R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,-11
+ ,7
+ ,57
+ ,1
+ ,5
+ ,-11
+ ,6
+ ,57
+ ,1
+ ,6
+ ,-12
+ ,3
+ ,57
+ ,3
+ ,6
+ ,-10
+ ,10
+ ,55
+ ,3
+ ,3
+ ,-15
+ ,0
+ ,65
+ ,1
+ ,4
+ ,-15
+ ,-2
+ ,65
+ ,1
+ ,7
+ ,-15
+ ,-1
+ ,64
+ ,0
+ ,5
+ ,-13
+ ,2
+ ,60
+ ,2
+ ,6
+ ,-8
+ ,8
+ ,43
+ ,2
+ ,1
+ ,-13
+ ,-6
+ ,47
+ ,-1
+ ,3
+ ,-9
+ ,-4
+ ,40
+ ,1
+ ,6
+ ,-7
+ ,4
+ ,31
+ ,0
+ ,0
+ ,-4
+ ,7
+ ,27
+ ,1
+ ,3
+ ,-4
+ ,3
+ ,24
+ ,1
+ ,4
+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,-2
+ ,-5
+ ,5
+ ,-2
+ ,2
+ ,-1
+ ,-1
+ ,6
+ ,0
+ ,2
+ ,1
+ ,5
+ ,5
+ ,1
+ ,2
+ ,-3
+ ,0
+ ,12
+ ,-1
+ ,3
+ ,-4
+ ,-6
+ ,8
+ ,-2
+ ,-1
+ ,-9
+ ,-13
+ ,17
+ ,-1
+ ,-4
+ ,-9
+ ,-15
+ ,22
+ ,-1
+ ,4
+ ,-7
+ ,-8
+ ,24
+ ,1
+ ,5
+ ,-14
+ ,-20
+ ,36
+ ,-2
+ ,3)
+ ,dim=c(5
+ ,323)
+ ,dimnames=list(c('consumentenvertrouwen'
+ ,'economischesituatie'
+ ,'werkloosheid'
+ ,'financielesituatie'
+ ,'spaarvermogen')
+ ,1:323))
> y <- array(NA,dim=c(5,323),dimnames=list(c('consumentenvertrouwen','economischesituatie','werkloosheid','financielesituatie','spaarvermogen'),1:323))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
consumentenvertrouwen economischesituatie werkloosheid financielesituatie
1 -28 -25 37 -16
2 -26 -23 33 -15
3 -27 -24 36 -16
4 -26 -24 37 -14
5 -27 -25 39 -14
6 -27 -25 39 -14
7 -27 -24 37 -16
8 -28 -24 37 -17
9 -26 -22 36 -15
10 -13 1 23 -9
11 -13 -5 21 -9
12 -14 -10 24 -7
13 -12 -10 25 -4
14 -16 -15 29 -9
15 -16 -13 24 -8
16 -12 -11 22 -6
17 -15 -15 28 -5
18 -18 -15 39 -7
19 -17 -16 36 -6
20 -10 -4 32 -1
21 -9 -5 27 -2
22 -13 -9 33 -1
23 -15 -14 36 -3
24 -12 -11 34 -2
25 -13 -7 34 -2
26 -10 -7 31 -1
27 -13 -9 37 -2
28 -11 -5 36 -1
29 -12 -10 35 0
30 -10 -9 32 1
31 -13 -10 35 -1
32 -12 -8 36 -1
33 -11 -9 35 0
34 -11 -10 32 0
35 -11 -10 28 1
36 -8 -5 24 1
37 -7 -6 25 2
38 -10 -10 29 1
39 -8 -10 28 2
40 -8 -9 25 1
41 -7 -10 22 0
42 -7 -8 22 2
43 -6 -8 22 1
44 -8 -8 23 0
45 -6 -4 22 1
46 -3 2 14 3
47 1 3 7 2
48 0 2 9 4
49 -3 -3 12 1
50 0 -1 9 4
51 0 1 6 2
52 -1 2 8 3
53 -1 -4 10 2
54 0 0 8 3
55 1 5 9 5
56 0 -1 11 5
57 2 3 6 3
58 3 6 6 4
59 2 7 9 5
60 4 7 7 5
61 3 3 8 4
62 4 8 2 6
63 3 3 2 5
64 1 0 7 4
65 2 1 6 4
66 4 4 4 7
67 3 4 8 8
68 2 1 9 5
69 -4 -17 11 4
70 -5 -16 14 1
71 -5 -13 18 2
72 -7 -15 23 0
73 -13 -31 25 -2
74 -11 -26 31 -1
75 -3 -5 18 2
76 -3 -5 19 3
77 -5 -6 23 2
78 -4 -5 24 2
79 -4 -5 25 5
80 -4 -7 26 4
81 -5 -6 27 5
82 -4 -8 23 2
83 -5 -6 27 6
84 -6 -12 34 7
85 -9 -15 34 1
86 -10 -15 37 1
87 -11 -16 41 0
88 -13 -19 43 -2
89 -13 -23 38 -1
90 -13 -23 39 -1
91 -11 -21 35 1
92 -12 -21 38 0
93 -14 -25 40 0
94 -20 -34 49 -1
95 -17 -30 51 -1
96 -16 -27 48 -1
97 -24 -40 54 -4
98 -24 -40 56 -6
99 -22 -34 56 -3
100 -25 -43 61 -7
101 -24 -39 57 -4
102 -25 -40 57 -5
103 -24 -40 52 -3
104 -25 -40 58 -5
105 -24 -35 60 -6
106 -26 -43 62 -7
107 -25 -44 48 -6
108 -24 -38 50 -8
109 -22 -37 50 -5
110 -20 -31 48 -5
111 -14 -20 40 -3
112 -13 -22 35 -2
113 -10 -9 33 -1
114 -10 -11 34 1
115 -11 -8 34 -1
116 -6 -3 28 -1
117 -2 3 26 3
118 -3 6 23 2
119 -2 -3 20 4
120 -4 -8 20 3
121 -7 -8 26 1
122 -8 -10 28 0
123 -7 -9 29 2
124 -4 -7 25 2
125 -7 -12 27 2
126 -5 -9 24 3
127 -6 -8 26 2
128 -12 -19 38 1
129 -12 -21 38 0
130 -16 -24 45 -4
131 -20 -30 53 -9
132 -16 -28 44 -6
133 -16 -27 43 -7
134 -18 -26 47 -6
135 -15 -27 40 -6
136 -12 -23 34 -3
137 -13 -26 38 -3
138 -13 -23 39 -4
139 -12 -21 35 -5
140 -11 -20 35 -4
141 -9 -14 36 -3
142 -9 -16 25 -5
143 -8 -17 24 -3
144 -8 -18 29 -2
145 -15 -25 44 -3
146 -16 -26 43 -5
147 -21 -36 57 -3
148 -21 -35 56 -3
149 -16 -27 47 -4
150 -13 -22 41 -2
151 -12 -25 38 -3
152 -8 -17 33 -2
153 -9 -14 36 -3
154 -1 -7 22 2
155 -5 -12 27 1
156 -9 -17 32 -1
157 -1 -8 21 2
158 3 -2 14 5
159 2 -1 10 3
160 3 1 14 3
161 5 0 12 3
162 5 -2 10 1
163 3 -5 12 3
164 2 -4 9 1
165 1 -9 14 2
166 -4 -16 23 2
167 1 -7 17 1
168 1 -7 16 2
169 6 3 7 4
170 3 -2 9 3
171 2 -3 9 3
172 2 -6 14 3
173 2 -7 12 2
174 -8 -24 23 -1
175 0 -13 12 1
176 -2 -14 15 3
177 3 -7 6 4
178 5 -1 6 4
179 8 5 1 6
180 8 6 3 4
181 9 5 -1 6
182 11 5 -4 6
183 13 9 -6 8
184 12 10 -9 4
185 13 14 -13 8
186 15 19 -13 10
187 13 18 -10 9
188 16 16 -12 12
189 10 8 -9 9
190 14 10 -15 11
191 14 12 -14 11
192 15 13 -18 11
193 13 15 -13 11
194 8 3 -2 11
195 7 2 -1 9
196 3 -2 5 8
197 3 1 8 6
198 4 1 6 7
199 4 -1 7 8
200 0 -6 15 6
201 -4 -13 23 5
202 -14 -25 43 2
203 -18 -26 60 3
204 -8 -9 36 3
205 -1 1 28 7
206 1 3 23 8
207 2 6 23 7
208 0 2 22 7
209 1 5 22 6
210 0 5 24 6
211 -1 0 32 7
212 -3 -5 27 5
213 -3 -4 27 5
214 -3 -2 27 5
215 -4 -1 29 4
216 -8 -8 38 4
217 -9 -16 40 4
218 -13 -19 45 1
219 -18 -28 50 -1
220 -11 -11 43 3
221 -9 -4 44 4
222 -10 -9 44 3
223 -13 -12 49 2
224 -11 -10 42 1
225 -5 -2 36 4
226 -15 -13 57 3
227 -6 0 42 5
228 -6 0 39 6
229 -3 4 33 6
230 -1 7 32 6
231 -3 5 34 6
232 -4 2 37 6
233 -6 -2 38 5
234 0 6 28 6
235 -4 -3 31 5
236 -2 1 28 6
237 -2 0 30 5
238 -6 -7 39 7
239 -7 -6 38 4
240 -6 -4 39 5
241 -6 -4 38 6
242 -3 -2 37 6
243 -2 2 32 5
244 -5 -5 32 3
245 -11 -15 44 2
246 -11 -16 43 3
247 -11 -18 42 3
248 -10 -13 38 2
249 -14 -23 37 0
250 -8 -10 35 4
251 -9 -10 37 4
252 -5 -6 33 5
253 -1 -3 24 6
254 -2 -4 24 6
255 -5 -7 31 5
256 -4 -7 25 5
257 -6 -7 28 3
258 -2 -3 24 5
259 -2 0 25 5
260 -2 -5 16 5
261 -2 -3 17 3
262 2 3 11 6
263 1 2 12 6
264 -8 -7 39 4
265 -1 -1 19 6
266 1 0 14 5
267 -1 -3 15 4
268 2 4 7 5
269 2 2 12 5
270 1 3 12 4
271 -1 0 14 3
272 -2 -10 9 2
273 -2 -10 8 3
274 -1 -9 4 2
275 -8 -22 7 -1
276 -4 -16 3 0
277 -6 -18 5 -2
278 -3 -14 0 1
279 -3 -12 -2 -2
280 -7 -17 6 -2
281 -9 -23 11 -2
282 -11 -28 9 -6
283 -13 -31 17 -4
284 -11 -21 21 -2
285 -9 -19 21 0
286 -17 -22 41 -5
287 -22 -22 57 -4
288 -25 -25 65 -5
289 -20 -16 68 -1
290 -24 -22 73 -2
291 -24 -21 71 -4
292 -22 -10 71 -1
293 -19 -7 70 1
294 -18 -5 69 1
295 -17 -4 65 -2
296 -11 7 57 1
297 -11 6 57 1
298 -12 3 57 3
299 -10 10 55 3
300 -15 0 65 1
301 -15 -2 65 1
302 -15 -1 64 0
303 -13 2 60 2
304 -8 8 43 2
305 -13 -6 47 -1
306 -9 -4 40 1
307 -7 4 31 0
308 -4 7 27 1
309 -4 3 24 1
310 -2 3 23 3
311 0 8 17 2
312 -2 3 16 0
313 -3 -3 15 0
314 1 4 8 3
315 -2 -5 5 -2
316 -1 -1 6 0
317 1 5 5 1
318 -3 0 12 -1
319 -4 -6 8 -2
320 -9 -13 17 -1
321 -9 -15 22 -1
322 -7 -8 24 1
323 -14 -20 36 -2
spaarvermogen
1 -33
2 -32
3 -32
4 -31
5 -31
6 -32
7 -32
8 -33
9 -31
10 -21
11 -17
12 -14
13 -10
14 -13
15 -19
16 -10
17 -13
18 -11
19 -9
20 -1
21 -3
22 -7
23 -6
24 -1
25 -11
26 -3
27 -1
28 -2
29 -2
30 -2
31 -4
32 -1
33 0
34 -3
35 -4
36 -4
37 -2
38 -3
39 4
40 3
41 3
42 -1
43 5
44 -2
45 2
46 -1
47 6
48 4
49 -2
50 4
51 3
52 0
53 7
54 5
55 3
56 9
57 7
58 8
59 8
60 10
61 11
62 5
63 9
64 7
65 8
66 12
67 10
68 10
69 8
70 11
71 10
72 8
73 5
74 12
75 10
76 8
77 8
78 10
79 12
80 13
81 7
82 13
83 11
84 13
85 11
86 10
87 15
88 11
89 10
90 12
91 14
92 11
93 8
94 3
95 15
96 11
97 0
98 4
99 7
100 12
101 5
102 2
103 0
104 5
105 4
106 7
107 0
108 -1
109 3
110 2
111 7
112 6
113 3
114 3
115 1
116 8
117 10
118 6
119 11
120 6
121 6
122 3
123 10
124 12
125 9
126 12
127 10
128 6
129 8
130 11
131 11
132 11
133 14
134 8
135 12
136 11
137 14
138 15
139 15
140 14
141 16
142 9
143 13
144 15
145 14
146 11
147 14
148 10
149 13
150 15
151 20
152 19
153 16
154 22
155 19
156 16
157 23
158 23
159 16
160 23
161 30
162 31
163 24
164 20
165 24
166 23
167 25
168 25
169 23
170 21
171 16
172 26
173 23
174 15
175 23
176 20
177 22
178 24
179 22
180 24
181 24
182 29
183 29
184 25
185 16
186 18
187 13
188 22
189 15
190 20
191 19
192 18
193 13
194 17
195 17
196 13
197 14
198 13
199 17
200 17
201 15
202 9
203 10
204 9
205 14
206 18
207 18
208 12
209 16
210 12
211 19
212 13
213 12
