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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+ ,1
+ ,3
+ ,-8
+ ,-10
+ ,28
+ ,2
+ ,4
+ ,-10
+ ,-10
+ ,29
+ ,1
+ ,-3
+ ,-7
+ ,-6
+ ,25
+ ,2
+ ,-2
+ ,-8
+ ,-5
+ ,24
+ ,1
+ ,-4
+ ,-11
+ ,-10
+ ,28
+ ,1
+ ,-4
+ ,-11
+ ,-10
+ ,32
+ ,0
+ ,-3
+ ,-11
+ ,-9
+ ,35
+ ,0
+ ,0
+ ,-12
+ ,-8
+ ,36
+ ,-1
+ ,-1
+ ,-13
+ ,-10
+ ,35
+ ,-1
+ ,-4
+ ,-10
+ ,-9
+ ,32
+ ,1
+ ,-2
+ ,-12
+ ,-10
+ ,35
+ ,0
+ ,-2
+ ,-11
+ ,-5
+ ,36
+ ,-1
+ ,-2
+ ,-13
+ ,-9
+ ,37
+ ,-2
+ ,-1
+ ,-10
+ ,-7
+ ,31
+ ,-1
+ ,-3
+ ,-13
+ ,-7
+ ,34
+ ,-2
+ ,-11
+ ,-12
+ ,-11
+ ,34
+ ,-2
+ ,-1
+ ,-15
+ ,-14
+ ,36
+ ,-3
+ ,-6
+ ,-13
+ ,-9
+ ,33
+ ,-1
+ ,-7
+ ,-9
+ ,-5
+ ,27
+ ,-2
+ ,-3
+ ,-10
+ ,-4
+ ,32
+ ,-1
+ ,-1
+ ,-17
+ ,-16
+ ,36
+ ,-6
+ ,-9
+ ,-18
+ ,-15
+ ,39
+ ,-7
+ ,-11
+ ,-15
+ ,-15
+ ,28
+ ,-5
+ ,-13
+ ,-12
+ ,-11
+ ,22
+ ,-6
+ ,-10
+ ,-16
+ ,-13
+ ,24
+ ,-8
+ ,-19
+ ,-16
+ ,-15
+ ,29
+ ,-9
+ ,-13
+ ,-12
+ ,-10
+ ,25
+ ,-4
+ ,-10
+ ,-14
+ ,-10
+ ,24
+ ,-7
+ ,-14)
+ ,dim=c(5
+ ,325)
+ ,dimnames=list(c('Indicator_van_het_consumentenvertrouwen'
+ ,'Algemene_economische_situatie'
+ ,'Werkloosheid_in_Belgie'
+ ,'Financiele_situatie_van_de_gezinnen'
+ ,'Spaarvermogen_van_de_gezinnen
')
+ ,1:325))
> y <- array(NA,dim=c(5,325),dimnames=list(c('Indicator_van_het_consumentenvertrouwen','Algemene_economische_situatie','Werkloosheid_in_Belgie','Financiele_situatie_van_de_gezinnen','Spaarvermogen_van_de_gezinnen
'),1:325))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Indicator_van_het_consumentenvertrouwen Algemene_economische_situatie
1 -25 -27
2 -24 -28
3 -17 -16
4 -14 -11
5 -16 -11
6 -13 -7
7 -10 -5
8 -10 -9
9 -12 -8
10 -12 -10
11 -20 -25
12 -16 -22
13 -12 -10
14 -14 -20
15 -7 -8
16 -9 -15
17 -9 -13
18 -4 -6
19 -3 0
20 1 5
21 -1 -1
22 -2 -5
23 1 4
24 -3 -3
25 -2 3
26 0 8
27 -2 3
28 -4 3
29 -4 7
30 -7 4
31 -9 -4
32 -13 -6
33 -8 8
34 -13 2
35 -15 -1
36 -15 -2
37 -15 0
38 -10 10
39 -12 3
40 -11 6
41 -11 7
42 -17 -4
43 -18 -5
44 -19 -7
45 -22 -10
46 -24 -21
47 -24 -22
48 -20 -16
49 -25 -25
50 -22 -22
51 -17 -22
52 -9 -19
53 -11 -21
54 -13 -31
55 -11 -28
56 -9 -23
57 -7 -17
58 -3 -12
59 -3 -14
60 -6 -18
61 -4 -16
62 -8 -22
63 -1 -9
64 -2 -10
65 -2 -10
66 -1 0
67 1 3
68 2 2
69 2 4
70 -1 -3
71 1 0
72 -1 -1
73 -8 -7
74 1 2
75 2 3
76 -2 -3
77 -2 -5
78 -2 0
79 -2 -3
80 -6 -7
81 -4 -7
82 -5 -7
83 -2 -4
84 -1 -3
85 -5 -6
86 -9 -10
87 -8 -10
88 -14 -23
89 -10 -13
90 -11 -18
91 -11 -16
92 -11 -15
93 -5 -5
94 -2 2
95 -3 -2
96 -6 -4
97 -6 -4
98 -7 -6
99 -6 -7
100 -2 0
101 -2 1
102 -4 -3
103 0 6
104 -6 -2
105 -4 2
106 -3 5
107 -1 7
108 -3 4
109 -6 0
110 -6 0
111 -15 -13
112 -5 -2
113 -11 -10
114 -13 -12
115 -10 -9
116 -9 -4
117 -11 -11
118 -18 -28
119 -13 -19
120 -9 -16
121 -8 -8
122 -4 -1
123 -3 -2
124 -3 -4
125 -3 -5
126 -1 0
127 0 5
128 1 5
129 0 2
130 2 6
131 1 3
132 -1 1
133 -8 -9
134 -18 -26
135 -14 -25
136 -4 -13
137 0 -6
138 4 -1
139 4 1
140 3 1
141 3 -2
142 7 2
143 8 3
144 13 15
145 15 13
146 14 12
147 14 10
148 10 8
149 16 16
150 13 18
151 15 19
152 13 14
153 12 10
154 13 9
155 11 5
156 9 5
157 8 6
158 8 5
159 5 -1
160 3 -7
161 -2 -14
162 0 -13
163 -8 -24
164 2 -7
165 2 -6
166 2 -3
167 3 -2
168 6 3
169 1 -7
170 1 -7
171 -4 -16
172 1 -9
173 2 -4
174 3 -5
175 5 -2
176 5 0
177 3 1
178 2 -1
179 3 -2
180 -1 -8
181 -9 -17
182 -5 -12
183 -1 -7
184 -9 -14
185 -8 -17
186 -12 -25
187 -13 -22
188 -16 -27
189 -21 -35
190 -21 -36
191 -16 -26
192 -15 -25
193 -8 -18
194 -8 -17
195 -9 -16
196 -9 -14
197 -11 -20
198 -12 -21
199 -13 -23
200 -13 -26
201 -12 -23
202 -15 -27
203 -18 -26
204 -16 -27
205 -16 -28
206 -20 -30
207 -16 -24
208 -12 -21
209 -12 -19
210 -6 -8
211 -5 -9
212 -7 -12
213 -4 -7
214 -7 -9
215 -8 -10
216 -7 -8
217 -4 -8
218 -2 -3
219 -3 6
220 -2 3
221 -6 -3
222 -11 -8
223 -10 -11
224 -10 -9
225 -13 -22
226 -14 -20
227 -20 -31
228 -22 -37
229 -24 -38
230 -25 -44
231 -26 -43
232 -24 -35
233 -25 -40
234 -24 -40
235 -25 -40
236 -24 -39
237 -25 -43
238 -22 -34
239 -24 -40
240 -24 -40
241 -16 -27
242 -17 -30
243 -20 -34
244 -14 -25
245 -12 -21
246 -11 -21
247 -13 -23
248 -13 -23
249 -13 -19
250 -11 -16
251 -10 -15
252 -9 -15
253 -6 -12
254 -5 -6
255 -4 -8
256 -5 -6
257 -4 -7
258 -4 -5
259 -4 -5
260 -5 -6
261 -3 -5
262 -3 -5
263 -11 -26
264 -13 -31
265 -7 -15
266 -5 -13
267 -5 -16
268 -4 -17
269 2 1
270 3 4
271 4 4
272 2 1
273 1 0
274 3 3
275 4 8
276 3 3
277 4 7
278 2 7
279 3 6
280 2 3
281 0 -1
282 1 5
283 0 0
284 -1 -4
285 -1 2
286 0 1
287 0 -1
288 -3 -3
289 0 2
290 1 3
291 -3 2
292 -6 -4
293 -8 -8
294 -6 -8
295 -7 -8
296 -7 -10
297 -8 -9
298 -8 -10
299 -10 -10
300 -7 -6
301 -8 -5
302 -11 -10
303 -11 -10
304 -11 -9
305 -12 -8
306 -13 -10
307 -10 -9
308 -12 -10
309 -11 -5
310 -13 -9
311 -10 -7
312 -13 -7
313 -12 -11
314 -15 -14
315 -13 -9
316 -9 -5
317 -10 -4
318 -17 -16
319 -18 -15
320 -15 -15
321 -12 -11
322 -16 -13
323 -16 -15
324 -12 -10
325 -14 -10
Werkloosheid_in_Belgie Financiele_situatie_van_de_gezinnen
1 57 -5
2 62 -5
3 50 -3
4 40 -3
5 46 -3
6 39 -2
7 35 -1
8 31 -3
9 35 -3
10 33 -4
11 47 -6
12 34 -5
13 31 -5
14 36 -2
15 24 1
16 22 -1
17 17 -1
18 8 -2
19 12 -1
20 5 1
21 6 0
22 5 -2
23 8 3
24 15 0
25 16 0
26 17 2
27 23 3
28 24 1
29 27 1
30 31 0
31 40 1
32 47 -1
33 43 2
34 60 2
35 64 0
36 65 1
37 65 1
38 55 3
39 57 3
40 57 1
41 57 1
42 65 -2
43 69 1
44 70 1
45 71 -1
46 71 -4
47 73 -2
48 68 -1
49 65 -5
50 57 -4
51 41 -5
52 21 0
53 21 -2
54 17 -4
55 9 -6
56 11 -2
57 6 -2
58 -2 -2
59 0 1
60 5 -2
61 3 0
62 7 -1
63 4 2
64 8 3
65 9 2
66 14 3
67 12 4
68 12 5
69 7 5
70 15 4
71 14 5
72 19 6
73 39 4
74 12 6
75 11 6
76 17 3
77 16 5
78 25 5
79 24 5
80 28 3
81 25 5
82 31 5
83 24 6
84 24 6
85 33 5
86 37 4
87 35 4
88 37 0
89 38 2
90 42 3
91 43 3
92 44 2
93 32 3
94 32 5
95 37 6
96 38 6
97 39 5
98 38 4
99 39 7
100 30 5
101 28 6
102 31 5
103 28 6
104 38 5
105 37 6
106 34 6
107 32 6
108 33 6
109 39 6
110 42 5
111 57 3
112 36 4
113 42 1
114 49 2
115 44 3
116 44 4
117 43 3
118 50 -1
119 45 1
120 40 4
121 38 4
122 29 4
123 27 5
124 27 5
125 27 5
126 32 7
127 24 6
128 22 6
129 22 7
130 23 7
131 23 8
132 28 7
133 36 3
134 60 3
135 43 2
136 23 5
137 15 6
138 7 8
139 6 7
140 8 6
141 5 8
142 -1 9
143 -2 11
144 -13 11
145 -18 11
146 -14 11
147 -15 11
148 -9 9
149 -12 12
150 -10 9
151 -13 10
152 -13 8
153 -9 4
154 -6 8
155 -4 6
156 -1 6
157 3 4
158 1 6
159 6 4
160 6 4
161 15 3
162 12 1
163 23 -1
164 12 2
165 14 3
166 9 3
167 9 3
168 7 4
169 16 2
170 17 1
171 23 2
172 14 2
173 9 1
174 12 3
175 10 1
176 12 3
177 14 3
178 10 3
179 14 5
180 21 2
181 32 -1
182 27 1
183 22 2
184 36 -3
185 33 -2
186 38 -3
187 41 -2
188 47 -4
189 56 -3
190 57 -3
191 43 -5
192 44 -3
193 29 -2
194 24 -3
195 25 -5
196 36 -3
197 35 -4
198 35 -5
199 39 -4
200 38 -3
201 34 -3
202 40 -6
203 47 -6
204 43 -7
205 44 -6
206 53 -9
207 45 -4
208 38 0
209 38 1
210 26 2
211 24 3
212 27 2
213 25 2
214 29 2
215 28 0
216 26 1
217 20 3
218 20 4
219 23 2
220 26 3
221 28 -1
222 34 -1
223 34 1
224 33 -1
225 35 -2
226 40 -3
227 48 -5
228 50 -5
229 50 -8
230 48 -6
231 62 -7
232 60 -6
233 58 -5
234 52 -3
235 57 -5
236 57 -4
237 61 -7
238 56 -3
239 56 -6
240 54 -4
241 48 -1
242 51 -1
243 49 -1
244 40 0
245 38 0
246 35 1
247 39 -1
248 38 -1
249 43 -2
250 41 0
251 37 1
252 34 1
253 34 7
254 27 6
255 23 2
256 27 5
257 26 4
258 25 5
259 24 2
260 23 2
261 19 3
262 18 2
263 31 -1
264 25 -2
265 23 0
266 18 2
267 14 1
268 11 4
269 9 5
270 8 8
271 4 7
272 6 4
273 7 4
274 2 5
275 2 6
276 8 4
277 7 5
278 9 5
279 6 4
280 6 3
281 11 5
282 9 5
283 8 3
284 10 2
285 8 3
286 6 2
287 9 4
288 12 1
289 9 4
290 7 2
291 14 3
292 22 1
293 23 0
294 22 1
295 22 2
296 22 0
297 25 1
298 28 2
299 29 1
300 25 2
301 24 1
302 28 1
303 32 0
304 35 0
305 36 -1
306 35 -1
307 32 1
308 35 0
309 36 -1
310 37 -2
311 31 -1
312 34 -2
313 34 -2
314 36 -3
315 33 -1
316 27 -2
317 32 -1
318 36 -6
319 39 -7
320 28 -5
321 22 -6
322 24 -8
323 29 -9
324 25 -4
325 24 -7
