641 0.027897821
[264,] 0.97417875 0.051642501 0.025821250
[265,] 0.96775588 0.064488236 0.032244118
[266,] 0.95995654 0.080086911 0.040043456
[267,] 0.96509942 0.069801163 0.034900581
[268,] 0.95676994 0.086460120 0.043230060
[269,] 0.94716378 0.105672434 0.052836217
[270,] 0.94886512 0.102269764 0.051134882
[271,] 0.93853264 0.122934710 0.061467355
[272,] 0.92828845 0.143423093 0.071711546
[273,] 0.91789837 0.164203266 0.082101633
[274,] 0.89929147 0.201417067 0.100708533
[275,] 0.88060493 0.238790135 0.119395068
[276,] 0.88816985 0.223660308 0.111830154
[277,] 0.88197338 0.236053230 0.118026615
[278,] 0.86745599 0.265088023 0.132544012
[279,] 0.85887489 0.282250229 0.141125114
[280,] 0.84802362 0.303952763 0.151976382
[281,] 0.85838867 0.283222656 0.141611328
[282,] 0.84136994 0.317260115 0.158630057
[283,] 0.82290303 0.354193936 0.177096968
[284,] 0.84468065 0.310638692 0.155319346
[285,] 0.91566776 0.168664488 0.084332244
[286,] 0.90763437 0.184731253 0.092365626
[287,] 0.91965238 0.160695232 0.080347616
[288,] 0.90304384 0.193912312 0.096956156
[289,] 0.88899277 0.222014460 0.111007230
[290,] 0.92573109 0.148537822 0.074268911
[291,] 0.90855087 0.182898254 0.091449127
[292,] 0.89614313 0.207713737 0.103856869
[293,] 0.86838149 0.263237025 0.131618512
[294,] 0.85210131 0.295797381 0.147898690
[295,] 0.85026195 0.299476100 0.149738050
[296,] 0.81203856 0.375922876 0.187961438
[297,] 0.77593530 0.448129405 0.224064703
[298,] 0.79094929 0.418101427 0.209050714
[299,] 0.77359731 0.452805385 0.226402693
[300,] 0.72090431 0.558191378 0.279095689
[301,] 0.66269629 0.674607426 0.337303713
[302,] 0.84285990 0.314280191 0.157140095
[303,] 0.90543568 0.189128635 0.094564317
[304,] 0.87315509 0.253689810 0.126844905
[305,] 0.83915266 0.321694677 0.160847338
[306,] 0.78908826 0.421823483 0.210911742
[307,] 0.84685019 0.306299629 0.153149814
[308,] 0.85497333 0.290053337 0.145026668
[309,] 0.91620679 0.167586423 0.083793212
[310,] 0.89309521 0.213809579 0.106904789
[311,] 0.92424054 0.151518925 0.075759463
[312,] 0.90648385 0.187032304 0.093516152
[313,] 0.87440371 0.251192587 0.125596293
[314,] 0.83432528 0.331349438 0.165674719
[315,] 0.82771579 0.344568422 0.172284211
[316,] 0.79109577 0.417808461 0.208904230
[317,] 0.70474605 0.590507909 0.295253955
[318,] 0.94877199 0.102456030 0.051228015
> postscript(file="/var/wessaorg/rcomp/tmp/18t5y1355590686.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/202om1355590686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32l2w1355590686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wk0j1355590686.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/50c5g1355590687.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 6
-0.370784292 -0.361016720 -0.096294477 0.418981186 0.182290746 0.442102793
7 8 9 10 11 12
0.201316944 -0.289101272 -0.022494526 0.019595987 0.025711786 -0.209708775
13 14 15 16 17 18
0.172931942 0.427462055 -0.076173822 0.200550480 0.211216517 -0.006319335
19 20 21 22 23 24
-0.247634101 -0.460500452 0.287718075 -0.453295960 -0.192997718 0.078244487
25 26 27 28 29 30
0.440904272 0.461138389 -0.767318837 -0.003045665 -0.250308491 0.507953504
31 32 33 34 35 36
-0.480914660 -0.459802475 0.050488580 0.300543191 -0.695986285 0.066276309
37 38 39 40 41 42
0.693627904 0.203907665 -0.024252397 -0.518131000 0.236733564 0.244902826
43 44 45 46 47 48
0.016245112 0.267389412 -0.209937057 -0.457357486 0.055632699 -0.183957359
49 50 51 52 53 54
0.926444693 0.446716097 -0.045924317 -0.284487806 0.233009732 -0.006040114
55 56 57 58 59 60
0.011537595 -0.472414676 0.282915850 0.050723660 -0.681156666 0.330409703
61 62 63 64 65 66
0.451215344 -0.299427861 -0.788787979 -0.027076891 0.235920805 -0.744056536
67 68 69 70 71 72
-0.479122512 0.278916408 0.013671872 -0.464089291 -0.193069838 0.569879439
73 74 75 76 77 78
0.167756049 0.458438874 -0.273259170 0.236746015 -0.246888796 0.271074866
79 80 81 82 83 84
-0.711739136 0.049908833 0.304662183 0.066035253 -0.915403030 0.602369408
85 86 87 88 89 90
0.216809024 0.231549910 -0.493684541 0.259507300 0.005461069 -0.228043722
91 92 93 94 95 96
-0.716365816 0.044784664 0.298648651 0.303080596 -0.156106721 0.348141628
97 98 99 100 101 102
0.443324543 0.464362316 -0.509603848 -0.245633760 -0.245909332 0.008843856
103 104 105 106 107 108
-0.240778375 -0.461488744 0.308721194 0.316561540 -0.457946285 0.313501842
109 110 111 112 113 114
0.195968066 0.461304933 -0.015332661 0.233558012 0.006044165 0.266702852
115 116 117 118 119 120
-0.471220343 0.049516861 0.570814996 -0.675166919 0.077041175 0.822560411
121 122 123 124 125 126
-0.294455742 0.712114887 -0.511060567 0.500156814 0.002552349 -0.481208537
127 128 129 130 131 132
-0.468552479 0.539551473 0.801236567 -0.426056882 0.338616780 0.838950074
133 134 135 136 137 138
-0.282827774 -0.273145584 0.232097744 0.238616710 0.257315473 -0.223694296
139 140 141 142 143 144
-0.466067322 0.295443440 0.320979625 0.299241399 -0.185197068 0.586631738
