R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,1
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+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,11
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,12
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,1
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6
+ ,2
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,3
+ ,-2
+ ,-5
+ ,5
+ ,-2
+ ,2
+ ,4
+ ,-1
+ ,-1
+ ,6
+ ,0
+ ,2
+ ,5
+ ,1
+ ,5
+ ,5
+ ,1
+ ,2
+ ,6
+ ,-3
+ ,0
+ ,12
+ ,-1
+ ,3
+ ,7
+ ,-4
+ ,-6
+ ,8
+ ,-2
+ ,-1
+ ,8
+ ,-9
+ ,-13
+ ,17
+ ,-1
+ ,-4
+ ,9
+ ,-9
+ ,-15
+ ,22
+ ,-1
+ ,4
+ ,10
+ ,-7
+ ,-8
+ ,24
+ ,1
+ ,5
+ ,11
+ ,-14
+ ,-20
+ ,36
+ ,-2
+ ,3)
+ ,dim=c(6
+ ,323)
+ ,dimnames=list(c('maand'
+ ,'consumentenvertrouwen'
+ ,'economischesituatie'
+ ,'werkloosheid'
+ ,'financielesituatie'
+ ,'spaarvermogen')
+ ,1:323))
> y <- array(NA,dim=c(6,323),dimnames=list(c('maand','consumentenvertrouwen','economischesituatie','werkloosheid','financielesituatie','spaarvermogen'),1:323))
> 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'
> #'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
> 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
consumentenvertrouwen maand economischesituatie werkloosheid
1 -28 1 -25 37
2 -26 2 -23 33
3 -27 3 -24 36
4 -26 4 -24 37
5 -27 5 -25 39
6 -27 6 -25 39
7 -27 7 -24 37
8 -28 8 -24 37
9 -26 9 -22 36
10 -13 10 1 23
11 -13 11 -5 21
12 -14 12 -10 24
13 -12 1 -10 25
14 -16 2 -15 29
15 -16 3 -13 24
16 -12 4 -11 22
17 -15 5 -15 28
18 -18 6 -15 39
19 -17 7 -16 36
20 -10 8 -4 32
21 -9 9 -5 27
22 -13 10 -9 33
23 -15 11 -14 36
24 -12 12 -11 34
25 -13 1 -7 34
26 -10 2 -7 31
27 -13 3 -9 37
28 -11 4 -5 36
29 -12 5 -10 35
30 -10 6 -9 32
31 -13 7 -10 35
32 -12 8 -8 36
33 -11 9 -9 35
34 -11 10 -10 32
35 -11 11 -10 28
36 -8 12 -5 24
37 -7 1 -6 25
38 -10 2 -10 29
39 -8 3 -10 28
40 -8 4 -9 25
41 -7 5 -10 22
42 -7 6 -8 22
43 -6 7 -8 22
44 -8 8 -8 23
45 -6 9 -4 22
46 -3 10 2 14
47 1 11 3 7
48 0 12 2 9
49 -3 1 -3 12
50 0 2 -1 9
51 0 3 1 6
52 -1 4 2 8
53 -1 5 -4 10
54 0 6 0 8
55 1 7 5 9
56 0 8 -1 11
57 2 9 3 6
58 3 10 6 6
59 2 11 7 9
60 4 12 7 7
61 3 1 3 8
62 4 2 8 2
63 3 3 3 2
64 1 4 0 7
65 2 5 1 6
66 4 6 4 4
67 3 7 4 8
68 2 8 1 9
69 -4 9 -17 11
70 -5 10 -16 14
71 -5 11 -13 18
72 -7 12 -15 23
73 -13 1 -31 25
74 -11 2 -26 31
75 -3 3 -5 18
76 -3 4 -5 19
77 -5 5 -6 23
78 -4 6 -5 24
79 -4 7 -5 25
80 -4 8 -7 26
81 -5 9 -6 27
82 -4 10 -8 23
83 -5 11 -6 27
84 -6 12 -12 34
85 -9 1 -15 34
86 -10 2 -15 37
87 -11 3 -16 41
88 -13 4 -19 43
89 -13 5 -23 38
90 -13 6 -23 39
91 -11 7 -21 35
92 -12 8 -21 38
93 -14 9 -25 40
94 -20 10 -34 49
95 -17 11 -30 51
96 -16 12 -27 48
97 -24 1 -40 54
98 -24 2 -40 56
99 -22 3 -34 56
100 -25 4 -43 61
101 -24 5 -39 57
102 -25 6 -40 57
103 -24 7 -40 52
104 -25 8 -40 58
105 -24 9 -35 60
106 -26 10 -43 62
107 -25 11 -44 48
108 -24 12 -38 50
109 -22 1 -37 50
110 -20 2 -31 48
111 -14 3 -20 40
112 -13 4 -22 35
113 -10 5 -9 33
114 -10 6 -11 34
115 -11 7 -8 34
116 -6 8 -3 28
117 -2 9 3 26
118 -3 10 6 23
119 -2 11 -3 20
120 -4 12 -8 20
121 -7 1 -8 26
122 -8 2 -10 28
123 -7 3 -9 29
124 -4 4 -7 25
125 -7 5 -12 27
126 -5 6 -9 24
127 -6 7 -8 26
128 -12 8 -19 38
129 -12 9 -21 38
130 -16 10 -24 45
131 -20 11 -30 53
132 -16 12 -28 44
133 -16 1 -27 43
134 -18 2 -26 47
135 -15 3 -27 40
136 -12 4 -23 34
137 -13 5 -26 38
138 -13 6 -23 39
139 -12 7 -21 35
140 -11 8 -20 35
141 -9 9 -14 36
142 -9 10 -16 25
143 -8 11 -17 24
144 -8 12 -18 29
145 -15 1 -25 44
146 -16 2 -26 43
147 -21 3 -36 57
148 -21 4 -35 56
149 -16 5 -27 47
150 -13 6 -22 41
151 -12 7 -25 38
152 -8 8 -17 33
153 -9 9 -14 36
154 -1 10 -7 22
155 -5 11 -12 27
156 -9 12 -17 32
157 -1 1 -8 21
158 3 2 -2 14
159 2 3 -1 10
160 3 4 1 14
161 5 5 0 12
162 5 6 -2 10
163 3 7 -5 12
164 2 8 -4 9
165 1 9 -9 14
166 -4 10 -16 23
167 1 11 -7 17
168 1 12 -7 16
169 6 1 3 7
170 3 2 -2 9
171 2 3 -3 9
172 2 4 -6 14
173 2 5 -7 12
174 -8 6 -24 23
175 0 7 -13 12
176 -2 8 -14 15
177 3 9 -7 6
178 5 10 -1 6
179 8 11 5 1
180 8 12 6 3
181 9 1 5 -1
182 11 2 5 -4
183 13 3 9 -6
184 12 4 10 -9
185 13 5 14 -13
186 15 6 19 -13
187 13 7 18 -10
188 16 8 16 -12
189 10 9 8 -9
190 14 10 10 -15
191 14 11 12 -14
192 15 12 13 -18
193 13 1 15 -13
194 8 2 3 -2
195 7 3 2 -1
196 3 4 -2 5
197 3 5 1 8
198 4 6 1 6
199 4 7 -1 7
200 0 8 -6 15
201 -4 9 -13 23
202 -14 10 -25 43
203 -18 11 -26 60
204 -8 12 -9 36
205 -1 1 1 28
206 1 2 3 23
207 2 3 6 23
208 0 4 2 22
209 1 5 5 22
210 0 6 5 24
211 -1 7 0 32
212 -3 8 -5 27
213 -3 9 -4 27
214 -3 10 -2 27
215 -4 11 -1 29
216 -8 12 -8 38
217 -9 1 -16 40
218 -13 2 -19 45
219 -18 3 -28 50
220 -11 4 -11 43
221 -9 5 -4 44
222 -10 6 -9 44
223 -13 7 -12 49
224 -11 8 -10 42
225 -5 9 -2 36
226 -15 10 -13 57
227 -6 11 0 42
228 -6 12 0 39
229 -3 1 4 33
230 -1 2 7 32
231 -3 3 5 34
232 -4 4 2 37
233 -6 5 -2 38
234 0 6 6 28
235 -4 7 -3 31
236 -2 8 1 28
237 -2 9 0 30
238 -6 10 -7 39
239 -7 11 -6 38
240 -6 12 -4 39
241 -6 1 -4 38
242 -3 2 -2 37
243 -2 3 2 32
244 -5 4 -5 32
245 -11 5 -15 44
246 -11 6 -16 43
247 -11 7 -18 42
248 -10 8 -13 38
249 -14 9 -23 37
250 -8 10 -10 35
251 -9 11 -10 37
252 -5 12 -6 33
253 -1 1 -3 24
254 -2 2 -4 24
255 -5 3 -7 31
256 -4 4 -7 25
257 -6 5 -7 28
258 -2 6 -3 24
259 -2 7 0 25
260 -2 8 -5 16
261 -2 9 -3 17
262 2 10 3 11
263 1 11 2 12
264 -8 12 -7 39
265 -1 1 -1 19
266 1 2 0 14
267 -1 3 -3 15
268 2 4 4 7
269 2 5 2 12
270 1 6 3 12
271 -1 7 0 14
272 -2 8 -10 9
273 -2 9 -10 8
274 -1 10 -9 4
275 -8 11 -22 7
276 -4 12 -16 3
277 -6 1 -18 5
278 -3 2 -14 0
279 -3 3 -12 -2
280 -7 4 -17 6
281 -9 5 -23 11
282 -11 6 -28 9
283 -13 7 -31 17
284 -11 8 -21 21
285 -9 9 -19 21
286 -17 10 -22 41
287 -22 11 -22 57
288 -25 12 -25 65
289 -20 1 -16 68
290 -24 2 -22 73
291 -24 3 -21 71
292 -22 4 -10 71
293 -19 5 -7 70
294 -18 6 -5 69
295 -17 7 -4 65
296 -11 8 7 57
297 -11 9 6 57
298 -12 10 3 57
299 -10 11 10 55
300 -15 12 0 65
301 -15 1 -2 65
302 -15 2 -1 64
303 -13 3 2 60
304 -8 4 8 43
305 -13 5 -6 47
306 -9 6 -4 40
307 -7 7 4 31
308 -4 8 7 27
309 -4 9 3 24
310 -2 10 3 23
311 0 11 8 17
312 -2 12 3 16
313 -3 1 -3 15
314 1 2 4 8
315 -2 3 -5 5
316 -1 4 -1 6
317 1 5 5 5
318 -3 6 0 12
319 -4 7 -6 8
320 -9 8 -13 17
321 -9 9 -15 22
322 -7 10 -8 24
323 -14 11 -20 36
financielesituatie spaarvermogen
1 -16 -33
2 -15 -32
3 -16 -32
4 -14 -31
5 -14 -31
6 -14 -32
7 -16 -32
8 -17 -33
9 -15 -31
10 -9 -21
11 -9 -17
12 -7 -14
13 -4 -10
14 -9 -13
15 -8 -19
16 -6 -10
17 -5 -13
18 -7 -11
19 -6 -9
20 -1 -1
21 -2 -3
22 -1 -7
23 -3 -6
24 -2 -1
25 -2 -11
26 -1 -3
27 -2 -1
28 -1 -2
29 0 -2
30 1 -2
31 -1 -4
32 -1 -1
33 0 0
34 0 -3
35 1 -4
36 1 -4
37 2 -2
38 1 -3
39 2 4
40 1 3
41 0 3
42 2 -1
43 1 5
44 0 -2
45 1 2
46 3 -1
47 2 6
48 4 4
49 1 -2
50 4 4
51 2 3
52 3 0
53 2 7
54 3 5
55 5 3
56 5 9
57 3 7
58 4 8
59 5 8
60 5 10
61 4 11
62 6 5
63 5 9
64 4 7
65 4 8
66 7 12
67 8 10
68 5 10
69 4 8
70 1 11
71 2 10
72 0 8
73 -2 5
74 -1 12
75 2 10
76 3 8
77 2 8
78 2 10
79 5 12
80 4 13
81 5 7
82 2 13
83 6 11
84 7 13
85 1 11
86 1 10
87 0 15
88 -2 11
89 -1 10
90 -1 12
91 1 14
92 0 11
93 0 8
94 -1 3
95 -1 15
96 -1 11
97 -4 0
98 -6 4
99 -3 7
100 -7 12
101 -4 5
102 -5 2
103 -3 0
104 -5 5
105 -6 4
106 -7 7
107 -6 0
108 -8 -1
109 -5 3
110 -5 2
111 -3 7
112 -2 6
113 -1 3
114 1 3
115 -1 1
116 -1 8
117 3 10
118 2 6
119 4 11
120 3 6
121 1 6
122 0 3
123 2 10
124 2 12
125 2 9
126 3 12
127 2 10
128 1 6
129 0 8
130 -4 11
131 -9 11
132 -6 11
133 -7 14
134 -6 8
135 -6 12