Spaarvermogen_van_de_gezinnen\r
1 -10
2 -2
3 3
4 -1
5 -3
6 -4
7 2
8 3
9 -1
10 -1
11 0
12 -4
13 -1
14 3
15 5
16 4
17 -4
18 -1
19 3
20 2
21 2
22 2
23 6
24 6
25 6
26 6
27 7
28 4
29 3
30 0
31 6
32 3
33 1
34 6
35 5
36 7
37 4
38 3
39 6
40 6
41 5
42 2
43 3
44 -2
45 -4
46 0
47 1
48 4
49 -3
50 -3
51 0
52 6
53 -1
54 0
55 -1
56 1
57 -4
58 -1
59 -1
60 0
61 3
62 0
63 8
64 8
65 8
66 8
67 11
68 13
69 5
70 12
71 13
72 9
73 11
74 7
75 12
76 11
77 10
78 13
79 14
80 10
81 13
82 12
83 13
84 17
85 15
86 6
87 9
88 6
89 11
90 12
91 13
92 11
93 16
94 16
95 19
96 14
97 15
98 12
99 14
100 16
101 13
102 13
103 15
104 12
105 13
106 12
107 15
108 10
109 8
110 11
111 8
112 13
113 9
114 8
115 8
116 6
117 8
118 6
119 12
120 16
121 10
122 11
123 13
124 12
125 13
126 19
127 12
128 16
129 12
130 18
131 18
132 14
133 9
134 10
135 9
136 15
137 17
138 17
139 13
140 14
141 13
142 17
143 17
144 13
145 18
146 19
147 20
148 15
149 22
150 13
151 18
152 16
153 25
154 29
155 29
156 24
157 24
158 22
159 24
160 22
161 20
162 23
163 15
164 23
165 26
166 16
167 21
168 23
169 25
170 25
171 23
172 24
173 20
174 24
175 31
176 30
177 23
178 16
179 23
180 23
181 16
182 19
183 22
184 16
185 19
186 20
187 15
188 13
189 10
190 14
191 11
192 14
193 15
194 13
195 9
196 16
197 14
198 15
199 15
200 14
201 11
202 12
203 8
204 14
205 11
206 11
207 11
208 8
209 6
210 10
211 12
212 9
213 12
214 10
215 3
216 6
217 6
218 11
219 6
220 10
221 8
222 1
223 3
224 3
225 6
226 7
227 2
228 3
229 -1
230 0
231 7
232 4
233 5
234 0
235 2
236 5
237 12
238 7
239 4
240 0
241 11
242 15
243 3
244 8
245 11
246 14
247 12
248 10
249 11
250 15
251 10
252 11
253 13
254 11
255 13
256 7
257 13
258 12
259 10
260 8
261 8
262 10
263 12
264 5
265 8
266 10
267 11
268 8
269 10
270 10
271 12
272 8
273 7
274 9
275 5
276 11
277 10
278 8
279 8
280 7
281 9
282 3
283 5
284 7
285 0
286 3
287 4
288 -2
289 4
290 6
291 -1
292 2
293 -2
294 5
295 -1
296 3
297 3
298 4
299 -3
300 -2
301 -4
302 -4
303 -3
304 0
305 -1
306 -4
307 -2
308 -2
309 -2
310 -1
311 -3
312 -11
313 -1
314 -6
315 -7
316 -3
317 -1
318 -9
319 -11
320 -13
321 -10
322 -19
323 -13
324 -10
325 -14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Algemene_economische_situatie
0.02441 0.24884
Werkloosheid_in_Belgie Financiele_situatie_van_de_gezinnen
-0.25174 0.24844
`Spaarvermogen_van_de_gezinnen\\r`
0.24883
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.96202 -0.23676 0.02243 0.27355 0.99226
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.024407 0.051294 0.476 0.635
Algemene_economische_situatie 0.248838 0.002820 88.231 <2e-16 ***
Werkloosheid_in_Belgie -0.251745 0.001388 -181.418 <2e-16 ***
Financiele_situatie_van_de_gezinnen 0.248441 0.009088 27.338 <2e-16 ***
`Spaarvermogen_van_de_gezinnen\\r` 0.248828 0.003005 82.804 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3558 on 320 degrees of freedom
Multiple R-squared: 0.9983, Adjusted R-squared: 0.9983
F-statistic: 4.646e+04 on 4 and 320 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,] 0.5198460236 0.9603079527 0.4801540
[2,] 0.3691260450 0.7382520899 0.6308740
[3,] 0.2380131592 0.4760263185 0.7619868
[4,] 0.3024536316 0.6049072631 0.6975464
[5,] 0.2431188482 0.4862376965 0.7568812
[6,] 0.1613826548 0.3227653097 0.8386173
[7,] 0.1360575762 0.2721151524 0.8639424
[8,] 0.1169240807 0.2338481614 0.8830759
[9,] 0.0922641765 0.1845283531 0.9077358
[10,] 0.0602013621 0.1204027242 0.9397986
[11,] 0.0700259182 0.1400518365 0.9299741
[12,] 0.0685685599 0.1371371197 0.9314314
[13,] 0.1032254596 0.2064509192 0.8967745
[14,] 0.1086677347 0.2173354693 0.8913323
[15,] 0.1466710442 0.2933420885 0.8533290
[16,] 0.1083069459 0.2166138919 0.8916931
[17,] 0.0879388766 0.1758777532 0.9120611
[18,] 0.0640316066 0.1280632131 0.9359684
[19,] 0.0922764395 0.1845528789 0.9077236
[20,] 0.2248072486 0.4496144973 0.7751928
[21,] 0.1794028278 0.3588056556 0.8205972
[22,] 0.1403722193 0.2807444385 0.8596278
[23,] 0.1253501841 0.2507003683 0.8746498
[24,] 0.1472852747 0.2945705494 0.8527147
[25,] 0.1176341069 0.2352682138 0.8823659
[26,] 0.0906894932 0.1813789865 0.9093105
[27,] 0.0840111020 0.1680222040 0.9159889
[28,] 0.0767011865 0.1534023730 0.9232988
[29,] 0.0583979874 0.1167959748 0.9416020
[30,] 0.0484567040 0.0969134080 0.9515433
[31,] 0.0390829211 0.0781658422 0.9609171
[32,] 0.0656458036 0.1312916071 0.9343542
[33,] 0.0550112820 0.1100225640 0.9449887
[34,] 0.0442036445 0.0884072890 0.9557964
[35,] 0.0517512175 0.1035024351 0.9482488
[36,] 0.0459774763 0.0919549527 0.9540225
[37,] 0.1067667113 0.2135334226 0.8932333
[38,] 0.1134433658 0.2268867316 0.8865566
[39,] 0.1036225178 0.2072450355 0.8963775
[40,] 0.0961690167 0.1923380334 0.9038310
[41,] 0.1150880974 0.2301761949 0.8849119
[42,] 0.1168956469 0.2337912938 0.8831044
[43,] 0.1188341244 0.2376682488 0.8811659
[44,] 0.1010394055 0.2020788109 0.8989606
[45,] 0.1001351678 0.2002703355 0.8998648
[46,] 0.1054651757 0.2109303514 0.8945348
[47,] 0.0890706782 0.1781413563 0.9109293
[48,] 0.0727342153 0.1454684306 0.9272658
[49,] 0.0616420689 0.1232841378 0.9383579
[50,] 0.0558205742 0.1116411484 0.9441794
[51,] 0.0488421887 0.0976843774 0.9511578
[52,] 0.0577852350 0.1155704699 0.9422148
[53,] 0.0510415582 0.1020831164 0.9489584
[54,] 0.0406233459 0.0812466918 0.9593767
[55,] 0.0546878817 0.1093757633 0.9453121
[56,] 0.0467296297 0.0934592594 0.9532704
[57,] 0.0389572801 0.0779145603 0.9610427
[58,] 0.0398424923 0.0796849846 0.9601575
[59,] 0.0341457378 0.0682914755 0.9658543
[60,] 0.0375007711 0.0750015421 0.9624992
[61,] 0.0314461166 0.0628922331 0.9685539
[62,] 0.0277934462 0.0555868924 0.9722066
[63,] 0.0282819899 0.0565639797 0.9717180
[64,] 0.0238375211 0.0476750422 0.9761625
[65,] 0.0237004636 0.0474009272 0.9762995
[66,] 0.0189322353 0.0378644706 0.9810678
[67,] 0.0166759676 0.0333519352 0.9833240
[68,] 0.0193416809 0.0386833618 0.9806583
[69,] 0.0198673245 0.0397346489 0.9801327
[70,] 0.0212050966 0.0424101933 0.9787949
[71,] 0.0170183955 0.0340367909 0.9829816
[72,] 0.0154153627 0.0308307253 0.9845846
[73,] 0.0152211769 0.0304423539 0.9847788
[74,] 0.0141771293 0.0283542586 0.9858229
[75,] 0.0187010786 0.0374021572 0.9812989
[76,] 0.0218913107 0.0437826213 0.9781087
[77,] 0.0198076265 0.0396152530 0.9801924
[78,] 0.0160078143 0.0320156286 0.9839922
[79,] 0.0157895709 0.0315791418 0.9842104
[80,] 0.0128563481 0.0257126963 0.9871437
[81,] 0.0130738048 0.0261476095 0.9869262
[82,] 0.0124423253 0.0248846507 0.9875577
[83,] 0.0164895617 0.0329791234 0.9835104
[84,] 0.0133781491 0.0267562982 0.9866219
[85,] 0.0284976933 0.0569953866 0.9715023
[86,] 0.0271303575 0.0542607151 0.9728696
[87,] 0.0311566230 0.0623132459 0.9688434
[88,] 0.0559717220 0.1119434439 0.9440283
[89,] 0.0598804282 0.1197608563 0.9401196
[90,] 0.0513092870 0.1026185740 0.9486907
[91,] 0.0437076303 0.0874152605 0.9562924
[92,] 0.0432706166 0.0865412332 0.9567294
[93,] 0.0447429151 0.0894858302 0.9552571
[94,] 0.0371738818 0.0743477635 0.9628261
[95,] 0.0308806397 0.0617612794 0.9691194
[96,] 0.0301439931 0.0602879861 0.9698560
[97,] 0.0260883286 0.0521766571 0.9739117
[98,] 0.0213509058 0.0427018116 0.9786491
[99,] 0.0186735541 0.0373471082 0.9813264
[100,] 0.0152031122 0.0304062244 0.9847969
[101,] 0.0139839189 0.0279678377 0.9860161
[102,] 0.0126238016 0.0252476031 0.9873762
[103,] 0.0181918178 0.0363836357 0.9818082
[104,] 0.0154209869 0.0308419738 0.9845790
[105,] 0.0154017284 0.0308034568 0.9845983
[106,] 0.0164119484 0.0328238968 0.9835881
[107,] 0.0138672073 0.0277344145 0.9861328
[108,] 0.0199584850 0.0399169699 0.9800415
[109,] 0.0256129014 0.0512258028 0.9743871
[110,] 0.0223275984 0.0446551969 0.9776724
[111,] 0.0234312948 0.0468625895 0.9765687
[112,] 0.0196184759 0.0392369518 0.9803815
[113,] 0.0163640312 0.0327280623 0.9836360
[114,] 0.0132648732 0.0265297464 0.9867351
[115,] 0.0114574071 0.0229148142 0.9885426