145 146 147 148 149 150
-0.527326870 -0.274446892 -0.499137303 0.005460034 0.268723205 -0.468479425
151 152 153 154 155 156
-0.458151406 0.305423789 0.320979625 0.330981405 -0.161924484 -0.405060635
157 158 159 160 161 162
-0.023878990 -0.014385995 -0.020218239 -0.237456927 -0.218958651 0.037530216
163 164 165 166 167 168
0.536602895 0.037453812 0.306768953 -0.428911647 0.589068743 0.099148178
169 170 171 172 173 174
0.219733259 -0.276421397 0.225201910 -0.242695877 0.508771418 0.249240896
175 176 177 178 179 180
0.272714679 -0.466912505 -0.206337332 -0.182674357 0.076514125 0.345660207
181 182 183 184 185 186
-0.038332811 -0.023369173 -0.008828612 -0.008438220 0.238156327 0.010811377
187 188 189 190 191 192
0.517133261 0.538214018 -0.219563934 0.043868295 0.058260193 0.062968906
193 194 195 196 197 198
-0.060693121 -0.292029731 -0.280823644 -0.522298205 -0.250252345 0.256504072
199 200 201 202 203 204
-0.226347217 -0.459514313 0.051251805 0.317210208 0.357074300 0.352419318
205 206 207 208 209 210
0.486868374 -0.499966785 0.015700428 0.259595360 -0.218017140 0.290040584
211 212 213 214 215 216
0.569324780 0.554718947 0.565999446 -0.167891459 -0.155235401 0.109083999
217 218 219 220 221 222
-0.015710601 -0.257820183 0.238129641 -0.232633982 0.537650369 0.544930107
223 224 225 226 227 228
-0.189998662 -0.435620456 0.335172678 -0.143616923 0.617821619 0.369574946
229 230 231 232 233 234
0.242537913 0.015192801 -0.228006307 0.035689367 -0.208959712 0.303121038
235 236 237 238 239 240
0.052735519 0.065402870 0.333522382 0.346622236 0.103888928 -0.124768624
241 242 243 244 245 246
-0.504274685 0.516911751 0.270792468 -0.478455106 0.529437227 -0.208591646
247 248 249 250 251 252
0.296640549 -0.443007192 -0.456703427 0.076553945 0.336291741 -0.137563207
253 254 255 256 257 258
-0.020767268 0.232536111 0.249213164 -0.497472325 -0.486552108 0.031422847
259 260 261 262 263 264
-0.202716803 -0.468393680 -0.451102165 -0.438286151 0.314980103 -0.136124531
265 266 267 268 269 270
0.210514329 -0.028249809 -0.521430435 0.225667037 0.005877559 -0.484752277
271 272 273 274 275 276
-0.229650100 0.258712500 -0.231208065 -0.225049543 -0.491849828 0.027151697
277 278 279 280 281 282
0.145586638 0.403748551 0.164200892 0.176454106 -0.304249397 -0.057666629
283 284 285 286 287 288
-0.035295697 0.246233185 -0.477116763 0.051597183 -0.415433506 -0.395433435
289 290 291 292 293 294
0.258979725 0.014605384 0.022139464 -0.456244941 0.561830258 -0.416983306
295 296 297 298 299 300
0.337034617 0.107267143 0.118970881 -0.623481830 -0.110036655 0.154419953
301 302 303 304 305 306
-0.219890042 0.037837176 -0.450696537 0.033275845 -0.216549227 0.291879912
307 308 309 310 311 312
-0.209977564 0.054618604 0.056988146 0.572338059 0.329404425 -0.168549190
313 314 315 316 317 318
-0.055416156 -0.294454708 0.441020898 0.210490299 0.229380447 -0.503749524
319 320 321 322 323 324
0.235409545 -0.253400936 -0.473190414 -0.445992639 -0.186457521 -0.175906690
325 326 327 328 329 330
0.179947202 -0.535117707 -0.025819602 -0.257874422 0.002297745 -0.224984086
331 332 333 334 335
0.029832220 -0.201599036 -0.196604453 -0.419304843 0.335420322
> postscript(file="/var/wessaorg/rcomp/tmp/6749n1355590687.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 -0.370784292 NA
1 -0.361016720 -0.370784292
2 -0.096294477 -0.361016720
3 0.418981186 -0.096294477
4 0.182290746 0.418981186
5 0.442102793 0.182290746
6 0.201316944 0.442102793
7 -0.289101272 0.201316944
8 -0.022494526 -0.289101272
9 0.019595987 -0.022494526
10 0.025711786 0.019595987
11 -0.209708775 0.025711786
12 0.172931942 -0.209708775
13 0.427462055 0.172931942
14 -0.076173822 0.427462055
15 0.200550480 -0.076173822
16 0.211216517 0.200550480
17 -0.006319335 0.211216517
18 -0.247634101 -0.006319335
19 -0.460500452 -0.247634101
20 0.287718075 -0.460500452
21 -0.453295960 0.287718075
22 -0.192997718 -0.453295960
23 0.078244487 -0.192997718
24 0.440904272 0.078244487
25 0.461138389 0.440904272
26 -0.767318837 0.461138389
27 -0.003045665 -0.767318837
28 -0.250308491 -0.003045665
29 0.507953504 -0.250308491
30 -0.480914660 0.507953504
31 -0.459802475 -0.480914660
32 0.050488580 -0.459802475
33 0.300543191 0.050488580
34 -0.695986285 0.300543191
35 0.066276309 -0.695986285
36 0.693627904 0.066276309
37 0.203907665 0.693627904
38 -0.024252397 0.203907665
39 -0.518131000 -0.024252397
40 0.236733564 -0.518131000
41 0.244902826 0.236733564
42 0.016245112 0.244902826
43 0.267389412 0.016245112
44 -0.209937057 0.267389412
45 -0.457357486 -0.209937057
46 0.055632699 -0.457357486
47 -0.183957359 0.055632699
48 0.926444693 -0.183957359
49 0.446716097 0.926444693
50 -0.045924317 0.446716097
51 -0.284487806 -0.045924317
52 0.233009732 -0.284487806
53 -0.006040114 0.233009732
54 0.011537595 -0.006040114
55 -0.472414676 0.011537595
56 0.282915850 -0.472414676
57 0.050723660 0.282915850
58 -0.681156666 0.050723660
59 0.330409703 -0.681156666
60 0.451215344 0.330409703
61 -0.299427861 0.451215344