136 -3 11
137 -3 14
138 -4 15
139 -5 15
140 -4 14
141 -3 16
142 -5 9
143 -3 13
144 -2 15
145 -3 14
146 -5 11
147 -3 14
148 -3 10
149 -4 13
150 -2 15
151 -3 20
152 -2 19
153 -3 16
154 2 22
155 1 19
156 -1 16
157 2 23
158 5 23
159 3 16
160 3 23
161 3 30
162 1 31
163 3 24
164 1 20
165 2 24
166 2 23
167 1 25
168 2 25
169 4 23
170 3 21
171 3 16
172 3 26
173 2 23
174 -1 15
175 1 23
176 3 20
177 4 22
178 4 24
179 6 22
180 4 24
181 6 24
182 6 29
183 8 29
184 4 25
185 8 16
186 10 18
187 9 13
188 12 22
189 9 15
190 11 20
191 11 19
192 11 18
193 11 13
194 11 17
195 9 17
196 8 13
197 6 14
198 7 13
199 8 17
200 6 17
201 5 15
202 2 9
203 3 10
204 3 9
205 7 14
206 8 18
207 7 18
208 7 12
209 6 16
210 6 12
211 7 19
212 5 13
213 5 12
214 5 13
215 4 11
216 4 10
217 4 16
218 1 12
219 -1 6
220 3 8
221 4 6
222 3 8
223 2 8
224 1 9
225 4 13
226 3 8
227 5 11
228 6 8
229 6 10
230 6 15
231 6 12
232 6 13
233 5 12
234 6 15
235 5 13
236 6 13
237 5 16
238 7 14
239 4 12
240 5 15
241 6 14
242 6 19
243 5 16
244 3 16
245 2 11
246 3 13
247 3 12
248 2 11
249 0 6
250 4 9
251 4 6
252 5 15
253 6 17
254 6 13
255 5 12
256 5 13
257 3 10
258 5 14
259 5 13
260 5 10
261 3 11
262 6 12
263 6 7
264 4 11
265 6 9
266 5 13
267 4 12
268 5 5
269 5 13
270 4 11
271 3 8
272 2 8
273 3 8
274 2 8
275 -1 0
276 0 3
277 -2 0
278 1 -1
279 -2 -1
280 -2 -4
281 -2 1
282 -6 -1
283 -4 0
284 -2 -1
285 0 6
286 -5 0
287 -4 -3
288 -5 -3
289 -1 4
290 -2 1
291 -4 0
292 -1 -4
293 1 -2
294 1 3
295 -2 2
296 1 5
297 1 6
298 3 6
299 3 3
300 1 4
301 1 7
302 0 5
303 2 6
304 2 1
305 -1 3
306 1 6
307 0 0
308 1 3
309 1 4
310 3 7
311 2 6
312 0 6
313 0 6
314 3 6
315 -2 2
316 0 2
317 1 2
318 -1 3
319 -2 -1
320 -1 -4
321 -1 4
322 1 5
323 -2 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) maand economischesituatie
0.10264 -0.01159 0.24918
werkloosheid financielesituatie spaarvermogen
-0.25144 0.24767 0.24861
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.91208 -0.25206 0.01189 0.26369 0.92331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.102639 0.056851 1.805 0.0720 .
maand -0.011585 0.005734 -2.020 0.0442 *
economischesituatie 0.249184 0.002765 90.110 <2e-16 ***
werkloosheid -0.251438 0.001372 -183.209 <2e-16 ***
financielesituatie 0.247674 0.008534 29.023 <2e-16 ***
spaarvermogen 0.248614 0.002860 86.925 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3546 on 317 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 4.128e+04 on 5 and 317 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,] 0.30104660 0.602093193 0.698953403
[2,] 0.17894324 0.357886473 0.821056763
[3,] 0.10976935 0.219538699 0.890230651
[4,] 0.21898068 0.437961359 0.781019321
[5,] 0.14002104 0.280042083 0.859978959
[6,] 0.14212103 0.284242065 0.857878968
[7,] 0.10554350 0.211086991 0.894456504
[8,] 0.08105633 0.162112663 0.918943668
[9,] 0.05615679 0.112313588 0.943843206
[10,] 0.15727317 0.314546341 0.842726829
[11,] 0.18877684 0.377553689 0.811223155
[12,] 0.23973021 0.479460413 0.760269793
[13,] 0.23057143 0.461142857 0.769428571
[14,] 0.26346089 0.526921780 0.736539110
[15,] 0.20788269 0.415765373 0.792117313
[16,] 0.17257507 0.345150144 0.827424928
[17,] 0.20146920 0.402938391 0.798530804
[18,] 0.19593931 0.391878628 0.804060686
[19,] 0.40631236 0.812624724 0.593687638
[20,] 0.34968913 0.699378252 0.650310874
[21,] 0.31975346 0.639506928 0.680246536
[22,] 0.34918120 0.698362399 0.650818800
[23,] 0.39086240 0.781724807 0.609137596
[24,] 0.37167814 0.743356282 0.628321859
[25,] 0.33331677 0.666633537 0.666683231
[26,] 0.31278373 0.625567453 0.687216273
[27,] 0.52675782 0.946484366 0.473242183
[28,] 0.47400399 0.948007976 0.525996012
[29,] 0.50732016 0.985359675 0.492679837
[30,] 0.45602244 0.912044877 0.543977562
[31,] 0.41115977 0.822319539 0.588840230
[32,] 0.48977858 0.979557159 0.510221421
[33,] 0.46346398 0.926927968 0.536536016
[34,] 0.41725763 0.834515259 0.582742371
[35,] 0.36912727 0.738254537 0.630872731
[36,] 0.33841806 0.676836122 0.661581939
[37,] 0.31000043 0.620000865 0.689999568
[38,] 0.39376664 0.787533271 0.606233364
[39,] 0.35019383 0.700387665 0.649806167
[40,] 0.32052549 0.641050981 0.679474509
[41,] 0.41832354 0.836647080 0.581676460
[42,] 0.38497244 0.769944878 0.615027561
[43,] 0.38612721 0.772254425 0.613872788
[44,] 0.44573455 0.891469099 0.554265451
[45,] 0.40845299 0.816905981 0.591547010
[46,] 0.37074331 0.741486613 0.629256693
[47,] 0.33242835 0.664856702 0.667571649
[48,] 0.37189135 0.743782691 0.628108655
[49,] 0.36205615 0.724112300 0.637943850
[50,] 0.32418037 0.648360740 0.675819630
[51,] 0.40348480 0.806969610 0.596515195
[52,] 0.43103750 0.862075008 0.568962496
[53,] 0.41739113 0.834782254 0.582608873
[54,] 0.46587024 0.931740478 0.534129761
[55,] 0.68972741 0.620545185 0.310272592
[56,] 0.65500390 0.689992204 0.344996102
[57,] 0.62860732 0.742785364 0.371392682
[58,] 0.74601997 0.507960050 0.253980025
[59,] 0.76012968 0.479740631 0.239870316
[60,] 0.75644739 0.487105218 0.243552609
[61,] 0.72451720 0.550965598 0.275482799
[62,] 0.72949533 0.541009346 0.270504673
[63,] 0.69929424 0.601411520 0.300705760
[64,] 0.78242802 0.435143956 0.217571978
[65,] 0.75555635 0.488887298 0.244443649
[66,] 0.75212561 0.495748781 0.247874391
[67,] 0.74730565 0.505388707 0.252694354
[68,] 0.72714453 0.545710949 0.272855475
[69,] 0.71108908 0.577821848 0.288910924
[70,] 0.69810909 0.603781813 0.301890907
[71,] 0.77415763 0.451684736 0.225842368
[72,] 0.74792119 0.504157623 0.252078811
[73,] 0.75236008 0.495279843 0.247639922
[74,] 0.72572717 0.548545651 0.274272825
[75,] 0.83652142 0.326957156 0.163478578
[76,] 0.89255264 0.214894716 0.107447358
[77,] 0.87808753 0.243824944 0.121912472
[78,] 0.86321617 0.273567660 0.136783830
[79,] 0.88455947 0.230881052 0.115440526
[80,] 0.87499931 0.250001382 0.125000691
[81,] 0.85614084 0.287718322 0.143859161
[82,] 0.84528059 0.309438825 0.154719413
[83,] 0.89677744 0.206445123 0.103222561
[84,] 0.88120700 0.237585995 0.118792998
[85,] 0.87712322 0.245753562 0.122876781
[86,] 0.86879612 0.262407767 0.131203884
[87,] 0.85157384 0.296852320 0.148426160
[88,] 0.85474811 0.290503773 0.145251887
[89,] 0.85156388 0.296872240 0.148436120
[90,] 0.85311817 0.293763667 0.146881833
[91,] 0.88573583 0.228528344 0.114264172
[92,] 0.87882879 0.242342416 0.121171208
[93,] 0.87362247 0.252755062 0.126377531
[94,] 0.85554156 0.288916883 0.144458441
[95,] 0.85042455 0.299150901 0.149575450
[96,] 0.86076282 0.278474367 0.139237184
[97,] 0.86152774 0.276944518 0.138472259
[98,] 0.86044286 0.279114270 0.139557135
[99,] 0.87304745 0.253905092 0.126952546
[100,] 0.87149959 0.257000819 0.128500409
[101,] 0.85744066 0.285118672 0.142559336
[102,] 0.86668138 0.266637249 0.133318624
[103,] 0.84797519 0.304049610 0.152024805
[104,] 0.83591076 0.328178473 0.164089236
[105,] 0.81438460 0.371230797 0.185615398
[106,] 0.80605361 0.387892772 0.193946386
[107,] 0.81723563 0.365528738 0.182764369
[108,] 0.79737999 0.405240027 0.202620013
[109,] 0.84305059 0.313898818 0.156949409
[110,] 0.88163805 0.236723890 0.118361945
[111,] 0.86774913 0.264501734 0.132250867
[112,] 0.93395117 0.132097663 0.066048831
[113,] 0.93075478 0.138490444 0.069245222
[114,] 0.95763133 0.084737342 0.042368671
[115,] 0.96431242 0.071375156 0.035687578
[116,] 0.97054662 0.058906750 0.029453375
[117,] 0.96464460 0.070710809 0.035355404
[118,] 0.96903231 0.061935374 0.030967687