[116,] 0.0098930564 0.0197861128 0.9901069
[117,] 0.0135098035 0.0270196070 0.9864902
[118,] 0.0184129355 0.0368258709 0.9815871
[119,] 0.0265318173 0.0530636346 0.9734682
[120,] 0.0246984513 0.0493969027 0.9753015
[121,] 0.0216035637 0.0432071274 0.9783964
[122,] 0.0196289664 0.0392579327 0.9803710
[123,] 0.0162398385 0.0324796770 0.9837602
[124,] 0.0192656597 0.0385313195 0.9807343
[125,] 0.0254873764 0.0509747527 0.9745126
[126,] 0.0247417593 0.0494835187 0.9752582
[127,] 0.0252837209 0.0505674418 0.9747163
[128,] 0.0242960628 0.0485921256 0.9757039
[129,] 0.0200733396 0.0401466793 0.9799267
[130,] 0.0234713769 0.0469427538 0.9765286
[131,] 0.0217399562 0.0434799124 0.9782600
[132,] 0.0193974572 0.0387949145 0.9806025
[133,] 0.0174683413 0.0349366825 0.9825317
[134,] 0.0243811951 0.0487623902 0.9756188
[135,] 0.0227974681 0.0455949362 0.9772025
[136,] 0.0220323832 0.0440647664 0.9779676
[137,] 0.0184110521 0.0368221041 0.9815889
[138,] 0.0150980085 0.0301960170 0.9849020
[139,] 0.0122829134 0.0245658268 0.9877171
[140,] 0.0099348555 0.0198697110 0.9900651
[141,] 0.0092261889 0.0184523777 0.9907738
[142,] 0.0119730540 0.0239461079 0.9880269
[143,] 0.0144558092 0.0289116183 0.9855442
[144,] 0.0117906881 0.0235813763 0.9882093
[145,] 0.0106050111 0.0212100221 0.9893950
[146,] 0.0090666363 0.0181332726 0.9909334
[147,] 0.0074261519 0.0148523039 0.9925738
[148,] 0.0060993796 0.0121987592 0.9939006
[149,] 0.0048983573 0.0097967146 0.9951016
[150,] 0.0048678445 0.0097356891 0.9951322
[151,] 0.0038626202 0.0077252403 0.9961374
[152,] 0.0032364359 0.0064728718 0.9967636
[153,] 0.0027717510 0.0055435019 0.9972282
[154,] 0.0032401726 0.0064803452 0.9967598
[155,] 0.0032824686 0.0065649371 0.9967175
[156,] 0.0031655467 0.0063310934 0.9968345
[157,] 0.0045549596 0.0091099191 0.9954450
[158,] 0.0038465053 0.0076930107 0.9961535
[159,] 0.0034984664 0.0069969328 0.9965015
[160,] 0.0030014108 0.0060028215 0.9969986
[161,] 0.0028141965 0.0056283931 0.9971858
[162,] 0.0022136422 0.0044272843 0.9977864
[163,] 0.0032196143 0.0064392286 0.9967804
[164,] 0.0037679075 0.0075358151 0.9962321
[165,] 0.0034687463 0.0069374925 0.9965313
[166,] 0.0027228803 0.0054457607 0.9972771
[167,] 0.0037566458 0.0075132916 0.9962434
[168,] 0.0029799684 0.0059599369 0.9970200
[169,] 0.0025188311 0.0050376622 0.9974812
[170,] 0.0021119122 0.0042238243 0.9978881
[171,] 0.0016399144 0.0032798289 0.9983601
[172,] 0.0012859082 0.0025718164 0.9987141
[173,] 0.0009993758 0.0019987516 0.9990006
[174,] 0.0011429184 0.0022858368 0.9988571
[175,] 0.0009328694 0.0018657389 0.9990671
[176,] 0.0009059305 0.0018118609 0.9990941
[177,] 0.0008944089 0.0017888178 0.9991056
[178,] 0.0008659994 0.0017319988 0.9991340
[179,] 0.0009773344 0.0019546689 0.9990227
[180,] 0.0010754257 0.0021508513 0.9989246
[181,] 0.0010551026 0.0021102052 0.9989449
[182,] 0.0008227110 0.0016454221 0.9991773
[183,] 0.0008921617 0.0017843235 0.9991078
[184,] 0.0007426431 0.0014852863 0.9992574
[185,] 0.0008361201 0.0016722401 0.9991639
[186,] 0.0012130113 0.0024260226 0.9987870
[187,] 0.0010554113 0.0021108226 0.9989446
[188,] 0.0009384251 0.0018768501 0.9990616
[189,] 0.0009090658 0.0018181315 0.9990909
[190,] 0.0008448171 0.0016896341 0.9991552
[191,] 0.0010105867 0.0020211733 0.9989894
[192,] 0.0008444142 0.0016888284 0.9991556
[193,] 0.0007728797 0.0015457595 0.9992271
[194,] 0.0006928363 0.0013856725 0.9993072
[195,] 0.0006227929 0.0012455858 0.9993772
[196,] 0.0005195664 0.0010391329 0.9994804
[197,] 0.0004468608 0.0008937215 0.9995531
[198,] 0.0012413610 0.0024827220 0.9987586
[199,] 0.0011527703 0.0023055406 0.9988472
[200,] 0.0013161554 0.0026323107 0.9986838
[201,] 0.0035544209 0.0071088417 0.9964456
[202,] 0.0049282233 0.0098564466 0.9950718
[203,] 0.0055147145 0.0110294291 0.9944853
[204,] 0.0062093488 0.0124186975 0.9937907
[205,] 0.0049202617 0.0098405234 0.9950797
[206,] 0.0069140028 0.0138280057 0.9930860
[207,] 0.0075641254 0.0151282508 0.9924359
[208,] 0.0167151833 0.0334303666 0.9832848
[209,] 0.0144843494 0.0289686989 0.9855157
[210,] 0.0299423286 0.0598846571 0.9700577
[211,] 0.0250165489 0.0500330978 0.9749835
[212,] 0.0383824025 0.0767648049 0.9616176
[213,] 0.0546306470 0.1092612941 0.9453694
[214,] 0.0464147803 0.0928295606 0.9535852
[215,] 0.0502555657 0.1005111315 0.9497444
[216,] 0.0483171842 0.0966343683 0.9516828
[217,] 0.0405577771 0.0811155541 0.9594422
[218,] 0.0369760579 0.0739521158 0.9630239
[219,] 0.0306476697 0.0612953394 0.9693523
[220,] 0.0360260695 0.0720521391 0.9639739
[221,] 0.0321713792 0.0643427584 0.9678286
[222,] 0.0281163470 0.0562326940 0.9718837
[223,] 0.0405090928 0.0810181857 0.9594909
[224,] 0.0367645245 0.0735290490 0.9632355
[225,] 0.0344434381 0.0688868762 0.9655566
[226,] 0.0410221026 0.0820442052 0.9589779
[227,] 0.0411274810 0.0822549620 0.9588725
[228,] 0.0346346051 0.0692692103 0.9653654
[229,] 0.0328536703 0.0657073405 0.9671463
[230,] 0.0305445949 0.0610891898 0.9694554
[231,] 0.0383717117 0.0767434233 0.9616283
[232,] 0.0395663206 0.0791326412 0.9604337
[233,] 0.0397273690 0.0794547380 0.9602726
[234,] 0.0368239738 0.0736479475 0.9631760
[235,] 0.0319453336 0.0638906671 0.9680547
[236,] 0.0273551264 0.0547102528 0.9726449
[237,] 0.0242980478 0.0485960956 0.9757020
[238,] 0.0196214249 0.0392428498 0.9803786
[239,] 0.0357142756 0.0714285513 0.9642857
[240,] 0.0324594150 0.0649188299 0.9675406
[241,] 0.0264907241 0.0529814482 0.9735093
[242,] 0.0244996237 0.0489992474 0.9755004
[243,] 0.0259499243 0.0518998486 0.9740501
[244,] 0.0239703548 0.0479407096 0.9760296
[245,] 0.0221783622 0.0443567245 0.9778216
[246,] 0.0378091230 0.0756182461 0.9621909
[247,] 0.0817768076 0.1635536151 0.9182232
[248,] 0.0688169439 0.1376338878 0.9311831
[249,] 0.0711115343 0.1422230686 0.9288885
[250,] 0.0614400807 0.1228801615 0.9385599
[251,] 0.0819523125 0.1639046250 0.9180477
[252,] 0.0813975682 0.1627951364 0.9186024
[253,] 0.0701383482 0.1402766965 0.9298617
[254,] 0.0671960507 0.1343921014 0.9328039
[255,] 0.0578979766 0.1157959533 0.9421020
[256,] 0.0662969079 0.1325938158 0.9337031
[257,] 0.0557976736 0.1115953472 0.9442023
[258,] 0.0736743950 0.1473487899 0.9263256
[259,] 0.0626014459 0.1252028918 0.9373986
[260,] 0.0752614178 0.1505228356 0.9247386
[261,] 0.0663521484 0.1327042969 0.9336479
[262,] 0.0629593319 0.1259186637 0.9370407
[263,] 0.0640299509 0.1280599017 0.9359700
[264,] 0.1053207949 0.2106415897 0.8946792
[265,] 0.0936112054 0.1872224109 0.9063888
[266,] 0.0769283542 0.1538567084 0.9230716
[267,] 0.1552362043 0.3104724086 0.8447638
[268,] 0.1433406192 0.2866812385 0.8566594
[269,] 0.1771597144 0.3543194288 0.8228403
[270,] 0.1848940767 0.3697881535 0.8151059
[271,] 0.2405508859 0.4811017718 0.7594491
[272,] 0.2069960271 0.4139920542 0.7930040
[273,] 0.1920521621 0.3841043243 0.8079478
[274,] 0.2263295071 0.4526590141 0.7736705
[275,] 0.1920706332 0.3841412664 0.8079294
[276,] 0.1630947260 0.3261894520 0.8369053
[277,] 0.1395694929 0.2791389857 0.8604305
[278,] 0.1439361424 0.2878722848 0.8560639
[279,] 0.1230552311 0.2461104622 0.8769448
[280,] 0.1167545732 0.2335091464 0.8832454
[281,] 0.2638049384 0.5276098769 0.7361951
[282,] 0.2426243428 0.4852486856 0.7573757
[283,] 0.2036372383 0.4072744766 0.7963628
[284,] 0.2917086632 0.5834173263 0.7082913
[285,] 0.2819755412 0.5639510824 0.7180245
[286,] 0.2422259922 0.4844519843 0.7577740
[287,] 0.2006313395 0.4012626789 0.7993687
[288,] 0.1656223544 0.3312447088 0.8343776
[289,] 0.1464398591 0.2928797182 0.8535601
[290,] 0.1732297149 0.3464594299 0.8267703
[291,] 0.1375120578 0.2750241156 0.8624879
[292,] 0.1147666140 0.2295332280 0.8852334
[293,] 0.1969087436 0.3938174872 0.8030913
[294,] 0.1558917850 0.3117835700 0.8441082
[295,] 0.3599748177 0.7199496353 0.6400252
[296,] 0.3186797249 0.6373594498 0.6813203
[297,] 0.2683915632 0.5367831263 0.7316084