62 -0.788787979 -0.299427861
63 -0.027076891 -0.788787979
64 0.235920805 -0.027076891
65 -0.744056536 0.235920805
66 -0.479122512 -0.744056536
67 0.278916408 -0.479122512
68 0.013671872 0.278916408
69 -0.464089291 0.013671872
70 -0.193069838 -0.464089291
71 0.569879439 -0.193069838
72 0.167756049 0.569879439
73 0.458438874 0.167756049
74 -0.273259170 0.458438874
75 0.236746015 -0.273259170
76 -0.246888796 0.236746015
77 0.271074866 -0.246888796
78 -0.711739136 0.271074866
79 0.049908833 -0.711739136
80 0.304662183 0.049908833
81 0.066035253 0.304662183
82 -0.915403030 0.066035253
83 0.602369408 -0.915403030
84 0.216809024 0.602369408
85 0.231549910 0.216809024
86 -0.493684541 0.231549910
87 0.259507300 -0.493684541
88 0.005461069 0.259507300
89 -0.228043722 0.005461069
90 -0.716365816 -0.228043722
91 0.044784664 -0.716365816
92 0.298648651 0.044784664
93 0.303080596 0.298648651
94 -0.156106721 0.303080596
95 0.348141628 -0.156106721
96 0.443324543 0.348141628
97 0.464362316 0.443324543
98 -0.509603848 0.464362316
99 -0.245633760 -0.509603848
100 -0.245909332 -0.245633760
101 0.008843856 -0.245909332
102 -0.240778375 0.008843856
103 -0.461488744 -0.240778375
104 0.308721194 -0.461488744
105 0.316561540 0.308721194
106 -0.457946285 0.316561540
107 0.313501842 -0.457946285
108 0.195968066 0.313501842
109 0.461304933 0.195968066
110 -0.015332661 0.461304933
111 0.233558012 -0.015332661
112 0.006044165 0.233558012
113 0.266702852 0.006044165
114 -0.471220343 0.266702852
115 0.049516861 -0.471220343
116 0.570814996 0.049516861
117 -0.675166919 0.570814996
118 0.077041175 -0.675166919
119 0.822560411 0.077041175
120 -0.294455742 0.822560411
121 0.712114887 -0.294455742
122 -0.511060567 0.712114887
123 0.500156814 -0.511060567
124 0.002552349 0.500156814
125 -0.481208537 0.002552349
126 -0.468552479 -0.481208537
127 0.539551473 -0.468552479
128 0.801236567 0.539551473
129 -0.426056882 0.801236567
130 0.338616780 -0.426056882
131 0.838950074 0.338616780
132 -0.282827774 0.838950074
133 -0.273145584 -0.282827774
134 0.232097744 -0.273145584
135 0.238616710 0.232097744
136 0.257315473 0.238616710
137 -0.223694296 0.257315473
138 -0.466067322 -0.223694296
139 0.295443440 -0.466067322
140 0.320979625 0.295443440
141 0.299241399 0.320979625
142 -0.185197068 0.299241399
143 0.586631738 -0.185197068
144 -0.527326870 0.586631738
145 -0.274446892 -0.527326870
146 -0.499137303 -0.274446892
147 0.005460034 -0.499137303
148 0.268723205 0.005460034
149 -0.468479425 0.268723205
150 -0.458151406 -0.468479425
151 0.305423789 -0.458151406
152 0.320979625 0.305423789
153 0.330981405 0.320979625
154 -0.161924484 0.330981405
155 -0.405060635 -0.161924484
156 -0.023878990 -0.405060635
157 -0.014385995 -0.023878990
158 -0.020218239 -0.014385995
159 -0.237456927 -0.020218239
160 -0.218958651 -0.237456927
161 0.037530216 -0.218958651
162 0.536602895 0.037530216
163 0.037453812 0.536602895
164 0.306768953 0.037453812
165 -0.428911647 0.306768953
166 0.589068743 -0.428911647
167 0.099148178 0.589068743
168 0.219733259 0.099148178
169 -0.276421397 0.219733259
170 0.225201910 -0.276421397
171 -0.242695877 0.225201910
172 0.508771418 -0.242695877
173 0.249240896 0.508771418
174 0.272714679 0.249240896
175 -0.466912505 0.272714679
176 -0.206337332 -0.466912505
177 -0.182674357 -0.206337332
178 0.076514125 -0.182674357
179 0.345660207 0.076514125
180 -0.038332811 0.345660207
181 -0.023369173 -0.038332811
182 -0.008828612 -0.023369173
183 -0.008438220 -0.008828612
184 0.238156327 -0.008438220
185 0.010811377 0.238156327
186 0.517133261 0.010811377
187 0.538214018 0.517133261
188 -0.219563934 0.538214018
189 0.043868295 -0.219563934
190 0.058260193 0.043868295
191 0.062968906 0.058260193
192 -0.060693121 0.062968906
193 -0.292029731 -0.060693121
194 -0.280823644 -0.292029731
195 -0.522298205 -0.280823644
196 -0.250252345 -0.522298205
197 0.256504072 -0.250252345
198 -0.226347217 0.256504072
199 -0.459514313 -0.226347217
200 0.051251805 -0.459514313
201 0.317210208 0.051251805
202 0.357074300 0.317210208
203 0.352419318 0.357074300
204 0.486868374 0.352419318
205 -0.499966785 0.486868374
206 0.015700428 -0.499966785
207 0.259595360 0.015700428
208 -0.218017140 0.259595360
209 0.290040584 -0.218017140
210 0.569324780 0.290040584
211 0.554718947 0.569324780
212 0.565999446 0.554718947
213 -0.167891459 0.565999446
214 -0.155235401 -0.167891459
215 0.109083999 -0.155235401
216 -0.015710601 0.109083999
217 -0.257820183 -0.015710601
218 0.238129641 -0.257820183
219 -0.232633982 0.238129641
220 0.537650369 -0.232633982
221 0.544930107 0.537650369
222 -0.189998662 0.544930107
223 -0.435620456 -0.189998662
224 0.335172678 -0.435620456
225 -0.143616923 0.335172678
226 0.617821619 -0.143616923
227 0.369574946 0.617821619
228 0.242537913 0.369574946
229 0.015192801 0.242537913
230 -0.228006307 0.015192801
231 0.035689367 -0.228006307
232 -0.208959712 0.035689367
233 0.303121038 -0.208959712
234 0.052735519 0.303121038