[119,] 0.97217395 0.055652098 0.027826049
[120,] 0.97870869 0.042582619 0.021291310
[121,] 0.99118320 0.017633598 0.008816799
[122,] 0.99156827 0.016863469 0.008431734
[123,] 0.99186747 0.016265067 0.008132534
[124,] 0.99728500 0.005430001 0.002715000
[125,] 0.99708480 0.005830397 0.002915198
[126,] 0.99673132 0.006537354 0.003268677
[127,] 0.99633063 0.007338737 0.003669368
[128,] 0.99590268 0.008194635 0.004097318
[129,] 0.99553477 0.008930469 0.004465234
[130,] 0.99479552 0.010408969 0.005204484
[131,] 0.99542743 0.009145139 0.004572570
[132,] 0.99522871 0.009542572 0.004771286
[133,] 0.99518586 0.009628286 0.004814143
[134,] 0.99491577 0.010168463 0.005084231
[135,] 0.99397150 0.012056996 0.006028498
[136,] 0.99608851 0.007822970 0.003911485
[137,] 0.99690228 0.006195442 0.003097721
[138,] 0.99648934 0.007021324 0.003510662
[139,] 0.99703251 0.005934978 0.002967489
[140,] 0.99626375 0.007472499 0.003736250
[141,] 0.99607339 0.007853211 0.003926606
[142,] 0.99652664 0.006946725 0.003473363
[143,] 0.99694458 0.006110834 0.003055417
[144,] 0.99683358 0.006332844 0.003166422
[145,] 0.99682579 0.006348411 0.003174206
[146,] 0.99676603 0.006467940 0.003233970
[147,] 0.99606174 0.007876530 0.003938265
[148,] 0.99627828 0.007443439 0.003721720
[149,] 0.99528132 0.009437351 0.004718675
[150,] 0.99405700 0.011886007 0.005943003
[151,] 0.99255581 0.014888378 0.007444189
[152,] 0.99157581 0.016848383 0.008424192
[153,] 0.99046795 0.019064096 0.009532048
[154,] 0.98827245 0.023455094 0.011727547
[155,] 0.99098030 0.018039397 0.009019698
[156,] 0.98883017 0.022339661 0.011169831
[157,] 0.98814462 0.023710766 0.011855383
[158,] 0.98937389 0.021252226 0.010626113
[159,] 0.99286457 0.014270859 0.007135430
[160,] 0.99129404 0.017411929 0.008705965
[161,] 0.99015919 0.019681627 0.009840814
[162,] 0.98921675 0.021566490 0.010783245
[163,] 0.98785904 0.024281917 0.012140959
[164,] 0.98629528 0.027409442 0.013704721
[165,] 0.98964786 0.020704283 0.010352142
[166,] 0.98924920 0.021501606 0.010750803
[167,] 0.98942688 0.021146236 0.010573118
[168,] 0.99061785 0.018764304 0.009382152
[169,] 0.98906935 0.021861303 0.010930651
[170,] 0.98702007 0.025959853 0.012979926
[171,] 0.98422506 0.031549888 0.015774944
[172,] 0.98526104 0.029477914 0.014738957
[173,] 0.98200179 0.035996412 0.017998206
[174,] 0.97842582 0.043148362 0.021574181
[175,] 0.97409919 0.051801627 0.025900813
[176,] 0.97006195 0.059876108 0.029938054
[177,] 0.96681893 0.066362131 0.033181066
[178,] 0.96031603 0.079367941 0.039683970
[179,] 0.96612487 0.067750261 0.033875130
[180,] 0.97321351 0.053572973 0.026786486
[181,] 0.97066787 0.058664255 0.029332127
[182,] 0.96477694 0.070446118 0.035223059
[183,] 0.95793976 0.084120472 0.042060236
[184,] 0.95007398 0.099852042 0.049926021
[185,] 0.94250810 0.114983792 0.057491896
[186,] 0.94358244 0.112835121 0.056417560
[187,] 0.94217914 0.115641726 0.057820863
[188,] 0.95963733 0.080725349 0.040362674
[189,] 0.95631840 0.087363204 0.043681602
[190,] 0.95110115 0.097797694 0.048898847
[191,] 0.94759050 0.104819004 0.052409502
[192,] 0.95345112 0.093097756 0.046548878
[193,] 0.94486826 0.110263472 0.055131736
[194,] 0.94376784 0.112464319 0.056232159
[195,] 0.94507653 0.109846948 0.054923474
[196,] 0.94675637 0.106487253 0.053243627
[197,] 0.95203825 0.095923493 0.047961746
[198,] 0.96343256 0.073134886 0.036567443
[199,] 0.95621620 0.087567609 0.043783804
[200,] 0.95050802 0.098983956 0.049491978
[201,] 0.94524414 0.109511721 0.054755860
[202,] 0.94050678 0.118986449 0.059493225
[203,] 0.95665155 0.086696907 0.043348453
[204,] 0.96826106 0.063477886 0.031738943
[205,] 0.97775411 0.044491780 0.022245890
[206,] 0.97369803 0.052603940 0.026301970
[207,] 0.96879169 0.062416620 0.031208310
[208,] 0.96320783 0.073584340 0.036792170
[209,] 0.95569670 0.088606602 0.044303301
[210,] 0.94940348 0.101193039 0.050596519
[211,] 0.94815370 0.103692595 0.051846298
[212,] 0.94325166 0.113496682 0.056748341
[213,] 0.95186540 0.096269192 0.048134596
[214,] 0.96424000 0.071520008 0.035760004
[215,] 0.95838955 0.083220901 0.041610450
[216,] 0.95960913 0.080781733 0.040390866
[217,] 0.96144546 0.077109083 0.038554541
[218,] 0.95439355 0.091212905 0.045606453
[219,] 0.97195010 0.056099805 0.028049902
[220,] 0.97253592 0.054928161 0.027464080
[221,] 0.96786787 0.064264269 0.032132134
[222,] 0.96068938 0.078621239 0.039310620
[223,] 0.95780547 0.084389058 0.042194529
[224,] 0.94882413 0.102351735 0.051175868
[225,] 0.94256271 0.114874573 0.057437287
[226,] 0.94073968 0.118520633 0.059260316
[227,] 0.92988412 0.140231753 0.070115876
[228,] 0.91747483 0.165050337 0.082525168
[229,] 0.92572850 0.148542991 0.074271495
[230,] 0.93275466 0.134490679 0.067245339
[231,] 0.92686330 0.146273396 0.073136698
[232,] 0.91495309 0.170093815 0.085046908
[233,] 0.93290057 0.134198862 0.067099431
[234,] 0.95532136 0.089357285 0.044678643
[235,] 0.95703087 0.085938259 0.042969129
[236,] 0.95681473 0.086370543 0.043185272
[237,] 0.97794707 0.044105865 0.022052933
[238,] 0.97281205 0.054375890 0.027187945
[239,] 0.98065022 0.038699560 0.019349780
[240,] 0.97874459 0.042510812 0.021255406
[241,] 0.97748583 0.045028338 0.022514169
[242,] 0.97440434 0.051191310 0.025595655
[243,] 0.97967018 0.040659641 0.020329821
[244,] 0.97669958 0.046600846 0.023300423
[245,] 0.97159709 0.056805824 0.028402912
[246,] 0.97236855 0.055262907 0.027631454
[247,] 0.97679168 0.046416634 0.023208317
[248,] 0.97596561 0.048068777 0.024034389
[249,] 0.97668114 0.046637718 0.023318859
[250,] 0.97434732 0.051305365 0.025652682
[251,] 0.96780258 0.064394846 0.032197423
[252,] 0.96724582 0.065508358 0.032754179
[253,] 0.96527431 0.069451382 0.034725691
[254,] 0.96461609 0.070767824 0.035383912
[255,] 0.96745713 0.065085737 0.032542868
[256,] 0.96166658 0.076666849 0.038333424
[257,] 0.95791265 0.084174698 0.042087349
[258,] 0.94799893 0.104002130 0.052001065
[259,] 0.95379174 0.092416515 0.046208257
[260,] 0.94628964 0.107420710 0.053710355
[261,] 0.93524362 0.129512768 0.064756384
[262,] 0.94229186 0.115416274 0.057708137
[263,] 0.93217375 0.135652490 0.067826245
[264,] 0.93886749 0.122265027 0.061132513
[265,] 0.92368713 0.152625743 0.076312872
[266,] 0.90572446 0.188551071 0.094275535
[267,] 0.90827317 0.183453659 0.091726830
[268,] 0.89228400 0.215432001 0.107716000
[269,] 0.86961022 0.260779562 0.130389781
[270,] 0.88091034 0.238179329 0.119089664
[271,] 0.85667997 0.286640055 0.143320027
[272,] 0.83612099 0.327758027 0.163879013
[273,] 0.81224042 0.375519153 0.187759576
[274,] 0.77535533 0.449289337 0.224644669
[275,] 0.74010875 0.519782503 0.259891252
[276,] 0.76320691 0.473586177 0.236793088
[277,] 0.73899805 0.522003904 0.261001952
[278,] 0.70457974 0.590840529 0.295420265
[279,] 0.68267767 0.634644651 0.317322326
[280,] 0.66883067 0.662338670 0.331169335
[281,] 0.68213691 0.635726179 0.317863089
[282,] 0.65500160 0.689996802 0.344998401
[283,] 0.61356090 0.772878207 0.386439103
[284,] 0.64022651 0.719546981 0.359773490
[285,] 0.83184304 0.336313927 0.168156964
[286,] 0.80143994 0.397120111 0.198560056
[287,] 0.80316636 0.393667283 0.196833641
[288,] 0.75736370 0.485272590 0.242636295
[289,] 0.70967694 0.580646130 0.290323065
[290,] 0.77454790 0.450904203 0.225452102
[291,] 0.73120582 0.537588367 0.268794184
[292,] 0.70256079 0.594878424 0.297439212
[293,] 0.63656912 0.726861759 0.363430880