[298,] 0.2475906096 0.4951812191 0.7524094
[299,] 0.2674160085 0.5348320171 0.7325840
[300,] 0.3229318154 0.6458636308 0.6770682
[301,] 0.2585558719 0.5171117437 0.7414441
[302,] 0.2088361568 0.4176723136 0.7911638
[303,] 0.2963695950 0.5927391901 0.7036304
[304,] 0.3965915226 0.7931830453 0.6034085
[305,] 0.8531665662 0.2936668675 0.1468334
[306,] 0.7747361031 0.4505277938 0.2252639
[307,] 0.6992211242 0.6015577516 0.3007789
[308,] 0.5920750072 0.8158499856 0.4079250
[309,] 0.6354705216 0.7290589567 0.3645295
[310,] 0.4678552694 0.9357105388 0.5321447
> postscript(file="/var/wessaorg/rcomp/tmp/15f941356045590.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jr761356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w4yb1356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4iaxi1356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5b5wm1356045590.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 = 325
Frequency = 1
1 2 3 4 5
-2.258365e-01 2.911043e-01 -4.569138e-01 -2.232428e-01 -2.151186e-01
6 7 8 9 10
2.770123e-02 -2.183617e-01 1.806648e-02 -2.284819e-01 1.414610e-02
11 12 13 14 15
-4.807973e-01 2.468751e-01 -2.409026e-01 -2.344285e-01 -4.844044e-01
16 17 18 19 20
-5.003161e-01 -2.660952e-01 2.282909e-01 -5.015117e-01 2.440289e-01
21 22 23 24 25
2.372447e-01 4.777350e-01 -2.440909e-01 5.313826e-03 -2.359715e-01
26 27 28 29 30
2.746998e-01 5.320918e-01 2.720147e-02 3.591017e-02 -2.156716e-01
31 32 33 34 35
2.993314e-01 -1.974135e-01 6.420320e-02 -4.072435e-01 9.196017e-02
36 37 38 39 40
-1.535529e-01 9.525345e-02 -1.586321e-01 -6.597572e-01 9.060951e-02
41 42 43 44 45
9.059886e-02 3.335849e-01 -4.047479e-01 5.888121e-01 -4.183909e-01
46 47 48 49 50
6.884279e-02 7.546132e-02 3.287832e-01 -4.513487e-01 -4.602630e-01
51 52 53 54 55
1.377792e-02 -5.028037e-01 2.335486e-01 -3.699304e-02 -5.175710e-02
56 57 58 59 60
-2.838781e-01 2.085063e-01 2.038730e-01 4.597167e-01 2.102890e-01
61 62 63 64 65
-3.424213e-02 -5.393088e-01 -2.653860e-01 -2.580093e-01 2.421763e-01
66 67 68 69 70
-2.359240e-01 -4.808527e-01 2.188944e-02 2.561103e-01 -4.814158e-01
71 72 73 74 75
2.305575e-02 2.774880e-01 -1.953594e-01 2.664148e-01 -4.783069e-01
76 77 78 79 80
-4.806577e-01 -4.827798e-01 -2.077514e-01 3.819114e-02 -4.672837e-01
81 82 83 84 85
-4.658830e-01 2.934136e-01 2.874163e-01 4.326717e-02 -1.984183e-01
86 87 88 89 90
2.918045e-01 4.183180e-02 -4.795335e-01 -4.571924e-01 2.967100e-01
91 92 93 94 95
-1.980496e-01 5.509531e-01 -4.509474e-01 3.103024e-01 5.694559e-01
96 97 98 99 100
-4.369841e-01 -1.856262e-01 5.522970e-02 3.128349e-01 3.044895e-01
101 102 103 104 105
5.020377e-02 4.923246e-02 3.083566e-01 -1.885646e-01 6.706865e-02
106 107 108 109 110
-1.858531e-01 6.649747e-02 3.088958e-01 3.123735e-01 5.695656e-01
111 112 113 114 115
-1.759989e-01 3.075590e-01 -4.506320e-01 -1.903548e-01 5.559652e-01
116 117 118 119 120
5.609880e-01 -1.981029e-01 2.857816e-01 -2.023355e-01 5.179203e-02
121 122 123 124 125
5.056180e-02 -2.058376e-01 -2.065851e-01 5.399193e-01 5.399299e-01
126 127 128 129 130
5.646143e-01 2.966988e-01 -2.021016e-01 2.912834e-01 5.470863e-02
131 132 133 134 135
-4.472172e-01 5.529352e-01 2.931791e-01 3.164787e-01 2.852472e-01
136 137 138 139 140
2.600216e-02 -4.759210e-01 -2.309529e-01 2.633772e-01 -2.335200e-01
141 142 143 144 145
-4.902934e-01 -2.398673e-01 -2.373321e-01 2.725591e-03 -2.460214e-03
146 147 148 149 150
4.529651e-03 1.633854e-03 -2.492004e-01 5.177419e-01 5.083267e-01
151 152 153 154 155
1.167455e-02 2.504034e-01 7.050232e-03 2.204874e-02 1.777348e-02
156 157 158 159 160
1.714639e-02 2.721690e-01 1.829139e-02 -2.307281e-01 -2.400426e-01
161 162 163 164 165
-4.863747e-01 2.599512e-01 2.538692e-01 5.184802e-01 -2.217925e-01
166 167 168 169 170
2.612454e-01 -2.317314e-01 2.744909e-01 2.780403e-02 5.279897e-01
171 172 173 174 175
-4.727818e-01 2.708188e-01 1.165471e-02 5.235349e-01 2.861812e-02
176 177 178 179 180
-2.136230e-01 -2.171779e-01 1.531352e-02 3.245546e-02 3.302179e-02
181 182 183 184 185
-4.711237e-01 -2.174043e-01 2.847559e-01 2.862222e-01 2.825789e-01
186 187 188 189 190
-4.683771e-01 -4.639602e-01 2.852375e-01 3.968983e-02 -4.550378e-01
191 192 193 194 195
-2.244838e-01 -4.649421e-01 5.197488e-01 -2.417173e-01 2.533817e-01
196 197 198 199 200
2.862222e-01 2.736037e-01 -4.779447e-01 -2.217297e-01 2.734274e-01
201 202 203 204 205
2.664162e-01 2.687334e-01 -2.225806e-01 -2.252468e-01 7.733786e-01
206 207 208 209 210
2.820811e-01 -4.671117e-01 7.768792e-01 5.284171e-01 -4.734941e-01
211 212 213 214 215
-4.742416e-01 2.243181e-02 5.282673e-01 -4.694213e-01 7.663478e-01
216 217 218 219 220
-2.297424e-01 7.629070e-01 2.613589e-02 -7.171547e-01 5.408432e-01
221 222 223 224 225
2.878173e-02 -4.747638e-01 2.772141e-01 2.467436e-02 2.650203e-01
226 227 228 229 230
2.568079e-02 5.178813e-01 2.655733e-01 2.550451e-01 -5.011238e-01
231 232 233 234 235
2.811120e-01 2.849578e-01 -4.716086e-01 -2.348206e-01 2.312971e-02
236 237 238 239 240
-2.206326e-01 -2.147713e-01 -4.626654e-01 5.221704e-01 5.171098e-01
241 242 243 244 245
2.893152e-01 -2.042462e-01 2.735500e-01 2.757223e-01 3.039614e-02
246 247 248 249 250
-7.197622e-01 -2.205692e-01 2.534141e-02 2.883252e-01 -4.538720e-01
251 252 253 254 255
2.860081e-01 2.819460e-01 5.471303e-01 -9.620172e-01 2.478839e-02
256 257 258 259 260
2.817345e-01 3.430272e-02 -7.147320e-01 2.765012e-01 -2.287498e-01
261 262 263 264 265
2.669917e-01 -2.339676e-01 5.119875e-01 2.359452e-01 5.076771e-01
266 267 268 269 270
-2.432608e-01 -5.041118e-01 -9.347309e-03 2.619765e-01 -4.816060e-01
271 272 273 274 275
-7.377997e-01 2.528383e-01 2.249186e-03 -7.490861e-01 -2.464080e-01
276 277 278 279 280
5.121681e-01 2.654568e-01 -7.333982e-01 8.646570e-03 2.524302e-01
281 282 283 284 285
-4.880295e-01 8.416961e-03 9.025231e-05 2.497187e-01 -2.534480e-01
286 287 288 289 290
-6.141443e-03 5.010602e-01 9.922601e-01 -2.454548e-01 1.443558e-03
291 292 293 294 295
-4.941514e-01 -2.367643e-01 2.540856e-01 1.210604e-02 2.566314e-01
296 297 298 299 300
2.558790e-01 -4.861658e-01 2.063840e-02 2.626180e-01 7.630168e-01
301 302 303 304 305
8.529883e-03 -7.402991e-01 2.662932e-01 2.620619e-02 -4.736188e-01
306 307 308 309 310
-4.812038e-01 5.201863e-01 -2.273001e-01 2.869385e-02 -7.245948e-01
311 312 313 314 315
5.164742e-01 5.107711e-01 1.784753e-02 -2.395685e-01 -4.870487e-01
316 317 318 319 320
2.602592e-01 -4.759514e-01 -2.500861e-01 2.406266e-03 2.339871e-01
321 322 323 324 325
2.301226e-01 -3.238003e-02 4.794954e-01 2.396370e-01 -2.714744e-01
> postscript(file="/var/wessaorg/rcomp/tmp/6ta5b1356045590.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 = 325
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.258365e-01 NA
1 2.911043e-01 -2.258365e-01
2 -4.569138e-01 2.911043e-01
3 -2.232428e-01 -4.569138e-01
4 -2.151186e-01 -2.232428e-01
5 2.770123e-02 -2.151186e-01
6 -2.183617e-01 2.770123e-02
7 1.806648e-02 -2.183617e-01
8 -2.284819e-01 1.806648e-02
9 1.414610e-02 -2.284819e-01
10 -4.807973e-01 1.414610e-02
11 2.468751e-01 -4.807973e-01
12 -2.409026e-01 2.468751e-01
13 -2.344285e-01 -2.409026e-01
14 -4.844044e-01 -2.344285e-01
15 -5.003161e-01 -4.844044e-01
16 -2.660952e-01 -5.003161e-01
17 2.282909e-01 -2.660952e-01
18 -5.015117e-01 2.282909e-01
19 2.440289e-01 -5.015117e-01
20 2.372447e-01 2.440289e-01
21 4.777350e-01 2.372447e-01
22 -2.440909e-01 4.777350e-01
23 5.313826e-03 -2.440909e-01
24 -2.359715e-01 5.313826e-03
25 2.746998e-01 -2.359715e-01
26 5.320918e-01 2.746998e-01
27 2.720147e-02 5.320918e-01
28 3.591017e-02 2.720147e-02
29 -2.156716e-01 3.591017e-02
30 2.993314e-01 -2.156716e-01
31 -1.974135e-01 2.993314e-01
32 6.420320e-02 -1.974135e-01
33 -4.072435e-01 6.420320e-02
34 9.196017e-02 -4.072435e-01
35 -1.535529e-01 9.196017e-02
36 9.525345e-02 -1.535529e-01
37 -1.586321e-01 9.525345e-02