235 0.065402870 0.052735519
236 0.333522382 0.065402870
237 0.346622236 0.333522382
238 0.103888928 0.346622236
239 -0.124768624 0.103888928
240 -0.504274685 -0.124768624
241 0.516911751 -0.504274685
242 0.270792468 0.516911751
243 -0.478455106 0.270792468
244 0.529437227 -0.478455106
245 -0.208591646 0.529437227
246 0.296640549 -0.208591646
247 -0.443007192 0.296640549
248 -0.456703427 -0.443007192
249 0.076553945 -0.456703427
250 0.336291741 0.076553945
251 -0.137563207 0.336291741
252 -0.020767268 -0.137563207
253 0.232536111 -0.020767268
254 0.249213164 0.232536111
255 -0.497472325 0.249213164
256 -0.486552108 -0.497472325
257 0.031422847 -0.486552108
258 -0.202716803 0.031422847
259 -0.468393680 -0.202716803
260 -0.451102165 -0.468393680
261 -0.438286151 -0.451102165
262 0.314980103 -0.438286151
263 -0.136124531 0.314980103
264 0.210514329 -0.136124531
265 -0.028249809 0.210514329
266 -0.521430435 -0.028249809
267 0.225667037 -0.521430435
268 0.005877559 0.225667037
269 -0.484752277 0.005877559
270 -0.229650100 -0.484752277
271 0.258712500 -0.229650100
272 -0.231208065 0.258712500
273 -0.225049543 -0.231208065
274 -0.491849828 -0.225049543
275 0.027151697 -0.491849828
276 0.145586638 0.027151697
277 0.403748551 0.145586638
278 0.164200892 0.403748551
279 0.176454106 0.164200892
280 -0.304249397 0.176454106
281 -0.057666629 -0.304249397
282 -0.035295697 -0.057666629
283 0.246233185 -0.035295697
284 -0.477116763 0.246233185
285 0.051597183 -0.477116763
286 -0.415433506 0.051597183
287 -0.395433435 -0.415433506
288 0.258979725 -0.395433435
289 0.014605384 0.258979725
290 0.022139464 0.014605384
291 -0.456244941 0.022139464
292 0.561830258 -0.456244941
293 -0.416983306 0.561830258
294 0.337034617 -0.416983306
295 0.107267143 0.337034617
296 0.118970881 0.107267143
297 -0.623481830 0.118970881
298 -0.110036655 -0.623481830
299 0.154419953 -0.110036655
300 -0.219890042 0.154419953
301 0.037837176 -0.219890042
302 -0.450696537 0.037837176
303 0.033275845 -0.450696537
304 -0.216549227 0.033275845
305 0.291879912 -0.216549227
306 -0.209977564 0.291879912
307 0.054618604 -0.209977564
308 0.056988146 0.054618604
309 0.572338059 0.056988146
310 0.329404425 0.572338059
311 -0.168549190 0.329404425
312 -0.055416156 -0.168549190
313 -0.294454708 -0.055416156
314 0.441020898 -0.294454708
315 0.210490299 0.441020898
316 0.229380447 0.210490299
317 -0.503749524 0.229380447
318 0.235409545 -0.503749524
319 -0.253400936 0.235409545
320 -0.473190414 -0.253400936
321 -0.445992639 -0.473190414
322 -0.186457521 -0.445992639
323 -0.175906690 -0.186457521
324 0.179947202 -0.175906690
325 -0.535117707 0.179947202
326 -0.025819602 -0.535117707
327 -0.257874422 -0.025819602
328 0.002297745 -0.257874422
329 -0.224984086 0.002297745
330 0.029832220 -0.224984086
331 -0.201599036 0.029832220
332 -0.196604453 -0.201599036
333 -0.419304843 -0.196604453
334 0.335420322 -0.419304843
335 NA 0.335420322
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.361016720 -0.370784292
[2,] -0.096294477 -0.361016720
[3,] 0.418981186 -0.096294477
[4,] 0.182290746 0.418981186
[5,] 0.442102793 0.182290746
[6,] 0.201316944 0.442102793
[7,] -0.289101272 0.201316944
[8,] -0.022494526 -0.289101272
[9,] 0.019595987 -0.022494526
[10,] 0.025711786 0.019595987
[11,] -0.209708775 0.025711786
[12,] 0.172931942 -0.209708775
[13,] 0.427462055 0.172931942
[14,] -0.076173822 0.427462055
[15,] 0.200550480 -0.076173822
[16,] 0.211216517 0.200550480
[17,] -0.006319335 0.211216517
[18,] -0.247634101 -0.006319335
[19,] -0.460500452 -0.247634101
[20,] 0.287718075 -0.460500452
[21,] -0.453295960 0.287718075
[22,] -0.192997718 -0.453295960
[23,] 0.078244487 -0.192997718
[24,] 0.440904272 0.078244487
[25,] 0.461138389 0.440904272
[26,] -0.767318837 0.461138389
[27,] -0.003045665 -0.767318837
[28,] -0.250308491 -0.003045665
[29,] 0.507953504 -0.250308491
[30,] -0.480914660 0.507953504
[31,] -0.459802475 -0.480914660
[32,] 0.050488580 -0.459802475
[33,] 0.300543191 0.050488580
[34,] -0.695986285 0.300543191
[35,] 0.066276309 -0.695986285
[36,] 0.693627904 0.066276309
[37,] 0.203907665 0.693627904
[38,] -0.024252397 0.203907665
[39,] -0.518131000 -0.024252397
[40,] 0.236733564 -0.518131000
[41,] 0.244902826 0.236733564
[42,] 0.016245112 0.244902826
[43,] 0.267389412 0.016245112
[44,] -0.209937057 0.267389412
[45,] -0.457357486 -0.209937057
[46,] 0.055632699 -0.457357486
[47,] -0.183957359 0.055632699
[48,] 0.926444693 -0.183957359
[49,] 0.446716097 0.926444693
[50,] -0.045924317 0.446716097
[51,] -0.284487806 -0.045924317
[52,] 0.233009732 -0.284487806
[53,] -0.006040114 0.233009732
[54,] 0.011537595 -0.006040114
[55,] -0.472414676 0.011537595
[56,] 0.282915850 -0.472414676
[57,] 0.050723660 0.282915850
[58,] -0.681156666 0.050723660
[59,] 0.330409703 -0.681156666
[60,] 0.451215344 0.330409703
[61,] -0.299427861 0.451215344
[62,] -0.788787979 -0.299427861
[63,] -0.027076891 -0.788787979
[64,] 0.235920805 -0.027076891
[65,] -0.744056536 0.235920805