214 13
215 11
216 10
217 16
218 12
219 6
220 8
221 6
222 8
223 8
224 9
225 13
226 8
227 11
228 8
229 10
230 15
231 12
232 13
233 12
234 15
235 13
236 13
237 16
238 14
239 12
240 15
241 14
242 19
243 16
244 16
245 11
246 13
247 12
248 11
249 6
250 9
251 6
252 15
253 17
254 13
255 12
256 13
257 10
258 14
259 13
260 10
261 11
262 12
263 7
264 11
265 9
266 13
267 12
268 5
269 13
270 11
271 8
272 8
273 8
274 8
275 0
276 3
277 0
278 -1
279 -1
280 -4
281 1
282 -1
283 0
284 -1
285 6
286 0
287 -3
288 -3
289 4
290 1
291 0
292 -4
293 -2
294 3
295 2
296 5
297 6
298 6
299 3
300 4
301 7
302 5
303 6
304 1
305 3
306 6
307 0
308 3
309 4
310 7
311 6
312 6
313 6
314 6
315 2
316 2
317 2
318 3
319 -1
320 -4
321 4
322 5
323 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) economischesituatie werkloosheid
0.02972 0.24923 -0.25148
financielesituatie spaarvermogen
0.24728 0.24858
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.96244 -0.24146 0.01919 0.26860 0.98559
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.029722 0.044137 0.673 0.501
economischesituatie 0.249226 0.002779 89.694 <2e-16 ***
werkloosheid -0.251480 0.001379 -182.379 <2e-16 ***
financielesituatie 0.247280 0.008573 28.845 <2e-16 ***
spaarvermogen 0.248577 0.002874 86.496 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3563 on 318 degrees of freedom
Multiple R-squared: 0.9984, Adjusted R-squared: 0.9984
F-statistic: 5.11e+04 on 4 and 318 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,] 0.18759544 0.375190871 0.812404565
[2,] 0.19718817 0.394376341 0.802811830
[3,] 0.10408644 0.208172878 0.895913561
[4,] 0.05895036 0.117900723 0.941049638
[5,] 0.14284805 0.285696098 0.857151951
[6,] 0.08517640 0.170352798 0.914823601
[7,] 0.08919641 0.178392815 0.910803593
[8,] 0.06714633 0.134292658 0.932853671
[9,] 0.05112624 0.102252485 0.948873757
[10,] 0.03674052 0.073481033 0.963259483
[11,] 0.11283483 0.225669656 0.887165172
[12,] 0.15002397 0.300047945 0.849976028
[13,] 0.19217809 0.384356185 0.807821908
[14,] 0.17896864 0.357937285 0.821031358
[15,] 0.22792390 0.455847800 0.772076100
[16,] 0.18658930 0.373178597 0.813410702
[17,] 0.14610504 0.292210085 0.853894958
[18,] 0.25455138 0.509102768 0.745448616
[19,] 0.31910302 0.638206035 0.680896982
[20,] 0.42450587 0.849011732 0.575494134
[21,] 0.38120049 0.762400980 0.618799510
[22,] 0.34901310 0.698026203 0.650986899
[23,] 0.36635922 0.732718433 0.633640783
[24,] 0.42011138 0.840222765 0.579888617
[25,] 0.40762672 0.815253433 0.592373284
[26,] 0.35942283 0.718845666 0.640577167
[27,] 0.32023175 0.640463491 0.679768255
[28,] 0.62255923 0.754881547 0.377440774
[29,] 0.57197392 0.856052156 0.428026078
[30,] 0.68237365 0.635252692 0.317626346
[31,] 0.64211273 0.715774533 0.357887266
[32,] 0.59312351 0.813752985 0.406876493
[33,] 0.63744002 0.725119958 0.362559979
[34,] 0.61342325 0.773153492 0.386576746
[35,] 0.56820603 0.863587941 0.431793971
[36,] 0.51913532 0.961729357 0.480864678
[37,] 0.47814070 0.956281392 0.521859304
[38,] 0.45861410 0.917228203 0.541385899
[39,] 0.57449138 0.851017233 0.425508616
[40,] 0.52767497 0.944650066 0.472325033
[41,] 0.51720459 0.965590828 0.482795414
[42,] 0.72136080 0.557278408 0.278639204
[43,] 0.70986632 0.580267356 0.290133678
[44,] 0.68001268 0.639974635 0.319987317
[45,] 0.69600260 0.607994797 0.303997399
[46,] 0.66452744 0.670945130 0.335472565
[47,] 0.62821864 0.743562720 0.371781360
[48,] 0.58936984 0.821260326 0.410630163
[49,] 0.64897051 0.702058986 0.351029493
[50,] 0.62606265 0.747874698 0.373937349
[51,] 0.58493761 0.830124783 0.415062391
[52,] 0.69007523 0.619849543 0.309924771
[53,] 0.68271641 0.634567190 0.317283595
[54,] 0.72040376 0.559192485 0.279596242
[55,] 0.71170360 0.576592808 0.288296404
[56,] 0.83270747 0.334585051 0.167292526
[57,] 0.80648290 0.387034205 0.193517103
[58,] 0.78864179 0.422716430 0.211358215
[59,] 0.86818993 0.263620144 0.131810072
[60,] 0.88050605 0.238987892 0.119493946
[61,] 0.87204170 0.255916601 0.127958300
[62,] 0.85627522 0.287449554 0.143724777
[63,] 0.87512932 0.249741350 0.124870675
[64,] 0.85959350 0.280812994 0.140406497
[65,] 0.88620847 0.227583064 0.113791532
[66,] 0.87084640 0.258307205 0.129153602
[67,] 0.88151768 0.236964631 0.118482316
[68,] 0.86841791 0.263164178 0.131582089
[69,] 0.86035413 0.279291739 0.139645870
[70,] 0.84532045 0.309359095 0.154679548
[71,] 0.83957640 0.320847193 0.160423596
[72,] 0.88796877 0.224062457 0.112031228
[73,] 0.87031747 0.259365059 0.129682529
[74,] 0.86507977 0.269840460 0.134920230
[75,] 0.84452849 0.310943027 0.155471514
[76,] 0.93240390 0.135192194 0.067596097
[77,] 0.94775366 0.104492675 0.052246337
[78,] 0.94334459 0.113310821 0.056655411
[79,] 0.93846528 0.123069436 0.061534718
[80,] 0.94331560 0.113368795 0.056684397
[81,] 0.93941060 0.121178790 0.060589395
[82,] 0.92829238 0.143415238 0.071707619
[83,] 0.92240845 0.155183110 0.077591555
[84,] 0.95438449 0.091231022 0.045615511
[85,] 0.94540757 0.109184868 0.054592434
[86,] 0.93969650 0.120606992 0.060303496
[87,] 0.93170025 0.136599505 0.068299752
[88,] 0.92372533 0.152549344 0.076274672
[89,] 0.91838766 0.163224688 0.081612344
[90,] 0.92122119 0.157557616 0.078778808
[91,] 0.92626786 0.147464282 0.073732141
[92,] 0.93895578 0.122088447 0.061044223
[93,] 0.93330238 0.133395241 0.066697620
[94,] 0.92933995 0.141320106 0.070660053
[95,] 0.91773710 0.164525792 0.082262896
[96,] 0.91659265 0.166814705 0.083407353
[97,] 0.92685912 0.146281752 0.073140876
[98,] 0.92349002 0.153019957 0.076509978
[99,] 0.91807366 0.163852674 0.081926337
[100,] 0.93575387 0.128492267 0.064246133
[101,] 0.92923485 0.141530293 0.070765147
[102,] 0.92279107 0.154417859 0.077208930
[103,] 0.93398098 0.132038034 0.066019017
[104,] 0.92274443 0.154511147 0.077255573
[105,] 0.91692279 0.166154429 0.083077215
[106,] 0.90384426 0.192311480 0.096155740
[107,] 0.89930674 0.201386528 0.100693264
[108,] 0.90615720 0.187685590 0.093842795
[109,] 0.89271201 0.214575985 0.107287993
[110,] 0.91670406 0.166591882 0.083295941
[111,] 0.94356687 0.112866256 0.056433128
[112,] 0.93392906 0.132141880 0.066070940
[113,] 0.96351173 0.072976532 0.036488266
[114,] 0.95899039 0.082019216 0.041009608
[115,] 0.97919413 0.041611735 0.020805867
[116,] 0.98124556 0.037508889 0.018754444
[117,] 0.98570346 0.028593075 0.014296537
[118,] 0.98248746 0.035025083 0.017512542
[119,] 0.98459898 0.030802042 0.015401021
[120,] 0.98633531 0.027329376 0.013664688
[121,] 0.98941804 0.021163915 0.010581958
[122,] 0.99548551 0.009028976 0.004514488
[123,] 0.99596541 0.008069188 0.004034594
[124,] 0.99578824 0.008423524 0.004211762
[125,] 0.99837869 0.003242627 0.001621313
[126,] 0.99809847 0.003803053 0.001901526
[127,] 0.99774017 0.004519659 0.002259829
[128,] 0.99753507 0.004929858 0.002464929
[129,] 0.99729463 0.005410739 0.002705369
[130,] 0.99707706 0.005845888 0.002922944
[131,] 0.99656207 0.006875852 0.003437926
[132,] 0.99703684 0.005926317 0.002963158
[133,] 0.99683367 0.006332659 0.003166329
[134,] 0.99669746 0.006605087 0.003302544
[135,] 0.99636228 0.007275443 0.003637722
[136,] 0.99583337 0.008333268 0.004166634
[137,] 0.99699086 0.006018277 0.003009139
[138,] 0.99733462 0.005330758 0.002665379
[139,] 0.99684442 0.006311166 0.003155583
[140,] 0.99715232 0.005695365 0.002847683
[141,] 0.99644060 0.007118793 0.003559397
[142,] 0.99634205 0.007315899 0.003657949
[143,] 0.99671541 0.006569177 0.003284588
[144,] 0.99711342 0.005773165 0.002886582
[145,] 0.99696653 0.006066936 0.003033468
[146,] 0.99687505 0.006249905 0.003124952