[294,] 0.60400756 0.791984886 0.395992443
[295,] 0.58314993 0.833700138 0.416850069
[296,] 0.50270628 0.994587442 0.497293721
[297,] 0.42705920 0.854118397 0.572940802
[298,] 0.47614102 0.952282050 0.523858975
[299,] 0.41558982 0.831179640 0.584410180
[300,] 0.33677078 0.673541554 0.663229223
[301,] 0.25829323 0.516586460 0.741706770
[302,] 0.49356206 0.987124117 0.506437942
[303,] 0.64824310 0.703513797 0.351756898
[304,] 0.53757867 0.924842664 0.462421332
[305,] 0.39460820 0.789216407 0.605391796
[306,] 0.29322974 0.586459483 0.706770259
> postscript(file="/var/www/rcomp/tmp/1jaqw1321901238.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ns2l1321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3zqw81321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47x361321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5fi9x1321901238.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 = 323
Frequency = 1
1 2 3 4 5 6
-0.391204175 -0.380026638 -0.117269761 0.401790679 0.165435513 0.425634615
7 8 9 10 11 12
0.180508588 -0.311618048 -0.042415388 -0.002941517 0.006416983 -0.222954297
13 14 15 16 17 18
0.163568300 0.411038396 -0.088924126 0.188543029 0.203658993 -0.020819660
19 20 21 22 23 24
-0.259265920 -0.470924427 0.277558114 -0.458711961 -0.200158274 0.070254858
25 26 27 28 29 30
0.432221395 0.452907244 -0.778066326 -0.013715777 -0.255322042 0.505091365
31 32 33 34 35 36
-0.487249576 -0.468436789 0.044606476 0.296904281 -0.696322007 0.063591370
37 38 39 40 41 42
0.691874538 0.202235539 -0.025589086 -0.521213196 0.232916988 0.245241194
43 44 45 46 47 48
0.012816834 0.263811743 -0.214907510 -0.459436031 0.050277046 -0.184198722
49 50 51 52 53 54
0.923305249 0.447502243 -0.049631733 -0.286187535 0.230754473 -0.007718799
55 56 57 58 59 60
0.011262886 -0.470855407 0.279380758 0.047125296 -0.683834605 0.327647052
61 62 63 64 65 66
0.447445085 -0.299181862 -0.788457664 -0.027229047 0.235120224 -0.741201207
67 68 69 70 71 72
-0.474311277 0.279286813 0.023963995 -0.462140742 -0.191417155 0.568301596
73 74 75 76 77 78
0.171877044 0.458196256 -0.277571308 0.235005276 -0.250800065 0.265810810
79 80 81 82 83 84
-0.711416971 0.049034508 0.306882807 0.062424096 -0.912077014 0.609775185
85 86 87 88 89 90
0.213164497 0.227676946 -0.501198198 0.250619255 0.002691695 -0.231513302
91 92 93 94 95 96
-0.716623987 0.042790644 0.299829741 0.307756132 -0.157887198 0.346288057
97 98 99 100 101 102
0.444648197 0.460001584 -0.512382704 -0.253324263 -0.246951819 0.007333593
103 104 105 106 107 108
-0.236390778 -0.463900223 0.300928068 0.310694172 -0.456042046 0.307276379
109 110 111 112 113 114
0.193177067 0.455395840 -0.023965002 0.229739183 -0.002777275 0.263265381
115 116 117 118 119 120
-0.480125416 0.036614673 0.562294519 -0.685855964 0.075654652 0.823904495
121 122 123 124 125 126
-0.299556848 0.706788255 -0.515019195 0.495218643 0.001441867 -0.482354863
127 128 129 130 131 132
-0.472176102 0.539817930 0.800217667 -0.435725170 0.320838300 0.828092363
133 134 135 136 137 138
-0.298133725 -0.285972081 0.220276872 0.232089987 0.251136758 -0.234332399
139 140 141 142 143 144
-0.479192380 0.284148334 0.307164311 0.286948351 -0.193524527 0.579531470
145 146 147 148 149 150
-0.535761244 -0.285239352 -0.502874391 0.002544683 0.259549146 -0.475989485
151 152 153 154 155 156
-0.466560849 0.295302062 0.307164311 0.324276541 -0.167512627 -0.411627533
157 158 159 160 161 162
-0.030857350 -0.017464231 -0.025168099 -0.246497807 -0.228901826 0.024910568
163 164 165 166 167 168
0.531872858 0.029764891 0.302329476 -0.430242491 0.580505152 0.092978250
169 170 171 172 173 174
0.212639777 -0.282076698 0.221762374 -0.248050797 0.503359051 0.248824430
175 176 177 178 179 180
0.269308360 -0.465115666 -0.205661637 -0.186409181 0.074761684 0.338158864
181 182 183 184 185 186
-0.041193218 -0.026991226 -0.010366680 -0.017126125 0.238799978 0.011888039
187 188 189 190 191 192
0.517714719 0.544244122 -0.213063863 0.053108006 0.066376635 0.071640482
193 194 195 196 197 198
-0.053905619 -0.280751187 -0.273195613 -0.514117166 -0.249036497 0.260612779
199 200 201 202 203 204
-0.220126146 -0.455769587 0.056508885 0.321765746 0.360689089 0.350251255
205 206 207 208 209 210
0.485704317 -0.500397811 0.011309210 0.259876843 -0.222871978 0.286044570
211 212 213 214 215 216
0.567080620 0.554429774 0.565444749 -0.169952325 -0.159773564 0.107654469
217 218 219 220 221 222
-0.015117269 -0.261312209 0.237151276 -0.235383189 0.532904503 0.540856622
223 224 225 226 227 228
-0.195142682 -0.442929969 0.329076834 -0.147375134 0.612059215 0.367498631
229 230 231 232 233 234
0.237470949 0.006996123 -0.234333034 0.030503875 -0.213448469 0.296769689
235 236 237 238 239 240
0.050227506 0.063088531 0.328565744 0.349259214 0.100473152 -0.128388322
241 242 243 244 245 246
-0.506322964 0.512386338 0.263562203 -0.485215241 0.526208624 -0.209362071
247 248 249 250 251 252
0.297767504 -0.446030898 -0.455623939 0.077153055 0.337455643 -0.138646760
253 254 255 256 257 258
-0.021477923 0.233747189 0.249237409 -0.496418102 -0.489329210 0.029963929
259 260 261 262 263 264
-0.205951569 -0.465543947 -0.454154716 -0.437937776 0.317339078 -0.138705836
265 266 267 268 269 270
0.211876596 -0.029692881 -0.522829353 0.225588103 0.003818726 -0.488878078
271 272 273 274 275 276
-0.233348846 0.260562920 -0.226963982 -0.222639835 -0.485413222 0.031799884
277 278 279 280 281 282
0.146797549 0.410048447 0.163412553 0.178262471 -0.300928515 -0.058373334
283 284 285 286 287 288
-0.031696037 0.247064397 -0.475364963 0.042583274 -0.424659450 -0.406345408
289 290 291 292 293 294
0.246879463 0.004274423 0.007762356 -0.470244845 0.549773688 -0.431517010
295 296 297 298 299 300
0.316769621 0.086962439 0.099117747 -0.637093252 -0.126830684 0.137708115
301 302 303 304 305 306
-0.237202071 0.018663344 -0.467017508 0.018090376 -0.230200708 0.281761300
307 308 309 310 311 312
-0.223708592 0.041056897 0.046451241 0.565408196 0.318734230 -0.179849250
313 314 315 316 317 318
-0.063618827 -0.299409836 0.433346255 0.204284146 0.221652480 -0.514042704
319 320 321 322 323
0.229026177 -0.253992130 -0.475761507 -0.449552177 -0.190253416
> postscript(file="/var/www/rcomp/tmp/6dgcw1321901238.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 = 323
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.391204175 NA
1 -0.380026638 -0.391204175
2 -0.117269761 -0.380026638
3 0.401790679 -0.117269761
4 0.165435513 0.401790679
5 0.425634615 0.165435513
6 0.180508588 0.425634615
7 -0.311618048 0.180508588
8 -0.042415388 -0.311618048
9 -0.002941517 -0.042415388
10 0.006416983 -0.002941517
11 -0.222954297 0.006416983
12 0.163568300 -0.222954297
13 0.411038396 0.163568300
14 -0.088924126 0.411038396
15 0.188543029 -0.088924126
16 0.203658993 0.188543029
17 -0.020819660 0.203658993
18 -0.259265920 -0.020819660
19 -0.470924427 -0.259265920
20 0.277558114 -0.470924427
21 -0.458711961 0.277558114
22 -0.200158274 -0.458711961
23 0.070254858 -0.200158274
24 0.432221395 0.070254858
25 0.452907244 0.432221395
26 -0.778066326 0.452907244
27 -0.013715777 -0.778066326
28 -0.255322042 -0.013715777
29 0.505091365 -0.255322042
30 -0.487249576 0.505091365
31 -0.468436789 -0.487249576
32 0.044606476 -0.468436789
33 0.296904281 0.044606476
34 -0.696322007 0.296904281
35 0.063591370 -0.696322007
36 0.691874538 0.063591370
37 0.202235539 0.691874538
