R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,3
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+ ,3
+ ,7
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+ ,0
+ ,8
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+ ,2
+ ,6
+ ,12
+ ,-2
+ ,3
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+ ,0
+ ,6
+ ,1
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+ ,15
+ ,0
+ ,6
+ ,2
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,3
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+ ,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'
+ ,'economischsituatie'
+ ,'werkloosheid'
+ ,'financielesituatie'
+ ,'spaarvermogen')
+ ,1:323))
> y <- array(NA,dim=c(6,323),dimnames=list(c('maand','consumentenvertrouwen','economischsituatie','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 = '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
> 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
maand consumentenvertrouwen economischsituatie werkloosheid
1 1 -28 -25 37
2 2 -26 -23 33
3 3 -27 -24 36
4 4 -26 -24 37
5 5 -27 -25 39
6 6 -27 -25 39
7 7 -27 -24 37
8 8 -28 -24 37
9 9 -26 -22 36
10 10 -13 1 23
11 11 -13 -5 21
12 12 -14 -10 24
13 1 -12 -10 25
14 2 -16 -15 29
15 3 -16 -13 24
16 4 -12 -11 22
17 5 -15 -15 28
18 6 -18 -15 39
19 7 -17 -16 36
20 8 -10 -4 32
21 9 -9 -5 27
22 10 -13 -9 33
23 11 -15 -14 36
24 12 -12 -11 34
25 1 -13 -7 34
26 2 -10 -7 31
27 3 -13 -9 37
28 4 -11 -5 36
29 5 -12 -10 35
30 6 -10 -9 32
31 7 -13 -10 35
32 8 -12 -8 36
33 9 -11 -9 35
34 10 -11 -10 32
35 11 -11 -10 28
36 12 -8 -5 24
37 1 -7 -6 25
38 2 -10 -10 29
39 3 -8 -10 28
40 4 -8 -9 25
41 5 -7 -10 22
42 6 -7 -8 22
43 7 -6 -8 22
44 8 -8 -8 23
45 9 -6 -4 22
46 10 -3 2 14
47 11 1 3 7
48 12 0 2 9
49 1 -3 -3 12
50 2 0 -1 9
51 3 0 1 6
52 4 -1 2 8
53 5 -1 -4 10
54 6 0 0 8
55 7 1 5 9
56 8 0 -1 11
57 9 2 3 6
58 10 3 6 6
59 11 2 7 9
60 12 4 7 7
61 1 3 3 8
62 2 4 8 2
63 3 3 3 2
64 4 1 0 7
65 5 2 1 6
66 6 4 4 4
67 7 3 4 8
68 8 2 1 9
69 9 -4 -17 11
70 10 -5 -16 14
71 11 -5 -13 18
72 12 -7 -15 23
73 1 -13 -31 25
74 2 -11 -26 31
75 3 -3 -5 18
76 4 -3 -5 19
77 5 -5 -6 23
78 6 -4 -5 24
79 7 -4 -5 25
80 8 -4 -7 26
81 9 -5 -6 27
82 10 -4 -8 23
83 11 -5 -6 27
84 12 -6 -12 34
85 1 -9 -15 34
86 2 -10 -15 37
87 3 -11 -16 41
88 4 -13 -19 43
89 5 -13 -23 38
90 6 -13 -23 39
91 7 -11 -21 35
92 8 -12 -21 38
93 9 -14 -25 40
94 10 -20 -34 49
95 11 -17 -30 51
96 12 -16 -27 48
97 1 -24 -40 54
98 2 -24 -40 56
99 3 -22 -34 56
100 4 -25 -43 61
101 5 -24 -39 57
102 6 -25 -40 57
103 7 -24 -40 52
104 8 -25 -40 58
105 9 -24 -35 60
106 10 -26 -43 62
107 11 -25 -44 48
108 12 -24 -38 50
109 1 -22 -37 50
110 2 -20 -31 48
111 3 -14 -20 40
112 4 -13 -22 35
113 5 -10 -9 33
114 6 -10 -11 34
115 7 -11 -8 34
116 8 -6 -3 28
117 9 -2 3 26
118 10 -3 6 23
119 11 -2 -3 20
120 12 -4 -8 20
121 1 -7 -8 26
122 2 -8 -10 28
123 3 -7 -9 29
124 4 -4 -7 25
125 5 -7 -12 27
126 6 -5 -9 24
127 7 -6 -8 26
128 8 -12 -19 38
129 9 -12 -21 38
130 10 -16 -24 45
131 11 -20 -30 53
132 12 -16 -28 44
133 1 -16 -27 43
134 2 -18 -26 47
135 3 -15 -27 40
136 4 -12 -23 34
137 5 -13 -26 38
138 6 -13 -23 39
139 7 -12 -21 35
140 8 -11 -20 35
141 9 -9 -14 36
142 10 -9 -16 25
143 11 -8 -17 24
144 12 -8 -18 29
145 1 -15 -25 44
146 2 -16 -26 43
147 3 -21 -36 57
148 4 -21 -35 56
149 5 -16 -27 47
150 6 -13 -22 41
151 7 -12 -25 38
152 8 -8 -17 33
153 9 -9 -14 36
154 10 -1 -7 22
155 11 -5 -12 27
156 12 -9 -17 32
157 1 -1 -8 21
158 2 3 -2 14
159 3 2 -1 10
160 4 3 1 14
161 5 5 0 12
162 6 5 -2 10
163 7 3 -5 12
164 8 2 -4 9
165 9 1 -9 14
166 10 -4 -16 23
167 11 1 -7 17
168 12 1 -7 16
169 1 6 3 7
170 2 3 -2 9
171 3 2 -3 9
172 4 2 -6 14
173 5 2 -7 12
174 6 -8 -24 23
175 7 0 -13 12
176 8 -2 -14 15
177 9 3 -7 6
178 10 5 -1 6
179 11 8 5 1
180 12 8 6 3
181 1 9 5 -1
182 2 11 5 -4
183 3 13 9 -6
184 4 12 10 -9
185 5 13 14 -13
186 6 15 19 -13
187 7 13 18 -10
188 8 16 16 -12
189 9 10 8 -9
190 10 14 10 -15
191 11 14 12 -14
192 12 15 13 -18
193 1 13 15 -13
194 2 8 3 -2
195 3 7 2 -1
196 4 3 -2 5
197 5 3 1 8
198 6 4 1 6
199 7 4 -1 7
200 8 0 -6 15
201 9 -4 -13 23
202 10 -14 -25 43
203 11 -18 -26 60
204 12 -8 -9 36
205 1 -1 1 28
206 2 1 3 23
207 3 2 6 23
208 4 0 2 22
209 5 1 5 22
210 6 0 5 24
211 7 -1 0 32
212 8 -3 -5 27
213 9 -3 -4 27
214 10 -3 -2 27
215 11 -4 -1 29
216 12 -8 -8 38
217 1 -9 -16 40
218 2 -13 -19 45
219 3 -18 -28 50
220 4 -11 -11 43
221 5 -9 -4 44
222 6 -10 -9 44
223 7 -13 -12 49
224 8 -11 -10 42
225 9 -5 -2 36
226 10 -15 -13 57
227 11 -6 0 42
228 12 -6 0 39
229 1 -3 4 33
230 2 -1 7 32
231 3 -3 5 34
232 4 -4 2 37
233 5 -6 -2 38
234 6 0 6 28
235 7 -4 -3 31
236 8 -2 1 28
237 9 -2 0 30
238 10 -6 -7 39
239 11 -7 -6 38
240 12 -6 -4 39
241 1 -6 -4 38
242 2 -3 -2 37
243 3 -2 2 32
244 4 -5 -5 32
245 5 -11 -15 44
246 6 -11 -16 43
247 7 -11 -18 42
248 8 -10 -13 38
249 9 -14 -23 37
250 10 -8 -10 35
251 11 -9 -10 37
252 12 -5 -6 33
253 1 -1 -3 24
254 2 -2 -4 24
255 3 -5 -7 31
256 4 -4 -7 25
257 5 -6 -7 28
258 6 -2 -3 24
259 7 -2 0 25
260 8 -2 -5 16
261 9 -2 -3 17
262 10 2 3 11
263 11 1 2 12
264 12 -8 -7 39
265 1 -1 -1 19
266 2 1 0 14
267 3 -1 -3 15
268 4 2 4 7
269 5 2 2 12
270 6 1 3 12
271 7 -1 0 14
272 8 -2 -10 9
273 9 -2 -10 8
274 10 -1 -9 4
275 11 -8 -22 7
276 12 -4 -16 3
277 1 -6 -18 5
278 2 -3 -14 0
279 3 -3 -12 -2
280 4 -7 -17 6
281 5 -9 -23 11
282 6 -11 -28 9
283 7 -13 -31 17
284 8 -11 -21 21
285 9 -9 -19 21
286 10 -17 -22 41
287 11 -22 -22 57
288 12 -25 -25 65
289 1 -20 -16 68
290 2 -24 -22 73
291 3 -24 -21 71
292 4 -22 -10 71
293 5 -19 -7 70
294 6 -18 -5 69
295 7 -17 -4 65
296 8 -11 7 57
297 9 -11 6 57
298 10 -12 3 57
299 11 -10 10 55
300 12 -15 0 65
301 1 -15 -2 65
302 2 -15 -1 64
303 3 -13 2 60
304 4 -8 8 43
305 5 -13 -6 47
306 6 -9 -4 40
307 7 -7 4 31
308 8 -4 7 27
309 9 -4 3 24
310 10 -2 3 23
311 11 0 8 17
312 12 -2 3 16
313 1 -3 -3 15
314 2 1 4 8
315 3 -2 -5 5
316 4 -1 -1 6
317 5 1 5 5
318 6 -3 0 12
319 7 -4 -6 8
320 8 -9 -13 17
321 9 -9 -15 22
322 10 -7 -8 24
323 11 -14 -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) consumentenvertrouwen economischsituatie
6.3267 -1.0973 0.2699
werkloosheid financielesituatie spaarvermogen
-0.2723 0.3054 0.2759
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1778 -3.0592 -0.1381 2.9245 6.4527
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.3267 0.4278 14.789 <2e-16 ***
consumentenvertrouwen -1.0973 0.5431 -2.020 0.0442 *
economischsituatie 0.2699 0.1380 1.955 0.0514 .