[147,] 0.99668756 0.006624882 0.003312441
[148,] 0.99607749 0.007845023 0.003922511
[149,] 0.99658285 0.006834304 0.003417152
[150,] 0.99568567 0.008628652 0.004314326
[151,] 0.99458142 0.010837154 0.005418577
[152,] 0.99320629 0.013587418 0.006793709
[153,] 0.99215288 0.015694248 0.007847124
[154,] 0.99101212 0.017975756 0.008987878
[155,] 0.98894238 0.022115240 0.011057620
[156,] 0.99142343 0.017153141 0.008576571
[157,] 0.98936236 0.021275271 0.010637636
[158,] 0.98843201 0.023135971 0.011567986
[159,] 0.99015605 0.019687895 0.009843948
[160,] 0.99272807 0.014543859 0.007271930
[161,] 0.99097851 0.018042985 0.009021492
[162,] 0.99030098 0.019398042 0.009699021
[163,] 0.98895445 0.022091109 0.011045554
[164,] 0.98793176 0.024136477 0.012068238
[165,] 0.98613390 0.027732191 0.013866095
[166,] 0.98975622 0.020487556 0.010243778
[167,] 0.98943186 0.021136277 0.010568139
[168,] 0.98952592 0.020948156 0.010474078
[169,] 0.99092753 0.018144947 0.009072473
[170,] 0.98962412 0.020751752 0.010375876
[171,] 0.98793922 0.024121557 0.012060778
[172,] 0.98517331 0.029653374 0.014826687
[173,] 0.98487500 0.030250003 0.015125002
[174,] 0.98162963 0.036740734 0.018370367
[175,] 0.97812721 0.043745581 0.021872791
[176,] 0.97389634 0.052207322 0.026103661
[177,] 0.96994710 0.060105794 0.030052897
[178,] 0.96698940 0.066021197 0.033010598
[179,] 0.96054659 0.078906814 0.039453407
[180,] 0.96593646 0.068127084 0.034063542
[181,] 0.97210516 0.055789681 0.027894841
[182,] 0.97020720 0.059585600 0.029792800
[183,] 0.96425878 0.071482449 0.035741224
[184,] 0.95732486 0.085350278 0.042675139
[185,] 0.94941317 0.101173654 0.050586827
[186,] 0.94089457 0.118210850 0.059105425
[187,] 0.93922709 0.121545813 0.060772906
[188,] 0.93572118 0.128557631 0.064278815
[189,] 0.95183910 0.096321806 0.048160903
[190,] 0.94739480 0.105210409 0.052605204
[191,] 0.94154297 0.116914065 0.058457032
[192,] 0.93787212 0.124255767 0.062127883
[193,] 0.94602559 0.107948823 0.053974411
[194,] 0.93626174 0.127476522 0.063738261
[195,] 0.93287302 0.134253961 0.067126980
[196,] 0.93120171 0.137596575 0.068798288
[197,] 0.92839792 0.143204159 0.071602079
[198,] 0.94086258 0.118274832 0.059137416
[199,] 0.94956647 0.100867053 0.050433526
[200,] 0.94046272 0.119074561 0.059537281
[201,] 0.93452272 0.130954565 0.065477283
[202,] 0.92714249 0.145715027 0.072857513
[203,] 0.92167750 0.156644996 0.078322498
[204,] 0.94027352 0.119452958 0.059726479
[205,] 0.95319230 0.093615391 0.046807695
[206,] 0.96348974 0.073020529 0.036510265
[207,] 0.95872557 0.082548854 0.041274427
[208,] 0.95338856 0.093222881 0.046611441
[209,] 0.94440446 0.111191080 0.055595540
[210,] 0.93542551 0.129148982 0.064574491
[211,] 0.92526874 0.149462516 0.074731258
[212,] 0.92832899 0.143342014 0.071671007
[213,] 0.91979096 0.160418076 0.080209038
[214,] 0.93354735 0.132905303 0.066452652
[215,] 0.95035896 0.099282076 0.049641038
[216,] 0.94285177 0.114296455 0.057148227
[217,] 0.94560090 0.108798196 0.054399098
[218,] 0.94511454 0.109770927 0.054885463
[219,] 0.93704837 0.125903258 0.062951629
[220,] 0.95275220 0.094495605 0.047247803
[221,] 0.94841610 0.103167809 0.051583905
[222,] 0.94454656 0.110906884 0.055453442
[223,] 0.93415305 0.131693899 0.065846950
[224,] 0.92643793 0.147124132 0.073562066
[225,] 0.91330375 0.173392499 0.086696249
[226,] 0.90241515 0.195169700 0.097584850
[227,] 0.89981445 0.200371107 0.100185553
[228,] 0.88367387 0.232652254 0.116326127
[229,] 0.86513328 0.269733434 0.134866717
[230,] 0.86791944 0.264161127 0.132080563
[231,] 0.86747435 0.265051298 0.132525649
[232,] 0.84951637 0.300967251 0.150483626
[233,] 0.82987175 0.340256501 0.170128251
[234,] 0.83697486 0.326050282 0.163025141
[235,] 0.89703932 0.205921369 0.102960684
[236,] 0.90795560 0.184088790 0.092044395
[237,] 0.90321938 0.193561235 0.096780618
[238,] 0.94779623 0.104407536 0.052203768
[239,] 0.93784544 0.124309127 0.062154564
[240,] 0.95183410 0.096331810 0.048165905
[241,] 0.94898761 0.102024786 0.051012393
[242,] 0.94892148 0.102157043 0.051078521
[243,] 0.93953765 0.120924710 0.060462355
[244,] 0.93917279 0.121654415 0.060827207
[245,] 0.92731725 0.145365497 0.072682749
[246,] 0.92235117 0.155297666 0.077648833
[247,] 0.93398321 0.132033579 0.066016790
[248,] 0.95015199 0.099696013 0.049848006
[249,] 0.94534773 0.109304535 0.054652267
[250,] 0.94344080 0.113118401 0.056559201
[251,] 0.94015378 0.119692432 0.059846216
[252,] 0.92770717 0.144585659 0.072292829
[253,] 0.92851752 0.142964963 0.071482481
[254,] 0.92833385 0.143332306 0.071666153
[255,] 0.93381229 0.132375425 0.066187712
[256,] 0.92743924 0.145121516 0.072560758
[257,] 0.91224934 0.175501319 0.087750659
[258,] 0.91612175 0.167756498 0.083878249
[259,] 0.90588966 0.188220688 0.094110344
[260,] 0.90257807 0.194843854 0.097421927
[261,] 0.89487016 0.210259683 0.105129842
[262,] 0.88217672 0.235646568 0.117823284
[263,] 0.88519111 0.229617782 0.114808891
[264,] 0.86752347 0.264953068 0.132476534
[265,] 0.87537878 0.249242431 0.124621215
[266,] 0.85162738 0.296745248 0.148372624
[267,] 0.82709431 0.345811371 0.172905685
[268,] 0.85527355 0.289452898 0.144726449
[269,] 0.82596944 0.348061120 0.174030560
[270,] 0.80676012 0.386479761 0.193239880
[271,] 0.84164763 0.316704746 0.158352373
[272,] 0.82021886 0.359562271 0.179781135
[273,] 0.80596603 0.388067942 0.194033971
[274,] 0.77540318 0.449193645 0.224596823
[275,] 0.73505200 0.529895994 0.264947997
[276,] 0.69864769 0.602704630 0.301352315
[277,] 0.71678176 0.566436490 0.283218245
[278,] 0.70035625 0.599287497 0.299643749
[279,] 0.65595629 0.688087419 0.344043710
[280,] 0.65960105 0.680797908 0.340398954
[281,] 0.68775240 0.624495198 0.312247599
[282,] 0.72016148 0.559677040 0.279838520
[283,] 0.70188372 0.596232567 0.298116283
[284,] 0.66610180 0.667796393 0.333898197
[285,] 0.68379476 0.632410476 0.316205238
[286,] 0.86986181 0.260276384 0.130138192
[287,] 0.84362150 0.312757008 0.156378504
[288,] 0.84458187 0.310836267 0.155418133
[289,] 0.80463402 0.390731955 0.195365977
[290,] 0.76044016 0.479119682 0.239559841
[291,] 0.82629306 0.347413874 0.173706937
[292,] 0.79211284 0.415774320 0.207887160
[293,] 0.76010661 0.479786780 0.239893390
[294,] 0.70053801 0.598923986 0.299461993
[295,] 0.67957528 0.640849444 0.320424722
[296,] 0.64994811 0.700103779 0.350051889
[297,] 0.57447577 0.851048468 0.425524234
[298,] 0.49551446 0.991028910 0.504485545
[299,] 0.56671734 0.866565317 0.433282659
[300,] 0.50969507 0.980609850 0.490304925
[301,] 0.42482985 0.849659703 0.575170149
[302,] 0.33629432 0.672588637 0.663705682
[303,] 0.59472020 0.810559601 0.405279800
[304,] 0.73138183 0.537236331 0.268618166
[305,] 0.62137450 0.757250991 0.378625496
[306,] 0.51457010 0.970859802 0.485429901
[307,] 0.37632285 0.752645695 0.623677153
[308,] 0.30213473 0.604269454 0.697865273
> postscript(file="/var/www/rcomp/tmp/1qc7c1321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2yb2h1321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3u6v31321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4k4p71321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5tiqb1321898366.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
-0.3348064127 -0.3350336608 -0.0840889919 0.4242538533 0.1764389869
6 7 8 9 10
0.4250161133 0.1673906889 -0.3367523302 -0.0783975169 -0.0492765141
11 12 13 14 15
-0.0511897494 -0.2909129355 0.2244186760 0.4585969111 -0.0530701329
16 17 18 19 20
0.2137651147 0.2179978120 -0.0183202431 -0.2679676208 -0.4896118918
21 22 23 24 25