[66,] -0.479122512 -0.744056536
[67,] 0.278916408 -0.479122512
[68,] 0.013671872 0.278916408
[69,] -0.464089291 0.013671872
[70,] -0.193069838 -0.464089291
[71,] 0.569879439 -0.193069838
[72,] 0.167756049 0.569879439
[73,] 0.458438874 0.167756049
[74,] -0.273259170 0.458438874
[75,] 0.236746015 -0.273259170
[76,] -0.246888796 0.236746015
[77,] 0.271074866 -0.246888796
[78,] -0.711739136 0.271074866
[79,] 0.049908833 -0.711739136
[80,] 0.304662183 0.049908833
[81,] 0.066035253 0.304662183
[82,] -0.915403030 0.066035253
[83,] 0.602369408 -0.915403030
[84,] 0.216809024 0.602369408
[85,] 0.231549910 0.216809024
[86,] -0.493684541 0.231549910
[87,] 0.259507300 -0.493684541
[88,] 0.005461069 0.259507300
[89,] -0.228043722 0.005461069
[90,] -0.716365816 -0.228043722
[91,] 0.044784664 -0.716365816
[92,] 0.298648651 0.044784664
[93,] 0.303080596 0.298648651
[94,] -0.156106721 0.303080596
[95,] 0.348141628 -0.156106721
[96,] 0.443324543 0.348141628
[97,] 0.464362316 0.443324543
[98,] -0.509603848 0.464362316
[99,] -0.245633760 -0.509603848
[100,] -0.245909332 -0.245633760
[101,] 0.008843856 -0.245909332
[102,] -0.240778375 0.008843856
[103,] -0.461488744 -0.240778375
[104,] 0.308721194 -0.461488744
[105,] 0.316561540 0.308721194
[106,] -0.457946285 0.316561540
[107,] 0.313501842 -0.457946285
[108,] 0.195968066 0.313501842
[109,] 0.461304933 0.195968066
[110,] -0.015332661 0.461304933
[111,] 0.233558012 -0.015332661
[112,] 0.006044165 0.233558012
[113,] 0.266702852 0.006044165
[114,] -0.471220343 0.266702852
[115,] 0.049516861 -0.471220343
[116,] 0.570814996 0.049516861
[117,] -0.675166919 0.570814996
[118,] 0.077041175 -0.675166919
[119,] 0.822560411 0.077041175
[120,] -0.294455742 0.822560411
[121,] 0.712114887 -0.294455742
[122,] -0.511060567 0.712114887
[123,] 0.500156814 -0.511060567
[124,] 0.002552349 0.500156814
[125,] -0.481208537 0.002552349
[126,] -0.468552479 -0.481208537
[127,] 0.539551473 -0.468552479
[128,] 0.801236567 0.539551473
[129,] -0.426056882 0.801236567
[130,] 0.338616780 -0.426056882
[131,] 0.838950074 0.338616780
[132,] -0.282827774 0.838950074
[133,] -0.273145584 -0.282827774
[134,] 0.232097744 -0.273145584
[135,] 0.238616710 0.232097744
[136,] 0.257315473 0.238616710
[137,] -0.223694296 0.257315473
[138,] -0.466067322 -0.223694296
[139,] 0.295443440 -0.466067322
[140,] 0.320979625 0.295443440
[141,] 0.299241399 0.320979625
[142,] -0.185197068 0.299241399
[143,] 0.586631738 -0.185197068
[144,] -0.527326870 0.586631738
[145,] -0.274446892 -0.527326870
[146,] -0.499137303 -0.274446892
[147,] 0.005460034 -0.499137303
[148,] 0.268723205 0.005460034
[149,] -0.468479425 0.268723205
[150,] -0.458151406 -0.468479425
[151,] 0.305423789 -0.458151406
[152,] 0.320979625 0.305423789
[153,] 0.330981405 0.320979625
[154,] -0.161924484 0.330981405
[155,] -0.405060635 -0.161924484
[156,] -0.023878990 -0.405060635
[157,] -0.014385995 -0.023878990
[158,] -0.020218239 -0.014385995
[159,] -0.237456927 -0.020218239
[160,] -0.218958651 -0.237456927
[161,] 0.037530216 -0.218958651
[162,] 0.536602895 0.037530216
[163,] 0.037453812 0.536602895
[164,] 0.306768953 0.037453812
[165,] -0.428911647 0.306768953
[166,] 0.589068743 -0.428911647
[167,] 0.099148178 0.589068743
[168,] 0.219733259 0.099148178
[169,] -0.276421397 0.219733259
[170,] 0.225201910 -0.276421397
[171,] -0.242695877 0.225201910
[172,] 0.508771418 -0.242695877
[173,] 0.249240896 0.508771418
[174,] 0.272714679 0.249240896
[175,] -0.466912505 0.272714679
[176,] -0.206337332 -0.466912505
[177,] -0.182674357 -0.206337332
[178,] 0.076514125 -0.182674357
[179,] 0.345660207 0.076514125
[180,] -0.038332811 0.345660207
[181,] -0.023369173 -0.038332811
[182,] -0.008828612 -0.023369173
[183,] -0.008438220 -0.008828612
[184,] 0.238156327 -0.008438220
[185,] 0.010811377 0.238156327
[186,] 0.517133261 0.010811377
[187,] 0.538214018 0.517133261
[188,] -0.219563934 0.538214018
[189,] 0.043868295 -0.219563934
[190,] 0.058260193 0.043868295
[191,] 0.062968906 0.058260193
[192,] -0.060693121 0.062968906
[193,] -0.292029731 -0.060693121
[194,] -0.280823644 -0.292029731
[195,] -0.522298205 -0.280823644
[196,] -0.250252345 -0.522298205
[197,] 0.256504072 -0.250252345
[198,] -0.226347217 0.256504072
[199,] -0.459514313 -0.226347217
[200,] 0.051251805 -0.459514313
[201,] 0.317210208 0.051251805
[202,] 0.357074300 0.317210208
[203,] 0.352419318 0.357074300
[204,] 0.486868374 0.352419318
[205,] -0.499966785 0.486868374
[206,] 0.015700428 -0.499966785
[207,] 0.259595360 0.015700428
[208,] -0.218017140 0.259595360
[209,] 0.290040584 -0.218017140
[210,] 0.569324780 0.290040584
[211,] 0.554718947 0.569324780
[212,] 0.565999446 0.554718947
[213,] -0.167891459 0.565999446
[214,] -0.155235401 -0.167891459
[215,] 0.109083999 -0.155235401
[216,] -0.015710601 0.109083999
[217,] -0.257820183 -0.015710601
[218,] 0.238129641 -0.257820183
[219,] -0.232633982 0.238129641
[220,] 0.537650369 -0.232633982
[221,] 0.544930107 0.537650369
[222,] -0.189998662 0.544930107