werkloosheid -0.2723 0.1372 -1.984 0.0481 *
financielesituatie 0.3054 0.1579 1.934 0.0540 .
spaarvermogen 0.2759 0.1378 2.002 0.0462 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.451 on 317 degrees of freedom
Multiple R-squared: 0.01466, Adjusted R-squared: -0.0008819
F-statistic: 0.9433 on 5 and 317 DF, p-value: 0.4531
> 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,] 0.1185482 0.2370963 0.88145185
[2,] 0.3130689 0.6261378 0.68693108
[3,] 0.2175922 0.4351844 0.78240781
[4,] 0.1336572 0.2673144 0.86634281
[5,] 0.6080473 0.7839054 0.39195268
[6,] 0.7180780 0.5638439 0.28192197
[7,] 0.6414380 0.7171241 0.35856203
[8,] 0.5522487 0.8955027 0.44775134
[9,] 0.4912262 0.9824523 0.50877384
[10,] 0.4185079 0.8370159 0.58149206
[11,] 0.3476038 0.6952076 0.65239618
[12,] 0.2839006 0.5678012 0.71609940
[13,] 0.2462387 0.4924775 0.75376127
[14,] 0.2167937 0.4335874 0.78320631
[15,] 0.2498305 0.4996609 0.75016954
[16,] 0.2922630 0.5845260 0.70773699
[17,] 0.4193314 0.8386628 0.58066860
[18,] 0.4206601 0.8413201 0.57933994
[19,] 0.6433293 0.7133413 0.35667066
[20,] 0.6283775 0.7432451 0.37162253
[21,] 0.5734803 0.8530394 0.42651970
[22,] 0.5378439 0.9243122 0.46215612
[23,] 0.4777231 0.9554462 0.52227689
[24,] 0.4195472 0.8390944 0.58045279
[25,] 0.4034730 0.8069460 0.59652700
[26,] 0.4519344 0.9038687 0.54806564
[27,] 0.4350101 0.8700202 0.56498989
[28,] 0.4695971 0.9391941 0.53040294
[29,] 0.4950143 0.9900287 0.50498566
[30,] 0.5005638 0.9988723 0.49943617
[31,] 0.4893306 0.9786613 0.51066937
[32,] 0.4888154 0.9776309 0.51118455
[33,] 0.4399128 0.8798255 0.56008723
[34,] 0.3936375 0.7872749 0.60636253
[35,] 0.3510785 0.7021570 0.64892151
[36,] 0.3282013 0.6564026 0.67179868
[37,] 0.2905960 0.5811920 0.70940399
[38,] 0.2533206 0.5066413 0.74667935
[39,] 0.2373216 0.4746431 0.76267844
[40,] 0.2261697 0.4523394 0.77383030
[41,] 0.2334735 0.4669470 0.76652651
[42,] 0.2451334 0.4902668 0.75486660
[43,] 0.2814480 0.5628961 0.71855197
[44,] 0.3008534 0.6017068 0.69914659
[45,] 0.2630199 0.5260397 0.73698015
[46,] 0.2281039 0.4562079 0.77189607
[47,] 0.1955647 0.3911294 0.80443529
[48,] 0.1663361 0.3326722 0.83366388
[49,] 0.1561574 0.3123147 0.84384263
[50,] 0.1421092 0.2842185 0.85789076
[51,] 0.1237360 0.2474720 0.87626402
[52,] 0.1464243 0.2928487 0.85357567
[53,] 0.1817785 0.3635569 0.81822155
[54,] 0.2604679 0.5209358 0.73953209
[55,] 0.3138789 0.6277579 0.68612106
[56,] 0.2885591 0.5771182 0.71144092
[57,] 0.2551252 0.5102503 0.74487483
[58,] 0.2316216 0.4632432 0.76837841
[59,] 0.2012880 0.4025759 0.79871203
[60,] 0.1890518 0.3781036 0.81094819
[61,] 0.2521346 0.5042692 0.74786542
[62,] 0.2437223 0.4874447 0.75627767
[63,] 0.2530876 0.5061753 0.74691235
[64,] 0.3172686 0.6345373 0.68273136
[65,] 0.3520677 0.7041355 0.64793226
[66,] 0.3599566 0.7199132 0.64004338
[67,] 0.3954331 0.7908663 0.60456686
[68,] 0.3720530 0.7441059 0.62794705
[69,] 0.3540647 0.7081295 0.64593527
[70,] 0.3196431 0.6392862 0.68035689
[71,] 0.2921370 0.5842740 0.70786301
[72,] 0.2646343 0.5292687 0.73536567
[73,] 0.2627568 0.5255137 0.73724317
[74,] 0.2552285 0.5104569 0.74477154
[75,] 0.2419931 0.4839863 0.75800687
[76,] 0.3239304 0.6478607 0.67606964
[77,] 0.3801830 0.7603661 0.61981697
[78,] 0.4000984 0.8001968 0.59990162
[79,] 0.4265912 0.8531823 0.57340885
[80,] 0.3989050 0.7978101 0.60109495
[81,] 0.3661752 0.7323505 0.63382476
[82,] 0.3327603 0.6655206 0.66723972
[83,] 0.3004426 0.6008853 0.69955736
[84,] 0.2826166 0.5652333 0.71738337
[85,] 0.2876537 0.5753074 0.71234629
[86,] 0.3081451 0.6162901 0.69185493
[87,] 0.3242165 0.6484331 0.67578347
[88,] 0.3829947 0.7659895 0.61700526
[89,] 0.4113444 0.8226889 0.58865556
[90,] 0.4113211 0.8226421 0.58867895
[91,] 0.4288101 0.8576202 0.57118989
[92,] 0.4083457 0.8166914 0.59165429
[93,] 0.3801578 0.7603156 0.61984219
[94,] 0.3491343 0.6982687 0.65086566
[95,] 0.3194890 0.6389779 0.68051105
[96,] 0.2929235 0.5858471 0.70707645
[97,] 0.2895129 0.5790258 0.71048711
[98,] 0.3072205 0.6144409 0.69277955
[99,] 0.3247196 0.6494392 0.67528039
[100,] 0.3962036 0.7924071 0.60379644
[101,] 0.4331695 0.8663391 0.56683047
[102,] 0.4386611 0.8773223 0.56133887
[103,] 0.4404163 0.8808326 0.55958372
[104,] 0.4190364 0.8380728 0.58096362
[105,] 0.3937053 0.7874106 0.60629470
[106,] 0.3619914 0.7239828 0.63800860
[107,] 0.3319947 0.6639894 0.66800529
[108,] 0.3069585 0.6139171 0.69304147
[109,] 0.2985729 0.5971458 0.70142709
[110,] 0.2816631 0.5633261 0.71833694
[111,] 0.2980228 0.5960457 0.70197716
[112,] 0.3773163 0.7546326 0.62268368
[113,] 0.4438268 0.8876536 0.55617319
[114,] 0.4492313 0.8984627 0.55076866
[115,] 0.4693756 0.9387512 0.53062442
[116,] 0.4473999 0.8947999 0.55260005
[117,] 0.4216788 0.8433576 0.57832122
[118,] 0.3939809 0.7879618 0.60601908
[119,] 0.3631270 0.7262541 0.63687296
[120,] 0.3427669 0.6855338 0.65723310
[121,] 0.3417317 0.6834633 0.65826834
[122,] 0.3337288 0.6674575 0.66627124
[123,] 0.3595841 0.7191683 0.64041586
[124,] 0.4254036 0.8508072 0.57459641
[125,] 0.4923192 0.9846384 0.50768082
[126,] 0.5244915 0.9510170 0.47550852
[127,] 0.5188255 0.9623491 0.48117455
[128,] 0.4992198 0.9984397 0.50078016
[129,] 0.4710349 0.9420698 0.52896509
[130,] 0.4402642 0.8805284 0.55973578
[131,] 0.4087213 0.8174425 0.59127873
[132,] 0.3865195 0.7730390 0.61348052
[133,] 0.3725169 0.7450339 0.62748305
[134,] 0.3799265 0.7598531 0.62007346
[135,] 0.3959024 0.7918047 0.60409763
[136,] 0.4547572 0.9095145 0.54524276
[137,] 0.5252680 0.9494640 0.47473201
[138,] 0.5567351 0.8865298 0.44326488
[139,] 0.5715486 0.8569027 0.42845136
[140,] 0.5606457 0.8787086 0.43935428
[141,] 0.5358565 0.9282869 0.46414346
[142,] 0.5085407 0.9829185 0.49145926
[143,] 0.4772519 0.9545038 0.52274812
[144,] 0.4502861 0.9005723 0.54971385
[145,] 0.4328196 0.8656393 0.56718036
[146,] 0.4316189 0.8632377 0.56838114
[147,] 0.4408888 0.8817775 0.55911124
[148,] 0.4689739 0.9379479 0.53102605
[149,] 0.5288651 0.9422698 0.47113491
[150,] 0.5576792 0.8846417 0.44232083
[151,] 0.5604077 0.8791846 0.43959229
[152,] 0.5518897 0.8962206 0.44811029
[153,] 0.5297777 0.9404446 0.47022228
[154,] 0.4986289 0.9972578 0.50137108
[155,] 0.4689337 0.9378673 0.53106633
[156,] 0.4431795 0.8863591 0.55682047
[157,] 0.4308859 0.8617717 0.56911413
[158,] 0.4218671 0.8437343 0.57813287
[159,] 0.4585220 0.9170440 0.54147798
[160,] 0.5153625 0.9692750 0.48463750
[161,] 0.5535185 0.8929630 0.44648148
[162,] 0.5772877 0.8454246 0.42271230
[163,] 0.5712645 0.8574710 0.42873548
[164,] 0.5572770 0.8854460 0.44272302
[165,] 0.5270925 0.9458150 0.47290750
[166,] 0.4954041 0.9908083 0.50459587
[167,] 0.4659349 0.9318698 0.53406511
[168,] 0.4373431 0.8746861 0.56265694
[169,] 0.4216550 0.8433101 0.57834495
[170,] 0.4244945 0.8489890 0.57550552
[171,] 0.4558739 0.9117479 0.54412607
[172,] 0.5481661 0.9036677 0.45183387
[173,] 0.5786496 0.8427007 0.42135036