0.2466495836 -0.5005405927 -0.2539901077 0.0052077282 0.4940759043
26 27 28 29 30
0.5037399962 -0.7388047734 0.0141097297 -0.2385209457 0.5105343854
31 32 33 34 35
-0.4940868383 -0.4867900808 0.0150990295 0.2556171383 -0.7490043129
36 37 38 39 40
-0.0010518960 0.7552194494 0.2538982414 0.0150988212 -0.4927090121
41 42 43 44 45
0.2493575721 0.2506548246 0.0064719207 0.2452713409 -0.2446997880
46 47 48 49 50
-0.5007201961 -0.0030637638 -0.2482840866 0.9855861378 0.4993932294
51 52 53 54 55
-0.0103605214 -0.2581754072 0.2473785560 -0.0026094952 0.0053358693
56 57 58 59 60
-0.4878128957 0.2495995744 0.0060652775 -0.7360013067 0.2638850789
61 62 63 64 65
0.5109705758 -0.2471333195 -0.7480331106 0.0014767166 0.2521941374
66 67 68 69 70
-0.7345906094 -0.4787974880 0.2621990724 -0.0043435629 -0.5030221081
71 72 73 74 75
-0.2434834291 0.5040804807 0.2349432824 0.5103727678 -0.2372896050
76 77 78 79 80
0.2640644741 -0.2335111763 0.2715884797 -0.7159256560 0.0327082969
81 82 83 84 85
0.2791451096 0.0220547357 -0.9624432506 0.5488350394 0.2773457355
86 87 88 89 90
0.2803619043 -0.4600993781 0.2794055142 0.0202074701 -0.2254671019
91 92 93 94 95
-0.7215513310 0.0258989452 0.2714927739 0.2680073354 -0.2088619079
96 97 98 99 100
0.2833302394 0.5083313141 0.5115418792 -0.4713836956 -0.2247195577
101 102 103 104 105
-0.2293410476 0.0128959582 -0.2419079020 -0.4813557402 0.2713317424
106 107 108 109 110
0.2696457552 -0.5090839735 0.2416575917 0.2562837504 0.5065468833
111 112 113 114 115
0.0157806040 0.2581310160 0.0136881432 0.2690596589 -0.4869036951
116 117 118 119 120
0.0180494754 0.5334618108 -0.7270661873 0.0240813771 0.7603757236
121 122 123 124 125
-0.2361864826 0.7582356568 -0.4741100284 0.5243654517 0.0191850524
126 127 128 129 130
-0.4759425397 -0.4777748428 0.5230531787 0.7716303244 -0.4769465552
131 132 133 134 135
0.2666447958 0.7630365611 -0.2361204163 -0.2352445613 0.2593149396
136 137 138 139 140
0.2602713297 0.2681359895 -0.2293589175 -0.4864493300 0.2656221698
141 142 143 144 145
0.2773131113 0.2440877607 -0.2470343629 0.5151557056 -0.4722116978
146 147 148 149 150
-0.2341745182 -0.4614923557 0.0321106971 0.2765358695 -0.4701850370
151 152 153 154 155
-0.4725525409 0.2775401512 0.2773131113 0.2841551452 -0.2193063571
156 157 158 159 160
-0.4754880050 0.0333241100 0.0357721489 0.0152272478 -0.2173454579
161 162 163 164 165
-0.2111189323 0.0303558328 0.5264726861 0.0116760865 0.2736149901
166 167 168 169 170
-0.4699103525 0.5283052167 0.0295456813 0.2765653781 -0.2299122930
171 172 173 174 175
0.2621991110 -0.2184964332 0.5207812110 0.2543523983 0.2634156975
176 177 178 179 180
-0.4817478181 -0.2340794564 -0.2265883411 0.0232531667 0.2743922126
181 182 183 184 185
0.0231395524 0.0258148780 0.0313927193 0.0111558289 0.2564087372
186 187 188 189 190
0.0185659154 0.5123962163 0.5288546974 -0.2410206359 0.0142043943
191 192 193 194 195
0.0158096575 0.0092422888 0.0110747808 -0.2262479724 -0.2309828105
196 197 198 199 200
-0.4836132777 -0.2308689686 0.2674689417 -0.2241881937 -0.4716621784
201 202 203 204 205
0.0291897791 0.2827949807 0.3113183450 0.2875450089 0.5514447925
206 207 208 209 210
-0.4459935156 0.0536090230 0.2904951886 -0.2042107784 0.2930570888
211 212 213 214 215
0.5637036556 0.5384565792 0.5378079336 -0.2092207368 -0.2110530398
216 217 218 219 220
0.0454216175 0.0507243965 -0.2080518143 0.2784010050 -0.2050685552
221 222 223 224 225
0.5517051199 0.5479595816 -0.1996848440 -0.4597914253 0.3013762448
226 227 228 229 230
-0.1859014803 0.5616771838 0.3056896661 0.3027542406 0.0607116119
231 232 233 234 235
-0.1921461034 0.0613931285 -0.1943671218 0.3040186607 0.0459237583
236 237 238 239 240
0.0473017735 0.3010353824 0.3115274570 0.0498158207 -0.1901672763
241 242 243 244 245
-0.4403496852 0.5668334580 0.3055431999 -0.4553166870 0.5448626888
246 247 248 249 250
-0.2018253274 0.2937236622 -0.4624669399 -0.4842435596 0.0380112455
251 252 253 254 255
0.2867019863 -0.2005938170 0.0439776326 0.2875119103 0.2914039727
256 257 258 259 260
-0.4660512384 -0.4713211077 0.0369888664 -0.2106316423 -0.4820885302
261 262 263 264 265
-0.4830778107 -0.4777272174 0.2658638674 -0.2009016001 0.2767446960
266 267 268 269 270
0.0230918691 -0.4818941532 0.2544480269 0.0216809635 -0.4831107011
271 272 273 274 275
-0.2394627898 0.2426763807 -0.2560831546 -0.2639477952 -0.5391171421
276 277 278 279 280
-0.0334017309 0.2083002630 0.4607363339 0.2011649920 0.2048626774
281 282 283 284 285
-0.2852699188 -0.0558267494 -0.0394488227 0.2282295979 -0.5048215401
286 287 288 289 290
0.0003114228 -0.4775621600 -0.4707675433 0.3114802481 0.0572445177
291 292 293 294 295
0.0481962197 -0.4408183302 0.5683107111 -0.4245061457 0.3107660493
296 297 298 299 300
0.0698741683 0.0705228139 -0.6763595793 -0.1781679655 0.0748691449
301 302 303 304 305
-0.1724106904 0.0713179642 -0.4254149104 0.0469615165 -0.2132736418
306 307 308 309 310
0.2876259604 -0.2307547295 0.0226379977 0.0165249169 0.5247541477
311 312 313 314 315
0.2656041841 -0.2451869277 -0.0013119765 -0.2480897096 0.4712109744
316 317 318 319 320
0.2312278581 0.2371136908 -0.5104171010 0.2206071679 -0.2730437765
321 322 323
-0.5058108399 -0.4905687177 -0.2431094680
> postscript(file="/var/www/rcomp/tmp/6cqo51321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 323
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.3348064127 NA
1 -0.3350336608 -0.3348064127
2 -0.0840889919 -0.3350336608
3 0.4242538533 -0.0840889919
4 0.1764389869 0.4242538533
5 0.4250161133 0.1764389869
6 0.1673906889 0.4250161133
7 -0.3367523302 0.1673906889
8 -0.0783975169 -0.3367523302
9 -0.0492765141 -0.0783975169
10 -0.0511897494 -0.0492765141
11 -0.2909129355 -0.0511897494
12 0.2244186760 -0.2909129355
13 0.4585969111 0.2244186760
14 -0.0530701329 0.4585969111
15 0.2137651147 -0.0530701329
16 0.2179978120 0.2137651147
17 -0.0183202431 0.2179978120
18 -0.2679676208 -0.0183202431
19 -0.4896118918 -0.2679676208
20 0.2466495836 -0.4896118918
21 -0.5005405927 0.2466495836
22 -0.2539901077 -0.5005405927
23 0.0052077282 -0.2539901077
24 0.4940759043 0.0052077282
25 0.5037399962 0.4940759043
26 -0.7388047734 0.5037399962
27 0.0141097297 -0.7388047734
28 -0.2385209457 0.0141097297
29 0.5105343854 -0.2385209457
30 -0.4940868383 0.5105343854
31 -0.4867900808 -0.4940868383
32 0.0150990295 -0.4867900808
33 0.2556171383 0.0150990295
34 -0.7490043129 0.2556171383
35 -0.0010518960 -0.7490043129
36 0.7552194494 -0.0010518960
37 0.2538982414 0.7552194494
38 0.0150988212 0.2538982414
39 -0.4927090121 0.0150988212
40 0.2493575721 -0.4927090121
41 0.2506548246 0.2493575721
42 0.0064719207 0.2506548246
43 0.2452713409 0.0064719207
44 -0.2446997880 0.2452713409
45 -0.5007201961 -0.2446997880
46 -0.0030637638 -0.5007201961
47 -0.2482840866 -0.0030637638
48 0.9855861378 -0.2482840866
49 0.4993932294 0.9855861378
50 -0.0103605214 0.4993932294
51 -0.2581754072 -0.0103605214
52 0.2473785560 -0.2581754072
53 -0.0026094952 0.2473785560
54 0.0053358693 -0.0026094952
55 -0.4878128957 0.0053358693
56 0.2495995744 -0.4878128957
57 0.0060652775 0.2495995744
58 -0.7360013067 0.0060652775
59 0.2638850789 -0.7360013067
60 0.5109705758 0.2638850789
61 -0.2471333195 0.5109705758
62 -0.7480331106 -0.2471333195
63 0.0014767166 -0.7480331106
64 0.2521941374 0.0014767166
65 -0.7345906094 0.2521941374
66 -0.4787974880 -0.7345906094
67 0.2621990724 -0.4787974880
68 -0.0043435629 0.2621990724
69 -0.5030221081 -0.0043435629
70 -0.2434834291 -0.5030221081
71 0.5040804807 -0.2434834291
72 0.2349432824 0.5040804807