[223,] -0.435620456 -0.189998662
[224,] 0.335172678 -0.435620456
[225,] -0.143616923 0.335172678
[226,] 0.617821619 -0.143616923
[227,] 0.369574946 0.617821619
[228,] 0.242537913 0.369574946
[229,] 0.015192801 0.242537913
[230,] -0.228006307 0.015192801
[231,] 0.035689367 -0.228006307
[232,] -0.208959712 0.035689367
[233,] 0.303121038 -0.208959712
[234,] 0.052735519 0.303121038
[235,] 0.065402870 0.052735519
[236,] 0.333522382 0.065402870
[237,] 0.346622236 0.333522382
[238,] 0.103888928 0.346622236
[239,] -0.124768624 0.103888928
[240,] -0.504274685 -0.124768624
[241,] 0.516911751 -0.504274685
[242,] 0.270792468 0.516911751
[243,] -0.478455106 0.270792468
[244,] 0.529437227 -0.478455106
[245,] -0.208591646 0.529437227
[246,] 0.296640549 -0.208591646
[247,] -0.443007192 0.296640549
[248,] -0.456703427 -0.443007192
[249,] 0.076553945 -0.456703427
[250,] 0.336291741 0.076553945
[251,] -0.137563207 0.336291741
[252,] -0.020767268 -0.137563207
[253,] 0.232536111 -0.020767268
[254,] 0.249213164 0.232536111
[255,] -0.497472325 0.249213164
[256,] -0.486552108 -0.497472325
[257,] 0.031422847 -0.486552108
[258,] -0.202716803 0.031422847
[259,] -0.468393680 -0.202716803
[260,] -0.451102165 -0.468393680
[261,] -0.438286151 -0.451102165
[262,] 0.314980103 -0.438286151
[263,] -0.136124531 0.314980103
[264,] 0.210514329 -0.136124531
[265,] -0.028249809 0.210514329
[266,] -0.521430435 -0.028249809
[267,] 0.225667037 -0.521430435
[268,] 0.005877559 0.225667037
[269,] -0.484752277 0.005877559
[270,] -0.229650100 -0.484752277
[271,] 0.258712500 -0.229650100
[272,] -0.231208065 0.258712500
[273,] -0.225049543 -0.231208065
[274,] -0.491849828 -0.225049543
[275,] 0.027151697 -0.491849828
[276,] 0.145586638 0.027151697
[277,] 0.403748551 0.145586638
[278,] 0.164200892 0.403748551
[279,] 0.176454106 0.164200892
[280,] -0.304249397 0.176454106
[281,] -0.057666629 -0.304249397
[282,] -0.035295697 -0.057666629
[283,] 0.246233185 -0.035295697
[284,] -0.477116763 0.246233185
[285,] 0.051597183 -0.477116763
[286,] -0.415433506 0.051597183
[287,] -0.395433435 -0.415433506
[288,] 0.258979725 -0.395433435
[289,] 0.014605384 0.258979725
[290,] 0.022139464 0.014605384
[291,] -0.456244941 0.022139464
[292,] 0.561830258 -0.456244941
[293,] -0.416983306 0.561830258
[294,] 0.337034617 -0.416983306
[295,] 0.107267143 0.337034617
[296,] 0.118970881 0.107267143
[297,] -0.623481830 0.118970881
[298,] -0.110036655 -0.623481830
[299,] 0.154419953 -0.110036655
[300,] -0.219890042 0.154419953
[301,] 0.037837176 -0.219890042
[302,] -0.450696537 0.037837176
[303,] 0.033275845 -0.450696537
[304,] -0.216549227 0.033275845
[305,] 0.291879912 -0.216549227
[306,] -0.209977564 0.291879912
[307,] 0.054618604 -0.209977564
[308,] 0.056988146 0.054618604
[309,] 0.572338059 0.056988146
[310,] 0.329404425 0.572338059
[311,] -0.168549190 0.329404425
[312,] -0.055416156 -0.168549190
[313,] -0.294454708 -0.055416156
[314,] 0.441020898 -0.294454708
[315,] 0.210490299 0.441020898
[316,] 0.229380447 0.210490299
[317,] -0.503749524 0.229380447
[318,] 0.235409545 -0.503749524
[319,] -0.253400936 0.235409545
[320,] -0.473190414 -0.253400936
[321,] -0.445992639 -0.473190414
[322,] -0.186457521 -0.445992639
[323,] -0.175906690 -0.186457521
[324,] 0.179947202 -0.175906690
[325,] -0.535117707 0.179947202
[326,] -0.025819602 -0.535117707
[327,] -0.257874422 -0.025819602
[328,] 0.002297745 -0.257874422
[329,] -0.224984086 0.002297745
[330,] 0.029832220 -0.224984086
[331,] -0.201599036 0.029832220
[332,] -0.196604453 -0.201599036
[333,] -0.419304843 -0.196604453
[334,] 0.335420322 -0.419304843
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.361016720 -0.370784292
2 -0.096294477 -0.361016720
3 0.418981186 -0.096294477
4 0.182290746 0.418981186
5 0.442102793 0.182290746
6 0.201316944 0.442102793
7 -0.289101272 0.201316944
8 -0.022494526 -0.289101272
9 0.019595987 -0.022494526
10 0.025711786 0.019595987
11 -0.209708775 0.025711786
12 0.172931942 -0.209708775
13 0.427462055 0.172931942
14 -0.076173822 0.427462055
15 0.200550480 -0.076173822
16 0.211216517 0.200550480
17 -0.006319335 0.211216517
18 -0.247634101 -0.006319335
19 -0.460500452 -0.247634101
20 0.287718075 -0.460500452
21 -0.453295960 0.287718075
22 -0.192997718 -0.453295960
23 0.078244487 -0.192997718
24 0.440904272 0.078244487
25 0.461138389 0.440904272
26 -0.767318837 0.461138389
27 -0.003045665 -0.767318837
28 -0.250308491 -0.003045665
29 0.507953504 -0.250308491
30 -0.480914660 0.507953504
31 -0.459802475 -0.480914660
32 0.050488580 -0.459802475
33 0.300543191 0.050488580
34 -0.695986285 0.300543191
35 0.066276309 -0.695986285
36 0.693627904 0.066276309
37 0.203907665 0.693627904
38 -0.024252397 0.203907665
39 -0.518131000 -0.024252397
40 0.236733564 -0.518131000
41 0.244902826 0.236733564
42 0.016245112 0.244902826
43 0.267389412 0.016245112
44 -0.209937057 0.267389412
45 -0.457357486 -0.209937057
46 0.055632699 -0.457357486
47 -0.183957359 0.055632699
48 0.926444693 -0.183957359