[174,] 0.5822438 0.8355123 0.41775616
[175,] 0.5722959 0.8554081 0.42770407
[176,] 0.5486108 0.9027783 0.45138915
[177,] 0.5187376 0.9625248 0.48126238
[178,] 0.4867563 0.9735126 0.51324372
[179,] 0.4578742 0.9157483 0.54212585
[180,] 0.4404362 0.8808725 0.55956376
[181,] 0.4237421 0.8474843 0.57625787
[182,] 0.4317074 0.8634148 0.56829258
[183,] 0.4659663 0.9319325 0.53403373
[184,] 0.5393004 0.9213993 0.46069965
[185,] 0.5775942 0.8448116 0.42240581
[186,] 0.6031007 0.7937985 0.39689927
[187,] 0.6049397 0.7901205 0.39506026
[188,] 0.6022247 0.7955506 0.39777530
[189,] 0.5772043 0.8455915 0.42279574
[190,] 0.5446837 0.9106327 0.45531634
[191,] 0.5119128 0.9761743 0.48808716
[192,] 0.4819907 0.9639814 0.51800928
[193,] 0.4667440 0.9334879 0.53325604
[194,] 0.4665748 0.9331495 0.53342523
[195,] 0.4835352 0.9670704 0.51646479
[196,] 0.5400093 0.9199815 0.45999073
[197,] 0.5775074 0.8449852 0.42249261
[198,] 0.6189153 0.7621694 0.38108469
[199,] 0.6169936 0.7660128 0.38300638
[200,] 0.6009195 0.7981610 0.39908049
[201,] 0.5762237 0.8475526 0.42377630
[202,] 0.5431431 0.9137138 0.45685688
[203,] 0.5172062 0.9655877 0.48279384
[204,] 0.4996833 0.9993667 0.50031666
[205,] 0.4983066 0.9966132 0.50169342
[206,] 0.4924512 0.9849025 0.50754876
[207,] 0.5072234 0.9855532 0.49277660
[208,] 0.5567452 0.8865096 0.44325482
[209,] 0.5966575 0.8066849 0.40334247
[210,] 0.6233044 0.7533911 0.37669557
[211,] 0.6188775 0.7622450 0.38112252
[212,] 0.6140037 0.7719926 0.38599629
[213,] 0.5835989 0.8328022 0.41640110
[214,] 0.5496270 0.9007459 0.45037296
[215,] 0.5149121 0.9701757 0.48508786
[216,] 0.4815417 0.9630834 0.51845832
[217,] 0.4764164 0.9528328 0.52358362
[218,] 0.4620357 0.9240714 0.53796430
[219,] 0.5154588 0.9690825 0.48454123
[220,] 0.5819998 0.8360003 0.41800017
[221,] 0.6193637 0.7612726 0.38063628
[222,] 0.6360610 0.7278779 0.36393896
[223,] 0.6458333 0.7083334 0.35416670
[224,] 0.6285244 0.7429512 0.37147559
[225,] 0.6050788 0.7898423 0.39492116
[226,] 0.5707670 0.8584661 0.42923305
[227,] 0.5359970 0.9280061 0.46400303
[228,] 0.5075512 0.9848976 0.49244880
[229,] 0.5110032 0.9779935 0.48899677
[230,] 0.5319282 0.9361437 0.46807185
[231,] 0.5722360 0.8555279 0.42776395
[232,] 0.6354641 0.7290718 0.36453588
[233,] 0.7122727 0.5754545 0.28772726
[234,] 0.6970782 0.6058437 0.30292183
[235,] 0.6771361 0.6457279 0.32286393
[236,] 0.6698846 0.6602308 0.33011540
[237,] 0.6369640 0.7260720 0.36303602
[238,] 0.6018317 0.7963367 0.39816835
[239,] 0.5744534 0.8510931 0.42554657
[240,] 0.5376302 0.9247395 0.46236976
[241,] 0.5058238 0.9883524 0.49417619
[242,] 0.5170991 0.9658018 0.48290092
[243,] 0.5902876 0.8194248 0.40971241
[244,] 0.6689570 0.6620861 0.33104303
[245,] 0.6868126 0.6263747 0.31318737
[246,] 0.6764071 0.6471858 0.32359288
[247,] 0.6527443 0.6945115 0.34725575
[248,] 0.6510544 0.6978912 0.34894562
[249,] 0.6368008 0.7263984 0.36319918
[250,] 0.5976502 0.8046995 0.40234977
[251,] 0.5571448 0.8857103 0.44285517
[252,] 0.5167187 0.9665627 0.48328133
[253,] 0.4803542 0.9607084 0.51964581
[254,] 0.4567973 0.9135946 0.54320269
[255,] 0.5376947 0.9246107 0.46230533
[256,] 0.6220706 0.7558589 0.37792943
[257,] 0.6215844 0.7568313 0.37841565
[258,] 0.6291315 0.7417371 0.37086854
[259,] 0.6703600 0.6592800 0.32963999
[260,] 0.6382170 0.7235659 0.36178295
[261,] 0.6080185 0.7839629 0.39198146
[262,] 0.5943771 0.8112458 0.40562289
[263,] 0.5541915 0.8916171 0.44580855
[264,] 0.5205113 0.9589774 0.47948869
[265,] 0.4818118 0.9636236 0.51818821
[266,] 0.4580091 0.9160182 0.54199090
[267,] 0.4519039 0.9038077 0.54809613
[268,] 0.5487725 0.9024550 0.45122749
[269,] 0.5776070 0.8447859 0.42239297
[270,] 0.5530275 0.8939451 0.44697255
[271,] 0.5462098 0.9075804 0.45379021
[272,] 0.5178976 0.9642048 0.48210241
[273,] 0.4910035 0.9820070 0.50899650
[274,] 0.4498987 0.8997974 0.55010131
[275,] 0.4001377 0.8002754 0.59986229
[276,] 0.3643199 0.7286398 0.63568008
[277,] 0.3273995 0.6547989 0.67260053
[278,] 0.3269023 0.6538045 0.67309775
[279,] 0.3385725 0.6771451 0.66142747
[280,] 0.4711853 0.9423705 0.52881475
[281,] 0.4749428 0.9498857 0.52505717
[282,] 0.4596153 0.9192306 0.54038468
[283,] 0.4216121 0.8432243 0.57838787
[284,] 0.3965863 0.7931726 0.60341371
[285,] 0.3546448 0.7092896 0.64535520
[286,] 0.3137482 0.6274965 0.68625175
[287,] 0.2659462 0.5318924 0.73405378
[288,] 0.2269152 0.4538304 0.77308479
[289,] 0.2139839 0.4279677 0.78601614
[290,] 0.1831123 0.3662246 0.81688768
[291,] 0.2035285 0.4070571 0.79647145
[292,] 0.3619139 0.7238278 0.63808609
[293,] 0.3838859 0.7677719 0.61611407
[294,] 0.3837141 0.7674282 0.61628592
[295,] 0.4452114 0.8904228 0.55478862
[296,] 0.4477615 0.8955229 0.55223854
[297,] 0.5352870 0.9294261 0.46471304
[298,] 0.6307738 0.7384524 0.36922620
[299,] 0.6773470 0.6453061 0.32265305
[300,] 0.7790805 0.4418390 0.22091948
[301,] 0.8223835 0.3552330 0.17761650
[302,] 0.7392049 0.5215902 0.26079510
[303,] 0.6588933 0.6822135 0.34110673
[304,] 0.8132936 0.3734127 0.18670635
[305,] 0.9154624 0.1690752 0.08453758
[306,] 0.9265819 0.1468361 0.07341806
> postscript(file="/var/wessaorg/rcomp/tmp/1gux61321896223.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2k8ac1321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3sxq51321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fr1y1321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54pfu1321896223.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
-5.235481975 -4.251299293 -2.956347383 -1.473446342 -0.756192086 0.519741657
7 8 9 10 11 12
1.315975209 1.800021695 3.019863345 3.945449966 4.916298168 5.546809230
13 14 15 16 17 18
-5.006203703 -3.601929106 -3.153057451 -1.942549957 -0.998489631 -0.235845447
19 20 21 22 23 24
0.457077942 1.075821837 2.938585145 3.061316893 4.367933375 5.620417194
25 26 27 28 29 30
-4.796993264 -3.835019088 -4.199620139 -2.386344981 -1.711947991 0.090362419
31 32 33 34 35 36
0.048032239 1.050073134 2.563575449 3.844280299 3.725548613 5.578688244
37 38 39 40 41 42
-4.639098002 -4.180800219 -3.495509437 -3.001039797 -1.145498794 -0.192256733
43 44 45 46 47 48
0.554778190 1.869487408 2.303093848 3.014122824 4.600881432 5.259253048
49 50 51 52 53 54
-4.294481523 -3.931132773 -3.401159463 -2.701221827 -1.163509426 -0.443885437
55 56 57 58 59 60
0.517459869 0.928468629 2.844512355 3.550851689 3.695310686 5.793322657
61 62 63 64 65 66
-4.922690180 -4.763868039 -4.310133304 -2.476188259 -1.197053666 -1.376648552
67 68 69 70 71 72
-0.138128083 1.762671557 2.438670443 2.976828464 4.227063330 6.096512045
73 74 75 76 77 78
-5.185923048 -3.943731092 -3.737383174 -2.218568159 -1.748555978 -0.200709947
79 80 81 82 83 84
-0.396380033 1.445126665 2.700542940 3.508780411 3.291432903 5.862414529
85 86 87 88 89 90
-5.235640385 -4.240001188 -4.052395404 -2.178175582 -1.489744286 -0.769289182
91 92 93 94 95 96
-0.366415306 1.486466441 2.743873786 3.725089503 4.170831153 5.745246495