38 -0.025589086 0.202235539
39 -0.521213196 -0.025589086
40 0.232916988 -0.521213196
41 0.245241194 0.232916988
42 0.012816834 0.245241194
43 0.263811743 0.012816834
44 -0.214907510 0.263811743
45 -0.459436031 -0.214907510
46 0.050277046 -0.459436031
47 -0.184198722 0.050277046
48 0.923305249 -0.184198722
49 0.447502243 0.923305249
50 -0.049631733 0.447502243
51 -0.286187535 -0.049631733
52 0.230754473 -0.286187535
53 -0.007718799 0.230754473
54 0.011262886 -0.007718799
55 -0.470855407 0.011262886
56 0.279380758 -0.470855407
57 0.047125296 0.279380758
58 -0.683834605 0.047125296
59 0.327647052 -0.683834605
60 0.447445085 0.327647052
61 -0.299181862 0.447445085
62 -0.788457664 -0.299181862
63 -0.027229047 -0.788457664
64 0.235120224 -0.027229047
65 -0.741201207 0.235120224
66 -0.474311277 -0.741201207
67 0.279286813 -0.474311277
68 0.023963995 0.279286813
69 -0.462140742 0.023963995
70 -0.191417155 -0.462140742
71 0.568301596 -0.191417155
72 0.171877044 0.568301596
73 0.458196256 0.171877044
74 -0.277571308 0.458196256
75 0.235005276 -0.277571308
76 -0.250800065 0.235005276
77 0.265810810 -0.250800065
78 -0.711416971 0.265810810
79 0.049034508 -0.711416971
80 0.306882807 0.049034508
81 0.062424096 0.306882807
82 -0.912077014 0.062424096
83 0.609775185 -0.912077014
84 0.213164497 0.609775185
85 0.227676946 0.213164497
86 -0.501198198 0.227676946
87 0.250619255 -0.501198198
88 0.002691695 0.250619255
89 -0.231513302 0.002691695
90 -0.716623987 -0.231513302
91 0.042790644 -0.716623987
92 0.299829741 0.042790644
93 0.307756132 0.299829741
94 -0.157887198 0.307756132
95 0.346288057 -0.157887198
96 0.444648197 0.346288057
97 0.460001584 0.444648197
98 -0.512382704 0.460001584
99 -0.253324263 -0.512382704
100 -0.246951819 -0.253324263
101 0.007333593 -0.246951819
102 -0.236390778 0.007333593
103 -0.463900223 -0.236390778
104 0.300928068 -0.463900223
105 0.310694172 0.300928068
106 -0.456042046 0.310694172
107 0.307276379 -0.456042046
108 0.193177067 0.307276379
109 0.455395840 0.193177067
110 -0.023965002 0.455395840
111 0.229739183 -0.023965002
112 -0.002777275 0.229739183
113 0.263265381 -0.002777275
114 -0.480125416 0.263265381
115 0.036614673 -0.480125416
116 0.562294519 0.036614673
117 -0.685855964 0.562294519
118 0.075654652 -0.685855964
119 0.823904495 0.075654652
120 -0.299556848 0.823904495
121 0.706788255 -0.299556848
122 -0.515019195 0.706788255
123 0.495218643 -0.515019195
124 0.001441867 0.495218643
125 -0.482354863 0.001441867
126 -0.472176102 -0.482354863
127 0.539817930 -0.472176102
128 0.800217667 0.539817930
129 -0.435725170 0.800217667
130 0.320838300 -0.435725170
131 0.828092363 0.320838300
132 -0.298133725 0.828092363
133 -0.285972081 -0.298133725
134 0.220276872 -0.285972081
135 0.232089987 0.220276872
136 0.251136758 0.232089987
137 -0.234332399 0.251136758
138 -0.479192380 -0.234332399
139 0.284148334 -0.479192380
140 0.307164311 0.284148334
141 0.286948351 0.307164311
142 -0.193524527 0.286948351
143 0.579531470 -0.193524527
144 -0.535761244 0.579531470
145 -0.285239352 -0.535761244
146 -0.502874391 -0.285239352
147 0.002544683 -0.502874391
148 0.259549146 0.002544683
149 -0.475989485 0.259549146
150 -0.466560849 -0.475989485
151 0.295302062 -0.466560849
152 0.307164311 0.295302062
153 0.324276541 0.307164311
154 -0.167512627 0.324276541
155 -0.411627533 -0.167512627
156 -0.030857350 -0.411627533
157 -0.017464231 -0.030857350
158 -0.025168099 -0.017464231
159 -0.246497807 -0.025168099
160 -0.228901826 -0.246497807
161 0.024910568 -0.228901826
162 0.531872858 0.024910568
163 0.029764891 0.531872858
164 0.302329476 0.029764891
165 -0.430242491 0.302329476
166 0.580505152 -0.430242491
167 0.092978250 0.580505152
168 0.212639777 0.092978250
169 -0.282076698 0.212639777
170 0.221762374 -0.282076698
171 -0.248050797 0.221762374
172 0.503359051 -0.248050797
173 0.248824430 0.503359051
174 0.269308360 0.248824430
175 -0.465115666 0.269308360
176 -0.205661637 -0.465115666
177 -0.186409181 -0.205661637
178 0.074761684 -0.186409181
179 0.338158864 0.074761684
180 -0.041193218 0.338158864
181 -0.026991226 -0.041193218
182 -0.010366680 -0.026991226
183 -0.017126125 -0.010366680
184 0.238799978 -0.017126125
185 0.011888039 0.238799978
186 0.517714719 0.011888039
187 0.544244122 0.517714719
188 -0.213063863 0.544244122
189 0.053108006 -0.213063863
190 0.066376635 0.053108006
191 0.071640482 0.066376635
192 -0.053905619 0.071640482
193 -0.280751187 -0.053905619
194 -0.273195613 -0.280751187
195 -0.514117166 -0.273195613
196 -0.249036497 -0.514117166
197 0.260612779 -0.249036497
198 -0.220126146 0.260612779
199 -0.455769587 -0.220126146
200 0.056508885 -0.455769587
201 0.321765746 0.056508885
202 0.360689089 0.321765746
203 0.350251255 0.360689089
204 0.485704317 0.350251255
205 -0.500397811 0.485704317
206 0.011309210 -0.500397811
207 0.259876843 0.011309210
208 -0.222871978 0.259876843
209 0.286044570 -0.222871978
210 0.567080620 0.286044570
211 0.554429774 0.567080620
212 0.565444749 0.554429774
213 -0.169952325 0.565444749
214 -0.159773564 -0.169952325
215 0.107654469 -0.159773564
216 -0.015117269 0.107654469
217 -0.261312209 -0.015117269
218 0.237151276 -0.261312209
219 -0.235383189 0.237151276
220 0.532904503 -0.235383189
221 0.540856622 0.532904503
222 -0.195142682 0.540856622
223 -0.442929969 -0.195142682
224 0.329076834 -0.442929969
225 -0.147375134 0.329076834
226 0.612059215 -0.147375134
227 0.367498631 0.612059215
228 0.237470949 0.367498631
229 0.006996123 0.237470949
230 -0.234333034 0.006996123
231 0.030503875 -0.234333034
232 -0.213448469 0.030503875
233 0.296769689 -0.213448469
234 0.050227506 0.296769689
235 0.063088531 0.050227506
236 0.328565744 0.063088531
237 0.349259214 0.328565744
238 0.100473152 0.349259214
239 -0.128388322 0.100473152
240 -0.506322964 -0.128388322
241 0.512386338 -0.506322964
242 0.263562203 0.512386338
243 -0.485215241 0.263562203
244 0.526208624 -0.485215241
245 -0.209362071 0.526208624
246 0.297767504 -0.209362071
247 -0.446030898 0.297767504
248 -0.455623939 -0.446030898
249 0.077153055 -0.455623939
250 0.337455643 0.077153055
251 -0.138646760 0.337455643
252 -0.021477923 -0.138646760
253 0.233747189 -0.021477923
254 0.249237409 0.233747189
255 -0.496418102 0.249237409
256 -0.489329210 -0.496418102
257 0.029963929 -0.489329210
258 -0.205951569 0.029963929
259 -0.465543947 -0.205951569
260 -0.454154716 -0.465543947
261 -0.437937776 -0.454154716
262 0.317339078 -0.437937776
263 -0.138705836 0.317339078
264 0.211876596 -0.138705836
265 -0.029692881 0.211876596
266 -0.522829353 -0.029692881
267 0.225588103 -0.522829353
268 0.003818726 0.225588103
269 -0.488878078 0.003818726
270 -0.233348846 -0.488878078
271 0.260562920 -0.233348846
272 -0.226963982 0.260562920
273 -0.222639835 -0.226963982
274 -0.485413222 -0.222639835
275 0.031799884 -0.485413222
276 0.146797549 0.031799884
277 0.410048447 0.146797549
278 0.163412553 0.410048447
279 0.178262471 0.163412553
280 -0.300928515 0.178262471
281 -0.058373334 -0.300928515
282 -0.031696037 -0.058373334
283 0.247064397 -0.031696037
284 -0.475364963 0.247064397
285 0.042583274 -0.475364963
286 -0.424659450 0.042583274
287 -0.406345408 -0.424659450
288 0.246879463 -0.406345408
289 0.004274423 0.246879463
290 0.007762356 0.004274423
291 -0.470244845 0.007762356
292 0.549773688 -0.470244845
293 -0.431517010 0.549773688