73 0.5103727678 0.2349432824
74 -0.2372896050 0.5103727678
75 0.2640644741 -0.2372896050
76 -0.2335111763 0.2640644741
77 0.2715884797 -0.2335111763
78 -0.7159256560 0.2715884797
79 0.0327082969 -0.7159256560
80 0.2791451096 0.0327082969
81 0.0220547357 0.2791451096
82 -0.9624432506 0.0220547357
83 0.5488350394 -0.9624432506
84 0.2773457355 0.5488350394
85 0.2803619043 0.2773457355
86 -0.4600993781 0.2803619043
87 0.2794055142 -0.4600993781
88 0.0202074701 0.2794055142
89 -0.2254671019 0.0202074701
90 -0.7215513310 -0.2254671019
91 0.0258989452 -0.7215513310
92 0.2714927739 0.0258989452
93 0.2680073354 0.2714927739
94 -0.2088619079 0.2680073354
95 0.2833302394 -0.2088619079
96 0.5083313141 0.2833302394
97 0.5115418792 0.5083313141
98 -0.4713836956 0.5115418792
99 -0.2247195577 -0.4713836956
100 -0.2293410476 -0.2247195577
101 0.0128959582 -0.2293410476
102 -0.2419079020 0.0128959582
103 -0.4813557402 -0.2419079020
104 0.2713317424 -0.4813557402
105 0.2696457552 0.2713317424
106 -0.5090839735 0.2696457552
107 0.2416575917 -0.5090839735
108 0.2562837504 0.2416575917
109 0.5065468833 0.2562837504
110 0.0157806040 0.5065468833
111 0.2581310160 0.0157806040
112 0.0136881432 0.2581310160
113 0.2690596589 0.0136881432
114 -0.4869036951 0.2690596589
115 0.0180494754 -0.4869036951
116 0.5334618108 0.0180494754
117 -0.7270661873 0.5334618108
118 0.0240813771 -0.7270661873
119 0.7603757236 0.0240813771
120 -0.2361864826 0.7603757236
121 0.7582356568 -0.2361864826
122 -0.4741100284 0.7582356568
123 0.5243654517 -0.4741100284
124 0.0191850524 0.5243654517
125 -0.4759425397 0.0191850524
126 -0.4777748428 -0.4759425397
127 0.5230531787 -0.4777748428
128 0.7716303244 0.5230531787
129 -0.4769465552 0.7716303244
130 0.2666447958 -0.4769465552
131 0.7630365611 0.2666447958
132 -0.2361204163 0.7630365611
133 -0.2352445613 -0.2361204163
134 0.2593149396 -0.2352445613
135 0.2602713297 0.2593149396
136 0.2681359895 0.2602713297
137 -0.2293589175 0.2681359895
138 -0.4864493300 -0.2293589175
139 0.2656221698 -0.4864493300
140 0.2773131113 0.2656221698
141 0.2440877607 0.2773131113
142 -0.2470343629 0.2440877607
143 0.5151557056 -0.2470343629
144 -0.4722116978 0.5151557056
145 -0.2341745182 -0.4722116978
146 -0.4614923557 -0.2341745182
147 0.0321106971 -0.4614923557
148 0.2765358695 0.0321106971
149 -0.4701850370 0.2765358695
150 -0.4725525409 -0.4701850370
151 0.2775401512 -0.4725525409
152 0.2773131113 0.2775401512
153 0.2841551452 0.2773131113
154 -0.2193063571 0.2841551452
155 -0.4754880050 -0.2193063571
156 0.0333241100 -0.4754880050
157 0.0357721489 0.0333241100
158 0.0152272478 0.0357721489
159 -0.2173454579 0.0152272478
160 -0.2111189323 -0.2173454579
161 0.0303558328 -0.2111189323
162 0.5264726861 0.0303558328
163 0.0116760865 0.5264726861
164 0.2736149901 0.0116760865
165 -0.4699103525 0.2736149901
166 0.5283052167 -0.4699103525
167 0.0295456813 0.5283052167
168 0.2765653781 0.0295456813
169 -0.2299122930 0.2765653781
170 0.2621991110 -0.2299122930
171 -0.2184964332 0.2621991110
172 0.5207812110 -0.2184964332
173 0.2543523983 0.5207812110
174 0.2634156975 0.2543523983
175 -0.4817478181 0.2634156975
176 -0.2340794564 -0.4817478181
177 -0.2265883411 -0.2340794564
178 0.0232531667 -0.2265883411
179 0.2743922126 0.0232531667
180 0.0231395524 0.2743922126
181 0.0258148780 0.0231395524
182 0.0313927193 0.0258148780
183 0.0111558289 0.0313927193
184 0.2564087372 0.0111558289
185 0.0185659154 0.2564087372
186 0.5123962163 0.0185659154
187 0.5288546974 0.5123962163
188 -0.2410206359 0.5288546974
189 0.0142043943 -0.2410206359
190 0.0158096575 0.0142043943
191 0.0092422888 0.0158096575
192 0.0110747808 0.0092422888
193 -0.2262479724 0.0110747808
194 -0.2309828105 -0.2262479724
195 -0.4836132777 -0.2309828105
196 -0.2308689686 -0.4836132777
197 0.2674689417 -0.2308689686
198 -0.2241881937 0.2674689417
199 -0.4716621784 -0.2241881937
200 0.0291897791 -0.4716621784
201 0.2827949807 0.0291897791
202 0.3113183450 0.2827949807
203 0.2875450089 0.3113183450
204 0.5514447925 0.2875450089
205 -0.4459935156 0.5514447925
206 0.0536090230 -0.4459935156
207 0.2904951886 0.0536090230
208 -0.2042107784 0.2904951886
209 0.2930570888 -0.2042107784
210 0.5637036556 0.2930570888
211 0.5384565792 0.5637036556
212 0.5378079336 0.5384565792
213 -0.2092207368 0.5378079336
214 -0.2110530398 -0.2092207368
215 0.0454216175 -0.2110530398
216 0.0507243965 0.0454216175
217 -0.2080518143 0.0507243965
218 0.2784010050 -0.2080518143
219 -0.2050685552 0.2784010050
220 0.5517051199 -0.2050685552
221 0.5479595816 0.5517051199
222 -0.1996848440 0.5479595816
223 -0.4597914253 -0.1996848440
224 0.3013762448 -0.4597914253
225 -0.1859014803 0.3013762448
226 0.5616771838 -0.1859014803
227 0.3056896661 0.5616771838
228 0.3027542406 0.3056896661
229 0.0607116119 0.3027542406
230 -0.1921461034 0.0607116119
231 0.0613931285 -0.1921461034
232 -0.1943671218 0.0613931285
233 0.3040186607 -0.1943671218
234 0.0459237583 0.3040186607
235 0.0473017735 0.0459237583
236 0.3010353824 0.0473017735
237 0.3115274570 0.3010353824
238 0.0498158207 0.3115274570
239 -0.1901672763 0.0498158207
240 -0.4403496852 -0.1901672763
241 0.5668334580 -0.4403496852
242 0.3055431999 0.5668334580
243 -0.4553166870 0.3055431999
244 0.5448626888 -0.4553166870
245 -0.2018253274 0.5448626888
246 0.2937236622 -0.2018253274
247 -0.4624669399 0.2937236622
248 -0.4842435596 -0.4624669399
249 0.0380112455 -0.4842435596
250 0.2867019863 0.0380112455
251 -0.2005938170 0.2867019863
252 0.0439776326 -0.2005938170
253 0.2875119103 0.0439776326
254 0.2914039727 0.2875119103
255 -0.4660512384 0.2914039727
256 -0.4713211077 -0.4660512384
257 0.0369888664 -0.4713211077
258 -0.2106316423 0.0369888664
259 -0.4820885302 -0.2106316423
260 -0.4830778107 -0.4820885302
261 -0.4777272174 -0.4830778107
262 0.2658638674 -0.4777272174
263 -0.2009016001 0.2658638674
264 0.2767446960 -0.2009016001
265 0.0230918691 0.2767446960
266 -0.4818941532 0.0230918691
267 0.2544480269 -0.4818941532
268 0.0216809635 0.2544480269
269 -0.4831107011 0.0216809635
270 -0.2394627898 -0.4831107011
271 0.2426763807 -0.2394627898
272 -0.2560831546 0.2426763807
273 -0.2639477952 -0.2560831546
274 -0.5391171421 -0.2639477952
275 -0.0334017309 -0.5391171421
276 0.2083002630 -0.0334017309
277 0.4607363339 0.2083002630
278 0.2011649920 0.4607363339
279 0.2048626774 0.2011649920
280 -0.2852699188 0.2048626774
281 -0.0558267494 -0.2852699188
282 -0.0394488227 -0.0558267494
283 0.2282295979 -0.0394488227
284 -0.5048215401 0.2282295979
285 0.0003114228 -0.5048215401
286 -0.4775621600 0.0003114228
287 -0.4707675433 -0.4775621600
288 0.3114802481 -0.4707675433
289 0.0572445177 0.3114802481
290 0.0481962197 0.0572445177
291 -0.4408183302 0.0481962197
292 0.5683107111 -0.4408183302
293 -0.4245061457 0.5683107111
294 0.3107660493 -0.4245061457
295 0.0698741683 0.3107660493
296 0.0705228139 0.0698741683
297 -0.6763595793 0.0705228139
298 -0.1781679655 -0.6763595793
299 0.0748691449 -0.1781679655
300 -0.1724106904 0.0748691449
301 0.0713179642 -0.1724106904
302 -0.4254149104 0.0713179642
303 0.0469615165 -0.4254149104
304 -0.2132736418 0.0469615165
305 0.2876259604 -0.2132736418
306 -0.2307547295 0.2876259604
307 0.0226379977 -0.2307547295
308 0.0165249169 0.0226379977
309 0.5247541477 0.0165249169
310 0.2656041841 0.5247541477
311 -0.2451869277 0.2656041841
312 -0.0013119765 -0.2451869277
313 -0.2480897096 -0.0013119765
314 0.4712109744 -0.2480897096
315 0.2312278581 0.4712109744
316 0.2371136908 0.2312278581