49 0.446716097 0.926444693
50 -0.045924317 0.446716097
51 -0.284487806 -0.045924317
52 0.233009732 -0.284487806
53 -0.006040114 0.233009732
54 0.011537595 -0.006040114
55 -0.472414676 0.011537595
56 0.282915850 -0.472414676
57 0.050723660 0.282915850
58 -0.681156666 0.050723660
59 0.330409703 -0.681156666
60 0.451215344 0.330409703
61 -0.299427861 0.451215344
62 -0.788787979 -0.299427861
63 -0.027076891 -0.788787979
64 0.235920805 -0.027076891
65 -0.744056536 0.235920805
66 -0.479122512 -0.744056536
67 0.278916408 -0.479122512
68 0.013671872 0.278916408
69 -0.464089291 0.013671872
70 -0.193069838 -0.464089291
71 0.569879439 -0.193069838
72 0.167756049 0.569879439
73 0.458438874 0.167756049
74 -0.273259170 0.458438874
75 0.236746015 -0.273259170
76 -0.246888796 0.236746015
77 0.271074866 -0.246888796
78 -0.711739136 0.271074866
79 0.049908833 -0.711739136
80 0.304662183 0.049908833
81 0.066035253 0.304662183
82 -0.915403030 0.066035253
83 0.602369408 -0.915403030
84 0.216809024 0.602369408
85 0.231549910 0.216809024
86 -0.493684541 0.231549910
87 0.259507300 -0.493684541
88 0.005461069 0.259507300
89 -0.228043722 0.005461069
90 -0.716365816 -0.228043722
91 0.044784664 -0.716365816
92 0.298648651 0.044784664
93 0.303080596 0.298648651
94 -0.156106721 0.303080596
95 0.348141628 -0.156106721
96 0.443324543 0.348141628
97 0.464362316 0.443324543
98 -0.509603848 0.464362316
99 -0.245633760 -0.509603848
100 -0.245909332 -0.245633760
101 0.008843856 -0.245909332
102 -0.240778375 0.008843856
103 -0.461488744 -0.240778375
104 0.308721194 -0.461488744
105 0.316561540 0.308721194
106 -0.457946285 0.316561540
107 0.313501842 -0.457946285
108 0.195968066 0.313501842
109 0.461304933 0.195968066
110 -0.015332661 0.461304933
111 0.233558012 -0.015332661
112 0.006044165 0.233558012
113 0.266702852 0.006044165
114 -0.471220343 0.266702852
115 0.049516861 -0.471220343
116 0.570814996 0.049516861
117 -0.675166919 0.570814996
118 0.077041175 -0.675166919
119 0.822560411 0.077041175
120 -0.294455742 0.822560411
121 0.712114887 -0.294455742
122 -0.511060567 0.712114887
123 0.500156814 -0.511060567
124 0.002552349 0.500156814
125 -0.481208537 0.002552349
126 -0.468552479 -0.481208537
127 0.539551473 -0.468552479
128 0.801236567 0.539551473
129 -0.426056882 0.801236567
130 0.338616780 -0.426056882
131 0.838950074 0.338616780
132 -0.282827774 0.838950074
133 -0.273145584 -0.282827774
134 0.232097744 -0.273145584
135 0.238616710 0.232097744
136 0.257315473 0.238616710
137 -0.223694296 0.257315473
138 -0.466067322 -0.223694296
139 0.295443440 -0.466067322
140 0.320979625 0.295443440
141 0.299241399 0.320979625
142 -0.185197068 0.299241399
143 0.586631738 -0.185197068
144 -0.527326870 0.586631738
145 -0.274446892 -0.527326870
146 -0.499137303 -0.274446892
147 0.005460034 -0.499137303
148 0.268723205 0.005460034
149 -0.468479425 0.268723205
150 -0.458151406 -0.468479425
151 0.305423789 -0.458151406
152 0.320979625 0.305423789
153 0.330981405 0.320979625
154 -0.161924484 0.330981405
155 -0.405060635 -0.161924484
156 -0.023878990 -0.405060635
157 -0.014385995 -0.023878990
158 -0.020218239 -0.014385995
159 -0.237456927 -0.020218239
160 -0.218958651 -0.237456927
161 0.037530216 -0.218958651
162 0.536602895 0.037530216
163 0.037453812 0.536602895
164 0.306768953 0.037453812
165 -0.428911647 0.306768953
166 0.589068743 -0.428911647
167 0.099148178 0.589068743
168 0.219733259 0.099148178
169 -0.276421397 0.219733259
170 0.225201910 -0.276421397
171 -0.242695877 0.225201910
172 0.508771418 -0.242695877
173 0.249240896 0.508771418
174 0.272714679 0.249240896
175 -0.466912505 0.272714679
176 -0.206337332 -0.466912505
177 -0.182674357 -0.206337332
178 0.076514125 -0.182674357
179 0.345660207 0.076514125
180 -0.038332811 0.345660207
181 -0.023369173 -0.038332811
182 -0.008828612 -0.023369173
183 -0.008438220 -0.008828612
184 0.238156327 -0.008438220
185 0.010811377 0.238156327
186 0.517133261 0.010811377
187 0.538214018 0.517133261
188 -0.219563934 0.538214018
189 0.043868295 -0.219563934
190 0.058260193 0.043868295
191 0.062968906 0.058260193
192 -0.060693121 0.062968906
193 -0.292029731 -0.060693121
194 -0.280823644 -0.292029731
195 -0.522298205 -0.280823644
196 -0.250252345 -0.522298205
197 0.256504072 -0.250252345
198 -0.226347217 0.256504072
199 -0.459514313 -0.226347217
200 0.051251805 -0.459514313
201 0.317210208 0.051251805
202 0.357074300 0.317210208
203 0.352419318 0.357074300
204 0.486868374 0.352419318
205 -0.499966785 0.486868374
206 0.015700428 -0.499966785
207 0.259595360 0.015700428
208 -0.218017140 0.259595360
209 0.290040584 -0.218017140
210 0.569324780 0.290040584
211 0.554718947 0.569324780
212 0.565999446 0.554718947
213 -0.167891459 0.565999446
214 -0.155235401 -0.167891459
215 0.109083999 -0.155235401
216 -0.015710601 0.109083999
217 -0.257820183 -0.015710601
218 0.238129641 -0.257820183
219 -0.232633982 0.238129641
220 0.537650369 -0.232633982
221 0.544930107 0.537650369
222 -0.189998662 0.544930107