294 0.316769621 -0.431517010
295 0.086962439 0.316769621
296 0.099117747 0.086962439
297 -0.637093252 0.099117747
298 -0.126830684 -0.637093252
299 0.137708115 -0.126830684
300 -0.237202071 0.137708115
301 0.018663344 -0.237202071
302 -0.467017508 0.018663344
303 0.018090376 -0.467017508
304 -0.230200708 0.018090376
305 0.281761300 -0.230200708
306 -0.223708592 0.281761300
307 0.041056897 -0.223708592
308 0.046451241 0.041056897
309 0.565408196 0.046451241
310 0.318734230 0.565408196
311 -0.179849250 0.318734230
312 -0.063618827 -0.179849250
313 -0.299409836 -0.063618827
314 0.433346255 -0.299409836
315 0.204284146 0.433346255
316 0.221652480 0.204284146
317 -0.514042704 0.221652480
318 0.229026177 -0.514042704
319 -0.253992130 0.229026177
320 -0.475761507 -0.253992130
321 -0.449552177 -0.475761507
322 -0.190253416 -0.449552177
323 NA -0.190253416
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.380026638 -0.391204175
[2,] -0.117269761 -0.380026638
[3,] 0.401790679 -0.117269761
[4,] 0.165435513 0.401790679
[5,] 0.425634615 0.165435513
[6,] 0.180508588 0.425634615
[7,] -0.311618048 0.180508588
[8,] -0.042415388 -0.311618048
[9,] -0.002941517 -0.042415388
[10,] 0.006416983 -0.002941517
[11,] -0.222954297 0.006416983
[12,] 0.163568300 -0.222954297
[13,] 0.411038396 0.163568300
[14,] -0.088924126 0.411038396
[15,] 0.188543029 -0.088924126
[16,] 0.203658993 0.188543029
[17,] -0.020819660 0.203658993
[18,] -0.259265920 -0.020819660
[19,] -0.470924427 -0.259265920
[20,] 0.277558114 -0.470924427
[21,] -0.458711961 0.277558114
[22,] -0.200158274 -0.458711961
[23,] 0.070254858 -0.200158274
[24,] 0.432221395 0.070254858
[25,] 0.452907244 0.432221395
[26,] -0.778066326 0.452907244
[27,] -0.013715777 -0.778066326
[28,] -0.255322042 -0.013715777
[29,] 0.505091365 -0.255322042
[30,] -0.487249576 0.505091365
[31,] -0.468436789 -0.487249576
[32,] 0.044606476 -0.468436789
[33,] 0.296904281 0.044606476
[34,] -0.696322007 0.296904281
[35,] 0.063591370 -0.696322007
[36,] 0.691874538 0.063591370
[37,] 0.202235539 0.691874538
[38,] -0.025589086 0.202235539
[39,] -0.521213196 -0.025589086
[40,] 0.232916988 -0.521213196
[41,] 0.245241194 0.232916988
[42,] 0.012816834 0.245241194
[43,] 0.263811743 0.012816834
[44,] -0.214907510 0.263811743
[45,] -0.459436031 -0.214907510
[46,] 0.050277046 -0.459436031
[47,] -0.184198722 0.050277046
[48,] 0.923305249 -0.184198722
[49,] 0.447502243 0.923305249
[50,] -0.049631733 0.447502243
[51,] -0.286187535 -0.049631733
[52,] 0.230754473 -0.286187535
[53,] -0.007718799 0.230754473
[54,] 0.011262886 -0.007718799
[55,] -0.470855407 0.011262886
[56,] 0.279380758 -0.470855407
[57,] 0.047125296 0.279380758
[58,] -0.683834605 0.047125296
[59,] 0.327647052 -0.683834605
[60,] 0.447445085 0.327647052
[61,] -0.299181862 0.447445085
[62,] -0.788457664 -0.299181862
[63,] -0.027229047 -0.788457664
[64,] 0.235120224 -0.027229047
[65,] -0.741201207 0.235120224
[66,] -0.474311277 -0.741201207
[67,] 0.279286813 -0.474311277
[68,] 0.023963995 0.279286813
[69,] -0.462140742 0.023963995
[70,] -0.191417155 -0.462140742
[71,] 0.568301596 -0.191417155
[72,] 0.171877044 0.568301596
[73,] 0.458196256 0.171877044
[74,] -0.277571308 0.458196256
[75,] 0.235005276 -0.277571308
[76,] -0.250800065 0.235005276
[77,] 0.265810810 -0.250800065
[78,] -0.711416971 0.265810810
[79,] 0.049034508 -0.711416971
[80,] 0.306882807 0.049034508
[81,] 0.062424096 0.306882807
[82,] -0.912077014 0.062424096
[83,] 0.609775185 -0.912077014
[84,] 0.213164497 0.609775185
[85,] 0.227676946 0.213164497
[86,] -0.501198198 0.227676946
[87,] 0.250619255 -0.501198198
[88,] 0.002691695 0.250619255
[89,] -0.231513302 0.002691695
[90,] -0.716623987 -0.231513302
[91,] 0.042790644 -0.716623987
[92,] 0.299829741 0.042790644
[93,] 0.307756132 0.299829741
[94,] -0.157887198 0.307756132
[95,] 0.346288057 -0.157887198
[96,] 0.444648197 0.346288057
[97,] 0.460001584 0.444648197
[98,] -0.512382704 0.460001584
[99,] -0.253324263 -0.512382704
[100,] -0.246951819 -0.253324263
[101,] 0.007333593 -0.246951819
[102,] -0.236390778 0.007333593
[103,] -0.463900223 -0.236390778
[104,] 0.300928068 -0.463900223
[105,] 0.310694172 0.300928068
[106,] -0.456042046 0.310694172
[107,] 0.307276379 -0.456042046
[108,] 0.193177067 0.307276379
[109,] 0.455395840 0.193177067
[110,] -0.023965002 0.455395840
[111,] 0.229739183 -0.023965002
[112,] -0.002777275 0.229739183
[113,] 0.263265381 -0.002777275
[114,] -0.480125416 0.263265381
[115,] 0.036614673 -0.480125416
[116,] 0.562294519 0.036614673
[117,] -0.685855964 0.562294519
[118,] 0.075654652 -0.685855964
[119,] 0.823904495 0.075654652
[120,] -0.299556848 0.823904495
[121,] 0.706788255 -0.299556848
[122,] -0.515019195 0.706788255
[123,] 0.495218643 -0.515019195
[124,] 0.001441867 0.495218643
[125,] -0.482354863 0.001441867
[126,] -0.472176102 -0.482354863
[127,] 0.539817930 -0.472176102
[128,] 0.800217667 0.539817930
[129,] -0.435725170 0.800217667
[130,] 0.320838300 -0.435725170
[131,] 0.828092363 0.320838300
[132,] -0.298133725 0.828092363
[133,] -0.285972081 -0.298133725
[134,] 0.220276872 -0.285972081
[135,] 0.232089987 0.220276872
[136,] 0.251136758 0.232089987
[137,] -0.234332399 0.251136758
[138,] -0.479192380 -0.234332399
[139,] 0.284148334 -0.479192380
[140,] 0.307164311 0.284148334
[141,] 0.286948351 0.307164311
[142,] -0.193524527 0.286948351
[143,] 0.579531470 -0.193524527
[144,] -0.535761244 0.579531470
[145,] -0.285239352 -0.535761244
[146,] -0.502874391 -0.285239352
[147,] 0.002544683 -0.502874391
[148,] 0.259549146 0.002544683
[149,] -0.475989485 0.259549146
[150,] -0.466560849 -0.475989485
[151,] 0.295302062 -0.466560849
[152,] 0.307164311 0.295302062
[153,] 0.324276541 0.307164311
[154,] -0.167512627 0.324276541
[155,] -0.411627533 -0.167512627
[156,] -0.030857350 -0.411627533
[157,] -0.017464231 -0.030857350
[158,] -0.025168099 -0.017464231
[159,] -0.246497807 -0.025168099
[160,] -0.228901826 -0.246497807
[161,] 0.024910568 -0.228901826
[162,] 0.531872858 0.024910568
[163,] 0.029764891 0.531872858
[164,] 0.302329476 0.029764891
[165,] -0.430242491 0.302329476
[166,] 0.580505152 -0.430242491
[167,] 0.092978250 0.580505152
[168,] 0.212639777 0.092978250
[169,] -0.282076698 0.212639777
[170,] 0.221762374 -0.282076698
[171,] -0.248050797 0.221762374
[172,] 0.503359051 -0.248050797
[173,] 0.248824430 0.503359051
[174,] 0.269308360 0.248824430
[175,] -0.465115666 0.269308360
[176,] -0.205661637 -0.465115666
[177,] -0.186409181 -0.205661637
[178,] 0.074761684 -0.186409181
[179,] 0.338158864 0.074761684
[180,] -0.041193218 0.338158864
[181,] -0.026991226 -0.041193218
[182,] -0.010366680 -0.026991226
[183,] -0.017126125 -0.010366680
[184,] 0.238799978 -0.017126125
[185,] 0.011888039 0.238799978
[186,] 0.517714719 0.011888039
[187,] 0.544244122 0.517714719
[188,] -0.213063863 0.544244122
[189,] 0.053108006 -0.213063863
[190,] 0.066376635 0.053108006
[191,] 0.071640482 0.066376635
[192,] -0.053905619 0.071640482
[193,] -0.280751187 -0.053905619
[194,] -0.273195613 -0.280751187
[195,] -0.514117166 -0.273195613
[196,] -0.249036497 -0.514117166
[197,] 0.260612779 -0.249036497
[198,] -0.220126146 0.260612779
[199,] -0.455769587 -0.220126146
[200,] 0.056508885 -0.455769587
[201,] 0.321765746 0.056508885
[202,] 0.360689089 0.321765746
[203,] 0.350251255 0.360689089
[204,] 0.485704317 0.350251255
[205,] -0.500397811 0.485704317