317 -0.5104171010 0.2371136908
318 0.2206071679 -0.5104171010
319 -0.2730437765 0.2206071679
320 -0.5058108399 -0.2730437765
321 -0.4905687177 -0.5058108399
322 -0.2431094680 -0.4905687177
323 NA -0.2431094680
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3350336608 -0.3348064127
[2,] -0.0840889919 -0.3350336608
[3,] 0.4242538533 -0.0840889919
[4,] 0.1764389869 0.4242538533
[5,] 0.4250161133 0.1764389869
[6,] 0.1673906889 0.4250161133
[7,] -0.3367523302 0.1673906889
[8,] -0.0783975169 -0.3367523302
[9,] -0.0492765141 -0.0783975169
[10,] -0.0511897494 -0.0492765141
[11,] -0.2909129355 -0.0511897494
[12,] 0.2244186760 -0.2909129355
[13,] 0.4585969111 0.2244186760
[14,] -0.0530701329 0.4585969111
[15,] 0.2137651147 -0.0530701329
[16,] 0.2179978120 0.2137651147
[17,] -0.0183202431 0.2179978120
[18,] -0.2679676208 -0.0183202431
[19,] -0.4896118918 -0.2679676208
[20,] 0.2466495836 -0.4896118918
[21,] -0.5005405927 0.2466495836
[22,] -0.2539901077 -0.5005405927
[23,] 0.0052077282 -0.2539901077
[24,] 0.4940759043 0.0052077282
[25,] 0.5037399962 0.4940759043
[26,] -0.7388047734 0.5037399962
[27,] 0.0141097297 -0.7388047734
[28,] -0.2385209457 0.0141097297
[29,] 0.5105343854 -0.2385209457
[30,] -0.4940868383 0.5105343854
[31,] -0.4867900808 -0.4940868383
[32,] 0.0150990295 -0.4867900808
[33,] 0.2556171383 0.0150990295
[34,] -0.7490043129 0.2556171383
[35,] -0.0010518960 -0.7490043129
[36,] 0.7552194494 -0.0010518960
[37,] 0.2538982414 0.7552194494
[38,] 0.0150988212 0.2538982414
[39,] -0.4927090121 0.0150988212
[40,] 0.2493575721 -0.4927090121
[41,] 0.2506548246 0.2493575721
[42,] 0.0064719207 0.2506548246
[43,] 0.2452713409 0.0064719207
[44,] -0.2446997880 0.2452713409
[45,] -0.5007201961 -0.2446997880
[46,] -0.0030637638 -0.5007201961
[47,] -0.2482840866 -0.0030637638
[48,] 0.9855861378 -0.2482840866
[49,] 0.4993932294 0.9855861378
[50,] -0.0103605214 0.4993932294
[51,] -0.2581754072 -0.0103605214
[52,] 0.2473785560 -0.2581754072
[53,] -0.0026094952 0.2473785560
[54,] 0.0053358693 -0.0026094952
[55,] -0.4878128957 0.0053358693
[56,] 0.2495995744 -0.4878128957
[57,] 0.0060652775 0.2495995744
[58,] -0.7360013067 0.0060652775
[59,] 0.2638850789 -0.7360013067
[60,] 0.5109705758 0.2638850789
[61,] -0.2471333195 0.5109705758
[62,] -0.7480331106 -0.2471333195
[63,] 0.0014767166 -0.7480331106
[64,] 0.2521941374 0.0014767166
[65,] -0.7345906094 0.2521941374
[66,] -0.4787974880 -0.7345906094
[67,] 0.2621990724 -0.4787974880
[68,] -0.0043435629 0.2621990724
[69,] -0.5030221081 -0.0043435629
[70,] -0.2434834291 -0.5030221081
[71,] 0.5040804807 -0.2434834291
[72,] 0.2349432824 0.5040804807
[73,] 0.5103727678 0.2349432824
[74,] -0.2372896050 0.5103727678
[75,] 0.2640644741 -0.2372896050
[76,] -0.2335111763 0.2640644741
[77,] 0.2715884797 -0.2335111763
[78,] -0.7159256560 0.2715884797
[79,] 0.0327082969 -0.7159256560
[80,] 0.2791451096 0.0327082969
[81,] 0.0220547357 0.2791451096
[82,] -0.9624432506 0.0220547357
[83,] 0.5488350394 -0.9624432506
[84,] 0.2773457355 0.5488350394
[85,] 0.2803619043 0.2773457355
[86,] -0.4600993781 0.2803619043
[87,] 0.2794055142 -0.4600993781
[88,] 0.0202074701 0.2794055142
[89,] -0.2254671019 0.0202074701
[90,] -0.7215513310 -0.2254671019
[91,] 0.0258989452 -0.7215513310
[92,] 0.2714927739 0.0258989452
[93,] 0.2680073354 0.2714927739
[94,] -0.2088619079 0.2680073354
[95,] 0.2833302394 -0.2088619079
[96,] 0.5083313141 0.2833302394
[97,] 0.5115418792 0.5083313141
[98,] -0.4713836956 0.5115418792
[99,] -0.2247195577 -0.4713836956
[100,] -0.2293410476 -0.2247195577
[101,] 0.0128959582 -0.2293410476
[102,] -0.2419079020 0.0128959582
[103,] -0.4813557402 -0.2419079020
[104,] 0.2713317424 -0.4813557402
[105,] 0.2696457552 0.2713317424
[106,] -0.5090839735 0.2696457552
[107,] 0.2416575917 -0.5090839735
[108,] 0.2562837504 0.2416575917
[109,] 0.5065468833 0.2562837504
[110,] 0.0157806040 0.5065468833
[111,] 0.2581310160 0.0157806040
[112,] 0.0136881432 0.2581310160
[113,] 0.2690596589 0.0136881432
[114,] -0.4869036951 0.2690596589
[115,] 0.0180494754 -0.4869036951
[116,] 0.5334618108 0.0180494754
[117,] -0.7270661873 0.5334618108
[118,] 0.0240813771 -0.7270661873
[119,] 0.7603757236 0.0240813771
[120,] -0.2361864826 0.7603757236
[121,] 0.7582356568 -0.2361864826
[122,] -0.4741100284 0.7582356568
[123,] 0.5243654517 -0.4741100284
[124,] 0.0191850524 0.5243654517
[125,] -0.4759425397 0.0191850524
[126,] -0.4777748428 -0.4759425397
[127,] 0.5230531787 -0.4777748428
[128,] 0.7716303244 0.5230531787
[129,] -0.4769465552 0.7716303244
[130,] 0.2666447958 -0.4769465552
[131,] 0.7630365611 0.2666447958
[132,] -0.2361204163 0.7630365611
[133,] -0.2352445613 -0.2361204163
[134,] 0.2593149396 -0.2352445613
[135,] 0.2602713297 0.2593149396
[136,] 0.2681359895 0.2602713297
[137,] -0.2293589175 0.2681359895
[138,] -0.4864493300 -0.2293589175
[139,] 0.2656221698 -0.4864493300
[140,] 0.2773131113 0.2656221698
[141,] 0.2440877607 0.2773131113
[142,] -0.2470343629 0.2440877607
[143,] 0.5151557056 -0.2470343629
[144,] -0.4722116978 0.5151557056
[145,] -0.2341745182 -0.4722116978
[146,] -0.4614923557 -0.2341745182
[147,] 0.0321106971 -0.4614923557
[148,] 0.2765358695 0.0321106971
[149,] -0.4701850370 0.2765358695
[150,] -0.4725525409 -0.4701850370
[151,] 0.2775401512 -0.4725525409
[152,] 0.2773131113 0.2775401512
[153,] 0.2841551452 0.2773131113
[154,] -0.2193063571 0.2841551452
[155,] -0.4754880050 -0.2193063571
[156,] 0.0333241100 -0.4754880050
[157,] 0.0357721489 0.0333241100
[158,] 0.0152272478 0.0357721489
[159,] -0.2173454579 0.0152272478
[160,] -0.2111189323 -0.2173454579
[161,] 0.0303558328 -0.2111189323
[162,] 0.5264726861 0.0303558328
[163,] 0.0116760865 0.5264726861
[164,] 0.2736149901 0.0116760865
[165,] -0.4699103525 0.2736149901
[166,] 0.5283052167 -0.4699103525
[167,] 0.0295456813 0.5283052167
[168,] 0.2765653781 0.0295456813
[169,] -0.2299122930 0.2765653781
[170,] 0.2621991110 -0.2299122930
[171,] -0.2184964332 0.2621991110
[172,] 0.5207812110 -0.2184964332
[173,] 0.2543523983 0.5207812110
[174,] 0.2634156975 0.2543523983
[175,] -0.4817478181 0.2634156975
[176,] -0.2340794564 -0.4817478181
[177,] -0.2265883411 -0.2340794564
[178,] 0.0232531667 -0.2265883411
[179,] 0.2743922126 0.0232531667
[180,] 0.0231395524 0.2743922126
[181,] 0.0258148780 0.0231395524
[182,] 0.0313927193 0.0258148780
[183,] 0.0111558289 0.0313927193
[184,] 0.2564087372 0.0111558289
[185,] 0.0185659154 0.2564087372
[186,] 0.5123962163 0.0185659154
[187,] 0.5288546974 0.5123962163
[188,] -0.2410206359 0.5288546974
[189,] 0.0142043943 -0.2410206359
[190,] 0.0158096575 0.0142043943
[191,] 0.0092422888 0.0158096575
[192,] 0.0110747808 0.0092422888
[193,] -0.2262479724 0.0110747808
[194,] -0.2309828105 -0.2262479724
[195,] -0.4836132777 -0.2309828105
[196,] -0.2308689686 -0.4836132777
[197,] 0.2674689417 -0.2308689686
[198,] -0.2241881937 0.2674689417
[199,] -0.4716621784 -0.2241881937
[200,] 0.0291897791 -0.4716621784
[201,] 0.2827949807 0.0291897791
[202,] 0.3113183450 0.2827949807
[203,] 0.2875450089 0.3113183450
[204,] 0.5514447925 0.2875450089
[205,] -0.4459935156 0.5514447925
[206,] 0.0536090230 -0.4459935156
[207,] 0.2904951886 0.0536090230
[208,] -0.2042107784 0.2904951886
[209,] 0.2930570888 -0.2042107784
[210,] 0.5637036556 0.2930570888
[211,] 0.5384565792 0.5637036556
[212,] 0.5378079336 0.5384565792
[213,] -0.2092207368 0.5378079336
[214,] -0.2110530398 -0.2092207368
[215,] 0.0454216175 -0.2110530398
[216,] 0.0507243965 0.0454216175
[217,] -0.2080518143 0.0507243965
[218,] 0.2784010050 -0.2080518143