97 98 99 100 101 102
-4.939192042 -3.887531706 -4.056161845 -2.715661775 -1.771764379 -0.465979012
103 104 105 106 107 108
0.210787712 0.978542349 2.852401693 3.839067213 4.019845789 5.929208804
109 110 111 112 113 114
-5.165998113 -3.859413270 -3.413424246 -2.167473416 -1.406233580 -0.204918330
115 116 117 118 119 120
0.050822785 1.622305669 3.074113669 2.759379433 4.478097672 6.317973778
121 122 123 124 125 126
-5.729127509 -3.608825566 -4.051398379 -1.940512056 -1.510495639 -1.075729245
127 128 129 130 131 132
-0.040975226 2.021017309 3.314267671 3.034789732 4.970424857 6.452702840
133 134 135 136 137 138
-5.611917311 -4.636795583 -3.085130341 -2.146955947 -1.173114954 -0.680965222
139 140 141 142 143 144
0.092639011 1.890588618 2.880964941 3.967445554 4.347771576 6.122013375
145 146 147 148 149 150
-6.003575440 -4.664737601 -4.078370351 -2.516829362 -1.162818393 -1.016941559
151 152 153 154 155 156
-0.001326487 1.837697373 2.880964941 3.774969696 4.230066631 4.990538628
157 158 159 160 161 162
-5.503415245 -4.555977652 -3.470115399 -2.754841704 -1.766627057 -0.436713070
163 164 165 166 167 168
1.043807728 1.574191342 2.778788906 2.908414121 5.085455214 5.507757559
169 170 171 172 173 174
-5.214430732 -4.754972994 -3.202695204 -2.790805503 -0.932402999 -0.198068878
175 176 177 178 179 180
0.797675782 0.907041412 2.196107390 3.219536186 4.471599192 5.805255621
181 182 183 184 185 186
-5.527651158 -4.529763008 -3.570119249 -2.428985509 -1.238595689 -0.556045627
187 188 189 190 191 192
1.021312680 1.908668363 2.148694754 3.373646857 4.382160406 5.396194711
193 194 195 196 197 198
-5.596791042 -4.952832396 -3.897150605 -3.163668729 -1.821498751 -0.298322933
199 200 201 202 203 204
0.104632407 0.854270948 2.390144699 3.674157689 4.603155046 5.728156120
205 206 207 208 209 210
-5.069471270 -5.185412409 -3.592389205 -2.324148404 -1.834860173 -0.283742337
211 212 213 214 215 216
0.910021771 1.969593727 2.975656077 3.159979548 4.194733567 5.421621102
217 218 219 220 221 222
-5.627627352 -4.825589333 -3.255092853 -2.841696173 -1.017456269 -0.011854047
223 224 225 226 227 228
0.172961191 0.950926227 2.721733292 3.121513610 4.965156157 5.670614550
229 230 231 232 233 234
-5.302887096 -4.569967943 -3.852303385 -2.598917495 -1.860325166 -0.292124595
235 236 237 238 239 240
0.421878986 1.414575217 2.706665625 3.598736777 4.427273150 5.123938981
241 242 243 244 245 246
-6.177824930 -4.077772064 -3.288431978 -3.080369061 -1.012314176 -0.872007925
247 248 249 250 251 252
0.671346014 0.911269810 1.939030713 3.420329858 4.695513951 5.127008540
253 254 255 256 257 258
-5.601702230 -4.325358183 -3.319964019 -3.132570990 -2.071576500 -0.565788256
259 260 261 262 263 264
0.172853899 0.899108773 1.966504962 2.910331403 4.734931785 5.148138557
265 266 267 268 269 270
-5.295588025 -4.530907644 -4.062186707 -2.211919087 -1.518033292 -1.027924455
271 272 273 274 275 276
0.264986560 1.810200277 2.232502622 3.275978248 4.044033027 5.591387291
277 278 279 280 281 282
-5.080198024 -3.869701039 -3.037963817 -2.071274172 -1.664626215 -0.281071332
283 284 285 286 287 288
0.625915066 1.876199758 1.988695281 3.649140502 4.042416528 5.044199540
289 290 291 292 293 294
-5.234400080 -4.509431753 -3.437264458 -3.023715358 -0.976482781 -1.070954555
295 296 297 298 299 300
0.859204941 1.551686388 2.545624038 2.647225769 4.235806707 5.506251332
301 302 303 304 305 306
-5.781807112 -4.466758546 -4.057822320 -2.440554412 -1.695118442 -0.190621443
307 308 309 310 311 312
0.355006256 1.614712378 2.601296433 4.084947125 4.877488060 5.370747920
313 314 315 316 317 318
-5.379608633 -4.702042433 -2.751344339 -2.071995127 -1.074396499 -0.873014292
319 320 321 322 323
0.968771418 1.344889056 2.038774851 3.002161052 4.295645299
> postscript(file="/var/wessaorg/rcomp/tmp/69yqs1321896223.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 -5.235481975 NA
1 -4.251299293 -5.235481975
2 -2.956347383 -4.251299293
3 -1.473446342 -2.956347383
4 -0.756192086 -1.473446342
5 0.519741657 -0.756192086
6 1.315975209 0.519741657
7 1.800021695 1.315975209
8 3.019863345 1.800021695
9 3.945449966 3.019863345
10 4.916298168 3.945449966
11 5.546809230 4.916298168
12 -5.006203703 5.546809230
13 -3.601929106 -5.006203703
14 -3.153057451 -3.601929106
15 -1.942549957 -3.153057451
16 -0.998489631 -1.942549957
17 -0.235845447 -0.998489631
18 0.457077942 -0.235845447
19 1.075821837 0.457077942
20 2.938585145 1.075821837
21 3.061316893 2.938585145
22 4.367933375 3.061316893
23 5.620417194 4.367933375
24 -4.796993264 5.620417194
25 -3.835019088 -4.796993264
26 -4.199620139 -3.835019088
27 -2.386344981 -4.199620139
28 -1.711947991 -2.386344981
29 0.090362419 -1.711947991
30 0.048032239 0.090362419
31 1.050073134 0.048032239
32 2.563575449 1.050073134
33 3.844280299 2.563575449
34 3.725548613 3.844280299
35 5.578688244 3.725548613
36 -4.639098002 5.578688244
37 -4.180800219 -4.639098002
38 -3.495509437 -4.180800219
39 -3.001039797 -3.495509437
40 -1.145498794 -3.001039797
41 -0.192256733 -1.145498794
42 0.554778190 -0.192256733
43 1.869487408 0.554778190
44 2.303093848 1.869487408
45 3.014122824 2.303093848
46 4.600881432 3.014122824
47 5.259253048 4.600881432
48 -4.294481523 5.259253048
49 -3.931132773 -4.294481523
50 -3.401159463 -3.931132773
51 -2.701221827 -3.401159463
52 -1.163509426 -2.701221827
53 -0.443885437 -1.163509426
54 0.517459869 -0.443885437
55 0.928468629 0.517459869
56 2.844512355 0.928468629
57 3.550851689 2.844512355
58 3.695310686 3.550851689
59 5.793322657 3.695310686
60 -4.922690180 5.793322657
61 -4.763868039 -4.922690180
62 -4.310133304 -4.763868039
63 -2.476188259 -4.310133304
64 -1.197053666 -2.476188259
65 -1.376648552 -1.197053666
66 -0.138128083 -1.376648552
67 1.762671557 -0.138128083
68 2.438670443 1.762671557
69 2.976828464 2.438670443
70 4.227063330 2.976828464
71 6.096512045 4.227063330
72 -5.185923048 6.096512045
73 -3.943731092 -5.185923048
74 -3.737383174 -3.943731092
75 -2.218568159 -3.737383174
76 -1.748555978 -2.218568159
77 -0.200709947 -1.748555978
78 -0.396380033 -0.200709947
79 1.445126665 -0.396380033
80 2.700542940 1.445126665
81 3.508780411 2.700542940
82 3.291432903 3.508780411
83 5.862414529 3.291432903
84 -5.235640385 5.862414529
85 -4.240001188 -5.235640385
86 -4.052395404 -4.240001188
87 -2.178175582 -4.052395404
88 -1.489744286 -2.178175582
89 -0.769289182 -1.489744286
90 -0.366415306 -0.769289182
91 1.486466441 -0.366415306
92 2.743873786 1.486466441
93 3.725089503 2.743873786
94 4.170831153 3.725089503
95 5.745246495 4.170831153
96 -4.939192042 5.745246495
97 -3.887531706 -4.939192042
98 -4.056161845 -3.887531706
99 -2.715661775 -4.056161845
100 -1.771764379 -2.715661775
101 -0.465979012 -1.771764379
102 0.210787712 -0.465979012
103 0.978542349 0.210787712
104 2.852401693 0.978542349
105 3.839067213 2.852401693