[206,] 0.011309210 -0.500397811
[207,] 0.259876843 0.011309210
[208,] -0.222871978 0.259876843
[209,] 0.286044570 -0.222871978
[210,] 0.567080620 0.286044570
[211,] 0.554429774 0.567080620
[212,] 0.565444749 0.554429774
[213,] -0.169952325 0.565444749
[214,] -0.159773564 -0.169952325
[215,] 0.107654469 -0.159773564
[216,] -0.015117269 0.107654469
[217,] -0.261312209 -0.015117269
[218,] 0.237151276 -0.261312209
[219,] -0.235383189 0.237151276
[220,] 0.532904503 -0.235383189
[221,] 0.540856622 0.532904503
[222,] -0.195142682 0.540856622
[223,] -0.442929969 -0.195142682
[224,] 0.329076834 -0.442929969
[225,] -0.147375134 0.329076834
[226,] 0.612059215 -0.147375134
[227,] 0.367498631 0.612059215
[228,] 0.237470949 0.367498631
[229,] 0.006996123 0.237470949
[230,] -0.234333034 0.006996123
[231,] 0.030503875 -0.234333034
[232,] -0.213448469 0.030503875
[233,] 0.296769689 -0.213448469
[234,] 0.050227506 0.296769689
[235,] 0.063088531 0.050227506
[236,] 0.328565744 0.063088531
[237,] 0.349259214 0.328565744
[238,] 0.100473152 0.349259214
[239,] -0.128388322 0.100473152
[240,] -0.506322964 -0.128388322
[241,] 0.512386338 -0.506322964
[242,] 0.263562203 0.512386338
[243,] -0.485215241 0.263562203
[244,] 0.526208624 -0.485215241
[245,] -0.209362071 0.526208624
[246,] 0.297767504 -0.209362071
[247,] -0.446030898 0.297767504
[248,] -0.455623939 -0.446030898
[249,] 0.077153055 -0.455623939
[250,] 0.337455643 0.077153055
[251,] -0.138646760 0.337455643
[252,] -0.021477923 -0.138646760
[253,] 0.233747189 -0.021477923
[254,] 0.249237409 0.233747189
[255,] -0.496418102 0.249237409
[256,] -0.489329210 -0.496418102
[257,] 0.029963929 -0.489329210
[258,] -0.205951569 0.029963929
[259,] -0.465543947 -0.205951569
[260,] -0.454154716 -0.465543947
[261,] -0.437937776 -0.454154716
[262,] 0.317339078 -0.437937776
[263,] -0.138705836 0.317339078
[264,] 0.211876596 -0.138705836
[265,] -0.029692881 0.211876596
[266,] -0.522829353 -0.029692881
[267,] 0.225588103 -0.522829353
[268,] 0.003818726 0.225588103
[269,] -0.488878078 0.003818726
[270,] -0.233348846 -0.488878078
[271,] 0.260562920 -0.233348846
[272,] -0.226963982 0.260562920
[273,] -0.222639835 -0.226963982
[274,] -0.485413222 -0.222639835
[275,] 0.031799884 -0.485413222
[276,] 0.146797549 0.031799884
[277,] 0.410048447 0.146797549
[278,] 0.163412553 0.410048447
[279,] 0.178262471 0.163412553
[280,] -0.300928515 0.178262471
[281,] -0.058373334 -0.300928515
[282,] -0.031696037 -0.058373334
[283,] 0.247064397 -0.031696037
[284,] -0.475364963 0.247064397
[285,] 0.042583274 -0.475364963
[286,] -0.424659450 0.042583274
[287,] -0.406345408 -0.424659450
[288,] 0.246879463 -0.406345408
[289,] 0.004274423 0.246879463
[290,] 0.007762356 0.004274423
[291,] -0.470244845 0.007762356
[292,] 0.549773688 -0.470244845
[293,] -0.431517010 0.549773688
[294,] 0.316769621 -0.431517010
[295,] 0.086962439 0.316769621
[296,] 0.099117747 0.086962439
[297,] -0.637093252 0.099117747
[298,] -0.126830684 -0.637093252
[299,] 0.137708115 -0.126830684
[300,] -0.237202071 0.137708115
[301,] 0.018663344 -0.237202071
[302,] -0.467017508 0.018663344
[303,] 0.018090376 -0.467017508
[304,] -0.230200708 0.018090376
[305,] 0.281761300 -0.230200708
[306,] -0.223708592 0.281761300
[307,] 0.041056897 -0.223708592
[308,] 0.046451241 0.041056897
[309,] 0.565408196 0.046451241
[310,] 0.318734230 0.565408196
[311,] -0.179849250 0.318734230
[312,] -0.063618827 -0.179849250
[313,] -0.299409836 -0.063618827
[314,] 0.433346255 -0.299409836
[315,] 0.204284146 0.433346255
[316,] 0.221652480 0.204284146
[317,] -0.514042704 0.221652480
[318,] 0.229026177 -0.514042704
[319,] -0.253992130 0.229026177
[320,] -0.475761507 -0.253992130
[321,] -0.449552177 -0.475761507
[322,] -0.190253416 -0.449552177
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.380026638 -0.391204175
2 -0.117269761 -0.380026638
3 0.401790679 -0.117269761
4 0.165435513 0.401790679
5 0.425634615 0.165435513
6 0.180508588 0.425634615
7 -0.311618048 0.180508588
8 -0.042415388 -0.311618048
9 -0.002941517 -0.042415388
10 0.006416983 -0.002941517
11 -0.222954297 0.006416983
12 0.163568300 -0.222954297
13 0.411038396 0.163568300
14 -0.088924126 0.411038396
15 0.188543029 -0.088924126
16 0.203658993 0.188543029
17 -0.020819660 0.203658993
18 -0.259265920 -0.020819660
19 -0.470924427 -0.259265920
20 0.277558114 -0.470924427
21 -0.458711961 0.277558114
22 -0.200158274 -0.458711961
23 0.070254858 -0.200158274
24 0.432221395 0.070254858
25 0.452907244 0.432221395
26 -0.778066326 0.452907244
27 -0.013715777 -0.778066326
28 -0.255322042 -0.013715777
29 0.505091365 -0.255322042
30 -0.487249576 0.505091365
31 -0.468436789 -0.487249576
32 0.044606476 -0.468436789
33 0.296904281 0.044606476
34 -0.696322007 0.296904281
35 0.063591370 -0.696322007
36 0.691874538 0.063591370
37 0.202235539 0.691874538
38 -0.025589086 0.202235539
39 -0.521213196 -0.025589086
40 0.232916988 -0.521213196
41 0.245241194 0.232916988
42 0.012816834 0.245241194
43 0.263811743 0.012816834
44 -0.214907510 0.263811743
45 -0.459436031 -0.214907510
46 0.050277046 -0.459436031
47 -0.184198722 0.050277046
48 0.923305249 -0.184198722
49 0.447502243 0.923305249
50 -0.049631733 0.447502243
51 -0.286187535 -0.049631733
52 0.230754473 -0.286187535
53 -0.007718799 0.230754473
54 0.011262886 -0.007718799
55 -0.470855407 0.011262886
56 0.279380758 -0.470855407
57 0.047125296 0.279380758
58 -0.683834605 0.047125296
59 0.327647052 -0.683834605
60 0.447445085 0.327647052
61 -0.299181862 0.447445085
62 -0.788457664 -0.299181862
63 -0.027229047 -0.788457664
64 0.235120224 -0.027229047
65 -0.741201207 0.235120224
66 -0.474311277 -0.741201207
67 0.279286813 -0.474311277
68 0.023963995 0.279286813
69 -0.462140742 0.023963995
70 -0.191417155 -0.462140742
71 0.568301596 -0.191417155
72 0.171877044 0.568301596
73 0.458196256 0.171877044
74 -0.277571308 0.458196256
75 0.235005276 -0.277571308
76 -0.250800065 0.235005276
77 0.265810810 -0.250800065
78 -0.711416971 0.265810810
79 0.049034508 -0.711416971
80 0.306882807 0.049034508
81 0.062424096 0.306882807
82 -0.912077014 0.062424096
83 0.609775185 -0.912077014
84 0.213164497 0.609775185
85 0.227676946 0.213164497
86 -0.501198198 0.227676946
87 0.250619255 -0.501198198
88 0.002691695 0.250619255
89 -0.231513302 0.002691695
90 -0.716623987 -0.231513302
91 0.042790644 -0.716623987
92 0.299829741 0.042790644
93 0.307756132 0.299829741
94 -0.157887198 0.307756132
95 0.346288057 -0.157887198
96 0.444648197 0.346288057
97 0.460001584 0.444648197
98 -0.512382704 0.460001584
99 -0.253324263 -0.512382704
100 -0.246951819 -0.253324263
101 0.007333593 -0.246951819
102 -0.236390778 0.007333593
103 -0.463900223 -0.236390778
104 0.300928068 -0.463900223
105 0.310694172 0.300928068
106 -0.456042046 0.310694172
107 0.307276379 -0.456042046
108 0.193177067 0.307276379
109 0.455395840 0.193177067
110 -0.023965002 0.455395840
111 0.229739183 -0.023965002
112 -0.002777275 0.229739183
113 0.263265381 -0.002777275
114 -0.480125416 0.263265381
115 0.036614673 -0.480125416
116 0.562294519 0.036614673
117 -0.685855964 0.562294519
118 0.075654652 -0.685855964
119 0.823904495 0.075654652
120 -0.299556848 0.823904495
121 0.706788255 -0.299556848
122 -0.515019195 0.706788255
123 0.495218643 -0.515019195
124 0.001441867 0.495218643
125 -0.482354863 0.001441867
126 -0.472176102 -0.482354863
127 0.539817930 -0.472176102