38 -6.597572e-01 -1.586321e-01
39 9.060951e-02 -6.597572e-01
40 9.059886e-02 9.060951e-02
41 3.335849e-01 9.059886e-02
42 -4.047479e-01 3.335849e-01
43 5.888121e-01 -4.047479e-01
44 -4.183909e-01 5.888121e-01
45 6.884279e-02 -4.183909e-01
46 7.546132e-02 6.884279e-02
47 3.287832e-01 7.546132e-02
48 -4.513487e-01 3.287832e-01
49 -4.602630e-01 -4.513487e-01
50 1.377792e-02 -4.602630e-01
51 -5.028037e-01 1.377792e-02
52 2.335486e-01 -5.028037e-01
53 -3.699304e-02 2.335486e-01
54 -5.175710e-02 -3.699304e-02
55 -2.838781e-01 -5.175710e-02
56 2.085063e-01 -2.838781e-01
57 2.038730e-01 2.085063e-01
58 4.597167e-01 2.038730e-01
59 2.102890e-01 4.597167e-01
60 -3.424213e-02 2.102890e-01
61 -5.393088e-01 -3.424213e-02
62 -2.653860e-01 -5.393088e-01
63 -2.580093e-01 -2.653860e-01
64 2.421763e-01 -2.580093e-01
65 -2.359240e-01 2.421763e-01
66 -4.808527e-01 -2.359240e-01
67 2.188944e-02 -4.808527e-01
68 2.561103e-01 2.188944e-02
69 -4.814158e-01 2.561103e-01
70 2.305575e-02 -4.814158e-01
71 2.774880e-01 2.305575e-02
72 -1.953594e-01 2.774880e-01
73 2.664148e-01 -1.953594e-01
74 -4.783069e-01 2.664148e-01
75 -4.806577e-01 -4.783069e-01
76 -4.827798e-01 -4.806577e-01
77 -2.077514e-01 -4.827798e-01
78 3.819114e-02 -2.077514e-01
79 -4.672837e-01 3.819114e-02
80 -4.658830e-01 -4.672837e-01
81 2.934136e-01 -4.658830e-01
82 2.874163e-01 2.934136e-01
83 4.326717e-02 2.874163e-01
84 -1.984183e-01 4.326717e-02
85 2.918045e-01 -1.984183e-01
86 4.183180e-02 2.918045e-01
87 -4.795335e-01 4.183180e-02
88 -4.571924e-01 -4.795335e-01
89 2.967100e-01 -4.571924e-01
90 -1.980496e-01 2.967100e-01
91 5.509531e-01 -1.980496e-01
92 -4.509474e-01 5.509531e-01
93 3.103024e-01 -4.509474e-01
94 5.694559e-01 3.103024e-01
95 -4.369841e-01 5.694559e-01
96 -1.856262e-01 -4.369841e-01
97 5.522970e-02 -1.856262e-01
98 3.128349e-01 5.522970e-02
99 3.044895e-01 3.128349e-01
100 5.020377e-02 3.044895e-01
101 4.923246e-02 5.020377e-02
102 3.083566e-01 4.923246e-02
103 -1.885646e-01 3.083566e-01
104 6.706865e-02 -1.885646e-01
105 -1.858531e-01 6.706865e-02
106 6.649747e-02 -1.858531e-01
107 3.088958e-01 6.649747e-02
108 3.123735e-01 3.088958e-01
109 5.695656e-01 3.123735e-01
110 -1.759989e-01 5.695656e-01
111 3.075590e-01 -1.759989e-01
112 -4.506320e-01 3.075590e-01
113 -1.903548e-01 -4.506320e-01
114 5.559652e-01 -1.903548e-01
115 5.609880e-01 5.559652e-01
116 -1.981029e-01 5.609880e-01
117 2.857816e-01 -1.981029e-01
118 -2.023355e-01 2.857816e-01
119 5.179203e-02 -2.023355e-01
120 5.056180e-02 5.179203e-02
121 -2.058376e-01 5.056180e-02
122 -2.065851e-01 -2.058376e-01
123 5.399193e-01 -2.065851e-01
124 5.399299e-01 5.399193e-01
125 5.646143e-01 5.399299e-01
126 2.966988e-01 5.646143e-01
127 -2.021016e-01 2.966988e-01
128 2.912834e-01 -2.021016e-01
129 5.470863e-02 2.912834e-01
130 -4.472172e-01 5.470863e-02
131 5.529352e-01 -4.472172e-01
132 2.931791e-01 5.529352e-01
133 3.164787e-01 2.931791e-01
134 2.852472e-01 3.164787e-01
135 2.600216e-02 2.852472e-01
136 -4.759210e-01 2.600216e-02
137 -2.309529e-01 -4.759210e-01
138 2.633772e-01 -2.309529e-01
139 -2.335200e-01 2.633772e-01
140 -4.902934e-01 -2.335200e-01
141 -2.398673e-01 -4.902934e-01
142 -2.373321e-01 -2.398673e-01
143 2.725591e-03 -2.373321e-01
144 -2.460214e-03 2.725591e-03
145 4.529651e-03 -2.460214e-03
146 1.633854e-03 4.529651e-03
147 -2.492004e-01 1.633854e-03
148 5.177419e-01 -2.492004e-01
149 5.083267e-01 5.177419e-01
150 1.167455e-02 5.083267e-01
151 2.504034e-01 1.167455e-02
152 7.050232e-03 2.504034e-01
153 2.204874e-02 7.050232e-03
154 1.777348e-02 2.204874e-02
155 1.714639e-02 1.777348e-02
156 2.721690e-01 1.714639e-02
157 1.829139e-02 2.721690e-01
158 -2.307281e-01 1.829139e-02
159 -2.400426e-01 -2.307281e-01
160 -4.863747e-01 -2.400426e-01
161 2.599512e-01 -4.863747e-01
162 2.538692e-01 2.599512e-01
163 5.184802e-01 2.538692e-01
164 -2.217925e-01 5.184802e-01
165 2.612454e-01 -2.217925e-01
166 -2.317314e-01 2.612454e-01
167 2.744909e-01 -2.317314e-01
168 2.780403e-02 2.744909e-01
169 5.279897e-01 2.780403e-02
170 -4.727818e-01 5.279897e-01
171 2.708188e-01 -4.727818e-01
172 1.165471e-02 2.708188e-01
173 5.235349e-01 1.165471e-02
174 2.861812e-02 5.235349e-01
175 -2.136230e-01 2.861812e-02
176 -2.171779e-01 -2.136230e-01
177 1.531352e-02 -2.171779e-01
178 3.245546e-02 1.531352e-02
179 3.302179e-02 3.245546e-02
180 -4.711237e-01 3.302179e-02
181 -2.174043e-01 -4.711237e-01
182 2.847559e-01 -2.174043e-01
183 2.862222e-01 2.847559e-01
184 2.825789e-01 2.862222e-01
185 -4.683771e-01 2.825789e-01
186 -4.639602e-01 -4.683771e-01
187 2.852375e-01 -4.639602e-01
188 3.968983e-02 2.852375e-01
189 -4.550378e-01 3.968983e-02
190 -2.244838e-01 -4.550378e-01
191 -4.649421e-01 -2.244838e-01
192 5.197488e-01 -4.649421e-01
193 -2.417173e-01 5.197488e-01
194 2.533817e-01 -2.417173e-01
195 2.862222e-01 2.533817e-01
196 2.736037e-01 2.862222e-01
197 -4.779447e-01 2.736037e-01
198 -2.217297e-01 -4.779447e-01
199 2.734274e-01 -2.217297e-01
200 2.664162e-01 2.734274e-01
201 2.687334e-01 2.664162e-01
202 -2.225806e-01 2.687334e-01
203 -2.252468e-01 -2.225806e-01
204 7.733786e-01 -2.252468e-01
205 2.820811e-01 7.733786e-01
206 -4.671117e-01 2.820811e-01
207 7.768792e-01 -4.671117e-01
208 5.284171e-01 7.768792e-01
209 -4.734941e-01 5.284171e-01
210 -4.742416e-01 -4.734941e-01
211 2.243181e-02 -4.742416e-01
212 5.282673e-01 2.243181e-02
213 -4.694213e-01 5.282673e-01
214 7.663478e-01 -4.694213e-01
215 -2.297424e-01 7.663478e-01
216 7.629070e-01 -2.297424e-01
217 2.613589e-02 7.629070e-01
218 -7.171547e-01 2.613589e-02
219 5.408432e-01 -7.171547e-01
220 2.878173e-02 5.408432e-01
221 -4.747638e-01 2.878173e-02
222 2.772141e-01 -4.747638e-01
223 2.467436e-02 2.772141e-01
224 2.650203e-01 2.467436e-02
225 2.568079e-02 2.650203e-01
226 5.178813e-01 2.568079e-02
227 2.655733e-01 5.178813e-01
228 2.550451e-01 2.655733e-01
229 -5.011238e-01 2.550451e-01
230 2.811120e-01 -5.011238e-01
231 2.849578e-01 2.811120e-01
232 -4.716086e-01 2.849578e-01
233 -2.348206e-01 -4.716086e-01
234 2.312971e-02 -2.348206e-01
235 -2.206326e-01 2.312971e-02
236 -2.147713e-01 -2.206326e-01
237 -4.626654e-01 -2.147713e-01
238 5.221704e-01 -4.626654e-01
239 5.171098e-01 5.221704e-01
240 2.893152e-01 5.171098e-01
241 -2.042462e-01 2.893152e-01
242 2.735500e-01 -2.042462e-01
243 2.757223e-01 2.735500e-01
244 3.039614e-02 2.757223e-01
245 -7.197622e-01 3.039614e-02
246 -2.205692e-01 -7.197622e-01
247 2.534141e-02 -2.205692e-01
248 2.883252e-01 2.534141e-02
249 -4.538720e-01 2.883252e-01
250 2.860081e-01 -4.538720e-01
251 2.819460e-01 2.860081e-01
252 5.471303e-01 2.819460e-01
253 -9.620172e-01 5.471303e-01
254 2.478839e-02 -9.620172e-01
255 2.817345e-01 2.478839e-02
256 3.430272e-02 2.817345e-01
257 -7.147320e-01 3.430272e-02
258 2.765012e-01 -7.147320e-01
259 -2.287498e-01 2.765012e-01
260 2.669917e-01 -2.287498e-01
261 -2.339676e-01 2.669917e-01
262 5.119875e-01 -2.339676e-01
263 2.359452e-01 5.119875e-01
264 5.076771e-01 2.359452e-01
265 -2.432608e-01 5.076771e-01
266 -5.041118e-01 -2.432608e-01
267 -9.347309e-03 -5.041118e-01
268 2.619765e-01 -9.347309e-03
269 -4.816060e-01 2.619765e-01
270 -7.377997e-01 -4.816060e-01
271 2.528383e-01 -7.377997e-01
272 2.249186e-03 2.528383e-01
273 -7.490861e-01 2.249186e-03
274 -2.464080e-01 -7.490861e-01
275 5.121681e-01 -2.464080e-01
276 2.654568e-01 5.121681e-01
277 -7.333982e-01 2.654568e-01
278 8.646570e-03 -7.333982e-01
279 2.524302e-01 8.646570e-03
280 -4.880295e-01 2.524302e-01
281 8.416961e-03 -4.880295e-01
282 9.025231e-05 8.416961e-03
283 2.497187e-01 9.025231e-05
284 -2.534480e-01 2.497187e-01
285 -6.141443e-03 -2.534480e-01
286 5.010602e-01 -6.141443e-03
287 9.922601e-01 5.010602e-01
288 -2.454548e-01 9.922601e-01
289 1.443558e-03 -2.454548e-01
290 -4.941514e-01 1.443558e-03
291 -2.367643e-01 -4.941514e-01
292 2.540856e-01 -2.367643e-01