106 4.019845789 3.839067213
107 5.929208804 4.019845789
108 -5.165998113 5.929208804
109 -3.859413270 -5.165998113
110 -3.413424246 -3.859413270
111 -2.167473416 -3.413424246
112 -1.406233580 -2.167473416
113 -0.204918330 -1.406233580
114 0.050822785 -0.204918330
115 1.622305669 0.050822785
116 3.074113669 1.622305669
117 2.759379433 3.074113669
118 4.478097672 2.759379433
119 6.317973778 4.478097672
120 -5.729127509 6.317973778
121 -3.608825566 -5.729127509
122 -4.051398379 -3.608825566
123 -1.940512056 -4.051398379
124 -1.510495639 -1.940512056
125 -1.075729245 -1.510495639
126 -0.040975226 -1.075729245
127 2.021017309 -0.040975226
128 3.314267671 2.021017309
129 3.034789732 3.314267671
130 4.970424857 3.034789732
131 6.452702840 4.970424857
132 -5.611917311 6.452702840
133 -4.636795583 -5.611917311
134 -3.085130341 -4.636795583
135 -2.146955947 -3.085130341
136 -1.173114954 -2.146955947
137 -0.680965222 -1.173114954
138 0.092639011 -0.680965222
139 1.890588618 0.092639011
140 2.880964941 1.890588618
141 3.967445554 2.880964941
142 4.347771576 3.967445554
143 6.122013375 4.347771576
144 -6.003575440 6.122013375
145 -4.664737601 -6.003575440
146 -4.078370351 -4.664737601
147 -2.516829362 -4.078370351
148 -1.162818393 -2.516829362
149 -1.016941559 -1.162818393
150 -0.001326487 -1.016941559
151 1.837697373 -0.001326487
152 2.880964941 1.837697373
153 3.774969696 2.880964941
154 4.230066631 3.774969696
155 4.990538628 4.230066631
156 -5.503415245 4.990538628
157 -4.555977652 -5.503415245
158 -3.470115399 -4.555977652
159 -2.754841704 -3.470115399
160 -1.766627057 -2.754841704
161 -0.436713070 -1.766627057
162 1.043807728 -0.436713070
163 1.574191342 1.043807728
164 2.778788906 1.574191342
165 2.908414121 2.778788906
166 5.085455214 2.908414121
167 5.507757559 5.085455214
168 -5.214430732 5.507757559
169 -4.754972994 -5.214430732
170 -3.202695204 -4.754972994
171 -2.790805503 -3.202695204
172 -0.932402999 -2.790805503
173 -0.198068878 -0.932402999
174 0.797675782 -0.198068878
175 0.907041412 0.797675782
176 2.196107390 0.907041412
177 3.219536186 2.196107390
178 4.471599192 3.219536186
179 5.805255621 4.471599192
180 -5.527651158 5.805255621
181 -4.529763008 -5.527651158
182 -3.570119249 -4.529763008
183 -2.428985509 -3.570119249
184 -1.238595689 -2.428985509
185 -0.556045627 -1.238595689
186 1.021312680 -0.556045627
187 1.908668363 1.021312680
188 2.148694754 1.908668363
189 3.373646857 2.148694754
190 4.382160406 3.373646857
191 5.396194711 4.382160406
192 -5.596791042 5.396194711
193 -4.952832396 -5.596791042
194 -3.897150605 -4.952832396
195 -3.163668729 -3.897150605
196 -1.821498751 -3.163668729
197 -0.298322933 -1.821498751
198 0.104632407 -0.298322933
199 0.854270948 0.104632407
200 2.390144699 0.854270948
201 3.674157689 2.390144699
202 4.603155046 3.674157689
203 5.728156120 4.603155046
204 -5.069471270 5.728156120
205 -5.185412409 -5.069471270
206 -3.592389205 -5.185412409
207 -2.324148404 -3.592389205
208 -1.834860173 -2.324148404
209 -0.283742337 -1.834860173
210 0.910021771 -0.283742337
211 1.969593727 0.910021771
212 2.975656077 1.969593727
213 3.159979548 2.975656077
214 4.194733567 3.159979548
215 5.421621102 4.194733567
216 -5.627627352 5.421621102
217 -4.825589333 -5.627627352
218 -3.255092853 -4.825589333
219 -2.841696173 -3.255092853
220 -1.017456269 -2.841696173
221 -0.011854047 -1.017456269
222 0.172961191 -0.011854047
223 0.950926227 0.172961191
224 2.721733292 0.950926227
225 3.121513610 2.721733292
226 4.965156157 3.121513610
227 5.670614550 4.965156157
228 -5.302887096 5.670614550
229 -4.569967943 -5.302887096
230 -3.852303385 -4.569967943
231 -2.598917495 -3.852303385
232 -1.860325166 -2.598917495
233 -0.292124595 -1.860325166
234 0.421878986 -0.292124595
235 1.414575217 0.421878986
236 2.706665625 1.414575217
237 3.598736777 2.706665625
238 4.427273150 3.598736777
239 5.123938981 4.427273150
240 -6.177824930 5.123938981
241 -4.077772064 -6.177824930
242 -3.288431978 -4.077772064
243 -3.080369061 -3.288431978
244 -1.012314176 -3.080369061
245 -0.872007925 -1.012314176
246 0.671346014 -0.872007925
247 0.911269810 0.671346014
248 1.939030713 0.911269810
249 3.420329858 1.939030713
250 4.695513951 3.420329858
251 5.127008540 4.695513951
252 -5.601702230 5.127008540
253 -4.325358183 -5.601702230
254 -3.319964019 -4.325358183
255 -3.132570990 -3.319964019
256 -2.071576500 -3.132570990
257 -0.565788256 -2.071576500
258 0.172853899 -0.565788256
259 0.899108773 0.172853899
260 1.966504962 0.899108773
261 2.910331403 1.966504962
262 4.734931785 2.910331403
263 5.148138557 4.734931785
264 -5.295588025 5.148138557
265 -4.530907644 -5.295588025
266 -4.062186707 -4.530907644
267 -2.211919087 -4.062186707
268 -1.518033292 -2.211919087
269 -1.027924455 -1.518033292
270 0.264986560 -1.027924455
271 1.810200277 0.264986560
272 2.232502622 1.810200277
273 3.275978248 2.232502622
274 4.044033027 3.275978248
275 5.591387291 4.044033027
276 -5.080198024 5.591387291
277 -3.869701039 -5.080198024
278 -3.037963817 -3.869701039
279 -2.071274172 -3.037963817
280 -1.664626215 -2.071274172
281 -0.281071332 -1.664626215
282 0.625915066 -0.281071332
283 1.876199758 0.625915066
284 1.988695281 1.876199758
285 3.649140502 1.988695281
286 4.042416528 3.649140502
287 5.044199540 4.042416528
288 -5.234400080 5.044199540
289 -4.509431753 -5.234400080
290 -3.437264458 -4.509431753
291 -3.023715358 -3.437264458
292 -0.976482781 -3.023715358
293 -1.070954555 -0.976482781
294 0.859204941 -1.070954555
295 1.551686388 0.859204941
296 2.545624038 1.551686388
297 2.647225769 2.545624038
298 4.235806707 2.647225769
299 5.506251332 4.235806707
300 -5.781807112 5.506251332
301 -4.466758546 -5.781807112
302 -4.057822320 -4.466758546
303 -2.440554412 -4.057822320
304 -1.695118442 -2.440554412
305 -0.190621443 -1.695118442
306 0.355006256 -0.190621443
307 1.614712378 0.355006256
308 2.601296433 1.614712378
309 4.084947125 2.601296433
310 4.877488060 4.084947125
311 5.370747920 4.877488060
312 -5.379608633 5.370747920
313 -4.702042433 -5.379608633
314 -2.751344339 -4.702042433
315 -2.071995127 -2.751344339
316 -1.074396499 -2.071995127
317 -0.873014292 -1.074396499
318 0.968771418 -0.873014292
319 1.344889056 0.968771418
320 2.038774851 1.344889056
321 3.002161052 2.038774851
322 4.295645299 3.002161052
323 NA 4.295645299
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.251299293 -5.235481975
[2,] -2.956347383 -4.251299293
[3,] -1.473446342 -2.956347383
[4,] -0.756192086 -1.473446342
[5,] 0.519741657 -0.756192086
[6,] 1.315975209 0.519741657
[7,] 1.800021695 1.315975209
[8,] 3.019863345 1.800021695
[9,] 3.945449966 3.019863345
[10,] 4.916298168 3.945449966
[11,] 5.546809230 4.916298168
[12,] -5.006203703 5.546809230
[13,] -3.601929106 -5.006203703
[14,] -3.153057451 -3.601929106
[15,] -1.942549957 -3.153057451
[16,] -0.998489631 -1.942549957
[17,] -0.235845447 -0.998489631