[219,] -0.2050685552 0.2784010050
[220,] 0.5517051199 -0.2050685552
[221,] 0.5479595816 0.5517051199
[222,] -0.1996848440 0.5479595816
[223,] -0.4597914253 -0.1996848440
[224,] 0.3013762448 -0.4597914253
[225,] -0.1859014803 0.3013762448
[226,] 0.5616771838 -0.1859014803
[227,] 0.3056896661 0.5616771838
[228,] 0.3027542406 0.3056896661
[229,] 0.0607116119 0.3027542406
[230,] -0.1921461034 0.0607116119
[231,] 0.0613931285 -0.1921461034
[232,] -0.1943671218 0.0613931285
[233,] 0.3040186607 -0.1943671218
[234,] 0.0459237583 0.3040186607
[235,] 0.0473017735 0.0459237583
[236,] 0.3010353824 0.0473017735
[237,] 0.3115274570 0.3010353824
[238,] 0.0498158207 0.3115274570
[239,] -0.1901672763 0.0498158207
[240,] -0.4403496852 -0.1901672763
[241,] 0.5668334580 -0.4403496852
[242,] 0.3055431999 0.5668334580
[243,] -0.4553166870 0.3055431999
[244,] 0.5448626888 -0.4553166870
[245,] -0.2018253274 0.5448626888
[246,] 0.2937236622 -0.2018253274
[247,] -0.4624669399 0.2937236622
[248,] -0.4842435596 -0.4624669399
[249,] 0.0380112455 -0.4842435596
[250,] 0.2867019863 0.0380112455
[251,] -0.2005938170 0.2867019863
[252,] 0.0439776326 -0.2005938170
[253,] 0.2875119103 0.0439776326
[254,] 0.2914039727 0.2875119103
[255,] -0.4660512384 0.2914039727
[256,] -0.4713211077 -0.4660512384
[257,] 0.0369888664 -0.4713211077
[258,] -0.2106316423 0.0369888664
[259,] -0.4820885302 -0.2106316423
[260,] -0.4830778107 -0.4820885302
[261,] -0.4777272174 -0.4830778107
[262,] 0.2658638674 -0.4777272174
[263,] -0.2009016001 0.2658638674
[264,] 0.2767446960 -0.2009016001
[265,] 0.0230918691 0.2767446960
[266,] -0.4818941532 0.0230918691
[267,] 0.2544480269 -0.4818941532
[268,] 0.0216809635 0.2544480269
[269,] -0.4831107011 0.0216809635
[270,] -0.2394627898 -0.4831107011
[271,] 0.2426763807 -0.2394627898
[272,] -0.2560831546 0.2426763807
[273,] -0.2639477952 -0.2560831546
[274,] -0.5391171421 -0.2639477952
[275,] -0.0334017309 -0.5391171421
[276,] 0.2083002630 -0.0334017309
[277,] 0.4607363339 0.2083002630
[278,] 0.2011649920 0.4607363339
[279,] 0.2048626774 0.2011649920
[280,] -0.2852699188 0.2048626774
[281,] -0.0558267494 -0.2852699188
[282,] -0.0394488227 -0.0558267494
[283,] 0.2282295979 -0.0394488227
[284,] -0.5048215401 0.2282295979
[285,] 0.0003114228 -0.5048215401
[286,] -0.4775621600 0.0003114228
[287,] -0.4707675433 -0.4775621600
[288,] 0.3114802481 -0.4707675433
[289,] 0.0572445177 0.3114802481
[290,] 0.0481962197 0.0572445177
[291,] -0.4408183302 0.0481962197
[292,] 0.5683107111 -0.4408183302
[293,] -0.4245061457 0.5683107111
[294,] 0.3107660493 -0.4245061457
[295,] 0.0698741683 0.3107660493
[296,] 0.0705228139 0.0698741683
[297,] -0.6763595793 0.0705228139
[298,] -0.1781679655 -0.6763595793
[299,] 0.0748691449 -0.1781679655
[300,] -0.1724106904 0.0748691449
[301,] 0.0713179642 -0.1724106904
[302,] -0.4254149104 0.0713179642
[303,] 0.0469615165 -0.4254149104
[304,] -0.2132736418 0.0469615165
[305,] 0.2876259604 -0.2132736418
[306,] -0.2307547295 0.2876259604
[307,] 0.0226379977 -0.2307547295
[308,] 0.0165249169 0.0226379977
[309,] 0.5247541477 0.0165249169
[310,] 0.2656041841 0.5247541477
[311,] -0.2451869277 0.2656041841
[312,] -0.0013119765 -0.2451869277
[313,] -0.2480897096 -0.0013119765
[314,] 0.4712109744 -0.2480897096
[315,] 0.2312278581 0.4712109744
[316,] 0.2371136908 0.2312278581
[317,] -0.5104171010 0.2371136908
[318,] 0.2206071679 -0.5104171010
[319,] -0.2730437765 0.2206071679
[320,] -0.5058108399 -0.2730437765
[321,] -0.4905687177 -0.5058108399
[322,] -0.2431094680 -0.4905687177
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3350336608 -0.3348064127
2 -0.0840889919 -0.3350336608
3 0.4242538533 -0.0840889919
4 0.1764389869 0.4242538533
5 0.4250161133 0.1764389869
6 0.1673906889 0.4250161133
7 -0.3367523302 0.1673906889
8 -0.0783975169 -0.3367523302
9 -0.0492765141 -0.0783975169
10 -0.0511897494 -0.0492765141
11 -0.2909129355 -0.0511897494
12 0.2244186760 -0.2909129355
13 0.4585969111 0.2244186760
14 -0.0530701329 0.4585969111
15 0.2137651147 -0.0530701329
16 0.2179978120 0.2137651147
17 -0.0183202431 0.2179978120
18 -0.2679676208 -0.0183202431
19 -0.4896118918 -0.2679676208
20 0.2466495836 -0.4896118918
21 -0.5005405927 0.2466495836
22 -0.2539901077 -0.5005405927
23 0.0052077282 -0.2539901077
24 0.4940759043 0.0052077282
25 0.5037399962 0.4940759043
26 -0.7388047734 0.5037399962
27 0.0141097297 -0.7388047734
28 -0.2385209457 0.0141097297
29 0.5105343854 -0.2385209457
30 -0.4940868383 0.5105343854
31 -0.4867900808 -0.4940868383
32 0.0150990295 -0.4867900808
33 0.2556171383 0.0150990295
34 -0.7490043129 0.2556171383
35 -0.0010518960 -0.7490043129
36 0.7552194494 -0.0010518960
37 0.2538982414 0.7552194494
38 0.0150988212 0.2538982414
39 -0.4927090121 0.0150988212
40 0.2493575721 -0.4927090121
41 0.2506548246 0.2493575721
42 0.0064719207 0.2506548246
43 0.2452713409 0.0064719207
44 -0.2446997880 0.2452713409
45 -0.5007201961 -0.2446997880
46 -0.0030637638 -0.5007201961
47 -0.2482840866 -0.0030637638
48 0.9855861378 -0.2482840866
49 0.4993932294 0.9855861378
50 -0.0103605214 0.4993932294
51 -0.2581754072 -0.0103605214
52 0.2473785560 -0.2581754072
53 -0.0026094952 0.2473785560
54 0.0053358693 -0.0026094952
55 -0.4878128957 0.0053358693
56 0.2495995744 -0.4878128957
57 0.0060652775 0.2495995744
58 -0.7360013067 0.0060652775
59 0.2638850789 -0.7360013067
60 0.5109705758 0.2638850789
61 -0.2471333195 0.5109705758
62 -0.7480331106 -0.2471333195
63 0.0014767166 -0.7480331106
64 0.2521941374 0.0014767166
65 -0.7345906094 0.2521941374
66 -0.4787974880 -0.7345906094
67 0.2621990724 -0.4787974880
68 -0.0043435629 0.2621990724
69 -0.5030221081 -0.0043435629
70 -0.2434834291 -0.5030221081
71 0.5040804807 -0.2434834291
72 0.2349432824 0.5040804807
73 0.5103727678 0.2349432824
74 -0.2372896050 0.5103727678
75 0.2640644741 -0.2372896050
76 -0.2335111763 0.2640644741
77 0.2715884797 -0.2335111763
78 -0.7159256560 0.2715884797
79 0.0327082969 -0.7159256560
80 0.2791451096 0.0327082969
81 0.0220547357 0.2791451096
82 -0.9624432506 0.0220547357
83 0.5488350394 -0.9624432506
84 0.2773457355 0.5488350394
85 0.2803619043 0.2773457355
86 -0.4600993781 0.2803619043
87 0.2794055142 -0.4600993781
88 0.0202074701 0.2794055142
89 -0.2254671019 0.0202074701
90 -0.7215513310 -0.2254671019
91 0.0258989452 -0.7215513310
92 0.2714927739 0.0258989452
93 0.2680073354 0.2714927739
94 -0.2088619079 0.2680073354
95 0.2833302394 -0.2088619079
96 0.5083313141 0.2833302394
97 0.5115418792 0.5083313141
98 -0.4713836956 0.5115418792
99 -0.2247195577 -0.4713836956
100 -0.2293410476 -0.2247195577
101 0.0128959582 -0.2293410476
102 -0.2419079020 0.0128959582
103 -0.4813557402 -0.2419079020
104 0.2713317424 -0.4813557402
105 0.2696457552 0.2713317424
106 -0.5090839735 0.2696457552
107 0.2416575917 -0.5090839735
108 0.2562837504 0.2416575917
109 0.5065468833 0.2562837504
110 0.0157806040 0.5065468833
111 0.2581310160 0.0157806040
112 0.0136881432 0.2581310160
113 0.2690596589 0.0136881432
114 -0.4869036951 0.2690596589
115 0.0180494754 -0.4869036951
116 0.5334618108 0.0180494754
117 -0.7270661873 0.5334618108
118 0.0240813771 -0.7270661873
119 0.7603757236 0.0240813771
120 -0.2361864826 0.7603757236
121 0.7582356568 -0.2361864826
122 -0.4741100284 0.7582356568
123 0.5243654517 -0.4741100284
124 0.0191850524 0.5243654517
125 -0.4759425397 0.0191850524
126 -0.4777748428 -0.4759425397
127 0.5230531787 -0.4777748428
128 0.7716303244 0.5230531787
129 -0.4769465552 0.7716303244
130 0.2666447958 -0.4769465552