293 1.210604e-02 2.540856e-01
294 2.566314e-01 1.210604e-02
295 2.558790e-01 2.566314e-01
296 -4.861658e-01 2.558790e-01
297 2.063840e-02 -4.861658e-01
298 2.626180e-01 2.063840e-02
299 7.630168e-01 2.626180e-01
300 8.529883e-03 7.630168e-01
301 -7.402991e-01 8.529883e-03
302 2.662932e-01 -7.402991e-01
303 2.620619e-02 2.662932e-01
304 -4.736188e-01 2.620619e-02
305 -4.812038e-01 -4.736188e-01
306 5.201863e-01 -4.812038e-01
307 -2.273001e-01 5.201863e-01
308 2.869385e-02 -2.273001e-01
309 -7.245948e-01 2.869385e-02
310 5.164742e-01 -7.245948e-01
311 5.107711e-01 5.164742e-01
312 1.784753e-02 5.107711e-01
313 -2.395685e-01 1.784753e-02
314 -4.870487e-01 -2.395685e-01
315 2.602592e-01 -4.870487e-01
316 -4.759514e-01 2.602592e-01
317 -2.500861e-01 -4.759514e-01
318 2.406266e-03 -2.500861e-01
319 2.339871e-01 2.406266e-03
320 2.301226e-01 2.339871e-01
321 -3.238003e-02 2.301226e-01
322 4.794954e-01 -3.238003e-02
323 2.396370e-01 4.794954e-01
324 -2.714744e-01 2.396370e-01
325 NA -2.714744e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.911043e-01 -2.258365e-01
[2,] -4.569138e-01 2.911043e-01
[3,] -2.232428e-01 -4.569138e-01
[4,] -2.151186e-01 -2.232428e-01
[5,] 2.770123e-02 -2.151186e-01
[6,] -2.183617e-01 2.770123e-02
[7,] 1.806648e-02 -2.183617e-01
[8,] -2.284819e-01 1.806648e-02
[9,] 1.414610e-02 -2.284819e-01
[10,] -4.807973e-01 1.414610e-02
[11,] 2.468751e-01 -4.807973e-01
[12,] -2.409026e-01 2.468751e-01
[13,] -2.344285e-01 -2.409026e-01
[14,] -4.844044e-01 -2.344285e-01
[15,] -5.003161e-01 -4.844044e-01
[16,] -2.660952e-01 -5.003161e-01
[17,] 2.282909e-01 -2.660952e-01
[18,] -5.015117e-01 2.282909e-01
[19,] 2.440289e-01 -5.015117e-01
[20,] 2.372447e-01 2.440289e-01
[21,] 4.777350e-01 2.372447e-01
[22,] -2.440909e-01 4.777350e-01
[23,] 5.313826e-03 -2.440909e-01
[24,] -2.359715e-01 5.313826e-03
[25,] 2.746998e-01 -2.359715e-01
[26,] 5.320918e-01 2.746998e-01
[27,] 2.720147e-02 5.320918e-01
[28,] 3.591017e-02 2.720147e-02
[29,] -2.156716e-01 3.591017e-02
[30,] 2.993314e-01 -2.156716e-01
[31,] -1.974135e-01 2.993314e-01
[32,] 6.420320e-02 -1.974135e-01
[33,] -4.072435e-01 6.420320e-02
[34,] 9.196017e-02 -4.072435e-01
[35,] -1.535529e-01 9.196017e-02
[36,] 9.525345e-02 -1.535529e-01
[37,] -1.586321e-01 9.525345e-02
[38,] -6.597572e-01 -1.586321e-01
[39,] 9.060951e-02 -6.597572e-01
[40,] 9.059886e-02 9.060951e-02
[41,] 3.335849e-01 9.059886e-02
[42,] -4.047479e-01 3.335849e-01
[43,] 5.888121e-01 -4.047479e-01
[44,] -4.183909e-01 5.888121e-01
[45,] 6.884279e-02 -4.183909e-01
[46,] 7.546132e-02 6.884279e-02
[47,] 3.287832e-01 7.546132e-02
[48,] -4.513487e-01 3.287832e-01
[49,] -4.602630e-01 -4.513487e-01
[50,] 1.377792e-02 -4.602630e-01
[51,] -5.028037e-01 1.377792e-02
[52,] 2.335486e-01 -5.028037e-01
[53,] -3.699304e-02 2.335486e-01
[54,] -5.175710e-02 -3.699304e-02
[55,] -2.838781e-01 -5.175710e-02
[56,] 2.085063e-01 -2.838781e-01
[57,] 2.038730e-01 2.085063e-01
[58,] 4.597167e-01 2.038730e-01
[59,] 2.102890e-01 4.597167e-01
[60,] -3.424213e-02 2.102890e-01
[61,] -5.393088e-01 -3.424213e-02
[62,] -2.653860e-01 -5.393088e-01
[63,] -2.580093e-01 -2.653860e-01
[64,] 2.421763e-01 -2.580093e-01
[65,] -2.359240e-01 2.421763e-01
[66,] -4.808527e-01 -2.359240e-01
[67,] 2.188944e-02 -4.808527e-01
[68,] 2.561103e-01 2.188944e-02
[69,] -4.814158e-01 2.561103e-01
[70,] 2.305575e-02 -4.814158e-01
[71,] 2.774880e-01 2.305575e-02
[72,] -1.953594e-01 2.774880e-01
[73,] 2.664148e-01 -1.953594e-01
[74,] -4.783069e-01 2.664148e-01
[75,] -4.806577e-01 -4.783069e-01
[76,] -4.827798e-01 -4.806577e-01
[77,] -2.077514e-01 -4.827798e-01
[78,] 3.819114e-02 -2.077514e-01
[79,] -4.672837e-01 3.819114e-02
[80,] -4.658830e-01 -4.672837e-01
[81,] 2.934136e-01 -4.658830e-01
[82,] 2.874163e-01 2.934136e-01
[83,] 4.326717e-02 2.874163e-01
[84,] -1.984183e-01 4.326717e-02
[85,] 2.918045e-01 -1.984183e-01
[86,] 4.183180e-02 2.918045e-01
[87,] -4.795335e-01 4.183180e-02
[88,] -4.571924e-01 -4.795335e-01
[89,] 2.967100e-01 -4.571924e-01
[90,] -1.980496e-01 2.967100e-01
[91,] 5.509531e-01 -1.980496e-01
[92,] -4.509474e-01 5.509531e-01
[93,] 3.103024e-01 -4.509474e-01
[94,] 5.694559e-01 3.103024e-01
[95,] -4.369841e-01 5.694559e-01
[96,] -1.856262e-01 -4.369841e-01
[97,] 5.522970e-02 -1.856262e-01
[98,] 3.128349e-01 5.522970e-02
[99,] 3.044895e-01 3.128349e-01
[100,] 5.020377e-02 3.044895e-01
[101,] 4.923246e-02 5.020377e-02
[102,] 3.083566e-01 4.923246e-02
[103,] -1.885646e-01 3.083566e-01
[104,] 6.706865e-02 -1.885646e-01
[105,] -1.858531e-01 6.706865e-02
[106,] 6.649747e-02 -1.858531e-01
[107,] 3.088958e-01 6.649747e-02
[108,] 3.123735e-01 3.088958e-01
[109,] 5.695656e-01 3.123735e-01
[110,] -1.759989e-01 5.695656e-01
[111,] 3.075590e-01 -1.759989e-01
[112,] -4.506320e-01 3.075590e-01
[113,] -1.903548e-01 -4.506320e-01
[114,] 5.559652e-01 -1.903548e-01
[115,] 5.609880e-01 5.559652e-01
[116,] -1.981029e-01 5.609880e-01
[117,] 2.857816e-01 -1.981029e-01
[118,] -2.023355e-01 2.857816e-01
[119,] 5.179203e-02 -2.023355e-01
[120,] 5.056180e-02 5.179203e-02
[121,] -2.058376e-01 5.056180e-02
[122,] -2.065851e-01 -2.058376e-01
[123,] 5.399193e-01 -2.065851e-01
[124,] 5.399299e-01 5.399193e-01
[125,] 5.646143e-01 5.399299e-01
[126,] 2.966988e-01 5.646143e-01
[127,] -2.021016e-01 2.966988e-01
[128,] 2.912834e-01 -2.021016e-01
[129,] 5.470863e-02 2.912834e-01
[130,] -4.472172e-01 5.470863e-02
[131,] 5.529352e-01 -4.472172e-01
[132,] 2.931791e-01 5.529352e-01
[133,] 3.164787e-01 2.931791e-01
[134,] 2.852472e-01 3.164787e-01
[135,] 2.600216e-02 2.852472e-01
[136,] -4.759210e-01 2.600216e-02
[137,] -2.309529e-01 -4.759210e-01
[138,] 2.633772e-01 -2.309529e-01
[139,] -2.335200e-01 2.633772e-01
[140,] -4.902934e-01 -2.335200e-01
[141,] -2.398673e-01 -4.902934e-01
[142,] -2.373321e-01 -2.398673e-01
[143,] 2.725591e-03 -2.373321e-01
[144,] -2.460214e-03 2.725591e-03
[145,] 4.529651e-03 -2.460214e-03
[146,] 1.633854e-03 4.529651e-03
[147,] -2.492004e-01 1.633854e-03
[148,] 5.177419e-01 -2.492004e-01
[149,] 5.083267e-01 5.177419e-01
[150,] 1.167455e-02 5.083267e-01
[151,] 2.504034e-01 1.167455e-02
[152,] 7.050232e-03 2.504034e-01
[153,] 2.204874e-02 7.050232e-03
[154,] 1.777348e-02 2.204874e-02
[155,] 1.714639e-02 1.777348e-02
[156,] 2.721690e-01 1.714639e-02
[157,] 1.829139e-02 2.721690e-01
[158,] -2.307281e-01 1.829139e-02
[159,] -2.400426e-01 -2.307281e-01
[160,] -4.863747e-01 -2.400426e-01
[161,] 2.599512e-01 -4.863747e-01
[162,] 2.538692e-01 2.599512e-01
[163,] 5.184802e-01 2.538692e-01
[164,] -2.217925e-01 5.184802e-01
[165,] 2.612454e-01 -2.217925e-01
[166,] -2.317314e-01 2.612454e-01
[167,] 2.744909e-01 -2.317314e-01
[168,] 2.780403e-02 2.744909e-01
[169,] 5.279897e-01 2.780403e-02
[170,] -4.727818e-01 5.279897e-01
[171,] 2.708188e-01 -4.727818e-01
[172,] 1.165471e-02 2.708188e-01
[173,] 5.235349e-01 1.165471e-02
[174,] 2.861812e-02 5.235349e-01
[175,] -2.136230e-01 2.861812e-02
[176,] -2.171779e-01 -2.136230e-01
[177,] 1.531352e-02 -2.171779e-01
[178,] 3.245546e-02 1.531352e-02
[179,] 3.302179e-02 3.245546e-02
[180,] -4.711237e-01 3.302179e-02
[181,] -2.174043e-01 -4.711237e-01
[182,] 2.847559e-01 -2.174043e-01
[183,] 2.862222e-01 2.847559e-01
[184,] 2.825789e-01 2.862222e-01
[185,] -4.683771e-01 2.825789e-01
[186,] -4.639602e-01 -4.683771e-01
[187,] 2.852375e-01 -4.639602e-01
[188,] 3.968983e-02 2.852375e-01
[189,] -4.550378e-01 3.968983e-02
[190,] -2.244838e-01 -4.550378e-01
[191,] -4.649421e-01 -2.244838e-01
[192,] 5.197488e-01 -4.649421e-01
[193,] -2.417173e-01 5.197488e-01
[194,] 2.533817e-01 -2.417173e-01
[195,] 2.862222e-01 2.533817e-01
[196,] 2.736037e-01 2.862222e-01
[197,] -4.779447e-01 2.736037e-01
[198,] -2.217297e-01 -4.779447e-01
[199,] 2.734274e-01 -2.217297e-01
[200,] 2.664162e-01 2.734274e-01
[201,] 2.687334e-01 2.664162e-01
[202,] -2.225806e-01 2.687334e-01
[203,] -2.252468e-01 -2.225806e-01