[18,] 0.457077942 -0.235845447
[19,] 1.075821837 0.457077942
[20,] 2.938585145 1.075821837
[21,] 3.061316893 2.938585145
[22,] 4.367933375 3.061316893
[23,] 5.620417194 4.367933375
[24,] -4.796993264 5.620417194
[25,] -3.835019088 -4.796993264
[26,] -4.199620139 -3.835019088
[27,] -2.386344981 -4.199620139
[28,] -1.711947991 -2.386344981
[29,] 0.090362419 -1.711947991
[30,] 0.048032239 0.090362419
[31,] 1.050073134 0.048032239
[32,] 2.563575449 1.050073134
[33,] 3.844280299 2.563575449
[34,] 3.725548613 3.844280299
[35,] 5.578688244 3.725548613
[36,] -4.639098002 5.578688244
[37,] -4.180800219 -4.639098002
[38,] -3.495509437 -4.180800219
[39,] -3.001039797 -3.495509437
[40,] -1.145498794 -3.001039797
[41,] -0.192256733 -1.145498794
[42,] 0.554778190 -0.192256733
[43,] 1.869487408 0.554778190
[44,] 2.303093848 1.869487408
[45,] 3.014122824 2.303093848
[46,] 4.600881432 3.014122824
[47,] 5.259253048 4.600881432
[48,] -4.294481523 5.259253048
[49,] -3.931132773 -4.294481523
[50,] -3.401159463 -3.931132773
[51,] -2.701221827 -3.401159463
[52,] -1.163509426 -2.701221827
[53,] -0.443885437 -1.163509426
[54,] 0.517459869 -0.443885437
[55,] 0.928468629 0.517459869
[56,] 2.844512355 0.928468629
[57,] 3.550851689 2.844512355
[58,] 3.695310686 3.550851689
[59,] 5.793322657 3.695310686
[60,] -4.922690180 5.793322657
[61,] -4.763868039 -4.922690180
[62,] -4.310133304 -4.763868039
[63,] -2.476188259 -4.310133304
[64,] -1.197053666 -2.476188259
[65,] -1.376648552 -1.197053666
[66,] -0.138128083 -1.376648552
[67,] 1.762671557 -0.138128083
[68,] 2.438670443 1.762671557
[69,] 2.976828464 2.438670443
[70,] 4.227063330 2.976828464
[71,] 6.096512045 4.227063330
[72,] -5.185923048 6.096512045
[73,] -3.943731092 -5.185923048
[74,] -3.737383174 -3.943731092
[75,] -2.218568159 -3.737383174
[76,] -1.748555978 -2.218568159
[77,] -0.200709947 -1.748555978
[78,] -0.396380033 -0.200709947
[79,] 1.445126665 -0.396380033
[80,] 2.700542940 1.445126665
[81,] 3.508780411 2.700542940
[82,] 3.291432903 3.508780411
[83,] 5.862414529 3.291432903
[84,] -5.235640385 5.862414529
[85,] -4.240001188 -5.235640385
[86,] -4.052395404 -4.240001188
[87,] -2.178175582 -4.052395404
[88,] -1.489744286 -2.178175582
[89,] -0.769289182 -1.489744286
[90,] -0.366415306 -0.769289182
[91,] 1.486466441 -0.366415306
[92,] 2.743873786 1.486466441
[93,] 3.725089503 2.743873786
[94,] 4.170831153 3.725089503
[95,] 5.745246495 4.170831153
[96,] -4.939192042 5.745246495
[97,] -3.887531706 -4.939192042
[98,] -4.056161845 -3.887531706
[99,] -2.715661775 -4.056161845
[100,] -1.771764379 -2.715661775
[101,] -0.465979012 -1.771764379
[102,] 0.210787712 -0.465979012
[103,] 0.978542349 0.210787712
[104,] 2.852401693 0.978542349
[105,] 3.839067213 2.852401693
[106,] 4.019845789 3.839067213
[107,] 5.929208804 4.019845789
[108,] -5.165998113 5.929208804
[109,] -3.859413270 -5.165998113
[110,] -3.413424246 -3.859413270
[111,] -2.167473416 -3.413424246
[112,] -1.406233580 -2.167473416
[113,] -0.204918330 -1.406233580
[114,] 0.050822785 -0.204918330
[115,] 1.622305669 0.050822785
[116,] 3.074113669 1.622305669
[117,] 2.759379433 3.074113669
[118,] 4.478097672 2.759379433
[119,] 6.317973778 4.478097672
[120,] -5.729127509 6.317973778
[121,] -3.608825566 -5.729127509
[122,] -4.051398379 -3.608825566
[123,] -1.940512056 -4.051398379
[124,] -1.510495639 -1.940512056
[125,] -1.075729245 -1.510495639
[126,] -0.040975226 -1.075729245
[127,] 2.021017309 -0.040975226
[128,] 3.314267671 2.021017309
[129,] 3.034789732 3.314267671
[130,] 4.970424857 3.034789732
[131,] 6.452702840 4.970424857
[132,] -5.611917311 6.452702840
[133,] -4.636795583 -5.611917311
[134,] -3.085130341 -4.636795583
[135,] -2.146955947 -3.085130341
[136,] -1.173114954 -2.146955947
[137,] -0.680965222 -1.173114954
[138,] 0.092639011 -0.680965222
[139,] 1.890588618 0.092639011
[140,] 2.880964941 1.890588618
[141,] 3.967445554 2.880964941
[142,] 4.347771576 3.967445554
[143,] 6.122013375 4.347771576
[144,] -6.003575440 6.122013375
[145,] -4.664737601 -6.003575440
[146,] -4.078370351 -4.664737601
[147,] -2.516829362 -4.078370351
[148,] -1.162818393 -2.516829362
[149,] -1.016941559 -1.162818393
[150,] -0.001326487 -1.016941559
[151,] 1.837697373 -0.001326487
[152,] 2.880964941 1.837697373
[153,] 3.774969696 2.880964941
[154,] 4.230066631 3.774969696
[155,] 4.990538628 4.230066631
[156,] -5.503415245 4.990538628
[157,] -4.555977652 -5.503415245
[158,] -3.470115399 -4.555977652
[159,] -2.754841704 -3.470115399
[160,] -1.766627057 -2.754841704
[161,] -0.436713070 -1.766627057
[162,] 1.043807728 -0.436713070
[163,] 1.574191342 1.043807728
[164,] 2.778788906 1.574191342
[165,] 2.908414121 2.778788906
[166,] 5.085455214 2.908414121
[167,] 5.507757559 5.085455214
[168,] -5.214430732 5.507757559
[169,] -4.754972994 -5.214430732
[170,] -3.202695204 -4.754972994
[171,] -2.790805503 -3.202695204
[172,] -0.932402999 -2.790805503
[173,] -0.198068878 -0.932402999
[174,] 0.797675782 -0.198068878
[175,] 0.907041412 0.797675782
[176,] 2.196107390 0.907041412
[177,] 3.219536186 2.196107390
[178,] 4.471599192 3.219536186
[179,] 5.805255621 4.471599192
[180,] -5.527651158 5.805255621
[181,] -4.529763008 -5.527651158
[182,] -3.570119249 -4.529763008
[183,] -2.428985509 -3.570119249
[184,] -1.238595689 -2.428985509
[185,] -0.556045627 -1.238595689
[186,] 1.021312680 -0.556045627
[187,] 1.908668363 1.021312680
[188,] 2.148694754 1.908668363
[189,] 3.373646857 2.148694754
[190,] 4.382160406 3.373646857
[191,] 5.396194711 4.382160406
[192,] -5.596791042 5.396194711
[193,] -4.952832396 -5.596791042
[194,] -3.897150605 -4.952832396
[195,] -3.163668729 -3.897150605
[196,] -1.821498751 -3.163668729
[197,] -0.298322933 -1.821498751
[198,] 0.104632407 -0.298322933
[199,] 0.854270948 0.104632407
[200,] 2.390144699 0.854270948
[201,] 3.674157689 2.390144699
[202,] 4.603155046 3.674157689
[203,] 5.728156120 4.603155046
[204,] -5.069471270 5.728156120
[205,] -5.185412409 -5.069471270
[206,] -3.592389205 -5.185412409
[207,] -2.324148404 -3.592389205
[208,] -1.834860173 -2.324148404
[209,] -0.283742337 -1.834860173
[210,] 0.910021771 -0.283742337
[211,] 1.969593727 0.910021771
[212,] 2.975656077 1.969593727
[213,] 3.159979548 2.975656077
[214,] 4.194733567 3.159979548
[215,] 5.421621102 4.194733567
[216,] -5.627627352 5.421621102
[217,] -4.825589333 -5.627627352
[218,] -3.255092853 -4.825589333
[219,] -2.841696173 -3.255092853
[220,] -1.017456269 -2.841696173
[221,] -0.011854047 -1.017456269
[222,] 0.172961191 -0.011854047
[223,] 0.950926227 0.172961191
[224,] 2.721733292 0.950926227
[225,] 3.121513610 2.721733292
[226,] 4.965156157 3.121513610
[227,] 5.670614550 4.965156157
[228,] -5.302887096 5.670614550
[229,] -4.569967943 -5.302887096
[230,] -3.852303385 -4.569967943
[231,] -2.598917495 -3.852303385
[232,] -1.860325166 -2.598917495
[233,] -0.292124595 -1.860325166