223 -0.435620456 -0.189998662
224 0.335172678 -0.435620456
225 -0.143616923 0.335172678
226 0.617821619 -0.143616923
227 0.369574946 0.617821619
228 0.242537913 0.369574946
229 0.015192801 0.242537913
230 -0.228006307 0.015192801
231 0.035689367 -0.228006307
232 -0.208959712 0.035689367
233 0.303121038 -0.208959712
234 0.052735519 0.303121038
235 0.065402870 0.052735519
236 0.333522382 0.065402870
237 0.346622236 0.333522382
238 0.103888928 0.346622236
239 -0.124768624 0.103888928
240 -0.504274685 -0.124768624
241 0.516911751 -0.504274685
242 0.270792468 0.516911751
243 -0.478455106 0.270792468
244 0.529437227 -0.478455106
245 -0.208591646 0.529437227
246 0.296640549 -0.208591646
247 -0.443007192 0.296640549
248 -0.456703427 -0.443007192
249 0.076553945 -0.456703427
250 0.336291741 0.076553945
251 -0.137563207 0.336291741
252 -0.020767268 -0.137563207
253 0.232536111 -0.020767268
254 0.249213164 0.232536111
255 -0.497472325 0.249213164
256 -0.486552108 -0.497472325
257 0.031422847 -0.486552108
258 -0.202716803 0.031422847
259 -0.468393680 -0.202716803
260 -0.451102165 -0.468393680
261 -0.438286151 -0.451102165
262 0.314980103 -0.438286151
263 -0.136124531 0.314980103
264 0.210514329 -0.136124531
265 -0.028249809 0.210514329
266 -0.521430435 -0.028249809
267 0.225667037 -0.521430435
268 0.005877559 0.225667037
269 -0.484752277 0.005877559
270 -0.229650100 -0.484752277
271 0.258712500 -0.229650100
272 -0.231208065 0.258712500
273 -0.225049543 -0.231208065
274 -0.491849828 -0.225049543
275 0.027151697 -0.491849828
276 0.145586638 0.027151697
277 0.403748551 0.145586638
278 0.164200892 0.403748551
279 0.176454106 0.164200892
280 -0.304249397 0.176454106
281 -0.057666629 -0.304249397
282 -0.035295697 -0.057666629
283 0.246233185 -0.035295697
284 -0.477116763 0.246233185
285 0.051597183 -0.477116763
286 -0.415433506 0.051597183
287 -0.395433435 -0.415433506
288 0.258979725 -0.395433435
289 0.014605384 0.258979725
290 0.022139464 0.014605384
291 -0.456244941 0.022139464
292 0.561830258 -0.456244941
293 -0.416983306 0.561830258
294 0.337034617 -0.416983306
295 0.107267143 0.337034617
296 0.118970881 0.107267143
297 -0.623481830 0.118970881
298 -0.110036655 -0.623481830
299 0.154419953 -0.110036655
300 -0.219890042 0.154419953
301 0.037837176 -0.219890042
302 -0.450696537 0.037837176
303 0.033275845 -0.450696537
304 -0.216549227 0.033275845
305 0.291879912 -0.216549227
306 -0.209977564 0.291879912
307 0.054618604 -0.209977564
308 0.056988146 0.054618604
309 0.572338059 0.056988146
310 0.329404425 0.572338059
311 -0.168549190 0.329404425
312 -0.055416156 -0.168549190
313 -0.294454708 -0.055416156
314 0.441020898 -0.294454708
315 0.210490299 0.441020898
316 0.229380447 0.210490299
317 -0.503749524 0.229380447
318 0.235409545 -0.503749524
319 -0.253400936 0.235409545
320 -0.473190414 -0.253400936
321 -0.445992639 -0.473190414
322 -0.186457521 -0.445992639
323 -0.175906690 -0.186457521
324 0.179947202 -0.175906690
325 -0.535117707 0.179947202
326 -0.025819602 -0.535117707
327 -0.257874422 -0.025819602
328 0.002297745 -0.257874422
329 -0.224984086 0.002297745
330 0.029832220 -0.224984086
331 -0.201599036 0.029832220
332 -0.196604453 -0.201599036
333 -0.419304843 -0.196604453
334 0.335420322 -0.419304843
> 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/7q6ff1355590687.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8mgz11355590687.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/98jct1355590687.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10iq2c1355590687.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1146zr1355590687.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/12pc681355590687.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/13nzdg1355590687.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/14ayin1355590687.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/15te9g1355590687.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/16qxyp1355590687.tab")
+ }
>
> try(system("convert tmp/18t5y1355590686.ps tmp/18t5y1355590686.png",intern=TRUE))
character(0)
> try(system("convert tmp/202om1355590686.ps tmp/202om1355590686.png",intern=TRUE))
character(0)
> try(system("convert tmp/32l2w1355590686.ps tmp/32l2w1355590686.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wk0j1355590686.ps tmp/4wk0j1355590686.png",intern=TRUE))
character(0)
> try(system("convert tmp/50c5g1355590687.ps tmp/50c5g1355590687.png",intern=TRUE))
character(0)
> try(system("convert tmp/6749n1355590687.ps tmp/6749n1355590687.png",intern=TRUE))
character(0)
> try(system("convert tmp/7q6ff1355590687.ps tmp/7q6ff1355590687.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mgz11355590687.ps tmp/8mgz11355590687.png",intern=TRUE))
character(0)
> try(system("convert tmp/98jct1355590687.ps tmp/98jct1355590687.png",intern=TRUE))
character(0)
> try(system("convert tmp/10iq2c1355590687.ps tmp/10iq2c1355590687.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.264 1.327 15.586