[204,] 7.733786e-01 -2.252468e-01
[205,] 2.820811e-01 7.733786e-01
[206,] -4.671117e-01 2.820811e-01
[207,] 7.768792e-01 -4.671117e-01
[208,] 5.284171e-01 7.768792e-01
[209,] -4.734941e-01 5.284171e-01
[210,] -4.742416e-01 -4.734941e-01
[211,] 2.243181e-02 -4.742416e-01
[212,] 5.282673e-01 2.243181e-02
[213,] -4.694213e-01 5.282673e-01
[214,] 7.663478e-01 -4.694213e-01
[215,] -2.297424e-01 7.663478e-01
[216,] 7.629070e-01 -2.297424e-01
[217,] 2.613589e-02 7.629070e-01
[218,] -7.171547e-01 2.613589e-02
[219,] 5.408432e-01 -7.171547e-01
[220,] 2.878173e-02 5.408432e-01
[221,] -4.747638e-01 2.878173e-02
[222,] 2.772141e-01 -4.747638e-01
[223,] 2.467436e-02 2.772141e-01
[224,] 2.650203e-01 2.467436e-02
[225,] 2.568079e-02 2.650203e-01
[226,] 5.178813e-01 2.568079e-02
[227,] 2.655733e-01 5.178813e-01
[228,] 2.550451e-01 2.655733e-01
[229,] -5.011238e-01 2.550451e-01
[230,] 2.811120e-01 -5.011238e-01
[231,] 2.849578e-01 2.811120e-01
[232,] -4.716086e-01 2.849578e-01
[233,] -2.348206e-01 -4.716086e-01
[234,] 2.312971e-02 -2.348206e-01
[235,] -2.206326e-01 2.312971e-02
[236,] -2.147713e-01 -2.206326e-01
[237,] -4.626654e-01 -2.147713e-01
[238,] 5.221704e-01 -4.626654e-01
[239,] 5.171098e-01 5.221704e-01
[240,] 2.893152e-01 5.171098e-01
[241,] -2.042462e-01 2.893152e-01
[242,] 2.735500e-01 -2.042462e-01
[243,] 2.757223e-01 2.735500e-01
[244,] 3.039614e-02 2.757223e-01
[245,] -7.197622e-01 3.039614e-02
[246,] -2.205692e-01 -7.197622e-01
[247,] 2.534141e-02 -2.205692e-01
[248,] 2.883252e-01 2.534141e-02
[249,] -4.538720e-01 2.883252e-01
[250,] 2.860081e-01 -4.538720e-01
[251,] 2.819460e-01 2.860081e-01
[252,] 5.471303e-01 2.819460e-01
[253,] -9.620172e-01 5.471303e-01
[254,] 2.478839e-02 -9.620172e-01
[255,] 2.817345e-01 2.478839e-02
[256,] 3.430272e-02 2.817345e-01
[257,] -7.147320e-01 3.430272e-02
[258,] 2.765012e-01 -7.147320e-01
[259,] -2.287498e-01 2.765012e-01
[260,] 2.669917e-01 -2.287498e-01
[261,] -2.339676e-01 2.669917e-01
[262,] 5.119875e-01 -2.339676e-01
[263,] 2.359452e-01 5.119875e-01
[264,] 5.076771e-01 2.359452e-01
[265,] -2.432608e-01 5.076771e-01
[266,] -5.041118e-01 -2.432608e-01
[267,] -9.347309e-03 -5.041118e-01
[268,] 2.619765e-01 -9.347309e-03
[269,] -4.816060e-01 2.619765e-01
[270,] -7.377997e-01 -4.816060e-01
[271,] 2.528383e-01 -7.377997e-01
[272,] 2.249186e-03 2.528383e-01
[273,] -7.490861e-01 2.249186e-03
[274,] -2.464080e-01 -7.490861e-01
[275,] 5.121681e-01 -2.464080e-01
[276,] 2.654568e-01 5.121681e-01
[277,] -7.333982e-01 2.654568e-01
[278,] 8.646570e-03 -7.333982e-01
[279,] 2.524302e-01 8.646570e-03
[280,] -4.880295e-01 2.524302e-01
[281,] 8.416961e-03 -4.880295e-01
[282,] 9.025231e-05 8.416961e-03
[283,] 2.497187e-01 9.025231e-05
[284,] -2.534480e-01 2.497187e-01
[285,] -6.141443e-03 -2.534480e-01
[286,] 5.010602e-01 -6.141443e-03
[287,] 9.922601e-01 5.010602e-01
[288,] -2.454548e-01 9.922601e-01
[289,] 1.443558e-03 -2.454548e-01
[290,] -4.941514e-01 1.443558e-03
[291,] -2.367643e-01 -4.941514e-01
[292,] 2.540856e-01 -2.367643e-01
[293,] 1.210604e-02 2.540856e-01
[294,] 2.566314e-01 1.210604e-02
[295,] 2.558790e-01 2.566314e-01
[296,] -4.861658e-01 2.558790e-01
[297,] 2.063840e-02 -4.861658e-01
[298,] 2.626180e-01 2.063840e-02
[299,] 7.630168e-01 2.626180e-01
[300,] 8.529883e-03 7.630168e-01
[301,] -7.402991e-01 8.529883e-03
[302,] 2.662932e-01 -7.402991e-01
[303,] 2.620619e-02 2.662932e-01
[304,] -4.736188e-01 2.620619e-02
[305,] -4.812038e-01 -4.736188e-01
[306,] 5.201863e-01 -4.812038e-01
[307,] -2.273001e-01 5.201863e-01
[308,] 2.869385e-02 -2.273001e-01
[309,] -7.245948e-01 2.869385e-02
[310,] 5.164742e-01 -7.245948e-01
[311,] 5.107711e-01 5.164742e-01
[312,] 1.784753e-02 5.107711e-01
[313,] -2.395685e-01 1.784753e-02
[314,] -4.870487e-01 -2.395685e-01
[315,] 2.602592e-01 -4.870487e-01
[316,] -4.759514e-01 2.602592e-01
[317,] -2.500861e-01 -4.759514e-01
[318,] 2.406266e-03 -2.500861e-01
[319,] 2.339871e-01 2.406266e-03
[320,] 2.301226e-01 2.339871e-01
[321,] -3.238003e-02 2.301226e-01
[322,] 4.794954e-01 -3.238003e-02
[323,] 2.396370e-01 4.794954e-01
[324,] -2.714744e-01 2.396370e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.911043e-01 -2.258365e-01
2 -4.569138e-01 2.911043e-01
3 -2.232428e-01 -4.569138e-01
4 -2.151186e-01 -2.232428e-01
5 2.770123e-02 -2.151186e-01
6 -2.183617e-01 2.770123e-02
7 1.806648e-02 -2.183617e-01
8 -2.284819e-01 1.806648e-02
9 1.414610e-02 -2.284819e-01
10 -4.807973e-01 1.414610e-02
11 2.468751e-01 -4.807973e-01
12 -2.409026e-01 2.468751e-01
13 -2.344285e-01 -2.409026e-01
14 -4.844044e-01 -2.344285e-01
15 -5.003161e-01 -4.844044e-01
16 -2.660952e-01 -5.003161e-01
17 2.282909e-01 -2.660952e-01
18 -5.015117e-01 2.282909e-01
19 2.440289e-01 -5.015117e-01
20 2.372447e-01 2.440289e-01
21 4.777350e-01 2.372447e-01
22 -2.440909e-01 4.777350e-01
23 5.313826e-03 -2.440909e-01
24 -2.359715e-01 5.313826e-03
25 2.746998e-01 -2.359715e-01
26 5.320918e-01 2.746998e-01
27 2.720147e-02 5.320918e-01
28 3.591017e-02 2.720147e-02
29 -2.156716e-01 3.591017e-02
30 2.993314e-01 -2.156716e-01
31 -1.974135e-01 2.993314e-01
32 6.420320e-02 -1.974135e-01
33 -4.072435e-01 6.420320e-02
34 9.196017e-02 -4.072435e-01
35 -1.535529e-01 9.196017e-02
36 9.525345e-02 -1.535529e-01
37 -1.586321e-01 9.525345e-02
38 -6.597572e-01 -1.586321e-01
39 9.060951e-02 -6.597572e-01
40 9.059886e-02 9.060951e-02
41 3.335849e-01 9.059886e-02
42 -4.047479e-01 3.335849e-01
43 5.888121e-01 -4.047479e-01
44 -4.183909e-01 5.888121e-01
45 6.884279e-02 -4.183909e-01
46 7.546132e-02 6.884279e-02
47 3.287832e-01 7.546132e-02
48 -4.513487e-01 3.287832e-01
49 -4.602630e-01 -4.513487e-01
50 1.377792e-02 -4.602630e-01
51 -5.028037e-01 1.377792e-02
52 2.335486e-01 -5.028037e-01
53 -3.699304e-02 2.335486e-01
54 -5.175710e-02 -3.699304e-02
55 -2.838781e-01 -5.175710e-02
56 2.085063e-01 -2.838781e-01
57 2.038730e-01 2.085063e-01
58 4.597167e-01 2.038730e-01
59 2.102890e-01 4.597167e-01
60 -3.424213e-02 2.102890e-01
61 -5.393088e-01 -3.424213e-02
62 -2.653860e-01 -5.393088e-01
63 -2.580093e-01 -2.653860e-01
64 2.421763e-01 -2.580093e-01
65 -2.359240e-01 2.421763e-01
66 -4.808527e-01 -2.359240e-01
67 2.188944e-02 -4.808527e-01
68 2.561103e-01 2.188944e-02
69 -4.814158e-01 2.561103e-01
70 2.305575e-02 -4.814158e-01
71 2.774880e-01 2.305575e-02
72 -1.953594e-01 2.774880e-01
73 2.664148e-01 -1.953594e-01
74 -4.783069e-01 2.664148e-01
75 -4.806577e-01 -4.783069e-01
76 -4.827798e-01 -4.806577e-01
77 -2.077514e-01 -4.827798e-01
78 3.819114e-02 -2.077514e-01
79 -4.672837e-01 3.819114e-02
80 -4.658830e-01 -4.672837e-01
81 2.934136e-01 -4.658830e-01
82 2.874163e-01 2.934136e-01
83 4.326717e-02 2.874163e-01
84 -1.984183e-01 4.326717e-02
85 2.918045e-01 -1.984183e-01
86 4.183180e-02 2.918045e-01
87 -4.795335e-01 4.183180e-02
88 -4.571924e-01 -4.795335e-01
89 2.967100e-01 -4.571924e-01
90 -1.980496e-01 2.967100e-01
91 5.509531e-01 -1.980496e-01
92 -4.509474e-01 5.509531e-01
93 3.103024e-01 -4.509474e-01
94 5.694559e-01 3.103024e-01
95 -4.369841e-01 5.694559e-01
96 -1.856262e-01 -4.369841e-01
97 5.522970e-02 -1.856262e-01
98 3.128349e-01 5.522970e-02
99 3.044895e-01 3.128349e-01
100 5.020377e-02 3.044895e-01
101 4.923246e-02 5.020377e-02
102 3.083566e-01 4.923246e-02
103 -1.885646e-01 3.083566e-01
104 6.706865e-02 -1.885646e-01
105 -1.858531e-01 6.706865e-02
106 6.649747e-02 -1.858531e-01
107 3.088958e-01 6.649747e-02
108 3.123735e-01 3.088958e-01
109 5.695656e-01 3.123735e-01
110 -1.759989e-01 5.695656e-01
111 3.075590e-01 -1.759989e-01
112 -4.506320e-01 3.075590e-01
113 -1.903548e-01 -4.506320e-01
114 5.559652e-01 -1.903548e-01
115 5.609880e-01 5.559652e-01
116 -1.981029e-01 5.609880e-01
117 2.857816e-01 -1.981029e-01
118 -2.023355e-01 2.857816e-01
119 5.179203e-02 -2.023355e-01
120 5.056180e-02 5.179203e-02
121 -2.058376e-01 5.056180e-02
122 -2.065851e-01 -2.058376e-01