[234,] 0.421878986 -0.292124595
[235,] 1.414575217 0.421878986
[236,] 2.706665625 1.414575217
[237,] 3.598736777 2.706665625
[238,] 4.427273150 3.598736777
[239,] 5.123938981 4.427273150
[240,] -6.177824930 5.123938981
[241,] -4.077772064 -6.177824930
[242,] -3.288431978 -4.077772064
[243,] -3.080369061 -3.288431978
[244,] -1.012314176 -3.080369061
[245,] -0.872007925 -1.012314176
[246,] 0.671346014 -0.872007925
[247,] 0.911269810 0.671346014
[248,] 1.939030713 0.911269810
[249,] 3.420329858 1.939030713
[250,] 4.695513951 3.420329858
[251,] 5.127008540 4.695513951
[252,] -5.601702230 5.127008540
[253,] -4.325358183 -5.601702230
[254,] -3.319964019 -4.325358183
[255,] -3.132570990 -3.319964019
[256,] -2.071576500 -3.132570990
[257,] -0.565788256 -2.071576500
[258,] 0.172853899 -0.565788256
[259,] 0.899108773 0.172853899
[260,] 1.966504962 0.899108773
[261,] 2.910331403 1.966504962
[262,] 4.734931785 2.910331403
[263,] 5.148138557 4.734931785
[264,] -5.295588025 5.148138557
[265,] -4.530907644 -5.295588025
[266,] -4.062186707 -4.530907644
[267,] -2.211919087 -4.062186707
[268,] -1.518033292 -2.211919087
[269,] -1.027924455 -1.518033292
[270,] 0.264986560 -1.027924455
[271,] 1.810200277 0.264986560
[272,] 2.232502622 1.810200277
[273,] 3.275978248 2.232502622
[274,] 4.044033027 3.275978248
[275,] 5.591387291 4.044033027
[276,] -5.080198024 5.591387291
[277,] -3.869701039 -5.080198024
[278,] -3.037963817 -3.869701039
[279,] -2.071274172 -3.037963817
[280,] -1.664626215 -2.071274172
[281,] -0.281071332 -1.664626215
[282,] 0.625915066 -0.281071332
[283,] 1.876199758 0.625915066
[284,] 1.988695281 1.876199758
[285,] 3.649140502 1.988695281
[286,] 4.042416528 3.649140502
[287,] 5.044199540 4.042416528
[288,] -5.234400080 5.044199540
[289,] -4.509431753 -5.234400080
[290,] -3.437264458 -4.509431753
[291,] -3.023715358 -3.437264458
[292,] -0.976482781 -3.023715358
[293,] -1.070954555 -0.976482781
[294,] 0.859204941 -1.070954555
[295,] 1.551686388 0.859204941
[296,] 2.545624038 1.551686388
[297,] 2.647225769 2.545624038
[298,] 4.235806707 2.647225769
[299,] 5.506251332 4.235806707
[300,] -5.781807112 5.506251332
[301,] -4.466758546 -5.781807112
[302,] -4.057822320 -4.466758546
[303,] -2.440554412 -4.057822320
[304,] -1.695118442 -2.440554412
[305,] -0.190621443 -1.695118442
[306,] 0.355006256 -0.190621443
[307,] 1.614712378 0.355006256
[308,] 2.601296433 1.614712378
[309,] 4.084947125 2.601296433
[310,] 4.877488060 4.084947125
[311,] 5.370747920 4.877488060
[312,] -5.379608633 5.370747920
[313,] -4.702042433 -5.379608633
[314,] -2.751344339 -4.702042433
[315,] -2.071995127 -2.751344339
[316,] -1.074396499 -2.071995127
[317,] -0.873014292 -1.074396499
[318,] 0.968771418 -0.873014292
[319,] 1.344889056 0.968771418
[320,] 2.038774851 1.344889056
[321,] 3.002161052 2.038774851
[322,] 4.295645299 3.002161052
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.251299293 -5.235481975
2 -2.956347383 -4.251299293
3 -1.473446342 -2.956347383
4 -0.756192086 -1.473446342
5 0.519741657 -0.756192086
6 1.315975209 0.519741657
7 1.800021695 1.315975209
8 3.019863345 1.800021695
9 3.945449966 3.019863345
10 4.916298168 3.945449966
11 5.546809230 4.916298168
12 -5.006203703 5.546809230
13 -3.601929106 -5.006203703
14 -3.153057451 -3.601929106
15 -1.942549957 -3.153057451
16 -0.998489631 -1.942549957
17 -0.235845447 -0.998489631
18 0.457077942 -0.235845447
19 1.075821837 0.457077942
20 2.938585145 1.075821837
21 3.061316893 2.938585145
22 4.367933375 3.061316893
23 5.620417194 4.367933375
24 -4.796993264 5.620417194
25 -3.835019088 -4.796993264
26 -4.199620139 -3.835019088
27 -2.386344981 -4.199620139
28 -1.711947991 -2.386344981
29 0.090362419 -1.711947991
30 0.048032239 0.090362419
31 1.050073134 0.048032239
32 2.563575449 1.050073134
33 3.844280299 2.563575449
34 3.725548613 3.844280299
35 5.578688244 3.725548613
36 -4.639098002 5.578688244
37 -4.180800219 -4.639098002
38 -3.495509437 -4.180800219
39 -3.001039797 -3.495509437
40 -1.145498794 -3.001039797
41 -0.192256733 -1.145498794
42 0.554778190 -0.192256733
43 1.869487408 0.554778190
44 2.303093848 1.869487408
45 3.014122824 2.303093848
46 4.600881432 3.014122824
47 5.259253048 4.600881432
48 -4.294481523 5.259253048
49 -3.931132773 -4.294481523
50 -3.401159463 -3.931132773
51 -2.701221827 -3.401159463
52 -1.163509426 -2.701221827
53 -0.443885437 -1.163509426
54 0.517459869 -0.443885437
55 0.928468629 0.517459869
56 2.844512355 0.928468629
57 3.550851689 2.844512355
58 3.695310686 3.550851689
59 5.793322657 3.695310686
60 -4.922690180 5.793322657
61 -4.763868039 -4.922690180
62 -4.310133304 -4.763868039
63 -2.476188259 -4.310133304
64 -1.197053666 -2.476188259
65 -1.376648552 -1.197053666
66 -0.138128083 -1.376648552
67 1.762671557 -0.138128083
68 2.438670443 1.762671557
69 2.976828464 2.438670443
70 4.227063330 2.976828464
71 6.096512045 4.227063330
72 -5.185923048 6.096512045
73 -3.943731092 -5.185923048
74 -3.737383174 -3.943731092
75 -2.218568159 -3.737383174
76 -1.748555978 -2.218568159
77 -0.200709947 -1.748555978
78 -0.396380033 -0.200709947
79 1.445126665 -0.396380033
80 2.700542940 1.445126665
81 3.508780411 2.700542940
82 3.291432903 3.508780411
83 5.862414529 3.291432903
84 -5.235640385 5.862414529
85 -4.240001188 -5.235640385
86 -4.052395404 -4.240001188
87 -2.178175582 -4.052395404
88 -1.489744286 -2.178175582
89 -0.769289182 -1.489744286
90 -0.366415306 -0.769289182
91 1.486466441 -0.366415306
92 2.743873786 1.486466441
93 3.725089503 2.743873786
94 4.170831153 3.725089503
95 5.745246495 4.170831153
96 -4.939192042 5.745246495
97 -3.887531706 -4.939192042
98 -4.056161845 -3.887531706
99 -2.715661775 -4.056161845
100 -1.771764379 -2.715661775
101 -0.465979012 -1.771764379
102 0.210787712 -0.465979012
103 0.978542349 0.210787712
104 2.852401693 0.978542349
105 3.839067213 2.852401693
106 4.019845789 3.839067213
107 5.929208804 4.019845789
108 -5.165998113 5.929208804
109 -3.859413270 -5.165998113
110 -3.413424246 -3.859413270
111 -2.167473416 -3.413424246
112 -1.406233580 -2.167473416
113 -0.204918330 -1.406233580
114 0.050822785 -0.204918330
115 1.622305669 0.050822785
116 3.074113669 1.622305669
117 2.759379433 3.074113669
118 4.478097672 2.759379433
119 6.317973778 4.478097672
120 -5.729127509 6.317973778
121 -3.608825566 -5.729127509
122 -4.051398379 -3.608825566
123 -1.940512056 -4.051398379
124 -1.510495639 -1.940512056
125 -1.075729245 -1.510495639
126 -0.040975226 -1.075729245
127 2.021017309 -0.040975226
128 3.314267671 2.021017309
129 3.034789732 3.314267671
130 4.970424857 3.034789732
131 6.452702840 4.970424857
132 -5.611917311 6.452702840
133 -4.636795583 -5.611917311
134 -3.085130341 -4.636795583
135 -2.146955947 -3.085130341
136 -1.173114954 -2.146955947
137 -0.680965222 -1.173114954
138 0.092639011 -0.680965222
139 1.890588618 0.092639011
140 2.880964941 1.890588618