128 0.800217667 0.539817930
129 -0.435725170 0.800217667
130 0.320838300 -0.435725170
131 0.828092363 0.320838300
132 -0.298133725 0.828092363
133 -0.285972081 -0.298133725
134 0.220276872 -0.285972081
135 0.232089987 0.220276872
136 0.251136758 0.232089987
137 -0.234332399 0.251136758
138 -0.479192380 -0.234332399
139 0.284148334 -0.479192380
140 0.307164311 0.284148334
141 0.286948351 0.307164311
142 -0.193524527 0.286948351
143 0.579531470 -0.193524527
144 -0.535761244 0.579531470
145 -0.285239352 -0.535761244
146 -0.502874391 -0.285239352
147 0.002544683 -0.502874391
148 0.259549146 0.002544683
149 -0.475989485 0.259549146
150 -0.466560849 -0.475989485
151 0.295302062 -0.466560849
152 0.307164311 0.295302062
153 0.324276541 0.307164311
154 -0.167512627 0.324276541
155 -0.411627533 -0.167512627
156 -0.030857350 -0.411627533
157 -0.017464231 -0.030857350
158 -0.025168099 -0.017464231
159 -0.246497807 -0.025168099
160 -0.228901826 -0.246497807
161 0.024910568 -0.228901826
162 0.531872858 0.024910568
163 0.029764891 0.531872858
164 0.302329476 0.029764891
165 -0.430242491 0.302329476
166 0.580505152 -0.430242491
167 0.092978250 0.580505152
168 0.212639777 0.092978250
169 -0.282076698 0.212639777
170 0.221762374 -0.282076698
171 -0.248050797 0.221762374
172 0.503359051 -0.248050797
173 0.248824430 0.503359051
174 0.269308360 0.248824430
175 -0.465115666 0.269308360
176 -0.205661637 -0.465115666
177 -0.186409181 -0.205661637
178 0.074761684 -0.186409181
179 0.338158864 0.074761684
180 -0.041193218 0.338158864
181 -0.026991226 -0.041193218
182 -0.010366680 -0.026991226
183 -0.017126125 -0.010366680
184 0.238799978 -0.017126125
185 0.011888039 0.238799978
186 0.517714719 0.011888039
187 0.544244122 0.517714719
188 -0.213063863 0.544244122
189 0.053108006 -0.213063863
190 0.066376635 0.053108006
191 0.071640482 0.066376635
192 -0.053905619 0.071640482
193 -0.280751187 -0.053905619
194 -0.273195613 -0.280751187
195 -0.514117166 -0.273195613
196 -0.249036497 -0.514117166
197 0.260612779 -0.249036497
198 -0.220126146 0.260612779
199 -0.455769587 -0.220126146
200 0.056508885 -0.455769587
201 0.321765746 0.056508885
202 0.360689089 0.321765746
203 0.350251255 0.360689089
204 0.485704317 0.350251255
205 -0.500397811 0.485704317
206 0.011309210 -0.500397811
207 0.259876843 0.011309210
208 -0.222871978 0.259876843
209 0.286044570 -0.222871978
210 0.567080620 0.286044570
211 0.554429774 0.567080620
212 0.565444749 0.554429774
213 -0.169952325 0.565444749
214 -0.159773564 -0.169952325
215 0.107654469 -0.159773564
216 -0.015117269 0.107654469
217 -0.261312209 -0.015117269
218 0.237151276 -0.261312209
219 -0.235383189 0.237151276
220 0.532904503 -0.235383189
221 0.540856622 0.532904503
222 -0.195142682 0.540856622
223 -0.442929969 -0.195142682
224 0.329076834 -0.442929969
225 -0.147375134 0.329076834
226 0.612059215 -0.147375134
227 0.367498631 0.612059215
228 0.237470949 0.367498631
229 0.006996123 0.237470949
230 -0.234333034 0.006996123
231 0.030503875 -0.234333034
232 -0.213448469 0.030503875
233 0.296769689 -0.213448469
234 0.050227506 0.296769689
235 0.063088531 0.050227506
236 0.328565744 0.063088531
237 0.349259214 0.328565744
238 0.100473152 0.349259214
239 -0.128388322 0.100473152
240 -0.506322964 -0.128388322
241 0.512386338 -0.506322964
242 0.263562203 0.512386338
243 -0.485215241 0.263562203
244 0.526208624 -0.485215241
245 -0.209362071 0.526208624
246 0.297767504 -0.209362071
247 -0.446030898 0.297767504
248 -0.455623939 -0.446030898
249 0.077153055 -0.455623939
250 0.337455643 0.077153055
251 -0.138646760 0.337455643
252 -0.021477923 -0.138646760
253 0.233747189 -0.021477923
254 0.249237409 0.233747189
255 -0.496418102 0.249237409
256 -0.489329210 -0.496418102
257 0.029963929 -0.489329210
258 -0.205951569 0.029963929
259 -0.465543947 -0.205951569
260 -0.454154716 -0.465543947
261 -0.437937776 -0.454154716
262 0.317339078 -0.437937776
263 -0.138705836 0.317339078
264 0.211876596 -0.138705836
265 -0.029692881 0.211876596
266 -0.522829353 -0.029692881
267 0.225588103 -0.522829353
268 0.003818726 0.225588103
269 -0.488878078 0.003818726
270 -0.233348846 -0.488878078
271 0.260562920 -0.233348846
272 -0.226963982 0.260562920
273 -0.222639835 -0.226963982
274 -0.485413222 -0.222639835
275 0.031799884 -0.485413222
276 0.146797549 0.031799884
277 0.410048447 0.146797549
278 0.163412553 0.410048447
279 0.178262471 0.163412553
280 -0.300928515 0.178262471
281 -0.058373334 -0.300928515
282 -0.031696037 -0.058373334
283 0.247064397 -0.031696037
284 -0.475364963 0.247064397
285 0.042583274 -0.475364963
286 -0.424659450 0.042583274
287 -0.406345408 -0.424659450
288 0.246879463 -0.406345408
289 0.004274423 0.246879463
290 0.007762356 0.004274423
291 -0.470244845 0.007762356
292 0.549773688 -0.470244845
293 -0.431517010 0.549773688
294 0.316769621 -0.431517010
295 0.086962439 0.316769621
296 0.099117747 0.086962439
297 -0.637093252 0.099117747
298 -0.126830684 -0.637093252
299 0.137708115 -0.126830684
300 -0.237202071 0.137708115
301 0.018663344 -0.237202071
302 -0.467017508 0.018663344
303 0.018090376 -0.467017508
304 -0.230200708 0.018090376
305 0.281761300 -0.230200708
306 -0.223708592 0.281761300
307 0.041056897 -0.223708592
308 0.046451241 0.041056897
309 0.565408196 0.046451241
310 0.318734230 0.565408196
311 -0.179849250 0.318734230
312 -0.063618827 -0.179849250
313 -0.299409836 -0.063618827
314 0.433346255 -0.299409836
315 0.204284146 0.433346255
316 0.221652480 0.204284146
317 -0.514042704 0.221652480
318 0.229026177 -0.514042704
319 -0.253992130 0.229026177
320 -0.475761507 -0.253992130
321 -0.449552177 -0.475761507
322 -0.190253416 -0.449552177
> 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/www/rcomp/tmp/7b51h1321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8n2271321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9ds401321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10l6ll1321901238.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/11wiu41321901238.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/www/rcomp/tmp/12fyau1321901238.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/www/rcomp/tmp/13gkst1321901238.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/www/rcomp/tmp/1405pb1321901238.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/www/rcomp/tmp/15arty1321901238.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/www/rcomp/tmp/16tv561321901238.tab")
+ }
>
> try(system("convert tmp/1jaqw1321901238.ps tmp/1jaqw1321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ns2l1321901238.ps tmp/2ns2l1321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zqw81321901238.ps tmp/3zqw81321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/47x361321901238.ps tmp/47x361321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fi9x1321901238.ps tmp/5fi9x1321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dgcw1321901238.ps tmp/6dgcw1321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b51h1321901238.ps tmp/7b51h1321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/8n2271321901238.ps tmp/8n2271321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ds401321901238.ps tmp/9ds401321901238.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l6ll1321901238.ps tmp/10l6ll1321901238.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.840 0.260 8.067