131 0.7630365611 0.2666447958
132 -0.2361204163 0.7630365611
133 -0.2352445613 -0.2361204163
134 0.2593149396 -0.2352445613
135 0.2602713297 0.2593149396
136 0.2681359895 0.2602713297
137 -0.2293589175 0.2681359895
138 -0.4864493300 -0.2293589175
139 0.2656221698 -0.4864493300
140 0.2773131113 0.2656221698
141 0.2440877607 0.2773131113
142 -0.2470343629 0.2440877607
143 0.5151557056 -0.2470343629
144 -0.4722116978 0.5151557056
145 -0.2341745182 -0.4722116978
146 -0.4614923557 -0.2341745182
147 0.0321106971 -0.4614923557
148 0.2765358695 0.0321106971
149 -0.4701850370 0.2765358695
150 -0.4725525409 -0.4701850370
151 0.2775401512 -0.4725525409
152 0.2773131113 0.2775401512
153 0.2841551452 0.2773131113
154 -0.2193063571 0.2841551452
155 -0.4754880050 -0.2193063571
156 0.0333241100 -0.4754880050
157 0.0357721489 0.0333241100
158 0.0152272478 0.0357721489
159 -0.2173454579 0.0152272478
160 -0.2111189323 -0.2173454579
161 0.0303558328 -0.2111189323
162 0.5264726861 0.0303558328
163 0.0116760865 0.5264726861
164 0.2736149901 0.0116760865
165 -0.4699103525 0.2736149901
166 0.5283052167 -0.4699103525
167 0.0295456813 0.5283052167
168 0.2765653781 0.0295456813
169 -0.2299122930 0.2765653781
170 0.2621991110 -0.2299122930
171 -0.2184964332 0.2621991110
172 0.5207812110 -0.2184964332
173 0.2543523983 0.5207812110
174 0.2634156975 0.2543523983
175 -0.4817478181 0.2634156975
176 -0.2340794564 -0.4817478181
177 -0.2265883411 -0.2340794564
178 0.0232531667 -0.2265883411
179 0.2743922126 0.0232531667
180 0.0231395524 0.2743922126
181 0.0258148780 0.0231395524
182 0.0313927193 0.0258148780
183 0.0111558289 0.0313927193
184 0.2564087372 0.0111558289
185 0.0185659154 0.2564087372
186 0.5123962163 0.0185659154
187 0.5288546974 0.5123962163
188 -0.2410206359 0.5288546974
189 0.0142043943 -0.2410206359
190 0.0158096575 0.0142043943
191 0.0092422888 0.0158096575
192 0.0110747808 0.0092422888
193 -0.2262479724 0.0110747808
194 -0.2309828105 -0.2262479724
195 -0.4836132777 -0.2309828105
196 -0.2308689686 -0.4836132777
197 0.2674689417 -0.2308689686
198 -0.2241881937 0.2674689417
199 -0.4716621784 -0.2241881937
200 0.0291897791 -0.4716621784
201 0.2827949807 0.0291897791
202 0.3113183450 0.2827949807
203 0.2875450089 0.3113183450
204 0.5514447925 0.2875450089
205 -0.4459935156 0.5514447925
206 0.0536090230 -0.4459935156
207 0.2904951886 0.0536090230
208 -0.2042107784 0.2904951886
209 0.2930570888 -0.2042107784
210 0.5637036556 0.2930570888
211 0.5384565792 0.5637036556
212 0.5378079336 0.5384565792
213 -0.2092207368 0.5378079336
214 -0.2110530398 -0.2092207368
215 0.0454216175 -0.2110530398
216 0.0507243965 0.0454216175
217 -0.2080518143 0.0507243965
218 0.2784010050 -0.2080518143
219 -0.2050685552 0.2784010050
220 0.5517051199 -0.2050685552
221 0.5479595816 0.5517051199
222 -0.1996848440 0.5479595816
223 -0.4597914253 -0.1996848440
224 0.3013762448 -0.4597914253
225 -0.1859014803 0.3013762448
226 0.5616771838 -0.1859014803
227 0.3056896661 0.5616771838
228 0.3027542406 0.3056896661
229 0.0607116119 0.3027542406
230 -0.1921461034 0.0607116119
231 0.0613931285 -0.1921461034
232 -0.1943671218 0.0613931285
233 0.3040186607 -0.1943671218
234 0.0459237583 0.3040186607
235 0.0473017735 0.0459237583
236 0.3010353824 0.0473017735
237 0.3115274570 0.3010353824
238 0.0498158207 0.3115274570
239 -0.1901672763 0.0498158207
240 -0.4403496852 -0.1901672763
241 0.5668334580 -0.4403496852
242 0.3055431999 0.5668334580
243 -0.4553166870 0.3055431999
244 0.5448626888 -0.4553166870
245 -0.2018253274 0.5448626888
246 0.2937236622 -0.2018253274
247 -0.4624669399 0.2937236622
248 -0.4842435596 -0.4624669399
249 0.0380112455 -0.4842435596
250 0.2867019863 0.0380112455
251 -0.2005938170 0.2867019863
252 0.0439776326 -0.2005938170
253 0.2875119103 0.0439776326
254 0.2914039727 0.2875119103
255 -0.4660512384 0.2914039727
256 -0.4713211077 -0.4660512384
257 0.0369888664 -0.4713211077
258 -0.2106316423 0.0369888664
259 -0.4820885302 -0.2106316423
260 -0.4830778107 -0.4820885302
261 -0.4777272174 -0.4830778107
262 0.2658638674 -0.4777272174
263 -0.2009016001 0.2658638674
264 0.2767446960 -0.2009016001
265 0.0230918691 0.2767446960
266 -0.4818941532 0.0230918691
267 0.2544480269 -0.4818941532
268 0.0216809635 0.2544480269
269 -0.4831107011 0.0216809635
270 -0.2394627898 -0.4831107011
271 0.2426763807 -0.2394627898
272 -0.2560831546 0.2426763807
273 -0.2639477952 -0.2560831546
274 -0.5391171421 -0.2639477952
275 -0.0334017309 -0.5391171421
276 0.2083002630 -0.0334017309
277 0.4607363339 0.2083002630
278 0.2011649920 0.4607363339
279 0.2048626774 0.2011649920
280 -0.2852699188 0.2048626774
281 -0.0558267494 -0.2852699188
282 -0.0394488227 -0.0558267494
283 0.2282295979 -0.0394488227
284 -0.5048215401 0.2282295979
285 0.0003114228 -0.5048215401
286 -0.4775621600 0.0003114228
287 -0.4707675433 -0.4775621600
288 0.3114802481 -0.4707675433
289 0.0572445177 0.3114802481
290 0.0481962197 0.0572445177
291 -0.4408183302 0.0481962197
292 0.5683107111 -0.4408183302
293 -0.4245061457 0.5683107111
294 0.3107660493 -0.4245061457
295 0.0698741683 0.3107660493
296 0.0705228139 0.0698741683
297 -0.6763595793 0.0705228139
298 -0.1781679655 -0.6763595793
299 0.0748691449 -0.1781679655
300 -0.1724106904 0.0748691449
301 0.0713179642 -0.1724106904
302 -0.4254149104 0.0713179642
303 0.0469615165 -0.4254149104
304 -0.2132736418 0.0469615165
305 0.2876259604 -0.2132736418
306 -0.2307547295 0.2876259604
307 0.0226379977 -0.2307547295
308 0.0165249169 0.0226379977
309 0.5247541477 0.0165249169
310 0.2656041841 0.5247541477
311 -0.2451869277 0.2656041841
312 -0.0013119765 -0.2451869277
313 -0.2480897096 -0.0013119765
314 0.4712109744 -0.2480897096
315 0.2312278581 0.4712109744
316 0.2371136908 0.2312278581
317 -0.5104171010 0.2371136908
318 0.2206071679 -0.5104171010
319 -0.2730437765 0.2206071679
320 -0.5058108399 -0.2730437765
321 -0.4905687177 -0.5058108399
322 -0.2431094680 -0.4905687177
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7672z1321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/81jvs1321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9v24u1321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10e7b81321898366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11e7cz1321898366.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12c1751321898366.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13kxhx1321898366.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14n63u1321898366.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15d5te1321898366.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16oo7j1321898366.tab")
+ }
>
> try(system("convert tmp/1qc7c1321898366.ps tmp/1qc7c1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yb2h1321898366.ps tmp/2yb2h1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u6v31321898366.ps tmp/3u6v31321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k4p71321898366.ps tmp/4k4p71321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tiqb1321898366.ps tmp/5tiqb1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cqo51321898366.ps tmp/6cqo51321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/7672z1321898366.ps tmp/7672z1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/81jvs1321898366.ps tmp/81jvs1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v24u1321898366.ps tmp/9v24u1321898366.png",intern=TRUE))
character(0)
> try(system("convert tmp/10e7b81321898366.ps tmp/10e7b81321898366.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.820 0.380 9.237