123 5.399193e-01 -2.065851e-01
124 5.399299e-01 5.399193e-01
125 5.646143e-01 5.399299e-01
126 2.966988e-01 5.646143e-01
127 -2.021016e-01 2.966988e-01
128 2.912834e-01 -2.021016e-01
129 5.470863e-02 2.912834e-01
130 -4.472172e-01 5.470863e-02
131 5.529352e-01 -4.472172e-01
132 2.931791e-01 5.529352e-01
133 3.164787e-01 2.931791e-01
134 2.852472e-01 3.164787e-01
135 2.600216e-02 2.852472e-01
136 -4.759210e-01 2.600216e-02
137 -2.309529e-01 -4.759210e-01
138 2.633772e-01 -2.309529e-01
139 -2.335200e-01 2.633772e-01
140 -4.902934e-01 -2.335200e-01
141 -2.398673e-01 -4.902934e-01
142 -2.373321e-01 -2.398673e-01
143 2.725591e-03 -2.373321e-01
144 -2.460214e-03 2.725591e-03
145 4.529651e-03 -2.460214e-03
146 1.633854e-03 4.529651e-03
147 -2.492004e-01 1.633854e-03
148 5.177419e-01 -2.492004e-01
149 5.083267e-01 5.177419e-01
150 1.167455e-02 5.083267e-01
151 2.504034e-01 1.167455e-02
152 7.050232e-03 2.504034e-01
153 2.204874e-02 7.050232e-03
154 1.777348e-02 2.204874e-02
155 1.714639e-02 1.777348e-02
156 2.721690e-01 1.714639e-02
157 1.829139e-02 2.721690e-01
158 -2.307281e-01 1.829139e-02
159 -2.400426e-01 -2.307281e-01
160 -4.863747e-01 -2.400426e-01
161 2.599512e-01 -4.863747e-01
162 2.538692e-01 2.599512e-01
163 5.184802e-01 2.538692e-01
164 -2.217925e-01 5.184802e-01
165 2.612454e-01 -2.217925e-01
166 -2.317314e-01 2.612454e-01
167 2.744909e-01 -2.317314e-01
168 2.780403e-02 2.744909e-01
169 5.279897e-01 2.780403e-02
170 -4.727818e-01 5.279897e-01
171 2.708188e-01 -4.727818e-01
172 1.165471e-02 2.708188e-01
173 5.235349e-01 1.165471e-02
174 2.861812e-02 5.235349e-01
175 -2.136230e-01 2.861812e-02
176 -2.171779e-01 -2.136230e-01
177 1.531352e-02 -2.171779e-01
178 3.245546e-02 1.531352e-02
179 3.302179e-02 3.245546e-02
180 -4.711237e-01 3.302179e-02
181 -2.174043e-01 -4.711237e-01
182 2.847559e-01 -2.174043e-01
183 2.862222e-01 2.847559e-01
184 2.825789e-01 2.862222e-01
185 -4.683771e-01 2.825789e-01
186 -4.639602e-01 -4.683771e-01
187 2.852375e-01 -4.639602e-01
188 3.968983e-02 2.852375e-01
189 -4.550378e-01 3.968983e-02
190 -2.244838e-01 -4.550378e-01
191 -4.649421e-01 -2.244838e-01
192 5.197488e-01 -4.649421e-01
193 -2.417173e-01 5.197488e-01
194 2.533817e-01 -2.417173e-01
195 2.862222e-01 2.533817e-01
196 2.736037e-01 2.862222e-01
197 -4.779447e-01 2.736037e-01
198 -2.217297e-01 -4.779447e-01
199 2.734274e-01 -2.217297e-01
200 2.664162e-01 2.734274e-01
201 2.687334e-01 2.664162e-01
202 -2.225806e-01 2.687334e-01
203 -2.252468e-01 -2.225806e-01
204 7.733786e-01 -2.252468e-01
205 2.820811e-01 7.733786e-01
206 -4.671117e-01 2.820811e-01
207 7.768792e-01 -4.671117e-01
208 5.284171e-01 7.768792e-01
209 -4.734941e-01 5.284171e-01
210 -4.742416e-01 -4.734941e-01
211 2.243181e-02 -4.742416e-01
212 5.282673e-01 2.243181e-02
213 -4.694213e-01 5.282673e-01
214 7.663478e-01 -4.694213e-01
215 -2.297424e-01 7.663478e-01
216 7.629070e-01 -2.297424e-01
217 2.613589e-02 7.629070e-01
218 -7.171547e-01 2.613589e-02
219 5.408432e-01 -7.171547e-01
220 2.878173e-02 5.408432e-01
221 -4.747638e-01 2.878173e-02
222 2.772141e-01 -4.747638e-01
223 2.467436e-02 2.772141e-01
224 2.650203e-01 2.467436e-02
225 2.568079e-02 2.650203e-01
226 5.178813e-01 2.568079e-02
227 2.655733e-01 5.178813e-01
228 2.550451e-01 2.655733e-01
229 -5.011238e-01 2.550451e-01
230 2.811120e-01 -5.011238e-01
231 2.849578e-01 2.811120e-01
232 -4.716086e-01 2.849578e-01
233 -2.348206e-01 -4.716086e-01
234 2.312971e-02 -2.348206e-01
235 -2.206326e-01 2.312971e-02
236 -2.147713e-01 -2.206326e-01
237 -4.626654e-01 -2.147713e-01
238 5.221704e-01 -4.626654e-01
239 5.171098e-01 5.221704e-01
240 2.893152e-01 5.171098e-01
241 -2.042462e-01 2.893152e-01
242 2.735500e-01 -2.042462e-01
243 2.757223e-01 2.735500e-01
244 3.039614e-02 2.757223e-01
245 -7.197622e-01 3.039614e-02
246 -2.205692e-01 -7.197622e-01
247 2.534141e-02 -2.205692e-01
248 2.883252e-01 2.534141e-02
249 -4.538720e-01 2.883252e-01
250 2.860081e-01 -4.538720e-01
251 2.819460e-01 2.860081e-01
252 5.471303e-01 2.819460e-01
253 -9.620172e-01 5.471303e-01
254 2.478839e-02 -9.620172e-01
255 2.817345e-01 2.478839e-02
256 3.430272e-02 2.817345e-01
257 -7.147320e-01 3.430272e-02
258 2.765012e-01 -7.147320e-01
259 -2.287498e-01 2.765012e-01
260 2.669917e-01 -2.287498e-01
261 -2.339676e-01 2.669917e-01
262 5.119875e-01 -2.339676e-01
263 2.359452e-01 5.119875e-01
264 5.076771e-01 2.359452e-01
265 -2.432608e-01 5.076771e-01
266 -5.041118e-01 -2.432608e-01
267 -9.347309e-03 -5.041118e-01
268 2.619765e-01 -9.347309e-03
269 -4.816060e-01 2.619765e-01
270 -7.377997e-01 -4.816060e-01
271 2.528383e-01 -7.377997e-01
272 2.249186e-03 2.528383e-01
273 -7.490861e-01 2.249186e-03
274 -2.464080e-01 -7.490861e-01
275 5.121681e-01 -2.464080e-01
276 2.654568e-01 5.121681e-01
277 -7.333982e-01 2.654568e-01
278 8.646570e-03 -7.333982e-01
279 2.524302e-01 8.646570e-03
280 -4.880295e-01 2.524302e-01
281 8.416961e-03 -4.880295e-01
282 9.025231e-05 8.416961e-03
283 2.497187e-01 9.025231e-05
284 -2.534480e-01 2.497187e-01
285 -6.141443e-03 -2.534480e-01
286 5.010602e-01 -6.141443e-03
287 9.922601e-01 5.010602e-01
288 -2.454548e-01 9.922601e-01
289 1.443558e-03 -2.454548e-01
290 -4.941514e-01 1.443558e-03
291 -2.367643e-01 -4.941514e-01
292 2.540856e-01 -2.367643e-01
293 1.210604e-02 2.540856e-01
294 2.566314e-01 1.210604e-02
295 2.558790e-01 2.566314e-01
296 -4.861658e-01 2.558790e-01
297 2.063840e-02 -4.861658e-01
298 2.626180e-01 2.063840e-02
299 7.630168e-01 2.626180e-01
300 8.529883e-03 7.630168e-01
301 -7.402991e-01 8.529883e-03
302 2.662932e-01 -7.402991e-01
303 2.620619e-02 2.662932e-01
304 -4.736188e-01 2.620619e-02
305 -4.812038e-01 -4.736188e-01
306 5.201863e-01 -4.812038e-01
307 -2.273001e-01 5.201863e-01
308 2.869385e-02 -2.273001e-01
309 -7.245948e-01 2.869385e-02
310 5.164742e-01 -7.245948e-01
311 5.107711e-01 5.164742e-01
312 1.784753e-02 5.107711e-01
313 -2.395685e-01 1.784753e-02
314 -4.870487e-01 -2.395685e-01
315 2.602592e-01 -4.870487e-01
316 -4.759514e-01 2.602592e-01
317 -2.500861e-01 -4.759514e-01
318 2.406266e-03 -2.500861e-01
319 2.339871e-01 2.406266e-03
320 2.301226e-01 2.339871e-01
321 -3.238003e-02 2.301226e-01
322 4.794954e-01 -3.238003e-02
323 2.396370e-01 4.794954e-01
324 -2.714744e-01 2.396370e-01
> 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/7hqxh1356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8lm4g1356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9jb291356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10brgj1356045590.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11phvo1356045590.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/1201541356045590.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/13xiw01356045590.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/14u3kc1356045590.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/159lmj1356045590.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/16cy981356045590.tab")
+ }
>
> try(system("convert tmp/15f941356045590.ps tmp/15f941356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jr761356045590.ps tmp/2jr761356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w4yb1356045590.ps tmp/3w4yb1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/4iaxi1356045590.ps tmp/4iaxi1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b5wm1356045590.ps tmp/5b5wm1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ta5b1356045590.ps tmp/6ta5b1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hqxh1356045590.ps tmp/7hqxh1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lm4g1356045590.ps tmp/8lm4g1356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jb291356045590.ps tmp/9jb291356045590.png",intern=TRUE))
character(0)
> try(system("convert tmp/10brgj1356045590.ps tmp/10brgj1356045590.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.303 1.258 15.573