141 3.967445554 2.880964941
142 4.347771576 3.967445554
143 6.122013375 4.347771576
144 -6.003575440 6.122013375
145 -4.664737601 -6.003575440
146 -4.078370351 -4.664737601
147 -2.516829362 -4.078370351
148 -1.162818393 -2.516829362
149 -1.016941559 -1.162818393
150 -0.001326487 -1.016941559
151 1.837697373 -0.001326487
152 2.880964941 1.837697373
153 3.774969696 2.880964941
154 4.230066631 3.774969696
155 4.990538628 4.230066631
156 -5.503415245 4.990538628
157 -4.555977652 -5.503415245
158 -3.470115399 -4.555977652
159 -2.754841704 -3.470115399
160 -1.766627057 -2.754841704
161 -0.436713070 -1.766627057
162 1.043807728 -0.436713070
163 1.574191342 1.043807728
164 2.778788906 1.574191342
165 2.908414121 2.778788906
166 5.085455214 2.908414121
167 5.507757559 5.085455214
168 -5.214430732 5.507757559
169 -4.754972994 -5.214430732
170 -3.202695204 -4.754972994
171 -2.790805503 -3.202695204
172 -0.932402999 -2.790805503
173 -0.198068878 -0.932402999
174 0.797675782 -0.198068878
175 0.907041412 0.797675782
176 2.196107390 0.907041412
177 3.219536186 2.196107390
178 4.471599192 3.219536186
179 5.805255621 4.471599192
180 -5.527651158 5.805255621
181 -4.529763008 -5.527651158
182 -3.570119249 -4.529763008
183 -2.428985509 -3.570119249
184 -1.238595689 -2.428985509
185 -0.556045627 -1.238595689
186 1.021312680 -0.556045627
187 1.908668363 1.021312680
188 2.148694754 1.908668363
189 3.373646857 2.148694754
190 4.382160406 3.373646857
191 5.396194711 4.382160406
192 -5.596791042 5.396194711
193 -4.952832396 -5.596791042
194 -3.897150605 -4.952832396
195 -3.163668729 -3.897150605
196 -1.821498751 -3.163668729
197 -0.298322933 -1.821498751
198 0.104632407 -0.298322933
199 0.854270948 0.104632407
200 2.390144699 0.854270948
201 3.674157689 2.390144699
202 4.603155046 3.674157689
203 5.728156120 4.603155046
204 -5.069471270 5.728156120
205 -5.185412409 -5.069471270
206 -3.592389205 -5.185412409
207 -2.324148404 -3.592389205
208 -1.834860173 -2.324148404
209 -0.283742337 -1.834860173
210 0.910021771 -0.283742337
211 1.969593727 0.910021771
212 2.975656077 1.969593727
213 3.159979548 2.975656077
214 4.194733567 3.159979548
215 5.421621102 4.194733567
216 -5.627627352 5.421621102
217 -4.825589333 -5.627627352
218 -3.255092853 -4.825589333
219 -2.841696173 -3.255092853
220 -1.017456269 -2.841696173
221 -0.011854047 -1.017456269
222 0.172961191 -0.011854047
223 0.950926227 0.172961191
224 2.721733292 0.950926227
225 3.121513610 2.721733292
226 4.965156157 3.121513610
227 5.670614550 4.965156157
228 -5.302887096 5.670614550
229 -4.569967943 -5.302887096
230 -3.852303385 -4.569967943
231 -2.598917495 -3.852303385
232 -1.860325166 -2.598917495
233 -0.292124595 -1.860325166
234 0.421878986 -0.292124595
235 1.414575217 0.421878986
236 2.706665625 1.414575217
237 3.598736777 2.706665625
238 4.427273150 3.598736777
239 5.123938981 4.427273150
240 -6.177824930 5.123938981
241 -4.077772064 -6.177824930
242 -3.288431978 -4.077772064
243 -3.080369061 -3.288431978
244 -1.012314176 -3.080369061
245 -0.872007925 -1.012314176
246 0.671346014 -0.872007925
247 0.911269810 0.671346014
248 1.939030713 0.911269810
249 3.420329858 1.939030713
250 4.695513951 3.420329858
251 5.127008540 4.695513951
252 -5.601702230 5.127008540
253 -4.325358183 -5.601702230
254 -3.319964019 -4.325358183
255 -3.132570990 -3.319964019
256 -2.071576500 -3.132570990
257 -0.565788256 -2.071576500
258 0.172853899 -0.565788256
259 0.899108773 0.172853899
260 1.966504962 0.899108773
261 2.910331403 1.966504962
262 4.734931785 2.910331403
263 5.148138557 4.734931785
264 -5.295588025 5.148138557
265 -4.530907644 -5.295588025
266 -4.062186707 -4.530907644
267 -2.211919087 -4.062186707
268 -1.518033292 -2.211919087
269 -1.027924455 -1.518033292
270 0.264986560 -1.027924455
271 1.810200277 0.264986560
272 2.232502622 1.810200277
273 3.275978248 2.232502622
274 4.044033027 3.275978248
275 5.591387291 4.044033027
276 -5.080198024 5.591387291
277 -3.869701039 -5.080198024
278 -3.037963817 -3.869701039
279 -2.071274172 -3.037963817
280 -1.664626215 -2.071274172
281 -0.281071332 -1.664626215
282 0.625915066 -0.281071332
283 1.876199758 0.625915066
284 1.988695281 1.876199758
285 3.649140502 1.988695281
286 4.042416528 3.649140502
287 5.044199540 4.042416528
288 -5.234400080 5.044199540
289 -4.509431753 -5.234400080
290 -3.437264458 -4.509431753
291 -3.023715358 -3.437264458
292 -0.976482781 -3.023715358
293 -1.070954555 -0.976482781
294 0.859204941 -1.070954555
295 1.551686388 0.859204941
296 2.545624038 1.551686388
297 2.647225769 2.545624038
298 4.235806707 2.647225769
299 5.506251332 4.235806707
300 -5.781807112 5.506251332
301 -4.466758546 -5.781807112
302 -4.057822320 -4.466758546
303 -2.440554412 -4.057822320
304 -1.695118442 -2.440554412
305 -0.190621443 -1.695118442
306 0.355006256 -0.190621443
307 1.614712378 0.355006256
308 2.601296433 1.614712378
309 4.084947125 2.601296433
310 4.877488060 4.084947125
311 5.370747920 4.877488060
312 -5.379608633 5.370747920
313 -4.702042433 -5.379608633
314 -2.751344339 -4.702042433
315 -2.071995127 -2.751344339
316 -1.074396499 -2.071995127
317 -0.873014292 -1.074396499
318 0.968771418 -0.873014292
319 1.344889056 0.968771418
320 2.038774851 1.344889056
321 3.002161052 2.038774851
322 4.295645299 3.002161052
> 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/7n5ps1321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8d32u1321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xz0i1321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107bu01321896223.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118l6n1321896223.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/12c8mh1321896223.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/13x1c71321896224.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/14z20n1321896224.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/158pfl1321896224.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/16tifr1321896224.tab")
+ }
>
> try(system("convert tmp/1gux61321896223.ps tmp/1gux61321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k8ac1321896223.ps tmp/2k8ac1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sxq51321896223.ps tmp/3sxq51321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fr1y1321896223.ps tmp/4fr1y1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/54pfu1321896223.ps tmp/54pfu1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/69yqs1321896223.ps tmp/69yqs1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n5ps1321896223.ps tmp/7n5ps1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d32u1321896223.ps tmp/8d32u1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xz0i1321896223.ps tmp/9xz0i1321896223.png",intern=TRUE))
character(0)
> try(system("convert tmp/107bu01321896223.ps tmp/107bu01321896223.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.338 0.588 9.999